Search Results (1,395)

The rapid evolution of smart transportation systems necessitates the integration of advanced sensing technologies capable of supporting the real-time, reliable, and cost-effective monitoring of road infrastructure. Fiber-optic sensor (FOS) technologies, given their high sensitivity, immunity to electromagnetic interference, and suitability for harsh environments, have emerged as promising tools for enabling intelligent transportation infrastructure. This review critically examines the current landscape of classical mechanical and electrical sensor realization in monitoring solutions. Focus is also given to fiber-optic-sensor-based solutions for smart road applications, encompassing both well-established techniques such as Fiber Bragg Grating (FBG) sensors and distributed sensing systems, as well as emerging hybrid sensor networks. The article examines the most topical physical parameters that can be measured by FOSs in road infrastructure monitoring to support traffic monitoring, structural health assessment, weigh-in-motion (WIM) system development, pavement condition evaluation, and vehicle classification. In addition, strategies for FOS integration with digital twins, machine learning, artificial intelligence, quantum sensing, and Internet of Things (IoT) platforms are analyzed to highlight their potential for data-driven infrastructure management. Limitations related to deployment, scalability, long-term reliability, and standardization are also discussed. The review concludes by identifying key technological gaps and proposing future research directions to accelerate the adoption of FOS technologies in next-generation road transportation systems. Full article

The rise in antimicrobial resistance (AMR) among the most clinically significant bacteria presents a global threat. The coexistence of resistance mechanisms to both carbapenems and colistin is particularly concerning, as these are last-line treatments, specifically reserved for the most challenging infections caused by clinically multidrug-resistant Enterobacterales. Natural aquatic environments have become environmental reservoirs for the transmission of AMR, particularly concerning mechanisms against these two types of critically important drugs. The crucial role of environmental settings as a driving force for the spread and evolution of AMR associated with these drugs is underestimated, and scientific knowledge on this topic is limited. This review aims to fill an important gap in the scientific literature and comprehensively consolidate the available data on carbapenem- and colistin-associated AMR in the aquatic environment. This study provides a comprehensive synthesis of the current knowledge by integrating bibliographic data with a detailed genomic analysis of 278 bacterial genomes sourced from natural waters. It explores the distribution of carbapenemase and mobile colistin resistance (mcr) genes, identifying their hosts, geographical spread, and complex gene–plasmid–host associations. This review distinguishes two critical host groups for genes that provide resistance to last-resort drugs, Enterobacterales and autochthonous aquatic microbiota, highlighting both confirmed and potential interactions between them. Crucially, genomic analysis highlights the alarming co-occurrence of carbapenem and colistin resistance in single cells and on single plasmids, contributing to the spread of multidrug resistance phenotypes. These findings clearly indicate that aquatic environments are not merely passive recipients but active, evolving hubs for high-risk AMR determinants. Future research should focus on the interplay between allochthonous vectors and autochthonous microbiota to better understand the long-term stabilization of carbapenemase and mcr genes. Such efforts, combined with advanced sequencing technologies, are essential to ensure that carbapenems and colistin remain viable treatment options in clinical settings. Full article

While the technological foundation for sludge valorization (anaerobic digestion and pyrolysis) is mature, a significant disconnect exists between traditional research and the advanced application of artificial intelligence. This study identifies that Machine Learning (ML) remains in a peripheral position, representing an untapped frontier for achieving predictive and circular systems. The methodology involved a quantitative bibliometric analysis of 190 Scopus-indexed documents (2005–2025). We analyzed indicators of scientific production, collaboration, and thematic evolution using Bibliometrix and VOSviewer 1.6.20. The results reveal a rapidly growing research field, predominantly led by Chinese institutions. The temporal analysis projects a productivity peak around 2033. Core topics include established technologies like anaerobic digestion and pyrolysis. However, network and keyword analyses reveal an emerging trend toward hydrothermal processes and, crucially, the early incorporation of ML. However, ML still occupies a peripheral position within the main scientific discourse, highlighting a gap between traditional research and the advanced application of artificial intelligence. The study systematizes existing knowledge and demonstrates that, although the technological foundation is mature, the deep integration of ML represents the future frontier for achieving sludge valorization systems that are more predictive, efficient, and aligned with the principles of the circular economy. Full article

Objective: We analyzed nanoparticle regulation research to examine the evolution of regulatory frameworks, identify major thematic structures, and evaluate current challenges in the governance of rapidly advancing nanotechnologies. By drawing parallels with the historical development of radiation regulation, the study aimed to contextualize emerging regulatory strategies and derive lessons for future governance. Methods: A total of 9095 PubMed-indexed articles published between January 2015 and October 2025 were analyzed using text mining, keyword frequency analysis, and topic modeling. Preprocessed titles and abstracts were transformed into a TF-IDF (Term Frequency–Inverse Document Frequency) document–term matrix, and NMF (Non-negative Matrix Factorization) was applied to extract semantically coherent topics. Candidate topic numbers (K = 1–12) were evaluated using UMass coherence scores and qualitative interpretability criteria to determine the optimal topic structure. Results: Six major research topics were identified, spanning energy and sensor applications, metal oxide toxicity, antibacterial silver nanoparticles, cancer nano-therapy, and nanoparticle-enabled drug and mRNA delivery. Publication output increased markedly after 2019 with interdisciplinary journals driving much of the growth. Regulatory considerations were increasingly embedded within experimental and biomedical research, particularly in safety assessment and environmental impact analyses. Conclusions: Nanoparticle regulation matured into a dynamic multidisciplinary field. Regulatory efforts should prioritize adaptive, data-informed, and internationally harmonized frameworks that support innovation while ensuring human and environmental safety. These findings provide a data-driven overview of how regulatory thinking was evolved alongside scientific development and highlight areas where future governance efforts were most urgently needed. Full article

As a potential strategic resource of critical metals, deep-sea cobalt-rich crusts represent one of the most promising metal reservoirs within oceanic seamount systems, and their metallogenic mechanism constitutes a frontier topic in deep-sea geoscience research. This review focuses on the cobalt-rich crusts from the Magellan Seamount region in the northwestern Pacific and synthesizes existing geological, mineralogical, and geochemical studies to systematically elucidate their mineralization processes and metal enrichment mechanisms from a microstructural perspective, with particular emphasis on cobalt enrichment and its controlling factors. Based on published observations and experimental evidence, the formation of cobalt-rich crusts is divided into three stages: (1) Mn/Fe colloid formation—At the chemical interface between oxygen-rich bottom water and the oxygen minimum zone (OMZ), Mn²⁺ and Fe²⁺ are oxidized to form hydrated oxide colloids such as δ-MnO₂ and Fe(OH)₃. (2) Key metal adsorption—Colloidal particles adsorb metal ions such as Co²⁺, Ni²⁺, and Cu²⁺ through surface complexation and oxidation–substitution reactions, among which Co²⁺ is further oxidized to Co³⁺ and stably incorporated into MnO₆ octahedral vacancies. (3) Colloid deposition and mineralization—Mn–Fe colloids aggregate, dehydrate, and cement on the exposed seamount bedrock surface to form layered cobalt-rich crusts. This process is dominated by the Fe/Mn redox cycle, representing a continuous evolution from colloidal reactions to solid-phase mineral formation. Biological processes play a crucial catalytic role in the microstructural evolution of the crusts. Mn-oxidizing bacteria and extracellular polymeric substances (EPS) accelerate Mn oxidation, regulate mineral-oriented growth, and enhance particle cementation, thereby significantly improving the oxidation and adsorption efficiency of metal ions. Tectonic and paleoceanographic evolution, seamount topography, and the circulation of Antarctic Bottom Water jointly control the metallogenic environment and metal sources, while crystal defects, redox gradients, and biological activity collectively drive metal enrichment. This review establishes a conceptual framework of a multi-level metallogenic model linking macroscopic oceanic circulation and geological evolution with microscopic chemical and biological processes, providing a theoretical basis for the exploration, prediction, and sustainable development of potential cobalt-rich crust deposits. Full article

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