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A Paradigm shift in Family therapy in India : Exploration from Socioeconomic, Cultural and Spiritual Perspectives
International Journal of Physical and Social Sciences Vol. 3, Issue 3,pp. 153-166 , ISSN No. 2249-5894 -
Exploring the effectiveness of mindfulness-based intervention among college students in India
This study investigated the effectiveness of an eight-week mindfulness-based intervention program on the trait mindfulness, psychological well-being and emotion regulation of college-going students. The experimental group participants were college-going students (N = 40) who enrolled for the intervention, and the participants in the control group (N = 40) were interested in the intervention and considered as a wait-list control group. The experimental group underwent mindfulness-based interventions, which included 1112 sessions, including brief exercises and meditations related to their trait mindfulness, emotion regulation, and psychological well-being. They received 23h of training per week for eight weeks. Repeated Measures of ANOVA together with an independent sample t-test were used to evaluate the effectiveness of this intervention programme. Further, Cohens d was used to calculate the effect size to explain the variance caused by the intervention program in trait mindfulness, emotion regulation, and psychological well-being. The results indicated that students significantly improved in their trait mindfulness, emotion regulation, and psychological well-being after receiving mindfulness training. In conclusion, the application of this eight-week mindfulness-based intervention sheds light on the common psychological issues confronted by college students in India, presenting itself as an advantageous tool for the professionals working in this field and offering positive effects on the overall well-being of college students. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Artificial Intelligence Involvement in Graphic Game Development
Games have always been a popular form of entertainment and with the advancements in technology, the integration of Artificial Intelligence (AI) in gaming has revolutionized the gaming industry. This research article aims to explore the various applications of AI in gaming and its impact on the industry and player experience. Unlike the typical straightforward nature of AI, this research paper takes a more human approach to discussing the topic. It delves into the evolution of AI in games and the various types of AI used in game development. These include rule-based AI, learning- based AI, and evolutionary AI, which have all contributed to the development of increasingly immersive gaming experiences. The benefits and challenges of using AI in games are also explored, considering the impact on player experience. While AI-powered opponents can provide a greater challenge, balancing the difficulty level is critical to ensuring the game remains enjoyable. The potential ethical concerns of using AI in games are also discussed, such as data privacy, bias, and fairness. Furthermore, this research paper looks into the future of AI in games and how it may shape the gaming industry and player experience in the years to come. With the continued development of AI techniques such as reinforcement learning and GANs, the possibilities for more immersive and engaging gaming experiences are endless. 2023 IEEE. -
Impact of Meltdown and Spectre Threats in Parallel Processing
Threat characterization is critical for associations, as it is an imperative move towards execution of data security. Vast majority of the current threat characterizations recorded threats in static courses without connecting risks to information system zones. The aim of this paper is to represent each threat in different areas of the information system the methodology to solve the problem. Data security is habitually represented to different kinds of threats which may cause distinctive types of harms that can prompt to critical monetary losses. Data security problems can go from small losses to entire data framework destruction. The effect of various threats vary extensively: some manipulate the integrity or confidentiality of information while others manipulate the accessibility of a framework. At present, associations are trying to comprehend what are the threats to their data resources are and what are the ways to get the significant intends to combat them which keep on representing a challenge. Springer Nature Switzerland AG 2020. -
Impact of Meltdown and Spectre Threats in Parallel Processing
[No abstract available] -
Optimal locations for PMUs maintaining observability in power systems
Population of Phasor Measurement Units (PMUs) in power systems are increasing day by day as PMUs measure the electrical quantities more accurately with time-stamping. The measurements done by PMU can be used for monitoring, controlling and for state estimation of the power system. Since the installation of PMUs demand high capital cost, their number and location to be chosen optimally is by minimizing investment without losing observability of the system. In this paper Integer Programming techniques used to solve Optimal Placement of PMU (OPP) problem. The OPP problem is solved for normal power system as well as for a few contingency conditions like one PMU outage, considering zero injection bus, outage of single line on various standard IEEE Bus Systems. The work is also trying to place PMUs under planned islanding in certain standard networks. 2016 IEEE. -
Further studies on circulant completion of graphs
A circulant graph C(n, S) is a graph having its adjacency matrix as a circulant matrix. It can also be interpreted as a graph with vertices v0, v1,,vn?1 that are in one-to-one correspondence with the members of Zn and with edge set {vivj: i ? j ? S}, where S known as the connection set or symbol, is a subset of non-identity members of Zn that is closed under inverses. This work extends the study of circulant completion and general formulae for calculating circulant completion numbers in two different perspectives, one in terms of circulant span and the other in terms of the adjacency matrix. (2024), (SciELO-Scientific Electronic Library Online). All Rights Reserved. -
On Equitable Chromatic Completion of Some Graph Classes
An edge of a properly vertex-colored graph is said to be a good edge if it has end vertices of different color. The chromatic completion graph of a graph G is a graph obtained by adding all possible good edges to G. The chromatic completion number of G is the maximum number of new good edges added to G. An equitable coloring of a graph G is a proper vertex coloring of G such that the difference of cardinalities of any two color classes is at most 1. In this paper, we discuss the chromatic completion graphs and chromatic completion number of certain graph classes, with respect to their equitable coloring. 2022 American Institute of Physics Inc.. All rights reserved. -
On Circulant Completion of Graphs
A graph G with vertex set as {v0, v1, v2,.., vn-1} corresponding to the elements of Zn, the group of integers under addition modulo n, is said to be a circulant graph if the edge set of G consists of all edges of the form {vi, vj} where (i-j)(modn)?S?{1,2,,n-1}, that is, closed under inverses. The set S is known as the connection set. In this paper, we present some techniques and characterisations which enable us to obtain a circulant completion graph of a given graph and thereby evaluate the circulant completion number. The obtained results provide the basic eligibilities for a graph to have a particular circulant completion graph. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predicting of Credit Risk Using Machine Learning Algorithms
Credit risk management is one of the key processes for banks and is crucial to ensuring the banks stability and success. However, due to the need for more rigid forecasting models with strong mapping abilities, credit risk prediction has become challenging for the banking industry. Therefore, this paper attempts to predict commercial banks credit risk (CR) by using various machine learning algorithms. Machine learning algorithms, namely linear regression, KNN, SVR, DT, RF, XGB, and MLP, are compared with and without feature selection and feature extraction techniques to examine their prediction capabilities. Various determinants of credit risk (features) have been extracted to predict credit risk, and these features have been used to train machine learning models. Findings revealed that the decision tree algorithm had the highest performance, with the lowest mean absolute error (MSE) value of 0.1637 and the lowest root mean squared error (RMSE) value of 0.2158. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
DETERMINANTS OF CREDIT RISK: EMPIRICAL EVIDENCE FROM INDIAN COMMERCIAL BANKS
Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a banks cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a banks credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value < 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a banks credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk. Tisa Maria Antony, Suresh G., 2023. -
Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques
In recent times, the use of Remote Sensing (RS) data obtained from Unmanned Aerial Vehicles (UAVs) has gained significant popularity in crop classification tasks, including crop mapping, yield prediction, and soil classification. The classification of food crops utilizing RS Imageries (RSI) is a major application of RS tools in crop growing. Meeting the conditions for investigating these data requires more difficult approaches, and Artificial Intelligence (AI) technologies offer the mandatory support. Because of the variation and division of crop planting, archetypal classification methods have fewer classification outcomes. This manuscript focuses on the design and execution of a Leveraging Enhanced Dipper Throat Optimization Algorithm with Dipper-Inspired Precision Classification for Remote-sensed Optimized (DIP-CROP) Processing methodology. The drive of the DIP-CROP algorithm is to classify distinct types of crops that exist in remote sensing. At first, the DIP-CROP model applies image processing using the Sobel Filter (SF) to eliminate the noise. Next, the presented DIP-CROP technique takes place SqueezeNet model is employed for the feature extractor. To classify the food crop types, the DIP-CROP approach utilizes a Multi-Head Attention-based Bi-directional Long Short Term Memory (MHA-BiLSTM) algorithm. For hyperparameter tuning of the MHA-BiLSTM classifier, the Enhanced Dipper Throat Optimization Algorithm (EDTOA) will be applied in this work. The optimization process utilizes Levy flight distribution, which is known for its faster convergence due to efficient exploration of the search space. Levy flights can be used to take larger steps in exploration, which prevents getting stuck in local minima and accelerates convergence. The performance of the DIP CROP method is examined experimentally using a benchmark database. Experimental results affirmed the superior solution of the DIP-CROP algorithm over existing methods. 2026 Seventh Sense Research Group. -
Metaheuristic Optimization of Deep Learning Models for Land Cover Classification Using Remote Sensing Data
Deep learning techniques have greatly advanced land-cover classification from remote sensing imagery, but their performance depends critically on choosing optimal hyperparameters. Manually tuning hyperparameters (e.g., learning rate, network depth, dropout rate) is time-consuming and often suboptimal. Metaheuristic algorithms offer an automated approach to this problem. In this work, we compare five metaheuristic optimizersParticle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), African Vulture Optimization Algorithm (AVOA), and an Enhanced Dipper Throat Optimization Algorithm (EDTOA)for hyperparameter tuning of convolutional neural networks (CNNs), a ResNet-50, and a U-Net. We evaluate these methods on two benchmark land-cover datasets: EuroSAT (patch-level multispectral image classification) and DeepGlobe (pixel-wise satellite image segmentation). Our data preprocessing includes normalization, data augmentation, and computing spectral indices (e.g., NDVI) to enrich the feature set. Each metaheuristic searches the hyperparameter space to maximize validation accuracy (for EuroSAT) or mean Intersection-over Union (mIoU) (for DeepGlobe). In addition to predictive performance, we analyze the computational cost (wall-clock time, epochs to convergence, GPU usage) of each optimizer to assess the trade-off between efficiency and accuracy. AVOA and EDTOA achieve the best results on both datasets (e.g., up to 98.5% accuracy on EuroSAT and 56% mIoU on DeepGlobe), outperforming the PSO, GA, and DE baselines while offering favorable cost-performance balance. These findings demonstrate that advanced metaheuristics can significantly improve deep model performance in land-cover classification. Our contributions include a comprehensive experimental comparison of five optimizers, a detailed methodology integrating spectral index features, a cost performance analysis, and reference results to guide future research. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Paired Domination Integrity of Graphs
The concept of vulnerability in a communication network plays an important role when there is a disruption in the network. There exist several graph parameters that measure the vulnerability of a communication network. Domination integrity is one of the vulnerability parameters that measure the performance of a communication network. In this paper, we introduce the concept of paired domination integrity of a graph as a new measure of graph vulnerability. Let G = (V, E) be a simple, connected graph. A set of vertices in a graph G, say S, is a paired dominating set if the following two conditions are satisfied: (i) every vertex of G has a neighbor in S and (ii) the subgraph induced by S contains a perfect matching. The paired domination integrity of G, denoted by PDI(G), is defined as PDI(G) = min{|S|+m(G?S): S is a paired dominating set of G}, where m(G?S) is the order of the largest component in the induced subgraph of G?S. In this paper, we determine few bounds relating paired domination integrity with other graph parameters and the paired domination integrity of some classes of graphs. 2025 World Scientific Publishing Company. -
Artificial Intelligence Based Automatic Question Paper Generation Using Natural Language Processing
Question paper generation is a crucial task in education, where the objective is to design an assessment that effectively evaluates students knowledge and understanding of various subjects. Traditional methods of question paper generation can be exceedingly difficult, time-consuming, and inappropriate and may not be fully optimized. They ensure a comprehensive assessment of students knowledge. The system also offers the flexibility to customize question papers based on specific preferences and requirements. This research introduces a comparative approach to question paper generation using Latent Semantic Analysis (LSA), Word Embedding, and Sequence-to-Sequence (Seq2Seq) models, leveraging the power of Artificial Intelligence (AI) and Natural Language Processing (NLP). This model compares their Semantic Representation Quality, Context Understanding, and Computational Complexity. Comparing these techniques shows that LSA offers simplicity but may lack precision, while word embedding balances complexity and semantic understanding. Seq2Seq models, despite their complexity, provide contextually rich mappings with the highest degree of fine-tuning potential. This comparative analysis underscores the importance of understanding the nuances and trade-offs of each approach, enabling educators to make informed decisions in adopting these technologies to enhance educational practices and student learning experiences. A few modules are included in this system, including the admin module, add user, subject selection, question entry, question management, paper management and difficulty level. By capitalizing on the capabilities of LSA, Seq2Seq models, and word embedding, educators can revolutionize the process of question paper generation, ultimately leading to more effective and impactful student learning outcomes. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
The Green Evolution: Transforming Supply Chains for a Sustainable Future
The paper analyses systemic, multi-stakeholder effort required to overcome continual challenges. Proactive solutions that prioritize supplier collaboration and transparency, aided by emerging technologies and public-policy guidance, can drive the necessary sustainability transformation. The required sustainability change can be initiated by proactive solutions that place a high priority on supplier collaboration and transparency, supported by new technology and recommendations from public policy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
HRM challenges and employee wellbeing in the tourism industry: Moderating role of organizational culture
The current study aims to understand the Challenges faced by Tourism sector employees and its effect on employee well being. Also the moderating effect of Organizational culture is measured. Staff members of airlines, hotels, and travel agencies provided the main data used in this quantitative analysis. A total of 122 individuals were deemed suitable for the study according to the Kregcie-Morgan table. Using the HRM Challenges, Employee Wellbeing, and Organizational Culture measure as a starting point, a carefully crafted questionnaire was developed. Gaskins' master validity table was used to evaluate the reliability and validity of the questionnaire. Analysis of the data was done using SPSS for factor analysis and confirmatory factor analysis with. The findings show that there is a negative impact of HRM challenges on the employee wellbeing. But when organization culture is introduced as a moderator, it dampens the effect of challenges on employee wellbeing. Therefore, to reduce the impact of challenges on employee wellbeing, Organizational culture is the key. 2025, IGI Global Scientific Publishing. -
Gen Z and the Road to Electric Vehicles: Navigating Practical Barriers
Electric Vehicles (EVs) are regarded as a solution to lowering a countrys carbon emissions in numerous countries worldwide. When compared to traditional fueled vehicles, also known as Internal combustion engine vehicles (ICEVs), EVs emit significantly less carbon. Numerous countries including India, are looking to increase EV adoption as part of their plan to curb their overall carbon output. However, transforming a substantial portion of a countrys vehicles to EVs, comes with its own set of challenges in areas like infrastructure required to maintain EVs as well as meeting the increased demand in energy in a sustainable and environment friendly manner. The present study dives into the exploration of perception among Gen Z towards adoption of EVs particularly related to factors such as cost, infrastructure, functionality and environment. The study signifies the importance of functionality features in electric vehicles such as facilities, technological features and benefits in the long run that have significant effect in effecting the perception of Gen Z in adopting electric vehicles. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
A concise study on the phytochemistry and antimicrobial efficiency of Artemisia absinthium L.: Phytochemical analysis of plant
This study is meant to elucidate the phytochemical and antibacterial characteristics of wormwood Artemisia absinthium L, a perennial herb from Asteraceae family that has been used extensively in traditional medicine. It has diverse phytochemical composition, including bitter sesquiterpenoid lactones like absinthin, as well as essential oil constituents including camphene, ?-cadinene, guaiazulene, ?-thujone, ?-thujone, and thujyl alcohol esters. Applications of A. absinthium in the past include its ability to treat a wide range of illnesses, from fever to gastrointestinal problems. This study highlights the presence of various phytochemical compounds in plant extract of A. absinthium, such as tannins, saponins, and terpenoids through standardised protocols. Remarkably, this study also reveals its antibacterial capabilities using agar well diffusion method against five different pathogenic bacterial strains, including Escherichia coli (MTCC 443), Salmonella typhi (not sequenced, procured from Chettinad Hospital, Chennai), Staphylococcus aureus (MTCC 3160), Enterococcus faecalis (MTCC 439), and Klebsiella pneumonia (MTCC 109). Testing it against these strains of bacteria highlighted its effectiveness in this area. A. absinthium presents a compelling topic for continued scientific investigation due to its complex phytochemical composition and antimicrobial efficiency. 2026, ScienceIn Publishing. All rights reserved. -
An Exploration of the Symbolic Power of Sand Play and Well-Being in Children: Analysis of Sand Play
This book chapter attempts to understand childrens inner world through the images, symbols, colors, and themes in their Sand Play. This essay covers sand play (both dry and wet sand tray) sessions of children during play therapy sessions. It provides an overview of the context of sand play, including background information, client referral, developmental age, the Play Therapy Dimension Model (PTDM), neuroscience, symbolism used, positioning, and placement of the symbols. This book chapter on SAND TRAY helps to engage effectively with children in line with PTDM, integrating theory into practice, understanding the therapists position and movement in the therapeutic process, and approaches using PTDM. Copyright 2026 by IGI Global Scientific Publishing. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Use of this publication to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.