Volume 13 | Issue 7 | July 2025

 Abstract

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This paper presents an experimental investigation into the flexural performance of built-up cold-formed steel (CFS) closed-section beams under four-point bending, with a focus on the effect of composite action using Expanded Polystyrene Lightweight Concrete (EPS-LWC). Three full-scale beams were examined: a bare steel built-up section and two composite variants utilizing the same steel cross-sectional area. The first composite beam (Beam-2) was fully infilled and externally encased with EPS-LWC to assess the contribution of composite interaction. The second composite beam (Beam-3) included a novel configuration featuring outward-facing lipped flanges embedded within the concrete matrix, intended to enhance mechanical interlock and strain transfer. Experimental results showed that EPS-LWC encasement significantly improved the flexural capacity, stiffness, and ductility of the CFS beam. Moreover, Beam-3 demonstrated superior performance over Beam-2, attributed to improved interface bonding and strain compatibility through the lipped flange interlock. These findings confirm the structural viability of EPS-LWC for composite steel applications and highlight the potential of flange detailing innovations in maximizing composite action in thin-walled CFS members.

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 Keywords

Cold-formed steel (CFS), EPS-LWC, composite action, flexural behavior, lipped flange interlock, lightweight concrete, built-up beams

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H5N1 [AVIAN FLU] is a type of influenza virus that causes a highly infectious, severe respiratory disease in birds called avian influenza (or "bird flu"). Human cases of H5N1 avian influenza occur occasionally, but it is difficult to transmit the infection from person to person. It is a highly pathogenic avian influenza {HPAI}. When people do become infected, the mortality rate is about 60%. INFLUENZA [bird flu], A subtype H5N1 has represented a growing alarm since its recent identification in Asia. Previously thought to infect only wild birds and poultry, H5N1 has now infected humans, cats, pigs and other mammals in an ongoing outbreak, often with a fatal outcome. In order to evaluate the risk factors for human infection with influenza virus H5N1, here we summarize 53 case patients confirmed with H5N1 infection during 2006. The review also compares the mortality rate among human cases from late 2003 until 15 June 2006 in different countries. Neither how these viruses are transmitted to humans nor the most effective way to reduce the risk for infection is fully understood. A wide range of serological and molecular methods have substantially aided in the identification of bird flu in humans. Candidate vaccines have been developed, yet are not ready for widespread use. Oseltamivir (brand name: Tamiflu) is the preferred drug for the management of human Influenza-like illness (ILI). Surveillance, mass awareness and pandemic preparedness abiding WHO recommendations are of paramount importance for the prevention of bird flu outbreaks.

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Avian influenza flu [AIV], Avian flu, Bird flu, H5N1, Oseltamivir.

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Creative Commons Attribution 4.0 and The Open Definition

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The study found a significant difference in attitudes towards dairy products among different age groups, rejecting the null hypothesis. However, the effect of education level on consumer attitude was not statistically significant, and income did not significantly affect attitudes. Age did not significantly influence buying intent, while education did not significantly influence it. Income had a statistically significant effect on buying intent. The results suggest that age has a significant impact on consumer behaviour towards dairy products, but education level and income do not significantly influence consumer behaviour.

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buying intention, attitude towards dairy product, consumer behaviour.

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Job satisfaction is the product of numerous attitudes employees develop toward various parts of their work. It strengthens emotional bonds and increases productivity in instructional processes, it has a significant impact on teachers' academic performance as it boosts their productivity in teaching and reinforces emotional connections. The current study aims to examine the job satisfaction of secondary school teachers in the Kurukshetra district by examining the effects of gender, school type, age, experience, and workplace location. The study was designed using the descriptive method. Teachers working in secondary schools in the Kurukshetra region of Haryana, India, make up the study's population. The study conducted on 160 secondary school teachers that are taken through a random sampling. The Job Satisfaction Scale, created by Dr. Parmod Kumar and D.N. Mutha in 1988, is the instrument used to collect data. The t-test and standard deviation are statistical methods used to analyse data. According to the study, Compared to instructors at public schools, private school teachers are happier in their positions. Additionally, it has been discovered that compared to their urban counterparts, teachers at secondary school in rural regions are happier in their jobs. Moreover, there is no significant variation in the job satisfaction of male and female secondary school teachers. Furthermore, studies reveal that teachers with less than 15 years of experience are happier in their positions than those with more than 15 years. In addition, the survey shows that teachers under 40 are more satisfied with their jobs than instructors over 40. The study looked at what needs to be done to address secondary education teachers' work satisfaction.

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Job satisfaction, Teachers at secondary school, gender, experience, workplace

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Creative Commons Attribution 4.0 and The Open Definition

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Network anomaly detection is an essential element of contemporary cybersecurity infrastructure, especially as organizations encounter more and more sophisticated and changing cyber threats. Conventional intrusion detection systems, which rely intensely on static signature databases and preconfigured rule sets, exhibit clear shortcomings in discovering new attack patterns and responding to dynamic threat environments. This research explores the optimization and utilization of conventional machine learning models for improved network anomaly detection, filling the research gap in which deep learning methods have dominated the potential that classical ML strategies can offer. With in-depth scrutiny of the UNSW-NB15 dataset comprising contemporary attack patterns in nine unique categories, this study compares six supervised machine learning classifiers: Logistic Regression, Decision Trees, Random Forests, Gaussian Naive Bayes, K-Nearest Neighbors (KNN), and LightGBM. The approach utilizes stringent data preprocessing pipelines, systematic feature engineering, and strenuous performance analysis across diverse metrics such as accuracy, precision, recall, and F1-score. Experimental findings show outstanding performance attainment, as Decision Trees, Random Forests, and LightGBM each recorded perfect accuracy at 100% and precision at 1.000, while KNN recorded 99.68% accuracy with 0.9968 precision and Gaussian Naive Bayes recorded 97.54% accuracy with 0.9764 precision. These results undermine the common belief that deep learning architectures of high complexity are required for successful anomaly detection, as it is shown that conventional ML models, if they are well optimized, can perform better with benefits in computational efficiency, interpretability, and deployment practicability. The work provides substantial practical implications for cybersecurity applications, especially in resource-limited settings and edge computing environments where light, interpretable models are desired. This research lays the groundwork for future-generation intrusion detection systems that strike a balance between outstanding detection rates and operational efficiency, offering actionable intelligence for security professionals while propelling the science of intelligent network security through evidence-based optimization of standard machine learning techniques.

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Network Anomaly Detection, Intrusion Detection System (IDS), Machine Learning, Supervised Learning, UNSW-NB15 Dataset, Feature Engineering, Class Imbalance, SMOTE, Random Forest, LightGBM, Decision Tree, K-Nearest Neighbors, Gaussian Naive Bayes, Cybersecurity

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The knowledge of anatomy in Ayurveda has been poorly understood in today's era. The lack of sufficient literatures regarding the subject has hindered its development. Despite the hurdles, still the concept of anatomy in Ayurveda is in depth and seeking for scientific exploration. The increasing incidence of joint diseases leading towards chronicity are best being treated through Ayurveda, but the concepts behind the results are not well understood due to lack of proper knowledge in anatomy. The description of various anatomical entities has been clearly mentioned in Samhitas in Ayurveda. Sushruta Samhita, the pioneer text of Ayurveda surgery, in its Shareera Sthana primarily has described about the human body anatomy. Apart from this, Charaka Samhita, the text of medicine has also lightened regarding the anatomy. In this article, the anatomical concepts of Kurpara Sandhi (elbow joint) are extracted from Sushruta Samhita and Charaka Samhita in order to organize the knowledge systematically. The concepts are correlated with the view of contemporary medical science and illustrated in best possible way.

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Kurpara Sandi, Elbow Joint, Ayurveda Surgery, Ayurveda Anatomy

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ABSTRACT Concrete is extensively used in the civil engineering sector worldwide due to its superior strength and durability. Fine aggregate, typically derived from natural river sand, plays a vital role in concrete production. However, excessive sand mining has led to serious environmental issues, including the depletion of natural resources, increased scour depth, and a heightened risk of flooding. To address these concerns and promote sustainability, industrial by-products such as copper slag--generated in large quantities by the copper industry--can be utilized as alternative materials. This study explores the potential of copper slag as a partial replacement for fine aggregate in M30 grade concrete. The experimental program involved replacing river sand with copper slag at varying proportions of 0%, 4%, 8%, 12%, and 16%. The mechanical properties, including compressive strength, tensile strength, and flexural strength, were assessed at 7 and 28 days. Results were analyzed and compared with conventional concrete to evaluate the impact of copper slag on the performance of concrete mixtures. The findings support the feasibility of using copper slag in concrete production, contributing to waste utilization and environmental sustainability.

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Keywords- Cement, Fine Aggregate, Coarse Aggregate, Copper Slag, Concrete, Compressive Strength, Split Tensile Strength, Flexural Strength.

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This article critically examines the urgent need to address population growth as a pivotal factor contributing to environmental degradation. It explores the demographic-ecological nexus through legal, constitutional, and international frameworks. The analysis draws upon judicial precedents, environmental statutes, and comparative policy approaches. The article proposes the establishment of a sustainable, rights-based legal framework that respects reproductive autonomy while addressing ecological pressures. Through a comprehensive doctrinal and policy-based review, the paper advances the argument that population control must be a central concern in environmental governance for the 21st century.

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Demographic Impact Assessment (DIA), Demographic-Ecological Nexus, Population Stabilization, Environmental Governance, Reproductive Rights, Sustainable Development, Rights-Based Legal Framework, Climate Change Mitigation, Environmental Impact Assessment (EIA), Population and Environmental Sustainability Act

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This study explores the behavioural dynamics underlying remittance decisions among international migrants from Kerala, using the Theory of Planned Behaviour (TPB) as its central framework. Applying a mixed-method approach, the research integrates quantitative data from structured surveys with qualitative insights from interviews and focus group discussions. The study examines how behavioural beliefs, normative pressures, and control perceptions shape migrants' intention to remit and how this intention translates into actual remittance behaviour. Empirical findings--supported by chi-square, regression, and structural equation modelling--reveal that remittance practices are strongly influenced by psychological drivers such as emotional fulfilment, familial responsibility, and financial aspirations, alongside demographic variables like income and employment type. The validated TPB framework confirms intention as a significant mediator between belief systems and remittance actions. These insights offer concrete implications for designing behaviourally informed remittance products, reducing transaction costs, and fostering financial inclusion among migrant populations.

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Creative Commons Attribution 4.0 and The Open Definition

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Remittance behaviour, migrant intention, Kerala migration, Theory of Planned Behaviour (TPB), structural equation modelling (SEM), behavioural finance

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Creative Commons Attribution 4.0 and The Open Definition