Volume 14 | Issue 5 |

 Abstract

Happiness is an essential aspect of human life and plays a significant role in determining the mental well-being and academic success of students. This study aims to compare the happiness levels of Undergraduate (UG) and Postgraduate (PG) students. A quantitative research design was used, and data were collected through a structured questionnaire based on a Likert scale. The data collected from BabasahebBhimraoAmbedkarUniversityLucknow. The findings indicate that PG students tend to have slightly higher happiness levels than UG students due to greater emotional maturity and clarity about career goals. However, academic pressure and stress affect both groups. The study suggests the need for mental health awareness programs and student support systems to enhance happiness levels.

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 Keywords

Happiness, Undergraduate Students, Postgraduate Students

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Li-Fi (Light Fidelity) technology is a cutting-edge kind of wireless connection that transmits data using light waves rather than conventional radio frequencies. It investigates the use of Arduino, a widely available and reasonably priced microcontroller platform, for Li-Fi data transfer. A straightforward and effective Li-Fi communication system that is simple to incorporate into a variety of applications is proposed in this research. The system is made up of an Arduino board interfaced with a light-emitting diode (LED) for data transmission and a photodiode for data receiving. In order to facilitate smooth data sharing between devices in a Li-Fi network, a communication protocol must be developed. Unlike the conventional Wireless Fidelity (Wi-Fi) architecture, Light Fidelity (Li-Fi) technology is a wireless communication system that uses the visible light spectrum to deliver data quickly and securely. This paper describes a Li-Fi communication system that makes use of a smart phone. By using the built-in smart phone flashlight as a source to send data and detecting the impact of employing external light detector sensors attached to an Arduino UNO circuit to receive data, this suggested method seeks to maximize the bit rate with high precision. In order to determine which light sensor could achieve a greater data bit rate and to test the system's performance under varying transmitter-receiver distances, four practical experiments were carried out. The evaluation findings showed that the suggested research outperformed the others in terms of data bit rate, reaching over 100 bps with 100% correctness.

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 Keywords

LiFi Technology, Visible Light Communication (VLC), Arduino UNO, Photodiode Sensor

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Abstract Background: Primary dysmenorrhea is a common gynecological problem among adolescent and young adult females, often affecting daily activities and quality of life. Non-pharmacological interventions such as Yoga and Jacobson's Progressive Muscle Relaxation Technique (JPMRT) have shown beneficial effects in reducing menstrual pain and associated symptoms. Methods: A randomized controlled trial was conducted among 34 college-going girls aged 18-25 years with primary dysmenorrhea. Participants were randomly allocated into two groups: Group A (Yoga) and Group B (JPMRT). Yoga was performed for 6 weeks, 4 sessions per week, while JPMRT was practiced twice daily during menstrual days for two consecutive cycles. Pain intensity was assessed using the Numerical Pain Rating Scale (NPRS-11) and menstrual symptoms using the Menstrual Symptom Scale (MSS) before and after the intervention. Paired and unpaired t-tests were used for statistical analysis. Results: Both groups showed statistically significant improvement (p < 0.0001). However, post-intervention comparison showed significantly greater reduction in NPRS (p = 0.0130) and MSS (p = 0.0054) scores in the Yoga group compared to the JPMRT group. Conclusion: Yoga was found to be more effective than Jacobson's Progressive Muscle Relaxation Technique in reducing pain intensity and menstrual symptoms in primary dysmenorrhea. Keywords: Primary dysmenorrhea, Yoga, Jacobson relaxation, NPRS, MSS

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Keywords: Primary dysmenorrhea, Yoga, Jacobson relaxation, NPRS, MSS

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ABSTRACT Cross border commerce expands very fast in recent decades, & parties litigate abroad more frequently for strategic or practical reasons. Indian courts therefore face growing number of suits & execution petitions based on foreign judgments. Code of Civil Procedure, 1908 gives statutory framework under Sections 13, 14 & 44A, but its public policy content remains contested. Section 13 adopts rule of conclusiveness subject to six exceptions, including clause (f) on breach of any law in force in India, which operates like broad public policy filter. Scholars describe this clause as "unsympathetic" because it allows refusal even for relatively technical violations of Indian law, rather than only for violations of fundamental norms. Indian courts, beginning with decisions such as Brijlal Ramjidas v. Govindram Gordhandas Seksaria & Duggamma v/s Ganeshayya, treated foreign judgments as conclusive only on matters directly adjudicated & within jurisdictional & natural justice limits. Later family law cases like Satya v/s Teja Singh & Y. Narasimha Rao v/s Y. Venkata Lakshmi linked refusal of recognition to deception of forum, lack of genuine jurisdiction & evasion of Indian matrimonial policy. More recent commercial decisions, including TransAsia Private Capital Ltd. v/s Gaurav Dhawan, show greater willingness to enforce foreign money decrees where due process & jurisdiction requirements are met. Parallel developments in arbitration law, from Renusagar Power Co. Ltd. v/s General Electric Co. through Shri Lal Mahal Ltd. v/s Progetto Grano Spa & Ssangyong Engineering & Construction Co. Ltd. v/s National Highways Authority of India, have narrowed notion of "public policy of India" for foreign awards. This article examines whether similar evolution is visible or desirable for foreign judgments, & proposes re reading of Section 13 that aligns with international best practices while preserving core Indian constitutional & statutory values.

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 Keywords

Foreign judgments; public policy; Section 13 CPC; recognition & enforcement; Indian courts.

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Advanced AI systems used in financial services increasingly rely on extended, autonomous reasoning rather than simple pattern recognition or prediction. These reasoning models generate intermediate deliberations--commonly referred to as chains of thought (CoT)--before producing outputs or taking actions. Recent research suggests that monitoring these reasoning traces can significantly improve the detection of misbehavior, misalignment, or unintended objectives compared to traditional output-only monitoring approaches [P1], [P3]. For model risk management (MRM), validation, and AI governance professionals, this presents both an opportunity and a risk. CoT monitoring provides a partial window into how advanced models reason, enabling earlier identification of behaviors such as reward hacking, deception, or policy-violating intent that may not be observable in final outputs alone [P1], [P3]. At the same time, the ability to monitor reasoning is not guaranteed to persist. Research shows that chains of thought are often incomplete, selectively unfaithful, or absent in precisely those settings where reliable oversight is most needed [P5]. Training choices, architectural shifts, and optimization pressures can further degrade monitorability over time [P1], [P4]. This white paper translates the emerging CoT monitorability literature into a governance-ready framework for financial institutions. It clarifies what CoT monitoring is--and is not--capable of delivering; outlines where it adds the most value for high-risk banking use cases; and proposes how monitorability can be operationalized as a distinct risk attribute within existing enterprise AI governance and model risk programs. Throughout, the paper emphasizes that CoT monitoring should be treated as one layer in a defense-in-depth strategy, rather than as a substitute for validation, testing, or explainability controls

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COT

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This paper presents TraceLine, a Blockchain and AI-integrated system designed for intelligent tracking and quality management of metal components in manufacturing environments. The system assigns unique identifiers to each part and monitors its lifecycle across multiple production stages using Data Matrix Codes (DMC). Blockchain technology is incorporated to ensure secure, decentralized, and tamper-proof storage of production data using distributed ledgers and smart contracts. Additionally, Artificial Intelligence (AI) enables anomaly detection, defect prediction, and process optimization through real time data analytics. The integration of Blockchain and AI enhances traceability, transparency, data integrity, and decision making, contributing to the development of secure and intelligen Industry 4.0 manufacturing systems.

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 Keywords

Blockchain, Artificial Intelligence, Industrial Traceability, Smart Manufacturing, Industry 4.0, Data Matrix Code (DMC), Predictive Maintenance, Anomaly Detection, Smart Contracts, Distributed Ledger Technology, Quality Control, Edge AI, Cloud AI

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As we grapple with some of the most pressing environmental and social challenges facing humanity, sustainable development has become a more urgent global imperative. Despite wide research on environmental domains of sustainable development, social dimensions that are important for its success received lesser attention. The critical appreciation of the existing literature in this paper is a systematic and holistic discourse on Sustainability issues with a focus on social sustainability. We look at poverty, inequality, gender, education, health and human rights as issues that impact on sustainable development. Mitigating these social aspects is essential for achieving sustainable development - we discuss that all members of society should be integrated into a collective socio-technical system;

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Sustainable Development, Social Issues, sustainable development nexus, Environmental Degradation, Environmental Challenges

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Abstract Background: Karnataka's forest outbreak has grown into something much bigger. Kyasanur Forest Disease now touches Kerala, Tamil Nadu, and Goa, placing community health nurses at the centre of managing this tick-borne fever in some of India's most isolated corners. Objective: This review examines how community health nurses handle kyasanur forest disease across South India their daily work, the obstacles they encounter, and where their capabilities might be strengthened. Methods: Published studies from 2014 to 2026 were examined, drawing from academic databases and government reports. The focus stayed on nursing practice, surveillance activities, and community interventions. Results: Community health nurses have become kyasanur forest disease's backbone. They manage house-to-house fever checks and deliver vaccines to tribal areas requiring hours of walking to reach. But workforce shortages, limited disease training, and supply problems create unnecessary hardships. Conclusion: Staying ahead of kyasanur forest disease and similar diseases demands investment in nursing capacity. This means proper training, sufficient resources, and recognition that these nurses often represent the only healthcare presence in vulnerable communities.

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 Keywords

Kyasanur Forest Disease; community health nursing; zoonosis; climate change; vector-borne disease; India; surveillance

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Low-light conditions significantly degrade the performance of object detection systems due to reduced visibility, noise, and poor contrast. This paper presents an adaptive image enhancement approach to improve object detection performance under varying illumination conditions. The proposed method dynamically selects enhancement techniques based on image brightness levels, applying gamma correction and histogram equalization accordingly. Experimental evaluation is conducted on low-light simulated datasets, and results demonstrate that the proposed adaptive method outperforms traditional fixed enhancement techniques in terms of detection accuracy. The study highlights the importance of preprocessing strategies in enhancing the robustness of computer vision systems in real-world scenarios.

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Low-light imaging, Object detection, Image enhancement, Adaptive processing, Computer vision

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This paper presents a Students Performance Analytics System designed to analyze and monitor student academic performance efficiently. The system uses structured data collected from multiple semesters and processes it using data analytics techniques to generate meaningful insights. The proposed system integrates tools such as Python, MySQL, and data visualization libraries to perform data cleaning, transformation, and analysis. It helps in identifying trends, comparing student performance, and detecting weak and strong areas. The system also provides graphical reports for better understanding and decision-making. This approach reduces manual effort and improves accuracy in performance evaluation. The developed system can be used by educational institutions to enhance academic monitoring and support data-driven decisions.

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 Keywords

Student Performance, Data Analytics, Python, MySQL, Data Visualization, Academic Analysis, SGPA, Educational System

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Creative Commons Attribution 4.0 and The Open Definition