Search Results (171)

To address the challenges of over-reliance on single-source data, strong spatial heterogeneity in scenic areas, and difficulty in dynamically capturing spatial topology and heterogeneous node relationships in forestry tourism resource carrying capacity prediction, this paper constructs a carrying capacity prediction framework that integrates a multi-source data fusion algorithm with an attention mechanism and a GAT-Transformer model. This framework employs a modal-level multi-head cross-attention mechanism to conditionally weight and fuse multi-source heterogeneous data in the node and time dimensions. It adaptively allocates the contribution of each information source based on the spatiotemporal context, suppressing noise and redundant interference. A weighted spatial graph is constructed based on fusion distance, trail connectivity, and traffic similarity. Neighborhood information is aggregated through a graph attention network to characterize spatial heterogeneity. The spatially enhanced node sequence is then input into a multi-layer Transformer encoder to capture the long-term temporal dependence and periodic patterns of carrying capacity. Finally, the prediction results are output through a regression layer. Systematic experiments were conducted using two years of multi-source observation data from Wulingyuan National Forest Park. The results show that the proposed method has low prediction error and good stability, exhibiting excellent performance in temporal scale adaptation, spatial generalization, and resistance to missing data and noise. Simultaneously, the model structure is lightweight, with low inference latency, achieving a good balance between prediction accuracy, interpretability, and engineering deployment. Full article

Background: Pacemakers enable continuous long-term surveillance of atrial fibrillation detected by implanted devices. Circulating biomarkers reflecting endothelial dysfunction, inflammation, and myocardial stress may help identify patients at risk for atrial fibrillation (AF) progression and higher arrhythmic burden. Methods: This analysis included patients from the prospective ACaSA study (NCT05127720) with a dual chamber pacemaker (Microport^® BOREA DR or TEO DR) and monitored weekly via remote monitoring technology (SMARTVIEW^®). Individuals with permanent AF or single-chamber systems were excluded. Baseline plasma concentrations of angiopoietin-2 (ANGPT2), growth differentiation factor-15 (GDF-15), fibroblast growth factor-23 (FGF-23), bone morphogenetic protein-10 (BMP10), and tumor necrosis factor–related apoptosis-inducing ligand receptor-2 (TRAIL-R2) were quantified using enzyme-linked immunosorbent assays. N-terminal pro-B-type natriuretic peptide (NT-proBNP) was measured using electrochemiluminescence immunoassay. Biomarkers were log₂-transformed, with values below assay detection limits imputed at half the lower limit of detection. Two endpoints were assessed following a 30-day blanking period: (1) progression to persistent AF, defined as ≥7 consecutive days with >99% daily AF burden, analyzed using Cox regression; and (2) AF burden, calculated as total AF time normalized to monitored days and categorized as <25%, 25–75%, or >75%, analyzed using multinomial logistic regression. Multivariable models were adjusted for age, sex, heart failure, diabetes, and prior myocardial infarction; Cox models were limited to age, sex, and heart failure due to fewer events. Results: A total of 223 patients were included (median age 75 years; 37.2% women). During follow-up, 28 patients (13.3%) progressed to persistent AF. Higher baseline ANGPT2 was the strongest predictor of progression (HR per doubling 1.83, 95% CI 1.27–2.66, p = 0.001), followed by GDF-15 (HR 1.52, 95% CI 1.03–2.24, p = 0.036). In the burden analysis, ANGPT2 demonstrated a pronounced graded relationship with arrhythmic load, with markedly increased odds of high (>75%) AF burden (OR 8.31, 95% CI 2.63–26.26, p < 0.001). GDF-15 independently predicted both medium (OR 2.05, p = 0.025) and high burden (OR 2.32, p = 0.037). NT-proBNP displayed a borderline association with high burden (OR 2.02, p = 0.061). No significant associations were observed for FGF-23, BMP10, or TRAIL-R2. Conclusions: In continuously monitored pacemaker patients, ANGPT2 and GDF-15 emerged as key biomarkers associated with AF disease severity. ANGPT2 was strongly linked to both progression to persistent AF and high AF burden, whereas GDF-15 consistently predicted higher AF burden and also contributed to risk of progression. These findings highlight endothelial and inflammatory pathways as potential markers of atrial disease progression. Full article

Zero Trust Network Access (ZTNA) has emerged as a fundamental paradigm for securing cloud-native and distributed computing environments. However, existing ZTNA implementations remain largely limited by static policy enforcement and opaque machine-learning-based anomaly detection mechanisms, which often lack contextual adaptability, policy awareness, and interpretable decision-making capabilities. These limitations create significant challenges in dynamic multi-cloud environments where access behavior continuously evolves and security decisions must be both accurate and explainable. To address these challenges, this study proposes Cognitive ZTNA framework, a unified neuro-symbolic trust enforcement framework that integrates transformer-based behavioral trust modeling with ontology-guided symbolic reasoning. The proposed architecture enables continuous trust evaluation by combining behavioral access patterns with explicit policy semantics through a hybrid trust fusion mechanism. This design allows the system to capture long-range behavioral dependencies while maintaining policy-compliant and interpretable access control decisions. The framework is evaluated using the CloudZT-Bench-2025 dataset, comprising 4.2 million cross-platform access events derived from enterprise security telemetry, AWS CloudTrail logs, and simulated adversarial scenarios. Experimental results demonstrate that Cognitive ZTNA achieves Precision = 0.96, Recall = 0.93, and F1-score = 0.95, significantly outperforming rule-based and machine-learning baselines while reducing the false positive rate to 0.03. In addition, the system maintains real-time feasibility with an average decision latency of 24 ms and explanation latency below 5 ms, while achieving 92% analyst-rated explanation sufficiency. These findings demonstrate that integrating behavioral intelligence with symbolic policy reasoning enables adaptive, interpretable, and policy-aware Zero Trust enforcement. The proposed framework therefore provides a practical foundation for next-generation ZTNA systems capable of supporting secure, transparent, and context-aware access control in modern cloud environments. Full article

Strategic Mine Planning (SMP) creates the long-term economic baseline for mining operations, yet economic variability necessitates Dynamic Mine Planning (DMP) to rapidly stress-test those financial assumptions. Currently, this capability is hindered by fragmented software ecosystems that require manual data handoffs, slowing iteration and breaking the audit trail between market data and valuation models. While Generative AI affords an opportunity to automate these workflows, its adoption in the mining industry is stalled by concerns over data quality and the risk of uncritical acceptance of automated outputs. Addressing these challenges, this paper describes the Mine Intelligence and Decision Support (MINDS) framework. We present MINDS as a modular reference architecture that uses Large Language Model (LLM) agents to orchestrate the economic evaluation process while maintaining strict engineering oversight. The system integrates a conversational interface with a multi-agent assessment layer that acts as an adversarial review, assessing price assumptions against market intelligence before generating economic valuation scenarios. A proof-of-concept using the Marvin copper benchmark evaluates the framework, demonstrating automated request-to-report orchestration, execution stability with an average debate latency of 10.69 s and a transparent decision audit trail. These findings show that MINDS can systematize economic scenario analysis without sacrificing the governance and verification required for definitive feasibility studies. Full article

As the trend towards larger wind turbines continues, the increasing length of wind turbine blades imposes higher demands on their structural properties. Long flexible wind turbine blades at the hundred-meter scale now typically employ composite materials. In recent engineering practice, wind turbine blade accidents occur frequently. As a result, the method for optimizing composite material layup of large wind turbine blade is attracting attention from both researchers and engineers. In the current research, thin-walled beam structural theory combined with pattern search algorithms are utilized to optimize the composite layup of large wind turbine blade structures under different loading cases. Utilizing the proposed optimization algorithm, the blade structure is capable of reducing weight while satisfying structural strength requirements. Based on the proposed optimization method, the impact of external loading on the structural optimization results are compared. The trailing edge section on the transition region is critical for wind turbine blade structural design. Increasing the thickness of the layup on spar caps is a feasible way to resist the flapwise loading. These findings provide valuable guidance for the structural design optimization of ultra-long flexible blades in large wind turbines and have positive significance for the safety and economy of wind farm operation, offering a more scientific, efficient, and practical approach to their design. Full article

White-tailed Deer (Odocoileus virginianus) overabundance has emerged as a significant ecological concern in recent decades. With current populations exceeding 30 million, White-tailed Deer (WTD) are now one of the most spatially abundant ungulate species across both natural and human-altered environments. High densities have led to considerable ecological and economic impacts, including forest understory degradation, biodiversity loss, and increased deer-vehicle collisions. This study examines the spatiotemporal distribution of WTD within three sites at Binghamton University, a heavily wooded campus in the Appalachian Upland region of New York State. To monitor population densities and movement patterns, a combination of remote sensing techniques was employed, including six Assark PH960W trail cameras and a DJI Mavic 3T UAV equipped with an uncooled VOx microbolometer thermal infrared (IR) sensor. Data were collected between 31 October 2024 and 10 March 2025, in relation to three deer culling events on 18 December 2024, 2 January 2025, and 9 January 2025. While Unoccupied Aerial Vehicle (UAV) based thermal imaging proved effective for estimating population dynamics, its utility is constrained by environmental and logistical limitations. In contrast, WiFi-enabled trail cameras provide a cost-efficient approach for capturing high-temporal resolution data at localized sites. Density estimates were derived from UAV thermal imaging and Random Encounter and Staying Time (REST) model calculations, ranging from 13.2 to 26.8 deer/km² across the region. Findings underscore the need for ongoing deer management strategies on campus to support long-term forest ecosystem health. Full article

This study examines the ripple effects of parkification, the transformation of post-industrial landscapes into public parks and green infrastructure, in Greenville at the Upper State region of South Carolina. As many Southern mill towns contend with industrial decline, environmental degradation, and complex land-use legacies, parkification has emerged as a pragmatic response to constraint rather than a conventional redevelopment strategy. Framed as a process rather than an isolated design outcome, parkification is understood here as a generative mechanism that produces cumulative spatial, ecological, and institutional change beyond individual project boundaries. Using a mixed-methods approach that integrates spatial and temporal mapping, archival research, site analysis, and semi-structured interviews with key stakeholders and decision-makers, this study traces how parkification unfolds across time and scale. Three interconnected case studies in Greenville, Falls Park on the Reedy, Conestee Nature Preserve, and the Swamp Rabbit Trail, are examined to address how post-industrial parkification contributes to greenway network formation and broader urban–regional transformation in the American South. The findings reveal that parkification consistently emerged from conditions of environmental constraint, including contamination, flooding, infrastructural legacies, and limited redevelopment feasibility. Early parkification projects functioned as generative landscape nodes that catalyzed the expansion of green space and connectivity rather than remaining isolated amenities. By establishing visible, accessible, and publicly valued landscapes, these projects enabled the extension of trails, river corridors, and preserved infrastructures, contributing to the formation of an interconnected regional greenway system. Institutional alignment among civic organizations, public agencies, and landscape professionals further supported the scaling and replication of parkification. Together, these findings position parkification as a process-based landscape strategy capable of driving the spread of green areas and long-term urban connectivity in post-industrial regions. Full article

This study investigates why companies sponsor individual athletes in sports with low media coverage and how such athletes secure sponsorship agreements. While sport sponsorship research has predominantly focused on mainstream sports and event-based contexts, limited attention has been given to individual athletes in niche sports. Using a qualitative research design, semi-structured expert interviews were conducted with Norwegian sponsors and elite athletes in long-distance running, trail running, and orienteering. The data were analyzed through qualitative content analysis, informed by the Sponsorship Motive Matrix and the Model of Athlete Brand Image. The findings indicate that sponsorship decisions are primarily driven by market-related motives, complemented by bond and society motives, with cost-effectiveness, authenticity, and value alignment playing important roles. Sponsors prioritize athlete performance, personality, and social media presence, while athletes emphasize financial support and performance optimization. Sponsorship activation is generally limited, and agreements are predominantly in-kind or hybrid. The study concludes that sponsorships in low-media-coverage sports are relational and selective, relying heavily on athlete-driven outreach and social media visibility. These findings extend existing sponsorship frameworks to an underexplored context and offer practical insights for sponsors and athletes in niche sports. Full article

Velocity, pressure, and density fields computed in earlier three-dimensional direct numerical simulations of a statistically stationary, planar, one-dimensional, low-Mach-number hydrogen–air flame propagating in small-scale, moderately intense, spatially decaying turbulence are filtered out using top-hat filters of four different widths. Certain source/sink filtered terms in the transport equations for resolved and subfilter-scale kinetic energies are analyzed. These are (i) the rate of inertial transfer of kinetic energy between resolved and subfilter scales, (ii) baropycnal work, (iii) subfilter-scale velocity–pressure–gradient term, and (iv) subfilter-scale pressure–dilatation term. These filtered terms are averaged over transverse planes and time or conditioned to the filtered combustion progress variable. Results show that terms (i) and (ii) work to transfer kinetic energy from smaller to larger scales (backscatter) and from larger to smaller scales, respectively, with the baropycnal work dominating the former term. These trends are observed for mean and conditional terms. The mean velocity–pressure–gradient term is positive and works to increase subfilter-scale kinetic energy due to combustion-induced thermal expansion. The pressure–dilatation term changes its sign from negative to positive at the leading and trailing edges, respectively, of the turbulent flame brush. Under conditions of the present study, the magnitudes of the mean velocity–pressure–gradient and pressure–dilatation terms are smaller when compared to the baropycnal work. Probability Density Functions (PDFs) for the explored filtered terms exhibit long tails, are highly skewed, and are characterized by a large kurtosis, thus implying significant intermittency of inter-scale energy transfer and energy exchange between internal and kinetic energy in the flame. These PDFs indicate that the intermittency of the inter-scale energy transfer and energy exchange depends substantially on mechanisms and scales of energy injection. Full article

A two-parameter environmental (measured in CO2eq—CO₂ is used in this paper to represent the carbon dioxide molecule as opposed to the chemical formula CO₂ as is common practice in LCA studies; CO2eq is an abbreviation for CO₂ equivalent and may be written as CO2e in the literature) and economic (measured in USD) analysis using life cycle analysis (LCA) and techno-economic analysis (TEA) of repurposed wind turbine blades for structural use in recreational trail bridges (e.g., on hiking trails and golf courses) is described in this paper. The US Department of Energy’s TECHTEST TEA/LCA software (v1.0) platform was used to compare three commercially available trail bridges (a steel truss bridge, an FRP pultruded truss bridge, and a glulam stringer bridge) with a bridge made from retired wind turbine blades (known as a BladeBridge). All bridges had a 50 ft (15.24 m) long by 6 ft (1.83 m) wide deck and were designed for a 90 psf (4.3 kN/m²) live load. The LCA functional unit was the assembled bridge, which was made ready to be shipped from the fabricator. Cradle-to-gate (A1–A3, i.e., raw material extraction, transportation, and manufacturing) system boundaries were used. For the BladeBridge, no embodied carbon was attributed to the blade itself (cut-off system allocation). For the TEA, a USD 660/tonne credit was attributed to the blade. The raw materials for each bridge were determined from detailed construction documents. Manufacturing and transportation energy were determined based on the equipment used for fabrication and geographical location. Direct labor for fabrication was calculated based on a weighted average of salaries taken from the US Bureau of Labor Statistics. The results indicate that raw materials had the biggest effect on embodied CO2eq and that labor had the largest impact on cost for all bridges. The results indicate that the BladeBridge is significantly less expensive to produce and releases less CO2eq into the environment (less Global Warming Potential (GWP)) than the three commercially available bridges. Additional TEA metrics for the BladeBridge, including Technology Readiness Level (TRL) and future market potential, were also evaluated and found to be positive for the BladeBridge technology. Full article

Bionic airfoils are an effective method to improve aerodynamic performance and reduce the noise of wind turbine blades. To explore the impact of the lower surface of bird wing airfoils on the aerodynamic performance and noise of blades, this study combines the upper surface of the NACA0018 airfoil with the lower surfaces of the teal, long-eared owl, and sparrowhawk (CBA-T, CBA-O, CBA-S) to create three new composite bionic airfoils (CBAs). The aerodynamic performance of these airfoils is evaluated, and the CBA-O airfoil is identified as having the best aerodynamic characteristics. A comparison of the noise and vortex structures of the CBA-O, owl wing airfoil, and NACA0018 is conducted, and the mechanisms behind the CBA-O airfoil performance improvement and noise reduction are explored. The results indicate that the CBAs enhance the aerodynamic performance of the airfoils. Before stall, the aerodynamic performance of the CBA-O improves the lift-to-drag ratio by 12.7% and 119.7% compared to the owl and NACA0018 airfoils, with its average SPL significantly lower than that of the NACA0018. The CBA-O has smaller vortex sizes at the trailing-edge, and the wake vortex develops more stably, effectively reducing both surface radiation noise and wake noise. Full article

With the deployment of mega-constellations, the proliferation of on-orbit Resident Space Objects (RSOs) poses a severe challenge to Space Situational Awareness (SSA). RSOs produce elongated and stripe-like signatures in long-exposure imagery as a result of their relative orbital motion. The accurate detection of these signatures is essential for critical applications like satellite navigation and space debris monitoring. However, on-orbit detection faces two challenges: the obscuration of dim RSOs by complex stray light interference, and their dense overlapping trajectories. To address these challenges, we propose the Shape-Aware Attention Network (SAANet), establishing a unified Shape-Aware Paradigm. The network features a streamlined Shape-Aware Feature Pyramid Network (SA-FPN) with structurally integrated Two-way Orthogonal Attention (TTOA) to explicitly model linear topologies, preserving dim signals under intense stray light conditions. Concurrently, we propose an Adaptive Linear Oriented Bounding Box (AL-OBB) detection head that leverages a Joint Geometric Constraint Mechanism to resolve the ambiguity of regressing targets amid dense, overlapping trajectories. Experiments on the AstroStripeSet and StarTrails datasets demonstrate that SAANet achieves state-of-the-art (SOTA) performance, achieving Recalls of 0.930 and 0.850, and Average Precisions (APs) of 0.864 and 0.815, respectively. Full article

Sequential twin-tunnel excavation has become increasingly common as urban rail networks expand, making both deformation control and construction-phase carbon management essential for sustainable underground development. This study investigates the spatiotemporal development of ground settlement induced by parallel Earth Pressure Balance shield tunnelling in a twin-tunnel section of the Hangzhou Metro, based on long-term field monitoring. The settlement process is divided into three stages—immediate construction settlement, time-dependent additional settlement, and long-term consolidation—each associated with distinct levels of energy input, grouting demand, and embodied-carbon release. Peck’s Gaussian function is used to model transverse settlement troughs, and Gaussian superposition is applied to separate the contributions of the leading and trailing tunnels. The results indicate that the trailing shield induces ahead-of-face settlement at approximately two excavation diameters and produces a deeper–narrower settlement trough due to cumulative disturbance within the overlapping interaction zone. A ratio-type indicator, the Twin-Tunnel Interaction Ratio (TIR), is proposed to quantify disturbance intensity and reveal its environmental implications. High TIR values correspond to amplified ground response, prolonged stabilization, repeated compensation grouting, and increased embodied carbon during construction. Reducing effective TIR through coordinated optimization of shield attitude, face pressure, and grouting parameters can improve both deformation control and carbon efficiency. The proposed framework links geotechnical behaviour with environmental performance and provides a practical basis for risk-controlled, energy-efficient, and low-carbon management of sequential shield tunnelling. Full article

This study investigates the aerodynamic and aeroacoustics behavior of automotive cooling modules in both conventional internal combustion engine (ICE) vehicles and electric vehicles (EVs), with a particular focus on installation effects. Numerical simulations based on the Lattice Boltzmann Method (LBM) are conducted to analyze noise generation mechanisms and flow characteristics across four configurations. The study highlights the challenges of adapting classical cooling module components to EV setups, emphasizing the influence of heat exchanger (HE) placement and duct geometry on noise levels and flow dynamics. The results show that the presence of the HE smooths the upstream flow, improves rotor loading distribution and disrupts long, coherent vortical structures, thereby reducing tonal noise. However, the additional resistance introduced by the HE leads to increased rotor loading and enhanced leakage flow through the shroud-rotor gap. Despite these effects, the overall sound pressure level (OASPL) remains largely unchanged, maintaining a similar magnitude and dipolar directivity pattern as the configuration without the HE. In EV modules, the inclusion of ducts introduces significant flow disturbances and localized pressure fluctuations, leading to regions of high flow rate and rotor loading. These non-uniform flow conditions excite duct modes, resulting in troughs and humps in the acoustic spectrum and potentially causing resonance at the blade-passing frequency, which increases the amplitude in the lower frequency range. Analysis of the loading force components reveals that rotor loading is primarily driven by thrust forces, while duct loading is dominated by lateral forces. Across all configurations, fluctuations at the leading and trailing edges of the rotor are observed, originating from the blade tip and extending to approximately mid-span. These fluctuations are more pronounced in the EV module, identifying it as the dominant source of pressure disturbances. The numerical results are validated against experimental data obtained in the anechoic chamber at the University of Sherbrooke and show good agreement. The relative trends are accurately predicted at lower frequencies, with slight over-prediction, and closely match the experimental data at mid-frequencies. Full article

Background/Objectives: Exercise supports physical and cognitive health through neurotrophin-mediated pathways, with irisin playing a key role in neuroprotection and synaptic plasticity. As adolescence represents a period of heightened neuroplasticity and metabolic adaptation, determining how different exercise modalities influence neurotrophic and cognitive responses is critical for health promotion in youth. This study aimed to compare the acute effects of low-intensity continuous training (LICT), short-interval high-intensity interval training (SI-HIIT), and long-interval HIIT (LI-HIIT) on circulating irisin levels and executive function in healthy late adolescent males. Methods: Eleven participants completed all conditions in a randomized crossover design with a 7-day washout. Venous blood samples and the Trail-Making Test, Parts A and B (TMT-A, TMT-B) were assessed pre- and postexercise, with continuous heart rate monitoring. Results: Post-exercise irisin levels were significantly greater in both HIIT protocols (SI-HIIT, p < 0.001; LI-HIIT, p < 0.038) than in the LICT protocol. Only the SI-HIIT group presented significantly shorter TMT-A (vs. LICT, p < 0.001; vs. LI-HIIT, p = 0.016) and TMT-B (vs. LICT, p < 0.001; vs. LI-HIIT, p < 0.001) completion times post-exercise. Conclusions: A single HIIT session elicited greater increases in circulating irisin and executive function compared with LICT. These findings highlight exercise intensity and interval structure as key factors for enhancing neurocognitive health, offering valuable insight for developing early-life training strategies to promote brain health. Full article