In the dynamic contemporary business landscape, the convergence of technology, finance, and management plays a pivotal role in organizational success. This research explores the multifaceted realm of strategic integration, emphasizing the intricate balance between these domains. The background sets the stage, elucidating the historical evolution and growing relevance of this integration. Various research methodologies, including case studies, surveys, interviews, and data analysis, are used to investigate practical aspects. The study delves into the role of technology, emphasizing digital transformation, innovation, and IT infrastructure. It dissects financial management, focusing on decision-making, risk management, and capital allocation. Additionally, management and leadership are discussed, with an emphasis on change management, strategic leadership, and skill development. Challenges, such as cultural disparities and regulatory complexities, are scrutinized, alongside opportunities like improved decision-making and enhanced productivity. Real-world case studies illustrate success stories and lessons learned. The paper concludes with findings, implications for businesses and management, and practical recommendations for navigating this convergence. This research contributes valuable insights into performance and competitiveness, facilitating a better understanding of key performance metrics and positioning strategies in the digital age.

The financial services industry is experiencing a swift adoption of artificial intelligence (AI) and machine learning for a variety of applications. These technologies can be employed by both public and private sector entities to ensure adherence to regulatory requirements, monitor activities, evaluate data accuracy, and identify instances of fraudulent behavior. The utilization of artificial intelligence (AI) and machine learning (ML) has the potential to provide novel and unforeseen manifestations of interconnectivity within financial markets and institutions. This can be represented by the adoption of previously disparate data sources by diverse institutions. The researchers employed convenience sampling as the sampling method. The form was filled out over the period spanning from July 2023 to February 2024, and it was designed to be both anonymous and accessible through online and offline platforms. To assess the reliability and validity of the measurement scales and evaluate the structural model, we employed Partial Least Squares (PLS) for model validation. Specifically, we have used the software package Smart-PLS 3 with a bootstrapping of 5000 samples to estimate the significance of the parameters. The results indicate a positive and direct connection between artificial intelligence (AI) and either financial services or financial institutions. On the contrary, machine learning (ML) exhibits a strong and positive association among financial services and financial institutions. Similarly, there exists a positive and direct connection between AI and investors, as well as between ML and investors.

This paper investigates the impact of financial inclusion on financial stability in BRICS countries from 2004 to 2020. Using a panel smooth transition regression model, the results reveal a U-shaped relationship between financial inclusion and financial stability. Financial inclusion reduces financial stability up to a threshold of 44.7%. Beyond this point, financial inclusion contributes to greater financial stability, through gradual transitions. Enhanced financial inclusion supports banks in stabilizing their deposit funding by facilitating access to more stable, long-term funds and alleviating the negative impacts of fluctuations in returns. Furthermore, the study examines the role of institutional quality in shaping the financial inclusion-financial stability nexus, indicating a significant positive effect, especially in the upper regime. These findings provide valuable insights for financial regulatory authorities, highlighting the importance of promoting financial inclusion in BRICS economies and adapting regulations to mitigate potential risks to global financial stability.

Liquid Metal Battery (LMB) technology is a new research area born from a different economic and political climate that has the ability to address the deficiencies of a society where electrical energy storage alternatives are lacking. The United States government has begun to fund scholarly research work at its top industrial and national laboratories. This was to develop Liquid Metal Battery cells for energy storage solutions. This research was encouraged during the Cold War battle for scientific superiority. Intensive research then drifted towards high-energy rechargeable batteries, which work better for automobiles and other applications. Intensive research has been carried out on the development of electrochemical rechargeable all-liquid energy storage batteries. The recent request for green energy transfer and storage for various applications, ranging from small-scale to large-scale power storage, has increased energy storage advancements and explorations. The criteria of high energy density, low cost, and extensive energy storage provision have been met through lithium-ion batteries, sodium-ion batteries, and Liquid Metal Battery development. The objective of this research is to establish that Liquid Metal Battery technology could provide research concepts that give projections of the probable electrode metals that could be harnessed for LMB development. Thus, at the end of this research, it was discovered that the parameter estimation of the Li//Cd-Sb combination is most viable for LMB production when compared with Li//Cd-Bi, Li-Bi, and Li-Cd constituents. This unique constituent of the LMB parameter estimation would yield a better outcome for LMB development.

Amyloidosis is a systemic disorder produced by the deposition of insoluble protein fibrils that fold and deposit in the myocardium. Patients with amyloidosis and cardiac involvement have higher mortality than patients without cardiac involvement. The two most prevalent forms of amyloidosis associated with cardiac involvement are AL amyloidosis, due to the deposition of immunoglobulin light chains, and ATTR amyloidosis, due to the deposition of the transthyretin (TTR) protein in mutated or senile form. This article aims to review the different cardiac imaging modalities (echocardiography, cardiac magnetic resonance imaging, nuclear medicine and tomography) that allow to determine the severity of cardiac involvement in patients with amyloidosis, the type of amyloidosis and its prognosis. Finally, we suggest a diagnostic algorithm to determine cardiac involvement in amyloidosis adapted to locally available diagnostic tools, with a practical and clinical approach.