Search Results (635)

In this case study, we used the Delta Simulation Model II (DSM2) to study the salt balance at the land–water interface in the river delta of California’s San Francisco Estuary. Drainage, a source of water and salt for adjacent channels in the study area, is affected by channel salinity. The DSM2 approach has been adopted by several hydrodynamic models of the estuary to enforce water volume balance between diversions, evapotranspiration and drainage at the land–water interface, but does not explicitly enforce salt balance. We found deviations from salt balance to be quite large, albeit variable in magnitude due to the heterogeneity of hydrodynamic and salinity conditions across the study area. We implemented a procedure that approximately enforces salt balance through iterative updates of the baseline drain salinity boundary conditions (termed loose coupling). We found a reasonable comparison with field measurements of drainage salinity. In particular, the adjusted boundary conditions appear to capture the range of observed interannual variability better than the baseline periodic estimates. The effect of the iterative adjustment procedure on channel salinity showed substantial spatial variability: locations dominated by large flows were minimally impacted, and in lower flow channels, deviations between baseline and adjusted channel salinity series were notable, particularly during the irrigation season. This approach, which has the potential to enhance the simulation of extreme salinity intrusion events (when high channel salinity significantly impacts drainage salinity), is essential for robustly modeling hydrodynamic conditions that pre-date contemporary water management infrastructure. We discuss limitations associated with this approach and recommend that—for this case study—further improvements could best be accomplished through code modification rather than coupling of transport and island water balance models. Full article

Nokoué Lake is located in the south of Benin and is fed by the Ouémé and Sô Rivers. Its hydrosedimentary dynamics were modelled using Telemac2D, incorporating the main environmental factors of this complex ecosystem. The simulations accounted for flow rates and suspended solids concentrations during periods of high and low water. The main factors controlling sediment transport were identified. The model was validated using field measurements of water levels and suspended solids. The results show that the north–south current velocity ranges from 0.5 to 1 m/s during periods of high water and 0.1 to 0.5 m/s during low-water periods. Residual currents are influenced by rainfall, river discharge, and tides. Complex circulation patterns are caused by increased river flow during high water, while tides dominate during low water and transitional periods. The northern, western, and south-eastern parts of the lake have weak residual currents and are, therefore, deposition zones for fine sediments. The estimated average annual suspended solids load for 2022–2023 is 17 Mt. The model performance shows a strong agreement between the observed and simulated values: R² = 0.91 and NSE = 0.93 for water levels and R² = 0.86 and NSE = 0.78 for sediment transport. Full article

With the advancement of intelligent transportation systems, vehicle trajectory data have become a key component in areas like traffic flow prediction, route planning, and traffic management. However, high-quality, publicly available trajectory datasets are scarce due to concerns over privacy, copyright, and data collection costs. The lack of data creates challenges for training machine learning models and optimizing algorithms. To address this, we propose a new method for generating synthetic vehicle trajectory data, leveraging traffic flow characteristics and road maps. The approach begins by estimating hourly traffic volumes, then it uses the Poisson distribution modeling to assign departure times to synthetic trajectories. Origin and destination (OD) distributions are determined by analyzing historical data, allowing for the assignment of OD pairs to each synthetic trajectory. Path planning is then applied using a road map to generate a travel route. Finally, trajectory points, including positions and timestamps, are calculated based on road segment lengths and recommended speeds, with noise added to enhance realism. This method offers flexibility to incorporate additional information based on specific application needs, providing valuable opportunities for machine learning in intelligent transportation systems. Full article

A VOC-contaminated shallow aquifer in an industrial site was investigated to evaluate its potential for natural attenuation. The shallow groundwater aquifer beneath the industrial site has been contaminated by dissolved volatile organic compounds (VOCs) such as trichloroethylene (TCE), cis-1,2-dichloroethylene (cis-DCE), and vinyl chloride (VC) for more than three decades. Monitoring and investigation were implemented during 2011–2024, aiming to propose future groundwater aquifer management strategies. This study included groundwater borehole investigation, well installation monitoring, hydraulic head measurements, slug tests, groundwater samplings, and microbial analyses. Microbial investigations identified the predominant group of microorganisms of Proteobacteria, indicating biodegradation potential, as demonstrated by the presence of cis-DCE and VC. BIOSCREEN was used to evaluate the process of natural attenuation, incorporating site-specific parameters. A two-layer groundwater flow model was developed using MODFLOW with hydraulic conductivities obtained from slug tests. The site has an average hydraulic head of 259.6 m amsl with a hydraulic gradient of 0.026, resulting in an average groundwater flow velocity of 11 m/y. Hydraulic conductivities were estimated during model calibration using the PEST pilot point technique. A reactive transport model, RT3D, was used to simulate dissolved TCE transport over 30 years, which can undergo sorption as well as biodegradation. Model calibration demonstrated a satisfactory fit between observed and simulated groundwater heads with a root mean square error of 0.08 m and a correlation coefficient (r) between measured and simulated heads of 0.81, confirming the validity of the hydraulic conductivity distribution. The TCE plume continuously degraded and gradually migrated southward, generating a cis-DCE plume. The concentrations in both plumes decreased toward the end of the simulation period at Source 1 (located upstream), while BIOSCREEN results confirmed ongoing natural attenuation primarily by biodegradation. The integrated MODFLOW-RT3D-BIOSCREEN approach effectively evaluated VOC attenuation and plume migration. However, future remediation strategies should consider enhanced bioremediation to accelerate contaminant degradation at Source 2 and ensure long-term groundwater quality. Full article

This study proposes an adaptive car-following model based on the unscented Kalman filter algorithm to enable coordinated speed control in vehicle platoons and to address key limitations present in conventional car-following models. Traditional models generally assume a fixed maximum speed within the optimal velocity function, which constrains effective platoon speed regulation across road segments with varying speed limits and lacks adaptability to dynamic scenarios such as changes in the platoon leader’s speed or substitution of the lead vehicle. The proposed adaptive model utilizes state estimation based on the unscented Kalman filter to dynamically identify each vehicle’s maximum achievable speed and to adjust inter-vehicle constraints, thereby enforcing a unified speed reference across the platoon. By estimating these maximum speeds and transmitting them to individual follower vehicles via vehicle-to-vehicle communication, the model promotes smooth acceleration and deceleration behavior, reduces headway variability, and mitigates shockwave propagation within the platoon. Simulation studies—covering both single-leader acceleration and intermittent acceleration scenarios—demonstrate that, compared with conventional car-following models, the adaptive model based on the unscented Kalman filter achieves superior speed synchronization, improved headway stability, and smoother acceleration transitions. These enhancements lead to substantial improvements in traffic flow efficiency and string stability. The proposed approach offers a practical solution for coordinated platoon speed control in intelligent transportation systems, with promising application prospects for real-world implementation. Full article

Fully mechanized mining involves high energy consumption, particularly during cutting and transportation. Scraper conveyors, crucial for coal transport, face energy efficiency challenges due to the lack of accurate dynamic coal flow models, which restricts precise energy estimation and optimization. This study constructs dynamic coal flow and scraper conveyor energy efficiency models to analyze the impact of multiple variables on energy consumption and lump coal rate. A dynamic coal flow model is developed through theoretical derivation and EDEM simulations, validated for parameter settings, boundary conditions, and numerical methods. The multi-objective optimization model for energy consumption is solved using the NSGA-II-ARSBX algorithm, yielding a 33.7% reduction in energy consumption, while the lump coal area is reduced by 27.7%, indicating a trade-off between energy efficiency and coal fragmentation. The analysis shows that increasing traction speed while decreasing scraper chain and drum speeds effectively lowers energy consumption. Conversely, simultaneously increasing both chain and drum speeds helps to maintain lump coal size. The final optimization scheme demonstrates this balance—achieving improved energy efficiency at the cost of increased coal fragmentation. Additional results reveal that decreasing traction speed while increasing chain and drum speeds results in higher energy consumption, while increasing traction speed and reducing chain/drum speeds minimizes energy use but may negatively affect lump coal integrity. Full article

Optimizing ambulance deployment is a critical task in emergency medical services (EMS), as it directly affects patient outcomes and system efficiency. This study proposes a cyber-secure, machine learning-based framework for predicting region-specific ambulance allocation and response times across South Korea. The model integrates heterogeneous datasets—including demographic profiles, transportation indices, medical infrastructure, and dispatch records from 229 EMS centers—and incorporates real-time IoT streams such as traffic flow and geolocation data to enhance temporal responsiveness. Supervised regression algorithms—Random Forest, XGBoost, and LightGBM—were trained on 2061 center-month observations. Among these, Random Forest achieved the best balance of accuracy and interpretability (MSE = 0.05, RMSE = 0.224). Feature importance analysis revealed that monthly patient transfers, dispatch variability, and high-acuity case frequencies were the most influential predictors, underscoring the temporal and contextual complexity of EMS demand. To support policy decisions, a Lasso-based simulation tool was developed, enabling dynamic scenario testing for optimal ambulance counts and dispatch time estimates. The model also incorporates the coefficient of variation (CV) of workload intensity as a performance metric to guide long-term capacity planning and equity assessment. All components operate within a cyber-secure architecture that ensures end-to-end encryption of sensitive EMS and IoT data, maintaining compliance with privacy regulations such as GDPR and HIPAA. By integrating predictive analytics, real-time data, and operational simulation within a secure framework, this study offers a scalable and resilient solution for data-driven EMS resource planning. Full article

Bamboo residues represent an abundant, renewable biomass feedstock that can be converted into hard carbon—an emerging anode material for sodium-ion batteries. This study presents a detailed techno-economic analysis of hard carbon production from bamboo residues across China’s ten most bamboo-rich provinces. Regional feedstock availability was estimated from provincial production statistics, while average transportation distances were derived using a square-root-area-based approximation method. The process includes hydrothermal pretreatment, acid washing, carbonization, graphitization, and ball milling. Material and energy inputs were estimated for each stage, and both capital and operating expenses were evaluated using a discounted cash flow model assuming a 15% internal rate of return. The resulting minimum selling price of bamboo-derived hard carbon ranges from 14.47 to 18.15 CNY/kg. Assuming 10% of bamboo residues can be feasibly collected and processed, these ten provinces could collectively support an annual hard carbon production capacity of approximately 1.04 million tons. The results demonstrate that bamboo residues are a strategically distributed and underutilized resource for producing cost-competitive hard carbon at scale, particularly in provinces with existing bamboo industries and supply chains. Full article

We investigate the transport coefficients $\alpha$ and $\beta$ in plasma systems with varying Reynolds numbers while maintaining a unit magnetic Prandtl number (${Pr}_{\mathrm{M}}$). The $\alpha$ and $\beta$ tensors parameterize the turbulent electromotive force (EMF) in terms of the large-scale magnetic field $\overline{\mathbf{B}}$ and current density as follows: $⟨\mathbf{u}\times \mathbf{b}⟩=\alpha \overline{\mathbf{B}}-\beta \nabla \times \overline{\mathbf{B}}$. In astrophysical plasmas, high fluid Reynolds numbers ($Re$) and magnetic Reynolds numbers ($R{e}_{\mathrm{M}}$) drive turbulence, where $Re$ governs flow dynamics and $R{e}_{\mathrm{M}}$ controls magnetic field evolution. The coefficients ${\alpha }_{\mathrm{semi}}$ and ${\beta }_{\mathrm{semi}}$ are obtained from large-scale magnetic field data as estimates of the $\alpha$ and $\beta$ tensors, while ${\beta }_{\mathrm{theo}}$ is derived from turbulent kinetic energy data. The reconstructed large-scale field $\overline{B}$ agrees with simulations, confirming consistency among $\alpha$, $\beta$, and $\overline{B}$ in weakly nonlinear regimes. This highlights the need to incorporate magnetic effects under strong nonlinearity. To clarify $\alpha$ and $\beta$, we introduce a field structure model, identifying $\alpha$ as the electrodynamic induction effect and $\beta$ as the fluid-like diffusion effect. The agreement between our method and direct simulations suggests that plasma turbulence and magnetic interactions can be analyzed using fundamental physical quantities. Moreover, ${\alpha }_{\mathrm{semi}}$ and ${\beta }_{\mathrm{semi}}$, which successfully reproduce the numerically obtained magnetic field, provide a benchmark for future theoretical studies. Full article

Estimating bed load in rivers is a critical aspect of river engineering. Numerous methods have been developed to quantify bed load transport, often yielding varying results depending on the bed surface texture and grain size. This study aims to investigate how vegetation on channel banks and bed material particle size influence bed load transport, bed shear stress, velocity distribution, and the Shields parameter. It also examines the impact of geometric changes in the channel cross-section on bed load transport capacity. To address these objectives, a novel simulation method was developed to analyze the effects of vegetated banks, bed material size, and channel geometry. Field investigations were carried out in two reaches of the Taleghan River in Iran—one with vegetated banks and one without. Complementary flume experiments were conducted at two scales, incorporating vegetation on the sidewalls. Results showed that Shields parameter distribution corresponded with bed load distribution across cross-sections. Increase in flow rate and the Shields parameter led to higher bedload transport rates. Near vegetated banks, flow velocity, shear stress, and bedload transport were significantly reduced, with velocity profiles showing distinct variations compared to non-vegetated sections. Full article

As air transportation demand continues to rise, congestion in air route networks has seriously compromised the safe and efficient operation of air traffic. Few studies have examined the spatiotemporal characteristics of congestion propagation under different time lag conditions. To address this gap, this study proposes a cross-segment congestion-propagation causal time-lag analysis framework. First, to account for the interdependency across segments in air route networks, we construct a point–line congestion state assessment model and introduce the FCM-WBO algorithm for precise congestion state identification. Next, the Multi-Channel Attention DSNG-BiLSTM model is designed to estimate the causal weights of congestion propagation between segments. Finally, based on these causal weights, two indicators—CPP and CPF—are derived to analyze the spatiotemporal characteristics of congestion propagation under various time lag levels. The results indicate that our method achieves over 90% accuracy in estimating causal weights. Moreover, the propagation features differ significantly in their spatiotemporal distributions under different time lags. Spatially, congestion sources tend to spread as time lag increases. We also identify segments that are likely to become overloaded, which serve as the primary receivers of congestion. Temporally, analysis of time-lag features reveals that because of higher traffic flow during peak periods, congestion propagates 36.92% more slowly than during the early-morning hours. By analyzing congestion propagation at multiple time lags, controllers can identify potential congestion sources in advance. They can then implement targeted interventions during critical periods, thereby alleviating congestion in real time and improving route-network efficiency and safety. Full article

In this study, we hypothesized and tested that physical parameters (flow, transport, and water depth) have a significantly greater influence on phosphorus (P) retention in wetlands than biogeochemical factors. Specifically, we evaluated the null hypothesis (H₀), that no significant difference exists between the influence of physical and biogeochemical parameters on phosphorus retention, against the alternative hypothesis (H₁), that physical parameters are more influential. We investigated two large wetlands (stormwater treatment areas, STAs) in south Florida: STA34C2A, which is dominated by emergent aquatic vegetation (EAV), and STA2C3, which is dominated by submerged aquatic vegetation (SAV). Building on Part 1, which mapped spatial flow resistance (K) as a vegetation-type-independent proxy for hydraulic resistance, this study (Part 2) applied a novel high-frequency (hourly) data approach with time-lagged regression modeling to estimate total phosphorus (TP) outflow concentrations. The key variables included inflow TP concentration, vegetation volume, water depth, nominal hydraulic residence time (HRT), hydraulic loading rate (HLR), phosphorus loading rate (PLR), and time lag (“P-spiral”). Multi-linear regression models for each STA identified inflow TP and water depth, a controllable physical parameter, as the most significant predictors of TP outflow, while the hour of day (a temporal proxy) contributed the least. Optimal model performance occurred with lag times of 8 and 9 days, producing R² values of 0.5788 (STA34C2A) and 0.5354 (STA2C3). In STA34C2A, high TP retention was linked to shallow water depth, dense EAV, and low K values, indicating high hydraulic resistance and reduced short circuiting. In contrast, lower TP retention in STA2C3 was associated with longer flow paths, sparse SAV, and high K values, suggesting less hydraulic control despite similar nominal HRTs. These results provide empirical support for rejecting the null hypothesis (H₀) in favor of the alternative (H₁): physical parameters, especially water depth, hydraulic resistance, and inflow dynamics, consistently exert a stronger influence on P removal than biogeochemical factors such as PLR. The findings highlight the importance of optimizing flow and depth controls in wetland design and management to enhance phosphorus removal efficiency in large, constructed wetland systems. Full article

This study focuses on the systematic conservation of historical architectural heritage in Heilongjiang Province, particularly addressing the challenges of point-based protection and spatial fragmentation. It explores the construction of a connected and conductive heritage corridor network, using historical building clusters across the province as empirical cases. A comprehensive analytical framework is established by integrating the nearest neighbor index, kernel density estimation, minimum cumulative resistance (MCR) model, entropy weighting, circuit theory, and network structure metrics. Kernel density analysis reveals a distinct spatial aggregation pattern, characterized by “one core, multiple zones.” Seven resistance factors—including elevation, slope, land use, road networks, and service accessibility—are constructed, with weights assigned through an entropy-based method to generate an integrated resistance surface and suitability map. Circuit theory is employed to simulate cultural “current” flows, identifying 401 potential corridors at the provincial, municipal, and district levels. A hierarchical station system is further developed based on current density, forming a coordinated structure of primary trunks, secondary branches, and complementary nodes. The corridor network’s connectivity is evaluated using graph-theoretic indices (α, β, and γ), which indicate high levels of closure, structural complexity, and accessibility. The results yield the following key findings: (1) Historical architectural resources in Heilongjiang demonstrate significant coupling with the Chinese Eastern Railway and multi-ethnic cultural corridors, forming a “one horizontal, three vertical” spatial configuration. The horizontal axis (Qiqihar–Harbin–Mudanjiang) aligns with the core cultural route of the railway, while the three vertical axes (Qiqihar–Heihe, Harbin–Heihe, and Mudanjiang–Luobei) correspond to ethnic cultural pathways. This forms a framework of “railway as backbone, ethnicity as wings.” (2) Comparative analysis of corridor paths, railways, and highways reveals structural mismatches in certain regions, including absent high-speed connections along northern trunk lines, insufficient feeder lines in secondary corridors, sparse terminal links, and missing ecological stations near regional boundaries. To address these gaps, a three-tier transportation coordination strategy is recommended: it comprises provincial corridors linked to high-speed rail, municipal corridors aligned with conventional rail, and district corridors connected via highway systems. Key enhancement zones include Yichun–Heihe, Youyi–Hulin, and Hegang–Wuying, where targeted infrastructure upgrades and integrated station hubs are proposed. Based on these findings, this study proposes a comprehensive governance paradigm for heritage corridors that balances multi-level coordination (provincial–municipal–district) with ecological planning. A closed-loop strategy of “identification–analysis–optimization” is developed, featuring tiered collaboration, cultural–ecological synergy, and multi-agent dynamic evaluation. The framework provides a replicable methodology for integrated protection and spatial sustainability of historical architecture in Heilongjiang and other cold-region contexts. Full article

In this study, a correction procedure for ship mass and its longitudinal location of center of gravity suitable for a simulation environment is proposed in OpenFOAM v6.0. The concept is implemented ensuring static equilibrium and an approximately zero-pitch moment on the ship before the simulation. The viscous flow field around the ship in calm water is simulated using the VOF (Volume of Fluid) free surface two-phase and SST (Shear Stress Transport) k–ω turbulence models. Using static mesh, the resistance error of medium and fine grids is 4%, on average, against the experimental value. As the sinkage and trim are predicted using dynamic mesh, the increasing ship’s resistance causes larger errors, except for the container ship. Through the proposed correction, the ship’s vertical motions are significantly improved, and the resistance error decreases for the dynamic simulation. For the container ship, the error of resistance and motion achieved is less than 1%. The sinkage and trim errors improve tremendously for the tanker and bulk carrier, and the resistance errors are reduced slightly, by less than 3%. In the end, the detailed flow field is analyzed, as well as the ship wave-making pattern and the nominal wake velocity distribution, and these are compared with the measurement data available. The characteristics of the flow phenomena are successfully modeled. The resistance value for each hull form satisfies the requirement of Verification and Validation, and the uncertainty values are estimated. Full article

The natural flow of sediment in the Ebro River has been altered by a variety of factors that have impacted the geomorphic and ecological balance of the delta. Ongoing restoration efforts in the delta would benefit if the flow of sediment in the river could be increased. Understanding the dynamics of sediment flow in the Ebro River is an important component in the design of effective management strategies for the Ebro Delta. This study estimates the sediment transport potential of the Ebro River under current and future conditions through numerical simulation. Historical data from the late 19th century indicate that the river once transported up to 28.1 million tons of sediment per year. However, due to water abstractions and flow regulations, the current sediment transport capacity is limited to 9 million tons annually, a reduction of 67%. Future projections suggest further decreases in flow and sediment transport potential, with reductions of up to 30% by 2060 and 50% by 2100, depending on climate conditions and water management practices. The findings underscore the need for integrated management strategies to mitigate the impacts of reduced sediment flow, emphasizing the importance of restoring sediment transport as a crucial component of the delta restoration efforts. Full article