Search Results (67)

Aiming at the problems of strong model uncertainty, unmodeled perturbation disturbances and unknown maneuvering of space non-cooperative targets in orbital rendezvous missions, an orbit prediction and control method combining active disturbance rejection and model predictive control (ADRC-MPC) is proposed in this paper. Firstly, the relative motion dynamic equations of chaser–target are established under a geocentric inertial coordinate system, and the lumped disturbance including space perturbation and unknown evasive maneuvers of the target is expanded into extended state variable. Secondly, an extended state observer (ESO) is designed to achieve real-time accurate estimation and feedforward compensation of time-varying lumped disturbances, which mitigates adverse influences induced by J2 zonal harmonics, atmospheric drag, solar radiation pressure, and target evasive maneuvers. Thirdly, a constrained MPC strategy incorporating thrust saturation, approach corridor, and collision safety zone constraints is developed. Sufficient conditions for closed-loop input-to-state stability (ISS) are theoretically derived via the small-gain theorem under the given assumptions, which ensures the uniform ultimate boundedness (UUB) of tracking errors. Finally, numerical simulations, comparative benchmarks against conventional MPC and proportional-derivative (PD) control, and Monte Carlo robustness tests are performed. Simulation results demonstrate that the proposed ADRC-MPC framework delivers superior rendezvous precision, stronger disturbance rejection, reduced propellant consumption, and improved robustness against initial state offsets and measurement noises, laying solid theoretical and technical foundations for autonomous orbital rendezvous with space non-cooperative targets. Full article

Urban passenger hubs, such as airports and railway stations, generate concentrated last-mile demand from arriving passengers to spatially dispersed urban destinations. Fluctuating passenger arrivals and changing vehicle availability can create a mismatch between accepted demand and available service capacity. This paper aims to coordinate zone-level pricing and vehicle dispatching, so that fare-responsive accepted demand can be better aligned with available vehicle resources, while balancing operator financial performance and service reliability. Under the zonal pricing scheme, the transit operator determines a quoted zone-level fare for each service zone at every decision epoch. Newly arriving service requests accept the service when the quoted fare does not exceed their maximum acceptable per-passenger fare, after which the fare is committed. A rolling-horizon optimization model jointly determines zone-level fares and dispatching plans as request states and vehicle states evolve over time. The fare discretization property reduces the continuous pricing decision to a finite candidate zone-level fare selection problem, and a customized Rolling-Horizon Adaptive Large Neighborhood Search (RH-ALNS) algorithm is developed to solve the resulting problem efficiently. Case studies based on Nanjingnan Railway Station in Nanjing, China, demonstrate the operational value of coordinating pricing and dispatching decisions. In the baseline case, the proposed method achieves a passenger service rate of 76.75%, an accepted-passenger fulfillment rate of 96.07%, and an operating surplus of 1.145 CNY per passenger-kilometer. Holding the RH-ALNS dispatching method fixed, dynamic zonal pricing increases the objective value by 4.39%, the operating surplus per passenger-kilometer by 5.46%, and accepted-passenger fulfillment by 3.63 percentage points relative to fixed zonal fares. The findings indicate that coordinating dynamic zonal pricing with vehicle dispatching can better align accepted demand with available vehicle resources and provide practical guidance for designing reliable, resource-efficient, and financially balanced hub-based demand-responsive last-mile transit services. Full article

Connected vehicles and vehicle-to-everything (V2X) communication create new opportunities for estimating urban origin–destination (OD) demand from continuously collected mobility data. However, in realistic deployment conditions, only a fraction of vehicles may be connected, making OD reconstruction a highly underdetermined problem under low penetration rates. This paper proposes a supervised deep-learning framework that reconstructs full OD matrices from synthetic connected-vehicle data in a simulated Manhattan network from New York City. Vehicle movement information is aggregated into intra-zonal and adjacent-zone traffic counts using K-means traffic analysis zones. These partial connected-vehicle observations, represented by the zonal movement matrix, outgoing and incoming zonal-movement summaries, diagonal movement counts, and penetration-rate features, form a compact input to a conditional Multi-Layer Perceptron (MLP) that predicts the complete OD matrix of all vehicles. The training objective separates OD spatial shape from total traffic volume and adds losses on marginals, diagonal elements, and log-space reconstruction to embed basic flow-conservation properties. A single conditional MLP is trained across multiple connected-vehicle penetration-rate scenarios by appending the penetration rate $\rho$ and $\log \left(\rho \right)$ to the input representation. The model is evaluated over ten random connected-vehicle sampling seeds. Results show that the proposed estimator remains stable down to 20% penetration, with test sMAPE increasing only from $20.70\pm 0.00\%$ at full penetration to $21.71\pm 0.38\%$ at 20% penetration. Marginal and total-flow errors increase more gradually as penetration decreases, while clear degradation appears below approximately 2–1% penetration. Baseline and ablation comparisons further show that penetration-rate conditioning and the conservation-aware loss are essential for improving OD reconstruction and total-flow consistency. Within the evaluated simulated Manhattan scenarios, these findings suggest the potential of conditional neural estimators for OD reconstruction under limited connected-vehicle penetration. Validation across longer periods, additional demand regimes, and real-world data is required before the results can be generalized to broader urban traffic conditions. Full article

The “spin-up” problem, in which convection-permitting models require hours to develop realistic clouds from large-scale initial fields, critically limits short-term severe weather forecasting. Cloud analysis can serve as a feasible approach to directly assimilate hydrometeor information from remote sensing retrievals. In this study, we leverage multi-source remote sensing data, including three-dimensional mosaic radar reflectivity, hourly averaged FY-2G satellite brightness temperature (black-body temperature, TBB), and FY-2G total cloud water products, within a stepwise cloud analysis initialization scheme. The scheme is implemented in a convective-scale ensemble forecasting system (CMA-Meso, 3 km resolution) for a heavy rainfall event. For each ensemble member, three-dimensional hydrometeor increments are independently generated from these remote sensing retrievals and gradually introduced over the first ten time steps, ensuring smooth coordination with the model’s dynamic thermal framework. Quantitatively, the scheme reduces near-surface Continuous Rank Probability Score (CRPS) errors, improves the overall predictive skill by 2.6–7.9% (maximum at the 12 h spin-up period), and increases ensemble spread by 2–5.8%, mitigating under-dispersion. Probabilistic precipitation forecasts show uniform area under the relative operating characteristic curve (AROC) improvements across all thresholds, 1.16–5.77% for light rain, 3.03–8.97% for moderate rain, and 6.00–12.07% for heavy rain, with these maxima consistently occurring at the 12 h spin-up time. Although Brier scores are marginally larger, these AROC gains confirm the enhanced discrimination of convective rainfall. At 500 hPa, CRPS reductions of 7.1–15.6% emerge after 24 h (largest 15.6% for geopotential height at 24 h), zonal wind CRPS is reduced by 2.2% at 12 h, and ensemble spread increases by 3.1–7.0% for all three variables. These improvements, particularly the pronounced benefits during the initial 12 h, demonstrate that the remote sensing-driven cloud analysis effectively shortens spin-up. Mechanistically, the gains arise from physically coordinated hydrometeor-latent heat perturbations and subsequent cloud radiation feedback that continuously regulate thermal-dynamic structures. This study establishes that assimilating diverse remote sensing data via cloud analysis is an effective approach for overcoming spin-up challenges in convective-scale ensembles. Full article

A novel strong-form meshless method based on Lagrange interpolation is proposed for vibro-acoustic coupling analysis. The governing equations of acoustic and structural domains, together with the coupling interface conditions, are discretized directly in differential form without relying on predefined meshes, thereby improving geometric flexibility. The computational domain is partitioned into local subdomains, within which Lagrange interpolation is employed to construct approximation functions from nodal distributions. In this manner, the proposed framework combines the flexibility of meshless formulations with a zonal parameterization strategy similar to that used in isogeometric analysis. Moreover, spatial derivatives and system matrices are directly evaluated and assembled at nodal points, avoiding numerical integration and simplifying the implementation procedure. Owing to the Kronecker delta property of Lagrange polynomials, boundary conditions can be imposed accurately and conveniently. For transient analysis, the Newmark-$\beta$ scheme is adopted to integrate both first- and second-order temporal terms, ensuring stable and accurate time integration. Finally, numerical examples, including comparisons with commercial software, demonstrate the accuracy and effectiveness of the proposed method for complex vibro-acoustic problems. Full article

Eutrophication remains a persistent water-quality problem in shallow lakes, where external inputs interact with internal loading and biogeochemical cycling. Although floating treatment wetlands (FTWs) are increasingly promoted as nature-based solutions for water remediation, their field-scale interpretation in hydrologically complex eutrophic lakes remains challenging. This study examined the spatial organization of water quality during the operation of a floating-island system in a eutrophic urban lake affected by polluted tributary inflow. The study was not designed to quantify isolated FTW removal efficiency, but to evaluate spatial water quality organization during FTW operation under real-use field conditions. Water quality was monitored over two growing seasons across six functionally defined zones, and spatial and temporal patterns were analyzed using descriptive statistics and linear mixed-effects models. The results showed parameter-specific spatial structuring rather than a uniform treatment response. The clearest inlet-lake contrasts were observed for electrical conductivity (EC), suspended matter (SM), and nitrate nitrogen (NO₃-N), whereas biochemical oxygen demand (BOD₅), ammonium nitrogen (NH₄-N), and total organic carbon (TOC) showed lower values at the inlet and higher values in downstream zones. Dissolved oxygen (DO), oxygen saturation (SO), chemical oxygen demand (COD), nitrite nitrogen (NO_2-N), and orthophosphate phosphorus (PO₄-P) showed moderate or non-robust zonal effects. These findings indicate that FTWs in shallow eutrophic lakes should be evaluated through functional zoning and parameter-specific interpretation rather than as isolated units with uniform removal responses. Full article

The provision of power and grid services requires the co-ordination between Day-Ahead Market (DAM) and Ancillary Service Market (ASM) to attain reserve services and technically feasible operating conditions for market players and for the network. In this context, this work proposes a multi-stage approach to evaluate the dispatched power to balance the forecast updates of renewable energy sources and load from DAM to ASM, taking into account network and Unit Commitment (UC) constraints. The DAM is solved considering a zonal market framework and neglecting the UC constraints. Then, a mechanism to adjust the ASM bids is developed, defining time-varying costs for each regulation. Finally, the ASM is modelled as a network-constrained UC and economic redispatch (NCUCER) optimization problem, aiming at minimizing the overall cost, in order to procure secondary reserve requirement and to adjust the DAM schedules, taking into account network and UC constraints and balancing forecast updates. DC load flow sensitivity factors are exploited to evaluate the influence of redispatch actions and forecast updates on the observed power flow. This procedure is applied to NREL 118-Bus Test System assessing its performances throughout a yearly time horizon. Full article

Outdoor ecological practice is essential for cultivating ecological literacy; however, there is currently a relative lack of comprehensive outdoor practical teaching case designs for class-based teaching. This study describes the design of an experimental teaching case for ecological education involving UAV-based field data collection. For the scheme, we selected the Xinhui Tangerine Peel Germplasm Resources Conservation Center in Jiangmen City, Guangdong Province as the study area, utilizing the DJI Phantom 4 RTK drone, which serves as the equipment for experimental teaching. The experiment is structured into three phases: indoor preparation, field execution, and data processing. Students from four groups collaboratively conducted aerial surveys across 24 partitioned plots, with flight altitudes stratified between groups to ensure safety and data integrity. (1) In the indoor preparation phase, appropriate single-flight operational units were defined. QGIS software (version 3.26.2) was employed for zonal mission planning, and suitable flight altitudes were estimated using contour data. (2) Field experiment phase. This involved conducting a comprehensive survey of the on-site environment, selecting suitable takeoff and landing points, dividing students into teams to carry out UAV-image-acquisition tasks, and assigning different altitudes for flight routes among the teams. (3) After the fieldwork, students processed imagery using Agisoft Metashape (version 2.0.1) to generate orthomosaics and digital surface models, and engaged in ecological interpretation of the results. The experimental design ensured orderly execution, complete data coverage, and active student participation. The results indicate the approach effectively enhanced students’ UAV operational skills, outdoor problem-solving abilities, and teamwork capabilities, while deepening their ecological understanding through real-world inquiry. This case provides a replicable model for integrating UAV technology into ecological education, contributing to the transformation of ecological awareness into actionable practice. Full article

This paper presents an optimization-based method to support distribution system operators (DSOs) in planning large-scale photovoltaic (PV) integration at the medium-voltage (MV) level. The PV hosting capacity (PV-HC) problem is formulated as a mixed-integer quadratically constrained program (MIQCP) without linearizing approximations to determine PV sizes and locations while enforcing operating limits and planning constraints, including candidate PV locations, per-unit PV capacity limits, active power exchange with the upstream grid, and PV power factor. Our method defines two HC solution classes: (i) sparse solutions, which allocate the PV capacity to a limited subset of candidate nodes, and (ii) non-sparse solutions, which are derived from locational hosting capacity (LHC) computations at all candidate nodes, and are then aggregated into conservative zonal HC values. The approach is implemented in a Hosting Capacity–Distribution Planning Tool (HC-DPT) composed of a Python–AMPL optimization environment and a Python–OpenDSS probabilistic evaluation environment. The worst-case operating conditions are obtained from probabilistic models of demand and solar irradiance, and Monte Carlo simulations quantify the performance under uncertainty over a representative daily window. To support integrated assessment, the index $G_{\exp }$ is introduced to jointly evaluate exported energy and changes in local distribution losses, enabling a system-level interpretation beyond loss variations alone. A strategy was also proposed to derive worst-case scenarios from zonal HC solutions to bound performance metrics across multiple PV integration schemes. Results from a real MV case study show that PV location policies, export constraints, and zonal HC definitions drive differences in losses, exported energy, and solution quality while maintaining computation times compatible with DSO planning workflows. Full article

Zonal day-ahead (DA) electricity markets followed by redispatch (RD) markets for congestion management are vulnerable to the strategic bidding behavior known as Inc-Dec gaming. Although previous literature has demonstrated the effects of Inc-Dec gaming, it has neglected the participation of flexible demand in RD markets. This paper addresses this gap by developing a novel multi-period bi-level optimization model of a strategic producer participating in DA and RD markets, accounting for the inherent time-coupling operating characteristics of demand flexibility (DF) in the RD market. This model includes an upper level problem determining the optimal bidding decisions of the strategic producer in the DA and RD markets, and two lower level problems representing the clearing process of the two markets. The role of DF in addressing Inc-Dec gaming is demonstrated through a small-scale case study involving a 2-node system and a 2-period market horizon (showing peak/off-peak dynamics), as well as a large-scale case study involving a modified IEEE RTS 24-node system and a daily (24-h) market horizon. Full article

With the gradual increase in coal production capacity, the problem of water damage from the coal seam roof is becoming more and more prominent. Neogene loose strata overlie coal seams in eastern China, and pressurized aquifers commonly lie at the bottom of the loose strata. The aquifers are mainly composed of unconsolidated sand, gravel, and weakly consolidated marl, which has strong permeability and an extremely unfavorable impact on safe production. Identifying the target area to prevent and control roof water damage can reduce the likelihood of water damage accidents in mines. This study takes the 8₅ mining district of Wobei mine as an engineering case. The discriminant indexes are selected for aquifer thickness, gradation coefficient, marlstone thickness, permeability, grouting quantity, and grouting termination pressure. A model integrating the newly proposed Crowned Porcupine Optimization (CPO, 2024), Convolutional Neural Network (CNN), and Bidirectional Long Short-Term Memory (BiLSTM) was constructed to predict unit water influx. A zonal map was generated based on the expected unit water influx of the fourth aquifer after grouting. In addition, the prediction results are compared with those from other models. Results indicate that the CPO-CNN-BiLSTM model achieves a higher accuracy and fewer errors in water abundance prediction, with an RMSE of 2.58 × 10⁻⁵ and an R² of 0.982 for the testing dataset. According to the prediction result, the fourth aquifer after grouting in the 8₅ mining district is divided into five water abundance zones. The strong and medium–strong water abundance zones are mainly distributed in the study area’s eastern region. A small portion of them is distributed in the northwestern and northern areas. This study provides a new insight for predicting the water abundance of thick loose aquifers and a theoretical basis for safe mining under thick loose aquifers. Full article

To promote a more decentralized energy system, the European Commission introduced the concept of Renewable Energy Communities (RECs). Meanwhile, the increasing penetration of Electric Vehicles (EVs) may significantly increase peak power demand and consumption ramps when charging sessions are left uncontrolled. However, by integrating smart charging strategies, such as Vehicle-to-Grid (V2G), EV storage can actively support the energy balance within RECs. In this context, this work proposes a comprehensive and scalable model for leveraging smart charging capabilities in RECs. This approach focuses on an external cooperative framework to optimize incentive acquisition and reduce dependence on Medium Voltage (MV) grid substations. It adopts a hybrid strategy, combining Mixed-Integer Linear Programming (MILP) to solve the day-ahead global optimization problem with local rule-based controllers to manage power deviations. Simulation results for a six-month case study, using historical demand data and synthetic charging sessions generated from real-world events, demonstrate that V2G integration leads to a better alignment of overall power consumption with zonal pricing, smoother load curves with a 15.5% reduction in consumption ramps, and enhanced cooperation with a 90% increase in shared power redistributed inside the REC. Full article

Inspired by real-world logistics scenarios, in this paper, we introduce a new variant of the Orienteering Problem known as the Multi-zone Orienteering Problem with Time Windows (MzOPTW). In the MzOPTW, customers are situated in distinct zones, each with multiple entrances and exits. Each customer has specific time window requirements; access to them will generate certain profits. This problem is to simultaneously determine which zones and customers to visit, select the zonal entrances and exits, and generate the routes for visiting each zone and its customers, all while maximizing total profits within a limited time frame. To tackle the MzOPTW, this paper develops an integer programming model. There are significant computational challenges in the strong interdependencies among zone selection, customer selection within zones, entrance and exit selection for each zone, the sequence of visits to zones and customers, and arrival and stay times. To address these challenges, this paper proposes a learning-enhanced metaheuristic algorithm called the Hybrid Ant Colony Optimization (HACO) algorithm, which incorporates Pointer Network learning. The HACO algorithm combines the global search capabilities of a population-based algorithm with the parallel decision-making abilities of the Pointer Network learning model. Additionally, a method to optimize zonal stay time limits is proposed to further enhance the solution. Experimental results demonstrate that the HACO algorithm outperforms comparative algorithms, achieving better solutions in 73% of the instances within the same time frame. Furthermore, the proposed optimization method for zonal stay time limits results in improvements in 78% of instances. Full article

Understanding the rheological properties of fresh cement slurries is essential to maintain optimal pumpability, achieve dependable zonal isolation, and preserve long-term well integrity in oil and gas cementing operations and the 3D printing cement and concrete industry. However, accurately and efficiently modeling the rheological behavior of cement slurries remains challenging due to the complex fluid properties of fresh cement slurries, which exhibit non-Newtonian and thixotropic behavior. Traditional numerical solvers typically require mesh generation and intensive computation, making them less practical for data-scarce, high-dimensional problems. In this study, a physics-informed neural network (PINN)-based framework is developed to solve the governing equations of steady-state cement slurry flow in a tilted channel. The slurry is modeled as a non-Newtonian fluid with viscosity dependent on both the shear rate and particle volume fraction. The PINN-based approach incorporates physical laws into the loss function, offering mesh-free solutions with strong generalization ability. The results show that PINNs accurately capture the trend of velocity and volume fraction profiles under varying material and flow parameters. Compared to conventional solvers, the PINN solution offers a more efficient and flexible alternative for modeling complex rheological behavior in data-limited scenarios. These findings demonstrate the potential of PINNs as a robust tool for cement slurry rheological modeling, particularly in scenarios where traditional solvers are impractical. Future work will focus on enhancing model precision through hybrid learning strategies that incorporate labeled data, potentially enabling real-time predictive modeling for field applications. Full article

The postagrogenic transformation of landscapes is one of the key problems leading to a decrease in soil fertility in the territory of Northwest Russia. In order to assess the degree of land degradation, field studies of soils from fallow lands in the Leningrad Region were carried out. Different evolutionary trends of ontogenesis of soils with types of soil parent materials were revealed. At morphological and micromorphological levels, degradation processes of old-arable horizons were noted, including secondary podzolization and decreasing Ap horizon thickness. Using a CHN analyzer, the stock levels of soil organic carbon and nitrogen of the studied chronoseries were estimated. The data obtained show that the carbon stocks of old-arable soils are lower than the benchmark ones due to the weak development of the Oi horizon. Carbon dynamics varied substantially by parent materials: soils on silt–clay materials showed a low 7.1% carbon decrease, while soils on sandy and bottom sediments increased by 139% and 163%, respectively, in old-arable horizons by the accumulation of coarse forms of carbon. For nitrogen, it was revealed that the highest stocks are observed in old-ploughed soils, which is due to the input of a large amount of plant residues from small-leaved forests. The content of biogenic elements in the soil showed separate evolutionary direction depending on parent materials: soils on silt–clay materials showed 7.6% phosphorus depletion and 15% potassium loss over 15–30 years, while soils on sandy materials demonstrated 18% phosphorus loss and 114% potassium increase during 30–86 years of fallow state. On the contrary, the content of nitrate and ammonium forms of nitrogen was higher than in the benchmark zonal soils, with nitrate nitrogen increasing by 150 times on sandy parent materials and ammonium nitrogen increasing by 102% in soils formed on bottom sediments over 35–70 years, which is due to the transformation of grass and forest plant residues. The duration of transformation and regradation of soils of fallow land depends on geogenic and bioclimatic conditions that determine the direction and speed of changes. Full article