bluuebunny/crossref_metadata_2025 huggingface dataset:
Arduino based Solar Tracking System with Aluminum Foil Reflectors and Cooling System for improvement of Efficiency
Authors: Naveen Kumar Gutha , Nireekshana Turaka , Anantha Lakshmi Venicharla
Published: February 2025

Fuel thirst and shrinking nature of conventional fuels has led many countries investigate for eco-friendly sources of energy. Solar radiation based power is one such clean, alternative, abundant and eco-friendly energy resource. Electricity and heat are two useful forms of solar energy. As electricity demand surges every minute, hour and day, the conversion of solar radiation based energy to electrical energy through photovoltaics is also becoming popular, therefore. But, deficiencies still exist which bring down the overall conversion efficiency. This includes overpricing of cells. Reflectors are off late used to improvise the heat focus rate on to the panels. In this case, lack of heat treatment has a direct effect on conversion efficiency. In this paper, we are presenting a scheme called aluminum foil reflected solar panel with tracking and cooling mechanism.
Adaptive Control Strategy for Real-Time Regulation of PEV Charging in Response to Fluctuating Renewable Energy Supply
Authors: Jayasri Nemala , Devi V.S. Anusuya , Tewari Preeti , Lakhanpal Sorabh , Abedi Tamam Ali Abd Ulabbas , Bodha Kapil
Published: February 2025

This study is concerned with the coordinated charging pattern of plugin electric vehicles (PEVs) by using a simulation and control framework. The first of these is to develop a novel control technique based on a grid structure to manage the charging power of PEVs in reaction to fluctuating renewable energy sources. The grid is assumed to control and communicate instantly and directly through a common control signal the electricity used for PEV charging. Based on the principle of market-based demand modeling, the subsequent theoretical formulation involves a system of partial differential equations for concurrent PEV charging. It is then applied to future real world driving data and compared to a PEV Monte Carlo model. Moreover, the principles of SM control are introduced to synthesize the robust output feedback controller for the system without state error. The fluctuating PEV count is addressed by focusing on the sole observable output: the instantaneous mismatch of supply and demand of renewable electricity by customers. The performance of the controller is evaluated in the present research based on a real wind power state trajectory through numerical simulations of the system.
Effect of Branch Inclination on Power output from Solar PV Tree: A flexible Testing Method
Authors: Madireddi Sowjanya , Reddy M Avinash , G Abhiteja , Reddy S Akhil , Swarupa M. Lakshmi
Published: February 2025

Solar PV tree has emerged as a solution by using only 1/10th of the space required to the issues of large space requirements for conventional solar panel arrangements for power harvesting. But pre-design and testing is not possible on prototype. Hence, in the present work one such flexible solar PV tree model is designed, fabricated and used for testing a special panel arrangement and the effect of various angles of inclination of the branches on which panels are arranged for higher power outputs. This model is also used for testing the effect on output by changing the connection between panels in series or parallel. The effect of inclination of the branches on power output is tested at an angle of 0°, 30°, 45°, 60°, 0°, 30°, 45°, 60°. The experiments reveal that when the panels are facing either east or west, maximum output is obtained at 30° (or 120°) and at 0° during mid-day. This testing model can be used for any design of the panel arrangements before making a prototype.
Designing a Solar Renewable Energy Controller Circuit Using a Peak Energy Point Tracking System
Authors: Murugan A.S.S. , Rajitha M. , Gangikunta Manohar , Shravani Ch. , Kar Rajib Kumar
Published: February 2025

Solar power, often known as photovoltaic (PV) systems, is a renewable energy source that has attracted a lot of interest from both industry and the scientific community. The goal of this project is to create a maximum power point tracking system for solar panels using a microcontroller and a DC/DC converter. A PV source with multiple voltage inputs ranging from 5V to 25V DC has had its maximum voltage established and maintained using the Perturb and Observe technique. The system was developed using an Arduino board and a converter, and it was simulated using Matlab Simulink.
Modeling and Simulation of Vehicle to Grid (V2G) Integration
Authors: Janardhan G. , Shanmugasundaram Ravivarman , Kumar J Senthil , Narendar R. , Kumar K Suresh
Published: February 2025

A Vehicle-to-Grid (V2G) integrated microgrid system, which represents a potential community-scale energy network, is modelled and simulated in this article. A diesel generator for base power generation, renewable energy sources (wind farms and photovoltaic cells), residential loads, and a V2G system with 100 electric cars make up the simulated microgrid. By controlling car battery charging and discharging, the V2G system provides grid support on a day of low demand for around 1,000 homes. While the PV farm and wind farm offer renewable energy based on real-time solar irradiance and wind profiles, the diesel generator maintains power balance by making up for variations in generation and consumption. Electric car charging and grid stability during power outages are the two functions of the V2G technology. Realistic charging patterns and energy availability are reflected in the modelling of five different vehicle-user profiles. Dynamic events like asynchronous machine starting, partial solar shading, and wind farm disconnection are all included in the 24-hour simulation. The system’s reaction to these occurrences is examined in the article, with particular attention paid to grid frequency stability, renewable power variability, and V2G’s function in load balancing and frequency control. The findings demonstrate how V2G integration may improve grid stability and lessen the erratic nature of renewable energy sources in energy systems of the future.
Enhanced Homomorphic Encryption for Cloud Security Through Individual Optimization
Authors: Radha Marepalli , Sunitha Vanamala , Manoj Kumar Konda , Venkata Rama Lakshmi Gundala , Prasanthi B.
Published: February 2025

Cloud computing is susceptible to a wide range of security issues since it is decentralized. Inappropriate actors may take advantage of these vulnerabilities. Using a method known as homomorphic encryption, which is a kind of encryption technology, it is possible to encrypt the data that can be accessed from the cloud server. This allows the data to be protected from unauthorized access. The study that has been suggested makes use of the technique of particle swarm optimization to achieve the goal of enhancing the encryption key. There are a lot of individuals who are acquainted with the population-dependent meta-heuristic algorithms that are known as particle swarm optimization techniques. These algorithms are also often referred to as PSO strategies. To developing these algorithms, the patterns of social behaviour that birds display serve as a source of inspiration. The hybrid homomorphic encryption techniques start with a collection of particles that are randomly spread to provide more effective outcomes. This is done to maximize the efficiency of the encryption. When establishing the starting point of these procedures, the quality that is being evaluated is considered as a factor. The results of the simulation make it abundantly evident that the hybrid encryption technique that was offered resulted in an improvement in the encryption process. This is the immediate consequence of the simulation's findings. When compared to the homomorphic encryption that is currently being used, this is a significant difference.
Next-Gen Arduino-Driven Drone with FS-16 Control: Advancing Aerial Precision and Versatility in Diverse Applications
Authors: Farman Md. Khaja , Sahithi Kannam , Nikhila Jarabana , Ramani Gaini , Reddy K. Pranavi , Reddy K. Pranavi , Kumar Devineni Gireesh
Published: February 2025

This research introduces an advanced drone system engineered with Arduino technology and governed by an FS-16 controller, designed to revolutionize aerial applications. The drone employs state-of-the-art sensors and real-time processing algorithms, ensuring exceptional flight stability, precise navigation, and robust obstacle avoidance capabilities. By harnessing the adaptability of the Arduino platform, we have crafted sophisticated control strategies that significantly enhance responsiveness and performance in diverse environments. The FS-16 controller provides a user-friendly interface, empowering operators with seamless control and customization options. This drone is poised for impactful applications in fields such as precision agriculture, environmental monitoring, and emergency response, underscoring its versatility and practicality. By marrying affordability with cutting-edge innovation, this project exemplifies the future potential of unmanned aerial vehicles, fostering advancements in technology and accessibility across various industries.
Flow Visualization In Closed Loop Pulsating Heat Pipe (CLPHP) Using Deep Learning Techniques
Authors: N Santhi Sree , P Bhramara , Medidi Veerabhadra Rao , Anthani Kamala Priya , G Avinash , K Sindhu
Published: February 2025

The present work describes an alternative method for recognizing and tracking the flow in PHP which is based on visualization to address the issue of examining individual flows graphically. For this an experimental analysis which was already carried out on an 8-turn Closed Loop Pulsating Heat Pipe (CLPHP) with copper tube capillary dimensions with 50% fill ratio in vertical mode changing the range of heat inputs by using water as the working fluid is taken as reference. The time and cost required for experimentation and simulation is significantly reduced by using deep learning method. YOLOv5 based Convolutional Neural Network (CNN) is used to identify and classify the flow in CLPHP. The results obtained by CNN are compared with existing experimental and CFD results.
Real-Time Face Mask Detection Using Deep Learning: Enhancing Public Health and Safety
Authors: Dodda Ratnam , C Raghavendra , U Raghavendra Swamy , Azmera Chandu Naik , M Sreenu , Nimmala Satyanarayana
Published: February 2025

This paper presents a deep learning-based system for real-time face mask detection, aimed at enhancing public health monitoring in environments where mask compliance is critical. Utilizing a Convolutional Neural Network (CNN) built with TensorFlow and Keras, the model effectively classifies individuals as mask-wearing or non-mask-wearing. Data preprocessing and augmentation techniques improve the model’s robustness across diverse input images, ensuring high performance and generalizability. Developed on Google Colab, the system leverages cloud-based resources for efficient model training and deployment, eliminating the need for extensive local hardware. It supports real-time image analysis and is scalable for continuous video monitoring, making it suitable for large-scale applications. Integration with Google Drive streamlines data management, simplifying updates and deployment. The proposed system provides an accessible solution for mask compliance monitoring in public spaces, offering accuracy, scalability, and ease of deployment. Future work will focus on enhancing the system with multi-class classification for mask types, IoT integration for automated responses, and edge device deployment to improve accessibility. This tool demonstrates the potential of AI in promoting health and safety in public settings.
Quantifying Recent State-of-Arts for Breast Cancer Segmentation, Detection and Classification: A Review
Authors: Chintakunta Job Prasanth Kumar , Lepakshi Vijayalakshmi A.
Published: February 2025

Researchers have been motivated to create effective, dependable, and scalable computer-aided diagnostic (CAD) systems given the rising incidence of breast cancer and its high death rates. In contrast to conventional evaluations that are subject to human mistake, CAD systems that use AI and visual computing can offer more precise diagnoses. But maintaining resilience in the face of complicated inputs is still difficult. Deep learning methods are the most effective for identifying and categorizing breast cancer, while there is still room for improvement in their generalizability. This study looks at state-of-the-art deep learning algorithms for breast cancer diagnosis, such as novel CNN-based segmentation, classification, and detection techniques. It highlights the advantages and disadvantages of improved deep networks, such as RNNs and transfer learning networks. Unlike traditional models, segmented region-of-interest (ROI) features can improve efficiency by addressing feature-level class imbalance. Hybrid deep models, designed to overcome issues like lack of contextual features and gradient vanishing, retain optimal feature sets for learning and prediction, resulting in highly precise and applicable findings for breast cancer diagnosis. Combining hybrid deep features with spatio-textural features yields even better results. These insights can guide future innovations in CAD solutions, ensuring higher accuracy and early detection while handling large data volumes consistently.
EEG Signal Acquisition and Analysis for Detecting and Classifying Mental Disorders
Authors: S Anshy Princella A. , Kuresan Harisudha , M Anand
Published: February 2025

Electroencephalography (EEG) is a critical tool in the diagnosis and monitoring of mental disorders, providing valuable insights into the brain’s electrical activity. However, traditional EEG electrodes face challenges in terms of signal quality, comfort, and stability, which can affect the accuracy of diagnosing conditions such as depression, schizophrenia, anxiety, and bipolar disorder. This project proposes a novel high-sensitivity, multi-layered electrode design tailored specifically for EEG applications. By combining advanced materials such as graphene-enhanced conductive polymers, silver nanowires, and integrated microfluidic channels on a flexible silicone substrate, this electrode offers improved signal-to-noise ratio (SNR), reduced contact impedance, and better comfort for long-term wear. The goal of this project is to enhance the accuracy of EEG signal acquisition for mental health diagnostics and to enable the differentiation of multiple mental disorders through machine learning-based signal analysis. The proposed electrode design significantly improves the quality of EEG signals, which are often subtle and prone to interference. By implementing microneedles for better skin contact and microfluidic channels for maintaining a stable interface, the electrode minimizes common artifacts and noise, enabling the capture of high-fidelity brainwave data. These features are essential for differentiating complex mental disorders, as even minor signal variations can be clinically relevant. This electrode design, with its enhanced sensitivity and stability, presents a significant advancement in EEG technology for mental health diagnostics. It not only improves the comfort and accuracy of EEG monitoring but also holds potential for personalized and early diagnosis of mental health conditions.
Power Smoothing Enhancement in Hybrid System Using Neural Network Controller
Authors: Gopalakrushna A. , Nayak N. Nithin , Vardhan B. Vishnu , Devi V Vimala
Published: February 2025

A novel hybrid energy system featuring a neural network (NN) controller for enhanced power smoothing is proposed in this research. The system integrates a wind turbine and a solar array to generate renewable electricity. However, the inherent variability of renewable energy sources can lead to power fluctuations that disrupt the stability of the power grid. To address this challenge, an NN controller is employed to optimize power smoothing within the system. By analysing historical data, the controller anticipates future power variations and dynamically adjusts the system’s operation to maintain a more consistent power output. The proposed hybrid system with improved power smoothing capabilities is anticipated to facilitate the integration of renewable energy into the power grid, fostering a more stable and sustainable energy landscape. The system is implemented using MATLAB Simulink software.
An AI-driven Robust Strategy for Automated Grape Leaf Disease Classification with Pesticide Recommendation System using Walrus-Optimized CNN
Authors: Uday Kaparthi , Venkateswarlu G. , Ramesh Gopisetty , Bojjwar Satish
Published: February 2025

Agricultural productivity is crucial for global economic development, but crop diseases can significantly impact output. Early detection is essential and so, Artificial Intelligence (AI) offers a solution by classifying leaf images as healthy or diseased types for aiding farmers. Grape leaf disease stands as the major issue behind large-scale orchard losses, impeding the healthy and sustained growth of the grape industry. This research outlines a new AI-driven model comprising data collection, pre-processing, image segmentation, disease classification, pesticide recommendation, and performance evaluation. It starts with extracting a leaf dataset, applies a Gaussian Filter for noise reduction, uses Mask R-CNN for image segmentation, and employs Walrus Optimization (WaOA)-driven Convolutional Neural Networks (CNNs) for disease classification. This model then suggests pesticides based on AI analysis and evaluates their performance. Ultimately, it aims to aid farmers by minimizing losses and enhancing production through timely disease detection and effective treatment recommendations.
Optimizing Combined Heat and Power Economic Dispatch Using a Differential Evolution Algorithm
Authors: Mahender K. , Rao Gundala Srinivasa , Kamalakar Gangula , Venkateshwarlu S. , Polasa Swathi , Pulluri Harish
Published: February 2025

The electrical load dispatch (ELD) optimizes the scheduling of various power plants to deliver the necessary power at the lowest operational cost. Implementing combined heat and power (CHP) units in contemporary power systems enhances energy efficiency and generates less environmental pollution compared to conventional units by producing electricity and heat at the same time. As a result, the ELD issue, including CHP units, transforms into a non-linear and non-convex challenge called CHP economic dispatch (CHPED), which aims to meet electrical and thermal demands at the lowest operational cost. The CHPED issue is examined by applying the Differential evolution (DE) approach. The algorithm's efficacy on 24-unit, 48-unit, and 96-unit systems is detailed. The results acquired are compared with other algorithms to evaluate the effectiveness of the DE algorithm. The findings proved the efficacy of the DE algorithms compared to other evolutionary algorithms.
A Synergistic Approach to Image Restoration DenseNet Enhanced Deep Image Prior
Authors: A Senthil Anandhi. , Jaiganesh M.
Published: February 2025

The DenseNet-Enhanced Deep Image Prior (Dense-DIP) model employs a combination of theories from DenseNet architecture and Deep Image Prior framework to achieve the best results in terms of image restoration. Such a cutting-edge method utilizes the densely connected layers of DenseNet for efficient recycling of features around the boned edges thus ensuring effective extraction of hierarchical features without compromising the finer details in the structures. The subtraction of Dense-DIP from pre-trained networks and large datasets for labeling is possible as the method begins with a random noise network that is directly optimized onto distorted images. Important elements are also skip connections and early stopping, which often prevent overfitting and improve the quality of restoration. The model has broad applicability, including image denoising, inpainting, super-resolution, deblurring with a large improvement in quantitative and perceptual quality.
An IoT System for Monitoring and Alerting Safety in Coal Mines
Authors: Ramesh Gopisetty , Bojjawar Satish , Swapna N. , Uday Kaparthi
Published: February 2025

Mines represent some of the most perilous workplaces globally, with numerous fatalities annually stemming from catastrophic explosions. Recent research indicates that, on average, approximately 12,000 individuals lose their lives in such mining disasters. It is imperative to continually monitor various factors like methane levels, temperature fluctuations, and fire hazards in underground mining operations. Given the intricate nature of coal mines and the diverse tasks undertaken within them, ongoing surveillance of the work environment is crucial. This study delves into the detection of harmful gases in critical areas and their impact on miners. Employing a real-time monitoring system facilitated by a wireless sensor network comprising multiple sensors, this research focuses on monitoring ambient conditions including temperature, vibrations, and toxic gas concentrations. Furthermore, this system offers early warnings, aiding miners in swiftly evacuating dangerous zones before any fatalities occur. Leveraging ZigBee technology, an IEEE 802.15.4 wireless networking standard well-suited for challenging environments, this system ensures effective communication within the mine.
Enhancing Road Safety at Traffic Signals Using IoT: A Smart Solution for Preventing Signal Violations and Reducing Accidents
Authors: Anilkumar G. , Jagmohan  , Karunakar P. , Rakesh V. , Rahimpasha Shaik , Krishnamoorthy Murugaperumal
Published: February 2025

This work aims to enhance road safety at traffic signals by leveraging IoT technology. Traffic signal violations, such as signal jumping, are a major cause of accidents, leading to injuries and loss of life. Our proposed system focuses on minimizing these risks by introducing a smart IoT-based solution that ensures compliance with traffic rules and promotes safe driving behavior. The system utilizes IoT-enabled actuators and sensors to monitor traffic conditions and enforce signal adherence. When a signal is violated, the system automatically triggers alerts and takes necessary actions to prevent potential collisions. By effectively managing traffic flow and improving the reliability of traffic signals, this project seeks to reduce the likelihood of accidents at intersections. In addition to improving safety, the system also enhances traffic management efficiency, making roads safer for drivers and pedestrians. The integration of IoT technology ensures real-time monitoring and adaptability to dynamic traffic conditions. Overall, this project underscores the importance of modernizing traffic signal systems to protect lives and foster a safer commuting environment.
Thermal and Sporadic Investigation of Silicon based Rubber Nano Composites
Authors: Tirgar Pravin , Priya R Karpaga , Kansal Lavish , Kumar Sunil Prashanth , Kumar Vankadara Sampath , Gouda Pramod Kumar
Published: February 2025

This paper comprises the details about the synthesis, thermal and sporadic investigations on the nano composites. For thermal analysis, the synthesized nano composite samples are subjected to Thermo gravitometric Analysis and Differential Scanning Calorimetry tests. Whereas breakdown tests, tracking test and dielectric breakdown tests are done through galvanic investigation. The effects of fillers of various concentrations in the range of 1%, 2% and 3% of their total volume subject to different frequency in particular from 1 kHz and 10 kHz for various components. The result shows the propitious behavior of polymer nano composites. The relative permittivity and loss tangent values are reduced radically in relation with frequency. The galvanic investigation of samples indicated the promising characteristics of tracking erosion test and electrical tests for various nano composites.
Optimizing Distributed Generation and Energy Storage in Microgrids through Intelligent Energy Administration Strategies
Authors: Raj Vijilius Helena , Reddy R. Akhilesh , Singh Navdeep , Gupta Navya , Al-Rubaye Taqi Mohammed Khattab , Agrawal Priyanka
Published: February 2025

An intelligent energy management strategy (IEAS), used for performing peak performance of microgrid, is proposed. SEMS mainly contains three modules -- energy retention system administration module, optimisation component, and power prediction module. From research done on the characteristics of solar PV production under various metrological scenarios, a module for power prediction one day ahead of time has been proposed. The mechanism of energy retention is its two most important characteristics: the retention must be improved across many time steps; energy pricing structures should be considered. Thus, the optimal way to operate are determined by using the ESS module. The storage device and the ESS financial performance can be evaluated by simultaneously considering ESSs of multiple-time limiting defined ESS. Thus, based on IEMS, the operations of DG, smart managing ESS and economical load dispatch is converted into a single-object optimisation problem. Finally, to get a workable approach of load management, an efficiency component for VE-GA is presented. This module generates control diagram of the dispersed generators and ESS, and give three different operation strategies.
Controller Design for Temperature Regulation in a Nonlinear Industrial Heat Exchanger System
Authors: Reddy Vakiti Srilatha , Subhadra Harivardhagini , Prananvanand S. , Juliet A. Vimala , Ramesh Gopisetty
Published: February 2025

A heat exchanger enables heat transfer between two fluids without mixing them. Its performance depends on factors like temperature difference, heat transfer area, and fluid flow rates. Heat exchangers are used in industries such as petroleum refining, food processing, nuclear power, thermal management in spacecraft, and electricity production. This study focuses on designing, modeling, and testing controllers for a nonlinear heat exchanger system. Initially, the baseline performance is evaluated, followed by an analysis of system behavior under various conditions. The transient response and error criteria of control strategies are assessed through setpoint tracking. MATLAB is used to implement the control algorithm, allowing real-time monitoring of the system’s input and output for different setpoints. The controller adjusts to ensure optimal heat transfer and performance.
Innovative marketing in intelligence urban logistics systems
Authors: Havrylenko Nataliia , Kasian Serhii , Opalko Viktoriia , Ptytsia Natalia , Lytvynenko Larysa
Published: February 2025

The significance of innovative marketing in the intelligent urban logistics systems is pointed out. The methodological basis of the study comprises the methods of expert assessments, generalisation and system analysis, the method of analogy, the method of morphological analysis, and the abstract and logical method. The scientific novelty of the proposed study is determined by determining the role and place of innovative marketing in economic formations and, primarily, in ensuring the management of intelligent urban logistics systems with the creation of an appropriate mechanism and characterisation of its components. It was determined that the problem of innovative marketing in the intellectual urban logistics systems is unresolved and requires the development of appropriate effective tools. The components of innovation marketing were characterised, on the basis of which the place and role of innovation marketing in managing the intelligent urban logistics systems were specified. The proposed mechanism for implementing innovative marketing in the management of intelligent urban logistics systems has a number of features that make it narrowly directed and non-universal in general.
Reduced component Nine Level Inverter with Level Shifted Modulation Controls
Authors: N Harikrishna , I Kumaraswamy
Published: February 2025

This article introduces a novel approach to nine-level inverters with trinary input and level shifted modulation control. The technique employs Level Shifted (LS) Pulse Width Modulation (PWM) on a newly proposed topology called Trinary Source Fed Nine-Level Inverter (TSFNLI). The TSFNLI utilizes trinary geometric DC links and minimizes the number of devices required. With eight switches, including 2 bidirectional switching devices, the suggested TSFNLI generates nine levels, achieving the highest voltage level compared to specific sequences while using fewer DC links and devices. Notably, the design produces a negative level without the need for additional circuits like an H-bridge, resulting in reduced voltage stress on switches. In each operating mode, only three switches conduct, leading to fewer switching shifts, lower switching and conduction losses. The LS-PWM technique is applied to the TSFNLI to enhance the Resultant Voltage quality, incorporating Alternate Phase Opposition Disposition (APOD) approaches Simulations conducted using MATLAB-SIMULINK reveal that the APOD methodology reduces THD and distortion factor, while offering higher fundamental RMS Resultant Voltage and Form Factor.
A Solar-Powered Vehicle: Design And Implementation
Authors: Sydu Shabbier Ahmed , Abdullah Ali Al-Jahwari Mohammed , Al-Housni Mohammed Badar Ali , Aljabri Abdulrahman Ali Khamis , Chenchireddy Kalagotla , Mora Madhuri
Published: February 2025

The design and operation of a solar-powered vehicle are examined in this paper, emphasizing the contribution of this technology to the global shift to sustainable energy sources. With the growing energy demands and environmental concerns, solar power has become an essential resource for solving these issues. Aerodynamic, lightweight designs and well-placed solar panels that effectively absorb sunlight to produce energy are combined in solar-powered vehicles. A significant limitation is addressed by this stored power, which allows for continuous operation even in situations with little sunlight. Air quality is greatly improved, and climate change is aided by solar-powered vehicles, which emit no emissions, in contrast to traditional vehicles that rely on fossil fuels. Decentralized solar energy also improves energy security by lowering dependency on volatile oil markets. This vehicle also has a delivery system and remote-control capability, which makes it possible to transport lightweight objects. A major advancement in mobility solutions, these characteristics highlight the usefulness and affordability of solar-powered transportation. In conclusion, solar-powered vehicles provide a useful and sustainable solution to today’s problems. They set the stage for a sustainable, effective, and flexible transportation future by incorporating remote control and delivery features.
Design of Efficient Single Switch DC-DC Converter for Electric Vehicles Charging
Authors: Yaramala Anil Kumar , Sirimalla Mahesh , Shetapagula Raviteja , Anugu Arun Kumar
Published: February 2025

Nowadays, power electronic converters are employed in various appliances to efficiently transform voltages. This paper focuses on designing a single-switch DC-DC converter with low ripple content, high voltage gain, and minimal power losses. This type of converter finds applications in diverse areas such as solar systems, electric vehicles, and DC micro grids. In this paper, we specifically design the single-switch converter for electric vehicle applications. Given the rising costs of fuel and diesel, there is a growing shift towards economical electric vehicles. This converter incorporates active elements, diodes, a single switch, and a Li-ion battery. The results demonstrate that the battery can be efficiently charged in less time with reduced ripple content and increased gain. These outcomes were achieved through simulations using MATLAB Simulink.
A Review of Emerging Techniques for Power Quality Improvement in Renewable Energy Integration
Authors: Kumar P. Rajesh , Shravani Chapala , Rajitha M. , Lokeshwar Reddy Ch.
Published: February 2025

The growing integration of renewable energy sources (RES) into power grids presents significant challenges to maintaining power quality (PQ) due to the inherent variability and intermittency of these energy resources. This paper provides a comprehensive review of emerging techniques aimed at improving power quality in systems with high levels of renewable energy integration. It examines state-of-the-art methods, including advanced control strategies, innovative compensation devices, and the latest developments in power electronic interfaces. Special emphasis is placed on the role of modern technologies, such as artificial intelligence (AI) and machine learning (ML), in enhancing the adaptability and robustness of PQ solutions. The effectiveness and limitations of various approaches, including the use of Flexible AC Transmission Systems (FACTS), Unified Power Quality Conditioners (UPQC), and dynamic voltage restorers (DVR), are critically analysed. Additionally, this review explores the integration of smart grid concepts and the deployment of energy storage systems as complementary measures to mitigate PQ issues. Future research directions are outlined, highlighting the need for further advancements in real-time monitoring, adaptive algorithms, and hybrid systems that combine multiple techniques for optimal performance. This review serves as a valuable resource for researchers, engineers, and policymakers seeking to understand and address the power quality challenges associated with renewable energy integration in modern and future power systems.
Moving Towards Sustainable Connectivity: Bibliometric Analysis of IoT-Enabled Financial Sustainability Trends
Authors: Priya  , Sharma Kavita , Bisht Vartika
Published: February 2025

The Internet of Things (IoT) is one of the biggest technical advances in recent years, improving our lives in many different ways. One important area of its application is sustainable development. Additionally, funds’ availability is as crucial for sustainable development as IoT. The relationship between technological advancements like big data, blockchain, artificial intelligence (AI), mobile platforms, and the IoT with finance is referred to as “digital finance”. The financial system has been digitalized for a while now. The capacity to quickly, accurately, affordably, and conveniently access vast amounts of complex data related to investments and sustainability consequences accelerates transparency and helps public institutions monitor the regulatory aspects of sustainable development. This study aims to investigate the characteristics of prior studies to comprehend the most recent developments in IoT and sustainable finance. A bibliometric analysis is performed on 306 research publications retrieved from the Scopus database and published between 2011 and 2024. Software tools like VOS-Viewer and Biblioshiny with R Studio are used for the analysis. The study is capable to summarise the traits and patterns of IoT and sustainable finance research. Moreover, the research identifies well-known authors, journals, and institutions and finds the research articles with the highest citation counts and the fastest-growing theme of the domain. This paper offers insightful recommendations to academicians for their future research.
Optimization Strategies for Electric Vehicles: A Review of Evolutionary, Swarm, and Hybrid Algorithms
Authors: Kumar A. Vijay , Manohar V. Joshi
Published: February 2025

Electric vehicles (EVs) are revolutionizing the automotive industry due to their environmental benefits and economic advantages. However, EV design and control present significant challenges, especially with regard to optimizing battery management, energy efficiency, thermal control, and cost. These challenges frequently feature non-linear relationships that traditional optimization techniques find difficult to handle effectively. This paper benchmarks the performance of selected non-linear optimization algorithms in addressing critical EV problems, including battery charging strategies, energy-efficient driving control, and thermal management. The analysis of optimization techniques is conducted in a chronological manner, highlighting their development from traditional methods to more sophisticated approaches. These techniques are evaluated against key metrics, including solution quality, convergence speed, computational cost, and robustness, illustrating how advancements over time have more effectively tackled the challenges associated with optimizing electric vehicle (EV) operation scenarios. This paper outlines the strengths and weaknesses of various optimization techniques, and offers insights and recommendations for their effective application in EV operation and design. Future research directions include exploring hybrid algorithms and adaptive optimization approaches to further improve EV performance.
Sustainable Water Quality Monitoring: A Time Series Analysis of Ecological Parameters in Saroornagar Lake
Authors: Priya K.T. Padma , Reddy P. Manikya , Swarupa M. Lakshmi
Published: February 2025

Further ecological studies of Saroornagar lake by using time series analysis had been carried out to evaluate the degree of pollution, diversity and shifting of species during 1980 to 2021. From the real-time data, most of necessary parameters showed increase in its concentration many-fold and some of the parameters were drastically decreased in its concentration. From biological parameters most of the species were eliminated from the habitat and some species showed high in its density. Particularly the Chlorophyceae and Euglenophyceae members withstand well and appeared in the form of thick blooms. Nutrient loading and high degree of pollution due to anthropogenic activities contributes to the deterioration of the lake and there is a major shift in species composition. The study clearly confirms the lake is highly polluted and designated as eutrophic.
The Role of Bio-Enzymes in Achieving Sustainable Development Goal 6: Enhancing Water Quality and Sanitation
Authors: Priya K.T. Padma , Motlagh Amin Hossaini , Swarupa M. Lakshmi
Published: February 2025

Global water stress levels are rising, with water pollution posing a significant challenge to achieving Sustainable Development Goal 6 (SDG 6). Sustainable management of lakes and other freshwater bodies is crucial for improving water sanitation and cleanliness. Bio-enzymes, non-toxic liquid enzymes, have shown a substantial impact on water quality in polluted lakes, naturally biodegrading organic matter. In this study, bio-enzymes were prepared using vegetable and fruit peels, with fermentation metabolites including carbohydrates, proteins, mineral salts, microbes, and organic acids. The pH of the bio-enzyme was recorded at 3.8, and the digestion period of the water sample was 60 days. Bio-enzyme solutions of 2% and 5% concentrations were applied initially and repeated after 20 days. Samples were analyzed after 20 and 40 days of application. Results showed significant improvements in dissolved oxygen (DO), biological oxygen demand (BOD), and chemical oxygen demand (COD) values, bringing them within permissible limits. The 5% bio-enzyme application proved more effective than 2% in water treatment. Therefore, bio-enzyme application presents an eco-friendly and sustainable method for enhancing water quality, contributing to the achievement of SDG 6 and advancing clean water and sanitation efforts.
The 1936 Winter Olympics in Garmisch-Partenkirchen: the other “Nazi Games” between boycott and success
Authors: Molinelli Edoardo
Published: February 2025

The IV Winter Olympics held in Garmisch-Partenkirchen from 7 to 16 February 1936 have long been regarded as a minor episode in the history of 20th-century sports, especially in comparison to the Games of the XI Olympiad, held in Berlin from 1 to 16 August 1936. While the Berlin Games have been the subject of many academic studies, the Garmisch-Partenkirchen event has received comparatively less attention, despite serving as a general rehearsal for the Summer Games. In the Winter Games, however, we can find many of the same themes that, on a larger scale, characterized the Berlin event, such as international relations, propaganda and political conflicts. This article will focus on the role that the 1936 Winter Olympics played within the complex relationship between Nazis and the Olympics, focusing primarily on the boycott attempt, the efforts of the organizers to hide the anti-Semitic persecutions and the success of the Games.
Clinical Collaboration Commentary: Breaking Down the Role of Pelvic Health Physical Therapy in Colorectal Care
Authors: Barten Julia
Published: February 2025

Pelvic health treatment demands an understanding of multiple pelvic subspecialties to adequately address. While there are many providers who identify as pelvic health physical therapists, not all are sufficiently able to manage bowel dysfunction. There is a limited number of certified pelvic health specialists within the United States. The goal of this clinical commentary is to break down the role pelvic health physical therapy (PHPT) plays in treatment for colorectal care. The “background” provides insight into the physical therapy (PT) education process and pelvic health specialization. The “methods” discuss PHPT treatment including patient education, examination techniques, value of tests and images, as well as common colorectal conditions. A table summarizes possible findings and treatment considerations for common colorectal conditions, methods for optimizing pelvic floor muscles (PFMs) are discussed, and pelvic devices are explained. The “results” of referring patients and expectation for goals is reviewed along with a “discussion” of room for improvement and “conclusion” of important takeaways. The depth of the vast subjects covered shall only be briefly summarized in this clinical commentary with the hope that it provides context, explanations, recommendations, and leads to future discussions and research that includes PHPT.
Highly transparent smart thermal emitter realized by thin vanadium dioxide
Authors: Nishikawa Kazutaka , Yatsugi Kenichi
Published: February 2025

Global energy shortage necessitates enhancing environmentally friendly and energy-saving technologies. This study demonstrates a smart thermal emitter (STE) with high visual transparency and temperature-dependent emissivity to realize smart thermal management in buildings or automobiles. The STE consisting of layered transparent conductive oxide (TCO), zinc sulfide (ZnS), and thin vanadium dioxide (VO2) films was fabricated using the sputtering process. The STE exhibited high transmittance in the visible region owing to the extremely thin VO2 film and the large optical bandgaps of TCO and ZnS. The average transmittance in the wavelength range of 400–800 nm was 0.57. Owing to the metal–insulator transition of VO2, a metal–insulator–metal structure resonating in the infrared region was formed above 70 °C, which increased the thermal emissivity. The thermal emissivity values at 29 and 91 °C in the infrared region within 2.5–15 μm were 0.29 and 0.66, respectively. The appearance of the STE remained almost unchanged even when the emissivity increased with temperature because the refractive index spectra of VO2 in the visible range hardly change by the metal–insulator transition. In addition, W-doped VO2 allowed the STE to operate under summer temperatures (48 °C). This scalable fabrication process and high visual transparency have significant potential in practical applications, such as windows or exterior coatings, for smart thermal management.
Anderson impurity solver integrating tensor network methods with quantum computing
Authors: Jamet François , Lindoy Lachlan P. , Rath Yannic , Lenihan Connor , Agarwal Abhishek , Fontana Enrico , Simkovic Fedor , Martin Baptiste Anselme , Rungger Ivan
Published: February 2025

Solving the Anderson impurity model typically involves a two-step process, where one first calculates the ground state of the Hamiltonian and then computes its dynamical properties to obtain Green’s function. Here, we propose a hybrid classical/quantum algorithm where the first step is performed using a classical computer to obtain the tensor network ground state as well as its quantum circuit representation and the second step is executed on the quantum computer to obtain Green’s function. Our algorithm exploits the efficiency of tensor networks for preparing ground states on classical computers and takes advantage of quantum processors for the evaluation of the time evolution, which can become intractable on classical computers. We demonstrate the algorithm using 24 qubits on a quantum computing emulator for SrVO3 with a multi-orbital Anderson impurity model within the dynamical mean field theory. The tensor network based ground state quantum circuit preparation algorithm can also be performed for up to 60 qubits with our available computing resources, while the state vector emulation of the quantum algorithm for time evolution is beyond what is accessible with such resources. We show that, provided that the tensor network calculation is able to accurately obtain the ground state energy, this scheme does not require a perfect reproduction of the ground state wave function on the quantum circuit to give an accurate Green’s function. This hybrid approach may lead to quantum advantage in materials simulations where the ground state can be computed classically, but where the dynamical properties cannot.
Phase-change metasurfaces for reconfigurable image processing
Authors: Liu Tingting , Qiu Jumin , Yu Tianbao , Liu Qiegen , Li Jie , Xiao Shuyuan
Published: February 2025

Optical metasurfaces have enabled high-speed, low-power image processing within a compact footprint. However, reconfigurable imaging in such flat devices remains a critical challenge for fully harnessing their potential in practical applications. Here, we propose and demonstrate phase-change metasurfaces capable of dynamically switching between edge-detection and bright-field imaging in the visible spectrum. This reconfigurability is achieved through engineering angular dispersion at electric and magnetic Mie-type resonances. The customized metasurface exhibits an angle-dependent transmittance profile in the amorphous state of Sb2S3 meta-atoms for efficient isotropic edge detection, and an angle-independent profile in the crystalline state for uniform bright-field imaging. The nanostructured Sb2S3-based reconfigurable image processing metasurfaces hold significant potential for applications in computer vision for autonomous driving systems.
Design of a new open-type active electromagnetic metamaterial for magnetic field control based on numerical method
Authors: Hu Jingtao , Zhang Chao , Liu Xiaokuo , Liu Haodong , Liu Guoqiang
Published: February 2025

Controlling magnetic field with active electromagnetic metamaterial (AEM) consisting of a group of wires with precise arrangements of currents, essential for a wide range of applications, is limited by the structure where the source is fully surrounded by the concentric cylindrical AEM. We propose an open-type AEM design method based on the transformed medium theory and finite-element calculation method to overcome this stringent limitation. We experimentally demonstrate that an open-type AEM with negative magnetic permeability controls the magnetic field of a straight current wire at a distance outside the AEM. Our strategy leads to focus on the magnetic field in empty space with various structural forms and may broaden the application scope of electromagnetic metamaterials.