Volume 14 | Issue 4 |

 Abstract

The construction industry plays a crucial role in fostering economic development by making substantial contributions to the expansion of infrastructure and overall national advancement. Nevertheless, the effectiveness and efficiency of construction labourers continue to be crucial elements in determining the success of a project and its overall performance. Efficiently managing labour is crucial in an industry with strict timetables and limited budgets, as it is essential for delivering desired project outcomes. The primary objective of this study is to investigate the influence of training and development programs on the efficiency of construction labour and the outcomes of projects. The study utilises a qualitative research methodology. This study employed a conceptual research approach, which entailed a thorough examination of articles, periodicals, and books published in the English language from 2019 to 2024. The goal was to synthesise information on the impact of training and development on employees' productivity, drawing insights from existing studies. This study highlights the significance of training and development programs in improving the skills, knowledge, and productivity of construction workers. These programs aim to provide labourers with up-to-date skills, safety protocols, and technological improvements, resulting in a more skilled and productive workforce. Such programs have a wide-ranging impact that goes beyond the performance of individual workers. They also affect project schedules, cost-effectiveness, and the quality of outputs. In conclusion, this study will provide essential knowledge about how training and development may improve the productivity and effectiveness of the construction workforce. It will emphasise the most effective methods and provide ways for industry stakeholders to enhance project performance.

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 Keywords

Training; Development; Project performance; Labour Efficiency; Skill development; Construction Industry.

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Influencer marketing has emerged as a strategic mechanism through which brands could reach out to people online. Brands partner with influencers on social media platforms like Instagram, YouTube, and Facebook because of their strength in reach and the ability to command trust from followers. Indeed, several studies have shown that an influencer's credibility, knowledge, attractiveness, and trustworthiness can strongly affect what people think, prefer, and eventually buy. Emotional connection and authenticity go hand in hand-especially among young audiences who are so much hooked to social media. This study uses a data-driven approach to figure out exactly why people buy products after seeing them on social media. By surveying a diverse group of social media users, a clear picture of modern shopping habits was developed. To make sense of the data, specific statistical methods like ANOVA, Regression analysis and correlation analysis were used to act like a high-tech filter.

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 Keywords

Influencer marketing, Consumer behaviour, social commerce, Purchase intension, social media engagement.

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Chronic Kidney Disease (CKD) remains one of the most serious complications observed among people living with HIV, affecting anywhere between 4% and 28% of the population depending on geographic and demographic factors. The early detection and accurate staging of CKD in this vulnerable group are critical--not only to slow disease progression but also to tailor antiretroviral regimens that minimize nephrotoxic burden. In this study, we explore how two unsupervised machine learning algorithms--K-means and hierarchical agglomerative clustering--can stratify a real-world cohort of 400 HIV-infected patients into clinically meaningful CKD stage groups without requiring pre-labeled training data. We describe the full analytical pipeline from raw data ingestion through preprocessing, feature engineering, dimensionality reduction, and clustering validation. Using a dominant-class mapping strategy, K-means achieved an accuracy of 78.25% and hierarchical clustering reached 77.50% when evaluated against clinical classification labels. While neither algorithm replaces traditional GFR-based staging, both reveal clinically coherent patient subgroups and offer a promising foundation for decision-support tools in resource-limited healthcare settings. We also present graphical outputs including confusion matrices, performance comparisons, and cluster distribution analyses to make the results accessible to both clinical and computational readers.

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 Keywords

Chronic Kidney Disease, HIV infection, K-means clustering, Hierarchical clustering, Unsupervised machine learning, CKD staging, Feature engineering, Clinical decision support.

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A normative survey was conducted to elucidate the level of job contentment among higher secondary school teachers. A sample of 400 teachers (include both gender) from Vellore district was selected by random sampling method. The modified Teacher's job satisfaction scale (Annamalai, 1999) consists of 40 test items under 5-point scale was used as tool for the survey. The data collected in person by the investigator from the sample was analysed by using SPSS. The findings reveals that the highly satisfied level of job contentment was evident from the response of higher secondary school teachers. There is no significant difference in job contentment of higher secondary school teachers with respect to gender, Type of School, Marital status and nature of main subject handling. (2) Higher secondary teachers working in rural based schools have highly satisfied level of job contentment than those working in urban based higher secondary schools. (3) Teachers below 45 years show highly satisfied level of job contentment than those Belonging to more than 45 years age group. (4) similarly Higher secondary teachers having 5-10 years of experience have highly satisfied level of job contentment than their counterpart of having more than 10 years of experience.

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 Keywords

Job contentment, job satisfaction, higher secondary teachers.

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Creative Commons Attribution 4.0 and The Open Definition

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The increasing sophistication and volume of cyber-attacks pose significant challenges to traditional signature-based security mechanisms. To address these limitations, this paper presents an AI-based cyber security approach that utilizes machine learning techniques for intelligent threat detection. The proposed framework analyzes network traffic and system behavior to identify both known and unknown cyber threats through anomaly and pattern recognition. Supervised and unsupervised learning models are employed to enhance detection accuracy while minimizing false positive rates. Relevant features are extracted from network packets and system logs to train the models effectively. Experimental evaluation conducted on benchmark intrusion detection datasets demonstrates that the proposed system achieves improved performance in terms of detection accuracy, precision, and recall when compared to conventional intrusion detection systems. The results indicate that machine learning-driven security solutions can provide adaptive and scalable defense mechanisms capable of responding to evolving cyber threats in real time. This study emphasizes the potential of integrating artificial intelligence into cyber security infrastructures to develop proactive and resilient threat detection systems.

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Cyber security , Machine Learning, Artificial Intelligence, Intrusion Detection System, Intelligent Threat Detection, Anomaly Detection, Network Security.

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Background: Acute appendicitis is among the most frequently encountered surgical emergencies worldwide, with peak incidence in individuals aged 21 to 30 years (approximately 36%), declining sharply in pediatric (0-10 years, ~3%) and elderly (41-75 years, ~2.3%) populations. When complicated by colonic obstruction, closed-loop physiology may precipitate ischemic injury, and up to 74-80% of such cases ultimately progress to bowel gangrene--a potentially fatal outcome arising from vascular compromise, septic invasion, or mechanical strangulation. Case Summary: We describe the case of a 30-year-old male who presented to the surgical emergency department with a two-day history of right iliac fossa pain accompanied by nausea, bilious vomiting, and low-grade fever. A working diagnosis of subacute appendicitis was established preoperatively; however, intraoperative findings revealed an unexpectedly severe picture of pan-colonic gangrene with multiple perforations, while the small intestine was completely spared. Management and Outcome: The patient underwent near-total colectomy with Hartmann's procedure and end ileostomy formation. Staged intestinal reconstruction via ileostomy closure with ileorectal anastomosis was performed six weeks later. The patient achieved full functional recovery at one-year follow-up without evidence of recurrence or major complication. Conclusion: This report underscores the diagnostic limitations of standard preoperative workup in atypical appendicitis and highlights the critical importance of thorough intraoperative exploration, prompt surgical decision-making, and aggressive staged management in preventing mortality from rare but catastrophic colonic complications.

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 Keywords

Acute appendicitis; Subacute appendicitis; Large bowel gangrene; Intestinal ischemia; Colonic obstruction; Closed-loop obstruction; Exploratory laparotomy; Near-total colectomy; Hartmann's procedure; Bowel necrosis; Case report

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Soil physicochemical properties significantly influence nutrient availability, microbial activity, and overall scosystem productivity. The present study investigates the physicochemical characteristics of soil collected from the Ranipokhar pond ecosystem located in Vaishali district. Bihat, India, Soil samples were analyzed for texture, colour, pf, electrical conductivity (EC), organic carbon (OC), and essential macro and micronutrients including nitrogen (N), phosphorus (P), potassium (K), sallar (5), aon (Fr), zec (Zn), manganese (Mn), and copper (Cu) Standard analytical techniques such as the Wakley-Black method for organic carbon, Kjeldahl method for nitrogen, Olsen method for phosphorus, and flame photometry for potassiam were applied. Soil texture was determined using the bydrometer method and soil colour was classified wing the Munsell Soil Colour Chart. The study revealed tut the soila moand Ranipokher pond ranged from sandy loam to clay loam with brown to greyish coloratam, indicating mesderate organic matter accumaslation. The soil pH find to be slightly allualise while electrical conductivity remained within acceptable limits for agricultural productivity Macnutrient analysis indicated moderate to high nitrogen and pota content, wherras phosphorus was present in moderate levels. Micronatriem assessment showel adequate iron and manganese concentrations but comparatively lower zinc availability in some samples. The findings provide baselime information on soil fertility statas and nutrient dynamics in the Ranipokhar ocosystem, which may assist in sustainable land-use planning and environmental monitoring in the Vaishali region.

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Soil fertility, organic carbon, micronutrient, electrical conductivity

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The System leverages Artificial Intelligence, Machine Learning, NLP, and Data Analytics to enhance academic auditing across a Computer Department. The system focuses on the Computer Department, analyzing student performance, faculty workload, and departmental metrics to ensure transparency and efficiency. It employs predictive analytics and anomaly detection to identify risks and generate AI-driven audit reports.

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Artificial Intelligence, AI-Driven Audit Reports, Anomaly Detection, Computer Department Metrics, Predictive Analysis.

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The purpose of this study was to examine how higher education institution (HEI) administrators use and make decisions about using artificial intelligence (AI) in their work, specifically within academic administrations across HEIs located within the Pune Metropolitan Region of Maharashtra. Both TAM and UTAUT were used as theoretical models for this research; a quantitative methodology was also employed. Results from PLS-SEM confirm strong path coefficients for the constructs of effort expectancy, performance expectancy, and social influence on administrator's intentions to adopt AI. Administrators' concern regarding data privacy and the lack of transparency related to algorithms that will drive institutional adoption were found to be statistically significant moderating factors. They significantly restricted the extent to which institutions adopted AI, regardless of whether or not they believed in its potential utility. The current study provides original empirical evidence concerning one of the fastest growing educational metropolitan zones in western India. As this region contains over 800 higher education institutions, many of whom are faced with the strategic imperative of embracing digital transformation, these findings have important implications for Human Resource Practitioners, Academic Policymakers, Institutional Leaders, and Regulatory Bodies charged with creating guidelines for integrating technology within Indian Higher Education. Future research directions include longitudinal studies of the same population over time; cross-sectional studies comparing multiple cities; and qualitative investigations examining the level of readiness of each institution to implement new technologies.

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 Keywords

Artificial Intelligence, Academic Administration, Higher Education Institutions, Technology Acceptance Model, Administrative Efficiency, Ethical Concerns, Decision-Making, Pune Metropolitan Region

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