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Modeling the impact of political risk components on major macroeconomic variables
The risks of the political conditions prevailing in an economy are found to have a significant impact on its stock market. Such political risks can distort the entire economy. This study investigated the impact of political risk on major macroeconomic variables which are the indicators of growth in any economy by considering the various components of political risk as given by World Bank's worldwide governance indicators. Using a panel data approach, it modeled the major macroeconomic variables of eleven emerging and frontier Asian economies with various components of political risk. The study found that irrespective of the inter-linkages among different macroeconomic variables, they were not affected by the same political risk components. Most importantly, it revealed that GDP did not respond to any of the political risk components, whereas the exchange rate was found to be affected by all the political risk components. The study also found that FDI, inflation, and real interest rate were affected by one or more political risk components. 2019 AESS Publications. All Rights Reserved. -
Navigating the Future of Intelligent and Smart Manufacturing A Comprehensive Bibliometric Analysis (2012-2024)
Intelligent and smart manufacturing is at the forefront of a digital revolution in the manufacturing industry. This chapter reveals how key technologies have developed and converged during the period 2012-2024 by analysing 1046 articles from the Web of Science database. Literature was compiled into ten distinct clusters: Convolutional Neural Networks (CNN), Technologies, Internet of Things (IoT), Services (Applications), Gateway, Industry 4.0, Heuristic, Cutting Force, Remote Maintenance, and Fabrication. Each cluster is measured by its degree, modularity, density, citation analysis metrics and help in predicting the impact and about their interrelations in research within these domains. Further, the major focus is on Industry 4.0 principles and the evolving research landscape that promptly helps in findings and the rapid adoption of advanced computational methods. With this analysis, the most influential authors, articles, and journals will provide insight for building collaborative networks and intelligent manufacturings intellectual structure. This study nonetheless delivers an overview of previously published research; in addition, it also illustrates trends and openings that provide a significant underpinning to support subsequent academic endeavours and their application in practice. 2025 selection and editorial matter, Alka Chaudhary, Vandana Sharma, and Ahmed Alkhayyat individual chapters, the contributors. -
CBDCs in a High-Evasion Economy: Seigniorage, Tax Collection, and Institutional Tensions
This chapter investigates the welfare implications of introducing Central Bank Digital Currency (CBDC) in an economy with tax evasion and varying degrees of central bank independence (CBI). The model considers scenarios with and without central bank independence, where the fiscal authority finances its expenditure through sales tax and/or seigniorage revenue from the central bank. The model analyses the equilibrium conditions for the coexistence of cash and CBDC, and it depends on the degree of CBI. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Environmental integration in supply chains: Collaborative strategies for energy efficiency and waste reduction
The chapter looks at why producers, transporters and distributors work with both government and nonprofit institutions to fix environmental problems in their operations. Stakeholders who work together achieve energy savings through resource efficiency and maintain water resources in a sustainable manner. This chapter examines practical strategies like renewable energy adoption and energy efficiency upgrades plus reverse supply chains and circular systems. It shows how several groups must work together to manage their environmental efforts to reach common targets. A combination of joint target setting, open discussion sharing and Collaborative Planning Forecasting Replenishment (CPFR) is spotlighted. Governments and NGOs work together to create supportive laws plus offer money benefits with help businesses to join forces. This section shows how small and medium- sized enterprises can solve financial hurdles through collective investments and green financing. Through real- world examples and practical methods, the chapter creates a way to combine sustainability with supply chains 2025, IGI Global Scientific Publishing. All rights reserved. -
Clinical Pattern Mining for Early Detection of Chronic Kidney Disease: A Data-Driven Diagnostic Framework
Early diagnosis of the Chronic Kidney Disease (CKD) is essential to avoid irreversible damage of the kidneys, but it is clear that the traditional threshold-based techniques of the diagnosis are not always able to detect a subtle pattern of biochemical changes, which indicate the early appearance of the disease. This paper provides an interpretable and data-intensive diagnostic model which incorporates clinical state transformation, frequent and contrast pattern mining, and phenotype-based clustering to reveal hidden signs of CKD progression. Continuous laboratory variables are discretized into clinically meaningful states, enabling transparent rule extraction and comparative analysis between CKD and non-CKD cohorts. The mined contrast patterns reveal distinctive early-stage abnormalities, including mild creatinine elevation, reduced urine specific gravity, albuminuria, and increased urea levels, which consistently differentiate diseased patients from healthy controls. Furthermore, K-means clustering identifies three clinically relevant renal phenotypes corresponding to early, moderate, and advanced biochemical deterioration. Sensitivity and comparative analyses demonstrate the robustness of the extracted patterns across varying support thresholds and against standard machine learning classifiers. The proposed framework offers a clinically interpretable and computationally efficient decision-support tool for early CKD detection and patient stratification using routinely collected clinical data. 2026 IEEE. -
Exploring Communication Authenticity Anxiety: A Data-DrivenPsychological Analysis of Al-Generated Content on StudentSelf-Perception and Expression
Generative artificial intelligence (AI) tools such as ChatGPT and Gemini are becoming more common in student communication, owing to the improvement that they offer in fluency and efficiency, but at the same time raise concerns about authenticity. Students struggle to put their authentic voice forward in the quest to enhance their work using these writing assistants. Many surveys have been conducted, which indicate widespread use of AI tools for education-related chores, yet these studies ignore the emotional effects related to this. The psychological discomfort related to authenticity in text-based communication is still not well examined and to address this gap, this study introduces a term called Communication Authenticity Anxiety and successfully examines its relationship with self-perception, academic stress, resilience, and AI dependence. Data were collected via a structured student survey and analyzed using exploratory factor analysis, regression modeling and machine learning techniques. Results show that self-perception and academic stress are the strongest predictors of authenticity anxiety, while resilience and AI dependence have weaker effects. These findings were further validated by Machine Learning models, with Random Forest achieving 75% accuracy and XGBoost achieving {9 2%}. This study, thus, successfully contributes to understanding the various psychological consequences of AI-generated content on student identity and expression, thereby providing valuable insights for crafting responsible educational policies. 2025 IEEE. -
Exploring Communication Authenticity Anxiety: A Data-DrivenPsychological Analysis of Al-Generated Content on StudentSelf-Perception and Expression
Generative artificial intelligence (AI) tools such as ChatGPT and Gemini are becoming more common in student communication, owing to the improvement that they offer in fluency and efficiency, but at the same time raise concerns about authenticity. Students struggle to put their authentic voice forward in the quest to enhance their work using these writing assistants. Many surveys have been conducted, which indicate widespread use of AI tools for education-related chores, yet these studies ignore the emotional effects related to this. The psychological discomfort related to authenticity in text-based communication is still not well examined and to address this gap, this study introduces a term called Communication Authenticity Anxiety and successfully examines its relationship with self-perception, academic stress, resilience, and AI dependence. Data were collected via a structured student survey and analyzed using exploratory factor analysis, regression modeling and machine learning techniques. Results show that self-perception and academic stress are the strongest predictors of authenticity anxiety, while resilience and AI dependence have weaker effects. These findings were further validated by Machine Learning models, with Random Forest achieving 75% accuracy and XGBoost achieving {9 2%}. This study, thus, successfully contributes to understanding the various psychological consequences of AI-generated content on student identity and expression, thereby providing valuable insights for crafting responsible educational policies. 2025 IEEE. -
Exploring the Blockchain-Enabled Metaverse: A Comparative Study of Leading Platforms
The integration of metaverse and secure-based blockchain is transforming several domains, including the area of virtual employment fairs. This chapter comprehensively examined technologies and covers the areas and platform that is both immersive and secure for job searchers and recruiters. It provides a novel case study of a virtual job fair, focusing on its system architecture with metaverse and blockchain. The Decentraland platform is focused and comprises essential elements for metaverse environment and blockchain network. This will help through analyzing as well as interactions between attendees, recruiters, and system administrators the operational process, with an improved security, transparency, and user engagement. The study recognizes promising advancements, yet it accentuates important obstacles and unsolved issues, such as expansion, data protection, and portability. These concerns must be addressed in order to fully exploit the promise of the metaverse and blockchain in revolutionizing virtual interactions. 2025 Scrivener Publishing LLC. -
Impact of corporate governance on financial performance of information technology companies
Corporate Governance is a broad term in todays competitive world. It is a series of processes, policies, rules, and regulations by which companies are managed and governed. In this perspective, the study attempts to analyze the impact of corporate governance on the financial performance of Information Technology (IT) Companies in India. Specifically, the study analyzed the impact of Board size, Board Composition, and Audit Committee Independence on Return on Assets and Return on Equity, which are considered as measures of financial performance. The findings of the study revealed that there is a significant and positive impact of Corporate Governance on Financial performance of IT companies, and Audit Committee Independence shows the most significant effect on Financial performance. The finding of the study endeavors to contribute to the limited literature available in the context of corporate governance in IT companies in India. BEIESP. -
Assessing global perceptions of India: Policy implications drawn from foreign tourism narratives
This study scrutinizes Indias growing appeal as a tourist destination, accentuated by government initiatives and innovative tourism policies like the e-visa program, Incredible India Campaign 2.0 and digital advancements in the travel sector. With the diminishing impact of COVID-19, there is a noticeable surge in various forms of tourism inbound, outbound and domestic. The primary focus is to understand the driving factors behind the choice of India as a destination for inbound tourists. This research delves into these motivations, providing a global perspective on Indias attractiveness. A mixed-method approach was employed, utilizing convenience sampling for data collection. The quantitative analysis was based on a survey, informed by a literature review, comprising 390 respondents from 10 diverse Indian destinations. Additionally, 25 qualitative interviews were conducted, aiming to enrich and triangulate the quantitative findings. Exploratory factor analysis (EFA) revealed five predominant motivations among inbound tourists: culinary interests, spiritual pursuits, budget-consciousness, cultural curiosity and natural allure. These findings were substantiated through thematic analysis. The outcomes have significant practical ramifications for destination managers and tourism policy developers in India. By understanding these key motivators, they can devise targeted strategies for enhancing the appeal of India to these specific tourist segments. This study not only aids in refining tourism promotion efforts but also contributes to the academic discourse on tourist motivation offering a fresh international perspective on Indias image as a tourist destination. by the author, licensee University of Lodz Lodz University Press, Lodz, Poland. -
Machine Learning Based Optimal Feature Selection for Pediatric Ultrasound Kidney Images Using Binary Coati Optimization
Chronic kidney disease (CKD) one of the most dangerous illnesses. Early detection is vital for improving survival rates and underscoring the need for an intelligent classifier to differentiate between normal and abnormal kidney ultrasound images. Features extracted from an image have a significant impact on classification accuracy. In this study, we present a Binary Coati optimization algorithm (BCOA) for feature selection in CKD, which focuses on reducing the high dimensionality features extracted from ultrasound images, including GLCM, GLRLM, GLSZM, GLDM, NGTDM, and first order, by employing BCOA-S shaped and BCOA-V shaped transfer functions that convert BCOA from a continuous search space to a binary form, which helps in the selection of optimal features to improve the classification performance while reducing the feature dimensionality. The reduced feature was evaluated using six machine-learning classifiers: Random Forest, Support Vector Machine, Decision tree, K-nearest Neighbor, XG-boost, and Nae Bayes. The efficiency of the proposed framework was assessed based on accuracy, precision, recall, specificity, f1 score and AUC curve. BCOA-V outperformed in terms of accuracy, precision, recall, specificity, F1 score and AUC curve by 99%,100%,97%,100%, 98%, and 98%, respectively. This makes it a superior choice for CKD diagnosis and is a valuable tool for feature selection in medical diagnosis. (2024), (Intelligent Network and Systems Society). All rights reserved. -
Optimizing Kidney Ultrasound images through Pre-Processing Filters
Medical image processing and analysis have greatly advanced in the past decade, significantly contributing to the diagnosis of various diseases.However, It is crucial to address the need for effective data management in the medical field due to the significant rise in data generation and storage. It necessitates the exploration of compression methods as a means of achieving efficient data handling. Consideration should be given to image processing approaches to minimize redundancy. Ultrasound imaging has gained importance in recent years, but the presence of artifacts in ultrasound images has complicated diagnoses. An evaluation has been performed to identify appropriate Pre-processing techniques for kidney images before extracting kidney features. Observing the sensitivity and calculating the PSNR and MSE of the filtered image are used to assess the applied methods. The results indicate that the median filter is ideal for image quality enhancement, while the Sobel filter is highly effective in detecting kidney edges. 2023 IEEE. -
Transforming Pediatric Healthcare with CKD using AI: A Systematic Mapping
Artificial intelligence has been used on a much larger scale, from self-driving cars to biometrics. The daily lifestyle of civilization has changed dramatically due to scientific growth. AI has been pushed to a wide range of applications rather than limited to certain areas and has benefited the health industry, resulting in improved outcomes. Heuristics, support vector machines, artificial neural networks, and natural language processing are some of the AI approaches employed. Kidney diseases and treatment can be challenging, especially when working with youngsters. Children with Chronic Kidney Disease (CKD) experience a wide range of symptoms classified as either transitory or nosologic. Some of its traits influence not only during childhood but also during adulthood in the long run. This study will focus on strategies utilized to identify, predict, and categorize the impacts of pediatric kidney disorders in terms of aetiology, clinical features, and medicines that might assist children in transition to adulthood smoothly. 2023 IEEE. -
Recent trends in photocatalytic water splitting using titania based ternary photocatalysts-A review
Hydrogen is considered as an ideal fuel, and its use has several advantages. While several methods are available for producing hydrogen, photocatalytic water splitting using semiconductor-based photocatalysts is one of the better methods. Among the various semiconductors, titania, having many desirable properties, is a widely explored photocatalyst material to fabricate ternary heterojunctions. Preventing the recombination of photoexcited charge carriers, reducing the band gap, and enhancing the migration of charges are steps needed to improve the efficiency of the photocatalysts. Various modifications have been made to the structural and chemical properties of the photocatalysts. While innovative synthetic protocols can bring about the desired changes, incorporating metal oxides and noble metals with varied morphologies into titania leads to multijunction photocatalysts. Structural modifications to titania include incorporation of various nanostructured materials, noble metal nanoparticles, transition metal chalcogenides, polymer materials, semiconductors like g-C3N4, quantum dots, etc. 2022 Hydrogen Energy Publications LLC -
Optimized Feature Selection for Kidney Ultrasound Image Classification Using Binary Coati Weighted Mean Vector Algorithm
The analysis of medical images presents many challenges, especially when making precise diagnoses. In pediatric Chronic Kidney Disease (CKD), early identification is critical because of its gradual progression to significant kidney failure. This study proposes a diagnostic framework for pediatric ultrasound image classification that incorporated machine learning and advanced feature selection methods. This approach is divided into four stages: Preprocessing, feature extraction, feature selection, and classification. Initially, pediatric kidney ultrasound images are enhanced using gaussian median filter. Radiomics features were then extracted, including Gray Level Co-Occurrence Matrix (GLCM), Gray Level Size Zone Matrix (GLSZM), Gray Level Run Length Matrix (GLRLM), Neighboring Gray Tone Difference Matrix (NGTDM), Gray Level Dependence Matrix (GLDM), and first-order statistics. To optimize this feature space, we introduce the Binary Coati Weighted Mean Vector (BinCoWmv) optimization algorithm, which uses a customized fitness function. Herein, the selected features were evaluated using different classifiers: Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), Nae Bayes (NB), K-Nearest Neighbor (KNN), and XG-Boost. Comparative evaluations with existing optimizers, such as the Coati Optimization Algorithm (COA), weighted average vector (INFO), Firefly Algorithm (FFA), and Harris Hawk Optimization (HHO), showed that BinCoWmv achieved a higher classification accuracy. Our framework improves diagnostic reliability and assists radiologist and nephrologist in the early detection of chronic kidney disease in children. 2025 Fizhan Kausar and Ramamurthy B. -
Coati Optimization Algorithm for Detecting Pediatric Kidney Abnormalities using Ultrasound Images
This study aimed to classify pediatric ultrasound images as normal or abnormal by identifying the optimal number of image texture features for analysis and developing an effective classification system using selected features. The experiment identified a successful feature selection and classification algorithm with a good performance. This study introduced a new approach for computer-assisted ultrasound image classification. Initially, a Gaussian median filter enhances the image quality and removes noise. For feature extraction, various features, including first-order derivatives, Gray Level Co-Occurrence Matrix (GLCM), Gray Level Run Length Matrix (GLRLM), Gray Level Dependence Matrix (GLDM), Gray Level Size Matrix (GLSZM), and Neighbouring gray tone difference matrix (NGTDM), were extracted using the Pyrandiomics Python package. The Coati optimization algorithm (COA) was employed as a feature selection technique. The Classification was performed using Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-nearest Neighbor (KNN), Nae Bayes (NB), and Extreme Gradient Boosting (XG-Boost) algorithms. Therefore, this study proposed a new machine learning classifier, the Extreme Gradient Neighborhood classifier (XGNC), using NB, KNN, and XG-Boost, with a classification accuracy of 97.91%, which outperformed the other classifiers mentioned in the study. The results indicated that the optimal feature selection and classifier choice yielded the most accurate computer-aided diagnosis of kidney abnormalities. 2025, Iquz Galaxy Publisher. All rights reserved. -
Punishing poverty: The economic disparity of the poor in the criminal justice system
Equality before law is one of the most significant features of the Indian constitution. Anyone who seeks justice must be provided with legal support without any discrimination. An accused is also assured of penalization based on the tenets of equality irrespective of his ethnicity, religion, economic, social background, etc. Poor parity has led to discriminatory approaches in awarding punishments to offenders belonging to economically marginalised sections of society. The low paying capacity of the poor offenders gives an upper edge to the rich offenders who has better paying capacity of fines or damages and suffer less severe repercussions through the justice system. This paper will conduct a comprehensive study to identify the discrepancies in the penalization process and its implications in the dispensation of justice. It will also explore the factors such as social background, ethnicity, and economic status which play an integral part in influencing the legal and sociological perspectives of the stakeholders of the justice delivery system. It will analyze the judicial trend and legislative framework to ensure equitable justice. It will conclude with suggestions and recommendations for the formulation of robust policies to ensure a just penal administration. 2026 The Author(s). -
The Preservative Technology in the Inventory Model for the Deteriorating Items with Weibull Deterioration Rate
An EOQ model for perishable items is presented in this study. The deterioration rate is controlled by preservative technology. This technology only enhances the life of perishable items. So, retailers invested in this technology to get extra revenue. The Weibull deterioration rate is considered for the ramp type demand. Shortages consider partially backlogged, and discount is provided to loyal customers. The concavity of the profit function is discussed analytically. Numerical examples support the solution procedure; then, Sensitivity analysis is applied to accomplish the most sensitive variable. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A study of Autoregressive Model Using Time Series Analysis through Python
A Time-series investigation is a simple technique for dividing information from reconsideration perceptions on a solitary unit or individual at ordinary stretches over countless perceptions. Timeseries examination can be considered to be the model of longitudinal plans. The most widely used method is focused on a class of Auto-Regressive Moving Average (ARMA) models. ARMA models could examine various examination questions, including fundamental cycle analysis, intercession analysis, and long-term therapy impact analysis. The model ID process, the meanings of essential concepts, and the factual assessment of boundaries are all depicted as specialized components of ARMA models. To explain the models, Multiunit time-series plans, multivariate time-series analysis, the consideration of variables, and the study of examples of intra-individual contrasts across time are all ongoing improvements to ARMA demonstrating techniques. [1] 2022 IEEE.