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Performance assessment of solar PV panels under varying environmental conditions: a laboratory and field-based approach for sustainable energy in mining operations

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Environmental Science and Pollution Research 2025年 1-20页

作者： Tripathi, Abhishek Kumar Aruna, Mangalpady Sharma, Sumit Kumar, Chandan Didwania, Mukesh Department of Mining Engineering Aditya University Andhra Pradesh Surampalem533437 India Department of Mining Engineering National Institute of Technology Karnataka Surathkal Mangalore575025 India Department of Mechanical Engineering Poornima College of Engineering Jaipur302022 India Department of Mechanical Engineering Swami Keshvanand Institute of Technology Management and Gramothan Jaipur302017 India

This study provides a novel and comprehensive assessment of solar photovoltaic (PV) panel performance under varying environmental conditions, integrating laboratory experiments with real-world field studies to address challenges specific to mining operations. The research uniquely explores the combined effects of shading, temperature, humidity, dust deposition, and tilt angle, delivering actionable insights for optimizing PV systems in harsh conditions. Laboratory experiments demonstrated that a parallel configuration significantly minimizes power losses under partial shading, while a rise in temperature from 35 to 75 °C resulted in a notable 21.34% and 29.12% power output reduction for monocrystalline and polycrystalline panels, respectively. Furthermore, increased humidity (65.40 to 98.20%) caused a 35.82% decline in power output due to scattering effects. Field studies conducted in a surface mining environment revealed that dust accumulation led to a substantial 43.18% drop in maximum power output after 5 days, emphasizing the importance of regular cleaning. Optimal energy capture was achieved at a 15° tilt angle, aligning with the site’s latitude. These findings underscore the novelty of using combined experimental approaches and field validation to improve PV performance in mining operations. Practical recommendations, including parallel configurations to mitigate shading losses, temperature regulation strategies, and frequent cleaning protocols, are proposed to enhance the sustainability and efficiency of renewable energy systems in challenging environments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Mine dust

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HARQ Soft Combining Using Bidirectional LSTMs

HARQ Soft Combining Using Bidirectional LSTMs

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International Conference on Smart Systems and Wireless Communication, SSWC 2024

作者： Ubaradka, Adithya S. Jamadagni, Hemang J. Sunil, Rohit Chandavarkar, B.R. Department of Computer Science and Engineering National Institute of Technology Karnataka Surathkal Mangalore India

ISBN: (纸本)9789819613472

Hybrid Automatic Repeat reQuest is used in modern wireless data communication to integrate both Automatic Repeat reQuest and high-rate Forward Error Correction mechanisms to enhance the reliability of data transmission. Unlike traditional ARQ, where an error-ridden frame is discarded upon reception, HARQ temporarily stores the erroneous frame in a buffer. When the re-transmission of the same frame occurs, these two frames are combined to generate a new frame, trying to minimize errors. This is HARQ with Soft Combining. Existing methods like Chase Combining (Type-I) and Incremental Redundancy (Type-II and Type-III) implement Log-Likelihood Ratio and Maximum Ratio Combining to combine two erroneous frames. This paper uses a Bidirectional Long Short-Term Memory model to combine two frames with high channel noise errors. This paper introduces the BiLSTM model, which aims to reduce the Bit Error Rate and provides an approach for integrating this model into the existing HARQ structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bidirectional Long Short-Term Memory Hybrid Automatic Repeat reQuest Soft Combining

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EffiCOVID-net: A highly efficient convolutional neural network for COVID-19 diagnosis using chest X-ray imaging

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Methods 2025年 240卷 81-100页

作者： Kumar, Sunil Bhowmik, Biswajit Maharshi Patanjali CPS Lab BRICS Laboratory Department of Computer Science and Engineering National Institute of Technology Karnataka Karnataka Mangalore 575025 India

The global COVID-19 pandemic has drastically affected daily life, emphasizing the urgent need for early and accurate detection to provide adequate medical treatment, especially with limited antiviral options. Chest X-ray imaging has proven crucial for distinguishing COVID-19 from other respiratory conditions, providing an essential diagnostic tool. Deep learning (DL)-based models have proven highly effective in image diagnostics in recent years. Many of these models are computationally intensive and prone to overfitting, especially when trained on limited datasets. Additionally, conventional models often fail to capture multi-scale features, reducing diagnostic accuracy. This paper proposed a highly efficient convolutional neural network (CNN) called EffiCOVID-Net, incorporating diverse feature learning units. The proposed model consists of a bunch of EffiCOVID blocks that incorporate several layers of convolution containing (3×3) filters and recurrent connections to extract complex features while preserving spatial integrity. The performance of EffiCOVID-Net is rigorously evaluated using standard performance metrics on two publicly available COVID-19 chest X-ray datasets. Experimental results demonstrate that EffiCOVID-Net outperforms existing models, achieving 98.68% accuracy on the COVID-19 radiography dataset (D₁), 98.55% on the curated chest X-ray dataset (D₂), and 98.87% on the mixed dataset (D_Mix) in multi-class classification (COVID-19 vs. Normal vs. Pneumonia). For binary classification (COVID-19 vs. Normal), the model attains 99.06%, 99.78%, and 99.07% accuracy, respectively. Integrating Grad-CAM-based visualizations further enhances interpretability by highlighting critical regions influencing model predictions. EffiCOVID-Net's lightweight architecture ensures low computational overhead, making it suitable for deployment in resource-constrained clinical settings. A comparative analysis with existing methods highlights its

关键词： Artificial intelligence CNN COVID-19 detection Deep learning Image classification Multi-scale features X-ray images

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Artificial Neural Networks Applied in the Detection of Breast Cancer 1st

Artificial Neural Networks Applied in the Detection of Breas...

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1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Kotian, Deepthi Bhat, Vinayambika S. Poojary, Dhanush Kavya, V. Mangalore Institute of Technology and Engineering Moodabidri Mangalore India

ISBN: (纸本)9783031756047

Breast cancer is a serious health issue for women everywhere, and effective treatment depends on early identification. For medical imaging, automated breast cancer identification is essential to speeding up diagnosis;however, current approaches frequently have extensive analysis durations. In order to address this, we investigate the usage of neural networks (NN) and concentrate on the backpropagation technique in particular. We found deficiencies in the sensitivity and specificity of the existing NN models by conducting a thorough literature analysis. Our method uses backpropagation to train a neural network (NN) for effective categorization of benign and malignant patterns in breast cancer images. Preliminary findings demonstrate decreased processing time and increased accuracy. A comparative study with current methods demonstrates our method’s effectiveness. To improve diagnostic precision even more, future improvements might include advanced NN architectures. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Lung cancer

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Bioremediation of Petroleum Hydrocarbon Contaminated Soil Using Bacillus subtilis Strain – Process Optimization by Box Behnken Design

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Iranian Journal of Science and technology - Transactions of Civil engineering 2025年第3期49卷 3073-3080页

作者： Bhaskar, S. Manu, B. Department of Civil Engineering National Institute of Technology Calicut P.O. – 673 601 Kerala Calicut India Department of Civil Engineering National Institute of Technology Karnataka P.O. Srinivasnagar Surathkal Mangalore 575025 India

Present study investigated bioremediation potential of Bacillus subtilis, a bacterial strain, in remediation of total petroleum hydrocarbons from oil contaminated soil. The study integrates the utilization of Bacillus subtilis for bioremediation of petroleum-contaminated soil with optimization using Box-Behnken experimental design. The process parameters were optimized using the Box-Behnken design, and the results were statistically interpreted using response surface methodology. Fifteen experimental runs were conducted with varying levels of pH (4.0 to 8.0), moisture content (40–70%), and salinity (0 g/L to 50 g/L). Lab - scale investigations were conducted utilizing PVC experiment vessel. Maximum hydrocarbon removal observed within 45 days was 83% under optimal conditions of pH 6, moisture content of 55%, and salinity of 25 g/L. A linear-quadratic regression model is used to fit the data for the process of Bioremediation, specifically for the removal of petroleum hydrocarbon from soil that has been contaminated. An analysis of variance (ANOVA) is conducted, and the adequacy of the model is evaluated. According to the model, the presence of moisture content and salinity has a beneficial impact, while the linear independent variables have an adverse impact. © The Author(s), under exclusive licence to Shiraz University 2025.

关键词： Bacillus subtilis Bioremediation Box – behnken design Contaminated soil Total petroleum hydrocarbon

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ADConv-Net: Advanced Deep Convolution Neural Network for COVID-19 Diagnostics Using Chest X-Ray and CT Images

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SN Computer Science 2025年第5期6卷 1-22页

作者： Kumar, Sunil Bhowmik, Biswajit Maharshi Patanjali CPS Lab BRICS Laboratory Department of Computer Science Engineering National Institute of Technology Karnataka Surathkal Karnataka Mangalore 575025 India

The worldwide COVID-19 epidemic has emerged as a significant concern, affecting daily lives and underscoring the importance of early diagnosis for effective treatment in medical and healthcare settings. Current diagnostic testing for COVID-19 is sluggish, typically requiring hours to get results. Detection of COVID-19 from medical imaging presents a challenging task that has gained substantial interest from experts worldwide. Essential imaging modalities for diagnosing COVID-19 include chest X-rays and computed tomography (CT) scans. By contrast, most of the chest radiography can be completed in within fifteen minutes. Thus, employing chest radiography gives a possibility for early and reliable diagnosis of COVID-19, intending to relieve therapeutic obstacles for patients and speed up the diagnostic process. Recently, deep learning (DL) techniques have been shown to be effective in image-based diagnostics. This paper proposed an advanced deep convolution neural network (ADConv-Net) for COVID-19 detection and categorization using chest X-ray and CT images. The proposed technique is not only capable of recognizing critical connections and similarities in image classification, but also leads to improved diagnostic accuracy. The proposed model undergoes thorough evaluation for standard performance metrics. After evaluation, the ADConv-Net model achieves high accuracies of 98.84% and 97.25% in training and testing for X-ray images and 99.41% and 98.87% in training and testing for CT images, respectively. Additionally, the proposed model demonstrates strong performance, with AUC values of 0.993 and 0.996 for X-ray and CT images, respectively. Further, the model also introduces a heatmap approach for displaying COVID-19 disease areas. Subsequently, radiologists can find COVID-19 disorders in chest X-ray and CT images with this approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial Intelligence Classification COVID-19 CT Images Deep Learning X-Ray Images

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Performance Analysis and Predictive Modeling of MPI Collective Algorithms in Multi-Core Clusters: A Comparative Study 17

Performance Analysis and Predictive Modeling of MPI Collecti...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Reddy, Manideep Raya V.S.R.S. Raju, Sanjay Ramapogu Girish, K.K. Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Lab Department of Computer Science and Engineering Surathkal Mangalore India

ISBN: (纸本)9798331531195

Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms - Flat Tree, Chain, and Binary Tree - by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical. © 2025 IEEE.

关键词： Scalability

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Study on temperature and updraft velocity in a solar chimney with steel spheres for heat energy storage

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Interactions 2025年第1期246卷 1-9页

作者： Rao P, Srinidhi Hebbar A, Gautama Acharya, Santhosh Marakini, Vikas V, Smitha G Krishnan, Arun Department of Mechanical Engineering Chameli Devi Group of Institutions Indore452020 India Nitte574110 India Department of Mechanical Engineering Mangalore Institute of Technology & Engineering Moodabidri D.K-574225 Affiliated to Visvesvaraya Technological University Karnataka Belagavi India Nitte574110 India

The most abundant solar energy can be considered as a big asset for dry and semi-dry regions. In the recent years, solar chimney which is a solar thermal energy driven electrical power generation plant that converts the solar thermal energy into electrical power are in use. In this work, the performance of the scaled model of solar chimney is studied with the usage of steel spheres filled with water as heat energy storage material. Initially the solar radiation intensity was measured with KDS-051 Global Radiation Sensor. The placements of the spheres were carried out in three different configurations with a fixed pattern and suitable distance of 30–35 cm between each sphere so that good results in term of temperature retained in collector is achieved. The configurations were 50%,60% and 70% of collector area respectively. The temperature data were collected using a ‘J’ type thermocouple at various sections of the solar chimney for a period of two months during sunny, cloudy and partly cloudy days. The temperature and updraft velocities of solar chimney was also monitored for overcast weather conditions. The results show that there is no rapid decrease in collector temperature during evening time due to the presence of steel spheres. Due to the larger surface area of steel spheres i.e. 70% coverage of collector area with heat energy storage material, the temperature of the collector was maintained upto 65 °C during evening time. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Solar chimneys

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Hardware Security in Evolving FinTech Landscape 5th

Hardware Security in Evolving FinTech Landscape

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Bhowmik, Biswajit Dongala, Joshitha Reddy Sudhama, Kruthika K. Antony, Reshma Tresa Girish, K.K. Maharshi Sushrut CAS Lab BRICS Laboratory Department of Computer Science and Engineering National Institute of Technology Karnataka Mangalore Surathkal575025 India

ISBN: (纸本)9789819745395

The assimilation of technology into the financial sector, often referred to as FinTech, has brought about a significant transformation. This shift has not only widened the scope of financial inclusivity but has also fundamentally reshaped the contours of financial solutions delivered. As FinTech solutions continue to empower individuals with greater control over their finances through mobile banking, digital wallets, and advanced data analytics, the security of these innovations becomes paramount. While software security has traditionally received more attention, this paper underscores the significance of hardware security, which serves as the foundational infrastructure for software security measures. It delves into the factors used to evaluate hardware security and outlines various categories of hardware attacks. A case study, focusing on point-of-sale (PoS) systems, exemplifies the importance of hardware security in FinTech. Ultimately, this research contributes to a comprehensive understanding of the evolving FinTech landscape and its implications for both financial inclusion and cybersecurity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cybersecurity

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Digital Glamour: Exploring the Intersection of Facial Makeup and Augmented Reality 1st

Digital Glamour: Exploring the Intersection of Facial Makeup...

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1st International Conference on Intelligent Systems in Computing and Communications, ISCComm 2023

作者： Bhat, Vinayambika S. Shetty, Deepthi Kotian, Deepthi Mangalore Institute of Technology and Engineering Moodabidri India

ISBN: (纸本)9783031756078

The COVID-19 global pandemic has affected the world in such a way that it has forced new systems to inhibit Augmented Reality (AR) technologies so as to avoid physical contact and also the business have turned to remote work mode. The Augmented Reality solution to help us through pandemic challenges and it may just represent the future of world business. Virtual try-on is an advanced way of trying a product. In this project, we examined and reviewed the existing works where they augment the face data and based on training models employed output is displayed as a blend of virtual and real object. Among many approaches, our emphasis is on models which inhibit deep learning and machine learning models with generative adversial networks which are faster and more accurate. We are comparing various technologies for e-business that are trying to create a virtual image using web camera. The proposed model will work in the following way: When a person enters beside camera’s vision, Camera will start capturing the live video stream of him which is nothing but a short video of specified frames. The deep learning algorithm works in background will capture the facial encodings of the person based on product which he wishes to try. Each time a new person enters into our portal (when he come’s infront of camera), dynamic input of his face video stream is taken and based on the product which he/she wishes to buy facial encodings of that part is extracted and virtual product will be blended along with specified facial part. Final product will be a virtual try-on experience where in a person could actually visualize how his product would look when he actually wears it. The enhanced product is created by integrating deep learning and classical machine learning approaches, with the input given to the convolutional neural network being processed. For better interface with the user a website built using HTML and CSS is used to develop a front-end framework. © The Author(s), under exclusi

关键词： Adversarial machine learning

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