The Three Gorges Reservoir region in China is the Yangtze River Economic Zone’s natural treasure trove. Its natural environment has an important role in development. The unique and fragile ecosystem in the Yangtze River’s Three Gorges Reservoir region is prone to natural disasters, including soil erosion, landslides, debris flows, landslides, and earthquakes. Therefore, to better alleviate these threats, an accurate and comprehensive assessment of the susceptibility of this area is required. In this study, based on the collection of relevant data and existing research results, we applied machine learning models, including logistic regression (LR), the random forest model (RF), and the support vector machine (SVM) model, to analyze landslide susceptibility in the Yangtze River’s Three Gorges Reservoir region to analyze landslide events in the whole study region. The models identified five categories (i.e., topographic, geological, ecological, meteorological, and human engineering activities), with nine independent variables, influencing landslide susceptibility. The accuracy of landslide susceptibility derived from different models and raster cells was then verified by the accuracy, recall, F1-score, ROC curve, and AUC of each model. The results illustrate that the accuracy of different machine learning algorithms is ranked as SVM > RF > LR. The LR model has the lowest generalization ability. The SVM model performs well in all regions of the study area, with an AUC value of 0.9708 for the entire Three Gorges Reservoir area, indicating that the SVM model possesses a strong spatial generalization ability as well as the highest robustness and can be adapted as a real-time model for assessing regional landslide susceptibility.

Digital twin is a problem of augmenting real objects with their digital counterparts. It can underpin a wide range of applications in augmented reality (AR), autonomy, and UI/UX. A critical component in a good digital-twin system is real-time, accurate 3D object tracking. Most existing works solve 3D object tracking through the lens of robotic grasping, employ older generations of depth sensors, and measure performance metrics that may not apply to other digital-twin applications such as in AR. In this work, we create a novel RGB-D dataset, called Digital Twin Tracking Dataset (DTTD), to enable further research of the problem and extend potential solutions towards longer ranges and mm localization accuracy. To reduce point cloud noise from the input source, we select the latest Microsoft Azure Kinect as the state-of-the-art time-of-flight (ToF) camera. In total, 103 scenes of 10 common off-the-shelf objects with rich textures are recorded, with each frame annotated with a per-pixel semantic segmentation and ground-truth object poses provided by a commercial motion capturing system. Through extensive experiments with model-level and dataset-level analysis, we demonstrate that DTTD can help researchers develop future object tracking methods and analyze new challenges. The dataset, data generation, annotation, and model evaluation pipeline are made publicly available as open source code at: https://github.com/augcog/DTTDv1.

The vertical axis hydrokinetic turbine is increasingly being used as a renewable energy device to harness tidal energy. In coastal regions with low tidal flow velocities, vertical-axis hydrokinetic turbines often exhibit low energy conversion efficiency, limiting their engineering applications. However, research in this field lacks systematic reviews and reliable solutions for improving efficiency. The paper, based on the traditional vertical axis hydrokinetic turbines, utilized numerical calculations and experimental methods to investigate the effects of blade helicity and airfoil curvature on the energy conversion efficiency of vertical axis hydrokinetic turbines in low flow velocity conditions. Additionally, an improved vertical-axis turbine model is proposed to enhance energy conversion efficiency in low-flow environments. The results indicate that increasing the blade helical angle and airfoil curvature can better optimize the flow conditions around the turbine, significantly improving the energy conversion efficiency of vertical axis turbines. The airfoil blade with a 20% curvature performs best at blade angle, with its power coefficient curve reaching higher peak values at several azimuth angles. At this point, the maximum efficiency reaches 24.42%. Compared to the conventional straight-blade design, the improved turbine model exhibits 6.13% increase in average energy capture efficiency, 3.70% increase in average dynamic torque, and 11.1% improvement in self-starting performance. Comparative analysis reveals that vertical-axis helical blade turbines exhibit superior hydrodynamic performance under low-flow conditions, effectively overcoming the limitations of conventional straight-blade turbines, including poor self-starting capability and low efficiency. This research provides valuable insights into improving the performance of vertical-axis turbines in low-flow environments and suggests innovative solutions for optimizing turbine design.

With the drastic change in global climate, the wide distribution of natural lakes over the Qinghai-Tibet Plateau (TP) has attracted extensive attention due to their high climate sensitivity. In this paper, we investigated the dynamics of Paiku Co, the largest inland lake in the Qomolangma Natural Reserve, with the associated response to climate change in the past three decades. The methods used contain the water index method, the spatial and temporal fusion model, the statistical mono-window algorithm, and multi-variable linear regression. Lake area fluctuated greatly in 1990–2000, followed by a continuous shrinkage in 2000–2010, and it stabilized after that (2010–2020). We forecasted that Paiku Co would enter a slow expansion period. Conjoint analysis with climate factors showed that the area variation of Paiku Co was not significantly related to precipitation change, but negatively related to the change of air temperature and lake temperature. We found that the lake change was not dominated by a single factor but showed different climate sensitivity in each period. Especially, there was a common inflection point around 2013 that might herald the occurrence of a new trend of climate change. This article provides new ideas and solutions for the research of lakes in the Qinghai-Tibet Plateau and offers a reference for water resource management.

Unmanned aerial vehicle (UAV) has become a mainstream data collection platform in precision agriculture. For more accessible UAV–visible imagery, the high spatial resolution brings the rich geometric texture features triggered large differences in same crop image's features. We proposed an encoder–decoder's fully convolutional neural network combined with a visible band difference vegetation index (VDVI) to perform deep semantic segmentation of crop image features. This model ensures the accuracy and the generalization ability, while reducing parameters and the operation cost. A case study of crop classification was conducted in Chengdu, China, where classified four crops, namely, maize, rice, balsam pear, and Loropetalum chinese, it was shown more effective results. In addition, this study explores a fine crop classification method based on visible light features, which is feasible with low equipment cost, and has a prospect of application in crop survey based on UAV low altitude remote sensing.