This study conducts a comprehensive analysis of the aquaculture industry across 11 coastal regions in eastern China from 2017 to 2021 to assess their adaptability and resilience in the face of climate change. Cluster analysis was employed to examine regional variations in aquaculture adaptation by analyzing data on annual average temperatures, annual extreme high/low temperatures, annual average relative humidity, annual sunshine duration, and total yearly precipitation alongside various aquaculture practices. The findings reveal that southern regions, such as Fujian and Guangdong, demonstrate higher adaptability and resilience due to their stable subtropical climates and advanced aquaculture technologies. In contrast, northern regions like Liaoning and Shandong, characterized by more significant climatic fluctuations, exhibit varying degrees of cluster changes, indicating a continuous need to adjust aquaculture strategies to cope with climatic challenges. Additionally, the study explores the specific impacts of climate change on species selection, disease management, and water resource utilization in aquaculture, emphasizing the importance of developing region-specific strategies. Based on these insights, several strategic recommendations are proposed, including promoting species diversification, enhancing disease monitoring and control, improving water quality management techniques, and urging governmental support for policies and technical guidance to enhance the climate resilience and sustainability of the aquaculture sector. These strategies and recommendations aim to assist the aquaculture industry in addressing future climate challenges and fostering long-term sustainable development.

Shipbuilding industry is characterized by high price competition, as well as tight deadlines for product design and production. The dominant positions in the civil shipbuilding market are occupied by the countries of Southeast Asia, and for a number of reasons, participants from other countries are uncompetitive. Thus, in order to ensure the sustainable development of companies in the global civil shipbuilding market, it is necessary to identify and analyze the main factors that provided the competitive advantages of industry leaders. Assessment of further directions of shipbuilding development is a necessary condition for the formation of competitive advantages of new market participants. The article analyzes the main directions of development of the world civil shipbuilding in the period after World War II, as well as prospects for the future. As a result of the analysis of the latest organizational management concepts, the concept of modular production in shipbuilding is proposed, and directions for further research are determined.

Islamic banking is one of the fastest-growing sectors of the financial industry. Several works have been written in this field, but none attempt to learn the entire Islamic banking and financial system. Furthermore, the study could not locate any publications investigating the conceptual and intellectual foundations of this emerging field of inquiry. The current study uses bibliometric methodologies to assess the current state of Islamic banking, financial research, and the upcoming trends. For the people who choose interest-free investments, the current research examines a conceptual research context on Islamic banking and finance at various planning and decision-making stages. One thousand research studies appearing in scholarly journals between 2005 and 2023 were reviewed for the purpose. In order to examine the works on Islamic banking and finance, bibliometric techniques were used, including analysis of citation network, content, co-citation, keyword, and publishing trends. By suggesting thirteen clusters, to enhance research on Islamic banking and finance to help interest-free investors learn more, the goal of the research is to promote the body of knowledge. The field of Islamic banking and finance has grown from a young lot to a prominent teaching and research tool. Investigating and identifying current research trends in this area is crucial. As institutions and society are placing more emphasis on Islamic banking to raise individual citizens’ responsibilities in developing interest-free investing strategies, the findings are crucial to the community of interest-free financiers. Further research urges with the studies not restricted to a thousand researches only.

Clustering technics, like k-means and its extended version, fuzzy c-means clustering (FCM) are useful tools for identifying typical behaviours based on various attitudes and responses to well-formulated questionnaires, such as among forensic populations. As more or less standard questionnaires for analyzing aggressive attitudes do exist in the literature, the application of these clustering methods seems to be rather straightforward. Especially, fuzzy clustering may lead to new recognitions, as human behaviour and communication are full of uncertainties, which often do not have a probabilistic nature. In this paper, the cluster analysis of a closed forensic (inmate) population will be presented. The goal of this study was by applying fuzzy c-means clustering to facilitate the wider possibilities of analysis of aggressive behaviour which is treated as a heterogeneous construct resulting in two main phenotypes, premeditated and impulsive aggression. Understanding motives of aggression helps reconstruct possible events, sequences of events and scenarios related to a certain crime, and ultimately, to prevent further crimes from happening.

This study investigates the public’s perceptions of digital innovations in pharmacy, with a focus on health informatics and medication management. Despite the rapid development of these technologies, a comprehensive understanding of how various demographics perceive and interact with them is lacking hence, this research aims to bridge this gap by offering insights into public attitudes and the factors influencing the adoption of digital tools in pharmacy practice, as KSA population and healthcare professionals after Covid-19 has observed the significant potential of digital health. A cross-sectional survey involving 1132 participants was conducted, employing SPSS for data analysis to ensure precise and reliable results. The findings indicate general optimism about the potential of digital innovations to enhance healthcare outcomes but concerns about data privacy and usability significantly affect user acceptance. The researchers recommended tailored educational programs and user-centered design to facilitate the adoption of digital pharmacy innovations. Key contributions include the identification of ‘Ease of Use’ and ‘Data Security and Privacy’ as predominant factors in the adoption of digital health tools.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.