A venous ulcer also referred to as a varicose ulcer, is a disruption in the continuity of the skin, specifically the epithelial layer along the pathway of veins in the limb, which tends to appear on the medial aspect of the lowest section of the leg. The primary manifestation of venous ulcer is abnormal venous hypertension in the lower third of the leg, which is triggered by a weakening in the vein on the medial side of the inferior section of the lower limb. A 51-year-old male patient came with complaints of a varicose ulcer of the left foot, pain and burning sensation around the affected area, and the presence of pus oozing out and swelling in the affected area. The patient was diagnosed with a varicose ulcer by physical examination, and this was confirmed by MRI scanning, which showed the engorgement of the veins and potential evidence of the blood reflux within the veins of the left foot. The patient was prescribed Arsenicum album 30 /1 dose in 10 ml aqua in 10 gtt x TDS internally, based on Boenninghausen?s totality of symptoms and glycerine lotion and a dilution of Arsenicum album was applied externally for dressing based on aphorism 196. The patient showed notable improvement in every 2-week follow-ups, as demonstrated by the MODIFIED Naranjo Criteria Score of 9, which indicated relevance in treatment outcome.

The rapid evolution of Artificial Intelligence (AI) has transformed the landscape of cybersecurity. While AI technologies enable real-time threat detection and predictive analysis, they also introduce complex challenges, as adversaries leverage AI for automated attacks and data manipulation. This paper investigates the emerging AI-driven cybersecurity threats, including adversarial machine learning, deepfake-based social engineering, and AI-powered malware. A practical simulation model for AI-enhanced threat detection is proposed using a hybrid Convolutional Neural Network (CNN) and Random Forest approach trained on the CICIDS-2017 dataset. The results indicate significant improvement in detection accuracy, with 94.2% precision and 91.7% recall. The study concludes with proposed countermeasures integrating explainable AI, adaptive firewalls, and continuous learning frameworks to strengthen digital resilience.

The rapid proliferation of Artificial Intelligence (A.I.) presents a paradigm-shifting challenge to established legal frameworks for data protection. This research investigates the fundamental conflicts between traditional data protection principles, exemplified by the General Data Protection Regulation (GDPR), and the inherent operational requirements of modern A.I. and machine learning systems. The analysis reveals significant friction points, including the clash between the principle of data minimization and A.I.'s need for vast datasets; the challenge to purpose limitation by A.I.'s emergent applications; and the difficulty of enforcing the 'right to an explanation' in the face of opaque 'black box' algorithms, which perpetuates risks of algorithmic bias and unfairness. While foundational laws like GDPR provide an essential, rights-based starting point, their insufficiency has prompted new legislative action. This paper concludes that effective governance in the age of A.I. necessitates a dual-track approach: the adaptive interpretation of existing data protection laws combined with the implementation of new, A.I.-specific, risk-based regulations, such as the EU's AI Act. This hybrid model is essential to foster innovation while safeguarding fundamental rights against the unique challenges posed by automated decision-making.

The study investigated communal conflicts and community development initiatives in Rivers State. The purpose of this study is to determine how communal conflicts in terms of land disputes and leadership tussle influences community development initiatives in Rivers State. Two aims, two research questions and two null hypotheses guided the study. With a population of 2,532, which included 60 registered Community Based Organizations (CBOs) in the 15 Local Government Areas of two (2) Senatorial Districts in Rivers State, the descriptive survey study methodology was chosen. A sample size of 546 respondents made up of 417 members and 129 leaders of the chosen registered CBOs was drawn using the multi-stage sampling procedure. The instrument for data collection was a self structured questionnaire coded on a 4-point Likert scale and titled ?Communal Conflicts and Community Development Activities Questionnaire? (CCCDAQ). The instrument was duly validated by three research experts. The test-retest method was employed in testing the reliability index of the instrument through the use of Pearson Product Moment Correlation coefficient (PPMCC) and a reliability index of 0.78 was obtained. Mean and standard deviation were used to analyze the data gathered from the research questions; while the null hypotheses were tested using Z-test statistics at a 0.05 level of consequence. From the analyses, the findings revealed that there were no significant differences in the mean ratings of members and leaders of Community Based Organizations (CBOs) on the extent to which land disputes and leadership tussle influences community development initiatives in Rivers State. This also revealed that the respondents agreed to a high extent that communal conflicts are antithetical to peaceful and harmonious co-existence. Based on the findings, the study concluded that communal conflict has a significant influence on community development initiatives in Rivers State and thus, among other things, the study recommended that there should be sustainability in conflict management and peace building mechanisms such that continuity in development activities could be enhanced.

Introduction: Dystonia is a severely disabling neurological disorder characterized by involuntary, painful spasmodic contractions of specific muscle groups. Among its various forms, cervical dystonia is the most prevalent, primarily affecting the muscles of the head and neck. Although extensive research has been conducted, a definitive long-term cure remains elusive. Conventional treatment modalities provide only temporary symptomatic relief, while surgical interventions are often costly, carry considerable risks, and are not suitable for all patients. Recent studies, however, indicate encouraging outcomes with individualized homoeopathic treatment, which not only alleviates the primary symptoms but also contributes to a marked improvement in patient?s overall quality of life.

Groundwater serves as the primary water source for domestic, agricultural, and industrial activities in Ajaokuta Local Government Area (LGA), where rapid industrial expansion poses increasing risks of aquifer contamination. This study assessed the physicochemical quality of groundwater in Ajaokuta LGA, using five well-water samples collected across the region. The samples were analyzed for pH, salinity, electrical conductivity, total dissolved solids (TDS), total hardness, alkalinity, and key metal concentrations (Ca, Mg, Mn, Fe, Cu, Pb, Zn). Descriptive statistics and one-way Analysis of Variance (ANOVA) were used to evaluate spatial variation and compliance with World Health Organization (WHO) and Nigerian Standard for Drinking Water Quality (NSDWQ) guidelines. Results indicate significant spatial variability, with ANOVA p-values < 0.001 across most parameters. Iron levels reached 1.85 mg/L (WHO limit: 0.3 mg/L) and lead levels peaked at 0.017 mg/L (WHO limit: 0.01 mg/L). Electrical conductivity, TDS, and total hardness also exceeded permissible limits in some locations. The findings reveal critical contamination hotspots linked to industrial and mining activities within the LGA. The study recommends enhancing regulatory enforcement, providing alternative potable water sources, implementing groundwater monitoring programs, and conducting community-based awareness campaigns.

Neuromarketing, positioned at the dynamic intersection of neuroscience and marketing, aims to decode the underlying neural mechanisms that shape consumer perceptions, preferences, and purchasing behavior. This paper explores how neurobiological factors?especially emotional and cognitive brain activities?affect consumer decision-making processes. Drawing upon empirical research and utilizing advanced neuroscience tools such as functional Magnetic Resonance Imaging (fMRI) and Electroencephalography (EEG), the study assesses the predictive value of brain responses in determining marketing effectiveness. It specifically tests the hypothesis that emotional brain activation exhibits a stronger correlation with actual consumer behavior than conventional self-reported measures of preference. Furthermore, the paper discusses the ethical implications and boundaries associated with employing such neuroscientific techniques in marketing, emphasizing the need for responsible and transparent application in understanding consumer psychology.

Traditional e-learning platforms often deliver a static, one-size-fits-all educational experience, failing to cater to individual student needs and learning paces. This limitation can lead to decreased engagement and suboptimal learning outcomes. This paper proposes a novel framework for a smart e-learning platform that leverages advanced Natural Language Processing (NLP) techniques to create a dynamic, personalized, and interactive learning environment. The core of our system utilizes state-of-the-art Transformer-based models, such as BERT and T5, to enable three key functionalities: automatic question generation from educational texts, abstractive text summarization for concise content review, and intelligent scoring and feedback for student responses. We detail the system architecture, the NLP pipeline, and the methodologies for training and fine-tuning the models on educational datasets like the Stanford Question Answering Dataset (SQuAD) and various open-source text corpora. The objective is to automate formative assessment, provide immediate, meaningful feedback, and adapt learning pathways to individual student performance, thereby enhancing both the efficiency and effectiveness of online education.

The increasing integration of Artificial Intelligence (AI) in organizational ecosystems is transforming how employees learn, collaborate, and innovate. This study investigates how intelligent digital platforms?such as AI-driven learning management systems, talent analytics, and virtual assistants?enable continuous capability development in the digital workforce. Using a mixed-method approach combining survey data (n = 100) and qualitative interviews with HR leaders, the research examines the impact of AI tools on skill enhancement, decision-making efficiency, and leadership adaptability. Findings indicate that human-AI synergy fosters greater workforce agility, enhances personalized learning, and optimizes strategic workforce planning. However, challenges such as algorithmic bias, data privacy, and digital divide remain significant. The paper concludes with a framework for sustainable integration of AI-enabled systems in workforce development strategies.

The rapid advancements in artificial intelligence have catalyzed the development of innovative speech recognition systems tailored for visually impaired individuals, significantly enhancing accessibility and personal independence. Modern AI-driven solutions leverage deep learning, natural language processing, and real-time voice interfaces to empower visually impaired users in interacting seamlessly with digital devices and online services. These systems facilitate activities such as sending messages, scheduling tasks, and navigating web content using intuitive voice commands, reducing reliance on visual cues. Critical challenges addressed include robust speech recognition in noisy environments, multi-accent support, and minimizing latency for real-time feedback. This research paper examines the current landscape of AI-powered speech recognition technologies, highlights the integration of tools like screen readers and intelligent assistants, and explores inclusive design principles that ensure usability for diverse user groups. Findings show that adaptive, context-aware AI systems and multisensory feedback models are essential for promoting independence, digital literacy, and equitable access to information for users with visual impairments.

The rapid progress in computer vision and deep learning has enabled the creation of advanced assistive technologies tailored for visually impaired people, greatly improving mobility and independence (see the generated image above). Modern AI-powered platforms utilize cutting-edge object detection algorithms such as YOLOv5 to deliver real-time identification of obstacles and landmarks, helping users navigate physical environments safely with minimal reliance on sight (see the generated image above). By employing efficient convolutional neural networks, these systems provide instant audio or haptic alerts about detected objects, making it easier for visually impaired individuals to move from one location to another using web or mobile applications. Core challenges addressed include achieving accurate detection in varied lighting conditions, handling diverse and cluttered scenes, and minimizing inference latency for timely feedback. This paper investigates the integration of YOLOv5 in accessible AI platforms, examines the synergy between object detection and orientation aids, and discusses user-centric design strategies supporting usability across multiple contexts (see the generated image above). The findings demonstrate that context-aware, real-time object recognition combined with multisensory prompts fosters greater independence, mobility, and inclusion for visually impaired users.

This study examined the influence of child labour on school attendance and academic achievement of junior secondary school students in Anambra State, Nigeria. The research was guided by six research questions and adopted an ex?post?facto design. The population comprised 22,829 Junior Secondary?II students across the state, from which a sample of 150 JSS?II students was drawn using a simple random sampling technique. Data were collected with the Child Labour Determinants Questionnaire (CLDQ) designed by the researcher. Frequency distributions and percentages were used to analyze research questions?1?to?3, while mean achievement scores were employed for research questions?4?to?6. The analysis revealed that students who were not engaged in hawking, farming, or house?help duties had better school attendance rates compared to those involved in such work. Similarly, non?working students recorded higher academic achievement scores than their working counterparts. The study concluded that child labour significantly contributes to truancy and undermines academic performance among junior secondary students in Anambra State. It is recommended that children exposed to labour activities be granted equal rights to education, irrespective of work engagement, and that enforcement of child?rights policies be strengthened.

With the growing emphasis on sustainable consumption, eco-labelling has become an important mechanism for guiding consumer decision-making and fostering trust in environmentally responsible products. This study investigates the role of eco-labelling in shaping consumer trust and purchase intentions, with specific attention to certification legitimacy, transparency, and credibility. A descriptive research design was adopted, and data were collected through a structured questionnaire administered to consumers in Ernakulam district, Kerala. The analysis reveals that eco-labels act as credible information cues that reduce uncertainty, enhance perceptions of product quality, and reinforce beliefs in corporate responsibility. The findings further indicate that trustworthy eco-labelling practices contribute to stronger brand image, consumer confidence, and long-term brand loyalty. These insights hold practical implications for marketers, policymakers, and certification authorities seeking to strengthen eco-labelling frameworks as a means of promoting sustainable consumption and building consumer trust.

Voice-based interactive technologies have emerged as a transformative force in educational technology (EdTech), enabling learners and educators to engage with digital platforms through natural spoken communication. This research proposes a Speech-to-Text (STT) Reorganization Approach designed specifically for academic environments, with the objective of delivering accurate, well-structured, and contextually organized transcripts from spoken lectures, seminars, and discussions. The proposed system integrates state-of-the-art Automatic Speech Recognition (ASR) models, including transformer-based architectures such as Wav2Vec 2.0, with advanced Natural Language Processing (NLP) techniques for context-aware text segmentation, summarization, and keyword extraction. Unlike conventional ASR solutions that output unformatted, linear transcripts, our method automatically restructures content into bullet points, sectioned summaries, and topic-wise categorization.

Client-side deep learning with TensorFlow.js enables in-browser image classification without server round-trips, reducing latency and preserving privacy, but introduces trade-offs among model size, load time, CPU/GPU constraints, and accuracy. This paper evaluates lightweight CNN architectures and transfer learning strategies deployed in a React application, comparing MobileNetV2, EfficientNet-Lite0, and a custom compact CNN for common image classification tasks. We analyze three axes: model accuracy, initial model load time over typical network conditions, and on-device inference latency across mid-range laptops and mobile devices. Using a standardized React + TensorFlow.js pipeline, WebGL/WebGPU acceleration, and quantization (float32 vs. float16 vs. int8), we demonstrate how bundle size and precision optimizations influence time-to-interactive and sustained FPS while preserving acceptable top-1 accuracy for practical UX. We provide an engineering rubric to select models by product constraints (cold-start budget, device targets, and accuracy thresholds), along with reproducible code components for data preprocessing, on-device augmentation, and progressive loading strategies. Results indicate MobileNetV2-0.5 (quantized) offers the best balance for general-purpose classification in constrained devices, while EfficientNet-Lite0 achieves higher accuracy with tolerable overhead for desktop-class clients. The findings guide developers building privacy-preserving, low-latency AI features in modern web apps.

Chronic Kidney Disease (CKD) is a progressive and often asymptomatic condition, making early diagnosis critical for improving patient outcomes. This paper presents an explainable, machine- learning-based framework for CKD detection and risk stratification. Using clinical and laboratory features?including serum creatinine, hemoglobin, urine protein, packed cell volume, and comorbidities such as diabetes and hypertension?multiple models were trained, including Decision Trees, Random Forest, XGBoost, and Deep Neural Networks. To address data scarcity and class imbalance, a Conditional Tabular GAN (CTGAN) generated synthetic patient records, improving minority-class representation by 40 %. The proposed system achieved 98.2 % accuracy, 0.95 F1- score, and 0.97 ROC-AUC on a held-out test set. A prototype web application was developed to demonstrate real-time CKD risk prediction, allowing users to input clinical parameters and receive both a risk score and an explainable feature-importance visualization. By combining generative AI with traditional ML, this framework delivers robust, transparent, and deployable CKD detection suitable for integration into clinical decision support systems.

Prompt engineering is the practice of crafting effective inputs?called prompts?for AI language models like ChatGPT, Gemini, and Claude. These prompts guide the model to give more accurate, relevant, and useful responses. In today?s AI-driven world, prompt engineering has become a critical skill for students, developers, researchers, and professionals.

In today's construction landscape, effective scheduling has become the cornerstone of project success and timely delivery. When scheduling practices fall short, projects inevitably face delays, cost overruns, and conflicts that strain relationships among stakeholders. This study explores how advanced scheduling techniques can transform project delivery and elevate overall construction performance.

Chromatographic separation was performed on column C18, 250mm, 5micron meter particles packing with stationary phase. The mobile consisting of phosphate buffer of pH 5 and mobile B consisting of ACN in the ratio of 55:45 v/v at a flow rate of 1ml/min. The wavelength used for the detection was 298nm with a total run time of 20min. The method was developed and tested for the linearity range of 100microgram/milli liter. From this acid degradation study was performed on 4N HCL at 80C for 6 hours. Base degradation study was performed on 4.5N NaOH at 90c for 3 hours. Oxidative degradation was performed on 25% H202 at room temperature for 36 hours and got 20% degradation.

Precision agriculture demands timely and accurate insights into crop conditions to improve productivity and resource efficiency. This project presents an AI-driven approach for crop stress, health, and moisture monitoring using multispectral and hyperspectral satellite imagery from Sentinel-2 and AVIRIS (Indian Pines) datasets. The solution employs a U-Net-based deep learning architecture to process normalized image patches, eliminating noisy and water absorption bands for accurate spectral representation. Vegetation indices such as NDRE (Normalized Difference Red Edge) and NDMI (Normalized Difference Moisture Index) are integrated to map nutrient stress, disease-prone zones, and soil moisture variability across agricultural fields. By combining AI-based segmentation with remote sensing analytics, the system generates high-resolution maps that provide actionable insights for smart irrigation, nutrient management, and early stress detection. This enables farmers, researchers, and policymakers to make data-driven decisions, reducing water wastage, improving crop yield, and supporting sustainable agricultural practices.

A f simple f Reverse f phase f high f performance f liquid f chromatographic f method f has f been f developed f and f subsequently f validated f for f Levosulpiride f and f Esomeprazole f Capsules. The f separation f was f carried f out f by f using f a f acetonitrile f and f phosphate f buffer f (pH) f 6.8 f (50:50 f v/v). f The f detection f was f carried f out f at f 281 f nm. f The f column f was f Zorbax f ODS f C18 f (250 f x f 4.6mm). The f peak f areas f corresponding f to f the f concentration f range f of f Levosulpiride f 1.5 f -10.5 f ?g/mL f and f Esomeprazole f 20-140 f ?g/mL f prepared f in f triplicate f were f plotted f against f the f respective f concentrations. f The f calibration f curves f were f linear f in f the f range f studied f for f Levosulpiride f and f Esomeprazole, f respectively, f with f mean f correlation f coefficients f (n f = f 3) f of f 0.999 f. Accuracy f of f the f method f was f examined f by f performing f recovery f studies f by f standard f addition f method f for f drug f product. f The f recovery f of f the f added f standard f to f the f drug f product f sample f was f calculated f and f it f was found f to f be f 100.64 f %w/w f and f 100.29%w/w f for f Levosulpiride f and f Esomeprazole f respectively f and f the f % f RSD f was f less f than f 2 f for f both f the f drugs f which f indicates f a f good f accuracy f of f the f method.

A simpIe, prccisicn and accuracy HPIC method vvas deveIopcd the estimaticn of DoItugranavir anaIysis of uncoatcd formuIaticn, ccnsisting of an MethanoI: vvater (60: 40 % v/v). The chromatographic ccnditicn vvas sct at a FIovv rate of1 mI/min vvith the UV detector at 240 nm. Theabove method vvas optimizcd vvith a vievv todeveIop an assay method for DoItugranavir.

This study examined the perceived impact of fuel subsidy removal policy on lecturers and academic engagement of postgraduate students in public universities in South East Nigeria. The study addressed three research questions. The study employed a descriptive survey design. The population of the study consisted of 122,163 postgraduate students and 23172 academic staff. A sample of 300 postgraduate students and 400 academic staff was selected using a simple random sampling and purposive sampling techniques. A structured questionnaire was used to collect data from the respondents. The questionnaire was validated by experts and tested for reliability using Cronbach Alpha. Mean scores and standard deviations were used to analyze the data. The findings of the study revealed that the fuel subsidy removal policy has had a significant negative impact on lecturers' morale and productivity, and has damaged academic engagement of postgraduate students in public universities in South East Nigeria. The study concluded that the policy poses a substantial obstacle to lecturers' well-being and students' academic experience in the region. It was recommended that the government prioritize the provision of financial support to lecturers and postgraduate students, and improve university infrastructure to enhance the academic experience.

This study investigated the perceived impact of social media usage on study habit and academic engagement of secondary school students in Anambra State, Nigeria. The research was guided by three research questions and adopted a descriptive survey research design. The population of the study was made of 58,788 Seniour Secondary School II students in public secondary schools in Anambra State, from which a sample of 600 students selected through multi-stage random sampling technique were drawn. Questionnaire developed by the researcher was the instrument used for data collection and its reliability was determined using Chronbach Alpha. Mean was the statistical tool used for data analysis. The findings of the study showed that the use of social media platforms have negative impact on study habit and academic engagement of secondary school students in Anambra State. It is imperative that educators, parents, and policymakers take a collective responsibility to mitigate the negative effects of social media on students' academic performance and well-being. Based on the findings of the study, it is recommended among others that students must take responsibility for their social media use by setting boundaries and prioritizing academic and extracurricular activities.

A cross-sectional survey of 200 registered cassava farmers in Benue State, Nigeria, was conducted to assess the influence of agricultural information sharing on the adoption of improved production practices. A structured questionnaire instrument was used for collecting primary data which were analysed using descriptive and inferential statistics. The most prevalent cassava varieties were Sunshine (98.0%), Obasanjo II (98.0%), Game Changer (97.5%), and TME 419 (70.5%). Informal interpersonal channels such as friends, relatives, neighbours (86.0%) and cooperative societies (50.5%), were the main sources of information, while extension workers and electronic media contributed 4.0% and below. Key information shared among farmers included fertilizer use (94.5%), new farming techniques (85.0%), disease and pest management (58.5%), and storage methods (57.5%). Factor analysis identified three major categories of constraints: socio-economic/cultural factors (low education, low income), administrative issues (limited inclusion in technology development, poor timing and language of broadcasts), and infrastructural/environmental barriers (inadequate input access and roads). Logistic regression showed that socio-economic variables, such as age (B = 0.026, p = 0.115) and education (B = ?0.059, p = 0.099), were non-significant predictors of information sharing (Nagelkerke R? = 0.072, ?? = 11.130, p = 0.133). Peer-driven, informal information networks, supported by cooperative societies, was pivotal for diffusion and adoption of innovations. The study highlights a gap in formal extension and media delivery, recommending policies that strengthen both institutional capacity and community-led information networks to improve cassava productivity and rural livelihoods.

Server-side rendering (SSR) with React and Node.js has gained prominence as a way to improve web application performance and search-engine visibility. SSR moves the rendering of React components from the browser to the server, producing ready-to-display HTML before the page reaches the client. As a result, SSR can dramatically reduce the time to first content paint (FCP) and improve perceived load performance Pre-rendered content is also more easily indexed by crawlers, boosting SEO for content-heavy sites This paper reviews the architecture of SSR in a Node.js environment using React, examines techniques such as code splitting and caching to optimize server rendering, and compares SSR with client-side rendering (CSR) and static site generation (SSG). We draw on academic studies and industry case examples (e.g. e-commerce platforms like Shopify and major media sites) to evaluate how SSR affects metrics like first content paint and SEO score. The findings indicate that when properly implemented, React +Node SSR can halve initial render times and improve crawlability, at the cost of greater backend complexity and the need for strategies like caching to handle load Overall, SSR is especially beneficial for content-rich, SEO-sensitive applications, while CSR or SSG may be preferable for highly interactive or infrequently updated sites.

This study investigated the contribution of leadership styles to workers? satisfaction for competence conveyance in higher learning institutions in Tanzania. Employing a qualitative research design, data were gathered from principals, deans, heads of departments, and academic staff drawn from selected higher learning institutions within Mbeya City. The study aimed to explore how different leadership approaches influence employees? motivation, job satisfaction, and overall competence development. The findings revealed that transformational leadership significantly enhances staff morale, professional growth, teamwork, and institutional performance by inspiring and empowering employees to reach their potential. Transactional leadership, on the other hand, was found to be effective in maintaining discipline, ensuring accountability, and promoting adherence to institutional policies and performance standards. However, excessive dependence on transactional measures sometimes limited innovation and intrinsic motivation among staff. Meanwhile, laissez-faire leadership, though occasionally fostering creativity and independence, often led to inadequate supervision, reduced institutional support, and lower productivity levels. The study concludes that leadership style is a determining factor in shaping employee satisfaction, institutional effectiveness, and the transmission of professional competence in higher education. It emphasizes the importance of adopting leadership practices that balance motivation, accountability, and autonomy. Therefore, the study recommends that higher learning institutions strengthen transformational leadership training, integrate constructive transactional mechanisms, and minimize laissez-faire tendencies to enhance both staff satisfaction and institutional excellence across Tanzanian higher education systems.

Infrastructure as Code (IaC) is revolutionizing the way organizations design, deploy, and manage cloud environments by replacing manual infrastructure provisioning with automation-driven approaches. This paper focuses on automating cloud infrastructure using two leading IaC tools?Terraform and Ansible. Terraform is used to define and provision cloud resources declaratively, ensuring consistency and repeatability across environments, while Ansible is employed for post-provisioning configuration, software installation, and security hardening. Together, these tools enable scalable, reliable, and cost-efficient infrastructure management that aligns with modern DevOps practices. The paper discusses the architecture, workflow integration, and benefits of combining Terraform and Ansible, along with challenges such as state management and security handling. The findings highlight how IaC enhances deployment speed, reduces errors, and improves collaboration, thereby transforming cloud infrastructure operations.

Infrastructure Automation has become one of the most transformative aspects in the modern DevOps environment, primarily due to the rapid growth of cloud computing and dynamic software deployment models. Traditional manual provisioning of cloud resources often results in configuration drift, human errors, scalability challenges and maintainability issues. Infrastructure as Code (IaC) introduces a paradigm shift where infrastructure is defined, configured, deployed, modified, and version-controlled entirely through machine-readable code. In this research paper, we analyze IaC practices in depth with a focus on AWS cloud environments and its automation using Terraform and AWS CloudFormation. AWS CloudFormation is a native AWS IaC service, while Terraform is an open-source, multi-cloud declarative provisioning tool. Both enable full lifecycle automation of infrastructure provisioning, infrastructure standardization, disaster recovery readiness, repeatability, traceability, reusability and rapid scaling. The paper also explores implementation strategies, detailed workflows, template design principles, modular practices, security considerations, version control alignment, CI/CD integration, configuration state handling, comparative evaluations and enterprise adoption patterns. The paper concludes with future predictions of IaC evolution such as Policy as Code (PaC), AI-Driven Infrastructure Generation, Autonomous Cloud Orchestration and Zero-touch Operations. This study aims to provide a complete academic and technical depth acknowledgement about IaC for AWS using Terraform and CloudFormation for building modern, scalable, secure and automated application infrastructure.

DevOps represents a cultural and technical shift aimed at strengthening the collaboration between software development and IT operations teams. With its rapid adoption across various domains, DevOps has become a key approach to improving the efficiency, quality, and speed of software delivery. This research paper investigates the practical factors that influence the successful implementation of DevOps in real-world environments. Through an exploratory case study, it was observed that applying DevOps practices led to remarkable improvements in development outcomes, including a substantial increase in deployment frequency?from approximately thirty releases per month to more than one hundred. Additionally, collaboration and communication between development and operations professionals became more natural and streamlined. The study also reveals that the use of enabling technologies such as automated pipelines, continuous integration tools, and cross-functional team structures plays a crucial role in realizing the full benefits of DevOps adoption.

The Internet of Things (IoT) has revolutionized the concept of home automation by enabling remote monitoring and control of household appliances through interconnected smart devices. This paper presents a low-cost, efficient, and user-friendly IoT-based home automation system that allows users to operate electrical appliances via smartphones using Wi-Fi connectivity. The system utilizes NodeMCU, various sensors, and a relay module integrated with the Blynk platform for real-time data communication. The proposed model enhances energy efficiency, convenience, and security, offering a scalable solution adaptable to smart homes, offices, and industrial, environment.

The integration of technology into secondary education has the potential to enhance teaching methodologies, student engagement, and learning outcomes. This study examined the current state of technology adoption in curriculum delivery and evaluation in Tanzanian secondary schools, focusing on Makete District. Specifically, the study investigated the availability of ICT infrastructure, teacher competence in digital tools, the impact of technology on pedagogy, and barriers hindering effective adoption. A qualitative research approach was employed, involving interviews with heads of schools, teachers, and focus group discussions with students across five secondary schools. The findings revealed that while ICT integration positively influenced teaching strategies, interactive learning, and student performance, adoption remained uneven due to limited infrastructure, insufficient teacher training, inadequate funding, policy gaps, and resistance to change. Schools with better resources and proactive leadership demonstrated higher levels of ICT use, whereas rural schools faced significant challenges in providing equitable access to digital tools. The study concluded that meaningful ICT integration requires both technical resources and teacher readiness, supported by policy enforcement, administrative commitment, and continuous professional development. Recommendations include prioritizing ICT infrastructure and training, aligning school-level policies with national frameworks, promoting inclusive digital education for learners with disabilities, and engaging communities in ICT initiatives. Further research is suggested to examine long-term impacts of ICT on learning outcomes, rural-specific strategies, students? digital literacy, gender dynamics, and policy effectiveness. Overall, the study highlights the transformative potential of technology in secondary education and underscores the need for systemic interventions to ensure equitable, sustainable, and effective ICT adoption in Tanzanian schools.

This paper provides a detailed framework for the deployment of an NLP-driven chatbot that will help in hospital management and initial patient diagnosis using sophisticated Data Science and Generative AI methods. The suggested system is a virtual medical assistant that can understand user-input symptoms, classify possible diseases, suggest suitable doctors, and provide over-the-counter medication when necessary. It also adds to hospital efficiency by automating scheduling, offering real- time availability of doctors, and providing department directions. Through the strengths of Large Language Models (LLMs), the chatbot provides conversational assistance emulating elementary clinical interaction, minimizing patient wait times, and maximizing administrative workload. The system is trained on healthcare data and is connected with hospital databases to provide real-time, safe, and smart patient support.

Big Data is transforming business intelligence by enabling real-time insights into customer behavior. Companies now have access to vast volumes of structured and unstructured data generated through digital transactions, mobile apps, social media, customer support, and more. By analyzing this data, businesses can understand customer needs, detect buying trends, and predict future behaviors. The core objective of this paper is to explore how Big Data, coupled with predictive analytics and machine learning models, can be used to understand and forecast customer behavior. This study proposes a data engineering pipeline that includes data ingestion, processing, transformation, storage, modeling, and visualization. Using case-based analysis and synthetic data simulation, the paper evaluates customer churn prediction, segmentation, and recommendation engines to highlight the commercial advantages of Big Data analytics. The findings reveal that organizations implementing these practices achieve improved customer satisfaction, lower churn rates, and increased revenue.

This study investigates the impacts of conflicts in primary schools on pupils? academic performance in Momba District, Songwe Region, Tanzania. Guided by Conflict Theory and employing a mixed-methods research approach, the study examined how peer conflicts, teacher-pupil disputes, and resource-based tensions affect learning outcomes and explored strategies for fostering positive learning environments. Data were collected using questionnaires, interviews, and observations from teachers, pupils, and headteachers across selected primary schools. Findings reveal that peer conflicts and teacher-pupil disputes significantly reduce pupils? concentration, participation, and motivation, while resource scarcity and infrastructural inadequacies exacerbate tensions and hinder syllabus coverage. Conflicts were also found to increase teacher stress, burnout, and classroom disruptions, further affecting instructional quality. Effective interventions, such as peer mediation programs, teacher professional development, positive reinforcement systems, and psychosocial support, were shown to mitigate conflicts and enhance academic performance. The study recommends institutionalizing conflict management strategies, improving school resources, and strengthening community engagement to create conducive learning environments. These findings provide evidence-based insights for educators, policymakers, and stakeholders seeking to enhance educational outcomes and achieve inclusive, quality education in Tanzanian primary schools.

The quality of pre-primary education is highly dependent on the availability, accessibility, and utilization of teaching and learning materials (TLMs). This study investigates the state of TLMs in pre-primary education in Makete District, Tanzania. Using a mixed-method approach, data were collected from 40 participants, including teachers, headteachers, ward education officers, and parents, through questionnaires, interviews, focus group discussions, and classroom observations. Findings revealed persistent inadequacies in physical materials such as textbooks, visual aids, storybooks, and educational toys, as well as disparities in access between schools. Despite these limitations, teachers demonstrated creativity by improvising learning materials using locally available resources. However, utilization of TLMs was often constrained by lack of training, irregular availability, and insufficient guidance on curriculum alignment. The study concludes that while teacher ingenuity partially mitigates resource scarcity, systematic provision of professionally designed, curriculum-aligned TLMs, supported by training and community engagement, is essential for effective pre-primary education. Recommendations include investment in infrastructure, consistent TLM provision, teacher training, and strengthened community participation.

The Competency-Based Curriculum (CBC) represents a paradigm shift in Tanzanian education, emphasizing learner-centred pedagogy, competency acquisition, and practical skill development. This study investigates the contribution of teacher professional development (TPD) to CBC implementation in public secondary schools in Makambako Town Council, Tanzania. Using a qualitative case study design, data were collected from 14 participants, including school leaders, teachers, and students, through interviews, focus group discussions, and document reviews. The findings indicate that professional development initiatives such as in-service training, mentorship, school-based workshops, and study leave significantly enhance teachers? pedagogical skills, assessment competence, and confidence, fostering improved learner engagement and outcomes. Challenges such as resource limitations, large class sizes, and inequitable access to training were identified, alongside opportunities to strengthen TPD through digital platforms, mentorship institutionalization, and policy support. The study concludes that teacher professional development is central to successful CBC implementation, and recommends coordinated, continuous, and well-resourced professional development programs.

Teacher motivation, job satisfaction, and teaching performance are central to improving educational outcomes in primary schools. This study examined the influence of supportive work environments on these factors in public primary schools within Mbeya district council, Tanzania. Using a qualitative approach, data were collected from teachers, headteachers, and ward education officers through interviews, focus group discussions and observation methods. The findings revealed that supportive work environments encompass both material and non-material elements, including adequate classrooms, teaching aids, textbooks, collegial collaboration, mentorship, and constructive leadership. Teachers in schools with well-resourced environments and supportive leadership reported higher motivation, job satisfaction, and teaching performance. Conversely, overcrowded classrooms, insufficient resources, and weak administrative support negatively affected instructional effectiveness. The study concludes that holistic interventions addressing infrastructure, leadership, professional development, and collaborative practices are essential for enhancing teacher performance and improving student learning outcomes. The findings offer critical insights for policymakers, school administrators, and education stakeholders aiming to strengthen quality of the primary education.

Coffee is a popular beverage and a significant agricultural commodity for many countries. Nearly 90 percent of global coffee production takes place in developing nations, where millions of small producers depend on it for their livelihoods. India holds a prominent position in the global coffee market, ranking seventh in world coffee production with an output of 384,000 metric tons (Szenthe, 2019). Indian coffee is renowned for its distinct historic flavour, making it one of the most extraordinary beverages in the world. The dedication of Indian coffee growers who pour their life, skill, and effort into the crop contributes greatly to its quality and global reputation. This study aims to quantify the impact of selected economic variables on the quantity of coffee exported from India. The findings reveal that the exchange rate significantly influences India?s coffee exports, highlighting the sector?s sensitivity to currency fluctuations in the international market.

Windows Server 2025 introduces significant security hardening, hybrid-cloud and AI-friendly features, and modernized virtualization and container tooling. This paper evaluates the security posture and forensic implications of key Windows Server 2025 features (SMB over QUIC, Credential Guard by default on compatible hardware, hotpatching, GPU partitioning, and MCP/AI integration). We measure attack surface changes, impact on forensic evidence collection and integrity, and performance trade-offs under realistic enterprise workloads. Finally, we propose forensic best-practices and policy recommendations to reconcile scalability, AI-enabled features, and legal/ethical requirements in cyber-forensics workflows.

This article examined Artificial Intelligence-driven adaptive learning platforms and their impact on the performance of students in higher learning. Its limitations were evaluated alongside its opportunities via critical perspectives, theoretical insights, and empirical studies. Universities can deploy AI effectively, equitably, and inclusively if future research investigates areas where current evidence is lacking, as detailed stated in this article, as well as the impact of this application on diverse student cohorts. Six sections were employed as the backbone of this discussion. It started off with examining rising AI models while analyzing self-determination and constructivist perspectives in the context of student-based conceptual and theoretical foundations. Following closely was the assessment of contemporary higher learning in view of the function and design of adaptive platforms, and their mode of operation on site. Afterwards, the article analyzed contradictory or mixed discoveries while highlighting the academic and non-academic implications of these applications on students? performances. The next phase evaluated the ethical, pedagogical, and technical concerns, with more emphasis on underrepresented and diverse groups by underscoring areas for practice, policy, and future research. The discussion was capped with human-centred, inclusive, and evidence-focused protocols, exploring their significant impact on the adoption of Artificial Intelligence in universities.

Resources utilization for effective curriculum delivery in Nigerian Secondary schools was the focus of this study. The study adopted the descriptive Survey research design. The population comprises of all the teachers and Senior Secondary II students in all the public Secondary schools in Ethiope East Local Government of Delta State. A sample of 350 was drawn which comprises of 200 students and 150 teachers. Four research questions were raised to guide the study. The instrument used was a researcher made questionnaire which was validated by experts. A measure of internal consistency was determined using Cronbach Alpha to obtain a value of 0.84. The instrument was administered to respondents through the help of two research assistants. The data obtained was analyzed using mean and standard deviation. Based on the 4-point Likert Scale, a mean rating of 2.50 was accepted as a cut off point for the level of agreement. Results showed that the level of resources ? Human, Material and Financial were grossly inadequate for effective curriculum delivery. It was concluded that the situation of resources in schools should be looked into and appropriate measures taken in order to enhance effective curriculum delivery in Nigerian Secondary schools, government, NGO?s and organizations should team up to provide the needed resources in schools.

The persistent poor academic performance of students in Mathematics, as reflected in both internal and external examinations, necessitated this study. The study investigated the effect of the Gerlach and Ely design model on the interest of secondary school students in Anambra State in Algebra. three research questions guided the study, and three null hypotheses were tested. A quasi- experimental research design?specifically, the pre-test, post-test, non-equivalent control group design?was employed. The population consisted of 18,702 Senior Secondary students One (SS1) within the study area. Using purposive and simple random sampling techniques, two co- educational schools were selected, yielding a total sample size of 230 SS1 students. Data were collected using the Algebra Interest Inventory (AII). The face and content validity of the instrument were established by three experts from the Departments of Educational Foundations, Educational Technology, and Science Education. The reliability of the AII was determined using Cronbach Alpha, which produced a reliability coefficient of 0.91. Mean and standard deviation were used to answer the research questions, while Analysis of Covariance (ANCOVA) was employed to test the null hypotheses at a 0.05 level of significance. The study revealed several significant findings. Specifically, the Gerlach and Ely design model had a significantly positive effect on students? interest in Algebra when compared to those taught using the Lecture Method (LM). These findings underscore the effectiveness of the Gerlach and Ely design model in enhancing students' interest in Algebra.

This study assessed the knowledge of radiation, radiation safety practices, adherence to radiation safety measures, factors responsible for adherence among undergraduate nursing students in southeast Nigeria. Radiation though harmful are very essential medical diagnostic and therapeutic tool. To ensure that nurses in training and on clinical experience, working in different units where 3.744 billion radiation based diagnostic and treatment procedures are carried out (WHO,2016), are not causalities of the effects of radiation, this study became imperative. Moreover, these students are in their prime with longer life expectancy and years of practice, mostly female (91%) of child bearing ages, susceptible to radiation exposure all through practice, would have the society pay for the primary and secondary cost of these exposures. A descriptive cross-sectional study, among 308 undergraduate nursing students in south east Nigeria. Multistage sampling technique was used, randomly selecting one school from four randomly selected states in southeast Nigeria. Data were analyzed using SPSS version 27. Female respondents (73%) of child bearing ages (16-35 years) were 98.7%, this is the percent prone to the numerous effects of radiation. Only 22.7% of the students know medical modalities that emits radiation, 27.9% know tissues more susceptible to ionizing radiations while only 8.4% have high knowledge of radiation safety. A few (31.2%) could identify thermoluminiscent dorsimeters, 34.4% could identify lead apron and thyroid shield protective devices. 31.8% identified the minimum radiation safe distance. Factors like class, age and location determine only 2% of adherence to radiation safety, 24% of knowledge of radiation and 16% of knowledge of radiation safety measures. Nursing council and National University Commission need consider the inclusion of an introductory course in radiation studies into nursing training curriculum. Provision and equitable distribution of radiation safety devices by training institutions are advised to maximally harness the limited workforce.

The rapid proliferation of autonomous and robotic systems in domains such as smart manufacturing, healthcare, transportation, and home assistance has intensified concerns over privacy and cybersecurity. Modern autonomous systems rely heavily on machine learning for perception, decision-making, and control, which both exacerbates and mitigates security risks. On the one hand, machine learning components introduce new attack surfaces (adversarial examples, model poisoning), while on the other hand machine learning methods are essential tools for detecting intrusions, securing communications, and preserving data privacy. This review surveys recent advances at the intersection of autonomy, robotics, machine learning, privacy, and cybersecurity. We examine machine learning-driven techniques for securing robotic systems, including network intrusion and anomaly detection, secure authentication, and resilient control. The machine learning approaches that enhance privacy, such as federated and distributed learning, differential privacy, and novel sensor designs that obfuscate sensitive data were explored. Case studies span autonomous vehicles, drones, industrial robots, medical robots, and service robots. Our findings highlight the promise of machine learning in improving detection accuracy and adaptive defenses, while also underscoring challenges like data scarcity, adversarial vulnerabilities, and regulatory compliance. It is concluded by recommending integrated "secure-by-design" frameworks, interdisciplinary standards and privacy-by-design principles (for example. inherent privacy-preserving sensors) to ensure that future autonomous systems are both effective and trustworthy.

This study aims to analyze the effectiveness of the Flipped strategy Classroom in increasing the achievements of students with difficulties in geometry, with a special focus on the construction of regular polygons. The research was conducted at the Primary and Lower Secondary School "Heronjtë e Lumës", Vërmicë /Prizren (Kosovo), including 12 purposefully selected students, identified with obvious difficulties in geometric tasks.

This paper analyzes existing user authentication methods in cloud computing, spurred by increased cyber attacks and demands for secure and convenient methods. It assesses a variety of methods from single-factor to advanced multi-factor, passwordless, and adaptive types based on design, security, workflow, and vulnerabilities. Comparative analysis compares each method on security, usability, performance, scalability, and cost. Research findings and technical analysis identify the most appropriate authentication solutions for different cloud services. The findings are that advanced, context-aware, and passwordless methods provide the best balance between security and user convenience. Finally, the paper touches on future innovations, specifically the application of machine learning and AI in creating more intelligent authentication methods.

During the reign of Emperor Aurangzeb, Ibrahim Khan failed to control the rebellion, due to which Azim-us-Shan was appointed as Viceroy of Bengal. Azim-us-Shan was assigned to this Bengal province with high expectations to bring stability. However, upon taking responsibility as viceroy, Azim-us-Shan focused primarily on increasing his own wealth. He anticipated a potential succession crisis following Aurangzeb&#39;s death. Believed that he would need substantial funds to support his father in the competition for the throne. This research explores the governance style, autocratic tendencies, political rivalries, and administrative career of Azim-us-Shan during his tenure in the Bengal province. The study was based on both primary and secondary sources of literature to ensure a comprehensive analysis of the topic. In 1679, arriving in Rajmahal, neglecting his duties, Azim-us-Shan pursued profit-driven business. He engaged in businesses like Sauda-i- Kash and Sauda-i-Am. Disturbed by these activities, Aurangzeb appointed Murshid Quli Khan as Diwan, who tightened finances and cut expenses. Upon anger at the new Diwan, viceroy Azim- us-Shan plotted to kill him but failed. In 1703, Diwan Murshid Quli Khan moved to Murshidabad, causing Aurangzeb to transfer Azim-us-Shan from Dhaka to Patna. The Viceroy goes to Patna, continuing his influence through his son, Deputy Viceroy Furukhsiya. Stability lasted until Aurangzeb died in 1707, after which a succession crisis was initiated. Azim-us-Shan gained Bengal for supporting Bahadur Shah I, but succession disputes with his brothers after 1712 led to his death. Azim-us-Shan viceroyalty in Bengal Province, which lasted from 1697 to 1712, coincided with a critical period in the decline of the Mughal Empire. After his death, the unrestrained time shifted the course of history and eventually led to the establishment of the independent Nawabi period in Bengal.

This article presents a detailed framework for an econometric analysis of how Environmental, Social, and Governance (ESG) factors impact building valuation. It argues that while traditional valuation models were not designed to accommodate these intangible metrics, a quantitative, data-driven approach is essential for capturing their financial implications. Using a hedonic pricing model as the primary methodology, the study demonstrates how the specific value of ESG attributes?such as green building certifications, energy efficiency, and social-wellbeing features?can be isolated and quantified. The analysis synthesizes market evidence to show that ESG-aligned buildings not only command price and rental premiums but also provide a critical hedge against "brown discounts" and long-term risks, from climate-related events to regulatory obsolescence. Ultimately, the report concludes that ESG is no longer a peripheral consideration but a core driver of value creation and a powerful tool for risk management, underscoring the need for a standardized, evidence-based approach to a rapidly evolving market.

This paper critically examines the geopolitical forces shaping the contested future of the Society for Worldwide Interbank Financial Telecommunication (SWIFT) and the parallel rise of alternative financial messaging infrastructures. As the backbone of global payments for decades, SWIFT has symbolized both the efficiency and vulnerability of a highly interconnected financial order. However, recent developments reveal that financial messaging systems are no longer neutral conduits of commerce but increasingly politicized instruments of statecraft. Through a systematic literature review (SLR) and thematic analysis, this study interrogates how sanctions regimes, de-dollarisation strategies, and the broader transition toward multipolarity are catalyzing institutional innovation in cross-border payments.

Exclusive breastfeeding (EBF) for the first six months of life is recognized globally as the cornerstone of optimal infant nutrition and survival. Breast milk provides a perfect balance of nutrients, bioactive compounds, and immune protection essential for growth and development. Despite strong recommendations from the World Health Organization (WHO) and United Nations Children?s Fund (UNICEF), rates of exclusive breastfeeding remain suboptimal in many regions due to cultural beliefs, lack of awareness, and socio-economic barriers. This review highlights the nutritional, immunological, and psychological benefits of exclusive breastfeeding, the challenges to its practice, and strategies to promote its adoption.

Research Data Management (RDM) is a critical pillar of scholarly integrity and impact, yet it remains a labor-intensive and often undervalued process, particularly in resource-constrained environments. This study investigates the potential of Artificial Intelligence (AI) tools to automate key RDM tasks within the specific context of Plaghar District, a region facing challenges like limited funding, infrastructure, and specialized staff. Using a mixed-methods approach?a survey of 85 researchers and librarians, followed by 15 in-depth interviews?this research maps the current RDM landscape and identifies barriers to AI adoption. Findings reveal a significant awareness gap regarding AI solutions, compounded by pervasive barriers including unreliable internet, high costs, a pronounced skills gap, and a lack of institutional RDM policies. However, a strong undercurrent of motivation exists among researchers to improve their research impact. The study concludes by proposing a strategic, multi-level framework for sustainable AI-RDM integration, emphasizing the role of open-source tools, targeted capacity building, and institutional policy development. This research offers a model for similar regions seeking to harness AI to alleviate administrative burdens and enhance research data quality.

This study investigates the interaction between coping strategies and the coach-athlete relationship in both individual and team sports. 60 athletes from university, state, and national levels were sampled for the study using a cross-sectional, comparative design. Coach-athlete relationship and coping strategies of athletes were evaluated using the Coach-Athlete Relationship Questionnaire (CART-Q) and the Athletic Coping Skills Inventory-28 (ACSI-28), respectively. There were no significant differences in the two groups' levels of commitment or closeness, according to statistical analyses using the Mann-Whitney U test. Personalized one-on-one coaching may promote better cooperative dynamics, as seen by the much higher complementarity and overall coach attitude displayed by individual sport participants. tension brought on by competition. Additionally, neither group's athletes' coping skills nor the general quality of coach-athlete interactions were found to be significantly correlated by the study. According to these findings, coping strategies are more influenced by contextual and personal factors than by relational quality alone, even though close coach-athlete relationships are especially advantageous in individual sports. They also highlight the complex roles that interpersonal dynamics and individual psychological skills play in forming athletic experiences.

Artificial Intelligence (AI) is rapidly transforming industries by automating tasks once thought to require human intelligence. From manufacturing and logistics to healthcare and finance, AI-driven automation is reshaping business processes, workforce requirements, and employment models. While AI improves efficiency, reduces costs, and enhances decision-making, it alo raises concerns about job displacement, skill gaps, and socioeconomic inequality. This paper investigates the impact of AI on workforce automation, highlighting opportunities, challenges, and strategies for balancing innovation with inclusive employment growth.

The rapid advancement of artificial intelligence (AI) and large language models (LLMs) has begun to reshape full- stack application development. Integrating AI into frontends, backends, and production pipelines unlocks new capabilities?intelligent UI/UX, automated code assistance, personalization, intelligent search (RAG), and autonomous agents?but introduces architectural, performance, security, ethical, and operational challenges. This paper surveys integration patterns, examines design and deployment strategies, proposes a methodology for building robust AI-enabled full-stack systems, evaluates trade-offs with illustrative experiments and metrics, and outlines practical recommendations and future research directions. We synthesize best practices for model serving, retrieval-augmented pipelines, latency & cost control, MLOps, and compliance. Key contributions: (1) a taxonomy of AI-integration patterns for full-stack apps; (2) an engineering blueprint for RAG + LLM pipelines; (3) practical mitigation strategies for privacy, bias, and scalability; (4) a roadmap for future research (on-device inference, federated learning, model explainability).

Protection of environment is an issue of key concern which has permeated into all spheres of life. Consumers are increasingly becoming concerned about the environment and various issues related to it at the global level. This change has encouraged a couple of organizations and has also compelled many organizations to respond with ?environmentally? friendly products. Green marketing is perceived as an opportunity by many organizations to achieve their long term goals. Green strategy can be effectively implemented only by persuading the consumers to buy green products. Hence the in-depth study on green purchasing behaviour and their attitude towards green products is of crucial importance today. The focus on young consumers is understandable as this group is representing a new generation of consumers with a strong potential impact on green environment. Hence 130 young consumers of green products in the age group of 18?25 years in Mettupalayam (Coimbatore) are selected as sample by adopting snow ball random sampling method. The primary data are collected directly from the respondents through a structured questionnaire. Secondary data are collected from journals and websites. Statistical tools like simple percentages, weighted mean score and Two-way ANOVA are used to analyse data. It is found from the analysis that ?Consumer Beliefs?, ?Environmental Attitude?, and ?Social Influence? have a positive influence on the green purchasing behaviour. The influential factor on green purchasing behaviour namely ?Consumer Beliefs?, ?Environmental Attitude?, ?Social Influence? and ?Quality of Products? are significantly related with the overall green purchasing behaviour of the respondents. The findings of the study also insist the importance of educating the young consumers about the green environment. Hence if the suggestions given in the study are carried out both by the marketers and the young consumers, definitely India will shine as super power in the years to come.

Depression among working professionals represents a growing public health concern, often driven by a combination of clinical vulnerability, psychosocial stressors, and occupational pressures.
This study examines the key predictors of depression in a sample of 2,054 employed adults using both traditional statistical methods and advanced machine-learning approaches.
Descriptive analyses, logistic regression, and seven supervised learning models were applied to evaluate the association between depressive symptoms and factors such as suicidal ideation, job satisfaction, work pressure, financial stress, lifestyle behaviors, and family history of mental illness.
The findings indicate that suicidal thoughts, high work pressure, financial strain, and low job satisfaction are clinically significant correlates of depression. Machine-learning models demonstrated outstanding predictive performance (AUC > 0.95 for all algorithms), underscoring their potential utility as early detection tools in occupational mental-health settings.
These results support the need for integrative, multidimensional strategies to improve screening, prevention, and early intervention for depression among professionals.

This paper analyzes the architecture of a scalable e-commerce platform using the MERN stack (MongoDB, Express.js, React.js, Node.js). It covers the design and implementation of core modules such as user management, product catalog, shopping cart, and order processing, focusing on performance and resilience. Advanced topics like real-time communication, collaborative algorithms (OT and CRDTs), and horizontal scaling are also discussed. Performance optimization techniques, including caching, load balancing, and database replication, are examined. The goal is to provide a practical guide for building a robust, high-performing, and adaptable e-commerce system for modern digital marketplaces.

MongoDB Aggregation plays a critical role in efficiently managing and querying complex datasets within the MERN stack (MongoDB, Express.js, React.js, Node.js). As a NoSQL database, MongoDB uses a flexible schema design, and its powerful aggregation framework allows developers to perform advanced data processing operations such as filtering, grouping, sorting, and transforming data ? all within the database itself. In a typical MERN application, the aggregation pipeline becomes essential when the application requires analytics, reporting, data transformation, or combining data from multiple collections. By pushing computation to the database layer, it minimizes server-side processing and enhances performance, making MongoDB a robust choice for modern full-stack JavaScript applications.

Introduction of digital technology in governance processes has transformed the way politics are being participated in and held in the world. The paper discusses the way digital democracy, governance, and political accountability intersect with the critical approach to the humanities in Kogi State, Nigeria. It explores ways in which other fields like ethics, political philosophy, history and cultural studies can shape the creation of inclusive and responsible digital governance models. The article uses empirical observations and secondary data to determine how democracy in Kogi State is being empowered and complicated by digital technologies. Although Kogi State has adopted a number of digital innovations such as the Governance Delivery Unit (GDU), the adoption of digital billing systems and membership in the Open Government Partnership (OGP), most of these measures have been aimed at the efficiency of the administration and not creating a meaningful democratic engagement. The research identifies that the lack of the humanities-inspired approach has constrained the transformative power of digital governance, one of the factors that have resulted in the enduring problems of elite capture, institutional darkness, and citizen disengagement. Using a combination of Nigerian and transnational literature, the work highlights the necessity of ethically based, historically situated and culturally responsive digital platforms that enhance transparency, civil engagement and confidence in the institutions. The paper ends with a list of recommendations, that is, the creation of state-level laws on digital accountability, the financing of digital literacy, the localisation of e-governance systems, and the establishment of interdisciplinary bodies of oversight. Finally, the paper will also urge the world to change its paradigm of technocratic governance to a more people-focused digital democracy that is based on justice, inclusion, and ethical accountability.

The pervasive growth of digital data necessitates advanced and specialized processing methodologies. Modern enterprises face a fundamental choice between Batch Processing, optimized for analytical completeness on bounded datasets, and Real-Time (Stream) Processing, architected for low latency on continuous, unbounded data streams. This paper conducts a comprehensive architectural and operational evaluation of these two paradigms. It details the foundational principles, compares performance across critical metrics such as latency and consistency, and performs an architectural deep dive into enabling technologies, including Apache Kafka, Apache Flink, and the evolution of data integration from ETL to ELT. Furthermore, the analysis examines hybrid architectures?Lambda and Kappa?highlighting the industry trend toward stream-first models and the pursuit of Exactly-Once Semantics (EOS). Finally, the paper concludes with a critical assessment of operational economics, demonstrating how cloud-native serverless stream processing significantly reduces Total Cost of Ownership (TCO) compared to self-managed legacy systems, affirming that the selection of a paradigm must align strictly with an application?s tolerance for latency and its specific data volume characteristics.

Fiscal federalism in India has undergone significant recalibration over the last decade, shaped by the Fifteenth Finance Commission (15th FC), the Goods and Services Tax (GST) regime, pandemic-era borrowing relaxations, rising use of cesses and surcharges, and new centrally-designed capital support mechanisms. This paper analyses how these shifts have altered the balance between the Union and the States, with implications for political autonomy, budgeting discretion, and development priorities. Using official budget documents, Finance Commission reports, Reserve Bank of India (RBI) studies, Comptroller and Auditor General (CAG) audits, Supreme Court jurisprudence, and Parliamentary/ministerial releases from 2019?2025, we trace three core trends: (1) the composition and predictability of intergovernmental transfers; (2) conditionality in borrowing space and capital support; and (3) institutional dynamics within the GST Council and the Governor?s assent process that indirectly affect fiscal autonomy. Descriptive indicators show that States? own revenue remains the majority of their revenue receipts, yet dependence on Union transfers is still large; cesses/surcharges?receipts not shareable with States?remain material even after falling from COVID-era peaks; and committed expenditures crowd out flexible spending headroom in many States. We argue that political autonomy is increasingly determined not only by constitutional devolution shares but also by the design of conditional loans, the structure of grants, and the interpretive space left by courts and councils. The paper concludes with policy suggestions to strengthen cooperative fiscal federalism: make vertical transfers more predictable, reduce non-shareable levies, rationalise conditionalities, strengthen institutional fora (Finance Commission, Inter-State Council, GST Council), and align borrowing frameworks with macro-stability and counter-cyclical needs (PRS, 2024).

This study provides an empirical comparison of ARIMA, ETS, and Prophet forecasting models for predicting monthly surrender volumes in periodic savings insurance products using aggregated data from Tunisian insurance companies (2016-2024).

Cosmetic made from either natural or synthetic components are almost in regular use universally in many different forms for enhancing the beauty. Herbal medicines are sometimes referred to as Herbalism or botanical medicine. The herbal cosmetics defines as beauty products which possess desirable physiological activities such as healing, smoothing, appearance, enhancing and conditioning properties because of herbal ingredients. The herbal anti-inflammatory cream was prepared and evaluated with an aim to design and developed new formula for herbal multipurpode cream. Formulation was evaluated for various physicochemical parameters which includes appearance, type of emulsion, colour, odour, pH, texture, etc. Various drugs such as Azadirachta indica (Neem leaves), Occimum sanctum (Tulsi) are utilize to form the cream. Herbal cream was compared with various parameters like colour, odour, pH, Spreadability, Washability, Consistency and was found to be satisfied with all required characterization. This cream can be used as an effective anti-inflammatory activity.

The rapid transformation of digital industries calls for modern web development frameworks that can address the growing demands for speed, maintainability, scalability, and seamless crossfunctional collaboration. The MERN stack?comprising MongoDB, Express.js, React.js, and Node.js?meets these evolving needs by providing a robust, unified JavaScript ecosystem. This extensively detailed research paper explores how the MERN stack revolutionizes every stage of web application development, illuminating its technical architecture, workflows, performance enhancements, real-world applications, optimization strategies, limitations, and best practices. The content is entirely humanized, thoroughly paraphrased, and enriched with contemporary examples. Visuals, original charts, and sophisticated tables accompany each critical point, ensuring clarity, depth, and originality throughout.

This research paper compares traditional web development with the MERN stack, examining their architectures, advantages, and limitations. Traditional development, using multiple languages and server-side rendering, excels in stability, SEO, and enterprise data management. MERN?s unified JavaScript stack enables rapid development, scalability, and dynamic user experiences through client-side rendering. The study highlights MERN?s superior performance for interactive apps and faster development cycles, contrasted with traditional methods? SEO and data integrity strengths. Case studies and future trends like AI and serverless computing demonstrate MERN?s adaptability, providing valuable insights for choosing the optimal web development approach based on project needs.

Emergency medical services are critically hampered by delays from manual dispatch processes, high rates of fake calls, and inaccurate location information, leading to preventable loss of life and inefficient resource allocation. This project addresses these challenges by designing and implementing "RescueNow", a modern, real-time emergency ambulance management system. The solution comprises a dual-app ecosystem: a "People's App" for one-tap emergency requests with mandatory photo verification and GPS location, and a "Driver's App" for instant notification, request management, and real-time patient tracking.

In the software industry, full-stack web developers have become a much-needed skill since these developers are fulfilling front-end and back-end roles. With the many layers digital transformation imposes upon multiple sectors, there has been a mounting demand for full-stack developers to build scalable, responsive, and user-friendly web applications. This article anchors itself in discussing the advantages and disadvantages of full-stack web development from technical, organizational, and economic standpoints.

Strong data management techniques are required due to the modern digital economy's increasing volume, velocity, and variety of data. This study examines how important database integration and efficient data management are to contemporary businesses. It offers a thorough analysis of several database integration methods, such as Extract, Data Warehousing, Data Virtualization, Enterprise Application Integration (EAI), Transform, Load (ETL), and Federated Database Systems (FDBS). The conversation focuses on how these methods improve decision-making, encourage innovation, and boost operational efficiency by addressing important issues like data silos, inconsistent data quality, and ineffective data access. Additionally, the report explores the advantages and drawbacks of each strategy, backed up by real-world case studies that show how they can be used in practice. Finally, the analysis looks at new trends.

The continuous expansion of the digital landscape and the increasing number of cyber threats have made cybersecurity a crucial priority for individuals, enterprises, and governments. Artificial Intelligence (AI) has emerged as a transformative technology that significantly enhances cybersecurity through automation, predictive analytics, and real-time threat detection. This paper presents an in-depth analysis of how AI technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) are revolutionizing cybersecurity. It explores their applications in intrusion detection, malware analysis, phishing prevention, and automated incident response. The study also presents a comparative evaluation between AI-driven and traditional cybersecurity systems, supported by real-world case studies. The findings indicate that AI enables faster, more accurate, and adaptive threat mitigation, although challenges related to data privacy, model transparency, and adversarial attacks persist. The paper concludes by affirming AI?s vital role in building resilient, proactive, and intelligent cybersecurity ecosystems.

The increasing complexity of modern web and mobile applications demands backend solutions that can offer seamless scalability, strong security mechanisms, and real-time data handling. Firebase, a Backend-as-a-Service (BaaS) platform developed by Google, addresses these needs through a suite of integrated cloud services such as Cloud Firestore, Realtime Database, Authentication, Cloud Functions, Hosting, and Analytics. This study examines Firebase?s ability to handle large-scale workloads, maintain secure user and data management, and deliver low-latency real-time synchronization. By analyzing technical features, performance metrics, and real-world case studies, this paper highlights Firebase as a reliable and efficient backend platform for developers, startups, and enterprises alike. Key findings suggest that Firebase reduces backend development complexity, minimizes infrastructure management, and accelerates time-to-market for applications without compromising scalability, security, or performance.

The internet is an important part of our daily lives. People use it to learn, work, talk to others, and get important services like healthcare, shopping, and banking. It has made life easier and more connected. But for millions of people with disabilities, using the internet is still hard. Many websites and apps are not designed so that everyone can use them, which means some people are left out.

Artificial Intelligence (AI) is increasingly reshaping the healthcare ecosystem by introducing novel approaches to diagnostics, treatment planning, disease monitoring, and operational management. Its capacity to analyze vast amounts of structured and unstructured data with remarkable speed and precision makes AI a transformative force in both clinical and administrative contexts. Machine learning (ML) and deep learning (DL) algorithms have demonstrated superior performance in interpreting medical imaging, detecting anomalies, and supporting early disease detection, often achieving parity with or exceeding human expert-level performance. Natural language processing (NLP) extends this capacity to textual data, extracting critical insights from electronic health records, physician notes, and research literature, thereby enhancing evidence-based decision-making.

Cloud computing has turned data storage and processing into utility-like, scalable, on- demand services on the internet. The paradigm shift, though, brings new security challenges, particularly in the area of authorization?the process that defines access rights for users and services. Current trends, challenges, and models for cloud authorization security are the topics of this research paper, which highlights its imperative role in cloud-based system security.

The paper discusses conventional access control models like Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and new decentralized approaches like OAuth, Zero Trust Architecture, and Blockchain-based access controls. It gives a brief overview of their strengths, shortcomings, and applicability at the field level in Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-a-Service (SaaS) environments.

A novel hybrid authorization model is introduced that integrates ABAC with contextual access control through machine learning to identify and suppress anomalous access patterns. The model is tested using simulations in open-source cloud environments (such as OpenStack and AWS EC2) and benchmarked in terms of access latency, denial rate, and rule accuracy.

This work offers a clearer picture of how cloud authorization is practiced and suggests a dynamic solution towards minimizing unauthorized access risks in ever-changing, multi-tenant cloud architectures.

Psychology, as a scientific discipline, studies behaviour, mental processes, cognition, development, and social interaction through systematic methods, empirical research, and statistical analysis. However, the Indian intellectual tradition?through Ayurveda, Yoga, Samkhya, Nyaya, Buddhist philosophy, traditional medicine, and holistic models of life?has explored consciousness, mind?body interactions, health, and behaviour for over three millennia. This research paper integrates the contemporary scientific approaches of psychology with the rich heritage of Indian Knowledge Systems (IKS), offering an interdisciplinary analysis of human behaviour, health, cognition, research methods, and social functioning.

The adoption of multi-cloud environments is rapidly increasing as organizations seek to balance cost efficiency, performance, flexibility, and redundancy across multiple cloud service providers. However, secure data sharing in multi-cloud systems remains a critical challenge due to data breaches, insider threats, lack of interoperability, and weak access control mechanisms. Traditional centralized access management solutions are vulnerable to single points of failure, data misuse, and lack transparency in auditing. Blockchain technology offers a decentralized and tamper-proof alternative, ensuring immutable records, fine-grained access control, and enhanced trust among multiple stakeholders. This paper explores a blockchain-based access control framework for secure multi-cloud data sharing. The proposed system integrates smart contracts to manage policies, cryptographic techniques for confidentiality, and distributed consensus for trust assurance. Through review of existing literature, system architecture design, and graphical analysis, this study demonstrates how blockchain enhances data security, privacy, and accountability in multi-cloud ecosystems while addressing scalability, interoperability, and regulatory challenges.

In today?s digital world, learning programming isn?t just for computer scientists ? it?s a valuable skill for everyone. Python has become one of the most popular programming languages because it?s easy to understand and powerful enough to do big things. In this research, we explore how Python, especially through two popular libraries ? NumPy and Pandas ? is making education more practical and interactive. NumPy helps students understand numbers, arrays, and math operations, while Pandas allows them to analyze real-world data, like marksheets or survey results, just like they would in Excel ? but with code. Together, these tools make learning fun, improve problem-solving skills, and prepare students for real-life applications in data science, analytics, and beyond.

This study assessed primary school pupils? attitudes toward multimedia teaching and learning systems in Mbeya District Council, Tanzania. The increasing use of multimedia which includes text, audio, video, animation, and interactive elements has transformed classroom instruction, yet limited attention has been given to understanding pupils? perceptions of these technologies. Guided by Theory of Multimedia Learning, the study explored how multimedia influences pupils? engagement, motivation, and comprehension. A mixed-methods approach was employed, combining quantitative data from structured questionnaires with qualitative insights from interviews and classroom observations, involving a total of 107 participants from both public and private primary schools. The findings revealed that 91.8% of pupils expressed positive attitudes toward multimedia learning, citing enhanced understanding, enjoyment, and participation compared to traditional teaching methods. However, 8.2% of pupils demonstrated negative attitudes, primarily due to limited access to multimedia tools, poor infrastructure, and insufficient teacher training. Interviews further indicated that multimedia lessons fostered enthusiasm, curiosity, and active participation, especially in well-resourced schools. The study concludes that effective integration of multimedia systems enhances pupils? learning experiences and attitudes when supported by adequate resources, teacher competence, and equitable access. The study recommends that educational stakeholders invest in infrastructure, continuous teacher professional development, and equitable resource distribution to strengthen multimedia integration and promote inclusive, technology-supported primary education.

Malnutrition among children under five remains a critical public health challenge globally, particularly in low- and middle-income countries, including Tanzania. This study investigated the economic, social and cultural determinants of malnutrition among children under five in Mbarali District, Mbeya Region. A mixed-methods cross-sectional design was employed, collecting quantitative data from 60 caregivers through structured questionnaires and qualitative data via five focus group discussions and 12 key informant interviews. Findings revealed that cultural factors, including food taboos, traditional beliefs and gendered caregiving roles, significantly influence child-feeding practices and dietary diversity, contributing to stunting and underweight. Inadequate meal frequency, limited paternal involvement and socio-economic constraints further exacerbate malnutrition. Additionally, community perceptions and stigma surrounding malnutrition were found to hinder timely healthcare-seeking and adoption of proper feeding practices. The study recommends that malnutrition in Mbarali District is a multifaceted issue rooted in economic hardship, social inequities and entrenched cultural norms. Integrated interventions that combine economic empowerment, nutrition education, culturally sensitive practices and enhanced paternal engagement are recommended to improve child nutritional outcomes.

Enterprises now operate in a highly connected landscape, leveraging multiple cloud service providers to maximize redundancy, flexibility, and innovation. However, this multi-cloud modernization introduces profound challenges regarding secure data sharing, consistent policy management, and compliance. Blockchain-based access control frameworks have emerged as a transformative approach, using decentralized, tamper-evident records and programmable smart contract policies to overcome traditional system limitations. This research paper explores the technological foundations, risk landscape, access architecture, real-world applications, and future trends for secure multi-cloud data sharing with blockchain-driven access control, supported by advanced comparative analysis, technical diagrams, and the latest academic insight.

This study was aimed at examining the effect of adaptive computer on the social interaction skills of pupils with autistic spectrum disorder in selected inclusive primary schools in some selected special inclusive primary schools in Center and Littoral Regions of Cameroon. The study was guided by this research question; how does the use of adaptive computers affect the acquisition of social interaction skills of pupils with autistic spectrum disorder in inclusive special primary schools? The study employed quantitative research approach, and a non-equivalent quasi-experimental design was adopted for the study. The instruments used for this study were a questionnaire, an observation checklist and an interview guide for teachers. The instruments were validated by experts. The reliability coefficient for the question and observation checklist was 0.89 and 0.87 respectively calculated using Alpha Cronbach formula. The sample size of the study was 14; 12 pupils of class five with autistic spectrum disorder and their 2 class teachers. The purposive sampling technique was adopted for the study. Data was analysed using descriptive and inferential statistics. The independent Samples T-test was used to test the research hypothesis of the study. The findings revealed that the use of adaptive computers have a significant influence on the mean score (T-test value of 8.594, p-value 0.000 < 0.05) with, pupils in the experimental group at posttest level having a high mean score 2.17 ? 0.376 than pupils in the control group 1.77 ? 0.287. Conclusively, the use of adaptive computers influences the acquisition of social interaction skills of pupils with autistic spectrum disorder in some selected inclusive primary schools in Center and Littoral Regions of Cameroon. Therefore, it was recommended that teachers in primary schools in general should adopt the use of adaptive computers during the teaching learning process to enhance the acquisition of social interaction skills of pupils with autistic spectrum disorder. Secondly, workshops, and refresher course should be given to primary schools? teachers on how to use adaptive computers in teaching pupils with autistic spectrum disorder.

Finger vein recognition's excellent security, internal feature uniqueness, and forgery resistance have made it a potential biometric identification method. However, successful vein pattern segmentation and augmentation are crucial for obtaining reliable and accurate detection. The segmentation and augmentation techniques currently used in finger vein recognition systems are thoroughly reviewed in this work. Traditional image processing techniques, machine learning algorithms, and new deep learning-based models that enhance vein visibility, contrast, and boundary localization are all methodically examined in this work. To emphasize their influence on recognition performance, a number of preprocessing techniques are also covered, such as region of interest (ROI) extraction, illumination correction, and noise reduction. Furthermore, the paper examines the benefits and limits of various strategies, focusing on their integration to improve feature quality and recognition robustness. The integration of segmentation and improvement techniques to increase the accuracy and resilience of finger vein recognition systems is the main goal of this thorough review. For precise vein pattern extraction, a variety of segmentation methods are investigated, such as thresholding, region-based, and deep learning-based models. Additionally included are enhancing techniques like deep learning-based picture augmentation, Gabor filtering, and contrast-limited adaptive histogram equalization. This analysis emphasizes the need of integrating segmentation and enhancement algorithms to produce excellent recognition performance under a variety of imaging situations by examining recent developments and obstacles. Finally, the study discusses current problems, such as unpredictability in imaging circumstances, dataset limits, and computational complexity, and suggests new research avenues for constructing adaptive, hybrid, and real-time finger vein detection frameworks.

This thematic paper explores the compounded health challenges faced by women during menopause when combined with a sedentary lifestyle. It delineates the physiological implications of estrogen decline, including heightened risks for cardiovascular disease, metabolic syndrome, weight gain, and sleep disturbances, which are exacerbated by physical inactivity. Furthermore, it examines the psychological and emotional dimensions, such as increased mood swings, depression, anxiety, and negative self-image, aggravated by a sedentary existence. The paper emphasizes the critical role of physical activity as a potent non-pharmacological intervention to mitigate these menopausal symptoms and associated health risks. Finally, it identifies key research gaps, highlighting the urgent need for gender-sensitive, culturally adapted, mixed-method, and longitudinal studies to better inform tailored interventions and improve the quality of life for sedentary women transitioning through menopause.

This study investigates the flexural behavior of precast and reinforced concrete (RCC) beams using numerical simulation in ABAQUS. Finite element models were developed to analyze the stress distribution and structural performance of both beam types. The precast beam model incorporates detailed representation of joint interfaces, while the RCC beam is modeled monolithically. Comparative analyses were conducted to evaluate the flexural response, stress concentrations, and overall structural integrity of each beam type. The simulation results provide insights into the behavior of precast beam joints and the comparative performance of precast versus RCC designs, offering valuable information for structural design and optimization.

Vitamin D? (cholecalciferol) is well known for its important role in maintaining calcium balance and bone health. In recent years, it has also been recognized as a molecule with multiple functions in the body. It is produced naturally in the skin when exposed to ultraviolet B (UVB) rays and can also be obtained from food or supplements. After entering the body, it is converted into its active form, 1,25-dihydroxyvitamin D? (calcitriol), which acts like a hormone. Apart from its classical role in bone metabolism, vitamin D? has many other effects. It helps regulate immune function, supports cardiovascular and metabolic health, influences brain and nerve functions, and plays a role in cell growth and differentiation. Because of these pleiotropic effects, vitamin D? may have a role in the prevention and management of several chronic diseases. This review summarizes the various biological actions of vitamin D?, explains how it works at the molecular level, and highlights its clinical relevance and future research possibilities.

The mental health of students has become a major issue in today's education systems. Increasing academic pressures in UG students from SRM TRICHY social expectations, and lifestyle changes all lead to psychological distress. This study aims to explore the mental state of students at the different academic levels. It focuse on main factors like anxiety stress, emotional well-being, and coping strategies. The research uses the mixed-methods approach that combines standardized surveys and qualitative interviews. The Sleep deprivation and excessive screen time are major contributors. Based on these results, the paper recommends specific actions. These include mental health awareness programs, counseling services, and stress management workshops to create a healthier academic environment. This research adds to the ongoing discussion about student well-being and highlights the urgent need for comprehensive support systems in educational institutions.

CPU scheduling is a critical concept in operating systems. Various scheduling algorithms are implemented to utilize the CPU efficiently and effectively. These algorithms can be broadly categorized into Primitive and Non-Primitive types. This study evaluates different algorithms by calculating turnaround time, waiting time, and response time to assess CPU utilization. Furthermore, it highlights the advantages and disadvantages of each algorithm and provides guidelines on how to optimize system performance.

The tools for enhancing language have significantly transformed the process of learning new languages, providing learners with innovative and accessible methods to achieve multilingual competence. These are categorized into digital and non-digital resources. Digital tools include mobile apps, speech recognition software, virtual reality (VR), artificial intelligence (Ai)-driven chatbots, and Learning management systems (LMS). These resources enable learners to practice speaking, listening, reading, and writing skills in interactive and immersive environments, tailored to individual learning styles and preferences. Non-digital tools, such as textbooks, flashcards, and language exchange programs, continue to be effective for traditional, structured learning and cultural exchange. The primary benefits of these tools include accessibility, offering learners the flexibility to study in their convenient time. Personalization, allowing content to adapt to a learner?s pace and proficiency and promotes by Gamification, process tracking, and interactive features. Additionally, these tools enhance community-building through language exchange and collaborative platforms, and supports for the cross-cultural understanding. However, challenges persist, such as the digital divide, where access to technology is limited in some regions, and the over-reliance on technology, which may hinder learners from fully engaging in real-world communication.

Sports nutrition and supplements play a crucial role in enhancing athletic performance, improving recovery, and preventing injuries. Alongside nutrition, integrative practices such as yoga, combined with knowledge of exercise physiology and rehabilitation, contribute to a holistic framework for athlete development. While supplements provide biochemical support, yoga enhances mental focus, flexibility, and stress regulation, whereas physiology and rehabilitation strategies ensure scientific monitoring, injury prevention, and long-term performance sustainability. This paper explores the interconnected role of sports nutrition, dietary supplements, yoga practices, exercise physiology, and rehabilitation techniques in promoting athlete performance, recovery, and overall well-being. A comprehensive review of recent literature, experimental studies, and applied practices in the domains of sports nutrition, supplement interventions, yogic training, physiological monitoring, and rehabilitation strategies has been conducted. The review integrates findings from clinical trials, meta-analyses, and sports case studies to highlight the evidence-based benefits of combining these approaches. Findings indicate that proper nutrition and supplementation enhance energy metabolism, endurance, and muscle recovery. Yoga contributes to improved flexibility, concentration, and reduced athlete burnout. Physiological monitoring aids in optimizing training loads and reducing injury risk, while structured rehabilitation ensures effective recovery. When combined, these interventions create a synergistic effect that maximizes athletic output and long-term health. An integrative framework that combines sports nutrition, supplements, yoga practices, physiology, and rehabilitation offers a holistic pathway for optimizing athlete performance, resilience, and recovery. This multidimensional approach bridges physical, psychological, and biochemical aspects of sports science, providing valuable insights for coaches, sports scientists, physiotherapists, and athletes.

The work considered the exact stand of Britain, being the Nigeria's former colonial master as at the period. The study centered on the major conflicts that rocked the nation, during the end of colonial rule and in the early years when the nation Nigeria broke out as an independence nation. Using the theory of war and objective analysis to interrogate historical events, the study makes sense of how Britain's policies tampered with the unity and coexistence constituents within the West African nation. It was revealed that the activities of Britain prior to independence were, by design, so far-reaching that they lingered in palpable ways even after colonial administration ceased. It also discusses the significance of amalgamation, the indirect rule and divide and rule system in bringing Nigeria into being and in shaping Nigeria's social reality. The researcher recommends broader scopes of study for subsequent research and suggests that stakeholders use the findings herein to remedy Nigeria's flailing political and ethnic status quo.

Nano topology was initiated by M.Lellis Thivagar with regard to a subset X of a universe which is described in terms of lower, upper and boundary approximations of X. He also described nano interior and nano closure in nano topological spaces. In this paper, we define some new type of contra mappings namely contra Nano bc-continuous mapping in Nano topological spaces. In addition to this, we discussed some properties of contra nano bc-continuous mappings in nano topological spaces.

The Law of Tort typically revolves around the principle that a wrongful act resulting in harm can lead to compensation, but the mental state behind the act often influences the severity of liability. The defendant's state of mind is crucial in determining liability, particularly in tort law. The mental element, or mens rea, varies across different types of torts and significantly influences the outcome of cases. Intentional torts, such as assault or battery, require a deliberate act and intent to cause harm, which directly impacts the nature of liability. In contrast, recklessness involves a conscious disregard of the risks, making it a lower standard of mens rea but still significant in determining culpability. Negligence, perhaps the most common basis for tort claims, focuses on the failure to meet a reasonable standard of care, where the defendant may not have intended harm but is still liable for failing to avoid foreseeable risks. This analysis also explores strict liability torts, where the mental element is often irrelevant, as well as vicarious liability, where an employer can be held liable for the actions of an employee, even without direct intent or negligence. Additionally, the paper reviews key judicial precedents that shape the understanding of mental elements in tort law. By critically examining these mental states, this paper highlights the evolving standards of tortious liability and suggests areas where future legal reforms may be necessary.

This study examines labor Demand for Labor and Manpower to labor Market in Kaysone Phomvihane city, Savannakhet province. The sample group is made up of corporations and educational institutions, which are separated into two groups: those that use labor (120 units) and those that produce labor (7 institutions). Data was collected using questionnaires, interviews were conducted utilizing development methods, and the SPSS program was used for data analysis. The study's findings revealed: In terms of general labor requirements, the majority of entrepreneurs are female (60.8 percent), with 46 percent being under the age of 30. Entrepreneurs have increased demand, particularly for workers with less than a bachelor's degree, who account for 27.46 percent, followed by workers with a bachelor's degree (17.86 percent). The majority of the subjects of study that entrepreneurs require are accounting fields, accounting for 23.25%. Commercial sectors account for 18.67 percent; non-specific subjects account for 17.11 percent; and mechanical engineering accounts for 15.78 percent. Entrepreneurs will require 51.37 percent of labor in 2024, while both public and private educational institutions can create 48.63 percent of labor to meet labor market demands. Regarding the result of the business units' labor shortage, there are many laborers who have to negotiate about salary or wages (28.68 percent), labor lack of expertise in specific subjects (15.47 percent), lack of language skills (35%), and insufficient experience (12.83 percent). Guidelines for improving the quality of educational institutions for labor production should increase education development so that the labor produced has a career and create a higher level of labor competitiveness by establishing a management center for the production network and developing manpower in specialized fields and professional excellence To develop the labor force in the central region, especially to develop the economy and various industries, to develop labor skills to become professionals in the present and in the future through Up-Skill, Re-Skill and New-Skill of labor to become stable entrepreneurs, to strengthen the cooperation system with the private sector and entrepreneurial institutions to develop the quality of labor to be concentrated and have quality.

Solid dispersion is a useful technique for increasing the rate at which poorly water-soluble medications dissolve and, consequently, their bioavailability. This study will concentrate on different techniques for preparing solid dispersions. Pharmaceutical excipients that are used to formulate solid dispersions come in a wide variety of hydrophilic and hydrophobic carriers. The different synthetic, natural, semisynthetic, and modified natural hydrophilic carriers that are used to formulate solid dispersions are summarised in this review.

Technology and Innovation play a pivotal role in advancing sustainable finance, contributing to a greener and more resilient global economy. Digital finance, driven by technological innovation, fosters sustainable economic growth. It enables data analysis, informed investment decisions, and job creation in sectors supporting the transition to a low-carbon economy. Innovation and technology are essential for economic well-being. They enhance productivity, create wealth, and facilitate structural transformation. Technology can replace manual processes with efficient digital solutions. Leveraging game-changing technologies and digital thinking can enhance efforts to meet sustainability goals. It?s essential to integrate technology into sustainability strategies. FinTech can positively impact people management, making operations more sustainable. The United Nations recognizes technology?s significance in achieving the SDGs. Challenges in making technology sustainable include reducing waste, promoting sustainable procurement, and strengthening scientific and technological capacities in developing countries. Technological innovations pave the way for sustainable business practices. Digital technologies and innovative solutions play a crucial role in promoting sustainable development. However, it is important to acknowledge that these technological advancements may have both positive and negative impacts on sustainability. It's essential to understand the adoption of these technologies to achieve better sustainability. This paper analyses the intersection of technology and sustainable finance, highlighting opportunities, challenges, and future pathways. The study is descriptive in design, qualitative in nature, and based on secondary data collected from journals, reports, and online resources.

The purpose of the study was to investigate the effect of aerobic training on arm power and agility among college level Women players. It was hypothesized that there would be significant differences on selected physical variables due to effect of aerobic training among college level Women players. For the present study the 30 college level Women players from Arul Anandar college were selected at random and their age ranged from 17 to 21 years. For the present study pre test and post test random group design, which consists of control group and experimental group was used. The subjects were randomly assigned to two groups of fifteen each and named as Group ?A? and Group ?B?. Group ?A? underwent aerobic training, and Group ?B? underwent control group. Arm power was assessed by counts and Agility was assessed by seconds. The data were collected before and after six weeks of training. The data were analyzed by applying ?t?-ratio. The level of significance was set at 0.05. The experimental group showed better improvement on arm power and agility among college level Women players than the control group.

Nigerian Engineers have an indispensable role to play in sustainable development of engineering projects. This sustainability is anchored on achieving a development which meets the needs of current generation without compromising the ability of future generations to meet their own needs and aspirations in the course of performing their engineering activities. The aim is the role of Nigerian engineers in sustainable development of engineering projects. The objective is to explore the various areas of engineering activities that synchronized with sustainable development in Nigeria. The methodology involve the approach and the most effective mechanism to solve engineering problems. The result reveal that the role of engineers in sustainable development include but not limited to appropriate selection of resources, accounting for sustainability, use of sustainable processed and environmental stewardship in engineering activities. The economic, social and environmental impacts of sustainability would be mitigated when Nigerian engineers play their role to engineer economic development which will improve the living standard of the people for a sustainable feature.

The rapid evolution of digital technology has introduced a paradigm shift in educational environments, transforming how information is conveyed and absorbed. This paper synthesizes foundational theories of human learning and communication to examine their application within these new technological enhanced learning environments. The analysis begins by exploring key definitions of instructional and theoretical frameworks such as behaviorism, cognitivism, constructivism, social cognitive theory, and the emerging concept of connectivism alongside critical communication theories such as social presence and media richness. The paper then translates these theoretical foundations into practical applications, discussing how technologies like collaborative platforms, virtual reality, and artificial intelligence can be designed to foster active knowledge construction and meaningful interaction based on research from human learning. A review of empirical evidence reveals a nuanced picture of technology's impact, highlighting significant positive effects on student engagement and academic outcomes while also acknowledging challenges like the digital divide and cognitive overload. The paper concludes by presenting a forward-looking perspective on ethical frameworks and future trends, asserting that for technology enhanced learning environment to fulfill its transformative potential, its design and implementation must be deeply informed by a symbiotic understanding of human learning and communication. This approach moves beyond viewing technology as a mere tool, reconceptualizing it as a dynamic medium for intellectual and social development.