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The Learning Organization for attaining inclusive growth: A new paradigm for the emerging educational market
This chapter aims to apply the concept of the learning organization for the higher educational institutions (HEIs) for attaining inclusive growth. For the theoretical underpinning, the concept of the professional learning community has been considered because there exists only a very thin difference with the learning organization concept. The researchers used a qualitative approach to analyze the literature resources to find the most promising variables associated with the learning organization and used Dedoose (qualitative software) to select the variables. It is found that variables such as knowledge management, inspired learning, team learning, and transcending organizational boundaries are the most important variables associated with a learning organization. The corporates which have implemented LO have been very successful and one of them is Royal Dutch Shell. The Indian Higher Educational Institutions are the backbone of the Indian economy. However, there is no yardstick to measure success. LO can be implemented to check the growth. This research work will serve as a base for motivating future researchers to make use of the LO concept for drafting educational policy for inclusive growth. If all HEIs are learning organizations, India will soon be a highly rich country. Indian HEI has not practised the concept of learning organization. Henceforth, the suggestions and recommendations will be a new yardstick, which can be used to measure the present status and to frame strategies to improvise and scale success. 2022 selection and editorial matter, Sudhir Rana and Avinash K Shrivastava. -
Efficacy of Nanomaterials and Its Impact on Nosocomial Infections
Nanotechnology provides the ability to manipulate the properties of materials by using their size, and this has lead research towards a massive amount of plausible uses for nanomaterials. Irresistible maladies can occur, and they create an impressive burden on general wellbeing worldwide. The incident of these ailments is higher in developing nations. Irresistible maladies might be caused by microscopic organisms, infections, and protozoa, and the diseases they cause are often resistant to traditional treatment bringing about protracted contamination and higher mortality risk. In connection to that, the patients infected with these smaller scale creatures, that may prove resilient for an extended period of time, can be transmitters of these diseases to others. The recuperating of irresistible maladies is possible by metal-based nanoparticles that are plausible therapeutics for the treatment of irresistible ailments and their natural productivity. Metal-based nanoparticles that have been accounted for with antibacterial movement include silver, iron, iron oxide, copper oxide, zinc oxide, aluminum oxide, titanium dioxide, gold, and gallium nanoparticles. Present day improvements in nanotechnology enable us to handle this issue at two levels: diagnostics and treatment. Elimination of irresistible microorganisms requires effortless and exact recognition of the irresistible agents for suitable treatment. Various nanomaterials have been considered for the management of and cautious measures for irresistible ailments. Recently, nanomaterials have improved the treatment, diagnostics, and avoidance of irresistible illnesses. Built nanoparticles have been progressively utilized in irresistible infection management caused by microorganisms. Progress in nanoparticle-based frameworks involve a confident research region with basic ramifications for the recuperating of bacterial contaminations. Nanosystems have been shown to be beneficial, and different approaches dependent on nanoparticles have been expanded to see unambiguous agents. Various purpose-of-care (POC) tests have been anticipated that can propose results earlier, simpler, and at less expense than known strategies and can even be used in difficult to reach areas for viral determination. Quorum sensing is a boosts reaction substance formulation strategy interrelated with population density that microorganisms use to authorize biofilms development. Research is ongoing concerning the antimicrobial movement of nanoparticles, contrasting it by methods for and the motivation behind the natural extract of therapeutic plants, and concentrating on anti-toxin protections of pathogenic microscopic organisms. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Grit as a protective factor against Nomophobia: Moderating effects of maximizing decision style
The present study examined the role of grit and decision-making styles in predicting nomophobia among young adults, considering gender and educational differences. The study also tested whether decision-making styles moderated the association between grit and nomophobia. Using a cross-sectional ex-post facto correlational design, data were collected using purposive sampling technique from 323 young adults (aged 1725) through online platforms. Participants completed the Three-Dimensional Grit Scale, the Maximizing Inventory, and the Nomophobia Questionnaire. Chi-square test showed that there was an association between gender and education. Mann-Whitney U test showed that men were higher in grit than women. Men and women did not differ in nomophobia or decision-making styles. Kruskal-Wallis ANOVA showed that grit, nomophobia, or decision-making styles did not differ concerning education. Spearmans correlation analysis showed that grit had a moderate positive correlation with satisficing. Nomophobia had a weak negative correlation with grit and a moderate positive correlation with Maximizing. Regression analysis showed grit as a negative predictor and Maximizing a positive predictor of nomophobia. Moderation analysis indicated that Maximizing style weakened the protective effect of grit on nomophobia, accounting for a small but significant variance in outcomes. The study highlights the interactive role of grit and decision-making styles in understanding nomophobia. Findings suggest that grit alone is insufficient as a protective factor against nomophobia. Its effectiveness depends on decision-making styles, with Maximizing acting as a risk enhancer. 2026 Taylor & Francis Group, LLC. -
Research progress of MXenes and MXenes-based catalysts for photocatalytic water splitting: A systematic review
The field of two-dimensional (2D) materials has witnessed remarkable growth over the years, especially on a class of materials known as MXenes. MXenes have garnered significant attention for their exceptional physicochemical properties, which include high electrical and thermal conductivity, large surface area, adjustable bandgap, and hydrophilicity. These characteristics have paved the way for a diverse range of applications, including photocatalysis, electrocatalysis, supercapacitors, sensing, and biomedicine. MXenes have been recognized to be particularly effective in applications such as photocatalytic hydrogen production through water splitting reactions. This involves using MXenes as cocatalysts to enhance the efficiency of the photocatalytic process. In this review, the various synthetic methods for producing MXenes and MXenes-based catalysts are summarized, shedding light on the versatility of their fabrication techniques. The underlying mechanisms of photocatalytic H2 evolution are explored, providing insights into how MXenes function as cocatalysts in these reactions. These mechanisms are crucial for understanding the enhancement of H2 production and improving the overall efficiency of the water splitting process. Furthermore, the review delves into the challenges that researchers face when utilizing MXenes and MXene-based materials for electrocatalytic water splitting. These challenges serve as motivation for further exploration and innovation in the field, driving the development of more efficient and sustainable electrocatalytic systems. In this discussion, the potential future applications of MXenes and their composites in electrocatalytic water splitting and other fields are explored. This suggests that ongoing research and advancements in MXene-based materials have the potential to revolutionize various technological areas, contributing to the development of cleaner energy sources and more efficient catalytic processes. 2024 Elsevier Ltd -
Mergers and acquisitions in India Information Technology Industry and its impact on shareholders wealth
International Journal of Research in Commerce, IT & Management Vol. 2, Issue 4, pp. 118-121 ISSN No. 2231-5756 -
The Executive Copilot: Investigating the Impact of Generative AI Assistants on Strategic Decision-Making, Leadership Efficiency, and Managerial Creativity
The generative artificial intelligence (AI) revolution has given rise to a class of technology - executive copilots that are designed to unclog the brains of overworked managers in the 21st century. This paper examines the effects of generative AI assistants on three intertwined facets of executive performance - strategic decision making, leadership effectiveness, and managerial creativity. Early literature suggests that AI systems could automate administrative tasks and offer data-driven insights; however, we still lack an understanding of their comprehensive impact on executive tasks and outcomes. To bridge this gap, the study uses a multimethod research design, drawing on survey data from 350 senior executives from various firms, controlled experimental studies of strategic problem solving, and in-depth case analysis of firms in finance, consulting, and technology sectors. Results suggest that AI copilots contribute significantly to faster decision-making and creative output by helping to synthesize information and sparking divergent thinking rapidly. Consistent with this, results reveal that a potential 'caveat' of overreliance on AI-generated recommendations might lead to less scrutiny and thus the fragility of the high-stakes decision-making in practice. Theoretically, the study extends the leadership augmentation theory to the context of generative AI. It extends the conceptualization of the executive copilot in terms of a complementary agent that adjusts the boundary of human-machine collaboration. Pragmatically, it has implications for executive education, corporate governance, and the design of responsible AI uptake strategies. Through methodical analysis of opportunities and risks, this study offers a nuanced view of how executive copilots change leadership practice in the era of intelligent augmentation. 2025 IEEE. -
A Comprehensive Review of Advanced Analytics for Predicting HRQoL in Cancer Survivors Using a Synergistic Approach
This systematic review explores the role applied and emerging methods including AI, Explainable AI and Quantum machine learning techniques in the prediction of Health-Related Quality of Life (HRQoL) of cancer survivors. It also gives possible benefits and limitation of using the advanced analytics to predict the HRQoL. In all, 141 research papers implemented in the last fifteen years with focus between the years 2008 to 2023 are analyzed. For the convenience, this literature review is divided into four primary categories - (i) Artificial intelligence, (ii) Explainable artificial intelligence, (iii) Quantum machine learning, and (iv) Synergistic integration. The third way the present systematic review paper differs from other papers in the domain is that the paper offers a direction of future research. Furthermore, the hypothetical illustration is provided in order to compare outcomes of the synergistic approach with the existing data. Consequently, this analysis provides beneficial insights for further research and development of the synergistic approach in both research and clinical practice. The assessment shows that there is a continued need for research focusing on improving the quality of life of those that survived cancer. 2025 IEEE. -
TenzinNet for handwritten Tibetan numeral recognition
Tibet is known for its enumerable collection of Nalanda based Buddhism manuscripts that need to be digitized for immortalization of the teachings of Buddha and various Buddhist scholars. Handwritten Tibetan numeral recognition is relatively unexplored as compared to Roman and Chinese numerals. Recognition of handwritten documents for digitalization has been under study from past many years. This work proposes a novel model using convolutional neural networks architecture named as TenzinNet to recognize handwritten Tibetan numerals. TenzinNet achieved an accuracy of 90.76% in recognizing Tibetan numerals using the proposed model. 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Regulating the gig economy: Addressing worker rights in India's quick commerce sector
The study examines the regulatory challenges faced by India's fast- growing quick commerce gig economy, with a focus on the rights and protections of workers. It examines the issue of gig workers and implementation gaps in existing frameworks and highlights the vulnerabilities of migrant workers. The study uses thematic analysis to explore aspects such as working conditions, workers' legal awareness, algorithmic management, and gender inclusion in the workforce. Findings reveal regional disparities in working conditions, discrepancies between legal recognition and practical enforcement, and persistent gender- based inequalities. The research underscores the need for tailored, collaborative efforts among various stakeholders to improve the situation of gig workers. This study aims to enhance the understanding of regulatory issues in the quick commerce sector and offers valuable insights for policymakers, platforms, and researchers to improve working conditions and ensure fair treatment for gig workers in India. 2025, IGI Global Scientific Publishing. All rights reserved. -
Driving better health outcomes for gig workers through strategic health initiatives
The gig economy offers flexibility and autonomy to workers but also presents significant challenges related to health and well-being, especially for delivery professionals who face irregular hours, physical strain, and limited access to healthcare. Immersive technologies, such as virtual reality (VR), augmented reality (AR), and artificial intelligence (AI), present innovative solutions to bridge these gaps. However, gig workers' adoption of these technologies remains underexplored. This paper applied the diffusion of innovation (DOI) theory to analyse the adoption patterns of immersive technologies within the gig economy and identified barriers, such as digital literacy and cost, alongside facilitators, like perceived usefulness and ease of integration. The paper provides insights into how these technologies can be effectively implemented in the gig workforce. The study highlights the role of platform policies and the broader regulatory landscape in shaping technology adoption, offering valuable recommendations for policymakers and technology developers. 2025 by IGI Global Scientific Publishing. All rights reserved. -
Health Communication as Capability: Gig Workers Freedoms Through Sens Approach
The rapid growth of the gig economy has increased the number of delivery-platform workers, whose precarious employment conditions expose them to health risks while limiting their ability to utilize existing health resources. This article employs Amartya Sens Capability Approach (CA) to reframe the health challenges faced by delivery workers, arguing that critical barrier is not the absence of medical facilities but the lack of communicative capability to access and use them. Within the CA, resources are only meaningful when individuals can convert them into valued functionings. For delivery workers, constraints such as time poverty, lack of paid leave, and informational asymmetries weaken this conversion process. We argue that health communication must be understood as a capability that directly enlarges workers substantive freedoms by equipping them with the knowledge, confidence, and navigational skills needed to make informed health choices. Health communication thereby turns access into utilization, and utilization into well-being. Health communication operates both as a valued function of being informed and able to engage with health systems and as an instrumental freedom that enhances the conversion of existing resources into achieved health outcomes. Recognizing health communication as a capability reshapes policy debates, highlighting the need to invest in service provision and communicative infrastructures that expand workers agency and real opportunities for well-being. 2025 Taylor & Francis Group, LLC. -
A fuzzy soft coronavirus alarm model
The entire world experienced a rampant outbreak of Covid-19 beginning in December 2019. The spread of this disease was so rapid and aggressive that many developed countries struggled to control it. However, some countries such as China and Australia have done a commendable job of controlling this virus. Various studies have been done in parallel to analyze strategies to curb the spread of the virus. In many locations, people displayed swarm intelligence. The collective behavior of people was mixed. Some people followed the instructions of the health authorities. In addition to the instructions, people in some localities developed self-organization to resist the spreading of the virus. This research work mainly focuses on the prediction of coronavirus spread in different districts of Kerala by use of a fuzzy approach as the fuzzy approach is considered the best tool that would not show imprecise data in any situation. The PRONE (Predicted Risk of New Event) indexing algorithm was used for finding the intensity of the spread in five districts of Kerala (Trivandrum, Ernakulam, Kozhikode, Kannur, and Kasargod) and was evaluated under the input parameters of immunity of person, food habits, financial factors, and age with the total number of infected people as the output variable. An eight-step algorithm is provided to determine the PRONE index. Kasargod is more vulnerable to the virus. The final results show that this proposed model better predicts virus spread. 2024 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
A modified fuzzy approach to prioritize project activities
Project management is an important task in business although project is not just confined to business. Due to the uncertainty of the various variables involved in a project, over several past decades research is going on in the search for an efficient project management model. Although numerous crisp models are easily implementable, the potential of fuzzy models are huge. In the case of software development, the variables involved are highly dynamic. In this paper, we propose a ranking based fuzzy model that can prioritize various activities. We use a popular crisp model to test the effectiveness of the fuzzy model proposed. Simulation is done through Java Server Pages (JSP). There is considerable computational and managerial advantage in implementing the fuzzy model. 2018 Authors. -
A fuzzy computing software quality model
Expectation of the quality of a software varies from user to user. A fuzzy approach to measure the quality of a software is very appropriate so that it can deal with non-crisp aspects of the various parameters. In the proposed model, ordered intuitionistic fuzzy soft sets (OIFSS) and relative similarity measures of OIFSS are considered in the backdrop of fuzzy multiple criteria decision making (FMCDM) approach. 2019 Author(s). -
An ordered ideal intuitionistic fuzzy software quality model
Software is one of the major factors in the development of computer - based systems and products. Measurement of the software quality is thus the key factor that has to be taken into account while developing a software system. Many software quality models with numerous quality parameters are under use to measure the performance of a software system, on the basis of which the software is valued. This study intends to make available a fuzzy multiple criteria decision making (FMCDM) approach to measure software quality and to propose new similarity measures between ordered ideal intuitionistic fuzzy sets (OIIFSs). The proposed model is applied to five live software projects so as to quantify the software quality of each project under fuzzy environment. IAEME Publication. -
A fuzzy approach to project team selection
Project team selection is a complex process in software engineering. The study uses a multiple criteria decision making (MCDM) approach for the selection of a project team under fuzzy environment. In this paper a FRI, FSS approaches are developed to the selection of project team. 2019, International Journal of Scientific and Technology Research. All rights reserved. -
Behavioral Biases in Financial Markets: Understanding the Impact of Cognitive Heuristic-Driven Biases and Emotional Biases in Shaping Investment Decisions
Conventional finance theories believe that the stock market is organized and that stock valuations provide all relevant facts. On the other hand, behavioral finance theories argue that stock valuations can be affected by behavioral biases, explicitly cognitive heuristic-driven biases and emotional biases. The stock market displays the current wellness of an economy, and investment decisions represent it. Investors unveil irrational actions in their investment decision strategies. The investment decision strategy itself is a cognitive procedure, as stock investors must form decisions informed by several possibilities that are available to them. This chapter provides theoretical underpinnings and an overview of the effect of behavioral biases on investors investment decision-making. This research provides an in-depth insight into cognitive heuristic-driven biases (Illusion of Control, Hindsight, Conservatism, House Money Effect, Self-Attribution, Gamblers Fallacy, Confirmation, Recency, Familiarity, and Religiosity) and emotional biases (Disposition Effect, Loss Aversion, Regret Aversion, Risk Perception, and Mental Accounting) impact investment decisions. The implications of this study could be helpful for financial markets and institutions as well as practitioners, such as equity investors and traders, portfolio and asset managers, securities analysts, wealth advisors, money managers, securities bankers, and brokers. In addition, it benefits regulators, policymakers, academicians, and researchers. The overall chapter offers a positive impact between behavioral biases and investment decisions, with distinct themes from earlier research, and contributes to generalization. Copyright 2026 by Nova Science Publishers, Inc. -
A SIGNIFICANT STUDY ON ROBUST MEASURE OF LOCATION PARAMETERS USING DATA DEPTH APPROACHES
Data depth procedures are statistical methods used to measure the centrality or depth of a point within a multivariate dataset. These procedures provide a way to quantify how deep or outlying a point is relative to the overall distribution of the data. This study explores various data depth procedures to find reliable location estimations in cases like with and without outliers. In this paper, various depth procedures, such as Mahalanobis depth, Halfspace depth, Euclidean depth, Simplicial depth, and Projection depth, are studied and compared. The efficiency of these depth functions is evaluated using real datasets and simulation studies with R software. 2025, Gnedenko Forum. All rights reserved. -
A Novel Preprocessing Technique to Aid the Detection of Infected Areas of CT Images in COVID-19 Patients Artificial Intelligence (AI) for Communication Systems
An innovative preprocessing method for discerning infected areas in CT images of COVID-19 is described in this abstract. The methodology being suggested exploits the capabilities of artificial intelligence (AI) to improve disease detection communication systems. By employing sophisticated AI algorithms to preprocess CT images, the method seeks to increase the precision and effectiveness of COVID-19-associated area detection. The incorporation of artificial intelligence (AI) into communication systems facilitates enhanced image analysis, resulting in improved diagnostic capabilities and treatment strategizing. The study's findings demonstrate the potential of preprocessing techniques powered by artificial intelligence in augmenting communication systems with the aim of enhancing healthcare outcomes. 2024 IEEE. -
Classic Models, Modern Threats: A Study on Adversarial Attack and Defense for Traditional ML Models
Adversarial attacks are a serious threat to machine learning models, both for conventional architectures, like neural networks, and for more sophisticated frameworks, like Vision Transformers (ViTs). Although a lot of work has been done to defend state-of-the-art deep learning models against attacks like Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Gaussian noise perturbations, classical machine learning models like logistic regression, support vector machines (SVMs), and decision trees are relatively less explored despite their extensive use in situations where low computational complexity and high interpretability are needed. This work presents a rigorous evaluation of the adversarial vulnerability of binary and other classical models on the MNIST dataset and explores the effectiveness of various defense mechanisms, including adversarial training, input pre-processing (Gaussian smoothing), and defensive distillation. Experiments demonstrate that adversarial training is the most effective defense that improves model robustness with classification accuracies of up to 96% in all attack scenarios. In contrast, defensive distillation and input preprocessing make modest gains, with accuracy levels ranging from 61 to 81% based on the nature of the attack. Through adversarial threat analysis of typical machine learning models, this work points out their inherent susceptibility to adversarial perturbations and introduces robust defense techniques. These results identify the necessity for robust security and reaffirm the practical viability of typical models in the scenario of resource-constrained environments, contributing towards a more complete picture of adversarial defenses for the entire spectrum of machine learning architectures. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.