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Traffic flow prediction is a prominent research area in intelligent transportation systems, significantly contributing to urban traffic management and control. Existing methods or models for traffic flow prediction predominantly rely on a fixed-graph structure to capture spatial correlations within a road network. However, the fixed-graph structure can restrict the representation of spatial information due to varying conditions such as time and road changes. Drawing inspiration from the attention mechanism, a new prediction model based on the mixed-graph neural network is proposed to dynamically capture the spatial traffic flow correlations. This model uses graph convolution and attention networks to adapt to complex and changeable traffic and other conditions by learning the static and dynamic spatial traffic flow characteristics, respectively. Then, their outputs are fused by the gating mechanism to learn the spatial traffic flow correlations. The Transformer encoder layer is subsequently employed to model the learned spatial characteristics and capture the temporal traffic flow correlations. Evaluated on five real traffic flow datasets, the proposed model outperforms the state-of-the-art models in prediction accuracy. Furthermore, ablation experiments demonstrate the strong performance of the proposed model in long-term traffic flow prediction. Full article

China’s rural revitalization strategy has entered a new stage of development, in which optimizing the layout of rural settlements constitutes both a critical component and an urgent task for promoting integrated urban–rural development. Important ecological function areas play a vital role in maintaining ecological security; however, research focusing on the evaluation and optimization of rural settlement suitability within these regions remains limited, thereby constraining their sustainable development. Accordingly, this paper selects Shiyan City, situated within the core water source area of China’s South-to-North Water Diversion Project, as a case study. From an ecological perspective, a suitability evaluation system for rural settlements is developed, specifically tailored to important ecological function areas. This system integrates ecological factors including geological hazards, vegetation coverage, soil and water conservation, and soil erosion. Utilizing GIS spatial analysis and the minimum cumulative resistance model, the study assesses the suitability of rural settlements within these important ecological function areas. Furthermore, it proposes corresponding optimization types and strategies for rural settlements in such areas. The findings indicate the following: (1) The rural settlements in the study area demonstrate a “large dispersed settlements and small clustered settlements” distribution pattern, exhibiting an overall high-density agglomeration, though their internal layout remains fragmented and disordered due to geographical and ecological constraints. (2) The spatial comprehensive resistance values in the study area exhibit significant heterogeneity, with a general pattern of lower values in the north and higher values in the south. The region was categorized into five suitability levels: high yield, highly suitable, generally suitable, less suitable and unsuitable. The highly suitable areas, despite their limited spatial extent, support the highest density of rural settlements. In contrast, unsuitable areas occupy a substantially larger proportion of the territory, reaching 46.83%. These areas are strongly constrained by topographic and ecological factors, limiting their potential for development, and the spatial layout of villages requires further optimization, with emphasis placed on ecological conservation and adaptive sustainability. (3) Rural settlements are categorized into four optimized types: Urban–rural integration settlements, primarily located in high yield areas, are incorporated into urban development plans after optimization. Adjusted and improved settlements, mainly in highly suitable areas, enhance service quality and stimulate economic vitality post-optimization. Relocation and renovation settlements, including those in generally suitable and less suitable areas, achieve concentrated living and improved ecological livability after optimization. Restricted development settlements, predominantly in unsuitable areas, focus on ecological conservation and regional ecological security post-optimization. This study integrates ecological function protection factors with spatial optimization zoning for rural settlements in the study area, providing scientific reference for enhancing residential safety and ecological security for rural residents in important ecological function areas. Full article

Vehicle-to-everything (V2X) communication, a current key area of research, has a large impact on traffic safety, traffic efficiency, autonomous driving technology development, and intelligent transport. In order to achieve the low-latency performance and high transmission efficiency required for V2X communication, channel estimation for transmission channels is particularly important. In this regard, this paper proposes an improved general orthogonal match pursuit (GOMP) channel estimation algorithm based on the base extension model for an orthogonal time frequency space (OTFS) system. Firstly, the channel matrix is decomposed using the basis expansion model. Then, the strong sparsity of the basis function is exploited for channel estimation using the GOMP algorithm, while the ordinal difference restriction method and the weak selectivity principle are introduced to improve the system. The obtained improved GOMP algorithm not only shows a greater improvement in terms of normalized mean square error (NMSE) and bit error rate (BER) performance but also greatly reduces computational complexity, enabling it to better satisfy the needs of V2X communication. Full article

In this paper, we propose a quantitative evaluation method for navigation safety in coastal waters based on unstructured grids. Initially, a comprehensive analysis was conducted on various factors affecting navigation safety, including natural conditions, traffic conditions, and marine hydro-meteorological conditions, to construct a multi-source fused spatiotemporal dataset. Subsequently, channel boundary extraction was performed using Constrained Delaunay Triangle–Alpha-Shapes, and the precise extraction of ship navigation areas was performed based on Constrained Delaunay Triangle–Voronoi diagrams. Additionally, temporal feature grids were constructed based on the spatiotemporal characteristics of marine hydro-meteorological data. Finally, unstructured grids for evaluating navigation safety were established through spatial overlay analysis. Based on this foundation, a quantitative analysis and evaluation model for comprehensive navigation safety assessment was developed using the fuzzy evaluation method. By calculating the fuzzy relation matrix and weight vectors, quantitative assessments were conducted for each grid cell, yielding safety risk levels from both spatial and temporal dimensions. An analysis was performed using maritime data within the geographic boundaries of lon.119.17–120.41° E and lat.34.40–35.47° N. The results indicated that the unstructured grid division and channel boundary extraction in the demonstrated sea area were closely related to parameters such as the ship traffic flow patterns and the spatiotemporal characteristics of the marine environmental factors. A quantitative evaluation and analysis of the 186 unstructured grid cells revealed that the high risk levels primarily corresponded to restricted navigation areas, the higher-risk grid cells were mainly anchorages, and the low to lower risk levels were primarily associated with channels and navigable areas for ships. Full article

Using Landsat series imagery and the deep learning model CITNet, this study conducted high-accuracy classification and spatiotemporal change analysis of wetlands on Chongming Island from 2000–2020 and explored the driving mechanisms by integrating climatic and anthropogenic factors. The results demonstrate that the total wetland area decreased by approximately 125.5 km² over the two decades. Among natural wetlands, tidal mudflats and shallow seawater zones continuously shrank, while herbaceous marshes exhibited a “decline recovery” trajectory. Artificial wetlands expanded before 2005 but contracted significantly thereafter, mainly due to aquaculture pond reduction. Wetland transformation was dominated by wetland-to-non-wetland conversions, peaking during 2005–2010. Driving factor analysis revealed a “human pressure dominated, climate modulated” pattern: nighttime light index (NTL) and GDP demonstrated strong negative correlations with wetland extent, while minimum temperature and the Palmer Drought Severity Index (PDSI) promoted herbaceous marsh expansion and accelerated artificial wetland contraction, respectively. The findings indicate that wetland changes on Chongming Island result from the combined effects of policy, economic growth, and ecological processes. Sustainable management should focus on restricting urban expansion in ecologically sensitive zones, optimizing water resource allocation under drought conditions, and incorporating climate adaptation and invasive species control into restoration programs to maintain both the extent and ecological quality of wetlands. Full article

Shale oil and gas reservoirs are characterized by low porosity and low permeability. The development of ultra-long horizontal wells can significantly increase reservoir contact area and enhance single-well production. Shale formations exhibit distinct bedding structures, high formation pressure, high rock hardness, and strong anisotropy. These characteristics result in poor drillability, slow drilling rates, and high costs when drilling horizontally, severely restricting efficient development. Therefore, accurately predicting the drillability of shale gas wells has become a major challenge. Currently, most scholars rely on a single parameter to predict drillability, which overlooks the coupled effects of multiple factors and reduces prediction accuracy. To address this issue, this study employs drillability experiments, mineral composition analysis, positional analysis, and acoustic transit-time tests to evaluate the effects of mineral composition, acoustic transit time, bottom-hole confining pressure, and formation drilling angle on the drillability of horizontal well reservoirs, innovatively integrating multiple parameters to construct a nonlinear model and introducing three intelligent optimization algorithms (PSO, AOA-GA, and EBPSO) for the first time to improve prediction accuracy, thus breaking through the limitations of traditional single-parameter prediction. Based on these findings, a nonlinear regression prediction model integrating multiple parameters is developed and validated using field data. To further enhance prediction accuracy, the model is optimized using three intelligent optimization algorithms: PSO, AOA-GA, and EBPSO. The results indicate that the EBPSO algorithm performs the best, followed by AOA-GA, while the PSO algorithm shows the lowest performance. Furthermore, the model is applied to predict the drillability of Well D4, and the results exhibit a high degree of agreement with actual measurements, confirming the model’s effectiveness. The findings support optimization of drilling parameters and bit selection in shale oil and gas reservoirs, thereby improving drilling efficiency and mechanical penetration rates. Full article

Water scarcity, soil salinization, and desertification threaten sustainable agricultural ecosystems of Hetao irrigation district, Yellow River Basin (YRB). Precise quantification of soil water dynamics and plant water consumption processes is essential for the agricultural sustainability of the irrigation district. Therefore, this study mainly focused on the crop coefficients and water consumption processes of three representative plant types in the Hetao irrigation district, each corresponding to a specific land system: Helianthus annuus (cultivated land), Tamarix chinensis (wasteland), and Phragmites australis (lake). The SIMDualKc model was calibrated and validated based on situ observation data (soil water content and yield) during 2018 (conventional conditions), 2023 and 2024 (deep water-saving conditions). Results show strong agreement between simulated and observed soil moisture and crop yields. The results indicate that the process curves of K_cb (basal crop coefficient) and K_cbadj (adjusted crop coefficient) nearly overlapped for the three plant types in 2018 and 2023. However, under the deep water-saving project implemented in 2024, the K_cbadj process curves for all three plant types exhibited a significant reduction (approximately 15%). Soil evaporation fractions (E/ET_cadj) were stable at 19–30% during the 2018, 2023, and 2024. The contribution of capillary rise to ET reached 38.61–43.18% in cultivated land (Helianthus annuus), 41.52–48.93% in wasteland (Tamarix chinensis), and 38.08–46.57% in lake boundary areas (Phragmites australis), which underscores the significant role of groundwater recharge in sustaining plant water consumption. Actual-to-potential transpiration ratios (T_a/T_p) during 2023–2024 decreased by 3–11% for Helianthus annuus, 5–12% for Tamarix chinensis, and 23% for Phragmites australis compared to T_a/T_p values in 2018. Capillary rise decreased approximately 10% during the whole system. Deep water-saving practices increased the groundwater depth and restricted groundwater recharge to plants via capillary rise, thereby impairing plant transpiration and growth. These findings provide scientific support for sustainable agriculture and ecological security in the Yellow River Basin. Full article

Background: Metabolic stress from amino acid (AA) insufficiency is increasingly linked to pathological angiogenesis, but specific essential AA (EAA) roles remain undefined. Neovascular age-related macular degeneration (AMD), a major cause of blindness driven by aberrant ocular neovascularization, has limited efficacy with current VEGFA-targeting therapies. We sought to identify specific EAAs that regulate pathological angiogenesis and dissect their mechanisms to propose new therapeutic strategies. Methods: Human retinal microvascular endothelial cells (HRMVECs) were used to identify angiogenesis-regulating amino acids through systematic EAA screening. The molecular mechanism was investigated using shRNA-mediated knockdown of key stress response regulators (HRI, PKR, PERK, GCN2) and ATF4. Angiogenesis was assessed via tubule formation and migration assays. Therapeutic potential was examined in a laser-induced choroidal neovascularization (CNV) mouse model, evaluated by fluorescein angiography and histomorphometry. Results: Deprivation of methionine, lysine, and threonine potently induced capillary-like tube formation (p < 0.01). Mechanistically, restriction of these three EAAs activated HRI and GCN2 kinases, converging on eIF2α phosphorylation to induce ATF4 and its target VEGFA. Dual, but not single, knockdown of HRI and GCN2 abolished eIF2α-ATF4 signaling and angiogenic responses. Restricting these EAAs exacerbated CNV area in mice. Conclusions: Our findings reveal a coordinated HRI/GCN2-ATF4-VEGFA axis linking EAA scarcity to vascular remodeling, establishing proof-of-concept for targeting this pathway in CNV. This work highlights the therapeutic potential of modulating specific AA availability or targeting the HRI/GCN2-ATF4 axis to treat CNV. Full article

During polar navigation, icebreakers frequently encounter ice ridges, which can significantly reduce navigation efficiency and even pose threats to structural safety. Therefore, studying the ramming of ice ridges by the icebreaker is of great importance. In this study, the ice ridge is decoupled into the consolidated layer and the keel for modeling. The consolidated layer is simplified as layered ice, and an innovative hybrid empirical–numerical method is used to determine the icebreaking loads. For the keel, a failure model is developed using the Mohr–Coulomb criterion in combination with the effective stress principle, accounting for shear failure in porous media and incorporating both cohesion and internal friction angle. The ship is restricted to surge motion only. A comparative analysis with the model test results was conducted to assess the accuracy of the method, with the predicted ice resistance showing deviation of 9.85% in the consolidated ice area and 10.48% in the keel area. Ablation studies were conducted to investigate the effects of different ice ridge shapes, varying retreat distances, and different ship drafts on the performance of ramming the ice ridge. The proposed method can quickly and accurately calculate ice ridge loads and predict their motion responses, providing a suitable tool for on-site rapid navigability assessment and for the design of icebreakers. Full article

Weather routing is a critical guarantee for the safe and economical navigation of ships. Existing methods for weather routing still face challenges in selecting the appropriate planning granularity. A granularity that is overly coarse may result in routes passing through coastal and island-rich waters, such as coastal zones and reefs, thus compromising navigational safety. Conversely, a granularity that is excessively fine leads to an exponential increase in computational complexity, rendering the problem intractable. To address this issue, this paper proposes an efficient method for weather routing in coastal and island-rich waters, guided by ship trajectory big data: First, an adaptive quadtree is used to partition the navigable space into an adaptive grid, based on which a route network is constructed using ship trajectory big data. Next, a ship motion model is introduced to build both static and dynamic marine environmental fields, which are used to dynamically update the time weights of the route network. Finally, using the updated route network as a guide, the method aims to minimize voyage time and employs an improved time-varying A* algorithm for weather routing. Experimental results show that the proposed method effectively adapts to coastal and island-rich waters, outperforming the baseline SIMROUTE in safety, optimization, and efficiency. Unlike SIMROUTE, which crosses restricted areas, it avoids such risks entirely. It achieves average reductions of 6.8% in route length and 4.3% in navigation time and is 5.8 times faster than SIMROUTE for fine-grained planning. This balances voyage time, safety, and efficiency, offering a practical weather routing solution. Full article

Rural poverty and unemployment remain persistent challenges in Indonesia, particularly in regions where agricultural development is uneven and land conversion accelerates socio-economic disparities. These conditions are highly relevant because rural areas serve as the backbone of food security, labour supply, and national economic stability. This study aims to address these issues by developing a flexible analytical framework that simultaneously models three indicators of rural development—rural poverty, rural unemployment, and agricultural sector growth—using a truncated spline nonparametric regression approach with longitudinal data from 2015 to 2023. The methodological approach integrates this regression with panel data across five Indonesian regions, allowing the analysis to capture nonlinear relationships and regional variations that conventional parametric models may overlook. The results indicate that population migration, land use change, and village fund allocation are the dominant drivers of rural development indicators, with nonlinear and region-specific effects. Village funds consistently reduce poverty and unemployment, while excessive land conversion restricts agricultural sector growth. The findings contribute to theory by demonstrating the advantages of flexible nonparametric approaches in modelling rural development dynamics, and to practice by offering empirical evidence for more targeted and adaptive policy interventions to alleviate poverty, reduce unemployment, and strengthen rural resilience. Full article

In high-density built environments, perceived density (PD)—shaped by physical, socio-cultural, and perceptual factors—often induces sensations of crowding, stress, and spatial oppression. Although green spaces are recognised for their stress-reducing effects, the influence of built-environment characteristics on public sentiment under stringent mobility restrictions remains inadequately explored. This study takes Chongqing, a representative mountainous metropolis in China, as a case to examine how natural and built environmental elements modulate emotional valence across varying PD levels. Using housing data (n = 4865) and geotagged Weibo posts (n = 120,319) collected during the 2022 lockdown, we constructed a PD-sensitive sentiment dictionary and applied Python’s Jieba package and natural language processing (NLP) techniques to analyse emotional scores related to PD. Spatial and bivariate autocorrelation analyses revealed clustered patterns of sentiment distribution and their association with physical density. Using entropy weighting, building density and floor area ratio were integrated to classify residential built environments (RBEs) into five tiers based on natural breaks. Key factors influencing positive sentiment across PD groups were identified through Pearson correlation heatmaps and OLS regression. Three main findings emerged: (1) Although higher-PD areas yielded a greater volume of positive sentiment expressions, they exhibited lower diversity and intensity compared to low-PD areas, suggesting inferior emotional quality; (2) Environmental and socio-cultural factors showed limited effects on sentiment in low-PD areas, whereas medium- and high-PD areas benefited from a significantly enhanced cumulative effect through the integration of socio-cultural amenities and transportation facilities—however, this positive correlation reversed at the highest level (RBE 5); (3) The model explained 20.3% of the variance in positive sentiment, with spatial autocorrelation effectively controlled. These findings offer nuanced insights into the nonlinear mechanisms linking urban form and emotional well-being in high-density mountainous settings, providing theoretical and practical guidance for emotion-sensitive urban planning. Full article

Under rapid urbanization, water-abundant cities face severe challenges of ecological space compression and ecosystem service (ES) degradation. This study focuses on Wuhan, a representative water-abundant city, integrating the PLUS model, InVEST model, correlation analysis, and geographically weighted regression (GWR) to simulate land use patterns in 2040 under three scenarios: natural development (ND), ecological protection (EP), and urban expansion (UE). We quantitatively assessed the spatiotemporal evolution of carbon storage (CS), water yield (WY), soil conservation (SC), and habitat quality (HQ), along with the trade-offs/synergies among these ES. The results reveal that the continuous expansion of construction land in Wuhan has extensively encroached upon cultivated land and water bodies. Although the woodland area increased, it was insufficient to offset the negative impacts of construction land expansion on ES. Under the ND scenario, ES declined by 1.89% to 5.33%. The EP scenario, by implementing ecological protection measures and restricting construction land expansion, enhanced ES by 1.4% to 10%. Conversely, the UE scenario saw construction land increase by over 60%, triggering a chain reaction of “urban expansion—reduction of cultivated land—encroachment on woodland/water bodies”, leading to a 4.77% to 10.75% decline in ES. Furthermore, this study uncovered complex interrelationships among ES: synergistic relationships generally prevailed among CS, SC, and HQ; trade-offs characterized the relationships between WY and both CS and HQ; and the relationship between WY and SC dynamically shifted between trade-off and synergy depending on land use patterns. Urban expansion (UE) intensified trade-off conflicts among ES, whereas ecological protection (EP) alleviated most trade-offs. However, water body expansion under EP weakened the synergy between CS and HQ due to the inherent characteristics of aquatic ecosystems (high HQ but low carbon sequestration). This research provides a scientific basis for water-abundant cities to coordinate development and ecological protection, informing the formulation of differentiated land use policies to optimize ES synergies. Full article

Rapid urbanization has led to substantial changes in land use, resulting in challenges related to the urban microclimate across multiple scales. Given the strong relationship between urban morphology and microclimatic conditions, designing appropriate urban fabric models plays a key role in supporting sustainable urban development. The spatial form and geometry of buildings influence external environmental conditions and affect the performance of urban architecture. This study investigates how morphological and geometric characteristics of urban form influence microclimate, using a case study approach. Data were obtained through a literature review and existing urban development plans. ENVI-met software was used to simulate microclimatic variables, which were treated as dependent factors. In parallel, morphological components—treated as independent variables—were analyzed using GIS Pro software. Findings reveal that the configuration of urban fabric has a notable impact on microclimate. Specifically, higher building density is associated with greater heat accumulation around structures. Urban areas with fragmented and highly granular layouts tend to trap more heat, thereby intensifying the urban heat island effect. Conversely, when buildings are spaced apart, increased wind flow helps reduce temperatures in central urban zones of urban development in District 9, Mashhad, Iran. The results also emphasize the importance of vegetation placement. While greenery can mitigate heat in ventilated areas, dense vegetation in wind-restricted zones may raise ambient temperatures. Overall, the study offers a simulation-based understanding of how urban form influences microclimate. These insights can assist urban planners and designers in creating environments that promote more favorable local climatic conditions. Full article

The increasing contradiction between ecological conservation and community development is a common challenge faced in most protected areas worldwide. Since 2019, China has used a “sky window” policy to alleviate the dilemma of environmental protection and sustainable production activities in national parks. This policy’s impact on household income in national park communities has received little attention. In this study, we aimed to evaluate the impact of the sky window policy on household income in Wuyi Mountain National Park communities in China and explore its mechanism of action in order to provide policy recommendations for achieving the protection goal of the national park and enabling win–win development of the community. Based on a total of 951 samples, which were collected through face-to-face interviews with 518 households in two periods, we used the difference-in-differences (DID) model to obtain consistent results and conducted robustness tests on the model by employing propensity score matching (PSM). The results showed that the “sky window” policy had a significant negative impact on the income of households in national park communities, which was mainly caused by the relaxation of restrictive regulations on farmers’ planting and breeding activities within national parks. The findings indicate that government departments in China need to further improve the laws and regulations regarding national park construction, establish a dynamic evaluation mechanism to regularly review the effects of the “sky window” policy, and make timely adjustments based on changes in the ecological environment of national parks and the development needs of local communities. At the same time, to ensure a stable source of income for residents, it is also necessary to establish a platform for realizing the value of ecological products, strengthen support for livelihood transformation, and establish long-term benefit linkage mechanisms. This study contributes to the research on the effective management of national parks, community welfare improvement, and sustainable development in developing countries. Full article