Research on community resilience has been ongoing for decades. Several studies have been carried out on resilience in different groups and contexts. However, few address the relationship between community resilience and depopulated rural areas. This study aims to dig deeper into this, considering the concrete impact of population decline in Spain. We carried out a systematic review of the most relevant contributions. A search protocol was developed and used to consult ten databases. Different combinations of terms such as ‘community resilience’, ‘rural’, and ‘depopulation’, or related terms, were used. 22 scientific texts were analysed. We obtained a set of publications that demonstrate the heterogeneity of research methods, approaches and analytical processes applied to the study of this relationship. A mostly qualitative approach was observed, either as the main technique or complementary to documentary reviews. The results underscore the complex nature of rural depopulation and related constructs. It emphasizes the specific importance of community resilience in these territories in terms of social capital, endogenous resources, sustainability, economic dynamism, local responsibility and effective governance. The findings identify a scarce mention to social intervention professions, which should have a more important role due to their core values. In the studies reviewed, it appears as an emerging and scientifically relevant area to explore, both for investigation and intervention purposes. The strength of a multidisciplinary approach to addressing the phenomena appears in the discussion as a main potential line of research.

This study informs the academic and policy debate on the policy effectiveness of exchange rate interventions on exchange rate levels and volatility. Using a constructed data set comprising daily data on exchange rates, monetary policy fundamentals, exchange rate intervention dates and magnitudes of those interventions as well as financial news speculation of such interventions, we empirically estimate the policy effectiveness of Bank of Japan interventions in the exchange rate over the 12-year period between 2010 and 2022. This allows us to investigate the policy effectiveness of a variety of exchange rate interventions, or news of exchange rate interventions, across different time-horizons. We find that policy interventions in the yen exchange rate are more effective over short-horizons than long-horizons, more effective when the policy objective is a competitive devaluation of the yen rather than a revaluation, and more effective at influencing the level of the yen against major world currencies other than the US dollar. In fact, for the yen-dollar rate, we find that policy interventions may have the unintended consequences of weakening the yen (when the policy intention is to strengthen it) and increasing volatility in the yen-dollar exchange rate.

The article presents an answer to the current challenge about needs to form methodological approaches to the digital transformation of existing industrial enterprises (EIE). The paper develops a hypothesis that it is advisable to carry out the digital transformation of EIE based on considering it as a complex technical system using model-based system engineering (MBSE). The practical methodology based on MBSE for EIE digital representation creation are presented. It is demonstrated how different system models of EIE is created from a set of entities of the MBSE approach: requirements—unctions—components and corresponding matrices of interconnections. Also the principles and composition of tasks for system architectures creation of EIE digital representation are developed. The practical application of proposed methodology is illustrated by the example of an existing gas distribution station.

In recent years, e-sports, as an emerging form of competition, has been rapidly integrated into the daily life of college students, and with its rich interactivity, instant feedback and teamwork, e-sports provides them with an effective channel for emotional catharsis and psychological regulation. This study takes students from four universities as the survey object and adopts quantitative research method to analyze the relationship between different types of e-sports activities and psychological stress resistance through questionnaire survey method combined with spss. The samples were randomly sampled, and a total of 500 valid questionnaires were collected. The results of the study show that: 1. In terms of participation, the ability of students to withstand academic stress and life stress is significantly improved, and e-sports is an effective way to regulate emotions and relieve stress; 2. the three types of games (First-person Shooter, Multiplayer Online Battle Arena, Real-Time Strategy Game) have different impacts on stress tolerance, of which FPS has the greatest impact on stress tolerance; 3. the frequency of playing e-sports affects your stress tolerance; 4. teamwork and strategy play an important role in e-sports resilience.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

With the rapid development of artificial intelligence (AI) technology, its application in the field of auditing has gained increasing attention. This paper explores the application of AI technology in audit risk assessment and control (ARAC), aiming to improve audit efficiency and effectiveness. First, the paper introduces the basic concepts of AI technology and its application background in the auditing field. Then, it provides a detailed analysis of the specific applications of AI technology in audit risk assessment and control, including data analysis, risk prediction, automated auditing, continuous monitoring, intelligent decision support, and compliance checks. Finally, the paper discusses the challenges and opportunities of AI technology in audit risk assessment and control, as well as future research directions.