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Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model's overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness. [ABSTRACT FROM AUTHOR]

Natural hazards are considered to have a strong link with climate change and human activities. With the rapid advancements in remote sensing technology, real-time monitoring and high-resolution remote-sensing images have become increasingly available, which provide precise details about the Earth's surface and enable prompt updates to support risk identification and management. This paper proposes a new network framework with Transformer architecture and a Residual network for detecting the changes in high-resolution remote-sensing images. The proposed model is trained using remote-sensing images from Shandong and Anhui Provinces of China in 2021 and 2022 while one district in 2023 is used to test the prediction accuracy. The performance of the proposed model is evaluated by using five matrices and further compared to both convention-based and attention-based models. The results demonstrated that the proposed structure integrates the great capability of conventional neural networks for image feature extraction with the ability to obtain global context from the attention mechanism, resulting in significant improvements in balancing positive sample identification while avoiding false positives in complex image change detection. Additionally, a toolkit supporting image preprocessing is developed for practical applications. [ABSTRACT FROM AUTHOR]

In recent years, mountainous areas in China have faced frequent geological hazards, including landslides, debris flows, and collapses. Effective simulation of these events requires a solver for shallow water equations (SWEs). Traditional numerical methods, such as finite difference and finite volume, face challenges in discretizing convection flux terms, while theory-based models need to account for various factors such as shock wave capturing and wave propagation direction, demanding a high-level understanding of the underlying physics. Previous deep learning (DL)-based SWE solvers primarily focused on constructing direct input–output mappings, leading to weak generalization properties when terrain data or stress constitutive relations change. To overcome these limitations, this study introduces a novel SWE solver that combines theory and data-driven methodologies. The core idea is to use artificial neural networks to compute convection flux terms, and to reduce modeling complexity. Theory-based modeling is used to tackle complex terrain and friction terms for the purpose of ensuring generalization. Our method surpasses challenges faced by previous DL-based solvers in capturing terrain and stress variations. We validated our solver's capabilities by comparing simulation results with analytical solutions, real-world disaster cases, and the widely used Massflow software-generated simulations. This comprehensive comparison confirms our solver's ability to accurately simulate hazard scenarios and showcases strong generalization on varying terrain and land surface friction. Our proposed method effectively addresses DL-based solver limitations while simplifying the complexities of theory-driven numerical methods, offering a promising approach for hazard dynamics simulation. [ABSTRACT FROM AUTHOR]

Foreign bodies often cause belt scratching and tearing, coal stacking, and plugging during the transportation of coal via belt conveyors. To overcome the problems of large parameters, heavy computational complexity, low classification accuracy, and poor processing speed in current classification networks, a novel network based on ESCBAM and multichannel feature fusion is proposed in this paper. Firstly, to improve the utilization rate of features and the network's ability to learn detailed information, a multi-channel feature fusion strategy was designed to fully integrate the independent feature information between each channel. Then, to reduce the computational amount while maintaining excellent feature extraction capability, an information fusion network was constructed, which adopted the depthwise separable convolution and improved residual network structure as the basic feature extraction unit. Finally, to enhance the understanding ability of image context and improve the feature performance of the network, a novel ESCBAM attention mechanism with strong generalization and portability was constructed by integrating space and channel features. The experimental results demonstrate that the proposed method has the advantages of fewer parameters, low computational complexity, high accuracy, and fast processing speed, which can effectively classify foreign bodies on the belt conveyor. [ABSTRACT FROM AUTHOR]

The location of debris flow occurring in a gully determines the observable differences in its formation, evolution and effects. In this piece of research, we propose a new method for debris flow gully classification based on the locations of the debris flows occurring in the gullies. It is termed the three-section method (TSM). It includes eight different types of gullies with different digital identifications (IDs) and susceptibility degrees (SDs). By taking the Jiangjia Gully (JJG), in Yunnan, China, as a case study site, the main gully and the sub-gullies at different levels were identified using a hydrological analysis method. Then, the gullies were divided into different types using the new classification TSM. The results show that there are seven different types of debris flow gullies in the JJG. The number of different types varied greatly in gullies at different levels. In particular, the topological diagram of debris flow gullies was drawn after simplifying the shape of the gullies, and it was a good way to understand the characteristics of debris flow gullies. Finally, the relationships were explored between the hypsometric integrals (HIs), surface exposures (SEs) and susceptibility degrees (SDs), and a new calculation model construction method for determining the degree of debris flow susceptibility was proposed. This model, using the above method, can not only be used to calculate the SDs of debris flows in the gullies, but can also be instrumental in pointing out the approximate locations of the debris flow commonly and easily occurring in the gullies. We hope that our research can provide a new concept for the assessment of debris flow susceptibility. [ABSTRACT FROM AUTHOR]

The study of land use/land cover (LULC) changes plays an important guiding role in regional ecological protection and sustainable development policy formulation. Especially, the simulation study of the future scenarios may provide a hypothetical prospect which could help to determine the rationality of current and future development policies. In order to support the ecological protection and high-quality development strategy of the Yellow River Basin proposed by the Chinese government, the Great Yellow River Region (GYRR) is taken as the research area. The multi-period land cover data are used to carry out the analysis of land cover changes. The MOLUSCE (Modules for Land Use Change Simulations) plugin of QGIS software is used to carry out a land cover simulation and prediction study for 2030 on a large regional scale. Finally, the land cover status in the mountainous areas of the GYRR is analyzed thoroughly. The results show a decrease in agricultural land and increase in forest land during the past 25 years from 1995 to 2020, and that this trend would continue to 2030. The landscape pattern index analysis indicates that the land cover in the GYRR has become more and more abundant, and the degree of fragmentation has become higher and higher, while landscape patches were more evenly distributed in the GYRR until 2020. On the other hand, the landscape pattern would tend to achieve a certain degree of stability in 2030. The decrease in farmland and the increase in forest land illustrate the efforts made by the GYRR residents and governments in improving the ecological environment under the policy of returning farmland to forests and grasslands. On the other hand, although the residential areas in the mountainous areas are far away from the mountain hazard historical points because of consideration during construction with the help of the development of disaster prevention and mitigation over the years, there could be problem of rapid and haphazard urbanization. It is worth mentioning here that the harmonious and sustainable development of people and land in the GYRR mountainous areas still requires a large amount of effort. [ABSTRACT FROM AUTHOR]

Carrying out mountain-hazards developing environment research is helpful for understanding the spatial characteristics of the mountain hazards so as to contribute to mountain-hazards prevention and mitigation and the safety of infrastructures and major projects. In this study, the Eurasian continent was selected as the research area to conduct regionalization research on mountain-hazards developing environments. Using peak ground acceleration (PGA), the annual average precipitation and topographic relief as root factors of mountain-hazards developing environments (known as PPR factors) to represent the characteristics of geological structures, climatic impacts and geomorphology, the regionalization of mountain-hazards developing environments of the Eurasian continent was conducted through the combination of computer automatic classification and later artificial cartographic generalization. Finally, 15 subregions were obtained. A preliminary judgment of the mountain-hazards susceptibility for each region according to the characteristics of PPR factors was made, and nine subregions were identified as the overall high-susceptibility areas of mountain hazards. Based on the analysis of the characteristics of PPR factors and the mountain-hazards susceptibility characteristics in different mountain-hazards developing environment subregions, the high susceptibility regions of mountain hazards could be divided into three types: arid and active-geologic regions, humid and active-geologic regions, and humid and inactive-geologic regions. We hope that our research provides support for subsequent works of more specific and reasonable mountain-hazards susceptibility, hazard and risk models construction for different types of mountain-hazards developing environments. [ABSTRACT FROM AUTHOR]

To understand the early warning signs of damage during the coal mass deformation process, an integrated monitoring procedure was designed that combines the apparent resistivity (AR) and acoustic emission (AE) of coal damage under multi-step loading. The spatiotemporal response characteristics of AR and the time-varying evolution of AE were studied and the varying correlation between the two was discussed. Additionally, the macro–microscopic mechanics of the AR response during the coal deformation process was explored. The results show that the AR and AE signals corresponded well with the applied load during the coal deformation process. In the early loading stage, variations in AR and AE signals were not apparent. As the applied load increased, the high-resistance area of AR increased and the AE signals became active. The local variation characteristics of AR could be used to indirectly invert the internal structure of the coal samples. The electrical variation in the loaded coal was mainly controlled by the conductive surface of cracks. The acoustic and resistivity methods can strongly complement the spatial and temporal dimensions of early warning systems for disasters. The AE technique can continuously monitor a test area for abnormal occurrences in the engineering site, and AR tomography images that are obtained can be used to locate inversions in the source coal in order to take pre-emptive action before disaster occurs. This research can provide new ideas for monitoring and early warning systems for coal and rock dynamic disasters. [ABSTRACT FROM AUTHOR]

Vein recognition technology identifies human vein characteristics under near-infrared light and compares it with stored vein information for personal identification. Although this has high anti-counterfeiting performance, it is possible to fabricate artificial hands that simulate vein characteristics to deceive the identity authentication system. In view of this potential deficiency, we introduced heart rate information to vein authentication, a means of living body detection, which can further improve the anti-counterfeiting effect of vein authentication. A hand vein transillumination imaging experiment was designed to prove its effectiveness. In the proposed method, a near-infrared light source is used to transilluminate the hand, and the transillumination images are collected by a common camera. Then, the region of interest is selected for gray-scale image processing, the feature value of each frame is extracted by superimposing and averaging the images, and then the one-dimensional pulse wave is drawn. Furthermore, the baseline drift phenomenon is filtered by morphological methods, and the maximum percentage frequency is determined by Fast Fourier Transform, that is, the pulse wave frequency. The heart rate value is then calculated, and finally, the stability of the heart rate detection result is evaluated. The experiment shows that the method produces accurate and stable results, demonstrating that it can provide living information (heart rate value) for vein authentication, which has great application prospects and development opportunities in security systems. [ABSTRACT FROM AUTHOR]

A catastrophic tailings dam failure disaster occurred in Brumadinho, Brazil on 25 January 2019, which resulted in over 270 casualties, 24,000 residents evacuated, and a huge economic loss. Environmental concerns were raised for the potential pollution of water due to tailings waste entering the Paraopeba River. In this paper, a detailed analysis has been carried out to investigate the disaster conditions of the Brumadinho dam failure using satellite images with different spatial resolutions. Our in-depth analysis reveals that the hazard chain caused by this failure contained three stages, namely dam failure, mudflow, and the hyperconcentrated flow in the Paraopeba River. The variation characteristics of turbidity of the Rio Paraopeba River after the disaster have also been investigated using high-resolution remote sensing images, followed by a qualitative analysis of the impacts on the downstream reservoir of the Retiro Baixo Plant that was over 300 km away from the dam failure origin. It is believed that, on the one hand, the lack of dam stability management at the maintenance stage was the main cause of this disaster. On the other hand, the abundant antecedent precipitation caused by extreme weather events should be a critical triggering factor. Furthermore, the spatiotemporal pattern mining of global tailings dam failures revealed that the Brumadinho dam disaster belonged to a Consecutive Hot Spot area, suggesting that the regular drainage inspection, risk assessment, monitoring, and early warning of tailings dam in Consecutive Hot Spot areas still need to be strengthened for disaster mitigation. [ABSTRACT FROM AUTHOR]

To solve the decline of rural development, an effective development mode is indispensable. Rural tourism is a key approach in poverty alleviation and rural revitalization in China. The comparative analysis of the 50 most beautiful villages as awarded by the Ministry of Agriculture of China shows that the rural tourism development mode with the integration of production, village and scenery realizes the sustainable development of rural areas. To examine this further, this study takes Qinggangshu Village as a case study and constructs a systematic rural tourism-based sustainable development model called Aims, Measures, Demands (AMD). The results show that Qinggangshu Village has changed from a single farming village to a mature tourism village due to the Beautiful Village Project's support. In this process, the production, village and scenery have made great change by promoting rural tourism development. Land consolidation and land asset activation is at the core of rural transformation and development, which can drive the reorganization and flow of labor and capital and can also make rebuilt villages more comfortable living spaces. Furthermore, a good landscape environment can stimulate development and competition. This study could be used as an example of attaining sustainable development for other rural areas. [ABSTRACT FROM AUTHOR]

Debris flows in a burned area, post-fire debris flows, are considered as one of the most dangerous geo-hazards due to their high velocity, long run-out distance, and huge destruction to infrastructures. The rainfall threshold to trigger such hazards is often reduced compared with normal debris flow because ashes generated by mountain fires reduce the permeability of the top soil layer, thus increasing surface runoff. At the same time, burnt material and residual debris have very poor geo-mechanical characteristics, e.g., their internal friction angle and cohesion are typically low, and thus an intense rainfall can easily trigger some debris flows. Studying post-fire debris flow enables us to get a deeper understanding of disaster management. In this paper, the debris flow that occurred in Montecito, California, USA, and was affected by the Thomas Fire was used as a case study. Five major watersheds were extracted based on the digital elevation model (DEM). Remote sensing images were used to analyze the wildfire process, the extent of the burned areas, and the burn severity. The hypsometric integral (HI) and short-duration rainfall records of the watersheds around Montecito when the post-fire debris flows occurred were analyzed. Steep terrain, loose and abundant deposits, and sufficient water supply are the important conditions affecting the formation of debris flows. Taking watersheds as the research objects, HI was used to describe the geomorphic and topographic features, open-access rainfall data was used to represent the water supply, and burn severity represented the abundance of material sources. An occurrence probability model of post-fire debris flow based on HI, short-duration heavy rainfall, and burn severity was developed by using a logistic regression model in post-fire areas. By using this model, the occurrence probability of the post-fire debris flow in different watersheds around Montecito was analyzed based on the precipitation with time. Especially, the change characteristics of occurrence probability of debris flows over time based on the model bring a new perspective to observe the obvious change of the danger of post-fire debris flows and it is very useful for early warning of post-fire debris flows. [ABSTRACT FROM AUTHOR]