Landslide Susceptibility Prediction Modeling Based on Self-Screening Deep Learning Model

Landslide susceptibility prediction has always been an important and challenging content. However, there are some uncertain problems to be solved in susceptibility modeling, such as the error of landslide samples and the complex nonlinear relationship between environmental factors. A self-screening graph convolutional network and long short-term memory network (SGCN-LSTM) is proposed int this paper to overcome the above problems in landslide susceptibility prediction. The SGCN-LSTM model has the advantages of wide width and good learning ability. The landslide samples with large errors outside the set threshold interval are eliminated by self-screening network, and the nonlinear relationship between environmental factors can be extracted from both spatial nodes and time series, so as to better simulate the nonlinear relationship between environmental factors. The SGCN-LSTM model was applied to landslide susceptibility prediction in Anyuan County, Jiangxi Province, China, and compared with Cascade-parallel Long Short-Term Memory and Conditional Random Fields (CPLSTM-CRF), Random Forest (RF), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD) and Logistic Regression (LR) models.The landslide prediction experiment in Anyuan County showed that the total accuracy and AUC of SGCN-LSTM model were the highest among the six models, and the total accuracy reached 92.38 %, which was 5.88%, 12.44%, 19.65%, 19.92% and 20.34% higher than those of CPLSTM-CRF, RF, SVM, SGD and LR models, respectively. The AUC value reached 0.9782, which was 0.0305,0.0532,0.1875,0.1909 and 0.1829 higher than the other five models, respectively. In conclusion, compared with some existing traditional machine learning, the SGCN-LSTM model proposed in this paper has higher landslide prediction accuracy and better robustness, and has a good application prospect in the LSP field

Analysis of spatial characteristics and influence mechanism of human settlement suitability in traditional villages based on multi-scale geographically weighted regression model: A case study of Hunan province

Traditional villages are invaluable cultural heritage sites in China, reflecting a rich historical and cultural legacy. However, rapid urbanization has led to the destruction and loss of numerous traditional villages. The remaining traditional villages also face challenges in preserving their original living environment. In order to assess human settlement suitability and identify potential factors, this paper proposes a village-scale model for assessing human settlement suitability. This paper used an eight-factor evaluation system, multi-criteria decision analysis and spatial visualization methods to evaluate the scores and spatial characteristics of human settlement suitability of 688 traditional villages in Hunan Province. This paper also examines the factors affecting cluster suitability using geographic detectors and multi-scale geographically weighted regression (MGWR) models. This study found that the quality of human settlement suitability varied widely. The overall quality is low and very few areas achieve medium or higher scores. Among them, the traditional villages in Huaihua city, Zhangjiajie junction, southern Hengyang city, northwestern Yongzhou city, central Chenzhou city and Changsha city exhibit higher scores. Secondly, the commercial facility density and normalized difference vegetation index (NDVI) are important factors driving the spatial changes. And there is a significant interaction effect between them. Additional factors impacting spatial variation include annual average temperature, per capita disposable income, and average house price. This comprehensive assessment reveals spatial characteristics and potential influencing factors. The results of the study provide an empirical basis and decision-making foundation for the protection and development of traditional villages, which will ultimately contribute to the sustainable development of China's rural areas

Cross-Lingual Named Entity Recognition Based on Attention and Adversarial Training

Named entity recognition aims to extract entities with specific meaning from unstructured text. Currently, deep learning methods have been widely used for this task and have achieved remarkable results, but it is often difficult to achieve better results with less labeled data. To address this problem, this paper proposes a method for cross-lingual entity recognition based on an attention mechanism and adversarial training, using resource-rich language annotation data to migrate to low-resource languages for named entity recognition tasks and outputting changing semantic vectors through the attention mechanism to effectively solve the long-sequence semantic dilution problem. To verify the effectiveness of the proposed method, the method in this paper is applied to the English–Chinese cross-lingual named entity recognition task based on the WeiboNER data set and the People-Daily2004 data set. The obtained F1 value of the optimal model is 53.22% (a 6.29% improvement compared to the baseline). The experimental results show that the cross-lingual adversarial named entity recognition method proposed in this paper can significantly improve the results of named entity recognition in low resource languages

Effect of alkali modification on the powder flowability of rapeseed straw cellulose fibers

Powder flowability of natural reinforced fibers has a significant effect on the processing and properties of the composite. Sodium hydroxide modified rapeseed straw reinforced fibers were prepared in four fiber sizes and the powder flowability before and after modification was tested. The results showed that the alkali modification reduced the total flowability index of the fibers by 6–11 within the test range, with the effect on each flowability index in the order of compression > angle of spatula > angle of repose > miformity. This effect was mainly attributed to morphological, chemical and surface characteristics. Moreover, the slimming effect of the alkali modification reduced the average particle size by 9.4% to 30.8%, exacerbating the anisotropy of the morphological structure. Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) analyses showed that pectin, lipids, hemicellulose and impurities were removed, exposing the clean rough fiber surface. Box plot analysis showed that the modification resulted in a uniform and regular distribution of shape factors. This work has valuable implications for the industrial application of straw cellulose fibers in the field of natural fiber composites

MasterSu: The Sustainable Development of Su Embroidery Based on Digital Technology

Su embroidery, as an intangible cultural heritage of China, is a treasure accumulated by human civilization, but it has been gradually fading from people’s view in recent years. To handle the problems of slow creative output, high learning difficulty, and low production efficiency, and to promote the sustainable development of Su embroidery, this study builds an automatic generation system of Su embroidery called MasterSu, based on the CorelDraw platform. The system can automate the generation of embroidery sketches through area texture filling and color recognition, which allows users to participate in the design process. Finally, the performance and usefulness of the system are verified through user experiments, and it is confirmed that the system can facilitate novice users to understand the embroidery culture, learn the embroidery techniques, and create their embroidery works through the system

Confined Iterative Self-Assembly of Ultrathick Freestanding Electrodes with Vertically Aligned Channels for High Areal Capacity Sodium-Ion Batteries

Electrodes with a high areal capacity are critical for developing high-energy-density sodium-ion batteries (SIBs). The free-standing thick electrode is a promising candidate among the state-of-art electrodes, since it has the advantages of high areal mass, binder-free, current-collector-free, and no carbon additive; thus, the whole mass of the electrode is an active material that can contribute to capacity. However, enhancing areal density by introducing thick electrodes impinges on the transport of charge carriers including ions and electrons. Here we designed and synthesized an ultrathick (1500 mu m) free-standing foam, which is a carbon framework with 1T-MoS2 nanosheets embedded in the vertically aligned channel wall. To tune the areal mass and morphology of the as-obtained foam, a confined iterative self-assembly strategy was proposed. The Na+ storage behavior was studied by using the as-obtained foam as a free-standing electrode against Na metal. Consequently, it displays good cycle stability even with a high areal mass of 20.74 mg cm(-2) and delivers an astonishing reversible areal capacity of 19.75 mAh cm(-2) at 0.01 A g(-1), which greatly exceeds that of most sodium storage materials. The proposed confined iterative self-assembly strategy for fabricating thick electrodes opens new avenues for high areal capacity batteries

Predictive Value of Folate Receptor-Positive Circulating Tumor Cells for the Preoperative Diagnosis of Lymph Node Metastasis in Patients with Lung Adenocarcinoma

Noninvasive assessments of the risk of lymph node metastasis (LNM) in patients with lung adenocarcinoma (LAD) are of great value for selecting individualized treatment options. However, the diagnostic accuracies of different preoperative LN evaluation methods in routine clinical practice are not satisfactory. Here, an assessment to detect folate receptor (FR)-positive circulating tumor cells (CTCs) based on ligand-targeted enzyme-linked polymerization is established. FR-positive CTCs have the potential to improve the specificity and sensitivity of diagnosing LNM in lung cancer patients. The addition of CTC level improved the diagnostic efficiency of the initial prediction model that comprises other clinical parameters. A nomogram for predicting preoperative LNM is established, which showed good prediction and calibration capacities and achieved an average area under the curve of 0.786. Good correlations are observed between the CTC level and nodal classifications, such as the number of positive LNs and the ratio of the number of positive LNs to removed LNs (LN ratio or LNR). The ligand-targeted enzyme-linked polymerization-assisted assessment of CTCs enables noninvasive detection and has a useful predictive value for the preoperative diagnosis of LNM in patients with LAD