Volume 14 | Issue 5 |

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People all over the world are now paying attention to the deaths of mothers and children. In low- and middle-income countries, maternal mortality is high, especially among teens and young adults. Healthcare professionals can use CTGs to keep an eye on the mother's heartbeat during pregnancy to make sure the baby is still alive and avoid these deaths. This study utilised machine learning techniques to conduct a risk factor analysis aimed at decreasing child and maternal mortality. This study assessed seven machine learning algorithms. Accuracy, precision, and recall were used to compare how well different categorisation algorithms worked. The random forest is the most accurate of the other algorithms, with an accuracy rate of 99.98%. At first, the dataset was not balanced. After using under sampling and oversampling methods, all of the algorithms worked very well. A primary objective of the current study was to forecast the risk factors associated with child and maternal mortality utilising clinical data. Ultrasound devices work by sending out a pulse and reading the response. This analysis is a good and cost-effective choice for healthcare professionals who want to keep mothers and children from dying.

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 Keywords

-- Maternal Mortality, Child Mortality Prediction, Machine Learning, Random Forest, Cardiotocography (CTG), Risk Factor Analysis, Clinical Data.

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Contemporary technologies, such as web-based learning, mobile learning, and the exploitation of smart boards, are the only means by which the educational process may be made easier in this age of digitalization. Both cyber and security are considered to be components of cybersecurity, which can be understood in two different ways. Information technology that includes computer systems, computer networks, computer software, and data is referred to as cyber. Ensuring the availability, integrity, and confidentiality of data should be the primary focus of any cybersecurity plan. The current investigation utilized a survey method known as normative surveying. As a sample for the research project, the researchers used students from the Education department at Vels College, which is located in the Chennai area. This survey collects information from 77 students, including those who are currently enrolled in Bachelor of Science in Education, Bachelor of Education, and Bachelor of Education programs. There appears to be a significant gap between the qualifications of students and the amount of time they spend online, according to the findings of the current study. The results of the study show that the factors that were selected have a substantial link with one another. In light of the fact that students are the workforce of the future, it is inevitable that the future cybersecurity landscape will be influenced by the digital behaviours and expertise that they currently possess.

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Cyber security, Cyber security awareness, Social media

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Abstract Objective: The objective of this study is to find out the Impact of qualification on Fundamental analysis aspect of Investment decision of the individual retail investors of Guwahati, Assam, India. Design/Methodology/Approach: The tested independent variables are different levels of qualification and the dependent variable is fundamental analysis aspect of investment decision making. Ordinal logistic regression is used to find the impact of these levels of qualification on the investment decision. Findings: Qualification level 1,2 and 3 i.e., up to graduate level, post graduate level and post graduate and above level of qualification has significant impact on the fundamental analysis aspect of investment decision making. This study showed that more factors can be used as separate variables to figure out how an investment decision is affected. Classifying people based on their qualification level alone is not enough to figure out how they choose investments. JEL Classification: G11

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Key words: Qualification level, Fundamental analysis, Investment decision

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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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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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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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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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