Search Results (1,754)

Improving cyclist safety conditions in the urban context is a key strategy to promote sustainable transport modes and reduce noise and environmental pollution. Recent plans have also addressed this point. In September 2020, the UN General Assembly declared the Decade of Action for Road Safety 2021–2030, aiming to reduce the number of road deaths by at least half. To achieve this task and highlight the risk factor, after collecting and pre-processing cyclist crash data in the city of Rome between 2013 and 2020, Random Forest and Ordered Logistic Regression models are proposed. The crash dataset is also enriched with vehicular speed and flows, and geographical information. A DBSCAN Clustering Analysis is also proposed to identify anomalous areas in the city. The findings show that the presence of cycle paths, the presence of anthropic activities, such as shops, schools, and universities, play a risk mitigation role. Conversely, vehicular speed and heavy vehicles emerge as the main detected risk factors. Finally, spatial analysis indicates that commercial activities reduce cycle path safety due to complex interactions with other road users. Furthermore, historic areas present unique risks driven by pedestrian flows and poor road surfaces, despite low vehicular traffic. Full article

A comprehensive study on the distribution characteristics and exploitation strategies of remaining oil was carried out in the Ordovician ultra-deep fault-controlled fractured–vuggy carbonate reservoir within the Shunbei No. 1 strike–slip fault zone. This research addresses challenges such as severe watered-out and gas channeling encountered during multi-stage development, marking a shift toward a development phase focused on residual oil recovery. By integrating seismic attributes, drilling, logging, and production performance data—and building upon previous methodologies of “hierarchical constraint and genetic modeling”—a three-dimensional geological model was constructed with a five-tiered architecture: strike–slip fault affected zone, fault-controlled unit, cave-like structure, cluster fillings, and fracture zone. Numerical simulations were subsequently performed based on this model. The results demonstrate that the distribution of remaining oil is dominantly controlled by the coupling between key geological factors—including fault kinematics, reservoir architecture formed by karst evolution, and fracture–vug connectivity—and the injection–production well pattern. Three major categories with five sub-types of residual oil distribution patterns were identified: (1) local low permeability, weak hydrodynamics; (2) shielded connectivity pathways; and (3) Well Pattern-Dependent. Accordingly, two types of potential-tapping measures are proposed: improve well control through optimized well placement and sidetrack drilling and reservoir flow field modification via adjusted injection–production parameters and sealing of high-permeability channels. Techniques such as gas (nitrogen) huff-and-puff, gravity-assisted segregation, and injection–production pattern restructuring are recommended to improve reserve control and sweep efficiency, thereby increasing ultimate recovery. This study provides valuable guidance for the efficient development of similar ultra-deep fractured–vuggy carbonate reservoirs. Full article

Rapid urban growth in many Global South cities strains waste systems and slows the shift to circular economy (CE) practice. Colombo, Sri Lanka, exemplifies this challenge, where overstretched state-led services coexist with neighborhood groups, NGOs, and informal collectors driving circular activities. This study adopts a layered social network diagnostic framework to examine how community-based waste management networks operate and how they might be reshaped to enable a city-wide CE. Using survey and interview data from 185 actors, information-sharing, collaboration, and resource-exchange networks are analyzed separately and in combination. The results reveal three principal findings: (i) Social-capital forms operate largely in parallel, with limited conversion between information, collaboration, and material exchange; (ii) the network exhibits “thin bridges and thick clusters,” in which a small number of NGO hubs mediate most cross-cluster connectivity; (iii) layers operate with mismatched coordination logics, producing gaps between awareness, collective action, and resource mobilization. As a result, ideas circulate widely but rarely translate into joint projects, local teams coordinate effectively yet remain isolated, and material flows depend on a narrow and fragile logistics spine. By diagnosing these structural misalignments, this study demonstrates a key novelty: scalable circular economy adoption depends not only on technology and policy but also on the design and alignment of underlying coordination networks. Full article

With the gradual rise of battery-powered ships, the high-power charging demand during berthing is poised to exacerbate the peak-to-valley difference in the port grid, possibly leading to grid congestion and logistical disruption. To address this challenge, this paper proposes a bi-level coordinated scheduling scheme across both logistical operations and energy flow dispatch. Initially, by developing a refined model for the dynamic power characteristics of quay crane (QC) clusters, the surplus power capacity that can be stably released through an orderly QC operational delay is quantified. Subsequently, a hybrid AC/DC ship–shore interconnection architecture based on a smart interlinking unit (SIU) is proposed to utilize the QC peak-shaving capacity and satisfy the increasing shore power demand. In light of these, at the logistics level a coordinated scheduling of berths, QCs, and ships charging is performed with the objective of minimizing port berthing operational costs. At the energy flow level, the coordinated delay in QC clusters’ operations and SIU-enabled power dispatching are implemented for charging power support. The case studies demonstrate that, compared with the conventional independent operational mode, the proposed coordinated scheduling scheme enhances the shore power supply capability by utilizing the QC peak-shaving capability effectively. Moreover, as well as satisfying the charging demands of electric ships, the proposed scheme significantly reduces the turnaround time of ships and achieves a 39.29% reduction in port berthing operational costs. Full article

In the context of high-quality urbanization, a key challenge for urban agglomerations is the structural mismatch between economic linkages and rapidly expanding information interactions, which may constrain the performance of coupled systems under shocks. Taking the Beijing–Tianjin–Hebei (BTH) urban agglomeration as a case, we construct an inter-city economic network from cross-city corporate investment ties and an information network from online attention flows, and further derive an economic–information coupled network using a coupling-coordination framework. Using social network analysis and resilience assessment (hierarchy, assortativity, clustering, and disruption simulations), we compare network structures in 2013 and 2023 and evaluate how the structural gap shapes coupled resilience. Results show that (i) economic ties strengthen steadily but moderately, whereas the information network expands faster and becomes more inclusive, widening the structural gap between “virtual” and “material” flows; (ii) despite a persistently high correlation between the two layers, coordination declines, indicating increasing local divergence in link organization; and (iii) resilience improves overall, but differentiation remains: the information network gains robustness through decentralization and redundancy, while the economic network is more sensitive to targeted removals of core nodes, and the coupled network exhibits intermediate performance. These findings suggest that enhancing BTH resilience requires strengthening cross-jurisdictional redundant links and reducing excessive dependence on core corridors to better translate information interactions into balanced economic connectivity. Full article

The Dartmoor Estate Tea plantation in Devon, UK, benefits from a unique microclimate and diverse soil conditions, which, together with its different processing methods, contribute to the distinctive flavours and chemical profiles of its teas. Objectives: The chemical diversity of Dartmoor tea was assessed via samples collected during processing of green and black tea. Methods: Leaf samples were collected during the processing of green and black tea and analysed using Flow Infusion Electrospray Ionisation Mass Spectrometry (FIE-MS). Results: For green tea processing, random forest regression identified features associated with the processing steps, resulting in a total of 272 m/z explanatory features. The analysis of black tea processing (4 h and overnight oxidation prior to roasting) yielded 209 discriminatory m/z features (4 h) and the model for the overnight oxidation and roasting treatments yielded 605 discriminatory m/z features. K-means clustering was performed on the percentage of relative abundance of the discriminatory m/z features. This grouped the discriminatory m/z features into 15 clusters of features showing similar trends across the processing stages. Functional and structural enrichment analysis was performed on each of the clusters and significant metabolic pathways included metabolism and biosynthesis of flavonoids, amino acids and lipids, the Pentose phosphate pathway, and the TCA cycle. Many discriminatory features were putatively classified as catechin-derived flavan-3-ols and flavonol glycosides. Conclusions: This research highlights the complex role that processing plays in shaping tea quality. It provides valuable insights into the metabolic pathways that influence tea production and emphasises how these factors determine the final chemical profile and sensory characteristics of tea. Full article

Designing high-speed aircraft for wide-speed-range operation remains a major aerodynamic challenge. This study investigates the unsteady aerodynamics of a continuously morphing airfoil from transonic to hypersonic regimes. A smooth morphing trajectory is constructed among transonic, supersonic, and hypersonic baseline shapes, and analyzed via high-fidelity unsteady Reynolds-averaged Navier–Stokes (URANS) simulations with a radial basis function (RBF) dynamic mesh. Two processes are examined: pure geometric morphing at fixed Mach numbers (Ma), and morphing coupled with flight acceleration. Key findings reveal two distinct adaptation features: (1) Transonic flow is highly sensitive to morphing (28.8% drop in lift-to-drag ratio), while supersonic flow is robust (<5% variation). (2) During coupled acceleration, the flow transitions smoothly—the shock evolves from a detached bow wave to an attached oblique structure, and the adaptive airfoil maintains a lift-to-drag ratio above 4 across Ma = 0.8–6. Additionally, wake vorticity transitions from organized shear layers to multi-scale clusters. These results elucidate the flow physics mechanism of continuous morphing and provide a framework for designing adaptive wide-speed-range aircraft. Full article

Water resources constitute a foundational strategic resource, and the efficiency of their spatial allocation profoundly impacts national sustainable development. This study integrates multi-regional input–output modeling, complex network analysis, and exploratory spatiotemporal data analysis methods to systematically examine the patterns, network structures, and spatiotemporal evolution characteristics of virtual water flows across 30 Chinese provinces from 2010 to 2023. Findings reveal the following: Virtual water flows underwent a three-stage evolution—“expansion–convergence–stabilization”—forming a “core–periphery” structure spatially: eastern coastal and North China urban clusters as input hubs, while East–Northeast–Northwest China served as primary output regions; The virtual water flow network progressively tightened and segmented, evidenced by increased network density, shorter average path lengths, and enhanced clustering coefficients and transitivity. PageRank analysis reveals significant Matthew effects and structural lock-in within the network; LISA time paths indicate stable spatial structures in most provinces, yet dynamic characteristics are prominent in node provinces like Guangdong and Jiangsu. Spatiotemporal transition analysis further demonstrates high overall system resilience (Type₀ transitions accounting for 47%), while abrupt transitions in provinces like Hebei and Liaoning are closely associated with national strategies and industrial restructuring. This study provides theoretical and empirical support for establishing a coordinated allocation mechanism between physical and virtual water resources and formulating differentiated regional water governance policies. Full article

Urban parks are central to public health and equity, yet less is known about how park travel distance, park “attractor” types, and time-of-day visitation rhythms co-evolved through and after the COVID-19 pandemic. Using anonymized smartphone mobility traces for public parks in Las Vegas, USA (2018–2022), we construct weekly origin–destination flows between census block groups (CBGs) and parks and link origins to socio-economic indicators. We first estimate visitor-weighted mean travel distance with a segmented time-series model that allows pandemic-related breakpoints. Results show that average park-trip distance (≈8.4 km pre-pandemic), including a substantial share of long-distance trips (≈52% of visits), contracted sharply at the onset of COVID-19, and that both travel radii and seasonal excursion peaks only partially rebounded by 2022. Next, cross-sectional OLS/WLS models (R² ≈ 0.08–0.14) indicate persistent socio-spatial disparities: CBGs with higher educational attainment and larger shares of Black and Hispanic residents are consistently associated with shorter park-trip distances, suggesting constrained recreational mobility for socially disadvantaged groups. We then identify a stable two-type park typology—local versus regional attractors—using clustering on origin diversity and long-distance share (silhouette ≈ 0.46–0.52); this typology is strongly related to visitation volume and temporal usage profiles. Finally, mixed-effects models of evening and late-night visit shares show that regional attractors sustain higher nighttime activity than local parks, even as citywide evening/late-night visitation dipped during the mid-pandemic period and only partly recovered thereafter. Overall, our findings reveal a durable post-pandemic re-scaling of park use toward more proximate, CBG-embedded patterns layered on enduring inequities in access to distant, destination-oriented parks. These insights offer actionable evidence for equitable park planning, targeted investment in high-need areas, and time-sensitive management strategies that account for daytime versus nighttime use. Full article

Population migration is an important indicator for measuring the interactions and connections between cities. Analyzing the spatiotemporal distribution pattern of the migration flows between cities is highly important to understanding urban development relationships and regional structures. From a spatiotemporal perspective, we conduct STFlowLISA (Space-Time Flow Local Indicator of Spatial Association) spatiotemporal autocorrelation analysis using population migration data from Hubei Province from 2018 to 2023 and, on this basis, calculate the spatiotemporal hub index and identify spatiotemporal clusters. The research aims to reveal the regional spatial structure shaped by migration flows and compare it with that of existing town system planning to evaluate deviations and provide a decision-making basis for the regional synergistic development of Hubei Province. The key findings include: (1) the population migration flows between Wuhan and its surrounding cities mostly exhibit a spatiotemporal distribution pattern of HH (high-value agglomeration), whereas the long-distance migration flows between eastern and western Hubei mostly follow a pattern of LL (low-value agglomeration), and these urban connections show improvement after the epidemic; (2) the spatiotemporal hubs of Hubei Province demonstrate a “core-periphery” structure with Wuhan as the absolute core, while Xiangyang’s role as a subcenter does not meet planning expectations; and (3) urban spatiotemporal clusters are dense in the east and sparse in the west, with Enshi and Shiyan showing disconnection from the main network, which deviates from the planned polycentric spatial pattern. By examining the spatiotemporal autocorrelation of migration flows, this study enriches the empirical understanding of regional spatial structure in Hubei Province within the framework of classical spatial theory and highlights the necessity of incorporating dynamic flow analysis into regional planning and policy-making. Full article

With the rapid growth of vessel traffic and the widespread adoption of the Automatic Identification System (AIS) in recent years, analyzing maritime traffic flow characteristics has become an essential component of modern maritime supervision. Clustering analysis is one of the primary data-mining approaches used to extract traffic patterns from AIS data. Addressing the challenge of assigning appropriate weights to the multidimensional features in AIS trajectories, namely latitude and longitude, speed over ground (SOG), and course over ground (COG). This study introduces an adaptive parameter optimization mechanism based on evolutionary algorithms. Specifically, Particle Swarm Optimization (PSO), a representative swarm intelligence algorithm, is employed to automatically search for the optimal feature-distance weights and the core parameters of Density-Based Spatial Clustering of Applications with Noise (DBSCAN), enabling dynamic adjustment of clustering thresholds and global optimization of model performance. By designing a comprehensive clustering evaluation index as the objective function, the proposed method achieves optimal parameter allocation in a multidimensional similarity space, thereby uncovering maritime traffic clusters that may be overlooked when relying on single-dimensional features. The method is validated using AIS trajectory data from the Xiamen Port area, where 15 traffic clusters were successfully identified. Comparative experiments with two other clustering algorithms demonstrate the superior performance of the proposed approach in trajectory pattern analysis, providing valuable reference for maritime regulatory and traffic management applications. Full article

Intercity population flows are playing an increasingly pivotal role in shaping the spatial evolution and structural dynamics of urban networks. Drawing upon Amap Migration Data (2018–2023), this study maps China’s urban networks using social network analysis and identifies their key drivers using a temporal exponential random graph model. The findings reveal three primary insights: First, the overall network exhibits “high connectivity and strong clustering” traits. Enhanced efficiency in intercity resource allocation fosters cross-regional factor flows, resulting in multi-tiered connectivity corridors. Industrial linkages and policy interventions drive the development of a polycentric and clustered configuration. Second, the individual city network exhibits a core–periphery dynamic structure. A diamond-shaped framework dominated by hub cities in the national strategic regions directs factor flows. Development of strategic corridors enables peripheral cities to evolve into secondary hubs by leveraging structural hole advantages, reflecting the continuous interplay between network structure and geo-economic factors. Third, driving factors involve nonlinear interactions within a multi-layered system. Path dependence in topology, gradient potential from nodal attributes, spatial counterbalance between geographic decay laws and multidimensional proximity, and adaptive self-organization are collectively associated with the transition of the urban network toward a multi-tiered synergistic pattern. By revealing the dynamic interplay between network topology and multidimensional driving factors, this study deepens and advances the theoretical connotations of the “Space of Flows” theory, providing an empirical foundation for optimizing regional governance strategies and promoting high-quality coordinated development of Chinese cities. Full article

The removal of residual free chlorine ions from industrial wastewater is a critical step toward achieving sustainable and environmentally compliant water reuse. Excess chlorine in sludge collector water causes corrosion of process equipment, inhibits biological treatment, and leads to toxic discharge effects. In this study, an intelligent control strategy was developed for an ion-exchange-based dechlorination process to dynamically regulate chlorine concentration in the effluent stream. A pilot-scale ion-exchange filtration unit, designed with a nominal capacity of 500 L h⁻¹, was constructed using a strong-base anion-exchange resin to selectively adsorb chloride and free chlorine ions. A total of 200 experimental observations were obtained to characterize the nonlinear relationship between inlet flow rate and outlet chlorine concentration under varying operational conditions. Based on these experimental data, an Adaptive Neuro-Fuzzy Inference System (ANFIS) model was developed in MATLABR2025 to simulate and control the ion-exchange process. Two model-optimization techniques, Grid Partition + Hybrid and Subtractive Clustering + Hybrid, were applied. The subtractive clustering approach demonstrated faster convergence and superior accuracy, achieving RMSE = 0.147 mg L⁻¹, MAE = 0.101 mg L⁻¹, and R² = 0.993, outperforming the grid-partition model (RMSE ≈ 0.29, R² ≈ 0.97). The resulting ANFIS model was subsequently integrated into a MATLAB/Simulink-based intelligent control system for real-time regulation of chlorine concentration. A comparative dynamic simulation was performed between the proposed ANFIS controller and a conventional PID (Proportional-Differential-Integral) controller. The results revealed that the ANFIS controller achieved a faster response (rise time ≈ 28 s), lower overshoot (≈6%), and shorter settling time (≈90 s) compared to the PID controller (rise time ≈ 35 s, overshoot ≈ 18%, settling time ≈ 120 s). These improvements demonstrate the ability of the proposed model to adapt to nonlinear process behavior and to maintain stable operation under varying flow conditions. Full article

Cloud computing environments generate high-dimensional, large-scale, and highly dynamic network traffic, making intrusion diagnosis challenging due to evolving attack patterns, severe traffic imbalance, and limited availability of labeled data. To address these challenges, this study presents an unsupervised, cloud-centric intrusion diagnosis framework that integrates autoencoder-based representation learning with density-based attack categorization. A dual-stage autoencoder is trained exclusively on benign traffic to learn compact latent representations and to identify anomalous flows using reconstruction-error analysis, enabling effective anomaly detection without prior attack labels. The detected anomalies are subsequently grouped using density-based learning to uncover latent attack structures and support fine-grained multiclass intrusion diagnosis under varying attack densities. Experiments conducted on the large-scale CSE-CIC-IDS2018 dataset demonstrate that the proposed framework achieves an anomaly detection accuracy of 99.46%, with high recall and low false-negative rates in the optimal latent-space configuration. The density-based classification stage achieves an overall multiclass attack classification accuracy of 98.79%, effectively handling both majority and minority attack categories. Clustering quality evaluation reports a Silhouette Score of 0.9857 and a Davies–Bouldin Index of 0.0091, indicating strong cluster compactness and separability. Comparative analysis against representative supervised and unsupervised baselines confirms the framework’s scalability and robustness under highly imbalanced cloud traffic, highlighting its suitability for future Internet cloud security ecosystems. Full article

Background: Axillary lymph node (ALN) metastasis is a critical prognostic determinant in breast cancer (BC) that informs surgical management. However, surgically clearing the axilla carries morbidity, so less invasive methods of risk-stratifying patients are needed. ALN fine needle aspiration (FNA) is currently used to detect BC metastases, but these samples also contain immune cells. Methods: Cells obtained via FNA from BC-patient-derived ALNs were analysed using flow cytometry. Results: FNA acquires sufficient leukocytes for comprehensive immunophenotyping of reactive, patient-derived ALNs. All CD4⁺ and CD8⁺ T-cell subsets (naïve, terminal effector, central memory, and effector memory) and rarer (<2%) natural killer (NK) and plasmacytoid dendritic cell (pDC) populations are represented. Importantly, the immune-cell profile of one reactive ALN appears to reflect the immune status of the patient’s axilla. Furthermore, FNA captures immune differences between patients with ≤1 or ≥2 metastatic ALNs. Increased numbers of naïve CD4⁺ T cells, but fewer terminal effector, central memory, and effector memory subpopulations, were obtained from patients with ≥2 metastatic ALNs. Moreover, despite their sparse distribution pattern on whole-section immunohistochemistry (WSI), FNA revealed that CD56⁺ NK cell activation receptors were decreased in patients with ≥2 metastatic ALNs. Finally, FNA captured a decrease in pDCs in patients with ≤1 metastatic ALNs, despite their clustered distribution pattern on WSI. Conclusions: FNA is not only feasible for sampling leukocytes from reactive, patient-derived ALNs, but also identifies immune-cell profiles that reflect axillary tumour burden in BC. Thus, this technique could be used to risk-stratify BC patients in the future. Full article