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Determinant of Capital Structure in Indian Manufacturing Sector
Asia-Pacific Journal of Management Research and Innovation Vol. 8, No. 3. pp 265-269, ISSN No. 2319-510X -
Generalized viscoelastic flow with thermal radiations and chemical reactions
Background: A generalized model of mathematical nature is considered to address the viscoelastic flow problem using fractional derivatives. Control/freedom of the flow mechanism is achieved with these derivatives. In simulations of industrial interest, more variations are available with fractional derivatives when compared with ordinary derivatives. Relaxation times are incorporated to handle the abrupt changes in the flow domain. Fluid flow is carried out under the influence of thermal radiations and when a heat source or sink is present. Chemical reactions of the first order are observed in the mathematical modeling of the flow. Methods: Flow is induced with the movement of the lower surface while applying force on the x-plane. Simulations of the governing mathematical problem are computed with the combination of finite element and finite difference algorithms. Significant Findings: It is noted that velocity, temperature, and concentration change with the variation of fractional order derivatives which was not possible with the classical derivatives. Moreover, with greater relaxation times, velocity, temperature, and concentration remained at a lower level. The modeled mechanism can be considered to avoid costly trials in chemical and polymer casting industries. 2023 Elsevier B.V. -
Integrating Explainable Machine Learning (XAI) in Stroke Medicine: Opportunities and Challenges for Early Diagnosis and Prevention
Stroke is a leading cause of mortality and disability worldwide, emphasizing the critical need for early diagnosis and prevention. Machine learning (ML) has demonstrated significant potential in improving stroke prediction and management by analysing complex datasets for risk stratification, diagnosis, and treatment planning. However, the adoption of ML in stroke medicine is limited by the opacity of these models, which can hinder clinical trust and decision-making. Explainable Artificial Intelligence (XAI) addresses this challenge by making ML models more interpretable and transparent, enabling healthcare professionals to understand, validate, and trust their outputs. This research work explores the integration of XAI in stroke medicine, highlighting its potential to enhance early diagnosis, personalized prevention strategies, and treatment planning. We discuss the opportunities XAI provides in identifying high-risk patients, uncovering critical predictors, and enabling informed clinical decisions. Furthermore, we examine challenges such as ensuring model reliability, addressing biases in stroke datasets, and navigating ethical considerations related to patient data privacy and algorithmic accountability. 2025 IEEE. -
Enhancing Software Cost Estimation using COCOMO Cost Driver Features with Battle Royale Optimization and Quantum Ensemble Meta-Regression Technique
This research suggests a unique method for improving software cost estimates by combining Battle Royale Optimisation (BRO) and Quantum Ensemble Meta-Regression Technique (QEMRT) with COCOMO cost driver characteristics. The strengths of these three strategies are combined in the suggested strategy to increase the accuracy of software cost estimation. The COCOMO model is a popular software cost-estimating methodology that considers several cost factors. BRO is a metaheuristic algorithm that mimics the process of the fittest people being selected naturally and was inspired by the Battle Royale video game. The benefits of quantum computing and ensemble learning are combined in the machine learning approach known as QEMRT. Using a correlation-based feature selection technique, we first identified the most important COCOMO cost drivers in our study. To get the best-fit model, we then used BRO to optimize the weights of these cost drivers. To further increase the estimation's accuracy, QEMRT was utilized to meta-regress the optimized model. The suggested method was tested on two datasets for software cost estimating that are available to the public, and the outcomes were compared with other cutting-edge approaches. The experimental findings demonstrated that our suggested strategy beat the other approaches in terms of accuracy, robustness, and stability. In conclusion, the suggested method offers a viable strategy for improving the accuracy of software cost estimation, which might help software development organizations by improving project planning and resource allocation. 2023 IEEE. -
A Comprehensive Review of IoT, Intelligent Systems, and Computing Applications in Enhancing Renewable Energy Sources
This chapter provides a thorough examination of the application of the Internet of Things (IoT), intelligent systems, and advanced computing in enhancing the effectiveness and sustainability of renewable energy sources such as wind, ocean, hydro, and solar energies. This study explores the incorporation of real-time monitoring, predictive maintenance, and energy forecasting facilitated by the Internet of Things (IoT) and intelligent systems. The integration of artificial intelligence (AI)-based analytics and cloud computing methodologies significantly improves the process of decision-making, grid management, and optimization of energy storage. This analysis highlights the significant impact of recent technological breakthroughs and case studies on the transformation of renewable energy generation and management, ultimately contributing to the development of a sustainable and intelligent energy landscape. 2026 River Publishers. -
AI-Driven Early Diagnosis of Acute Liver Failure: A Machine Learning Perspective
The liver performs a valuable role in operating proper metabolism. This organ in the human body is responsible for maintaining and preserving overall health and well-being. However, when it fails to function optimally, it can cause severe and significant health complications. Liver diseases are multifactorial conditions that can be challenging to diagnose and treat. Early detection of any disease is beneficial for effective treatment and diagnosis of patients' conditions. Machine Learning algorithms create a great platform for analyzing medical data that helps improve disease detection procedures. This paper aims to get a better understanding of ML algorithms for detecting diseases associated with the liver. The paper tries to explore various machine learning techniques for predicting accurate liver diseases. It uses various parameters as symptoms and calculates ALF (Acute Liver Failure) based on the parameters and ALF predicts in-case the person is suffering from a Liver disease or not. Accuracy was calculated with various ML techniques i.e. Logistic Regression Classification, KNN Classification, Decision Tree, Random Forest and Support Vector Machine. Among all these, Logistic Regression was found to be most effective in identifying and predicting the outcome of the dataset compared to other algorithms. SVM has a higher cross-validation score but Accuracy, precision and recall are very low thus, cannot select this model. 2025 IEEE. -
Cloud Job Scheduling Using Deadline-Based Task Optimisation Algorithm in Internet of Things
The cloud-based Internet of Things (IoT) gadgets are becoming increasingly significant in todays current environment. Thoroughly examining the ever-changing relationship among these two domains, this literature review sheds light on how the field of research is developing and how important both domains are to defining our digital future. The analysis delves into the various uses of cloud-based computing in conjunction with IoT devices, highlighting how these two technologies have the combined power to transform companies, improve productivity, and improve user experiences. Blending cloud-based resources with IoT has become essential for advancement, from connected houses to industrial automation. The article provides a detailed overview of the complexities involved in this merger, highlighting the importance of computing in the cloud in tackling issues like data protection, immediate analysis, and resource optimisation. This study also points out significant gaps in current understanding, highlighting the need for more investigation to fully realise the promise of cloud computing when combined with IoT devices. Essentially, this analysis of the literature highlights the critical role in determining the integration of cloud technology and IoT devices by giving a more efficient and optimal scheduling Deadline-Based Task scheduling algorithm, which has proved to have the least average waiting time of five units when compared to all the scheduling algorithms taken into consideration. The beginning of a new era characterised by connectivity and data-driven decision-making, and the key to realising the full potential of IoT applications is to comprehend and leverage the power of cloud technology. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Artificial Intelligence-Driven Perspectives on Maternal Health: Revealing Important Aspects and Improving Pregnancy Results via Machine Learning
A number of factors, including genetic, environmental and social ones, affect the intricate biological process of pregnancy. The developing foetuss health as well as the mothers must be maintained in the necessary secure equilibrium of these variables. The mothers health, which encompasses her mental as well as physical health, lifestyle decisions, money, social support systems and educational attainment, will determine whether the pregnancy ends well. Medical research has changed as a result of the long-awaited tools for processing for complicated datasets that have been made possible by recent advancements in machine learning models. These models have the ability to identify correlations between characteristics that are difficult for traditional analytical techniques to uncover. Therefore, scientists can improve their understanding of the elements influencing conception and create diagnostic tools by utilizing machine learning technology for timely intervention and customized treatment. Machine learning encompasses various techniques, such as logistic regression, linear regression, random forest, K-Nearest Neighbours and gradient boosting classifier. While Random Forest is an effective way to handle big databases with multiple dimensions and interactions, KNN classifiers are excellent for more organic, data-driven cluster finding of relevant instances and association investigation between various parameters and pregnancy outcomes. Logistic regression only explains the ways in which individual factors affect pregnancy outcomes; it cannot handle binary outcomes as well as linear regression does. We will look for significant determinants of pregnancy outcomes and assess each models performance. Important elements will also be expanded upon. Pregnant patients care, professional practice and improved program decisions may all benefit from this information. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Modeling and computational fluid dynamic analysis on a non-AC bus coach system
The main objective of this paper is to reduce the drag force and enhance the uniform airflow inside an existing non-air-conditioning bus coach system. The redesigning of an existing bus carried out by considering the forces that reduce the moment of the bus. Modeling and meshing was carried out using solid works and Hypermesh software, respectively. Finally, the problem is simulated using Ansys fluent software and analysis is carried out for different bus models. The noteworthy findings state that the air resistance of the vehicle is found to be 812.74 N and coefficient of drag is 0.67 are less as compared to existing bus model. 2020 Wiley Periodicals LLC -
A Novel Approach for Implementing Conventional LBIST by High Execution Microprocessors
The major VLS I circuits like sequential circuits, linear chips and op amps are very important elements to provide many logic functions. Today's competitive devices like cell phone, tabs and note pads are most prominent and those are used to get function the 5G related operations. In this work lower built-in self-test (LBIS T) mechanism is used to designing a microprocessor. The proposed methodology is giving performance measure like power efficiency 97.5%, improvement of delay is 2.5% and 32% development of area had been attained. This methodology attains more out performance and compete with present technology. The proposed equipment and execution for our approach requiring a constrained range overhead (lower than 3% power) over conventional LBIS T. 2022 IEEE -
Bioprospecting Soil Bacteria for Protease Production Using Agro-Waste: Toward Sustainable Detergent Formulations
Purpose: Microbial proteases, particularly from soil-dwelling Bacillus species, are preferred over plant- and animal-derived enzymes due to their high yield, stability, and cost-effectiveness for large-scale production in industries. This study aimed to isolate and characterize potent protease-producing bacteria from soil and explore their application in developing a sustainable, bio-based stain remover. The formulation incorporates waste (citrus fruit peel and flower), promoting the valorization of agro-waste as part of a sustainable waste management strategy. Methods and Results: Soil samples collected from market waste disposal sites in Madurai, Tamil Nadu, yielded eight distinct bacterial isolates, among which strain S-5 showed the highest proteolytic activity on skimmed milk agar. Molecular identification confirmed the isolate as Bacillus aerius based on 16S rRNA sequencing.The crude enzyme extract obtained after 48h of incubation exhibited maximum proteolytic activity at pH 11, with an activity of 0.928U/mL. This confirms that enzyme production improves at higher pH levels. A biodegradable stain remover was prepared by combining the crude protease extract with citrus peel extract in water and ethanol formulations. The prepared formulation effectively removed oil, paint, and dye stains from cotton cloth within 20min of treatment without mechanical rubbing, whereas control samples showed minimal stain removal. Ethanol-based formulations demonstrated higher cleaning efficiency compared to water-based extracts, showing extensive stain removal in all replicates, while control treatments showed only minor or minimal removal. Conclusion: The integration of microbial proteases from soil-derived bacteria with agro-waste components produced an eco-friendly stain remover, offering a sustainable alternative to chemical detergents and promoting waste valorization in circular economy-based green product development. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
GC-MS profiling of metabolites in blue and white varieties of heirloom butterfly pea (Clitoria ternatea L.) seeds
The Butterfly Pea is a tropical legume, a perennial herbaceous plant commonly found in Southeast Asia. The plant and its products are rich in bioactive ingredients, attracting the industrial and biopharmaceutical sectors due to their various applications. In this study, the blue and white flowered variety seeds of Butterfly Pea methanolic extract were comprehensively screened to identify the bioactive compounds and their drug-like properties. The methanolic extract was prepared by the cold maceration method, and the crude dried extract was subjected to GC-MS analysis for seed metabolite profiling. The chromatogram analysis revealed 39 abundant phytoconstituents, demonstrating the diverse chemical composition of the Butterfly Pea seeds. Among the identified compounds, the relatively abundant bioactive components in the blue variety seeds were stearic acid (64.6%), methyl stearate (54.0%), hexadecanoic acid, methyl ester (48.2%), and ethriol (35.9%). the white variety seeds primarily included palmitic acid (71.0%), hexadecanoic acid, methyl ester (53.4%), methyl stearate (42.0%), and hydrocinnamic acid (30.5%). Additionally, both varieties exhibited a diverse array of shared compounds reflecting their phylogenetic proximity. These metabolites are associated with key bioactivities in plant signaling and defense, playing vital roles in growth regulation, stress adaptation, and exhibiting potential antidiabetic properties. The research highlights the potential of the butterfly pea seeds as a valuable resource of active metabolites for vast research and therapeutic applications. 2025, Indian journals. All rights reserved. -
Effect of Magneto Convection Nanofluid Flow in a Vertical Channel
An analytical study of the effect of the magneto-convective flow of immiscible fluids through a vertical channel has been investigated in the presence of a chemical reaction. One region is saturated by electrically conducting incompressible fluid, and the other is saturated by nanofluid in a vertical channel with constant transport properties. The coupled nonlinear governing equations are solved by the regular perturbation method, with the Brinkman number as a perturbation parameter since its value is always less than unity. The results are discussed in detail using plots to analyze the flow phenomena. The increase in thermal and mass Grashof numbers enhances the fluid velocity and temperature profile, whereas Hartman number, solid volume fraction, and chemical reaction parameters exhibit the opposite effect. The effect of an increase in the nanoparticle volume fraction opposes the fluid flow and diminishes the temperature distribution due to the enhanced viscosity of the nanofluid. The Author(s), under exclusive licence to Springer Nature India Private Limited 2024. -
Social Media Addiction and ParentPeer Attachment in Telangana Adolescents: A Cross-sectional Investigation
Background: The ubiquity of social media in contemporary life has raised concerns about its potential negative impacts, particularly among adolescents. While the impact of attachment on adolescents social media use has been studied in Western and South Asian contexts, there is a paucity of research on this relationship in the Indian context. Aims: This study aimed to find the relationship between sociodemographic factors, attachment to parents and peers, and social media addiction among adolescents in Telangana, India. Methods: A random cluster sampling method was used to survey 264 6th to 12th grade students in two schools. Data was collected using the Parent and Peer Attachment Inventory and the Social Media Disorder Scale. Chi-square analysis, Pearsons correlation, and multiple regression analysis were done to achieve the research objective. Results: The study found no association between sociodemographic factors (age, gender, socioeconomic status, family type, and number of siblings) and social media addiction. However, significant negative correlations were found between social media addiction and dimensions of attachment to parents and peers, except for communication with friends. Multiple regression revealed that attachment dimensions explained 15.7% of the total variance. The variables, Trust in the father and Alienation from the mother independently and significantly predicted social media addiction. Conclusion: The findings underscore the importance of attachment relationships in understanding social media addiction among Indian adolescents. The results reveal that fathers and mothers attachments to adolescents predict adolescent social media addiction differentially. Further research, especially longitudinal studies, is needed to explore these relationships in greater depth. 2025 Indian Journal of Social Psychiatry. -
Determination of heavy metals in various tissues of locally reared (country) chicken in major districts of Karnataka, India: Assessment of potential health risks
Food is one of the most prevalent ways that humans are exposed to metals. Heavy metals including cadmium, iron, zinc, lead, and mercury are harmful to humans and have a detrimental impact on health because they accumulate in biological organs. The concentration levels of these heavy metals were tested in different edible parts of the country (locally raised) chicken from various districts in Karnataka, India, namely Bengaluru, Tumakuru, Mangaluru, and Udupi, using an Atomic-Absorption Spectrophotometer (AAS). Heavy metal concentrations in various chicken parts were found to be below detectable limits (BDL)-0.0062, 0.027-3.178, and 0.262-2.103 ppm for Cd, Fe, and Zn, respectively, whereas Hg and Pb were BDL. The content of Zn was found to be significantly higher in all chicken samples from all examined districts, followed by Fe and Cd. Hg and Pb concentrations, on the other hand, were below the detection level in all samples. The estimated daily intakes (EDIs) of the observed metals from country chicken consumption were found to be lower than their respective FAO/WHO reference oral doses (RfD). The non- carcinogenic health hazards posed by the tested metals to the target population were estimated using the Hazard Quotient (HQ) and Hazard Index (HI) values. The HQ and HI values observed in this estimation were less than one, indicating that exposure to these heavy metals through the consumption of country chicken is unlikely to provide possible health concerns to the examined regions human population. 2023, Universidade Federal do Parana. All rights reserved. -
Psychological Problems Among Children Three Years After the Earthquake in Nepal
Background: Frequent disasters and weak mental health system pose a risk to psychological health in Nepal. In 2015, a massive earthquake of 7.6 magnitude occurred in Nepal, which caused large scale destruction to human life and property. Limited research in children after disasters in Nepal prevent health professionals from implementing new evidence-based trauma treatments. Aim: The study aimed to identify the long term emotional problems experienced by earthquake-affected children in Nepal. The role of gender, severity of exposure, socioeconomic status and type of family in relation to emotional problems were also examined in the selected group. Methods: A purposive sampling was used to select 454 children (4th and 5th standard) from two highly affected wards in Kathmandu Metropolitan City. Information about exposure to the earthquake was collected from children using the Level of Exposure Scale while the parents completed the Nepali version of the Strengths and Difficulties Questionnaire (SDQ/ 4-17). Results: The effect of exposure to the earthquake was identified in the children even after three years. Boys had higher conduct, hyperactivity-inattention and peer problems while girls had high pro-social behaviour. Emotional problems were greater for those belonging to a lower socio-economic status. Among the variables, gender was a better predictor of emotional problems in earthquake-affected children. Conclusions: Emotional problems such as conduct problems, hyperactivity-inattention, peer problems are present in the earthquake-affected children in Kathmandu. Future researchers and clinicians need to monitor the children affected by the earthquake to recognise vulnerable groups and implement appropriate trauma-focused interventions. 2021, Indian Association for Child and Adolescent Mental Health. All rights reserved. -
Forest Fire Prediction Using Machine Learning and Deep Learning Techniques
Forests are considered synonyms for abundance on our planet. They uphold the lifecycle of a diversity of creatures, including mankind. Destruction of such forests due to environmental hazards like forest fires is disastrous and leads to loss of economy, wildlife, property, and people. It endangers everything in its vicinity. Sadly, the presence of flora and fauna only increase the fire spread capability and speed. Early detection of these forest fires can help control the spread and protect the nearby areas from the damage caused. This research paper aims at predicting the occurrence of forest fires using machine learning and deep learning techniques. The idea is to apply multiple algorithms to the data and perform comparative analysis to find the best-performing model. The best performance is obtained by the decision tree model for this work. It gave an accuracy of 79.6% and a recall score of 0.90. This model was then implemented on front-end WebUI using the flask and pickle modules in Python. The front-end Website returns the probability that a forest fire occurs for a set of inputs given by the user. This implementation is done using the PyCharm IDE. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Sexual violence in cyberspace: breaking the silence of international law
The increasing dependence of the world on digital technology and the internet has consequently led to the juxtaposition of the problematic social structure in online transactions and communications. This has resulted in increasing cases of cybersexual violence against women. The article argues that the effects of cybercrimes are transnational and, therefore, the traditional domestic criminal law is rather inept in preventing crime and punishing offenders. This increases the obligation of international law, which has so far remained silent on the issue. The articles conclusion suggests that the proposed World Convention on Cybercrime should include cybersexual violence as a core crime. This would serve as a beginning for addressing the threat, effect, and extent of the crime of cybersexual violence. The article concludes that the masculinist normative structure of international law is to blame for its culture of silence. Copyright 2024 Inderscience Enterprises Ltd. -
Characterization of signed paths and cycles admitting minus dominating function
Let G = (V, E, ?) be a finite signed graph. A function f: V ? {?1, 0, 1} is a minus dominating function (MDF) of G if f(u) + Pv?N(u) ?(uv)f(v) ? 1 for all u ? V. In this paper we characterize signed paths and cycles admitting an MDF. 2020 Azarbaijan Shahid Madani University -
All-Optical Plasmonic Neurosensor for Self-Learning Anomaly Detection in Smart IoT Systems
An integrated plasmonic neurosensing platform is introduced to enable ultrafast, self-learning anomaly detection within next-generation Internet of Things (IoT) environments. The research attempts to design an all-optical plasmonic neurosensor that can monitor irregularities as well as at the same time learns in hardware without the aid of electronics. The big picture is to develop an ultra-fast energy-saving sensorial unit that can scale to large tissues of IoT network applications and, autonomously, adjusts to varying conditions. The most significant invention of the paper is that localized surface plasmon resonance (LSPR) nanostructures are proposed to combine both nonlinear optical memory-effect and physical learning in sensor plasmonic gap. The technique is a hybrid between FDTD/FEM electromagnetic modelling, nanoimprint based production of sub-20-nm bow-tie antennas, nonlinear optical modulation experimental studies, and scalability analysis on the network level. A simulated system determined the optimal bow-tie configuration that resonated at 817nm with a field enhancement of approximately 28x with gap dimensions of 10nm long. Fabricated devices attained resonance of 823nm with Q-factor of 18.7. A refractive-index modulation was achieved of 3.1 10? and overall shift of the resonance at 51nm of 50 cycles in optical learning. The IoT level testing had 94.6% anomaly-detection errors and 47 ps response time, whereas the scalability experiment enabled the growth of bandwidth linearly with WDM and 92% fabrication yield. These findings provide an answer to the consequences that will lead to ultra-dense self-learning photonic IoT designs. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2026.