Search Results (87,458)

Thoracic aortopathies, including aneurysm and dissection, are complex vascular disorders characterized by structural alterations of the aortic wall that disrupt normal haemodynamics. Altered shear stress, turbulent flow, and endothelial dysfunction promote thrombus formation and modulate systemic hemostasis via platelet activation and the von Willebrand factor–ADAMTS13 axis. The Total Thrombus-Formation Analysis System (T-TAS) is a microfluidic, flow-dependent assay that quantitatively evaluates thrombus formation under physiological shear conditions. Although studied in various cardiovascular contexts, its application in aortopathies remains largely unexplored, and no prospective studies have validated its clinical utility. Integrating T-TAS with computational haemodynamic approaches, such as two-way fluid–structure interaction simulations, enables assessment of the interplay between blood flow, vessel wall mechanics, pulse wave propagation, and local shear patterns. Patient-specific modelling, including individualized flow profiles, pressure distributions, and wall properties, may enhance mechanistic insights. Genetic variants in Fibrillin-1 gene (FBN1), Transforming Growth Factor Beta Receptor 1/2 (TGFBR1/2), Actin Alpha 2 (ACTA 2), and Myosin Heavy Chain 11 (MYH11) further contribute to structural vascular heterogeneity and diverse systemic haemostatic phenotypes, highlighting the need for personalized assessment. T-TAS should currently be considered an exploratory research tool rather than a validated diagnostic or prognostic method. This narrative review proposes a hypothesis-generating framework integrating structural, haemodynamic, molecular, and functional perspectives. Combining flow-based thrombosis assays with advanced modelling may inform future translational studies, improve mechanistic understanding of thrombus formation, and support personalized risk stratification and management in patients with thoracic aortopathies. Full article

Traditional drug discovery processes are typically expensive, time-consuming, and have a very high failure rate. Artificial intelligence (AI) is currently reshaping this field in unprecedented ways, promising to significantly improve the efficiency and success rate of drug development. This article systematically compares and analyzes the application of AI for two major drug types: small molecule vs. peptide drugs. It explores their applications in several key stages of drug development, including virtual screening, lead compound optimization, de novo drug design, ADMET (absorption, distribution, metabolism, excretion, and toxicity) property prediction, and chemical synthesis planning. While both drug types benefit from AI-driven approaches, fundamental differences exist in molecular representation, data availability, key challenges, and model adaptability. For small molecule drugs, AI focuses on drug efficacy, synthetic feasibility, and accurate structure–activity relationship prediction. In contrast, for peptide drugs, AI faces more unique biological challenges, such as inherent flexibility, complex biological functions, stability, and immunogenicity. Finally, this article provides a forward-looking perspective on the future of AI-driven drug discovery, highlighting the immense potential of basic models, multimodal integrated systems, and autonomous discovery platforms, which will collectively drive the next wave of precision drug development. Full article

Building Information Modelling (BIM) offers a pivotal opportunity to automate Life Cycle Assessment (LCA) within the Architecture, Engineering, and Construction (AEC) industry. However, seamless integration is persistently hindered by a semantic gap, a critical misalignment between the object-oriented, geometric definitions of BIM and the process-based material data required by Life Cycle Inventory (LCI) databases. This paper presents a comprehensive review of data interoperability challenges and evaluates advanced methodologies designed to bridge this divide, moving beyond simple tool comparison to analyse structural integration barriers. Through a systematic review of 124 primary studies published between 2010 and 2025, this research inductively derives the BIM-LCA Interoperability Triad. This framework analyses causal dependencies across three dimensions, including Semantic and Ontological Structures, Workflow and Temporal Integration, and System Architecture and Interoperability. Furthermore, by establishing a comparative challenge–solution matrix, the analysis reveals a maturity paradox in current methodologies. While semi-automated commercial plugins dominate practice due to accessibility, they frequently function as opaque black boxes with limited transparency. Conversely, advanced approaches utilising Semantic Web technologies and Machine Learning demonstrate superior capability in resolving terminological mismatches but currently face significant barriers regarding infrastructure and expertise. This study contributes a novel theoretical model for understanding integration failures. It concludes that future research must pivot from static schema mapping towards AI-driven semantic healing, dynamic Digital Twins, and explicit system boundary harmonisation to achieve truly automated, context-aware environmental assessments and support whole-life circularity. Full article

Screw compressors are critical equipment in oil and gas production and transportation, where efficiency losses caused by rotor geometry, inlet pressure pulsations, and harsh climatic conditions can accumulate into substantial annual energy penalties and reliability degradation. This study provides a quantitative assessment of these coupled effects within a unified multiphysics framework that combines time-accurate transient CFD simulations based on a fixed Cartesian immersed-boundary formulation with a climate-calibrated offline physics-based digital twin—functioning as a digital shadow with one-way data flow from archival SCADA records—a reduced-order seasonal model with no real-time updating, calibrated against a full calendar year of SCADA records and validated against a held-out cold-season dataset (October–December 2022, T_amb = −15 to +8 °C); summer-period predictions rely on calibrated extrapolation beyond the validation window—an integration not previously demonstrated for oil-flooded screw compressors. Two rotor profile configurations (Type A and Type B) were analyzed to quantify geometry-driven differences in static pressure distribution, leakage tendency, and pulsation sensitivity. Transient suction conditions were modeled using harmonic and quasi-random inlet pressure disturbances to evaluate pressure amplification, phase lag, leakage intensification, and efficiency degradation. Seasonal performance was assessed by integrating temperature-dependent gas properties, oil viscosity behavior, and external heat transfer into an annual climatic load framework. The results show that inlet oscillations are amplified inside the chambers (pressure amplification factor П_р ≈ 1.95; П_р up to 2.3 under quasi-random excitation), reducing mass flow and volumetric efficiency by 8–10% and decreasing polytropic efficiency from 0.78 to 0.69–0.71, while increasing leakage by up to 27% and raising peak contact pressures to 167–171 MPa. Seasonal variability (+30 to −30 °C) increased suction density by 38% but raised drive power by ~9% due to viscosity-driven mechanical losses, producing an energy penalty up to 10.8% and an estimated annual additional consumption of approximately 186 MWh per compressor, decomposed as: cold-season contribution ~113 MWh (±10 MWh, directly field-validated against October–December 2022 SCADA data) and summer-season contribution ~51 MWh (calibrated extrapolation; additional uncertainty unquantified and not included in the ±10 MWh bound). The full annual figure of 186 MWh should be interpreted as a model-based estimate rather than a fully validated result. These findings demonstrate that rotor design optimization and mitigation of nonstationary suction effects, coupled with climate-aware offline physics-based digital shadow operation, represent high-priority levers for improving efficiency and reducing energy penalties in field conditions; reliability implications require further validation against summer-season field measurements. Full article

Interlayer bond strength is critical for ensuring the safety and durability of 3D-printed concrete (3DPC) structures. However, there remains a lack of real-time prediction methods addressing interlayer performance under the combined effects of interval time and environmental factors during the in situ printing process. To address this issue, this study conducted experiments considering various printing interval times and environmental conditions, incorporating monitoring of dielectric constant and water evaporation, alongside interlayer splitting tensile tests. By integrating the SHAP interpretability algorithm with nonlinear regression analysis, the results indicate that the printing interval time is the dominant factor inducing interlayer strength decay (with a contribution rate of 68.6%), while relative humidity emerges as the primary environmental variable (with a contribution rate of 21.3%). Mechanism analysis reveals that prolonged printing intervals intensify the hydration of the lower deposited layer, leading to reduced interfacial moisture content and loss of plasticity. Furthermore, environmental evaporation significantly regulates this process, with high-humidity environments notably mitigating the moisture loss and strength reduction caused by time delays. Based on the correlation mechanism between moisture and strength, a dimensionless general prediction model for 3DPC interlayer strength was established, incorporating printing interval time and an evaporation index (goodness of fit, R² = 0.96). Consequently, a digital twin quality inversion scheme based on companion specimen monitoring and printing timestamps was proposed. This study quantifies the intrinsic relationships among printing interval time, environmental conditions, and interlayer strength, offering a novel approach for determining the construction window and achieving non-destructive quality prediction for 3DPC in complex environments. Full article

Fractures as critical discontinuous structural planes in rock masses, directly govern their stability and serve as the core controlling factor in rock mechanics engineering. Existing deep learning models for fracture extraction face persistent challenges, including imbalanced integration of deep and shallow features, limited suppression of background noise, inadequate multi-scale feature representation, and large parameter sizes—making it difficult to strike a balance between detection accuracy and deployment efficiency. Focusing on the Wanshanshan quarry in Yunnan, this study first constructs a high-precision digital model using close-range photogrammetry and 3D real-scene reconstruction. A lightweight yet high-accuracy intelligent detection method, termed MSEM-Deeplabv3+, is then proposed for rock mass fracture extraction. The model adopts lightweight MobileNetV2 as the backbone network, incorporating inverted residual modules and depthwise separable convolutions, resulting in a parameter size of only 6.02 MB and FLOPs of 30.170 G—substantially reducing computational overhead. Furthermore, the proposed MAGF (Multi-Scale Attention Gated Fusion) and SCSA (Spatial-Channel Synergistic Attention) modules are integrated to enhance the representation of fracture details and semantic consistency while effectively suppressing multi-source and multi-scale background interference. Experimental results demonstrate that the proposed model achieves an mPA of 89.69%, mIoU of 83.71%, F1-Score of 90.41%, and Kappa coefficient of 80.81%, outperforming the classic Deeplabv3+ model by 5.81%, 6.18%, 4.53%, and 9.2%, respectively. It also significantly surpasses benchmark models such as U-Net and HRNet. The method accurately captures fine and continuous fracture details, preserves the spatial distribution of long-range continuous fractures, and maintains robust performance on the CFD cross-scene dataset, showcasing strong adaptability and generalization capability. This approach effectively mitigates the risks associated with manual high-altitude inspections and provides a lightweight, high-precision, non-contact intelligent solution for fracture detection in high-steep rock slopes. Full article

Predicting acute toxicity across species is essential for early-stage drug safety evaluation. While recent efforts have primarily focused on improving predictive accuracy, they often fail to address two critical issues: the substantial divergence in toxicity mechanisms among different species, and the inherent noise present in experimental data. To bridge this gap, we introduce a Probabilistic Multitask Active Learning (PMAL) framework for multi-species acute toxicity prediction. Our framework integrates two key modules: a Probabilistic Multitask Learning (PML) component which jointly models the predictive distributions of multiple toxicity endpoints from a probabilistic viewpoint, and an Uncertainty-based Active Learning (UAL) component which strategically selects the most informative compounds for experimental annotation based on predictive uncertainty. Empirical evaluations demonstrate that PMAL surpasses state-of-the-art methods and is capable of providing well-calibrated uncertainty estimates for small molecules across diverse toxicity endpoints. Beyond advancing multi-species toxicity prediction, the core design principles of PMAL offer a generalizable paradigm for learning in noisy multi-task environments. Full article

Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article

The Draa Sfar polymetallic mine, located near Marrakech in Morocco, represents the deepest currently operating underground mine in North Africa, with workings extending beyond depths of −1200 m. At such depths, mining activities are conducted within weak, highly anisotropic foliated black pelites, where recurrent instability mechanisms, most notably rib buckling and crown deterioration, are frequently observed, especially in drifts developed parallel to the foliation planes. In this context, the present study integrates detailed structural field observations with two-dimensional finite-element modelling using RS2 in order to analyse excavation-scale stability within these schistose pelitic rocks. Both numerical simulations and field evidence indicate that increasing depth-related confinement, together with a dominant in situ stress regime, favours stress channelling and localized damage development, while the pronounced transverse weakness of the pelites exerts a primary control on failure kinematics, including schistosity-parallel spalling, asymmetric rib buckling, and shear along inclined foliation intersecting the excavation back. Instability processes are further intensified by excavation geometry and mine layout: angular, square-shaped profiles and foliation-parallel drift orientations generate steeper stress gradients and greater convergence compared to arched sections, while proximity to stopes and adjacent openings enhances mining-induced stress redistribution and associated deformation. Intersection areas emerge as the most critical configurations, where the superposition of stress perturbations and structurally controlled damage mechanisms accelerates wall convergence and roof sagging. Overall, these findings demonstrate that drift stability cannot be adequately evaluated using generic design criteria when excavation geometry, interaction effects, and structural anisotropy exert a dominant influence on mechanical behaviour. Consequently, a fully integrated approach that combines drift geometry optimisation, detailed structural mapping, site-calibrated numerical modelling, and in situ monitoring is required to achieve reliable stability assessment and control. Full article

Artificial levees along major rivers are critical for flood-risk mitigation, yet many aging structures have poorly constrained internal composition and material heterogeneity, limiting the reliability of conventional safety assessments. This study develops a quantitative, non-destructive framework for characterizing levee internal structure by integrating electrical resistivity tomography (ERT) with borehole (BH) observations. ERT profiles were combined with borehole measurements of grain size (D50) and water content to investigate subsurface compositional variability and to evaluate relationships between sedimentological and geophysical parameters. Grain-size data from borehole samples were modeled using four predictive approaches—random forest regression (RFR), artificial neural networks (ANN), linear regression (LR), and support vector regression (SVR)—based on ERT-derived resistivity and moisture information. The results reveal pronounced internal heterogeneity within the investigated levees and demonstrate consistent relationships between sediment composition, water content, and electrical resistivity. Among the tested models, the ensemble-based RFR provided the highest predictive performance (R² = 0.81). These findings indicate that D50 characteristics of levee materials can be reliably inferred from ERT data using machine learning, reducing the need for destructive sampling. The proposed approach offers a transferable methodology for levee assessment and supports future applications in non-destructive monitoring, spatially explicit flood-risk analysis, and climate-resilient flood-protection management. Full article

Tooth development or odontogenesis is a complex morphogenetic process that requires tightly regulated interactions between the oral epithelium and mesenchyme of neural crest origin. In this narrative review, we compile existing knowledge regarding gene regulatory networks and epigenetic factors throughout tooth development from initiation to eruption. Signaling between the epithelium and mesenchyme is mediated by four conserved pathways—Wnt/β-catenin, bone morphogenetic protein (BMP), fibroblast growth factor (FGF), and Sonic hedgehog (Shh)—which operate iteratively and interact through extensive crosstalk at each developmental stage. Transcription factors, such as PAX9, MSX1, PITX2, and LEF1, interpret these signals to control cell fate decisions and differentiation. Epigenetic modifications, including DNA methylation, histone modifications, and microRNA-mediated regulation, provide additional layers of control that fine-tune gene expression programs. Unlike existing reviews that address these regulatory mechanisms separately, here we integrate signaling pathways, transcription factor networks, epigenetic regulation, human genetic disorders, dental stem cell biology, and recent single-cell transcriptomic insights into a unified framework. We discuss opportunities to apply developmental biology knowledge towards regenerative dentistry goals, including iPSC-derived dental models and spatially resolved multi-omics approaches, while acknowledging the considerable gap between preclinical findings and clinical applications. Full article

Cultural heritage, with its humanistic values, is seen as a tool for preserving historical memory and reinforcing cultural identity, while its socio-economic values have a significant impact on the tourism industry. However, the contemporary global context, characterized by rapid and often unsustainable development, has intensified challenges such as tourism massification, urbanization, and climate change. To address these challenges, the authors assume that contemporary society should find a balanced development model in which heritage management becomes an integrated part of sustainable tourism practices. Although the relationship between heritage, tourism, and sustainability has been extensively explored for more than four decades, existing research remains fragmented and lacks an integrated conceptual framework that systematically explains the interconnections between sustainable tourism and heritage management. Therefore, the main goal of this paper is to develop a conceptual framework and conduct a comprehensive literature review that synthesizes these processes, contributing to the existing body of knowledge and addressing identified research gaps. The conducted research indicates that contemporary approaches should enhance integrated heritage management plans, effective visitor management strategies, carrying capacity assessments, and continuous monitoring of tourism impacts. In this context, sustainable tourism and heritage management represent a coordinated process of planning and governance aimed at ensuring the long-term conservation of cultural and natural heritage resources while enabling responsible tourism development. By reviewing and synthesizing existing literature, this paper contributes to the theoretical advancement of sustainable tourism and heritage management studies through the development of an integrated conceptual framework that addresses existing research gaps and incorporates contemporary academic insights. Full article

Intelligent oilfield development is experiencing an increasingly deep integration between localized automation and integrated, data-centric ecosystems. To systematically delineate the knowledge structure and technological trajectories within this field, this study analyzes 225 high-quality publications. This study innovatively employs a custom toolchain based on the Dart language for heterogeneous data cleaning and standardization, ensuring high accuracy and scientific rigor in the analysis samples. The investigation reveals a distinct dual-core driving mechanism underpinning recent advancements: a cognitive cluster centered on Artificial Intelligence and Deep Learning for complex data interpretation and prediction, and a decision-making cluster focused on Operational Optimization and Predictive Modeling for production enhancement. These two clusters respectively encompass eight sub-clusters: “artificial intelligence,” “machine learning,” “deep learning,” “performance,” “enhanced oil recovery,” “model,” “optimization,” and “predication.” This dual-core framework signifies a paradigm shift from experience-based practices to a synergistic “AI-enabled + mathematical optimization” approach. The analysis further explores emerging trends, including the potential of deep reinforcement learning for dynamic decision-making and the critical role of cybersecurity and model robustness in safety risk management. By mapping the current landscape and core mechanisms, this study provides a foundational reference for researchers and practitioners to navigate the future development of intelligent oilfields towards more resilient and efficient ecosystems. Full article

Given its substantial contribution to traffic accidents, one of the main goals of intelligent driver-assistance systems has become the detection and mitigation of driver fatigue to enhance driving safety and comfort. Among various approaches, vision-based facial analysis using deep learning has emerged as an effective and non-intrusive method for identifying driver drowsiness, as a key manifestation of fatigue. However, current drowsiness detection models do not account for demographic factors like gender, even though recent research has shown gender behavioral differences such as eye closure duration, blink frequency, yawning patterns, and facial muscle relaxation. In this paper, we present a fine-grained multi-stream transformer architecture that incorporates gender-awareness and shifted-windows attention for spatial feature fusion. Integrating gender embedding, by modulating the region-based features, allows the model to effectively learn gender-conditioned drowsiness features to minimize bias and diluted representations. Using the NTHU-DDD dataset, we evaluated two-stream and three-stream variants for gender-aware and gender-agnostic across three facial region contexts: the face region with a 20% margin, bare face region, and key facial regions (face, eyes, and mouth). A comprehensive ablation study was conducted to identify the most effective model setup. The results demonstrate that incorporating gender embedding improves detection performance, achieving an accuracy of 95.47% on the evaluation set. Moreover, using the proposed three-stream model (SWT-DD-3S) produced better results. Full article

The preservation of rooster semen quality during short-term liquid storage remains a challenge in poultry reproductive biotechnology because sperm cells rapidly lose functional competence under ambient conditions. This deterioration is largely associated with oxidative stress and lipid peroxidation of sperm membranes, which are particularly vulnerable in avian species due to their high polyunsaturated fatty acid content and limited cytoplasmic antioxidant defenses. Selenium is an essential trace element involved in cellular antioxidant protection through its incorporation into several selenoproteins that regulate redox balance and protect cellular structures from oxidative injury. The present study evaluated the effects of selenium methionine supplementation on rooster semen quality during liquid storage at 25 °C. Semen was diluted using a standard poultry semen extender composed of sodium glutamate, glucose, potassium acetate, magnesium acetate, and potassium citrate. Selenium methionine was incorporated into the semen extender at concentrations of 0.5%, 1%, and 2% (w/v) at the time of semen dilution prior to storage. Semen quality was assessed at 0, 4, 8, 12, and 24 h of storage. Functional parameters, including total sperm motility, sperm viability, and dead sperm percentage, together with kinematic variables (VSL, VCL, VAP, ALH, LIN, and STR), were analyzed using computer-assisted sperm analysis (CASA). Structural integrity was evaluated through acrosome and plasma membrane integrity tests, while sperm physiological status and apoptotic progression were assessed using Annexin V-FITC/propidium iodide flow cytometry. Significant effects of storage time, selenium methionine concentration, and their interaction were observed for multiple semen quality parameters (p < 0.05). Among the tested concentrations, supplementation with 0.5% selenium methionine consistently produced the most favorable results, maintaining higher sperm motility, viability, and membrane integrity while reducing dead sperm percentage and apoptotic progression during storage, with protective effects particularly evident at 8, 12, and 24 h compared with the control and higher concentrations. Polynomial contrast analysis indicated predominantly non-linear dose–response relationships, with quadratic and cubic components providing the best model fit (R² = 0.90–0.99; p < 0.0001), suggesting a hormetic antioxidant effect. Overall, these findings indicate that selenium methionine supplementation in semen extender improves the stability of rooster semen during short-term liquid storage at ambient temperature, with 0.5% showing the most consistent protective effects among the concentrations evaluated. Full article