Search Results (60)

Earthquake Early Warning Systems (EEWS) represent one of the most effective technological solutions for mitigating the impacts of strong ground motion in seismically active regions. This study presents the design, implementation, and comprehensive evaluation of a real-time earthquake early warning system for Izmir-a region in Western Anatolia characterized by complex tectonic structures and high seismic hazard-using multi-station seismic acceleration data. The proposed framework integrates multi-threaded data acquisition, signal preprocessing, Min-Max normalization, and Euclidean distance-based similarity analysis to enable rapid detection of anomalous seismic patterns during the early P-wave phase. The system architecture consists of distributed sensor inputs, centralized real-time processing, similarity-based anomaly detection, and user-oriented visualization and alerting modules. The performance of the system was evaluated using both real and synthetic seismic datasets. Instrumental earthquake catalog from the 12 June 2017 Karaburun (Mw 6.2) and 30 October 2020 Samos (Mw 6.6) earthquakes demonstrate that the system can generate early warnings 18 s and 13 s prior to strong ground shaking, respectively. In addition, synthetic seismic scenarios were employed to assess system robustness under varying noise levels, station configurations, and signal conditions. The results indicate that the proposed framework maintains stable detection performance and low false-positive rates across diverse operational scenarios. The methodology emphasizes computational efficiency and inter-station waveform coherence analysis, providing a lightweight alternative to conventional magnitude-based approaches. By avoiding computationally intensive source inversion, the system achieves low-latency performance while preserving detection reliability. The proposed EEWS demonstrates strong generalization capability, scalability, and practical applicability for real-time deployment in earthquake-prone urban environments. Full article

The public deployment of electric vehicle charging stations must simultaneously balance construction economics, user accessibility, queueing pressure, feeder security, tail risk under demand uncertainty, and spatial fairness. These criteria are strongly coupled, yet most existing studies either rely on static optimization with limited behavioral realism or use multi-agent reinforcement learning for short-term charging operation rather than for long-term siting. This paper proposes a preference-conditioned multi-agent deep deterministic policy gradient (PC-MADDPG) framework for the urban charging station siting problem in a coupled traffic–distribution environment. Candidate charging sites are modeled as cooperative agents under centralized training and decentralized execution. Each agent outputs a continuous pile-allocation action, which is repaired into an integer expansion plan under a budget constraint. The environment evaluates each plan through attraction-based demand assignment, queue approximation, LinDistFlow-style feeder analysis, and a six-objective performance vector, including annual net cost, travel burden, service inconvenience, grid penalty, CVaR of unmet charging demand, and equity loss. On a reproducible benchmark with 12 demand zones, 10 candidate sites, an 11-bus radial feeder, and 16 stochastic daily scenarios, the proposed framework generates a non-dominated archive with 42 unique feasible plans. A representative PC-MADDPG solution opens 5 of 10 candidate sites and installs 20 fast-charging piles, achieving 99.88% mean demand coverage with an annual profit of 2.083 M$ and a maximum line utilization of 0.999. Relative to the NoGrid ablation, the selected full model reduces grid penalty by 23.87% and equity Gini by 51.08%, with only a 0.35% profit concession. Relative to the NoRisk ablation, the CVaR of unmet demand is lowered by 69.70%. Compared with a demand-greedy baseline, the proposed method reduces grid penalty by 11.72% and equity Gini by 25.19% while preserving similar demand coverage. These results provide proof-of-concept evidence, on a reproducible coupled benchmark, that preference-conditioned multi-agent learning can serve as a practical many-objective siting engine for charging-infrastructure planning when coupled traffic and feeder constraints are explicitly modeled. Full article

As a typical example of a high-density city, Macau’s medical resource allocation system, a key component of the city’s complex socio-technical system, suffers from significant spatial imbalances, which restricts the overall effectiveness of the medical service system. Based on the perspective of systems science theory, regards the allocation of medical resources as a dynamic system with multiple coupled factors. It comprehensively utilizes systems research methods such as POI data mining and space syntax analysis and employs techniques such as kernel density analysis and spatial structure coupling models to systematically evaluate the spatial structure, resource accessibility, and service balance of Macau’s medical service system. It found that (1) the Macau Peninsula has concentrated core medical resources, such as the Conde de São Januário Hospital (CHCSJ) and Kiang Wu Hospital, which form a core subsystem with high service saturation. Excessive concentration of resources has led to high concentration of a certain type of facility. (2) Taipa Island and the Cotai Reclamation Area have created an extended subsystem of medical resources along with urban development. However, the northern area does not have enough facilities, and its internal structure is not balanced. (3) Coloane Island has only basic health stations remaining, forming a marginal subsystem with scarce medical resources, which has a significant hierarchical gap with the core and extended subsystems. This spatial pattern of “saturated Macau peninsula, expanded Taipa Island, and sparse Coloane Island” is essentially a concrete manifestation of the imbalance between the medical resource allocation system and the urban spatial development system. Therefore, based on system optimization theory, it proposes constructing a multi-level, networked spatial system for medical facilities to promote the coordinated operation of various regional medical subsystems and achieve overall functional optimization and a balanced layout for Macau’s medical service system. This research analyzes the imbalance mechanism of high-density urban public service systems using systems science methods, providing not only a scientific basis for the precise optimization of Macau’s medical resource allocation system but also a practical reference for the planning and governance of similar high-density urban public service systems under a systems thinking framework. Full article

Numerical weather prediction (NWP) models are essential for precipitation forecasting but are constrained by coarse spatial resolutions (10–50 km), which fail to capture fine-scale variations required for regional disaster prevention, particularly in complex terrain. While statistical and machine learning downscaling methods have been developed to bridge this resolution gap, they predominantly operate as “black boxes” without explicit physical guidance, leading to predictions that violate meteorological principles and systematic underestimation of extreme precipitation events. To address these limitations, this study aims to develop a Physics-Informed Machine Learning framework that explicitly integrates multi-scale topographic modulation and physical consistency constraints into precipitation downscaling. Specifically, a Random Forest model enhanced with Multi-Scale Structural Similarity (MS-SSIM) loss and Physical Constraint Enhancement (MSSSIM-PCE-RF) was constructed. The model introduces elevation gradient weights at low-resolution layers and micro-topographic parameters (slope, surface roughness) at high-resolution layers, while enforcing physical consistency between precipitation intensity, radar reflectivity, and ground observations via the Z-R relationship. Based on hourly data from 2252 meteorological stations in Jiangxi Province (2021–2022), coupled with topographic factors (DEM, slope, aspect) and Normalized Difference Vegetation Index (NDVI), a technical framework of “data fusion–feature synergy–machine learning–spatial reconstruction” was established. Results demonstrate that the MSSSIM-PCE-RF model achieves a validation R² of 0.9465 and RMSE of 0.1865 mm, significantly outperforming the conventional RF model (R² = 0.9272). Notably, errors in high-altitude, steep-slope, and high-vegetation areas are reduced by 45.3%, 42.0%, and 43.1%, respectively, with peak precipitation period errors decreasing by 37.2%. Multi-scale topographic analysis reveals significant orographic lifting effects at 250–1000 m elevations, peak precipitation at 12–15° slopes, and abundant precipitation on south/southeast aspects. By explicitly embedding topographic modulation and physical consistency constraints, the model effectively alleviates systematic underestimation of extreme precipitation in complex terrain, providing high-resolution data support for transmission line disaster prevention and micro-meteorological risk assessment. Full article

The Three Gorges Reservoir (TGR) in China is one of the world’s largest hydropower projects. Interval inflow, originating from ungauged areas between the upstream gauging control stations (Zhutuo, Beibei, Wulong) and the TGR dam site, is a critical component of total reservoir inflow, but its hydrological characteristics have not been fully clarified. The accurate estimation and prediction of interval inflow are essential for reservoir safety and flood control operations. Using daily hydrological data from 2009 to 2017, we propose an integrated analytical framework combining (i) flow travel time estimation using cross-correlation analysis, (ii) multi-scale statistical characterization, and (iii) K-means clustering with bootstrap validation and algorithm comparison. This framework systematically identified hydrological regimes of interval inflow and their associated flood control risks. The key findings are as follows. (1) The optimal flow travel time from the upstream gauging stations to the dam site is 1 day (correlation coefficient $\rho =0.9809,p<0.001$), and it remains stable across different flow regimes. (2) The interval inflow exhibited a highly right-skewed distribution (mean 1279 m³/s, standard deviation 1651 m³/s) and contributed on average 10.1% to the total inflow. The contribution ratio exhibited an inverted U-shaped relationship with increasing total inflow, peaking at 11.4% when the total inflow ($Q$) was 13,014 m³/s. The quartile thresholds were 5788 m³/s, 9575 m³/s, and 16,869 m³/s (corresponding to Q1, Q2, and Q3, respectively), and the 10th and 90th percentiles (P10 and P90) were 4865 m³/s and 24,625 m³/s, respectively. (3) Five distinct hydrological patterns (C1–C5) were successfully identified, among which Cluster C4 (5.7% of days) was defined as the high-impact pattern based on reservoir operational criteria, with a mean $I$ of 6425 m³/s, a mean $R$ of 27.8% (up to 44% in extreme events), a mean flood duration of 5.8 days, a mean flood volume of 36.1 × 10⁸ m³, and a flashiness index of 1.48. (4) C4 is predominantly triggered by localized heavy rainfall, and its flashy nature implies a substantially shorter forecast lead time compared with mainstream-dominated floods, posing major challenges to real-time reservoir operations. This study demonstrates that interval inflow risk is pattern-dependent and that the proposed framework provides a scientific basis for developing pattern-specific reservoir operation strategies. The proposed framework is transferable to other large river-type reservoirs facing similar ungauged interval inflow challenges. Full article

With the rapid increase in renewable energy penetration and the expansion of multi-regional interconnected power systems, there is a growing need to forecast the power output of renewable energy power plant clusters within a region. Existing methods primarily utilize spatio-temporal correlations between stations to directly predict cluster output, but they still have the following shortcomings: (1) lack of analysis and utilization of the similar output characteristics between wind and solar power stations; and (2) inadequate integration of individual plant characteristics and adaptability across different prediction spatial scales. Therefore, this study proposes a method for forecasting and correcting daily power generation zones of wind–solar clusters based on output similarity clustering. First, the output similarity characteristics of wind and solar plants within the cluster are evaluated, and a similarity matrix is constructed to cluster the plants into sub-clusters. Second, a single-site power prediction model based on the BiLSTM model and multi-task learning is constructed to aggregate preliminary power prediction results from individual sites within sub-clusters. Finally, a cluster power prediction correction model based on the GCN-Transformer model is developed to refine preliminary predictions using spatio-temporal correlations between sub-clusters. Simulation results demonstrate that the proposed method, through its integrated approach combining clustering partitioning, multi-task learning, and spatio-temporal correlation correction within a comprehensive forecasting workflow, achieves improvements of 15.2323%, 19.0581%, and 0.0283% over the baseline GCN model in terms of MAE, RMSE, and R-score, respectively. This effectively enhances the accuracy of power forecasting for wind-solar power plant clusters. Full article

Suspended sediment transport in large rivers is characterized by complex cross-sectional patterns. This study investigates the cross-sectional distribution of the suspended sediment concentration (SSC), based on 15 measurement campaigns at six stations along a 67 km reach of the middle Rhine in Germany. Utilizing a multi-method approach, we conducted turbidity and acoustic backscatter measurements, in situ particle size data, recorded water quality parameters such as electrical conductivity, and took 495 pump-based water samples over a period of 2.5 years. Statistical analysis of this comprehensive dataset shows that lateral differences have greater importance for the cross-sectional SSC distribution than vertical differences, suggesting that incomplete river mixing is of greater importance than vertical stratification for uncertainties in load calculations. We demonstrate that surface measurements are consistently representative for the whole water column and that applying the traditional Rouse equation for vertical extrapolation from surface measurements leads to large errors. We conclude that efficient monitoring programs should prioritize covering the lateral SSC distribution for more accurate load calculations and offer practical recommendations for improved SSC monitoring in similar conditions. Full article

The analysis of changing trends of river runoff and sediment discharge and the exploration of their causes are of great significance for formulating sustainable development measures for river basin systems. Based on methods such as trend test, mutation detection, and regression analysis, this study conducts a systematic comparative research on the water–sediment processes in the river reach where large-scale cascaded reservoirs connected end to end are located in the upper Yangtze River, and obtains the following key research progress: For the study reach (between Sanduizi and Xiangjiaba Stations), during the period of 1966–2023, the change rates of annual incoming and outgoing runoff were 2.88 × 10⁸ m³·yr⁻¹ and −0.186 × 10⁸ m³·yr⁻¹, respectively, accounting for 0.017% and 0.013% of the annual average runoff. The changing trends were not significant. During the same period, the change rates of Suspended Sediment Load (SSL) at the inlet and outlet of this river reach were −8.0 × 10⁵ t·yr⁻¹ and −46 × 10⁵ t·yr⁻¹, respectively, accounting for 1.25% and 2.45% of their respective annual average sediment discharge. The SSL showed a significant decreasing trend, which was particularly characterized by a sharp reduction at the outlet. The massive sediment retention and multi-mode operation of cascaded reservoirs are the fundamental reasons for the variation in the water–sediment relationship and the sharp decrease in annual SSL in this reach, and they also lead to an obvious adjustment of water and sediment in the river basin that “cuts peaks and fills valleys” within a year. Climate change and other human activities have reduced the sediment input in the study reach. Looking forward to the next few decades, climate factors will remain the dominant factor affecting the inter-annual variation in runoff in the study area. In contrast, human activities such as reservoir operation will continue to fully control the sediment output of the river reach and also restrict the annual distribution of water and sediment. The results of this study can provide a reference for predicting the changing trends of water and sediment in similar river reaches with cascaded reservoir groups and formulating effective river management measures. Full article

Assessing the accessibility of urban metro stations is essential for optimizing metro system planning and improving travel efficiency for residents. This study proposes an innovative evaluation framework—the CWM-GRA-TOPSIS model—for comprehensive metro station accessibility assessment. First, a multi-dimensional indicator system is established, encompassing three key dimensions, to-metro accessibility, by-metro accessibility, and land use accessibility, which are further refined into 14 secondary indicators for detailed analysis. A Combination Weighting Method (CWM) is then introduced, integrating the Analytic Hierarchy Process (AHP) for subjective weighting and the Criteria Importance Through Intercriteria Correlation (CRITIC) method for objective weighting, with their integration optimized through Game Theory. Subsequently, the overall accessibility of metro stations is evaluated and ranked using a hybrid Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The proposed method is applied to Wuhan, China, to demonstrate its effectiveness and applicability. Results show that the CWM-GRA-TOPSIS model, by balancing objectivity and practical relevance, provides a more reliable and systematic approach for identifying accessibility disparities and formulating targeted improvement strategies for urban metro systems. Full article

The accuracy of Precise Orbit Determination (POD) for Global Navigation Satellite Systems (GNSS) critically depends on optimal tracking station selection. This study proposed and validates a novel framework that integrates a hybrid-weight Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) model with spherical k-means clustering, effectively resolving the challenge of balancing station data quality with uniform spatial distribution. The framework generates by first a comprehensive quality score for each station based on 40 indicators and then selects the top-scoring station from distinct geographical clusters to construct a well-distributed, high-quality network. To validate the methodology, we performed multi-GNSS POD using networks of 30, 60, and 90 stations selected by the proposed framework. The accuracy was assessed via two independent methods: orbit comparisons (Root Mean Square, RMS) against final Analysis Center (AC) orbits and Satellite Laser Ranging (SLR) validation. The results demonstrate that the optimized 60-station network (e.g., RMS of ~2.5, 5.3, 2.1, and 5.4 cm for GPS, GLONASS, Galileo, and BDS, respectively) achieves an accuracy comparable to that of a 90-station network. Moreover, a 30-station globally uniform network outperforms a 90-station network of high-quality but spatially clustered stations. This study provides an objective and quantitative solution for establishing efficient and reliable GNSS tracking networks, directly benefiting ACs and other high-precision applications. Full article

Fractional-order chaotic systems have emerged as powerful tools in secure communications and multimedia protection owing to their memory-dependent dynamics, large key spaces, and high sensitivity to initial conditions. However, most existing fractional-order image encryption schemes rely on fixed-order chaos and conventional solvers, which limit their complexity and reduce unpredictability, while also neglecting the potential of variable fractional-order (VFO) dynamics. Although similar phenomena have been reported in some fractional-order systems, the coexistence of hidden attractors and stable equilibria has not been extensively investigated within VFO frameworks. To address these gaps, this paper introduces a novel discrete variable fractional-order dark matter–dark energy (VFODM-DE) chaotic system. The system is discretized using the piecewise constant argument discretization (PWCAD) method, enabling chaos to emerge at significantly lower fractional orders than previously reported. A comprehensive dynamic analysis is performed, revealing rich behaviors such as multistability, symmetry properties, and hidden attractors coexisting with stable equilibria. Leveraging these enhanced chaotic features, a pseudorandom number generator (PRNG) is constructed from the VFODM-DE system and applied to grayscale image encryption through permutation–diffusion operations. Security evaluations demonstrate that the proposed scheme offers a substantially large key space (approximately $2^{249}$) and exceptional key sensitivity. The scheme generates ciphertexts with nearly uniform histograms, extremely low pixel correlation coefficients (less than 0.04), and high information entropy values (close to 8 bits). Moreover, it demonstrates strong resilience against differential attacks, achieving average NPCR and UACI values of about 99.6% and 33.46%, respectively, while maintaining robustness under data loss conditions. In addition, the proposed framework achieves a high encryption throughput, reaching an average speed of 647.56 Mbps. These results confirm that combining VFO dynamics with PWCAD enriches the chaotic complexity and provides a powerful framework for developing efficient and robust chaos-based image encryption algorithms. Full article

This work presents an analysis of the sub-ionospheric VLF transmitter signal disturbances which were detected more than one week before the Turkey–Syria EQ occurrence. We have applied the multi-terminator method when considering amplitude and phase variations of the TBB transmitter signal (Turkey), selected because of a good signal to noise ratio for the amplitude, a stable phase variation, and a ray-path propagation crossing the pre-seismic sensitive region, estimated from the combination of the Dobrovolsky area and the Fresnel zone. New spectral features, i.e., inflexions and jumps, are considered in this study, besides the minima and maxima investigated in. The spectral occurrence probabilities are derived at three specific locations: Graz facility, TBB station and EQ epicenter. We show that two main precursors occurred from 27 to 30 January, and from 31 January to 3 February. More important are the prior precursors detected from 23 January to 25/26 January, where anomaly fluctuations were found to be similar to those at the EQ epicenter area, approximately. A forecasting model is proposed, in which the main steps can provide, in the presence of spectral anomalies, first hints regarding the longitudinal locations of the seismic preparation zone. Full article

With the increasing demand for near real-time atmospheric water vapor monitoring, this study evaluates the performance of the open-source PRIDE PPP-AR software (version 3.0.5) for retrieving Zenith Total Delay (ZTD) and Integrated Water Vapor (IWV) over the African continent over a one-year period. PRIDE PPP-AR is compared with established PPP-AR and PPP solutions, including CSRS-PPP, IGN-PPP, and NGL and using GipsyX, ERA5, and IGS products as references. A robust methodology combining time series processing and statistical evaluation was adopted. Multiple tools were leveraged to ensure a comprehensive performance analysis of GNSS data from seven stations in Africa, where such studies remain scarce. The results show that PRIDE PPP-AR achieves ZTD accuracy comparable to GipsyX (RMSE < 6 mm, R² ≈ 0.99) and performs at a similar level to NGL and CSRS-PPP. Compared to the other solutions, PRIDE PPP-AR has an accuracy similar to CSRS-PPP and NGL, but slightly better than IGN-PPP, in line with ERA5 and IGS references. For IWV retrieval, comparisons with ERA5 indicate RMSE values of about 1.5 to 2.7 kg/m², depending on station location and climatic conditions. IWV variability tends to increase towards the equator, where the recorded fluctuations are higher than in subtropical zones. In addition, collocated radiosonde (RS) measurements in Abidjan confirm good agreement, further validating the reliability of the software. This study highlights the potential of GNSS meteorology, in providing reliable spatiotemporal IWV monitoring and indicates that the PRIDE PPP-AR is ready for the high precision meteorological applications in African regions. These results offer promising prospects for spatiotemporal studies through African multi-GNSS networks and the PRIDE PPP-AR approach. Full article

The precise extractions of tree components such as wood (i.e., trunk and branches) and leaves are fundamental prerequisites for obtaining the key attributes of trees, which will provide significant benefits for ecological and physiological studies and forest applications. Terrestrial laser scanning technology offers an efficient means for acquiring three-dimensional information on tree attributes, and has marked potential for extracting the detailed tree attributes of tree components. However, previous studies on wood–leaf separation exhibited limitations in unsupervised adaptability and robustness to complex tree architectures, while demonstrating inadequate performance in fine branch detection. This study proposes a novel unsupervised model (NE-PC) that synergizes geometric features with graph-based path analysis to achieve accurate wood–leaf classification without training samples or empirical parameter tuning. First, the boundary-preserved supervoxel segmentation (BPSS) algorithm was adapted to generate supervoxels for calculating geometric features and representative points for constructing the undirected graph. Second, a node expansion (NE) approach was proposed, with nodes with similar curvature and verticality expanded into wood nodes to avoid the omission of trunk points in path frequency detection. Third, a path concatenation (PC) approach was developed, which involves detecting salient features of nodes along the same path to improve the detection of tiny branches that are often deficient during path retracing. Tested on multi-station TLS point clouds from trees with complex leaf–branch architectures, the NE-PC model achieved a 94.1% mean accuracy and a 86.7% kappa coefficient, outperforming renowned TLSeparation and LeWos (ΔOA = 2.0–29.7%, Δkappa = 6.2–53.5%). Moreover, the NE-PC model was verified in two other study areas (Plot B, Plot C), which exhibited more complex and divergent branch structure types. It achieved classification accuracies exceeding 90% (Plot B: 92.8 ± 2.3%; Plot C: 94.4 ± 0.7%) along with average kappa coefficients above 80% (Plot B: 81.3 ± 4.2%; Plot C: 81.8 ± 3.2%), demonstrating robust performance across various tree structural complexities. Full article

This paper analyzes the exploitability, economic viability, and impact of fleet-based 5G network extensions implemented in maritime environments, focusing on the Baltic Sea and Mediterranean as a case study. Through cost–benefit analysis and business model validation, we demonstrate how multi-hop 5G connectivity can reduce communication costs while improving service quality for maritime operators. Our findings indicate that implementing vessel-based 5G relay stations can achieve 80–90% coverage in key maritime corridors with a break-even period of 2–3 years. The study reveals that combining vessel-to-vessel relaying with strategic floating base stations can reduce connectivity costs by up to 40% compared to traditional satellite solutions, while enabling new revenue streams through premium services. We provide a detailed economic framework for evaluating similar implementations across different maritime routes and suggest policy recommendations for facilitating cross-border 5G maritime networks and introduce key use cases value creation for network extension. Full article