CHRIST (Deemed to be University), is a deemed to be university in Bangalore, Karnataka, India. Founded in 1969 as Christ College, the University Grants Commission (UGC) of India conferred autonomy to Christ College in 2004. On 22 July 2008 it was declared as an institution deemed to be university under section 3 of UGC Act 1956 by the Ministry of Education (India). The university is under the management of the priests of the Catholic religious order, Carmelites of Mary Immaculate (CMI), part of Syro Malabar Major Archiepiscopal Church. In 2016, the University was accredited by National Assessment and Accreditation Council with A Grade.
The convergence of Electric Vehicles (EVs) and the smart grid through Vehicle-to-Grid (V2G) networks introduces critical cybersecurity vulnerabilities. Traditional security mechanisms present single points of failure and are inadequate for the dynamic and high-volume data environment of V2G ecosystems. This paper proposes an integrated security framework that synergizes blockchain technology with a deep learning-based anomaly detection system to strengthen V2G communication. A permissioned blockchain architecture establishes a decentralized and immutable ledger for secure messaging between EVs. A deep Long Short-Term Memory (LSTM) autoencoder model is deployed to perform real-time multivariate time-series analysis on grid command data and energy flow metrics. The proposed framework was evaluated using a synthetic V2G network dataset. EV Electrical Vehicles Dataset (3k records, 2025)” from Kaggle, which was augmented by the authors to generate V2G transaction fields, digital signatures, grid frequency deviations, and three attack types (False Data Injection, replay, and coordinated load spikes). The study relies entirely on synthetic data and simulated communications; real-world validation is a necessary next step (see Sections 6.8 and Appendix E). In this proof-of-concept simulation, the proposed framework achieved 91.48% resilience to attacks, 97.1% detection F1-score, 77.73 kbps communication overhead, 92.48% detection accuracy, and 134.26 ms transaction latency. These results are obtained from a proof-of-concept simulation and should be interpreted as promising indications; real-world validation on physical hardware and live grid data is required before any operational claims can be made.
India's expanding data centre economy powers AI, cloud computing, e-governance and digital services, but also carries heavy environmental costs through electricity use, cooling, water consumption, diesel backup, land use and e-waste. This chapter examines greenwashing by “environmental hustlers” who use claims such as “green cloud,” “carbon neutral” or “renewable powered” without verifiable proof. It argues for data centre-specific sustainability disclosures, independent audits, clear legal standards for green claims and stronger enforcement under consumer, environmental, ESG and corporate laws.
The chapter examines how the healthcare system in India has been commercialised and how this has impacted equity, ethics and health of the people. Using mixed-methods data with expenditure data, policy documents and research analytics, it investigates how the preeminence of the private hospitals has driven healthcare up the Care-Commerce Continuum to models focused on profits. Among the most critical problems are excessive out-of-pocket expenses, lack of accessibility, practice malpractices, and the ineffective regulations. It points out the concentration of resources in the cities, the COVID inequities, and the effects of medical tourism. The chapter suggests a theoretical model of creating the contexts and evidence-based solutions to strengthen regulation, state investment, and ethics- restoring healthcare back to being a public good.
This study investigates the enablers and barriers influencing the acceptance of Agentic AI in the hospitality sector using a mixed-methods research design. In the first phase, thirty-two in-depth interviews were conducted with hospitality managers to identify the key drivers and barriers of Agentic AI adoption. Based on these insights, a theoretical model grounded in Behavioral Reasoning Theory (BRT) was developed and subsequently validated through a survey of 980 consumers. The findings reveal that autonomy, personalization, social influence, and perceived usefulness are the primary drivers of Agentic AI adoption, whereas technological anxiety, perceived privacy risk, perceived hallucination, and traditional barriers impede its acceptance. This study contributes to the literature on advanced technology adoption in hospitality by extending the application of BRT to the context of Agentic AI. It also offers practical insights for hospitality managers and technology developers seeking to design and implement Agentic AI solutions that effectively promote consumer adoption.
Machine-learning classifiers, natural language processing systems, and knowledge-graph architectures are now identifying, at a scale that no conventional audit can manage, statistical and textual patterns associated with greenwashing. This chapter assesses whether AI-generated inconsistency detection could result in corporate liability under Indian and United States law, and argues that it could, but only through evidential pathways that neither system has yet tested. India has deliberately designed its BRSR Core disclosure framework, coupled with its 2024 CCPA greenwashing guidelines and the under-use of the PFUTP Regulations, to create a better infrastructure for algorithmic verification. It is also a formalised evidential one by virtue of the Bharatiya Sakshya Adhiniyam 2023's dual-certificate structure. The technical stages common to both systems are separated from the jurisdictionally specific certification and liability-attribution stages that follow.
