bluuebunny/crossref_metadata_2025 huggingface dataset:
IoT Network Security Anomaly Detection and Classification using Deep Learning
Authors: Karthik Meduri
Published: February 2025

The Internet of Things (IoT) is an expanding network of interconnected devices exposed to growing cyber security threats. Integrating AI-powered solutions presents a promising avenue for enhancing anomaly detection and classification. This study delves into developing a comprehensive methodology leveraging machine learning and deep learning techniques. Utilizing the BoTNeTIoT-L01 dataset, meticulously curated from IoT devices, the research focuses on data gathering, preprocessing, and exploratory data analysis to unearth underlying patterns and anomalies within network traffic data. Subsequently, a suite of machine learning models, including Logistic Regression, LightGBM (Light Gradient-Boosting Machine), and Decision Tree, along with a deep learning model optimized with the Adam optimizer, is employed to detect and classify anomalies effectively. The comparative analysis underscores the superior performance of advanced models such as LightGBM and Decision Tree, showcasing their efficacy in accurately identifying security threats within IoT environments. The study also addresses pertinent technical challenges, ethical considerations, and future directions, emphasizing the imperative for responsible deployment and ongoing innovation in AI-powered IoT security solutions.
Towards an Enhanced Adaptive Learning Framework: Integrating Blended Learning and Educational Technology
Authors: Rahimah Abd Halim
Published: February 2025

The quality of education today significantly impacts student performance and academic achievements. Adaptive learning has emerged as a strategic approach to enhance educational quality by replacing conventional one-size-fits-all methods, which often fail to address diverse student needs. However, existing adaptive learning frameworks predominantly rely on e-learning platforms, despite many educators still favouring face-to-face instruction in the classroom as their primary platform. Therefore, this research article aims to propose an adaptive learning framework that integrates a blended learning environment while leveraging educational technology. This research uses literature reviews, interviews, and questionnaires to develop the proposed adaptive learning framework, the Adaptive-Decision Support System-Learning Framework (A-DSS-LF). The A-DSS-LF comprises four primary components: the learning process, learning assessment, adaptive decision support system, and adaptive profile—the validation of the proposed A-DSS-LF involves experts from the education field. Presentations and discussions were conducted to gather their feedback and reach a consensus on the framework's validity. The findings of the Fuzzy Delphi Analysis reveal that the proposed A-DSS-LF garnered a positive reception from experts. However, valuable feedback was also offered for potential enhancements to the framework. The proposed A-DSS-LF provides educators and students with alternative learning strategies within a blended learning environment, customised to meet students' individual needs. It also leverages existing technology as a supportive learning tool.
An Examination of Virtual Learning Platform (VLP) Usage, Engagement Factors, and Effectiveness in Education: An Analysis Using the Delone and Mclean Model
Authors: Gilda J. Taupa
Published: February 2025

The current educational landscape integrates learning management systems (LMS) to cater to the needs and demands of the digital and virtual era. Central Bicol State University of Agriculture’s LMS, the Virtual Learning Portal (VLP) is implemented to teach and learn, offering flexible and accessible avenues for students and instructors. This research explores the utilization, engagement factors, and effectiveness of VLPs within educational settings. Employing the updated DeLone and Mclean Model as a guiding framework, the study delves into the multifaceted dimensions of VLP usage, scrutinizing the frequency and patterns of engagement among students and instructors alike. Additionally, the research aims to assess the perceived effectiveness of Learning Management Systems (LMS) incorporated within VLPs. The researcher utilized online surveys and data mining techniques to gather information from a sample of LMS users, comprising students and faculty members. The information collected is then evaluated using statistical techniques to assess the correlations between the various parameters and ascertain their impact on LMS success. The results showed that system information and service quality influence user satisfaction and behavioral intention to utilize the LMS. Further evidence that a great LMS experience enhances learning outcomes is the positive correlation between user happiness and learning effectiveness. The implications of this research highlighted the importance of prioritizing system quality, information quality, and service quality in LMS design and implementation. It focuses on the significance of ongoing evaluation and enhancement of these elements to guarantee a satisfying user experience and increased learning efficacy. The results also show that user happiness is a critical factor in determining how well an LMS performs, indicating the necessity of a user-centered design and individualized learning strategies within the LMS. This research contributes to the existing body of knowledge by providing insights into evaluating LMS success using the updated DeLone and McLean model.
An Enhancement of CNN Algorithm for Rice Leaf Disease Image Classification in Mobile Applications
Authors: Kayne Uriel K. Rodrigo
Published: February 2025

This study focuses on enhancing rice leaf disease image classification algorithms, which have traditionally relied on convolutional neural network (CNN) models. We employed transfer learning with MobileViTV2_050 using ImageNet-1k weights—a lightweight model that integrates CNN’s local feature extraction with Vision Transformers’ global context learning through a separable self-attention mechanism. Our approach resulted in a significant 15.66% improvement in classification accuracy for MobileViTV2_050-A, our first enhanced model trained on the baseline dataset, achieving 93.14%. Furthermore, MobileViTV2_050-B, our second enhanced model trained on a broader rice leaf dataset, demonstrated a 22.12% improvement, reaching 99.6% test accuracy. Additionally, MobileViTV2-A attained an F1-score of 93% across four rice labels and a Receiver Operating Characteristic (ROC) curve ranging from 87% to 97%. In terms of resource consumption, our enhanced models reduced the total parameters of the baseline CNN model up to 92.50%, from 14 million to 1.1 million. These results indicate that MobileViTV2_050 not only improves computational efficiency through its separable self-attention mechanism but also enhances global context learning. Consequently, it offers a lightweight and robust solution suitable for mobile deployment, advancing the interpretability and practicality of models in precision agriculture.
Transformational Leadership Qualities, Performance of School Administrators and Teachers’ Self-Efficacy: Basis for Transformational Leadership Model
Authors: Mari Daisy O. Corral
Published: February 2025

The  purpose of this study is to examine the relationship between transformational leadership and teachers’ self-efficacy through analysing school administrators’ performance - in terms of instructions, research and extension - and quality. The study made use of a combination of the descriptive and correlational methods of research. The descriptive design helped the researcher to characterize the leadership performance of the school administrators and their qualities. The correlational approach was used to determine the magnitude of the relationship between the leadership performances and qualities of the school administrators, and teachers’ efficacy of the participating state universities. The study utilized the descriptive-correlational method as a quantitative research design. It involved participants who were school administrators and regular faculty members of state universities in the province of Cavite, with a total enumeration of 226 participants accounted in the study with the participation of 51 school administrators. Questionnaires were the primary instruments in data gathering which were developed by the researcher. The data collected were analyzed using the use of median, frequency, percentage, Kruskal-Wallis, Friedman’s, and Spearman Rank Correlation Coefficient. The data revealed that as per the level of the following transformational leadership quality of the school administrators, the majority of the participants constitutes Very Satisfactory level of transformational leadership by their leaders and Inspirational Motivation obtained Excellent verbal interpretation, and this implies that participants were satisfied as to transformational qualities of their leaders, with a high regard for Inspirational Motivation. In terms of performance, leaders manifesting high performance in the three-fold areas obviosuly showed Very Satisfacory performance in terms of Instructions, Research and Extension, Over-all results showed that the school administrators’ performance and qualities highly influenced teachers’ efficacy.
Web-Based Document Tracking System for Catanduanes State University with QR-Code Technology: In Alignment with Republic Act No. 11032
Authors: Gemma G. Acedo
Published: February 2025

Tracing in a university setting is usually beset with inaccuracies, misplaced files, and delay retrieval. This research is, therefore, designed to develop a web-based document tracking system that integrates QR-code technology specifically tailored for Catanduanes State University (CatSU). It shall be designed to further enhance efficiency in document tracking to complement administrative processes and purport compliance with Republic Act No. 11032, otherwise known as the Ease of Doing Business and Efficient Government Service Delivery Act of 2018. Through document tracking, the system reduces the risk of human error and allows transparency of both sent and received documents. Developed using HTML, CSS, JavaScript, and PHP, along with a centralized database implemented on an Apache server, QR codes uniquely identify each document for real-time status tracking. The system evaluation, performed on ISO/IEC 25010 software quality criteria, gave it a rating of "Excellent." Overall, the mean score of critical parameters of Functional Suitability, Performance Efficiency, Compatibility, Usability, Reliability, Security, Maintainability, and Portability are in the range of 4.94. This high rating signifies that the system can further document management and administrative procedures at CatSU. Other recommendations include expanding the system's capabilities, training the users, and the development of mobile access. Overall, the DTS provides an effective solution for the document tracking needs of CatSU, which enables transparency, accuracy, and compliance with RA 11032, besides the potential of the backbone toward scalability and further improvements.
Recent Advancements and Applications of Electronic Nose Systems in Environmental Monitoring and Pollution Detection: A Comprehensive Survey
Authors: Kasthuri S
Published: February 2025

This article reviews some of the recent developments in Electronic Nose (E-nose) technology, which is used for environmental monitoring and pollution control and detection. An E-nose model is a sophisticated way of emulating the human sense of smell, which is used to detect VOCs (volatile organic compounds) and hazardous gases mostly used in industrial and urban settings. The use of machine learning models and wireless sensor networks (WSN) has improved their scalability and increased their efficiency for use in real-time pollution control. The survey spans the period from 2020 to 2024 on the available technological advances for the different types of chemical sensors, particularly their sensing materials consisting of metal-oxide semiconductors (MOS) and graphene-based arrays, and their operating software platforms that bring about real-time monitoring using IoT. The study also reviews the experimental findings and the associated theoretical advances in the areas of chemical detection, specifically the study of machine learning techniques such as support vector machines (SVM) and artificial neural networks (ANN) that are observed to enhance the precision of chemical detection and the performance of sensors. This review is derived from 40 peer-reviewed papers, goes through a phase of preliminary selection from different major academic databases, including IEEE Xplore, MDPI, ScienceDirect, and SpringerLink. The literature is divided into major categories based on the thematic approach, including sensor advancements, machine learning integration, and IoT/WSN applications. The reviews also explore the newest technologies, such as energy-harvesting and edge computing, which work for the improvement of the energy efficiency of E-nose systems. The survey reports that sensor technologies, specifically graphene-based sensors and low-power wireless communication protocols, have greatly increased the scalability and deployability of E-nose systems in large and complex environments. The integration of machine learning has also helped in mitigating issues such as sensor drift and improving the system’s ability to classify VOCs. However, challenges still remain, such as the requirement of better energy management and drift compensation mechanisms, among other things. The survey identifies key future research areas including the need for self-calibrating sensors and larger, more labelled datasets for currently used machine learning models that are struggling to adequately generalize their learning to different environments.
Management, Strategies, Tactics and Communication Technologies in Media
Authors: Liudmyla Mialkovska
Published: February 2025

Introduction: The relevance of the research topic is due to the rapid development of digital technologies, which significantly change the media and communication space, requiring a revision of traditional approaches to interaction with the audience. Given the changing information load and consumer preferences, there is a need to adapt communication strategies to modern challenges.
Objectives: The aims: to determine the effectiveness of modern media and communication strategies and offer recommendations for their optimisation in digital transformation.
Methods: The methodology includes content analysis and comparative analysis, which allowed us to systematise information and identify critical factors for the success of communication campaigns.
Results: Results showed that integrating traditional and new media, personalised content, targeting and visual communication significantly increases the effectiveness of interaction with the audience. Particular attention is paid to crowdsourcing and the audience's active involvement in the content creation, which helps build trust and loyalty to the brand.
Conclusions: The practical significance of the findings lies in developing recommendations for optimising media strategies that can be used in business, politics and social projects to improve the effectiveness of communications and achieve goals.
Navigating through the Dynamic Landscape of Public Administration Governance through Innovative Adaptation and Resilience Practices and Strategies in Africa
Authors: Eustache Tanzala Kikasu
Published: February 2025

Effective public administration governance in the 21st century requires adapting to an ever-changing landscape of complex societal needs, technological advancements, and shifting political contexts. In the face of rapid urbanization, demographic changes, and shifting global dynamics, African public administrations are being called upon to navigate an increasingly complex governance landscape. This paper explores the imperative of innovative adaptation and resilience practices in African public administration governance. It also examines the role of adaptive governance, digital transformation, and human-centered design in fostering a resilient and responsive public sector and highlights the importance of building trust, engaging citizens, and promoting diversity and inclusivity in public administration. This paper argues that traditional approaches to governance are no longer sufficient to address the multifaceted challenges facing African countries, and that innovative strategies are needed to build resilience and adapt to an ever-changing environment. It reviews existing literature on public administration governance in Africa, highlighting the key challenges and opportunities that arise from demographic shifts, technological advancements, and changing societal expectations. It then presents case studies of successful adaptation and resilience strategies employed by New Zealand and Singapore governments, highlighting the importance of inclusive and participatory approaches, digitalization, and capacity building. Ultimately, this research aims to provide a roadmap for policymakers and practitioners seeking to navigate the complexities of public administration governance and create a more agile, effective, and citizen-centric government. The paper concludes by outlining a framework for navigating the dynamic landscape of public administration governance in Africa, emphasizing the need for ongoing learning, collaboration, and innovation to ensure effective and sustainable governance practices. Through embracing a culture of continuous learning, experimentation, and collaboration, public administrators can develop the capacity to respond to emerging challenges and seize opportunities for improvement.
A Comparative Analysis of Predictive Monitoring Systems Utilizing Machine Learning and Deep Learning Algorithms
Authors: Pranita Bhosale
Published: February 2025

This study offers an in-depth comparative examination of predictive monitoring systems utilizing sophisticated machine learning (ML) and deep learning (DL) algorithms. The investigation delves into the efficacy, advantages, and constraints of ML algorithms, exemplified by Random Forest and Extreme Gradient Boosting will be contrasted with Deep Learning (DL) algorithms including artificial neural networks (ANNs) and LSTM, recurrent neural networks (RNNs) in this study. By evaluating these algorithms across diverse domains, the research aims to discern optimal strategies for predictive monitoring, considering factors like efficiency, real-time processing, and adaptability. The findings contribute valuable insights for practitioners and researchers, informing the selection and deployment of algorithms in predictive monitoring systems
Rain Attenuation Investigation for Ka band Satellite Transmission in Tropical Region
Authors: Khairunnisa Aziding
Published: February 2025

Growing demands in satellite communication are driving a shift towards higher frequency bands, as lower bands become oversaturated. It is crucial to explore higher frequency bands beyond 10 GHz in order to satisfy the rapidly increasing demand for dependable and high-capacity satellite communication. But to explore into higher frequency bands, a problem has been encountered where there is a significant deterioration of Ka-Band signal quality caused by continuous heavy rainfall in the tropical regions. Rain attenuation caused a great loss in signal compared to other attenuation. The objective of this study is to determine the fade margin estimation for the measured data. This research compares and analyses a set of data from 2022 measured in Cyberjaya. This study also conducted a comparison of rain attenuation estimation utilizing ITU-R model. Fade margin estimation at 99.9% availability obtained from this research is 20 dB at Ka-band frequency.
Towards Safer Aging: A Hybrid KNN Model for Pre-Impact Fall Detection Enhanced by Class Balancing
Authors: R Ranjith
Published: February 2025

Falls among the elderly are a significant public health issue, often resulting in serious injuries or even fatalities. Detecting falls before they happen (pre-impact) can enable timely interventions and help minimize the severity of injuries. This research focuses on creating an effective pre-impact fall detection system for elderly individuals by utilizing class balancing techniques and a hybrid k-nearest neighbors (KNN) approach. To address the challenges posed by imbalanced datasets, methods like SMOTE, ADASYN, and RUS were employed, while the hybrid KNN model was used for accurate classification. The system was tested using the KFall dataset, which includes sensor data from accelerometers and gyroscopes. The hybrid KNN model demonstrated an impressive 99.8% accuracy in detecting pre-impact falls, surpassing traditional KNN and other standard methods. Moreover, the use of class balancing significantly enhanced the detection of minority classes. This study highlights the potential of combining class balancing techniques with hybrid KNN models for reliable pre-impact fall detection. These findings could pave the way for developing wearable devices aimed at preventing falls and improving safety for elderly individuals.
Advancements and Challenges in Optimal node localization for Energy-efficient data transmission in IoT assisted Wireless Sensor Network: A Survey
Authors: Surabhi Sushant Sawant
Published: February 2025

Optimal node localization is a key area of research focused on enhancing energy efficiency and data transmission accuracy within Internet of Things  (IoT)-assisted Wireless Sensor Networks (WSNs). Despite advancements in localization methodologies, the field encounters significant challenges, including adapting to dynamic environmental conditions, managing energy constraints, and ensuring scalability across various network topologies. This survey provides a systematic review of around 30 research articles, focusing on the techniques used, challenges faced, and outcomes achieved in node localization for WSNs. By critically examining existing methods such as clustering, routing, and hybrid optimization approaches often integrated with machine learning this research highlights each strategy's strengths and limitations, providing insights into their applicability and effectiveness in real-world scenarios. Ultimately, the survey aims to guide future research toward developing more robust, adaptable, and energy-efficient localization solutions, contributing to enhanced network performance and prolonged sensor lifetimes in IoT-integrated WSN environments.
An Empirical Study on the Economic Impact of Cybersecurity Breaches and Computer Fraud on SMEs
Authors: Nidhi Sonkar
Published: February 2025

An empirical study of the economic impact of cybersecurity breaches and computer fraud on Small and Medium Enterprises (SMEs) is presented in this research paper. As the need for digital infrastructure continues to rise, SMEs are ever increasingly finding themselves being hit by cyber attacks, resulting in huge financial losses and operational disruptions. Using SME data from various sectors, the study examines the extent and nature of these impacts. We examine key areas of focus associated with breaches, including direct financial costs, indirect costs (such as reputational damage) and broader impacts on business continuity. The research is done by employing quantitative analysis methods to identify trends and correlations of frequency of cyber attacks and financial resilience of SMEs. These findings highlight the need for robust cybersecurity measures as well as provide lessons for policymakers and business owners on how to reduce risks. As such, it further contributes to the increasingly voluminous SME cybersecurity literature by providing a foundation for further research and strategic policy development.
Industry 4.0 Digital Technologies as Enablers of Sustainable Supply Chain Operations Performance
Authors: Piyusha Nayyar
Published: February 2025

The objective of this paper is to find the best industry 4.0 digital technologies as enablers amongst the available alternatives based on sustainable supply chain operations performance. Numerous sustainability advantages are provided by these technologies, from the possibility of reducing energy consumption and waste generation to increased prospects for recycling and industrial symbiosis. Using twenty sustainable supply chain performance criteria, we identified ten industry 4.0 digital technologies and used the Multi-criteria Decision Making (TODIM) technique to rank the best of them in a hierarchical fashion. In order to assist newly emerging firms in identifying the best industry 4.0 practices, this study analyzes the dominance of industry 4.0 digital technologies based on the significance and dominance of sustainable supply chain operations performance measures. The results show that the most important Industry 4.0 digital technologies for a sustainable supply chain are IoT, Big Data Analytics, and Blockchain; hence, enterprises should prioritize these technologies more to reap their benefits.
Integration of AI Functions for Automatic Identification of Morphological Parameters in Medical Images Using MATLAB
Authors: Dafina Xhako
Published: February 2025

Introduction: Artificial intelligence (AI) is transforming medical imaging by automating complicated and challenging processes with remarkable accuracy. This paper examines the adoption of AI algorithms using MATLAB for the automated recognition of morphological structures in medical imaging.
Methods: This research utilizes advanced machine learning (ML) and deep learning (DL) methods to address essential tasks like image segmentation, feature extraction, and parameter estimation. MATLAB's intuitive interface and powerful computational capabilities make it easy to create and test models for specific medical imaging tasks. Segmentation and parameter prediction are carried out with great accuracy in experiments carried out on publically available datasets, such as brain MRI and lung CT scans. In many instances, the Dice coefficients surpass 0.9. While tackling issues like data quality, model interpretability, and computational efficiency, this study emphasizes the benefits of MATLAB for developing and implementing AI solutions in healthcare.
Results: The results emphasize MATLAB's capability to improve diagnostic accuracy and operational effectiveness in clinical environments, facilitating future advancements in customized medicine. There has to be effective software for automated analysis and interpretation of the ever-increasing amount of medical imaging data.
Conclusions: The purpose of this research is to examine the feasibility of using MATLAB's AI features for the automated detection of morphological parameters in medical images. Here, we compare AI-driven algorithms to more conventional image processing approaches for typical morphological metrics like area, perimeter, shape descriptors, and object recognition, and we find that the former are more accurate and efficient. To verify the efficacy of the suggested AI model, statistical analysis is carried out on a dataset consisting of medical photographs.
Hermit Crab Optimizer Approach: Harmonics Reduction of Hybrid Renewable Energy Sources
Authors: G. Balram
Published: February 2025

Micro Grids (MGs) have gained a lot of popularity recently because of their benefits in terms of high transmission efficiency and efficient power conversion. However, the nonlinear properties of renewable energy sources (RESs), the increased usage of power electronic devices, and unanticipated fluctuations in load are what lead to the formation of stability issues in the MG. This manuscript presents the optimization approach to improve the performance of microgrid with renewable energy sources (RES). The proposed method is the Hermit Crab Optimizer (HCO) Algorithm. Here, Solar PV and Wind Turbine is utilized as power sources, supported by DC/DC and AC/DC converters for efficient energy conversion. The main objective of proposed technique is to minimize the THD and maximize the efficiency of system. The HCO algorithm is used to optimize the duty cycle of the DC to DC converters within the system. By then, the proposed method's performance is put into practice using the MATLAB platform, and it is contrasted with a number of existing methods, including Salp Swarm Algorithm (SSA), Ant Lion Optimizer (ALO), and Particle Swarm Optimization (PSO). From the result, the THD of proposed method is -17.75. The THD of existing PSO is 17.81465, ALO is -17.81469 and SSA is -17.81545.
KnowbasAI: Knowledge-Based AI system for Custom Recommendations
Authors: Ekta Dalal
Published: February 2025

Ontology-based recommendation engines are a type of recommendation system that incorporates ontologies to enhance the recommendation process. An ontology is a formal representation of knowledge that defines the relationships between various concepts within a specific domain. In the context of recommendation engines, an ontology organizes and structures information about users, items, and their attributes, facilitating more intelligent and context-aware recommendations. KnowbasAI is an innovative Knowledge-Based AI System that revolutionizes custom recommendations in diverse domains. Leveraging a comprehensive ontology-driven knowledge base, KnowbasAI captures structured and unstructured data, including user interactions, product descriptions, and domain-specific knowledge. By assimilating this wealth of information, the system gains a profound understanding of user preferences and item attributes. KnowbasAI's recommendation engine uses a hybrid strategy that combines content-based filtering with collaborative filtering techniques. While content-based filtering aligns user preferences with pertinent items, collaborative filtering discovers user commonalities. Even for inexperienced or specialized users, the integration of various approaches guarantees precise and varied recommendations. KnowbasAI's clear and understandable suggestion approach is a key benefit. By using cutting-edge methods, the system offers insightful analyses of the decision-making process, boosting user confidence in the suggestions. With about 90% accuracy, it assists in achieving privacy and personalized recommendations for the users.
Forty-two-year frequencies of river buffaloes calving in the Terecay ranch, Venezuela
Authors: Becerra Renny Jesús , Reggeti-Gómez Inés Valentina , Reggeti Gómez Francisco Miguel , Montiel-Urdaneta Néstor Simón
Published: February 2025

Although buffaloes are polyestrous, they exhibit a distinct seasonal variation in displaying estrus, conception rate, and calving, greatly influenced by physiological, sanitary, nutritional, management, and particularly bioenvironmental factors, such as temperature, humidity, precipitation, evaporation, and daylight duration. Venezuela, a tropical country, is located in the intertropical zone of the northern hemisphere, between the Equator and the Tropic of Cancer, between coordinates 00° 38’ 53” and 12° 11’ 22” LN and 59° 48’ 10” and 73° 25’ 00” LW. The area (Terecay ranch) under study has two well-defined seasons concerning precipitation, with a bimodal behavior, a rainy season (From May to October with an average of 1,441.3 mm/year) and a season with very little or no precipitation (From November to April with an average of 107.4 mm/year). In order to calculate the calving distribution, the records of 13,720 calvings of buffaloes between 1980-2022 corresponding to the Terecay farm located in the Esteros de Camaguan of Guárico State were used. For calculating calving frequency, the PROC FREC procedure of the statistical package SAS version 9.1 was used, and a polynomial regression analysis was performed to evaluate the prediction of future behavior of the calving distribution. The percentage of calving according to the months of the year were: January: 6.96%; February: 4.34%; March: 2.3%; April: 0.95%; May: 0.98%; June: 1.34%; July: 4.39%; August: 11.44%; September: 16.38%; October: 22.32%; November: 15.69% and December 12.91%. The months with the highest average of calving percentages were September (16.38%), October (22.32%), and November (15.69%), and the months with the lowest average percentages were April (0.95%) and May (0.98%). The equation of the polynomial regression analysis was y = -3.0656x4 + 64.363x3 - 360.05x2 + 417.41x + 810.37 with an R² = 0.9571. With this R² value, it can be inferred that the calving behavior will be maintained during these 42 years. Conclusions are that the frequency of calving showed a very marked seasonality between July and January, with 90.09% and 9.91% between February and June. Calving seasonality should be preserved if environmental, nutritional, sanitary, and management conditions are maintained, especially without hormonal interventions.
Constitution and brief history of the College of Pharmacists of Cartagena and La Unión (1900-1904)
Authors: López Azorín Fernando
Published: February 2025

On June 6, 1900, a Royal Order authorized the creation of a College of Physicians and another of Pharmacists for the towns of Cartagena and La Unión in the province of Murcia. The College of Pharmacists was established on September 1, 1900 with a Governing Board chaired by Eduardo Romero Germes, Gonzalo Robles Fernández as secretary, first member Antonio Rodríguez Comellas, second member Alfonso Carrión Inglés and treasurer José Díaz Benzal. Starting from the pharmacists of both towns in the last third of the 19th century, the history of the application for the College, the constitution process, the news about its operation and the cause of its dissolution on May 30, 1904 are reviewed. In addition to the list of registered pharmacists, the biography of the five members of its Governing Board is recalled and the constitution of the College is analyzed in relation to the cantonal uprising of 1873.

Keywords: College of Pharmacists of Cartagena and La Unión; Medical-Pharmaceutical Academy of Cartagena; Government Board; Eduardo Romero Germes
Identification of Staphylococcus aureus and prevalence of Staphylococcus aureus resistant to methicillin and other patterns of resistance to antibiotics in clinical isolates of diabetic foot
Authors: Segura Ramón Edder Guadalupe , Martínez González Gabriel , Almeida  Villegas Jorge
Published: February 2025

Objective: To determine the prevalence of methicillin-resistant Staphylococcus aureus (MRSA) in diabetic foot wounds and analyze antibiotic resistance patterns.
Introduction: The global incidence of diabetes mellitus, particularly type II, has significantly increased, leading to complications such as diabetic foot ulcers. These ulcers often become infected, with MRSA being a common and challenging pathogen. Understanding the prevalence and resistance patterns of MRSA in diabetic foot infections is crucial for effective treatment.
Methodology: Diabetic foot wound samples were collected from 65 patients in the Valley of Toluca, Mexico. Samples were cultured and analyzed using chromogenic agar, BHI, salt and mannitol, calf blood, EMB, and MacConkey agars. Strain identification and antibiotic sensitivity testing were performed using the Vytek automated system. Oxacillin and cefoxitin were used to detect methicillin resistance. Statistical analyses, including Kolmogorov-Smirnov, Shapiro-Wilk tests, and Spearman correlation, were conducted to evaluate relationships between clinical factors and antibiotic resistance.
Results: Of the 65 samples, 56.9% were from men and 43.1% from women, with 97.1% having type II diabetes. MRSA prevalence was 63%. Resistance rates were highest for ampicillin (100%), clindamycin (89%), erythromycin (87%), and gentamicin (73%). Statistical analysis showed no significant correlation between oxacillin resistance and glycemic control, erythromycin resistance (MLS resistance), hypertension, or gender.
Conclusion: The high prevalence of MRSA in diabetic foot wounds underscores the need for targeted infection control and appropriate antibiotic therapy. The lack of correlation between resistance and clinical factors suggests a multifactorial nature of antibiotic resistance, necessitating broader clinical and microbiological considerations for future studies.
Keywords: Diabetic Foot; Bacterial resistance; MRSA
Perwujudan Partnership dalam Perkawinan untuk Mencapai Bonum Coniugum sebagai Tujuan Perkawinan Katolik di Paroki Katedral Santa Maria Penolong Abadi Samarinda
Authors: Lalang Lily marselina , Avan Komela , Devung G. Simon
Published: February 2025

Perwujudan partnership oleh pasangan suami istri dalam mewujudkan partnership untuk mencapai bonum coniugum dalam aspek kesetaraan, komunikasi, dan kerjasama. Dalam penelitian ini, ditemukan bahwa perwujudan partnership oleh pasangan suami istri dalam aspek kesetaraan. Dalam aspek komunikasi, pasangan suami istri berbicara terbuka satu sama lain, berbagi perasaan, pemikiran, dan keinginan mereka dengan jujur, menghargai pendapat satu sama lain, mendengarkan dengan penuh perhatian, dan komunikasi yang kuat dan istimewa melebihi komunikasi secara verbal, yaitu komunikasi melalui sentuhan. Dalam aspek kerjasama, pasangan suami istri bekerja sama dalam menghadapi tantangan, mengelola kehidupan sehari hari, merencanakan masa depan, dan kerjasama yang istimewa dengan melayani pasangan yang sedang sakit dan pasangan yang sedang sakit juga merespon dengan baik. Perbedaan suku, adat, pribadi, dan keluarga menjadi kendala dalam aspek kesetaraan. Kesibukan, kurangnya waktu untuk berkomunikasi, kurangnya keterbukaan, dan kurangnya kemampuan mendengarkan menjadi kendala dalam aspek komunikasi. Perbedaan pendapat, dan ego menjadi kendala dalam aspek Kerjasama
Perbandingan Pesan Ensiklik Fratelli Tutti dan Tradisi Lonto Leok Masyarakat Manggarai
Authors: Mbukut Antonius , Atu Laurentius Florido
Published: February 2025

Paus Fransiskus melalui Ensiklik Fratelli Tutti menyerukan pentingnya membangun hidup persaudaraan dan persahabatan sosial di antara manusia. Namun, nilai tentang hidup persaudaraan itu sebenarnya juga sudah terkandung dalam kearifan-kearifan lokal tertentu. Karena itu, menelaah pesan-pesan Ensiklik Fratelli Tutti dalam konteks lokal merupakan hal yang penting untuk dilakukan agar pesan-pesan tersebut dapat dengan mudah diselami dan diterapkan dalam kehidupan masyarakat sesuai dengan realitas kebudayaan mereka. Salah satu kearifan tradisional yang memiliki nilai tentang persaudaraan dan persahabatan sosial ialah tradisi lonto leok di Manggarai. Tujuan dari penulisan ini adalah mengkaji pesan-pesan Ensiklik Fratelli Tutti dalam perspektif tradisi lokal tersebut. Penelitian ini menggunakan pendekatan kualitatif dengan metode komparasi. Pengumpulan data dilakukan melalui wawancara dan studi kepustakaan. Hasil yang ditemukan dalam penelitian ini adalah adanya seruan yang sama antara pesan yang digaungkan dalam Ensiklik Fratelli Tutti dengan nilai yang terkandung dalam lonto leok. Keduanya memiliki tujuan yang sama, yakni menumbuhkan rasa persaudaraan dan persahabatan sosial dalam kehidupan masyarakat. Dengan demikian, kesadaran untuk membangun relasi yang harmonis di antara manusia dapat dihidupkan kembali dengan mengangkat nilai-nilai yang ada dalam tradisi lokal seperti lonto leok.
MESENTERIC LYMPHADENITIS A REVIEW
Authors: Kumari Dr. Rupam , Singh Dr. Lallan , Jaiswal Dr. Binod Kumar
Published: February 2025

Mesenteric adenitis is a syndrome characterized by right lower quadrant pain secondary to an inflammatory condition of mesenteric lymph nodes. The signs and symptoms of Mesenteric lymphadenitis are similar to acute appendicitis. Surgeon may operate upon such cases which apparently give a sign-symptom complex of acute appendicitis, and will find at operation a normal appendix but with enlargement of the mesenteric lymph nodes, most especially those nodes distributed about the terminal ileum and the appendicocecal junction. Individuals with mesenteric adenitis or ileocecitis often present with a history of fever and abdominal pain, often localizing to the right lower quadrant. The illness needs to be differentiated from appendicitis, intraabdominal abscess formation, and diverticulitis. The patient recovers, and recovery is attributed to the appendectomy. Can one evaluate these cases to such a degree that in many cases surgical procedures are unnecessary? And is one able to define a sign-symptom complex which might very well encompass the diagnosis of primary acute mesenteric adenitis? Or are the symptoms and signs so bizarre. Aims And Objective of Review: To know the aetiology, clinical presentation, diagnostic criteria and management of primary acute mesenteric adenitis.
AN OBSERVATIONAL STUDY TO ASSESS THE ENVIRONMENTAL HEALTH STATUS OF CHILDREN IN SELECTED ANGANWADI CENTERS AT GWALIOR
Authors: kumari Khushboo , sarojini K
Published: February 2025

This study was conducted to evaluate the environmental health status of children between the ages of 2 to 6 years coming to Anganwadi centers in Gwalior, Madhya Pradesh with particular reference to sanitation, water, and waste. A cross-sectional observational design was used, and 150 children and their corresponding Anganwadi centers were selected by the use of stratified random sampling. The investigators used self-developed structured questionnaires to obtain data from the caregiver and Anganwadi worker respondents, and checklists for the physical assessment of the environmental conditions of the centers. The correlations between environmental factors and child health outcomes were tested by descriptive and inferential statistics. The study established that there were huge differences between the rural and urban areas with the urban areas having relatively better sanitation facilities, water quality, and waste disposal systems. Large-scale statistical analysis revealed that there is a significant association between environmental quality and childhood diseases with sanitation having the strongest relationship (r = 0.72), followed by water (r = 0.65) and waste disposal (r = 0.58). These findings underscore the need for enhancing the physical environment of Anganwadi centers, especially in rural settings to enhance the health of children. The findings of the study point to the direction that it is imperative to tailor the intervention to improve sanitation, water, and waste disposal in the Anganwadi centers. More future studies should be conducted on long-term outcomes of environmental changes on child health taking into consideration other determinants of child development such as nutrition and immunization.
Personality Traits And Self-Esteem As Predictors Of Life Satisfaction In Indian Youth Students
Authors: Pandey Dr. Kashi Nath , Tripathi Dr. Rohini , Singh Rakesh Kumar
Published: February 2025

India is a country where there is a lack of comprehensive studies in the literature on well-being. The aim of this study is to examine the relationship between life satisfaction, self-esteem and the Big Five personality traits among Indian youth. The participants were 200 Hindu men and women from colleges and universities in Varanasi (UP), India. After the study, it was found that the level of life satisfaction of female participants was higher than that of male participants. After analysing the results, it was found that self-esteem explained about 18.5% of the variance in life satisfaction and the Big Five personality traits could explain about 14.3% of the variance. We found that the Big Five traits, neuroticism and conscientiousness were the most significant predictors of life satisfaction. Additionally, as per the Big Five personality traits, life satisfaction was predicted by self-esteem. Based on the findings of this study, neuroticism was found to have a positive relationship with life satisfaction and self-esteem. Based on previous studies, we will examine the significance of the data on the relationship between personality traits and other aspects of well-being.
The importance of nursing in healthcare systems
Authors: Almetari Amirah Awad Abdulrahman
Published: February 2025

Nursing is an international, contemporary, dynamic and vital component of health systems. Like other health workers, nurses contribute to protecting and promoting health; preventing illness, injury and disability; alleviating suffering and palliating illness; and restoring health and productivity. However, social, economic and political structures too often undermine nursing and other health workers’ competence, confidence, and authority, and thwart their keenest endeavours to serve the people in need of care and cure. For centuries now, nurses and midwives have listened, observed and acted where action was needed, and, when faced with unmet need of such proportion, made loud that voice. And, for centuries, the answers have been variations on the same theme of deference, disdain or downright hostility. Even today, in the early twenty-first century, nurses, particularly younger, female, marginalised and migrant nurses, continue to experience this. Compounded by structural inequalities of gender and social class, their attempts to push for reform rarely gain traction.
Pengaruh Ukuran Perusahaan dan Likuiditas Terhadap Struktur Modal Dengan Profitabilitas Sebagai Variabel Intervening
Authors: Wahyuningsih Wahyuningsih , Meliza Meliza , Mahirun Mahirun
Published: February 2025

The food and beverage sector is one of the industries that plays a strategic role in supporting economic growth in Indonesia. The growth of this sector is driven by high domestic demand and government support through policies that promote the development of the food and beverage industry. The objective of this research is to identify and analyze the effect of firm size and liquidity on capital structure, with profitability as an intervening variable, in food and beverage sector companies listed on the Indonesia Stock Exchange (IDX) for the 2019-2023 period. This research employs a quantitative approach. The sample consists of 14 food and beverage companies listed on the IDX during the 2019-2023 period. Data collection techniques use secondary data in the form of annual reports from 2019-2023. The data analysis methods include descriptive statistical tests, classical assumption tests, multiple linear regression analysis, and hypothesis testing using SPSS tools. The results of this study indicate that firm size and liquidity have a significant negative effect on capital structure, while profitability has an insignificant negative effect on capital structure. Firm size and liquidity significantly negatively affect capital structure, with profitability as an intervening.
Conceptual Paper: Impact of Hotel Frontliners' Social Attractiveness on Consumer Participation: Mediating Role of Consumer Appreciation and Moderating Influence of Interpersonal Trust
Authors: Mengqi Shi , Ariffin Ahmad Azmi Mohd
Published: February 2025

Underpinned in the Stimulus-Organism-Response (S-O-R) framework, this conceptual paper presents a model that explores how consumers’ intentions to participate and their level of appreciation are influenced by their perceptions of service frontliners in the context of hotels. The model is based on established theories and draws from a wide array of literature in psychology, sociology, and service marketing. This study examines the relationship between the social attractiveness of hotel frontliners (Stimulus) and consumer participation intentions (Response), with consumer appreciation (Organism) acting as a mediating variable. In this context, social attractiveness is analysed through three dimensions: likability, similarity, and friendliness. Beyond S-O-R, interpersonal trust is considered as a moderator to strengthen social attractiveness’ influence toward consumer participation.