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On the secure vertex cover pebbling number
A new graph invariant called the secure vertex cover pebbling number, which is a combination of two graph invariants, namely, secure vertex cover and cover pebbling number, is introduced in this paper. The secure vertex cover pebbling number of a graph, G, is the minimum number m so that every distribution of m pebbles can reach some secure vertex cover of G by a sequence of pebbling moves. In this paper, the complexity of the secure vertex cover problem and secure vertex cover pebbling problem are discussed. Also, we obtain some basic results and the secure vertex cover pebbling number for complete r-partite graphs, paths, Friendship graphs, and wheel graphs. 2023 World Scientific Publishing Co. Pte Ltd. All rights reserved. -
Probing the soft state evolution of 4U 1543-47 during its 2021 outburst using AstroSat
4U 1543-47 underwent its brightest outburst in 2021 after two decades of inactivity. During its decay phase, AstroSat conducted nine observations of the source spanning from 2021 July 1 to September 26. The first three observations were performed with an offset of 40 arcmin with AstroSat/LAXPC, while the remaining six were on-axis observations. In this report, we present a comprehensive spectral analysis of the source as it was in the High/Soft state during the entire observation period. The source exhibited a disc-dominated spectra with a weak high-energy tail (power-law index ?2.5) and a high inner disc temperature (?0.84 keV). Modelling the disc continuum with non-relativistic and relativistic models, we find inner radius to be significantly truncated at >10 Rg. Alternatively, to model the spectral evolution with the assumption that the inner disc is at the innermost stable circular orbit, it is necessary to introduce variation in the spectral hardening in the range ?1.5-1.9. 2023 The Author(s). -
An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
As object detection has gained popularity in recent years, there are many object detection algorithms available in today's world. Yet the algorithm with better accuracy and better speed is considered vital for critical applications. Therefore, in this article, the use of the YOLOV4 object detection algorithm is combined with improved and efficient inference methods. The YOLOV4 state-of-the-art algorithm is 12% faster compared to its previous version, YOLOV3, and twice as faster compared to the EfficientDet algorithm in the Tesla V100 GPU. However, the algorithm has lacked performance on an average machine and on single-board machines like Jetson Nano and Jetson TX2. In this research, we examine the performance of inferencing in several frameworks and propose a framework that effectively uses hardware to optimize the network while consuming less than 30% of the hardware of other frameworks. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Next generation employability andcareer sustainability inthehospitality industry 5.0
Purpose: With an industry 5.0 revolution taking place in the hospitality industry, a shift from manual to cognitive labor is anticipated, characterized by greater sustainability, resilience and a human-centric approach. In this regard, hospitality educators' ability and willingness to teach novel topics such as automation at work, upskilling of employees, man-machine interaction and service robots have become more important than ever. This study aims to interpret the perspectives of hospitality educators about bridging the gap in the employability skills of (next-gen) hospitality graduates and the concerns relating to career sustainability in times of transition. Design/methodology/approach: A case study method was used given the novelty of the topic in a developing country like India. A qualitative survey with open-ended questions, is employed to understand the viewpoints of Indian hospitality educators, including those with more than 15years of teaching experience. In-depth interviews were conducted with 23 hospitality educators to reach the theoretical saturation point. MAXQDA software was used to analyze the qualitative data collected in the study. Findings: The findings reveal the challenges and motivations of hospitality educators in adapting to frequently changing business environments. In doing so, it sheds light on the methods employed to create a generation of hospitality graduates aligned with the changing dynamics of the industry. Originality/value: The paper presents the viewpoints of hospitality educators in India in relation to a futuristic approach to next-gen employability and career sustainability. Whilst numerous studies have focused on the role of robots and artificial intelligence in replacing the human component of the service environment, the concept of people working alongside advanced technologies is fairly new and needs to be fully explored. 2023, Emerald Publishing Limited. -
Scaling new heights: personal transformation through high altitude trekking in the Himalayas
In recent times, the human-nature continuum is being explored and studies have shown different kinds of terrains and nature evoke different emotional responses in individuals. Trekking in high-altitude mountains is one kind of nature and is special in terms of the height, the extent of naturalness and the experience of living in the wilderness that is involved. The current study focuses on understanding the experience of high-altitude trekking for novice Indian trekkers. The semi-structured interview data from eight participants who had gone on four different treks in the Himalayas was analysed using Interpretative Phenomenological Analysis. The following themes- motivational factors, preparation, environmental shift, social relationships, psychological impact and physical impact- emerged with personal transformation being the essence of the experience. The themes bring forth the various psychological benefits of interacting with nature whilst facilitating social connection. The research emphasises the psychosocial benefits of the trekking experience and paves the way for a holistic approach towards health and well-being in theoretical and therapeutic approaches in the Indian scenario. The Author(s) under exclusive licence to Outdoor Education Australia 2025. -
Integrating AI into Corporate Social Responsibility (CSR) for Ethical and Sustainable Business Practices
The rapid advancement of artificial intelligence (AI) technologies has significantly transformed various facets of business operations, including corporate social responsibility (CSR). As businesses strive to align their growth strategies with ethical, social, and environmental responsibilities, AI emerges as a powerful tool to enhance the effectiveness of CSR initiatives. This research investigates the integration of AI into CSR, exploring its potential to drive more sustainable business practices, improve transparency, and foster ethical decision-making within organizations. By employing a combination of qualitative and quantitative research methods, this study examines how AI-powered analytics, automation, and decision-making frameworks can optimize CSR efforts. Key areas of exploration include AI's role in enhancing supply chain sustainability, optimizing resource allocation, detecting unethical business practices, and enabling real-time monitoring and reporting of CSR initiatives. 2026, IGI Global Scientific Publishing. -
Economic growth and higher education in south asian countries: Evidence from econometrics
South Asian economies has witnessed very slow growth over the years and the gap has widened manifold between other nations of Asia particularly East Asian nations and South Asian nations. This paper examines co-integration between the economic growth and reach of higher education in South Asian nations explaining this disparity. The research employed an econometric panel co-integration investigation to analyse the long run relationship of higher education and economic growth among these nations. The research confirmed positive long run causality between the economic growth of the South Asian nations and gross enrolment ratio of higher education. So, if the South Asian nations continue with their existing pattern of paying less attention to higher education by allocating low share of investment on it, poor human capital formation would result in growing further economic disparity between developed and South Asian nations where rich nations would remain richer and poor nations would remain poor with the gap remaining unabridged. This research will serve as an aid to policy makers, educators and financers of South Asian nations to bridge the gap between high-and low-income nations. The focus on the quantum of spending on higher education by the government will help improve the reach of tertiary education and build economic prosperity in these nations. 2020, Sciedu Press. All rights reserved. -
Linkage between enterpreneurial orientation and export performance of South Asian countries
Purpose: South Asian economies has witnessed export dependence over the past several years and the dependence has increased manifold. Export performance is the most preferred modes of internationalisation in developing economies as it is directly linked to getting access to international markets with limited resources and capabilities thereby contributing to the economic productivity of the country. This paper examines co-integration between the export performance and entrepreneurial orientation in South Asian nations explaining it as the main enabler of export. Entrepreneurial Orientation has been considered an important criterion for promoting export as EO requires innovation, proactiveness and risk taking which provides competitive advantage to enterprises. Design/Methodology/Approach: The research employed an econometric panel cointegration investigation to analyse the long run relationship of economic orientation and export performance among these nations. Findings: The research confirmed positive long run causality between the innovativeness, proactiveness and risk taking as three dimensions of entrepreneurial orientation and export concentration ratio as an indicator for export performance among South Asian nations. So, if these developing nations continue to diversify their product & market mix in exporting products and services the concentration ratio would improve that would result in growing further economic productivity. Practical implications: This research will serve as an aid to policy makers and entrepreneurs of South Asian nations to focus on the diverse mix of variety of products, services and markets to help South Asian nations prosper. Originality/Value: The policy makers and entrepreneurs of South Asian nations have accorded high priority to export performance. This research is one of the few studies that highlights access to EO as the basis for better export performance of South Asian nations. 2021, Allied Business Academies. All rights reserved. -
Biomedical Waste Management: Legal and Regulatory Framework and Remedial Strategies
The present chapter begins with conceptual analysis of legal and regulatory framework from Indian as well as international perspectives. Follow through comparative analysis of Basel Convention on the Control of Trans-Boundary Movement of Hazardous Waste and Their Disposal, 1992; Convention on the Import into Africa and the Control of Trans-Boundary Movement and Management of Hazardous Wastes within Africa, Bamako, 1998; Convention on Persistent Organic Pollutants (POPs), Stockholm 2004; with Biomedical Waste Management Rules 2016 and (Amendment 2018) of India. The chapter also presents the legal and regulatory frameworks from the perspective of the United Kingdom, Indonesia, Kenya, and Sri Lanka as case studies. The chapter focuses on addressing SDG 3 (Good Health and Wellbeing), SDG 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation, and Infrastructure), SDG 10 (Reduced Inequalities), SDG 11 (Sustainable Cities and Communities), SDG 12 (Responsible Consumption and Production), SDG 14 (Life Below Water), SDG 15 (Life on Land), SDG 16 (Peace, Justice, and Strong Institutions), and SDG 17 (Partnerships for the Goals). 2025 Moharana Choudhury, Ankur Rajpal, Srijan Goswami, Arghya Chakravorty and Vimala Raghavan. -
Intersecting Barriers: Gender, Religion and the Political Under-representation of Muslim Women in Local Governance in Bihar
Womens reservation policies have substantially expanded female political participation in India, yet the representation of Muslim women continues to remain disproportionately low across levels of governance. Drawing on detailed administrative data from the 2016 and 2021 Panchayat elections in Bihar, this study examines the institutional, structural and behavioural mechanisms that shape Muslim womens political inclusion. Using a supply-side framework, the analysis formalizes two key determinants of contest entry, past co-ethnic competitiveness and demographic potential, and shows how these factors jointly influence womens decisions to contest elections. The results highlight the central role of institutional design and strategic expectations in shaping minority womens political agency, even in communities where demographic conditions appear favourable for political representation. 2026 Lokniti, Centre For The Study Of Developing Societies -
Transforming Customer Relations: Emotional AI and Behavioural Insights as Strategic Enablers in the Automotive Industry
This research examines how emotional AI deepens customer relations within the American automotive industry and how behavioural insights (BLI) mediate this process. Underlying framework: In the automotive industry, prioritising customer needs is crucial. Using emotional AI that analyses emotions and behavioural patterns can contribute to customer loyalty and satisfaction. The research employs a quantitative survey research approach. The sample pool comprised 237 customer experience managers representing various automotive companies in Texas. Emotional AI, business intelligence (BI), and consumer research are assessed using survey questionnaires, and the responses are recorded electronically. Descriptive regression and correlation analysis methods are employed to understand the connections between emotional AI, BLI, and customer relations. Evidence: The survey results indicated a positive relationship between the developed emotional AI and BLI, highlighting aspects of customer relations, including satisfaction and trust levels. The researchs findings suggest that emotional AI and BI could be a strategic intervention for enhancing customer loyalty. 2026 Haitham M. Alzoubi and Shanmugan Joghee and 2026 The authors. -
Fuzzy Logic Based Energy Storage Management for Parallel Hybrid Electric Vehicle
For the parallel hybrid electric vehicle, the various control strategies for energy management are illustrated with the implementation of fuzzy logic. The controller is designed and simulated in two modes for the economy and fuel optimisation. In order to manage the energy in HEV with three separate energy sources - batteries, Fuel cell and a supercapacitor system, - this article intends to create a fuzzy logic controller. By considering a complete system, the operating efficiency of the components need to be optimized. the control strategy implementation will be performed by the forward-facing approach. The fuel economy is optimised by maximising the operating efficiency in this strategy while other strategies does not have this extra aspect. The ability controller for parallel hybrid vehicles is mentioned in this research to enhance fuel economy. Although the earlier installed power controllers optimise operation, they do not fully utilise the capabilities. Hybrid vehicles can be equipped with a variety of power and energy sources such as batteries, internal combustion engines, fuel cell systems, supercapacitor systems or flywheel systems. The Authors, published by EDP Sciences, 2024. -
Enhancement of efficiency of military cloud computing using lanchester model
Cloud computing is a technology that uses centrally processed computing resources over the Internet by a large number of users. Because many requests are concentrated on cloud servers, they must be properly distributed to avoid degradation of quality. Load balancing categorizes requests from users according to established algorithms and assigns appropriate virtual machines. Because load balancing algorithms are developed according to the cloud's usage environment, various algorithms are being utilized. Recently, government agencies are also interested in introducing cloud technologies beyond private sectors. Many militaries have selected Cloud as its basic task to apply new technologies such as AI to military operations. However, there is no precedent for military cloud development, and the lack of doud technology research considering the operational environment has delayed the progress of cloud adoption. The algorithm presented by this paper makes the combat power, which varies according to the importance of the operation, an important variable. This variable makes each user's access to computing resources different. Although similar to other dynamic algorithms, the impact of priorities is so big that the degree of imbalance between tasks was higher. 2020 IEEE. -
Enhancing image compression through a novel Structural Fidelity Weighted Ensemble (SFWE) model
With the explosion of digital images across multiple sectors like social media, health care, medical imaging, and remote sensing, there is a demand to optimise the storage and transmission of images. In this paper, a novel Structural Fidelity Weighted Ensemble model is proposed to dynamically adjust the weights between SVD and PCA outputs to enhance the quality of reconstructed images.Unlike traditional static fusion techniques, the proposed SFWE deploys a fast bounded scalar optimization strategy so as to dynamically estimate the optimal fusion weights thereby ensuring non-negativity and simplex constraints while significantly reducing computational overhead compared to Sequential Quadratic Programming(SQP) or constrained gradient descent methods.Validation was done across multiple benchmarks datasets namely, USC-SIPI Sequences (grayscale TIFF), Kodak, BSDS500, DRIVE (Digital Retinal Images for Vessel Extraction), and ISPRS Potsdam which cover natural, medical, and remote-sensing images. Per-image processing, runtime measurement, and compressed ratio (CR) were produced automatically by the provided evaluation pipeline;The SFWE method provides greater image quality and structural fidelity across diverse datasets, attaining a PSNR of 40 dB and SSIM of 0.95, outperforming existing approaches such as Discrete Cosine Transform (DCT), Wavelet Transform, Singular Value Decomposition (SVD), and Principal Component Analysis and JPEG2000 + CNN models. In addition, it also maintains a good compression ratio leading to an effective balance between the reduction in file size as well as visual quality of the images, which confirms enhanced structural preservation across diverse image types. To implement a novel ensemble model (SFWE) that optimally balances the outputs of SVD and PCA for doing effective image compression. To achieve a higher SSIM (0.95) and good PSNR (40 dB) compared to compression techniques such as DCT, Wavelet, SVD, PCA, and JPEG2000 + CNN. To ensure adaptive high-quality reconstruction across multiple datasets, demonstrating its suitability for diverse image-intensive applications. 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/ -
Integrated Approach of Brain Disorder Analysis by Using Deep Learning Based on DNA Sequence
In order to research brain problems using MRI, PET, and CT neuroimaging, a correct understanding of brain function is required. This has been considered in earlier times with the support of traditional algorithms. Deep learning process has also been widely considered in these genomics data processing system. In this research, brain disorder illness incliding Alzheimer's disease, Schizophrenia and Parkinson's diseaseis is analyzed owing to misdetection of disorders in neuroimaging data examined by means fo traditional methods. Moeover, deep learning approach is incorporated here for classification purpose of brain disorder with the aid of Deep Belief Networks (DBN). Images are stored in a secured manner by using DNA sequence based on JPEG Zig Zag Encryption algorithm (DBNJZZ) approach. The suggested approach is executed and tested by using the performance metric measure such as accuracy, root mean square error, Mean absolute error and mean absolute percentage error. Proposed DBNJZZ gives better performance than previously available methods. 2023 Authors. All rights reserved. -
Integrated photonic devices for cancer detection
[No abstract available] -
2D Photonic Crystal for the Detection of Infectious Virus and Bacterial Diseases
In this paper, photonic crystal (PhC) sensor for analysis and modelling for viral and bacterial detection is proposed. Optical biosensors detect cancer, Bacillus cereus, malaria, typhoid, tuberculosis, etc. Optical biosensors work by shifting the peak resonance wavelength with modest refractive index changes. Because viral pathogens rapidly mutate and replicate in the human cell nucleus, sensors that offer accurate results for viral and bacterial diseases in seconds are in high demand. Hence, optical biosensors provide fast, sensitive results. The sensor detects influenza H1N1, hepatitis B (HBV), and typhoid, respectively. A maximum sensitivity of 443.33nm/RIU with a quality factor of 1309 is obtained. Simulations are performed using finite-difference time-domain (FDTD). The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Optimization and Design of a Sustainable Industrial Grid System
Electricity is a multifaceted form of energy and is used globally, with a continuously growing demand. Electrical power grids are there for more than 150 years. The generated electrical power is delivered to different industrial, commercial, and residential sectors, thereby fulfilling the ever-growing demand. In this research paper, the design and optimization of an industrial grid for various electrical loads is discussed. The electrical grid ensures a stable power supply to the loads by providing quality power with the minimum total harmonic distortion (THD) possible. A complete study of the short circuit current has been done in two different electrical grid systems, as it is seen that the short circuit current depends on the impedance of the transformer which feeds the load. These two designs of a single diagram will be simulated by using a power system analyzer, the Electrical Transient Analyzer Program (ETAP) software. The different electrical parameters, like choosing the optimised rated generator, cables, and transformers, are done. Load flow analysis is performed on both the design to evaluate the THD, short circuit fault, as well as to choose the right protection circuit for the system. 2022 Samat Iderus et al. -
An Optimized Algorithm for Selecting Stable Multipath Routing in MANET Using Proficient Multipath Routing and Glowworm Detection Techniques
Mobile Ad Hoc Networks (MANETs) depend on the selected and constant path with an extended period and the flexibility of the battery power condensed in searching end nodes, leading to numerous link failures. This kind of link damages occurs, and it also affects the packet success rate. We presented a Proficient Multipath Routing and Glowworm detection (PMGWD) technique to overcome such a Manets failure. Initially, a proposed Proficient Multipath Routing (PMR) technique identifies the damaged or failure routes and continues communication inefficiently. Secondly, the Glowworm detection node technique is implemented for both fault node identification and for extending the nodes network lifetime. Another reason to select the glowworm optimization is to update the node based on the glow to improve its neighbor its search space. Lastly, the PMGWD technique is utilized for identifying an optimal route and fault nodes in the manet. It is achieved to correct the identification of fault nodes using the glowworm detection node technique, and it helps to explore more paths for the optimal route by using proficient multipath routing. Hence, this proposed PMGWD technique is used to perform a problem-free communication process in a network system. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Evaluating Building Damage Classification Accuracy: A Benchmarking Study of UNet
Building damage classification must be done accurately and quickly in order to support disaster response and recovery activities. Deep learning models, particularly U-Net, have demonstrated strong potential in automating damage assessment from satellite and aerial imagery. This study benchmarks the accuracy of U-Net in classifying building damage across multiple datasets, evaluating its performance against ground truth labels. Key factors such as data preprocessing, augmentation techniques, and model variations are analyzed to determine their impact on classification accuracy. The results provide insights into the strengths and limitations of variations in U-Net for damage assessment, highlighting areas for improvement and future research directions 2025 IEEE.