Daily Archives: March 26, 2026

Authors: Nimaful N Samuel, Hanyabui Augustine

Abstract: Distributed energy resources (DERs)—including distributed photovoltaics, behind-the-meter storage, flexible demand, and electrified end uses—are transforming U.S. distribution systems while exposing a persistent planning and interconnection constraint: hosting capacity. Hosting capacity is commonly defined as the amount of DER that can be accommodated without adversely impacting power quality or reliability under specified control configurations and without requiring infrastructure upgrades. Yet hosting capacity is not an immutable feeder attribute; it is strongly sensitive to analytical methods (snapshot vs. time-series; deterministic vs. probabilistic), modeling assumptions (e.g., inverter settings), data quality, and governance choices regarding what constitutes an acceptable violation or mitigation. This article provides a secondary analysis synthesizing peer-reviewed research, national laboratory reports, interconnection standards resources (IEEE 1547 family implementation guidance), and public regulatory/utility records to develop an integrated technical–economic–regulatory framework for expanding hosting capacity through complementary strategies: targeted grid reinforcement and non-wires alternatives (NWAs). Comparative case evidence from New York’s Brooklyn-Queens Demand Management program, California’s integration capacity analysis ecosystem, and Hawaii’s hosting-capacity mapping and inverter experience is used to extract transferable mechanisms and failure modes. Synthesized findings indicate that hosting capacity should be communicated as a scenario-dependent range; that advanced inverter functionality and flexible demand can expand feasible DER penetration but require validated settings, telemetry, and verification; and that integrated distribution planning linking hosting capacity analytics to locational value and benefit-cost screening improves comparability between wires and non-wires portfolios while strengthening transparency for interconnection stakeholders. (Electric Power Research Institute [EPRI], 2018; Jain et al., 2020; Narang et al., 2021).

DOI: https://doi.org/10.5281/zenodo.19235202

Published by: vikaspatanker

Authors: Dr.M.Suganthi(Ap/Cse), K.Niranjana, T.Nisha, S.Prarthana

Abstract: College food courts often struggle with long waiting queues, overcrowding during peak hours, inefficient order management, and the absence of real-time order tracking; these challenges result in increased waiting time for students and difficulty for administrators in managing multiple food orders effectively, especially during busy lunch and break hours. This paper presents a Smart Food Court Ordering and Management System, a web-based platform designed to simplify food ordering and improve food court management within a college environment. The proposed system allows students to view the food menu, which includes food name, image, price, availability status, waiting time, and quality information, and place orders through an online or offline mode. The system also displays the current food court crowd level as high, medium, or low to help students decide the best time to place their orders. An admin management module enables administrators to monitor student orders, update order status such as waiting, preparing, or ready, manage food availability, and update crowd levels through an interactive dashboard. All system data, including student login details, food menu information, order records, order status updates, and food availability, are stored in a MySQL database using phpMyAdmin within the XAMPP control panel. The system operates as a web application without requiring additional hardware and aims to improve efficiency in food ordering, reduce waiting time, and enhance the overall food court experience for both students and administrators within the campus environment.

DOI: https://doi.org/10.5281/zenodo.19235190

 

Published by: vikaspatanker

Publishing a research paper is more than just completing your writing; it’s about sharing your ideas with the right audience. Many researchers, especially beginners, feel confused when it comes to choosing where and how to publish. With so many platforms available online, the real challenge is not finding a website, but finding the right one.

Submit Your Paper  / Check Publication Charges 

Today, research publishing has become much easier and faster. Digital platforms allow authors to submit their work, track progress, and reach a global audience without complicated processes. But at the same time, this convenience has also increased the risk of low-quality or fake platforms. That’s why understanding the basics of research publishing is very important.

Understanding How Publishing Works

When you submit your research paper to a publishing platform, it usually goes through a process called peer review. In this process, experts in your field check your work for quality, accuracy, and originality. This step is important because it ensures that only valuable and genuine research gets published.

Some platforms also offer open access publishing, which means your research becomes freely available to everyone. This increases visibility and helps your work reach more readers, including students, researchers, and professional,

Key Features to Look For

Before submitting your paper, always check a few important things. A good publishing platform will clearly mention its review process, publication timeline, and author guidelines. Transparency is a strong sign of credibility.

Indexing is another important factor. If a platform is indexed in well-known academic databases, it means your research will be easier to find and more widely recognized. This adds value to your work and improves your academic profile.

You should also check whether the platform provides proper certificates or publication proof. This is especially useful for students and professionals who need documentation for academic or career purposes.

Common Mistakes to Avoid

Many beginners make the mistake of choosing platforms that promise “instant publication” without proper review. While fast publishing may sound attractive, it often comes at the cost of quality and credibility.

Another common issue is paying high fees without verifying the authenticity of the platform. Not all paid platforms are bad, but you should always ensure that you are getting proper value in return, such as genuine review, indexing, and visibility.

Always prepare your paper according to the given format and guidelines. A well-structured paper has a higher chance of acceptance. Make sure your content is original and free from plagiarism, as most platforms strictly check for it.

Take time to read and understand submission requirements before applying. A small mistake in formatting or documentation can delay the process.

Research publishing is an important step in building your academic or professional journey. Instead of rushing, focus on choosing a reliable platform that values quality and transparency. A well-published paper not only shares your knowledge but also builds your credibility and confidence. it’s not just about getting published—it’s about getting published the right way.

Published by: Neeraj Mishra

Authors: Kishore A, Nawfees MI, Dr. S. Thilagavathi

Abstract: Early and accurate disease prediction is a major challenge in modern healthcare systems. Delayed diagnosis often leads to higher treatment costs and lower patient survival rates. Artificial Intelligence (AI) and Machine Learning (ML) techniques are widely used to help with medical decision-making by analyzing complex healthcare datasets. However, traditional machine learning models often face issues with inefficient feature selection, poor hyperparameter tuning, and slow convergence during optimization. This is especially true when working with high-dimensional medical data. To tackle these challenges, this paper presents an AI-based disease prediction framework that uses quantum-inspired optimization techniques. This approach combines classical machine learning classifiers with optimization strategies based on quantum computing principles, such as probabilistic state representation and superposition-based search. These quantum-inspired methods allow for efficient exploration of the solution space, which leads to better feature selection and optimized model parameters. We evaluate the proposed framework using a publicly available healthcare dataset from Kaggle. We compare the performance of traditional machine learning models and quantum-inspired optimized models using accuracy, precision, recall, and F1-score metrics. The experimental results show that the quantum-inspired optimized model consistently performs better than conventional approaches. This study demonstrates that quantum-inspired optimization provides a practical and scalable solution for improving AI-driven disease prediction systems without the need for actual quantum computing hardware.

DOI: https://doi.org/10.5281/zenodo.19232740

Published by: vikaspatanker

Authors: Muneeswaran B, Shanmuga Eswari M

Abstract: This research presents an innovative hybrid machine learning framework that amalgamates density-based clustering with ensemble regression and logistic classification to improve the precision of student performance prediction. We use DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering on the StudentPerformanceFactors dataset to find hidden student behavioural phenotypes. These phenotypes are then used as engineered features for supervised learning models. An automated hyperparameter tuning system uses silhouette score maximisation to systematically test different DBSCAN settings and find the best density parameters (eps=1.0, min_samples=5) without any human input. The final cluster assignments are used in both a RandomForestRegressor to predict test scores and a Logistic Regression model to classify performance into categories. This creates a hybrid framework that captures both clear academic metrics and more subtle behavioural patterns. Experimental validation shows performance gains that are statistically significant. The hybrid RandomForest gets an MSE of 4.45 on test data that wasn't used to train it, and the hybrid Logistic Regression gets an accuracy of 82.3%. Feature importance analysis shows that Attendance (33.4%), Hours_Studied (23.9%), and Previous_Scores (9.8%) are the most important predictors. DBSCAN_Cluster also adds useful discriminative power. Five-fold cross-validation verifies model robustness (CV-MSE=4.88±0.12). This study enhances educational data mining by implementing unsupervised learning for supervised improvement, providing interpretable student groupings that uncover density-based behavioural phenotypes affecting academic performance. The proposed framework shows that it can be used in real life for early intervention systems by giving teachers useful student types based on regular academic data.

Published by: vikaspatanker

Authors: Omkar Ganesh arnikar, Siddhi Rupesh Datar, Ishwari Sanjay Karad, Kiran bapu karhe

Abstract: The IoT-based full-range audio system with gesture control is a smart audio system that allows users to control music using hand gestures without physical touch. An ESP32 microcontroller works as the main controller, while an APDS9960 gesture sensor detects hand movements such as up, down, left, right, and near to perform functions like play/pause, next track, previous track, and volume control. The audio signal is processed using a 3-way active crossover and amplified by TPA3116D2 class-D amplifiers to drive a subwoofer, midrange speaker, and tweeter, producing clear full-range sound. The system is powered using a 12-0-12 transformer and voltage regulation circuits. This project combines IoT technology, gesture-based control, and high-quality audio output to create a modern and user-friendly sound system.

DOI: https://doi.org/10.5281/zenodo.19229877

Published by: vikaspatanker