We develop an integrated neural network landslide susceptibility assessment (LSA) method that integrates temporal dynamic features of interferometry synthetic aperture radar (InSAR) deformation data and the spatial features of landslide influencing factors. We construct a time-distributed convolutional neural network (TD-CNN) and bidirectional gated recurrent unit (Bi-GRU) to better understand the temporal dynamic features of InSAR cumulative deformation, and construct a multi-scale convolutional neural network (MSCNN) to determine the spatial features of landslide influencing factors, and construct a parallel unified deep learning network model to fuse these temporal and spatial features for LSA. Compared with the traditional MSCNN method, the accuracy of the proposed model is improved by 1.20%. The performance of the proposed model is preferable to MSCNN. The area under the receiver operating characteristic curve (AUC) of the testing set reaches 0.91. Our LSA results show that the proposed model clearly depicts areas with very high susceptibility landslides. Further, only 10.18% of the study area accurately covers 84.79% of historical landslide areas. Subjective consequences and objective indicators show that the proposed model that is integrated time-series InSAR deformation dynamic features can make full use of landslide characteristics and effectively improve the reliability of LSA. 

An integrated neural network method for landslide susceptibility assessment based on time-series InSAR deformation dynamic features

Construction activities of accelerated urbanization in Shenzhen have increased the landslide risk area, which has intensified the potential threat to human and natural environment. However, the risk of landslides in Shenzhen is poorly evaluated. In this paper, a landslide risk evaluation (LRE) model is constructed using landslide susceptibility map (LSM) and landslide vulnerability. In the experiment, a stacking ensemble learning (SEL) model is constructed based on convolutional neural network (CNN), multilayer perceptron (MLP), gated recurrent unit (GRU) and support vector machine regression (SVR) to generate LSM by using topography, geology, human engineering activities, time-series precipitation and time-series normalized difference vegetation index (NDVI). Road network, building distribution density and annual average precipitation data are used to evaluate landslide vulnerability based on entropy weight method. In this study, multiple statistical indicators are used to evaluate the performance of the LSM model, and Interferometric Synthetic Aperture Radar (InSAR) deformation data are utilized to verify the LRE results in Shenzhen. The results show that the SEL method has more refined results for LSM, with a best overall evaluation accuracy, especially in the receiver operating characteristic curve (ROC), where the accuracy is improved by nearly 8%. In LRE of Shenzhen, very high, high, moderate, low and very low risk areas account for 0.283%, 0.451%, 0.859%, 36.890% and 61.517%, respectively. In most of very high risk area, InSAR deformation results show a clear concentrated deformation trend with a large deformation rate. Research results can provide technical and data support for landslide disaster prevention in Shenzhen. 

https://ieeexplore.ieee.org/ielx7/4609443/4609444/10171365.pdf

Landslide risk evaluation in Shenzhen based on stacking ensemble learning and InSAR

Cancer is widely recognized as one of the most devastating diseases, necessitating the development of intelligent diagnostic techniques, targeted treatments, and early prognosis evaluation to ensure effective and personalized therapy. Conventional treatments, unfortunately, suffer from limitations and an increased risk of severe complications. In light of these challenges, boron neutron capture therapy (BNCT) has emerged as a promising approach for cancer treatment with unprecedented precision to selectively eliminate tumor cells. The distinctive and promising characteristics of BNCT hold the potential to revolutionize the field of oncology. However, the clinical application and advancement of BNCT technology face significant hindrance due to the inherent flaws and limited availability of current clinical drugs, which pose substantial obstacles to the practical implementation and continued progress of BNCT. Consequently, there is an urgent need to develop efficient boron agents with higher boron content and specific tumor-targeting properties. Researchers aim to address this need by integrating tumor-targeting strategies with BNCT, with the ultimate goal of establishing BNCT as an effective, readily available, and cutting-edge treatment modality for cancer. This review delves into the recent advancements in integrating tumor-targeting strategies with BNCT, focusing on the progress made in developing boron agents specifically designed for BNCT. By exploring the current state of BNCT and emphasizing the prospects of tumor-targeting boron agents, this review provides a comprehensive overview of the advancements in BNCT and highlights its potential as a transformative treatment option for cancer.

The Dawn of a New Era: Tumor-Targeting Boron Agents for Neutron Capture Therapy.

Accurate prediction of the trend of Normalized Difference Vegetation Index (NDVI) time series in the Yellow River Basin (YRB) is crucial for the assessment of the hydrological and ecological environment in this region. Currently, the NDVI time series prediction model is primarily based on traditional models and single-variable neural network models. Nevertheless, these models present challenges in considering the limitations of multiple factors, causing the NDVI time series prediction results to lack reliability. To predict NDVI time-series in the YRB of China, this study constructed a multilayer multivariate Long-Short Term Memory (LSTM) neural network model including climatic components. The initial important climatic elements in this region were identified using GeoDetector. Then, the relationship between NDVI and climatic factors in the YRB of China is established. Finally, numerical scale data are used to train and predict a multilayer multivariate LSTM model with climatic components. According to the results, the three-layer multivariate LSTM neural network NDVI time series prediction model developed in this study has the best performance among the evaluated indices. When compared to existing time series prediction models, the proposed model in this study takes into account the common constraint effect of various climate factors on NDVI. This leads to a significantly improved prediction accuracy, presenting new opportunities for enhancing the prediction model. By analyzing the NDVI time series prediction outcomes for the YRB, it has been determined that the ecological environment of the area will continuously improve in the future. This study offers significant technological and theoretical backing for assessing the hydrological and ecological environment of the YRB and comparable ecologically vulnerable regions in China. 

LSTM time series NDVI prediction method incorporating climate elements: A case study of Yellow River Basin, China

Fast and efficient landslide detection plays an important role in post-disaster rescue and risk assessment. Existing convolution neural network (CNN) based landslide detection methods are difficult to exploit global long-distance dependencies due to limited receptive fields. Considering that landslide occurrence is susceptible to local and global conditions, we propose a novel multi-scale feature fusion scene parsing (MFFSP) framework to explore information at different scales by coupling CNN with Transformer to learn local and global clues for landslide detection based on satellite data. In the encoder, we design three modules, visual geometry module (VGM), residual learning module (RLM), and Transformer module (TRM) to exploit multi-scale features. Specifically, VGM and RLM are constructed based on convolution operations to explore local features by learning low-level and middle-level information, while TRM is built based on self-attention mechanism to learn long-distance dependencies. In the decoder, TRM and VGM are further extended to motivate the model to mine long-distance dependencies and detailed spatial information by deeply fusing features from multiple scales. To demonstrate the performance of the model, we employ two study areas with four test regions to conduct experiments and compare with seven state-of-the-art deep learning models. Extensive experiments demonstrate that MFFSP greatly outperforms other algorithms. In addition, we conduct numerous ablation experiments, proving that MFFSP fully combines the complementary advantages of CNN and Transformer to mine robust features. 

MFFSP: Multi-scale feature fusion scene parsing network for landslides detection based on high-resolution satellite images

Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results. 

A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images

Boron neutron capture therapy (BNCT) is regarded as a revolutionary means for high-accuracy cancer therapy with cell level selectivity. It has unique therapeutic effects for some malignance, such as glioblastoma multiforme, melanoma, recurrent head and neck malignance. It can also be used for cancer treatment of deep organ, such as liver and lung. Even though its principle was proposed about <sc>80 years</sc> ago, it has never been utilized in routine clinic therapy in hospital until 2020. BNCT, as a binary therapeutic method, greatly depends on both high-quality neutron beam and highly target-selective boron drug. Tumor killing in cell-level accuracy can be achieved only when the two elements work together and collaborate closely. To realize such therapeutic effect, BNCT sets high requirement on both neutron source and boron drug. The research on boron drug is related with multi-disciplines, such as chemistry, biology, medicine, radiology, pharmacy and physics. This article focuses on the neutron source for BNCT. Commonly, there are two types of BNCT neutron sources: reactor-based neutron source and accelerator-based neutron source. Up to now, almost all clinic trails of BNCT therapy were performed with the former. Due to limited resources of reactor neutron source, only less than 2000 cases BNCT treatments have been carried out in the world since BNCT method was invented. Thanks for the intense beam proton accelerator development, accelerator based neutron source can provide better beam quality, and especially importantly it can be installed in a hospital environment, which is essential for wide-range application of BNCT. In accelerator-based BNCT field, China has a sound base owing to more than <sc>20 years</sc> research and development in high intensity neutron source for spallation neutron source and accelerator-driven subcritical system. It is meaningful work to transfer the related technology to accelerator-based BNCT (AB-BNCT). In this paper we will firstly introduce the demand on neutron beam specification recommended by IAEA. Then the difficulties in BNCT slow development in passing decades are reviewed. New era of BNCT is coming and one can expect a prosperous future, owing to accelerator-based BNCT. The core technology of AB-BNCT is explored and recent research achievements on BNCT research and development in China are reported. Various types of accelerators now can be used for BNCT facility, including electro-static high voltage, cyclotron and RF linac. Their pro and con are reviewed. Majorly two kinds of neutron generation targets are utilized for BNCT, and the characteristics comparison of these two are analyzed. On the newly constructed BNCT research platform in China, new boron drug research and development work are conducted in many institutes, universities and pharmacy companies. And in recent, a new AB-BNCT facility is under construction for clinic trails in a hospital. So we can have an optimistic viewpoint for BNCT future in China.

Status and outlook: Research and development on the neutron source for BNCT

Ancient glass is highly susceptible to weathering by the burial environment, with a large number of internal elements exchanging with environmental elements, resulting in changes in the proportions of their composition. In order to analyse the classification patterns of High potassium-rich glass and Lead and barium enriched glass, and to classify each type of glass into appropriate subcategories, Decision tree was first chosen to broadly classify the decision factors for the glass types, and then lasso-logistic was chosen for the exact solution. K-means clustering was used in combination with Factor Analysis Method to achieve subcategories for both types of glass. Finally, using the above model, the chemical composition of the unknown glass is used to identify the type.

/pdf/glass-classification-and-identification-based-on-lasso-2zt0iih4.pdf

Glass classification and identification based on Lasso-logistic model and K-means clustering

硼中子俘获治疗的发展机遇与挑战：含硼药物的研发

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A landslide extraction method of channel attention mechanism U-Net network based on Sentinel-2A remote sensing images

Status and outlook: Research and development on the neutron source for BNCT

Glass classification and identification based on Lasso-logistic model and K-means clustering

硼中子俘获治疗的发展机遇与挑战：含硼药物的研发