Search Results (2,240)

Regional development disparities remain a persistent global challenge, yet existing assessment approaches often face a trade-off between spatial detail and temporal coverage. Conventional socioeconomic statistics provide relatively reliable information but are typically limited by coarse spatial representation and low update frequency, whereas high-resolution remote sensing-based studies often focus on individual time points, making it difficult to capture the temporal evolution of regional development. Remote sensing observations provide valuable opportunities for regional development assessment by offering extensive spatial coverage and repeated observations over time. To address this gap, this study proposes a multisource remote sensing framework for characterizing the spatiotemporal dynamics of regional development in Chongqing Municipality across four temporal nodes (2014, 2016, 2018, and 2020). We first construct a county-level Development Intensity Index (DII) using socioeconomic indicators derived from statistical data. Subsequently, we integrate nighttime light, DEM, NDVI, and POI data to generate a 500 m gridded Comprehensive Spatial Development Index (CSDI), which captures spatial heterogeneity at a fine spatial scale. The CSDI exhibits strong correspondence with the DII, and its spatial validity is further corroborated through visual interpretation of Google Earth imagery. Results indicate that areas with higher development levels are predominantly concentrated in Chongqing’s central urban core, while less-developed counties are concentrated in the northeastern and southeastern peripheries. Although a general upward trend in development is observed across the study period, notable spatial disparities persist. Overall, the proposed CSDI-based framework offers an effective and replicable approach for gridded regional development assessment, with implications for targeted regional planning and differentiated policy design. Full article

An automated cartridge-based Vcheck M Canine Vector 8 Panel for qualitative detection of Leishmania spp. DNA in canine lymph node aspirates, an off-label specimen type, using laboratory qPCR as the comparator, was evaluated. Fifty-seven residual lymph node aspirate suspensions from dogs investigated for suspected canine leishmaniosis (CanL) were tested. Reference qPCR detected L. infantum DNA in 29 samples. Vcheck M was positive in 22/29 qPCR-positive samples and negative in 28/28 qPCR-negative samples, corresponding to positive percent agreement/sensitivity of 75.9% (95% CI, 56.5–89.7) and negative percent agreement/specificity of 100.0% (95% CI, 87.7–100.0). Agreement was substantial (Cohen’s kappa, 0.76), and discordance was asymmetric (McNemar p = 0.016). Vcheck-negative/qPCR-positive results were mainly observed at low qPCR parasite loads: 6/7 discordant samples contained ≤30 parasites/mL, whereas all samples with ≥500 parasites/mL were Vcheck positive. Among Vcheck-positive clinical samples, Vcheck Ct correlated inversely with log10 qPCR parasite load (Spearman rho = −0.77; p < 0.001). In a single-run dilution series, the lowest instrument-positive L. infantum standard was 10³ parasites/mL. Purified L. major, L. braziliensis, and L. tropica DNA were also detected. Vcheck M showed high specificity as a rapid rule-in test for Leishmania spp. detection in canine lymph node aspirates. However, negative results should not exclude infection in symptomatic or strongly suspected dogs and should be confirmed by qPCR, particularly when low parasite burden is plausible or when the result is critical for diagnostic or therapeutic decision-making. Full article

This study proposes a metaheuristic-based route optimization framework that balances earthworks in highway design by referencing a constant-slope “zero polygon.” The aim is to distribute excavation and embankment volumes evenly between fixed endpoints. The positions of Point of Intersection nodes and curve radii are optimized using the Social Spider Optimization (SSO) algorithm, guided by an objective function that minimizes the weighted signed distances between the route and the zero polygon while enforcing geometric constraints such as minimum curve radius, tangent length, and alignment continuity through penalty terms. Applications on two terrains with varying slopes demonstrate that the calibrated model substantially improves the cut–fill balance compared with both manual and pre-calibration solutions: the absolute difference between excavation and embankment volumes decreased from thousands to hundreds of cubic meters, and the cut–fill ratio fell below 0.1 in representative cases. Coupling a swarm-intelligence search with an interpretable geometric reference line, the framework offers a reproducible decision-support tool for preliminary corridor design. It enhances transparency and reduces reliance on trial-and-error practice. It targets preliminary, open-terrain corridor selection under a constant longitudinal slope without vertical curves, and does not yet incorporate land-use, environmental, or geotechnical constraints. Full article

Automotive millimeter-wave radar produces sparse point clouds with Doppler velocity and radar cross-section (RCS), but graph detectors typically use a shared representation for semantic prediction and box regression despite their different propagation requirements. We propose multi-GSO spectral filtering (MGSF), a residual module that filters radar features over geometry-, Doppler-, and RCS-defined graph shift operators and fuses diffusion and residual components with a node-adaptive gate. MGSF-TD applies full multi-GSO refinement to semantic prediction and geometry-only refinement to box regression. On the complete RadarScenes validation set, MGSF-TD improves the official RadarGNN checkpoint from 60.19 to 60.59 mAP and from 74.06 to 75.10 mean foreground F1 (FG-F1). Across three MGSF-TD training seeds, the FG-F1 margin under RCS noise increases from $+1.15$ at 3 dBsm to $+2.38$ at 20 dBsm; seed-42 full-validation mAP margins are $+0.24$, $+1.07$, and $+1.82$. Controls show that geometry-only diffusion explains part of the gain and the RCS operator contributes most clearly at low-to-moderate noise, whereas a parameter-matched widened baseline matches or exceeds MGSF-TD under severe RCS and Doppler corruption. Cross-sensor diagnostics reproduce the Doppler failure trend but not the severity-dependent RCS gain. MGSF-TD therefore offers a balanced, physically interpretable operating point rather than a universal robustness gain. Full article

The escalating penetration of distributed photovoltaic (PV) systems has intensified grid-connected voltage violations, posing severe challenges to the stability of distribution networks. This paper first investigates the mechanisms of voltage violations at 35 kV substations and 380 V consumer-side terminals under high-penetration scenarios. It is demonstrated that PV integration elevates line voltage, with the voltage profile at any given node being governed by the equivalent net load—defined as the offset between total demand and PV generation—downstream of that node. Subsequently, the impacts of critical operating conditions, including PV penetration levels, line impedance, and dynamic meteorological variations, are quantitatively analyzed. Simulation results characterize voltage fluctuation patterns under diverse variables, such as varying PV outputs, line parameters, and interconnection points, thereby validating the theoretical derivation. Finally, an integrated management strategy, coupling coordinated reactor compensation with voltage-source inverter (VSI) control, is proposed. Simulation results across multi-dimensional complex scenarios verify the effectiveness of the proposed strategy in suppressing voltage violations and enhancing grid resilience. Full article

Robot person following (RPF) in dense crowds requires a mobile robot to maintain an appropriate relative position with respect to a moving target while avoiding surrounding pedestrians and satisfying rear-following and social constraints. This paper proposes PPO-GAT-Follow, an interaction-aware reinforcement learning framework for dense-crowd RPF under geometric visibility loss with available target-relative pose estimates. The follower, target pedestrian, and surrounding pedestrians are represented as graph nodes, and a graph attention encoder models their local interactions. A task-oriented reward mechanism jointly accounts for target maintenance, visibility preservation, collision avoidance, proximity-aware social compliance, rear position maintenance, post-arrival stabilization, and action stability. Experiments are conducted in IR-SIM under fixed-route and random-route settings, with comparisons against MPC, DWA, SFM, and an adapted SARL baseline. In the fixed-route setting with 12 background pedestrians, PPO-GAT-Follow achieves a task success rate of $98.8\%$ and a collision rate of $1.1\%$, improving task success by 10.9 percentage points over MPC. In the random-route setting at the training density, it achieves $83.1\%$ task success and an SPL of $0.815$, outperforming MPC by 18.3 percentage points in task success; at this density, it also surpasses SARL in the main task-level metrics. Zero-shot evaluations across crowd densities, together with structural and reward ablations, reward weight sensitivity analysis, tolerance shift tests, multi-seed training, and stress testing under target pose noise and heterogeneous pedestrian dynamics, further demonstrate the effectiveness and reliability of the proposed framework. Gazebo-based validation also demonstrates system integration feasibility with localization, point cloud-based surrounding pedestrian perception, tracking, and UWB-like target-relative pose input. Nevertheless, visual target identification, re-identification, and perception-level occlusion recovery remain outside the scope of the present validation. Full article

Coastal cities protected by artificial structures are vulnerable to wave overtopping during typhoons, where flooding can occur even when the still water level stays below the structure crest. This study develops EurOtop-based wave overtopping modules within the cross-scale SCHISM-WWM modelling framework and applies the coupled model to Marine City, Busan, South Korea. At each time step, the module extracts the local water level and wave conditions at a coastal-structure pair-node, computes the overtopping or overflow discharge from the EurOtop formulae while accounting for wave direction, roughness reduction, and crest-width transfer losses, and injects it as a landward source term for inland flood propagation. The framework is evaluated against two typhoon events. For Typhoon Hinnamnor in 2022, comparison with the CCTV-based overtopping record reproduces the timing and intensity of overtopping. For Typhoon Chaba in 2016, validation against field-surveyed inundation marks shows that the modelled maximum inundation depth matches the observed spatial pattern and the survey-point observations to within a mean absolute difference of about 12 cm. These results demonstrate the framework’s applicability to overtopping-induced compound flooding in urbanised coastal cities and, since it is fully coupled within the SCHISM-WWM codebase, its capacity to incorporate further drivers such as pluvial and fluvial flooding. Full article

With the accelerating commercialization of autonomous driving technology, robotaxis have emerged as a significant force in reshaping urban transportation systems. However, their service efficiency and system resilience depend heavily on the spatial layout and network structure of pick-up points. Utilizing the Waymo Open Motion Dataset comprising 2,316,135 motion trajectories, this study proposes a multi-stage analytical framework to systematically identify autonomous vehicle pick-up points and evaluate the vulnerability of the constructed network. First, trajectories are stratified using kinematic criteria, and K-Means clustering is applied to 12 kinematic and geometric features to distinguish genuine pick-up and drop-off (PUDO) events from traffic-related stops. The identified pick-up points are then aggregated into spatial grid nodes to construct an undirected, unweighted network. Finally, network vulnerability is assessed by simulating random failures and three types of targeted attacks. The findings reveal that: (1) identifies 21,503 candidate pick-up points exhibiting pronounced curbside-departure characteristics from 111,321 stop-to-go trajectories. (2) The network exhibits global sparsity and high local clustering; the largest connected component (LCC) encompasses 66.1% of nodes, forming a primary service area covering the urban core, while the remaining 33.9% are scattered across 377 isolated fragments. (3) The network demonstrates strong robustness against random failures but is highly vulnerable to targeted attacks on high-betweenness centrality nodes. Removing merely the top 5% of such nodes reduces the LCC to 36.7%, and at 20% removal the LCC drops to 3.5% with near-complete loss of global efficiency. This study contributes a reproducible, machine learning-based methodology for extracting pick-up points from trajectory data and reveals structural vulnerabilities in autonomous driving service networks from a complex network perspective, providing quantitative evidence for enhancing the resilience of future urban intelligent transportation systems. Full article

The generalized Burgers–Huxley (gBH) equation arises as a canonical model in nerve-pulse propagation (generalizing the Hodgkin–Huxley/FitzHugh–Nagumo excitable-media framework), in population dynamics with Allee-threshold reaction kinetics, and in nonlinear wave propagation in dispersive media; accurate benchmark solutions are essential for quantitative predictions in these domains. We couple the fourth-order exponential time differencing scheme ETDRK4 with a Chebyshev collocation spatial discretization and a linear boundary-lifting procedure to solve the gBH equation on a bounded interval with non-homogeneous Dirichlet data. On the canonical Ismail–Raslan–Rabboh travelling-wave benchmark the scheme attains $L^{\infty }$ errors at the level of floating-point round-off (∼10⁻¹⁹ absolute, ∼10⁻¹⁵ relative) with as few as $N=2$ collocation points and a single time step of size $\Delta t=1.0$—that is, three total nodes and one ETDRK4 advance. In strongly nonlinear regimes ($\gamma =0.1, 0.3, 0.5, 0.9$) the scheme exhibits approximately $\mathcal{O}(\Delta t^{2.45})$ temporal convergence across all four parameter values, consistent with the classical Hochbruck–Ostermann order reduction for exponential integrators on parabolic PDEs with non-homogeneous Dirichlet data. Used as a high-accuracy probe, the scheme provides a diagnostic of independent interest: the wave-speed formula of Wang, Zhu and Lu, still appearing as the exact-solution benchmark in numerical studies as recently as 2020, does not satisfy the partial differential equation. The corrected formula stated by Deng and verified symbolically by Appadu and Tijani is the unique value that makes the travelling-wave ansatz a genuine solution. We derive the residual associated with Wang’s formula in closed form, $R=\frac{\gamma A_1}{2}(A_2-A_2^{\mathrm{W}})(1-v^2)$, and show both analytically and numerically that reported errors for schemes benchmarked against Wang’s formula coincide with the analytical wave-profile gap $\frac{\gamma A_1}{2}|A_2-A_2^{\mathrm{W}}|$ rather than with true scheme accuracy. At the Ismail benchmark this gap equals $3.748\times {10}^{-7}$, which matches the N- and $\Delta t$-independent plateau observed when the scheme is measured against Wang’s profile. In the nerve-pulse and excitable-media interpretation, the two formulas correspond to action-potential propagation speeds of opposite sign at the Ismail benchmark, underscoring that the correction is not a mere algebraic curiosity but changes the qualitative physical prediction of the model. Full article

To address the problem that hotel recommendations in tourism activities do not consider the spatial relationship between hotels and scenic spots and the cost of tour routes, we construct a tourism hotel recommendation model based on ISTING-AGNES machine learning and an IDFST optimal route algorithm. Firstly, a scenic spot spatial clustering model based on the ISTING-AGNES machine learning algorithm is constructed, including a scenic spot ISG spatial topological model based on the neighborhood cell growth algorithm and an ISTING-AGNES machine learning algorithm based on the scenic spot ISG spatial topological model, which can realize spatial dimension reduction in tourist cities and construct tourism sub-regions for recommending scenic spots and hotels. Secondly, taking the tourism sub-regions as the modeling scope, a tourism hotel recommendation model based on the IDFST optimal route algorithm is constructed in which a closeness model between the tourists’ interests and the attributes of scenic spots in the sub-region is established to recommend the most matched scenic spots for tourists. Then, based on the recommended scenic spots, a tourism sub-interval optimal route algorithm based on IDFST and a tourism hotel recommendation model based on the optimal route decision forest algorithm are established to search for the global optimal tour route—with hotels as the starting and ending points and scenic spots as nodes—and to recommend the hotel with the most cost-effective tour route for tourists. The experiments prove that the constructed algorithm can output the hotel with the best geospatial location and the lowest tour route cost. Under the experimental conditions, compared with the hotel recommended by the weighted centroid positioning algorithm, the cost optimization rate reaches 5.98%. Compared with the greedy mountain climbing algorithm and the greedy BFS algorithm, the cost optimization rates reach 14.73% and 16.03%, proving that the constructed algorithm is feasible and advantageous over the traditional algorithms. Full article

Industrial Internet of Things (IIoT) edge computing supports latency-sensitive services through task offloading to distributed edge resources. However, large-scale emergencies such as node failures and traffic surges may trigger cascading failures, leading to severe performance degradation and poor post-crisis recovery. Existing offloading methods mainly optimize operational efficiency under normal conditions while overlooking resilience against cascading disruptions. To address this issue, we propose Cascade-Emergency Resilient Offloading (CERO), an adversarial deep reinforcement learning framework for resilient task offloading in IIoT edge computing. Distinct from existing works, CERO introduces a structure-aware shared node encoder to capture heterogeneous topological roles of edge nodes, providing critical structural information for cascade-aware decision making, and incorporates cascade-oriented adversarial training to enhance robustness against compound disturbances. CERO integrates structure-aware state representation, minimax adversarial training, and potential-based reward shaping to learn resource-allocation policies balancing task efficiency and system resilience. By interacting with dynamically generated crisis scenarios, the agent learns resilient offloading policies and achieves high post-crisis recovery performance after cascading disruptions. All performance evaluations are conducted via discrete-event simulation experiments. Simulation results for normal, single-crisis, and compound-crisis scenarios show that CERO achieves comparable task efficiency under normal conditions and significantly superior post-crisis recovery performance compared to conventional rule-based strategies. In the hardest compound-crisis case involving simultaneous node failures and load surges, CERO achieves a post-recovery task-completion rate of 97.8%, surpassing the best rule-based baseline by more than 63 percentage points. Statistical significance is confirmed by the Wilcoxon signed-rank test with Bonferroni correction over 10 independent runs. These results demonstrate that CERO effectively improves the robustness and recoverability of IIoT edge-computing systems under cascading emergency scenarios. Full article

Vehicle Ad Hoc Networks (VANETs) face critical challenges in trust management, privacy preservation, and scalability, particularly with the integration of 5G networks in Intelligent Transportation Systems (ITS). Traditional centralized trust models present single points of failure and privacy concerns that compromise network security and user anonymity. This paper presents a novel decentralized trust model leveraging blockchain technology, Interplanetary File System (IPFS) integration, and post-quantum cryptographic algorithms to address these limitations. Our proposed TrustChain-VANET framework implements advanced privacy-preserving encryption techniques including threshold and homomorphic encryption, geographical sharding for scalability, and edge-assisted consensus mechanisms. Performance evaluation demonstrates significant improvements: 40% reduction in authentication latency (90–120 ms vs. 150–300 ms), 90% malicious node detection rate (+15% improvement), 300% increase in transaction throughput (2000–2150 TPS), and 100% scalability enhancement supporting up to 5000 nodes. The system integrates seamlessly with 5G network slicing (URLLC, eMBB, mMTC) while maintaining quantum resistance through CRYSTALS-Dilithium, KYBER, and FALCON algorithms. Real-world deployment considerations including OBU computational constraints, standardization gaps, and energy efficiency are comprehensively analyzed. Results indicate that the proposed decentralized approach provides robust security, enhanced privacy, and improved scalability for next-generation vehicular networks, making it suitable for large-scale ITS deployment. The main contribution of this work is the development of a unified TrustChain-VA 48NET framework. The proposed framework integrates blockchain-based trust management, IPFS-assisted storage, 5G network slicing, Mobile Edge Computing (MEC), geographical sharding, and post-quantum cryptographic mechanisms within a single architecture for next-generation VANET environments. While these technologies have been investigated separately in previous studies, this work presents a consolidated framework that analyzes their interoperability, identifies integration challenges, and evaluates their combined impact on trust management, scalability, privacy preservation, and deployment feasibility in Intelligent Transportation Systems. Full article

Rice-milling quality is conventionally inspected post-process, leaving operators unable to correct breakage as it occurs. We present and quantitatively validate a cloud-mediated closed-loop architecture that couples a YOLO26 machine-vision pipeline to Arduino-based actuator control on a laboratory rice mill. The image-acquisition node uploads frames to a cloud repository; an inference and analysis node retrieves them, runs YOLO26 detection with a hybrid post-process classifier to estimate the broken-rice fraction, and issues a command to an Arduino microcontroller that drives PWM-modulated motor and vibrator actuators. The detector achieved a mean Average Precision of 0.951 (peak precision 0.99, peak recall 0.98) on a held-out test set of 100 images. In a matched comparison against an open-loop baseline ($n=196,000$ kernels, broken fraction 21.04%), closed-loop operation ($n=114,000$ kernels) reduced the broken fraction to 6.19%, an absolute improvement of 14.85 percentage points (two-proportion z-test: $z=112.8$, $p<0.001$, 95% CI for the absolute reduction: 14.62–15.08 pp). Dynamic analysis identified a near-linear plant gain of 1.0–1.5% breakage per 1% PWM, providing the empirical basis for future formal PID and Model Predictive Control synthesis. The principal empirical contribution is a quantitative characterisation of the PWM-to-breakage transfer relationship of a rice-milling actuator under deep-learning-derived quality feedback, together with a matched open-loop/closed-loop demonstration that this feedback loop moves the laboratory prototype from non-compliant to Grade A-equivalent quality at constant throughput. The lab-scale prototype is not yet industrial; a roadmap to pilot-scale deployment is outlined. Full article

The Bohr radius is normally presented as a non-relativistic length scale. Less widely discussed is that Bohr’s 1913 work also indicated how the radius formula changes when the orbital velocity is not negligible compared with the speed of light. We revisit this relativistic prescription and show that, for a point nucleus and a one-electron Coulomb field, it gives $a_{0,r}^{\left(Z\right)}={\displaystyle \frac{a_0}{Z}}\sqrt{1-Z^2{\alpha }^2},$ which is exactly the most-probable radius obtained independently from the Dirac $1s_{1/2}$ radial probability density. The two radii are calculated independently and are found to be analytically identical. This equality does not derive the Dirac result from the Bohr model; rather, it shows that Bohr’s relativistic circular-orbit prescription selects the same radial scale as the maximum of the Dirac probability distribution. We also show that the same construction extends to the node-free circular Dirac excited states, for which $r_{\mathrm{mp}}^D(n,\kappa =-n)=(a_0/Z)n\sqrt{n^2-Z^2{\alpha }^2}$. We emphasize throughout that most-probable radii are distinct from expectation values and from empirical radii of many-electron atoms. Full article

The optimal expansion of unbalanced three-phase distribution networks in non-interconnected zones requires the simultaneous resolution of two highly complex planning decisions: the selection of feeder routes and the sizing of conductors. This problem, formulated as a non-convex mixed-integer nonlinear program (MINLP), poses significant computational challenges due to the combinatorial explosion of radial topologies, discrete conductor choices, and the nonlinearity of three-phase power-flow equations. While metaheuristics offer flexible exploration, they lack optimality guarantees and repeatability, whereas exact MINLP solvers provide rigorous solutions but become computationally intractable for systems of realistic size. To overcome these limitations, this paper introduces a novel hybrid exact–metaheuristic framework that synergistically combines the global exploration capabilities of the Equilibrium Optimizer (EO) with the rigorous evaluation power of an exact MINLP model. In this cascade architecture, EO efficiently navigates the discrete space of radial topologies, while the exact MINLP stage, solved using BONMIN with an interior-point branch-and-bound scheme, optimizes conductor selection and evaluates the full annualized cost, rigorously enforcing voltage, ampacity, and physical constraints. The proposed methodology was validated on 10-, 30-, 50-, and 110-node test systems derived from real Colombian non-interconnected zones (Nuquí, Leticia, San Andrés, and a large-scale urban case). Comparative analysis against pure metaheuristics (SSA, GWO, VSA) and standalone MINLP demonstrates that EO-MINLP consistently yields the lowest total annualized costs, achieving savings of up to 0.42%, 0.71%, and 1.36% over the best pure metaheuristic for the 10-, 30-, and 50-node systems, respectively. Crucially, the hybrid strategy dramatically enhances scalability, reducing the standalone MINLP computational time by 15.79%, 78.68%, and 88.95% for these cases, while preserving solution quality and improving repeatability (standard deviation reduced from over 1.2% to as low as 0.11%). For the challenging 110-node system, where the standalone MINLP proved computationally infeasible, the proposed method successfully delivered a feasible, high-quality solution with a standard deviation of just 0.43%, confirming its practical applicability to large-scale planning. These results demonstrate that the EO-MINLP framework provides a robust, scalable, and economically superior tool for the cost-effective design of unbalanced distribution networks, effectively bridging the gap between the flexibility of stochastic search and the rigor of mathematical programming. Full article