March 2025

This study examines the role of meditation in students' emotional intelligence, self-awareness, and individual development.The study was conducted at Excel Public School, Kathmandu, using a qualitative, descriptive approach. The school was chosen through purposive sampling, and data were collected from stakeholders over six months through interviews, focus group discussions, and observations.According to research, meditation dramatically enhances kids' capacity to focus, manage difficult emotions, act appropriately in class, and be socially adaptive. Teachers saw increases in attention, patience, and interpersonal interactions, while students reported feeling less stressed, more self-aware, and more confident about their ability to study. Despite challenges like time constraints and administrative roadblocks, the effectiveness of mindfulness treatments is maximized through systematic, teacher-delivered sessions and continuous feedback.A mindful classroom environment generates emotional resilience, reduces acting-out, and promotes collaboration among students. Expanding meditation programs requires highly trained instructors who can modify techniques across ages and integrate mindfulness into curriculum. New developments could include technology-based tools and interdisciplinary approaches to maximize long-term impact. This study highlights the importance of sustained mindfulness programs in education; pointing to meditation's capacity for creating a supportive and engaged learning environment.

The advent of cloud computing in healthcare has made it possible for storage to become scalable and cost effective, which allows sensitive healthcare data to be accessed remotely and promotes collaboration among healthcare providers. The challenge in this data during the time of storage and retrieval will need to be secured and have privacy because it is at risk from cyber threats and unauthorized access existing solutions that do not protect healthcare data from vulnerabilities during decryption and usage.The study proposes developing a methodology for retrieving healthcare data from cloud storage for sensitive information. The system generates a key that will enable a decryption process by using the Blowfish algorithm in order to perform fast and secure decryption of healthcare data. Auditing and monitoring mechanisms are implemented to keep track of unauthorized activities while Threat Intelligence Platforms(TIPs) are used for detecting threats in real time. Further, sensitive information is protected from other forms of misappropriation through data masking. Thus, it suffices to say that all these components make secure handling of healthcare data in compliance with privacy regulation possible.The outcomes show that the key generation time increases linearly with the increase in prime sizes and varies from 200 ms for 0 bits to 1400 ms at 2000 bits. Similarly, the decryption times have risen with the rise of the number of files: the decryption time is 0.82 seconds for 0 files at one point, whereas, for 1000 files, it goes up to 9.33 seconds. There is a suitable match between the predicted CPU usage values and the actual observed values, and thus it confirms the efficiency of the system.This study provides secure methodologies for retrieving health care data from clouds using decryption, monitoring, and masking for sensitive information. This would add value by giving a framework for acquiring fairly secure health data from the cloud in a way that advances health data protection and minimizes vulnerabilities.

Financial fraud activities are a serious threat to the security and integrity of online banking systems. Traditional fraud detection approaches, such as rule-based and simple machine learning models, are not effective in detecting changing patterns of fraud and suffer from high false positive rates and scalability. To overcome these drawbacks, this research introduces BankSafeNet, a Dual-Autoencoder and Transformer-Based Anomaly Detection System for detecting financial fraud. The suggested framework utilizes a dual-autoencoder architecture to learn transaction patterns and identify anomalies, while a transformer-based classification model learns sequential relationships in transaction data. The system provides a fraud probability score and marks suspicious transactions for investigation. Measured on the PaySim dataset, the developed model records 99.45% accuracy, 99.54% precision, 99.37% recall, and 99.45% F1-score, performing much better than conventional fraud detection methods. The model also has a false positive rate (FPR) of 0.469% and a false negative rate (FNR) of 0.634%, which prove it to be highly resilient in terms of reducing false positives while its fraud detection correctness remains high. The findings demonstrate the effectiveness of BankSafeNet in furnishing an scalable, real-time fraud detection platform that complements financial security of digital transactions.

Ransomware attacks have emerged as a major cybersecurity threat in terms of the massive financial and data losses it has inflicted across the globe. Such attacks cannot easily be detected by traditional detection techniques, including signature-based and rule-based detection, because these are issues that rely heavily on predefined characteristics and static rules for their identification purposes. These were thus conventional systems that turned out to be poor in adaptability, having high false-positive rates, and insufficient detection when it came to the ever-evolving ransomware attacks. To overcome such limitations, we introduce in this study an improved framework for detecting and preventing ransomware through deep behavioural analysis using Convolutional Neural Network Bidirectional Long Short-Term Memory (CNN-BiLSTM). Here, the CNN would extract spatial features from different system activity logs, whereas the BiLSTM would capture sequential dependencies to improve the accuracy and robustness of the detection. The current proposed system identifies behaviour related to the ransomware domestication instantly and further integrates it with prevention and response mechanisms to counteract the threats before encryption either occurs or can take place. The experimental results indicate that the method realizes the detection accuracy level of 97.5%, which beats the traditional model. The proposed approach outperforms the traditional methods with 18% improved detection rate and 22% of false-positive reduction, making ransomware defence much more reliable. This contribution to much-needed next-generation protection against ransomware is scalable, intelligent, and proactive, thus increasing cyberspace resilience against sophisticated ransomware threats in real-world applications.

Introduction: This research examines the Santhal tribe of India while exploring their cultural and sociological patterns with indigenous populations from countries within South and Southeast Asia which include Nepal, Bangladesh, Myanmar, Thailand and Vietnam. Objectives: This research objective evaluates common traditions within these groups along with their oral storytelling, farming activities and religious beliefs and their ecological stewardship. The ethnographic research compared different cultural groups through qualitative analysis of data that included literature review findings and interview and observational data to find common patterns between cultures and their resilience measures. Discussion: The study shows that community structures alongside sustainable resource management alongside traditional beliefs formed a common cultural value system to protect the environment. The exchange analyzes both the difficulties that globalization along with modernization creates by causing displacement and cultural erosion and shows indigenous adaptability and cultural blending to sustain their traditions. Conclusion: The study demonstrates that indigenous tribes provide essential knowledge about sustainable practices and biodiversity protection and cultural unity which should be integrated into worldwide sustainability systems. Researchers and officials handling environmental advoccacy and cultural protection can use this study as their reference point.

Efficient resource allocation is crucial for cloud data centers performance, scalability, and cost effectiveness. Traditional load balancing systems frequently fail to adjust changing workloads resulting in poor resource use, increased latency and excessive energy consumption. Adaptive Load Balancing Algorithm that optimizes resource allocation in cloud systems is implemented. It combines heuristic and metaheuristic techniques to efficiently distribute workloads among virtual machines resulting in lower response times and balanced computing loads. Centralised Control System monitors real time system data and dynamically reallocates resources in response to workload needs. Performance evaluation shows suggested solution reduces delay by 35 percentage from 120 ms to 85 ms and reduces energy consumption by 30 percent from 200W to 140W. This increases efficiency of resources from 65 to 98 percent. Suggested adaptive technique improves distribution of loads, energy usage and level of service in cloud computing settings. It provides fault tolerance by dynamically shifting jobs during server failures, reducing downtime and increasing system dependability. Comparative research with existing load balancing approaches confirms its advantages in managing large scale dynamic workloads demonstrating suitability for implementation in cloud data centers.

The disaster management includes a complex set of activities, which are carried out both in the pre and post disaster stage.The disaster emergency management practitioners and scholars refer to these activities as the four phases of the comprehensive emergency management cycle, which include mitigation, preparedness, response and recovery. Mitigation and preparedness are pre-disaster activities, while response and recovery constitute post-disaster activities.The governmental institutions response role is very important than the private sphere activity for handling the disaster management.The Kerala(state) floods took place in the month of august ,2018. Bothe the Kerala(state)government and the central government of Indiahad played an immense response role by various policies for successfully dealtthis state’sfloods in 2018 in India.

Effective community development requires active participation and ownership from local stakeholders. In Nigeria, Ward Development Committees (WDCs) have the potential to drive sustainable community development and social impact. However, these committees often face various challenges in terms of capacity, resources, and effectiveness. This study aims to empower WDCs in Nigeria, enhancing their ability to promote sustainable community development and social impact. By strengthening community governance and promoting participatory development, this initiative seeks to improve the lives of citizens and contribute to Nigeria's sustainable development goals. Ward Development Committees are critical to improving health outcomes and fostering sustainable community development in Nigeria. This study evaluates an intervention aimed at strengthening WDCs to enhance maternal, newborn, and child health (MNCH) outcomes and fortify community health systems. Using a mixed-methods approach, the study was conducted across five states: Bauchi, Ebonyi, Sokoto, Kebbi, and the Federal Capital Territory (FCT). The interventions comprised capacity-building, resource mobilization, community engagement, and implementation of optimized MNCH Week activities. Key achievements included the training of 510 WDC members, transportation of 33,508 pregnant women for antenatal care, and mobilization of over 44 million NGN for health initiatives. Additionally, 606 sensitization activities that reached 24,860 individuals, significantly increasing community participation. However, challenges such as disparities in WDC performance, resource constraints, and weak coordination mechanisms were identified, alongside limitations in state-level support and follow-up mechanisms. To address these issues, the study recommends tailored capacity-strengthening programs, enhanced collaboration with local governments, and increased funding for WDC activities. The findings underscore the transformative potentialities of empowering WDCs to strengthen health systems, reduce maternal and child mortality, and achieve sustainable, community-driven development in Nigeria.

The Apology Strategies of Malaysian and Iraqi undergraduate students represent an investigation of cultural norms which affect spoken Apology Speech Acts. The study evaluated apology methods used between Malaysian and Iraqi students while studying the effects of collectivism and hierarchy on these techniques. The research included 120 participants sorted into equal groups of 60 students from Malaysia and Iraq ranging in age from 18 to 23 who studied different academic subjects. Data collection used Discourse Completion Tasks (DCTs), surveys, semi-structured interviews, followed by combined quantitative and qualitative analysis of these data. The study produced substantial variations between the chosen apology methods of both populations. The majority of Malaysian research participants (58.33%) deployed indirect apologetic approaches that incorporated both social harmony markers and hedging techniques due to their collectivist and harmonious cultural values. The Iraqi student participants showed preference toward directness (63.33%) as well as formal language methods that explicitly expressed responsibility, in accordance with their society’s hierarchical structure. The research demonstrated Malaysian students worked to protect their group harmony yet Iraqi students emphasized individual performance combined with proper respect to authority figures. The research results affect both intercultural communication practices as well as second-language acquisition methods. Language education professionals benefit from cultural enlightenment about apology strategies to create training materials and educational curricula. The findings gather from this study help people from different cultures understand each other better while reducing communication errors between people of different backgrounds. The research adds knowledge to cross-cultural pragmatics through its exploration of cultural forces that direct language strategies in apology situations.