Search Results (1,390)

The geographic distribution patterns of microorganisms and their underlying mechanisms are central topics in microbiology, crucial for understanding ecosystem functioning and predicting responses to global change. Cryoconite absorbs solar radiation to form cryoconite holes, and because it lies within these relatively deep holes, it faces limited interference from surrounding ecosystems, often being seen as a fairly enclosed environment. Moreover, it plays a dominant role in the biogeochemical cycling of key elements such as carbon and nitrogen, making it an ideal model for studying large-scale microbial biogeography. In this study, we analyzed bacterial communities in cryoconite across a transcontinental scale of glaciers to elucidate their biogeographical distribution and community assembly processes. The cryoconite bacterial communities were predominantly composed of Proteobacteria, Cyanobacteria, Bacteroidota, and Actinobacteriota, with significant differences in species composition across geographical locations. Bacterial diversity was jointly driven by geographical and anthropogenic factors: species richness exhibited a hump-shaped relationship with latitude and was significantly positively correlated with the Human Development Index (HDI). The significant positive correlation may stem from nutrient input and microbial dispersal driven by high-HDI regions’ industrial, agricultural, and human activities. Beta diversity demonstrated a distance-decay pattern along spatial gradients such as latitude and geographical distance. Analysis of community assembly mechanisms revealed that stochastic processes predominated across continents, with a notable scale dependence: as the spatial scale increased, the role of deterministic processes (heterogeneous selection) decreased, while stochastic processes (dispersal limitation) strengthened and became the dominant force. By integrating geographical, climatic, and anthropogenic factors into a unified framework, this study enhances the understanding of the spatial-scale-driven mechanisms shaping cryoconite bacterial biogeography and emphasizes the need to prioritize anthropogenic influences to predict the trajectory of cryosphere ecosystem evolution under global change. Full article

High rock slopes are extensively distributed in areas of major engineering constructions, such as transportation infrastructure, hydraulic projects, and mining operations. The stability and failure evolution mechanism during their multi-stage excavation process have consistently been a crucial research topic in geotechnical engineering. In this paper, a series of two-dimensional rock slope models, incorporating various combinations of slope height and slope angle, were established utilizing the Discrete Element Method (DEM) software PFC2D. This systematic investigation delves into the meso-mechanical response of the slopes during multi-stage excavation. The Parallel Bond Model (PBM) was employed to simulate the contact and fracture behavior between particles. Parameter calibration was performed to ensure that the simulation results align with the actual mechanical properties of the rock mass. The research primarily focuses on analyzing the evolution of displacement, the failure modes, and the changing characteristics of the force chain structure under different geometric conditions. The results indicate that as both the slope height and slope angle increase, the inter-particle deformation of the slope intensifies significantly, and the shear band progressively extends deeper into the slope mass. The failure mode transitions from shallow localized sliding to deep-seated overall failure. Prior to instability, the force chain system exhibits an evolutionary pattern characterized by “bundling–reconfiguration–fracturing,” serving as a critical indicator for characterizing the micro-scale failure mechanism of the slope body. Full article

Introduction: Overweight and obesity are becoming increasingly prevalent. Incretin-based obesity treatments—glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual glucagon-like peptide-1 receptor/glucose-dependent insulinotropic polypeptide receptor agonists (GIP/GLP-1 RAs or dual agonists)—are a major stride in the evolution of obesity management. However, like weight loss with other means, they are associated with an inadvertent significant loss of lean body mass, including muscle. This has led to a resurgence in research for the preservation of lean body mass, the loss of which occurs with weight loss. The purpose of this narrative review is to discuss the mechanisms involved with lean body loss and capture the research landscape of the different classes of pharmacological agents being developed to address this problem. Methodology: We queried PubMed, Medline, and Scopus for randomized controlled trials and phase II or phase III trials using key words to capture the breath of this topic—obesity, weight loss, muscle loss, lean mass, and muscle preservation. Animal studies were excluded. We analyzed the studies conducted to date. Results: Weight loss, regardless of the method used to achieve it, is inadvertently accompanied by lean body mass loss, to varying degrees. There are several mechanisms that govern the loss of lean body mass and, more specifically, the loss of muscle mass; as such, several classes of medications have been explored, targeting different pathways and receptors—including bimagrumab (activin receptor agonist), tesamorelin (growth hormone releasing hormone agonists), and enobosarm (selective androgen receptor modulator). Most of these drugs are in the early phases of research development, but some show great promise. Conclusion: This narrative review attempts to detail the physiology of muscle mass loss when accompanied by weight loss and identify pharmacological targets that can be utilized to minimize it with mechanisms, effects, side effects, and research developmental progress. Full article

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article

In recent years, infrared detection technology for embankment leakages has become a popular research topic. The seepage and heat transfer characteristics of embankments under seepage failure conditions form the theoretical basis of infrared detection technology for leakage hazards. Nevertheless, a majority of prior research has relied on predetermined seepage pathways, which fail to accurately simulate the actual scenarios encountered in engineering practice. Accordingly, taking a typical soil embankment in the Dongting Lake area as the research object, a seepage damage test of the embankment body and surface soil of the embankment foundation was conducted. The mechanical and seepage damage of the embankment soil was established. FLAC3D6.0 software was used to develop a coupled numerical model of the unsaturated seepage, temperature, and stress of the embankment based on the damage model. The distribution laws of the seepage and temperature fields in the embankment body and foundation were calculated and analyzed. The results of this study show that there is a strong correlation between seepage, temperature, and structure during local seepage failure and even the overall structural failure of the embankment. Moreover, the evolution of the downstream embankment toe and surface temperature shows a phased change. By capturing this feature, it is possible to quickly screen the seepage location of an embankment to thereby provide a basis for determining the infrared detection indicators of the embankment. Full article

Driven by the urgent need for industrial transformation and emerging technologies, the construction engineering market is rapidly evolving toward intelligent building systems. This study employs latent Dirichlet allocation (LDA) methodology to analyze 474 blockchain-related research abstracts from Web of Science and Scopus databases, identifying eight key research topics: (1) industry adoption and implementation challenges; (2) smart contracts and payment mechanisms; (3) emerging technologies and digital transformation; (4) construction supply chain integration and optimization; (5) building modeling and technology integration; (6) modular integrated construction (MIC) applications; (7) project data and security management; and (8) construction industry sustainability and circular economy (CE). Using the autoregressive integrated moving average (ARIMA) model, the study forecasts trends for the top three research topics over the next 36 months. The results indicate strong positive growth trajectories for industry adoption and implementation challenges (Topic 1) and project data and security management (Topic 7), while emerging technologies and digital transformation (Topic 3) demonstrate sustained growth. This study offers a thorough examination of the present landscape and emerging research trends of blockchain in construction, and establishes an overall framework to comprehensively summarize its research and application in the construction industry. The results provide actionable insights for both practitioners and researchers, facilitating a deeper understanding of blockchain’s evolution and implementation prospects, and supporting the advancement of innovation within the industry. Full article

To achieve China’s carbon peak and carbon neutrality goals, it is essential to increase the market penetration of New Energy Vehicles (NEVs) and understand consumer attitudes. Based on a big data set of over 20,000 online user reviews, this study employs the Latent Dirichlet Allocation (LDA) model to extract themes, popular brands, and focal points across different time windows. The research constructs a data-driven threshold filtering mechanism that integrates topic probability, frequency, keyword weight, and cross-temporal topic similarity to quantify consumer reviews, enabling an in-depth analysis of the dynamic evolution of attitudes in the NEV market. The findings reveal a dual shift in consumer sentiment: first, a transition in focus from basic configurations and aesthetics toward quality experience; and second, a shift in purchasing decisions toward a socially driven model dominated by word-of-mouth and family collaboration. Full article

Implant-based breast reconstruction (IBBR) remains the most common form of post-mastectomy reconstruction worldwide, offering patients a reliable and accessible option to restore breast contour. Advances in surgical technique, biomaterials, and implant technology have driven rapid evolution in the field, with the dual goals of improving aesthetic outcomes and minimizing patient morbidity. The prepectoral plane has been popularized due to the eliminated risk of animation deformity and reduced postoperative pain. Some concerns remain regarding mastectomy flap thickness and long-term oncologic and aesthetic outcomes. Concurrently, nipple-sparing mastectomy has improved aesthetic results and enabled surgeons to move beyond just restoring breast form and improve functional recovery as well, as demonstrated by surgical efforts aimed at restoring nipple–areolar complex (NAC) sensation. Adjunctive use of biologic matrices and synthetic meshes has broadened reconstructive options, while next-generation implants seek to further enhance outcomes. Balanced against these innovations are important oncologic and systemic safety concerns, including breast implant-related cancers and the ongoing debate over breast implant illness (BII). This review highlights eight current “hot topics” in implant-based breast reconstruction: (1) prepectoral reconstruction, (2) nipple-sparing mastectomy, (3) oncoplastic techniques, (4) nipple–areolar complex (NAC) neurotization, (5) biologic matrices and synthetic meshes, (6) next-generation implants, (7) optimizing aesthetic outcomes, and (8) implant-associated cancer and systemic concerns. Together, these areas define the current landscape of innovation, controversy, and future directions in implant-based reconstruction. Full article

Community detection in social networks is one of the most important topics of network science. Researchers have developed numerous methods from various perspectives. However, the existing methods often overlook the team information encoded as a special type of user relation in the social network, which plays an important role in community formation and evolution. In this paper, we propose a novel community detection algorithm called Team-aware Community Detection (TaCD). Our model constructs a multi-view network by encoding the user interaction information as the user view and the team information as the team view. To measure the consistency across the two views, we use the Jaccard similarity to establish a cross-view coupling. Based on the constructed 2-view network, we use multi-view modularity to discover team-aware community structure, and solve the optimization problem using the well-known Generalized Louvain approach. Another contribution of this paper is the collection of a new SCHOLAT dataset, which consists of several social networks with team information and is publicly available for testing purposes. Our experimental results on several SCHOLAT networks with team information demonstrate that TaCD outperforms the existing community detection algorithms. Full article

The rapid diffusion of digital technologies, including big data, blockchain, and artificial intelligence, unlocks significant potential for marketing innovation in e-commerce while simultaneously raising fresh governance challenges. Digital platforms, as core infrastructures for online transactions and marketing interactions, have therefore come under increasing regulatory scrutiny amid tensions between technological progress and social stability. This study compiles a comprehensive Chinese Digital Platform Policy dataset consisting of national-level policy documents issued from 2000 through July 2025. We introduce a time-dimension topic clustering approach using density-based LDA algorithm to construct a policy corpus with reduced thematic overlap and develop a document-level policy intensity index by quantifying and aggregating the salience of domain-specific terms across documents. Validation exercises confirm the intensity measure strongly correlates with e-commerce transaction value and with digital innovation, with statistically significant lags consistent with policy implementation and firm adaptation. Beyond offering an empirically grounded metric, our analysis traces the dynamic co-evolution of regulation and technology adoption and identify composition effects—the joint influences of enabling and disciplining policy elements—on market outcomes. We argue that such effects also reconfigure the mix of marketing innovations. Collectively, the corpus and measurement framework provide a foundation for analyzing how regulatory innovation shapes the trajectory of marketing innovation and e-commerce development. Full article

The cross-fertilization of video games and tourism has expanded in recent years, with digital narratives increasingly shaping real-world travel behavior, yet the mechanisms linking mythological video games to pre-trip travel intention remain underexplored. Using the Chinese mythological game Black Myth: Wukong as a case, this study examines how digital myth narratives relate to overseas audiences’ perceptions of, and travel intentions towards, Chinese tourist destinations in a cross-cultural context. Based on a large corpus of YouTube comments, we integrate topic modeling, sentiment analysis, and interpretable machine learning to identify semantic cues associated with travel intention. The results indicate that multidimensional perceptions elicited by digital myth narratives are associated with a gradual evolution of destination image from cognitive to affective and then intentional. Cultural symbol perception, cross-cultural understanding, aesthetic appreciation, and emotional resonance show positive relationships with travel intention and appear as important predictors in the model. SHAP analysis further suggests a nonlinear threshold effect, whereby the probability that a comment is classified as expressing travel intention increases when overall perception reaches a relatively high level. Embedding the cognition–emotion–intention path within a digital game context, this study provides empirical evidence on destination image and behavioral intention in digital narrative settings and offers implications for cross-cultural communication and sustainable tourism planning. Full article

The global transition to renewable energy sources is essential to carbon neutrality and ensuring energy security. First, the paper presents a comprehensive literature review of the main technological breakthroughs in bioenergy, hydro energy, solar energy, onshore and offshore wind energy, ocean energy, and geothermal energy, selecting the latest papers published. Next, key scientific challenges, environmental and economic constraints, and future research priorities for each of the six renewable energies were outlined. Then, to emphasize the important contribution of renewable energies to total energy production and the proportions of each type of renewable energy, the evolution of global electricity generation from all six renewable sources between 2000 and 2023 was analyzed. Thus, in 2023, the global electricity generation weight of each renewable energy in total renewable energy ranks hydro energy (47.83%) first, followed by onshore and offshore wind energy (25.8%), solar energy (18.19%), bioenergy (7.07%), geothermal energy (1.1%), and ocean energy (0.01%). After that, the bibliometric analysis, conducted between 1 January 2021 and 1 October 2025 on the Web of Science (WoS) database and using the PRISMA approach and VOSviewer version 1.6.20 software, enabled the identification of the most cited papers, publications and citation number by WoS categories, topics, correlation with Sustainable Development Goals, authors’ affiliation, publication title, and publisher. Furthermore, the paper presents a network visualization of the link between co-occurrences and all keywords, imposing minimum thresholds of 10, 20, and 30 occurrences per keyword, and computes the network density based on the number of edges and nodes. Finally, additional analysis included the most used keywords in different co-occurrences, a word cloud of occurrences by total link strength, regression of occurrences versus total link strength, and correlations between citations and documents and between citations and authors. Carbon neutrality and a resilient energy future can only be achieved by integrating renewable sources into hybrid systems and optimized smart grids. Each technological progress stage will bring new challenges that must be addressed cost-effectively. Full article

Effective post-disaster management requires continuous and reliable monitoring of the evolving disaster situation. While remote sensing provides objective measurements of ground deformation, social media data offer dynamic insights into public perception and disaster progression. However, integrating these complementary data sources to achieve sustained monitoring of disaster remains a challenge. To address this, we propose a novel framework that combines Sentinel-1 SAR data with Sina Weibo posts to improve dynamic earthquake impact assessment. Physical damage was quantified using D-InSAR-derived deformation. Disaster-related locations were identified using a fine-tuned pre-trained language model, and public sentiment was inferred through prompt-based few-shot learning with a large language model. Spatiotemporal analysis was performed to examine the relationship between sentiment dynamics and varying levels of physical damage, followed by an analysis of topic transitions within regional semantic networks to compare discussion patterns across areas. A case study of the 2023 Jishishan earthquake demonstrates the framework’s capability to continuously track disaster evolution: regions experiencing severe physical damage exhibit clear concentrations of negative sentiment, whereas increases in positive sentiment coincide with areas where rescue operations are effectively underway. These findings indicate that integrating the two data sources improves continuous disaster monitoring and situational awareness, thereby supporting emergency response. Full article

This review study promotes the sustainability of civil infrastructure by advancing the materials science of alternative cementitious materials. Supported by extensive global research and industrial trials, geopolymer cement has emerged as a promising approach to reducing the ecological impact of ordinary Portland cement (OPC) due to its superior engineering properties and eco-friendly benefits from industrial waste utilization. Geopolymers are inorganic polymers formed by the polymerization of aluminosilicate precursors, such as fly ash (FA), slag, and metakaolin, in the presence of alkaline activating solutions. This work integrates findings across multiple domains, including precursor chemistry, microstructural evolution, mechanical and durability performance, and sustainability metrics like carbon footprint and energy consumption. A key contribution of this review is the comparative evaluation of FA-based and slag-based GPC systems against OPC concrete, emphasizing the factors influencing their mechanical and durability properties, while also distinguishing differences in environmental impact, microstructural development, and overall performance. The findings highlight that slag-based systems generally exhibit lower environmental impacts, especially in energy demand and emissions, while regional differences in precursor availability constrain how widely the LCA and economic results can be applied. Building on previous reviews that have considered these topics, this study jointly examines technical performance and sustainability indicators and identifies regional variations that influence feasibility. The synthesis provides a balanced, evidence-based assessment of the potential and limitations of GPC as a lower-carbon alternative to OPC, supporting efforts to reduce the climate impact of future concrete construction. Full article

This study aims to map the evolution of pico-hydropower and Michell–Banki (cross-flow) turbine research from 2000 to 2025 through a combined bibliometric analysis and qualitative mini-review. In total, 1036 Scopus-indexed records were initially identified and refined to 922 relevant publications for analysis. Bibliometric mapping with CiteSpace, VOSviewer, and Bibliometrix identified publication trends and seven major thematic clusters (dominated by topics such as cross-flow turbine design, renewable energy integration, and asynchronous generators), while a qualitative mini-review of key studies provided contextual depth. The analysis detected 25 keywords with strong citation bursts, indicating a shift in focus over the last decade from traditional electrical regulation toward digitalization and additive manufacturing. The mini-review distilled three dominant lines of inquiry geometric design optimization, hydraulic performance characterization, and socio-economic evaluation and highlighted critical knowledge gaps, including the absence of standardized flow–head–efficiency (Q–H–η) performance data, sparse reporting of economic metrics like levelized cost of energy (LCOE), and limited high-altitude (above 3000 m) validation of pico-hydro systems. This study’s integrative approach is unique compared to prior bibliometric or technical reviews, providing a comprehensive overview of the pico-hydropower landscape and outlining a future research agenda to standardize experimental protocols, integrate economic analysis, and extend cross-flow turbine deployments to high-Andean regions. Full article