Daily Archives: May 21, 2026

Authors: S. Santhosh Kumar, Dr. R. Senthamil Selvi

Abstract: The construction site is considered a risky place for employees, and the risks are associated with falling objects, machines, and exposure to harmful substances. Monitoring the implementation of Personal Protective Equipment (PPE) standards, including helmets, vests, gloves, boots, and masks, is of critical importance in preventing accidents and injuries. The conventional approach to monitoring the implementation of these standards is through manual observation, which is associated with time delays and human error. This study proposes an intelligent framework for the implementation of PPE standards and safety monitoring using an improved YOLOv11 deep learning model for the detection and classification of different types of PPE in real-time construction site video feeds. The model is trained on a diverse dataset to cater to complex backgrounds, lighting, occlusion, and multiple PPE pose angles, ensuring the model performs well in diverse site environments. The framework helps improve workplace safety by ensuring compliance, reducing the probability of accidents caused by negligence, and promoting regulatory compliance, thereby creating a culture of consistent PPE usage and safe work practices across the construction industry.

DOI: http://doi.org/10.5281/zenodo.20325549

Published by: vikaspatanker

Authors: Mrs. Parul Gupta, Safiya Naaz, Priya Upadhyay, Mohd. Arshad, Tanu

Abstract: The rapid degradation of environmental quality driven by industrialization and urbanization demands continuous, real-time monitoring of key atmospheric and ecological parameters. This paper presents the design and implementation of a low-power, solar-powered IoT-based environmental monitoring system built around the STM32 microcontroller. The proposed system integrates a suite of sensors to measure temperature, humidity, atmospheric pressure, air quality, UV radiation, and soil moisture. Data is transmitted wirelessly over Wi-Fi and LoRa protocols to a cloud-based dashboard for real-time visualization and historical analysis. The system is entirely powered by a solar photovoltaic panel coupled with a lithium-ion battery and a power management unit, ensuring uninterrupted autonomous operation in remote locations without access to the electrical grid. Experimental results demonstrate reliable data acquisition with a sampling accuracy exceeding 97%, an end-to-end data transmission latency of less than 2 seconds, and continuous operation exceeding 72 hours on battery backup under cloudy conditions. The proposed system offers a cost-effective, scalable, and energy-autonomous alternative to conventional environmental monitoring stations.

DOI: https://doi.org/10.5281/zenodo.20325332

Published by: vikaspatanker

Authors: Mrs. Pragati Sharma, Aman Chandel, Harsh Sharma, Priya Upadhyay, Safiya Naaz, Sunny Kumar, Tanu Saini, Vashu Dhiman

Abstract: The escalating degradation of environmental quality in educational institutions and public spaces demands cost -effective, real-time monitoring solutions. Conventional systems rely on centralised infrastructure or mobile applications that fail to deliver localised, immediate feedback. This paper presents AgroNexus, an IoT-driven environmental monitoring and public display platform that integrates the ESP32 microcontroller with four sensing modules—DHT22 (temperature and humidity), MQ135 (air quality), a rain sensor (precipitation detection), and DS3231 (real-time clock)—to deliver continuous data acquisition, threshold-based alerting, and live display via a six-panel P10 LED matrix. Experiments conducted in a simulated campus environment demonstrate that AgroNexus achieves high sensor accuracy, low false-alert rates, and sub-three-second display refresh cycles, outperforming single-sensor baselines across all evaluation metrics. The framework is economical, scalable, and readily deployable in smart campuses, industrial zones, and public spaces, establishing a transparent and auditable pipeline for environmental awareness.

DOI: http://doi.org/10.5281/zenodo.20325235

Published by: vikaspatanker

Authors: Sudarshan K, Sushmitha H Y, Varshanth Gowda M L, Vinay C N

Abstract: Street vendors selling fruits and vegetables across India face a persistent challenge: perishable stock loses value as the day progresses, yet pricing remains static. This paper presents Smart Vendor AI, a complete end-to-end system that combines inventory management, point-of-sale operations, analytics dashboards, sales forecasting, and AI-assisted product quality assessment within a unified web-based platform.. The pipeline consists of six sequential layers: a fine-tuned YOLOv8s model for ripeness classification, a signal engine that converts raw predictions into weighted freshness scores, a deterministic market con- text module, an XGBoost pricing model trained on 5,000 realistic scenarios, a rule-based decision engine, and a FAISS-backed retrieval-augmented generation module powered by LLaMA 3.3 70B. Experiments on banana and tomato datasets show classifi- cation accuracy of 99.3% and 98.6% respectively. The system delivers specific, actionable vendor instructions—including an exact discount percentage and an inventory action string—without requiring any technical knowledge from the user. Results indi- cate meaningful potential to reduce the 30–40% annual revenue loss that vendors typically incur through spoilage and mispricing.

Published by: vikaspatanker

Authors: Mahmud Danladi, Salihu Sarki Ubayi, Mahmud Danladi

Abstract: The construction industry is highly susceptible to uncertainties and risks that significantly influence project delivery in terms of cost, time, quality, safety, and sustainability. This study examined the analysis of risk management in project construction within the Nigerian construction industry. Specifically, the study identified the types of risks associated with project construction, examined the factors affecting risk management, and evaluated the effects of risk management on construction project performance. A descriptive quantitative research design was adopted. Data were collected through structured questionnaires administered to 80 construction engineers involved in risk management practices, out of which 74 valid responses were retrieved, representing a response rate of 92.5%. Descriptive statistical tools including frequency distribution, percentage analysis, mean item score, and standard deviation were used for data analysis. The findings revealed that inadequate site investigation, inadequate specification, contractor’s experience, weather implications, natural disasters, new technology, and shortage of resources were among the most significant risks affecting construction projects. Resource availability, project complexity, and time compression were identified as the major factors affecting risk management implementation. Furthermore, the study established that risk management strongly affects project cost, completion time, productivity, project quality, health and safety, and environmental sustainability. The study concluded that effective risk management is essential for successful construction project delivery and recommended proper site investigation, adequate resource allocation, experienced workforce engagement, and proactive risk management strategies to improve project outcomes in Nigeria.

DOI: https://doi.org/10.5281/zenodo.20324799

Published by: vikaspatanker

Authors: Sweta Pandey, Meentu Grover

Abstract: Digital payment systems have transformed the way people conduct financial transactions in their daily lives. The rapid growth of internet technology, smartphones, and financial technology has increased the adoption of digital payments across the world. In India, digital payment methods such as Unified Payments Interface (UPI), mobile wallets, internet banking, debit cards, and credit cards have become highly popular due to convenience, speed, and security. This paper examines the impact of digital payments on daily life and analyses how cashless transactions have influenced consumer behaviour, business activities, and economic growth. The study highlights the advantages of digital payments, including faster transactions, financial inclusion, transparency, reduced dependency on cash, and improved online shopping experiences. It also discusses challenges such as cyber fraud, privacy concerns, internet dependency, and lack of digital literacy among certain sections of society. The paper concludes that digital payments have significantly improved the efficiency and convenience of daily financial activities and will continue to play an important role in the future digital economy.

Published by: vikaspatanker

Authors: Priya kumari, Sohail Verma

Abstract: Job satisfaction is one of the most important aspects influencing employee performance, mental well-being, and overall quality of life. In the modern competitive work environment, employees often face workload pressure, stress, long working hours, and work-life imbalance, which directly affect their domestic and family life. This study examines the relationship between job satisfaction and employees’ domestic life and analyses how workplace conditions influence family relationships, personal happiness, and social well-being. The paper highlights factors such as salary, working conditions, job security, organizational support, work-life balance, and employee recognition in determining job satisfaction levels. The study also discusses how satisfied employees maintain healthier family relationships, lower stress levels, and improved domestic harmony, whereas job dissatisfaction may lead to emotional stress, conflicts, and reduced quality of life at home. The findings suggest that organizations should focus on employee welfare, flexible work policies, and supportive work environments to improve both job satisfaction and domestic well-being.

Published by: vikaspatanker

Authors: Assistant Professor. Sukanya H N, Adithya N, Akash H S, Farazulla Khan, G P Chinmayaradhya

Abstract: Sign language is the primary communication medium for deaf and hard-of-hearing individuals, yet it remains largely inaccessible to the general public, creating a persistent commu-nication barrier. This paper presents a real-time sign language detection system that leverages computer vision and machine learning to recognise hand gestures and convert them into readable text or speech with minimal latency. The proposed framework follows a structured processing pipeline comprising data acquisition, key-frame extraction, skin-colour-based hand segmentation, face-region elimination, morphological filtering, and noise reduction. Discriminative spatial features are derived using fuzzy triangular membership functions, and gesture recognition is performed by a K-Nearest Neighbour (Mediapipe) classifier trained on a self-collected dataset of two-handed dynamic signs. For real-time operation, the system employs the MediaPipe library for hand-landmark detection and a Convolutional Neural Network (CNN) trained with TensorFlow/Keras for gesture classification. Experimental evaluation demonstrates an overall gesture recognition accuracy of approximately 92%, with a high-confidence detection of 99.6% for the “Peace” gesture and an average detection-plus-translation latency of approximately 150 ms per frame. The system requires no specialised sensors or gloves, making it cost-effective and practically deployable in educational institutions, healthcare facilities, and public service environments. Results confirm the feasibility and effectiveness of the proposed approach as an assistive communication solution for hearing-impaired individuals.

Published by: vikaspatanker

Authors: Pratyush Ranjan Sahu, Nishith Das

Abstract: The incorporation of agro-industrial residues like spent mushroom substrate (SMS) into nutrient management strategies provides a sustainable pathway for intensive agriculture. A field experiment was carried out during the Kharif of 2025 at GIET University, Odisha, to assess the impact of SMS-based compost supplemented with zinc (Zn), boron (B), and neem cake on the physiological, yield, and economic indices of maize (Zea mays L., var. VNR 4226). Utilizing a Randomized Block Design (RBD) with eight treatments and three replications, the study revealed that integrating SMS with micronutrients and the Recommended Dose of Fertilizers (RDF) significantly augmented crop performance. Treatment 8 (T8) (SMS@7t/ha + dried plant debris@2t/ha + cow dung@1t/ha + 5% Zn + B + RDF) delivered the highest plant stature (217.47 cm), maximum dry matter accumulation (237.63 g/plant), and superior yield attributes. This resulted in an exceptional kernel yield of 8.17 t/ha, a 175% increase over the FYM control. Soil chemical properties, notably available phosphorus, improved considerably under SMS regimes. Economically, T8 yielded the highest net monetary returns (₹1,18,642/ha), whereas T6 (RDF + 5% neem cake) optimized the Benefit-Cost ratio (2.38). These findings advocate for the integrated use of fortified SMS compost to enhance maize productivity and soil health.

Published by: vikaspatanker

Authors: Jayanth L, Kaviraj M Kumkumgar, Kuldeep Raj M S, Madhuraj H R, Dr. Mohammad Rafi. H. Kerur

Abstract: This project (Phase 2) presents the fabrication of an innovative, portable noodle making machine aimed at providing a cost-effective and user-friendly solution for small-scale and home-based noodle production. Traditional noodle machines tend to be expensive, and require considerable expertise, limiting their accessibility especially for households and micro-entrepreneurs. To address this gap, the proposed machine utilizes a lightweight frame built from available materials, with food-grade stainless steel components for all parts in contact with the dough. The core mechanism involves a threaded extrusion system powered by a small electric motor, which efficiently transforms freshly prepared dough into uniformly shaped noodles. The process comprised conceptual sketches, noodle quality, and portability. Test results demonstrate consistent noodle extrusion with ease of operation and quick cleaning, making it suitable for diverse environments including homes, street vendors, and small eateries. The modular construction further enhances maintainability and transport convenience. This project not only offers a practical fabrication approach but also supports entrepreneurial activities by enabling affordable fresh noodle production. Overall, the project contributes an innovative, accessible, and sustainable noodle-making solution that promotes food variety and small business empowerment.

Published by: vikaspatanker