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One- and two-dimensional carbon nanomaterials are attracting considerable attention because of their extraordinary electrical, mechanical and thermal properties, which could lead to a range of important potential applications. Synthetic processes associated with making these materials can be quite complex and also consume large amounts of energy, so a major challenge is to develop simple and efficient methods to produce them. Here, we present a self-templated, catalyst-free strategy for the synthesis of one-dimensional carbon nanorods by morphology-preserved thermal transformation of rod-shaped metal–organic frameworks. The as-synthesized non-hollow (solid) carbon nanorods can be transformed into two- to six-layered graphene nanoribbons through sonochemical treatment followed by chemical activation. The performance of these metal–organic framework-derived carbon nanorods and graphene nanoribbons in supercapacitor electrodes demonstrates that this synthetic approach can produce functionally useful materials. Moreover, this approach is readily scalable and could be used to produce carbon nanorods and graphene nanoribbons on industrial levels. A rod-shaped metal–organic framework can be converted into one-dimensional carbon nanorods through a catalyst-free thermal transformation in which the morphology of the material is preserved. The as-synthesized nanorods can be unravelled to form 2–6-layer graphene nanoribbons by ultrasonication in the presence of KOH, followed by thermal activation.

Landslides are catastrophic natural hazards that often lead to loss of life, property damage, and economic disruption. Image-based landslide investigations are crucial for determining landslide susceptibility and risk. In practice, satellite images have been widely utilized for such investigations; however, they still require significant labor and time resources. In this study, we propose an image-based two-phase data-driven framework for detecting and segmenting landslide regions using satellite images. In phase I, an object detection algorithm, Faster-RCNN, is trained to detect the landslide location within the large-scale satellite images. The bounding boxes of each landslide location are proposed and visualized. In phase II, we crop the satellite images into small images using the location information of the bounding boxes. Next, we use a boundary detection algorithm to identify the boundary information of each detected loess landslide to strengthen the segmentation performance. Finally, we improve the architecture of the segmentation U-Net by integrating additional inception blocks with dilation to enhance the landslide segmentation performance. A total of 150 local loess landslide occurrences in northern China are selected as our case study to validate the effectiveness, efficiency, and universality of the proposed two-phase framework. Segmentation of loess landslides is considered a challenging task due to the intrinsic nature of vague boundary information. The proposed framework is compared with the conventional U-Net and other recent benchmarking landslide segmentation algorithms. Computational results indicate that the proposed framework produces more accurate segmentation of loess landslides compared with the other tested benchmarking algorithms.

Aiming at the intrusion detection problem of the wireless sensor network (WSN), considering the combined characteristics of the wireless sensor network, we consider setting up a corresponding intrusion detection system on the edge side through edge computing. An intrusion detection system (IDS), as a proactive network security protection technology, provides an effective defense system for the WSN. In this paper, we propose a WSN intelligent intrusion detection model, through the introduction of the k-Nearest Neighbor algorithm (kNN) in machine learning and the introduction of the arithmetic optimization algorithm (AOA) in evolutionary calculation, to form an edge intelligence framework that specifically performs the intrusion detection when the WSN encounters a DoS attack. In order to enhance the accuracy of the model, we use a parallel strategy to enhance the communication between the populations and use the Lévy flight strategy to adjust the optimization. The proposed PL-AOA algorithm performs well in the benchmark function test and effectively guarantees the improvement of the kNN classifier. We use Matlab2018b to conduct simulation experiments based on the WSN-DS data set and our model achieves 99% ACC, with a nearly 10% improvement compared with the original kNN when performing DoS intrusion detection. The experimental results show that the proposed intrusion detection model has good effects and practical application significance.

This study aimed to enhance the prevention and control of pulmonary tuberculosis (PTB) and provide more effective and accurate methods in Changshu City. The PTB patients' information came from the China Information System for Disease Control and Prevention (CISDCP). The demographic data for Changshu city and towns came from the Suzhou Statistical Yearbook and the LandScan platform. ArcGIS was used for global spatial autocorrelation analysis and local spatial autocorrelation analysis. Univariate logistic regression and multivariate logistic regression were used to analyze the influencing factors of cured PTB patients. The receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to analyze the predictive efficacy and clinical benefit of the indicators. XGBoost analysis was performed to explore the feature importance of key metrics for PTB outcome. A total of 3943 PTB cases were included. The annual incidence rate of new PTB in Changshu city was 27.081 per 100,000. Changshu High-tech Industrial Development Zone in Jiangsu Province and Shajiabang town were the high-high aggregation areas and hot spot areas. Diagnosis delay, TB strain types, and drug sensitivity were independent predictors of the cure of new PTB patients. The central and southern areas of Changshu were the high-high cluster areas and hot spots for PTB. Shorter diagnosis delay days and mycobacterium tuberculosis (MTB) promote the cure of tuberculosis, while drug sensitivity was a risk factor for its cure.

Convolution neural network (CNN) is an effective and popular deep learning method which automatically learns complicated non-linear mapping from original inputs to given labels or ground truth through a series of convolutional layers. This study focuses on detecting landslides from high-resolution optical satellite images using CNN-based methods, providing opportunities for recognizing latent landslides and updating large-scale landslide inventory with high accuracy and time efficiency. Considering the variety of landslides and complicated backgrounds, attention mechanisms originated from the human visual system are developed for boosting the CNN to extract more distinctive feature representations of landslides from backgrounds. As deep learning needs a large number of labeled data to train a learning model, we manually prepared a landslide dataset which is located in the Bijie city, China. In the dataset, 770 landslides, including rock falls, rock slides, and a few debris slides, were interpreted by geologists from the satellite images and digital elevation model (DEM) data and further checked by fieldwork. The landslide data was separated into a training set that trains the attention boosted CNN model and a testing set that evaluates the performance of the model with a ratio of 2:1. The experimental results showed that the best F1-score of landslide detection reached 96.62%. The results also proved that the performance of our spatial-channel attention mechanism was fairly over other recent attention mechanisms. Additionally, the effectiveness of predicting new potential landslides with high efficiency based on our dataset is demonstrated.

Chloroplasts are important for photosynthesis and for plant immunity against microbial pathogens. Here we identify a haustorium-specific protein (Pst_12806) from the wheat stripe rust fungus, Puccinia striiformis f. sp. tritici ( Pst ), that is translocated into chloroplasts and affects chloroplast function. Transient expression of Pst_12806 inhibits BAX-induced cell death in tobacco plants and reduces Pseudomonas -induced hypersensitive response in wheat. It suppresses plant basal immunity by reducing callose deposition and the expression of defense-related genes. Pst_12806 is upregulated during infection, and its knockdown (by host-induced gene silencing) reduces Pst growth and development, likely due to increased ROS accumulation. Pst_12806 interacts with the C-terminal Rieske domain of the wheat TaISP protein (a putative component of the cytochrome b6-f complex). Expression of Pst_12806 in plants reduces electron transport rate, photosynthesis, and production of chloroplast-derived ROS. Silencing TaISP by virus-induced gene silencing in a susceptible wheat cultivar reduces fungal growth and uredinium development, suggesting an increase in resistance against Pst infection. Chloroplasts are important for plant immunity against microbial pathogens. Here Xu et al. identify, in the wheat stripe rust fungus, a haustorium-specific protein that is translocated into chloroplasts and affects chloroplast function by interacting with a putative component of the plant cytochrome b6-f complex.

The thermal transformation of metal–organic frameworks (MOFs) generates a variety of nanostructured materials, including carbon-based materials, metal oxides, metal chalcogenides, metal phosphides and metal carbides. These derivatives of MOFs have characteristics such as high surface areas, permanent porosities and controllable functionalities that enable their good performance in sensing, gas storage, catalysis and energy-related applications. Although progress has been made to tune the morphologies of MOF-derived structures at the nanometre scale, it remains crucial to further our knowledge of the relationship between morphology and performance. In this Review, we summarize the synthetic strategies and optimized methods that enable control over the size, morphology, composition and structure of the derived nanomaterials. In addition, we compare the performance of materials prepared by the MOF-templated strategy and other synthetic methods. Our aim is to reveal the relationship between the morphology and the physico-chemical properties of MOF-derived nanostructures to optimize their performance for applications such as sensing, catalysis, and energy storage and conversion. Nanomaterials derived from metal–organic frameworks (MOFs) show good performance in sensing, gas storage, catalysis and energy-related applications. In this Review, the influence of the morphology of MOF-derived nanostructures on their performance is elucidated, and the opportunities in this field are discussed.

The initial collision between Indian and Asian continents marked the starting point for transformation of land-sea thermal contrast,uplift of the Tibet-Himalaya orogen,and climate change in Asia.In this paper,we review the published literatures from the past 30 years in order to draw consensus on the processes of initial collision and suturing that took place between the Indian and Asian plates.Following a comparison of the different methods that have been used to constrain the initial timing of collision,we propose that the tectono-sedimentary response in the peripheral foreland basin provides the most sensitive index of this event,and that paleomagnetism presents independent evidence as an alternative,reliable,and quantitative research method.In contrast to previous studies that have suggested collision between India and Asia started in Pakistan between ca.55 Ma and50 Ma and progressively closed eastwards,more recent researches have indicated that this major event first occurred in the center of the Yarlung Tsangpo suture zone（YTSZ） between ca.65 Ma and 63 Ma and then spreading both eastwards and westwards.While continental collision is a complicated process,including the processes of deformation,sedimentation,metamorphism,and magmatism,different researchers have tended to define the nature of this event based on their own understanding,an intuitive bias that has meant that its initial timing has remained controversial for decades.Here,we recommend the use of reconstructions of each geological event within the orogenic evolution sequence as this will allow interpretation of collision timing on the basis of multidisciplinary methods.

The Qianjiangping landslide is a large planar rock slide which occurred in July 14, 2003 shortly after the water level reached 135 m in the Three Gorges Reservoir, China. The landslide destroyed 4 factories and 129 houses, took 24 lives, and made 1,200 people homeless. Field investigation shows that the contributing factors for the landslide are the geological structure of the slope, the previous surface of rupture, the water level rise, and continuous rainfall. In order to reveal the mechanism and failure process of the landslide, numerical simulation was conducted on Qianjiangping slope before sliding. Based on the characteristics and the engineering conditions of the landslide, the topography and the geological profiles of Qianjiangping slope before sliding is reconstructed. The seepage field of Qianjiangping slope before sliding was simulated with the Geostudio software. The results show that ground water table rises and bends to the slope during the rise of water level, and the slope surface becomes partially saturated within the period of continuous rainfall. Using the ground water table obtained above, the failure process of Qianjiangping slope is simulated with the Flac3D software. The results demonstrate that the shear strain increment, displacement, and shear failure area of the slope increased greatly after the water level rose and continuous rained, and the landslide was triggered by the combined effect both of water level rise and continuous rainfall. The development of shear strain increment, displacement, and shear failure area of the slope shows that the landslide was retrogressive in the lower part of the slope and progressive in the upper part of the slope.

Halide perovskites exhibit unique slow hot-carrier cooling properties capable of unlocking disruptive perovskite photon–electron conversion technologies (e.g., high-efficiency hot-carrier photovoltaics, photo-catalysis, and photodetectors). Presently, the origins and mechanisms of this retardation remain highly contentious (e.g., large polarons, hot-phonon bottleneck, acoustical–optical phonon upconversion etc.). Here, we investigate the fluence-dependent hot-carrier dynamics in methylammonium lead triiodide using transient absorption spectroscopy, and correlate with theoretical modeling and first-principles calculations. At moderate carrier concentrations (around 10 18  cm −3 ), carrier cooling is mediated by polar Fröhlich electron–phonon interactions through zone-center delayed longitudinal optical phonon emissions (i.e., with phonon lifetime τ LO around 0.6 ± 0.1 ps) induced by the hot-phonon bottleneck. The hot-phonon effect arises from the suppression of the Klemens relaxation pathway essential for longitudinal optical phonon decay. At high carrier concentrations (around 10 19  cm −3 ), Auger heating further reduces the cooling rates. Our study unravels the intricate interplay between the hot-phonon bottleneck and Auger heating effects on carrier cooling, which will resolve the existing controversy. Harvesting excess energy from above-bandgap photons can break solar cells’ conventional efficiency limits. Using transient spectroscopy, modelling and ab-initio calculations, Fu et al., unravel the interplay between hot phonon bottleneck and Auger heating effects on hot-carrier cooling in halide perovskites.