Browse Items (14421 total)
Sort by:
-
ARTIFICIAL INTELLIGENCE IN NEUROCOGNITIVE REHABILITATION: AI Applications in Assessment, Monitoring, and Therapy
The global rise in neurocognitive disorders, due to aging populations, traumatic brain injuries, and neurodegenerative diseases, demands innovative rehabilitation strategies. Artificial intelligence (AI) is transforming neurocognitive rehabilitation by enabling early detection, real-time monitoring, and personalized therapy through technologies such as machine learning, natural language processing, neuroimaging, and wearable sensors. This chapter explores how AI-powered tools enhance neuropsychological assessments, support continuous monitoring through multimodal data streams, and enable adaptive, patient-centered therapeutic interventions. Additionally, it evaluates the ethical challenges and implementation barriers associated with AI integration in clinical practice. By examining the interplay between AI and neurorehabilitation, the chapter underscores the transformative potential of interdisciplinary, data-driven approaches in cognitive healthcare. 2026 selection and editorial matter, K. Jayasankara Reddy; individual chapters, the contributors. All rights reserved. -
Integrating cyber-physical systems with intelligent transportation: Challenges and opportunities
Cyber-physical systems (CPS) are revolutionizing the transportation sector, wherein physical processes are combined with computational systems to create efficient, reliable, and safe transportation solutions. This chapter discusses the ways in which CPS impact contemporary transportation development. The theoretical and practical aspects of CPS have been considered as they follow with the intelligent traffic management systems and driverless cars within this scope of work. The first half of the chapter is then applied to architectural design in CPS, discussing how elements of the physical worldinteraction with cars and roads, for exampleare coupled with cyber systems, such as cloud computing, IoT, and communication networks. Important technical breakthroughs in these areas highlight the key aspects that make real-time decision-making and optimization of systems possible: 5G, edge computing, and artificial intelligence. The chapter also reviews simulation-based techniques in analyzing vehicle behavior and traffic flow, which encompasses insights into how CPS might improve traffic safety and efficiency. Simulations can study very complex transportation scenarios like collision avoidance and control of traffic without the need for real data. The chapter discusses cybersecurity risks, legal issues, and the need for standardized infrastructure to support intelligent transportation systems. It also focuses on the challenges presented by laws and policies in the field of CPS. The interaction of drivers, passengers, and traffic operators with these devices further helps grasp the human factor as well as the experience of a CPS user. The final section of the chapter discusses future directions of CPS research and development, specifically regarding how blockchain technology and quantum computing might advance transportation networks. This chapter will, therefore, give the reader a holistic understanding of how CPS may change the face of transportation in the future by bringing its non-data-driven components to the fore. 2026 selection and editorial matter, Jossy George, Kamal Upreti, Ramesh Chandra Poonia, Ankit Gautam, and Danish Nadeem; individual chapters, the contributors. -
Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security
Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security tech-niques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame of the sequenced set of frames to overcome the above-said problems. This research offers a novel Edge-Net autoencoder to select convoluted and dominant features of the input diffused structure. First, this pro-posedmethodistestedwiththeCross-ethnicityFaceAnti-spoofing (CASIA), Fetal alcohol spectrum disorders (FASD) dataset. This database has three models of attacks: distorted photographs in printed form, photographs with removed eyes portion, and video attacks. The images are taken with three different quality cameras: low, average, and high-quality real and spoofed images. An extensive experimental study was performed with CASIA-FASD, 3 Diagnostic Machine Aid-Digital (DMAD) dataset that proved higher results when compared to existing algorithms. 2023, Tech Science Press. All rights reserved. -
The computational model of nanofluid considering heat transfer and entropy generation across a curved and flat surface
The entropy generation analysis for the nanofluid flowing over a stretching/shrinking curved region is performed in the existence of the cross-diffusion effect. The surface is also subjected to second-order velocity slip under the effect of mixed convection. The Joule heating that contributes significantly to the heat transfer properties of nanofluid is incorporated along with the heat source/sink. Furthermore, the flow is assumed to be governed by an exterior magnetic field that aids in gaining control over the flow speed. With these frameworks, the mathematical model that describes the flow with such characteristics and assumptions is framed using partial differential equations (PDEs). The bvp4c solver is used to numerically solve the system of non-linear ordinary differential equations (ODEs) that are created from these equations. The solutions of obtained through this technique are verified with the available articles and the comparison is tabulated. Meanwhile, the interpretation of the results of this study is delivered through graphs. The findings showed that the Bejan number was decreased by increasing Brinkman number values whereas it enhanced the entropy generation. Also, as the curvature parameter goes higher, the speed of the nanofluid flow diminishes. Furthermore, the increase in the Soret and Dufour effects have enhanced the thermal conduction and the mass transfer of the nanofluid. 2023, The Author(s). -
Therapeutic romanticism: Khalil Gibrans journey from rebellion to spiritual healing
This article argues that Khalil Gibrans Romanticism functions not merely as literary expression but as a poetic mechanism for processing trauma, cultivating resilience, and fostering relational awareness, especially in contexts of exile and existential fragmentation. To operationalize this reading, the study employs a literary micro-model: Van der Kolk and Fisleriss Dissociation and the Fragmentary Nature of Traumatic Memories used to map The Madman (1918) as the ruptured phase, where fragmentation, irony, and self-estrangement externalize pain; while Seligmans PERMA model frames The Prophet (1923) as the integrative phase, where meaning, relationships, and transcendence articulate universalist healing. By juxtaposing alienation and flourishing, the analysis highlights Gibrans movement from rebellion to harmony, revealing poetrys potential as a therapeutic medium for personal and collective crises. 2025 National Association for Poetry Therapy. -
Challenging Colonial Hegemony through Khalil Gibrans Beautiful and Rare Sayings
The paper examines how Khalil Gibrans Arabic book (Al-Badai waal-Taraif), translated as Beautiful and Rare Sayings, is a rebellious call for the rebirth of the Arab nation. In this work, Gibran expresses his strong political views and dreams of liberty, urging Arabs to awaken from their slumber, regain their freedom, and build their future independently, without relying on foreign powers. Having witnessed the damaging effects of colonialism on the Arab world, Gibran realized how it threatened the future of his people and sought to reform his nation based on the values of liberty and justice. He criticizes oppressive systems such as the decline of the Ottoman Empire and European imperialism, which had long prevented the unity and peace of the Arab world. Through many insights in the book, Gibran gives voice to the pain of his people while guiding them toward the path of freedom and inspiring them with the broad aspirations they can achieve. This work represents the revolutionary spirit of its time, providing a counter-narrative to those in power who seek to silence the opposite. Additionally, this paper explores how Gibrans use of language and metaphors critiques the social and political conditions of the Arab world, reflecting his vision of unity beyond ethnic divisions. By analyzing Beautiful and Rare Sayings, the paper highlights Gibrans role as a cultural mediator, navigating colonial hegemony and inspiring cultural awakening and identity within the Arab context. 2024 selection and editorial matter, Dr. L. Santhosh Kumar, Ms. Minu A., Dr. Barnashree Khasnobis, Dr. Preetha M. and Dr. Merrin R. S.; individual chapters, the contributors. -
A comparative study of the impact of thermal indices on Indian coral ecosystem
Coral reefs have been the diversified ecosystem in the planet. Advantages are opportunities in tourism, coastal protection and fisheries production. Corals, as key ingredient is sourced got drug manufacturing. Its distribution is evident in locations of where sea water temperature ranges between 16C to 30C. Their presence is >0.2% of ocean area and supports >25% of marine species. India has five reef formations. Globally, last two decades have seen an increase in reporting reef deterioration. The reason significantly attributed to be climate change, apart other challenges such as pollution, sedimentation, oil spillage, etc. Such events lead to widespread mortality of corals. Mortality during bleaching events are inevitable and varied; depends on intensity of such events. The primary reason is due to significant rise in average sea surface temperature (SST). Recovery takes time after such events, and it becomes worse with recurring events. The reefs of Indian seas have reported events of severe bleaching during 1998, 2010 and 2016. IPCC reviews show mass bleaching will be prominent in future due to elevated SST. This work tries to compare the HS values of a few regions. The data collected is from 2001 to 2017. A few significant observations are drawn which could further help us to extend the work to take help from Artificial Intelligence to make predictions for the future. This study uses the indices derived out of SST to look at relative risk faced by Indian reefs. The need for comprehensive and localized actions will be discussed. 2021 Author(s). -
Machine Learning-Enabled NIR Spectroscopy. Part 3: Hyperparameter by Design (HyD) Based ANN-MLP Optimization, Model Generalizability, and Model Transferability
Data variations, library changes, and poorly tuned hyperparameters can cause failures in data-driven modelling. In such scenarios, model drift, a gradual shift in model performance, can lead to inaccurate predictions. Monitoring and mitigating drift are vital to maintain model effectiveness. USFDA and ICH regulate pharmaceutical variation with scientific risk-based approaches. In this study, the hyperparameter optimization for the Artificial Neural Network Multilayer Perceptron (ANN-MLP) was investigated using open-source data. The design of experiments (DoE) approach in combination with target drift prediction and statistical process control (SPC) was employed to achieve this objective. First, pre-screening and optimization DoEs were conducted on lab-scale data, serving as internal validation data, to identify the design space and control space. The regression performance metrics were carefully monitored to ensure the right set of hyperparameters was selected, optimizing the modelling time and storage requirements. Before extending the analysis to external validation data, a drift analysis on the target variable was performed. This aimed to determine if the external data fell within the studied range or required retraining of the model. Although a drift was observed, the external data remained well within the range of the internal validation data. Subsequently, trend analysis and process monitoring for the mean absolute error of the active content were conducted. The combined use of DoE, drift analysis, and SPC enabled trend analysis, ensuring that both current and external validation data met acceptance criteria. Out-of-specification and process control limits were determined, providing valuable insights into the models performance and overall reliability. This comprehensive approach allowed for robust hyperparameter optimization and effective management of model lifecycle, crucial in achieving accurate and dependable predictions in various real-world applications. Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). -
Machine LearningEnabled NIR Spectroscopy. Part 2: Workflow for Selecting a Subset of Samples from Publicly Accessible Data
Abstract: An increasingly large dataset of pharmaceuticsdisciplines is frequently challenging to comprehend. Since machine learning needs high-quality data sets, the open-source dataset can be a place to start. This work presents a systematic method to choose representative subsamples from the existing research, along with an extensive set of quality measures and a visualization strategy. The preceding article (Muthudoss et al. in AAPS PharmSciTech 23, 2022) describes a workflow for leveraging near infrared (NIR) spectroscopy to obtain reliable and robustdata on pharmaceutical samples. This study describes the systematic and structured procedure for selecting subsamples from the historical data. We offer a wide range of in-depth quality measures, diagnostic tools, and visualization techniques. A real-world, well-researched NIR dataset was employed to demonstrate this approach. This open-source tablet dataset (http://www.models.life.ku.dk/Tablets) consists of different doses in milligrams, different shapes, and sizes of dosage forms, slots in tablets, three different manufacturing scales (lab, pilot, production), coating differences (coated vs uncoated), etc. This sample is appropriate; that is, the model was developed on one scale (in this research, the lab scale), and it can be great to investigate how well the top models are transferable when tested on new data like pilot-scale or production (full) scale. A literature review indicated that the PLS regression models outperform artificial neural network-multilayer perceptron (ANN-MLP). This work demonstrates the selection of appropriate hyperparameters and their impact on ANN-MLP model performance. The hyperparameter tuning approaches and performance with available references are discussed for the data under investigation. Model extension from lab-scale to pilot-scale/production scale is demonstrated. Highlights: We present a comprehensive quality metrics and visualization strategy in selecting subsamples from the existing studies A comprehensive assessment and workflow are demonstrated using historical real-world near-infrared (NIR) data sets Selection of appropriate hyperparameters and their impact on artificial neural network-multilayer perceptron (ANN-MLP) model performance The choice of hyperparameter tuning approaches and performance with available references are discussed for the data under investigation Model extension from lab-scale to pilot-scale successfully demonstrated Graphical Abstract: [Figure not available: see fulltext.]. 2023, The Author(s). -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
DDoS Intrusions Detection in Low Power SD-IoT Devices Leveraging Effective Machine Learning
Security and privacy are significant concerns in software-defined networking (SDN)-applied Internet of Things (IoT) environments, due to the proliferation of connected devices and the potential for cyberattacks. Hence, robust security mechanisms need to be developed, including authentication, encryption, and distributed denial of service (DDoS) attack detection, tailored to the constraints of low-power IoT devices. Selecting a suitable tiny machine learning (TinyML) algorithm for low-power IoT devices for DDoS attack detection involves considering various factors such as computational complexity, robustness in dealing with heterogeneous data, accuracy, and the specific constraints of the target IoT device. In this paper, we present a two-fold approach for the optimal TinyML algorithm selection leveraging the hybrid analytical network process (HANP). First, we make a comparative analysis (qualitative) of the machine learning algorithm in the context of suitability for TinyML in the domain of SD-IoT devices and generate the weights of suitability for TinyML applications in SD-IoT. Then we evaluate the performance of the machine learning algorithms and validate the results of the model to demonstrate the effectiveness of the proposed method. Finally, we see the effect of dimensionality reduction with respect to features and how it affects the precision, recall, accuracy, and F1 score. The results demonstrate the effectiveness of the scheme. 1975-2011 IEEE. -
Hybrid Renewable Source Powered Dual Input Single Output Converter With High Voltage Gain for Rural Healthcare Facilities
Rural dwellers need a well-equipped healthcare service for a decent life. Most of the rural areas located in southern parts of India away from the grid connection thereby lack in electricity. Unreliable electric power leads to the limited access or inaccessibility of most essential medical equipment in the clinic. The deficiency has also reduced rural healthcare centers ethics criteria. This research work finds all the available resources in the rural healthcare clinic and proposes hybrid solar PV source and supercapacitor-based approaches to make sure of reliable energy access and uninterrupted power supply. Any healthcare facilities include an emergency room, waiting hall, nursing room, consulting room, delivery room, male and female room, and a testing lab. It may take a daily average energy consumption of 16 kWh with 3 kW peak demand. In the input side, solar PV system with an H-type clamped capacitorbased boost converter is proposed for the reduction of input current ripples and power switch conduction losses. At the load side, a capacitor with a switch (switched capacitor) is considered to reduce voltage stress of the components present in the topology and to attain high gain. This research work adopts interleaved structure-based capacitors for current ripple reduction, and the series structure is considered to attain high gain. The proposed novel converter takes the input voltage of 40 V and produces the output voltage of 280 V. The DC link output is then connected with the voltage source inverter (VSI) to get a desired output. The proposed novel converter is employed to run a 3? induction motor for the AC load with the rating of 400 V, 15 A AC power. MATLAB R2015a software is preferred for the simulation analysis. Copyright 2025 K. M. D. Riyaz Ali et al. Journal of Electrical and Computer Engineering published by John Wiley & Sons Ltd. -
Artificial Intelligence in Detecting and Mitigating Online Child Sexual Abuse: Approaches and Solutions
Previous research papers have discussed whether Artificial Intelligence (AI) -based tools like Chat Bots, Law-U Model, and Sweetie. 20 have the potential to mitigate online child sexual abuse. The literature review indicates that AI tools promise good intervention and prevention strategies for several tech-based companies like Google and Microsoft. However, there is a lack of systematic study on AI tools' potential uses, limitations, and legal risks. This paper conducts a systematic literature review to explore the uses and limitations of AI-based interventions in combating online child sexual abuse. It explores the legal and ethical risks of deploying such technological innovations from the viewpoint of data protection, privacy, and security. The authors use the PRISMA technique and thematically answer the research questions. Data are collected from reliable sources such as Statista and the World Health Organisation. The findings of this paper highlight the potential uses of AI for law agencies, forensic experts, victims, and technology companies. The research reports the absence of a sufficient legal framework for the governance and accountability of AI tools. The findings further indicate the need for clarification in the law regarding the legal status of AI tools like Sweetie 2.0. Lastly, this paper offers a framework for harmonizing AI usage with human rights standards. 2025 IEEE. -
Applying a Multi-Agent Simulation Model for Examining Restorative Justice-Based Intervention in the Criminal Justice System: A Legal and Technological Perspective
Previous studies on Restorative Justice (RJ) have focused on the theoretical underpinnings of RJ and its processes. Several systematic literature reviews on RJ point out its potential to assist in victim healing much better compared to the traditional criminal justice system. However, the potential and viability of RJ largely remain in the theoretical landscape. Few empirical studies or simulations have been conducted to explore the viability of this practice in the legal domain. Furthermore, apart from purely studying RJ, literature also points towards its potential use in addressing child sexual abuse cases (CSA) by providing a child and victim-centric approach. However, the practicality of this claim remains scant in present times. Given the gap in the global discourse on the use of RJ as an intervention strategy in the criminal justice system, this paper outlines a computational framework for including RJ into the legal system. The paper does so by applying a Multi-Agent Simulation model (MAS). By utilising JADE for agent orchestration and NetLogo for a visual structure, the framework encodes multiple stakeholders such as accused/ offender, victim, counsellors and judges as autonomous agents with state vectors, utility performance and ACL-communication. The criminal justice system is compared to the restorative justice system using metrics like resolution rate, time, victim healing, rehabilitation and reintegration into the community. Through this paper, a foundation is laid for the potential of RJ in CSA. This paper will enable law and policy makers to consider introducing alternative practices like RJ. 2026 IEEE. -
The Impact of AI on Digital Marketing Across Various Industries: Unveiling New Possibilities Across Industries
Artificial Intelligence (AI) is revolutionizing digital marketing by enhancing the effectiveness of strategies through automation, data- driven insights, and personalized customer engagement. This paper presents a cross- industry analysis to understand the impact of AI on digital marketing effectiveness across various sectors. The primary objective of this research is to examine how AI-powered tools and techniques are reshaping marketing practices and optimizing consumer interactions. Industries such as retail, healthcare, finance, and entertainment are leveraging AI for customer segmentation, personalized recommendations, chatbots, predictive analytics, and automated content creation. By evaluating case studies and industry reports, this study aims to identify key trends and best practices adopted by successful organizations. This research also investigates the challenges faced by companies when implementing AI in digital marketing, such as data privacy concerns, ethical implications, and the integration of AI with existing marketing strategies. 2026, IGI Global Scientific Publishing. All rights reserved. -
CORPORATE SOCIAL RESPONSIBILITY: BRIDGING MANAGEMENT PRACTICES AND COMMUNITY DEVELOPMENT
The paper discusses Corporate Social Responsibility (CSR) as a mediating strategy between corporate management and community development in the diverse situations. Indeed, acknowledging the fact that CSR is no longer a pure concept of voluntary charity, but rather a significant part of business logic, the study was conducted with a qualitative and posthumanistic approach through the prism of situated inquiry. Semi-structured interviews with document and artefact analysis, and some field observation was conducted in India, Nigeria, and Saudi Arabia with 27 participants who were corporate managers and NGO and community stakeholders. Findings show that on one hand CSR is integrated as a strategy with organizational objectives, but on the other hand the perceptions differ so much. Interactions historically and the level of participatory planning tended to focus relationships. Digital dashboards were a form of technology that mediated transparency but posed the threat of dehumanizing engagement. There were continuation tensions between performance-based strategies and community-based ethics. The research comes to the conclusion that effective CSR presupposes a combination of strategic intent and trust-building and shared responsibility. It is suggested that collaborative project design, long-term partnership, and proper technological support are to be used as, rather than a substitute to direct community interaction. This issue is to be investigated in the future with respect to how non-human forces and time processes can determine the impressions and results of CSR. On the whole, the study highlights that the transformative aspect of CSR is adaptive, relational, and circumstentially-sensitive practices. 2025. This is an open-access article distributed under the terms of the Creative Commons Attribution License. (https://cre-ativecommons.org/licenses/by/4.0/). -
Application of SWOT And Breakeven Analysis in Strategic Decision-Making: A Quantitative Approach
This chapter investigates the extent to which the conjunction of SWOT analysis and quantitative analysis (such as breakeven analysis) enhances the quality of strategic decision-making. In addition to the usual limitations of a qualitative SWOT analysis, the paper embraces financial feasibility tools and promotes a multi-criteria decisionmaking approach. After a brief initial chapter noting the rationale for combining qualitative and quantitative analyses in the strategic planning process, the chapter continues with the theoretical foundation relating to strategic decision-making models (including SWOT, breakeven analysis, Analytic Hierarchy Process (AHP), Hybrid SWOT-AHP, and Quantitative Strategic Planning Matrix (QSPM). A casestudy approach with examples from both the private and public sectors illustrates the practical application of these tools through numerical data tables, breakeven calculations, and decision matrices. 2026 by IGI Global Scientific Publishing. -
Crystallographic and computational investigation of a bent-core Schiff base Ni(ii) complex with DNA and protein binding studies
The rational design and synthesis of a three-ring bent-core Schiff base ligand, (E)-4-(trifluoromethyl)phenyl-3-((4-butoxy-2-hydroxybenzylidene)amino)-2-methylbenzoate (HL), and its mononuclear Ni(ii) complex, [Ni(L)2] (1), are described. The presence of a polar CF3 group and a flexible butoxy chain imparts amphiphilic character to HL and induces aggregation-induced emission (AIE) behavior. Coordination with NiCl2 yields a square-planar complex, as confirmed by spectroscopic methods, single-crystal X-ray diffraction analysis, and topological analysis. Fluorescence and SEM studies substantiate the aggregation propensity of HL. Density functional theory (DFT) and natural bond orbital (NBO) analyses reveal pronounced ligand-to-metal charge transfer in (1) and a moderate HOMOLUMO gap of 4.00 eV, indicative of kinetic stability and optoelectronic relevance. Complex (1) exhibits strong binding affinity toward duplex DNA and serum proteins (BSA and HSA), evidenced by red-shifted fluorescence enhancement at 475 nm and low detection limits (0.0750.188 M). Molecular docking further supports stable BSA binding (?8.52 kcal mol?1), highlighting the potential of this Ni(ii) system for biomolecular recognition. This journal is The Royal Society of Chemistry, 2026 -
Wireless Network Security Using Load Balanced Mobile Sink Technique
Real-time applications based on Wireless Sensor Network (WSN) technologies are quickly increasing due to intelligent surroundings. Among the most significant resources in the WSN are battery power and security. Clustering stra-tegies improve the power factor and secure the WSN environment. It takes more electricity to forward data in a WSN. Though numerous clustering methods have been developed to provide energy consumption, there is indeed a risk of unequal load balancing, resulting in a decrease in the networks lifetime due to network inequalities and less security. These possibilities arise due to the cluster heads limited life span. These cluster heads (CH) are in charge of all activities and control intra-cluster and inter-cluster interactions. The proposed method uses Lifetime centric load balancing mechanisms (LCLBM) and Cluster-based energy optimization using a mobile sink algorithm (CEOMS). LCLBM emphasizes the selection of CH, system architectures, and optimal distribution of CH. In addition, the LCLBM was added with an assistant cluster head (ACH) for load balancing. Power consumption, communications latency, the frequency of failing nodes, high security, and one-way delay are essential variables to consider while evaluating LCLBM. CEOMS will choose a cluster leader based on the influence of the fol-lowing parameters on the energy balance of WSNs. According to simulated find-ings, the suggested LCLBM-CEOMS method increases cluster head selection self-adaptability, improves the networks lifetime, decreases data latency, and bal-ances network capacity. 2023, Tech Science Press. All rights reserved. -
A PV-Powered Single Phase Seven-Level Invertera's Photocurrent and Injected Power
The PV inverter in this study is linked to the grid and its performance analysis is evaluated using a PI controller. It is a single phase multi-level PV inverter. The major objective of this research is to increase efficiency and eliminate harmonics caused by DC link voltage fluctuations created by Maximum Power Point Tracking (MPPT) during foggy situations. PV inverters generate and inject actual power into the main grid. This study uses a transformer-less photovoltaic inverter to cut down on losses, cost, and size. A transformer-less multilayer inverter is described in this paper. There is no high-frequency leakage current since that inverter can distribute both actual and reactive electricity. MATLAB/Simulink software was used to analyze and assess the effects of various PV-based seven-level techniques on the devicea's Maximum Power Point Tracking (MPPT) performance. The Authors, published by EDP Sciences, 2024.