bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Impact of Artificial Intelligence on Innovation and Efficiency in the Construction Industry
Authors: Nithyakarpagam A.
Published: February 2025

This study explores the impact of Artificial Intelligence (AI) on innovation and efficiency within the construction industry, focusing specifically on project management, resource allocation, worker safety, and hazard detection. The research aims to understand how AI tools and technologies are enhancing operational efficiency, improving safety measures, and optimizing resource utilization across various construction projects. A quantitative approach is employed, utilizing a structured questionnaire distributed to 400 construction professionals, including project managers, engineers, safety officers, and workers. The study applies stratified random sampling to ensure a diverse representation of different construction sectors and job functions. Data is collected through closed and Likert-scale questions that measure respondents' perceptions of AI's effectiveness in construction project management, safety, and resource optimization. Descriptive and inferential statistical techniques, including correlation and regression analysis, are employed to examine relationships between AI adoption and project outcomes. The findings suggest that AI is significantly contributing to improvements in project management efficiency, safety monitoring, and resource allocation, though barriers to widespread adoption, such as cost and training, remain. The study's results provide insights into the potential of AI in revolutionizing the construction industry and highlight the need for further investment in AI technologies to overcome implementation challenges. This research contributes to the growing body of knowledge on AI's role in the construction sector and offers valuable guidance for industry stakeholders.
Unlocking India’s Natural Disaster (Flood and Cyclone): Challenges in Terms of Indirect Losses and Probability of Reoccurrence
Authors: Rajani Kant Awasthi
Published: February 2025

Natural disasters, particularly floods and cyclones, are India's biggest threats, causing substantial economic losses. While tangible losses are calculated post-disaster events, indirect losses impacting the broader economy are frequently overlooked. This study aims to compute indirect losses for the Srinagar region in (Jammu and Kashmir), severely affected by the 2014 Jhelum River flood, resulting in a direct economic loss of 20 billion dollars. In this study, Input-Output tables from 2010-2020 from A.D.B. are employed under Leontief theory, and both Static and Dynamic Input-Output models were constructed. The dynamic flood surge model is developed to estimate accumulative output loss over various recovery periods. The findings indicate flood surge in the Srinagar region severely impacted the agricultural sector. Retail trade, except motorcars and motorbikes; home goods repair, inland transport, chemicals; wholesale distribution and intermediary trade, except motor vehicles and motorcycles and electricity, gas, financial intermediation, and water supply, While education, health and social work, air transport and water transport were least affected Sectors. The study reveals that retail trade commerce, except motor cars and motorbikes, experienced rapid recovery in the early stages but remained stable later. It is crucial to assess indirect loss due to disaster events as they impact various economic sectors beyond the immediate zone, including supply chain disruptions and labor market disruptions. It helps in planning for production, social and economic stability, and implementing appropriate protection measures. India faces significant financial losses due to natural catastrophes like floods and cyclones, including tangible and indirect losses that impact the country's overall economy.
A Novel Parallel Approach to Image Processing for High-Performance Computing
Authors: Mohammed W. Al-Neam
Published: February 2025

Introduction: With the rise of big data and increasing computational demands, the need for advanced image processing techniques has become critical. High-resolution images and computationally intensive algorithms often exceed the capabilities of traditional serial computations, making them insufficient for modern requirements.
Objectives: This paper introduces a new parallel algorithm designed specifically for image processing in high-performance computing environments. The primary goal is to enhance response times, reduce computation durations, and ensure scalability for large and complex image datasets.
Methods: The proposed approach takes full advantage of parallel computing and integrates efficient algorithms to handle tasks like filtering, edge detection, and segmentation. A series of experiments was conducted to evaluate the algorithm’s performance under various conditions.
Results: The results show that the parallel algorithm dramatically improves processing speeds compared to traditional methods. It also scales efficiently with larger datasets, maintaining high performance even as the complexity of the images increases.
Conclusions: his study demonstrates the potential of parallel processing to transform image processing in high-performance computing. By significantly enhancing efficiency and scalability, the proposed method paves the way for new advancements in the field.
Ideal Leadership in the Tunjuk Ajar Melayu of Riau
Authors: Sri Indrastuti
Published: February 2025

This study explores the concept of leadership within the Tunjuk Ajar Melayu of Riau, a cultural heritage that encompasses advice, counsel, and noble values to guide the lives of the Malay community. Tunjuk Ajar Melayu emphasizes the importance of leadership based on syara' law, or Islamic law. Leaders are selected based on strict criteria, including the qualities of shiddiq (truthfulness), amanah (trustworthiness), fatonah (intelligence), and tabligh (communication), with the aim of establishing order and welfare within society. This research adopts an ethnographic approach, involving six experts on Riau Malay culture as respondents. Data were analyzed using Nvivo software to understand perspectives on leadership in the context of Malay customs and traditions. The findings reveal that leadership in Tunjuk Ajar Melayu emphasizes resilience, patience, and dedication, yet its application in both government and private institutions is increasingly rare. This study recommends stronger efforts to integrate the values of Tunjuk Ajar Melayu into local regulations and policies, in order to maintain the relevance of traditional values in the modern context.
LightGBM-Based Anomaly Detection System for Modern Network Traffic Using UNSW-NB15 Dataset
Authors: Venkata Sai Rahul Trivedi Kothapalli
Published: February 2025

Intrusion detection systems play a vital role in identifying unauthorized activities and safeguarding against emerging threats. This study highlights the capabilities of the UNSW-NB15 dataset, a modern benchmark for network anomaly detection, capturing diverse network traffic patterns. Unlike earlier research that often lacked uniform validation or relied on limited evaluation methods, we leverage the LightGBM classifier for binary anomaly detection, integrating advanced feature engineering, preprocessing, and selection techniques. Our approach was evaluated using various experimental configura- tions, including ten-fold cross-validation on training, testing, and combined datasets, yielding F1 scores of 97.21%, 98.33%, and 96.21%, respectively. Furthermore, when trained exclusively on the training data, our model achieved a 92.96% F1 score on the independent test dataset, demonstrating strong generalization to unseen data. Comparative analysis reveals that our method surpasses prior models across multiple metrics, underscoring its effectiveness in identifying anomalies within contemporary network environments.
Comparing Two Samples of Circular Data Using Watson's Test
Authors: Waad Mariai Yahya Alsultany
Published: February 2025

The investigation focuses on the spatial differences in wind directions: a comparison of circular wind data from two locations over a period of 30 days. Utilizing Watson's U² test, this non-parametric approach to circular data, questions whether directional variations occur between the two sites in a significant way. Initial exploratory data visualization with polar plots does suggest distinct wind patterns at each site: the first location is marked with a broad distribution of wind directions, whereas the second location exhibits clustering within certain angular ranges. Visual differences on these plots point to unique directional characteristics between sites, thus warranting further rigorous statistical analysis. The U² test statistic is then computed and compared with critical values to determine significance. This approach provides much more solid justification than simply observing spatial differences in wind pattern by eye alone. Results From the results, it is seen that there exists significant spatial variation in wind directionality between the two locations, hence a practical extension. Hazard assessment in meteorology stands to benefit from this result in adding value to existing weather forecast models. For environmental planning, especially in areas where wind direction affects dispersion, having accurate wind data will improve the accuracy in such planning. Understanding the details of localized wind patterns can also add benefit to the wind power industry because knowledge of such information informs about the best placement of turbines, site selection, and energy-capture strategies. The methodology used in this research study will allow the identification and analysis of spatial wind variations through Watson's U² test on circular data, thereby contributing to better decision-making in environmental, meteorological, and energy-related areas.
BERT-based Job Recommendation System Using LinkedIn Dataset
Authors: Liala Almalki
Published: February 2025

Amidst rapid technological progress, bridging the gap between job seekers and employers has become increasingly important as the job market evolves. This research presents a job recommendation system that leverages the Bidirectional Encoder Representations from Transformers (BERT) model, a powerful Natural Language Processing (NLP) framework. The system ensures precise and personalized recommendations by understanding the semantic relationships within job descriptions and user profiles. The model integrates contextual matching of skills and preferences, addressing the limitations of traditional content-based methods. Evaluation results demonstrate effectiveness in recommendation accuracy. This research proposes an alternative approach to the job-matching process by harnessing BERT’s bidirectional contextual capabilities. The presented work has significant implications for human resources platforms, job portals, and recruitment solutions in an increasingly digital workforce ecosystem.
Stock Market Forecasting with Differential Graph Transformer: A Novel Approach to Temporal and Spatial Stock Data Analysis
Authors: Satti Rama Gopala Reddy
Published: February 2025

Forecasting stock market trends are a difficult endeavor because of the intricate interrelations and ever-changing characteristics of financial markets. This paper presents the Differential Graph Transformer (DGT), a novel deep learning model that integrates temporal attention with differential graph attention methods to understand time-series dynamics and interstock relations. Utilizing global and local correlation matrices based on mutual information and Pearson coefficients, the DGT surpasses conventional models in forecasting stock prices. Experiments performed on the S&P500 dataset indicate that the DGT results in a 13.5% lower Root Mean Squared Error (RMSE) and a 12.2% reduction in Mean Absolute Error (MAE) when compared to baseline GRU models. Significantly, the DGT utilizing local mutual information matrices demonstrates the highest performance, validating its capacity to accurately model short-term interstock dependencies. This research highlights the capability of differential attention mechanisms in enhancing stock market predictions.
Advancing Security and Efficiency in IoT Healthcare Applications with Challenges Benefits and Latency Reduction Techniques
Authors: Safa Hussein Oleiwi
Published: February 2025

With the expansion of the Internet of Things (IoT) and cloud computing, conventional reliance on centralized cloud data centres for the storage, analysis, and real-time processing of vast data volumes presents significant challenges. This is particularly applicable to the elevated latency and stringent security demands necessary for real-time IoT healthcare applications. Critical and time-sensitive applications, including e-healthcare, telemedicine, and robotic surgery, necessitate ultra-low latency and stringent security measures. Suboptimal processing, connectivity, and networks impede the performance of these applications. Moreover, conventional cloud designs frequently fail to provide the necessary Quality of Service (QoS) for IoT healthcare systems. Consequently, this essay also explores latency reduction and security improvement techniques in IoT healthcare focusing on the need for information transmission in the respective areas. It wishes to list the first principles for approaches that reduce latency and protect communications, and computational architectures that can operate with such systems. It also explores the features that are crucial for understanding latency and security, as well as comparing several methods of addressing latency reduction and improvement in security alongside their effectiveness. It critically assesses previous approaches, identifies gaps in the literature and emphasizes unanswered questions in the study that may be useful in other works in this field. This research incorporates these findings into propelling concepts relative to IoT device connection and enhancing general paradigms relating to the better employment of healthcare applications. These findings support the need to design future IoT healthcare systems that will be latent sensitive, security-enhanced, and high-performance systems due to emerging characteristics of IoT in healthcare.
Effectiveness of Interactive Learning Methods in National Defense Education
Authors: Eka Yogaswara
Published: February 2025

The national defense curriculum has faced ongoing difficulties in effectively engaging learners and improving comprehension due to the reliance on traditional lecture-based teaching methods. Studies suggest that interactive learning techniques, including group discussions, localized case studies, and simulations, can greatly enhance both learner engagement and understanding. This research aims to assess the effectiveness of these interactive methods in improving educational outcomes in the context of national defense training. Using a qualitative literature review, data from scholarly articles and institutional reports were analyzed to compare the impact of interactive approaches against traditional methods. The results reveal that group discussions encourage collaboration and critical thinking, boosting comprehension by 30%. Localized case studies add real-world relevance by connecting theoretical concepts with practical applications, increasing comprehension by 40%. Simulations, which immerse learners in realistic scenarios, develop key skills such as teamwork and decision-making, resulting in a 50% rise in engagement levels. While challenges such as limited resources and the need for trained educators persist, leveraging digital tools presents a viable opportunity to expand the application of these methods. This study advocates for integrating interactive methods into the national defense curriculum, equipping educators with proper training, implementing digital learning platforms, and incorporating culturally relevant content to optimize learning outcomes. Interactive methods provide a transformative approach to fostering active participation and equipping learners for real-world challenges.
Pandemic Disruptions and Supply Chain Resilience: A Retail Food Perspective
Authors: Jaafar Pyeman
Published: February 2025

The COVID-19 pandemic has significantly disrupted global supply chains, particularly in the retail food sector, highlighting the need for resilient and adaptive supply chain management strategies. This study investigates the impact of pandemic-related disruptions on the Malaysian retail food supply chain, focusing on identifying key drivers of disruption and developing optimized mitigation strategies to enhance resilience. Using secondary data from annual reports of 15 Malaysian retail food companies, the research employs financial metrics such as Current Ratio, Quick Ratio, Net Profit Margin, and Return on Assets (ROA) to analyze pre-pandemic (2017–2019) and during-pandemic (2020–2022) performance. The study utilizes both correlation and regression models, including Linear Regression and Random Forest, to predict financial performance and assess the effectiveness of various supply chain strategies. Key findings reveal that liquidity management, operational efficiency, and cost control were critical factors influencing ROA during the pandemic. The analysis also highlights the importance of inventory management and fixed asset utilization in maintaining supply chain resilience. Furthermore, the research develops a machine learning model for predictive analytics, integrating the results into an interactive Power BI dashboard to facilitate data-driven decision-making. The dashboard provides visualizations of financial trends, model performance, and attribute contributions, enabling stakeholders to identify vulnerabilities and implement targeted strategies. The study concludes that businesses must adopt proactive measures, such as diversifying suppliers, optimizing inventory, and leveraging digital technologies, to mitigate the impact of future disruptions. By enhancing supply chain resilience, Malaysian retail food companies can better navigate economic uncertainties and ensure the continuity of operations in the face of global challenges.
FRICE: Enhancing AI Trust through a Robust Accountability System
Authors: Ravirajsinh Vaghela
Published: February 2025

In the paper presented, a mathematical framework has been formulated on the basis of the FRICE Model to allow the evaluation of accountability in AI systems across five dimensions of assessment-Fairness, Robustness, Impact, Compliance and Explanatory Effectiveness-becoming necessary currently. The framework proposed here focuses on algorithmic assessment methods to ensure transparency, reliability and ethical governance in AI-based decision-making processes. Accountability score computation in the FRICE model employs a structured mathematical method, such as fairness-by Statistical Parity Difference (SPD) and Equal Opportunity Difference (EOD)-robustness by adversarial accuracy tests-impact by positive and negative outcome assessments-compliance and explanatory effectiveness. These features embed the principles of ethics into the system by ensuring compliance with legal stipulations and clarity in decision explanations. This framework takes weighted aggregation techniques incorporating parameters into account and translates them into a holistic accountability score, reflecting the performance of the system as a whole. Each parameter is presented together with its detailed formulas to ensure reproducibility and adaptation to various AI applications. Trade-offs will equally hinge on how ethical considerations are deeply integrated into the designs with fairness, inclusivity, and transparency to mitigate biases and ensure equitable results.
An Optimized Genetic Neural Network for Brain Tumour Detection
Authors: B. Thimma Reddy
Published: February 2025

Brain tumour classification is an important function of medical research, contributing to timely and accurate treatment decisions. This research uses a convolutional neural network (CNN) optimized by genetic algorithms to classify MRI scanned images of brain tumors into four groups. The CNN algorithm uses hierarchical feature extraction layers, capturing complex tumor-specific patterns greater than traditional methods. By programmatically adjusting genetic algorithm learning rates and dropout rates, the models are normalized and enhanced in real-time to ensure efficient training, reduction of redundancy for the model work well, ensure high levels of input, dealing with changes in brightness and contrast. A grayscale MRI scans dataset was used to train and validate the system, demonstrating the ability to generalize to unseen data obtaining significant classification accuracy Visualization methods including confusion matrices and class-wise accuracy analysis for insights a delve into the predictive capabilities of the genetic algorithm Integration Therapy - highlights a new approach to improve the performance of CNNs in image analysis. The results highspot the capability of such machine learning methods to improve brain tumor diagnostic tools, providing a foundation for future developments in automated healthcare systems.
Mental Health and Employee Wellbeing: A Bibliometric and Thematic Content Analysis
Authors: Nikita Munda
Published: February 2025

Introduction: Mental health and employee wellbeing have become critical determinants of organizational success, significantly impacting productivity, engagement, and retention, particularly in high-pressure work environments. With escalating workplace stressors and global challenges, there is a growing recognition of the need for effective strategies to enhance employee wellbeing.
Objectives: This study aims to explore the evolving research landscape on mental health and employee wellbeing. It seeks to identify key themes, collaborative patterns, and gaps in the literature to provide actionable insights for improving workplace wellbeing.
Methods: The research employs a bibliometric and thematic content analysis from 1980 to 2024 of 254 scholarly articles published across 180 journals. A systematic review of collaborative research patterns involving 994 authors was conducted, coupled with a thematic analysis to identify recurring themes such as workplace culture, leadership support, and stress management practices.
Results: The findings indicate an annual growth rate of 8.34% in research publications, reflecting increasing interest in mental health and employee wellbeing. Collaborative research patterns and thematic analysis highlight the interconnected roles of workplace culture, leadership support, and stress management in promoting employee wellbeing. The study also identifies gaps in the literature, such as limited focus on cultural and industry-specific wellbeing strategies.
Conclusions: This research underscores the importance of mental health strategies in fostering healthier workplaces and improving employees’ quality of life. By bridging theoretical insights and practical applications, the study provides organizations with data-driven interventions to address wellbeing challenges. It contributes to the broader discourse on mental health within organizational frameworks and offers a roadmap for future research in this vital area.
Mindful Innovations: Exploring Employee Mindfulness, Risk Attitude, and Participation in Sustainable Practices
Authors: Nidhi Srivastava
Published: February 2025

Introduction: In the face of mounting environmental challenges, organizations increasingly recognize the importance of employee engagement in driving sustainability initiatives. Employee mindfulness has emerged as a key factor influencing risk attitudes and participation, thereby fostering sustainable innovation and enhancing organizational performance.
Objectives: This study aims to investigate the role of employee mindfulness in promoting sustainable innovation. Specifically, it examines how mindfulness influences risk-taking attitudes and participation in sustainability efforts, contributing to organizational performance and reputation.
Methods: Data were collected through structured questionnaires distributed via surveys to employees in the Indian energy sector, chosen through purposive convenience sampling. A total of 441 valid responses were analyzed using Smart PLS-SEM to evaluate the relationships among mindfulness, risk attitudes, participation, and sustainable innovation.
Results: The analysis reveals that mindfulness significantly enhances employees’ awareness, reflection, and willingness to take risks, enabling them to embrace uncertainties in sustainability initiatives. Furthermore, active participation in decision-making fosters a sense of ownership, which is critical for the successful implementation of sustainable innovations. Organizations that cultivate mindfulness among employees show improved engagement, performance, and reputation.
Conclusions: This study contributes to the theoretical understanding of mindfulness in workplace sustainability, with a particular focus on high-demand sectors like energy. It underscores the importance of mindfulness training to enhance employees’ risk-taking and participation capabilities, thereby addressing sustainability challenges and securing a competitive advantage.
Sectoral Analysis of Economic and Operating Efficiency Post Merger in the Indian Banking and IT Sector – A Quantitative Research with Special Focus to India
Authors: Akankshya Arun Das
Published: February 2025

Mergers and acquisitions (M&A) are strategic management decisions aimed at enhancing corporate performance by consolidating resources, reducing operational costs, and achieving economies of scale. The study examines the sectoral analysis of economic and operating efficiency in the Indian banking and IT sector post-merger, utilizing quantitative research to evaluate the performance changes. The objective of the study is to investigate the pre- and post-merger financial performance of selected banks and IT firms in India and to determine the influence of M&A on the Non-Performing Assets (NPA) ratio of Indian banks and IT firms. Additionally, it analyze the effects of the M&A announcement decision on the security prices of selected banks and IT firms. The research focuses on key financial metrics including liquidity position, non-performing assets (NPA), total investments, total assets, and security prices to assess the impact of M&A on economic and operational efficiency. Furthermore, the study highlights substantial increases in security prices for both banking and IT firms post-merger, indicating heightened investor confidence and market perception of enhanced operational synergies. The findings state that there is a significant difference in the financial performance of selected banks and IT firms in India before and after mergers, and M&A has a significant impact on the NPA ratio of India banks and IT firms, leading to a decrease in NPA levels post-merger. It also states that the M&A announcement decision has a positive effect on the security prices of selected banks and IT firms. These trends underscore the effectiveness of mergers in driving financial performance and operational efficiencies within these sectors of the Indian economy. Overall, the research contributes to the understanding of how strategic consolidations can lead to economic growth and improved operational effectiveness in the banking and IT sectors of India.
Advancements and Challenges in Epileptic Seizure Detection Techniques: A Survey
Authors: Pratibha Sonawane
Published: February 2025

Epileptic seizure detection is a crucial area of research aimed at identifying and predicting seizure events through advanced techniques, primarily utilizing electroencephalogram (EEG) signal. Despite significant progress, the field faces numerous challenges, including the need for diverse and comprehensive datasets, high computational complexity, and difficulties in generalizing models across various patient populations. This survey systematically reviews approximately 30 research articles, focusing on the methodologies employed, the challenges encountered, and the results obtained in seizure detection. By critically analyzing the strengths and limitations of existing approaches such as deep learning, machine learning, and hybrid models this research provides valuable insights into current practices and identifies opportunities for enhancing the effectiveness and reliability of seizure detection systems in clinical settings. Ultimately, the research aims to inform future developments in this vital domain, facilitating improved patient outcomes through timely and accurate seizure prediction.
Ridge Regressive Quadratic Multivalued Feature Matching Pursuit for Skill-based Employability Identification in Higher Education
Authors: Bijithra. N. C
Published: February 2025

Skill-based employability identification involves evaluating students' skills and determining their suitability for a specific job or industry. Data mining techniques have been developed for predicting student employability based on certain skills. Skills identification is a crucial step for students in understanding employability. However, accurate and time-efficient prediction of student employability has become a pivotal focus for educational institutions. This paper introduces a novel approach using data mining techniques called Ridge Regressive Quadratic Multivalued Projection Matching Pursuit (RRQMPMP) to identify skill-based employability for students in higher education with better accuracy and minimum time consumption. The proposed RRQMPMP technique includes two major processes namely preprocessing and feature selection. First, the number of features and student data are collected from the dataset. Then the preprocessing steps are executed, including three processes namely missing data handling, duplicate data removal, and normalization to clean the input dataset. The Ridge regressive imputation method is employed to handle missing data in the dataset. Subsequently, duplicate and non-duplicate data points are distinguished from the dataset using a simple matching distance measure. Finally, quadratic mean feature scaling is developed for the normalization process. With the preprocessed dataset, the feature selection step is performed by applying a Russell-Rao index multivalued projection matching pursuit. Based on the Russell-Rao similarity index value, pertinent and impertinent features are identified. Finally, pertinent features are selected for skill-based student employability prediction to achieve higher accuracy and minimize time consumption as well as space complexity. An experimental evaluation is carried out with respect to accuracy, error rate, time complexity, and space complexity for different numbers of student data. The quantitatively analyzed results indicate that the performance of the proposed RRQMPMP technique increases the accuracy of skill-based student employability prediction with minimum time and space complexity compared to conventional methods.
ConsensusPlus: Blockchain Driven Secure Data Sharing Protocol for Internet of Vehicles
Authors: B. Ramesh
Published: February 2025

The rapid expansion of the Internet of Vehicles (IoV) creates a distinct set of difficulties and opportunities for improving vehicular communication and data security. "ConsensusPlus" provides a revolutionary blockchain-based protocol for secure data sharing within the IoV. This protocol uses a proprietary consensus mechanism to enable real-time, reliable data sharing and integrity across a network of vehicles and infrastructure components. The integration of blockchain technology, which provides a decentralized framework for managing vehicular data in a transparent, immutable, and secure manner, is key to ConsensusPlus. By utilizing blockchain, the protocol improves data privacy and resilience to manipulation and cyber threats, which is critical in the IoV scenario where data sensitivity and security are key. ConsensusPlus also enables the smooth integration of existing vehicular communication protocols, such as Dedicated Short-Range Communications (DSRC) and Cellular V2X, with blockchain technology. This integration assures that the protocol is not only compatible with existing technologies, but also adaptable to future advances in vehicular communications. The ConsensusPlus protocol's performance has been evaluated through a number of simulations and real-world deployments, which have shown considerable gains in data security and operational efficiency inside the IoV. This protocol not only provides a reliable solution for secure data sharing, but it also promotes a scalable and efficient communication framework for the growing needs of the Internet of Vehicles. The implementation of ConsensusPlus could be a critical step in achieving a completely secure, decentralized, and efficient digital vehicular ecosystem.
Comparative Study of Common Core Curriculum Implementation in Urban and Rural Basic Schools in Ghana: Unveiling the Integration of Global Citizenship Education
Authors: Abudu Amadu Musah , Bariham Iddrisu , Ibrahim Mohammed Gunu
Published: February 2025

AbstractThis study examined stakeholders' perspectives on the integration of Global Citizenship Education (GCE) into the Common Core Curriculum (CCC) in urban and rural basic schools in Ghana. A quantitative research approach was adopted and data was collected from 460 participants through a structured questionnaire. The analysis employed t-tests, ANOVA and Fisher’s F-tests to interpret the findings. The t-tests revealed significant differences in collaborative learning (t (458) = 3.008, p = 0.003), competency-based assessment opportunities (t (458) = -2.461, p = 0.014), and stakeholder engagement (t (458) = -2.087, p = 0.037), all favouring urban contexts. To test for variance differences, Fisher’s F-tests indicated significant disparities, such as in critical thinking and problem-solving skills (F (212, 246) = 2.215, p < 0.0001) and interpersonal skills (F (212, 246) = 0.029, p < 0.0001), reflecting urban schools' advantage due to greater resource availability and support systems. The study concludes that, although the CCC aims to standardize educational quality, rural settings face significant limitations in its effective implementation. It was recommended that adequate resources, enhanced teacher training, and context-sensitive policies should be provided to rural schools to promote equitable educational outcomes and ensure that the curriculum meets diverse educational needs across Ghana.
Extracellular vesicles (EVs) in seminal plasma of buffalo bulls possess spermatozoa specific fertility associated proteins and enzymes
Authors: Kumar Rakesh , Ahmad Mir , Subramaniam Bala , Pal Ankit , Patel Aditya , Shiva Shiva , Chauhan Vitika , Karanwal Seema , Bhakat Mukesh , Datta Tirtha K.
Published: February 2025

Buffaloes are the backbone of the Indian dairy and live-stock industry. They contribute nearly 53% of milk and 30% of meat production in India. Bull fertility is vital to productivity, sustainability, and efficient buffalo farming. The current study aims to assess the extracellular vesicles (EVs) based fertility marker in the seminal plasma of buffalo bulls. The seminal plasma of buffalo bulls of contrasting fertility was utilized to purify EVs by size exclusion chromatography (SEC). Diffraction light scattering (DLS), nano-tracking assay (NTA), and Western blotting were used to confirm seminal plasma EVs. A pure population of EVs was obtained in 7-12 SEC fractions with an average EV size of 161nm. The NTA analysis provides a quantitative abundance of EVs, and the EVs population was 1.5x109 to 2.9x1011 EVs/ml in seminal plasma. Utilizing the Western blotting approach, the abundance of SPAM-1 protein in EVs was significantly high in high fertile (HF) bulls as compared to low fertile (LF) group (p <0.05). However, disintegrin and metalloproteinase domain-containing protein 7 (ADAM-7) and acrosomal protein SP-10 were not significantly different in seminal EVs of HF and LF bulls. Similarly, the abundance of Gelectin-3 and aldose reductase (AR) was comparatively higher in HF groups, although it was insignificant. The fertility factor, CD9 protein, was significantly abundant in the seminal EVs of HF vis a vis LF buffalo bulls, suggesting that CD9 of seminal EVs origin could help assess the fertility status of buffalo bulls. Enzymatic phospholipase A2 (PLA2) activity in HF was higher than in LF seminal EVs, indicating superior PLA2 activity in highly fertile bull semen. Uptake of EV-associated fertility proteins by spermatozoa was assessed. We observed that protein cargo was successfully assimilated with sperm. Finally, LC-MS/MS-based high throughput proteome was performed to identify the differential abundance of protein cargo in seminal EVs of contrasting fertility bulls. We identified the top twenty differently abundant proteins in seminal EVs of HF bulls vis-à-vis LF bulls. The identified proteins were strongly related to sperm functions. Thus, the present study supports that buffalo seminal EVs possess vital fertility-associated proteins and enzymes, which could be important markers for assessing buffalo bull fertility.
Association between sperm motility in thawed semen and pregnancy rate in Mediterranean buffaloes
Authors: Quintero-Moreno Armando , Osorio Meléndez Carla , Nava-Trujillo Héctor , Rubio Guillén Jorge , Gutiérrez-Añez Juan Carlos , Camacho de Gutiérrez Adriana
Published: February 2025

Progressive individual motility is the main parameter used to determine the reproductive potential of a buffalo bull. This information contributes to deciding to use and process or not the ejaculate obtained and therefore assume that the spermatozoa from the seminal sample could achieve pregnancy in inseminated buffaloes. The association of sperm motility to fertility and/or pregnancy rate (PR) has yielded controversial results. To contribute to knowledge in buffalo reproduction, an experiment was carried out where four sperm motility descriptors (VCL: curvilinear velocity, VSL: straight-line velocity, VAP: average path velocity, and LIN: linearity) were evaluated using a Computerized Semen Analysis System (Sperm Class Analyzer®, CASA System, Barcelona, Spain) in samples obtained from 8 frozen-thawed straws of 8 Mediterranean buffaloes. The PR data for each buffalo bull were obtained from 2 farms where the management and FTAI protocol employed was handled by the same professionals and straws of similar provenance and proper nitrogen thermos management were used. The PR data for each buffalo bull was confirmed by ultrasonography, organized in Excel® and analyzed with SAS®, associating VAP to PR, using a Chi-square analysis. To achieve the objective, the PR obtained was categorized from highest to the lowest value (A: PR>70%, B: PR>60≤70%, C: PR>50≤60%, D: PR>40≤50%), in addition to creating sperm subpopulations (Sp) based on the quality of sperm movement, for which it was decided to use the VAP, since it is a variable that integrates the VCL and the VSL and indirectly assumes the LIN, creating the following categories: Z= Sp with VAP value ≥ 80 mμ/s, Y: Sp with a VAP value ≥ 50 and < 80 mμ/s, X= Sp with a VAP ≥30 and < 50 mμ/s, W=Sp with a VAP ≥ 10 <30 mμ/s, V= VAP <10 mμ/s. The SAS® GLM procedure was used to assess the VAP velocity according to the fertility rate obtained. The results show that the buffalo bulls with high fertility (A and B) presented in their ejaculates sperm with the best VAP (Z: 14.6% and Y: 15.84%) and less quantity of immobile sperm (W: 7.14% and V: 1.63%) (P<0.01). In contrast, the buffalo bulls with lower fertility (C and D) had a considerable decrease in sperm with high velocity (Z: 4.84%) or immobile (6.24%) (p>0.01). There is an evident association between the average speed (VAP) provided by the CASA system and the PR in this species. This type of analysis provides the opportunity to quantify sperm movements and group them to generate more accurate sperm motility values, which allows the prediction of the fertility and/or PR in this species.
Are the acute phase proteins related to anestrus in primiparous water buffaloes? Preliminary results
Authors: Díaz-Huerta Frank , Meléndez-Becerra Anthony , Dijak Justin , Nava-Trujillo Héctor
Published: February 2025

Primiparous water buffaloes are a group with more riskfor low reproductive performance, mainly due to a long periodof postpartum anestrus, which could have a multifactorial or-igin. It is unknown if inflammatory diseases may promote thelong anestrus period in primiparous buffaloes. Therefore, this preliminary analysis aimed to compare C reactive protein andfibrinogen levels, two acute phase proteins, and body condi-tion score and cholesterol levels in primiparous water buffaloesduring the early postpartum and after a long anestrus period. Tothis, ten primiparous water buffaloes from a commercial farm inLa Fria, Tachira, Venezuela, were submitted to a reproductiveultrasonographic evaluation; no signs of uterine disease or cor-pora lutea were observed in any buffalo; and therefore, thesewere considered in anestrus and grouped as early postpartum(n=4, mean of postpartum days 15±2.70) or prolonged anestrus(n=6, mean of postpartum days 124.5±36.55, p< 0.05). More-over, the body condition score was evaluated using a 1 to 5scale with 0.25 increments (1 = thin, 5 = fat), and blood sam-ples were collected to measure the levels of C-reactive protein(by ELISA), fibrinogen (by Clauss assay), and cholesterol (byColorimetric method). All the means were compared by T-testusing R studio; additionally, a correlation analysis was carriedout. No differences were observed for any of the studied vari-ables. Body condition score was lower than 3 points in bothgroups (2.31±0.51 and 2.33±0.37 for early and late postpartum,respectively, p=0.947). The cholesterol level was 142.75±9.74mg/dL and 156±12.26 mg/dL, respectively (p=0.133). C-reac-tive protein averaged 60±24 mg/dL and 33±18 mg/dL for earlyand late postpartum buffaloes, respectively (p=0.137). At thesame time, fibrinogen levels were 318.5±161.77 mg/dL forearly postpartum and 318.83±69.78 mg/dL, respectively (p =0.914). C-reactive protein and cholesterol levels were negative-ly correlated (r = -0.71, p=0.048). Because no differences wereobserved in the variables studied in both postpartum moments,it is possible to suggest that a body condition score lower than3.0 and persistent levels of C-reactive protein, fibrinogen, andcholesterol could be related to a prolonged period of anestrusin primiparous water buffaloes. However, given the reducednumber of animals included and the absence of cyclic females,the results reported should be taken cautiously and verified ina larger population.
Integrasi Nilai Kristiani dalam Pendidikan Kewirausahaan: Membentuk Karakter Wirausahawan
Authors: Ariandy Vincentius , . Mario , Tarihoran Emmeria
Published: February 2025

Pada era digital ini segala bidang yang ada di dunia mulai berkembang dan teknologi yang ada memperngaruhi hidup manusia, begitu juga dalam bidang keagamaan. Pada sharing regio jawa keuskupan malang dalam PKKI ke 12 menemukan bahwa orang muda katolik harus mulai ikut andil dalam tugas pewartaan di era digital. Gereja ingin agar Orang Muda Katolik di ajak untuk terlibat dalam pewartaan dalam media digital. Dalam dokumen Internet: A New Forum for Proclaming the Gospel oleh Yohanes Paulus II menyampaikan bahwa gereja harus mulai berani menyebrangi lautan dalam era digital, artinya gereja harus berkembang sesuai dengan perkembangan jaman demi keselamatan semua umatnya. Sebagai calon katekis dan masa depan gereja mahasiswa STP- IPI Malang tentunya memerlukan keterlibatan itu. Penelitian awal yang telah dilakukan terlihat bahwa lebih dari 90% mahasiswa STP- IPI Malang memiliki media sosial Instagram. Maka penulis mengangkat tema pengaruh media sosial Instagram terhadap keterlibatan Mahasiswa IPI Malang Prodi PKK dalam pewartaan. penelitian ini menggunakan metode kuantitatif dengan pengolahan data 60 responden menggunakan analisi Regresi.
Mengintegrasikan Katekese, Pastoral, dan Tindakan Sosial: Model Pendampingan Katekis untuk Menciptakan Transformasi Umat
Authors: Saputra Yohanes Chandra Kurnia
Published: February 2025

Latar belakang penelitian ini berfokus pada pentingnya integrasi katekese, pastoral, dan tindakan sosial dalam membentuk umat Katolik yang bertanggung jawab sosial dan berlandaskan iman. Tujuan penelitian ini adalah untuk mengeksplorasi bagaimana ketiga elemen tersebut dapat bekerja bersama dalam mengubah kehidupan umat, baik secara spiritual maupun sosial. Metode yang digunakan adalah studi literatur yang mengkaji berbagai teori dan praktik terkait katekese, pastoral, dan aksi sosial dalam konteks Gereja Katolik. Hasil penelitian menunjukkan bahwa integrasi katekese, pastoral, dan tindakan sosial memiliki dampak positif dalam membentuk karakter iman umat Katolik. Ketiga elemen ini saling mendukung dalam memperdalam pemahaman agama dan mendorong partisipasi umat dalam kegiatan sosial. Kolaborasi yang baik antara ketiganya mampu menciptakan perubahan positif dalam kehidupan spiritual dan sosial umat. Simpulan penelitian ini menunjukkan bahwa integrasi katekese, pastoral, dan tindakan sosial merupakan kunci dalam pembentukan karakter iman umat Katolik. Implementasi yang efektif dari ketiga elemen ini dapat meningkatkan kedalaman spiritual dan keterlibatan umat dalam kegiatan sosial.
Intervensi Gereja Katolik dalam Menyelesaikan Kasus Perselingkuhan: Studi Kontekstual di Manggarai
Authors: Febrian Mulyadi Angsemin  , Robertus Mirsel
Published: February 2025

Gereja Katolik berperan strategis dalam membentuk tatanan sosial dan moral masyarakat Manggarai, terutama dalam menyelesaikan konflik seperti kasus perselingkuhan yang mengancam stabilitas keluarga. Pendekatan inkulturasi, seperti penggunaan elemen adat Compang dalam ajaran Injil, memungkinkan harmonisasi nilai agama dan budaya lokal. Dalam penyelesaian perselingkuhan, Gereja berfungsi sebagai mediator, bekerja sama dengan tokoh adat untuk menyelesaikan konflik secara damai. Melalui homili, pendidikan keluarga, dan sakramen rekonsiliasi, Gereja mengajarkan nilai kesetiaan pernikahan. Selain itu, kelompok kategorial seperti Marriage Encounter memberikan pendampingan spiritual bagi pasangan bermasalah, memperkuat komitmen berdasarkan pandangan pernikahan sebagai sakramen. Meski demikian, resistensi terhadap inkulturasi menjadi tantangan, menuntut pendekatan yang lebih inklusif. Penelitian ini mengeksplorasi bagaimana teologi kontekstual Gereja berkontribusi pada resolusi konflik dan harmoni sosial di masyarakat Manggarai.
Deklarasi Istiqlal dan Reinforcement Dialog Inter-religius di Indonesia
Authors: Koli Donatus Doni
Published: February 2025

Pada 5 September 2024, di Masjid Istiqlal, berlangsung peristiwa bersejarah yang disaksikan oleh para pemimpin dan tokoh multiagama, di mana Pemimpin Gereja Katolik Sedunia, Paus Fransiskus, dan Imam Besar Masjid Istiqlal, Nassarudin Umar, menandatangani Deklarasi Bersama Istiqlal. Dokumen ini menjadi simbol kuat komitmen lintas agama dalam memperkuat dialog, membangun perdamaian, serta meneguhkan peran agama dalam menjawab isu sosial-kemanusiaan di Indonesia. Karya tulis ini mengkaji makna dan implikasi deklarasi tersebut sebagai fondasi bagi penguatan dialog inter-religius, yang tidak hanya sebatas wacana, tetapi juga mendorong kerja sama konkret dalam membangun masyarakat harmonis dan inklusif. Dengan menganalisis isi deklarasi serta konteks sosialnya, tulisan ini menunjukkan bahwa peran agama yang aktif dalam menjawab tantangan zaman dapat memperkuat sinergi antarumat beragama. Deklarasi ini diharapkan menjadi inspirasi bagi inisiatif dialog dan aksi nyata demi menciptakan kehidupan yang lebih damai dan berkeadilan.