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Boundary recognition visually delineates the horizontal extent of subsurface anomalies, providing a foundational basis for interpreting gravity data. Compared to gravity observations, gravity gradient data offers the benefits of multiple components and the enhancement of shortwave information in graphical representations. In our study, we investigated the delineation of geological structure boundaries in the lunar Oceanus Procellarum region using gravity gradient techniques. First, based on the observations from the Gravity Recovery and Interior Laboratory (GRAIL) mission, we obtained high-precision synthetic full tensor values of the gravity gradient as our primary research data. Moreover, to reduce curvature errors during large-scale terrain forward modeling, we applied the spherical prism of tesseroid to eliminate the effects of near-surface topography. Grounded on the Bouguer anomalies of gravity gradient, four distinct boundary recognition methods have been employed to explore the geological structure of the lunar Oceanus Procellarum region. It includes the Theta map method, the directional Theta map method, the combination of total horizontal derivative and the modulus of full tensor gravity gradient, and the improved edge detection method based on the Theta map method.The common feature of these methods is the utilization of the multi-component data combination from gravity gradient tensors, which can enhance the accuracy of edge detection. From the investigation results presented in the paper, we have found the following: 1) The improved edge detection method, based on the Theta map principle, enables better identification of the center of subsurface anomaly bodies during boundary recognition, utilizing a consistent single color in graphic displays. 2) According to the results of boundary recognition, numerous strip anomalies exist in the Oceanus Procellarum area that show less correlation with topographic relief. One possible explanation for these graphical anomalies of gravity gradient is that they serve as channels for the upwelling of mantle material during lunar evolution. [ABSTRACT FROM AUTHOR]

The increasing role of unmanned aerial vehicle (UAV) swarms in modern warfare poses a significant challenge to ground and air defense systems. Considering complex terrain environments and multi-sensor resources including radar and photoelectric systems constraints, a novel multi-sensor dynamic scheduling algorithm is proposed in this paper. Firstly, a transmission model with Fresnel zone under complex terrain and sensor models for radar/photoelectric systems are established. Considering the constraints of 6 factors, such as pitch angle, array scanning angle and threat levels, a detection model is developed subsequently. Secondly, to meet the real-time requirements of ground and air defense systems, a fast calculation method for Fresnel zone clearance using adaptive buffer is achieved. Thirdly, an improved Hungarian algorithm is proposed to solve the combinatorial optimization problem of sensor scheduling. Finally, simulation experiments are conducted to evaluate the algorithm performance under different conditions. The results demonstrate that the proposed approach significantly reduces the sensor switching rate while achieving a high sensor-UAV matching rate and high-threat matching rate. Furthermore, the simulation results verify the effectiveness of the proposed algorithm when applied to multi-sensor scheduling for defending UAV swarms. • Modern militaries need advanced sensor scheduling solutions to defend UAV. • Considering the Fresnel zone under complex terrain is crucial. • Adaptive buffer enables rapid calculation of Fresnel zone clearance. • Multiple sensor constraints make scheduling problems realistic. • Improved algorithm effectively enhances matching rates while reducing switching. [ABSTRACT FROM AUTHOR]

• This paper identified the innovation networks and innovation environment as the core components of the regional innovation system. • A two-region, three-sector New Economic Geography model is developed by integrating the digital economy, innovation cooperation, R&D elements flow, and innovation output into a unified framework. • Digital economy promotes innovation output through promoting intra-regional and inter-regional cooperation and attracting the flow of R&D elements personnel and capital. • Underdeveloped cities benefit more from digital economy, while large central cities function as conduits and generate spillover effects to interconnected regions. This paper explored the paradigm shift in spatial innovation dynamics driven by the rapid emergence of the digital economy (DIGE) through the lens of the regional innovation system (RIS). By integrating DIGE, RIS, and innovation output into a unified analytical framework, this study examined the impact and mechanisms of DIGE on innovation output across 330 Chinese cities from 2011 to 2020. Results show that DIGE development significantly contributes to innovation output. This contribution is mediated by DIGE's strengthening of the RIS, primarily through its facilitation of integration into innovation networks and enhancement of the innovation environment. Regional heterogeneities are evident in the innovation-promoting influence of DIGE, with less developed cities, cities with underdeveloped infrastructure, and smaller cities benefiting more significantly. While the direct impact of DIGE on large central cities is subtle, their pivotal positions enable them to radiate and disseminate innovation spillovers to connected cities through extensive cooperative networks. This study underscored the potential of leveraging DIGE, especially in disadvantaged regions, to cultivate robust and synergistic RIS and act as catalysts for augmented innovation output. [Display omitted] [ABSTRACT FROM AUTHOR]

The aim of the article is to show the use of geostatistical methods in estimating real estate in a quite intensively urbanized area. The article presents a geostatistical analysis of residential premises, which focuses on the impact of the differential terrain model on the value of residential real estate. The research carried out consisted of literature studies, including Internet data, and the collection of data that could be presented in the form of cartographic images using a spatial analysis tool. The analyzes included land properties with multi-unit buildings, which, after taking into account the terrain model, were generated on polygons taking into account the appropriate geodetic reference system. The research results were developed in cartographic and descriptive form, taking into account elements of spatial interpolation. The study results confirmed the hypothesis that the estimation of residential premises should take into account the differential terrain model. And this should certainly take place on the seaside real estate market, where the level of the premises and the visibility of the sea are crucial in the context of their value. [ABSTRACT FROM AUTHOR]

When legged robots perform complex tasks in unstructured environments, falls are inevitable due to unknown external disturbances. However, current research mainly focuses on the locomotion control of legged robots without falling. This paper proposes a comprehensive decision-making and control framework to address the falling over of quadruped robots. First, a capturability-based fall prediction algorithm is derived for planar single-contact and 3D multi-contact locomotion with a predefined gait sequence. For safe fall control, a novel contact-implicit trajectory optimization method is proposed to generate both state and input trajectories and contact mode sequences. Specifically, incorporating uncertainty into the system and terrain models enables mitigating the non-smoothness of contact dynamics while improving the robustness of the resulting trajectories. Furthermore, a model-free deep reinforcement learning-based approach is presented to achieve fall recovery after the robot completes a fall. Experimental results demonstrate that the proposed fall prediction algorithm accurately predicts robot falls with up to 95% accuracy approximately 395ms in advance. Compared to classical locomotion controllers, which often struggle to maintain balance under significant pushes or terrain perturbations, the presented framework can autonomously switch to the fall controller approximately 0.06s after the perturbation, effectively preventing falls or achieving recovery with a threefold reduction in touchdown impact velocity. These findings highlight the effectiveness of the proposed framework in enhancing the stability and safety of legged robots in unstructured environments. • The proposed fall prediction approach exhibits significantly enhanced reliability and flexibility. • The proposed controller can autonomously generate contact sequences, reducing damage from falls. • The designed reinforcement learning-method improves efficiency and robustness in fall recovery. [ABSTRACT FROM AUTHOR]

This paper introduces an implementation of different GIS tools into Streamlit application: FCNN Terrain Classification, Earth Engine Dataset Parsing, and GIS Timelapse Animations. This toolkit is integrated with terrain multi-classification models using Fully Convolutional Neural Networks (FCNNs) for imagery data into Streamlit microservices. The proposed methodology involves labeled and unlabeled data collection from ESA WorldCover and Sentinel-2 MSI on the Google Earth Engine, compressing datasets into TFRecords format with 9 diverse terrain types, and handling Google Cloud training computations. The experimental results demonstrate the effectiveness of the CNN-based approach, achieving a tolerable from 60% up to 80% accuracy of the model and robust classification performance. The simplicity and efficiency of the proposed method make it suitable for real-world tasks requiring reliable and fast GIS analytics. [ABSTRACT FROM AUTHOR]

Terrain is a vital element in construction of virtual scene in the digital era. Despite considerable progress has been made in Generative Adversarial Network (GAN) based terrain modeling methods, their quality and controllability still cannot meet up the requirements of many emerging industries. The present work proposes a novel disentangled generative model, named as StyleTerrain, for achieving controllable high-quality terrain generation. It introduces disentangled representation learning into GAN-based terrain modeling methods for the first time. The model has been evaluated quantitatively. The results show a significantly short perceptual path length and the effectiveness of the disentanglement mechanism in controllable terrain generation, indicating that latent space disentanglement is a promising future direction for achieving generation controllability in GAN-based terrain modeling methods. [Display omitted] • Disentanglement is introduced into GAN-based terrain modeling for the first time. • StyleTerrain achieves superior generation quality and controllability. • Disentanglement is promising in future development of GAN-based terrain modeling. [ABSTRACT FROM AUTHOR]

The mobility of vehicles in off-road environments is critical for many applications. Predicting the tire-soil interaction is a challenge, especially in non-linear deformable terrain. This paper presents an overview of the bibliographic references on tires–deformable soil interactions after the 2000's, identifying the gaps in the literature. The capabilities and challenges of different modeling frameworks used for mobility (i.e., empirical, semi-empirical, and physics-based) are discussed; special emphasis is given to continuum-based frameworks. A summary of terrain material models used to approximate the behavior of coarse and fine-grained soils is provided with practices used to characterize such materials. A review of tire models for deformable soil navigation and the tire-soil interfaces is provided. Strategies to validate all these models are presented. Finally, the application of these studies for assessing the sensitivity concerning input parameters (e.g., velocity, inflation pressure, and normal load), multi-pass, multi-layered soils, wet soils, and fully integrated multi-body vehicle models are discussed. The final contribution of this review paper is a summary table that synthesizes the extensive bibliographic review. Overall, this work highlights a lack of physics-based trafficability studies in wet and plastic deformable soils. Moreover, studies on contact adhesion, stone picking, multipass, and cornering also need improvement. [ABSTRACT FROM AUTHOR]

• A methodology for seafloor topography mapping is proposed. • Data used are satellite gravity anomaly, shipborne gravity anomarly, and shipborne depth. • The accuracy of the seafloor depth determination is better than the available depth models. Seafloor topography mapping has a vital role in territorial management, geological investigation, natural hazard research, and marine construction; therefore, improving the accuracy of seafloor topography mapping is essential. The main objective of this research is to assess the possibility of improving the accuracy of seafloor topographic mapping for the central sea area of Vietnam and its surroundings based on the combination of satellite-derived gravity anomaly data and shipborne data. Firstly, the best satellite-derived gravity anomaly model for the study area is determined by comparing its gravity with the shipborne gravity anomaly. Then, the systematic deviation in the satellite-derived gravity anomaly is eliminated and fitted. Then, the substance density contrast (SDC) between the seabed and the seawater is derived. Subsequently, the seafloor depth was computed using the Gravity Geological method. In the next step, the computed depth is fitted with the shipborne depth to improve the accuracy and reduce the systematic deviation. The result shows that the best satellite-derived gravity anomaly model for the study area is the DTU17GRAV, with an accuracy of ± 5.06 mGal. The most suitable SDC in the study area is 1.40 g/cm3. The final seafloor topographic map with 1′×1′ grid is obtained with the root mean square deviation (RMSD) of ± 83.96 m, which is lower than that of the global terrain model of GEBCO 2022 (RMSD = ±152.88 m). Therefore, it is concluded that the combination of satellite-derived gravity anomaly and shipborne data can significantly improve the accuracy of seafloor mapping for complex topographic areas. The results of this research are helpful for seabed applications and territorial management in the region. [ABSTRACT FROM AUTHOR]

We present a novel proposal for modeling complex dynamic terrains that offers real-time rendering, dynamic updates and physical interaction of entities simultaneously. We can capture any feature from landscapes including tunnels, overhangs and caves, and we can conduct a total destruction of the terrain. Our approach is based on a Constructive Solid Geometry tree, where a set of spheres are subtracted from a base Digital Elevation Model. Erosions on terrain are easily and efficiently carried out with a spherical sculpting tool with pixel-perfect accuracy. Real-time rendering performance is achieved by applying a one-direction CPU–GPU communication strategy and using the standard depth and stencil buffer functionalities provided by any graphics processor. • Real-time rendering using the standard depth and stencil buffers. • Real-time dynamic updates and physical interaction on complex dynamic terrains. • Erosions with a spherical sculpting tool with pixel-perfect accuracy. • The model easily captures tunnels, overhangs and terrain erosion. [ABSTRACT FROM AUTHOR]

• Dynamic factor characterizes vehicle potential that can be created by powertrain. • Approach adds the conjoint effect of the engine, transmission, and driveline. • Includes power distribution among the wheels caused by driveline configuration. • Design factors assess effects on vehicle performance and power utilization. Vehicle terrain mobility characteristics, provided by the powertrain and running gear, are realized in dynamic interactions between the wheels and terrain. Approaches to modeling and simulation of vehicle-terrain interaction and mobility characteristics as well as engineering approaches to design powertrain sub-systems together pre-determine a vehicle's technical success or failure before it touches the ground. This article develops a vehicle mobility design technique, applicable to both manned and unmanned platforms, concerned with powertrain power conversion and realization in tire-terrain interactions. The modeling component is based on multi-drive-wheel vehicle longitudinal dynamics combined with terramechanics and powertrain characteristics. The approach advances the conventional dynamic factor by introducing the conjoint effect of the engine, transmission, and driveline system on vehicle traction and acceleration performance in terrain conditions where circumferential wheel forces and tire slippages may differ from each other. The vehicle design component of the proposed technique introduces drivetrain, driveline, and powertrain design factors that assess the influence of the drivetrain and driveline systems on traction, acceleration performance, power conversion, and realization at the wheels. The vehicle-design-for-mobility technique is completed by examining indices of mobility margins and performance. An analysis of several 8x8 armored personal carriers and 4x4 off-road vehicles illustrates the proposed technique. [ABSTRACT FROM AUTHOR]

This paper constructs the economic model to consider the economy in cities from the waste management perspective. Specifically, we analyze the link between migration, natural capital, and waste management by applying the new economic geography model. We show the results; the population distribution pattern in the long run varies depending on the congestion effect of natural capital and waste management's technological level. In particular, a full agglomeration equilibrium realizes in the long run for higher technological levels of waste management (lower congestion effects), an interior asymmetric equilibrium does for intermediate technological levels (intermediate congestion effects), and the symmetric dispersion equilibrium realizes for the lower technological levels (higher congestion effects). [ABSTRACT FROM AUTHOR]

Salt marshes are critical coastal ecosystems that provide numerous services. They are governed by complex biogemorphological interactions on multiple spatiotemporal scales. To simulate long-term salt marsh development in numerical models, smaller- and shorter-term vegetation processes are often neglected. This study investigates the importance of spatiotemporally and seasonally varying vegetation dynamics on decadal salt marsh development in an integrated numerical biogeomorphological model simulating a representative, idealized case of the Dutch Western Scheldt Estuary. The focus lies on the influence of seasonal vegetation dynamics, interspecific interactions between two salt marsh species, and the stabilizing effect of below-ground biomass with increasing vegetation age on critical shear stress, which results in spatially non-uniform erosion resistance of sediment and vegetation. By incorporating seasonal growth periods and varying characteristics of vegetation, we demonstrate the impact of species-specific traits on vegetation stability and morphological changes. Introducing an additional pioneer species that can establish further seaward reveals the sheltering effect and the long-term implications of this zonation for marsh development. Additionally, the influence of spatially and temporally varying below-ground biomass on sediment stability and vegetation's resistance to uprooting is significant for the biogeomorphological interactions. Comparison of the different model simulations to topographical data from the Dutch Western Scheldt Estuary shows that the model results are representing the morphological variability occurring in the field. Our findings highlight the complex biogeomorphological feedback that govern salt marsh development and show that models that neglect shorter-term seasonal and grid-scale (25 m2) spatially varying processes may overlook critical interactions that influence the lateral extent and overall morphological development of salt marshes. Therefore, this study underscores the need to integrate seasonal dynamics, species diversity, and below-ground biomass variability into biogeomorphological models to enhance predictive accuracy. This approach provides a more comprehensive understanding of the large-scale, long-term consequences of seasonal and spatiotemporally varying vegetation processes, which is essential for informing conservation and management strategies under changing climatic conditions. • Short-term dynamics are crucial for accurate long-term marsh development models. • The stabilizing effect of below-ground biomass influences marsh biogeomorphology. • Seasonal vegetation growth and wave conditions shape long-term marsh development. • Pioneer species introduction impacts vegetation stability and marsh morphology. [ABSTRACT FROM AUTHOR]

Computational Fluid Dynamics (CFD) numerical simulation is a cost-effective and reproducible technique that has become a pivotal tool for researching wind characteristics in complex terrains, which is a critical area of investigation for wind resistance in large-span bridges. To ensure reliable those CFD analysis, it is crucial to rely on effective transitional curve. In this study, a novel transition curve based on the Bernstein basis function is proposed, which exhibits superior transition capabilities compared to existing transition curves applied in ideal terrains under comprehensive indices. Subsequently, this curve was applied to numerically simulate the wind field in a gorge terrain bridge site. The simulated spatial mean wind field was compared with three years of measurement data along a long-span bridge. The findings indicate that in the absence of transitional curves in terrain models of varying scales, the simulated wind speed distribution along the bridge can differ significantly from the actual measurements. However, the addition of various transitional curves yields superior results, with the recommended transitional section performing the best, with the relative error in wind speed for the dominant wind direction of less than 10%. Furthermore, the recommended transitional curve demonstrates strong applicability to terrain models of different sizes. • A novel transition curve using the Bernstein function for CFD simulation is proposed. • A comparison is made between the numerical outcomes and the field measurements. • The proposed curve exhibits superior capabilities in ideal and real terrains. • The transitional curve shows robustness to terrain models of different sizes. [ABSTRACT FROM AUTHOR]

• Compares ground vehicle terrain mobility modelling and simulation (M&S) methods. • Covers Eastern & Western historical and recent developments in mobility M&S. • The Eastern approach is covered in this Part I of the article. • The Western approach is covered in this Part II of the article. For many decades, different approaches, fundamentals, and expressions have been developed in various countries for military vehicle modelling and simulation (M&S) as a core component of ground vehicle design for mobility. The political division of the world into the West and East that existed before the Soviet Union breakdown in 1991 had not facilitated collaboration between researchers and engineers of both sides, and, thus, they created and practiced their own approaches. The war in Ukraine urgently prompted analysis of the origins and essence of the Western and Eastern technical paradigms, which being conceptually different to ground vehicle mobility, had predetermined the development of vehicle M&S methods and techniques in their parts of the world, specifically for studying dynamic interactions of vehicles with severely uncertain terrains, which impact vehicle behavior and performance, and, thus, may either facilitate mission accomplishment or lead to its failure. Furthermore, this analysis of the technical paradigms aims to further advance M&S fundamentals for next generation combat vehicles as described in the U.S. Army's 2019 Modernization Strategy. Part I of this article considers the Eastern approach and the Western approach is presented in Part II. [ABSTRACT FROM AUTHOR]

The complexity of image scene information presents challenges for the trafficability assessment and path planning of Mars rovers. To ensure the operational safety of Mars rovers and extract terrain features from complex image scenes, this paper develops an end-to-end deep learning model, using the deep convolutional neural networks ResNet50 and DeepLabV3 + as the framework, with images from the Zhurong rover's navigation camera as the training and test datasets. A deep learning model suitable for classification and segmentation of terrain in the Mars Utopia Planitia region has been established and applied to planetary geology research. The classification accuracy of model exceeds 83.90 % and segmentation accuracy exceeds 80 %. Subsequently, an analysis of 1309 raw images from the navigation camera yielded 203,744 individual estimates of rock abundance, the model evaluates the rock abundance in the Utopia Planitia region, where the Zhurong rover is located, at 10.94 %. The terrain classification model proposed in this study provides both engineering and scientific value for future rovers on the Utopia Planitia. [ABSTRACT FROM AUTHOR]

• Compares ground vehicle terrain mobility modelling and simulation (M&S) methods. • Covers Eastern & Western historical and recent developments in mobility M&S. • The Eastern approach is covered in this Part I of the article. • The Western approach is covered in this Part II of the article. For many decades, different approaches, fundamentals, and expressions have been developed in various countries for military vehicle modelling and simulation (M&S) as a core component of ground vehicle design for mobility. The political division of the world into the West and East that existed before the Soviet Union breakdown in 1991 had not facilitated collaboration between researchers and engineers of both sides, and, thus, they created and practiced their own approaches. The war in Ukraine urgently prompted analysis of the origins and essence of the Western and Eastern technical paradigms, which being conceptually different to ground vehicle mobility, had predetermined the development of vehicle M&S methods and techniques in their parts of the world, specifically for studying dynamic interactions of vehicles with severely uncertain terrains, which impact vehicle behavior and performance, and, thus, may either facilitate mission accomplishment or lead to its failure. Furthermore, this analysis of the technical paradigms aims to further advance M&S fundamentals for next generation combat vehicles as described in the U.S. Army's 2019 Modernization Strategy. Part I of this article considers the Eastern approach and the Western approach is presented in Part II. [ABSTRACT FROM AUTHOR]

Geographic information and topographical understanding are crucial for effective field environmental monitoring and autonomous navigation. Unfortunately, implementing this knowledge is often difficult due to unsolved problems such as understanding disorganized and unstructured mountainous regions. Traditional three-dimensional (3D) scene understanding methods from 3D point clouds or fused data are energy-consuming and unsuitable for a resource-constrained unmanned aerial vehicle (UAV) with limited computation, memory, and energy. This study proposes a methodology to understand unstructured gorges and reconstruct them in 3D environments from a low-cost monocular camera. The new approach assigns mountain texture projections to clusters and reshapes surfaces of unstructured mountains to follow geometric constraints regarding position and orientation. The relative relationships between surfaces and vanishing points are then analyzed to understand and reconstruct amorphous gorge scenes. This method is practical and efficient for navigating amorphous gorges based on understanding unstructured mountains. Fortunately, this method requires no prior training and is robust to color and illumination. We compared the percentage of incorrectly classified pixels with the ground truth. Experimental results have demonstrated that our approach can successfully understand amorphous gorges, meeting the requirements for autonomous flying in mountainous environments. • A method is presented to understand amorphous gorge scenes and generate a topographic map. • The algorithm can reconstruct unstructured mountains without prior training. • The approach is robust against changes in illumination and color without camera's parameters. [ABSTRACT FROM AUTHOR]

This study presents the impact of step-like ground slopes on free-field motions at different excitation frequencies. Furthermore, numerical analyses were also performed to evaluate how variations in dynamic soil displacements due to kinematic interaction affect the seismic response of the building. To analyze the complex wave propagation mechanism within the scope of this study, 1-D and 2-D wave propagation models were developed using the geometry and the dynamic soil properties based on the literature. The 2-D finite element model of topographical irregularities was calibrated in terms of lateral acceleration behavior using both recorded values and previously computed data by researchers. The evaluation of seismic ground response includes the amplification effects on acceleration-time histories and the response spectra utilized in seismic codes. Additionally, the 2-D dynamic response of buildings with different periods was investigated considering the influence of topography-soil-structure interaction resulting from rock-outcrop motions. Results indicate significant role of particular factors in the variability of intense amplification. • The effect of topographical irregularities on seismic ground vibrations was investigated at various frequencies. • The calibrated 2-D finite element solutions exhibited better performance in terms of measured records. • The amplification effects on lateral acceleration and response spectra due to rock-outcrop motions were evaluated. • Resonance effects resulting from the vibrating soil-structure system were observed. • Identified key factors contributing to the variability of intense seismic amplification. [ABSTRACT FROM AUTHOR]

The construction of three-dimensional numerical models for complex terrains and geological bodies has always posed a challenge in geoengineering. This study introduces an efficient discretization approach that facilitates the establishment of material point models for three-dimensional complex terrain and geological conditions. The proposed approach incorporates three structural components and employs an efficient algorithm for data processing to characterize terrain and geological bodies within the model. This enables the construction of complex, heterogeneous discrete models using elevation and material information as input data. A detailed procedure for implementing the proposed three-dimensional material point discretization approach is outlined. Additionally, robust metrics for discretization deviation and efficiency evaluation are introduced to assess the accuracy and efficiency of the constructed discretized models. The efficacy of the proposed approach is verified and evaluated through both two-dimensional and three-dimensional examples, demonstrating its accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

Earthquakes can initiate slow-moving landslides and cause them to transition into rapid failures. Although observations are limited, the literature suggests that strong earthquakes are more likely to trigger nearby failures, while smaller earthquakes may increase susceptibility. However, understanding the role of seismic disturbances requires considering other environmental conditions. This study focuses on the 2008 M w 7.9 Wenchuan earthquake, using a data-driven multivariate approach to analyze slow-moving landslides. Our findings highlight topographic relief, road proximity, river distance, average monthly precipitation, lithology, and distance to the earthquake surface rupture as key factors. While the distance to the surface rupture is inversely related to landslide occurrence, it is the least influential variable, suggesting that most slow-moving landslides near the rupture may not have failed during the earthquake. Our finding also suggests that persistent disturbances from road networks may be as significant as the impact of a strong earthquake in influencing the existence of slow-moving landslides. • Spatial distribution of slow-moving landslides was analyzed in a data-driven model. • We aimed at identifying the role of the Wenchuan earthquake. • Distance to roads is the second most influential variable in the model after relief. • Distance to surface rupture is the least influential variable. [ABSTRACT FROM AUTHOR]

• A new 3DJG-CT wake model suitable for complex terrain wind farm is proposed. • The topology anisotropy effect on wake development over complex terrain is revealed. • Two phases of field experiment are conducted using LiDARs. • The good performance of 3DJG-CT model is verified by the field measured data. • The correlation between wake development and background flow field is quantified. The topology anisotropy of complex terrain has a tremendous impact on the wake characteristics in real complex terrain wind farm. To efficiently and precisely evaluate the terrain effect on the wake development, a new three-dimensional Jensen-Gaussian wake model for complex terrain (3DJG-CT) was proposed with two improved assumptions and combined with the CFD simulation, building upon the 3DJG-U wake model. The wake centerline is determined by the velocity components (u x , u y , u z) extracted from the CFD simulation results of the background flow field, which also provide the local wind velocity U local (x , y , z) to replace the inflow wind shear model in our earlier 3DJG model. Two phases of field measurement were conducted to reveal the impact of the topology anisotropy on the 3D wake characteristics. By comparing the predictions of the 3DJG-CT model and previous models with the field measurement data, the results show that the 3DJG-CT wake model has good performance in wake profile prediction. Due to the dominant impact of the hill-blocking effect compared to the momentum mixing effect, the downstream wake deficits may be larger than the upstream wake deficits on the leeward slope, which is opposite to the situation in flat terrain. In the windward slope scenario, the wind speed at hub height at downstream distances of 5.5D and 7D is 6.64%% and 8.27% higher than the upstream inflow velocity at hub height, because of the positive coupling effect of the wind speed-up effect and the momentum mixing effect. Better quantification of the wake effect in complex terrain can be supplied in this study. [ABSTRACT FROM AUTHOR]

In this paper, a terrain-adaptive motion planner is developed specifically for articulated construction vehicles (ACVs) to address instability issues caused by elevation changes on unstructured construction sites—challenges that traditional 2D motion planners struggle to manage effectively. The proposed planner adopts a modular framework, incorporating a terrain elevation model, an articulated vehicle kinematic model, and a posture response model. These models collaboratively capture the dynamic interactions between the vehicle and the terrain. The planner utilizes a multi-objective evaluation function to enhance the vehicle's 3D motion stability, especially in challenging terrains. By considering real-time vehicle-terrain interactions, this function estimates and optimizes the vehicle's stability. The planner's effectiveness is validated through field tests with a scaled-down ACV prototype, demonstrating significant improvements in stability and confirming its potential for practical application on unstructured terrains. • Developed a DWA-based motion planner to ensure stable operation of ACVs on unstructured terrains. • Extended trajectory representation to 3D space by coupling vehicle motion response with terrain excitation. • Optimized a multi-objective evaluation to balance different planning constraints. • Established a modular integrated architecture to support real-time updates. • Validated the planner's effectiveness through field tests with a scaled-down prototype. [ABSTRACT FROM AUTHOR]

• Deviation of three atmospheric dispersion models supported by wind tunnel experiments. • Significant deviation appears among three models for uneven terrain in particular. • CFD-LES predicted stronger turbulent dispersion than CFD-RANS method. • Atmospheric dispersion characteristic influenced by complex terrain was given. • Predictive tritium distribution driven by hourly varying meteorological and topographical data. Tritium discharge from the fusion system and its environmental impact receive wide attention. Considerable deviation exists in various atmospheric dispersion models for evaluating near-surface tritium concentration distribution. To quantitatively find out the diversity of these models, a performances comparison was performed based on the case of tritium discharge from the ITER site. Wind tunnel experiments were preliminarily performed to test the performances of these models. Annual average tritium concentration and individual radiation dose were assessed for some selected residential areas adjacent to the ITER site. It is indicated that atmospheric dispersion simulation by the CFD method is significantly influenced by the modeling of the turbulence. Atmospheric dispersion predicted by the CFD-RANS model with default parameters is weaker, resulting in higher downwind pollutant concentration, compared with the CFD-LES model, Gaussian plume model and Lagrangian puff model. Wind tunnel experimental results relatively support the CFD-LES model with strong turbulent dispersion. CFD-LES model is superior in reflecting the effects of complex topography in high resolution and shows strong turbulent dispersion. Individual radiation dose under normal operation at selected residential areas near ITER was estimated to be much lower than the natural radiation level and also the ITER dose limit even considering the uncertainty margin. [Display omitted] [ABSTRACT FROM AUTHOR]

To study the morphological characteristics of the largest canyons in the Southern and Central Basins of Lake Baikal, we analysed a digital relief model based on high-resolution multibeam bathymetric surveying. We evaluate the role of tectonic, lithological and climatic controlling factors in the formation and evolution of canyons. Tectonic activity and seismicity act as a catalyst for canyon formation and for the development of sublacustrine gravity flows and turbidity currents, and alter the morphological appearance of already existing canyons. The influence of tectonics on the formation of the Baikal canyons is particularly noticeable on the western slope of Lake Baikal, where canyons are mainly located along faults and follow their directions. Gravity flows and turbidity currents become more important during earthquakes and periods of lower lake level. When canyon heads are located close to river mouths, riverine processes have a significant impact on the evolution of the canyon. The topography of the original surface and the lithology of the substrate also play a significant role in the evolution of canyons and in shaping the canyon morphology. During periods of lower lake level (during Pleistocene glacial periods) riverine processes were responsible for initiating the formation of many canyons in Lake Baikal. The canyons developed under the complex influence of the factors considered. One or another factor could be dominant at different stages of their evolution, while the role of the other factors became secondary. These roles could interchange over time. The Baikal canyons show a great deal of resemblance to canyons in marine environments, in terms of morphological characteristics as well as of canyon-forming processes. We propose a morphogenetic classification of the underwater valleys and canyons of Lake Baikal. [ABSTRACT FROM AUTHOR]

A perpendicular jet impingement on a granular bed covering uneven terrain causes complex particle erosion and diffusion behaviours. Such a situation is most likely to occur at a plume-regolith interaction in planetary exploration missions. A plume due to a retrorocket impinges on regolith over a planetary surface in a soft-landing sequence. In this study, we focus on the understanding of basic terrain effects and investigate numerically a subsonic jet impingement on the granular bed covering a rectangular hump using a two-way coupled fluid-particle interaction method. Moreover, the experiments are conducted to determine the suitable numerical parameters: turbulence parameters, friction coefficient of particles, and coefficient of restitution. Both particle erosion and diffusion behaviours are numerically investigated at several hump positions and heights, and consequently the hump position strongly influence the particle diffusion and erosion behaviours. Erosion due to the bearing capacity failure continues to occur from the initial elapsed time in the case of the jet impingement around the hump close to a jet axis. This is because the gas interaction with the hump leads to the velocity reduction of a diffusion gas velocity, and it is lower than a particle velocity in most of the regions. Additionally, the deposited particles in a cavity formed by the close hump are difficult to be diffused because the particles stagnate by the downward impinging jet flow. • The particles in a cavity are difficult to be diffused if a jet covers the cavity. • The particle erosion and diffusion behaviours are influence by a hump position. • The deposited particles are upwardly diffused by a hump. [ABSTRACT FROM AUTHOR]

Autonomous safe landing of UAVs is an important and challenging task in unknown environments, as almost no prior scene information can be leveraged for navigation. Most existing methods cannot address this issue completely, due to terrain uncertainty and system complexity. In this paper, we present a novel and complete framework for UAVs landing, which is built on point cloud in coarse-to-fine manner. Besides, our framework is designed with modularity and has four modules: point cloud preprocessing, coarse landing site selection, fine terrain evaluation, and landing optimal model. Specifically, a composite preprocessing scheme is applied to simultaneously filter noise, generate 3D Octo-map and plan the path on the raw point cloud. To balance the accuracy and real-time of the landing system, only promising coarse landing locations are automatically selected by adopting the proposed multi-stage process in grid-map. Based on the result of coarse stage, a fine-grained 3D validation is modeled by multiple terrain factors, which can further improve landing safety. Finally, a novel landing optimal model fuses terrain condition, fuel consumption, and second landing validation to determine the final landing sites during descent. Extensive experiments have been successfully conducted on different real-world and unknown environments, verifying that our method can select safe landing sites for UAVs robustly. Additionally, the system is further evaluated under normal, emergency, and rescue situations respectively to highlight different landing requirements. • A UAVs landing framework is proposed using a coarse-to-fine manner. • We use the multi-stage process to select coarse landing locations automatically. • A fine-grained terrain evaluation is introduced considering multiple factors. • An optimal model is designed to determine the final landing sites. [ABSTRACT FROM AUTHOR]

In emergency communication scenarios, the deployment of multiple unmanned aerial vehicles (UAVs) can rapidly provide communication coverage and restore network connectivity. Though existing research has paid attention to the issue of maintaining connectivity in multi-UAV networks, there are still some problems that have not been fully addressed. This paper investigates the scenario of deploying multiple UAVs with integrated sensing and communications (ISAC) abilities to provide data collection and communication coverage services for ground mobile users in a search mission. The aim is to maximize the sum of data collected from all users and minimize the adjustment cost of the UAV network while ensuring Backbone network connectivity during the search. By modeling the effect of 3-D terrains, the simulation environment presented in this paper is more realistic. Moreover, this paper assumes that the terrain within the search area is unknown, thus the search trajectory of the mobile user is also unknown. As a consequence, centralized methods are infeasible. Accordingly, this paper proposes a distributed dynamic adjustment scheme, named QoS-Connectivity-Guaranteed Distributed Adjustment (QCDA), to ensure both quality of service and network connectivity. The efficiency of the proposed scheme is validated through simulations, and its performance in terms of data collection, communication satisfaction ratio, the number of active UAVs as well as the adjustment cost of the UAV network is better than the baselines. [ABSTRACT FROM AUTHOR]

We propose a rapid and cost-effective Horizontal-to-Vertical Spectral Ratio (HVSR) method that uses microtremors (ambient noise) for mapping intercalated sedimentary sequences with lateral heterogeneity for a depth of about 100 m in Lower reaches of Narmada Valley, western India. The elastic parameters for various units/layers at a given site (1D) were derived theoretically based on HVSR inversion, built on the concepts of Diffuse Field Assumption and retrieval of the imaginary part of Green's function. This approach enables efficient forward calculation, delves into the relationship between the HVSR curve and elastic parameters, and accelerates the inversion process. A site-specific priori knowledge gathered during the field visits is further integrated. We successfully derive overall shear wave velocity and density models inferring three sedimentary Units and associated Subunits. The key highlight of this study is successfully distinguishing low-velocity sedimentary Units/Subunits within a sedimentary sequence. [Display omitted] • Velocity inversion in a sedimentary sequence is inferred based on Microtremor HVSR studies. • HVSR peak resonant frequency and Vs – ρ relationship together assist to classify sediment sequence into Units and Subunits. • Integration of results from HVSR studies with priori field knowledge abets to identify the sediment types. • An inclusive terrain model is attempted by connecting the depths interfaces of Units and Subunits to build cross-sections. [ABSTRACT FROM AUTHOR]

Disaster relief presents many unique logistics challenges, including damaged transportation infrastructure, multiple conflicting goals, and secondary disasters caused by spontaneous disaster relief. The delivery and distribution of humanitarian relief materials faced by nonprofit organizations (NPOs) poses a critical challenge due to the highly unpredictable nature of disasters, conflicting missions, and the need to navigate government regulations in disaster management. In this paper, we propose a novel multi-objective disaster relief modeling system for NPOs, considering (1) prioritization (providing medical services to seriously injured victims), (2) effectiveness (degree of satisfaction), (3) efficiency (operational costs), and (4) equity (cost of maintaining order). To incorporate the practical constraints of disaster relief operations, we include factors such as road damage, transportation capacity limitations, and route flow saturation as part of the model formulation. We then utilize fuzzy multi-objective programming to optimize the proposed disaster relief model, considering two relief distribution schemes: a common NPOs' spontaneous relief scheme and a newly designed traffic control scheme with government regulation. Based on the real-world scenario of the 2008 Wenchuan earthquake, we demonstrate that the designed traffic control scheme exhibited remarkable improvement among the NPOs' four objectives compared to the spontaneous relief scheme. Moreover, in the process of solving fuzzy multi-objective programming problems, artificial intelligence technology is employed to handle the fuzziness in multi-objective and automatically adjust parameters, making the solution results better and in line with practical situations. And we also validate the proposed model can be solved in polynomial time, meeting the engineering practice requirements, and can be easily extend to most disaster relief distribution scenarios. [Display omitted] • Fuzzy multi-objective programming model for humanitarian relief materials' delivery and distribution was built. • Prioritization, effectiveness, efficiency and equity were considered as the NPOs objectives. • Roadway capacity is considered as a constraint condition. • The NPOs' spontaneous relief scheme and traffic control scheme were discussed specially. • Delivery and distribution of humanitarian relief materials was analyzed based on 2008 Wenchuan earthquake. [ABSTRACT FROM AUTHOR]

• The determinants of smart roadway retrofitting on injury severity are examined in hazardous and non-hazardous terrain. • Random parameters logit models with heterogeneity in the means and variances are established. • Temporally unconstrained parameters model is more suitable compared to partially temporally constrained parameters model. • Road safety policies are suggested to be designed and implemented based on terrain and road development level. The advancement of intelligent road systems in developing countries poses unique challenges in identifying risk factors and implementing safety strategies. The variability of factors affecting crash injury severity leads to different risks across levels of roadway smartness, especially in hazardous terrains, complicating the adaptation of smart technologies. Therefore, this study investigates the temporal instability of factors affecting injury severities in crashes across various terrains, with a focus on the evolution of road smartness. Crash data from selected complex terrain regions in Shaanxi Province during smart road adaptation were used, and categorized into periods before, during, and after smart road implementations. A series of mixed logit models were employed to account for unobserved heterogeneity in mean and variance, and likelihood ratio tests were conducted to assess the spatio-temporal instability of model parameters across different topographic settings and smart processes. Moreover, a comparison between partially constrained and unconstrained temporal modeling approaches was made. The findings reveal significant differences in injury severity determinants across terrain conditions as roadway intelligence progressed. On the other hand, certain factors like pavement damage, truck and pedestrian involvement were identified that had relatively stable effects on crash injury severities. Out-of-sample predictions further emphasize the need for modeling across terrain and roadway development stages. These insights are crucial for developing tailored safety measures for smart road retrofitting in different terrain conditions, thereby supporting the transition towards smarter road systems in developing regions. [ABSTRACT FROM AUTHOR]

Satellite-based precipitation products (SPPs), such as the NOAA Climate Prediction Center (CPC) Morphing technique (CMORPH), have greatly extended our ability to monitor global precipitation. However, their performance over complex terrain remains highly uncertain. To improve accuracy, CPC has recently upgraded its operational SPP to the second generation CMORPH–CMORPH2. In addition to such efforts, the reliability of SPPs can be further enhanced by developing robust uncertainty estimates. This study employs a censored, shifted gamma distribution (CSGD)-based error modeling framework to develop error models for both CMORPH2 and its predecessor, CMORPH1, at a 6-hourly scale over Northern California—a typical complex terrain region that challenges most SPPs. Using the Stage IV reference precipitation, the relative improvements of CMORPH2 over CMORPH1 are quantified by comparing their CSGD-derived error statistics. The comparison shows that CMORPH2 outperforms CMORPH1 by reducing the overall bias and detection errors, and by better capturing the orographic gradients of precipitation over the Coast Ranges and Sierra Nevada. Validation results show that the trained error models can appropriately represent the bias and random errors; however, the models for both CMORPH2 and CMORPH1 show reduced skills in the high-altitude Sierra Nevada. With high-resolution regional climate simulations, the uncertainty estimates are further improved by incorporating them as covariates. The simulated precipitation shows substantial improvement of the uncertainty estimates over most areas, including the challenging high-altitude Sierra Nevada. Following precipitation, the simulated integrated water vapor transport (IVT) and convective available potential energy (CAPE) also offer modest improvements, mainly along the coast. Independent verification further demonstrates the robustness of the uncertainty estimates during heavy precipitation events. As NOAA is currently reprocessing CMORPH2 to produce an updated global precipitation record from 1991 onward, this error modeling framework holds potential for broader applications in quantifying the uncertainty of CMORPH2 for estimating orographic precipitation over extended periods and locations. • We developed parametric models for CMORPH uncertainty estimates in complex terrain. • The second-generation CMORPH exhibits superior performance over its predecessor. • The uncertainty estimates of CMORPH are reliable even in heavy precipitation events. • WRF simulations improve the uncertainty estimates for orographic precipitation. [ABSTRACT FROM AUTHOR]

Downward shortwave radiation (DSR) is the main energy source for the Earth's system and a dominant component of the radiation budget. Although many algorithms have been proposed for estimating DSR, most models only consider either the radiative effects of terrain or atmosphere, and no research has considered the conditions of clouds on the mountainside. In this paper, a high-resolution Mountain Radiative Transfer model with Clouds (MRTC) is proposed to characterize the cloud-terrain coupling effects on DSR under different surface positions relative to the clouds. To operate the MRTC, a look-up table is employed to obtain initial radiative parameters. The comparison with Monte-Carlo based radiative transfer indicates that MRTC can be applied to conditions of clouds on the mountainside. And the reliability of MRTC under conditions when clouds are above mountains is also validated against in-situ measurements, with an RMSE of 149.9 W / m 2 , a bias of 28.3 W / m 2 , and an R 2 of 0.74. By using more accurate cloud cover data extracted from All-Sky images, the accuracy of MRTC has improved, with the RMSE decreasing to 115.8 W / m 2 , and the R 2 increasing to 0.8. This suggests that the uncertainty in satellite cloud products significantly contributes to errors in DSR estimations. MRTC is expected to more accurately represent cloud-terrain coupling effects on DSR, and improve the accuracy of DSR estimation over rugged terrain under cloudy skies. • A high-resolution mountain radiative transfer model with clouds (MRTC) is proposed. • The coupling effects of clouds and terrain on DSR are fully considered. • The first time the conditions of clouds on the mountainside are handled in DSR estimation. • Validation against a 3D radiative transfer model and in-situ measurements shows good agreement. • Uncertainty in cloud products and the complex 3D cloud structures significantly affect the accuracy of estimation. [ABSTRACT FROM AUTHOR]

• Terrace erosion is impacted by the dispersive nature of soils. • Gully and pipe erosion deviate the attention for sedimentation. • Sedimentation was a magnitude larger than erosion. • Leaving plastic sheets on fields increases geomorphic processes. Abandonment of agricultural terraces is one of the main drivers of erosion processes and land degradation in semiarid environments. Soil erosion is a complex process and it is difficult to capture erosion rates including the different phases of the erosion process, depending on the spatio-temporal scale of observation. In this work erosion processes were studied in a small abandoned cascade terraced system in SE Spain regarding its sedimentation rate. Here agriculture practices included plastic covers and the activities were abandoned 18 years before the field study. The objectives were to determine erosion and sedimentation rates in the system and explore the relationships between erosion vulnerability and soil physicochemical properties of the parent material. The methodological approach included the study of erosion features such as rilling, gullying, and piping. A budget per terrace was estimated, using dGPS surface height measurements, drone images and derived terrain modeling, detailed sampling, and GIS techniques; and the determination of material properties such as structural stability, slaking, bulk density, soil organic matter, pH, electrical conductivity, gypsum, carbonate content, and SAR. The results show that gullying and piping result in high detachment rates (5.5–38.9 ton Ha−1 ·yr−1), but deposition was found to be at least as important or larger than detachment for several terraces (31.7–130.4 ton Ha−1 yr−1). Even though not a full exploration of all components of the sediment budget could be done, the 'on terrace' sedimentation rates were high. This means that just mapping gullying or erosional features as indicators for degradation rates can be misleading. Additional information on temporary sedimentation is also crucial to get reliable erosion models, especially at finer spatial scales. Furthermore, this also demonstrates that leveling and abandonment of fields on dispersive substrates accelerates land degradation and that leaving the plastic on the fields also has negative environmental impacts concerning erosion. [ABSTRACT FROM AUTHOR]

• A method for modeling agricultural unstructured terrain was proposed. • The color information and FLANN were incorporated to optimize the SURF. • Achieve fast and accurate image feature extraction and matching. • The constructed agricultural unstructured terrain point cloud was optimized. The global terrain model is the cornerstone and crucial background data towards acquiring autonomous tractors in an agricultural unstructured environment. However, there are severe perspective distortions, variable local feature changes, affine deformations between images, and a lack of manually labeled features during the data-matching process of terrain modeling. In this paper, a feature fusion-based optimization method for modeling agricultural unstructured terrain was proposed on the basis of Unmanned Aerial Vehicle (UAV) oblique photography technology. Firstly, the color information of the image and the Fast library for Approximate Nearest Neighbors (FLANN) were incorporated to optimize the Speed Up Robust Feature (SURF). Secondly, the optimized SURF feature was combined with the Maximally Stable Extremal Regions (MSER) to achieve the fast and accurate feature extraction and matching between the affine deformation images. Finally, the agricultural unstructured terrain was constructed and optimized by adopting the bilateral filtering algorithm. The experimental results indicated that the algorithm proposed in this paper had better matching effect and matching efficiency in the data matching process, which in turn achieved the fast and accurate construction of the global 3D model of agricultural unstructured terrain. [ABSTRACT FROM AUTHOR]

• Equipped with the principle of wiring cost minimization, a hybrid computational model consisting of a deep convolution neural network and a self-organizing map is constructed to simulate human VTC. • A central feature of the model is the biological constraint of neurons' lateral wiring span, which significantly shapes the morphology of the topographical structure in the model. • The topographical structure of both fine-scale category-selective regions and large-scale abstract functional maps is largely replicated in the model. • Categorical selectivity and view-invariant representations of the category-selective regions are generally reserved in the model. Our minds represent miscellaneous objects in the physical world metaphorically in an abstract and complex high-dimensional object space, which is implemented in a two-dimensional surface of the ventral temporal cortex (VTC) with topologically organized object selectivity. Here we investigated principles guiding the topographical organization of object selectivities in the VTC by constructing a hybrid Self-Organizing Map (SOM) model that harnesses a biologically inspired algorithm of wiring cost minimization and adheres to the constraints of the lateral wiring span of human VTC neurons. In a series of in silico experiments with functional brain neuroimaging and neurophysiological single-unit data from humans and non-human primates, the VTC-SOM predicted the topographical structure of fine-scale category-selective regions (face-, tool-, body-, and place-selective regions) and the boundary in large-scale abstract functional maps (animate vs. inanimate, real-word small-size vs. big-size, central vs. peripheral), with no significant loss in functionality (e.g., categorical selectivity and view-invariant representations). In addition, when the same principle was applied to V1 orientation preferences, a pinwheel-like topology emerged, suggesting the model's broad applicability. In summary, our study illustrates that the simple principle of wiring cost minimization, coupled with the appropriate biological constraint of lateral wiring span, is able to implement the high-dimensional object space in a two-dimensional cortical surface. [ABSTRACT FROM AUTHOR]

• Use of SoLIM-generated soil data in the SWAT model produces reliable results. • Forested areas resulted in higher output uncertainty compared to grasslands and paddy fields. • Higher slope introduces increased uncertainty in the hydrological processes. • Uncertainty patterns differ during flood and non-flood seasons simulated with SWAT. • Evaporation, Φ d , and K sat are key contributors to uncertainty in the SWAT simulation. Hydrological models are essential tools for understanding natural processes, yet they often encounter with uncertainties due to limited input data and simplified assumptions. Soil data is a crucial input parameter for hydrological models, but it faces cartographic challenges due to lack of standardized soil mapping methods. To explore the applicability of soil datasets derived from the Third Law of Geography in hydrological models, the Soil Landscape Inference Model (SoLIM) based on this principle is used to generate a soil dataset. This dataset is then utilized as input data for a semi-distributed hydrological model (Soil and Water Assessment Tool, SWAT) to assess the impact of soil dataset uncertainty on performance of the hydrological model. This study, utilized the Fengyu River Watershed in Yunnan Province, China as an example, showed that: 1) Although the integration of SoLIM-generated soil data into the SWAT model introduces sensitivity in sediment yield, the overall results remain reliable. This suggests that soil datasets based on the Third Law of Geography can serve as input for semi-distributed hydrological models. 2) Uncertainty introduced by SoLIM-generated soil data affects parameters such as evaporation, total porosity of the soil layer (Φ d), and groundwater infiltration coefficient (K sat). These variations influence the sensitivity of SWAT's predictions regarding streamflow and water quality. 3) Factors influencing model outcomes include steeper slopes in certain areas, which enhance streamflow velocity and flow rates, resulting in more dynamic and non-linear responses. Additionally, soil water dynamics exhibit higher temporal and spatial variability in forests compared to pasture and rice paddy fields, where vegetation tends to be more uniform, maintaining stable soil water loss pathways and reducing uncertainty in streamflow predictions. The study provides a foundation for studies addressing the difficulties in acquiring high-precision soil datasets for such models. [ABSTRACT FROM AUTHOR]

• A semi-empirical terramechanics approach for complex terrain profiles is presented. • The approach is an extension of the semi-empirical models. • The terrain can be defined using an arbitrary heightfield. • The method can capture complex terrain geometries and gaps in the contact patch. • Comparison to the least square plane method highlight proposed approach's strengths. Dynamic simulations of various types of off-road vehicles, from planetary rovers to agricultural equipment, have long relied on well-established semi-empirical terramechanics models. While these models do have drawbacks and reliability issues that have been addressed by numerous works in the decades since the models were first introduced, semi-empirical approaches remain one of the few ways to simulate realistic wheel-soil interaction in real-time. One of their drawbacks is their assumption that the terrain is a flat plane. The models work by integrating normal and shear stresses along the wheel-terrain contact patch. The normal stress at each point along the contact patch is determined using an equation that computes soil pressure based on semi-empirical parameters, the dimensions of the wheel and the sinkage, which is determined based on the distance between the point and the plane that defines the terrain. Other works simplify the rough terrain contact problem by defining an equivalent contact plane at each time step in order to continue to be able to use semi-empirical models - modified to work with slanted planes - to compute the interaction forces. In this work, we propose a new, modified version of the semi-empirical model in which interaction forces for a wheel travelling on rough terrain can be computed without the need to use an equivalent contact plane. To highlight the advantages of our proposed approach, we compare our simulation results to the results of simulations using an existing approach for modelling a wheel travelling over rough terrain using traditional semi-empirical models. [ABSTRACT FROM AUTHOR]

• A software application was developed to simulate sprinkler water distribution on undulating hilly terrain. • A water application rate conversion from flat to hilly terrain was deduced by the principle of water volume conservation. • An indoor experimental platform was set up to replicate the sprinkler arrangement on a hill, devoid of plant canopy. The hilly terrain modifies the droplet landing positions in comparison to flat ground, complicating the prediction of sprinkler water distribution on such uneven regions. Utilizing a digital elevation model combined with droplet dynamics and evaporation models, droplet landing positions were calculated on both flat and uneven terrains. From the droplet landing positions and the corresponding water application rates on flat terrain, the water application rate on hilly terrains was deduced using the principle of water volume conservation. Subsequently, a software application was developed to assist in this prediction. Using the radial water distribution data from an individual sprinkler under various operating pressures and the elevation data of a hill, the software predicts the water distribution for either an individual sprinkler or an entire solid-set sprinkler irrigation system on the hill. This allows for the calculation of the average water application rate (h ¯) and distribution uniformity (UCS) for combined sprinklers. The simulated water application rate on hilly terrains was validated using an experimental platform that replicates the sprinkler arrangement on a hill, devoid of plant canopy. Our findings indicate that the simulated water application rate for a sprinkler on hilly terrain aligns closely with the observed values, achieving a confidence coefficient (c) of 0.96. The developed software reliably predicts sprinkler water distribution on hilly terrains with significant accuracy for the conditions studied. We compared the UCS of the flat, hilly slope, and homogeneous slope terrains and found that the hilly slope better reflects the uneven water distribution owing to the undulating characteristics of the hilly terrain. [ABSTRACT FROM AUTHOR]

192 coupled wind and fire CFD simulations were performed using ANSYS Fluent to study the wind and fire interaction in an open car park. The natural ventilation was insufficient for removal of smoke and heat for the majority of investigated fires. Blockage effect was observed for wind directions parallel to the open walls, which resulted in reduced smoke removal and higher smoke temperatures. With known probabilities of all the investigated wind speed and directions, we have estimated the percentage of time in which fire outcomes are acceptable. This value was coined as the car park operational uptime, and was 84.9 %–92.8 % in relation to the fire with HRR = 1.4 MW; 36.5 %–56.1 % at 4.0 MW; 34.1 %–54.4 % at 6.0 MW and 12.13–25.5 % at 8.8 MW. In 6.22 % of wind cases, the velocity inside the car park exceeded 3.0 m/s, which could potentially contribute to the growth of the fire. Limitations of the study include the assumed wind probability distribution relevant to the Warsaw region, the single location of the fire used in the assessment and the implicit modelling of topography and architecture through an introduced wind profile and terrain roughness model. • Qualitative method of natural smoke control assessment of operational uptime is proposed. • Operational uptime of natural smoke control ranged from 84.9 % to 92.8 % for 1.40 MW design fire. • Operational uptime of natural smoke control ranged from 12.1 % to 25.5 % for 8.80 MW design fire. • Wind perpendicular to open facades yielded the best results. • Wind parallel to open facades caused smoke entrapment in the car park and the worst outcomes. [ABSTRACT FROM AUTHOR]

Accurate large-area Digital Terrain Models (DTMs) are crucial for many science applications. Spaceborne Synthetic Aperture Radar (SAR) platforms are often used to create these DTMs as they provide an effective tool to collect surface elevations across a wide extent. However, SAR-derived digital elevation models (DEMs) cannot accurately measure ground elevations in the presence of forests. This work demonstrates an approach to estimate terrain elevations from 12 m TanDEM-X by using a convolutional neural network (CNN) trained with ground elevations from ICESat-2 – a spaceborne laser altimeter. This approach demonstrated the ability to estimate terrain elevations from TanDEM-X DEMs for the greater North Carolina area. The CNN estimated terrain saw an improvement in RMSE from 11.28 m to 4.42 m within the entire area of interest, and a focused improvement in RMSE from 12.78 m to 4.95 m in forested areas when compared to ICESat-2. The CNN model outperformed linear, random forest, and gradient boosted regression models using comparable model inputs. This work combines 12-m TanDEM-X data with ICESat-2 profiles, resulting in a new DTM product with accuracy approaching that of reference elevations obtained from satellite laser altimetry in the southeastern United States. • Investigates use of CNN to create DTM with TanDEM-X and ICESat-2 in North Carolina. • CNN outperformed linear, random forest, and gradient boosted regression models tested. • CNN reduced RMSE from 11.28 m to 4.42 m with convolutional neural network. • Establishes possible framework for creating global DTMs. [ABSTRACT FROM AUTHOR]

Filtered Geomorphic Reference Models (FGRMs), extracted from High-Pass Filters based on the Fast Fourier Transform (HPF-FFT), provide a semi-automated and objective detection of a wide range of geoforms and geomorphic situations (flat areas, slopes, depressions, or valley bottoms). The method is sensitive enough to reveal numerous inflection points that enable the identification of subtle changes in relief polarity, resulting in accurate representations of real land depressions. The effectiveness of this method has been demonstrated in lunar landscapes, utilizing altimetry extracted from high-precision Planetary Digital Elevation Models (PDEMs) to construct crater inventories with well-defined FGRMs. However, when analysing lunar environments with closely-spaced geomorphic features, the FGRMs display blurring and generalization effects, reducing their capability to identify all existing crater depressions. This limitation controls the ability of FGRMs, to generate reliable crater inventories. This study examines such effects in three experiments. Using Synthetic Digital Relief Models (SDRMs) constructed by means of a MATLAB script two experiments are performed. In Experiment_1 different scenarios of proximity between aligned craters are analysed. The marks, or footprints, formed by craters of identical size and shape (i.e., circular bowl-shaped depressions with central peaks) are considered, resulting in simplified PDEMs. Various scenarios of crater centre separation (proximity) and the presence of internal features within craters are also analysed. Experiment_2 validates the method with different proximities, locations and crater sizes. Experiment_3 validates the approach in a sampled area of LRO lunar PDEM corresponding to Zelinskiy crater (Mare Ingenii area). To address these blurring effects, a hybrid methodology is employed, generating FGRMs extracted from HPF-FFT, in conjunction with other GIS and remote sensing tools, such as singular point zoning, aspect-slope zoning, and edge-limits zoning. As a result, the correspondence between the FGRMs and the synthetic forms is examined, aiding the comprehension of the graphical response provided by filtering in these cratered areas. The methodology demonstrates that FGRMs derived from HPF-FFT are sensitive to capturing connections between shapes as soon as the crater features start to merge, while blurring becomes evident when the craters are located at a proximity of <20 model units. Additionally, the results highlight the method's utility in accurately locating crater footprints and morphologically characterizing inner crater features, even when crater centres are closely spaced. This approach effectively mitigates blurring effects and enables the precise localization of crater marks and their internal features. The validations carried out in complex relief and high precise DEMs provide a use in real situation. [Display omitted] • SDRMs of nearby craters at different proximity distances are generated using a MATLAB script. • FGRs of craters extracted from FFT filtering yield inaccurate inventories due to blurring effects. • Different scenarios of crater proximity are designed to evaluate the answer provided by filtering. • Combined use of FFT filtering, GIS and remote sensing tools is sensitive to blurring. • The morphological characterisation of simulated craters improving crater inventories. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have demonstrated the potential to recover finer textural details from lower-resolution images by superresolution (SR). Given similar grid-based data structures, some researchers have transferred image SR methods to digital elevation models (DEMs). These efforts have yielded better results than traditional spatial interpolation methods. However, terrain data present inherently different characteristics and practical meanings compared with natural images. This makes it unsuitable for existing SR methods on perceptually visual features of images to be directly adopted for extracting terrain features. In this paper, we argue that the problem lies in the lack of explicit terrain feature modeling and thus propose a terrain feature-aware superresolution model (TfaSR) to guide DEM SR towards the extraction and optimization of terrain features. Specifically, a deep residual module and a deformable convolution module are integrated to extract deep and adaptive terrain features, respectively. In addition, explicit terrain feature-aware optimization is proposed to focus on local terrain feature refinement during training. Extensive experiments show that TfaSR achieves state-of-the-art performance in terrain feature preservation during DEM SR. Specifically, compared with the traditional bicubic interpolation method and existing neural network methods (SRGAN, SRResNet, and SRCNN), the RMSE of our results is improved by 1.1% to 23.8% when recovering the DEM from 120 m to 30 m, by 4.9% to 22.7% when recovering the DEM from 60 m to 30 m, and by 7.8% to 53.7% when recovering the DEM from 30 m to 10 m. The source code that has been developed is shared on Figshare (https://doi.org/10.6084/m9.figshare.19597201). [ABSTRACT FROM AUTHOR]

Ground-Based Synthetic Aperture Radar (GB-SAR) enables to monitor a scene in a distance up to a few km and with an accuracy of around one mm. It is mainly used for landslide monitoring but also used to monitor man-made structures such as bridges and dams. Due to the particular distance-dependent imaging concept and related geometric distortion effects, the interpretation of the SAR images can be difficult, especially in the case of complex man-made structures. The geocoding of the SAR data enables to project the 2D SAR image on a 3D digital elevation model (DEM) or a 3D terrain model and highly improves the interpretation of the observed deformation. It also allows to fuse SAR-based observations with other data sources. The purpose of this paper is to present a new algorithm to geocode GB-SAR data in complex scenarios. The method introduces two new components, a Bayesian statistical approach and a ray-tracing algorithm. The first one uses an a priori function on the received intensity to improve the geocoding in case of strong layover, while the latter considers areas in the radar shadow to be excluded from geocoding. Moreover, ray-tracing provides the starting point for the analysis of signals with multiple reflections which are also discussed in this paper. Our algorithm is applied to and evaluated with two case studies. The first one demonstrates the improved geocoding of GB-SAR data on the Linach dam in the Black Forest in Germany, which is characterized by a rather complex architecture. The second case study shows the application of the method at the Enguri dam in Georgia, which is characterized by its large dimensions and a dam height over 270 m. [ABSTRACT FROM AUTHOR]

Intra-articular (IA) glucocorticoids (GC) are commonly used for clinical management of both osteoarthritis and rheumatoid arthritis, but their efficacy is limited by the relatively short duration of action and associated side effects. To provide sustained efficacy and to improve the safety of GCs, we previously developed a N -(2-hydroxypropyl)methacrylamide (HPMA) copolymer-based dexamethasone (Dex) prodrug. Serendipitously, we discovered that, by increasing the Dex content of the prodrug to unusually high levels, the aqueous solution of the polymeric prodrug becomes thermoresponsive, transitioning from a free-flowing liquid at 4 °C to a hydrogel at 30 °C or greater. Upon IA injection, the prodrug solution forms a hydrogel (ProGel-Dex) that is retained in the joint for more than 1 month, where it undergoes gradual dissolution, releasing the water-soluble polymeric prodrug. The released prodrug is swiftly internalized and intracellularly processed by phagocytic synoviocytes to release free Dex, resulting in sustained amelioration of joint inflammation and pain in rodent models of inflammatory arthritis and osteoarthritis. The low molecular weight (6.8 kDa) of the ProGel-Dex ensures rapid renal clearance once it escapes the joint, limiting systemic GC exposure and risk of potential off-target side effects. The present study illustrates the translational potential of ProGel-Dex as a potent opioid-sparing, locally delivered adjuvant analgesic for sustained clinical management of arthritis pain and inflammation. Importantly, the observed thermoresponsive properties of the prodrug establishes ProGel as a platform technology for the local delivery of a broad spectrum of therapeutic agents to treat a diverse array of pathological conditions. [Display omitted] • HPMA copolymer-based dexamethasone prodrug demonstrates thermoresponsive properties. • The thermoresponsive prodrug (ProGel-Dex) can be intraarticularly administered. • ProGel-Dex forms hydrogel and remains in the joint for more than one month. • ProGel-Dex provides sustained pain relief in three models of arthritis. • ProGel-Dex does not have any measurable glucocorticoid side effects. [ABSTRACT FROM AUTHOR]

• A framework for dynamic simulation of vehicle-terrain systems is discussed. • We develop and implement a cone index-based (CI) model for dynamics simulation. • CI model does not show oscillations and reaches the steady state smoothly. • We compare the CI model to the existing Wong-Reece models. • Methods qualitatively agree in transient states and maximum tractive effort. Simulation of wheel-ground and vehicle-ground interactions is very important in many applications. Achieving accuracy and efficiency is challenging for both soft and hard terrains. This is not only because of the simulation and numerical challenges, but also due to the questionable nature of the existing terrain models. For example, the most widely used terramechanics model is not a representative constitutive relation for a full range of dynamic conditions and applications, but rather a parametrization of steady state conditions. In general, the selection and development of the proper constitutive model and the parametrization of the ground properties are very challenging. Here, we present a unified framework for general wheel-ground interaction which can be used with different terramechanics models. The framework is based on a complementarity formulation and also uses the concept of kinematic constitutive relations, beside the other known concepts for modelling and parametrizing the soil properties. The framework makes it possible to consider the appropriate modelling of the terrain for a broad range of dynamic behaviours and simulation conditions. We will illustrate the material with several examples for off-road conditions. [ABSTRACT FROM AUTHOR]

Posttraumatic Stress Disorder (PTSD) is highly co-morbid with chronic pain conditions. When present, PTSD significantly worsens chronic pain outcomes. Likewise, pain contributes to a more severe PTSD as evidenced by greater disability, more frequent use of harmful opioid analgesics and increased pain severity. The biomechanism behind this comorbidity is incompletely understood, however recent work strongly supports the widely-accepted role of expectation, in the entanglement of chronic pain and trauma symptoms. This work has shown that those with trauma have a maladaptive brain response while expecting stress and pain, whereas those with chronic pain may have a notable impairment in brain response while expecting pain relief. This dynamical expectation model of the interaction between neural systems underlying expectation of pain onset (traumatic stress) and pain offset (chronic pain) is biologically viable and may provide a biomechanistic insight into pain-trauma comorbidity. These predictive mechanisms work through interoceptive pathways in the brain critically the insula cortex. Here we highlight how the neural expectation-related mechanisms augment the existing models of pain and trauma to better understand the dynamics of pain and trauma comorbidity. These ideas will point to targeted complementary clinical approaches, based on mechanistically separable neural biophenotypes for the entanglement of chronic pain and trauma symptoms. [ABSTRACT FROM AUTHOR]

Surface relief in forms of extrusions and intrusions as the substantial feature of early fatigue damage is one of the most important phenomena studied in metal fatigue. The most common surface relief models in bulk metals are agreed to be correlated with the formation of typical dislocation patterns as persistent slip bands (PSBs), while little is known about the fundamental mechanisms at submicron and even nanometer scales where dislocation pattern formation is fully inhibited. Here, as exampled with thin Au films, the underlying fatigue damage mechanism at small scales is investigated through the quantitative characterization of fatigue damages. Continuous generation and migration of vacancies is found to be crucial for the shape of extrusion/intrusions and kinetics of their growth at submicron and even nanometer scales. Due to the degraded dislocation interaction and intensified vacancy diffusion, the delayed vacancy accumulation in the small-scale metal interior suppresses the extrusion and interface void formation in thinner films, which finally leads to the superior ability to support tremendous surface relief and strong fatigue resistance. The finding of the vacancy-dominated fatigue mechanism at small scales extends our understanding of the metal fatigue mechanisms down to the submicron and even nanometer scales and suggests a novel interface engineering strategy by vacancy behavior modulation for fatigue-tolerance material design. [Display omitted] [ABSTRACT FROM AUTHOR]