Search Results (41)

Background/Objectives: This study examines the academic discourse surrounding Critical Incident Stress Debriefing (CISD) and Critical Incident Stress Management (CISM) for first responders using Latent Dirichlet Allocation (LDA) topic modeling. It aims to uncover latent topical structures in the literature and critically evaluate assumptions to identify gaps and limitations. Methods: A corpus of 214 research article abstracts related to CISD/M was gathered from the Web of Science Core Collection. After preprocessing, we used Orange Data Mining software’s LDA tool to analyze the corpus. We tested models ranging from 2 to 10 topics. To guide interpretation and labeling, we evaluated them using log perplexity, topic coherence, and LDAvis visualizations. A four-topic model offered the best balance of detail and interpretability. Results: Four topics emerged: (1) Critical Incident Stress Management in medical and emergency settings, (2) psychological and group-based interventions for PTSD and trauma, (3) peer support and experiences of emergency and military personnel, and (4) mental health interventions for first responders. Key gaps included limited focus on cumulative trauma, insufficient longitudinal research, and variability in procedural adherence affecting outcomes. Conclusions: The findings highlight the need for CISD/M protocols to move beyond event-specific interventions and address cumulative stressors. Recommendations include incorporating holistic, proactive mental health strategies and conducting longitudinal studies to evaluate long-term effectiveness. These insights can help refine CISD/M approaches and enhance their impact on first responders working in high-stress environments. Full article

Technology roadmapping is conducted by systematic mapping of technological evolution through patent analytics to inform innovation strategies. This study proposes an integrated framework combining hierarchical Latent Dirichlet Allocation (LDA) modeling with multiphase technology lifecycle theory, analyzing 113,449 Derwent patent abstracts (2008–2022) across three dimensions: technological novelty, functional applications, and competitive advantages. By segmenting innovation stages via logistic growth curve modeling and optimizing topic extraction through perplexity validation, we constructed dynamic technology roadmaps to decode latent evolutionary patterns in AI-powered programmable manipulators (B25J classification) within an innovation trajectory. Key findings revealed: (1) a progressive transition from electromechanical actuation to sensor-integrated architectures, evidenced by 58% compound annual growth in embedded sensing patents; (2) application expansion from industrial automation (72% early stage patents) to precision medical operations, with surgical robotics growing 34% annually since 2018; and (3) continuous advancements in adaptive control algorithms, showing 2.7× growth in reinforcement learning implementations. The methodology integrates quantitative topic modeling (via pyLDAvis visualization and cosine similarity analysis) with qualitative lifecycle theory, addressing the limitations of conventional technology analysis methods by reconciling semantic granularity with temporal dynamics. The results identify core innovation trajectories—precision control, intelligent detection, and medical robotics—while highlighting emerging opportunities in autonomous navigation and human–robot collaboration. This framework provides empirically grounded strategic intelligence for R&D prioritization, cross-industry investment, and policy formulation in Industry 4.0. Full article

Coastal cities are increasingly exposed to the risks posed by climate change, including rising sea levels, intensified storms, and coastal erosion. In this context, risk communication plays a crucial role, as it can shape public perception, promote preparedness, and influence both emergency responses and long-term mitigation strategies. This study investigated how disaster-related risks are framed in the media, focusing on the case of Naples, Italy, following a severe coastal storm surge that struck the city’s waterfront on December 2020. Using Dynamic Latent Dirichlet Allocation (DLDA), the research analyzed 297 newspaper articles published between 2020 and 2024 to examine the evolution of media narratives over time. The findings reveal four dominant patterns: (1) a prevailing economic discourse centered on financial damages and compensations, with limited references to resilience planning; (2) a temporal framing that presents the storm as a sudden, exceptional event, disconnected from historical precedents or future climate projections; (3) a lack of emphasis on the social experiences and vulnerabilities of local residents; and (4) minimal discussion of tourists’ exposure to risk, despite their presence in high-impact areas. These results highlight key limitations of media-driven risk communication and underscore the need for more inclusive, forward-looking narratives to support urban resilience and climate adaptation in coastal cities. This research offers valuable insights for urban planners, policymakers, journalists, and disaster risk reduction professionals, helping them to better align communication strategies with long-term adaptation goals and the needs of diverse urban populations. Full article

The Point of Interest (POI) recommendation system is a critical tool for enhancing user experience by analyzing historical behaviors, social network data, and real-time location information with the increasing demand for personalized and intelligent services. However, existing POI recommendation systems face three major challenges: (1) oversimplification of user preference modeling, limiting adaptability to dynamic user needs, (2) lack of explicit arrival time modeling, leading to reduced accuracy in time-sensitive scenarios, and (3) complexity in trajectory representation and spatiotemporal mining, posing difficulties in handling large-scale geographic data. This paper proposes NextMove, a novel POI recommendation model that integrates four key modules to address these issues. Specifically, the Probabilistic User Preference Generation Module first employs Latent Dirichlet Allocation (LDA) and a user preference network to model user personalized interests dynamically by capturing latent geographical topics. Secondly, the Self-Attention-based Arrival Time Prediction Module utilizes a Multi-Head Attention Mechanism to extract time-varying features, improving the precision of arrival time estimation. Thirdly, the Transformer-based Trajectory Representation Module encodes sequential dependencies in user behavior, effectively capturing contextual relationships and long-range dependencies for accurate future location forecasting. Finally, the Next Location Feature-Aggregation Module integrates the extracted representation features through an FC-based nonlinear fusion mechanism to generate the final POI recommendation. Extensive experiments conducted on real-world datasets demonstrate the superiority of the proposed NextMove over state-of-the-art methods. These results validate the effectiveness of NextMove in modeling dynamic user preferences, enhancing arrival time prediction, and improving POI recommendation accuracy. Full article

Media platforms provide an effective way to gauge public perceptions, especially during mass disruption events. This research explores public responses to the 2023 Ohio train derailment event through Twitter, currently known as X, and Google Trends. It aims to unveil public sentiments and attitudes by employing sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) and topic modeling using Latent Dirichlet Allocation (LDA) on geotagged tweets across three phases of the event: impact and immediate response, investigation, and recovery. Additionally, the Self-Organizing Map (SOM) model is employed to conduct time-series clustering analysis of Google search patterns, offering a deeper understanding into the event’s spatial and temporal impact on society. The results reveal that public perceptions related to pollution in communities exhibited an inverted U-shaped curve during the initial two phases on both the Twitter and Google Search platforms. However, in the third phase, the trends diverged. While public awareness declined on Google Search, it experienced an uptick on Twitter, a shift that can be attributed to governmental responses. Furthermore, the topics of Twitter discussions underwent a transition across three phases, changing from a focus on the causes of fires and evacuation strategies in Phase 1, to river pollution and trusteeship issues in Phase 2, and finally converging on government actions and community safety in Phase 3. Overall, this study advances a multi-platform and multi-method framework to uncover the spatiotemporal dynamics of public perception during disasters, offering actionable insights for real-time, region-specific crisis management. Full article

In modern society with a highly developed Internet, online public opinions on swine epidemic diseases have become one of the important influencing factors for the fluctuation of pork prices. In this paper, the Baidu AI large model, Time-Varying Parameter-Stochastic Volatility-Vector Auto Regression (TVP-VAR) and Latent Dirichlet allocation (LDA) approaches are employed to investigate the dynamic impact of online public opinion regarding live swine epidemic diseases on fluctuations in pork price. The results show that: (1) Online public attention and negative sentiment exert significant time-varying impacts on pork price fluctuations, with these impacts being most pronounced in the short term and gradually diminishing over the medium and long term. (2) During the outbreaks of swine epidemic diseases, the impulse impact of online public attention and negative sentiment on pork price fluctuations exhibits distinct stage-specific characteristics. Initially, the impact is negative and subsequently turns positive before eventually waning. (3) The online discourse surrounding swine epidemic diseases can be categorized into four topics including disease transmission, vaccine technology, industry development, and disease prevention and control. Online public attention towards these four topics associated with negative sentiments generally contributes to variations in pork prices. Based on findings, several policy recommendations are proposed, including the timely release of swine epidemic disease information, the establishment and enhancement of the online public opinion monitoring and early warning system, as well as adherence to routine prevention and control of pig epidemic diseases. Full article

This research aims to investigate the roles of ontology and Semantic Web Technologies (SWT) in modern knowledge representation and data management. By analyzing a dataset of 10,037 academic articles from Web of Science (WoS) published in the last 6 years (2019–2024) across several fields, such as computer science, engineering, and telecommunications, our research identifies important trends in the use of ontologies and semantic frameworks. Through bibliometric and semantic analyses, Natural Language Processing (NLP), and topic modeling using Latent Dirichlet Allocation (LDA) and BERT-clustering approach, we map the evolution of semantic technologies, revealing core research themes such as ontology engineering, knowledge graphs, and linked data. Furthermore, we address existing research gaps, including challenges in the semantic web, dynamic ontology updates, and scalability in Big Data environments. By synthesizing insights from the literature, our research provides an overview of the current state of semantic web research and its prospects. With a 0.75 coherence score and perplexity = 48, the topic modeling analysis identifies three distinct thematic clusters: (1) Ontology-Driven Knowledge Representation and Intelligent Systems, which focuses on the use of ontologies for AI integration, machine interpretability, and structured knowledge representation; (2) Bioinformatics, Gene Expression and Biological Data Analysis, highlighting the role of ontologies and semantic frameworks in biomedical research, particularly in gene expression, protein interactions and biological network modeling; and (3) Advanced Bioinformatics, Systems Biology and Ethical-Legal Implications, addressing the intersection of biological data sciences with ethical, legal and regulatory challenges in emerging technologies. The clusters derived from BERT embeddings and clustering show thematic overlap with the LDA-derived topics but with some notable differences in emphasis and granularity. Our contributions extend beyond theoretical discussions, offering practical implications for enhancing data accessibility, semantic search, and automated knowledge discovery. Full article

In this paper, we investigated the role of generative AI in education in academic publications extracted from Web of Science (3506 records; 2019–2024). The proposed methodology included three main streams: (1) Monthly analysis trends; top-ranking research areas, keywords and universities; frequency of keywords over time; a keyword co-occurrence map; collaboration networks; and a Sankey diagram illustrating the relationship between AI-related terms, publication years and research areas; (2) Sentiment analysis using a custom list of words, VADER and TextBlob; (3) Topic modeling using Latent Dirichlet Allocation (LDA). Terms such as “artificial intelligence” and “generative artificial intelligence” were predominant, but they diverged and evolved over time. By 2024, AI applications had branched into specialized fields, including education and educational research, computer science, engineering, psychology, medical informatics, healthcare sciences, general medicine and surgery. The sentiment analysis reveals a growing optimism in academic publications regarding generative AI in education, with a steady increase in positive sentiment from 2023 to 2024, while maintaining a predominantly neutral tone. Five main topics were derived from AI applications in education, based on an analysis of the most relevant terms extracted by LDA: (1) Gen-AI’s impact in education and research; (2) ChatGPT as a tool for university students and teachers; (3) Large language models (LLMs) and prompting in computing education; (4) Applications of ChatGPT in patient education; (5) ChatGPT’s performance in medical examinations. The research identified several emerging topics: discipline-specific application of LLMs, multimodal gen-AI, personalized learning, AI as a peer or tutor and cross-cultural and multilingual tools aimed at developing culturally relevant educational content and supporting the teaching of lesser-known languages. Further, gamification with generative AI involves designing interactive storytelling and adaptive educational games to enhance engagement and hybrid human–AI classrooms explore co-teaching dynamics, teacher–student relationships and the impact on classroom authority. Full article

Cultural ecosystem services (CESs) are essential for the sustainable development and management of urban green spaces. However, there remains a gap in leveraging big data and unsupervised machine learning to comprehensively evaluate perceived CESs. This study introduces a hybrid research methodology integrating latent dirichlet allocation (LDA) and importance–performance analysis (IPA) to analyze 20,087 user-generated reviews of Fragrance Hill Park in Beijing from Meituan. The key findings are the following: (1) ten types of CESs were identified, including five related to personal well-being, four to public well-being, and one bridging both categories; (2) the most significant dimensions were “recreational activities”, “aesthetic appreciation”, “physical well-being”, and “mental well-being”; (3) users expressed positive sentiments toward “history and culture”, “mental well-being”, and “religious engagement”, while “social relations” received the most negative feedback; (4) IPA results highlight “recreational activities” and “aesthetic appreciation” as priority areas for improvement. This study provides a scalable, data-driven framework for evaluating CESs in urban green spaces. The insights gained can inform urban green space management and policy decisions to enhance user experiences and promote sustainable urban development. Full article

The stable long-term preservation of plant cells is crucial for biopharmaceuticals and food security. Therefore, the long-term cryopreservation of plant cells using a cryoprotective agent (CPA) is a crucial area of study. However, research on low-toxicity CPAs remains limited. We analyzed 1643 abstracts related to plant-cryopreservation (PCP) research published from 1967 to May 2023, spanning 56 years, from academic citation databases, with the search conducted in May 2023. Grouping these abstracts by five-year intervals revealed an increase in PCP papers until 2015, followed by a decline in the 2020s. In order to confirm the declining trend, we performed text-mining analysis using the Latent Dirichlet Allocation (LDA) algorithm, which identifies underlying topics across diverse documents to aid decision-making and classified the abstracts into three distinct topics: Topic 1, “Seed bank”; Topic 2, “Physiology”; and Topic 3, “Cryopreservation protocol”. The decline, particularly in “Cryopreservation protocol” research, is an important observation in this study. At the same time, this decrease may be due to the limited scope of Topic 3. However, we expect improvements with the development of new CPAs. This expectation is based on numerous ongoing studies focused on developing new CPAs for the cryopreservation of various animal and medical cell lines, with particular attention on polysaccharides as components that could reduce the required concentrations of existing CPAs. Full article

As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like “system” and “energy” are among the most frequent, signaling that Topic 1 revolves around data systems and IoT technologies, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the application of technologies, as indicated by terms such as “technologies”, “industry” and “research”. It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on machine learning and IoT applications. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on smart technologies and systems. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on digital transformation. Topic 6 focuses on technical aspects such as modeling, system performance and prediction algorithms. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out. Full article

Academic publications from the Web of Science Core Collection on “e-business” and “artificial intelligence” (AI) are investigated to reveal the role of AI, extract latent themes and identify potential research topics. The proposed methodology includes relevant graphical representations (trends, co-occurrence networks, Sankey diagrams), sentiment analyses and latent topics identification. A renewed interest in these publications is evident post-2018, with a sharp increase in publications around 2020 that can be attributed to the COVID-19 pandemic. Chinese institutions dominate the collaboration network in e-business and AI. Keywords such as “business transformation”, “business value” and “e-business strategy” are prominent, contributing significantly to areas like “Operations Research & Management Science”. Additionally, the keyword “e-agribusiness” recently appears connected to “Environmental Sciences & Ecology”, indicating the application of e-business principles in sustainable practices. Although three sentiment analysis methods broadly agree on key trends, such as the rise in positive sentiment over time and the dominance of neutral sentiment, they differ in detail and focus. Custom analysis reveals more pronounced fluctuations, whereas VADER and TextBlob present steadier and more subdued patterns. Four well-balanced topics are identified with a coherence score of 0.66 using Latent Dirichlet Allocation, which is a probabilistic generative model designed to uncover hidden topics in large text corpora: Topic 1 (29.8%) highlights data-driven decision-making in e-business, focusing on AI, information sharing and technology-enabled business processes. Topic 2 (28.1%) explores AI and Machine Learning (ML) in web-based business, emphasizing customer service, innovation and workflow optimization. Topic 3 (23.6%) focuses on analytical methods for decision-making, using data modeling to enhance strategies, processes and sustainability. Topic 4 (18.5%) examines the semantic web, leveraging ontologies and knowledge systems to improve intelligent systems and web platforms. New pathways such as voice assistance, augmented reality and dynamic marketplaces could further enhance e-business strategies. Full article

The study aimed to explore the relationship and interaction between humans and nature in specific areas. Latent Dirichlet allocation topic recognition and SnowNLP sentiment analysis were used to extract the topics and analyze the sentiments from visitors’ online reviews of Wuyishan National Park. The conclusions were as follows: (1) The tourists mainly expressed positive emotions toward Wuyishan National Park, and the tourists acknowledged its ecological environment and natural and cultural heritage value. (2) The tourists’ comments focused on four themes: tourism activities and facilities, natural and cultural heritage value, characteristic tourism products, and tourism management and services. Natural experience was the main tourism activity in Wuyishan National Park, while cultural activities were related to the tea culture. (3) The tourist facilities, ticket and reservation mechanism, and management and services of Wuyishan National Park were the main concerns of the tourists. The study suggested that Wuyishan National Park could be transformed from a tourist destination into a comprehensive national park that provides recreational experiences and environmental education. This should be conducted by (1) developing detailed natural and cultural education and experience products and (2) improving public service functions and enhancing the public welfare of the national park. Full article

A conducive business environment is crucial for establishing a sustainable market economy. This paper aims to quantitatively analyze the evolution of China’s business environment policy, offering valuable insights for future optimization. Focusing on 1335 national-level business environment policies from 2006 to 2023, this study employed a social network analysis (SNA) and the latent Dirichlet allocation (LDA) model to investigate policy changes in terms of the issuing rhythm, collaborating departments, and policy attention. The findings reveal: (1) China has established a mature policy system for its business environment, with both an increasing number and improving impact of related policies. (2) A governance network of the business environment has gradually formed, with the participation of differentiated departments and the domination of one central unit, ensuring efficient collaboration and clear instruction. (3) The attention of the business environment policy has shifted from attracting foreign investment and developing the industrial chain to simplifying bureaucratic approval procedures, smoothing trade circulation, and ensuring legal reliability. This paper’s results serve as an empirical contribution to the field of business environment policy research. Full article

In the post-sharing era, the evolution of the shared micro-mobility industry has transitioned from expanding market share to providing precise services. The focus on user satisfaction has shifted from a singular emphasis on functional utility to diversified product values. Product perceived value has emerged as a comprehensive factor for enhancing user satisfaction, aiding companies in formulating precise services, mitigating urban resource wastage, and promoting sustainable urban development. From the perspective of product perceived value, this study combined the analysis of factors affecting user satisfaction of shared micro-mobility and the interaction between these aspects and carried out the following two studies. Research No. 1: By mining the user review data on app platforms related to shared micro-mobility and adopting the latent dirichlet allocation (LDA) algorithm, we have initially identified 17 major factors affecting the satisfaction of users and summarized these factors into four research topics constituting product perceived value. Research No. 2: On the basis of Research No. 1, the content of the American Customer Satisfaction Index (ACSI) was expanded, and a user satisfaction research model focusing on the perceived value of shared micro-mobility products was constructed. Afterwards, by using the data collected in questionnaire surveys, structural equation modeling (SEM) was used to model the user satisfaction of shared micro-mobility through SEM, which was deployed to establish an empirical analysis. It is found that (1) both user expectation and product quality can positively affect the perceived value of products through interactive experience; (2) factors such as user expectation, product quality, interactive experience, and subjective consciousness can positively affect user satisfaction through the perceived value of products, with user expectation delivering the greatest influence; and (3) subjective consciousness has a direct positive effect on users’ willingness to continuously use a product but no significant effect on user satisfaction. These findings expand the user satisfaction theoretical model in the field of shared micro-mobility, constitute suggestions for product development and service promotion in the shared micro-mobility industry, and can provide new ideas and methods for the sustainable development of urban transportation. Full article