Volume 13 | Issue 4

 Abstract

This literature review examines the relationship between spirituality and well-being in workplace settings, focusing on studies published between 2004 and 2024. Spirituality at work encompasses an individual's quest for meaning, purpose, and connectedness through their professional roles. Research indicates that integrating spiritual practices and values into the workplace can significantly enhance employee well-being and organizational performance. Key findings suggest that workplace spirituality contributes to various dimensions of well-being, including psychological resilience, job satisfaction, and overall mental health. Practices such as mindfulness, meditation, and reflective thinking have been linked to reduced stress and burnout, fostering a more engaged and productive workforce. Moreover, organizations that embrace spiritual values like justice, integrity, and compassion often experience improved morale and stronger interpersonal relationships among employees. The review also highlights the importance of aligning personal and organizational values to create a supportive work environment. Employees who perceive their workplace as supportive of their spiritual growth are more likely to experience higher levels of job satisfaction and reduced stress. Additionally, fostering a sense of community and belonging within the workplace contributes to enhanced emotional stability and job performance. In conclusion, integrating spirituality into workplace practices offers a holistic approach to enhancing employee well-being and organizational effectiveness. Future research should explore the differential impact of various spiritual traditions on workplace outcomes and investigate how organizations can strategically incorporate spiritual accommodations to support a more engaged and fulfilled workforce.

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The Role of Spirituality and Meditation in Enhancing Employee Well-Being and Reducing Stress: A Literature Review

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ABSTRACT: Scientific progress in electronic commerce and communication networks led to credit cards becoming the most popular payment instrument supporting in-store and electronic purchasing transactions. The number of credit card payment fraud incidents has substantially escalated because of current transaction trends. The yearly financial losses from fraud damages credit card issuers to such an extent that they need an advanced system with a complete fraud detection framework. Electronic payments have become a leading factor that drives the increase in fraudulent transactions. The urgent need exists for new methods which can detect credit card transaction fraud before it happens. Our fundamental research develops a transaction fraud system which integrates machine learning elements with original feature modification strategies. Our method received validation through experiments conducted with two real-world public credit card transaction datasets where one of them contained actual fraud cases. Our system achieved the highest success rate compared to other competing methods when processing both datasets. Our proposed method proves highly effective based on the achieved results. The tool from our approach strengthens credit card issuer capabilities to recognize fraudulent transactions thus safeguarding their customers while simultaneously minimizing loss and regulatory expense. A protected transaction environment for credit cards can emerge from implementing the procedures we have recommended.

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CCFD = Credit Card Fraud Detection ML = Machine Learning

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The Israel-Palestine conflict remains one of the most enduring and complex disputes in modern international relations, marked by a long history of territorial contestation, population displacement, and cycles of violence. Amidst escalating tensions and repeated allegations of human rights abuses, the International Criminal Court (ICC) has emerged as a potential mechanism for legal accountability. This study investigates the role and effectiveness of the International Criminal Court in addressing serious international crimes--genocide, war crimes, and crimes against humanity--within the context of the Israel-Palestine conflict. The research begins by exploring the historical roots of the conflict, marked by colonial legacies, territorial disputes and repeated outbreaks of violence, particularly in Gaza and the West Bank. It examines how both Israeli military operations and Palestinian armed groups have been accused of violating international humanitarian law, resulting in civilian casualties, forced displacement, infrastructure destruction. The study focuses on the ICC's legal framework under the Rome Statute and its jurisdiction over crimes committed in Palestine following its accession to the ICC in 2015. It explores the complexities of prosecuting crimes in a politically sensitive and asymmetrical conflict, where powerful actors like Israel and the United States reject the Court's jurisdiction. The principle of complementarity, which requires the ICC to act only when national systems fail to prosecute, is critically assessed, particularly in light of claims that Israel's domestic investigations lack impartiality. Through a case study analysis, this study evaluates the ICC's progress, challenges, and limitations in investigating alleged atrocities committed by both Israeli forces and Palestinian groups. It draws comparisons with other ICC cases, such as Sudan and Ukraine, to highlight patterns of geopolitical influence and selective justice. Finally, the study contends that while the ICC promotes global accountability and deters impunity, its effectiveness is limited by political pressures and enforcement gaps. Legal tools alone are insufficient; long-term peace and justice in Israel-Palestine necessitate international cooperation, grassroots advocacy, and political solutions that address deep-seated inequalities. The study concludes with recommendations for reforming the ICC and improving its ability to handle complex conflicts.

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War crimes, Genocide, Crimes Against Humanity, International Criminal Court (ICC), Israel-Palestine conflict, International Humanitarian Law

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Preterm infants are exposed to an atypical sensory environment in the neonatal intensive care unit (NICU), which places them at risk for disrupted neurobehavioral development. Emerging evidence highlights the crucial role of early physiotherapy-led sensory modulation strategies in promoting organized brain development and optimizing behavioral outcomes. This narrative review explores the current understanding of sensory modulation, reviews physiotherapy-based interventions in NICU, discusses their impact on neurobehavioral outcomes, and identifies future directions. Although the body of evidence remains limited and heterogeneous, findings support the integration of structured sensory-based physiotherapy practices within NICU care to improve long-term developmental trajectories in preterm infants.

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Keywords: preterm infants, physiotherapy, sensory modulation, neurobehavioral development, neonatal intensive care unit.

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This paper introduces a Smart Waste Management System (SWMS) aimed at optimizing waste sorting and processing through automation. The system utilizes advanced sensor technologies, including metal, moisture, and color sensors, to accurately identify and classify waste materials on a conveyor belt. An IR sensor controls waste separation, directing materials into designated chambers for further processing. Organic waste is incinerated in a combustion chamber, generating heat that powers a Thermoelectric Generator (TEG) for energy recovery. Food waste is routed to a bio-gas chamber for anaerobic digestion, producing renewable bio-gas. This integrated approach not only enhances sorting efficiency but also reduces landfill dependence and contributes to sustainability by recovering energy from waste, offering an eco-friendly, scalable solution to modern waste management challenge.

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Smart Waste Management, Automated Sorting, Sensor Technologies, Energy Recovery, Biogas Production, Thermoelectric Generator (TEG), Waste-to-Energy, Sustainability, Waste Separation, Anaerobic Digestion and Renewable Energy.

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The proposed BIST architecture focuses on minimizing power consumption during the testing phase while maintaining high fault coverage. It leverages the capabilities of the Verilog hardware description language to model and simulate the design. The research investigates various power reduction techniques, including test pattern compression, selective clock gating, and power-aware test scheduling, to optimize power consumption during testing. This research paper presents the design and implementation of a Built-In Self- Test (BIST) architecture specifically tailored for low-power Very Large-Scale Integration (VLSI) applications. The increasing demand for energy-efficient electronic devices and the proliferation of portable systems have necessitated the development of power-aware design techniques. BIST, a onchip testing technique, plays a crucial role in ensuring the quality and reliability of integrated circuits. The implementation is performed on a field-programmable gate array (FPGA) platform, demonstrating the feasibility and practicality of the proposed design. The research contributes to the field of low- power VLSI design by providing a comprehensive BIST architecture that effectively reduces power consumption during testing. The proposed design is compatible with standard Verilog synthesis and is readily applicable to a wide range of low-power VLSI applications. The findings from this study can guide designers in developing energy-efficient and reliable integrated circuits, promoting sustainability, and extending battery life in portable devices.

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BIST Architecture , Low Power Emphasis, Verilog Programming, Autonomous Testing, Functional and Power Testing.

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Cerebral palsy (CP) is a common cause of motor and postural disorders in children. Kinesio taping (KT) has emerged as a relatively new therapeutic technique used in the rehabilitation of children with CP, often as an adjunct to other therapies. This narrative review explores the evidence regarding the effectiveness of trunk KT application in improving various aspects in children with CP, such as sitting posture, trunk control, balance, and functional mobility, based on available research. Evidence from randomised controlled trials and pilot studies suggests that trunk KT may offer benefits in certain areas, particularly sitting posture and dynamic balance, when integrated into rehabilitation programmes.

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Keywords: Cerebral palsy, Kinesio taping, Paediatric rehabilitation, Physiotherapy, Trunk control.

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Modern lifestyles expose individuals to various environmental and chemical toxins through processed foods, intoxicating beverages, food adulterants, preservatives, pesticides, and synthetic drugs. Combined with irregular dietary and sleep patterns, these factors contribute to declining immunity and the rise of chronic diseases. If not efficiently eliminated, these toxins accumulate in the body, transforming into Dushivisha-a latent form of toxicity that disrupts physiological balance. Allergic skin diseases are a significant health concern affecting individuals across all age groups, primarily resulting from hypersensitivity of the immune system to specific physical or chemical agents. Classical medical texts, including the Charaka Samhita and Sushruta Samhita, provide detailed insights into the role of Dushivisha in the pathogenesis of allergic skin disorders. Various dermatological conditions attributed to Dushivisha are documented in these texts, including Kustha (a broad category of skin diseases), Mandal (raised circular lesions), Visharpa (erysipelas-like eruptions), Bhinna Varna (skin discoloration), Shonit Dushti (vitiation of blood), Kitibha (psoriasis-like conditions), and Kotha (urticaria or hives).The manifestation and progression of these conditions are influenced by aggravating factors such as exposure to eastern winds, indigestion, cold temperatures, daytime sleep, and the consumption of unwholesome or incompatible foods. These factors contribute to the persistence and exacerbation of Dushivisha, leading to chronic skin hypersensitivity and inflammation. Understanding the role of Dushivisha in allergic skin disorders underscores the importance of detoxification, dietary regulation, and holistic interventions. This review explores the classical perspective of Dushivisha and its relevance to modern dermatological conditions.

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Ayurveda, Dushivisha, Allergic Skin Diseases, Cumulative poison, toxins

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Liposomal drug delivery systems represent a significant advancement in modern pharmacotherapy, providing controlled and targeted delivery of both hydrophilic and hydrophobic drugs. Due to their biocompatibility and ability to encapsulate various therapeutic agents, liposomes have found applications across oncology, infectious diseases, and gene therapy. This review explores the classification of liposomes, advances in drug loading strategies, surface modifications, clinical uses, and future research directions. While several formulations have already gained FDA approval, challenges in manufacturing, stability, and regulatory approval persist. Ongoing innovations, such as stimuli-responsive liposomes and personalized nanomedicine, are poised to further elevate liposomes as a cornerstone in drug delivery technology.

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Liposomes, Drug Delivery, PEGylation, Targeted Therapy, Nano medicine, Controlled Release

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In the Present study an attempt has been made to investigate the Impact of Family environment on Emotional intelligence of Pre-Service secondary school teachers. To achieve the objective of present study a sample of 1113 Pre-service secondary school teachers selected randomly from different B.Ed. Training institutions situated in five districts of Himachal Pradesh. The requisite data is collected from the selected subjects by using the Emotional Intelligence Scale constructed and standardized by the researcher herself and Family Environment Scale developed by Bhatia and Chadha (2012). For analysis of the data, the statistical technique Independent sample t-test was employed. The findings of the study revealed that type of family environment significantly affects the emotional intelligence of Pre-service secondary school teachers. The present study further highlights the suggestions for creating good family environment as well as interventions that promote emotional intelligence among pre-service secondary school teachers.

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Emotional Intelligence, Family Environment, Pre-service secondary school teachers.

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Natural Language Processing (NLP) has emerged as a transformative field within artificial intelligence, enabling machines to understand, interpret, and generate human language. Recent advancements in NLP, driven by deep learning architectures and large language models (LLMs), have significantly enhanced its capabilities across various domains. This paper explores key trends shaping the field in 2025, including real-time language translation, fine-tuning of deep learning models for specialized tasks, semantic search optimization, reinforcement learning applications, and the integration of NLP with other AI domains such as computer vision and robotics. Additionally, the paper highlights practical applications of NLP in healthcare (e.g., medical record analysis and telemedicine), tourism (e.g., automated translation services), and customer service (e.g., chatbots and sentiment analysis). Despite its rapid progress, NLP faces challenges related to bias, explainability, and ethical considerations. Future research directions emphasize transparency, fairness, and scalability to ensure responsible development and deployment of NLP technologies.

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applications,architectures,scalability,analysis,nlp,optimization learning domains,robotics,technologies

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Automated data governance's role in helping cloud based financial data lakes achieve compliance and security is researched. The research addresses the way automated systems handle challenges such as data quality, privacy and regulatory compliance. The scalability of disposal systems is also authorized to help such systems adapt to more and more volumes of data and exchange in regulations. There is less human error involved in data disposal and it continues to be monitored mechanically and approach is inside limits in terms of ensuring data security. The research determines recommended practices for implementing automated disposal frameworks that prioritized maximal data shelter and regulative compliance. Maintaining the unity and credentials of the fiscal data are authorized in the ever-changing regulative environs and these are authorized practices.

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financial data lakes, automated data governance, data quality, compliance, security, scalability, governance frameworks, privacy, data protection, regulatory compliance.

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The study explores the budgeting challenges and financial habits of CA Article students in Mumbai, a city known for its high cost of living. It is important to understand the budgeting habits of students undergoing CA practical training in Mumbai and face financial challenges. CA Articleship is a very vital part of any CA Student's journey and hence it is important to focus on the problems students face in this period. The purpose of the study is to examine the income, expenses & saving pattern of students and to identify challenges and strategies used for financial management. A structured questionnaire was used to collect primary data and interviewed a few students as well after which inferences have been drawn. Data from surveys and interviews reveal that while stipends are a primary income source, they are often insufficient to cover essential expenses like rent and transportation. The findings highlight the need for better financial planning tools and institutional support to alleviate these challenges, ultimately contributing to improved financial well-being among CA students.

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Budgeting, CA Articleship, Cost of Living, Financial Management.

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Customer loss or churn is an enormous issue for most firms currently. Regarding this, the computation of the churn rates will most definitely help the level of customers retained in and also improve the level of profit generation. It explains the most often-used algorithms of machine learning algorithms: logistic regression and random forests for modeling churning from historical data. It elaborates on specific features like demographics and history of transactions, which help to increase results on this model. That too, at a machine learning platform, if proactive retention exists, outputs are even more superior compared to normal practice. Further work might be related to incorporating real-time analytics along with deep learning to create even better predictions for predictability related to churn in the future

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Customer Churn Prediction, Machine learning, Logistic Regression, KNN Algorithm, Random Forest Algorithm, ROC Curve.

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Abstract Cloud computing has revolutionized data storage and sharing by providing scalable and cost-effective solutions. However, ensuring the security of shared data remains a critical challenge due to threats such as unauthorized access, data breaches, and insider attacks. This paper explores various security mechanisms for secure data sharing in cloud environments, including encryption techniques, access control models, identity management, and blockchain-based solutions. Furthermore, it analyzes the role of homomorphic encryption, attribute-based encryption (ABE), and multi-factor authentication (MFA) in enhancing security. The paper also discusses emerging threats and potential future advancements in securing cloud data sharing. The findings highlight the importance of integrating robust cryptographic techniques with advanced access control policies to achieve confidentiality, integrity, and availability in cloud-based data sharing systems.

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Keywords: Cloud computing, Data security, Encryption, Access control, Blockchain, Secure data sharing.

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In addition to being dangerous, skin conditions including melanoma, eczema, and impetigo are frequently communicable, so early and precise diagnosis is essential for successful treatment. However, diagnosis usually calls for specialized understanding of dermatology, and even experts sometimes misclassify disorders, which can result in improper therapy. Our project suggests a deep learning and image processing-based skin disease detection system that uses a Convolutional Neural Network (CNN) model for precise classification in order to overcome this difficulty. This system, which is a personal computer-based program, can be implemented in places with limited resources or in remote locations, improving access to first diagnostic assistance. The system evaluates and categorizes the condition by examining a user-provided photograph of the affected skin area, providing pertinent medical information along with the projected disease.

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AI/ML, Python, Frameworks, etc.

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Malware is an increasing threat to cybersecurity, causing significant harm to systems, data, and networks worldwide. As cyberattacks become more sophisticated, it's crucial to develop reliable and effective methods for detecting and preventing malware. This project addresses this issue by using the Random Forest algorithm, a well-established machine learning technique known for its ability to handle complex datasets and deliver high accuracy. The model is trained to analyse features extracted from executable files, such as file size, code structure, and patterns in behaviour, to distinguish between benign and malicious software. One of the key advantages of the Random Forest approach is its ability to adapt to new and evolving types of malware, ensuring it stays effective as threats change. Additionally, its resistance to overfitting and scalability make it a robust choice for detecting even the most sophisticated malware. Ultimately, this project aims to enhance cybersecurity by providing a flexible, precise, and scalable solution for malware detection.

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Malware Detection, Random Forest Algorithm, Machine Learning, Predictive Accuracy, Malicious Software, Malware Identification.

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The Himalayan regions have been most vulnerable to the impacts of climate change in recent years. In this region, environmental justice, gender parity, and climate change adaptation are intertwined challenges. Women often face greater vulnerability to climate change impacts and are less likely to have access to resources needed for adaptation, hindering their ability to build resilience. The impacts of climate change are leading to severe consequences such as scarcity of resources, food insecurity and gender inequalities. Women, often bearing the brunt of climate change-related impacts due to their roles in resource management and subsistence livelihoods and face increased vulnerability and lack access to decision-making and resources, hindering their ability to adapt to changing conditions. Addressing this requires a holistic approach that integrates gender concerns into climate change policies, empowers women, and promotes equitable resource management. In this paper, the author examines environmental justice from gender perspectives and adaptation in the prevailing climate change scenario.

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Climate Change, environmental justice, gender inequality, and resource management

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The system processes 3D skeletal data from sign language gestures, constructs spatio-temporal graphs, and applies graph convolutional networks (GCNs) to extract spatial and temporal features. The trained model classifies sign language gestures into predefined categories, making sign language recognition more efficient and scalable.

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Sign Language Recognition, Spatiotemporal Graph Convolutional Networks (ST-GCN), 3D Skeleton Data, Gesture Recognition, Human-Computer Interaction, Deep Learning, Motion Capture, Assistive Technology

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In order to meet the consumers of a better life the traditional furniture is developing in the direction of intelligence, but the traditional furniture with single function such as courtyard sunshade has been developing in the direction of Intelligence. The researchers purpose to change the traditional sunset, where is an automatic sunset system add an environmental monitoring system of for the sun shapes and increase voice playback function which realize the data exchange between the sun shade and the user through Bluetooth direction of Smart home . This paper uses design platform and provides the designs scheme of the intelligent as well as the beginning method of the use of the end of the paper . The feasibility of the user and the device and the flexibility of the designs scheme will be proved by Arduino and open source development platform which can be used in the development of traditional home intelligence. It also aims in proceeding protection to doors, windows and especially vehicles.

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Home intelligence, Sun shade

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