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Skill Enhancement is an Essential Aspect of the Emerging Curriculum to Resolve the Talent Crunch and Foster Entrepreneurship Among Young Graduates
Human capital management is one of the most important aspects of the booming economies in the current scenario. However, a problem faced over the last decade is the lack of skilful employees required by the industries in the changing market trends. The World is rapidly progressing with innovation and technological advancements from time to time. Our young graduates need to gear up and gain momentum to match the ever-changing needs of the new business models. The biggest challenge in many countries is recruiting skilful resources, resulting in a recent Talent crunch and another important problem is a shortage of entrepreneurs. When these issues are discussed, it is very important to bridge the gap and make the path to success clear by transforming human capital into skilful capital, which could be achieved by redesigning the curriculum and tailoring it to integrate academic knowledge with industry interface. This paper is an attempt to highlight the importance of Curriculum design in improving the skills for employability and entrepreneurship among students to bridge the gap between the industry, job seekers and the role of educational institutions in building an individual's employability and growth, as they are the prime sources of skills and knowledge. 2025 selection and editorial matter, Kennedy Andrew Thomas, Joseph Chacko Chennattuserry and Joseph Varghese Kureethara; individual chapters, the contributors. -
Artificial intelligence in education: Personalizing learning and overcoming ethical challenges
This may be the breakthrough that AI in education can give to personalized learning, offering learners experiences tailored according to their individual needs and styles of learning. AI-driven technologies like adaptive learning platforms and intelligent tutoring systems enhance engagement, deepen understanding, and facilitate real-time feedback. However, the speedy development of AI in education also poses significant ethical challenges, including data privacy and algorithmic bias, as well as the automation of learning processes. It investigates the need for fairness and transparency in AI algorithms, protecting student data, and minimizing biases that might occur in the automated systems. Balancing innovation with ethical considerations will unlock new possibilities in education with AI while at the same time safeguarding the integrity of the learning environment. The chapter sets out key issues at the interface of AI and education, which provide a balanced overview of both its transformative potential and the challenges that need to be addressed for its responsible use. 2026, IGI Global Scientific Publishing. All rights reserved. -
Demystifying Data Justice: Legal Response To India's Privacy And Security Standards: Challenges In Cloud Computing
Data is the new oil of this economy. Cloud Computing acts in the capacity of storing databases, in operational analytics, networking and intelligence. Indian cloud computing market is valued at 2.2 billion dollars, which is said to scale by 30 percent in 2022. It's therefore pertinent to understand Indian's data protection landscape in the light of Personal Data Protection Bill, 2018 to answer the questions of ownership, controlling, processing of data in order to reflect upon the liability, obligations, and compliances by intermediaries, dispute resolution forums, data portability and indemnification. The authors will explore by means of doctrinal method, the challenges posed on the content regulatory mechanism for the internet architecture which paves responsibility of data classification into lawful and unlawful, with the exception of section 79 of Information Technology Act. The authors will further examine the encryption standard tools exhibiting data security and the obstacles created by the 40-bit limit encryption standard as part of the DoT's telecom licensing conditions and section 84A IT Act, 2008, to provide suggestions towards pragmatic delimitation. Cloud computing being the next growth frontier of the IT industry, makes it more evident to enable cloud forensics in entrusting with investigations and establishing confidence within the end-users. Goal 16 of SDG's deal with Promote just, peaceful and inclusive societies. The Electrochemical Society -
Irreducible tensor approach to study ? + d ? d + ? 0
The study of photoproduction of mesons plays an important role in understanding the properties of strong interactions. Pion photoproduction on deuterons has been studied theoretically for several decades. At the VEPP - 3 storage rings, tensor analysing powers in ? + d ? d + ?0 have recently been measured. In light of these advances, we suggest adopting an irreducible tensor technique to explore the reaction ? + d ? d + ?0 at close to threshold energies. Our method, which is model-independent, works well for predictions regarding spin observables. By describing the differential cross section in terms of multipole amplitudes, the angular dependence of the cross section will be studied. 2023 Author(s). -
Theoretical Studies on Pion Photoproduction on Deuterons
The study of nuclear reactions between elementary particles and atomic nuclei plays an important role in understanding the interdisciplinary area of Nuclear Physics and Particle Physics. The study of photoproduction of mesons has a long history going back to 19500s. It was in the next decade, studies on photoproduction of ? meson on deuteron started. Since then coherent and incoherent photoproduction of ? meson on deuteron have been studied theoretically and experimentally. The study of photoproduction of pions describes the coupling among photon, meson and nucleon fields and also gives information about strong interactions that indirectly hold the nucleus together. A thorough investigation of the photoproduction process is firmly believed to give first hand information on two important aspects, one being the threshold of ? photoproduction amplitude and the other being propagation of low-energy pions in nuclear medium. The purpose of the present contribution is to theoretically study pion photoproduction on deuterons using model independent irreducible tensor formalism developed earlier to study the photodisintegration of deuterons[1]. Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0) -
Between Empires: The Treaty of Lausanne and Its Reflection on Indias Path to Independence
This chapter reconceptualizes the Treaty of Lausanne (1923) as a global inflection point whose significance extended beyond the Near East into debates within the Indian independence movement. Commonly understood as the diplomatic settlement that secured international recognition for the Turkish Republic under Mustafa Kemal Atatk, Lausanne is treated here not as the cause of subsequent religious reforms, but as the formal acknowledgment of a new political order already unfolding through earlier Kemalist measures, including the abolition of the Sultanate (1922) and the proclamation of the Republic (1923). Rather than arguing that Lausanne directly enabled the abolition of the Caliphate or determined the course of Indian nationalism, this chapter suggests that it symbolically reflected a broader reconfiguration of sovereignty in the postwar world. In British India, these developments coincided with the decline of the Khilafat Movement and prompted leaders such as Mahatma Gandhi and Maulana Abul Kalam Azad to reassess strategies grounded in transnational religious solidarity. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
The Vanishing Point of Ecology Featuring Carbon Criminals: A Study of Ecocide in the South Asian Countries with a Special Focus on the Indian Landscape
The abounding ecosystem is increasingly being transformed by global warming, which has also resulted in sea level rise, melted glaciers, heat waves, altered precipitation patterns, and other climate impacts. India now ranks fifth in terms of climate change vulnerability. Weakening our natural defenses against climate-related calamities, it has already lost approximately one-third of its coastline, a third of its grasslands, and is losing wetlands at a pace of 23% annually. In recent times several scholarly works have framed this transgression under the rubrics of ecocide. Legal acceptance of ecocide is growing, albeit slowly, in India. Carbon criminals and climate crimes mirror exploration and hypothesis from a wide variety of disciplines; to break down four explicit concerns it plans to state-corporate environment-related crimes: such as extraction of non-renewable energy sources and rising fossil fuel by-products; political failure owing to mitigation of carbon emissions. Green criminology lays its focus to identify various environmental crimes and adjudges the liability of the States dissuading actions leading to the repercussions. Climate change reflects profound class and social inequalities leading to ecocidal tendencies. The chapter aims to discuss the dynamics involving carbon crimes and ecocide by identifying the perpetrators, issuing of responsibility, and responses to the causes of climate injustice across the South Asian countries and Indias pressing concerns on it. Further, endeavors are made to review legislations and offer operational solutions to achieving climate justice. The chapter undertakes South Asian countries as a territorial scope to assess the ecological damage. 2026 selection and editorial matter, Shanthakumar Sanjeevi and Dhanya S, individual chapters, the contributors. -
ByWalk: Unriddling Blind Overtake Scenario with Frugal Safety System
Safety is crucial, and the truth is ineluctable with its practicality. We strive forward to rev up the safety protocols even more in the field of Road Safety in particular. Countries like India face around 5,00,000 accidents, which lead to 1,80,000 demises each year. The two-lane one-way roads present a risk of the overtaking vehicle crashing onto an incoming car (from the opposite direction) that the overtaking vehicle is unaware of. We seek to achieve two equivocal milestones with our idea in the blind overtake issue, namely, technological aid and economic feasibility. This makes our concept equally impactful in all situations. The technological precision and advancement will help anyone with enough resources to use them tangibly, and economic feasibility ensures a threshold of safety levels that must be put into action. In fact, we are slightly inclined toward the frugality of the architecture paradigm of our idea because safety is everyones right. On the economic side, we propose an LED board-based solution that presents enough information about the incoming vehicle with which a blind overtake condition can be avoided. Besides, we put forward the idea of vehicle-to-vehicle communication for streaming the video content to the trailing cars with smarter selection and added ease to the drivers. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comparative Study of Machine Learning Models for Predicting Earthquake Magnitudes
This study evaluates the effectiveness of machine learning models in predicting earthquake magnitudes, aiming to address the challenges posed by traditional seismological methods. By leveraging geospatial and temporal data, the research compares the performance of Random Forest (RF), Artificial Neural Network (ANN), and K-Nearest Neighbours (KNN) using Mean Absolute Error (MAE) as the primary metric. Random Forest demonstrated the lowest MAE of 0.3291, showcasing its ability to handle complex, non-linear patterns better than ANN (0.3660) and KNN (0.3758). The analysis highlights the importance of geospatial and temporal factors in improving prediction accuracy, offering insights into their predictive significance. While traditional methods struggle with high-dimensional data, this study eliminates these limitations by employing machine learning models capable of extracting meaningful patterns. These findings underscore the potential of ensemble methods like Random Forest for enhancing earthquake prediction systems. Future research will explore hybrid approaches and real-time data integration to further advance predictive accuracy in seismology. 2025 IEEE. -
The Development of ID System for Detecting Attacks in WSN Through Ontology Method and its Strategy
Cybercriminals are becoming increasingly targeted by the rapid expansion of the Internet of Things (IoT), leading to an increase in cyberattacks targeting IoT devices and their communication channels These attacks, if failure to detect may result in significant service disruption, financial loss or damage to sensitive data. Real-time intrusion detection is essential to ensure reliability, security and profitability of IoT applications. This study introduces a new intrusion detection system designed for IoT devices that uses deep learning (DL). Utilizing ontology in wireless sensor networks (WSN), this intelligent system detects suspicious activities that pose a threat to connected IoT devices with configuration-neutral design provides ease of use, while the test performance analysis is simulated and real-world. It highlights its strong performance in determining admissions. The effectiveness of the system against many types of attacks such as denial of service, workholes, blackholes, opportunistic service attacks, etc. is confirmed by experimental research and furthermore, the system achieves F1 scores, accuracy and the number of memories. This advanced deep learning intrusion detection system shows great promise to improve IoT network security due to its high detection rate. 2024 IEEE. -
The Effect of Customer Satisfaction on Use Continuance in Bank Chatbot Service
Chatbots are text-based conversational agents that use Natural Language Processing to converse with customers. Chatbot has revolutionized the service industry by providing a customer-centric environment and a cost-effective business pattern to service providers. This technology is still maturing and has already influenced a lot of businesses due to its effective human-like interaction in different sectors. The banking industry too has adopted this very well. However, the acceptance level of this service is relatively slow among banking customers when compared to other sectors. This study focuses on the role of customer satisfaction factors that influence the use continuance of Chatbot services in the banking sector. A quantitative research design, using a purposive sampling method with a sample size of 422 respondents was considered. The data was analysed using SPSS and JMP. The results gave some new perspectives that will help the service providers to identify the antecedents that influence the use continuance of Chatbot service. IJCESEN. -
Service Quality, the Most Driving Factor for Use Continuance of Chatbot in Banking Sector
The challenges faced by businesses in todays context are numerous. There are many uncertainties. However, to achieve sustainable development many businesses have adopted artificial intelligence to improve customer experience. Particularly the banking industry has implemented AI in the form of Chatbot. A conversational application that answers the queries of the customers. This study aims to analyze the factors that influence the customers use continuance of Chatbot services in the banking industry. The survey evaluated various determinants such as Service Quality, Customer Satisfaction, Word-of-Mouth and Use Continuance. Data was collected from 232 bank customers in Bangalore, Karnataka, South India, through online and offline surveys using a random sampling method. The collected data was analyzed using IBM SPSS, and AMOS. The result shows that Service Quality has a significant effect on Satisfaction. And Satisfaction acts as a catalyst that impacts the Use Continuance of bank Chatbots. Loyalty and Word-of-mouth show mild mediation between Satisfaction and Use Continuance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Service Quality, the Most Driving Factor for Use Continuance of Chatbot in Banking Sector
The challenges faced by businesses in todays context are numerous. There are many uncertainties. However, to achieve sustainable development many businesses have adopted artificial intelligence to improve customer experience. Particularly the banking industry has implemented AI in the form of Chatbot. A conversational application that answers the queries of the customers. This study aims to analyze the factors that influence the customers use continuance of Chatbot services in the banking industry. The survey evaluated various determinants such as Service Quality, Customer Satisfaction, Word-of-Mouth and Use Continuance. Data was collected from 232 bank customers in Bangalore, Karnataka, South India, through online and offline surveys using a random sampling method. The collected data was analyzed using IBM SPSS, and AMOS. The result shows that Service Quality has a significant effect on Satisfaction. And Satisfaction acts as a catalyst that impacts the Use Continuance of bank Chatbots. Loyalty and Word-of-mouth show mild mediation between Satisfaction and Use Continuance. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A comparison of distressed and non distressed married couples on marital quality emotional intelligence and conflict resolution styles
Aim: To compare and study the marital quality, emotional intelligence newlineand conflict resolution styles of distressed and non-distressed married couples; and to examine the interrelationships between these variables. Method: The study utilized a cross sectional, between group, mixed method research design. The sample consisted of 43 heterosexual married couples (N=86) in non-clinical settings, in the age range of 20-60 years, living in Bangalore, who met the inclusion and exclusion criteria, were newlinerecruited through purposive/ snowball sampling. The participants were administered a demographic data sheet, the Marital Quality Scale (Shah, 1995), the Emotional Intelligence Scale (Schutte, Malouff, Hall, newlineHaggerty, Cooper, and Golden, 1998), the Conflict Resolution Scale (Kurdek, 1994) and a Semi-structured Interview Schedule for qualitative data (prepared by the researcher). Quantitative and Qualitative analysis was carried out. The MQS cutoff score of 80 was used to divide the sample into distressed and nondistressed couples. newlineResults: The two groups significantly differed on the conflict resolution styles. A significant relationship was found between marital quality and conflict resolution styles of distressed as well as non distressed group. There was a significant positive relationship between withdrawal as a newlineconflict resolution style and marital quality among distressed wives. Correlations between marital quality and conflict resolution styles among non-distressed couples showed that withdrawal had a significant positive newlinerelationship with the marital quality of the husband. From the correlation between the emotional intelligence and conflict resolution styles of distressed couples, compliance had a significant positive relationship with the wife s emotional intelligence. Among non-distressed wives conflict engagement was negatively correlated and positive problem solving was positively correlated with their emotional intelligence. -
Adaptive Risk-Aware Ride Assignment (ARARA) Algorithm to Improve Efficiency to Lower Cancellation Rates in Bengaluru
Ride cancellations on urban mobility platforms like Rapido, OLA, Uber and other service provide platforms are negatively impacting user experience, driver earnings, and platform efficiency due to high cancellation of rides. This study addresses the challenge by developing a machine learning based adaptive userride matching algorithm that is trained on real world ride dataset from Bengaluru. The dataset includes features such as ride time, source, destination, distance, fare, payment method, and ride status. Through data preprocessing and feature engineering, key patterns influencing ride cancellations are identified. A classification model is developed to predict the likelihood of cancellation before ride assignment by using few Machine learning models among various model XGBoost and Logistic Regression outperformed with nearly 9 0% accuracy. Later to enhance the performance in allocation based on cancellation prediction the ARARA algorithm suggests that reallocates rides dynamically based on cancellation risk using inference and assignment Algorithm. Experimental results shows that how to reduce cancellation rates and improved accuracy by choosing best allocation based on top three best captains for allocation to optimize chances of cancellation. This framework can be integrated by ride platforms to enhance service reliability and optimize fleet efficiency. 2025 IEEE. -
Securing Trust in the Connected World: Exploring IoT Security for Privacy in Connected Environments
This abstract delves into IoT security measures to ensure privacy in connected environments. It examines encryption, authentication, access control, and data privacy techniques. Key considerations include end-to-end security, vulnerability mitigation, regulatory compliance, and user trust. By addressing these challenges, trust can be established in the connected world, enabling the widespread adoption of IoT technologies while safeguarding user privacy. 2024 IEEE. -
Thermal and entropy generation of non-Newtonian magneto-Carreau fluid flow in microchannel
The heat flow in microchannels can be established in numerous applications such as micro air vehicles, mechanicalelectromechanical systems, cooling of electronic devices and micro heat exchanger systems. Heat flow optimization deliberates the function of entropy generation minimization (EGM) in engineering applications. Hence, this paper investigates the heat transport of non-Newtonian magneto-Carreau fluid in a microchannel with EGM. Mathematical modeling incorporates the Carreau fluid model. Further, viscous heating, Joule heating and convective heating aspects are also analyzed. The physical features of entropy production in the flow of non-Newtonian Carreau fluid in a microchannel are the major focus of this model. Dimensionless variables are executed for the simplicity of basic equations. The subsequent system is treated by using finite element method. Behaviors of effective parameters on velocity, Bejan number, entropy generation rate and temperature are interpreted. It is established that EGM is occurred for larger values of Weissenberg number. The Carreau fluid exponent is positively related to Bejan number, whereas it is negatively related to EG, temperature and velocity fields. 2020, Akadiai Kiad Budapest, Hungary. -
Brinkman-Forchheimer slip flow subject to exponential space and thermal-dependent heat source in a microchannel utilizing SWCNT and MWCNT nanoliquids
This communication examines the impact of carbon nanotubes (single-wall carbon nanotubes [SWCNT] and multi-wall carbon nanotubes [MWCNT]) on magnetohydrodynamic Brinkman and Forchheimer flow in a planar microchannel with multiple slips. Flow through a porous medium is modeled via Brinkman and Forchheimer theory. The impacts of thermal-dependent heat source (THS) and exponential space-dependent heat source (ESHS) are deployed. Aspects of Joule and viscous dissipations are also retained. The dimensionless equations are solved using the Runge-Kutta-Fehlberg joint with shooting methodology. The significance of various nondimensional parameters on the flow distributions as well as skin-friction and Nusselt number is illustrated and analyzed. Closed form solution of momentum quantity is developed for a particular case. Obtained numerical results are in perfect agreement with analytical results. Further, the results of SWCNT and MWCNT are compared. 2019 Wiley Periodicals, Inc. -
Parsing the Promise of Mindfulness for Obsessive-CompulsiveDisorder: From Heterogeneous Evidence to Mechanistic Precision
This extended commentary critically evaluates recent mindfulness-based interventions (MBIs) for obsessive-compulsive disorder (OCD), with particular attention to the synthesis by Reis et al. (Expert Review of Neurotherapeutics, 24(7), 735741,2024). Drawing on a meta-analysis by Chien et al. (Journal of Obsessive-Compulsive and Related Disorders, 32, 100712,2022) and 10 randomized controlled trials, this review highlighted substantial heterogeneity across intervention types, delivery formats, and outcome measures. Key distinctions were identified among mindfulness-based cognitive therapy, acceptance and commitment therapy, and mindfulness-based exposure and response prevention (MB-ERP). Notable discrepancies between self-reported and clinician-rated outcomes, divergent theoretical frameworks, and the need for greater mechanistic precision were underscored. Integration of mindfulness within ERP emerges as a theoretically promising but still preliminary strategy to enhance inhibitory learning, reduce covert compulsions, and strengthen distress tolerance and treatment engagement. A forward-looking research agenda was proposed, emphasizing mechanism-matched trials, optimization of intervention sequencing, culturally adapted protocols, and scalable digital MB-ERP platforms with fidelity monitoring. This approach aimed to support the development of individualized, effective, and durable mindfulness-based treatments for OCD. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025. -
Aggressive driving and ADHD symptoms in young male drivers: Examining the roles of personality traits and driving anger
Introduction Aggressive driving behaviors are linked to attention-deficit/hyperactivity disorder (ADHD) symptoms, yet the moderating roles of personality traits and driving anger remain underexplored, particularly among two-wheeler riders in low- and middle-income countries (LMICs). This study examined associations between aggressive driving violations and ADHD symptom severity, focusing on neuroticism and driving anger as moderators. Methods A cross-sectional survey was conducted with 150 male postgraduate two-wheeler riders in India. ADHD symptoms were assessed using the Adult ADHD Self-Report Scale, aggressive driving violations via the Extended Driver Behaviour Questionnaire, driving anger using the Deffenbacher Driving Anger Scale, and personality traits through the 10-item Big Five Inventory. Multiple regression and moderation analyses were performed. Results Aggressive driving violations significantly predicted ADHD symptom severity (p < .001), independent of driving anger and neuroticism. A marginal interaction with neuroticism (p = .068) suggested a stronger association at lower neuroticism levels. Driving anger did not significantly moderate this relationship. Age and helmet non-use were also independently associated with ADHD symptoms (p = .045 and p = .024, respectively). Conclusions Aggressive driving violations show a stable association with ADHD symptom severity in young male two-wheeler riders in an LMIC context, with preliminary evidence for neuroticism as a moderator. These findings underscore the need for personality-informed interventions addressing self-regulatory and behavioral aspects of driving behavior in ADHD populations. 2025 Elsevier Ltd.