IIMT University

Socio-emotional health refers to an individual's ability to understand, manage, and express emotions effectively while maintaining positive relationships with others. In contemporary organizations, it has emerged as a critical factor influencing both team performance and overall organizational excellence. A workforce with strong socio-emotional health demonstrates higher levels of self-awareness, empathy, emotional regulation, and social competence, which are essential for collaboration and productivity.

Digital transformation in microbiology laboratories is changing the generation, processing, and application of microbial data for research, diagnostics, and other industrial purposes. This chapter describes how IoT, Automation and digital workflow technologies have been integrated into this field (e.g. serverless computing platforms, electronic lab notebooks, wireless connectivity, connected sensors, automated instruments, cyber-physical infrastructure (CPI), etc.). It details how these technologies increase the accuracy, repeatability and efficiency of laboratory data and decrease human error and turnaround time. Additionally, the book details different system architectures, data interoperability, and cloud-based platforms that allow for seamless data acquisition and analysis throughout the microbiological workflow. The chapter examines challenges related to data security, standardisation, scalability, and regulatory compliance.

This paper examines the impact of M&A activities on Indian firms' sustainability performance, considering the triple bottom line approach grounded in Agency Theory and Resource-Based View (RBV). To achieve this objective, the large panel data of 342 NSE-listed M&A firms from the period 2014 to 2023 is considered, with the event window of (-1, +3). The empirical results provide evidence that corporate governance variables and the ESG score of M&A firms are positively correlated; however, some variation exists. Promoter ownership, institutional ownership, and FII positively impact performance post-M&A, with FII showing improved sustainability performance, especially when considering overall ESG. Board size and CEO duality negatively affect ESG post-M&A, while board diversity, especially female representation, has a stronger positive impact on sustainability performance after M&A. However, when each of the three components of sustainability, that is, E (environmental), S (social), and G (governance), is examined, it is found that different ESG parameters are affected differently. The research explores a new subject, which links Mergers and Acquisitions to sustainability, to expand knowledge in these two fields. The research draws its theoretical foundation from Agency Theory and Resource-Based View (RBV) to show that corporate governance systems function as valuable, rare, and inimitable resources, which lead to better sustainable performance for firms. The research applies theoretical frameworks to establish relationships between post-acquisition governance systems and ESG performance, which enables a better understanding of governance integration approaches that lead to sustainable long-term results in Indian emerging markets.

A new technique enabling to improve feature selection process based on weighted intuitionistic fuzzy (IF) similarity relation (WIFSR) is suggested in the present study. Firstly, we discuss a novel WIFSR by improving the idea of IF similarity relation. Secondly, IF granular structure (IFGS) is established on the basis of WIFSR. Thirdly, IF rough set model is outlined based on the idea of aforesaid IFGS. Next, positive region is computed based on the lower approximation of IF rough set. Then, dependency of decision dimension over set of conditional dimension is calculated based on positive region and cardinality of the decision system. With granular structures, features/dimensions can be represented with different levels of abstraction to provide a dynamic and flexible selection approach. Moreover, we present a WIFSR designed to measure the similarity between features by taking into account their relevancy and non-redundancy. Proposed approach effectively addresses the problem of feature selection by measuring degree of dependency between features in IFGS framework. Mathematical validation is illustrated for all the established notions. Proposed method is experimentally evaluated on various datasets, and we successfully demonstrate its efficiency in terms of determining the inherent features while safeguarding them from later uncertainty and noise. Our experimental results illustrate that the suggested approach, in the context of accuracy and standard deviation, outperforms the existing feature selection methods. At the end, a new scheme is demonstrated to enhance the overall prediction performances of machine learning methods for antiviral peptides.

Broadband metamaterial absorbers provide efficient solar absorption but suffer from radiative losses at moderate temperatures (375 K), while strong thermal emission at high temperatures is essential for effective solar thermal conversion. Achieving spectral selectivity with sustained photothermal efficiency across this wide temperature range remains a key challenge. Here, we propose a selective refractory metamaterial absorber (SRMA) with a three-layer structure (TiN-Si3N4-W). Simulation results demonstrate a weighted average absorption efficiency of 96.70% under AM 1.5 solar illumination over a wavelength range of 200-2500 nm using the Finite Element Method (FEM). To further assess its photovoltaic potential application, the short-circuit current density (Jsc) at different angles of incidence is evaluated for a solar cell configuration incorporating the proposed absorber, yielding an optimum value of 37.57 mA/cm2. The proposed absorber exhibits a low emission of 9.7% at 375 K while sustaining high emission at elevated temperatures. It also demonstrates a photothermal conversion efficiency above 90% over a broad temperature range of 200-1200 K under high solar concentration. At the same time, the design is thermally stable, polarization-insensitive, and angle-insensitive up to 60 degrees across all polarization states. These characteristics highlight the intrinsic spectral selectivity of the proposed absorber, making it a promising candidate for applications ranging from moderate-temperature solar thermal energy harvesting to high-temperature solar thermophotovoltaic (STPV) systems.