Analyzing the dynamic effect of energy endowment-demand distortion on sustainable development: Insights from China's regional disparity.

The distribution of China's energy resources is concentrated in the central and western regions, whereas the energy demand is predominantly centered in the eastern regions. Under the ambitious "carbon neutrality" goal, how to effectively quantify and respond to the impact of this energy "endowment-demand" distortion (EEDD) on economy and environment has become a key issue. We introduce an EEDD index that quantifies the distortion characteristics of China's energy endowment and demand. Based on the EEDD index, a panel vector auto regression-generalized method of moments (PVAR-GMM) model is adopted to assess the interactive effects between regional EEDD and sustainable development variables. The obtained results indicate that the energy endowment-demand distortions are progressively worsening across most provinces. Interestingly, we discover that the EEDD has significant beneficial effects on regional sustainable development. Moreover, the EEDD is found to be significantly influenced by the sustainability-related variables. These impacts between EEDD and sustainable development variables demonstrate significant regional heterogeneity. This study provides crucial empirical evidence for advancing the comprehensive and sustainable development of regional energy, environment, and economy.

Research on the influence of different sampling resolution and spatial resolution in sampling strategy on landslide susceptibility mapping results.

Landslides, recognized as a significant global natural disaster, necessitate an exploration of the impact of various resolution types in sampling strategies on Landslide Susceptibility Mapping (LSM) results. This study focuses on the segment from Zigui to Badong within the Three Gorges Reservoir Area, utilizing two resolution types: sampling resolution and spatial resolution, The Support Vector Machine (SVM) is employed to obtain LSM results, which are then analyzed using Receiver Operating Characteristic (ROC) curve, specific category accuracy and statistical methods. Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used to verify the reliability of the results. Additionally, five common machine learning models, including Logistic Regression (LR), are used to conduct experiments on four sampling resolutions (10 m,30 m,50 m and 70 m) to further investigate the effect of sampling resolution on LSM results. These are evaluated using a comprehensive quantitative method. The results reveal that increasing spatial resolution improves the prediction accuracy, while increasing sampling resolution produces a contrary effect. Furthermore, the impact of spatial resolution on LSM results is more pronounced than that of sampling resolution. Finally, Fanjiaping landslide and Huangtupo landslide are selected as references for comparative analysis, with the results aligning with engineering reality.

Comparative study on landslide susceptibility mapping based on unbalanced sample ratio.

The Zigui-Badong section of the Three Gorges Reservoir area is used as the research area in this study to research the impact of unbalanced sample sets on Landslide Susceptibility Mapping (LSM) and determine the sample ratio interval with the best performance for different models. We employ 12 LSM factors, five training sample sets with different sample ratios (1:1, 1:2, 1:4, 1:8, and 1:16), and C5.0, Support Vector Machine (SVM), Logistic Regression (LR), and one-dimensional Convolution Neural Network (CNN) models are used to obtain landslide susceptibility index and landslide susceptibility zoning in the study area, respectively. The prediction performance of the model is evaluated by the receiver operating characteristic curve area under the curve value, five statistical methods, and specific category precision. The results show that the CNN, SVM, and LR models in the sample ratio of 1:2 achieve better performance than on the balanced sample set, which indicates the importance of the unbalanced sample set in training the LSM modeling. The C5.0 model is always in a state of overfitting in this study and needs to be further studied. The conclusions put forward in this study help improve the scientificity and reliability of LSM.

Land use classification of open-pit mine based on multi-scale segmentation and random forest model.

The mining industry production is an important pillar industry in China, while its extensive production activities have led to several ecological and environmental problems. Earth observation technology using high-resolution satellite imagery can help us efficiently obtain information on surface elements, surveying and monitoring various land occupation issues arising from open-pit mining production activities. Conventional pixel-based interpretation methods for high-resolution remote sensing images are restricted by "salt and pepper" noise caused by environmental factors, making it difficult to meet increasing requirements for monitoring accuracy. With the Jingxiang phosphorus mining area in Jingmen Hubei Province as the studied area, this paper uses a multi-scale segmentation algorithm to extract large-scale main characteristic information using a layered mask method based on the hierarchical structure of the image object. The remaining characteristic elements were classified and extracted in combination with the random forest model and characteristic factors to obtain land occupation information related mining industry production, which was compared with the results of the Classification and Regression Tree model. 23 characteristic factors in three aspects were selected, including spectral, geometric and texture characteristics. The methods employed in this study achieved 86% and 0.78 respectively in overall extraction accuracy analysis and the Kappa coefficient analysis, compared to 79% and 0.68 using the conventional method.

Study on landslide susceptibility mapping based on rock-soil characteristic factors.

This study introduces four rock-soil characteristics factors, that is, Lithology, Rock Structure, Rock Infiltration, and Rock Weathering, which based on the properties of rock formations, to predict Landslide Susceptibility Mapping (LSM) in Three Gorges Reservoir Area from Zigui to Badong. Logistic regression, artificial neural network, support vector machine is used in LSM modeling. The study consists of three main steps. In the first step, these four factors are combined with the 11 basic factors to form different factor combinations. The second step randomly selects training (70% of the total) and validation (30%) datasets out of grid cells corresponding to landslide and non-landslide locations in the study area. The final step constructs the LSM models to obtain different landslide susceptibility index maps and landslide susceptibility zoning maps. The specific category precision, receiver operating characteristic curve, and 5 other statistical evaluation methods are used for quantitative evaluations. The evaluation results show that, in most cases, the result based on Rock Structure are better than the result obtained by traditional method based on Lithology, have the best performance. To further study the influence of rock-soil characteristic factors on the LSM, these four factors are divided into "Intrinsic attribute factors" and "External participation factors" in accordance with the participation of external factors, to generate the LSMs. The evaluation results show that the result based on Intrinsic attribute factors are better than the result based on External participation factors, indicating the significance of Intrinsic attribute factors in LSM. The method proposed in this study can effectively improve the scientificity, accuracy, and validity of LSM.

A landslide susceptibility map based on spatial scale segmentation: A case study at Zigui-Badong in the Three Gorges Reservoir Area, China.

China experiences frequent landslides, and therefore there is a need for landslide susceptibility maps (LSMs) to effectively analyze and predict regional landslides. However, the traditional methods of producing an LSM are unable to account for different spatial scales, resulting in spatial imbalances. In this study, Zigui-Badong in the Three Gorges Reservoir Area was used as a case study, and data was obtained from remote sensing images, digital elevation model, geological and topographic maps, and landslide surveys. A geographic weighted regression (GWR) was applied to segment the study area into different spatial scales, with three basic principles followed when the GWR model was applied for this propose. As a result, 58 environmental factors were extracted, and 18 factors were selected as LSM factors. Three of the most important factors (channel network basic level, elevation, and distance to river) were used as segmentation factors to segment the study area into 18 prediction regions. The particle swarm optimization (PSO) algorithm was used to optimize the parameters of a support vector machine (SVM) model for each prediction region. All of the prediction regions were merged to construct a GWR-PSO-SVM coupled model and finally, an LSM of the study area was produced. To verify the effectiveness of the proposed method, the outcomes of the GWR-PSO-SVM coupled model and the PSO-SVM coupled model were compared using three evaluation methods: specific category accuracy analysis, overall prediction accuracy analysis, and area under the curve analysis. The results for the GWR-PSO-SVM coupled model for these three evaluation methods were 85.75%, 87.86%, and 0.965, respectively, while the results for the traditional PSO-SVM coupled model were 68.35%, 84.44%, and 0.944, respectively. The method proposed in this study based on a spatial scale segmentation therefore acquired good results.

Optimal path for controlling pollution emissions in the Chinese electric power industry considering technological heterogeneity.

The electric power industry is not only an important part in the Chinese economic system but also the key industry with the highest emissions of air pollutants in China. This paper aims to control the pollution emissions of the Chinese electric power industry and enhance its electric-generation capacity though pollution-emission allocation patterns and inefficiency elimination. The data envelopment analysis centralized allocation model (DEA-CA) under metafrontier framework is adopted to distribute pollution emissions and electric-generation capacity considering technological heterogeneity at regional and national levels. The empirical result shows that the emission reduction responsibility is directly proportional to regional power generation performance. The metafrontier framework allocates emission permits to combine the national and regional, which makes the adjustment of each province more reasonable. At last, the relationship between the aggregate optimal electricity capacity and the pollution emission control coefficient is shown to follow an inverted U-shape curve, which implies that a modest emission control policy might be more appropriate for the electric power industry to achieve the joint optimizing goal of electricity generation enhancement and pollution emission control.

CU Partition Mode Decision for HEVC Hardwired Intra Encoder Using Convolution Neural Network.

The intensive computation of High Efficiency Video Coding (HEVC) engenders challenges for the hardwired encoder in terms of the hardware overhead and the power dissipation. On the other hand, the constrains in hardwired encoder design seriously degrade the efficiency of software oriented fast coding unit (CU) partition mode decision algorithms. A fast algorithm is attributed as VLSI friendly, when it possesses the following properties. First, the maximum complexity of encoding a coding tree unit (CTU) could be reduced. Second, the parallelism of the hardwired encoder should not be deteriorated. Third, the process engine of the fast algorithm must be of low hardware- and power-overhead. In this paper, we devise the convolution neural network based fast algorithm to decrease no less than two CU partition modes in each CTU for full rate-distortion optimization (RDO) processing, thereby reducing the encoder's hardware complexity. As our algorithm does not depend on the correlations among CU depths or spatially nearby CUs, it is friendly to the parallel processing and does not deteriorate the rhythm of RDO pipelining. Experiments illustrated that, an averaged 61.1% intraencoding time was saved, whereas the Bjøntegaard-Delta bit-rate augment is 2.67%. Capitalizing on the optimal arithmetic representation, we developed the high-speed [714 MHz in the worst conditions (125 °C, 0.9 V)] and low-cost (42.5k gate) accelerator for our fast algorithm by using TSMC 65-nm CMOS technology. One accelerator could support HD1080p at 55 frames/s real-time encoding. The corresponding power dissipation was 16.2 mW at 714 MHz. Finally, our accelerator is provided with good scalability. Four accelerators fulfill the throughput requirements of UltraHD-4K at 55 frames/s.

A Combination of Geographically Weighted Regression, Particle Swarm Optimization and Support Vector Machine for Landslide Susceptibility Mapping: A Case Study at Wanzhou in the Three Gorges Area, China.

In this study, a novel coupling model for landslide susceptibility mapping is presented. In practice, environmental factors may have different impacts at a local scale in study areas. To provide better predictions, a geographically weighted regression (GWR) technique is firstly used in our method to segment study areas into a series of prediction regions with appropriate sizes. Meanwhile, a support vector machine (SVM) classifier is exploited in each prediction region for landslide susceptibility mapping. To further improve the prediction performance, the particle swarm optimization (PSO) algorithm is used in the prediction regions to obtain optimal parameters for the SVM classifier. To evaluate the prediction performance of our model, several SVM-based prediction models are utilized for comparison on a study area of the Wanzhou district in the Three Gorges Reservoir. Experimental results, based on three objective quantitative measures and visual qualitative evaluation, indicate that our model can achieve better prediction accuracies and is more effective for landslide susceptibility mapping. For instance, our model can achieve an overall prediction accuracy of 91.10%, which is 7.8%-19.1% higher than the traditional SVM-based models. In addition, the obtained landslide susceptibility map by our model can demonstrate an intensive correlation between the classified very high-susceptibility zone and the previously investigated landslides.