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Industrial site selection holds strategic importance in the layout of industrial facilities. Scientific decision-making in site selection not only enhances the economic and technical feasibility of a project but also lays the foundation for sustainable development. However, industrial site selection is considered an NP-hard problem. The criteria used to evaluate site suitability, the methods proven effective under different conditions, big data sources introduced, and the key data gaps, methodological limitations, and research priorities to improve decision quality are important for researchers and engineers. Based on the Web of Science (WOS) core collection as the data source, this paper retrieved the literature related to the themes of “industrial site selection” and “facility location decision making,” and selected 149 highly relevant papers. It systematically categorizes three mainstream site selection methods: operations research-based methods; the application of geographic information systems in site selection; and the application of artificial intelligence in site selection. On this basis, this paper provides a systematic review of the overall industrial site selection process and methodologies, aiming to offer references for subsequent site selection analysis research and practical site selection work. An “MCDM–GIS–AI” technology convergence roadmap is also proposed for industrial site selection to identify remaining research gaps and offer a set of “good-practice guidelines” to inform both practical applications and future analytical studies. Full article

Smart cities offer new opportunities for urban governance and sustainable development. However, at the current stage, the construction and development of smart cities generally exhibit a technology-driven tendency, neglecting real resident demand, which contradicts the “human-centric” principle. Traditional top-down methods of demand collection struggle to capture the dynamics and heterogeneity of public demand. At the same time, government service platforms, as one dimension of smart city construction, have accumulated massive amounts of user-generated data, providing new solutions for this challenge. This paper aims to construct a big data-driven analytical framework for dynamically identifying and accurately forecasting core resident demand. The study uses Xi’an City, Shaanxi Province, China, as a case study, utilising user messages from People.cn spanning 2011 to 2023. These messages cover various domains, including urban construction, healthcare, education, and transportation, as the data source. The People.cn message board is China’s most significant nationwide online political platform. Its institutionalised feedback mechanism ensures data content focuses on highly representative specific grievances, rather than the broad emotional expressions on social media. The study employs user messages from People.cn from 2011 to 2023 as its data source, encompassing urban construction, healthcare, education, and transportation. First, a large language model (LLM) was used to preprocess and clean the raw data. Subsequently, the BERTopic model was applied to identify ten core demand themes and construct their monthly time series, thereby overcoming the limitations of traditional methods in short-text semantic recognition. Finally, by integrating variational mode decomposition (VMD) with support vector machines (SVMs), a hybrid demand forecasting model was established to mitigate the risk of overfitting in deep learning when forecasting small-sample time series. The empirical results show that the proposed LLM-BERTopic-VMD-SVM framework exhibits excellent performance, with the goodness-of-fit (R2) on various demand themes ranging from 0.93 to 0.96. This study proposes an effective analytical framework for identifying and forecasting resident demand. It provides a decision-support tool for city managers to achieve proactive and fine-grained governance, thereby offering a viable empirical pathway to promote the transformation of smart cities from technology-centric to human-centric. Full article

Based on data from Chinese corporate bonds issued between 2014 and 2023, this study examines how ESG rating disagreement affects credit spreads. The results indicate that such disagreement significantly increases spreads through financial risk and information asymmetry channels, though this effect is mitigated by higher bond ratings. The impact is more pronounced in developed regions, highly marketized areas, less polluted and less competitive industries, non-Big Four audited firms, small enterprises, and state-owned enterprises. Increases in credit spreads are mainly driven by environmental and social rating disagreements, with the governance dimension playing a limited role. Full article

Big data analytics has become a cornerstone of modern industries, driving advancements in business intelligence, competitive intelligence, and data-driven decision-making. This study applies Neural Topic Modeling (NTM) using the BERTopic framework and N-gram-based textual content analysis to examine job postings related to big data analytics in real-world contexts. A structured analytical process was conducted to derive meaningful insights into workforce trends and skill demands in the big data analytics domain. First, expertise roles and tasks were identified by analyzing job titles and responsibilities. Next, key competencies were categorized into analytical, technical, developer, and soft skills and mapped to corresponding roles. Workforce characteristics such as job types, education levels, and experience requirements were examined to understand hiring patterns. In addition, essential tasks, tools, and frameworks in big data analytics were identified, providing insights into critical technical proficiencies. The findings show that big data analytics requires expertise in data engineering, machine learning, cloud computing, and AI-driven automation. They also emphasize the importance of continuous learning and skill development to sustain a future-ready workforce. By connecting academia and industry, this study provides valuable implications for educators, policymakers, and corporate leaders seeking to strengthen workforce sustainability in the era of big data analytics. Full article

This study investigates the interaction between digital–intelligent integration and carbon productivity in 23 prefecture-level cities across the middle reaches of the Yellow River from 2013 to 2022, focusing on a resource-dependent region transitioning towards low-carbon development. The aim is to examine how digital technologies contribute to improving carbon productivity and reducing environmental pollution. An entropy-weighted index system was used to assess digital–intelligent transformation and carbon productivity. A coupling coordination model was applied to measure their joint performance, with spatial autocorrelation and spillover analyses used to detect regional patterns and intercity linkages. Data were sourced from official yearbooks, environmental bulletins, and urban big-data platforms. The results show a steady improvement in coordination between digital–intelligent integration and carbon productivity, with significant progress in 2018 and 2020 following national policy initiatives. Core cities showed higher coordination and generated positive spillovers, while peripheral cities lagged, resulting in noticeable spatial agglomeration. These findings highlight the growing coupling between digital–intelligent development and carbon productivity, reinforced by policy initiatives but accompanied by regional disparities. This study suggests that policies should focus on enhancing data infrastructure in core cities, improving regional cooperation, and bridging gaps in peripheral areas. It offers insights into the role of digital technologies in achieving low-carbon development in resource-dependent urban regions. Full article

The accelerated development of digital technologies during the 21st century has intensified requirements for Human Settlements (HS) infrastructure advancement in China’s maritime urban centers, driven by national objectives to forge a cohesive, technologically integrated state framework. This transformation has changed people’s work, learning, and entertainment patterns, leading to the rise in complex networks of pseudo human settlements (PHS). Traditional approaches to environmental research are insufficient for understanding the interactions between PHS and reality human settlements (RHS), which are interdependent and shape urban development. This study utilizes advanced methods such as the entropy weight method to determine indicator weights, the coupling coordination degree model to quantify the interaction intensity, the geo-detector to identify driving factors, and ArcGIS for spatial analysis to assess the interaction between PHS and RHS in 53 coastal cities from 2011 to 2022. The results show: (1) The coupling coordination degree rose initially but later declined, reflecting temporal differentiation; (2) The coordination of settlements varies across regions; (3) A migration trend from the northeast to southwest, with faster coordination improvement in the southwest; (4) Socio-economic development drives the coupling coordination, with big data technology enhancing the relationship. The findings guide sustainable urban development in coastal cities. Full article

This review presents a synthesis of global research on precision forestry, a field that integrates advanced technologies to enhance—rather than replace—established tools and methods used in the operational forest management and the wood products industry. By evaluating 210 peer-reviewed publications indexed in Web of Science (up to 2025), the study identifies six main categories and eight components of precision forestry. The findings indicate that “forest management and planning” is the most common category, with nearly half of the studies focusing on this topic. “Remote sensing platforms and sensors” emerged as the most frequently used component, with unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) systems being the most widely adopted tools. The analysis also reveals a notable increase in precision forestry research since the early 2010s, coinciding with rapid developments in small UAVs and mobile sensor technologies. Despite growing interest, robotics and real-time process control systems remain underutilized, mainly due to challenging forest conditions and high implementation costs. The research highlights geographical disparities, with Europe, Asia, and North America hosting the majority of studies. Italy, China, Finland, and the United States stand out as the most active countries in terms of research output. Notably, the review emphasizes the need to integrate precision forestry into academic curricula and support industry adoption through dedicated information and technology specialists. As the forestry workforce ages and technology advances rapidly, a growing skills gap exists between industry needs and traditional forestry education. Equipping the next generation with hands-on experience in big data analysis, geospatial technologies, automation, and Artificial Intelligence (AI) is critical for ensuring the effective adoption and application of precision forestry. Full article

Forest and grassland fire behavior prediction is increasingly critical under climate change, as rising fire frequency and intensity threaten ecosystems and human societies worldwide. This paper reviews the status and future development trends of wildfire behavior modeling and prediction technologies. It provides a comprehensive overview of the evolution of models from empirical to physical and then to data-driven approaches, emphasizing the integration of multidisciplinary techniques such as machine learning and deep learning. While conventional physical models offer mechanistic insights, recent advancements in data-driven models have enabled the analysis of big data to uncover intricate nonlinear relationships. We underscore the necessity of integrating multiple models via complementary, weighted fusion and hybrid methods to bolster robustness across diverse situations. Ultimately, we advocate for the creation of intelligent forecast systems that leverage data from space, air and ground sources to provide multifaceted fire behavior predictions in regions and globally. Such systems would more effectively transform fire management from a reactive approach to a proactive strategy, thereby safeguarding global forest carbon sinks and promoting sustainable development in the years to come. By offering forward-looking insights and highlighting the importance of multidisciplinary approaches, this review serves as a valuable resource for researchers, practitioners, and policymakers, supporting informed decision-making and fostering interdisciplinary collaboration. Full article

In the context of global sustainable development strategies and the rise of intelligent construction, it has become increasingly urgent for universities to adapt construction management curricula to meet the demands of this new era. However, prior education-reform-based studies rarely offer a systematic, educator-centered prioritization of knowledge areas, limiting actionable guidance for course sequencing and credit-hour allocation. To address this gap, this study identifies eight essential knowledge categories for construction management education through a comprehensive literature review and a survey of faculty members with strong theoretical and practical experience. An improved Analytic Hierarchy Process (AHP) model, weighted by the Consistency Ratio (CR), is applied to prioritize these areas. Results show that Fundamentals of Construction (18.50%), BIM (18.08%), and AI and Big Data (17.07%) received the highest importance values. These findings emphasize the need for curriculum reorientation to align with intelligent construction. This study contributes to modernizing construction management education and offers practical insights for curriculum development, ensuring alignment with industry trends and technological advancements. Full article

Building healthy communities is crucial for creating healthy cities and improving residents’ well-being. Old residential areas, with their substantial stock and elevated health risks, require urgent environmental upgrades. However, the relationship between community health promotion factors and resident sentiment, a crucial indicator of subjective well-being, in old residential areas remains poorly understood. By integrating big data-based community health promotion factors and Weibo data within the 15-min living circle of old residential areas in Xi’an, we developed an XGBoost model and employed SHAP analyses to interpret predictive outcomes. Results show that healthy facilities were dominant influencing factors in old residential areas. Densities of parking, supermarkets, education, package stations, and scenic spots exhibit nonlinear relationships with positive sentiment, indicating clear threshold effects and saturation effects. Two dominant patterns were observed in interactions between dominant factors and their strongest interacting factors. Four environment–sentiment patterns were identified for targeted planning interventions. It is recommended that planners and policymakers account for density phases and synergistic combinations of the dominant factors to optimize community health within old residential areas. The findings offer empirical support and planning insights for fostering healthy, sentiment-sensitive retrofit in old residential areas within the 15-min living circle. Full article

Advantages in CMOS MOSFET-based electronics served as a basis for modern ubiquitous computerization. At the same time, theoretical and practical developments in material science, analytical chemistry and molecular biology have presented the possibility of applying Boolean logic and information theory findings on a molecular basis. Molecular computing, both organic and inorganic, has the advantages of high computational density, scalability, energy efficiency and parallel computing. Carbon-based and carbohydrate molecular machines are potentially biocompatible and well-suited for biomedical tasks. Molecular computing-enabled sensors, medication-delivery molecular machines, and diagnostic and therapeutic nanobots are at the cutting edge of medical research. Highly focused diagnostics, precision medicine, and personalized treatment can be achieved with molecular computing tools and machinery. At the same time, traditional electronics and AI advancements create a highly effective computerized environment for analyzing big data, assist in diagnostics with sophisticated pattern recognition and step in as a medical routine aid. The combination of the advantages of MOSFET-based electronics and molecular computing creates an opportunity for next-generation healthcare. Full article

In the era of big data, the rapid proliferation of user-generated content enriched with geolocations offers new perspectives and datasets for probing the spatiotemporal dynamics of tourist mobility. Mining large-scale geospatial traces has become central to tourism geography: it reveals preferences for attractions and routes to enable intelligent recommendation, enhance visitor experience, and advance smart tourism, while also informing spatial planning, crowd management, and sustainable destination development. Using Mount Huangshan—a UNESCO World Cultural and Natural Heritage site—as a case study, we integrate GPS trajectories and geo-tagged photographs from 2017–2023. We apply a Density-Field Hotspot Detector (DF-HD), a Space–Time Cube (STC), and spatial gridding to analyze behavior from temporal, spatial, and fully spatiotemporal perspectives. Results show a characteristic “double-peak, double-trough” seasonal pattern in the number of GPS tracks, cumulative track length, and geo-tagged photos. Tourist behavior exhibits pronounced elevation dependence, with clear vertical differentiation. DF-HD efficiently delineates hierarchical hotspot areas and visitor interest zones, providing actionable evidence for demand-responsive crowd diversion. By integrating sequential time slices with geography in a 3D framework, the STC exposes dynamic spatiotemporal associations and evolutionary regularities in visitor flows, supporting real-time crowd diagnosis and optimized spatial resource allocation. Comparative findings further confirm that Huangshan’s seasonal intensity is significantly lower than previously reported, while the high agreement between trajectory density and gridded photos clarifies the multi-tier clustering of route popularity. These insights furnish a scientific basis for designing secondary tour loops, alleviating pressure on core areas, and charting an effective pathway toward internal structural optimization and sustainable development of the Mount Huangshan Scenic Area. Full article

Entrepreneurship plays a significant role in the economic development of emerging economies, particularly by addressing persistent issues such as youth unemployment and growth challenges. Developing nations perceive their startup ecosystems as critical engines of economic progress. Policymakers in these countries strive to reduce uncertainties and mitigate risks that could impede the growth of this essential sector. However, they face a significant obstacle: the lack of accurate and reliable data necessary to comprehend the challenges and requirements of the startup ecosystem. To effectively navigate these challenges, policymakers must utilize advanced analytical tools and technologies, including big data analytics, artificial intelligence, and machine learning. These technologies are crucial for the comprehensive collection and analysis of data from diverse sources. This research aims to identify current trends and challenges within the startup ecosystem in developing countries through the meticulous collection and analysis of news data on the topic. To achieve this objective, we developed a detailed plan to collect news data on Iran’s startup ecosystem spanning from 2017 to 2022. By employing advanced natural language processing techniques, we intended to conduct a thorough analysis of the collected data. Our goal is to extract significant insights that will inform and shape effective policymaking. Full article

As a critical technological foundation for electric vehicles, power battery state estimation primarily involves estimating the State of Charge (SOC), the State of Health (SOH) and the Remaining Useful Life (RUL). This paper systematically categorizes battery state estimation methods into three distinct generations, tracing the evolutionary progression from single-state to multi-state cooperative estimation approaches. First-generation methods based on equivalent circuit models offer straightforward implementation but accumulate SOC-SOH estimation errors during battery aging, as they fail to account for the evolution of microscopic parameters such as solid electrolyte interphase film growth, lithium inventory loss, and electrode degradation. Second-generation data-driven approaches, which leverage big data and deep learning, can effectively model highly nonlinear relationships between measurements and battery states. However, they often suffer from poor physical interpretability and generalizability due to the “black-box” nature of deep learning. The emerging third-generation technology establishes transmission mechanisms from microscopic electrode interface parameters via electrochemical impedance spectroscopy to macroscopic SOC, SOH, and RUL states, forming a bidirectional closed-loop system integrating estimation, prediction, and optimization that demonstrates potential to enhance both full-operating-condition adaptability and estimation accuracy. This progress supports the development of high-reliability, long-lifetime electric vehicles. Full article

Objective: This study investigates how artificial intelligence (AI) research relates to firm value, focusing on dominant thematic trends, theoretical foundations, and global collaboration patterns. Methods: A PRISMA-guided systematic review was conducted on 219 peer-reviewed articles published between 2013 and May 2025 in the Web of Science Social Sciences Citation Index. Bibliometric techniques, including co-word, co-citation, and collaboration network analyses, were performed using the bibliometrix (version 4.2.3) in R (version 4.4.2) package to map key concepts, intellectual structures, and international research partnerships. Results: The literature is primarily grounded in strategic management theories such as the resource-based view (RBV) and dynamic capabilities. Emerging research streams emphasize digital transformation, big data analytics, and decision support systems. Frequently co-occurring terms include “firm performance,” “artificial intelligence,” “dynamic capabilities,” “information technology,” and “decision-making.” Collaboration mapping highlights the United States, United Kingdom, and China as leading hubs, with increasing contributions from emerging economies such as India, Malaysia, and Saudi Arabia. The alignment between co-word and co-citation structures reflects a shift from foundational theory to applied AI capabilities in firm-value creation. Implications: By integrating a systematic review with advanced bibliometric and science-mapping methods, this work establishes a structured foundation for theory development, empirical testing, and policy formulation in AI-driven business landscapes. Full article