Adequate sanitation is crucial for human health and well-being, yet billions worldwide lack access to basic facilities. This comprehensive review examines the emerging field of intelligent sanitation systems, which leverage Internet of Things (IoT) and advanced Artificial Intelligence (AI) technologies to address global sanitation challenges. The existing intelligent sanitation systems and applications is still in their early stages, marked by inconsistencies and gaps. The paper consolidates fragmented research from both academic and industrial perspectives based on PRISMA protocol, exploring the historical development, current state, and future potential of intelligent sanitation solutions. The assessment of existing intelligent sanitation systems focuses on system detection, health monitoring, and AI enhancement. The paper examines how IoT-enabled data collection and AI-driven analytics can optimize sanitation facility performance, predict system failures, detect health risks, and inform decision-making for sanitation improvements. By synthesizing existing research, identifying knowledge gaps, and discussing opportunities and challenges, this review provides valuable insights for practitioners, academics, engineers, policymakers, and other stakeholders. It offers a foundation for understanding how advanced IoT and AI techniques can enhance the efficiency, sustainability, and safety of the sanitation industry.

This study systematically examines the literature of electric vehicle (EV) purchase intention and consumer behavior using a bibliometric method to unveil three main research questions: 1) identifying influential publications, authors, and journals; 2) analyzing the thematic evolution of research over time; and 3) identifying emerging research directions. The main objective is to provide a comprehensive understanding of the current state of knowledge and to guide future research in this evolving field. A comprehensive bibliometric analysis was conducted, using Scopus statistics analysis, R-Studio Biblioshiny and VOSviewer, comprising 687 publications authored by 1743 researchers representing 34 different countries with the dataset sourced from the Scopus database from 2010 to 2023. To achieve a nuanced understanding of the research landscape, a multifaceted approach was adopted, including detailed citation analysis, author co-citation analysis, keyword analysis, and thematic mapping. Through meticulous analysis, this study identifies the most influential publications, authors, and journals in the domain of EV purchase intentions and consumer behaviors. It also traces the evolution of themes over time and identifies emerging research directions, providing valuable insights into the trajectory and future avenues of inquiry within this field. The findings contribute to a deeper understanding of the dynamics shaping research in the realm of EVs. The insights gained contribute significantly to advancing knowledge in this crucial domain, offering theoretical insights and practical implications for policymakers, businesses, manufacturers, and academics.

The study of authoritarian leadership has undergone significant development, with researchers exploring its different dimensions and consequences. This leadership style, characterized by a top-down approach and centralized decision-making authority, has been extensively examined in psychology, organizational behavior, and management literature. Scholars have delved into the effects of authoritarian leadership on various aspects of organizations such as employee satisfaction, motivation levels, productivity rates, turnover rates, and team dynamics. The research landscape surrounding authoritarian leadership has witnessed a recent surge in interest as scholars strive to understand its intricate connections with different variables. The primary objective of this study is to conduct a comprehensive bibliometric analysis on authoritarian leadership, aiming to identify the key research areas, influential authors, prominent journals in the field, and citation patterns. To our knowledge, no bibliometric analysis on authoritarian leadership can be found in the Scopus database, highlighting the novelty of our research. Through a source-based examination of scholarly articles and their citations pertaining to authoritarian leadership, this analysis offers valuable insights into the current state of research in this domain. By focusing on publications from the past decade onwards, we aim to uncover trends and potential gaps within existing literature while also providing guidance for future research endeavors. Our research findings will provide valuable insights into the phenomenon of authoritarian leadership, contributing to a deeper understanding of its implications. By delving into this topic, we hope to pave the way for future studies and investigations in this field that can build upon our findings and expand knowledge even further.

Developing “New Quality Productive Forces” (NQPFs) has been accepted as a new theory to accelerate the high-quality development in China. In current, China’s high-quality development mainly relies on the traction of the digital economy. In view of this, developing NQPFs in China’s digital economy sector requires locate and remove some obstacles, such as the insufficient utilization of data, inadequate algorithm regulation, the mismatched supply and demand of regional computing power and the immature market environment. As a solution, it is necessary to allocating data property rights in a market-oriented way, establishing a user-centered algorithm governance system, accelerating the establishment of the national integrated computing network, and maintaining fair competition to optimize the market environment.

This study investigates the significance of data analytics in digital marketing for sustainable business growth. Data analytics has become an indispensable instrument in the world of digital marketing, offering organisations the means to achieve sustainable growth while minimising their environmental impact. We gathered data from 273 marketing and business consultants, chosen for their expertise in digital channels and data analytics, using a survey research design. The questionnaire, which was validated through expert review and pilot testing, assessed the relationship between data analytics utilization and its impact on competitive advantage and business optimization. We conducted statistical analyses, including descriptive and inferential statistics, using SPSS version 25.0. Findings reveal a significant correlation between data analytics adoption in digital marketing and sustainable business competitive advantage, as well as a notable impact on business optimization. Recommendations emphasise the strategic importance of customer segmentation and predictive analytics in leveraging data analytics for targeted marketing campaigns and proactive adjustments to market trends. This study underscores the indispensability of data analytics in the evolving digital marketing landscape, offering actionable insights for businesses seeking sustainable growth and competitive advantage.

The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI's capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.