Search Results (1,540)

The remotely operated vehicle (ROV), a vital deep-sea platform, offers key advantages, including operational duration via continuous umbilical power, high task adaptability, and zero human risk. It has become indispensable for deep-sea scientific research and marine engineering. To enhance surveys of cobalt-rich crusts (CRCs) on complex seamount terrains, the 4500-m-class Haima ROV integrates advanced payloads, such as underwater positioning systems, multi-angle cameras, multi-functional manipulators, subsea shallow drilling systems, sediment samplers, and acoustic crust thickness gauges. Coordinated control between deck monitoring and subsea units enables stable multi-task execution within single dives, significantly improving operational efficiency. Survey results from Caiwei Guyot reveal the following: (1) ROV-collected data were highly reliable, with high-definition video mapping CRCs distribution across varied terrains. Captured crust-bearing rocks weighed up to 78 kg, drilled cores reached 110 cm, and acoustic thickness measurements had a 1–2 cm margin of error compared to in situ cores; (2) Video and cores analysis showed summit platforms (3–5° slopes) dominated by tabular crusts with gravel-type counterparts, summit margins (5–10° slopes) hosting gravel crusts partially covered by sediment, and steep slopes (12–15° slopes) exhibiting mixed crust types under sediment coverage. Thicker crusts clustered at summit margins (14 and 15 cm, respectively) compared to thinner crusts on platforms and slopes (10 and 7 cm, respectively). The Haima ROV successfully investigated CRC resources in complex terrains, laying the groundwork for seamount crust resource evaluations. Future advancements will focus on high-precision navigation and control, high-resolution crust thickness measurement, optical imaging optimization, and AI-enhanced image recognition. Full article

For efficient radio network planning, empirical path loss (PL) prediction models are utilized to predict signal attenuation in different environments. Alternatively, machine learning (ML) models are proposed to predict path loss. While empirical models are transparent and require less computational capacity, their predictions are not able to generate accurate forecasting in complex environments. While ML models are precise and can cope with complex terrains, their opaque nature hampers building trust and relying assertively on their predictions. To fill the gap between transparency and accuracy, in this paper, we utilize glass box ML using Microsoft research’s explainable boosting machines (EBM) together with the PL data measured for a university campus environment. Moreover, polar coordinate transformation is applied in our paper, which unravels the superior explanation capacity of the feature transmitting angle beyond the feature distance. PL predictions of glass box ML are compared with predictions of black box ML models as well as those generated by empirical models. The glass box EBM exhibits the highest performance. The glass box ML, furthermore, sheds light on the important explanatory features and the magnitude of their effects on signal attenuation in the underlying propagation environment. Full article

Military settlements along the Ming Great Wall are typical representatives of the construction of the ancient Chinese military defense system. The location of the military fortification is complex, and the settlements are scattered and affected by multiple factors. The academic community lacks systematic research on the military settlements along the Ming Great Wall. Existing studies focus on local protection, especially the regional connectivity and overall defense mechanism of the military settlements in the Hexi Corridor. This study incorporates the distribution, morphology, and function of the military settlements in the Hexi Corridor into a unified analytical framework to explore the coordinated defense mechanism under the spatial attributes of the military settlements. Additionally, this study looks at the distribution pattern of 173 local military settlements using tools such as the kernel density index, the Moran index, and the buffer zone. It also conducts statistical analyses of 85 existing settlements to determine their scale and morphological index and uses 18 typical settlements as examples to investigate their spatial morphology using space syntax. This study’s findings indicate that (1) military settlements are spread out in a straight line, which is affected by many things such as terrain, water systems, oasis, and the Great Wall; (2) military facilities and environmental factors are strongly connected and linked in space; (3) military settlements have obvious cluster characteristics, and most are relatively regular quadrilaterals; and (4) the organizational logic of the internal space form is consistent. The main blocks are highly accessible, and the overall space is recognizable and has certain defensive characteristics. This study systematically constructed an analytical framework for multi-scale collaborative defense mechanisms, revealing a collaborative defense model of “linear distribution–hierarchical defense–functional coordination”. This demonstrates the top–down strategic thinking of the ancient Chinese military system and provides a new perspective for the study and protection of linear military heritage corridors. Full article

Due to the complex operating environment of combine harvesters, uneven terrain, multiple vibration sources, and complex transmission systems, failures easily occur in critical working components, especially the bolted connections of the vibrating screen. To address these issues, this study first established a bolt-tightening mechanical model. Secondly, a finite element simulation of the preload force was performed using Ansys Workbench software (2023R2). The simulation results showed that the bolt head area exhibits a ring-shaped strain distribution. To determine the critical state of bolt loosening, a single-bolt loosening test was conducted. The experimental results indicated that when the bolt pressure decreased to 78.4 N and the torque decreased to 0.5 N·m, bolt loosening intensified, and the pressure value showed a sharp decreasing trend. These pressure and torque values can be defined as the bolt loosening threshold, providing an important reference basis for subsequent monitoring and early warning. Finally, to more realistically simulate actual working conditions, a combine harvester field vibration test was conducted. By arranging triaxial acceleration sensors on the bolted connections of the vibrating screen, acceleration signals were collected under both low-speed and high-speed field operating conditions. Time–frequency analysis was performed on the signals to extract characteristic values for each measurement point. The field vibration test results showed that the characteristic values of the transmission shaft bolt structure of the vibrating screen were at a relatively high level, indicating that this part is subjected to a large vibration load. Furthermore, frequency domain feature analysis revealed that the vibration frequency components in this area are complex, which further increases the risk of bolt loosening. This study provides an in-depth analysis of the loosening characteristics and vibration characteristics of the vibrating screen’s bolted connections in combine harvesters. The results provide an important theoretical basis and technical support for the online monitoring of failures in the vibrating screen’s bolt structure. Full article

Accurate acquisition of two-dimensional digital maps of disaster sites is crucial for rapid and effective emergency response. The construction of two-dimensional digital maps using unmanned aerial vehicle (UAV) aerial images is not affected by factors such as signal interference, terrain, or complex building structures, which are common issues with methods like single-soldier image transmission or satellite imagery. Therefore, this paper investigates a method for modeling two-dimensional digital maps based on UAV aerial images. The proposed Canny edge-enhanced Speeded-Up Robust Features (C-SURF) algorithm in this method is designed to enhance the number of feature extractions and the accuracy of image registration. Compared to the SIFT and SURF algorithms, the number of feature points increased by approximately 44%, and the registration accuracy improved by about 16%, laying a solid foundation for feature-based image stitching. Additionally, a novel image stitching method based on the novel energy function is introduced, effectively addressing issues such as color discrepancies, ghosting, and misalignment in the fused image sequences. Experimental results demonstrate that the signal-to-noise ratio (SNR) of the fused images based on the novel energy function can reach an average of 36 dB. Full article

This article draws on over 40 years of the author’s experience with hydroinformatics tools for water and sustainability challenges, including flooding. It aims to spark discussion on urban flood risk and resilience rather than provide a literature review or definitive answers. Assessing urban flood risk and resilience is complex due to the spatio-temporal nature of rainfall, urban landscape features (e.g., buildings, roads, bridges and underpasses) and the interaction between aboveground and underground drainage systems. Flood simulation methods have evolved to analyse flood mitigation schemes, damage evaluation, flood risk mapping and green infrastructure impacts. Advances in terrain mapping technologies have improved flood analyses. Despite investments in flood management infrastructure, a residual flood risk remains, necessitating an understanding of the recovery and return to normality post-flood. Both risk and resilience approaches are essential for urban flood planning and management. Future challenges and opportunities include both technological and governance solutions, with artificial intelligence advancements offering the potential for digital twins to better protect urban communities and enhance the recovery from flood disasters. Full article

Pine wilt disease, a highly destructive forest disease with rapid spread, currently has no effective treatments. Infected pine trees usually die within a few months, causing severe damage to forest ecosystems. A rapid and accurate detection algorithm for diseased trees is crucial for curbing the spread of this disease. In recent years, the combination of drone remote sensing and deep learning has become the main methods of detecting and locating diseased trees. Previous studies have shown that increasing network depth cannot improve accuracy in this task. Therefore, a lightweight semantic segmentation model based on a CNN-Transformer hybrid architecture was designed in this study, named EVitNet. This segmentation model reduces network parameters while improving recognition accuracy, outperforming mainstream models. The segmentation IoU for discolored trees reached 0.713, with only 1.195 M parameters. Furthermore, considering the diverse and complex terrain where diseased trees are distributed, a fine-tuning model approach was adopted. After a small amount of training, the IoU on new samples increased from 0.321 to 0.735, greatly enhancing the practicality of the algorithm. The model’s segmentation speed in the task of discolored trees identification meets the requirements of real-time performance, and its accuracy exceeds that of mainstream semantic segmentation models. In the future, it is expected to be deployed on drones for real-time recognition, accelerating the entire process of discovering and locating infected trees. Full article

The extreme rainstorm flood disaster in Beijing on 31 July 2023 resulted in 33 fatalities and 18 missing persons, with direct economic losses across the Beijing–Tianjin–Hebei metropolitan area exceeding RMB 10 billion. Despite its inland location, which is not conventionally classified as a flood-prone zone, Beijing is not conventionally considered a flood-prone region, yet it frequently experiences flood disasters, which has led to confusion and perplexity. This article collects records of historical flooding disasters in Beijing over the past 1000 years, spanning the Jin, Yuan, Ming, and Qing dynasties, the Republics of China, and the founding of New China up to the present, aiming to analyze the basic patterns and characteristics of regional historical flooding disasters. Taking the extreme rainstorm flood disaster in Beijing on 31 July 2023 as an example, this research employs a multidisciplinary approach, including field investigations and numerical simulations, to dissect the disaster-causing mechanisms. The study shows that the significant characteristics of historical flood disasters in Beijing are concentrated in the plain area and the high-frequency outbreaks (below the 3-year return period). Flood disaster events under the participation of typhoons accounted for a high proportion and caused great harm. The extreme rainstorm flood disaster in Beijing on 31 July 2023 was an extreme weather event under the complex coupling of typhoons and terrain. The residual typhoon circulation stayed on the mainland for nearly 70 h, providing abundant precipitation conditions for Beijing. Water vapor is blocked by the Yanshan–Taihang Mountains, uplifting and converging, forming a strong precipitation center in the Piedmont, which aggravates the regional local precipitation intensity. The research results can provide a reference for the scientific prevention and control of typhoon rainstorm flood disasters in Beijing. Full article

Geothermal resources are crucial for sustainable energy development, yet accurately detecting geothermal anomalies in complex terrains remains a significant challenge. This study develops a multi-source thermal infrared approach to enhance geothermal anomaly detection using Landsat 8 and ASTER land surface temperature (LST) data. The Yangbajing–Yangyi Basin in Tibet, characterized by high altitude and rugged topography, serves as the study area. Landsat 8 winter time-series data from 2013 to 2023 were processed on the Google Earth Engine (GEE) platform to generate multi-year average LST images. After water body removal and altitude correction, a local block thresholding method was applied to extract daytime geothermal anomalies. For nighttime data, ASTER LST products were analyzed using global, local block, elevation zoning, and fault buffer strategies to extract anomalies, which were then fused using Dempster–Shafer (D–S) evidence theory. A joint daytime–nighttime analysis identified stable geothermal anomaly regions, with results closely aligning with known geothermal fields and borehole distributions while predicting new potential anomaly zones. Additionally, a 21-year time-series analysis of MODIS nighttime LST data identified four significant thermal anomaly areas, interpreted as potential magma chambers, whose spatial distributions align with the identified anomalies. This multi-source approach highlights the potential of integrating thermal infrared data for geothermal anomaly detection, providing valuable insights for exploration in geologically complex regions. Full article

Urban morphology, including land surface, building heights, vegetation, water bodies, and terrain, exerts a significant influence on the urban thermal environment. The complex and nonlinear pathways through which these factors exert influence present significant challenges in urban climate studies. However, existing studies of statistical approaches to the urban thermal environment have primarily focused on linear relationships, often overlooking the complex and nonlinear effects of these factors. Additionally, previous research on those approaches has not adequately addressed the seasonal and diurnal variations in land surface temperature, nor has it examined the extent to which urban morphology influences these variations. While simulation-based approaches can address these nonlinearities and temporal variations, they require large parameter sets and extensive high-resolution input data, making them computationally demanding. This gap limits the ability to develop targeted and effective urban heat mitigation strategies. Recent advancements in remote sensing technologies have revolutionized our ability to analyze these complexities using medium-resolution data products. In this study, we apply a polynomial regression model with an elastic net to represent the impact of terrain and urban morphological factors on the urban thermal environment, considering its seasonal and diurnal variations, taking the case of the Osaka Metropolitan Area. This approach is unique in terms of capturing the nonlinearity of the impacts based on earth observation data by remote sensing and efficiently captures complex relationships while maintaining interpretability and reducing computational overhead. The study leverages MODIS Terra thermal infrared data from 2018, supplemented by Sentinel-2 and Copernicus Land Cover data. The results reveal significant seasonal and diurnal variations in the thermal environment, indicating that building height influences LST non-monotonically, with daytime cooling effects in dense urban areas (0.12 to 0.19 °C decrease) but nighttime heat retention in suburban zones (0.06 to 0.13 °C increase). Similarly, vegetation coverage reduces nighttime LST more effectively, particularly beyond a critical density threshold (NDVI > 0.4). These findings suggest that by optimizing urban design by considering building height effects, strategic design of vegetation coverage can help mitigate heat/cold stress and improve thermal comfort throughout seasons. These findings contribute to sustainable urban development and heat mitigation efforts by providing data-driven insights into urban morphology’s impact on the thermal environment. Full article

Limited by water and heat conditions, the southwest alpine valley area has a dry climate, complex terrain, low vegetation coverage, and a very fragile ecological environment. The runoff plots of different slope gradients (10°, 15°, and 20°), slope lengths (2, 5, and 10 m) and reverse slope terrace (RST) in the Lancang River arid valley were taken as the objects. Through in situ observation of the slope runoff and sediment yield of six natural erosive rainfalls, the contribution rate of different factors was quantified, and the effect mechanism was revealed. The main results were as follows: (1) Sediment yields of different rainfalls were closely correlated with rainfall type and duration. Under the conditions of heavy rain (rain II and III), there was a critical slope gradient, and the maximum sediment yield was achieved when the slope gradient was 15°. (2) The runoff and sediment reduction benefits of horizontal terraces were 24.88% and 46.25%, and these benefits were increased by 1.47 times and 1.30 times after setting the RST, and the sediment reduction benefits increased significantly with the increase in the number of RSTs (p < 0.05). (3) In this study, rainfall intensity contributed the most to the runoff yield rate (34.5%), followed by slope length (15.1%) and horizontal terrace (7.2%). Slope length, rain intensity, and horizontal terrace order contributed 25.9%, 18.0%, and 11.4% to the sediment yield rate, respectively. (4) There was a significant linear correlation between sediment yield and runoff yield on different slopes (p < 0.05). The critical runoff yield rate decreased with the increase in slope length, the RST significantly increased the critical runoff yield rate (2.91 times), and it increased with the increase in RST numbers. This study can provide a scientific basis and reference for the prevention and control of soil and water loss and ecological restoration on the slope of the arid valley in the southwest alpine and canyon area. Full article

The Heyu ore-concentrated area in western Henan, situated within the East Qinling metallogenic belt, represents a strategic fluorite resource base currently confronting severe challenges of reserve depletion. Given this critical status, this study focuses on enhancing exploration of concealed fluorite deposits through an innovative aeromagnetic approach. Prioritizing aeromagnetic surveys across 280 km² of rugged terrain achieved 100% coverage, demonstrating cost-efficiency in regional-scale exploration of fault-controlled fluorite systems. By systematically analyzing mineralization mechanisms and integrating processed magnetic data with geological constraints, we characterized magnetic anomaly patterns specific to fluorite-bearing structures. Key findings include: distinctive “low-density, low-magnetic” signatures of fluorite deposits (2.42 g/cm³, 15.57 × 10⁻⁵ SI) contrasted sharply with host granites (2.58 g/cm³, 2612 × 10⁻⁵ SI); identification of two deep-seated prospecting targets (Y-1 and Y-2) through residual anomaly analysis, spatially correlating with fault intersections; and successful borehole validation revealing 11.5 m-thick fluorite zones at 300–500 m depths. The established geological–geophysical model provides dual functionality: enabling precise delineation of deep-seated exploration targets, and offering actionable guidelines for sustainable resource development in ore-concentrated areas. This work pioneers a technical pathway for fluorite exploration in complex terrains, underscoring geophysics’ indispensable role in deep mineral targeting while setting a benchmark for analogous metallogenic provinces. Full article

To address the issues of low trajectory tracking accuracy and difficulties in tuning control parameters for crawler robots operating in uneven terrains, this paper proposes a trajectory tracking control method. The method is based on improved particle swarm optimization and sliding mode active disturbance rejection control (SPSO-SMADRC). Firstly, considering the influence of disturbances such as terrain undulations and soil inhomogeneity on trajectory deviation, the kinematic and dynamic models of the crawler robot are established. A vector field guidance approach is employed to transform the trajectory tracking task into a heading control problem. The heading angle is adaptively adjusted based on the position deviation and path curvature. A nonlinear extended state observer is introduced to estimate external disturbances. A velocity-based SMADRC controller is designed to dynamically regulate the robot’s linear and angular velocities. This allows real-time correction of the robot’s motion. To overcome the tendency of the standard particle swarm optimization (PSO) algorithm to fall into local optima during controller parameter tuning, a nonlinear dynamic adjustment strategy was adopted. This strategy adaptively adjusts the inertia weight and learning factors, enhancing the algorithm’s global search capability. Comparative experiments were conducted using two types of curved trajectories: U-shaped and V-shaped paths. The experimental results show that, under the proposed SPSO-SMADRC method, the crawler robot achieved maximum position errors of 8.28 cm and 9.26 cm, average position errors of 1.41 cm and 2.94 cm, and maximum heading angle deviations of 0.56 rad and 0.87 rad. The standard deviations of the position errors were 3.19 and 4.28, respectively. Compared with conventional PSO-based SMADRC and standard SMADRC methods, the proposed approach improved the navigation tracking accuracy. In the U-shaped trajectory, the maximum position error was reduced by 19.22% and 38.21%, the average position error by 40.00% and 65.53%, and the heading angle error by 28.21% and 74.66%. In the V-shaped trajectory, the maximum position error was reduced by 17.39% and 38.95%, the average position error by 51.71% and 52.04%, and the heading angle error by 80.58% and 84.49%. These results demonstrate that the proposed SPSO-SMADRC method significantly enhances trajectory tracking performance and system robustness. It provides effective support for high-precision autonomous navigation of crawler robots in complex and unstructured environments. Full article

Building extraction plays a pivotal role in enabling rapid and accurate construction of urban maps, thereby supporting urban planning, smart city development, and urban management. Buildings in remote sensing imagery exhibit diverse morphological attributes and spectral signatures, yet their reliable interpretation through single-modal data remains constrained by heterogeneous terrain conditions, occlusions, and spatially variable illumination effects inherent to complex geographical landscapes. The integration of multi-modal data for building extraction offers significant advantages by leveraging complementary features from diverse data sources. However, the heterogeneity of multi-modal data complicates effective feature extraction, while the multi-scale cross-modal feature fusion encounters a semantic gap issue. To address these challenges, a novel building extraction network based on multi-modal remote sensing data called SDA-les (AGAFMs) was designed in the decoding stage to fuse multi-modal features at various scales, which dynamically adjust the importance of features from a global perspective to better balance the semantic information. The superior performance of the proposed method is demonstrated through comprehensive evaluations on the ISPRS Potsdam dataset with 97.66% F1 score and 95.42% IoU, the ISPRS Vaihingen dataset with 96.56% F1 score and 93.35% IoU, and the DFC23 Track2 dataset with 91.35% F1 score and 84.08% IoU. Full article

This narrative review delves into the multifaceted landscape of obstetric and gynecological malpractice, focusing on stakeholders’ expectations, legal implications, and clinical considerations. Through a comprehensive analysis of the relevant literature, we evaluated 25 articles, culminating in a comprehensive understanding of the primary drivers behind malpractice litigation in this field. The review highlights the complex nature of these issues and their implications for various stakeholders. The key findings reveal the critical role of meeting medical care standards to avoid harm to patients, along with factors such as diagnostic errors, mismanagement of complications, and deficiencies in patient counseling contributing to malpractice allegations. Additionally, issues related to surgical procedures, informed consent, and documentation are explored. The review underscores the importance of collaboration, education, and accountability in mitigating the impact of malpractice and upholding patient safety in obstetric and gynecological practice. Full article