Search Results (11,105)

Station-based car-sharing has been shown to reduce resource-intensive private car ownership. However, only a small proportion of the population uses station-based car-sharing, which could be improved by redesigning the service to reduce walking distances and increase availability. We developed a method for designing an efficient and cost-effective station-based car-sharing network for smart cities that emphasizes user comfort and convenience, while reducing the number of needed cars. To quantify the placements, we created a high-resolution synthetic population for Munich, Germany as a case study. The population was based on census and OpenStreetMap data, and each person was assigned to a suitable mobility plan derived from two mobility surveys. Since car ownership and station-based car-sharing are particularly associated with trips for vacations, we supplemented the mobility plans with long-distance travel data from a one-year tracking dataset. This allowed us to perform a spatial and temporal analysis of the theoretical potential of various station placements for station-based car-sharing. The tested station networks varied in user comfort, especially in the distance to the nearest station and the group size of car-sharing users. Our findings indicate that the best trade-off between convenience and efficiency is a station design with a group size of 217–949 people. We further found that the car-sharing fleet size is strongly influenced by long-distance trips, and that a substitution rate of 1:1.25 to 3.3 with private cars is possible. Full article

Recent space missions demand higher pointing accuracy, smoother attitude transitions and lower energy consumption than those typically achievable with conventional control approaches. This motivates the exploration of intelligent and nonlinear control methods. The FuzzyBuzz experiment investigates the application of fuzzy logic for spacecraft attitude control using NASA’s Astrobee robotic system aboard the International Space Station. Unlike traditional control methods, fuzzy logic introduces a rule-based approach capable of handling uncertainties and nonlinearities inherent in space environments, making it particularly suited for autonomous operations in microgravity. The objective of FuzzyBuzz is to evaluate the effectiveness of fuzzy controllers compared to traditional linear ones, such as Proportional–Integral–Derivative (PID) and $H_{\infty }$ controllers. In addition, a comparison with a nonlinear controller based on a Model Predictive Control (MPC) strategy is considered. The controllers will be tested through predefined attitude maneuvers, evaluating precision, energy efficiency, and real-time adaptability. This work presents the design of the FuzzyBuzz experiment, including the software architecture, simulation environment, experiment protocol, and the development of a fuzzy logic-based attitude control system for Astrobee robots. The proposed fuzzy controller and a PID controller are optimized using a Multi-Objective Particle Swarm Optimization (MOPSO) method, providing a range of operational points with different trade-offs between two metrics, related to convergence time and energy consumption. Results show that the PID controller is better suited for scenarios demanding low convergence times, whereas the fuzzy controller provides smoother responses, reduced steady-state error, and maintains convergence under significant parametric uncertainties. Results from $H_{\infty }$ and MPC controllers will be reported once the in-orbit experiment is performed. Full article

Non-motorized mobility networks increasingly serve as critical infrastructure for sustainable regional development that integrates recreational, environmental, and transportation functions across diverse geographical contexts. To enhance the spatial planning efficiency and support evidence-based policy development, this study develops a Geographic Information Systems (GIS)-based analytical framework to evaluate the connectivity and accessibility of Korea’s integrated non-motorized mobility system. The model systematically maps 606 walking courses, 60 cycling routes, and 66 water activity sites nationwide, and examines their spatial relationships with major transportation hubs, including Korea Train e-Xpress (KTX) stations and airports within 20–30 km buffer zones. Using proximity analysis, connectivity mapping, and origin–destination (OD) cost matrix modeling, the framework identifies intermodal distance structures and spatial integration patterns. The analysis reveals a hybrid network configuration characterized by localized multimodal clustering alongside regional accessibility gaps, with urban–coastal regions demonstrating stronger connectivity than inland–rural areas. This study proposes a data-driven Korean mobility network framework that integrates walking, cycling, and water routes with the existing transportation infrastructure. These findings demonstrate how GIS-based tools can support evidence-based sustainable mobility policies and regional tourism planning on a national scale. Full article

This study investigated the extraction of aluminum from aluminum silicate-rich coal ash from the ash-slag waste of the Almaty CHP-2 power station using microwave-assisted alkaline leaching. The high chemical stability of the quartz and mullite phases in the ash leads to high energy consumption during conventional acid–base treatment. To improve the kinetic parameters of the leaching process, amorphous graphite was therefore used as an active additive, which effectively absorbs microwave energy. The experiments were conducted in the temperature range of 50–200 °C, in 1–6 M NaOH solution, and over a period of 5–30 min. The amount of amorphous graphite varied between 5 and 20 wt%. The proportion of amorphous graphite varied between 5 and 20 wt%. Upon microwave irradiation, the graphite-free ash reached a temperature of 200 °C within approximately 12 min, whereas this temperature was reached in the system with 15% amorphous graphite after only 8–9 min. At low alkali concentrations (1–2 M NaOH), the aluminum transfer into solution in the graphite-free system was approximately 18%–35%. With increasing NaOH concentrations to 3–4 M, the aluminum removal efficiency increased to 38%–58%. Under the same temperature conditions, the leaching process was significantly accelerated by the addition of amorphous graphite; thus, at temperatures near 200 °C and in a 5–6 M NaOH solution, 70%–72% of aluminum was removed. The leaching kinetics were analyzed using the shrinking core model. The results showed that the apparent activation energy of the reaction decreased from 54 kJ/mol to 32 kJ/mol in the presence of graphite. These results suggest that microwave-assisted alkaline leaching in the presence of amorphous graphite is an energy-efficient and promising method for aluminum recovery from coal ash. Full article

The study presents a comprehensive spatio-temporal assessment of meteorological drought in the Upper Vistula basin, a region located in southern Poland. The analysis was based on monthly precipitation data from 30 meteorological stations covering the period 1961–2022. These data were used to calculate the Standardized Precipitation Index (SPI) for accumulation periods of 3, 6, 9, 12, 24, and 48 months. Drought events were identified using run theory, adopting a threshold of SPI < −1 for all accumulation periods. On this basis, drought characteristics were determined, including the number of identified drought episodes (N), average drought duration (ADD), average drought severity (ADS), and average drought intensity (ADI). The multi-scale analysis revealed a clear dependence of drought characteristics on the time scale. Short-term droughts (SPI-3 and SPI-6) occurred frequently and were characterized by high monthly intensity but short duration. In contrast, long-term droughts (SPI-24 and SPI-48) occurred less frequently, but were marked by much longer duration and greater cumulative severity, despite lower average intensity. Spatial analyses showed substantial heterogeneity of drought characteristics within the Upper Vistula basin. The western and south-western parts of the region were particularly exposed to frequent short-term droughts, whereas long-term droughts were less frequent, but more regional in nature and resulted from accumulated, multi-year precipitation deficits affecting groundwater resources and catchment retention. The presented findings provide valuable information for improving drought monitoring systems and adaptation strategies in the Upper Vistula basin and in other climatically diverse regions of Central Europe. Full article

Vertical docking of the autonomous underwater shuttle (AUS) for deep-sea data relay relies heavily on ultra-short baseline (USBL) acoustic positioning, whose measurements can be intermittently unavailable and contaminated by outliers in complex underwater environments. This paper proposes a neural network-enhanced robust navigation framework to improve AUS navigation reliability during acoustically guided vertical docking under USBL outages. First, a model-aided batch maximum a posteriori trajectory estimation method (MA-BMAP) is developed to generate learning quality supervision under sensor-limited conditions. Based on the estimated trajectories, a long short-term memory (LSTM)-based horizontal velocity predictor is integrated into a robust fusion filter with online ocean current estimation, enabling stable state estimation during USBL outages and robust rejection of abnormal USBL measurements. The proposed framework is validated through simulations and field trials in lake and sea environments. In sea trials, during two representative 200 s USBL outage intervals, the end-of-window horizontal position errors are 7.86 m and 4.14 m, respectively, corresponding to AUS-to-docking station distances of 244 m and 51 m. In addition, the introduced USBL outliers are successfully detected and rejected. The results indicate that the proposed method enables accurate and stable navigation during USBL unavailability and rapid recovery once USBL measurements resume, demonstrating its practicality for vertical docking missions. Full article

Sea-surface reflections and wind–wave motion render maritime channels strongly time-varying and statistically non-stationary, while nearshore deployments face sparse infrastructure and co-channel multiuser interference. This study integrates reconfigurable intelligent surfaces (RISs) with rate-splitting multiple access (RSMA) for joint online resource allocation. A physics-inspired time-varying channel model is established by embedding fractional Brownian motion-driven slow statistical drift and reflection-phase perturbations. With imperfect, delayed channel state information (CSI) and discrete RIS phase quantization, a proportional-fairness utility maximization problem is formulated to jointly optimize shore base-station precoding, RIS phase shifts, and RSMA common-rate allocation. To cope with strong non-convexity, high dimensionality, mixed continuous–discrete coupling, and partial observability, a fractal-aware recurrent robust Actor–Critic (FRRAC) algorithm is developed. FRRAC encodes short observation histories using a gated recurrent unit and incorporates a lightweight Hurst-proxy estimator to capture slow channel statistics for robust value evaluation and policy learning. Truncated quantile critics and mixed prioritized–uniform replay further improve value robustness, training stability, and sample efficiency. Simulation results show that FRRAC converges faster and more stably under both conventional and fractal non-stationary channel modeling, and outperforms representative baselines across the objective and multiple statistical metrics, validating its effectiveness for joint resource optimization in maritime RIS–RSMA systems. Full article

It is inconceivable that an unsafe mode of transport could be sustainable. Therefore, in this century, where sustainability is at the forefront, occupational health and safety is more important than ever. However, the most used traditional risk analysis methods cannot be applied to every area due to their shortcomings. In this study, a new proposal of interval-valued intuitionistic fuzzy failure mode and effects analysis (IVIF FMEA) within the framework of fuzzy logic overcomes the inadequacies of the traditional method, particularly in uncertainty assessment. In this proposed IVIF FMEA method, the effects of historical incident data are incorporated to improve risk prioritization through static decision-maker weighting and dynamic risk parameter weighting. Then, this novel method is implemented for the operational risks of the underground stations within a metropolitan city in Türkiye. When the proposed method is compared with traditional FMEA and classical IVIF FMEA, failure modes (FMs) related to platform surveillance, working at height, and chemical handling are identified as requiring priority action. To ensure safer and more sustainable transport operations, the prioritization of safety measures is as important as their implementation. The obtained results further offer a transferable and methodologically robust basis for advancing sustainability-oriented safety governance by enabling more evidence-based risk prioritization within urban transportation systems. Full article

This work presents the development of a wrist-worn wireless sensor system for high-accuracy indoor human zone tracking. The proposed system employs machine learning techniques to combine data from multiple sources, including a Received Signal Strength Indicator (RSSI) from wireless signals, three-axis acceleration, and three-axis angular velocity. A prototype wearable wireless sensor device was implemented using a SparkFun Thing Plus-XBee3 microcontroller supporting the Zigbee/IEEE 802.15.4 standard at 2.4 GHz, integrated with a six-degree-of-freedom IMU sensor (MPU-6050). Experiments using one wrist-worn sensor as a transmitter and one base station as a receiver were conducted in a two-story residential building environment covering three zones (i.e., staircase area, living room, and dining room) under static and dynamic test scenarios. Classification performances of 33 machine learning classifiers with different data feature groups and window sizes were evaluated. The results demonstrate the achievement of wrist-worn wireless sensor system development. The system exhibits high communication reliability with a packet delivery ratio (PDR) of 99.99% and can efficiently track data signals in real time. Results indicate that using only raw RSSI data achieves 75.0% accuracy in classifying human zones. However, when statistical RSSI features and accelerometer data fusion are applied, accuracies significantly increase to 98.7% (static scenario, wide neural network with a window size of 25) and 99.6% (dynamic scenario, Fine k-NN). These results demonstrate the system’s potential for indoor human tracking applications. Full article

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved $R^2\in (0.53,0.75)$ across PM_2.5, PM₁₀, NO₂, and O₃, representing from 40% (for O₃) to 408% (for PM_2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates ($\alpha$) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM₁₀ and O₃, $p<0.05$). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived $\alpha$ parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design. Full article

The growing scale and complexity of industrial infrastructure make efficient and reliable inspections a critical challenge. Inspection task demands often vary dynamically, requiring efficient and demand-responsive inspection strategies to ensure stable operation. However, existing UAV inspection approaches typically deploy UAV base stations (UAV-BSs) based on fixed inspection frequencies, which are inadequate for adapting to such dynamic demands and may reduce inspection efficiency. Moreover, these approaches often rely on historical inspection data, whose empirical distributions may deviate from the true distributions, thereby compromising solution robustness. To address these issues, this paper proposes a two-stage distributionally robust optimization (TDRO) framework for joint UAV-BS deployment and inspection routing in dynamic environments. The framework accounts for uncertainties in both inspection frequency and distributional perturbations. Uncertainty sets constructed based on probability metrics are employed to capture deviations between empirical and true distributions, forming the foundation of the two-stage distributionally robust optimization model. The resulting model is solved using column-and-constraint generation (C&CG) integrated with column generation (CG), yielding robust deployment decisions and an effective trade-off between total system cost and inspection efficiency. Simulation results show that the framework effectively addresses inspection frequency uncertainty, reducing the total objective by 5.50% on average, with a further 2.16% reduction when distributional perturbations are considered. Full article

Hydropower is a cornerstone of global renewable energy; however, reservoir sedimentation directly undermines its benefits and operational lifespan. A critical, often overlooked, aspect of sedimentation is the compaction of fine-grained deposits, which introduces systematic discrepancies between standard siltation calculation methods. This study addresses this gap by developing a machine learning-based model to quantify sediment compaction and correct siltation estimates using the Xiluodu Hydropower Station on the Jinsha River, China, as a case study from 2014 to 2020. Based on hydrological, sediment, and fixed-section monitoring data, we applied five machine learning algorithms (Linear Regression, Neural Network, Random Forest, Gradient Boosting, and Support Vector Regression) to establish a relationship between the compaction thickness and the following key predictors: Year, Cumulative Sediment Thickness, Annual Sediment Thickness, and Distance to the Dam. The results demonstrate that the Neural Network (NN) model significantly outperforms traditional models, effectively capturing complex, nonlinear compaction dynamics with strong predictive accuracy (test R² = 0.766, RMSE = 0.047 m) and no significant overfitting. SHAP analysis revealed the dominant influences of consolidation time (years) and overburden stress (Cumulative Sediment Thickness), linking the model’s predictions to fundamental geotechnical principles. Applying the NN model to correct for the cross-sectional volume method markedly improved its consistency with the independent sediment transport method, reducing the average relative difference from −33.7% to −6.5% (2016–2020). This study provides the first quantitative, continuous (198 km, 221 sections) assessment of reservoir-scale sediment compaction, confirming its widespread existence and demonstrating its critical role in the long-standing methodological discrepancies. Our study transformed compaction from an acknowledged phenomenon into a quantifiable correction, offering a novel, data-driven framework to enhance the accuracy of reservoir sedimentation assessments globally. Full article

The transition towards sustainable energy systems is critically dependent on the reliable integration of renewable energy sources into the power grid. With the increasing penetration of renewable generation, hybrid grid-connected systems comprising grid-following (GFL) and grid-forming (GFM) converters have become essential in modern power stations. This paper addresses a key challenge to sustainable grid operation: maintaining stability and power delivery during grid faults. When faults cause voltage sags at the point of common coupling (PCC), different low-voltage ride-through (LVRT) strategies significantly impact both the voltage support capability and the active power transmission capacity, which are vital for a stable and resilient energy supply. To address this, the paper proposes a coordinated LVRT strategy for GFL/GFM converters that adapts to varying grid requirements, thereby promoting sustainable grid integration. First, the topology and control strategies of the hybrid system are briefly described. The conventional LVRT control strategies for both GFL and GFM converters are then improved. Based on the severity of the grid voltage sag, the converters’ active and reactive power output are adaptively adjusted. This dual-function approach not only effectively limits fault currents, protecting sensitive equipment, but also prioritizes the continuous transmission of active power, thereby minimizing the loss of renewable generation during faults and supporting grid stability. Subsequently, through an analysis of the voltage and active power characteristics of different LVRT modes, a coordinated strategy is designed. Unlike single-converter LVRT control, the proposed method flexibly selects and adjusts control modes to meet specific grid code requirements, ensuring robust voltage support and maximizing the utilization of clean energy even under adverse conditions. Finally, the effectiveness of this coordinated control strategy in ensuring a sustainable and resilient grid connection is validated through MATLAB R2022b/Simulink simulations. Full article

This study proposes a four-dimensional Historical Building Defect Information Modeling (HBDIM) framework designed to support the documentation, diagnosis, and conservation of deteriorated historic stucco elements. The framework integrates multi-source digital documentation techniques, including terrestrial laser scanning (TLS), high-resolution photogrammetry, and automated total station measurements with laboratory-based material diagnostics to create a unified digital environment for defect detection and conservation assessment. The approach was applied to the Baron Empain Palace in Egypt as a representative case study of complex architectural heritage affected by material deterioration. Within the HBDIM workflow, point cloud processing and defect-oriented information modeling were used to identify and spatially localize deterioration features such as cracking, erosion, and material loss. Laboratory investigations—including computed tomography (CT), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray fluorescence (XRF)—were conducted to evaluate the effectiveness of calcium hydroxide nanoparticle consolidation treatments and to relate microstructural material behavior to spatially mapped defects within the digital model. Mechanical testing demonstrated a significant improvement in material performance, with treated stucco samples exhibiting an average compressive strength increase of approximately 69.06% compared to untreated specimens. The results demonstrate that integrating digital documentation, defect-oriented modeling, and material diagnostics within a four-dimensional framework provides a robust platform for linking geometric deterioration patterns with material-level conservation performance. By embedding diagnostic data and treatment outcomes within a temporally structured digital model, the HBDIM approach supports preventive conservation strategies, long-term monitoring, and data-driven decision-making in sustainable heritage management. Full article

Background/Objectives: We aimed to determine risk factors and to design a clinically based predictive model for a failed vacuum assisted delivery (VAD). Methods: We conducted a retrospective cohort study in a single tertiary university-affiliated medical center between 2011 and 2023. The study population consisted of singleton pregnancies with a VAD trial. The study group comprised cases of a failed VAD, defined as the occurrence of any of the following: (1) more than two vacuum cup detachments; (2) extraction duration exceeding 20 min; or (3) abandonment of the vacuum attempt by the operating physician, with conversion to urgent cesarean delivery (CD). The control group comprised cases of successful VADs. Factors associated with failed VAD were examined by univariate and multivariate analyses. A prediction score was developed to predict failed VAD. A receiver-operating characteristic curve (ROC) was utilized for the model. Internal validation was performed by means of a 70/30 train–test split, with model performance evaluated on the validation set using ROC analysis. Results: A total of 131,019 women delivered in our center during the study period. VAD was attempted in 8885 (6.8%) cases, of which 172 (1.9%) failed trials that led to urgent CDs. Several risk factors for a failed VAD were identified, including induction of labor, fetal head station below +2 cm relative to the ischial spines, duration of the second stage of delivery >3.5 h, preeclampsia, birthweight >3750 g, and male gender. The prediction score demonstrated good discriminatory performance, with an AUC of 0.723 (95% CI 0.637–0.810). Internal validation using a 30% holdout cohort revealed that the model maintained good performance, with an AUC of 0.764 (95% CI 0.619–0.909; p < 0.001). Conclusions: Our model has the potential to assist obstetricians with VAD decision-making and parturient counseling, as well as identifying parturients at high risk for complicated deliveries. Full article