Search Results (6,611)

Background: Severe fever with thrombocytopenia syndrome (SFTS) has become an increasing public health threat in China, with Yantai City representing a major endemic focus. A fine-scale, long-term epidemiological analysis integrating human case data with vector surveillance is essential for understanding local transmission dynamics. Methods: We conducted a retrospective analysis using 12-year (2013–2024) county-level SFTS surveillance data from Yantai City. Temporal trends were analyzed by Joinpoint regression. Concurrent field surveillance of Haemaphysalis longicornis (2019–2024) was used to quantify local SFTSV infection rates in ticks. Associations between SFTS incidence and environmental/livestock factors were evaluated using Spearman’s correlation and multivariable negative binomial regression. Results: A total of 1964 SFTS cases were reported. The annual incidence rate increased from 0.65 to 5.12 per 100,000 population, with an average annual percentage change (AAPC) of 13.56% 2013–2024, showing the most substantial rise among the elderly. Marked spatial heterogeneity was observed, with county-level mean incidence ranging from 0.30 to 5.23 per 100,000. The SFTSV infection rate in ticks surged from 0.54% in 2019 to 3.24% in 2024, and showed a strong positive correlation with human incidence both seasonally (ρ = 0.998) and across counties (ρ = 0.79), a pattern likely driven by shared environmental factors. Multivariable analysis identified grassland coverage (adjusted IRR [aIRR] = 1.21), woodland coverage (aIRR = 2.31), goat density (aIRR = 1.49), and tick infection rate (aIRR = 1.65) as independent risk factors, while urban land was protective (aIRR = 0.83). The overall case fatality rate was 8.86%, showing a declining trend, but was significantly higher in males (10.90%) than in females (7.04%), particularly among the elderly. Conclusions: SFTS incidence in Yantai increased significantly over the past decade, characterized by a heightened burden on the elderly and strong spatiotemporal clustering. Risk is independently mediated by ecological interfaces, notably woodland/grassland habitats and goat rearing. These findings delineate high-risk areas and populations, offering crucial insights for developing targeted public health strategies. Full article

Understanding power consumption conditions is necessary for optimizing UAV energy use, particularly during flight under varying weather conditions and environmental factors. Maintaining UAV energy while accounting for multiple influencing variables and vulnerability to weather conditions provides an appropriate case study for advanced predictive modeling. This study investigates UAV power consumption during hovering flight by forecasting power usage using a MDFA network to improve prediction accuracy and better adapt to rapid weather-induced variations. To capture intricate temporal dependencies and recurrent oscillatory behavior, the integrated model combines multi-scale dilated convolutions with a Fourier-enhanced mechanism. According to the experimental results, this model achieves 3% error reductions under all tested flight conditions, indicating a significant improvement in performance. Overall, the MDFA model consistently showed better performance under high power consumption conditions than under low power consumption conditions, and it produced the lowest error in heavy flight compared to low and medium flight. Full article

Vegetation elements on buildings such as green roofs are increasingly recognized as nature-based solutions to address urban environmental challenges. Green roofs can be adapted to diverse climates and building types. Their implementation in Slovakia has been rising, yet it remains limited in scale and technological ambition. Projects funded from public resources often remain conventional, with rare ambition to implement novel stormwater management systems and solutions that enhance biodiversity. Currently, the majority of investments in green roofs are limited to the private sector, while public institutions lag behind. Thus, public buildings with novel green systems and elements can still be considered non-conventional, innovative, and influential. This study investigates the development of green roofs on public buildings in the city of Nitra, Slovakia, from the first installation in 1992 to recent projects in the 2020s. By systematically mapping all existing public green roofs and conducting qualitative narrative interviews with key stakeholders, this research aims to identify the main motivations, actors, and barriers behind the implementation of green roofs in public investments. The novelty of this research lies in its mixed-methods approach, combining quantitative and qualitative analyses to draw conclusions from a comprehensive dataset. By capturing all existing examples within their spatial and temporal context, rather than relying on a random subsample of case studies, this study provides a highly representative evaluation of green roof adoption. Preliminary findings provide insights into the temporal and spatial diffusion patterns of green roofs in a medium-sized Central European city and highlight the main drivers of public decision-making. The results contribute to a better understanding of how urban sustainability initiatives emerge in public sector contexts and aim to inform policy and planning to initiate and boost more green roof implementation. Full article

The proliferation of fake reviews on e-commerce and social platforms has severely undermined consumer trust and market integrity, necessitating robust and interpretable real-time detection mechanisms with multi-sensor data fusion capabilities. While traditional machine learning approaches have shown promise in identifying fraudulent reviews, they often lack transparency and fail to leverage the rich contextual knowledge embedded in large-scale datasets. In this paper, we propose KE-MLLM (Knowledge-Enhanced Multimodal Large Language Model), a unified framework that integrates knowledge-enhanced prompting with parameter-efficient fine-tuning for explainable fake review detection. Our approach employs LoRA (Low-Rank Adaptation) to fine-tune lightweight large language models (LLaMA-3-8B) on review text, while incorporating multimodal behavioral sensor signals including temporal patterns, user metadata, and social network characteristics for comprehensive anomaly sensing. To address the critical need for interpretability in fraud detection systems, we implement a Chain-of-Thought (CoT) reasoning module that generates human-understandable explanations for classification decisions, highlighting linguistic anomalies, sentiment inconsistencies, and behavioral red flags. We enhance the model’s discriminative capability through a knowledge distillation strategy that transfers domain-specific expertise from larger teacher models while maintaining computational efficiency suitable for edge sensing devices. Extensive experiments on two benchmark datasets—YelpChi and Amazon Reviews from the DGL Fraud Dataset—show that KE-MLLM achieves strong performance, reaching an F1-score of 94.3% and an AUC-ROC of 96.7% on YelpChi and outperforming the strongest baseline in our comparison by 5.8 and 4.2 percentage points, respectively. Furthermore, human evaluation indicates that the generated explanations achieve 89.5% consistency with expert annotations, suggesting that the framework can improve the interpretability and practical usefulness of automated fraud detection systems. The proposed framework provides a useful step toward more accurate and interpretable fake review detection and offers a practical reference for building more transparent and accountable AI systems in high-stakes applications. Full article

Background/Objectives: The management and prognosis of ALK-rearranged non-small-cell lung cancer have substantially improved over the past decade. However, challenges remain in timely molecular identification, prediction of treatment response, and understanding resistance mechanisms. This systematic review evaluates and synthesizes the evidence on artificial intelligence (AI) approaches leveraging imaging, pathology, molecular, and clinical data in this setting. Methods: A systematic search was conducted for peer-reviewed studies published between 2020 and 2025. Eligible studies involved human subjects and applied AI, machine learning, or deep learning methods to predict ALK status or treatment-related outcomes using imaging, pathology, molecular, or multimodal data. Study selection followed the PRISMA 2020 guidelines. Data were extracted on study design, data modality, AI methodology, clinical objectives, and performance metrics. Bibliometric co-occurrence analysis was performed to characterize thematic patterns and temporal trends. Results: Thirteen studies met the inclusion criteria, most of which were retrospective and single-center. AI approaches were applied to radiologic, pathologic, molecular, or multimodal data. Models predicting ALK status reported area under the curve values ranging from 0.73 to 0.99, while prognostic and treatment-response models reported moderate to high discriminative performance. Bibliometric analysis identified two dominant research themes focused on molecular characterization and computational methodology, with a recent shift toward treatment-specific and integrative analyses. External validation and clinical implementation remained limited across studies. Conclusions: AI shows promising potential to support diagnosis, prognostication, and treatment assessment in ALK-rearranged lung cancer. However, methodological heterogeneity, limited external validation, and a lack of prospective studies currently constrain clinical translation. Full article

From an active-health perspective, regular walking is a key pathway for mitigating chronic disease risks and promoting sustained health benefits. Existing studies have primarily assessed urban walkability using static or aggregated measures of walking intensity, which insufficiently capture the capacity of urban spaces to continuously support walking behavior over time. This study aims to identify urban walking support types by incorporating the temporal structure of walking behavior beyond intensity alone. Crowdsourced walking trajectory data are used to construct a multidimensional behavioral indicator system integrating walking intensity, temporal stability, and rhythmic characteristics over an annual period. An unsupervised clustering framework combining nonlinear dimensionality reduction and density-based clustering is applied to identify distinct walking support types, while interpretable machine-learning models are employed to examine the relative roles of built-environment factors in differentiating these types. The results indicate that urban walking support does not vary continuously along a single intensity dimension but instead forms discrete spatial types shaped by multiple behavioral temporal characteristics. These types exhibit clear differences in temporal walking structures and associated environmental constraints. By emphasizing behavioral temporal stability and rhythm, this study provides a process-oriented understanding of urban walking support and supports typology-based spatial identification beyond intensity-based assessments. Full article

The world is undergoing rapid transformations driven by climate change, socio-economic pressures, and geopolitical tensions. Monitoring these dynamics is essential to understand and anticipate territorial change. Although initiatives such as the European Union’s Open Data program promote spatiotemporal datasets (e.g., population, land use), analyzing and interpreting these data over time remains complex and requires technical expertise, limiting their accessibility. This research proposes Semantic Web-based methods to detect and annotate trends in spatiotemporal series, thereby assisting in the systematic analysis of temporal patterns. We introduce the SETT ontology (SEmantic Trajectory of Territory) and its OFF-SETT framework (Ontological Framework For SETT), enabling the formal description of territorial trends and their publication as semantic trajectories in the Linked Open Data cloud. The study delivers (i) a generic methodology for detecting and describing trajectories in spatiotemporal datasets; (ii) a framework for automatically generating knowledge graphs capturing these trajectories; (iii) a knowledge graph describing trajectories of demographic and satellite-derived variables (e.g., temperature, water, vegetation) for study areas in France and Switzerland; and (iv) a web-based geovisualization platform. The approach shows that Semantic Web technologies bridge complex spatiotemporal analysis and public accessibility. By publishing territorial trajectories as knowledge graphs, it fosters transparency, interoperability, and reuse of data, supporting informed decision-making and citizen engagement. Full article

Soil erosion on the Yunnan–Guizhou Plateau (YGP) has a significant impact on the water sources and ecological safety of Southeast Asia and South China. With the influence of climate change, this erosion has been altered, which will create uncertainty regarding soil erosion management and social development in China and Southeast Asia. However, existing research still lacks simulations of soil erosion in large-scale regions, as well as a systematic understanding of the spatiotemporal characteristics of future soil erosion under climate change. Therefore, a coupled model of the Shared Socioeconomic Pathways (SSPs) and the Revised Universal Soil Loss Equation (RUSLE) at the regional scale of the YGP is proposed in this study. By analyzing the erosion patterns in the YGP, this research determines the optimal future scenario and corresponding mitigation strategies, thereby offering a localized practical reference for soil erosion control in the YGP and its alignment with the UN SDGs. The results show the following: (i) Temporally, soil erosion on the YGP will improve in the future. The overall soil erosion moduli of the YGP decrease by 196.86, 367.03, and 391.72 t/(km²·a) under the scenarios of SSPs1-1.9, SSPs2-4.5, and SPPs5-8.5, respectively. (ii) Spatially, soil erosion in the southwestern and central-northern parts of the YGP will be significantly improved in the future. The soil erosion moduli of the karstic and non-karstic areas gradually become close to each other, with the difference in soil erosion moduli between them in SSPs1-1.9, SSPs2-4.5, and SSPs5-8.5 being reduced from 671.65 t/(km²·a) to 623.79, 592.21, and 611.92 t/(km²·a), respectively. (iii) Among the different SSP scenarios, the SSPs2-4.5 scenario aligns most closely with the principles of sustainable development, making it the most desirable pathway. To ensure the long-term effectiveness of soil erosion control under changing climate and socioeconomic conditions, future strategies should take the SSPs2-4.5 scenario as a core reference and implement resilient portfolios of mitigation measures. Full article

Battery state-of-health (SoH) estimation is central to transportation electrification because it conditions safety limits, warranty accounting, power capability management, and long-horizon fleet optimization. Although deep temporal architectures can achieve high laboratory accuracy, field deployment is frequently limited by laboratory (Lab)-to-field (L2F) domain shift that alters input statistics, feature definitions, and noise regimes. Under such a shift, predictors may remain strongly monotonic, preserving degradation ordering and become operationally unreliable due to systematic output distortion (e.g., compression/warping of the SoH scale). A deployment-complete L2F transfer learning pipeline is presented, built around a gated Temporal Convolutional Network (TCN)–Transformer fusion backbone, domain-specific adapters and heads, alignment-regularized fine-tuning, and row-level inference via sliding-window overlap averaging. To address the dominant deployment failure mode, a Safe Calibration stage robustly filters calibration pairs and selects among candidate calibrators under a strict do-no-harm criterion. On an unseen deployment stream (2154 labeled rows), overlap-averaged raw inference achieves MAE = 0.0439, RMSE = 0.0501, and R² = 0.7451, consistent with mid-to-high SoH range compression, while Safe Calibration (Isotonic-Balanced selected) corrects nonlinear scaling without violating monotonic structure, improving to MAE = 0.0188, RMSE = 0.0252, and R² = 0.9357 to obtain a complete understanding of the challenges due to domain shifts, evaluation is extended to include other architecture baselines such as TCN-only, Transformer-only, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and a Ridge regression baseline. Also added is explicit alignment and calibration ablations that include CORAL off/on, that is, none vs. Safe-Global vs. Context-Aware under identical leakage-safe splits and the same overlap-averaged deployment inference operator. This work goes beyond peak-score reporting and looks at the robustness of a pipeline under domain shift, which is quantified across four random seeds and multiple deployment streams, with uncertainty summarized via mean ± std and bootstrap confidence intervals for Mean of Absolute value of Errors (MAE)/Root of the Mean of the Square of Errors (RMSE) computed from per-example absolute errors. Full article

Mining at the Red Dog Mine generated a 60 million-tonne waste rock stockpile that produces acid rock drainage with pH values typically below 3. The drainage chemistry is controlled by the competing kinetics of acid-generating iron sulfide weathering and acid-neutralizing carbonate and phosphate dissolution. To evaluate the interaction of these reactions, waste rock was collected from the stockpile by drilling a borehole from the surface to a depth of 52 m, terminating at the shale bedrock. A temporal paste pH test was conducted to extend the utility of the static paste pH test to a continuous (30 min) measurement of pH and ORP over a 24-h period. The 24-h paste pH results revealed multiple acid-generating and acid-neutralizing reactions: pH values ranged from 3.31 to 6.96. Mineralogical analysis indicated initial acidic conditions in 12 of the depth intervals (upper and lower zones) were due to the release of stored acidity from soluble iron sulfate minerals. Subsequent pH increases were driven by calcite dissolution and likely phosphate and clay mineral acid-neutralizing reactions. Conversely, late-stage pH decreases in the lower middle zone indicated the presence of highly reactive/available iron sulfide surfaces, which allowed for earlier acid generation compared to less reactive/available iron sulfide minerals in other zones. The utility of this temporal paste pH test and associated mineral analysis is to understand the mineralogical controls on early temporal acid generation to guide batch reactor testing of remaining acid potential under saturated conditions. This sequential approach provides critical information for predicting long-term acid generation and information management of the stockpile for mine site remediation and closure. Full article

Endoscopic images are often degraded by uneven illumination, motion blur, and tissue occlusion, which obscure critical anatomical details and complicate surgical manipulation. This issue is particularly pronounced in single-port endoscopic surgery, where the imaging capability of the camera is further constrained by limited working space. While deep learning-based enhancement methods have demonstrated impressive performance, most existing approaches remain too computationally demanding for real-time surgical use. To address this challenge, we propose an efficient stepwise endoscopic image enhancement framework that introduces an implicit degradation representation as an intermediate feature to guide the enhancement module toward high-quality results. The framework further exploits the temporal continuity of endoscopic videos, based on the assumption that image degradation evolves smoothly over short time intervals. Accordingly, high-quality degradation representations are estimated only on key frames at fixed intervals, while the representations for the remaining frames are obtained through fast inter-frame propagation, thereby significantly improving computational efficiency while maintaining enhancement quality. Experimental results demonstrate that our method achieves an excellent balance between enhancement quality and computational efficiency. Further evaluation on the downstream segmentation task suggests that our method substantially enhances the understanding of the surgical scene, validating that implicitly learning and degradation representation propagation offer a practical pathway for real-time clinical application. Full article

Cyber–Physical Systems of Systems (CPSoS) integrate autonomous constituent systems to accomplish complex missions. Nonetheless, decentralized coordination and continuous evolution create intricate dependencies that make behavior difficult to analyze. Current semi-formal modeling approaches, despite being easy to understand and widely accessible, lack semantic precision and are not computationally checkable to guarantee time-critical properties. Furthermore, current formal methods are often fragmented: they analyze behavior either at the individual CPS level or the collective CPSoS level, failing to provide a multi-level specification. To address these limitations, we propose an integrated framework combining SysML and Maude rewriting logic. SysML provides structural and behavioral specification capabilities, while Maude enables rigorous semantics, executable models, and formal verification. First, our approach proposes MM-CPSoS, a meta-model that unifies CPS and CPSoS entities with explicit temporal constraints. Dynamic behavior is captured through evolution patterns governing mission progression across both levels. Then, we encode SysML models into Maude as object-oriented configurations and conditional rewrite rules, enabling linear temporal logic (LTL) model checking of temporal properties. Finally, we demonstrate our approach through a Time-Aware Road Crisis Management System (TaRCiMaS²). Full article

This paper proposes the Unified Observation Layer Theory (UOLT), a structural framework for understanding observation not as an act of cognition, measurement, or subjectivity, but as a layered condition through which the world becomes visible. Contemporary theories across physics, philosophy, and cognitive science often treat observation as a primary explanatory principle, implicitly assuming that what is observed constitutes the world itself. Such approaches repeatedly encounter paradoxes concerning objectivity, incompleteness, and the limits of visibility. UOLT argues that these paradoxes do not arise from epistemic failure or insufficient data, but from a structural confusion between distinct layers of observation. UOLT introduces a three-layer model consisting of an Invisible Layer, a Projection Layer, and a Visible Layer. The Invisible Layer refers to structural conditions that do not appear directly within a given observational configuration, yet are presupposed by the coherence of what becomes established within it. The Projection Layer specifies the conditions under which certain structural relations become stably manifest, including selection, emphasis, and exclusion. The Visible Layer corresponds to the domain in which objects, quantities, causality, language, and time are articulated as established. By separating these layers, UOLT explains why observation can never access the totality of the world, why visibility does not imply completeness, and why similar structural paradoxes emerge across otherwise distinct domains. Importantly, UOLT does not compete with or replace existing physical or philosophical theories. Instead, it repositions them as descriptions operating within the Visible Layer, without reducing the Invisible Layer to hidden variables or metaphysical entities. Unified Observation Layer Theory offers a non-temporal, non-reductive account of observation that clarifies the structural conditions under which reality appears coherent despite being only partially visible. In doing so, it provides a framework for reconsidering objectivity, visibility, and world formation without privileging observation as an ultimate ground. This paper does not aim to propose a unified theory, but to clarify the structural conditions under which observation becomes possible. Full article

Understanding how Sustainable Development Goals (SDGs) interact—through synergies or trade-offs—is critical for coordinating economic growth, social equity, and environmental protection at the regional scale. However, empirical evidence on the structure, directionality, and spatial heterogeneity of SDG interactions remains limited, particularly in policy-supported regions undergoing development transitions. This study addresses this gap by examining SDG interactions in the former Central Soviet Area of Jiangxi Province, China. Using panel data from eight prefecture-level cities spanning 2001–2022, we construct a multi-dimensional SDG evaluation framework encompassing economic development, social equity and livelihood security, resource utilization and environmental protection, and sustainable cities and communities. A two-stage analytical approach is employed: Spearman’s rank correlation analysis is used to identify synergistic and trade-off relationships among SDGs, and a Geographically and Temporally Weighted Regression (GTWR) model is applied to estimate directional influences and their spatiotemporal heterogeneity among development dimensions. The results indicate that synergistic relationships dominate the regional SDG interaction network, while trade-offs are comparatively limited and selectively concentrated in specific goal pairings. Marked spatial heterogeneity is observed, with stronger synergies in the east than in the west. Functional-zone analysis reveals that ecological and cultural conservation zones exhibit the strongest synergies, whereas industrial transformation zones face pronounced trade-offs, particularly between food security (SDG2) and income inequality (SDG10). GTWR results further demonstrate directional asymmetry among development dimensions, with social equity exerting a stronger influence on economic development than the reverse, and relatively weaker feedback from economic growth to resource and environmental outcomes. Overall, this study provides a systematic, spatiotemporally explicit assessment of SDG interactions in a policy-supported regional context. By integrating interaction analysis with spatiotemporal modeling, it offers a robust empirical basis for understanding where, how, and in which direction SDGs interact, thereby contributing to more context-sensitive approaches to regional sustainable development. Full article

The endemic Minuartia nifensis, the only known gynodioecious species of its genus, offers a suitable model for understanding the relationships between floral characteristics, pollination, and mating systems in species with narrow distributions and single populations. We analyzed population structure, floral morphology, pollen viability, stigma receptivity, mating system components, and pollinator assemblages using field observations, morphometric measurements, controlled pollination experiments (autogamy, allogamy, apomixis and open pollination), and standardized pollinator surveys. The population exhibited an approximately balanced hermaphrodite–female ratio (0.97:1) and clear sexual dimorphism, with hermaphrodite flowers significantly larger than female flowers. Despite this dimorphism, pollinator visitation was similar between morphs, with 52.54% of visits to hermaphrodite flowers and 47.46% to female flowers. A total of 1734 visits by seven visitor species were recorded, of which approximately 95% of potentially effective pollen transfer was attributable to three bee taxa. Pollen viability, stigma receptivity, and visitation frequency peaked between 12:00 and 14:00, accounting for 58% of total insect visits. Controlled pollination experiments showed highest reproductive success under cross-pollination and limited success under self-pollination, indicating a mixed but predominantly outcrossing mating system. Together, these results suggest that gynodioecy in M. nifensis may be supported by floral differentiation, temporal reproductive traits, and pollinator-mediated pollen transfer. Full article