International Journal of Research in Engineering, Science and Management
Call for Papers Publish in Multidisciplinary Journal Volume 9, Issue 5, May 2026 7000+ Articles | 10,000+ Authors | 30+ Countries Publication Process Submit Manuscript ISSN (Online): 2581-5792 Publication Frequency: Monthly Subject/Scope: Multidisciplinary Areas Publication Format: Online CrossRef DOI is assigned to all published articles Free e-certificates for all authors Fast Publication: Within 3 Days Article Publication Fee (APC): ₹1600 (India), $52 (Africa), $59 (Others) International Journal of Research in Engineering, Science and Management, is an open-access International Journal for Paper Publication. To publish a paper, the authors can submit research papers, case studies, survey papers, academic project works, scholarly articles, academic articles, original or extended versions of previously published papers in conferences, and basic advances from all the areas of academic disciplines. With a broad scope, the journal is focused on multidisciplinary research, from the viewpoints in the advancement of research. The journal welcomes articles from all the academic disciplines. The journal aims to provide a platform for authors to publish research work online. Students at graduate and post-graduate levels are welcome to publish project papers. Academicians, and researchers, who carry out research can submit and publish research work in the IJRESM journal. The authors who wish to publish a paper in IJRESM, are advised to read the publication process and submit a paper for publication.
Assessment of Construction Quality through Drone Technology – Comparing Drone Based and Ground-Based Inspections
In the last few years, drone technology has emerged as a vital aspect of the construction industry in view of their effective and safe ability to access site-generated real-time site information. These Unmanned Aerial Vehicles, commonly known as drones, are being utilized in various activities such as site monitoring, tracking, safety surveillance, and inspection. Their ability to access hard-to-reach areas, which was earlier considered unsafe, makes them a more preferable tool. At construction site execution level, quality assessment is one of the most prominent areas to concentrate on because it impacts structures directly with implications for safety, durability, as well as satisfaction for clients. Under customary quality assessment methods, personnel concentrate on making manual observations during construction activity execution. These activities often prove to be lengthy and risky for personnel. The advent of drone construction tactics enables construction personnel to use drone cameras to observe site conditions. While there is an increase in the application of drones for monitoring and safety purposes, their effectiveness with respect to construction quality assessment is still evolving from traditional ground-based inspections. Clearly understanding which quality parameters can be reliably evaluated using drone-based inspection and which aspects need close human observation is essential for effective implementation. This study focuses on the evaluation of the role of drone technology in construction quality assessment by comparing drone-based inspections with conventional manual inspection practices.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Integration of Tausug Adat (Customary Governance) and Indigenous Knowledge Systems in the Social Science Curriculum: A Framework for Culturally Responsive Education in Sulu
Globally, the preservation of Indigenous Knowledge Systems and Practices (IKSPs) is recognized as a critical pillar for the survival of both humanity and the planet. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO, 2021), indigenous knowledge represents a sophisticated set of understandings, interpretations, and meanings that are integral to the identity of communities and the conservation of global biodiversity. Furthermore, the United Nations Declaration on the Rights of Indigenous Peoples (UNDRIP) emphasizes that indigenous peoples have the inherent right to maintain, control, protect, and develop their cultural heritage and traditional knowledge. Despite these international protections, globalization continues to threaten the "intellectual patrimony" of tribal communities, often replacing sustainable ancestral practices with homogenized modern systems that fail to account for local ecological and social realities.
Copyright (c) 2026 Merry-Rose Lozada Sibayan, Nedzma Bakil Jinnul, Babylyn Nisal Aragon, Honey Mae Samson Jimlan, Sara Mae Kidjani Sali, Sitti Raisa Asaddil Annil https://creativecommons.org/licenses/by/4.0
Vibration Reduction through Centrifugal Fan Balancing in Painting Line Manufacturing
In the context of predictive maintenance, vibration analysis provides insight into the condition of rotating components by interpreting vibration amplitude and frequency characteristics. This helps maintenance professionals identify and classify damage before it reaches critical levels. According to ISO 10816, vibration severity can be systematically evaluated to determine whether a machine is operating within acceptable limits. This study successfully investigated and resolved high vibration levels in a rigid-mounted centrifugal fan used in a cement plant. Initial vibration measurements revealed critical levels of unbalance and misalignment, with horizontal amplitudes as high as 7.50 mm/s at the vertical fan drive far exceeding ISO 10816 acceptable limits. A structured static balancing procedure was then conducted using trial weights (5 g, 17 g, and 20 g), with the 5 g weight yielding the most stable response.
Copyright (c) 2026 Rahmat, Pedro Da Silva, Acim Maulana https://creativecommons.org/licenses/by/4.0
Manipulative Personalization in Social Commerce: Examining Consumer Distrust, Brand Switching, and Negative Word-of-Mouth
For brand success in the digital age, companies are using more personalized marketing efforts to engage customers more effectively and drive sales. Yet, when consumers experience personalization strategies as manipulative, they may have detrimental effects including brand distrust, switching to alternative brands, and the proliferation of negative word of mouth. The research seeks to examine how perceived manipulative personalization is related to consumer distrust, brand switching behavior, and negative word of mouth. Based on survey research, the research will investigate how consumers respond to various personalization tactics and how their perceptions determine their loyalty and communication. The research will provide actionable recommendations for marketers who want to use ethical personalization practices without eroding consumer trust.
Copyright (c) 2026 Om K. Sorathiya, Nandini Rajput, Naman Khandelwal, Saumyashree Senapati, Sachiket Bontha https://creativecommons.org/licenses/by/4.0
Formulation and Evaluation of Antifungal Soap Using Latex of Calotropis gigantea L.
The present study aimed to develop and evaluate an antifungal herbal soap incorporating latex of Calotropis gigantea using the melt and pour method. The formulation was prepared using a glycerin soap base with varying concentrations (2–5% w/w) of latex as the active antifungal agent. The prepared soaps were evaluated for physicochemical parameters including pH, foam height, hardness, and stability. Antifungal activity was assessed against Candida albicans and Aspergillus niger using the agar well diffusion method and minimum inhibitory concentration (MIC) studies. The results indicated that the formulated soap exhibited acceptable physicochemical properties with pH in the range of 6.8–7.4 and good foaming ability. The antifungal activity showed a concentration-dependent increase, with maximum zones of inhibition of 18 mm and 16 mm against Candida albicans and Aspergillus niger, respectively. Stability studies confirmed that the formulation remained stable under accelerated conditions without significant changes. Skin irritation studies revealed no adverse reactions, indicating safety for topical use. The study concludes that Calotropis gigantea latex can be effectively utilized in herbal soap formulation to develop a natural, safe, and potent antifungal product.
Copyright (c) 2026 Rutika M. Kharnare, Vinod A. Bairagi, Rahul B. Chavhan, Tushar S. Shivde, Sushrit M. Khalane, Shubham R. Lengare https://creativecommons.org/licenses/by/4.0
The Effects of Developmentally Appropriate Learning Materials on Reading Proficiency of Grade 2 Low Emerging Learners
This study examined the effects of Developmentally Appropriate Learning Materials (DALMs) on the reading proficiency of Grade 2 low emerging learners. Despite ongoing literacy interventions, many early-grade learners continue to struggle with foundational reading skills, highlighting the need for more effective instructional approaches. This study employed a quantitative single-group pretest–posttest design involving ten Grade 2 learners identified as low emerging readers through the Comprehensive Rapid Literacy Assessment (CRLA). The intervention consisted of structured reading sessions using DALMs such as storybooks, phonics games, and culturally relevant materials. Data were collected through pretest and posttest scores, running records, and observation checklists. Results revealed a significant improvement in reading proficiency, with mean scores increasing from 2.80 to 17.10. A paired sample t-test indicated a statistically significant difference (p < .001) between pretest and posttest scores. Regression analysis further showed that prior DALM exposure significantly predicted subsequent reading performance (R² = .775). The findings suggest that DALMs are effective and sustainable tools for enhancing reading skills among struggling learners. The study recommends integrating DALMs into early literacy instruction to address persistent reading difficulties.
Copyright (c) 2026 Mae Ann E. Tado, Danilyn P. Jamco, Mitzi P. Jarabelo, Giftphany C. Tado https://creativecommons.org/licenses/by/4.0
An Assessment of Organisational Support and Challenges in Digital Health Procurement and Cybersecurity Implementation in Health Institutions in Bayelsa State
The growing use of digital technologies in healthcare has increased the need for effective procurement systems and strong cybersecurity measures to protect patient information and support quality service delivery. This study examined the challenges affecting digital health procurement and cybersecurity practices in health institutions in Bayelsa State, with emphasis on organisational support and possible strategies for improvement. A descriptive cross-sectional survey design was used, and data were collected from 196 health personnel through a structured questionnaire. The study covered key areas such as procurement processes, cybersecurity policies, staff training, funding, availability of skilled personnel, management enforcement, incident response planning, and system audits. Data were analyzed using descriptive statistics, including mean scores and standard deviation. The findings revealed that organisational support for both procurement and cybersecurity is generally low. Procurement systems are affected by inadequate funding, bureaucratic delays, lack of transparency, weak monitoring, and corruption. Similarly, cybersecurity practices are weakened by poor training, low awareness of cyber risks, weak policy enforcement, shortage of skilled personnel, and lack of regular system audits and response plans. Despite these challenges, respondents strongly supported practical strategies such as increased funding, regular staff training, adoption of digital procurement systems, enforcement of cybersecurity policies, and continuous monitoring. The study concludes that although awareness is high, implementation remains weak due to insufficient institutional support. It recommends stronger management commitment, improved funding, clear policies, and continuous capacity building to enhance data security and strengthen digital health systems.
Copyright (c) 2026 Lucky Ebiteinye Dogiye, Biobelemoye Jack-Gbarabe, Chinyere G. N. Idiapho https://creativecommons.org/licenses/by/4.0
Multi-Variable Traffic Control: Simulating the Impact of Weather and Incidents via Type-1 Fuzzy Logic
Basic The effective management of urban traffic remains an important obstacle, due to the dynamic and probabilistic nature of typical urban settings. Conventional traffic control systems, which are primarily based on fixed-timing or simple sensor-actuated adaptive mechanisms, frequently disregard external factors such as adverse weather conditions and unexpected road incidents. In this research, a multi-variable traffic control framework is proposed. The system is designed to maximize the intersection throughput by incorporating the multiple variables into the fuzzy logic simulation system. The developed system uses fuzzy logic controller that can handle the linguistic variables and non-linear inputs. Inputs are traffic density, precipitation intensity, visibility conditions, and incident severity. The controller employs a large set of rules to adaptively adjust signal timings to compensate for the reduced roadway capacity and longer stopping distances that are common in rain or fog. Simulation analyses were carried out to evaluate the performance of the fuzzy logic model compared to the existing control methods. The results demonstrate that the multi-variable fuzzy strategy performs significantly better than conventional methods, particularly under peak demand and extreme weather conditions.
Copyright (c) 2026 Ashish Gad https://creativecommons.org/licenses/by/4.0
Stock Market Reaction to Union Budget Announcements: An Event Study
Union Budget plays particularly key role in shaping India's economic environment because it presents government's plans related to taxation, public spending, and overall fiscal policy. These announcements strongly influence investor expectations and often lead to noticeable movements in stock market. This study aims to understand how Indian stock market reacts to Union Budget announcements by using Event Study Methodology. Daily stock market data were collected for selected time periods before and after budget announcement dates. analysis was carried out using estimation and event windows to measure market behaviour accurately. Market Model (Regression Approach) was applied to estimate expected returns by regressing index returns on market returns during estimation window. Abnormal returns were then calculated as difference between actual and model-estimated returns, while cumulative abnormal returns (CAR) were used to evaluate market's reaction to budget-related information. results show that stock market experiences increased volatility and significant price movements around budget announcement day. These changes reflect how investors interpret policy decisions and adjust their investment strategies accordingly. findings also indicate that Indian stock market responds quickly to publicly available information, which supports semi-strong form of market efficiency. Overall, study provides useful insights into investor behaviour and demonstrates strong impact of major government policy announcements on stock market performance.
Copyright (c) 2026 Shubham Warke, Jahanvi Deshmukh, Ayush Jariwala, Anannya Joshi, Vedpriya Bargaje https://creativecommons.org/licenses/by/4.0
RECreate: A Blockchain-Based Marketplace for Renewable Energy Credits with Intelligent Peer-to-Peer Trading Using Model-Based Multi-Agent Reinforcement Learning
Renewable Energy Credit (REC) markets suffer from high transaction costs (3–5%), slow settlement (2–3 days), and exclusion of small-scale producers. This paper presents RECreate, a blockchain-based marketplace integrating business-to-business (B2B) REC trading with intelligent peer-to-peer (P2P) energy exchange. The B2B component lever- ages Polygon blockchain smart contracts with zero-knowledge proofs for privacy-preserving verification. The P2P component introduces MB-MASAC (Model-Based Multi-Agent Soft Actor-Critic), combining Temporal Fusion Transformer (TFT) forecasting with differential attention for proactive multi-agent coordination. Over 7,200 simulated days, RECreate achieves 99% reduction in settlement time, 93% decrease in verification costs, and 80% reduction in transaction fees. For P2P trading, MB-MASAC achieves 42.7% cost reduction ($755 annual savings per household), 15.1% lower battery volatility extending lifespan by 20–25%, and 8.4% MAPE for 24-hour forecasting.
Copyright (c) 2026 Saachi Peswani, Khushi Parekh, Abhishek Sharma, Param Gogia, Sunil Ghane https://creativecommons.org/licenses/by/4.0
FreelanceAI: System Design and Technology Acceptance Analysis of a Freelance Platform
The proliferation of the digital economy has led to an increased need for online freelance platforms connecting the client and professional workforce. Such platforms have the capability to help people offer their services remotely and also help companies hire from across the globe. But at the same time, such online systems depend on user adoption and technology acceptance. In this paper, we present a model for designing and implementing a system which would act as a platform to interact between freelancers and clients. The platform offers features like user-friendly interface, security, job listing functionalities, and other project-related capabilities. Further, technology acceptance is considered as a means for evaluating the user's acceptance towards the platform. The findings show that a well-designed platform helps to enhance the users' participation and make the process of freelance work more efficient. Thus, it could be stated that such a system holds high potential in helping collaborate and support the online freelance market.
Copyright (c) 2026 Paramjeet Kaur, Rani Sharma, Rishabh Mishra, Abhijeet Saxena, Shivang Gupta https://creativecommons.org/licenses/by/4.0
Assessing the Impact of Quality Management Systems on Production Efficiency: A Case Study of a Manufacturing Company in Cavite
This study examines the impact of Quality Management Systems (QMS) on production efficiency in a manufacturing company in Cavite, Philippines. A descriptive-correlational quantitative research design was employed using survey data collected from 30 engineers and supervisors. Statistical analysis was conducted using SPSS to evaluate the relationship between QMS implementation and production efficiency indicators such as defect rate, machine downtime, and output performance. The results revealed a high level of QMS implementation and a statistically significant moderate positive correlation (r = 0.572, p < 0.01) between QMS and production efficiency. These findings indicate that effective implementation of QMS practices contributes to improved operational performance. The study recommends strengthening employee training, enhancing preventive maintenance systems, and adopting real-time monitoring tools to further optimize production efficiency.
Copyright (c) 2026 Almira E. Balatico, Renz M. Ancheta, Michael John M. Villar https://creativecommons.org/licenses/by/4.0
BOOKVERSE: A Full-Stack Web-Based Book Marketplace with Collaborative Filtering Recommendation System
The rapid growth of e-commerce platforms has transformed how users discover and purchase products online. However, traditional online bookstores often lack intelligent mechanisms for personalized recommendations. This paper presents BOOKVERSE, a full-stack web-based book marketplace integrating user-based collaborative filtering with real-time user interaction data. The system enables users to browse, search, and purchase books while generating personalized recommendations based on behavioral data such as browsing history and cart activity. A cosine similarity-based approach is used to identify similar users and recommend relevant books. The platform also includes a rule-based chatbot for assistance and an admin analytics dashboard for monitoring system performance. Experimental evaluation demonstrates a precision of 0.72 and an average response time of 0.35 seconds, indicating satisfactory performance. The proposed system enhances user engagement and improves the efficiency of book discovery.
Copyright (c) 2026 Shivesh Mishra Suman, Rakesh Kumar Arora https://creativecommons.org/licenses/by/4.0
Tooth Whitening in Clinical Practice: An Evidence-Based Review of Efficacy and Safety
Tooth whitening is one of the most commonly requested cosmetic dental procedures. Despite its popularity, misconceptions regarding safety, efficacy, and long-term effects remain prevalent. This review aims to provide an evidence-based overview of tooth whitening methods, mechanisms of action, indications, contraindications, and safety considerations. Both in-office and at-home bleaching techniques are effective when used under professional supervision, with minimal adverse effects. Patient selection, appropriate concentration of bleaching agents, and post-treatment care play critical roles in achieving optimal outcomes.
Copyright (c) 2026 Pathuri. L. S. Akshatha https://creativecommons.org/licenses/by/4.0
This project focuses on developing a comprehensive Smart City Citizen Engagement Platform to streamline civic issue reporting, tracking, and management. The system is built using a Java Full-Stack architecture, leveraging Spring Boot for the scalable backend and React/Angular/Vue.js for the responsive frontend. A crucial aspect of this project is the automation of the application's build and deployment process using containerized environments and GitLab CI/CD pipelines. The proposed system utilizes Docker to create reproducible, isolated environments for the microservices and leverages GitLab CI/CD automation to ensure continuous integration and deployment (CI/CD). This framework significantly enhances developer productivity, reduces human errors, and accelerates feature release cycles.
Copyright (c) 2026 Nandini Anand Rao, Somesh Vernekar, Kaif Mohammed, Amol Bhosle https://creativecommons.org/licenses/by/4.0
BidSmart: A Full-Stack Real-Time Online Auction Platform with Automated Lifecycle Management and Role-Based Access Control
BidSmart is a full-stack web-based online auction platform designed with a three-tier user architecture (Buyer, Seller, Admin). The system enables verified sellers to create time-based auctions while buyers can browse, bid, and monitor auctions in near real-time. The platform implements an automated auction state machine (UPCOMING → ACTIVE → ENDING_SOON → CLOSED) with scheduled background tasks for state transitions and winner notifications. Key features include JWT-based stateless authentication, role-based access control, KYC-based seller verification with document uploads, a multi-channel notification system, watchlist management, and multi-image auction listings. The backend is built with Java 21, Spring Boot 4, Spring Security 6, PostgreSQL, and Flyway migrations, while the frontend uses React 18 with TypeScript, TanStack React Query for server state management, and Shadcn/UI with Tailwind CSS. The system ensures bid integrity through transactional validation, optimistic concurrency control, and database-level constraints. This paper presents the design, architecture, and implementation of a scalable, secure, and user-friendly auction platform.
Copyright (c) 2026 Tanushi Agnihotri, Smriti Gupta, Aayush Singh, Saurabh Bahadur https://creativecommons.org/licenses/by/4.0
An AGI-Inspired Cognitive Safety System for Real-Time Logistics and Warehouse Accident Prevention
Industrial logistics and warehouse environments are still plagued by accidents owing to the reactive characteristics of the safety systems in place, which are primarily rule-based or frame-based. The present paper discusses an architecture for a cognitive safety system inspired by AGI for accident prevention in industrial environments in real-time. The architecture is based on a combination of structured perception, prediction of the future state of the world, symbolic reasoning, experience-based memory, and safety-constrained decision planning in a closed loop. The architecture also simulates the future state of the world before executing an action, considers alternative courses of action, and improves its behavior through self-evaluation following a decision. The architecture also includes modules for modeling human compliance with safety warnings, learning the operational costs of safety interventions, and zero-shot learning across different environments. The architecture also includes the injection of deliberate errors and the monitoring of the stability of the decisions. The architecture is designed to operate in real-time and is tested in representative scenarios in a warehouse environment. The results show the effectiveness of the cognitive safety architecture in the prevention of accidents in industrial environments.
Copyright (c) 2026 R. Ramya, M. Baskar https://creativecommons.org/licenses/by/4.0
In today’s fast-paced digital landscape, where customers share their opinions instantly across multiple channels, understanding customer sentiment in real-time has become more than just a competitive advantage, it is essential for building trust, enhancing customer satisfaction, and safeguarding brand reputation. Businesses can no longer rely solely on periodic surveys or delayed feedback; they need systems that can listen, interpret, and respond to the voice of the customer as it happens. This paper introduces a comprehensive real-time customer sentiment analysis system that leverages the power of machine learning, natural language processing (NLP), and streaming data technologies. By integrating advanced deep learning models such as Bidirectional LSTM and BERT, the system can accurately detect nuanced sentiments expressed in text, from social media posts and online reviews to customer service feedback. The approach goes beyond simple positive or negative classification, capturing subtle emotional tones that reflect genuine customer experiences. To gather timely insights, the system continuously collects live data streams using APIs from a variety of social media platforms and feedback channels. These data streams are then processed in real-time, allowing organizations to visualize emerging trends and sentiment patterns through interactive dashboards. Such visualizations empower decision-makers to spot potential issues before they escalate, gauge public opinion instantly, and tailor responses to meet customer expectations proactively. Beyond monitoring sentiment, this research highlights the broader impact of combining NLP with scalable data processing frameworks. By enabling responsive engagement, organizations can improve operational efficiency, strengthen customer relationships, and make informed strategic decisions grounded in real-time intelligence. Ultimately, the proposed system demonstrates how modern AI-driven tools can transform raw customer feedback into actionable insights, bridging the gap between businesses and the people they serve.
Copyright (c) 2026 Sadula Sai Sidhartha Reddy, Tarun, Gousiya Begum, Thishitha https://creativecommons.org/licenses/by/4.0
Landslide Detection in Remote Areas Using LoRa and Sensor Networks
Landslides pose a significant threat to lives and infrastructure in hilly, remote regions, necessitating more proactive safety measures. This project introduces an intelligent IoT system that monitors environmental stability in real time by integrating a NodeMCU ESP8266 with soil moisture, tilt, and atmospheric sensors. By leveraging LoRa technology for long-range data transmission and Firebase for cloud storage, the system ensures that critical information is accessible even from isolated locations. An integrated AI model analyzes these data streams to predict landslide probabilities, triggering immediate alerts via a web dashboard, local buzzers, and LCD displays. This combination of smart sensing and predictive modeling creates a reliable, high-tech early warning system that empowers communities to manage disaster risks more effectively.
Copyright (c) 2026 S. Jesna, R. Raj Krishna, Sahithya S. Dev, M. V. Vinay Krishna, S. Mahesh https://creativecommons.org/licenses/by/4.0
Strategic Analysis of Productivity and Operations of Free Medical Oxygen Services of the City Government of Cabanatuan
This research analyzes the operational effectiveness and productivity of the free medical oxygen program launched by the Cabanatuan City Government in October 2021, supplying Manuel V. Gallego Cabanatuan City General Hospital (MVGCCGH) and the residents of the city. Using a mixed-methods approach, the study combines quantitative data on tank refills (2021–2026), infrastructure assessment, and stakeholder feedback. The findings of the study revealed that the free medical oxygen program has a 16-tank daily supply-demand gap, slightly lower oxygen purity than design specifications, and challenges including unreliable power, supply chain vulnerabilities, and limited technical expertise. A SWOT analysis identifies strengths (stable funding, high uptime) and opportunities (government grants, digital tools) to inform evidence-based recommendations. These include expanding capacity, enhancing power reliability, upgrading monitoring systems, and improving staff training. Implementing these strategies will ensure consistent, high-quality access to free medical oxygen, even during health crises, and improve health outcomes for Cabanatuan City's citizens.
Copyright (c) 2026 Rizaldy M. Matadling Jr., Patrick F. Pingol, Michael John M. Villar https://creativecommons.org/licenses/by/4.0
CareChain AI: An AI Powered Universal Health Record System
Due to the fact that healthcare data is often spread out across many hospitals (as well as laboratories) and in many different formats (i.e., PDFs, images, handwritten prescriptions), it is often difficult to obtain urgent/critical patient information during emergencies. This fragmentation of data may cause delays in diagnosis and inefficient clinical decision making; additionally, patients are not as engaged in their own healthcare when the data they need to manage their own health is fragmented. Current digital health systems only focus on storing data, without providing the contextual intelligence and safety mechanisms that can assist users in understanding complex medical data. In response to these issues and challenges, this paper presents CareChain AI, an AI-based universal healthcare record management system that serves both to consolidate and intelligently process lifelong patient medical records in a secure (encrypted) and patient-centered manner. CareChain AI integrates Optical Character Recognition (OCR) functions to extract text from various documents, along with a Retrieval-Augmented Generation (RAG) framework to allow for natural language queries from a user-friendly interface. As text is extracted from documents, it is converted into vector embeddings and stored using FAISS indexes for highly efficient document retrieval methods.
Copyright (c) 2026 Prithviraj Kshirsagar, Sudesh Jambhulkar, Tanishq Dhote, Krish Rawat, Nitish Das https://creativecommons.org/licenses/by/4.0
Modeling and Creation of a Remote Monitoring Network for Industrial Enterprises
The problems of designing and setting up an IoT-based remote monitoring network are examined in this essay. In industrial and environmental monitoring systems, it is crucial to ensure dependable real-time data transmission, boost network stability, and improve energy efficiency. This paper proposes a multi-tiered Internet of Things architecture including sensor nodes, a data transmission module, and a server component. Equations for figuring out the number of nodes, evaluating coverage area, computing energy usage, and predicting transmission latency are provided, along with a monitoring system. Furthermore, a system for dynamically regulating transmission frequency under both normal and critical situations has been established through the development of an adaptive data transmission algorithm. A model network is suggested to lessen network load, and it permits the sensible use of energy resources and improved data transmission dependability. The findings of the experimental assessment show that the monitoring system's efficiency is much greater than that of traditional options.
Copyright (c) 2026 Djumaniyazov Otabek Baxtiyarovich https://creativecommons.org/licenses/by/4.0
A Survey of Serverless Data Pipelines on AWS: Automating CSV Processing with Scalable and Secure Architecture
Serverless architecture has emerged as a transformative approach for building scalable, secure, and cost-efficient data pipelines in cloud environments. This paper presents a comprehensive survey of recent advancements in AWS-based serverless data processing, with a focus on automating CSV ingestion, transformation, and visualization. The proposed architecture leverages Amazon S3 for multi-zone data storage, AWS Lambda for real-time cleaning, AWS Glue and Glue Crawler for schema-aware transformation, and Amazon QuickSight for interactive dashboards. Designed to operate without Athena or Step Functions, the pipeline demonstrates significant improvements in latency, modular scalability, and governance. Performance benchmarks show up to 25% reduction in transformation latency and enhanced schema adaptability across evolving datasets. A comparative analysis of state-of-the-art serverless frameworks is included, identifying critical gaps in orchestration-free automation, cost-performance optimization, and visualization accessibility. Finally, the paper outlines future research directions to develop lightweight, secure, and domain-adaptable serverless pipelines for enterprise and IoT analytics.
Copyright (c) 2026 Komal Chaudhary, Sristi Vashisth, Yash Saxena, Akansha Singh, Khushi Bansal https://creativecommons.org/licenses/by/4.0
Serverless Data Pipelines on AWS: Automating CSV Processing with Scalable and Secure Architecture
Copyright (c) 2026 Komal Chaudhary, Sristi Vashisth, Yash Saxena, Akansha Singh, Khushi Bansal; Khushi Bansal https://creativecommons.org/licenses/by/4.0
Zero Trust Network Access (ZTNA) is a modern cybersecurity approach that addresses the weaknesses of traditional perimeter-based security models that rely on implicit trust. This project proposes an AI-driven ZTNA framework that enhances security through strict identity verification and continuous monitoring. The system uses artificial intelligence and machine learning to analyze user behavior, device health, and contextual data in real time to make risk-based access decisions. It integrates multi-factor authentication, policy-based access control, and anomaly detection to prevent unauthorized access. The framework also enables secure remote access and improves overall network security and access management.
Development of Nutraceutical Jelly Using Tulsi, Honey and Lemon
This study focuses on the development of a functional herbal jelly using Tulsi (Ocimum sanctum), honey, and lemon juice. Tulsi is known for its antioxidant, antimicrobial, and anti-inflammatory properties, while honey acts as a natural sweetener with added nutritional benefits. Lemon juice provides acidity, enhances flavor, and supports gel formation. The jelly was prepared using tulsi extract, honey, sugar, pectin, and citric acid in different proportions to obtain an optimized formulation. The preparation involved extraction, mixing, heating, and gel formation. The developed samples were analyzed for physicochemical properties such as pH, total soluble solids, acidity, and moisture content. Sensory evaluation was conducted to assess color, taste, aroma, texture, and overall acceptability. The results showed that the optimized jelly had good texture, balanced sweetness and acidity, and high acceptability. The addition of tulsi and honey improved the functional and nutritional properties without affecting product quality. The study concludes that Tulsi–Honey–Lemon jelly can be successfully developed as a value-added functional food with good sensory qualities and potential for commercial production.
Copyright (c) 2026 Kotla Venkat, A. Swaroopa Rani, D. Jagadeesh https://creativecommons.org/licenses/by/4.0
Reimagining Human Resource Management in the Age of Technological Disruption: A Digital HR Framework
Rapid technological disruption is significantly transforming human resource management all over the world. Organizations are inevitably challenged to leverage digitally enabled HR management systems. Currently, there is a conceptual framework that attempts to explain the process by which technological advancements, competitive demands, digital readiness of employees, the needs of cost efficiency, and decision demands with regard to data all influence digital HR management. Some of the intermediating factors considered include HR strategies and HR process automation, while digital leadership is an influencing factor that is captured within the study. Instances of digital HR management include the use of artificial intelligence for recruitment, digital performance management, e-learning, and HR management statistics. Some of the bottom-line outcomes include productivity, organizational flexibility, employee experience, cost efficiency, and decision making.
Copyright (c) 2026 P. Vaijayanthi, B. S. Dhanushree https://creativecommons.org/licenses/by/4.0
Machine Learning-Based Analysis for Thermal Properties of MWCNTs/Polymer Composites
This article discusses thermal prediction and data-based prediction of E-glass fiber polymer composites reinforced with multi-walled carbon nanotube (MWCNT). The hand lay-up technique was used to make the laminates by the use of woven roving E-glass mats oriented at 0º/90º, 0º/45, and 0º/135º. The volume fractions of MWCNTs into the polymer matrix were 0%, 1%, 3%, 5% and 7%, which developed fifteen different composite configurations. The Thermogravimetric Analysis (TGA) was used to find out the thermal properties of the material including weight loss, degradation and decomposition behavior. These findings indicate that the orientation of fibers and MWCNT content are crucial determinants of thermal stability and that increased resistance exists at maximized filler loadings. A number of machine learning models were developed and trained using experimental data to increase predictive power. The performance of the models was measured using standard evaluation metrics and the importance of features analysis was conducted to identify important parameters on the thermal behavior. The integrated experimental and machine learning method offers a developed framework to predict thermal properties and optimize composite design, valuable information to be used in the advanced structural and thermal applications.
Copyright (c) 2026 Satish Geeri, Gantyada Amaladevi https://creativecommons.org/licenses/by/4.0
Design of an ESP32-Instrumentation Device for Data Acquisition and Wireless Monitoring of Human Foot Pronation
This paper presents the design and experimental prototyping of a low-cost ESP32-based wireless instrumentation device for real-time acquisition and monitoring of human foot pronation. The system integrates five FSR 402 force sensors into a smart cotton sock, interfaced with an ESP32-WROOM-32 microcontroller. A rule-based classification algorithm analyzes sensor activation patterns to distinguish neutral pronation, over-pronation, under-pronation, and error states. Pronation data is transmitted wirelessly via Bluetooth to an Android smartphone running the Serial Bluetooth Terminal application. Experimental validation on three human subjects with clinically confirmed neutral, over-pronated, and under-pronated foot types demonstrated 100% classification accuracy under normal walking conditions. The system achieves wireless transmission latency below 100ms, reliable operation up to 10 meters line-of-sight, and over 4 hours of continuous operation on a 2000mAh power bank. Total material cost is under $25 USD. The device offers a non-invasive, portable, and reproducible solution for biomechanical monitoring in sports medicine, physiotherapy, and home-based rehabilitation.
Copyright (c) 2026 Ndofor Mariette Mata, Mbihi Jean, Lienou Tchawé Jean Pierre https://creativecommons.org/licenses/by/4.0
Protection of transmission and distribution lines is an important issue in modern power systems, as a large percentage of system faults occur on these lines. Locating the exact point of fault is often difficult; however, rapid fault detection helps in preventing serious damage to electrical equipment. Accurate fault location also assists maintenance personnel in repairing faults quickly and identifying regions where faults frequently occur, thereby reducing outage duration and improving system reliability. This work focuses on detecting the position of faults in distribution lines using an Arduino-based system, and the fault information is transmitted to a control unit using an Arduino Uno controller. The system determines fault location based on voltage variations across series resistors using Ohm’s law. The processed signal is then given to the Arduino Uno microcontroller. When a short circuit occurs, the current through the series resistors changes according to the fault distance and connected load. If the resistance falls below a predefined threshold, a relay connected to the Arduino disconnects the line. Additionally, an LCD module displays the line condition, and a buzzer provides an alert when a fault is identified.
Copyright (c) 2026 Manvitha Poka, Mohana Krishna Punna, Mahendra Ponugoti, Abdul Nehal Shaik, A. Sai Anusha https://creativecommons.org/licenses/by/4.0