IIMT University

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.

Liver disorders represent a major global health challenge, highlighting the need for safer, multi-targeted, and affordable therapeutic options. Indian traditional medicine systems—Ayurveda, Siddha, and Unani—have historically utilized a wide range of medicinal plants for liver protection. This review provides a comprehensive and updated synthesis of Indian hepatoprotective plants and polyherbal formulations, uniquely integrating traditional knowledge with modern biomolecular insights, which forms the novelty of this work. Literature was systematically gathered from Google Scholar, PubMed, ScienceDirect, and Wiley using keywords related to hepatoprotection, pathophysiology, and biomolecular mechanisms. Key botanicals such as Phyllanthus niruri, Picrorhiza kurroa, and Silybum marianum demonstrate potent antioxidant, anti-inflammatory, and antifibrotic activities, attributed to flavonoids, polyphenols, alkaloids, and other bioactive constituents. The review also highlights the evolution of traditional remedies into standardized formulations, including Liv.52 and Phyllanthus-based products, underscoring progress toward evidence-based phytopharmaceuticals. Importantly, mechanistic pathways such as Nrf2 activation, NF-κB suppression, and inhibition of TGF-β-mediated fibrosis are discussed to provide molecular clarity. Despite promising therapeutic potential, critical gaps remain regarding clinical validation, dosage standardization, quality assurance, and regulatory oversight. By bridging traditional ethnopharmacology with contemporary scientific evidence, this review emphasizes the relevance of Indian medicinal plants in liver disorder management and identifies future directions for translational research.

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.

In fire safety, understanding the behaviour of concrete exposed to high temperatures is essential. This study experimentally explored the mechanical properties of Alkali-Activated Concrete (AAC) and utilized Recurrent Neural Network (RNN)based Long Short-Term Memory (LSTM) techniques to predict the mechanical properties of AAC at elevated temperatures. The LSTM models accurately predicted compressive, flexural, and split tensile strengths, with coefficients of determination (R2) exceeding 0.9 for training and testing datasets. Specifically, R2 values were 0.9838 and 0.9134 for compressive strength, 0.9965 and 0.9861 for flexural strength, and 0.9743 and 0.9852 for split tensile strength in training and testing, respectively. The models also yielded low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, further underscoring their predictive reliability. Error analysis across all mechanical properties affirmed the LSTM models' robustness in capturing AAC's complex behaviour under thermal stress. These results suggest that LSTM networks are highly effective tools for predicting material properties crucial for structural fire safety and sustainable construction, offering a promising approach for improving the resilience and safety of AAC structures in extreme conditions.