Nirma University

Computational advancement is the need of the present century and has played an important role in transforming the medical industry and health research. The application of engineering principles to biology using computational techniques has led to the development of in silico modeling and simulation. This chapter discusses the role of computational advancements in medical research and the importance of artificial intelligence and machine learning in modeling and simulation of diseases with personalized healthcare. In silico modeling and simulation provide precise predictions about the underlying signaling mechanisms involved in various diseases. This leads to early detection, as well as time-efficient and cost-effective solutions for healthcare practitioners. Computational techniques enhance targeted drug therapy in the pharmaceutical industry, facilitating drug design, development, and testing. Although in silico modeling and simulations are trending nowadays, challenges and limitations remain, such as the accuracy of the model, the depth of complex biological models, effective and efficient datasets, the lack of data availability, patient concerns, consent, and finally, the validation of the data as the model persists. Keeping the constraints in mind, the health informatics field has boosted the development and analysis of much more complex models like those related to cancer and diabetes. For advancing the medical industry, the impact of in silico models would bring a revolution in patient care. This chapter has attempted to cover everything, from significance to constraints and difficulties in in silico modeling.

With the increasing prevalence of type 1 and type 2 diabetes, management, particularly for those receiving insulin therapy, becomes a global health concern. Since the advancement of in silico models, diabetes treatment has greatly improved. Computer simulations forecast the insulin dosages needed for bolus and basal infusion based on real-time glucose dynamics. By replicating glucose-insulin dynamics and taking into account factors like age, activity, food intake, insulin resistance, and more, these models—like the UVA/PADOVA simulator—have completely changed the way people with diabetes are treated. These algorithms not only forecast insulin dosages but also help with medication customization, thereby improving patient outcomes. When factors like stress and physical activity are taken into account, in silico models offer vital information on the ideal basal and bolus insulin dosages. Additionally, even in the absence of exact insulin-to-carbohydrate ratios, reinforcement learning models have demonstrated potential in predicting bolus insulin doses, increasing the accuracy of insulin delivery. Despite improvements, data validation, device integration, and the requirement for individualized care continue to pose challenges to these models' ability to accurately forecast insulin dosages. Several in silico modeling techniques, their uses, and the significance of tailored care in maximizing diabetes management are covered in this research.

ABSTRACT In recent years, the growth and popularity of electric vehicles (EVs) has soared owing to the facilitation of zero‐emission carbon for people commuting on the road, preserving the environment from air pollution and hazardous gases. However, uncertain EV energy demands and their dynamic arrival times impact the ancillary operations and stability of the charging station (CS). Thus, it becomes a challenging task to schedule EVs for charging with their dynamic charging prices, traveling time, and waiting time efficiently and optimally. Thus, we propose an optimal EV selection scheme for trustworthy charging by implementing the hybrid game theory. The hybrid game theory is bifurcated into stage 1 and stage 2, in which stage 1 includes a coalition game to generate EV clusters or coalitions based on the parameters of state‐of‐charge (SoC), energy demand, and penalty factor. Then, the trust values are determined to select the EV pair fairly. Furthermore, stage 2 highlights the zero‐sum game theory, which aims to optimize the payoff at saddle point and formulate strategies for EV pair (generated in stage 1), ensuring the optimal EV selection for trustworthy charging. Moreover, we have utilized the blockchain network to secure the EV optimal payoff by implementing smart contract in Remix Integrated Development Environment (IDE). The hybrid game theory ensures the optimal and efficient EV selection using coalition game to select EV pair then apply zero‐sum game to optimize the payoff at saddle point condition. Next, we implement the hybrid game theory in Python 3.9 to simulate the results with the help of various factors such as trust value comparison, profit comparison based on strategies, convergence comparison, and profit comparison with the traditional approach.

Abstract The construction and demolition (C&D) sector is a major contributor to material consumption and waste generation, making it a critical focal point for circular economy (CE) transitions. In India, the introduction of Extended Producer Responsibility (EPR) under the Construction and Demolition Waste Management Rules 2025 represents a significant policy shift toward lifecycle-based material governance. However, the effectiveness of EPR in delivering circular outcomes remains uncertain. This study presents a systematic review of academic literature and policy documents to critically examine the design, implementation, and performance of EPR oriented C&D waste governance in India within a global context. Following the PRISMA 2020 protocol, 130 sources, including peer reviewed studies and official policy documents, were systematically analyzed. The review develops a conceptual framework that links policy intent, EPR design, enterprise readiness, and circular outcomes, highlighting enterprise readiness as a key mediating factor. A critical policy analysis reveals that while India’s regulatory framework aligns with international circular economy principles, significant implementation gaps persist due to ambiguous responsibility allocation, weak enforcement mechanisms, and underdeveloped markets for secondary construction materials. Comparative analysis with the European Union and Japan demonstrates that EPR effectiveness is strongly associated with institutional clarity, capability building mechanisms, and enforcement intensity. To address these gaps, the study proposes a Circular Capability Maturity Model (CCMM) as a structured pathway for assessing and enhancing enterprise readiness for EPR implementation. By shifting the focus from compliance-based regulation to capability oriented governance, the paper contributes a theoretically grounded and policy relevant framework to advance circular construction transitions in emerging economies.