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Event-Triggered Polynomial Model Predictive Control for Multi-Agent Navigation
This paper proposes an event-triggered polynomial model predictive control method for collision-free point-to-point multi-agent navigation. In this control method, each control input to each agent is a polynomial whose coefficients are updated in an event-triggered manner. For each agent, we design an event-triggering rule that guarantees non-Zeno behavior of inter-event times. At each event, the controller updates the coefficients of the polynomial control law corresponding to a subset of agents by solving one or more finite horizon optimization problems. We also ensure feasibility of the optimization problems solved at each event. Through numerical simulations, we illustrate the results and compare the proposed method with other existing methods. 2025 IEEE. -
Engineered MOF-199 Modified Electrodes for Enhanced Electrochemical Immunosensing of Lactoferrin via Signal Amplification
The detection of lactoferrin (LF), an essential immunological and nutritional biomarker, demands highly sensitive analytical platforms for low-level detection. In this study, a label-free electrochemical immunosensing platform was constructed by immobilizing anti-lactoferrin antibodies (Anti-LF-Ab) on a multiwalled carbon nanotube and a metalorganic framework (MWCNT/MOF-199) nanocomposite-modified glassy carbon electrode (GCE). The synergy between MWCNTs and MOF-199 provided abundant active sites for antibody immobilization and enhanced electron transfer, yielding an eightfold current increase compared to bare GCE. Electrochemical analyses confirmed efficient charge transfer and stable antibody binding. Under optimized conditions, the immunosensor exhibited exceptional analytical performance with a low detection limit of 5.24 ng ml?1 and a quantification limit of 17.46 ng ml?1 across a wide detection range of 050 ng ml?1. The platform demonstrated strong analytical reliability, including excellent repeatability (RSD < 5%), reproducibility and operational stability over multiple measurement cycles for LF detection in food diagnostics. In addition, Monte Carlo simulations confirmed the stability of the layer-by-layer assembly, supporting the robustness of the engineered sensing interface. 2025 The Author(s). Published on behalf of The Electrochemical Society by IOP Publishing Limited. -
Leveraging QSPR-guided ZIF selection for MWCNTs/ZIF-8 platforms for electrochemical immunosensing of lactoferrin
This study presents a data-driven workflow integrating quantitative structureproperty relationship (QSPR) modelling with Monte Carlo (MC) adsorption simulations to guide zeolitic imidazolate framework (ZIF) selection for lactoferrin (LF) immunosensing.MC simulations calculated adsorption energies (Eads) of LF across 27 ZIFs, represented using MOFid/MOFkey-encoded SMILES notation, enabling construction of a predictive QSPR model (top-performing model: PLS, R2=0.891, Q2=0.888). The model successfully ranked ZIFs according to predicted LF affinity, with ZIF-8 emerging as the optimal candidate based on computational predictions, structural robustness, and synthetic accessibility. Following computational validation through molecular docking, ZIF-8 was integrated with multiwalled carbon nanotubes (MWCNTs) on a glassy carbon electrode (GCE), enabling noncovalent immobilization of anti-LF antibodies. Electrochemical measurements performed using square-wave voltammetry (SWV) with a ferri/ferrocyanide [Fe(CN)6] 3?/4- redox probe demonstrated a linear response over 1060ng/mL LF (R2=0.994), a limit of detection (LOD) of 4.78ng/mL, and recoveries of 95105% in spiked milk samples with reproducibility ?8% RSD (n=3). Shelf-life studies showed 74% signal retention after four weeks of storage at 4C. Electrochemical analysis revealed a synergistic enhancement of charge transfer (Rct) in the MWCNTs/ZIF-8 composite (0.572k? vs. 12.79k? for bare GCE). This work demonstrates a transferable, computationally driven framework for screening framework materials in MOF/ZIF-based biosensors, bridging predictive materials design with experimental device fabrication for broad applications in clinical diagnostics and food quality monitoring. 2026 Published by Elsevier B.V. -
Post-Listing Performance of IPOs in Indias Financial Services Sector (20212024): An Industry-Specific Empirical Study
The number of initial public offerings (IPOs) made in the Indian financial services sector between 2021-2024 has increased, due to heightened investor activity. Although the volume of IPO issues has increased, there is limited research on their short-term performance, particularly in the context of industry-specific analysis. This paper will evaluate the performance of IPOs that issue financial services companies in India during the listing period of 20 days using the event study methodology. The daily returns of IPOs and the index (NIFTY 50) are taken to compute market-adjusted short-run performance (MASRP), wealth relative (WR), abnormal returns (AR), and cumulative abnormal returns (CAR). The regression analysis shows that only oversubscription can have a significant effect on the revenue of an IPO in terms of returns. This research has added value by using sector-specific evidence, which may be useful to investors, issuers, and regulators when evaluating short-run IPO efficiencies. 2026 by IGI Global Scientific Publishing. All rights reserved. -
The Role of IOT in Creating SC'S through Ultra Fast Updation of the Status for Accurate Action Plan
The idea of a smart city includes the merging of technologies and advances aimed at improving urban efficiency, scientific progress, the preservation of the environment, and social inclusion. Coined in the year 2000, the term became widely used in politics, business, management, and urban planning groups to drive tech-based changes in urban areas. It reacts to the difficulties posed by postindustrial communities handling problems such as pollution to the environment, demographic changes, population growth, health care monetary crises, and resource shortages. Beyond technical answers, the smart city idea includes non-technical innovations for healthy urban life. Particularly encouraging is the application that uses Internet of The circumstances (IoT)based sensors in healthcare, applying machine learning for effective data management. This paper discusses the application of AI-powered Ai and Wireless Sensor Networks, more commonly known as the field of health care, acting as a basic study to understand the impact of IoT in smart cities, especially in healthcare, for the sake of future research. 2024 IEEE. -
Blockchain technology as a panacea in tourism industry
The purpose of this study is to investigate how a customer's travel experience in the tourist sector affects customer satisfaction based on service value through the implementation of Blockchain Technology. 250 questionnaires were administered to tourists, tour operators, travel agents, hoteliers, and transportation companies using the Cluster Sampling method to obtain a sample size of 238. Blockchain technology, Customer Satisfaction, and Service Value are the critical variables observed to validate the hypothesis using Correlation, ANOVA & Regression Analysis. There is a strong correlation between the value of a service and blockchain technology. Secure payment is the most important component in developing and improving service value among customers and is the best predictor of service value. 2024 Srinesh Thakur, Anvita Electronics, 16-11-762, Vijetha Golden Empire, Hyderabad. -
Advancements in Solar-Powered UAV Design Leveraging Machine Learning: A Comprehensive Review
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have seen significant innovations in recent years. Among these innovations, the integration of solar power and machine learning has opened up new horizons for enhancing UAV capabilities. This review article provides a comprehensive overview of the state-of-the-art in solarpowered UAV design and its synergy with machine learning techniques. We delve into the various aspects of solar-powered UAVs, from their design principles and energy harvesting technologies to their applications across different domains, all while emphasizing the pivotal role that machine learning plays in optimizing their performance and expanding their functionality. By examining recent advancements and challenges, this review aims to shed light on the future prospects of this transformative technology. The Authors, published by EDP Sciences, 2024. -
Perception of Climate Finance: An Empirical Approach
Climate finance is an alternative financing source in which private and public at domestic and global levels invest their funds to support mitigation of and adapt to present and upcoming climate change. It is an enormous challenge since it is incredibly susceptible to climate impact. The main challenge lies in identifying risks of climate change, appropriate response measures, and prioritizing them to control climate change. The paper aims to determine the perception of climate finance among the public while assessing India's current situation concerning climate change. A well-structured questionnaire was prepared, and data were collected from 253 respondents in Chennai city from December 2020 to February 2021 using a convenience sampling method. A chi-square tool was used to examine the association between the demographic profiles of the respondents and the respondents' perception of climate change-related activities. Type of family, age, and number of family members are significantly associated with most statements connected to the perception of climate finance. The majority of the respondents had insufficient knowledge about climate change policies. Forty-two per cent of the respondents believed that the investment made in climate finance is used effectively for sustainable development. It explores the present scenario of climate finance in India during the Covid 19 pandemic period. The study results will be helpful to the social investment companies, and the regulators frame suitable strategic policies. 2022 by authors, all rights reserved. -
IndiaA Growing Hub for Global Education
Purpose/Objective To understand the stakeholders perspective of different methods by which internationalization of education has been happening in India. Design/Methodology/Approach A qualitative research approach based on the interview method is adopted, along with a review of historical data on the growth of different formats. Findings India needs to regain its lost glory from the pre-independence era as a global education hub by working on all three formats of internationalization of higher education: encouraging student exchange programs, enabling Indian Universities to expand overseas, and welcoming foreign institutions to set up campuses in India. Originality This research will be the first of its kind to present themes emerging from student interviews and opinions about the three formats of internalization of higher education in India. This research contributes to the ongoing transformation of India's higher education landscape, aligning it with global standards and aspirations. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Rationality of the Terrorist Group and Governments Policy: A Game Theoretic Approach
The two ideas of the rationality of terrorist organisations and the costly leader game are used in this paper to construct a game theoretic model. It is an addition to the literature on terrorism and leader-follower games, in which the government and a terrorist organisation are the two players. Terrorist group can be rational or irrational. In case it is rational, it does the cost-benefit analysis and is open to negotiation. Only in this case, the government chooses to not spend on counter-terrorist measures. The irrational group has lexicographic preferences, which means that it prefers a successful attack to attract attention and recruits at the beginning or finish of its operation. Consequently, it is assumed that the irrational group will always attack. the irrational terrorist organisation has the option of either choosing not to mimic the rational group or choosing to do so at a psychological cost. Although the irrational group dislikes imitation. It seeks to duplicate the rational group so that the government withdraws and cuts back on spending on counter-terrorism. A costly leader model is set up in the paper, where the government can incur a cost to gather information about the type of terrorist group. In this framework, the paper provides policy prescriptions concerning counter-terrorist measures that the government should take in case the type of terrorist group being rational or irrational is unknown and it highlights the importance of intelligence. 2024 Walter de Gruyter GmbH. All rights reserved. -
Case study: Impact of Industry 4.0 and its impact on fighting COVID-19
The emerging development in industrial technology for automation and data sharing is known as Industry 4.0. It incorporates the Internet of Things, Cyber-physical systems, and Cloud computing, all of which contribute to the development of a "smart factory". Customers, distributors, vendors, and stakeholders in the supply chain would be capable of connecting and can exchange data easily through Industry 4.0. The COVID-19 pandemic is quickly spreading and posing a threat to people all over the world. Employment and activities in all markets have been disrupted, putting economies all over the world in serious jeopardy. To combat the pandemic, retailers will benefit from Industry 4.0 because it will help to mitigate the impact of identified risks. I4.0 executives were focused on gaining a competitive edge, rising efficiency, lowering prices, and, ensuring profitability as their primary aim was to enhance the productivity of business during the time before the COVID-19 crisis. Our Government has imposed new behavioral trends including social distancing, isolation and, lockdown. The Government needs additional financial resources to combat pandemics as a result of these actions, there has been a global economic slowdown. This chapter enlightens the significance and technologies of Industry 4.0, showing how those technologies and applications help in attaining a better society. It also explains how Industry 4.0 helps in accomplishing sustainable manufacturing and the management tactics it used to boost the company's efficiency, as well as the effects of COVID-19. 2023 Bentham Science Publishers. All rights reserved. -
An Enhanced A3C-LSTM Framework with Attention for Dynamic Portfolio Allocation in Equity Markets
Portfolio optimization in dynamic financial markets presents a significant challenge for traditional models. This paper introduces an advanced deep reinforcement learning framework for portfolio management based on an enhanced Asynchronous Advantage Actor-Critic (A3C) algorithm. This paper integrate's a Long Short-Term Memory layer and a multi-head attention mechanism into the actor-critic architecture to more effectively capture temporal dependencies and feature importance within financial time-series data. The model's novelty lies in its enriched state representation, which includes a comprehensive set of technical indicators, inter-asset correlation matrices, and market regime analysis. Furthermore, we employ a sophisticated riskadjusted reward function, incorporating penalties for drawdown and volatility alongside a bonus based on the Sortino ratio. The agent was trained and tested in a simulated environment using historical daily price data from five major S&P 500 stocks. Experimental results demonstrate that our agent successfully learns a robust and adaptive allocation strategy, significantly outperforming an equal-weight benchmark in terms of overall return, Sharpe ratio, and maximum drawdown. This study underscores the potential of sophisticated DRL architectures to navigate complex market dynamics and optimize for riskadjusted performance. 2025 IEEE. -
Soft Voting Ensemble for Heart Disease Detection Using RF, CatBoost, and XGBoost
Global health reports constantly list heart disease as a major threat to human health. With the help of reliable information and early detection methods the disease's impact can be reduced. The study suggests an RCX(Random Forest, CatBoost, XGBoost) Ensemble machine learning method to figure out how likely a person is to get heart disease. Data for the system came through the UCI Repository containing records that were from Cleveland,Switzerland,Veterans Affairs(VA),Hungarian and statlog medical centers. As the raw datasets were not usable for performing operations, Data was put through preprocessing stages-which had involved removing missing values,making all labels uniform(standardizing them) and employing the SMOTE Technique for artificially creating new minority samples to balance the dataset. To explore an effective approach possible,models including Logistic Regression, Naive Bayes, Ran- dom Forest,CatBoost and gradient boosting(XGBoost) were trained and tested on the datasets. Using the Recursive Feature Elimination (RFE) method 8 out of 14 relevant features were selected. The models were improved through hyperparameter tuning and Among all models used the best results had come from a soft voting ensemble that combined Random Forest, CatBoost and XGBoost. The RCX ensemble model which was developed, has been shown to give accuracy and ROC AUC results of 91.18% and 96.21% respectively, showing stronger results compared to individual models. Metrics and Indicators like accuracy, precision, recall, F1-Score and the ROC AUC were used for comparing performance during testing periods. The data was visualized by using Python libraries such as matplotlib and seaborn for confusion matrices, heatmaps,and bar graphs for better understanding of the data at a glance. 2025 IEEE. -
Deep learning based classification of microplastic in edible food using optical microscopy images
Microplastics (MPs), a prevalent pollution in food, water, and ecosystems around the world, have become a serious environmental and health concern. The traditional detection and classification techniques are labor-intensive by nature and do not support extensive, large-scale monitoring. The main emphasis of this study is to generate a novel image dataset via a simple extraction method that will be useful for classification applications in high-consumption edible food by integrating with the deep-learning model. This study compares the efficacy of several Deep learning (DL) architectures, including MobileNetV2, ResNet101V2, ResNet50V2, InceptionV3, EfficientNetB0, and a baseline Convolutional Neural Network (CNN) in classification into three groups: threads, beads, and fragments. The best performance was recorded by MobileNetV2, ResNet101V2, and ResNet50 V2, all with 98 percent test accuracy and weighted F1-scores of 0.986 and 0.983, respectively, which is a strong and consistent MPs classification. The outcome indicates that the DL models, especially ResNet101V2 and MobileNetV2, outperform the baseline CNN in terms of classification accuracy (98%). The present study provides strong, scalable opportunities for Artificial Intelligence (AI) based solutions for the assessment and reduction of MPs contamination globally in edible food. The Author(s) 2026. -
Hand Sign Recognition to Structured Sentences
Computer vision is not just a concept of deep learning; it has wide applications such as motion recognition, object recognition, video indexing, video media understanding, and recognition-based intelligence. -However, vision-based systems are a challenging field for research and accurate results. Recent areas of interest are human action recognition or human hands gesture recognition techniques using video data set, still, an image data set, spatiotemporal methods, features in RGB, deep learning methods. Hand action recognition has applications such as communication systems to shorten the bridge gap for people with speech disabilities by using a vision-based system to recognize hand sign language and convert it to text, forming structured sentences which will be easy to understand and communicate. 2023 IEEE. -
Big Data Paradigm in Cybercrime Investigation
Big Data is a field that provides a wide range of ways for analyzing and retrieving data as well as hidden patterns of complex and large data collections. As cybercrime and the danger of data theft increase, there is a greater demand for a more robust algorithm for cyber security. Big Data concepts and monitoring are extremely useful in discovering patterns of illegal activity on the internet and informing the appropriate authorities. This chapter investigates privacy and security in the context of Big Data, proposing a paradigm for Big Data privacy and security. It also investigates a classification of Big Data-driven privacy and security of each algorithm. In this section, we first define Big Data in the contexts of police, criminology, and criminal psychology. The chapter will look at how it might be used to analyze concerns that these paradigms confront carefully. We provide a conceptual approach for assisting criminal investigations, as well as a variety of application situations in which Big Data may bring fresh insights into detecting facts regarding illegal incidents. Finally, this chapter will explore the implications, limits, and effects of Big Data monitoring in cybercrime investigations. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Featuring Machine Learning Models to Evaluate Employee Attrition: A Comparative Analysis of Workforce Stability Relating Factors
Employee attrition is a problem for most organizations as it affects morale, productivity, and business continuity. In addressing this, the study made use of machine learning techniques such as Clear AI, Random Forest, and logistic regression in designing a prediction model to predict who is the next to leave within an organization. The HR data relating to demographics, performance metrics, job roles, and conditions of work was sourced from publicly available website Kaggle.com for the study. Data preprocessing included scaling, outlier detection, and balancing the dataset using SMOTE. Multiple machine learning models were trained and evaluated by checking on accuracy, F1-score, and the ROC-AUC curve. The best model that was tested was Random Forest, which gave an accuracy of 85.71%. Additional insights from feature importance highlighted the significant effect of overtime, marital status, and stock options on attrition. Among the remaining key drivers are workload, work-life balance, and financial incentives. These findings suggest the need for focused HR strategies, such as reduction of overtime, mentorship programs, and career development opportunities, to reduce attrition rates and improve employee satisfaction. This study provides a robust methodology in predicting attrition and delivers actionable insights into designing interventions that improve workforce stability and organizational efficiency. 2025, Iquz Galaxy Publisher. All rights reserved. -
SALF: A Blockchain-Based Framework for Scalable Academic Credential Management and Institutional Governance
This study introduces SALF (Secure Academic Ledger Framework), a technically innovative blockchain-based system engineered to overcome persistent challenges in academic credential management, including latency bottlenecks, governance opacity, and integration inflexibility. SALF pioneers a hybrid on-chain/off-chain architecture optimized for low-latency operations while preserving blockchain immutability, and it employs a role-based smart contract suite tailored to institutional hierarchies. Unlike prior frameworks, SALF integrates a degree-based incentive mechanism that quantifies data quality metricslegibility, correctness, and non-redundancyto ensure equitable institutional participation and discourage centralization. Built upon a Proof of Authority (PoA) consensus model, SALF achieves high performance under load, maintaining a throughput of over 30 transactions per second (TPS) and P95 latency below 300 milliseconds. RESTful APIs ensure real-time interoperability with existing systems such as ERPs and academic dashboards. Compared to benchmark systems like EduCert-Chain and EduCopyRight-Chain, the proposed framework achieves a 41.3% reduction in latency and maintains stable throughput under high-load conditions, even as other systems exhibit significant degradation or integration constraints. These distinctive technical contributions position SALF as a scalable, governance-aware, and future-ready infrastructure for decentralized academic credentialing across heterogeneous institutions. 2025, Interdisciplinary Publishing Academia. All rights reserved. -
Women and Fairness: Navigating an Unfair World
[No abstract available]