Search Results (36,254)

With the acceleration of urbanization, public transit systems face prominent challenges, including insufficient passenger flow prediction accuracy and low scheduling efficiency. This study analyzes passenger flow variation patterns from both spatial and temporal dimensions, constructs spatiotemporal matrices, and employs matrix dimensionality reduction methods to extract key features. We propose a passenger flow prediction model based on GCN-AM-LSTM and a dynamic real-time intelligent scheduling strategy. For passenger flow prediction, the model first utilizes Graph Convolutional Networks (GCNs) to extract spatial features of the transit network, then employs Attention Mechanism-enhanced Long Short-Term Memory networks (AM-LSTM) to perform weighted extraction of temporal features, and finally integrates external factors such as weather conditions to generate prediction outputs. For scheduling optimization, a dynamic real-time scheduling mode is adopted: the foundational framework optimizes dynamic departure timetables using a multi-objective particle swarm optimization algorithm, which is then combined with real-time passenger flow data to adjust departure intervals at the route level and implement stop-skipping strategies at the station level. Validation was conducted using Xiamen BRT Line 1 as a case study. Experimental results demonstrate that the proposed GCN-AM-LSTM prediction model reduces Mean Absolute Error (MAE) by 14% and 22% compared to CNN and LSTM models, respectively, achieving significantly improved prediction accuracy. Regarding scheduling optimization, the number of departures decreased by 15.24%, passenger waiting time costs were reduced by 3.7%, and transit operating costs decreased by 3.19%, effectively balancing service quality and operational efficiency. Full article

The increasing frequency and intensity of extreme climate events have posed more geohazards worldwide. It is therefore crucial to quantify and map risk to reduce disaster-related losses. The main objective of this study is to propose a quantitative framework to conduct risk assessment of buildings and infrastructures impacted by geohazards. A debris flow hazard in Tianjin, North China was taken as a case study. A physically based model and the Gumbel extreme value distribution were utilized to construct a range of extreme rainfall and runoff scenarios. The FLO-2D and ABAQUS software were subsequently employed to simulate the surging behavior of the debris flow and assess the structural vulnerability of buildings, respectively. Furthermore, the number of elements at risk and economic values were estimated to generate risk maps. The results revealed that variations in peak discharge in the channel evidently affected flow velocity and depth, thus elevating the debris flow intensity and the likelihood of the materials threatening buildings. The stiffness degradation of concrete was strategically used as the indicator to quantify structure vulnerability and effectively present the dynamic responses under the impacts of the debris flow. Under a 100-year return period rainfall scenario, the proportion of very high- and high-risk areas reached 31%, with the estimated economic loss approximately ¥167.7 million. This highlighted the critical role that extreme rainfall played in shaping both the spatial distribution and severity of debris flow risks. The proposed method provides a scientific basis for enhancing the resilience of mountainous regions to compound natural disasters exacerbated by climate change. Full article

Clouds are a key factor affecting solar radiation, and their dynamic variations directly cause uncertainty and fluctuations in photovoltaic (PV) power output. To improve PV power prediction accuracy, this paper proposes an enhanced short-term photovoltaic power forecasting approach based on a hybrid neural network architecture using features extracted from satellite cloud images. First, a dual-layer image fusion method is developed for satellite cloud images from different wavelengths and spectral bands, effectively improving fusion accuracy. Second, texture descriptors derived from the Gray-Level Co-occurrence Matrix and multiscale information obtained via the wavelet transform are employed for feature extraction from fused images. Combined with a residual network (ResNet), an optical flow method, as well as an LSTM-based temporal modeling module, multidimensional features of the predicted cloud images are obtained. An improved Bayesian optimization (IBO) algorithm is then employed to derive the optimal fused features, thereby improving the matching between cloud image features and PV power. Third, an enhanced hybrid architecture integrating a convolutional neural network and long short-term memory units with a multi-head self-attention mechanism is developed. Numerical weather prediction (NWP) meteorological features are incorporated, and a tilted irradiance model is introduced to calculate the solar irradiance received by PV modules for use in near-term photovoltaic power forecasting. Finally, measurements collected at a photovoltaic power plant located in Hebei Province are used to validate the proposed method. The results show that, relative to the SA-CNN-MSA-LSTM and BO-CNN-LSTM models, the developed approach lowers the RMSE to an extent of 22.56% and 4.32%, while decreasing the MAE by 24.84% and 5.91%, respectively. Overall, the proposed model accurately captures the characteristics of predicted cloud images and effectively improves PV power prediction accuracy. Full article

Stratigraphic forward modeling (SFM) is a numerical approach used to reconstruct sedimentary basin evolution by simulating the infilling and tectonic evolution process of strata. The challenge is that existing approaches inevitably require trade-offs among modeling fidelity and computational cost. We present a novel clastic stratigraphic forward modeling (CSFM) approach to reducing computational cost while retaining key flow and transport behaviors relevant to stratigraphic architecture. In CSFM, Lagrangian water particles affect momentum and sediment, while a fixed Eulerian grid stores topographic elevation and lithologic fractions. A simplified form of the Navier–Stokes equations is proposed to compute the trajectories of fluid particles, which can greatly reduce the computational cost. Sediment dynamics are represented by coupled suspended load and bedload modules. To validate CSFM, we constructed a synthetic alluvial fan model and performed stratigraphic forward modeling on it. Five lake-level cycles were imposed and results showed that cyclic sand–clay couplets and isolated channel sand bodies were formed during repeated progradation and backstepping. These results are consistent with established sedimentological knowledge, confirming the geological plausibility of CSFM. Full article

Ground vortex formation beneath aeroengine intakes during near-ground operations represents an energy-related aerodynamic issue, as it degrades inlet flow quality, induces pressure distortion, and reduces the effective utilization of incoming kinetic energy. This study investigates the unsteady characteristics of ground vortex flow under headwind conditions and its influence on foreign object ingestion (FOI) in an aeroengine intake. Three-dimensional unsteady Reynolds-averaged Navier–Stokes (URANS) simulations coupled with a Lagrangian Discrete Phase Model (DPM) are employed to resolve the interaction between intake-induced vortices and dispersed particles near the ground. The results indicate that the ground vortex rapidly develops into a quasi-periodic state, generating significant unsteady total pressure distortion at the intake face, with peak fluctuations reaching approximately 10% of the mean value. This flow non-uniformity reflects a deterioration of inlet energy distribution and is detrimental to downstream compression efficiency. Particle ingestion behavior is strongly dependent on particle density and diameter. Low-density and small particles are more readily entrained into the vortex core and ingested, whereas particles with higher density or larger size exhibit increased inertia and reduced sensitivity to vortex-induced energy transport. The ingestion region is biased toward the lower portion of the intake, consistent with the vortex core location. These findings provide insight into vortex-induced energy distortion and FOI mechanisms, offering guidance for improving aeroengine intake design and energy-efficient operation during near-ground conditions. Full article

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs. Full article

The increasing cost of energy and the need for low-carbon solutions have strengthened interest in wastewater as a stable and underutilized source of recoverable heat. This study assesses the technical feasibility, economic viability, and environmental benefits of a wastewater heat recovery system based on a case study of the Gorzyce municipal wastewater treatment plant in Poland. Water-to-water heat pump configurations and application scenarios are analyzed together with data-driven forecasting of wastewater outflow using artificial neural networks (MLP and RBF). Operational data from 2025 were used to estimate thermal potential and support system sizing. RBF networks provided more accurate flow forecasts than MLP models, improving reliability of energy recovery planning. Results show that even with a 1 K cooling depth, the annual heat recovery potential reaches about 1.16 GWh. The proposed heat pump system achieved the COP values of 3.0–3.4 and seasonal COP around 3.2, confirming high technical performance supported by stable wastewater temperatures. The recovered heat can fully cover the facility’s heating demand, demonstrating clear technical feasibility. The economic analysis indicates annual savings of about EUR 2310 compared to gas heating, with a simple payback period of roughly 13 years, reduced to 7–8 years when combined with on-site photovoltaics. Environmental benefits include CO₂ emission reductions of about 5.5 tones per year. Overall, wastewater heat recovery supported by predictive modeling and renewable electricity is a practical, cost-effective, and environmentally friendly solution for municipal infrastructure. Full article

The continuous increase in power density of electronic devices imposes stringent requirements on the design of lightweight, high-efficiency heat sinks. To overcome the limitations of conventional single-gradient or monomaterial heat sinks—namely, their suboptimal heat-transfer efficiency and poor structural adaptability—this study proposes a dual-gradient, triply periodic minimal surface (TPMS)-based multimaterial heat sink architecture fabricated from CuCrZr and AlSi₇Mg. Thermal performance was quantified experimentally using infrared thermography, while the underlying flow-field mechanisms were investigated numerically via computational fluid dynamics (CFD) simulations employing the standard k–ε turbulence model. With the TPMS material volume ratio fixed at 3:3 (CuCrZr:AlSi₇Mg), the Z-axis gradient configuration P-Z4-5 delivered the best overall thermal performance, achieving a heat-transfer coefficient (HTC) of 1557.63 W·m⁻²·K⁻¹ and a thermal resistance as low as 1.83 K·W⁻¹ at an inlet velocity of 5 m·s⁻¹. In contrast, the Y-axis gradient configuration P-Y3-6 yielded the most uniform temperature distribution, exhibiting a maximum surface temperature difference of only 21.5 °C under the same inlet condition. Velocity and turbulence distribution analyses reveal that the dual-gradient design enhances both the narrow-tube effect and flow-induced disturbances; furthermore, increasing the inlet velocity from 5 m·s⁻¹ to 21.65 m·s⁻¹ significantly intensifies vorticity-driven fluid mixing. Among all configurations evaluated, P-Z4-5 exhibited the highest j/f factor (i.e., the ratio of Colburn j-factor to Fanning friction factor), followed by P-Z3.5-5.5 and P-Z3-6. These findings establish a promising new pathway for the development of high-performance, lightweight heat sinks tailored for next-generation high-power electronics. Full article

Carbon dioxide (CO₂) emissions resulting from natural gas flaring are significant contributors to atmospheric greenhouse gases, posing a substantial risk to the Earth’s climate by exacerbating global warming. As a response, both the oil industry and government authorities are actively exploring cost-effective strategies to address this issue through carbon capture, utilization, and storage (CCUS), as well as reducing natural gas flaring and CO₂ leaks in the oil fields to mitigate the adverse consequences of greenhouse gas emissions. This study presents a numerical investigation of CO₂ utilization for enhanced oil recovery (EOR) and associated CO₂ retention in unconventional reservoirs, using the Bakken Formation as a representative case. A compositional reservoir model is developed to simulate CO₂ Huff-n-Puff (HnP) processes in a fractured horizontal well. The model incorporates dual-porosity and dual-permeability formulations, fluid–rock interactions, and an equation-of-state-based compositional framework to capture multiphase flow behavior. Key operational parameters, including reservoir pressure, injection rate, injection duration, and CO₂ molecular diffusion, are systematically evaluated to assess their impact on oil recovery and CO₂ retention. The results show that lower bottom-hole pressures enhance oil recovery through increased drawdown, while operating pressures near the minimum miscibility pressure (MMP) improve CO₂ solubility and overall retention. Extended injection durations and higher diffusion coefficients increase CO₂ dissolution in the oil phase but exhibit diminishing marginal benefits beyond an optimal injection time. The study quantifies residual and solubility trapping mechanisms during the operational timeframe of CO₂-EOR and provides mechanistic insights into optimizing CO₂-HnP performance in tight formations. The proposed framework establishes a technical basis for integrating CO₂-EOR with emission mitigation strategies in unconventional reservoirs. Full article

As China’s electric vehicle (EV) market transitions from rapid growth to high-quality development, competition among brands is shifting from purely technological aspects to more holistic expressions involving spatial design and brand positioning. This study investigates the coupling mechanism between spatial aesthetics and brand positioning and its influence on consumer purchase intention. Drawing on Gibson’s theory of spatial aesthetics and the Technology Acceptance Model (TAM), we develop a theoretical framework that integrates perceived usefulness and perceived ease of use of spatial aesthetics with brand cognition. Empirical analysis is conducted using coupling coordination degree modeling and multiple regression, based on 1576 valid questionnaires collected from 4S dealerships of nine major EV brands in China. The results indicate that spatial aesthetic elements—such as environmental visual flow, invariance, and affordance—positively affect consumers’ perceptions of technology and brand recognition. Furthermore, the degree of coupling between spatial aesthetics and brand positioning perceptions significantly enhances purchase intention, particularly among consumers of safety-oriented and luxury EV models. These findings confirm the synergistic effect of spatial experience and brand strategy in shaping consumer behavior, enriching the theoretical understanding of EV consumer psychology and offering practical guidance for strategic decision-making in product design and brand communication. Full article

The rapid digitalization of power systems and the growing penetration of variable renewable energy sources have intensified the need for flexible and resilient smart-grid architectures capable of coordinating cross-sector energy flows. This review aims to provide a system-level synthesis of the artificial-intelligence-enabled integration of smart grids and green hydrogen, explicitly addressing coordination across physical infrastructure, digital control layers, market mechanisms, and environmental constraints. Following the PRISMA 2020 framework, 142 high-relevance studies published between 2010 and 2025 were systematically screened and classified into five interdependent thematic pillars: demand-side flexibility, ICT and IoT infrastructures, cybersecurity and resilience, communication and control performance, and AI-based optimization and decision-making. The synthesis reveals three principal findings. First, while core technologies such as photovoltaics, battery storage, and proton exchange membrane electrolyzers exhibit high component-level maturity, system-integration readiness remains limited by interoperability, communication latency, cybersecurity compliance, and market eligibility constraints. Second, electrolyzers can technically provide fast-response and multi-timescale flexibility services, yet their economic viability depends strongly on market product granularity, settlement intervals, and regulatory frameworks. Third, environmental and resource constraints, including water availability and material criticality, are emerging as binding factors that must be embedded directly into planning and optimization models. Overall, the review positions artificial intelligence as a cross-layer coordination mechanism that links operational control, digital observability, market participation, and sustainability boundaries, providing an integrated architecture to guide scalable and resilient smart grid–hydrogen deployment. Full article

This study investigates the separation performance of a novel hydrocyclone design incorporating side filtration flow. Experiments were conducted using black silicon carbide powder in an 18.5 mm diameter hydrocyclone, while computational fluid dynamics (CFD) simulations were performed using FLUENT to analyze the flow behavior. The cylindrical section of the hydrocyclone was modified into a porous filter column, allowing controlled side filtrate discharge. The Volume of Fluid (VOF) multiphase model and Large Eddy Simulation (LES) turbulence model were applied to capture the flow field, while the Discrete Phase Model (DPM) was used to track particle motion and assess classification efficiency. Experimental results showed that when the side filtration flow rate was approximately 1/200 of the feed flow rate, the cumulative particle size distribution at the overflow shifted toward smaller particle sizes, indicating improved separation of fine particles. Simulations further revealed an optimal side flow ratio of 0.004–0.005: higher side flow reduced rotational velocity and classification efficiency, while lower side flow provided insufficient pressure relief. Particle tracking demonstrated that side filtration reduced particle recirculation in the cylindrical region, accelerating underflow discharge. These findings highlight the potential of side filtration for enhancing hydrocyclone classification efficiency, providing quantitative insights for future design optimization. Full article

Background/Objectives: To assess the diagnostic performance of dynamic susceptibility contrast (DSC) perfusion MRI parameters and machine learning methods for differentiating intracranial dural metastases (IDMs) from meningiomas. Methods: This retrospective diagnostic accuracy study included 56 patients (mean age: 57.6 ± 11.2 years; 20 men) with dural-based intracranial lesions (65 lesions): 18 patients with IDM (27 lesions) and 38 patients with meningiomas (38 lesions). All patients underwent DSC perfusion MRI. Relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), diffusion metrics, and dynamic time–signal intensity curve parameters were extracted. Group comparisons were performed using nonparametric statistical tests. Machine learning models, including linear discriminant analysis (LDA), were developed using patient-level grouped nested cross-validation to avoid data leakage. Diagnostic performance was evaluated using out-of-fold receiver operating characteristic (ROC) analysis, calibration assessment, and clinically oriented thresholds prioritizing metastasis sensitivity. Results: rCBV_mean and rCBF_mean were significantly higher in meningiomas than in dural metastases (median rCBV_mean: 4.71 vs. 2.95; median rCBF_mean: 3.44 vs. 2.02; both p < 0.001). Diffusion metrics and dynamic perfusion parameters, including wash-in time, percentage signal recovery, and wash-out slope, did not differ significantly between groups (p > 0.05). Univariate ROC analysis demonstrated strong discrimination for both rCBF_mean (AUC: 0.82; 95% CI: 0.72, 0.90) and rCBV_mean (AUC: 0.82; 95% CI: 0.72, 0.91). An LDA model integrating rCBF_mean and rCBV_mean achieved an out-of-fold AUC of 0.81 (95% CI: 0.72, 0.89) and improved specificity (85%) at a fixed metastasis sensitivity of 85%. Conclusions: DSC perfusion MRI-derived rCBF and rCBV are robust biomarkers for differentiating IDMs from meningiomas. An interpretable machine learning model integrating these parameters improves diagnostic specificity while maintaining high sensitivity. Full article

Current research lacks systematic understanding of cross-scale correlations between micro-texture geometry and macro-lubrication behavior. This study presents a multi-scale collaborative optimization methodology for gear Micro-Textured Meshing Interface (MTMI). An objective function targeting macroscopic interfacial performance is formulated, and a topology optimization strategy is employed to achieve optimal MET configuration. The homogenization analysis captures the modulating effects of MET on local flow and stress fields, while topology optimization transcends conventional parametric geometric constraints, enabling the generation of non-regular MET topological patterns tailored to complex operating conditions, thereby ensuring optimal macroscopic ASLBC. The proposed scheme is validated through numerical simulations of two representative problems capturing distinct lubrication regimes: (1) IEL, characterizing transient load-bearing dynamics governed by temporally evolving MET configurations; and (2) ASLBC, elucidating steady-state load-bearing capacity modulation via spatially heterogeneous MET distributions. A Taylor expansion-based surrogate model is developed to efficiently explore the MET configuration design space, significantly enhancing computational efficiency and solution accuracy for multi-scale optimization. While the gradient-based algorithm cannot guarantee global optimality, extensive numerical simulations and cross-validation studies demonstrate consistent convergence toward high-performance MET configurations, with sensitivity analyses of design parameters further confirming the engineering applicability of the optimized solutions. Full article

Forests play a vital role in influencing wind flow by modifying turbulence intensity and vertical wind shear. Because wind turbines are susceptible to these conditions, accurately characterising wind flow in forested environments is vital to ensuring structural reliability and realistic energy-yield assessments. In Latvia, where approximately 51.3% of the territory is covered by forests; the likelihood of wind turbine deployment in such areas is considerable. However, wind behaviour within and above forests is complex and strongly influenced by canopy effects, which in turn affect wake dynamics, structural fatigue, and power production. Advancing research in this field is therefore crucial for improving the accuracy of wind resource assessment and supporting evidence-based engineering solutions that enable the sustainable development of wind energy. Wind conditions were evaluated using NORA3 reanalysis data. Wake effects were simulated with the Jensen wake model to estimate annual energy production (AEP), which then informed levelised cost of energy (LCOE) calculations at various hub heights. The results indicate clear seasonal variability and show that increasing hub height leads to higher AEP and lower LCOE, owing to higher wind speeds and reduced turbulence. For forest heights of 0–25 m, the AEP loss increases from 7.8% (hub height = 199 m) to 22.9% (hub height = 114 m). Higher hub heights are also less sensitive to canopy-induced variability, reducing the impact of forest-related turbulence on energy production. Full article