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E-Commerce and Consumer Trust Impact of Industry 4.0 on MSME Sales and Business Practices in India
The role of Industry 4.0 in business practices and consumer preference is gaining high importance in Indias MSME economy. This study examines if e-commerce implementation has influenced MSMEs sales volume, which encouraged them to shift from offline to online business. This study suggests global regulatory norms to promote e-commerce practices in consumer markets. In order to study this issue, data from 407 respondents were collected and processed using advanced statistical software IBM SPSS and AMOS, paying special attention to the inter-relation between Industry 4.0 interventions and consumer behavior. Advanced statistical software, including Structural Equation Modeling and path analysis, describe how Industry 4.0 influences company practices, consumer confidence, and sales in the MSME economy. Advanced research demonstrates high inter-relation between Industry 4.0-initiated improvement and consumer confidence, and they demonstrate insights into complexity about how technological innovations influence corporate operations and consumer attitudes. Findings of this study demand stringent regulations that enhance effective standards and consumer psycho-logical well-being in e-commerce. This study contributes to the building block of effective utilization of e-commerce in Indias fast-evolving industry, and it stresses the top priority for comprehensive frameworks that address the challenges emerging from advanced technologies. Firms can navigate complexity in e-commerce interactions better by acknowledging the implications and establishing trust. Lastly, this study highlights the key role of e-commerce in shaping consumer behavior and demands global regulatory norms to make e-commerce practices in consumer markets effective and sustainable. Findings provide a road map to policymakers and firms to frame and implement policies that enhance customer confidence and encourage long-term prosperity in the MSME economy. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Partition Refugees in Jammu Cry for Protection of Land and Job Rights
Article 370 was presented as an obstacle in the complete integration of Kashmir and it had denied citizenship and land rights to around 1.5 lakh West Pakistan Refugees (all Hindus) since their migration in 1947. However, after the lapse of merely five years of the abrogation, these WPRs, the new citizens of Jammu and Kashmir, have started feeling disillusioned and demanding protection of lands and jobs under Article 371 of the Indian Constitution. 2024 Economic and Political Weekly. All rights reserved. -
Networks Simulation: Research Based Implementation using Tools and Approaches
The advancements in computer networks and communication technology keep network-related research in high demand. Protocols are designed to improve the environment and it is mandatory to test their effectiveness before deploying them. Deploying an untested protocol in a full-fledged real environment is not desirable as there exists uncertainty about its success. Simulation software is one of the essential tools in network research areas. It gives a platform for testing and observing newly developed protocol's behavior with less cost and risk. Different kinds of network simulators are available., some are exclusive for wired or wireless., and some are for both. There are many simulators available hence selecting the most appropriate simulation tool among them is a difficult task. This paper focuses on giving a detailed review of popular simulation tools. 2022 IEEE. -
A Review of Channel Estimation Mechanisms in Wireless Communication Networks
The fluctuating nature of wireless networks influences network performance. Estimation of channel condition is essential for many reasons. The accurate estimation and prediction help to improve the performance, like better rate adaptation in Wi-Fi, improved video streaming, reduce energy consumption, and better scheduling. There are many different approaches introduced past two decades. In this paper, we are focusing on providing a brief review of different channel estimation approaches and their importance in improving performance. 2021 IEEE. -
Stability of porous medium convection in polarized dielectric fluids with non-classical heat conduction
International Journal of Mathematical Archive Vol.4, Issue 4, pp.136-144, ISSN No. 2229-5046 -
Directional injection-driven contaminants transport in groundwater system with asymptotically varying dispersion coefficients
This work examines the effect of asymptotic dispersion for different contaminants, like heavy metals, biological, and radiological types, in heterogeneous groundwater systems. The migration of contaminants within groundwater systems is controlled by advection, dispersion, and sorption phenomena, and these mechanisms are mathematically modeled using the Advection-Dispersion Equation (ADE). Using the Thomas algorithm, a numerical simulation with the Peaceman - Rachford Alternating Direction Implicit (PR-ADI) scheme is applied to solve the ADE under the directional injection boundary (axial input sources). The study on asymptotic dispersion coefficients revealed a broader plume evolution. Non-linear sorption models depended on the saturation limit, and various parameters revealed physically relevant results. The iso-concentration figures depict flow patterns for diverse directional hydrological inflows, supporting stability interpretations. This study introduces a computational approach for modeling contaminant transport in groundwater systems, emphasizing asymptotic field conditions that introduce heterogeneity, coupled with nonlinear sorption effects on the plume morphology. The results highlight how plume morphology responds to variable dispersion and velocity, offering guidance for field-scale aquifer analysis and water quality management. The study is also aligned with Sustainable Development Goal 6 - Clean water and sanitation. 2025 Elsevier B.V. -
A Pilot Feasibility Study of Reconnecting to Internal Sensations and Experiences (RISE), a Mindfulness-Informed Intervention to Reduce Interoceptive Dysfunction and Suicidal Ideation, among University Students in India
Although 20% of the worlds suicides occur in India, suicide prevention efforts in India are lagging (Vijayakumar et al., 2021). Identification of risk factors for suicide in India, as well as the development of accessible interventions to treat these risk factors, could help reduce suicide in India. Interoceptive dysfunctionor an inability to recognize internal sensations in the body has emerged as a robust correlate of suicidality among studies conducted in the United States. Additionally, a mindfulness-informed intervention designed to reduce interoceptive dysfunction, and thereby suicidality, has yielded promising initial effects in pilot testing (Smith et al., 2021). The current studies sought to replicate these findings in an Indian context. Study 1 (n = 276) found that specific aspects of interoceptive dysfunction were related to current, past, and future likelihood of suicidal ideation. Study 2 (n = 40) was a small, uncontrolled pre-post online pilot of the intervention, Reconnecting to Internal Sensations and Experiences (RISE). The intervention was rated as highly acceptable and demonstrated good retention. Additionally, the intervention was associated with improvements in certain aspects of interoceptive dysfunction and reductions in suicidal ideation and eating pathology. These preliminary results suggest further testing of the intervention among Indian samples is warranted. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
EXPERIENTIAL TOURISM: Nature-based Tourism Trends in India
The rise of experiential tourism in recent years is seemingly gaining momentum. Tourism providers are marketing immersive travel experiences by engaging visitors with the destinations history, residents, and environment. Experience tourism entails immersing oneself in the essence of the destination in its truest sense. Authenticity involving emotions and values is of great importance for tourism marketing. Experiential travel rejects the idea of the traditional visitor experiences and gets back to the roots of travel. Over the last decade, nature-based tourism has become a widespread phenomenon and contributes more than US$120 billion to global GDP. The recent pandemic has additionally stimulated the demand for spending time and nature. Thus, experiential nature-based tourism could play an important role in the sustainable recovery of global tourism. This chapter discusses Indias critical experiential tourism trends through the marketing perspective and provides innovative marketing solutions for nature-based experiential tourism planning and development. Extensive and comprehensive literature has been used in this study to identify and critically reflect on vital nature-based experiences worldwide and the recent trends in the market. 2023 Taylor and Francis. -
Exploring the economy of creativity and culture in the light of Industry 5.0: a systematic literature review of the setup ofcreative industries
Purpose: This study aims at stirring up the existing research conducted in the field of creative economy (CE) and also in the context of Industry 5.0. CE encompasses all the creative industries/businesses which form a major part of the knowledge-based economy. The functionalities of these setups, their global trends and developments are to be assessed for a better understanding of its present circumstances and its prospective opportunities by augmenting Industry 5.0 and its core principles. This provides a comprehensive illustration to enhance the economic, social, creative and sustainable performances of the creative industry. In addition, the study also seeks to identify the dynamics of creative units and how it could highly contribute to the glorification of the creative and cultural history in the Indian economic backdrop. Design/methodology/approach: The study adopts a systematic literature review process to fulfill the research objective. Four critical databases in Scopus such as Emerald Insight, Springer Link, Sage Publications and Taylor and Francis have been chosen for the review process. Following the critical literature review process, the chosen articles from each database have been retrieved for an exhaustive analysis within a time frame of 20132023 to evaluate the research evolution on the subject area. Findings: The paper identified various research dimensions and perspectives of the researchers in the area of study. This gives a platform to extensively evaluate the capabilities and functionalities of the sector for strategy building and enhancing returns from the sector. Research limitations/implications: As the methodology was restricted to top 5 articles from 5 important databases, the study was limited to only those articles and the other open-access peer-reviewed articles/journals/databases have not been considered which is a major limitation. Alongside, as the time frame was restricted for a period of 10years and only English language papers were chosen, prior study has not been considered, which is also a key limitation to the study. Practical implications: Policymakers, i.e. government and institutions, can understand the existence and contribution of the CE in different geographical regions for a specified period of time. This helps them understand the new revolution, Industry 5.0, and how they could merge their concepts to bring innovations in the sector and support in building sustainable cities in the emerging economies. Originality/value: As the paper works on bringing out the viewpoints of multiple authors and research works, it is considered to be a novel study as none of the previous studies, especially systematic literature review works, have been done only in high-quality journals of Scopus database. Therefore, the study holds high-quality information which can be significantly used by creative business units. 2024, Emerald Publishing Limited. -
Leveraging Java for Developing Privacy-Preserving and Cross-Platform Machine Learning Applications
The growing focus on data privacy and system interoperability has created a clear need for machine learning (ML) applications. These applications must be able to protect sensitive data while maintaining consistent performance in various computing environments. Java is considered as a strong choice due to its platform independence, strong static typing, and rich ecosystem of development tools and libraries. This study checks how Java supports privacy-preserving techniques such as federated learning, differential privacy, and homomorphic encryption, instilling confidence in its role for secure ML development. It also reviews Java-based libraries, including Weka, Deeplearning4j, and Apache Spark, highlighting their role in building safe, scalable, and portable ML solutions. Through architectural analysis, benchmark-based evaluation, and comparisons with other programming languages, the study demonstrates the strengths of Java to deliver secure, scalable, and interoperable privacy-sensitive machine learning applications. 2025 IEEE. -
Comprehensive Analysis of Canine Parvovirus Outbreaks: Predictive Modeling and Evaluation Metrics
This paper addresses the persistent threat of Canine Parvovirus (CPV) to canine health, exploring a spectrum of outcomes from recovery to fatalities. Employing a fusion of machine learning techniques and comprehensive evaluation metrics, we present a robust analysis of CPV outbreaks. Our methodology involves the development of a deep learning-based predictive model designed to anticipate CPV case outcomes based on symptoms and diverse contributing factors, with performance monitoring through visualization techniques. The study delves into the intricacies of a dataset featuring diverse features such as age, breed, symptoms, treatment, and geographic location. Through meticulous preprocessing and feature encoding, we establish a powerful deep learning model proficient in discerning intricate patterns within the data. Model evaluation encompasses key metrics, including accuracy, precision, recall, F1-score, confusion matrix, Cohens Kappa, and Matthews Correlation Coefficient, providing a comprehensive assessment of predictive capabilities. Our findings highlight the models proficiency in anticipating CPV outcomes, suggesting potential enhancements in decision-making within veterinary practice. Insights derived from this research contribute to the refinement of CPV diagnosis, treatment, and prevention strategies, ultimately benefiting the well-being of canine companions. The projects results demonstrate the efficacy of the proposed models in forecasting the prevalence and survival rate of the CPV virus in dogs using basic parameters. This approach eliminates the need for costly and time-consuming laboratory tests, typically requiring 1224h for results, showcasing a practical and efficient solution for CPV management. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Intelligent predictive hiring model and personality assessment
Selecting the right candidates is essential for organisational success, yet traditional hiring methods often fall short. This research introduces an advanced approach integrating natural language processing (NLP), personality assessment, and deep learning to improve candidate selection. NLP extracts key attributes from job descriptions and resumes, while personality assessments evaluate candidate suitability. A fusion of LSTM and RNN models predicts job fit using a dataset of job roles, resumes, and MBTI personality types. Pre-processing includes tokenisation, encoding, and data splitting for training and testing. The model architecture combines embedding layers with LSTM units, optimised using binary cross-entropy loss and accuracy metrics. Results show that this fusion model outperforms traditional algorithms, improving job matching accuracy. This research enhances recruitment by leveraging AI-driven insights. Future work will refine predictive models, integrate additional data, and address ethical concerns to ensure fairness and transparency, fostering a more efficient and equitable hiring process. Copyright 2026 Inderscience Enterprises Ltd. -
Effect of Doping in Aluminium Nitride (AlN) Nanomaterials: A Review
Piezoelectric materials can generate electrical charges when subjected to mechanical pressure through the piezoelectric effect. In addition to generating electricity from environmental vibrations, they are also used as nano energy generators for micro electro mechanical systems (MEMS). Aluminum Nitride (AlN) with a doping element exhibits unique physical and chemical properties. It is used to manufacture many electromechanical devices. They are ideal candidates for many applications, including MEMS resonators and microwave filters, due to their large piezoelectric coefficient and low resistance. A number of material properties led to its selection, including high thermal conductivity, good mechanical strength, high resistance, corrosion resistance, and the largest piezoelectric coefficient. A piezoelectric coefficient d33 characterizes the piezoelectric response of AlN thin films. By doping this material, a wide range of applications have been explored. The Electrochemical Society -
Addressing the complexities of postoperative brain MRI cavity segmentationa comprehensive review
Postoperative brain magnetic resonance images (MRI) is pivotal for evaluating tumor resection and monitoring post-surgical changes. The segmentation of surgical cavities in these images poses challenges due to artifacts, tissue reorganization, and heterogeneous appearances. This study explores challenges and advancements in postoperative brain MRI segmentation, examining publicly accessible datasets and the efficacy of various deep learning models. The analysis focuses on different U-Net models (U-Net, V-Net, ResU-Net, attention U-Net, dense U-Net, and dilated U-Net) using the EPISURG dataset. The training dice scores are as follows: U-Net 0.8150, attention U-Net 0.8534, V-Net 0.7602, ResU-Net 0.7945, dense U-Net 0.83, dilated U-Net 0.80. The study thoroughly assesses existing postoperative cavity segmentation models and proposes a fine-tuning approach to enhance the performance further, particularly for the best-performing model, attention U-Net. This fine-tuning involves introducing dilated convolutions and residual connections to the existing attention U-Net model, resulting in improved results. These improvements underscore the necessity for ongoing research to select and adapt efficient models, retrain specific layers with a comprehensive collection of postoperative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis
Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G. -
Advancing Brain Tumor Segmentation in MRI Scans: Hybrid Attention-Residual UNET with Transformer Blocks
Accurate segmentation of brain tumors is vital for effective treatment planning, disease diagnosis, and monitoring treatment outcomes. Post-surgical monitoring, particularly for recurring tumors, relies on MRI scans, presenting challenges in segmenting small residual tumors due to surgical artifacts. This emphasizes the need for a robust model with superior feature extraction capabilities for precise segmentation in both pre-and post-operative scenarios. The study introduces the Hybrid Attention-Residual UNET with Transformer Blocks (HART-UNet), enhancing the U-Net architecture with a spatial self-attention module, deep residual connections, and RESNET50 weights. Trained on BRATS20 and validated on Kaggle LGG and BTC_ postop datasets, HART-UNet outperforms established models (UNET, Attention UNET, UNET++, and RESNET 50), achieving Dice Coefficients of 0.96, 0.97, and 0.88, respectively. These results underscore the models superior segmentation performance, marking a significant advancement in brain tumor analysis across pre-and post-operative MRI scans. 2024 by the authors of this article. -
Exploring the Adaptability of Attention U-Net for Post-operative Brain Tumor Segmentation in MRI Scans
This study explores the adaptability of a segmentation model, originally trained on pre-operative MRI data, in post-operative recurrent brain tumor segmentation. We utilized the Attention U-Net model for this study. In pre-operative training, the model achieved a Dice Coefficient of 0.92 and an IOU of 0.86 for brain tumor MRI segmentation. Due to the surgical artifacts in post-operative data, performance reduced with Dice Coefficient of 0.54 and an IOU of 0. To improve the performance, the model's architecture is fine-tuned by introducing dilated convolutions and residual connections. This refinement yielded improvements in results, with a Dice Coefficient of 0.68 and an IOU of 0.62 in the post-operative context. This improvement underscores the need for further research to select and adapt efficient models, retrain specific layers with an extensive collection of post-operative images, and fine-tune model parameters to enhance feature extraction during the encoding phase. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
A new framework for contour tracing using Euclidean distance mapping
In this paper, a new fast, efficient and accurate contour extraction method, using eight sequential Euclidean distance map and connectivity criteria based on maximal disk, is proposed. The connectivity criterion is based on a set of point pairs along the image boundary pixels. The proposed algorithm generates a contour of an image with less number of iterations compared to many of the existing methods. The performance of the proposed algorithm is tested with a database of handwritten character images. In comparison to two standard contour tracing algorithms (the Moore method and the Canny edge detection method), the proposed algorithm found to give good quality contour images and require less computing time. Further, features extracted from contours of handwritten character images, generated using the proposed algorithm, resulted in better recognition accuracy. Copyright 2021 Inderscience Enterprises Ltd. -
An Exploratory Study of Emotional Labour Among Therapists and Counsellors in India
Background: Emotional labour has been extensively investigated in the service sector, where employees manage their emotions to ensure a positive customer experience. However, there is a dearth of research into how therapists perform emotional labour during therapy sessions. Thus, the aim of this study was to explore psychotherapists' and counsellors' experiences of performing emotional labour in therapeutic settings. Method: The study used a qualitative research design with an exploratory approach. Semi-structured interviews were conducted with four clinical psychologists and four counsellors. The interviews were conducted via video call and lasted about 4560 min. Thematic analysis was used to identify emerging themes. Results: The analysis revealed that therapists experience an array of emotions during sessions. However, the expression of these emotions is guided by professional norms and emotional display rules. Participants disclosed that they use several techniques to manage their emotions both during and after sessions and that participating in emotional labour yielded both favourable and unfavourable outcomes for the therapists. Conclusion: The findings presented in this study provide insight into emotional labour and inform professionals on how this can negatively impact them if not sufficiently addressed. The study highlights the need for further investigation. In the meantime, therapists and counsellors would benefit from integrating the study's findings into their respective practices. 2025 British Association for Counselling and Psychotherapy. -
FITNESS TRAINERS PHYSICAL ATTRACTIVENESS AND GYM GOERS EXERCISE INTENTION
In line with the law of attraction, physical attractiveness has been widely used in marketing as well as advertising due to its potency in persuading consumers to take action. However, would physical attractiveness of a fitness trainer influence gym goers intention to exercise? This question motivated this research. Based on recent literature reviews, several research constructs were identified to form a research framework to investigate the physical attractiveness phenomena in the fitness industry. Hypothetically, the impact of the physical attractiveness of a fitness trainer on gym goers exercise intention is postulated to be mediated by trainers perceived expertise, trustworthiness, likeability and perceived health. Questionnaires were administered among gym-goers from 10 randomly selected fitness centres across three districts of Melaka State in Malaysia, and 192 final sample data were obtained. Data analysis reveals fitness trainers perceived expertise and likeability significantly mediates the relationship between the physical attractiveness of fitness trainers and gym goers exercise intention. Physical attractiveness of fitness trainers does impact the exercise intention of gym goers indirectly. Implications of the findings to theory and practice are also discussed in this paper, as well as suggestions for future studies. 2022, Universiti Malaysia Sarawak. All rights reserved.