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Decoding Cognitive Control and Cognitive Flexibility as Concomitants for Experiential Avoidance in Social Anxiety
Background and objectives: Avoidance is regarded as a central hallmark of social anxiety. Experiential avoidance is perilous for social anxiety, specifically among university students (young adults). Additionally, cognitive control and cognitive flexibility are crucial components of executive functions for a fulfilling and healthy lifestyle. The current research is a modest attempt to understand how cognitive flexibility and cognitive control affect the emergence of experiential avoidance in social anxiety in young adults. Methods: Using an ex-post facto design, the Social Phobia Inventory was employed to screen university students with social anxiety based on which one hundred and ninety-five were identified. Thereafter, participants completed the standardized measures on experiential avoidance, cognitive control and cognitive flexibility. Results: A stepwise multiple regression analysis was computed wherein the cognitive control predicts an amount of 5% of variance towards experiential avoidance, whereas a 10% of additional variance has been contributed by cognitive flexibility. Interpretation and Conclusions: The statistical outcome indicated that cognitive control is positively associated with experiential avoidance which is a negative correlate to cognitive flexibility among university students. Both also emerged as significant predictors of experiential avoidance and add a cumulative variance of 15% towards the same. This conclusion supports the need for improved and efficient management techniques in counseling and clinical settings. The Author(s) 2024 -
Scaling the bars: The relationship between women entrepreneur well-being and work-life balance
This chapter aims to focus on women entrepreneurs, who sometimes confront the double constraints of managing both company and personal duties. This study examines the particular difficulties faced by female entrepreneurs in striking a sustainable balance, emphasising the absence of support networks like accessible childcare as well as gender stereotypes and cultural expectations. The study assesses how their productivity, well-being, and company performance are affected by a poor work-life balance. It also looks into the tactics used by female business owners to overcome these obstacles, such delegation, selfcare, and time management. In order to promote a more inclusive entrepreneurial ecosystem, the chapter also suggests legislative initiatives, such as flexible work schedules and improved support networks. 2025, IGI Global Scientific Publishing. -
Leveraging AI and Machine Learning for Healthcare Accessibility: Enhancing Clinical Decision Support Systems in Rural Africa
Healthcare in rural Africa is hindered by resource scarcity, limited infrastructure, and a shortage of trained professionals, contributing to high mortality and morbidity rates. This study examines the transformative potential of artificial intelligence (AI) and machine learning (ML) in clinical decision support systems (CDSS) to address these challenges. Focusing on diseases prevalent in the region, such as malaria, HIV/AIDS, and noncommunicable illnesses like diabetes, the research develops and evaluates AI-enhanced CDSS to improve diagnostic accuracy, treatment planning, and healthcare accessibility. This research contributes a framework for deploying AI-driven CDSS in resource-limited settings, with implications for enhancing global health outcomes. 2026 selection and editorial matter, Wasswa Shafik, Adel Ben Youssef, Chithirai Pon Selvan and Pushan Kumar Dutta; individual chapters, the contributors. -
Enhancing Stock Market Price Prediction with Advanced Machine Learning Techniques: A Comparative Study
The non-linearity and intrinsic volatility of financial markets make accurate stock price prediction an important but challenging undertaking. This research proposes a Gated Recurrent Unit (GRU)-based model to forecast the stock prices of Tata Consultancy Services (TCS) using 18 years of historical data sourced from Yahoo Finance, comprising features such as Date, Open, High, Low, Close, Adjusted Close, and Volume. The methodology includes data preprocessing steps such as feature selection using Recursive Feature Elimination (RFE), normalization with standard scaling, and data splitting into 70% training and 30% testing sets. The proposed GRU model was evaluated and benchmarked against existing models including Long Short-Term Memory (LSTM), Linear Regression (LR), and Decision Tree (DT), using performance metrics such as Root Mean Squared Error (RMSE) and R2 score. Experimental outcomes revealed that the GRU model achieved the best performance with an RMSE of 0.045, outperforming LSTM (38.19), LR (8.66), and DT (5.22). The study's findings have important implications for algorithmic trading and well-informed investment choices, since the GRU model effectively captures temporal trends in stock data while minimizing prediction mistakes. 2025 IEEE. -
Reinforcement Learning-Driven Innovation Clusters: Strategic Planning for Sustainable Corporate Growth
This paper explores the role of reinforcement learning (RL) in optimizing innovation clusters to foster sustainable corporate growth. We go on to establish how RL allows organizations to optimize core performance metrics (innovation output, profit growth, sustainability impact and resource allocation efficiency), and show in dynamic datasets how a network of simulated strategic decisions were made in an innovation ecosystem. Moreover, it highlights the ability of RL to adapt to ever changing industries and implement long term strategic plans besides traditional strategic practices. The results demonstrate that RL-based methods contribute to unleashing innovation and profitabilising the companies, but also to more sustainable operations, bringing into proportion the growth and social responsibility. These results demonstrate RL as an implication tool with a strong future for optimizing corporate strategies that serves as an incentive for further innovation, translating into long-term viability and success. 2025 IEEE. -
Enhancing Mobile DeFi Transactions Through Blockchain Adoption: A Pythagorean Fuzzy AHP Study
Blockchain technology has emerged as a pivotal enabler for innovation in mobile decentralized finance (DeFi) ecosystems. This study ememploys the Pythagorean Fuzzy Analytic Hierarchy Process (PF-AHP) to identify and rank critical enablers driving blockchain adoption for enhancing mobile DeFi transactions. A structured three-stage methodology evaluates twenty-four sub-criteria across operational, managerial, and strategic dimensions. Results emphasize managerially focused factorssuch as reduced foreign exchange (FX) transfer costs and open-source adaptabilityas the most critical enablers, followed by operational drivers like transparency. Sensitivity analysis confirmed the ro-robustness of these findings. The results offer actionable insights for fintech practitioners, digital strategists, and policymakers seeking to optimize blockchain-based mobile financial platforms. By contributing to improved financial inclusion, operational efficiency, and regulatory alignment, the study supports broader welfare enhancement in the digital economy. The proposed PF-AHP framework provides an empirical decision-making tool to guide innovation and strategic planning in financial technology services. Yogesh Kumar Jain et al. -
A multi-preference integrated algorithm for deep learning based recommender framework
Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short-term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi-preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1-score. Consequently, the MPIA can be integrated with real time recommender systems. 2022 John Wiley & Sons, Ltd. -
Blockchain for Securing Healthcare Data Using Squirrel Search Optimization Algorithm
The Healthcare system is an organization that consists of important requirements corresponding to security and privacy, for example, protecting patients medical information from unauthorized access, communication with transport like ambulance and smart e-health monitoring. Due to lack of expert design of security protocols, the healthcare system is facing many security threats such as authenticity, data sharing, the conveying of medical data. In such situa-tion, block chain protocol is used. In this manuscript, Efficient Block chain Network for securing Healthcare data using Multi-Objective Squirrel Search Optimization Algorithm (MOSSA) is proposed to generate smart and secure Healthcare system. In this the block chain is a decentralized and the distributed ledger device that consists of various blocks linked with digital signature schemes, consensus mechanisms and chain of hashing, offers highly reliable storage capabilities. Further the block chain parameters, such as block size, transac-tion size and number of block chain channels are optimized with the help of MOSSA. With the evolution of the MOSSA provide new features for enhancing security and scalability. The simulation process is executed in the JAVA platform. The experimental result of the proposed method shows higher throughput of 26.87%, higher efficiency of 34.67%, lowest delay of 22.97%, lesser computational overhead of 37.03%, higher storage cost of 34.29% when compared to the existing method such as Block chain-ECIES-HSO, Block chain-hybrid GO-FFO, Block chain-SDN-HSO algorithm for healthcare technologies. 2022, Tech Science Press. All rights reserved. -
IoT Infrastructure to Energize Electromobility
Mobile technology is becoming more sophisticated as it advances. A comparison of different mobility scenarios was conducted. This chapter examined how electric vehicles interact with local energy systems in Stuttgart. Utilizing a travel demand model, a charging profle based on mobility patterns was generated for electric vehicles. During a quarter, charging demand and standard household load profles were used to analyze peak hour load fow for 349 households. Considering that peak loads and charging capacity are usually separated in time, greater charging capacity might lead to lower utilization of transformers. Furthermore, a study was conducted to determine if the existing infrastructure was adequate for future demand, focusing on substation transformer reserves. Electromobility is a rapidly growing and evolving application domain of the Internet of Things, with a huge market potential in various areas. It incorporates many stakeholders from manufacturers to the players of the energy market with all sorts of physical and virtual resources. It is essential to allow these devices and systems to collaborate to create advanced e-mobility services. The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. -
Machine Learning in Cyber Threats Intelligent System
Cybercriminals disrupt services, exfiltrate sensitive data, and exploit victim machines and networks to perform malicious activities against organizations. A malicious adversary seeks to steal, destroy, or compromise business assets that have a specific financial, reputational, or intellectual value. As a result, organizations are complementing their perimeter defenses with threat intelligence platforms to address these security challenges and eliminate security blind spots for their systems. Any type of information useful for identifying, assessing, monitoring, and responding to cyber threats is considered cyber threat intelligence. Organizations can benefit from increased visibility into cyber threats and policy violations. An organizations threat intelligence allows them to prevent or mitigate various types of cyberattacks. The use of machine learning and artificial intelligence is a key component of cybersecurity conflict, which together allows attackers and defenders to function at new speeds and scales. In spear-phishing attacks, relatively frivolous machine learning algorithms have been used to overwhelming effect as adversarial artificial intelligence. This chapter discusses the various cyber threats, cyber security attack types, publicly available datasets for research work, and machine learning techniques in cyber-physical systems. 2024 selection and editorial matter, S. Vijayalakshmi, P. Durgadevi, Lija Jacob, Balamurugan Balusamy, and Parma Nand; individual chapters, the contributors. -
Applications of Digital Technologies and Artificial Intelligence in Cryptocurrency - A Multi-Dimensional Perspective
The paradigm shift requires spreading the light of decentralized ledger technology, extraordinarily implementing cryptocurrencies, and being visible as a game-changer. Blockchain technology, along with cryptocurrencies like Bitcoin, Ethereum, and Litecoin, is a tool for global economic transformation that is rapidly gaining traction in the finance industry. However, these technologies have had low popularity in the consumer market. Many platforms have been misunderstood and ignored when there is an obvious hole in among them. The basic idea behind cryptocurrency is that it is a network-based, totally virtual exchange medium that utilizes cryptographic algorithms such as Secure Hash Algorithm 2 (SHA-2) and Message Digest 5 (MD5) to secure the data. Transactions within the blockchain era are secure, transparent, traceable, and irreversible. Cryptocurrencies have gained a reputation in practically all sectors, including the monetary sector, due to these properties. The uncertainty and dynamism of their expenses, however, hazard investments substantially despite cryptocurrencies growing popularity amongst approval bodies. Studying cryptocurrency charge prediction is fast becoming a trending subject matter in the global research community. Several device mastering and deep mastering algorithms, like Gated Recurrence Units (GRUs), Neural nets (NNs), and nearly short-term memory, were employed by the scientists to analyze and forecast cryptocurrency prices. As a part of this chapter, we discuss numerous aspects of cryptographic protection and their related issues. Specifically, the research addresses the state-of-the-art by examining the underlying consensus mechanism, cryptocurrency, attack style, and applications of cryptocurrencies from a unique perspective. Secondly, we investigate the usability of blockchain generation by examining the behavioral factors that influence customers decision to use blockchain-based technology. To identify the best crypto mining strategy, the research employs an Analytic Hierarchy Process (AHP) and Fuzzy-TOPSIS hybrid analytics framework. Furthermore, it identifies the top-quality mining methods by evaluating providers overall performance during cryptocurrency mining. 2023 Scrivener Publishing LLC. -
Classification of Breast Invasive Ductal Carcinomas Using Histopathological Images Based on Deep Learning Techniques
Women suffer from cancer, which is the main reason for death for females around the world. With the use of artificial intelligence, it is possible to predict and detect all types of cancers in the near future. It is not just women who can heal, and most breast cancers are caused by the most vulnerable type of breast. Eighty percent of all diagnoses of carcinoma are invasive ductal carcinomas (IDCs). In this paper, deep learning techniques are extended to support visible semantic evaluation of tumor areas, using convolutional neural networks (CNNs).A CNN is skilled ended a large number of photo covers (tissue areas) after Whole Slide Images (WSI) to study ranked part-based total image. About 600 normal image patches and 200 breast invasive ductal carcinomas are selected for the experiment. It was intended to amount classifier correctness in the detection of IDC tissue areas in Whole Slide Images. We achieved excellent measurable outcomes for an automated finding of IDC areas with our technique. The results are evaluated based on performance measures and compared with a different number of neurons, and the results are highlighted. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Effects of a Mindfulness-based Intervention on Well-being Among Rural Adolescents with Academic Anxiety
Background: Academic anxiety revolves around scholastic work and performance and can be detrimental to students health and overall subjective well-being. It has been found to be significantly high in adolescents, leading to consequences that prove to be detrimental to their academic performance, focus, and overall self-esteem. This phenomenon acts as a vicious cycle impacting all aspects of a students life. Method: The current study aimed to explore mindfulness-based intervention (MBI) as a possible option to deal with academic anxiety in rural adolescent students and improve their overall subjective well-being. A total of 600 students were screened for academic anxiety and a total of 47 students were subjected to an eight-week MBI. MBI aims to bring more present-moment awareness and cultivate overall well-being and thereby works against anxiety. Mixed repeated measures ANOVA was carried out to compare pre, post, and follow-up scores. Result: The results indicated a significant effect of MBI on adolescents, suggesting a significant decline in academic anxiety from pre-to-post and an increase in mindfulness and subjective well-being from pre-to-post and follow-up assessments. Conclusion: Academic anxiety and subjective well-being improved significantly with the MBI intervention, thereby implication that MBI is a feasible option for rural adolescents with academic anxiety. 2024 The Author(s). -
Effects of Mindfulness-based Intervention on Academic Anxiety: Enhancing Well-being of Rural Adolescents
Academic worry has been reported to be highly prevalent among adolescents, and it negatively affects their well-being. In comparison to urban adolescents, rural adolescents experience a lesser degree of academic anxiety. At the same time, very little attention is given to this problem of rural adolescents due to the lack of resources to provide such type of care. The poor resources-driven rural area requires a compact, more easily comprehensible and more inclusive intervention programme that can aid a group of students at a time and be more beneficial and effective. Therefore, in this study, mindfulness-based intervention (MBI) is used, which is indigenous, inclusive and compact, as an intervention to enable adolescents to deal with academic anxiety and improve their well-being. In this study, 47 rural school adolescents with academic anxiety underwent an 8-week MBI after the initial screening process and assessment with the Children and Adolescent Mindfulness Measure and WarwickEdinburgh Mental Wellbeing Scale. Post and 2-month follow-up assessment after intervention showed a significant decline in academic anxiety and an increase in mindfulness and well-being. 2025 SAGE Publications. -
Smart Metering System with Google Assistant
This paper presents a unique research problem in the area of automation system by using IoT. The mentioned approach utilizes Google assistant, which is incorporated within Google home which uses voice-controlled inputs and voice feedbacks. This paper discusses a new method to develop a smart energy meter at a distributor level and to make use of this technology to monitor the power consumption of each device individually which can help the user to monitor the electricity usage in real time and thus helps to save electricity and reduce cost on your electricity bill. 2020, Asian Research Association. All rights reserved. -
One Pot Hydrothermal Synthesis and Application of Bright-yellow-emissive Carbon Quantum Dots in Hg2+ Detection
Carbon quantum dots (CQD) have drawn great interest worldwide for their extensive application as sensors due to their extraordinary physical and chemical characteristics, good biocompatibility, and high fluorescence in nature. Here, we demonstrate a technique for detecting mercury (Hg2+) ion using a fluorescent CQD probe. Ecology is concerned about the accumulation of heavy metal ions in water samples due to their harmful effects on human health. Sensitive identification and removal of metal ions from water samples are required to reduce heavy metals risk. To find out Mercury in the water sample, carbon quantum dots were used and synthesized by 5-dimethyl amino methyl furfuryl alcohol and o-phenylene diamine through the hydrothermal technique. The synthesized CQD shows yellow emission when exposed to UV irradiation. Mercury ion was used to quench carbon quantum dots, and it was found that the detection limit was 5.2 nM with a linear range of 15100 M. The synthesized carbon quantum dots were demonstrated to efficiently detect Mercury ions in real water samples. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Green Synthesis of Bioinspired Nanoparticles Mediated from Plant Extracts of Asteraceae Family for Potential Biological Applications
The Asteraceae family is one of the largest families in the plant kingdom with many of them extensively used for significant traditional and medicinal values. Being a rich source of various phytochemicals, they have found numerous applications in various biological fields and have been extensively used for therapeutic purposes. Owing to its potential phytochemicals present and biological activity, these plants have found their way into pharmaceutical industry as well as in various aspects of nanotechnology such as green synthesis of metal oxide nanoparticles. The nanoparticles developed from the plants of Asteraceae family are highly stable, less expensive, non-toxic, and eco-friendly. Synthesized Asteraceae-mediated nanoparticles have extensive applications in antibacterial, antifungal, antioxidant, anticancer, antidiabetic, and photocatalytic degradation activities. This current review provides an opportunity to understand the recent trend to design and develop strategies for advanced nanoparticles through green synthesis. Here, the review discussed about the plant parts, extraction methods, synthesis, solvents utilized, phytochemicals involved optimization conditions, characterization techniques, and toxicity of nanoparticles using species of Asteraceae and their potential applications for human welfare. Constraints and future prospects for green synthesis of nanoparticles from members of the Asteraceae family are summarized. 2023 by the authors. -
Bioactive nanoparticles derived from marine brown seaweeds and their biological applications: a review
The biosynthesis of novel nanoparticles with varied morphologies, which has good implications for their biological capabilities, has attracted increasing attention in the field of nanotechnology. Bioactive compounds present in the extract of fungi, bacteria, plants and algae are responsible for nanoparticle synthesis. In comparison to other biological resources, brown seaweeds can also be useful to convert metal ions to metal nanoparticles because of the presence of richer bioactive chemicals. Carbohydrates, proteins, polysaccharides, vitamins, enzymes, pigments, and secondary metabolites in brown seaweeds act as natural reducing, capping, and stabilizing agents in the nanoparticles synthesis. There are around 2000 species of seaweed that dominate marine resources, but only a few have been reported for nanoparticle synthesis. The presence of bioactive chemicals in the biosynthesized metal nanoparticles confers biological activity. The biosynthesized metal and non-metal nanoparticles from brown seaweeds possess different biological activities because of their different physiochemical properties. Compared with terrestrial resources, marine resources are not much explored for nanoparticle synthesis. To confirm their morphology, characterization methods are used, such as absorption spectrophotometer, X-ray diffraction, Fourier transforms infrared spectroscopy, scanning electron microscope, and transmission electron microscopy. This review attempts to include the vital role of brown seaweed in the synthesis of metal and non-metal nanoparticles, as well as the method of synthesis and biological applications such as anticancer, antibacterial, antioxidant, anti-diabetic, and other functions. Graphical abstract: (Figure presented.). The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024. -
Bioactive Compounds and Biological Activities of Arrowroot (Maranta arundinacea L.)
Arrowroot is one of the most widely studied herbal species belonging to the family Marantaceae, which originated from South America and is mainly found in tropical areas. Species belonging to the Maranta genus attaining worldwide attention due to the bioactive compounds are present in their rhizomes. The nutritional values of the Maranta arundinacea plant parts were explored in traditional medicine and culinary practices. Maranta arundinacea flour is a good source of fiber, starch, and carbohydrate and is extensively utilized as a major ingredient in food products. It is also used as an alternative to wheat as the flour is gluten-free. Dietary fibers present in the Maranta arundinacea are beneficially used in the treatment of digestive disorders such as celiac disease and immune disorders. Its known to stimulate the production of IgM by immune cells. Maranta arundinacea is commonly used for weight management as it is protein-rich and has fewer calories. The rhizome contains substantial amounts of sodium, magnesium, phosphorus, potassium, calcium, iron, and zinc. The processed starch from the Maranta arundinacea rhizomes is broadly used in nutritional food products as well as in pharmacological applications. The bioactive compounds present in the Maranta arundinacea rhizome make it the subject of novel pharmaceutical studies. The current chapter tries to emphasize the general morphology, nutritional benefits and processing, bioactive compounds, and biological activities of the Maranta arundinacea. Springer Nature Switzerland AG 2024. -
Artemisia stelleriana-mediated ZnO nanoparticles for textile dye treatment: a green and sustainable approach
Textile effluents being one of the major reasons for water pollution raises major concern for water bodies and the habitation surrounding them. The lack of biologically safer treatment solutions creates a major concern for the disposal of these effluents. The present study focuses on the degradation of textile dyes using leaf extract of Artemisia stelleriana-assisted nanoparticles of zinc oxide (ZnO-NPs). ZnO NPs synthesized were confirmed using spectroscopic, X-ray diffraction and microscopic analysis. The current research utilizes widely used major textile dyes, Reactive Yellow-145 (RY-145), Reactive Red-120 (RR-120), Reactive Blue-220 (RB-220) and Reactive Blue-222A (RB-222A), which are released accidentally or due to the non-availability of cost-effi-cient, dependable and environment-friendly degradation methods, making this work a much-needed one for preventing the discharge before treatment. The biosynthesized ZnO-NPs were top-notch catalysts for the reduction of these dyes, which is witnessed by a gradual decrease in absorbance maximum values. After 320 min, ZnO-NPs under UV light exposure showed 99, 95, 94 and 45% degradations of RY-145, RR-120, RB-220 and RB-222A dyes, respectively. The phytotoxicity study conducted at two trophic levels revealed that the A. stelleriana-mediated ZnO-NPs have great potential for the degradation of textile dyes, allowing them to be scaled up to large-scale treatments. 2023 The Authors.