Search Results (4,600)

Distributed acoustic sensing (DAS) is increasingly used for third-party intrusion (TPI) detection in oil and gas pipeline monitoring, but labeled DAS data are often scarce, leading to overfitting, poor generalization, and increased false alarms and missed detections. Conventional data augmentation, GAN-based synthesis, and transfer learning may generate physically implausible samples or fail to cover the event feature space. To address this, we propose a physics-guided large language model (LLM) prompt-engineering framework for DAS data augmentation and pipeline intrusion detection. The framework establishes a physically grounded feature-indicator framework for DAS disturbance-event classification by mapping primary event mechanisms to measurable signal indicators, and then uses a standardized four-module prompt template to guide LLM-based synthesis-script generation. A two-stage iterative verification procedure is further introduced to constrain the generated samples in terms of physical-mechanism compliance and feature-parameter consistency. Synthetic data are combined with real data to train a lightweight PatchTransformer model for TPI detection, while an additional CNN is used to assess cross-architecture applicability. Using the public DAS1K benchmark with five-fold stratified cross-validation and a univariate controlled experiment (0–800 synthetic samples per category), the results show that the use of synthetic data improves detection performance overall. The configuration with 600 synthetic samples per category achieves 92.27% accuracy and 92.38% macro-F1, outperforming the conventional augmentation baseline by 4.74 and 4.86 percentage points, respectively. An additional CNN experiment also showed consistent performance gains across the tested augmentation settings, indicating that the benefit of the proposed synthetic data was not restricted to the PatchTransformer architecture. These findings indicate that LLM-assisted data augmentation can effectively improve the generalization of DAS-based pipeline intrusion detection when field-labeled samples are scarce. Full article

Depth-averaged 2D models have shown considerable success at predicting the presence of tidal sand ridges in the nearshore environment while only requiring minimal efforts of parameter tuning. 3D models, on the other hand, fail to predict the growth of coarse-grain sand waves unless unrealistic assumptions on eddy viscosity are applied. When a vertically varying eddy viscosity profile is adopted in the model, sand waves will invariably be the fastest growing mode unless suspended load dominates, which contradicts observations. Through both numerical and analytical approaches, this paper will show that the residual circulation in the vertical plane due to mass continuity and vertical shear is a possible underlying mechanism that accounts for the dominant growth of sand waves. The strength of this residual circulation is proportional to the shear of the tidal velocity. A consequence is that sand ridges are more likely to develop in certain models with a small slip parameter in the bottom boundary condition (hence, less shear in tidal velocity), and such a parameter choice often, though not necessarily, leads to stronger mixing due to model configurations. Hence, sand waves are favoured in coastal seas due to the strong shear of the tidal current. The above findings suggest that there could be some unresolved processes that cause a transition in the bedform-building mechanism, which eventually inhibits the growth of sand waves and promotes the growth of sand ridges. Full article

The accelerating digital transformation of urban economies is reshaping labor markets at unprecedented speed, generating skills mismatches, employment volatility, and widening inclusion gaps that current smart city governance frameworks are insufficiently equipped to address. While the smart city literature has advanced substantially in the areas of digital infrastructure, mobility, and e-government services, the governance of labor market transitions in data-driven urban environments remains conceptually underdeveloped. In particular, no integrated analytical framework currently links smart city governance, labor market intelligence, and workforce resilience into a coherent tool for assessing urban preparedness for technology-driven employment change. This study addresses that gap by developing the Urban Workforce Readiness Framework (UWRF)—an integrated conceptual model designed to evaluate how prepared urban labor markets are for accelerating digital and technological transformation. Methodologically, the framework is constructed through a structured synthesis of peer-reviewed scholarship published between 2015 and 2025 across five domains—smart city governance, labor market regulation, human capital development, workforce resilience, and data-driven public administration—complemented by a thematic review of policy documents issued by the OECD, ILO, European Commission, and World Bank. On this basis, the UWRF identifies five interdependent dimensions of urban workforce readiness: (i) digital infrastructure capacity, (ii) labor market intelligence and analytics, (iii) workforce skills adaptability, (iv) institutional governance capacity, and (v) social inclusion mechanisms. A multi-criteria operationalization is proposed, enabling comparative diagnostic assessment across cities and supporting evidence-based prioritization of policy interventions. The analysis demonstrates that institutional governance capacity and real-time labor market intelligence function as critical mediators within the system: in their absence, even substantial investments in digital infrastructure fail to produce resilient, inclusive, or sustainable labor market outcomes. Theoretically, the study extends data-driven governance scholarship beyond service delivery into the domain of workforce management, thereby integrating three traditionally separate research streams—smart city studies, labor market governance, and digital public administration—under a single analytical architecture. Practically, the UWRF provides policymakers, municipal authorities, labor market institutions, and urban planners with a structured diagnostic instrument for aligning digital transformation strategies with sustainable and equitable employment outcomes, and offers a replicable foundation for future empirical validation across diverse urban contexts. Full article

High-precision mapping of tree species composition is essential for sustainable forest management, biodiversity assessment, and ecosystem monitoring. Airborne hyperspectral imagery provides rich spectral and spatial information that enables detailed species discrimination. However, traditional single-stream convolutional neural networks (CNNs) often fail to fully exploit multi-dimensional features and are susceptible to spectral redundancy. In this study, we propose the MS-CBAM-TSCNet (Multi-Stream Convolutional Block Attention Deep Neural Network), a novel architecture specifically designed for tree species classification using aerial hyperspectral data. The proposed model integrates three parallel processing streams: a 1D spectral branch for capturing reflectance signatures, a 2D spatial branch for modeling contextual patterns, and a 3D spectral–spatial branch enhanced with Convolutional Block Attention Modules (CBAMs) to adaptively recalibrate channel-wise and spatial–spectral features. An adaptive gated fusion mechanism with attention-based weighting is introduced to dynamically combine the complementary representations extracted from the three streams, improving robustness to spectral redundancy and class imbalance. The method was evaluated on an airborne HyMap hyperspectral dataset (125 bands, with 4 m spatial resolution) acquired over a mixed boreal forest in Karlsruhe, Germany, comprising five dominant tree species. Using five-fold cross-validation on an augmented dataset, the MS-CBAM-TSCNet achieved an overall accuracy of 96.8%, a Kappa coefficient of 0.96, and a macro F1-score of 0.966, outperforming conventional 1D, 2D, and 3D CNNs, as well as a hybrid CNN-SVM approach across all evaluation metrics. An ablation study further confirms the complementary contributions of the multi-stream architecture, CBAM attention, and adaptive gated fusion. Pixel-wise classification maps demonstrate improved boundary delineation and reduced misclassification in mixed stands, highlighting the effectiveness of the proposed framework for operational forest inventory and ecological monitoring. Full article

Remote sensing image object detection is a core task in computer vision, which plays a vital role in intelligent transportation, port monitoring, and infrastructure management. However, rotated dense objects in remote sensing scenes suffer from severe challenges, including arbitrary 0–360° pose variations, large-scale differences, dense spatial aggregation, and blurred boundaries. Traditional Convolutional Neural Networks rely on fixed sampling grids and receptive fields, failing to adaptively capture the dynamic morphological and pose features of tilted targets. Meanwhile, existing methods struggle to address feature coupling and boundary misjudgment among densely arranged objects, leading to degraded detection accuracy. To tackle these bottlenecks, we propose an adaptive detection framework named PDNet for rotated dense object detection. The framework integrates four key designs: First, a Pose-Aware Dynamic Sampling Mechanism (PDSM) is developed to estimate the target principal axis in real time and learn a deformable offset field, which dynamically adjusts the convolution sampling pattern and receptive field shape to adapt to target pose variations. Second, a Dense-Scene Feature Modulation Mechanism (DSFM) constructs a dynamic weight field based on local feature responses to enhance discriminative target features and suppress inter-target interference in dense regions. Third, the Strip-Based Context Attention (SCA) module fuses global and local contextual information to strengthen the representation of small and weak targets. Fourth, a boundary-aware rotation loss function is designed to optimize the regression accuracy of rotated bounding boxes via pixel-level supervision. Extensive experiments on DOTA-v1.0, AI-TOD achieve 79.86% mAP and 25.95% AP, outperforming state-of-the-art methods. Full article

Background and Clinical Significance: Iatrogenic aorto-ostial dissection is a rare but potentially life-threatening complication of percutaneous coronary intervention (PCI), most commonly involving the right coronary artery. Although ostial stenting is generally considered the standard bailout strategy, failure of initial sealing may occur in selected anatomical settings and remains poorly understood. A focused narrative review of the literature was conducted through PubMed/MEDLINE, Scopus and Web of Science to identify reports of PCI-related aorto-coronary dissection with particular attention to dissection morphology, propagation mechanisms, bailout strategies, and outcomes after ostial stenting; Case Presentation: A 76-year-old man presented with non-ST-elevation myocardial infarction. Coronary angiography showed severe ostial right coronary artery (RCA) disease and significant left anterior descending artery stenosis. Following drug-eluting stent implantation in the RCA, extensive aorto-ostial dissection with retrograde extension into the sinus of Valsalva occurred. Initial ostial stenting failed to seal the dissection and was complicated by hyperacute stent thrombosis. After successful rewiring of the true lumen, a second overlapping drug-eluting stent was implanted using a stent-in-stent technique, followed by prolonged balloon inflation, achieving complete sealing and stabilization. Serial computed tomography angiography confirmed stability, and staged PCI of the LAD was successfully performed five days later; Conclusions: Failure of primary sealing may depend not only on procedural factors but also on dissection morphology. Transverse dissections with wide entry tears may be less effectively sealed by a single ostial stent, whereas overlapping stenting with prolonged balloon inflation may represent a more effective bailout strategy. Full article

The widespread occurrence of antibiotics in the environment threatens public health and ecosystem safety. This review summarizes the global occurrence of antibiotic contamination across the different environmental media, i.e., water systems, solid wastes, and soils, and provides a comprehensive analysis of physical, chemical, and biological removal methods, including their mechanisms, application advantages and disadvantages. It is deduced that physical methods aid in antibiotic enrichment, which leads to residual accumulation and fails to achieve complete degradation. In comparison, chemical methods are more efficient and rapid, but they are largely limited by high costs and secondary pollution. Biological methods, despite being appealing due to their low costs and environmental friendliness, may generate and spread antibiotic-resistant bacteria. To overcome the disadvantages of these conventional treatment methods, this review emphasizes the significant potential of integrated antibiotic removal systems, such as coupled advanced oxidation processes (AOPs), physical methods combined with AOPs and chemical methods combined with biological methods, which could achieve superior treatment performance. Future research should focus on optimizing and simplifying coupled systems and developing innovative treatment methods to enhance removal efficiency, reduce operational costs, and minimize secondary toxicity, thereby enabling effective antibiotic pollution remediation. This review summarizes the global state of antibiotic residues and stresses the importance of combined treatment methods for enhancing antibiotic degradation and removal, providing the valuable insights for green and efficient antibiotic treatment. Full article

Thermal therapies require precise prediction of the temperature and stress distributions in skin tissue to ensure efficacy while minimizing tissue damage. Classical bioheat models rely on oversimplified boundary assumptions, such as thermally insulated surfaces and mechanically free membranes, that fail to capture the physiological environment. This study develops a fractional-order dual-phase-lag bioheat model that incorporates clinically realistic conditions, namely simultaneous convective, radiative, and evaporative heat losses, active epidermal cooling, and subcutaneous mechanical restraint modeled through a Winkler elastic foundation. Both the Caputo and the Atangana–Baleanu (ABC) fractional derivatives are employed to represent memory effects in biological tissues. Analytical solutions in the Laplace–Fourier domain are obtained using displacement potential functions, with numerical inversion carried out via the Stehfest algorithm and Gaussian quadrature. The results show that realistic boundary conditions substantially alter the thermomechanical response: convective and evaporative cooling reduce surface temperatures and penetration depths, whereas active cooling permits deeper heating without epidermal damage. The Winkler foundation yields higher compressive stresses than traction-free models, and the ABC operator produces smoother responses than the Caputo operator. Overall, the model reveals the trade-offs between thermal efficacy and mechanical safety, thereby bridging bioheat modeling and clinical practice. Full article

Climate change compels the built environment to adopt adaptive Building-Integrated PhotoVoltaic (BIPV) designs balancing energy yields with indoor environmental quality. However, current dynamic facades suffer from algorithmic opacity and an operational bias prioritising generation over human comfort. This study systematically reviews the energy–comfort nexus within intelligent building skins. Following PRISMA 2020 guidelines, we searched nine databases (including Scopus and Web of Science) for empirical studies (2016–early 2026) evaluating dynamic BIPV control. Methodological quality and bias risk were assessed via a rigorous framework evaluating control transparency and simulation validity. From 2423 initial records, 92 high-quality studies were selected and structured into six adaptive BIPV design clusters (e.g., Double-Skin Facades, kinetic shading, semi-transparent glazing). The synthesis reveals a polarisation between thermo-mechanical integration and computational–geometric optimisation. An energy-centric bias persists. Power generation dominates over 92% of research, whereas glare evaluation remains below 20%. Additionally, nearly 60% of studies fail to explicitly define their control parameters. Current evidence limitations include a heavy reliance on idealised 1D simulations, a strong Northern Hemisphere geographical concentration, and a scarcity of robust data on dynamic bifacial systems. Bridging the gap between PV hardware and intelligent control, this study delivers a comprehensive theoretical framework and an actionable roadmap. Full article

Amniotic fluid embolism (AFE) is a rare but catastrophic obstetric emergency characterized by sudden cardiorespiratory collapse, disseminated intravascular coagulation (DIC), and a high case-fatality rate. Because no confirmatory test exists, the diagnosis remains clinical and one of exclusion. We report a case of presumed AFE in a 39-year-old primigravida with uterine myomas, obesity, and chronic hypertension who underwent an elective primary cesarean delivery under spinal anesthesia. During manipulation of the placenta, the patient developed abrupt cardiovascular collapse requiring cardiopulmonary resuscitation, with return of spontaneous circulation followed by profound coagulopathy and refractory uterine atony. Management included goal-directed transfusion within a massive transfusion protocol, uterotonic therapy, a failed B-Lynch suture, supracervical hysterectomy, and a subsequent right oophorectomy for an ovarian-vein hemorrhage identified on imaging. Laboratory studies demonstrated an overt consumptive coagulopathy consistent with the International Society on Thrombosis and Haemostasis (ISTH) criteria, while a normal serum tryptase argued against an anaphylactic mechanism. The neonate was delivered in good condition (Apgar scores 9, 10, and 10 at 1, 5, and 10 min; umbilical-artery pH 7.38) and required no neonatal intensive care. The mother achieved full hemodynamic and neurological recovery. This case illustrates that survival from presumed AFE is achievable through early recognition, high-quality resuscitation, prompt correction of coagulopathy, and decisive surgical hemostasis, and it highlights the diagnostic reasoning required to distinguish AFE from its principal differential diagnoses. Full article

Aging of asphalt pavements is a complex, multi-scale degradative process driven by the synergistic effects of various environmental stressors. Traditional laboratory-accelerated aging protocols often employ static parameters that fail to accurately replicate dynamic, region-specific climatic conditions. To bridge the gap between laboratory simulations and actual field performance, this study investigates the aging behaviors of base binder and SBS-modified binder under multi-factor coupled environmental conditions. Field observations were conducted across six distinct climatic regions in Gansu Province, alongside an indoor second-order orthogonal regression composite design that evaluated the interactive effects of temperature, ultraviolet (UV) radiation, humidity, and aging time. Rheological evaluations revealed that for the base binder, the synergistic coupling of UV radiation, elevated temperatures, and high humidity significantly accelerates oxidative hardening and embrittlement far beyond the impact of any single factor. Conversely, SBS-modified binder demonstrated a non-linear, U-shaped rheological response governed by a competitive mechanism between UV/thermal-induced polymer scission and moisture/time-driven matrix oxidation. Fourier Transform Infrared (FT-IR) spectroscopy corroborated these macroscopic findings at the molecular level, tracking the simultaneous evolution of carbonyl and sulfoxide indices alongside the degradation of the polybutadiene segments in the modified binder. Ultimately, a quadratic polynomial regression model was established to precisely correlate natural field aging with equivalent indoor accelerated aging times based on specific regional climatic data. Full article

In Virtual Power Plants (VPPs), computing the Optimal Energy Flow (OEF) for integrated electricity–heat systems is essential but challenged by highly asymmetric topologies and multi-timescale characteristics. Traditional data-driven models often fail to extract cross-domain coupling features and violate physical laws, yielding infeasible solutions. To address these challenges, this paper proposes an efficient OEF framework based on a Hierarchical Physics-Informed Heterogeneous Graph Neural Network (HPI-HGNN). First, a lossless mapping mechanism transforms physical networks into a heterogeneous graph, utilizing generalized edge attributes and feature projection to resolve dimensional discrepancies. Second, an attention-driven feature fusion algorithm is developed to adaptively evaluate path sensitivities and deeply extract cross-domain features. Finally, a hierarchical constraint mechanism embeds polar-coordinate power flow and thermo-hydraulic balance equations directly into hidden layers. This approach enables mechanism-guided optimization through layer-wise cross-gradient feedback. Simulations on a coupled IEEE 33-bus power and 32-node thermal system show that HPI-HGNN improves prediction accuracy for key variables (voltage magnitude, phase angle, and temperature) by 42.58–78.51% compared to a baseline PINN. Furthermore, it effectively suppresses voltage and temperature limit violations to a near-zero level, ensuring a highly accurate and physically reliable solver for secure online VPP scheduling. Full article

Background: The natural motion of the human knee involves a combination of rotation and translation, resulting in a variable Instantaneous Center of Rotation (ICR) throughout the gait cycle. Traditional prosthetic knee designs often fail to reproduce this complex kinematic behavior. Objectives: This study aims to propose a customized, biomimetic knee mechanism through the synthesis of a four-bar linkage capable of approximating the physiological ICR trajectory with high precision. Methods: A Genetic Algorithm (GA) was implemented to optimize the geometric parameters of the four-bar mechanism, specifically its link lengths and inter-link angles. The optimization process is based on Freudenstein’s equation, which analytically relates the input and output angles of the linkage to the lengths of its links. The desired ICR trajectory was derived from experimental data, and the objective function minimized the Euclidean error between the generated and target trajectories. Results: The proposed method yielded customized mechanisms that closely approximate the target ICR curves, achieving an overall mean Euclidean tracking error of 1.95% (±1.68%) across diverse patient profiles, with a best-case optimization error as low as 0.355%. Furthermore, the GA demonstrated high computational efficiency, converging on optimal geometric configurations in an average execution time of just 3.98 min. Conclusions: These numerical results validate the robustness of the GA in navigating the design space while strictly adhering to kinematic constraints, Grashof’s condition, and anatomical motion limits. The integration of Freudenstein’s equation with GA-based optimization techniques enables the customized synthesis of four-bar linkages with a high capacity to reproduce the physiological kinematics of the knee. This computational approach could be highly beneficial for the design of polycentric knee prostheses, as it reduces design and manufacturing time by providing the initial parameters for the development of the four-bar mechanism, ultimately ensuring a better biomechanical fit between prosthetic and natural human movement. Full article

Mechanisms for the maintenance of chronic viruses are poorly understood, particularly for archaeal viruses. Here, we identify the product of Sulfolobus spindle-shaped virus 1 (SSV1) ORF a291 as a putative virally encoded toxin required for growth inhibition but dispensable for viral replication and virion production. Viruses lacking ORF a291 replicated their genomes and formed morphologically normal spindle-shaped particles, yet failed to inhibit the growth of uninfected Saccharolobus solfataricus. Substitution of residues at a predicted N-terminal signal peptide cleavage site abolished growth suppression without affecting replication, suggesting that secretion is essential for toxin function. Despite primary sequence divergence among fusellovirus toxin candidates, analysis of protein structure predictions revealed a conserved hydrolase-like fold across SSV1, SSV9 and SSV10 toxins. These findings demonstrate functional separation of viral replication and host growth suppression and support a model in which chronic archaeal viruses modulate host competition through antagonistic factors. This work expands the known diversity of putative viral toxins and suggests that fuselloviruses employ conserved strategies to promote persistence in extreme environments. Full article

Intense rainfall and the resulting increase in ground saturation can significantly modify the mechanical performance of rock masses in natural slopes, particularly when fractured material is present. Extended infiltration reduces shear strength along discontinuities and increases pore-water pressures, raising the probability of large-scale landslides. When a tunnel is built within or near an unstable slope, the response of both structures becomes coupled, and this tunnel–slope interaction has proven to be an important aspect in the design and safety assessment of underground infrastructure in mountainous regions. This study evaluates the static and seismic performance of a tunnel–slope system in a fractured shale–limestone slope that failed after heavy rainfall. Since ground exploration was limited, the observed failure was reproduced through a back-analysis within a performance-based design (PBD) framework to calibrate representative geomechanical parameters. These parameters were then used in three-dimensional finite difference models to simulate the tunnel construction process and the seismic response of the system. During construction, the interaction between the tunnel and the slope was found to be minor. Under seismic loading, however, the simulations revealed notable interaction effects: slope displacements accumulate in the zone where the tunnel runs closest to the unstable critical section, and the stresses in the tunnel lining increase as a result of both the interaction with the slope and the curvature of the alignment. These results indicate that tunnel–slope interaction should be explicitly considered in the analysis and design of underground infrastructure whenever the tunnel lies within about four diameters of an unstable slope. Full article