The business life cycle is examined through a comprehensive literature review in this academic study. Our initial approach involves searching for relevant articles on firm life cycle and strategy using the Web of Science and Scopus databases. We conduct bibliometric analyses to identify key contributors and recurring keywords. Subsequently, we select twenty-seven research papers to explore the Theory Development, Characteristics, Context, and Methodology (TCCM) framework for firm life cycle and strategy. Our analysis summarizes corresponding business strategies for each stage, including the use of Initial Management Control Systems (MCS) in the introduction phase. As companies grow, a high inventory-to-sales ratio may hinder effectiveness, but it proves beneficial in the growth and revival stages. Mature companies excel in green process innovation and engage more in Corporate Social Responsibility (CSR) activities. In the decline stage, firms use cost efficiencies, asset retrenchment, and core activity focus for recovery, signaling commitment to a successful turnaround. However, there is a research gap in exploring appropriate global strategies for various life cycle stages, providing an opportunity for additional articles to thoroughly investigate this relationship and assess multinational enterprises’ success trajectories throughout their life cycles.

Amidst an upsurge in the quantity of delinquent loans, the financial industry is experiencing a fundamental transformation in the approaches utilised for debt recovery. The debt collection process is presently undergoing automation and improvement through the utilisation of Artificial Intelligence (AI), an emergent technology that holds the potential to revolutionise this sector. By leveraging machine learning, natural language processing, and predictive analytics, automated debt recovery systems analyse vast quantities of data, generate forecasts regarding the likelihood of recovery, and streamline operational processes. Debt collection systems powered by AI are anticipated to be compliant, precise, and effective. On the other hand, conventional approaches are linked to increasing expenditures and inefficiencies in operations. These solutions facilitate efficient resource allocation, customised communication, and rapid data analysis, all while minimising the need for human intervention. Significant progress has been made in data analytics, predictive modelling, and decision-making through the application of artificial intelligence (AI) in debt recovery; this has the potential to revolutionize the financial sector’s approach to debt management. The findings of the research underscore the criticality of artificial intelligence (AI) in attaining efficacy and precision, in addition to the imperative of a data-centric framework to fundamentally reshape approaches to debt collection. In conclusion, artificial intelligence possesses the capacity to profoundly transform the existing approaches utilized in debt management, thereby guaranteeing financial institutions’ sustained profitability and efficacy. The application of machine learning methodologies, including predictive modelling and logistic regression, signifies the potential of the system.

A method for studying the resilience of energy and socio-ecological systems is considered; it integrates approaches developed at the International Institute of Applied Systems Analysis and the Melentyev Institute of Energy Systems (MESI) of the Siberian Branch of the Russian Academy of Sciences. The article discusses in detail the methods of using intelligent information technologies, in particular semantic technologies and knowledge engineering (cognitive probabilistic modeling), which the authors propose to use in assessing the risks of natural and man-made threats to the resilience of the energy sector and social and ecological systems. More attention is paid to the study and adaptation of the integral indicator of quality of life, which makes it possible to combine these interdisciplinary studies.

Being supposedly the ground for an exchange system that does not depend on central, top-down regulation, cryptocurrencies increasingly need new algorithmic and policy-driven rules to maintain their trustworthiness and capacity to exhibit empirically supported growth. The present paper offers a conceptual and philosophical discussion on whether and how cryptosystems could be able to generate resilient development in a way that is coherent with a non-reductionist view of positive economics. As proposed, a plausible way to understand them can be achieved considering their complexity and their concrete, local features, which have to be grasped both in terms of formal and material specificity.

Depression is a mental disorder caused by various causes with significant and persistent depressed mood as the main clinical feature, and is the most common mental illness worldwide and in our country. The number of patients with depression worldwide was as high as 350 million in 2017, and the number of patients with depression in our country was nearly 100 million in 2019. The greatest danger of depression is self-injurious and suicidal behaviour, and this behaviour carries a high medical burden. Medication is the most costly treatment for depression in China, and while it is an effective way to treat patients with depression, it has many side effects and poor patient compliance. Non-pharmacological treatments commonly used in clinical practice include physiotherapy and psychotherapy. Physiotherapy is commonly used in non-convulsive electroconvulsive therapy, but its clinical efficacy is uncertain and it can also cause adverse effects such as heart failure and arrhythmias, which are poorly tolerated by patients. Psychotherapy is also a common non-pharmacological therapy. Cognitive therapy is a common form of psychotherapy, but the cycle of cognitive therapy is too long, the cost to the patient is high, and the patient’s cognitive ability has certain requirements. Music therapy is a combination of art and science. It is a cross-discipline that combines body, movement, dance and psychology and is a method of psychotherapy that has biological, psychological and social functions to compensate for deficiencies. Music therapy sees a fundamental connection between mind and body and emphasises that what affects the body also affects the mind. When mind-body integration is lacking, individuals will suffer from a variety of psychological disorders. Therefore, the core principles of music therapy emphasise that holistic individual health is embodied in the integration of mind and body, that body movement is expressive and communicative, and that music therapy uses body movement as a method of assessing the individual and as a means of clinical intervention.

The Lancaster mutual teaching model originated in late 18th century England and quickly spread to the American colonies after receiving positive responses in Europe. In the 1820s, renowned Spanish physician, educator, and publisher Manuel Codorniú Ferreras brought it to Mexico, making outstanding contributions to the newly independent nation in educational philosophy, system, and methods. In the mid-19th century, with the absence of a centralized institution for public education in Mexico, the Lancaster Company took on the significant responsibility of guiding the direction of national public education development. Although this function did not persist for too long due to political changes in Mexico, the educational system continued to play an important role in the Mexican education sector. The Lancaster Company and its teaching system exerted a positive and profound influence on the democratization and secularization of education in Mexico, laying important foundations for the modernization and reform of Mexican education.