Authors: Nagalakshmi.S, Kuvvarapu rasmitha, Anbarasan. P

Abstract: Electrical distribution conditions that feed inducting appliances like induction motors and responding industrial devices often have poor power factor due to slow response time to the supply voltage. These conditions of operation cause a high conduction loss, a low stability of the voltage, and a low capacity utilization and energy waste. To help avoid such inefficiencies, a Automatic Power Factor Monitoring and Correction System based on Sequential Load Balancing is introduced as an autonomous driven real-time compensation structure.The suggested research structure incorporates an embedded controller based on Arduino to monitor the acquisition of voltage and current under the control of calibrated sensing modules. Phase difference testing on sensed waves can be used to compute power factor correctly as well as to estimate the amount of reactive compensation that is necessary. Relay-controlled capacitor stages are switched in in sequence when it is detected that there is deviation from target operating limits and hence, the reactive power injection is in an incremental fashion but not abrupt transiently. These simulated balancing ensure that overcompensation is prevented and operational stability is maintained at different loading conditions of variable loads.The measured electrical quantities such as the magnitude of voltage, current level, and post-corrected power factor are displayed on a liquid crystal interface whereas remote monitoring is also facilitated via wireless communication modules which are connected to the serial protocols. This is proved by experimental validation of a correction accuracy of 96.8, and the values of power factor are kept in the range of 0.95 or more after implementation under dynamic load variation. Measured data prove the significant decrease of the line losses and the increase of the voltage regulation. The derived implementation confirms its appropriateness in the industrial and domestic power distribution setting that needs affordable, high quality power factor improvement.

 

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Authors: Glecemae Rubino, Adriann R. Dela Peña, Reymark M. Petecio, Allana C. Dimarao, Hearth B. Musketer

Abstract: This study investigated the potential of overripe tomato waste as a sustainable bioenergy source through the use of a Microbial Fuel Cell (MFC) system for electricity generation. An experimental quantitative research design was employed to evaluate the electrical performance of an overripe tomato-based MFC in comparison with a mud-based MFC used as a positive control. Key parameters measured included voltage output, current production, and power density, along with microbial activity indicators such as bacterial growth rate, electron transfer efficiency, and biofilm formation. A total of ten trials were conducted under controlled conditions to ensure data reliability and consistency. Statistical analyses, including frequency distribution, mean computation, and independent sample t-tests at a 0.05 level of significance, were used to determine differences in electricity generation between substrates. Results of the study aim to demonstrate the feasibility of converting organic tomato waste into bioelectricity, supporting sustainable energy development, waste reduction, and low-cost renewable energy solutions, particularly for urban communities such as Davao City.

DOI: https://doi.org/10.5281/zenodo.18875721

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Authors: Annie Threse Edwis

Abstract: A CPW antenna with overall dimensions of 30 mm × 25 mm × 2 mm is designed using FR4 as the substrate material. Two rectangular ground planes, each having dimensions of 12 mm × 9.5 mm, are printed on the top surface of the substrate. A signal strip of width 5 mm is used to excite the antenna, and the gap between the signal strip and the ground planes is maintained at 0.5 mm. The proposed antenna structure consists of an elliptical patch connected to the central signal strip along with two rectangular CPW ground planes on the top side of the FR4 substrate. The elliptical patch, which is united with the signal strip, resonates at 6.25 GHz. The designed antenna operates over several wireless communication bands, including 5.15–5.35 GHz (5.2 GHz WLAN), 5.725–5.825 GHz (5.8 GHz WLAN), 5.25–5.85 GHz (5.5 GHz WiMAX), 5.725–5.875 GHz (ISM band), and 5.15–5.85 GHz (5.5 GHz Wi-Fi). The impedance (Z) and VSWR characteristics demonstrate good radiation performance at the resonant frequency of 6.25 GHz. Furthermore, the gain plot of the CPW radiator indicates efficient radiation across various wireless bands. The co-polarization level is significantly higher than the cross-polarization level at 6.25 GHz, confirming that the proposed antenna is suitable for multiple wireless communication applications.

 

 

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Authors: Professor Yash Deshmukh, Aishwarya Bhalekar, Vaibhav Dhok, Atharva Gaikwad, Epshita Gaikwad

Abstract: Industrial machines require continuous monitoring to avoid damage due to overcurrent, overheating, or abnormal voltage conditions. This paper presents a GSM-based machine protection system that monitorsmachine parameters and sends real-time alerts to theoperator using SMS. A microcontroller continuously checks sensor values, and when unsafe conditions are detected, the system automatically shuts down the machine through a relay and informs the user remotely. This system improves safety, reduces maintenance cost, and enables remote monitoring of industrial equipment.

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Authors: Om Vichare, Aniket Nale, Soham Deolalikar, Siddhant Kamble

Abstract: Maintaining optimal indoor humidity is essential for human comfort, health, and the safety of electronic devices. Conventional humidifiers lack intelligent monitoring and safety mechanisms, and typically require manual operation. This paper presents the design and implementation of a Smart Room Humidifier using the ESP32 microcontroller. The system continuously monitors ambient humidity using a DHT11 sensor and automatically controls a 5 V ultrasonic mist maker through a transistor-driven switching circuit. A water level sensor prevents dry operation, and a buzzer alerts the user when the water level is low. The ESP32’s built-in Wi-Fi module enables web- based remote ON/OFF control, while a 0.96-inch OLED display provides real-time readings of humidity, temperature, water level status, and system state. The system is powered by a 5 V buck converter; regulated 3.3 V for sensor modules is supplied directly by the ESP32. Experimental testing confirms reliable performance, safe operation, and effective humidity control. The system is low-cost and well-suited for indoor domestic applications.

DOI:

 

 

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Authors: Kunal Vishwajit Uke, Shreyas Anil Sonwalkar, Abhishek Avinash Yadav, Krushna Ganesh Vibhute, Prof. Chetna Sharma

Abstract: Industrial automation demands efficient material handling and quality control mechanisms to enhance productivity and reduce operational costs. This paper presents the design and implementation of an automated colour-sorting machine using Arduino microcontroller technology integrated with optical colour sensors. The system employs a conveyor belt mechanism that transports objects through a detection zone where a TCS3200 colour sensor identifies the colour of each item. Based on the detected colour signature, the Arduino controller processes the sensor data and activates corresponding servo motors to divert items into designated collection bins. The proposed system achieves high-speed sorting with accuracy exceeding 95%, significantly reducing manual labour requirements and minimizing classification errors. Experimental results demonstrate the system's effectiveness in sorting multiple colours simultaneously with minimal delay. This automated solution finds applications in food processing, pharmaceutical packaging, recycling industries, and quality control operations where colour-based segregation is essential.

 

 

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Authors: Saukhya, Santhosh, Sai Surya, Abdul Rahman Sharikh, Abhijit raj N

Abstract: Auto Go is a tech-enabled autorickshaw rental and fleet management platform designed to address the growing urban transportation challenges in Indian metropolitan cities, with an initial focus on Bengaluru. The project’s core objective is to facilitate affordable and flexible autorickshaw access for independent drivers and small businesses, thereby reducing the financial barrier posed by vehicle ownership and promoting economic opportunity. The service will provide a digital platform — including a website and mobile app — to enable customers to book autorickshaws under daily, weekly, or monthly rental agreements. Additionally, the platform will integrate payment processing, customer support, and vehicle tracking, improving transparency and operational efficiency.

 

 

Published by: vikaspatanker

Mallesh Miryala
Affiliation	SalesForce Technical Head, San Francisco , California, USA
Email-Id:	miryalaca@gmail.com
Publication: Mallesh Miryala. “Engineering-Grade Delivery for Salesforce in Integration-Heavy Enterprises: Metadata Graphs, Contract Tests, and Deterministic Operations”. International Journal of Scientific Research & Engineering Trends, Volume 11 issue 6, 2025. Mallesh Miryala. “Operational Graph Patterns For Continuity And Fulfillment In Large Enterprises: A Field-Based Reference Architecture”. International Journal of Scientific Research & Engineering Trends, Volume 11 issue 6, 2025.

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Authors: Priti Birajdar, Ambika Kshirsagar, Shravani Raut, Harshada Raykar, Prajakta Bhadale

Abstract: This paper presents an Artificial Intelligence (AI) based system designed for the early detection of brain-related diseases such as Alzheimer's disease, Parkinson's disease, brain tumors, and stroke using medical imaging and machine learning techniques. Early diagnosis of neurological disorders is critical for effective treatment and improved patient outcomes. Traditional diagnostic approaches rely heavily on manual interpretation of MRI scans, which may lead to delayed detection and human error. The proposed system integrates Deep Learning models, particularly Convolutional Neural Networks (CNN), to analyze MRI images and detect abnormalities at an early stage. The architecture consists of image preprocessing, feature extraction, classification, and result visualization modules. The system aims to assist neurologists by providing accurate and fast predictions.

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Authors: Ch.Srilakshmi, Nithish Kanth M, Rupesh J, Tharun CR

Abstract: Retinal vessel segmentation is essential for the early diagnosis of diseases such as diabetic retinopathy, hypertensive retinopathy, and age-related macular degeneration. Manual segmentation of fundus images is time-consuming and prone to variability, limiting large-scale screening. This paper presents RETINASEG, a deep learning-based system for automated pixel-level segmentation of retinal vessels from fundus images. The proposed framework combines image enhancement techniques such as contrast normalization, CLAHE, and noise reduction with an encoder–decoder architecture based on U-Net and transformer-enhanced models. To address challenges including thin vessel detection and class imbalance, data augmentation and class-balanced loss functions are employed during training. Experimental results on DRIVE and STARE datasets demonstrate strong performance, achieving high accuracy and robustness across datasets. A web-based interface with real-time visualization and explainable AI support further enhances clinical usability. RETINASEG enables scalable, reliable, and automated retinal analysis for early disease detection and tele-ophthalmology applications.

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