By Comité Editorial

Introduction. It is an essential requirement during the teaching process in the postgraduate the continuous scientific updating of the faculty members, both from the thematic and pedagogical point of view. Teachers must have skills to transmit their knowledge to students. The faculty is essential to achieve quality in postgraduate teaching. Objective. To characterize the postgraduate training of the University of Medical Sciences of Havana. Methods. Observational, descriptive, retrospective study, where the specialties, settings and faculty of each of the medical schools and postgraduate training centers of the university were described during the year 2021. The primary source for data collection was the databases of the Postgraduate Department of the UCMH. Results. The study included 11 faculties of Medical Sciences and 4 Postgraduate Centers, with 265 accredited scenarios and training in 69 specialties. In the year 2021, of 6 108 teachers, only 6.4% are consultants, 7.1% are associate profesor and 31.9% assistants. 18.9% of the teachers have a research category and 8.5% are doctors of science (PhD). The tutor/resident ratio was 0.69. The distribution of teachers with higher categories, PhDs in science and teachers with research category shows great variability, depending on the postgraduate training center. Conclusions. During the year 2021, postgraduate training at the UCMH was characterized by its heterogeneity, with 69 specialties, several training centers; where the quality of the faculty depends on the training scenario.

By Lissette Cárdenas de Baños, Rossana Planas Labrada, Niurka de la C Almaguer Fernández, María Teresa Dieguez Calderón, Sergio González-García

Deep Learning (DL) has revolutionized crop management practices, with disease detection and classification gaining prominence due to their impact on crop health and productivity. Addressing the limitations of traditional methods, such as reliance on handcrafted features, sensitivity to small datasets, limited adaptability, and scalability issues, deep learning enables accurate disease detection, real-time monitoring, and precision agriculture practices. Its ability to analyze and extract features from images, handle multimodal data, and adapt to new data patterns paves the way for a more sustainable and productive agricultural future. This study evaluates six pre-trained deep-learning models designed for stage-wise classification of white-scale date palm disease (WSD). The study assesses key metrics such as accuracy, sensitivity to training data volume, and inference time to identify the most effective model for accurate WSD stage-wise classification. For model development and assessment, we employed a dataset of 1,091 colored date palm leaflet images categorized into four distinct classes: healthy, low infestation degree, medium infestation degree, and high infestation degree. The results reveal the MobileNet model as the top performer, demonstrating superior accuracy and inference time compared to the other models and state of the art methods. The MobileNet model achieves high classification accuracy with only 60% of the training data. By harnessing the power of deep learning, this study enhances disease management practices in date palm agriculture, fostering improved crop yield, reduced losses, and sustainable food production.

By Abdelaaziz Hessane, Mohamed Khalifa Boutahir, Ahmed El Youssefi, Yousef Farhaoui, Badraddine Aghoutane

The main objective of this research was to determine the relationship between Aggression (A) and School Coexistence (CE). The research was conducted with a quantitative approach, basic type, correlational level, non-experimental design, cross-sectional and hypothetical deductive method. Non-probabilistic convenience sampling was applied considering a sample made up of 60 6th grade students from educational institution No. 20595 "José Gabriel Condorcanqui", elementary level, from the San Mateo district. The validity of the expert judgment and the confirmation of reliability were met, through Cronbach's Alpha (aggressiveness = -0.613 and school coexistence = -0.711). The survey technique was used and through two instruments (questionnaires) the data were collected via Google forms. The results obtained were (P = 0.000, Rho = -0.407), it is concluded that there is a significant negative and moderate inverse correlation between the study variables.

By Yolanda Yauri-Paquiyauri, Nerio Enriquez-Gavilan, Brian Meneses-Claudio, Aydeé Lopez-Curasma, Julio Romero-Sandoval

Medical professionals often rely on Magnetic Resonance Imaging (MRI) to obtain non-invasive medical images. One important use of this technology is brain tumor segmentation, where algorithms are used to identify tumors in MRI scans of the brain. The foundation model Pipeline is based on U-Net Architecture to handle medical image segmentation and has been fine-tuned in the research paper to segment brain tumors. The model will be further trained on various medical images to segment images for various bio-medical purposes and used as part of the Generative AI functional model framework. Accurate segmentation of tumors is essential for treatment planning and monitoring, and this approach can potentially improve patient outcomes and quality of life.

By Sanjeev Kumar Bhatt, Dr. S. Srinivasan, Dr. Piyush Prakash

Faced with the scale of cases of acute poisoning, whether accidental or voluntary and requiring admission to the emergency services, the integration of the in silico approach in the process of diagnosis, prognosis and treatment is of paramount importance. This approach, centered on artificial intelligence (AI), is based on prediction based on significant clinical data, thus supporting practitioners and helping them to target the most likely toxic substances. The objective is to make a prediction upstream of the confirmation stage, which would require biological and toxicological investigations that are often costly and time-consuming. With this in mind, our work focuses on the development of a Machine Learning (ML) algorithm capable of predicting the causative toxic agent, providing essential information on the predominant clinical signs. Although many studies in the literature have addressed the use of technology and artificial intelligence in diagnosis, monitoring and pharmacology, we did not find any publications concerning the use of artificial intelligence for the diagnosis or the aid in the diagnosis of poisoning cases. This innovation will therefore constitute the strong point of our research work. Our machine learning algorithm is based on a prediction process based on the in-depth analysis of clinical data provided by the clinical examination of the patient as soon as he arrives at the emergency room. By taking into account a set of parameters such as the symptoms present, the medical history and the circumstances surrounding the exposure, the model can establish relevant links between the clinical signs and the potential toxic agents. By emphasizing the speed and accuracy of prediction, while recognizing the crucial importance of biological and toxicological analysis to confirm diagnoses, our approach has the potential to optimize clinical management by directing the physician to appropriate measures more quickly. As a decision support tool, it offers a relevant first predictive assessment from the patient's admission.

By Rajae Ghanimi, Khalil Chouikri, Ilyas Ghanimi, Fadoua Ghanimi, Abdelmajid Soulaymani

This work aims to determine the rheological properties of the industrial phosphoric acid slurry and its behavior under the operating conditions of the phosphoric acid production process. For that, several experimental tests on the slurry were carried out, using a Rheometer (Anton Paar), which testing the effect of temperature and solid content. The results show that, for a fixed solids rate, the viscosity of the slurry decreases with temperatures from 75°C to 82°C and increases for temperatures above 82°C considered as the maximum temperature required by the process. This phenomenon is due to the morphological change of the gypsum which corresponds to the range of calcium sulfate hemihydrate formation. For a fixed temperature, the viscosity increases with increasing slurry solid content (31% to 37%). The viscosity increases with the shear gradient. Increasing the solid charge in the slurry increases its resistance to flow and movement. Thus, the slurry has a higher tendency to settle. A comparative study of four rheological models, Casson, Bingham, Ostwald and Herschel-Buckley, led to the selection of the Herschel-Bulkley model. This predicts the behavior of the phosphate slurry with a correlation coefficient of 99.9% and a MAE less than 4%. Overall, the results show that the threshold flow of the slurry is negligible, and its behavior is nonlinear. Thus, the slurry is a non-Newtonian fluid, with a dilatant rheological behavior. The various tests carried out enabled us to measure the viscosity of the phosphoric acid suspension for different solids contents and at different temperatures. The results obtained enabled us to study the rheological behavior and develop an artificial neural network model to control the viscosity of the slurry at the phosphoric acid attack tank.

By Ahmed Bichri, Hamid Mazouz, Souad Abderafi

The goal of handwriting recognition has been a top priority for those who want to enter data into computer systems for more than thirty years. In several fields, the advent of handwriting recognition technology is highly anticipated. OCR technology has made it possible for computers to recognize characters as visual objects and collect data about their unique characteristics in recent years. In particular, several studies in this field have focused on Arabic writing. The use of machines to examine handwritten papers is the first step in the character identification process. The identification of specific Arabic characters is the main goal of this particular investigation. In computer vision, Arabic character recognition is very important since it's necessary to correctly recognize and classify Arabic letters and characters in manuscripts. In this research, an innovative approach based on identifying Arabic character characteristics using BoVW (bag of visual words) and SIFT (Scale Invariant Feature Transform) features is proposed. These features are clustered using k-means clustering to produce a dictionary. Following that, SVM (Support Vector Machine) is utilized to classify the word images in a visual codebook created using these terms. The proposed approach is an innovative method to deal with the difficulties associated with Arabic hand-writing recognition. The utilization of BoVW and SIFT features is expected to enhance the system's robustness in recognizing and classifying Arabic characters. The proposed approach will be experimentally evaluated using a dataset that includes a variety of Arabic characters written in various styles. The results of this study will offer important new perspectives on the effectiveness and practicality of the approach suggested.

By Othmane Farhaoui, Mohamed Rida Fethi, Imad Zeroual, Ahmad El Allaoui

Introduction: Motivation is fundamental in the entrepreneurship of the students of the National University of Cañete. Motivation is the drive that a person feels to achieve their goals.
Objective: The contribution that this study will provide will be to encourage students to undertake, in turn new jobs will be generated, a source of income and greater economic development in the province of Cañete. This research work was carried out at the National University of Cañete, which is in the district of San Vicente, belonging to the province of Cañete – department of Lima. The objective was to determine how motivation is associated with entrepreneurship in students in the final cycles of Administration at the National University of Cañete, 2021.
Method: The type of research used was basic, with a quantitative - deductive, correlational - descriptive level and non-experimental - cross-sectional design. The variables used were motivation and entrepreneurship, the dimensions of the research were the following: need for achievement, need for affiliation, need for power, innovation, risk management and proactivity. The population was made up of 178 students from the last cycles of the professional Administration degree at the National University of Cañete. The total sample was 73 students. For the data collection process, the questionnaire was used as an instrument and the survey of the study variables according to the Likert scale was used as a technique. Likewise, in the descriptive and inferential analysis of the data, Microsoft Excel programs and SPSS version 25 statistics were used.
Results: Finally, to determine the correlation between the variables, the Pearson parametric test was used, through which the following results were obtained with a P-Value = 0.00, that is, a P < 0.05, which establishes that motivation is associated with entrepreneurship of the students of the last administration cycles of the National University of Cañete.
Conclusions: In conclusion, the results of this research reveal a clear and significant association between motivation and entrepreneurship among students in the final cycles of Administration at the National University of Cañete. Additionally, the importance of entrepreneurship education in the academic curriculum to prepare students for an entrepreneurial future and the potential impact on local economic development by generating employment and income opportunities is highlighted. These findings establish a solid foundation for future research and emphasize the relevance of promoting entrepreneurship in academic and community settings.

By Lisbhet Mendoza-Cabello, Segundo Ríos-Ríos , Hugo Morán-Requena, Filiberto Ochoa-Paredes, Fernando Ochoa-Paredes, Yrene Uribe-Hernandez

Diabetic Foot Ulcer is considered a critical complication of diabetes, characterized by injuries and frequent exposure of the diabetic patient’s foot. Approximately 20 % of diabetic patients may develop foot ulcers, with around 10 % requiring hospitalization due to additional complications. Typically, these ulcers affect individuals who have had diabetes for more than ten years. Neglecting or leaving Diabetic Foot Ulcers untreated can result in severe damage, leading to worsened infections and potentially necessitating amputation, often accompanied by multiple complications that may even result in mortality. Therefore, early prediction of foot-threatening risks is crucial to prevent worsening situations. In this work visualization methods are applied for a better understanding of the dataset to draw meaningful insights and to observe the behavior of amputation risks in diabetic patients. The feature values fluctuate, so selecting the best feature from a combination of statistical and graphical data analysis is not trivial. Data visualization techniques (data-driven approach), and statistical analysis were used to select important features, that lead to leg amputation. The Machine learning models were implemented to forecast foot ulcers depending on clinical outcomes. A predicted accuracy of 85 % is observed using Ensemble Methods.

By Sri Lalitha Y, Gayatri P, Laxmi Bindu I, Ganapathi Raju

The primary objective of this study is to delve into the intricate interplay between workforce mobility and the spatial proximity to agglomerations, and their collective impact on the residential attractiveness of small cities in Morocco. Initially, we meticulously estimated the net migration rate, a robust and widely acknowledged metric within scholarly discourse, employed to gauge the territorial magnetism. Subsequently, employing this metric as the dependent variable, we embarked on a thorough examination of how the mobility of the workforce and territorial proximity to agglomerations synergistically shape the attractiveness of small cities. The assessment of the net migration rate unearthed a pattern of dispersion, a phenomenon that catalyzed our adoption of quantile regression modeling. Therefore, our rigorous analysis has unveiled a compelling revelation: the geographical proximity of small cities exerts a pronounced influence on their allure. Specifically, a closer adjacency to agglomeration zones invariably results in an augmented residential attractiveness. Furthermore, our research has discerned a robust correlation between heightened workforce mobility and an amplified migratory interest in small Moroccan cities. These compelling findings challenge the prevailing notion that the residential magnetism of small cities in Morocco hinges solely on their socio-economic profile. Instead, it underscores the profound impact wielded by their spatial disposition and the dynamic movements of the workforce.

By Sohaib Khalid, Driss Effina

The "Parturition Detection Sensor Belt," also known as the "Labor Pain Detection Sensor Belt," represents a novel advancement in maternal health monitoring. "Parturition Detection Sensor Belt" designed to simultaneously predict oxytocin levels and monitor uterine muscle contractions. This innovative system combines real-time prediction of oxytocin levels and simultaneous monitoring of uterine muscle contractions to provide a comprehensive solution for parturition detection. By integrating cutting-edge sensor technology and deep learning algorithms, the system offers precise, non-invasive monitoring during labor. The oxytocin level predictions aid in understanding maternal well-being, while the real-time uterine muscle contraction monitoring ensures early detection of labor progression. This interdisciplinary approach leverages advancements in biomedical engineering and data analysis, holding promise for improving the safety and care of expectant mothers. The "Parturition Detection Sensor Belt" has the potential to revolutionize the field of obstetrics by offering a versatile tool for healthcare providers, enhancing maternal health, and facilitating data-driven research in this critical domain. . A correlation is developed between oxytocin release and muscle contraction which turns out to be nearly 0.899836. This infers that the two factors that we are considering as important parameters are having a strong association with each other.

By Harshavardhan D, Saisree K, Ragavarshini S

Fetal electrocardiograms (ECG) provide crucial information for the interventions and diagnoses of pregnant women at the clinical level. Maternal signals are robust, making retrieval and detection of Fetal ECGs difficult. In this article, we propose a solution based on Machine Learning by adapting the k-means clustering to detect the fetal ECG by recording the ECGs. In our first preprocessing part, we tried normalized and segmented ECG waveform. Next, we used the Euclidean distance to measure similarity. To identify a certain number of centroids in our data, the results classified into two classes are represented in the last part through graphs and compared with other algorithms, such as the CNN classifier, to demonstrate the effectiveness of this innovative approach, which can be deployed in real-time.

By Mohammed Moutaib, Mohammed Fattah, Yousef Farhaoui, Badraddine Aghoutane, Moulhime El Bekkali

In recent years, there has been a persistent increase in the number of road accidents worldwide. The US National Highway Traffic Safety Administration reports that distracted driving is responsible for approximately 45 percent of road accidents. In this study, we tackle the challenge of automating the detection and classification of driver distraction, along with the monitoring of risky driving behavior. Our proposed solution is based on the Pyramid Scene Parsing Network (PSPNet), which is a semantic segmentation model equipped with a pyramid parsing module. This module leverages global context information through context aggregation from different regions. We introduce a lightweight model for driver distraction classification, where the final predictions benefit from the combination of both local and global cues. For model training, we utilized the publicly available StateFarm Distracted Driver Detection Dataset. Additionally, we propose optimization techniques for classification to enhance the model’s performance.

By Abdelhak Khadraoui, Elmoukhtar Zemmouri

Introduction: this paper explores the effectiveness of character recognition models for document digitization, leveraging diverse machine learning and deep learning techniques. The study, driven by the increasing relevance of image classification in various applications, focuses on evaluating Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning. The research employs a challenging French alphabet dataset, comprising 82 classes, to assess the models' capacity to discern intricate patterns and generalize across diverse characters. Objective: This study investigates the effectiveness of character recognition models for document digitization using diverse machine learning and deep learning techniques. Methods: the methodology initiates with data preparation, involving the creation of a merged dataset from distinct sections, encompassing digits, French special characters, symbols, and the French alphabet. The dataset is subsequently partitioned into training, test, and evaluation sets. Each model undergoes meticulous training and evaluation over a specific number of epochs. The recording of fundamental metrics includes accuracy, precision, recall, and F1-score for CNN, RNN, and VGG16, while SVM and KNN are evaluated based on accuracy, macro avg, and weighted avg. Results: the outcomes highlight distinct strengths and areas for improvement across the evaluated models. SVM demonstrates remarkable accuracy of 98.63%, emphasizing its efficacy in character recognition. KNN exhibits high reliability with an overall accuracy of 97%, while the RNN model faces challenges in training and generalization. The CNN model excels with an accuracy of 97.268%, and VGG16 with transfer learning achieves notable enhancements, reaching accuracy rates of 94.83% on test images and 94.55% on evaluation images. Conclusion: our study evaluates the performance of five models—Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and VGG16 with transfer learning—on character recognition tasks. SVM and KNN demonstrate high accuracy, while RNN faces challenges in training. CNN excels in image classification, and VGG16, with transfer learning, enhances accuracy significantly. This comparative analysis aids in informed model selection for character recognition applications.

By Ali Benaissa, Abdelkhalak Bahri, Ahmad El Allaoui, My Abdelouahab Salahddine

This research addressed the analysis of the perception and resilience of key actors in the implementation of biosecurity protocols to enhance tourist services in risky situations. A case study approach was used, and in-depth interviews were conducted to gather significant data, which were processed using the Atlas.ti software. The findings of the research underscore the essential importance of implementing biosecurity protocols for the success and growth of accommodation establishments, reaffirming their commitment to the safety and well-being of all involved. These protocols are also crucial for a safe and sustainable reactivation of the gastronomic sector. Despite regulatory limitations, providers of recreational and complementary tourism services demonstrate a clear willingness to adapt and implement biosecurity measures, ensuring a secure tourist experience. It is concluded that biosecurity protocols are fundamental for the economic reactivation of tourism establishments in the district, instilling confidence and safety in tourists, which encourages travel and visits to these places. Additionally, the significance of personnel training and the need for a well-structured contingency plan to effectively respond to risky situations in the tourism industry are highlighted.

By Laydi Diana Milagros Caycho Araujo, Miriam Viviana Ñañez Silva

Introduction: Artificial intelligence (AI) encompasses the concept of automated machines that can perform tasks typically carried out by humans, doctor-patient communication will increasingly rely on the integration of artificial intelligence (AI) in healthcare, especially in medicine and digital assistant systems like chatbots. The objective of this study is to explore the understanding, utilization, and apprehensions of future doctors at the Faculty of Medicine in Casablanca regarding the adoption of artificial intelligence, particularly intelligent chatbots. Methods: A cross-sectional study was conducted among students from the 1st to 5th year at the Faculty of Medicine and Pharmacy in Casablanca. Probability sampling was implemented using a clustered and stratified approach based on the year of study. Electronic forms were distributed to randomly selected groups of students. Results: Among the participants, 52% of students fully agreed to utilize chatbots capable of answering health-related queries, while 39% partially agreed to use chatbots for providing diagnoses regarding health conditions. About concerns, 77% of the respondents expressed fear regarding reduced transparency regarding the utilization of personal data, and 66% expressed concerns about diminished professional autonomy. Conclusion: Moroccan Medical students are open to embracing AI in the field of medicine. The study highlights their ability to grasp the fundamental aspects of how AI and chatbots will impact their daily work, while the overall attitude towards the use of clinical AI was positive, participants also expressed certain concerns.

By Berrami Hind, Zineb Serhier, Manar Jallal, Mohammed Bennani Othmani

In 2011, the German government launched the visionary initiative known as Industry 4.0, with the goal of positioning itself at the forefront of cutting-edge manufacturing and the shift towards digital transformation. In the wake of this transformative wave, numerous manufacturers are continuously exploring avenues to bolster their capabilities and remain competitive in the market. This empirical study adopts a maturity model inspired by the Economic Development Board's Singapore Smart Industry Readiness Index. The model empowers companies to perform self-assessments, facilitating a systematic and comprehensive alignment with the principles of Industry 4.0. The research delves into the assessment of Industry 4.0 maturity within the Moroccan clothing industry, examining clustering index factors and the influence of key factors on companies' self-assessment. The results classify 252 Moroccan Clothing enterprises into three distinct categories, highlighting a strong positive correlation among process, technology, and organization. Significantly, a majority of the 252 companies evaluated using the maturity model still appear to be in early stages or partially mature, necessitating significant improvements and a reevaluation of their Industry 4.0 transformation strategies. Conclusively, the Singapore Smart Industry Readiness Index proves to be a valuable tool for conducting self-assessments within Moroccan-based enterprises. These findings offer practical guidance for both industry practitioners and researchers seeking to navigate the complexities of Industry 4.0 maturity and grouping.

By Younes JAMOULI; Samir TETOUANI, Omar CHERKAOUI, Aziz SOULHI

The impact of COVID 19 on university higher education has been noticeable in the transformation of the operating system from face-to-face education to distance education. This mixed-approach research sought to determine the relationship between university academic tutoring and vocational training, as well as to describe and analyze the work conducted in academic tutoring at a public university in the Faculty of Business Sciences, Agrarian Sciences and Engineering to know the strategies they execute and elaborate proposals. A survey was administered to a random sample of 227 tutors. Then, 25 tutors and 25 tutors were selected, applying a semi-structured interview. The results indicate that there is a high positive correlation between academic tutoring and vocational training (0.728) and between academic tutoring and the dimensions: personal (0.712); professional (0, 671) and academic (0, 679). It is concluded that this should follow an organizational process that allows individual and group orientation activities, as well as co-referral when specialized help is required. The tutors must meet certain qualities that allow an empathic approach with the tutors; and academic management should provide continuous training for tutors to improve their strategies.

By Miriam Ñañez-Silva, Brian Meneses-Claudio

The introduction and widespread use of social media has altered how information is generated and disseminated, along with the expansion of the Internet. Through social media, information is now more quickly, cheaply, and easily available. Particularly harmful content includes misinformation propagated by social media users, such as false news. Users find it simple to post comments and false information on social networks. Realising the difference between authentic and false news is the biggest obstacle. The current study's aim of identifying bogus news involved the deployment of a capsule neural network. However, with time, this technique as a whole learns how to report user accuracy. This paper offers a three-step strategy for spotting bogus news on social networks as a solution to this issue. Pre-processing is executed initially to transform unstrsuctrured data into a structured form. The second part of the project brought the HFEM (Combined Feature Extraction Model), which also revealed new relationships between themes, authors, and articles as well as undiscovered features of false news. based on a collection of traits that were explicitly and implicitly collected from text. This study creates a capsule neural network model in the third stage to concurrently understand how creators, subjects, and articles are presented. This work uses four performance metrics in evaluations of the suggested classification algorithm using on existing public data sets.

By Uma Maheswari R, Sudha N

Coronary Artery Disease (CAD) is an increasingly prevalent ailment that has a significant impact on both longevity and quality of life. Lifestyle, genetics, nutrition, and stress are all significant contributors to rising mortality rates. CAD is preventable through early intervention and lifestyle changes. As a result, low-cost automated solutions are required to detect CAD early and help healthcare professionals treat chronic diseases efficiently. Machine learning applications in medicine have increased due to their ability to detect data patterns. Employing machine learning to classify the occurrence of coronary artery disease could assist doctors in reducing misinterpretation. The research project entails the creation of a coronary artery disease diagnosis system based on machine learning. Using patient medical records, we demonstrate how machine learning can help identify if an individual will acquire coronary artery disease. Furthermore, the study highlights the most critical risk factors for coronary artery disease. We used two machine learning approaches, Catboost and LightGBM classifiers, to predict the patient with coronary artery disease. We employed various data augmentation methods, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAE), to solve the imbalanced data problem. Optuna was applied to optimize hyperparameters. The proposed method was tested on the real-world dataset Z-Alizadeh Sani. The acquired findings were satisfactory, as the model could predict the likelihood of cardiovascular disease in a particular individual by combining Catboost with VAE, which demonstrated good accuracy compared to the other approaches. The proposed model is evaluated using a variety of metrics, including accuracy, recall, f-score, precision, and ROC curve. Furthermore, we used the SHAP values and Boruta Feature Selection (BFS) to determine essential risk factors for coronary artery disease.

By Louridi Nabaouia, Douzi Samira, El Ouahidi Bouabid

Video classification holds a foundational position in the realm of computer vision, involving the categorization and labeling of videos based on their content. Its significance resonates across various applications, including video surveil-lance, content recommendation, action recognition, video indexing, and more. The primary objective of video classification is to automatically analyze and comprehend the visual information embedded in videos, facilitating the efficient organization, retrieval, and interpretation of extensive video collections. The integration of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks has brought about a revolution in video classification. This fusion effectively captures both spatial and temporal dependencies within video sequences, leveraging the strengths of CNNs in extracting spatial features and LSTMs in modeling sequential and temporal information. ConvLSTM and LRCN (Long-term Recurrent Convolutional Networks) are two widely embraced architectures that embody this fusion. This paper seeks to investigate the impact of convolutions on LSTM networks in the context of video classification, aiming to compare the performance of ConvLSTM and LRCN.

By Manal Benzyane, Mourade Azrour, Imad Zeroual, Said Agoujil

The main objective of this research was to determine the relationship between pedagogical management (PM) and directive leadership (ML). The research was conducted with a quantitative approach, basic type, correlational level, non-experimental design, cross-sectional and hypothetical deductive method. Non-probabilistic convenience sampling was applied considering a sample made up of 60 teachers from secondary level educational institutions of Network 6, UGEL 06, of the Ate district. The validity of the expert judgment and the confirmation of the reliability were fulfilled through Cronbach's Alpha (pedagogical management = 0.974 and directive leadership = 0.909). The survey technique was used and through two instruments (questionnaires) the data were collected via Google forms. The results obtained were (P = 0.000, Rho = 0.586), it is concluded that there is a moderate positive significant correlation between the study variables.

By Nerio Enriquez-Gavilan, Yolanda Yauri-Paquiyauri, Brian Meneses-Claudio, Aydeé Lopez-Curasma, Julio Romero-Sandoval

Introduction: This research was carried out on the topic: “Balanced scorecard in the business development of MSMEs in the district of San Vicente de Cañete, 2021”.
Objective: Determine the influence of the balanced scorecard on the business development of MSMEs in the province of Cañete, 2021, so that the balanced scorecard indicators are considered the choice of MSMEs for an improvement in decision making.
Method: In this scenario, an applied methodology was developed, with a quantitative approach, a non-experimental, transversal and correlational design. A questionnaire was used as a survey of 68 managers of MSMEs, which was made up of 23 questions on a Likert scale, these being validated with expert judgment.
Results: The results achieved allowed us to confirm the hypotheses raised that the balanced scorecard has a significant influence on the business development of MSMEs in the province of Cañete, 2021, as well as the secondary hypotheses were confirmed.
Conclusions: The same ones that stated that the balanced scorecard significantly influences economic profitability, product quality, resource optimization and innovation in MSMEs in the province. de Cañete, 2021.

By Rayda Villalobos-Castro , Segundo Ríos-Ríos, Fernando Ochoa-Paredes, Miguel Vargas-Tasayco, Yrene Uribe-Hernandez

In 1952, Alan Turing who is considered as a father of Computer Science, based on his previous scientific research on the theory of computation, he emphasized how important is the analysis of pattern formation in nature and developed a theory. In his theory, he described specific patterns in nature that could be formed from basic chemical systems. Turing in his previous studies in the theory of computation, he had constantly worked on symmetrical patterns that could be formed simultaneously and realized the necessity for further analysis of pattern formation in biological problems. However, it was until the late 1960s, when John Conway was the first to introduce the "Game of Life", an innovative mathematical game based on cellular automata, having a purpose to utilize the fundamental entities, called as cells, in two possible states described as "dead" or "alive". This paper tries to contribute to a better understanding of the "Game of Life" by implementing algorithmic approaches of this problem in PASCAL and Python programming languages. Also, inside the paper numerous variations and extensions of the Conway's Game of Life are proposed that introduce new ideas and concepts. Furthermore, several machine learning algorithms to learn patterns from large sets of Game of Life simulations and generate new rules or strategies are described in detail.

By Serafeim A. Triantafyllou

Predicting fetal and maternal electrocardiograms (ECGs) is crucial in advanced prenatal monitoring. In this study, we explore the effectiveness of Convolutional Neural Networks (CNNs), using a carefully developed methodology to predict the category of fetal (F) or maternal (M) ECGs. In the first part, we trained a CNN model to predict fetal and maternal ECG images. In the following sections, the study results will be revealed. The CNN model demonstrated its ability to effectively discriminate between fetal and maternal patterns using automatically learned features.

By Mohammed Moutaib, Mohammed Fattah, Yousef Farhaoui, Badraddine Aghoutane, Moulhime El Bekkali

The main economic powers are focusing on a sustainable economic recovery following the crises triggered by systemic risks. In this context of global renewal, the opportunity arises to promote long-term collective goals and avoid unsustainable setbacks in the social, economic, and environmental realms. This article aims to conduct a critical analysis of the scientific production on country branding and its contribution to sustainable development. From 1991 to 2023, there is an interesting scientific contribution from researchers worldwide, although the years 2022 and 2023 lack production. Through scientometrics analysis using data from Web of Science (JCR and ESCI), 103 articles were identified in the knowledge categories "Country Brand" and "Sustainable Development." Laws such as Price, Zipf, Lotka, Bradford, and the Hirsch index were applied. The results reveal contributions from authors and institutions at a global level, highlighting the international relevance of the subject. Global precedents in country branding research are emphasized, aiming to establish a connection between this field and the sustainable development of nations. With this article, the authors seek to rekindle interest in this theme, promoting a comprehensive approach to the sustainable future of nations.
Keywords: Country brand, sustainable development, brand image, brand value.

By Ángel Acevedo-Duque, Agustín Álvarez-Herranz, Enrique Marinao-Artigas

In an environment full of competitiveness, data is a valuable asset for any company looking to grow. It represents a real competitive economic and strategic lever. The most reputable companies are not only concerned with collecting data from heterogeneous data sources, but also with analyzing and transforming these datasets into better decision-making. In this context, the data lake continues to be a powerful solution for storing large amounts of data and providing data analytics for decision support. In this paper, we examine the intelligent data lake management system that addresses the drawbacks of traditional business intelligence, which is no longer capable of handling data-driven demands. Data lakes are highly suitable for analyzing data from a variety of sources, particularly when data cleaning is time-consuming. However, ingesting heterogeneous data sources without any schema represents a major issue, and a data lake can easily turn into a data swamp. In this study, we implement the LDA topic model for managing the storage, processing, analysis, and visualization of big data. To assess the usefulness of our proposal, we evaluated its performance based on the topic coherence metric. The results of these experiments showed our approach to be more accurate on the tested datasets.

By Amine El Haddadi, Oumaima El Haddadi, Mohamed Cherradi, Fadwa Bouhafer, Anass El Haddadi, Ahmed El Allaoui

Business intelligence (BI) has long been a crucial factor in bolstering organizational competitiveness, offering strategic insights that shape decision-making and propel business expansion. The advent of cloud computing has further amplified data sharing and collaboration. This study advocates for the adoption of Cloud BI as an innovative tool to bolster the economic growth of small- and medium-sized enterprises (SMEs) in Morocco. We emphasize the interconnectedness of these businesses' performance with the overall well-being of the Moroccan economy, underscoring the need for regulatory bodies to prioritize not only financial support but also a keen focus on technological advancements. We explore how technological integration can enhance the competitive edge of SMEs. Finally, we conclude by presenting a framework that incorporates the migration of BI to the cloud within the realm of Cloud BI. Drawing inspiration from prior research, we propose modifications tailored to address the specific concerns of SMEs in embracing cloud BI technology.

By Najia Khouibiri, Yousef Farhaoui

Introduction: The document presents a comprehensive review of the utilization of Artificial Intelligence (AI) in personalized learning within the educational context. The study aims to investigate the various approaches to using ML algorithms for personalizing educational content, the impact and implications of these approaches on student performance, and the challenges and limitations associated with AI in personalized learning. The research questions are structured around these three broad areas, focusing on the AI methods used in education, their impact on students' academic outcomes, and the challenges and limitations associated with AI.

By Zainab Rasheed, Sameh Ghwanmeh, Abedallah Zaid Abualkishik

In this article, we propose a method that helps us to analyze the homogeneity of gray levels locally by calculating a coefficient for each pixel based on the nature of neighboring pixels. This principle of encoding pixels according to their adjacent neighbors is described the nature of the distribution of gray levels within the image and measures their degree of homogeneity locally. This allows us to detect the different regions of the image and their contours based on the coefficient of homogeneity of the gray levels. In addition, this allows us to exploit these homogeneity coefficients to restructure regions of the image, extract and enhance the image contours while reducing the noise present in the image. This homogeneity study principle has several functions in the study and analysis of image texture, as do other methods of homogeneity assessment, such as the local contrast descriptor (LCD) and the co-occurrence matrix.

By Abdelhamid El Beghdadi, Mohammed Merzougui, Ahmad El Allaoui

Introduction: science and scientific production are spaces given to men for centuries, although in the 21st century, there are gaps in this sense.
Aim: to describe from a gender perspective the scientific production in the academic journals of Cuban medical universities, period 2017-2021.
Method: a descriptive bibliometric study was carried out. The universe consisted of all the academic journals of Cuban medical universities in the period 2017-2021, they were reviewed between December 2022 and March 2023. The sampling was non-probabilistic and the sample was 19. Only regular numbers were considered.
Results: women were 19% of the magazine directors and 44% of the editorial board composition. 57.5% of the editorials were written by men. 2.6% of the articles dealt with the gender approach. 61.8% of the authors were women and 38.2% were men. Women were 55.7% of the lead authors. In 32.3% of the articles, women predominated as authors and 23.7% were written only by women; gender equality represented 14.1%.
Conclusions: an empowerment of women in Cuba is evident as they are the majority within scientific production in medical universities. However, this is not reflected in the direction of the magazines and the composition of their editorial committees, where men predominate, nor in publications with a gender focus, which is still insufficient.

By Yamilé Rodríguez Sotomayor, Lee Yang Díaz-Chieng, Luis Ernesto Paz Enrique, Hilda Lidia Iznaga Brooks, Katsuyori Pérez Mola, Jimmy Javier Calás Torres

This article examines the trend around the adoption of machine learning in the hotel business in light of the significance of new technologies. According to previous research, the hospitality industry uses a variety of chemicals for cleaning. Cleaning supplies are the housekeeping department's primary tool in their daily routine to keep rooms and common areas clean and tidy. Guest and staff don't know the harmfulness of these chemicals. Providing hospitality that meets the needs of guests requires not only a positive attitude, but also high-quality and excellent services that keep guests warm, relaxed, and comfortable. But in some incidents, we find that the guest and staff health is affected by the chemicals. Also, no one worked on predicting the chemical's effects on staff and guest health in the hospitality sector with the use of Machine Learning models. For this purpose, data is collected from different hotels of Delhi NCR in India. There were two distinct fields utilized for assessment and instruction. For the investigation, machine learning methods were employed. The research project employed five machine learning methods. The newly developed MHC-CNN algorithm achieved the highest accuracy (93.75) in comparison to other cutting-edge machine learning techniques. The created technique can be expanded upon and applied in many hotels all around the world.

By Mr. Rohit, Kapil Sethi, Mudassir Khan, Ashish Raina

Arabic character identification presents a significant obstacle to the comprehension and analysis of Arabic text. This paper presents an improved technique that generates Freeman code from handwritten Arabic characters. This code provides the shortest code length without losing character information, accounting for all handwritten Arabic character variants. We tested this code using a set of Arabic characters in various formats to identify Arabic characters in order to take use of the code generated by our enhanced method. We also performed a comparison between our Freeman code and codes generated in other related research. In light of this, the code that we obtained correctly represents the Arabic letter in all of its variants, including the ones that the algorithms in previous publications did not consider. Consequently, our novel method based on Freeman coding represents a significant advancement in Arabic character recognition. Furthermore, our method provides a successful way of identifying and presenting Arabic characters.

By Mohamed Rida Fethi, Othmane Farhaoui, Imad Zeroual, Ahmad El Allaoui

Introduction: The thesis “Strategic planning and organizational culture of Bodega y Viñedos Santa María S.A.C. of the district of Lunahuaná - Cañete 2021” highlights that strategic planning is a structured process through which an organization considers where it wants to go and how it is going to achieve it. Likewise, organizational culture is that culture (set of values, beliefs, and other representative characteristics of a group of people) that are reflected within an organization and that identify it as such.
Objective: Determine how strategic planning is associated with the organizational culture of Bodega y Viñedos Santa María S.A.C. of the district of Lunahuaná - Cañete 2021.
Method: Research with a quantitative approach, basic type, non-experimental cross-sectional design, and correlational level. The population was made up of 10 people from the company and the research sample was made up of the entire population. Two surveys were used: strategic planning and organizational culture, composed of 28 and 60 questions for each instrument, respectively, which were validated by expert judgment and reliability through Cronbach's alpha, respecting ethical considerations.
Results: The results obtained consider that strategic planning is significantly associated with the organizational culture of Bodega y Viñedos Santa María S.A.C. of the district of Lunahuaná - Cañete 2021, because Spearman's Rho statistical test is 0.764, which according to the correlation analysis table is considered a very strong positive correlation. In relation to the dimensions: involvement, consistency, adaptability, and mission, with strategic planning it was found that they have a significant association.
Conclusions: In conclusion, there is an association between both variables. Therefore, the implementation of strategic planning in micro and small businesses (MYPE) establishes the procedure to follow and the appropriate organizational culture contributes to the fulfillment of what is planned, allowing the continuous improvement of the company and the scope of business success.

By Ana Chaman-Bardalez, Alberto Ramón-Osorio, Segundo Ríos-Ríos, Miguel Vargas-Tasayco, Yrene Uribe-Hernandez

Photovoltaic systems play a pivotal role in renewable energy initiatives. To enhance the efficiency of solar panels amid changing environmental conditions, effective Maximum Power Point Tracking (MPPT) is essential. This study introduces an innovative control approach based on an Artificial Neural Network (ANN) controller tailored for photovoltaic systems. The aim is to elevate the precision and adaptability of MPPT, thereby improving solar energy harvesting. This research integrated an ANN controller into a photovoltaic system in order dynamically optimize the operating point of solar panels in response to environmental changes. The performance of the ANN controller was compared with traditional MPPT approaches using simulation in Simulink/Matlab. The results of the simulation showed that the ANN controller performed better than the traditional MPPT techniques, highlighting the effectiveness of this method for dynamically changing solar panel performance. The ANN particularly demonstrates higher precision and adaptability when environmental conditions vary. The strategy consistently achieves and maintains the maximum power point, enhancing overall energy harvesting efficiency. The integration of an ANN controller marks a significant advance in solar energy control. The study highlights the superiority of the ANN controller through rigorous simulations, demonstrating increased accuracy and adaptability. This approach not only proves effective, but also has the potential to outperform other MPPT strategies in terms of stability and responsiveness.

By BENCHIKH Salma , JAROU Tarik, BOUTAHIR Mohamed Khalifa, NASRI Elmehdi, LAMRANI Roa

In this article, we realize the epistemological weakness of pedagogy, after reviewing the specialized bibliography in which there is a very wide spectrum of references with respect to its nature, from those that justify and proclaim its scientific character, to the positions that deny it. For a discipline to be recognized as scientific, it is necessary that the body of knowledge that constitutes it has been produced with the application of the scientific method, so that the hypotheses it supports have passed the test of falsity or some other test, such as verifiability or corroboration that allows it to be distinguished from what is not science. The central hypotheses of pedagogy have not passed these requirements, so it is not possible to defend their scientific character. On the other hand, the terminological and conceptual proliferation that we have found, highlights the weakness of its epistemological bases.

By Elias Mejia-Mejia, Francis Díaz-Flores, Brian Meneses-Claudio

Plant diseases appear to be posing a serious danger to the production and availability of food globally. The main factor affecting the quality and productivity of agricultural products is the health of the plants. In this paper, we describe a modified plant disease detection using deep convolutional neural networks in real time. By employing image processing techniques to enlarge the plant illness photos, the plant disease sets of data were initially produced. To recognise plant illnesses, a system called Convolutional Neural Network combined with Wolf Optimisation algorithm (CNN-WO) was used. Finally, the Whale Optimization algorithm (WO) is used to maximise and optimizes getting input. And it is given to CNN's learning rate for classification process. This paper presents an image segmentation and classification technique to automatically identify plant leaf diseases. The suggested strategy increased accuracy, sensitivity, precision, F1 measure, and specificity of plant disease detection. According to this study, HCNNWO real detectors have improved, which would require deep learning. It would be an effective method for determining plant illnesses and other diseases within plants. According to the evaluation report, the suggested method offers good reliability. To evaluate how well the suggested algorithm performs in comparison to cutting-edge techniques such as SVM, BPNN and CNN, experiments are conducted on datasets that are openly accessible.

By E.Banu, Dr.A.Geetha

The Internet of Things (IoT) is a decentralized network of physically connected devices that communicate with other systems and devices over the internet. As the number of IoT-based devices continues to grow at an exponential rate, this technology has the potential to improve nearly every aspect of daily life, from smart networks and transportation to home automation and agriculture. However, the absence of adequate security measures on all levels of the IoT poses a significant security risk, with the potential for cyber-attacks and data theft. While scholars have suggested various security measures, there are still gaps that need to be addressed. In this study, we analyzed previous research and proposed metamodels for security, IoT, and machine learning. We then proposed a new IoT-based smart agriculture model with integrated security measures to mitigate cyber- attacks and increase agricultural output. Our model takes into account the unique features of the smart farming domain and offers a framework for securing IoT devices in this specific application area. Moreover, in order to mitigate a range of cyber security attacks across various layers of IoT, we introduced two certificate-based schemes named CBHA and SCKA for smart agriculture. A comparative analysis of their security with existing literature demonstrates their superior robustness against diverse attacks. Additionally, security testing utilizing scyther affirms the resilience and security of both CBHA and SCKA, establishing them as viable options for ensuring security in smart agriculture.

By Khaoula Taji, Badr Elkhalyly, Yassine Taleb Ahmad, Ilyas Ghanimi, Fadoua Ghanimi

Introduction: The agricultural sector in Peru has witnessed a notable increase in the production of oranges, which has promoted the essential use of convolutional neural networks (CNN). The ability to interpret images by visual artificial intelligence has been fundamental for the analysis and processing of these images, especially in the detection and classification of fruits, standing out in the specific case of oranges.
Objective: Conduct a systematic literature review (RSL) to evaluate the neural networks used in the classification of oranges in Peru.
Method: An RSL was carried out using the PICO strategy to search the Scopus database. The selection criteria included studies that used convolutional neural networks to classify the quality status of oranges in the Peruvian context.
Results: All the studies reviewed were based on the use of convolutional neural networks (CNN) for fruit classification, using various architectures and techniques. Some studies focused on a single specific fruit, while others addressed the classification of multiple types of fruits, highlighting the importance of the number and variety of images for training the networks.
Conclusions: Convolutional neural networks show effectiveness in orange classification, but the quality of the images and the variety of data are essential to improve accuracy.

By Franklin Moza-Villalobos, Juan Natividad-Villanueva, Brian Meneses-Claudio

Introduction: Motivation is fundamental in this research that was carried out in the province of Cañete, Lima-Peru, with the purpose of finalizing the capacity for technological innovation that influences the level of entrepreneurship of the students of the Faculty of Business Sciences of the professional school of administration and accounting, knowing their skills and experiences when having or carrying out business ideas and the active participation of students in an incubator.
Objective: This research seeks to analyze students in terms of their entrepreneurial skills and abilities in addition to identifying the role that teachers play in terms of encouraging and encouraging initiative behavior towards creativity and entrepreneurship.
Method: This research work is quasi-experimental; it is to determine how the capacity for technological innovation influences the level of entrepreneurship of students at the National University of Cañete. Manage the type of control and compare with the experimental one. We worked with a population of 200 students, of which the questionnaire was applied to a sample of 80 students with 20 Likert-type items, handling 3 important dimensions with the independent variable.
Results: There are 6 dimensions of which the high level of responses is managed (83.8%), being effective, presenting a better result in the experimental than the control with (41.3%). The limitations of the students focus on the level of entrepreneurship, the skills and attitudes of (88.8%), entrepreneurial capacity of (87.5%) and entrepreneurial experience of (81.3) in their levels of effectiveness.
Conclusions: In conclusion, this research carried out in the province of Cañete, Lima-Peru, has shown that the capacity for technological innovation has a significant impact on the level of entrepreneurship of the students of the faculty of business sciences of the professional school of administration and accounting from the National University of Cañete. The findings highlight the importance of integrating technological innovation and entrepreneurship in higher education to prepare students for the business world and foster entrepreneurship in the next generation of professionals.

By Luz Martínez-Ríos, Jorge Franco-Medina, Segundo Ríos-Ríos , Hugo Morán-Requena , Fernando Ochoa-Paredes , Yrene Uribe-Hernandez

Introduction: As technology usage grows at an exponential rate, cybersecurity has become a primary concern. Cyber threats have become increasingly advanced and specific, posing a severe risk to individuals, businesses, and even governments. The growing complexity and sophistication of cyber-attacks are posing serious challenges to traditional cybersecurity methods. As a result, machine learning (ML) techniques have emerged as a promising solution for detecting and preventing these attacks.
Aim: This research paper offers an extensive examination of diverse machine learning algorithms that have the potential to enhance the intelligence and overall functionality of applications.
Methods: The main focus of this study is to present the core principles of distinct machine learning methods and demonstrate their versatile applications in various practical fields such as cybersecurity systems, smart cities, healthcare, e-commerce, and agriculture. By exploring these applications, this paper contributes to the understanding of how machine learning techniques can be effectively employed across different domains. The article then explores the current and future prospects of ML in cybersecurity.
Results: This paper highlights the growing importance of ML in cybersecurity and the increasing demand for skilled professionals who can develop and implement ML-based solutions.
Conclusion: Overall, the present article presents a thorough examination of the role of machine learning (ML) in cybersecurity, as well as its current and future prospects. It can be a valuable source of information for researchers, who seek to grasp the potential of ML in enhancing cybersecurity.

By Md Alimul Haque, Sultan Ahmad, Deepa Sonal, Hikmat A. M. Abdeljaber, B.K.Mishra, A.E.M. Eljialy, Sultan Alanazi, Jabeen Nazeer

The healthcare sector plays a pivotal role in both generating and relying on vast amounts of data, emphasizing the significance of collecting, managing, and sharing information. Technological advancements have facilitated the transformation of healthcare data into electronic health records (EHRs). These digital records are disseminated among various stakeholders, including patients, healthcare professionals, providers, insurance companies, and pharmacies. Given the sensitivity of healthcare information, the assimilation of new technologies is paramount. Blockchain technology, with its immutable nature and decentralized features, has emerged as a promising solution to instigate changes in the healthcare system. In the healthcare domain, where confidentiality is crucial, strict regulations are in place to safeguard patient privacy. Frameworks like the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) are designed to mitigate the risks associated with health data breaches. Although blockchain's characteristics, such as enhanced interoperability, anonymity, and access control, can improve the overall landscape of health data management, it is imperative for blockchain applications to adhere to existing regulatory frameworks for practical implementation. This paper delves into the examination of the compliance of blockchain-based EHR systems with regulations like HIPAA and GDPR. Additionally, it introduces a Blockchain-based EHR model specifically crafted to seamlessly align with regulatory requirements, ensuring its viability and effectiveness in real-world scenarios.

By Nehal Ettaloui, Sara Arezki, Taoufiq Gadi

Text-to-speech conversion can be done with two approaches: dictionary-based (database) approach and grapheme-to-phoneme (G2P) mapping. One of the drawbacks of this approach is its performance depends on the size of the dictionary or database. In the case of domain specific conversion, a simple rule -based technique is used to play pre-recorded audio for each equivalent token. It is easy to design but its limitation is mapping with the sound database and availability of the audio file in the database. In general, grapheme to phoneme conversion can be used in any domain. Advantages are the limited size of the database required, ease of mapping and compliance with domain. However, G2P suffers from pronounce ambiguity (formation of audio output). This paper will discuss about the grapheme-to -phoneme mapping and its application in text to speech conversion system. In this work, Assamese (an Indian scheduled Unicode language) is used as the experimental language and its performance is analysis with another Unicode language (Hindi). English (ASCII) language will be used as a benchmark to compare with the target language.

By Chandamita Nath, Bhairab Sarma

In recent years, the demand for solar energy has increased considerably. This growing demand has created a corresponding need for solar panel systems that not only demonstrate efficiency, but also guarantee reliability. However, The per-formance and durability of solar panels can be significantly affected by diverse faults such as surface defects, cracks, hot spots and accumulations of dust. Thus, early detection is crucial to ensure optimal operation of solar panels. In this study, we propose an intelligent system for detecting surface defects on solar panels us-ing the Visual Geometry Group (VGG) models. A camera is utilized to capture images of solar panels in both normal and defective states, these images are sub-sequently fed into the trained VGG model, which analyzes and processes them to identify defects on the surface of the solar panel. The experimental results show that the VGG19 model outperforms the VGG16 model in detecting faulty solar panels. VGG19 achieved a precision of 80%, a recall of 1, and an F1 score of 89%, while VGG16 achieved a precision of 79%, a recall of 92%, and an F1 score of 85%. Furthermore, the system demonstrated a high accuracy for the VGG19 in detecting surface panels in their normal state, while for the VGG16 it only achieved 90%. The results demonstrate the ability of the VGG19 model to detect surface defects on solar panels based on visual analysis.

By Naima El Yanboiy, Mohamed Khala, Ismail Elabbassi, Nourddine Elhajrat, Omar Eloutassi, Youssef El Hassouani, Choukri Messaoudi

In this paper, we aim to provide a comprehensive analysis of image classification, specifically in the context of emergency vehicle classification. We have conducted an in-depth investigation, exploring the effectiveness of six pre-trained Convolutional Neural Network (CNN) models. These models, namely VGG19, VGG16, MobileNetV3Large, MobileNetV3Small, MobileNetV2, and MobileNetV1, have been thoroughly examined and evaluated within the domain of emergency vehicle classification. The research methodology utilized in this study is carefully designed with a systematic approach. It includes the thorough preparation of datasets, deliberate modifications to the model architecture, careful selection of layer operations, and fine-tuning of the model compilation. To gain a comprehensive understanding of the performance, we conducted a detailed series of experiments. We analyzed nuanced performance metrics such as accuracy, loss, and training time, considering important factors in the evaluation process. The results obtained from this study provide a comprehensive understanding of the advantages and disadvantages of each model. Moreover, they emphasize the crucial significance of carefully choosing a suitable pre-trained Convolutional Neural Network (CNN) model for image classification tasks. Essentially, this article provides a comprehensive overview of image classification, highlighting the crucial significance of pre-trained CNN models in achieving precise outcomes, especially in the demanding field of emergency vehicle classification.

By Ali Omari Alaoui, Omaima El Bahi, Mohamed Rida Fethi, Othmane Farhaoui, Ahmad El Allaoui, Yousef Farhaoui

Diabetes is a persistent medical condition that arises when the pancreas loses its ability to produce insulin or when the body is unable to utilize the insulin it generates effectively. In today's world, diabetes stands as one of the most prevalent and, unfortunately, one of the deadliest diseases due to certain complications. Timely detection of diabetes plays a crucial role in facilitating its treatment and preventing the disease from advancing further. In this study, we have developed a diabetes prediction model by leveraging a variety of machine learning classification algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, to determine which algorithm yields the most accurate predictive outcomes. we employed the famous PIMA Indians Diabetes dataset, comprising 768 instances with nine distinct feature attributes. The primary objective of this dataset is to ascertain whether a patient has diabetes based on specific diagnostic metrics included in the collection. In the process of preparing the data for analysis, we implemented a series of preprocessing steps. The evaluation of performance metrics in this study encompassed accuracy, precision, recall, and the F1 score. The results from our experiments indicate that the K-nearest neighbors’ algorithm (KNN) surpasses other algorithms in effectively differentiating between individuals with diabetes and those without in the PIMA dataset.

By Mariame Oumoulylte, Abdelkhalak Bahri, Yousef Farhaoui, Ahmad El Allaoui

In Korea, aid projects to developing countries at central government level are increasing in number significantly every year, yet at local government level their scale is extremely small. Recognizing this problem, this study aims to set policy priorities to strengthen official development assistance (ODA) at local government level in Korea. Accordingly, we analysed the important issues relating to ODA projects at local government level by performing topic modelling analysis method. On the basis of these analysis results, policy priorities were derived using the AHP method. The analysis suggests that in Korea, in order to revitalize ODA projects at local government level, a dedicated department that can professionally handle these projects must be established within each local authority. Furthermore, it is important to recruit and deploy professional administrators who can utilize these dedicated departments to discover new ODA projects.

By Young-Chool Choi, Kim, Gamin, Jeon, Yunseo, Cavallini, Yona

Acute poisoning by pesticides in Morocco is an important public health issue, because the use of pesticides has become both massive and anarchic. This is the cause of deaths whose incidence is unfortunately increasing. Unfortunately, these deaths are not always accidental. Pesticides are also used as a means of suicide; according to the WHO, these are means suicide chemicals most used in the world, since, out of the 800,000 suicides recorded per year, more than a third are caused by this type of product. Even more serious, these suicides are currently being observed among children and teenagers. Faced with this alarming figure, and in order to prevent deaths and improve emergency treatment of cases of pesticide poisoning, it becomes important to use the potential of artificial intelligence in the treatment of these admissions. Our approach is essentially based on machine learning algorithms, including decision support software capable of predicting, based on major clinical signs, the most likely pesticide responsible of the intoxication in the triage room. This, before moving on to the confirmation stage based on biological and toxicological investigations, which are often costly and time-consuming.

By Rajae Ghanimi, Fadoua Ghanimi, Ilyas Ghanimi, Abdelmajid Soulaymani

Introduction: Educational landscapes have been transformed by technological advancements, enabling adaptive and flexible learning through AI-based and decision-oriented adaptive learning systems. The increasing importance of this solutions is underscored by the pivotal role of the learner model, representing the core of the teaching-learning dynamic. This model, encompassing qualities, knowledge, abilities, behaviors, preferences, and unique distinctions, plays a crucial role in customizing the learning experience. It influences decisions related to learning materials, teaching strategies, and presentation styles. Objective: This study meets the need for applying AI-driven adaptive learning in education, implementing a novel method that uses self-esteem (ES), emotional intelligence (EQ), and demographic data to predict student performance and adjust the learning process. Methods: Our study involved collecting and processing data, constructing a predictive machine learning model, implementing it as an online solution, and conducting an experimental study with 146 high school students in computer science and French as foreign language. The aim was to tailor the teaching-learning process to the learners' performance. Results: Significant correlations were observed between self-esteem, emotional intelligence, demographic data, and final grades. The predictive model demonstrated a 90% accuracy rate. In the experimental group, the results indicated higher scores, with an average of 15.78/20 compared to the control group's 12.53/20 in computer science. Similarly, in French as a foreign language, the experimental group achieved an average of 13.78/20, surpassing the control group's 10.47/20. Conclusion: The achieved results motivate the creation of a multifactorial AI-driven adaptive learning platform. Recognizing the necessity for improvement, we aim to refine the predicted performance score through the incorporation of a diagnostic evaluation, ensuring an optimal grouping of learners.

By Aymane Ezzaim, Aziz Dahbi, Abdelfatteh Haidine, Abdelhak Aqqal

This article introduces a methodology for constructing a trained dataset to facilitate Tifinagh script recognition using the Tesseract OCR engine. The Tifinagh script, widely used in North Africa, poses a challenge due to the lack of built-in recognition capabilities in Tesseract. To overcome this limitation, our approach focuses on image generation, box generation, manual editing, charset extraction, and dataset compilation. By leveraging Python scripting, specialized software tools, and Tesseract's training utilities, we systematically create a comprehensive dataset for Tifinagh script recognition. The dataset enables the training and evaluation of machine learning models, leading to accurate character recognition. Experimental results demonstrate high accuracy, precision, recall, and F1 score, affirming the effectiveness of the dataset and its potential for practical applications. The results highlight the robustness of the OCR system, achieving an outstanding accuracy rate of 99.97%. The discussion underscores its superior performance in Tifinagh character recognition, exceeding the findings in the field. This methodology contributes significantly to enhancing OCR technology capabilities and encourages further research in Tifinagh script recognition, unlocking the wealth of information contained in Tifinagh documents.

By Ali Benaissa, Abdelkhalak Bahri, Ahmad El Allaoui, My Abdelouahab Salahddine

This paper aims to conceptualize, design, and implement a Data Mining (DM) system integrated with machine learning within the realm of school management. The primary objective is to support the educational community and decision-makers in addressing the issue of school dropout and enhancing success rates at the certificate levels in Morocco, specifically focusing on the bachelor's degree examination in the qualifying cycle. The proposed system categorizes students five months prior to the exam date, facilitating targeted academic interventions for those at risk of course repetition or discontinuation. The DM system, operational throughout the school year, enhances the precision and effectiveness of schools and provincial administrations by identifying areas requiring additional support to improve end-of-year success rates and student performance. Project development is rooted in the collection and analysis of existing data from various departmental information systems, utilizing classification and regression algorithms to predict learner performance, success rates, and overall outcomes at the conclusion of certificate levels.

By Mohamed Sabiri, Yousef Farhaoui, Agoujil Said

In the twenty-first century, Collective intelligence (CI) arose as a social phenomenon to assist organizations in managing future uncertainty. It pushes a broad diverse group to come up with new solutions that outperform those uncovered within the organization itself. Accordingly, CI has been widely acknowledged as a means to foster innovation, and develop, and sustain an organization's creative potential. This paper aims to conduct a literature review to examine the existing body of literature regarding the ways collective intelligence improves innovation. The findings emphasized the importance of collective intelligence in fueling a firm’s knowledge and innovation in all of its forms to overcome public and private organizational challenges. Furthermore, our review underlined the mediating role of information technology in taking full advantage of collective intelligence via digital platforms. In addition, our analysis pointed out the multifaceted traits of collective intelligence as reflected in the literature under several terms, including crowdsourcing. Our research revealed several gaps in the current literature, including insufficient analysis and modeling of the relationship between the two concepts. Finally, we concluded our paper by identifying the limits of our research and suggesting avenues for future studies on collective intelligence and innovation.

By Ghita Ibrahimi, Wijdane Merioumi, Bouchra Benchekroun

Skin cancer represents one of the primary forms of cancer arising from various dermatological disorders. It can be further categorized based on morphological characteristics, coloration, structure, and texture. Given the rising incidence of skin cancer, its significant mortality rates, and the substantial costs associated with medical treatment, the imperative lies in early detection to promptly diagnose symptoms and initiate appropriate interventions. Traditionally, skin cancer diagnosis and detection involve manual screening and visual examination conducted by dermatologists. these techniques are complex, error-prone, and time-consuming. Machine learning algorithms, particularly deep learning approaches, have been applied to analyze images of skin lesions, detect potential cancerous growths, and provide predictions regarding the likelihood of malignancy. In this paper, we have developed an optimized deep convolutional neural network (DCNN) specifically tailored for classifying skin lesions into benign and malignant categories. Thereby, enhancing the precision of disease diagnosis. Our study encompassed the utilization of a dataset comprising 3,297 dermoscopic images. To enhance the model's performance, we applied rigorous data preprocessing techniques and softmax activation algorithms. The suggested approach employs multiple optimizers, including Adam, RMSProp, and SGD, all configured with a learning rate of 0.0001. The outcomes of our experiments reveal that the Adam optimizer outperforms the others in distinguishing benign and malignant skin lesions within the ISIC dataset, boasting an accuracy score of 84%, a loss rate of 32%, a recall rating of 85%, a precision score of 85%, a f1-score of 85%, and a ROC-AUC of 83%.

By Mariame Oumoulylte, Ali Omari Alaoui, Yousef Farhaoui, Ahmad El Allaoui, Abdelkhalak Bahri

In the context of digital transformation, to assess the current state of manufacturing companies, a readiness model is proposed in this paper. Using a literature review and a framework considering maturity as an 'input' enabler and not as an 'output'. Three dimensions are considered in this model (Organization maturity, Technology maturity, and Process Maturity), to assess the company readiness (Ready or Not ready). Allowing compagnies to identify their readiness for Industry 4.0 (I4.0) adoption, by developing a decision support model, is the goal of this research. This model based on Fuzzy Inference System, considers the three decision criteria and then ranks the enterprise according to its output indicator. For the validation of this proposed model, an experimental study was conducted to assess the readiness of 2 manufacturing companies, a multinational in automotive sector and an SME in Apparel sector. The proposed model meets the desired objective and is therefore retained for the evaluation of the readiness to I4.0 in different manufacturing contexts.

By Younes JAMOULI, Samir TETOUANI, Omar CHERKAOUI, Aziz SOULHI

This study explores an innovative approach to anomaly severity classification within the realm of solar power optimization. Leveraging established machine learning algorithms—including Isolation Forest (IF), Local Outlier Factor (LOF), and Principal Component Analysis (PCA)—we introduce a novel framework marked by dynamic threshold fine-tuning. This adaptive paradigm aims to refine the accuracy of anomaly classification under varying environmental conditions, addressing factors such as dust storms and equipment irregularities. The research builds upon datasets derived from Errachidia, Morocco. Results underscore the effectiveness of dynamically adjusting severity thresholds in optimizing anomaly classification and subsequently improving the overall efficiency of solar power generation. The study not only reaffirms the robustness of the initial framework but also emphasizes the practical significance of fine-tuning anomaly severity classification for real-world applications in solar energy management. By providing a more nuanced perspective on anomaly detection, this research advances our understanding of the intricate precision required for optimal solar power generation efficiency. The findings contribute valuable insights into the broader field of machine learning applications in renewable energy, offering a pathway for the refinement of existing frameworks for enhanced sustainability and operational effectiveness.

By Mohamed Khalifa Boutahir, Abdelaaziz Hessane, Imane Lasri, Salma Benchikh, Yousef Farhaoui, Mourade Azrour

Diagnosing plant diseases is a challenging task due to the complex nature of plants and the visual similarities among different species. Timely identification and classification of these diseases are crucial to prevent their spread in crops. Convolutional Neural Networks (CNN) have emerged as an advanced technology for image identification in this domain. This study explores deep neural networks and machine learning techniques to diagnose plant diseases using images of affected plants, with a specific emphasis on developing a CNN model and highlighting the importance of hyperparameters for precise results. The research involves processes such as image preprocessing, feature extraction, and classification, along with a manual exploration of diverse hyperparameter settings to evaluate the performance of the proposed CNN model trained on an openly accessible dataset. The study compares customized CNN models for the classification of plant diseases, demonstrating the feasibility of disease classification and automatic identification through machine learning-based approaches. It specifically presents a CNN model and traditional machine learning methodologies for categorizing diseases in apple and maize leaves, utilizing a dataset comprising 7023 images divided into 8 categories. The evaluation criteria indicate that the CNN achieves an impressive accuracy of approximately 98.02%.

By Khaoula Taji , Fadoua Ghanimi

Emotion recognition being a complex task because of its valuable usages in critical fields like Robotics, human-computer interaction and mental health has recently gathered huge attention. The selection and optimization of suitable feature sets that can accurately capture the underlying emotional states is one of the critical challenges in Emotion Recognition. Metaheuristic optimization techniques have shown promise in addressing this challenge by efficiently exploring the large and complex feature space. This research paper proposes a novel framework for emotion recognition that uses metaheuristic optimization. The key idea behind metaheuristic optimization is to explore the search space in an intelligent way, by generating candidate solutions and iteratively improving them until an optimal or near-optimal solution is found. The accuracy & robustness of emotion identification systems can be enhanced by optimizing the metaheuristic optimization. The major contribution of this research is to develop a Chiropteran Mahi Metaheuristic optimization which emphasizes the weights updating in the classifier for improving the accuracy of the proposed system.

By Prachi Jain, Vinod Maan

Plant diseases are important factors because they significantly affect the quality, quantity, and yield of agricultural products. Therefore, it is important to detect and diagnose these diseases at an early stage. The overall objective of this study is to develop an acceptable deep learning model to correctly classify diseases on tomato leaves in RGB color images. To address this challenge, we use a new approach based on combining two deep learning models VGG16 and ResNet152v2 with transfer learning. The image dataset contains 55000 images of tomato leaves in 5 different classes, 4 diseases and one healthy class. The results of our experiment are promising and encouraging, showing that the proposed model achieves 99.08% accuracy in training, 97.66% in validation, and 99.0234% in testing

By Noredine Hajraoui, Mourade Azrour, Ahmad El Allaoui

Paulo Freire's dialogical pedagogy provides teachers with a framework for their professional practice, offering educators strategies for teaching and learning. The main objective of this research is to determine the contributions of Paulo Freire's dialogical pedagogy to teaching praxis from bibliometric analysis, in terms of increasing impact and incidence in educational processes, knowing its structure, production, and utilization of information for pedagogical practices. A descriptive bibliometric study in Scopus database was conducted, applying a technique of exploratory and descriptive bibliographic document collection to analyze research related to the research topics. A total of 781 documents were retrieved from the Scopus database on the topic under study, of which 32,5% were open access, involving 1317 authors, with an average of 8,1 citations per document (1,42 Field-Weighted Citation Impact). Original articles represented three-quarters of the total documents, indicating research with new contributions to knowledge, while 12.4% were book chapters and the remaining 11.8% were Reviews, Books, Conference Papers, Editorials, and Errata. The top 10 countries with the highest number of published documents in the research area are the United States, United Kingdom and Australia. The analysis carried out revealed that there is significant progress in the area of research related to dialogic pedagogy and its scientific evolution.

By Yanir Bayona Arévalo, Matilde Bolaño García

Introduction: cognitive accessibility is part of the general accessibility framework. Cognitive accessibility means that services are simple, consistent, clear, multimodal, error tolerant, and focused, with all users in mind.
Objectives: to validate a questionnaire on cognitive accessibility to be applied to health professionals.
Methods: The study is of a quantitative approach, with a non-experimental and cross-sectional design, developed between March and June 2022. The sample consisted of 130 health professionals from Argentina, selected through purposive sampling.
Results: the validation process was carried out in three stages. Internal consistency analysis (reliability) was performed using Cronbach's Alpha. The descriptive results with the 17 items showed a variance of 4.445 for each item, a total variance of 13.049, with a total Cronbach's Alpha of 0.701, indicating that the instrument presents internal consistency.
Conclusions: It was possible to verify that the scores of both Cronbach's Alpha and the factorial analysis allow us to affirm that the instrument has the necessary metric aspects to be used in future research, considering that it had a prior assessment by expert criteria. It can be assumed that this article becomes the starting point for future studies, in which it is intended to continue the line of research, which allows the analysis of cognitive accessibility in the context of health professionals.

By Sonia Castellanos, Claudia Figueroa

Introduction: data science careers are on the rise due to the growing demand for technical skills in this area. Data science careers focus on collecting, organizing, and analyzing data to identify patterns and trends, which allows organizations to make informed decisions and develop effective solutions.
Aim: to analyze the thematic specialization of institutions with academic programs in the area of data science.
Methods: The Scopus database was used to conduct a bibliometric analysis aimed at examining the thematic specialization of institutions with academic programs in the field of data science. SciVal, a bibliometric analysis tool, was employed to extract the relevant data. The study period ranged from 2012 to 2021.
Results: Nine higher education institutions were found to offer undergraduate or graduate degrees in the field of data science. There was no correlation found between RSI and Field-Weighted Citation Impact (r=0.05355; P=0.8912; 95% CI: -0.6331 to 0.6930). Therefore, it cannot be claimed that specialization in the subject area studied influences the greater impact of research. On the other hand, recent accreditation did not influence greater specialization (r=0.1675; P=0.6667; 95% CI: -0.5588 to 0.7484). Additionally, no differences were found regarding academic level.
Conclusions: The analysis of the thematic specialization of institutions with academic programs in the field of data science shows low scientific production in this field. Moreover, more than half of the analyzed higher education institutions have thematic specialization below the global average. This suggests that there is still a long way to go for these institutions to achieve adequate specialization and compete internationally in the field of data science.

By Denis Gonzalez-Argote

Introduction: Netnography is a research method that has emerged in response to the growing popularity of online communication and social networks.
Aim: To analyze communication patterns about netnography in the Scopus database.
Methods: A bibliometric study was conducted in the Scopus database on netnography. The analysis was conducted globally, by country, and by institution.
Results: A total of 11173 documents and 2213 authors were recovered. 35,1% of the documents were open access. The global field-weighted citation impact was 1,27. the most productive ones in the following order: United Kingdom (275 documents), United States (223 documents), Australia (165 documents), Brazil (100 documents), and France (83 documents).
Conclusions: The results show that netnography is an emerging area of research, with a wide geographic and thematic diversity, that has experienced steady growth in recent years and is being explored in a variety of contexts, from market research to the analysis of social dynamics online.

By Carlos Alberto Gómez Cano, Verenice Sánchez Castillo, Tulio Andrés Clavijo Gallego

Introduction: Higher education is undergoing significant changes due to the existence of agents promoting substantial changes in universities.
Objective: To evaluate the scientific production of Bolivian universities in the Scopus database through a bibliometric study.
Methods: A descriptive bibliometric study was conducted. The time period analyzed was documents with publication dates between January 1, 2000, and December 31, 2020.
Results: Private universities predominated in Bolivia with 54%, and the remaining 46% were public universities. The private university with the highest scientific production was the Universidad Católica San Pablo with 5.3%, followed by the Universidad Privada de Bolivia with 4.4%. Most articles were original, with 1,797 documents, 43,141 citations, and 24.01 citations per document, followed by conference articles.
Conclusions: The scientific production of Bolivian universities in Scopus during the 2000-2020 period had much lower indicators than expected, with low productivity and scientific performance. The universities with the highest scientific production over the 20-year period were the Universidad Mayor de San Andrés, followed by the Universidad Mayor de San Simón. The number of published documents had a growth trend over time. Original articles predominated, and the predominant language of publication was English. According to the research area, the best indicators were in agriculture, social sciences, and medicine.

By Jhossmar Cristians Auza-Santiváñez, María Victoria Santivañez-Cabezas, Aaron Eduardo Carvajal Tapia, Boris Adolfo Llanos Torrico, Germán José Martín Rico Ramallo, Judith Marlene Aliaga Ramos

Introduction: the years 2020 and 2021 were characterized by the COVID-19 epidemic in Cuba, which caused the adaptation of academic courses, with the premise of making the training process more flexible, based on the suspension of face-to-face activities and the modification of the teaching curriculum.
Objective: to describe the state exam results during the epidemic period.
Methods. An observational, descriptive, retrospective study was carried out based on analyzing the promotion reports and the official models 36.19 and 36.20 of the Postgraduate Department, corresponding to 2020-2021.
Results: 173 residents took the state examination, 111 from medical specialties and 62 from stomatological specialties, with promotion of 100%. 49.7% obtained final grades above 95 points and 78.0% above 90 points in the state exam. The residents of the Dermatology and Intensive and Emergency Medicine specialties received the best teaching results.
Conclusions: The Faculty of Medical Sciences "Miguel Enríquez", during the epidemic period, graduated, with quality, all the residents who took the state exam.

By Daisy Bencomo-García, Lissette Cárdenas-de Baños, Niurka Hernández-Labrada, Jhossmar Cristians Auza Santivañez, Idrian García-García, Sergio González-García

Introduction: CONICET has been fundamental in the training of a large number of researchers and the promotion of science in Argentine society.
Objective: Describe the relative cost per published article and citation received for articles published by authors affiliated with CONICET.
Methods: A bibliometric study was carried out in which the scientific production of CONICET was analyzed in the Scopus database and the CONICET budget from 2016 to 2021.
Results: A decrease in the CONICET budget was observed, only recovering in the last year but without reaching the historical maximum studied. On the other hand, as previously mentioned, it was commented that the citations decreased despite the increase in the number of articles. Faced with this panorama, the theoretical cost of an article and that of a bibliographical citation can be presented. So, for example, for the year 2021, the cost of publishing an article was 41,014.09 USD, and the cost of a citation was 9,442.77 USD.
Conclusions: We cannot minimize the budgetary expenses of a government institution of thousands of workers to simple final products that are articles when in between are the expenses of salaries, awareness campaigns, building construction and its maintenance or things that have nothing to do with it. With science (or yes) how to pay the water bill of an institute; but if we can get closer to a theoretical cost of the articles and citations produced by Argentine scientists.

By Javier Gonzalez-Argote

Introduction: Implementing renal POCUS in critical care is a valuable tool complementing the physical examination of critical patients. As it is noninvasive, accessible, innocuous, and economical, it makes it possible to assess, at the bedside of patients, renal perfusion via ultrasound measurements such as the renal resistance index (RRI) and intrarenal venous Doppler (IRVD), which are considered early predictors of the acute renal lesion.
Goals: Determine the relationship between the renal resistance index (RRI) and the degree of acute renal lesion according to KDIGO in neurocritical patients. Correlate the alterations to intrarenal venous Doppler (IRVD) flow with the degree of the acute renal lesion, according to KDIGO.
Methods: An observational, analytical, prospective, longitudinal study was carried out in an ICU with an influx of neurocritical patients. Forty-three (43) patients participated. Their renal resistance index (RRI) and intrarenal venous Doppler (IRVD) were measured upon admission, 72 hours later, and 7 days after admission. Which of these tools better predicts acute renal lesions according to KDIGO was assessed.
Results: In the study with 43 critical patients, no significant correlation was found between the RRI value and the acute renal lesion, according to KDIGO. On the contrary, a significant relation was found between intrarenal venous Doppler (IRVD) upon admission, 72 hours later, and 7 days after admission with the acute renal lesion according to KDIGO, with a value of r: 43=0.95 (P=0.54); 0.49 (P=0.001); 0.58 (P=0.000). When analyzing via the classification tree, it was determined that the variables better predicting the risk of suffering from an acute renal lesion before its occurrence are the measurement of intrarenal venous Doppler (IRVD) 7 days after admission and the value of the accumulated water balance.
Conclusions: There is a positive and significant correlation between intrarenal venous Doppler (IRVD) and the acute renal lesion. Intrarenal venous Doppler (IRVD) and the accumulated water balance better predict the risk of suffering from an acute renal lesion in critical patients. In contrast, the renal resistance index (RRI) was unrelated to the acute renal lesion in the studied population.

By Jorge Márquez Molina, Jhossmar Cristians Auza-Santivañez, Edwin Cruz-Choquetopa, Jose Bernardo Antezana-Muñoz, Osman Arteaga Iriarte, Helen Fernández-Burgoa

Introduction: cancer is a disease caused by neoplastic cells that multiply uncontrollably, invading other tissues autonomously and at a distance. There are many types of cancer that can be prevented by avoiding certain risk factors.
Objective: to describe the epidemiological behavior of patients with cancer diagnosis in Guantanamo province in 2019.
Methods: an observational, descriptive and cross-sectional study was conducted in patients diagnosed with cancer in the province of Guantánamo, belonging to the country Cuba, during the year 2019. The universe was composed by the 1697 cases reported in that period. The variables age, sex, municipality and main location of the cancer were studied. The primary source of data was the Health Statistical Yearbook of Guantánamo Province.
Results: it was observed that the age group older than 60 years had the highest incidence, with 1176 patients, which represents 69,29 %. The male sex was the most representative, with 870 patients, equivalent to 51,26 %. Prostate cancer was the most prevalent cancer in the male population, with 220 patients, representing 25,28 %.
Conclusions: cancer is an important health problem for the Guantanamo population, especially in the age group over 60 years old. Male sex has a higher incidence, and prostate, breast and skin cancer are the most frequent in the population studied.

By Eduardo Enrique Chibas-Muñoz, Annier Jesús Fajardo-Quesada, Karina Vidal-Díaz, Nayaxi Reyes-Domínguez

The purpose of the study was to demonstrate the benefits of a model for scientific research in the sense that a construction system with a vegetated enclosure could benefit the internal environment of Juliaca in winter. To do this, we used an experimental procedure to compare the thermal resistance of a fifth-scale adobe high Andean house without vegetation and a house built in the climatic zone with vegetated facades. It simultaneously records the internal surface temperature, the internal air temperature, and the external environmental conditions. The results obtained show that the use of photosystems in buildings is an effective passive technique to reduce energy consumption due to its ability to insulate and protect internal thermal conditions.

By Alioska Jessica Martínez García, Yeny Roxana Estrada Cahuapaza, Grover Marín Mamani, Vitaliano Enríquez Mamani, Kely Lelia Cotacallapa Ochoa, Francisco Curro Pérez

Introduction: ultrasonography is a useful tool during cardiopulmonary resuscitation, however, its analysis from a bibliometric perspective is scarce.
Objective: to analyze the scientific production on ultrasound and cardiopulmonary resuscitation in the Scopus database in the period between 2012 and 2021.
Methods: a descriptive, retrospective, bibliometric study was conducted on the scientific production on ultrasound and cardiopulmonary resuscitation in journals indexed in Scopus. A Scopus search strategy was used to retrieve the records. The search period was established as 2012 - 2021.
Results: the years with the highest productivity were 2020 and 2021 (17.7% both). There was a predominance of original articles (75.1%). The largest number of documents corresponds to the area of Medicine (73.5%). The most productive countries were the United States (Ndoc=49) and China (Ndoc=17). The most productive journals were Critical Care Medicine and Resuscitation. There were 160 authors, 1 with 5 articles, 1 with 4 articles, 10 with 3 articles, 32 with 2 or 2 articles and the rest with 1 article.
Conclusions: there is a low scientific production on ultrasound and cardiopulmonary resuscitation in journals indexed in the Scopus database, characterized by the publication of original articles, Medicine as the area with the highest production and journals from developed countries as the most productive. Existence of a small group of very productive authors who publish on this topic.

By Guillermo Alejandro Herrera Horta, Reinolys Godínez Linares, Daniel Sánchez Robaina, Roxana de la Caridad Rodríguez León

Predicting Freshman student’s aptitude for computing is critical for researchers to understand the underlying aptitude for programming. Dataset out of a questionnaire taken from various Senior students in a high school in the city of Kanchipuram, Tamil Nadu, India was used, where the questions related to their social and cultural backgrounds and their experience with computers. Several hypotheses were also generated. The datasets were analyzed using three machine learning algorithms namely, Backpropagation Neural Network (BPN) and Recurrent Neural Network (RNN) (and its variant, Gated Recurrent Network (GNN)) with K-Nearest Neighbor (KNN) used as the classifier.  Various models were obtained to validate the underpinning set of hypotheses clusters. The results show that the BPN model achieved a high degree of accuracies on various metrics in predicting Freshman student’s aptitude for computer programming.

By Lakshmi Narasimhan, G. Basupi

Thanks to the security policy of the marketing information system which includes physical, administrative and logical safeguards, organizations are today able to design marketing and sales strategies that enable them to effectively respond and satisfy their customers' needs and expectations in a timely and cost effective manner and this by protecting the relevant information and data circulating in the said information system against any attempt at attack or malicious intrusion which seeks only to harm its reliability, confidentiality, integrity, availability and credibility. Indeed with this security policy we arrive easily to identify each discrepancy observed in the behavior of persons accessing this marketing information system as well as each mismatch between the service provided to users and the service expected by them, a context that pushes this security system to generate automatically some countermeasures such as encryption, decryption, hashing, electronic signature, intrusion detection and prevention and certification.

By Khalid Lali, Abdellatif Chakor

In this paper, an attempt has been made to highlight the importance of the English language and ICT in the entrepreneurial endeavors of Moroccan women. The development of ICT and the rise of English as the major lingua franca of worldwide business have been followed by a considerable increase in academic interest in a variety of topics connected to language choice and usage in the business and professional sectors. In business, it's important for Moroccan women entrepreneurs to be good at ICT and speak English well. The integration of digital technologies into female entrepreneurship has developed a new approach called "digital entrepreneurship." This method offers numerous benefits for Moroccan female entrepreneurs, namely economic development, women's empowerment, and access to worldwide markets. The power of English and new digital paradigms has changed how Moroccan businesswomen work and communicate with each other. This has changed business practices and given Moroccan businesswomen new opportunities.

By Moulay Driss Hanafi, Khalid Lali, Houda Kably, Abdellatif Chakor

Introduction: The rapid and precipitous increase in the number of scientific journals has outlined the hardship of perpetrating periodic assessments of their scientific production or that of a specific area of knowledge. Scientific production is directly related to scientific activity.
Objective: Describe the production on fractures in the Cuban Journal of Orthopedics and Traumatology.
Methods: A metric, descriptive and cross-sectional analysis of the articles published on fractures in the Cuban Journal of Orthopedics and Traumatology (RCOT) was carried out from from 2013 to 2022.
Results: A total of 37 articles were collected; 19 (51.4%) original articles, 12 (32.4%) case presentations, 4 (10.8%) bibliographic reviews, 1 (2.7%) special article and 1 (2.7%) letter to the editor. The year with the highest scientific production was 2023 (n=19; 51.4%), in the period 2015-2019 no scientific contributions were reported. The total originality rate is 51.4%, with 2013 being the year with the highest rate with 100%, although together with 2014 they represent the most unproductive years within the productive ones, 2014 has an originality rate of 0%.
Conclusions: The production on fractures in the RCOT had a notable representation in the period 2020-2021 and showed a tendency to progress in this regard; but work still needs to be done in order to endorse optimal visibility of the contents on this topic, as well as a greater citation of these, to which the publication in English and the persuasion of collaborators of other nationalities could greatly contribute.

By Lázaro Horta-Martínez, Melissa Sorá-Rodriguez

Noise pollution generated by vehicular traffic can have a significant impact on the quality of life of residents, affecting their physical and emotional well-being. To determine the relationship between vehicular noise pollution and the perception of the population in the cities of Juliaca and Puno, a descriptive and correlational study was carried out. Data were collected using registration forms and questionnaires, using 10 representative sampling points on the roads with the highest traffic and surveying 584 randomly selected people. The results revealed sound pressure levels that exceed the limits established by regulations in both cities. Minimum values of 67.84 dB in Puno and 68.03 dB in Juliaca, and maximum values of 83.86 dB and 78.83 dB, respectively, were found. In addition, a positive but low correlation (r = 0.142) was identified between noise pollution and population perception. These findings highlight the exposure of the population to vehicular noise pollution levels that exceed the permissible limits, which can have negative consequences for health and well-being. It is necessary to implement effective measures to reduce noise pollution and improve the quality of life of residents in both cities. These results provide valuable information for the development of appropriate mitigation strategies.

By Néstor Eloy Gonzales Sucasaire

ChatGPT (Generative Pre-Trained Transformer) is a chatbot that is being widely used by the public. This technology is based on Artificial Intelligence and is capable of having conversational interactions with its users just like humans, but in the form of automated text. Because of this capability, online forums such as Brainly and the like can be overtaken by these smart chatbots. Therefore, this study was conducted to determine the positive and negative sentiments towards ChatGPT using Naive Bayes Classification algorithm on 5000 Twitter users. Data was collected by scraping technique and Python programming language was used in data analysis. The results showed that the majority of Twitter users had a positive sentiment of 57.6% towards ChatGPT, while the negative sentiment reached 42.4%. The resulting classification model had an accuracy of 80%, indicating a good classification model in determining sentiment probabilities. These findings provide a basis for the development of better AI chatbot technology that can meet user needs. The results of this study provide insights into user sentiment towards ChatGPT and can be used as a reference for future AI chatbot development.

By Adhitia Erfina, Muhamad Rifki Nurul

Introduction: in the care of women with precursor lesions of cervical cancer, preventing possible progression to invasive cancer without over-treating the high chances of regression is extremely important. Over time, different treatments and protocols have been tested in order to obtain the best results in the control of this condition with conservative techniques.
Objective: to identify predictors of residual disease depending on the intrinsic characteristics of the patients with conization, due to high-grade epithelial lesions or with microinvasion.
Methods: A prospective descriptive study was conducted to determine the relationship between the diagnosis of residual disease and clinical- epidemiological variables dependent on the patient, in 1090 patients with high-grade cervical epithelial lesions who were treated at the "Héroes del Baire" General Teaching Hospital on the Isle of Youth (Cuba) during the period 2014-2019.
Result: a linear trend of the association of age and residual disease was observed, as well as an association with glandular disease, histological severity and infection by oncogenic serotypes of the human papillomavirus. HPV infection (OR=11.3), history of previous lesion (OR=9.8), persistence of viral infection (OR=4.9) and glandular involvement (OR=3.1) were the factors that showed the greatest association with residual disease.
Conclusion: the severity and size of the lesion, the glandular extension and the persistent infection by the human papilloma virus were the predictive factors that contribute to the existence of residual lesion.

By Heenry Luis Dávila Gómez, Lidia Esther Lorié Sierra, Georgia Díaz-Perera Fernández, Jorge Bacallao Gallestey, Eliany Regalado Rodríguez

Introduction: Epidemiological risk stratification in health is a tool effective in identifying where the main problems lie in a program health, to distribute resources where they are most needed. kidney disease chronic is a metabolic endocrine syndrome, brings disability to people who
suffer, has become one of the main causes of death in the world, in our country has seen an increase in the last ten years.
Objective: Stratify mortality with CKD in Cuba and characterize some sociovariables demographics from 2011-2020.
Method: The universe consisted of 35031 deceased with CKD in Cuba, percentages, crude, specific and specific rates were calculated. standardized by age, sex, causes of death, by province of residence and color of the skin. The stratification by provinces was classified as very high risk, high risk, medium and low risk.
Results: There was a total of 35031 deaths, the risk of die older in men, older adults with black skin color. The main cause of death hypertensive kidney disease. The standardized rates showed slow and sustained increase in all provinces. Very high risk provinces
Artemisa (22.15), Cienfuegos (19.36) and the Isla de la Juventud Special Municipality (18.72).
Conclusions: Risk stratification presented differences in the country, the main cause of death was hypertensive kidney disease, older adults have higher risk of dying, although it is important to pay attention to the group that includes working age.

By María del Carmen Marín Prada, Nayra Condori-Villca, Francisco Gutiérrez Garcia, Carlos Antonio Rodriguez García, Miguel Ángel Martínez Morales, Jhossmar Cristians Auza-Santiváñez, Fidel Aguilar-Medrano

The objective of this study was to establish strategic guidelines to solve the existing vehicular mobility problems in the District of Riohacha, proposing the adoption of advanced technologies to optimize traffic management in the city. The methodology of the study consisted in the application of surveys and the review of relevant bibliography. The results allowed the identification of various intelligent traffic control tools used in different regions of the world, determining their applicability and benefits for the context of Riohacha, where there was a notable lack of traffic signals. It was concluded that the implementation of the technological tools proposed in this study could offer effective solutions to the mobility challenges faced by the District of Riohacha.

By Silfredo Damian Vergara Danies, Daniela Carolina Ariza Celis, Liseth Maria Perpiñan Duitama

The purpose of this study is to examine certain aspects associated with the 4.0 Revolution in the field of health data, with particular emphasis on decision-making and organizational models implemented in the information systems of the health sector. This analysis is conducted in the context following the implementation of the Federal Unified Program for the Computerization of the Digital Medical Record, which establishes a unified registry of patient data. The practices and tools used in document management of health data, biometric and genetic data are identified and examined, which, due to their sensitive nature, require rigorous protection. Various aspects related to the responsible provision of health services are discussed. For efficient and effective management of health systems, both public and private, the implications of using technologies in health from the perspective of safety and privacy are considered.

By María del Carmen Becerra, Alicia Aballay, María Romagnano

Different biotic indices have been used for the integral analysis of hydrographic basins in Colombia, using benthic microinverters and physicochemical parameters. A comprehensive analysis of water quality was performed in the low Marquesote river basin, using benthic macroinvertebrates and the ETP index (Ephemeroptera-Trichoptera-Plecoptera) as a biological indicator and some physicochemical parameters. Work was worked on the middle and lower basin on the Marquesote River at two times of the year (dry and rainy); standardized methods for water physicochemical variables were applied, for benthic fauna collected using a Surber network, and identifying them up to the taxonomic level of family. The study observed 22 families and 1388 individuals, where 49% represent PTSD, indicating regular water quality; however, physicochemical variables had wide variation, noting that pH showed the greatest variability based on an analysis of major components. Environmental quality in the Marquesote river basin is compromised according to the indicators used, a more detailed study of the sources of pollution and dynamics of macroinvertebrates could provide a greater ecological knowledge of the basin.

By Stephany Romero Tobias, Geomar Molina-Bolívar, Iris Jiménez-Pitre

Introduction: In manufacturing companies, specifically in the brewery, there are processes that involve the handling and use of chemical agents, such as carbon dioxide (CO2), this is the reason why workers are exposed to this agent. In the studied company, an accident was caused by exposure to this substance.
Objective: To propose an epidemiological surveillance program for the prevention of occupational accidents and diseases in workers exposed to carbon dioxide (CO2) in a Venezuelan brewery.
Methods: A qualitative-quantitative, field, descriptive, feasible project-type research was carried out, with the epidemiological surveillance program as the unit of analysis. Documentary review, direct observation and the interview were used as data collection techniques, and the observation guide, the sociodemographic form and the field diary were used as instruments.
Results: The machine room has 18 workers, which shows that the workforce is composed of men over 40 years of age. Among the main causes of consultation of workers to the medical service are headache with 24.1%, followed by fatigue with 20.6% and then dizziness with 13.7%.
Conclusion: We propose an Epidemiological Surveillance Program aimed at machine room workers exposed to Carbon Dioxide (CO2), since there is no system that collects complete information on the working conditions and health of its workers, thus failing to comply with the legal framework governing the subject.

By María Eugenia Ramírez, Misael Ron, Gladys Mago, Estela Hernandez–Runque, María Del Carmen Martínez, Evelin Escalona

A secure E-health care system is satisfying by maintaining data authenticity and privacy. Authentic users only access and edit medical records, any alteration in the medical records may result in a misdiagnosis and, as a result, harm the patient's life. Biometric method and watermarking modes are utilized to satisfy goal, such as Discrete Wavelet Transform (DWT), Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Least Significant Bit (LSB). In this work focused on a biometric watermarking system where the iris code of the sender programmed as a sender authentication key. The confidentiality of the patient information is safeguarded via encrypting it with an XOR algorithm and embedding the key in the DCT image. The algorithm has demonstrated which is suggested system has met earlier constraints. We used samples of original watermarked images with PSNR value, embedding time and extraction time, the lowest embedding time was 0.0709 and the PSNR value was 49.2369.

By Amal Fadhil Mohammed, Hayder A Nahi, Akmam Majed Mosa, Inas Kadhim

Aim: The objective of this study was to evaluate the adherence to the preprint publication format by a sample of Brazilian researchers.
Methods: Searches were carried out, in September 2021, on the MedArxiv, OSF, and SciELO preprints platforms, looking for publications in preprint format by all Brazilian researchers of graduate programs in dentistry (n=211) who were productivity fellows in 2021 (PQ). Searches were performed by typing the authors’ full names and the possible variations, as indicated by each author's curriculum, openly available on the Lattes website platform. The Friedman test, with the Durbin-Conover post-hoc (α=0.05) was applied in order to compare the three platforms. Spearman's correlation test (α=0.05) was performed to assess the possible correlations between the number of preprints and age, career stage, and the researcher’s scholarship level variables.
Results: From the 211 researchers searched, 22 (10.4%) published 1 (one) preprint on at least one platform. A total of 39 published preprints were found at MedArxiv (n=19, 48.7%), SciELO preprints (n=18, 46.2%), and OSF platforms (n=2, 5.1%). There was no difference between the adherence to MedArxiv and SciELO preprints (p = 0.731). However, the OSF platform presented the lowest adherence, statistically differing from MedArxiv (p=0.008) and SciELO preprints platforms (p=0.003). In addition, no correlation was found between the publication of preprints and the researcher's age (p=0.128), career stage (p=0.248), or the researcher's scholarship level (p=0.661).
Conclusion: It was possible to observe a low adherence to the preprints’ publications by Brazilian researchers’ productivity fellows of graduate programs in dentistry.

By Jaisson Cenci, Daiane Silva Santos Da Cruz, Pedro Dentice Da Silva Leite, Maximiliano Sérgio Cenci, Anelise Fernandes Montagner

The objective was to establish the impact of the management of the tourism system on the environmental awareness of the population of Lunahuana-Cañete, period 2022, the method used was a basic study, the design was without any experiment, in a single time and descriptive, quantitative and deductive approach. The population and test was 120 workers who work in the tourism and gastronomic areas, a non-probabilistic sensal sampling was used. As a result, 86.6% of respondents stated that the management of the tourism system is well implemented and basically implemented; the cultural, economic, environmental and social dimensions are between basically implemented and very well implemented. The environmental awareness variable was rated with 60.0% medium level, 36.7% high level, 3.3% low level, and the cognitive, affective, active and behavioral dimensions were rated as high level with an average of over 70%. The inferential statistical results indicate that the management of the Tourist System has a significant influence on the environmental awareness of the inhabitant, in the same way for the specific premise 1, it was confirmed that the cultural dimension is linked in a preponderant way with the environmental awareness of the inhabitant, for the specific premise 2, it was confirmed that the cultural dimension is associated in an important way with the environmental awareness of the inhabitant, for the specific premise 3, it was confirmed that the environmental dimension is linked with the environmental awareness of the inhabitant. And finally for the specific premise 4, it was confirmed that the social dimension is linked to the environmental awareness of the inhabitant, all the premises or hypotheses refer to the inhabitant of Lunahuana, Cañete, Lima, 2022.

By Filiberto Fernando Ochoa Paredes, Manuel Enrique Chenet Zuta, Segundo Waldemar Rios Rios, Anwar Julio Yarin Achachagua

Introduction: Malnutrition is a global problem that affects millions of people around the world, especially the elderly. Among the possible consequences of malnutrition in the elderly is sarcopenia or loss of muscle mass.
Objective: To characterize the trends and impact of scientific production on malnutrition in ambulatory older adults with progression to sarcopenia published in Scopus between 2019 and 2022 in the Latin American context.
Method: An observational, descriptive, cross-sectional, bibliometric study was carried out. The data used in the study in question were obtained from the Dimensions database. Pearson's linear correlation was used to perform the trend analysis of the data.
Results: The most productive years were 2020 (175 articles) and 2021 (160 articles), with the least productive being 2022 (31 articles). The year with the highest number of citations was represented by 2019 (15795 citations) for 53.74% and the year with the lowest number was 2022 (2141 citations) for 7.29%. Of the total citations, 6552 were considered self-citations. The results corroborate the hegemony of countries like Brazil (176 articles) and Mexico (110 articles). Cuba ranks 14th in Latin America with respect to the production of articles on the subject of study.
Conclusions: A low Latin American scientific production on malnutrition in ambulatory older adults with progression to sarcopenia was evidenced in journals indexed in Scopus, with published articles and citations that follow a direct line towards reduction.

By Emilio Manuel Zayas Somoza, Vilma Fundora Álvarez, Roberto Carlos Morejón Alderete

Background: The use and abuse of social networks are harming the mental health of university students.
Objective: To adapt and validate the Social Media Addiction Scale (SMAS) for the Chilean context to have a reliable instrument to measure addiction to social networks. The sample comprised 686 university students (mean age = 28.04, SD = 8.4), 71.1% female, 28.4% male, and 0.5% other genders.
Methods: Confirmatory factor analysis (CFA) using the weighted least squares means and variances method (WLSMV) was used for this study.
Results: Reliability was Cronbach's alpha (α) 0.841. The SMAS yielded two factors that explained 53.433% of the variance. The CFA yielded very good fit indicators such as CFI = .959, TLI = .949, and RMSEA .060.
Conclusions: Based on the results described above, we can affirm that the SMAS is a good instrument to measure social network addiction in college students.

By Jonathan Martínez-Líbano, Nicole González Campusano, Javiera Pereira Castillo, Juan Carlos Oyanedel, María-Mercedes Yeomans-Cabrera

This research aims to address educational inequalities and improve the quality of education in the country, through a comprehensive and systematic review of the literature related to the topic of "Digital Tools and Meaningful Learning" in the current educational context. The key findings of each study were examined, including the methodologies used, the results obtained and the relevant conclusions. The papers were categorized and grouped according to common themes and emerging trends in the relationship between digital tools and meaningful learning. Special attention was paid to the limitations and challenges identified in the literature. In conclusion, the use of digital tools in the classroom can contribute significantly to the teaching-learning process, as long as they are implemented effectively and existing educational inequalities are addressed. Effective strategies implemented in Latin America to close the digital divide and reduce educational inequalities were identified.

By Juan Carlos Cotrina Aliaga, Danny Alonso Lizarzaburu Aguinaga, Teresa Marianella Gonzales Moncada, Jorge Luis Ilquimiche Melly, Yoni Magali Maita Cruz, Segundo Pio Vasquez Ramos

Introduction: speech and language therapist services has been extensively described. However, in Chile the evolution and characteristics of these services at hospital level and especially during the last years (pre and post COVID-19) are unknown. The exploration of these data could contribute to the development of strategies and decision making at the local level.
Objective: to determine the evolution and characteristics of speech and language therapist services between the years 2015-2022 in a Chilean high complexity hospital.
Methods: by means of a quantitative, transectional and descriptive design, 96 databases corresponding to the Monthly Statistical Records (REM) between January 2015 and December 2022 were reviewed. The variables analyzed were: number of initial and intermediate evaluations, hospital rehabilitation sessions, home rehabilitation and procedures-activities performed.
Results: an oscillating increase in the number of speech and language therapist services performed between 2015-2022 was observed. Preference is given to hospital rehabilitation sessions (95,626 services) followed by initial evaluations (11,550). By specific area, the highest number of benefits was obtained by swallowing rehabilitation (22,594), while individual and group auditory rehabilitation only presented 7 and 11 records respectively.
Conclusions: The analysis of the REM exhibits an incremental evolution of the registry of speech and language therapist services, especially since the last three years (2020 onwards), this despite the fluctuations observed during the previous years (2015-2019). This increase would be directly related to the increase in the hiring of professionals, improvement of supplies and equipment, incorporation of the speech therapist to pathologies with explicit health guarantees (GES) and the need for professional staffing due to the COVID-19 contingency.

By Jorge Burdiles-Aguirre, Nicole Hidd-Cuitiño, Jaime Crisosto-Alarcón, Carlos Rojas

Introduction: Migration is a social phenomenon that affects the structure and distribution of the population, driven by the search for better opportunities and living conditions. In this regard, irregular migration poses a challenge for host countries, as it involves the entry of individuals without the appropriate documentation, potentially compromising national security and border control.
Objective: To evaluate the application of the unsupervised DBSCAN algorithm to classify foreigners based on the level of risk of irregular immigration at the National Migration Superintendence of Peru.
Methods: We use the DBSCAN algorithm on a dataset from the National Immigration Superintendence, classifying foreigners into clusters according to their level of risk of irregular immigration. In addition, we use the Silhouette, Davies-Bouldin, and Calinski-Harabasz coefficients to evaluate the quality of the classification.
Results: DBSCAN classified foreigners into four clusters based on the level of risk of irregular immigration: high, medium-high, medium-low, and low. The performance of the Silhouette index was 0.5338, the Davies-Bouldin index was 0.6213, and the Calinski-Harabasz index was 3680.2359.
Conclusions: We show that the use of DBSCAN in the National Immigration Superintendence effectively classified foreigners according to the level of risk of irregular immigration. This tool supports informed decisions of immigration inspectors, favoring Peruvian immigration regulation.

By Miguel Valles-Coral, Ulises Lazo-Bartra, Lloy Pinedo, Jorge Raul Navarro-Cabrera, Luis Salazar-Ramírez, Fernando Ruiz-Saavedra, Pierre Vidaurre-Rojas, Segundo Ramirez

Introduction: In the current era, Artificial Intelligence (AI) has profoundly transformed the operation and management of business processes, being essential for competitiveness. This article focuses on quantitatively evaluating the impact of AI on the automation of business processes, seeking to support decision making.
Objective: This study aims to carry out a quantitative evaluation of the impact of AI on business processes. Robust methods are used to measure and analyze key variables related to AI adoption.
Methods: The methodology combines secondary data and company surveys. Public business databases are accessed and financial data is collected, in addition to analyzing Key Performance Indicators (KPI). A random selection of companies is made for the surveys, a structured questionnaire is used and the data is subjected to rigorous statistical analysis.
Result: Quantitative results show significant impact of AI on business processes. The average reduction in operating costs reaches 26%, the improvement in the quality of products and services is 30%, and an average increase of 20% in profit margins is observed. Possible moderators that influence these results are identified.
Conclusion: This quantitative study supports the strategic importance of AI in business, demonstrating substantial improvements in efficiency, quality and decision making. Despite its limitations, it offers a solid framework for decision-making and future research in the field of AI and business automation.

By Justiniano Felix Palomino Quispe, Domingo Zapana Diaz, Leopoldo Choque-Flores, Alisson Lizbeth Castro León, Luis Villar Requis Carbajal, Edwin Eduardo Pacherres Serquen, Arturo García-Huamantumba, Elvira García-Huamantumba, Camilo Fermín García-Huamantumba, Carlos Enrique Guanilo Paredes

The objective of this research is to know the effectiveness of business strategies from the perspective of the Balanced ScoreCard in medium-sized companies in the city of Ambato. The research consisted of an explanatory methodology with a qualitative and quantitative approach with a descriptive research design, the study population was based on micro-enterprises in the province of Tungurahua, in this sense for the sample, 22 medium-sized companies. of the city of Ambato were determined. A survey structured by 16 questions distributed in four dimensions was applied: financial perspective, customer perspective, internal processes perspective and learning and growth perspective. Among the main results is the low financial perspective (27.3%). The customer perspective is the lowest point in 45.4% of companies, on the other hand, there is a very low perspective of internal processes (27.3%) and finally, the management focused on learning and knowledge perspective maintains the same deficiencies of the previous dimensions (22.7%). In this context, it is concluded that the companies selected for the study have a low level of use of the Balanced ScoreCard perspectives. The results of the correlation analysis show that the financial perspective varies directly with the change in customer perspectives and internal processes and the customer perspective varies directly with respect to internal processes.

By Freddy Lalaleo, Amanda Martínez

The objective was to establish the impact of the Management of the tourist System on the environmental awareness of the population of Lunahuana-Cañete period 2022, the method that was used is a basic study, the design without any experiment, in a single time and descriptive , quantitative and deductive approach. The population and test was 120 workers who work in the field of tourism, gastronomy, a proven non-probabilistic sensible was used. As a result, 86.6% of those surveyed state that the management of the Tourism system is well implemented and basically implemented, both the cultural, economic, environmental and social dimensions are between basically implemented and very well implemented. The environmental awareness variable was qualified with 60.0% at a medium level, 36.7% at a high level, and 3.3% with a low level, and the cognitive, affective, active, and behavioral dimensions were qualified as a high level on average. greater than 70%. The inferential statistical results indicate that the management of the Tourist System significantly influences the environmental awareness of the inhabitant, in the same way for the specific premise 1, it is established that the cultural dimension is significantly related to the environmental awareness of the inhabitant, for the specific premise 2 , it is guaranteed that the cultural dimension is significantly related to the environmental awareness of the population, for specific premise 3, it is guaranteed. The environmental dimension is related to the environmental awareness of the population. And finally for the specific premise 4 it is guaranteed that the social dimension is related to the environmental awareness of the resident, all the premises or hypotheses are about the resident of Lunahuana, Cañete, Lima, 2022.

By Filiberto Fernando Ochoa Paredes, Segundo Waldemar Rios Rios, Manuel Enrique Chenet Zuta, Anwar Julio Yarin Achachagua, Soledad del Rosario Olivares Zegarra

Introduction: Fraud detection in financial transactions has become a critical concern in today's financial landscape. Machine learning techniques have become a key tool for fraud detection given their ability to analyze large volumes of data and detect subtle patterns.
Objective: Evaluate the performance of machine learning techniques such as Random Forest and Convolutional Neural Networks to identify fraudulent transactions in real time.
Methods: A real-world data set of financial transactions was obtained from various institutions. Data preprocessing techniques were applied that include multiple imputation and variable transformation. Models such as Random Forest, Convolutional Neural Networks, Naive Bayes and Logistic Regression were trained and optimized. Performance was evaluated using metrics such as F1 score.
Results: Random Forests and Convolutional Neural Networks achieved an F1 score greater than 95% on average, exceeding the target threshold. Random Forests produced the highest average F1 score of 0.956. It was estimated that the models detected 45% of fraudulent transactions with low variability.
Conclusions: The study demonstrated the effectiveness of machine learning models, especially Random Forests and Convolutional Neural Networks, for accurate real-time fraud detection. Its high performance supports the application of these techniques to strengthen financial security. Future research directions are also discussed.

By Roberto Carlos Dávila-Morán, Rafael Alan Castillo-Sáenz, Alfonso Renato Vargas-Murillo, Leonardo Velarde Dávila, Elvira García-Huamantumba, Camilo Fermín García-Huamantumba, Renzo Fidel Pasquel Cajas, Carlos Enrique Guanilo Paredes

Introduction: The scientific production of universities serves as a key indicator of their commitment to research and knowledge generation. It represents the collective efforts of educators, researchers, and students contributing to the advancement of science and technology.
Objective: To analyze the scientific production in the Scopus database of the Universidad Nacional Amazónica de Madre de Dios (UNAMAD).
Methods: The research adopted a bibliometric and retrospective approach. An analysis of Scopus-indexed documents was conducted, evaluating the number of publications, authors, journals of publication, types of documents, language of publication, authorship order, funding sources, knowledge areas to which the documents belong, and co-authorship networks.
Results: A total of 172 documents indexed in the Scopus database were identified, indicating a rising trend in production in recent years. Most documents were original articles, published in foreign journals in the English language, with collaboration from researchers affiliated with UNAMAD and no declared funding sources. Additionally, a higher number of documents were observed in the fields of Social Sciences and Environmental Sciences.
Conclusions: In recent years, there has been a notable increase in UNAMAD's scientific production in the Scopus database. Nevertheless, it remains relatively limited in comparison to other universities in the Amazonian region and throughout Peru.

By Edwin Gustavo Estrada-Araoz, Marilú Farfán-Latorre, Willian Gerardo Lavilla-Condori, Jhemy Quispe-Aquise, Maribel Mamani-Roque, Franklin Jara-Rodríguez

Introduction: Social responsibility is related to organizations' commitment to society and the environment. Recent research has shown the relationship between organizations' performance and some indicators such as economic performance or corporate image.
Objective: This study analyzes the research on social responsibility to know the trend of studies.
Method: Based on qualitative and quantitative research and with bibliometric techniques, a statistical analysis is made with the Vosviewer program of 1639 publications from the Scopus database to map the research based on publications, authors, and citations.
Results: The geographical distribution shows that the United States and the United Kingdom have the most published documents. They have the greatest scientific impact and a strong collaboration network. From the above, it is evident that social responsibility research has been approached from different angles to verify its relationship with economic, societal, or environmental variables. There is a wide field of knowledge that scholars can address.
Conclusions: The results indicate that central research topics include the connection of social responsibility with advancing technologies, globalization, and climate change. Mapping the co-occurrence of keywords by authors reveals four clusters related to ethics and social responsibility, corporate governance, corporate social responsibility, and sustainable development.

By Rolando Eslava Zapata, Rómulo Esteban Montilla, Edixon Chacón Guerrero, Carlos Alberto Gómez Cano, Edgar Gómez Ortiz

Introduction: Scientific publications are considered the final step of a research and are an excellent tool to characterize the scientific output of a university. Objective. To characterize the scientific production of the faculty of the Faculty of Medical Sciences "Miguel Enriquez", based on their scientific publications, in the period 2016-2022.
Methods: A descriptive, cross-sectional, retrospective, observational, descriptive study was carried out. The universe was constituted by the publications of the faculty professors, grouped by teaching departments. Articles, complete books and chapters, and monographs were included. The publications were analyzed according to the time of dedication of the professor to the teaching activity, and annual indexes of scientific productivity were calculated.
Results: A total of 845 scientific publications were counted in a faculty composed of 444 professors from 17 teaching departments. In a quarter of them, the main author was from the Diagnostic Means department. The number of authorships per professor was 1487 during the period, with the Clinical Sciences Department standing out. Most of the works were published in journals indexed in prestigious international databases (Groups I-II), with a predominance of publications by full-time professors. The highest indicators of annual productivity, both per department and per professor, were obtained by the Diagnostic Means and Graduate and Research departments. Professors with a scientific degree and full professors and researchers were the most productive.
Conclusions: The analysis of seven years of scientific publications of the faculty of the "Miguel Enriquez" Faculty shows that there is a diminished scientific production, which mainly corresponds to the professors of higher rank or category.

By Idrian García-García, Sergio González-García, Hamna Coello-Caballero, Lisbel Garzón-Cutiño, Lourdes Hernández-Cuétara

Introduction: Currently, work with young people plays an important role in advancing the Sustainable Development Goals.
Objective: Describe the scientific-technological contributions of the Student Scientific Group: "Technology and Science" linked to the achievement of the Sustainable Development Goals.
Methods: Observational, descriptive and cross-sectional study, from January 2021 to March 2023 at the University of Medical Sciences of Cienfuegos, Cuba. Study variables: scientific advice and technological support in carrying out virtual health scientific events, technological solutions, virtual courses and work with digital social networks as spaces for the exchange of knowledge and community work in health promotion and prevention.
Results: The GCE is made up of 14 medical science students. The contributions linked to the objectives are shown (3,4,11). Advice and training for the development of events in the different interactive virtual platforms were highlighted. They supported developers and programmers in the creation of Android Mobile Applications. They stood out in the preparation of professors and teachers for the work with the Virtual Teaching-Learning Environments. They gave training courses on digital social networks to the members of the chair of the University for the Elderly and they joined the community work in health promotion and prevention.
Conclusions: Inspiring positive changes and transformations of the new generations in society is vital in order to contribute to the achievement of the Sustainable Development Goals.

By Yuleydi Alcaide Guardado, Luis Enrique Jiménez-Franco, Claudia Díaz de la Rosa, Enrique Acosta Figueredo, Juan Luis Vidal Martí

Introduction: Malnutrition is a global problem that affects millions of people around the world, especially the elderly. Among the possible consequences of malnutrition in the elderly is sarcopenia or loss of muscle mass.
Objective: To characterize the trends and impact of scientific production on malnutrition in ambulatory older adults with progression to sarcopenia published in Scopus between 2019 and 2022 in the Latin American context.
Methods: An observational, descriptive, cross-sectional, bibliometric study was carried out. The data used in the study in question were obtained from the Dimensions database. Pearson's linear correlation was used to perform the trend analysis of the data.
Results: The most productive years were 2020 (175 articles) and 2021 (160 articles), with the least productive being 2022 (31 articles). The year with the highest number of citations was represented by 2019 (15795 citations) for 53.74% and the year with the lowest number was 2022 (2141 citations) for 7.29%. Of the total citations, 6552 were considered self-citations. The results corroborate the hegemony of countries like Brazil (176 articles) and Mexico (110 articles). Cuba ranks 14th in Latin America with respect to the production of articles on the subject of study.
Conclusions: A low Latin American scientific production on malnutrition in ambulatory older adults with progression to sarcopenia was evidenced in journals indexed in Scopus, with published articles and citations that follow a direct line towards reduction.

By Emilio Manuel Zayas Somoza, Vilma Fundora Álvarez, Roberto Carlos Morejón Alderete

This article presented the implementation, results, and usability evaluation of a software solution designed to manage oncological cases between healthcare centers. The software was developed to facilitate the exchange of clinical and administrative data for patients referred to the Arturo López Peréz Foundation (FALP) through a charitable program. The software underwent iterative development and included features such as user roles, patient list, progress tracking, document upload and viewer, chat, DICOM viewer, sharing, download, and API integration. The usability of the software was evaluated using the System Usability Scale (SUS) questionnaire, which showed high levels of usability and user satisfaction. The software proved successful in facilitating the coordination and continuity of care for patients referred to FALP and received positive feedback from users. The results of this study highlight the effectiveness and value of the software solution in improving case management and information exchange in the Chilean healthcare system. Future plans include expanding the software for internal patient management at FALP and extending its use to other institutions.

By Sergio Peñafiel, Analia Hurtado, Marcela Aguirre, Inti Paredes, Vladimir Pizarro

This study aimed to develop a predictive model for cancer stage using data from a Chilean cancer registry. Several factors, including cancer type, patient age, medical history, and time delay between diagnosis and treatment, were examined to determine their association with cancer stage. Multiple supervised multi-class classification methods were tested, and the best-performing models were identified. The results showed that the random forest, SVM polynomial, and composite models performed well across different stages, although distinguishing between Stages II and III was more challenging. The most important features for predicting cancer stage were found to be cancer type, TNM variables, and diagnostic extension. Variables related to treatment timing and sequence also showed some importance. It was emphasized that the results of predictive models should be interpreted carefully to avoid overprediction or underprediction. Clinical context and additional information should be considered to enhance the accuracy of predictions. The small dataset and limitations in data availability posed challenges in accurately predicting cancer stage for different cancer types. Implementing the predictive model can have various benefits, including informing treatment decisions, assessing disease severity, and optimizing resource allocation. Further research and expansion of the model's scope were recommended to improve its performance and impact. Overall, the study emphasized the potential of predictive models in cancer staging and highlighted the need for ongoing advancements in this field.

By Marcela Aguirre, Sergio Peñafiel, April Anlage, Emily Brown, Cecilia Enriquez-Chavez, Inti Paredes

Introduction: The innovative implementation of a Hospital-based cancer registry (HBCR) at the Arturo López Pérez Oncology Institute (FALP), showcasing the transition from a manual data extraction model to a semi-automation of the process. The purpose of this publication is to compare both methodologies by assessing their efficiency and accuracy. Methods: The analysis was conducted by comparing the complete dataset of the FALP HBCR from 2017 to 2021. The efficiency variable is analyzed, taking into account the total execution time of the registration process, and the precision variable was measured through the internal data consistency method using the IARCcrg Tools Software. Results: In terms of efficiency, the analysis reveals that in 2017, employing a manual approach without automation, it was necessary to analyze 13,061 cases over 144 weeks with an average of 4 registrars to achieve a total of 3,211 cases fully registered. In contrast, over the subsequent 4 years (2018 to 2021), with varying degrees of automation, 65,088 cases were analyzed within 115 weeks, employing an average of 8 registrars, resulting in 13,537 fully registered. This method demonstrated to be 3 times more efficient. Regarding precision, the manual approach exhibited a 5% error rate in registered cases, whereas the automated approach showed a 0.6% error rate during the 2018-2021 period. Conclusion: The obtained results highlight the significant impact of semi-automating the tumor registration process through the utilization of tools for data capture and processing, achieving a threefold increase in efficiency and reducing errors to 0.6%.

By Carolina Villalobos, Carla Cavallera, Matías Espinoza, María Francisca Cid, Inti Paredes

This paper aims to validate and subsequently design a Risk Scoring System based on Lohman et al.'s risk calculator for patients undergoing brain or spinal tumor surgery.Three models were tested: replication of Lohman's methodology, modification of risk groups, and development of a custom risk calculator. The replication of Lohman's instrument did not show significant correlations with adverse events in the study population. However, the adapted risk calculator demonstrated promising predictive performance for unplanned reoperation at 30 days, indicating good utility. The study suggests the potential applicability of the adapted risk calculator for predicting unplanned reoperation within 30 days for patients undergoing brain or spinal tumor surgery. Further research with larger samples and less missing data is recommended to confirm and enhance the utility of the proposed risk calculator. The results could be used to optimize decision-making and improve the quality of care for neuro-oncological surgery patients.

By Rodrigo Lagos, Matías Espinoza, Alejandro Cubillos

This paper discusses the benefits of surgery streaming and tele-mentoring, as well as the use of 5G technology in surgical procedures. The paper describes the advantages of using wireless 5G broadband as a low-latency and large-bandwidth capacity connection, which can solve problems with cables and large equipment in the surgery room. The Chilean oncology clinic Fundación Arturo López Pérez coordinated an international project with Japanese companies NTT Data and Allm Inc. to implement a proof of concept using 5G technology for the transmission of an oncological surgery. This project consisted of the installation of a local 5G network, its configuration and testing, and the realization of the first broadcast of a robotic partial nephrectomy in Latin America using the 5G broadband. The paper provides details on the hardware infrastructure and components used in the project.

By Nicolás Bravo, Inti Paredes, Luis Loyola, Gonzalo Vargas

Several institutions and countries have recognized the need to integrate disruptive technologies in the training of health professionals. An elective course on disruptive technologies in health for nursing was developed, structured in 5 units: a) innovation in health and nursing, b) creation of apps and virtual environments, c) digital manufacturing for nursing, d) sensors and internet of things, and e) data science in health. For its implementation, the didactic model proposed by Jorba and Sanmartí was considered; and for the evaluation of the units and the impact of the course, Urquidi's extended model of technological adoption was used. Forty-four students participated (39 women and 5 men), with an average age of 23 years. According to the technology acceptance model, statistically significant differences were found between the pre- and post-intervention groups in all dimensions of the model (Wilcoxon test, p < 0.05). In addition, a positive correlation was found between ease of use, subjective norm and intention to use the technologies taught. The implementation of the disruptive technologies course proved to be effective in the development of technological skills among nursing students in Chile.

By Jorge Contreras, Andrés Cepeda

The accurate diagnosis of bipolar disorder is extremely challenging, due to unpredictable mood swings, behaviors, sleep, judgment, and inability to think, which makes it difficult to make a proper diagnosis. This paper aims to investigate the application of ensemble classifiers in classifying bipolar disorder and to compare their performance with existing methods. Herein, the work involves a thorough analysis of diagnostic precision and performance metrics. According to a study, an existing classifier achieved an accuracy rate of 87% in bipolar disorder classification. In addition, the two most widely used classifiers, which are Random Forest and Decision Tree, achieved accuracy rates of 90% and 86%, respectively. These results highlight the performance baseline against which the proposed ensemble classifier is evaluated. Notably, the proposed ensemble classifier shows excellent results in bipolar disorder classification thereby, achieving an impressive accuracy rate of 98%. This considerable improvement in accuracy marks a significant stride in diagnostic precision, showcasing the potential of ensemble classifiers in enhancing bipolar disorder detection. The results of this study have given substantial implications for the field of mental health diagnosis, offering a promising avenue for a more accurate and reliable classification of bipolar disorder. This research reinforces the significance of advanced machine learning techniques and their potential to revolutionize the approach to diagnose and to manage mental health conditions.

By Lingeswari Sivagnanam, N. Karthikeyani Visalakshi

In recent times, various techniques have been employed in agriculture to address different aspects. These techniques encompass strategies to enhance crop yield, identify hidden pests, and implement effective pest reduction methods, among others. Presented in this study a novel strategy which focuses on identification of plant leaf infections in agricultural fields using drones. By employing cameras on drones with high resolution, we take precise pictures of plant leaves, ensuring comprehensive coverage of the entire area. These images serve as datasets for Deep Learning algorithms, including Convolutional Neural Networks(CNN), Resnet, ReLu enabling the early detection of infections. The deep learning models leverage the captured images to identify and classify infections at their initial stages. The usage of R-CNN and ResNet technology in agriculture field has brought the tremendous change when we detect the disease in earlier stage of crop. Thus the farmer can take the pest preventive measures in the beginning stage to avoid crop failure.

By V. Sushma Sri, V. Hima Sailu, U. Pradeepthi, P. Manogyna Sai, Dr. M. Kavitha

The postures are important for conveying emotions, expressing artistic intent, and preserving appropriate technique. Posture recognition in dance is essential for several reasons, as it improving the performance and overall artistic expression of the dancer. The Samapadam, Aramandi, and Muzhumandi are three postures that serve as the foundation for the Bharathanatyam dance style. This work proposes a model designed to recognize the posture portrayed by the dancer. The proposed methodology employs the pre-trained 2D-CNN model fine-tuned using the Bharathanatyam dance image dataset and evaluates the model performance.

By M. Kalaimani, AN. Sigappi

Introduction: In the current era of globalization and digitalization, international logistics faces unique challenges and opportunities. The growing demand for efficient supply chain management, combined with the need to reduce costs and improve services, has led to the adoption of advanced technologies such as Artificial Intelligence (AI). AI has become a key catalyst in the transformation of logistics, giving way to what is known as Logistics 4.0. This paper explores the most recent trends of AI in international logistics and its integration into education, with a specific focus on the San Mateo University Foundation.
Methods: This mixed study, combining qualitative and quantitative methods, begins with quantitative data collection and analysis, followed by a qualitative phase. The qualitative approach focuses on students' perceptions of logistics training, while the quantitative approach describes how they perceive AI tools. The research included students and companies in Bogota, analyzing their familiarity with AI and its implementation in practice.
Results: The findings indicate that AI is increasingly relevant in logistics, especially in process automation and data-driven decision making. Most companies surveyed have a good understanding of AI, but less than half implement it in their operations. Students recognize the importance of AI in logistics and its positive impact on education. There is consensus on the role of AI in improving educational quality, highlighting its usefulness in optimizing processes and personalizing learning.
Conclusions: The research highlights the crucial role of AI in modern logistics and its ability to improve operational efficiency. The integration of AI in international business education is critical to enrich students' learning experience and prepare them for the challenges of the labor market. The blended methodology used is effective in gaining a holistic view of AI integration in logistics and its educational impact. The conclusions provide guidelines for curriculum development in international business with a focus on international logistics, aligning curricula with emerging trends in logistics and AI.

By Ricardo Javier Albarracín Vanoy

The implementation of Information Technology (IT) in university education encompasses multiple aspects, from the incorporation of accessible technologies to the disruptive transformation of learning through emerging technologies. This article proposes a conceptual framework that describes four phases of IT adoption by university teachers: Technology Adoption, Online Collaboration and Feedback, Technology Exploration and Experimentation, and Adoption of Emerging Technologies. Each phase is detailed, starting from the integration of accessible technological tools to the incorporation of emerging technologies such as artificial intelligence, virtual and augmented reality, to create innovative and transformative learning experiences. This article is based on bibliographic references that support each phase and underline the importance of personalizing learning, promoting interaction between students and teachers, and applying project-based approaches to enrich the educational process.

By Mario Macea-Anaya, Ruben Baena-Navarro, Yulieth Carriazo-Regino, Julio Alvarez-Castillo, Jhoan Contreras-Florez

Global organizations currently face the challenge of managing massive volumes of data and knowledge efficiently. The consolidation of the knowledge society is manifesting itself in an evident way, driving university institutions to reconfigure both their academic and administrative processes in order to achieve excellence in their functions. In this context, the central purpose of this research is to present a comprehensive systematic review of the implementation of Quality Management Systems (QMS) in the field of higher education. In order to address this issue with the utmost rigor, a systematic review was carried out incorporating the fundamental pillars outlined in the PRISMA statement. In an initial phase, a selection of 883 papers was carried out from preeminent documentary sources, namely: Scopus, IEEE and Web Science. Subsequently, the final review was confined to a corpus of 23 research papers. The results derived from this thorough review show that the paradigm embodied by the ISO 9001 model prevails as the most predominant approach, with 69.56% representativeness in the set of studies analyzed. In contrast, the EFQM, TQM and Malcom Baldrige models showed a more modest presence, each accounting for 4.35% of the total number of studies examined. In addition, fundamental aspects have been identified that both facilitate and condition the process of implementing QMS.

By Daniel Cristóbal Andrade-Girón, William Joel Marín-Rodriguez, Marcelo Zúñiga-Rojas, Edgar Tito Susanibar-Ramirez, Irina Patricia Calvo-Rivera

Augmented reality is a technology that combines elements of the real and virtual world to enhance the user experience by providing additional information and enriching interaction. In education, AR has been used to enhance the teaching of complex concepts by providing interactive content and immersive experiences. This review examines various aspects related to the implementation of AR in higher education, including its educational benefits, impact on student motivation and engagement, and its effectiveness in achieving learning objectives. Associated challenges and limitations, such as device availability and effective experience design, are also explored. The results indicate that AR can improve content comprehension and retention, encourage active student participation, and enhance collaborative learning. However, significant challenges are identified, such as the initial investment in technology and the need for adequate teacher training. In addition, diversity in institutional infrastructure and resources may limit the widespread adoption of AR in higher education. In conclusion, augmented reality in higher education offers promising potential to enhance teaching and learning, but its successful implementation requires careful considerations of pedagogy, accessibility, and overcoming technological barriers. It highlights the need for further research to thoroughly understand its impact and maximize its benefits in academic training.

By William Joel Marín-Rodriguez, Daniel Cristóbal Andrade-Girón, Marcelo Zúñiga-Rojas, Edgar Tito Susanibar-Ramirez, Irina Patricia Calvo-Rivera, Jose Luis Ausejo-Sanchez, Felix Gil Caro-Soto

The digitalization of production operations is considered today as a decisive condition capable of stimulating the spontaneous and regular use of an effective and operational marketing information system. Certainly, thanks to digitalisation, companies can: increase their profitability; simplify working methods, automate production processes and interactions between the various employees responsible for monitoring the smooth running of production activities as well as between the latter and the heads of the marketing department who prepare the marketing strategies to be executed. Indeed, if companies want to increase their sales volumes and be able to take advantage of the new opportunities that digital will offer them, they are encouraged and better than ever to quickly computerize their production processes. To do this, they must rely on well-documented marketing strategies that emphasize customer orientation and ensure that the latter receives a personalized offer while benefiting from the operational functionalities provided by marketing information systems.

By Khalid Lali, Abdellatif Chakor, Hayat El Boukhari

Introduction: Epidemiology plays a fundamental role in public health by providing evidence for decision making. However, the lack of access to data limits the evaluation and replicability of epidemiological studies.
Objective: Establish recommended practices for the open publication of epidemiological research data and reports, in order to maximize their value and accessibility.
Method: A systematic review of open publication guidelines was conducted. Good practices were identified in the stages of collection, storage, publication and dissemination of epidemiological information.
Results: Consensus was found on the importance of using standardized instruments, documenting metadata, storing data in repositories with open licenses, assigning digital identifiers and publishing in open access journals.
Conclusions: The adoption of these recommended practices will substantially improve the quality, replicability and use of epidemiological research. This will strengthen transparency, scientific collaboration and evidence-based decision making.

By Lucio-Arnulfo Ferrer-Peñaranda, Lindomira Castro Llaja, Mercedes-Lulilea Ferrer-Mejía, Zoila Rosa Díaz Tavera, Fernando Martin Ramirez Wong, Leonardo Velarde Dávila, Roberto Carlos Dávila-Morán

Telemedicine technology is one of the key achievements of recent years. This technology is based on biomedical devices that contain essential components, in-cluding antennas. Biomedical antennas ensure the exchange of data between de-vices installed on the human body and the external environment. This paper pre-sents the study and design of a flexible circular patch antenna implanted on a bio-sourced substrate for industrial, scientific, and medical applications. The frequen-cy chosen for the study is 2.45GHz. Return loss and radiation pattern measure-ments. An improvement in the gain of this antenna is also investigated in this study. This antenna offers adequate performance to meet the needs of 5G users. This antenna is printed on a polyester substrate with a thickness of h=2.85cm, a relative permittivity εr=3.2, a loss tangent equal to 0.003, and a patch radius equal to 2.11cm. In addition, this antenna provides the following results: reflection co-efficient S11=-26.59dB, bandwidth BW=0.12GHz, gain G=5.6, directivity D=5.8dB, and efficiency η=96.55%.

By Salah-Eddine Didi, Imane Halkhams, Abdelhafid Es-Saqy, Mohammed Fattah, Younes Balboul, Said Mazer, Moulhime El Bekkali

Purpose: Libraries may become obsolete in the twenty-first century unless they begin to harness new technology and improve information and service delivery. This paper examines the potential application and implications of artificial intelligence for contemporary library services in Nigeria.
Methods: This paper adopts the expository research approach to evaluate the application and implication of artificial intelligence in contemporary library services in Nigeria. Through systematic analysis of literature, the study addresses how academic libraries can utilize artificial intelligence to support innovative library services.
Findings: The column emphasizes that, academic libraries in Nigeria have not yet adopted and applied AI, in spite of the potential that it holds for libraries. Given that there has been relatively little study linking artificial intelligence (AI) to librarianship, this may be because there is a low degree of awareness and adoption of AI's importance in libraries.
Conclusions: This column is the original idea from the authors and does not reflect on any copyrighted materials. The column recommended that, academic libraries in Nigeria should fully embrace artificial intelligence like chatbots, barcodes, RFIDs, and robots for delivering quality services and libraries should also leverage on the opportunities presented by artificial intelligence to reconnect their remote users, and consequently re-establish their relevance among the user community.

By Solomon Olusegun Oyetola, Bolaji David Oladokun, Charity Ezinne Maxwell, Solomon Obotu Akor

The fourth industrial revolution has paved the way for emerging technologies, and among them, blockchain stands out for its unprecedented ability to create and trade value in library organizations. This research paper explores the potential application of blockchain technologies in 21st-century library services. By conducting a systematic analysis of the literature, this study examines how libraries can harness blockchain to support innovative services and meet global demands. The study suggests that the recent advancements in blockchain have led to a fourth generation of the technology, which possesses disruptive capabilities across diverse fields, including library and information science. The paper proposes that blockchain can enhance library services such as collection development, circulation services, research, data management, and storage. It is important to note that this paper represents the original ideas of the authors and does not rely on copyrighted materials. Furthermore, it highlights that blockchain remains a vast and underexplored area of research, presenting both challenges and opportunities for library professionals seeking to provide diverse library services.

By Vivien Oluchi Emmanuel, Maryjane Efemini, Dauda Oseni Yahaya, Bolaji David Oladokun

Low- and middle-income countries (LMICs) are only involved in 3-4% of mental health research, and the engagement of Early Career Psychiatrists (ECPs) is declining. A total of 21,986 documents have been published in Sri Lanka with 395,157 citations, resulting in 18 citations per publication (CPP) and a 1.48 Field-Weighted Citation Impact (FWCI). The highest number of documents are published in Medicine (n=5704), followed by Computer Science (n=4847) and Engineering (n=4598). Some of the main barriers to research include a lack of technical support, funding, and a poor research culture. In order to improve research output and quality, international collaborations, grants, and research infrastructure are urgently needed.

By Waseem Hassan