Spatial Flow Research Articles

Complex network analysis of fossil fuel functional regions in the United States during the period 2017 to 2022

In this paper we use complex network analysis to describe fossil fuel spatial flows among 132 places covering the entire United States in 2017 and in 2022. These spatial flows are for crude petroleum, gasoline, and oil fuels. The analysis shows that all three fuels have different network topology. For all six networks we find major hubs of crude petroleum and its products, gasoline and fuel oils, concentrated in areas with large reserves such as the south-central part of the US. Using modularity, a network cluster identification metric, we show that spatial interactions can be used to delineate functional regions and their differences across fuel types. These functional regions evolve over time in response to the shifting US role as a major producer and net exporter of fossil fuels, expansion of the domestic pipeline network, and increases in fuel production and refinement locations. The modal split of the fuels examined in this paper shows the dominant role pipelines play for crude petroleum, transporting approximately 83 % of tonnage in 2017 and increasing to almost 89 % in 2022. In contrast, gasoline and oil fuels modal split hovers at around 60 % of tonnage transported by tanker truck followed by other modes including pipelines. Our analysis shows geographic clustering of major hubs and their functional regions along the Gulf Coast in Texas and Louisiana. These are in places that are often the locations of natural disasters. This together with the rapid increase of a few hubs as gateways to fossil fuel US exports makes them prime candidates in disrupting fossil fuel supply chains worldwide and amplifies vulnerability of fossil fuel supply chains. The spatial clustering trends shown in this paper provide added evidence of the source of short-term negative impacts in places such as Chicago in Illinois and Corpus Christi in Texas. This offers added information for government intervention.

Integrating supply and beneficiaries into assessing the benefit of water purification service: a spatial flow perspective

Integrating supply and beneficiaries into assessing the benefit of water purification service: a spatial flow perspective

Evaluating the Diagnostic Potential of Four-Dimensional Flow Magnetic Resonance Imaging in Aortic Stenosis Diagnosis: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis evaluates the potential of four-dimensionalflow magnetic resonance imaging(4DFM) in assessing aortic stenosis (AS) compared to traditional imaging modalities like two-dimensional phase contrast MRI (2D MRI) and transthoracic echocardiography (TTE). AS is a common and severe valvular heart disease, particularly in older adults, requiring accurate diagnosis for proper clinical management. Conventional imaging methods have limitations in capturing complex flow dynamics, prompting the need for advanced modalities like 4DFM. The objectives of the review were to determine whether 4DFM offers superior diagnostic metrics, including peak aortic jet velocity, transvalvular pressure gradients, and aortic valve area (AVA), and to identify potential advantages of 4DFM in overcoming the limitations of traditional modalities. This review included six cohort studies with 285 participants, examining the diagnostic accuracy of 4DFM in terms of peak aortic jet velocity, transvalvular pressure gradients, and aortic valve area (AVA). Studies were selected from MEDLINE (PubMed), Cochrane Library, and Google Scholar databases between December 2010 and October 2024. The study pool was limited by stringent inclusion criteria focusing on cohort studies that directly compared 4DFM with TTE or 2D MRI for AS assessment. The National Institutes of Health Quality Assessment Tool and Cochrane ROBINS-I tool were used to assess bias. Quantitative results showed that 4DFM typically measured higher AVA values than TTE, with a mean difference of 0.48 cm² (95% CI: -0.16 to 1.12). For mean pressure gradients, 4DFM reported slightly higher measurements in individual studies, but pooled results showed no significant difference compared to TTE (mean difference: 3.32 mmHg, 95% CI: -2.30 to 8.93). In terms of peak aortic jet velocity, 4DFM demonstrated a pooled mean difference of -0.18 m/s (95% CI: -0.44 to 0.08) compared to TTE. High heterogeneity was observed across studies (e.g., I² = 97% for peak velocity, I² = 93% for AVA), likely due to differences in patient populations, imaging protocols, and software for data analysis.4DFM demonstrates potential as a complementary imaging tool, particularly in complex AS cases where conventional methods like TTE may provide inconclusive results. Its capacity to capture intricate flow dynamics and deliver high spatial resolution could inform clinical decision-making, potentially influencing practice guidelines to integrate 4DFM as a supplementary tool. Limitations such as high costs, specialized training requirements, and access challenges currently restrict widespread adoption. Limitations of this review include small sample sizes, high heterogeneity, and variability in patient populations and imaging protocols. Despite these challenges, 4DFMI demonstrated superior spatial resolution and complex cardiovascular flow assessment, suggesting it could serve as a valuable complement to TTE for more detailed AS evaluation, particularly in complex cases.Future studies should aim to standardize imaging protocols, incorporate larger and more diverse populations, and conduct cost-benefit analyses to support the integration of 4DFM into clinical practice, potentially shaping future diagnostic guidelines.

Behavior recognition algorithm based on a dual-stream residual convolutional neural network

Abstract In the process of behavior recognition, the recognition operation may be carried out in various environments such as sunny, cloudy, and night. Since traditional recognition algorithms are judged by identifying the pixels of the image, the intensity of the light will affect the image. The brightness and contrast of the display thus interfere with the recognition results. Therefore, traditional algorithms are easily affected by the lighting environment around the recognition object. To improve the accuracy and recognition rate of the behavior recognition algorithm in different lighting environments, a convolutional neural network (CNN) algorithm using a dual-stream method of time flow and spatial flow is studied here. First, we collect behavioral action data sets and preprocess the data. The core of the behavior recognition algorithm of the dual-stream residual CNN is to use the time stream and the spatial stream to fuse behavioral features and eliminate meaningless data features. After processing Perform feature selection on the data, select the acoustic wave and light-sensing features of the data, and finally, use the extracted features to classify and identify using the two-stream residual CNN and the traditional behavior recognition method. The behavior recognition algorithm based on the dual-stream residual CNN was tested on the data of four groups of people. For the behavioral feature map with a data volume of 50, the behavior recognition algorithm of the dual-stream residual CNN was effective in various environments under different lighting conditions. The recognition accuracy can reach 83.5%, which is 12.3% higher than the traditional. The behavior recognition algorithm of the dual-stream residual CNN takes 17.25 s less than the conventional recognition algorithm. It is concluded that behavior recognition based on dual-stream residual CNNs can indeed improve the recognition accuracy and recognition speed in environments with different lighting conditions than traditional behavior recognition.

Numerical modelling of pump-driven tsunami generation and fluid-structure-interaction in idealized urbanized coastal areas during run-up

Tsunami wave inundations are still one of the most devastating natural disasters worldwide. Tsunamis striking a settlement frequently devastate much of its infrastructure. In instances where infrastructure withstands the tsunami’s actions, it acts as a flow resistance for the wave’s run-up, altering inundation dynamics and flow depth. Accurately predicting the complex dynamics of tsunami wave run-up in densely populated urban areas is paramount for informing effective evacuation protocols and conducting comprehensive hazard and risk assessments. In pursuit of improving wave run-up prediction capabilities, this study delves into the three-dimensional numerical modelling of wave run-up of non-breaking, long tsunami waves in urbanized areas. Leveraging insights from a physical experiment with pump-driven wave generation and idealized infrastructure, a novel pressure-based wave generation boundary condition is developed. The boundary condition achieves an average of 4.9% accuracy in replicating the water surface elevation from experiments. Additionally, it attains an average 1.5% precision in reproducing flow velocities, furthermore reproducing the spatial flow dynamics accurately. Physical experiment wave run-up is modelled with an average 6.9% deviation for both simulations with and without idealized infrastructure. 63.0% higher non-linearity waves than in the physical experiments are additionally investigated to highlight the boundary conditions capabilities of high non-linearity wave generation, change in run-up reduction for higher non-linearity waves for infrastructure interaction and furthermore in-depth flow field characteristics during tsunami inundation. Finally, the study highlights deviations from analytically calculated wave run-up, emphasizing the necessity for numerical and physical experimental evaluation for both high non-linearity waves and tsunami infrastructure interaction, ultimately fostering both resilience and preparedness against tsunami hazards.

Translating the narrative of tolerance in designing a museum environment

The study explores the design of a museum environment based on the narrative of 'tolerance.' Museums deliver in-depth and interactive messages through creating spatial experiences that provides information and engages visitor's emotions. This academic design study utilises the narrative architecture approach to compose and create holistic spatial flow that guide visitors through various sensory experiences. The design of the museum environment consists of five core spaces with different flows of sensorial narrative related to the experience of tolerance, named Susila, Sahayanda, Sabah, Sadrah, and Segianya. The sequence between spaces allows for a coherent and progressive narrative of tolerance, enabling visitors to feel a deep involvement from one space to another. Sensorially, manipulation and organisation of spatial scales bring different experiences of light, texture, sound, and visuals to create a comprehensive and memorable experience. The application of narrative architecture allows the museum environment to evolve, not only as a place to exhibit artefacts but also as a dynamic educational platform. It provides important information about the history of tolerance-related events and reflectively enriches how the visitors experience the space. Connections between the conveyed narrative of tolerance and the space create a vibrant experience of the overall museum environment.

Particle image velocimetry study of the flow control effects of singular and multiple curved serration models

This paper reports on preliminary observations of an investigation of flows associated with models that mimic serrations on the leading edge of barn owl feathers. The objective was to use particle image velocimetry measurements to determine the capacity of singular and multiple curved (three-dimensional) serration models to modify the noise-reducing indicators of a narrow-channeled flow past a cylinder. Four models were tested: 3 singular serration models of respective angles of inclination, α = 24°, 27.5° and 31°, and a model consisting of an array of 3 serrations of α = 24°, 27.5° and 31°. Each case was subjected to flow of Reynolds number (based on the serration height and maximum velocity of the flow) of ∼2,000, simulating the flow regime of local flow around barbs of real barn owl flights. A planar particle image velocimetry technique was used to capture the midspan plane velocities to determine the effects of each model. The results show that using singular serration models of inclination angles than 30° may lead to disorganized spatial structures and enhanced turbulence levels. On the other hand, an array of only 3 curved serrations of different geometries can modify the spatial flow structure into a well-ordered one, resulting in a 50% reduction in turbulence intensities. These initial results suggest that under complex flow conditions, the insertion of single and multiple curved serrations can lead to significant flow changes that may result in potential noise modifications.

Simulating unsteady flows on a superconducting quantum processor

Recent advancements of quantum technologies have triggered tremendous interest in exploring practical quantum advantage. The simulation of fluid dynamics, a highly challenging problem in classical physics but vital for practical applications, emerges as a potential direction. Here, we report an experiment on the digital simulation of unsteady flows with a superconducting quantum processor. The quantum algorithm is based on the Hamiltonian simulation using the hydrodynamic formulation of the Schrödinger equation. With the median fidelities of 99.97% and 99.67% for parallel single- and two-qubit gates respectively, we simulate the dynamics of a two-dimensional (2D) compressible diverging flow and a 2D decaying vortex with ten qubits. Note that the former case is an inviscid potential flow, and the latter one is an artificial vortical flow with an external body force. The experimental results well capture the temporal evolution of averaged density and momentum profiles, and qualitatively reproduce spatial flow fields with moderate noises. This work demonstrates the potential of quantum computing in simulating more complex flows, such as turbulence, for practical applications.

Relaxation-weighted MRI analysis of biofilm EPS: Differentiating biopolymers, cells, and water

Biofilms are a highly complex community of microorganisms embedded in a protective extracellular polymeric substance (EPS). Successful biofilm control requires a variety of approaches to better understand the structure-function relationship of the EPS matrix. Magnetic resonance imaging (MRI) is a versatile tool which can measure spatial structure, diffusion, and flow velocities in three dimensions and in situ. It is well-suited to characterize biofilms under natural conditions and at different length scales. MRI contrast is dictated by T1 and T2 relaxation times which vary spatially depending on the local chemical and physical environment of the sample. Previous studies have demonstrated that MRI can provide important insights into the internal structure of biofilms, but the contribution of major biofilm components—such as proteins, polysaccharides, and cells—to MRI contrast is not fully understood. This study explores how these components affect contrast in T1-and T2-weighted MRI by analyzing artificial biofilms with well-defined properties modeled after aerobic granular sludge (AGS), compact spherical biofilm aggregates used in wastewater treatment. MRI of these biofilm models showed that certain gel-forming polysaccharides are a major source of T2 contrast, while other polysaccharides show minimal contrast. Proteins were found to reduce T2 contrast slightly when combined with polysaccharides, while cells had a negligible impact on T2 but showed T1 contrast. Patterns observed in the model biofilms served as a reference for examining T2 and T1-weighted contrast in the void spaces of two distinct AGS granules, allowing for a qualitative evaluation of the EPS components which may be present. Further insights provided by MRI may help improve understanding of the biofilm matrix and guide how to better manage biofilms in wastewater, clinical, and industrial settings.

Five key issues of assessing nature’s contribution to people toward landscape sustainability

Landscape sustainability aims to consistently provide long-term, landscape-specific ecosystem services. The concept of nature’s contributions to people (NCP), broader than that of ecosystem services, has sparked methodological controversy regarding the new assessment framework of NCP. Accordingly, for further exploration of the essence of landscape sustainability, a way forward in better assessing NCP that differs from previous assessments of ecosystem service is necessary. Five key issues for assessing NCP are summarized in this paper, including: 1) how to determine generalized or landscape-specific parameters; 2) how to identify different people’s needs from a landscape; 3) how to deal with the complexity of non-material NCP at landscape scale; 4) how to evaluate the relationship between different NCP from a landscape; and 5) how to assess landscape multifunctionality based on NCP. The way to deal with these key issues is discussed, including: determining generalized or landscape-specific parameters based on research targets; tracking nature’s contributions to different people’s needs considering spatial flow; forming new spatial data for non-material NCP; evaluating the relationship between different NCP depending on threshold of benefit; and assessing landscape multifunctionality based on the relations of NCP.

Probing Spatial Energy Flow in Plasmonic Catalysts from Charge Excitation to Heating: Nonhomogeneous Energy Distribution as a Fundamental Feature of Plasmonic Chemistry.

Plasmonic catalysts use light to drive chemical reactions. One critical question is how light energy moves at nanoscales in these complex systems, leading to chemical transformations. In this contribution, we map out this energy flow by developing approaches to measure spatial temperature distributions in heterogeneous plasmonic catalysts, consisting of three-dimensional networks of plasmonic nanoparticles anchored on an oxide support. We survey the local temperatures of molecules adsorbed on catalytically active plasmonic nanoparticles, the nanoparticles themselves, and the catalyst support, under steady-state continuous-wave illumination. We reveal the existence of large temperature gradients, in which the local temperatures of the molecules, nanoparticles, and the surrounding environment can vary greatly. We show that these temperature gradients are a natural consequence of plasmon relaxation, involving the interconversion between electromagnetic light energy, electronic excitations, and heating of various entities as these electronic excitations relax. The presence of these gradients is a fundamental and unique feature of gas-phase plasmonic catalysis.

Comparison of groundwater distribution, exchange and storage between cropland and forestland over the past 115 years

ABSTRACT Using the US Geological Survey groundwater model, we compared spatial distribution, flow exchange, and storage of groundwater between cropland and forestland in the Upper Yazoo River Watershed, Mississippi, from 1900 to 2014. Under normal climate, average groundwater head declined 2.7 m in cropland but only 1 m in forestland over the 115 years. The average groundwater flow from forestland to cropland surpassed the reverse flow by a factor of 238, owing to a higher elevation in forestland and intensive groundwater pumping in cropland. Cropland was a net groundwater sink while forestland was a net groundwater source under all climates. Our findings underscore the discernible impacts of climate changes on groundwater storage and suggest that afforestation in the region would be an alternative for saving groundwater resources and supplying more waters to croplands. This study offers valuable insights into groundwater supply planning not only in Mississippi but also in comparable situations worldwide.

Exploration of initiation and stabilization properties of oblique detonation in a combustor with hydrogen fuel pre-injection

This study delves into the innovative aspects of hydrogen fuel injection and mixing within the duct of an oblique detonation engine (ODE) from both theoretical and numerical perspectives. By solving the multi-species reactive Reynolds-Averaged Navier-Stokes (RANS) equations alongside a hydrogen combustion model, the fluid dynamics and combustion processes in the combustor are thoroughly examined. The analysis shows that employing ramp-cantilever injectors for fuel delivery leads to fuel accumulation in the gas stream core, leaving a deficiency near the wall area. By supplementing ramp-cantilever injectors with wall injectors, fuel distribution adjacent to the wall is significantly enhanced, enabling the successful establishment of a detonation mode. However, the confined space and geometric constraints generate complex flow patterns, particularly due to boundary layer separation. The combustor operates in a stable configuration featuring an oblique shock-oblique detonation-separation shock (OSW-ODW-SSW) structure. Conversely, an oblique shock-oblique detonation-normal detonation-separation shock (OSW-ODW-NDW-SSW) arrangement is found to be unstable. Adjusting the position of the expansion corner on the upper wall forward stabilizes the unstable configuration, maintaining the ODW. Spatial flow inhomogeneities, especially equivalence ratio variations, are identified as crucial factors that can hinder ODW initiation and stability. These findings emphasize the pivotal role of optimizing fuel distribution, refining geometrical design, and ensuring flow uniformity in guaranteeing the efficiency and stability of oblique detonation engines. The outcomes provide valuable insights for future improvements in ODE design and operation.

Quantifying Dissolution Dynamics in Porous Media Using a Spatial Flow Focusing Profile

AbstractThe diverse range of patterns in porous media formed by dissolution processes depends on the relative magnitude of flow, transport, and chemical reactions at pore surfaces. However, distinguishing between regimes often relies solely on qualitative, visual comparisons of emergent structures. Here, we propose a quantitative measure capable of identifying different regimes using the concept of the spatial flow focusing profile, which segments the medium into cross sections along the flow direction to calculate the flow focusing index for each section. We employ this measure in numerical simulations of a dissolving porous medium using a pore network model. We obtain a morphological phase diagram of dissolution patterns, which we characterize using the flow focusing profile. In particular, we demonstrate that analyzing the temporal changes in the profile allows one to quantitatively distinguish between wormholing and channeling. The transition between them is shown to be affected by the heterogeneity of the system.

Analysis of the Laws of Planar Composition in Interior Design

The purpose of this paper is to discuss the laws of planar composition in interior design, analyze the role and use of basic elements such as points, lines and surfaces in interior design, as well as the analysis of the laws of composition such as contrast and symmetry, proportion and balance, rhythm and repetition. Firstly, the thesis introduces the importance of points, lines and surfaces as constituent elements in interior design, and discusses their influence on spatial atmosphere, visual effect and overall sense. Secondly, through the analysis of the compositional laws of contrast and symmetry, proportion and balance, rhythm and repetition, the paper elaborates on the principles and effects of the application of these laws in interior design. Then, the thesis discusses the application of planar composition in different styles, including modern minimalist style, European classical style, Chinese traditional style, etc., showing the characteristics and design techniques of planar composition in different styles. Finally, the paper discusses the optimal use of interior space from the perspective of plane composition, emphasizing the importance of the unity of functionality and aesthetics, spatial layout and flow design, and the collocation of materials and colors. Through in-depth analysis of the laws of plane composition in interior design, this paper aims to provide designers with certain theoretical guidance and practical reference, with a view to achieving better design effects and space utilization.

Study on the ecosystem service flow based on the relationship of between supply and demand in Yangtze River Economic Belt

Ecosystems supply goods and services to humans and are the basis for sustainable development of human society. The study of the supply of ecosystem services and the demand and consumption of ecosystem services by human society, and the analysis of the supply and demand characteristics and flow relationships of ecosystem service flows are of great significance for the management of regional ecosystems and the development of ecological compensation. Taking the Yangtze River Economic Belt as an example, this paper calculates the supply and demand indices of ecosystem services in 2015 and 2020, and determines the ecosystem spatial flow paths and flow volumes from the ecosystem supply area to the demand area based on various methods and models such as the minimum cumulative resistance (MCR) model and distance decay model. The results indicate that 1). In 2015 and 2020, the supply and demand of ecosystem services in the Yangtze River Economic Zone show an increasing trend numerically, and there is spatial heterogeneity in the spatial distribution. In terms of ecosystem service supply per unit area, the midstream region is higher than the upstream and downstream regions. In terms of the demand for ecosystem services per unit area, the downstream is higher than the midstream and upstream. 2). From the supply-demand balance of ecosystem services in the Yangtze River Economic Zone, the midstream region is mainly the area of surplus supply of ecosystem services, and the downstream region is mainly the area of deficit supply. From 2015 to 2020, the number of areas with balanced supply and demand of ecosystem services in the Yangtze River Economic Belt decreases and the number of areas with unbalanced supply and demand increases, which is related to the changes in the level of economic development and land use patterns. 3). The flow of ecosystem services in the Yangtze River Economic Belt shows an increasing trend, from 726.59 billion yuan in 2015 to 1,450.54 billion yuan in 2020, with Jiangxi Province being the main ecosystem service supply area and Zhejiang Province being the main ecosystem service demand area in the Yangtze River Economic Belt.

Nonlinear parametric models of viscoelastic fluid flows.

Reduced-order models (ROMs) have been widely adopted in fluid mechanics, particularly in the context of Newtonian fluid flows. These models offer the ability to predict complex dynamics, such as instabilities and oscillations, at a considerably reduced computational cost. In contrast, the reduced-order modelling of non-Newtonian viscoelastic fluid flows remains relatively unexplored. This work leverages the sparse identification of nonlinear dynamics (SINDy) algorithm to develop interpretable ROMs for viscoelastic flows. In particular, we explore a benchmark oscillatory viscoelastic flow on the four-roll mill geometry using the classical Oldroyd-B fluid. This flow exemplifies many canonical challenges associated with non-Newtonian flows, including transitions, asymmetries, instabilities, and bifurcations arising from the interplay of viscous and elastic forces, all of which require expensive computations in order to resolve the fast timescales and long transients characteristic of such flows. First, we demonstrate the effectiveness of our data-driven surrogate model to predict the transient evolution and accurately reconstruct the spatial flow field for fixed flow parameters. We then develop a fully parametric, nonlinear model capable of capturing the dynamic variations as a function of the Weissenberg number. While the training data are predominantly concentrated on a limit cycle regime for moderate , we show that the parametrized model can be used to extrapolate, accurately predicting the dominant dynamics in the case of high Weissenberg numbers. The proposed methodology represents an initial step in applying machine learning and reduced-order modelling techniques to viscoelastic flows.

Effects of a spoiler attachment on the wake flow of a bed-mounted horizontal pipe

This research investigated the spatial flow field of a pipe with a spoiler attachment and the modifications in the wake flow field of a bed-mounted horizontal pipe due to the attachment of a spoiler to the pipe for two different approach flow conditions. To attain the above-mentioned objective, the spatial evolution of the mean and turbulence flow properties in the flow field of the cylinder with an attached spoiler was determined. In addition, the mean flow and turbulence properties of a pipe without a spoiler attachment were compared with the properties of a pipe with an attached spoiler of height 0.2 D. For this purpose, two approach flow Reynolds numbers based on the pipe's diameter (D) and the free-stream velocity (U∞), Re∞ = 8850 and 11650, were considered. Three-dimensional (3D) velocity measurements were recorded with an acoustic Doppler velocimetry (ADV) between locations 4 D upstream (u/s) and 12 D downstream (d/s) of the pipe. It is observed that the length of the recirculation region is increased extensively due to the spoiler on the pipe from 6 D and 6.4 D to 10.7 D. The peak streamwise velocities for both Reynolds numbers are located near the free-water surface, as separated flows are deflected upward and result in enhanced free-stream velocities, which are higher than their approach flow free-stream velocities. In addition to that, the strength of the backflow is enhanced by 23% due to the attachment of the spoiler as compared to that of the pipe without the spoiler. The streamwise and vertical turbulence intensities are increased by 24% and 36%, respectively, and the wake flow field is found to be highly anisotropic. The streamwise turbulence intensities, Reynolds shear stress (RSS), turbulent kinetic energy (TKE), and turbulence indicator are attaining their peaks near the top level of the spoiler in the wake region. The triple velocity correlations indicate that the switching of sweeps and ejection events takes place near the top level of the spoiler. Therefore, it can be concluded that the spoiler attachment has increased the turbulence level in the wake region, and their highest magnitudes are located near the top level of the spoiler.

A progressive segmentation network for navigable areas with semantic–spatial information flow

Segmentation of safe navigable areas is a crucial technology for scene parsing in autopilot systems. However, existing segmentation methods often fail to adequately exploit the complementary relationships between multiscale features in complex wild environments, resulting in insufficient information fusion. In pursuit of a resolution, a Progressive Segmentation Network (PSNet) is proposed for navigable areas segmentation, which builds a semantic–spatial information flow branch to dynamically exploit the complementary relationships between multiscale features for progressive guided learning. Specifically, PSNet consists of four essential modules, including the Local Capturer and Global dependence Builder (LCGB), Multi-Directional Pooling Module (MDPM), Fusion-wise Module (FWM), and Spatial Weight Aggregation Module (SWAM). To facilitate efficient information dissemination, the LCGB was utilized to capture dense spatial information, and the MDPM was proposed to extract global geometric information of obstacles. These pieces of information serve as prior knowledge to guide learning. Additionally, we propose the FWM based on attention fusion unit (AFU) and contribution weights unit (CWU) to construct the complementary relationships between multiscale features and obtain rich multiscale fusion information. SWAM is proposed to enhance the significant spatial features from FWM and achieve impressive segmentation results. Extensive experimental results on the wild datasets demonstrate that PSNet outperforms state-of-the-art methods in recognizing safe navigable areas. The code for PSNet will be available at https://github.com/lv881314/PSNet.

Flow prediction of mountain cities arterial road network for real-time regulation

This study aims to enhance the understanding of vehicle path selection behavior within arterial road networks by investigating the influencing factors and analyzing spatial and temporal traffic flow distributions. Using radio frequency identification (RFID) travel data, key factors such as travel duration, route familiarity, route length, expressway ratio, arterial road ratio, and ramp ratio were identified. We then proposed an origin–destination path acquisition method and developed a route-selection prediction model based on a multinomial logit model with sample weights. Additionally, the study linked the traffic control scheme with travel time using the Bureau of Public Roads function—a model that illustrates the relationship between network-wide travel time and traffic demand—and developed an arterial road network traffic forecasting model. Verification showed that the prediction accuracy of the improved multinomial logit model increased from 92.55 % to 97.87 %. Furthermore, reducing the green time ratio for multilane merging from 0.75 to 0.5 significantly decreased the likelihood of vehicles choosing this route and reduced the number of vehicles passing through the ramp. The flow prediction model achieved a 97.9 % accuracy, accurately reflecting actual volume changes and ensuring smooth operation of the main airport road. This provides a strong foundation for developing effective traffic control plans.