Search Results (1,131)

Against the dual backdrop of rapid population aging and legacy neighborhood renewal, morphologically planning-locked legacy neighborhoods in high-density cities face persistent imbalances in outdoor activity spaces that undermine aging-in-place participation and health equity. This study advances a Spatial Pathology framework. Using nine representative communities in Longtan Subdistrict, Dongcheng District, Beijing, we develop a GIS-assisted spatial audit, a systematic behavioral observation protocol with temporal-intensity metrics, and a validated perception instrument. These tools form a closed evidentiary loop with explicit indicator definitions, formulas, and decision thresholds, alongside a reproducible analytic and visualization pipeline. Tri-dimensional baselines revealed substantial inter-community disparities: Spatial Quality Index (SQI) ranged from 43.3 to 77.0; activity intensity varied from 1.5 to 15.7 persons/100 m²·hour; and overall satisfaction scores spanned 3.88–4.49. It quantifies and identifies three core paradoxes in outdoor activity spaces within this context: (1) the Functional Failure Paradox with FFI exceeding +0.5 and ELR surpassing 60% in dormant communities; (2) the Value Misalignment Paradox where Facilities & Equipment showed the strongest satisfaction impact (β = 0.344) yet the largest unmet-need gap (VQGI > +8); (3) the Practice–Perception Decoupling Paradox evidenced by a negative correlation (r = −0.38) between usage intensity and satisfaction. These paradoxes reveal the spatial roots of planning-locked legacy neighborhoods—compound mechanisms of planning inertia, decision–demand information gaps, and elderly adaptability masking environmental deficits. We translate the diagnosis into typology-specific prescriptions—reactivating dormant spaces via “route–node–plane” continuity and proximal micro-spaces; decongesting peak periods through elastic zoning and equipment redistribution; and precision calibration of facilities and walking loops—implemented through co-creation and light-touch stewardship. This provides evidence-based, precision-targeted intervention pathways for micro-renewal of aging neighborhoods, supporting localized implementation of UN Sustainable Development Goals (SDG 11 Sustainable Cities; SDG 10 Reduced Inequalities). This methodological framework is transferable to other high-density aging cities, offering theoretical scaffolding and empirical reference for multi-source geographic data-driven urban spatial analysis and equity-oriented age-friendly retrofitting. Full article

The resilience of urban transportation networks refers to the system’s ability to resist, absorb, and recover performance when facing external shocks. Traditional methods have obvious limitations in temporal granularity, data fusion, and predictive capabilities. To address this, this study proposes a minute-level real-time resilience measurement model driven by ride-hailing big data. First, the spatio-temporal characteristics of urban ride-hailing data are analyzed, and a transportation cost indicator is introduced to construct a multidimensional road network resilience measurement framework encompassing transport supply–demand, efficiency, and cost. Second, a high-precision hybrid LSTM-Transformer prediction model integrating spatio-temporal attention mechanism is developed, and a time-varying node identification method based on RMSE curves is proposed to accurately capture the disturbance onset time and recovery completion time. Finally, empirical validation shows that, taking Taixing City as an example, the model achieves minute-level resilience measurement with an average prediction accuracy of 96.8%, making resilience assessment more precise and sensitive. The research results provide a scientific basis for urban traffic management departments to formulate emergency response strategies and improve road network recovery efficiency. Full article

Tree fruit and nut crops are a critical component of the global economy, producing at least 400 million tonnes of produce in 2022 and nourishing a growing population of approximately 8 billion humans every year. Improved cultivars and growing practices depend upon an understanding of the molecular basis of tree traits and physiology. Over the past 20 years, the proliferation of reference genomes for tree fruit and nut crop species has transformed the study of genetics in these crops, providing a platform for resequencing analyses of large populations, enabling comparative genomic analyses between distant plant species, and allowing the development of molecular markers for use in breeding. However, reference bias and poor transferability of markers limit widespread applicability in many instances. As third-generation sequencing has become more accurate and accessible, a greater number of reference genomes have become available, enabling higher-quality assemblies and wider sampling of genomic diversity. To facilitate the effective use of multiple closely related genomes to create a reference and comparative genomics platform, tools have been developed for the creation of pangenome graphs, a data structure using nodes connected by edges to represent multiple genomes and their sequence variations. Pangenome graphs allow for singular representations of diversity within a species or even a wider genus. Pangenomic analyses at the genus-scale (e.g., Malus, Citrus) have been conducted for Malus and Citrus, and more tree fruit and nut species are likely to follow. As the number of genome sequences and pangenome resources increases, the importance of generating great quantities of transcriptomic and phenomic data will increase as well. This data is essential in the drive to connect genes to traits and overcome traditional breeding bottlenecks, which is needed to develop improved tree fruit and nut crops, which can satisfy global demand. Full article

Achieving reliable interoperability between architectural and structural models remains one of the main challenges in BIM-based design workflows. Despite the widespread adoption of Building Information Modeling, the automatic transfer of information between modeling software and FEM analysis tools continues to generate inconsistencies, information loss, and the need for manual interventions. This study examines these issues through the case study of a reinforced-concrete residential building located in Palermo, used to evaluate BIM-to-FEM exchanges between Revit^®, Robot Structural Analysis^®, PRO_SAP^®, and JASP^®. The interoperability tests highlight significant limitations in both native and IFC-based workflows. The direct Revit–Robot link ensures good geometric consistency but still requires manual correction of analytical axes, connections, and boundary conditions. Indirect transfers via IFC exhibit greater instability: both IFC2x3 Coordination View 2.0 and IFC4 Reference View show difficulties in correctly interpreting structural elements and do not adequately preserve analytical relationships, resulting in unconnected slabs, disconnected nodes, and missing constraint information. In PRO_SAP^®, several elements are also absent after IFC import. To address these issues, the study proposes a workflow based on the integration of Revit^® and JASP^® aimed at generating a reliable federated model. This model was further validated in Navisworks^®, Solibri Anywhere^®, BIM Vision^®, and Enscape^® to assess its correct interpretation across different software environments. This approach enhances interdisciplinary coordination, supports clash detection, facilitates immersive VR-based review, and centralizes architectural, structural, and MEP models into a unified environment. The results show that structured workflows and careful management of native and IFC transfers significantly improve model reliability and reduce design inconsistencies. Full article

To address challenges in agent path planning within complex environments—particularly slow convergence speed, high path redundancy, and insufficient smoothness—this paper proposes KDB-RRT*, a novel algorithm built upon RRT.* This method integrates a bidirectional search strategy with a three-layer optimization framework: ① accelerated node retrieval via KD-tree indexing to reduce computational complexity; ② enhanced exploration efficiency through goal-biased dynamic circle sampling and a bidirectional gravitational field guidance model, coupled with adaptive step size adjustment using a Sigmoid function for directional expansion and obstacle avoidance; and ③ trajectory optimization employing DP algorithm pruning and cubic B-spline smoothing to generate curvature-continuous paths. Additionally, a multi-level collision detection framework integrating Separating Axis Theorem (SAT) pre-judgment, R-tree spatial indexing, and active obstacle avoidance strategies is incorporated, ensuring robust collision resistance. Extensive experiments in complex environments (Z-shaped map, loop-shaped map, and multi-obstacle settings) demonstrate KDB-RRT’s superiority over state-of-the-art methods (Optimized RRT*, RRT*-Connect, and Informed-RRT*), reducing average planning time by up to 97.9%, shortening path length by 5.5–21.4%, and decreasing inflection points by 40–90.5%. Finally, the feasibility of the algorithm’s practical application was further verified based on the ROS platform. The research results provide a new method for efficient path planning of intelligent agents in unstructured environments, and its three-layer optimization framework has important reference value for mobile robot navigation systems. Full article

To address the challenges to power system frequency stability under high penetration of renewable energy, this paper proposes a coordinated inertia support strategy for wind–PV–thermal storage systems, overcoming the limitations of conventional inertia parameter adjustment. The core of the strategy lies in optimizing unit control activation logic and establishing a scenario-adaptive batch activation mechanism. Specifically, virtual inertia characteristic models for wind, PV, and storage units are developed, with key parameters optimized via fuzzy-logic-based coordinated control. An inertia demand assessment model under frequency security constraints is constructed to quantify the minimum system inertia requirement. Furthermore, disturbance reference power is generated based on the inertia reserve capability of each unit, and disturbance intervals are classified to achieve coordinated optimal allocation of virtual inertia. Simulation results on a built 3-machine, 9-node system demonstrate that the proposed strategy can intelligently coordinate the activation timing, role assignment, and regulation resources of wind, PV, and storage according to the type and severity of disturbances. Under various scenarios such as sudden load increase and decrease, the system effectively mobilizes resources to maintain frequency within the secure range while avoiding frequent actions of any single unit. The results verify that the strategy significantly enhances the system’s capability to handle bidirectional power disturbances and provide frequency support, offering a practical solution for inertia management in renewable-dominated power systems. Full article

Accurate indoor floor classification is essential for wireless positioning systems. However, the performance of conventional received signal strength indictor (RSSI)-based fingerprinting approaches is often limited by signal fluctuations and insufficient feature representation. To address these challenges, this paper introduces a heterogeneous graph neural network (GNN) framework that models WiFi signals using two types of nodes: reference points and Media Access Control (MAC) address. The edges between reference points and MAC addresses are weighted by normalized RSSI values, allowing the model to capture signal strength interactions and perform relation-aware message passing. Through this graph-based representation, the model can learn spatial and signal dependencies more effectively than conventional vector-based approaches. The proposed model was extensively evaluated under both benchmark and practical settings. On small-scale datasets, it achieved performance comparable to that of a conventional convolutional neural network trained on large-scale datasets, confirming its effectiveness with limited samples. In addition, the proposed model consistently outperformed other models under noisy conditions, achieving 93.88% accuracy on the widely used UJIIndoorLoc dataset and 97.3% accuracy in real-time experiments conducted at a test site. These values are significantly higher than those achieved using conventional machine learning (ML) baselines, highlighting the ability of the proposed model to handle real-world signal variations. These findings highlight that the heterogeneous GNN effectively captures spatial and signal-level dependencies, offering a robust and scalable solution for accurate indoor floor classification. Overall, this work presents a promising pathway for improving the performance and reliability of future wireless positioning systems. Full article

As critical national energy arteries, long-distance large-scale cross-regional gas transmission networks are characterized by high operating pressures, extensive spatial coverage, and complex topological structures. Thus, the multi-hazard profiles threatening its safety and reliability operation differ significantly from those of local urban gas distribution networks. This research develops a resilience assessment framework capable of quantifying resistance, adaptation, and recovery capacities of such energy systems. The framework establishes performance indicator systems based on design parameters, installation environments, and construction methods for long-distance trunk pipelines and key facilities such as storage facilities. Furthermore, based on complex network theory, the size of the largest connected component and global efficiency of the transmission network are selected as core topological metrics to characterize functional scale retention and transmission efficiency under disturbances, respectively, with corresponding quantification methods proposed. A cross-regional pipeline transmission network within a representative municipal-level administrative region in China is used as a case for empirical analysis. The quantitative assessment results of pipeline and network resilience are analyzed. The research indicates that trunk pipeline resilience is significantly affected by characteristic parameters, the laying environment, and installation methods. It is notably observed that installation methods like jacking and directional drilling, used for road or river crossings, offer greater resistance than direct burial but considerably lower restoration capacity due to the complexity of both the environment and the repair processes, which increases time and cost. Moreover, simulation-based comparison of recovery strategies demonstrates that, in this case, a repair-time-prioritized strategy more effectively enhances overall adaptive capacity and restoration efficiency than a node-degree-prioritized strategy. The findings provide quantitative analytical tools and decision-support references for resilience assessment and optimization of cross-regional energy transmission networks. Full article

Reliability evaluation of power distribution systems is computationally intensive, as standard Monte Carlo simulations require extensive sampling to accurately estimate rare event-based indices like SAIDI and SAIFI. This paper introduces a multi-objective cross-entropy approach for reliability evaluation of power distribution systems, aiming to accelerate reliability evaluation by optimizing importance sampling reference parameters. The multi-objective approach aims to optimize a set of objective functions related to systemic and load point reliability indices. A deduction of an analytical solution for the optimization of reference parameters of the cross-entropy method is developed, taking into account the standard hypotheses used in reliability assessments. The proposed method has been validated on a real 181-node Brazilian distribution feeder. Results show that the proposed approach can accelerate the convergence of estimates for reliability indices in comparison with the crude Monte Carlo approach and the single-objective CE method. Full article

Against the backdrop of global rural decline and China’s Rural Revitalization Strategy (2017–2050), this study examines the spatial form evolution and development mechanism of Central Village in Huotong Town, Ningde. To achieve this, it employs field surveys, spatial mapping, and hierarchical element analysis to analyze the village’s scale, architectural texture, street interfaces, and landscape nodes. Results show three evolutionary stages: pre-2017 traffic-guided disorderly expansion, 2017–2020 policy-driven orderly planning, and post-2020 stock optimization, forming an “oval radial” structure constrained by the southern hills and Huotong River. The spatial structure shifted from a “kinship-based agglomeration” to a multi-center model, yet contradictions like historical texture damage and excessive street commercialization emerged. Its development is driven by four linked factors: policy, the market, socio-culture issues, and nature. This study offers references for similar rural settlements’ spatial planning and sustainability. Full article

China’s High-Speed Railway (HSR), the world’s largest HSR system and a core component of national transportation, exhibits vulnerability in operational robustness to uncertain events such as natural disasters. Existing hypernetwork-based studies on HSR robustness often assume full connections among nodes within hyperedges, an assumption that deviates from reality. Using China’s HSR line and station data, this paper constructs a real-world HSR hypernetwork and three reconstructed hypernetworks with distinct internal hyperedge structures. It also proposes a cascading failure model accounting for internal hyperedge structures and quantifies economic feasibility through an optimization cost index. Experimental results show the real-world HSR hypernetwork has scale-free properties. Sub-line density within hyperedges shows a positive correlation with robustness, where denser sub-lines enhance robustness. Prioritizing sub-line deployment around hub stations offers the most economical solution. This paper is the first to provide an HSR hypernetwork robustness optimization scheme from the perspective of internal hyperedge structures, offering theoretical reference for research on transportation networks with similar topological characteristics. Full article

In resource-constrained wireless sensor networks, efficient link scheduling is a well-studied challenge. This problem is NP-hard, indicating that NP (Nondeterministic Polynomial Time) refers to problems whose solutions can be verified in polynomial time but are computationally difficult to find, and traditional methods seldom yield optimal solutions within practical time limits. This research introduces an innovative novel link scheduling strategy based on the Tuna Swarm Optimization (TSO-LS) algorithm to optimize the link scheduling performance of wireless sensor networks. This work enhances the tuna swarm algorithm’s search process by incorporating characteristics of the link scheduling problem, resulting in specialized algorithmic improvements for this scenario. This research presents three principal improvements to the algorithm: first, optimizing the individual update mechanism to expedite scheduling solutions; second, refining the leading individual selection strategy to elevate global scheduling quality; and third, maintaining population diversity to prevent convergence on suboptimal scheduling schemes. In the experimental section, TSO-LS is compared with the Genetic Algorithm, Particle Swarm Optimization, Enhanced Particle Swarm Optimization and Ant Colony Optimization. The results show that TSO-LS achieves a 13.3% improvement in energy efficiency and a 12.5% decrease in average latency. Under different experimental conditions, the TSO-LS strategy shortens the average latency to 10.5 ms, demonstrating outstanding overall performance. Furthermore, this strategy reduces node consumption from 0.41 mJ to 0.32 mJ, significantly extending the overall lifespan of the network. Full article

Lung cancer is one of the most commonly diagnosed malignancies worldwide and continues to be a leading cause of cancer-related mortality. Precise staging is crucial for predicting outcomes and directing treatment decisions. Current international guidelines mainly recommend imaging techniques like CT and PET-CT, with limited references to transcutaneous ultrasound, which is only suggested in particular clinical cases. Ultrasound provides real-time imaging, high resolution in near-field structures, and the ability to assess thoracic wall infiltration, supraclavicular and cervical lymph nodes, pleural effusions, and metastatic lesions. Furthermore, ultrasound-guided biopsies can enable quick and safe histological confirmation of accessible lesions. Based on these advantages and a review of current literature, we propose that integrating ultrasound into staging algorithms could improve diagnostic efficiency, decrease invasive procedures, and support prompt treatment planning. We also highlight the need for further research in this area. Full article

In order to solve the problem of continuous and stable energy supply of underwater sensor nodes, a shear mode bidirectional piezoelectric energy harvesting structure based on underwater vortex-induced vibration is proposed. The structure was investigated through fluid–solid and piezoelectric coupling numerical simulations using ANSYS 2020, and the relationship between the vibration mode, thickness, flow velocity, spacing diameter ratio of the bluff body and energy harvester (L/D) was studied. The vibration and piezoelectric characteristics of parallel and tandem energy harvesters were analyzed. The results verify that the piezoelectric material with d₁₅ mode has higher output power than that with d₃₃ and d₃₁ modes, under the same conditions, the output power has increased by 50%. When the flow velocity is 1.1 m/s, the bluff body is a wavy cylinder, the L/D is 2, and the maximum voltage and output power values generated by the energy harvesting structure are 56.97 V and 3.25 mW, respectively. Its power density reaches 1.35 mW/cm³, superior to similar-scale collectors reported in the recent literature. In the case of the double energy harvesting structure, it can capture vortex-induced vibration energy in both flow and lateral directions, thereby expanding the working bandwidth and improving the overall energy capture efficiency; the parallel output voltage is also the largest. When L/D is 1.5 and the flow velocity is 1.1 m/s, the maximum output voltage values of the upper and lower energy harvesting structures are 78.65 V and 83.05 V, respectively, and the corresponding output power is 6.19 mW and 6.90 mW. The above simulation results verify that the shear mode energy harvesting structure and its array can appropriately increase the open-circuit output voltage of the structure, which provides a new reference scheme for the study of underwater vortex-induced vibration piezoelectric energy harvesting structures. Full article

This study investigates the impact of different aggregation operators (commonly referred to as fuzzy operators) on the application of fuzzy signatures. Fuzzy signatures are specialized multidimensional data structures that symbolically represent data. As a use case, the study focuses on robot environment representation and path planning, presenting the results obtained by applying various aggregation operators including $minimum$, $maximum$, $algebraic$ $product$ and $arithmetic$ $mean$ on the normalized values obtained from the robot sensors. The comparison highlights their effects on the computational load and path lengths of the path planning task. The findings reveal that the most efficient aggregation operator, in terms of computational load and the path length, is the $algebraic$ $product$ aggregation operator. Specifically, the $algebraic$ $product$ consistently yielded the shortest paths (as low as 22 nodes) and the lowest execution times (down to 0.0913 s), demonstrating superior efficiency compared to the $maximum$ operator, which resulted in path lengths up to 34 nodes and execution times reaching 0.1923 s. This represents an improvement of up to 35.3% reduction in path length and 52.5% reduction in execution time when comparing the $algebraic$ $product$ to the $maximum$ operator based on observed extreme values. Furthermore, this work provides the first empirical comparison of fuzzy aggregation operators specifically for fuzzy signature-based mobile robot path planning. Full article