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Resilience: Future Trends and Challenges in Small and Medium Enterprises
Small- and medium-sized enterprises (SMEs) are essential drivers of innovation, employment, and economic growth in the global economy. However, the rapid technological advancements associated with Industry 5.0 introduce unprecedented challenges and vulnerabilities for these businesses. This chapter delves into the resilience of SMEs, with a focus on the future trends and challenges that will shape their survival and growth in this ever-evolving environment. By leveraging secondary data from reputable databases such as Scopus and Web of Science, this study synthesizes the available literature to deliver a thorough analysis of SME resilience. In addition to digital transformation, this chapter discusses the growing importance of sustainability in building resilience. It advocates for the adoption of sustainable practices that mitigate environmental risks while aligning with the increasing demand for corporate social responsibility. This chapter also underscores the necessity of fostering a resilient organizational culture capable of withstanding economic and political uncertainties. By leveraging data from previous studies, this chapter offers practical recommendations for enhancing SME resilience. It can be a critical resource for policymakers, business leaders, and researchers seeking to understand and address the factors that will determine the future success and sustainability of SMEs in the age of Industry 5.0. 2026 Mohit Sharma, Rishi Chaudhry, Raj Kumar, Nitika Malik and Kuldeep Chaudhary -
Rendering support for the empowerment of rural women to overcome life impediments
The socio-cultural and economic landscape of rural India is not totally conducive for women. Surveys and reports in this regard would suggest the continuous existence of gender-based discrimination and its negative effects on the status and livelihood of rural women. Recognizing this situation, the Centre for Social Action (CSA) has pioneered its efforts to sensitize and involve youth (especially students) in the mitigation of rural issues and has been supporting women in select villages by promoting Self Help Groups (SHGs). These SHGs, in turn, organize capacitybuilding and empowerment programmes for the women to enhance their livelihood and socio-economic well-being. The present study is designed to understand the impact of CSA's intervention on the status and livelihood of women in the select project sites. Towards this end, we collected data from a sample of 150 women beneficiaries of CSA's initiative using a structured interview schedule. The study used a mixedmethod design. The study's outcome indicates a positive correlation between women's participation in CSA initiatives and their status and livelihood improvements. The results are encouraging that they would help one formulate effective models similar to this one for the empowerment of women. 2024 Nova Science Publishers, Inc. -
Co Adaptation Vs Signal Altering Regularization Layer in Deep Learning: A Trade Off Analysis via Node Redundancy and Transfer Learning
This study investigates the trade-off between incorporating regularization layers-such as Dropout, R-Drop, and Gaussian Dropout-and the phenomenon of co-adaptation in deep learning models. While regularization is designed to enhance generalization by disrupting hidden layer activations and reducing overfitting, it may also introduce node redundancy, potentially diminishing the model's capacity to learn efficiently. Conversely, co-adaptation, though often considered undesirable, may help preserve beneficial internal representations that contribute to learning generalizable data patterns-particularly in transfer learning scenarios-where regularization may inadvertently hinder such learning. Using the CIFAR-10 dataset, this study conducts an empirical analysis of how various regularization strategies influence neuron redundancy and downstream transfer performance. The results indicate that, although regularization effectively controls overfitting, excessive distortion in hidden representations can impair the model's ability to generalize across tasks. These findings provide insights into the need for balanced regularization strategies that maintain useful structure while minimizing detrimental redundancy. 2025 IEEE. -
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Analyzing the therapeutic significance of Strelitzia reginae Banks: Exploring its physico-chemical properties, elemental makeup, and antimicrobial activity
Plants constituting biologically active molecules with curative value have overtime showed advantage as subject of researches. Strelitzia reginae (Bird of Paradise) is a member of the Strelitziaceae family. Several South African tribes used plant parts to treat the venereal diseases and inflamed glands. The study aimed to investigate therapeutic potential of leaf and root extracts of S. reginae by assessing the physico-chemical properties, elemental analysis. Elemental analysis was carried out by Atomic Absorption Spectrometry (AAS) method, quantitative phytochemical analysis was carried out using, Gas Chromatography and Mass Spectrometry (GC-MS) analysis. The leaf and root of S. reginae were extracted using soxhlet technique of extraction and was further concentrated with a rotary evaporator. Standard protocols assessed the plants elemental compounds, physico-chemical properties, qualitative and quantitative phytochemicals, GC-MS analysis, antioxidant activity using DPPH (2,2-diphenyl-1-picrylhydrazyl) radical scavenging assay, phosphomolybdate assay, ferric reducing power assay (FRAP), metal chelating assay, and antimicrobial potential by well diffusion test. The results of AAS exhibited that the leaf and root contain more calcium and less of cadmium content. Preliminary phytoconstituents showed the presence of medicinally important alkaloids, anthraquinones, tannins, carbohydrates, flavonoids, saponins, phenols, proteins, and amino acids. The quantitative phytochemical analysis revealed that the leaf has higher total phenolic, flavonoid, chlorophyll, carbohydrates, protein, and proline contents than root. GC-MS analysis verifies the existence of bioactive components like squalene, hexatriacontane, phytol, hexacosane, heptacosane, and octacosane. DPPH, phosphomolybdate assay, FRAP and metal chelating antioxidant analysis revealed excellent activity in leaf and in root sample. As various South African tribes employed plant parts to treat sexual diseases and swollen glands, the antimicrobial property was investigated for the first time using a well-diffusion approach, and both plant parts revealed significant antibacterial and antifungal efficacy against recognized strains. The current study showed S. reginaes therapeutic potential and asked for more pharmacological and biological research to boost the importance of the worlds unevaluated herbal plants. 2024, Indian journals. All rights reserved. -
Assessing oral acute toxicity and histopathological effects of Strelitzia reginae Aiton leaf extracts in Zebrafish (Danio rerio Hamilton)
Strelitzia reginae, commonly known as the Bird of Paradise, is a decorative shrub endemic to southern Africa. This study marks the first comprehensive investigation into the safety of S. reginae leaf extract through oral acute toxicity assessments and histopathological examinations in Zebrafish (Danio rerio). The interest in this research arises from the historical use of S. reginae components by various indigenous South African societies to treat conditions like swollen glands and sexual problems. GC-MS analysis was used along with traditional methods to look at the phytochemical parts of S. reginae. The results showed the presence of several substances, such as eicosane, hexacosane, 1-octadecene, and neophytadiene. Notably, the analysis also identified certain chemicals with potential cytotoxic properties, such as octacosane and bis (2-ethylhexyl) phthalate. Drawing upon the biological similarities between Zebrafish and humans, who share a majority of their genes, this study represents the first attempt to evaluate the toxicity and histopathology of S. reginae using D. rerio as the test model, aligning with the OECD recommendations outlined in Article 203. The oral acute toxicity tests were done using ethanolic leaf powdered extracts of S. reginae. Higher concentrations (1200 mg/L) were toxic, but lower doses were less harmful to D. rerio. As observed in the histopathology examination, exposure to higher concentrations of S. reginae extract induced severe histological abnormalities in the Zebrafish's gills, liver, kidneys, intestines, and brain. This work contributes greatly to our understanding of S. reginae's safety profile and its potential therapeutic applications for enhancing well-being. 2024 Horizon e-Publishing Group. All rights reserved. -
Carbon Dioxide Neutralization across the Global Supply Chain
The increased impacts of climatic changes and global warming has led many organizations to adopt green initiatives in several areas of their business processes. Many multinational companies are moving towards reduction of carbon emission across its various operations. Carbon neutrality is the process where steps are taken to achieve net zero carbon dioxide emissions. This article proposes measures to achieve carbon neutrality across the supply chain globally. As part of its sustainability initiative, organizations have decided to reduce carbon consumption across their plants. This calls for estimation of carbon dioxide emissions and reducing the carbon footprint in the entire supply chain process. It also involves gauging Green House CO2 emissions during the transportation process for all TMC regions and Transportation models used by various companies. The main calculations include total CO2 emissions, CO2 Emissions per Ton. Of Goods Transported, CO2 Emissions per Transport Km. These calculations are done based on factors such as Full Truck Load, Less Truck Load, Sea mode of transportation and Air mode of transportation. An analysis is performed on the resulting calculation figures for different modes of transportation such as road, air and sea. The analysis shows that there is an increase in overall CO2e for Air mode of transportation. The least increase in overall Co2 is Sea mode of transportation. Through this analysis, it helps the company to take better decisions regarding the mode of transportation that they need to adopt to achieve carbon neutrality. The Electrochemical Society -
REVIEW OF CENSORING SCHEMES: CONCEPTS, DIFFERENT TYPES, MODEL DESCRIPTION, APPLICATIONS AND FUTURE SCOPE
Survival analysis is one of the key techniques utilized in the domains of reliability engineering, statistics, and medical domains. It focuses on the period between the initialization of an experiment and a subsequent incident. Censoring is one of the key aspects of survival analysis, and the techniques created in this domain are designed to manage various censoring schemes with ease, ensuring accurate and insightful time-to-event data analysis. The statistical efficiency of parameter estimates is improved by accurately incorporating censoring information by making use of the available data. This paper reviews the concepts, model descriptions, and applications of conventional and hybrid censoring schemes. The introduction of new censoring schemes from conventional censoring schemes has evolved by rectifying the drawbacks of the previous schemes, which are explained in detail in this study. The evolution of hybrid censoring schemes through the combination of various conventional censoring schemes, the data structures, concepts, methodology, and existing literature works of hybrid censoring schemes are reviewed in this work. 2024, Gnedenko Forum. All rights reserved. -
Advancing Astronomical Science - Machine Learning-Based Classification of Variable Stars for Scientific Innovation and Research
Variable star classification is an important part of astrophysics and gives astrophysicists a way of studying stellar evolution, structure and dynamics. Due to the availability of large scale surveys such as Gaia DR3, machine learning techniques are used in automation of the classification process. In this study, RF, SVM, MLP and XGBoost (XGBClassifier) models are evaluated for classification of variable stars. The data set used in this work was collected from Gaia DR3 using Astroquery and the ability of these models is evaluated for different star classes. The result shows that the XGBoost had the best accuracy of 91% compared to RF (89.98%), MLP (88%) and SVM (83%). A comparison of various metrics such as precision, recall and F1-score of each method is also provided to address their strengths and weaknesses. This work further emphasises the need of sophisticated machine learning techniques in astrophysical data analysis and discusses problems of certain kinds of variable star classification. KeywordsGaia, variable stars, Classification, Machine Learning, Random Forest, XGBoost, Multi-Layer Perceptron, Support Vector Machine. 2025 IEEE. -
Bayesian and non-bayesian inference of the generalized Lomax distribution under a unified hybrid censoring scheme with applications in failure times in biomedical and aerospace materials
The unified hybrid censoring scheme is a combination of different types of censoring schemes used in reliability testing. This paper presents the statistical inference of generalized Lomax distribution under unified hybrid censoring scheme. The point and interval estimates of the parameters ?,?, and ? of the generalized Lomax distribution have been studied for unified hybrid censored data. In point estimation, the maximum likelihood estimation method is used for computing the estimates, and Tierney and Kadane estimation method is used for Bayes estimation. A 100(1-?)% approximate confidence interval and Bayesian credible intervals for the parameters ?,?, and ? have been computed in the interval estimation part. Mean squared errors are computed for all the estimates and comparison of estimates have been done. The results indicate that the Bayesian estimation method yields more accurate and reliable parameter estimates compared to the maximum likelihood approach. Finally, data representing failure times of fatigue fracture of Kevlar 373/epoxy and failure times of aircraft windshields have been used for point and interval estimations of all parameters as application of real-life scenarios. The Author(s) under exclusive licence to The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden 2025. -
Examining the Relationship between Academic Expectations and Suicidal Ideation among College Students in India Using the Interpersonal Theory of Suicide
Objective: As the second most populous country in the world, India accounts for over 20% of the global suicide deaths. Notably, young adults make up 38% of those who die by suicide in India. Yet, the literature on factors associated with suicide within this age group in India is limited. The Interpersonal Theory of Suicide (IPTS) posits thwarted belongingness and perceived burdensomeness as constructs that heighten the risk for suicide. Testing mechanisms that may mediate the relationship between common stressors for young adults in India, such as academic expectations, and suicidal ideation are important to better understand factors contributing to suicide risk within this country. Method: Indian college students (N = 432, M age = 19.41, 73.1% male) completed questionnaires on academic expectations, thwarted belongingness, perceived burdensomeness, collectivism, and suicidal ideation. Results: Current suicidal ideation was endorsed at a rate of 38%. Academic expectancy from the self, perceived burdensomeness, and thwarted belongingness was significantly associated with suicidal ideation. The only significantly mediated pathway was academic expectancy from others to suicidal ideation through perceived burdensomeness. Collectivism was not a significant moderator in the model. Discussion: The sample endorsed high rates of suicidal ideation, highlighting the need for culturally appropriate interventions. Thwarted belongingness, perceived burdensomeness, and academic expectations from oneself may be relevant treatment targets for reducing suicidal ideation among college students in India.HIGHLIGHTS Over one-third of Indian university students endorsed suicidal ideation. Suicidal ideation related to ones own more than others academic expectations. Results offer support for the Interpersonal Theory of Suicide within this context. 2022 International Academy for Suicide Research. -
Impact of Node Failures on Productivity in Multilayer Supply Chain Networks: An Influence Network Analysis in the Indian Electronics Sector
Supply chain networks are essential for the delivery of goods and information, but disruptions such as natural disasters or trade embargoes can severely impact them. Resilience of entire networks under different types of disruptions when nodes or edges fail has been extensively studied. However, the extent to which the failure of a particular company affects another company of interest within a network has not been widely explored. To address this, we created a multilayer physical supply chain network of companies in the Indian electronics industry. Through systematic node removal simulations, we examined how the productivity of one company is impacted by the removal of another. Extending these simulations to include all possible combinations of companies yielded an influence network that represents interdependence among nodes in terms of productivity. We observed that removing a critical node could lead to not only a decrease but, quite counter-intuitively, an increase as well in the productivity of affected nodes. This study identifies the factors that influence these productivity changes and offers insights to supply chain managers to maintain network resilience in the face of node failures. 2025, Binghamton University Libraries. All rights reserved. -
Modeling Flood-Induced Cascading Disruptions in the Indian Electronics Supply Chain Using Influence Network Analysis
This study investigates flood induced disruptions in the Indian electronics supply chain using influence network analysis. Monsoon floods are recurring hazards that significantly impact economic activities, logistics, and industrial productivity. This study integrates district-level rainfall data (2020 to 2025) with supply chain network models to quantify cascading failures. The methodology applies rainfall thresholds (? 300 mm/month) to identify flood-prone districts and constructs a stochastic influence matrix representing inter-firm dependencies. Flood propagation dynamics are modeled iteratively with a propagation coefficient (? = 0.6) and convergence threshold (? = 10-4). The resulting disruption profiles are mapped onto company-level revenues calibrated to India-specific scales, adjusted for disruption durations (two months per year). This approach produces district and company-level economic loss estimates consistent with observed flood impacts (e.g., Chennai 2015 flood losses of USD 3 to 5 billion). Key contributions include linking meteorological hazards to systemic supply chain failures, demonstrating economic vulnerabilities at district and sectoral scales, and providing a framework for resilience planning. 2026 Binghamton University Libraries. All rights reserved. -
Artificial intelligence for blockchain and cybersecurity powered IoT applications
The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper-proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity. 2025, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouard, Abhishek Kumar, Vandana Sharma and Keshav Kaushik. All rights reserved. -
Artificial intelligence for blockchain and cybersecurity powered IoT applications
The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper-proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity. 2025, Mariya Ouaissa, Mariyam Ouaissa, Zakaria Boulouard, Abhishek Kumar, Vandana Sharma and Keshav Kaushik. All rights reserved. -
Credit card fraud detection using ANN
Fraud on its own was and is devastating a lot of businesses, be them small or large. Particularly in the field of finance where we can see constant attacks on both individuals and enterprises alike. As such, credit cards are the most targeted as they are linked to both personal information and accounts. It is also evident to say that credit card fraud detection research is very much needed to deter and mitigate the impact of fraud on the financial field in general. It is important to identify frauds before it is too late so that the stolen credit card cannot be used for fraudulent transactions. To effectively detect these fraud transactions, we use a data consisting of fraudulent and non-fraudulent transactions to create a model that classifies these transactions with a high accuracy based on a machine learning technique. We used Artificial Neural Network with Logistic Regression to measure and in order to achieve high accuracy, we refined the parameters using the algorithms Back-propagation which has proved to have a high accuracy rate giving the model the ability to distinguish a fraudulent transaction from a normal one. BEIESP. -
Corporate social responsibility assurance, board characteristics and social performance disclosure. Evidence of listed firms in India
The study examines board characteristics, corporate social responsibility (CSR) assurance and social performance disclosure of listed firms before and after mandatory CSR reporting in India. We used the Indian stock market as the testing grounds and applied panel regression and difference-in-differences to analyse 960 firm-year observations between 2010 and 2021. The first findings show that independent board directors and total board size are insignificant in CSR assurance engagement in a mandatory CSR policy period. However, CEO duality is less than likely causing CSR assurance engagement. The second findings show that CSR assurance engagement more than likely causes an increase in social performance disclosure before mandatory CSR policy implementation and increases social performance after policy implementation. The third findings show that the interactive effect of board characteristics (independent directors, total board size and CEO duality) and CSR assurance engagement causes an increase in social performance disclosure. The study sought clarity on the impact of CSR assurance and mandatory CSR reporting on information asymmetry problems to stakeholders. The study also contributes new knowledge on the influence of the interactive effect of board characteristics and CSR assurance on the social performance disclosure of listed firms in India. 2022 John Wiley & Sons Ltd. -
Radiation effects on 3D rotating flow of Cu-water nanoliquid with viscous heating and prescribed heat flux using modified Buongiorno model
In this article, the three-dimensional (3D) flow and heat transport of viscous dissipating Cu-H2O nanoliquid over an elongated plate in a rotating frame of reference is studied by considering the modified Buongiorno model. The mechanisms of haphazard motion and thermo-migration of nanoparticles along with effective nanoliquid properties are comprised in the modified Buongiorno model (MBM). The Rosseland radiative heat flux and prescribed heat flux at the boundary are accounted. The governing nonlinear problem subjected to Prandtls boundary layer approximation is solved numerically. The consequence of dimensionless parameters on the velocities, temperature, and nanoparticles volume fraction profiles is analyzed via graphical representations. The temperature of the base liquid is improved significantly owing to the existence of copper nanoparticles in it. The phenomenon of rotation improves the structure of the thermal boundary layer, while, the momentum layer thickness gets reduced. The thermal layer structure gets enhanced due to the Brownian movement and thermo-migration of nanoparticles. Moreover, it is shown that temperature enhances owing to the presence of thermal radiation. In addition, it is revealed that the haphazard motion of nanoparticles decays the nanoparticle volume fraction layer thickness. Also, the skin friction coefficients found to have a similar trend for larger values of rotation parameter. Furthermore, the results of the single-phase nanoliquid model are limiting the case of this study. 2021, The Author(s). -
Development of ?-carrageenan-based transparent and absorbent biodegradable films for wound dressing applications
Wound healing remains a critical challenge in healthcare, requiring advanced wound dressings with superior properties like transparency, absorbency, and biocompatibility. However, gaps exist in the use of marine-derived biopolymers for sustainable dressings. This study addresses this gap by combining ?-carrageenan (KC) with polyvinyl pyrrolidone (PVP) to develop transparent and absorbent biodegradable films through solvent casting and lyophilization techniques. Lyophilized films exhibited superior absorbency (9.17 g/cm2) and moisture management, with a water vapour transmission rate of 3990.67 g/m2/24 h, while solvent-cast films showed 78 % transmittance, enabling wound visualization. Mechanical testing revealed high tensile strength (31.5 MPa) and folding endurance (410 folds), ensuring durability. In vitro bactericidal assays confirmed efficacy against MRSA and E. coli, and in vivo tests on Wistar rats showed complete wound healing within 16 days with 91.1 % closure, outperforming untreated controls (76.7 %). This is the first study to explore lyophilized KC-PVP films for wound dressing applications, demonstrating potential for drug release, absorbency, and biodegradability. The innovative combination of biopolymers and fabrication techniques offers a sustainable, high-performance solution for wound care. 2024 Elsevier B.V. -
N'-[(1E)-1-(2-Fluorophenyl)Ethylidene]Pyridine-3-Carbohydrazide /
Acta Crystallographica Section E, Vol-E70(o115), ISSN-1600-5368