Search Results (4,894)

Trajectory similarity calculation, a cornerstone of trajectory data mining, is pivotal for diverse applications such as clustering, classification, and retrieval. While existing representation learning-based methods offer notable advantages in efficiency and accuracy, preserving the fidelity of similarity computation when processing large-scale trajectory data remains a significant challenge. To address this, this paper introduces a novel hybrid network architecture integrating Long Short-Term Memory (LSTM) and attention mechanisms to learn discriminative latent representations of trajectories. Moreover, we propose an Adaptive Contrastive Trajectory Learning (ACTL) module that dynamically refines the learning process through batch-adaptive temperature scaling and strategic hard negative mining, substantially improving boundary discrimination and robustness to data perturbations. Experimental validation on two real-world datasets, Porto and Chengdu, demonstrates the superiority of our model over state-of-the-art (SOTA) baselines in both similarity trajectory search and k-Nearest Neighbor (k-NN) query evaluations. The model exhibits exceptional performance, particularly under conditions of high noise and with large trajectory volumes, underscoring its practical applicability in demanding scenarios. Full article

Generative artificial intelligence (GenAI) is increasingly transforming creative industries through its ability to generate high-quality content, raising critical questions about authorship, ownership, and the future of creative labor. This paper addresses these challenges by conducting a systematic bibliometric review of 119 peer-reviewed articles on GenAI in the creative sectors, published between 2023 and 2025. The study applies PRISMA 2020 guidelines and keyword co-occurrence analysis using VOSviewer to identify thematic clusters and map research trends. The central research question is how the academic literature conceptualizes the role and impact of GenAI within creative industries and how this has evolved over time. Findings reveal nine major thematic areas, ranging from technical implementations to ethical, economic, and institutional perspectives. The analysis shows that recent research emphasizes not only the technological capacities of GenAI, but also its implications for value creation, creative agency, and industry structures. The main contribution of the paper lies in offering a structured overview of current research trajectories, clarifying conceptual ambiguities, and highlighting understudied areas—particularly regarding the intersection of GenAI, platform economies, and labor dynamics. The review also identifies a methodological gap in comparative empirical studies and proposes directions for future research. By mapping the evolving discourse on GenAI in creative industries, this study contributes to both scholarly understanding and policy development. It provides a foundation for interdisciplinary inquiry and a forward-looking agenda for critically assessing GenAI’s role in reshaping creative work. Full article

This study assesses the innovation performance and convergence dynamics across 237 European regions (NUTS 2 level) from 2016 to 2023, explicitly accounting for the structural and behavioural changes triggered by the COVID-19 pandemic. The article provides a novel regional-level assessment of how an unprecedented external shock reshaped innovation trajectories before and after the pandemic. To this end, the analysis combines Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), sigma-convergence measures, and a Difference-in-Differences (DiD) framework within an integrated multi-method empirical approach to evaluate shifts in regional innovation patterns over time. The results reveal a highly uneven distribution of innovation activities, with increasing polarization in the post-pandemic period. Northern and Western European regions strengthened their competitive advantage through robust digital infrastructure, strong human capital, and substantial R&D investments. In contrast, many Southern and Eastern European regions faced heightened structural barriers, leading to a widening innovation gap. Nevertheless, several regions exhibited notable resilience and achieved significant innovation catch-up, providing new empirical evidence on heterogeneous regional adaptive dynamics supported by targeted regional policies and improved local capabilities. The sigma-convergence analysis indicates a general increase in overall disparities, as reflected by rising dispersion in the Regional Innovation Index (RII) during 2020–2023. However, according to the DiD estimation, regions most severely affected by COVID-19 experienced a statistically significant relative increase (approximately 2.17%) in innovation performance, highlighting the pandemic’s role as a catalyst for accelerated digital transformation and innovation adjustment at the regional level. Full article

Background: Hypoxic–ischemic (HI) brain injury triggers a dynamic, multi-phase response involving early microglial efferocytosis followed by extracellular matrix (ECM) deposition and scar formation. Arginase-1 (ARG1), a key enzyme in tissue repair, is implicated in both processes, yet its role in neonatal microglia remains poorly defined. We characterize ARG1-linked pathways in neonatal microglia, identifying distinct efferocytic and fibrotic phases post-HI. Methods: HI was induced in P9 mice using the Vannucci model, and brains were collected at 24 h (D1) and 5 days (D5). Spatially resolved single-cell transcriptomics (seqFISH) was performed using a targeted panel enriched for microglial, ARG1-pathway, efferocytosis, and profibrotic genes. Cell segmentation, clustering, and spatial mapping were conducted using Navigator and Seurat. Differential expression, GSEA, and enrichment analyses were used to identify time- and injury-dependent pathways. Results: Spatial transcriptomics identified 12 transcriptionally distinct cell populations with preserved neuroanatomical organization. HI caused the expansion of microglia and astrocytes and the loss of glutamatergic neurons by D5. Microglia rapidly activated regenerative and profibrotic programs—including TGF-β, PI3K–Akt, cytoskeletal remodeling, and migration—driven by early DEGs such as Cd44, Reln, TGF-β1, and Col1a2. By D5, microglia adopted a collagen-rich fibrotic state with an upregulation of Bgn, Col11a1, Anxa5, and Npy. Conclusion: Neonatal microglia transition from early efferocytic responses to later fibrotic remodeling after HI, driven by the persistent activation of PI3K–Akt, TGF-β, and Wnt/FZD4 pathways. These findings identify microglia as central regulators of neonatal scar formation and highlight therapeutic targets within ARG1-linked signaling. Full article

The global shipping industry faces severe carbon emission challenges. Harbor tugs, as significant contributors to port emissions, require improved energy efficiency. However, their sailing conditions are complex and dynamic, making temporal feature characterization difficult with traditional static or simplistic clustering methods. To address this, this study proposes a novel method for constructing typical sailing conditions by integrating an enhanced clustering approach with Hidden Markov Models (HMM). First, kinematic segments are extracted from processed ship speed data, and key features are selected and reduced via Principal Component Analysis (PCA). Subsequently, an improved clustering model combining the Whale Optimization Algorithm (WOA) and K-means++ is developed to categorize segments into six distinct condition types. These clustered states then serve as the hidden states of an HMM, whose learned transition matrix synthesizes a 3600 s typical sailing condition profile. The constructed profile is validated through multi-dimensional comparison with original data, demonstrating high fidelity in statistical characteristics, temporal properties, and distribution similarity. The results confirm that the proposed method can accurately replicate the operational patterns of harbor tugs. This study provides a reliable data foundation for the energy efficiency assessment and optimization of harbor tugs and offers a new methodological perspective for constructing operational profiles for ships and other mobile machinery. Full article

The evolution of landscape patterns in plateau lake basins directly influences the sustainable provision of ecosystem services. Revealing and predicting the impacts of landscape changes on ecosystem service value (ESV) under different development scenarios are essential for maintaining regional ecological security, enhancing ESV, and formulating policies for ecological conservation and restoration. As a typical representative of China’s plateau lake basin, the Erhai Lake Basin faces multiple challenges arising from rapid urbanization, tourism commercialization, and agricultural modernization. It is therefore crucial to understand its potential future landscape dynamics and their effects on ecosystem services. Based on landscape data, natural environmental data, and socio-economic data, we applied GIS-based spatial analysis and the equivalent factor method to simulate and assess landscape pattern changes and corresponding variations in ESV in 2030, 2040, and 2050 under three distinct scenarios. Local spatial autocorrelation analysis was further employed to identify the spatial clustering patterns of ESV. There were three findings: (1) From 2030 to 2050, forest increased continuously under the natural evolution scenario (NES) and ecological protection scenario (EPS) but declined under the economic growth scenario (EGS). Farmland expanded under the NES and EGS, whereas it decreased under the EPS. Grassland declined across all three scenarios, while built-up area showed consistent expansion. (2) In all simulated years, the total ESV of the Erhai Lake Basin ranked as EPS > NES > EGS. Between 2030 and 2050, total ESV exhibited an increasing trend under the EPS but declined under the other two scenarios, with the sharpest reduction under the EGS. Forests and water body were the main contributors to total ESV, while farmland and grassland played a critical role in driving ESV dynamics—the scale and direction of their transformation directly determined the overall ESV trends. (3) Across the three scenarios, ESVs all exhibit significant spatial heterogeneity. Local Moran’s I analysis indicated a dominant cluster of high values (HH) or a cluster of low values (LL), with LL clusters mainly concentrated in the northern basin and the western side of Erhai Lake, and HH clusters primarily located within the lake area. This study, through multi-scenario simulations, elucidates the spatiotemporal dynamics of landscape and ESV changes, providing valuable insights for green transformation, landscape spatial allocation, ecological restoration, and sustainable development in the Erhai Lake Basin. Full article

Artificial Intelligence (AI) is increasingly shaping materials research, particularly in the development and optimization of Glass Fibre Reinforced Polymer (GFRP) bars used as innovative alternatives to steel reinforcement. Despite this growing intersection, no prior bibliometric study has systematically mapped how AI contributes to the advancement of GFRP technologies. This paper fills this gap through a comprehensive bibliometric analysis based on 102 Scopus-indexed publications from 2015 to 2025. Following PRISMA guidelines, the study combines performance analysis and science mapping using VOSviewer to identify publication dynamics, leading journals, key contributors, and thematic clusters. The results reveal a tenfold growth in annual output (compound annual growth rate, CAGR = 10.1%) and five dominant research directions: (1) machine learning in structural analysis, (2) AI-driven composite materials modeling, (3) smart damage detection, (4) mechanical characterization, and (5) advanced deep learning frameworks. China, India, and the United States collectively account for more than half of global publications, highlighting strong international collaboration. The findings demonstrate that AI has evolved from an exploratory tool to a transformative driver of innovation in GFRP research. This study provides the first quantitative overview of this emerging field, identifies critical gaps such as sustainability integration and standardization, and proposes future directions to foster cross-disciplinary collaboration toward intelligent and sustainable composite structures. Full article

Offshore wind-farm performance remains constrained by persistent wake deficits and turbulence that compound across intra-farm, intra-cluster, and inter-cluster scales, particularly under atmospheric neutral–stable stratification. A concept is advanced whereby offshore wind-farm yield may be augmented by pairing conventional horizontal-axis wind turbines (HAWTs) with lighter-than-air parafoil systems that entrain higher-momentum air and re-energise wakes, complementing yaw/induction-based wake control and enabling higher array energy density. A concise synthesis of wake physics and associated challenges motivates opportunities for active momentum re-injection, while a review of kite technologies frames design choices for lift generation and spatial keeping. Stability and control, spanning static and dynamic behaviours, tether dynamics, and response to extreme meteorological conditions, are identified as key challenges. System-integration pathways are outlined, including alignment and mounting options relative to turbine rows and prevailing shear. A staged validation programme is proposed, combining high-fidelity numerical simulation with wave-tank testing of coupled mooring–tether dynamics and wind-tunnel experiments on scaled arrays. Evaluation metrics emphasise net energy gain, fatigue loading, availability, and Levelized Cost of Energy (LCOE). The paper concludes with research directions and recommendations to guide standards and investment, and with a quantitative assessment of the techno-economic significance of kite–HAWT integration at scale. Full article

Soursop fruit (Annona muricata L.) exhibits a rapid loss of firmness during postharvest ripening, mainly attributed to pectin depolymerization and cell wall restructuring. Among the enzymes involved, rhamnogalacturonan lyase (RGL), belonging to the PF06045 protein family, contributes to the degradation of rhamnogalacturonan I (RG-I), a key structural component of pectin. However, the regulatory mechanisms and transcriptional dynamics of RGL genes in tropical fruits remain poorly characterized. This study aimed to evaluate RGL in three soursop varieties (GUANAY-1, GUANAY-2, and GUANAY-3) during postharvest ripening through integrative bioinformatic, transcriptional, and enzymatic analyses. Bioinformatic analysis identified five soursop genes containing the PF06045 domain, designated RGL1–RGL5, which were grouped into three phylogenetic clusters. Differential expression analysis revealed that RGL1, RGL2, and RGL3 were differentially expressed, while functional enrichment analysis indicated that these genes are mainly associated with lyase activity and cell wall polysaccharide disassembly. Quantitative polymerase chain reaction (qPCR) revealed variety-dependent transcriptional patterns. RGL2 showed expression peaks on day 5 in GUANAY-1 and GUANAY-3 and on day 7 in GUANAY-2, while RGL3 reached its maximum expression on day 5 in all varieties. Enzymatic activity also varied among varieties, showing concordance with RGL2 and RGL3 expression in GUANAY-1, a delayed maximum in GUANAY-2, and a progressive decline in GUANAY-3. Principal component analysis explained 87.2% of the total variation, with enzymatic activity contributing mainly to PC1 and RGL2 and RGL3 expression to PC2. Overall, these results demonstrate differential regulation of RGL among soursop varieties and confirm its central role in RG-I degradation during postharvest fruit softening. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

Sthenoteuthis oualaniensis is a major commercial species in the high-seas fisheries of the northwestern Indian Ocean. However, its spatiotemporal distribution exhibits strong uncertainty under climate and environmental variability, complicating the understanding of fishing ground dynamics. To investigate the spatiotemporal distribution of S. oualaniensis under climate change, this study analyzed commercial fishing data from 2016 to 2024. The results indicate that the core distribution area of the species is consistently concentrated within 14–19° N and 61–65° E. From 2016 to 2024, the fishing ground expanded annually and shifted overall toward the east and north, with its centroid showing a persistent northeastward trajectory. Global spatial autocorrelation analysis revealed positive and significant Moran’s I values for all years, demonstrating a strong spatial clustering pattern. Hotspot analysis shows that high-abundance areas were primarily located north of 14° N, with an overall northeastward migration trend. Hotspots expanded continuously from 2016 to 2023, but sharply contracted in 2024, shifting further northeast and becoming restricted to 63–68° E and 19–21° N. The GAM results indicate that CPUE in the region is jointly influenced by spatiotemporal drivers and multiple environmental factors. These findings confirm that the spatiotemporal distribution and population dynamics of S. oualaniensis are shaped by the combined effects of environmental variability and temporal–spatial factors, with environmental influences playing a particularly crucial role. Full article

Crowdsourced geospatial platforms constitute complex socio-technical systems in which data quality and reliability emerge from collective user behavior rather than centralized control. This study proposes a system-oriented, unsupervised machine learning framework to assess the reliability of crowdsourced building data using only intrinsic indicators. The framework is demonstrated through a large-scale analysis of OpenStreetMap building polygons in Tehran. Six intrinsic indicators—reflecting contributor activity, temporal dynamics, semantic instability, and geometric evolution—were normalized using fuzzy membership functions and objectively weighted based on their discriminative influence within a K-means clustering process. Five reliability classes were identified, ranging from very low to very high reliability. The resulting classification exhibited strong internal validity (average silhouette coefficient = 0.58) and pronounced spatial coherence (Global Moran’s I = 0.85, p < 0.001). This approach eliminates dependence on authoritative reference datasets, enabling scalable, reproducible, and feature-level reliability assessment in open geospatial systems. The framework provides a transferable methodological foundation for trust-aware analysis and decision-making in participatory and data-intensive systems. Full article

The increasing penetration of photovoltaic (PV) generation, energy storage systems, and flexible loads within modern buildings demands advanced control strategies capable of harnessing dynamic assets while maintaining grid reliability. This Perspective article presents a comprehensive overview of reinforcement learning-driven (RL-driven) control methods for DC flexible microgrids—focusing in particular on building-integrated systems that shift from AC microgrid architectures to true PV–Energy storage–DC flexible (PEDF) systems. We examine the structural evolution from traditional AC microgrids through DC microgrids to PEDF architectures, highlight core system components (PV arrays, battery storage, DC bus networks, and flexible demand interfaces), and elucidate their coupling within building clusters and urban energy networks. We then identify key challenges for RL applications in this domain—including high-dimensional state and action spaces, safety-critical constraints, sample efficiency, and real-time deployment in building energy systems—and propose future research directions, such as multi-agent deep RL, transfer learning across building portfolios, and real-time safety assurance frameworks. By synthesizing recent developments and mapping open research avenues, this work aims to guide researchers and practitioners toward robust, scalable control solutions for next-generation DC flexible microgrids. Full article

The wnt gene family encodes a group of highly conserved secreted glycoproteins that play essential roles in vertebrate development, including tissue patterning, cell differentiation, and gonadal regulation. However, the genomic organization, evolutionary dynamics, and functional roles of Wnt signaling components in flatfish remain poorly understood. In this study, we performed a comprehensive genome-wide identification, evolutionary characterization, expression profiling, and functional analysis of wnt genes in Cynoglossus semilaevis, a flatfish species exhibiting ZW/ZZ sex determination and temperature-induced sex reversal. A total of 20 wnt genes were identified and classified into 13 subfamilies, displaying conserved structural organization and phylogenetic relationships consistent with other teleosts. Chromosomal mapping revealed lineage-specific WNT clusters, including a unique wnt3–wnt7b–wnt5b–wnt16 block, as well as syntenic associations with reproduction-related genes (e.g., adipor2, sema3a, nape-pld, erc2, lamb2), suggesting coordinated genomic regulation. Tissue transcriptome analysis demonstrated strong sex- and tissue-biased expression patterns, with wnt5a predominantly expressed in ovaries and wnt5b specifically upregulated in pseudo-male testes. Functional assays revealed that knockdown of wnt5a or wnt5b induced testis-specific genes (sox9b, tesk1) and suppressed ovarian markers (foxl2, cyp19a1a), indicating antagonistic regulatory roles in gonadal fate determination. Promoter analysis identified yy1a as a selective repressor of wnt5b, but not wnt5a, providing a mechanistic basis for paralog divergence. Furthermore, pull-down combined with LC–MS/MS analysis showed that WNT5b interacts with proteins enriched in ribosome biogenesis and ubiquitin-mediated proteolysis, suggesting a role in translational regulation and protein turnover during spermatogenesis. Together, these findings establish WNT5 signaling—particularly wnt5b—as a key driver of testicular development in C. semilaevis and provide new insights into the molecular mechanisms underlying sex differentiation and sex reversal in flatfish. Full article

Research on road transport infrastructures is rapidly evolving as electrification, automation, and digital connectivity reshape how systems are designed, operated, and managed. This study presents a combined bibliometric, scientometric, and network analysis of 2755 publications published between 2021 and 2025 to map the intellectual structure, main contributors, and dominant technological themes shaping contemporary road transport research. Using data from the Web of Science Core Collection, co-occurrence mapping, thematic analysis, and collaboration networks were generated using Bibliometrix and VOSviewer. The results reveal strong growth in research output, with China, the United States, and Europe forming the core of high-impact publication and collaboration networks. Six bibliometric clusters were identified and consolidated into three overarching domains: road transport systems, emphasizing vehicle dynamics, control, and real-time computational frameworks; energy and efficiency-oriented mobility research, focusing on electrification, optimization, and infrastructure integration; and emerging digital technologies, including IoT, AI, and autonomous vehicles. The analysis highlights persistent research gaps related to interoperability, cybersecurity, large-scale deployment, and governance of intelligent transport infrastructures. Overall, the findings provide a data-driven overview of current research priorities and structural patterns shaping next-generation road transport systems. Full article