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Changing landscape patterns would alter ecosystem components and functions, affecting the supply of ecosystem services. Understanding the spatial and temporal variations of ecosystem services is an important basis for ecosystem management and planning decisions and is of great significance for the realization of regional sustainable development. Based on Landsat TM/OLI remote sensing images from 1990, 2000, 2010, and 2020 in the Li River Basin, we explored the spatial and temporal variabilities of ecosystem services in the Li River Basin over the past 30 years, from both horizontal and vertical dimensions, using modified equivalence factor method and spatial autocorrelation analysis. The research findings are as follows: (1) Forestland has consistently been the dominant landscape type in the Li River Basin, with its area continuously increasing, while farmland, water bodies, and grassland have decreased, and construction land and bare land have increased. (2) The value of ecosystem services in the Li River Basin exhibited an initial increase followed by a decrease trend, with a net increase of 9.20 × 108 yuan. Forestland contributed the most to the value of ecosystem services. (3) Hydrological regulation and climate regulation are the dominant functions of the Li River Basin's ecosystems, accounting for over 50% of the total contribution. (4) The value of ecosystem services per unit area increases with increasing slope and elevation. The segments with slopes ranging from 15 to 25 degrees and elevation zones between 200 and 500 m have the highest total value of ecosystem services. (5) The overall level of ecosystem services in the Li River Basin is relatively high and continues to rise, but areas with a low ecosystem service value are gradually concentrated. (6) The Moran's I values for ecosystem services in all four periods are greater than 0, indicating a significant positive spatial autocorrelation. The overall pattern of ecosystem services is relatively stable, but there are significant spatial variations, which are characterized by lower values in the central area and higher values in the surrounding areas. The research findings provide a scientific basis for watershed ecological environment construction, optimal allocation of land resources, and sustainable landscape management. [ABSTRACT FROM AUTHOR]

Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network (CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator (SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database (OQMD) (https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultra-low lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials. [ABSTRACT FROM AUTHOR]

Background and aims: Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) is a promising method of treating gastric outlet obstruction. However, no study has investigated gastrointestinal anastomosis formation after EUS-GE. We aimed to evaluate the formation of gastrointestinal anastomosis after EUS-GE in a porcine model. Methods: Retrieval anchor-assisted EUS-GE was performed on 15 Bama mini pigs. Five pigs each were randomly euthanized 3, 7, and 14 days postoperatively to evaluate the formation of gastrointestinal anastomosis and measure the anastomotic distance. The expression of transforming growth factor-β1(TGF-β1) and Smad3 in the anastomosis site were examined by immunohistochemistry. Results: EUS-GE was successfully performed in all 15 pigs. The mean procedure time was 29.2 ± 6.0 (range 18–40) minutes. The anastomotic distance was 34.0 ± 3.6 cm in 14 pigs. The site of gastroenterostomy of one pig was at the ileum. For pigs euthanized 3 days postoperatively, the structure was mechanically maintained by a stent. For pigs euthanized 7 or 14 days postoperatively, the stomach and small intestine were anastomosed to form a stable structure. The level of TGF-β1 and Smad3 in the anastomosis site gradually increased from 3 to 14 days after EUS-GE. TGF-β1 and Smad3 expression had a significant difference between 3 days, 7 days, and 14 days after EUS-GE (P < 0.05). Conclusions: For EUS-GE, the stomach and small intestine were initially linked together mechanically and spontaneously anastomosed to form a stable structure 7 days postoperatively. TGF-β1 and Smad3 play an important role in the formation of a stable structure of gastrointestinal anastomosis. [ABSTRACT FROM AUTHOR]

Background and objectives: Up till now, there are still controversies about the specific indication of endoscopic resection for small gastric subepithelial tumors (gSETs) originating from muscularis propria. We aimed to investigate the safety of endoscopic resection and postoperative pathology analysis. Method: The patients with primary small gSETs originating from muscularis propria, treated by endoscopic resection in the endoscopic center of Shengjing Hospital between January, 2011 and September, 2019 were enrolled. The complete resection rate, adverse events and clinicopathological features were recorded. Result: A total of 936 patients with 972 gastric SETs ≤ 2 cm originating from muscularis propria were included in our study. All the lesions were successfully treated by endoscopic resection. Nearly half of lesions were proved to be gastrointestinal stromal tumor (GIST) [n = 411 (42.3%)] according to postoperative pathology. All the objects were further subdivided into 2 groups, ≤ 1 cm, > 1 and ≤ 2 cm gSETs. The risk of gastric GIST of intermediate/high risk in the group (> 1 and ≤ 2 cm gSETs) is 8.41 times as that of gastric GIST in the group (the size of gastric ≤ 1 cm gSETs) (P < 0.05). Conclusion: Endoscopic resection is a safe and effective treatment for small gSETs. gSETs (1–2 cm) is more risky than gSETs (≤ 1 cm) and should be resected. This should be evaluated by further studies. [ABSTRACT FROM AUTHOR]

Extracting facade maps from 3D point clouds is a fast and economical way to describe a building's surface structure. Existing methods lack efficiency, robustness, and accuracy, and depend on many additional features such as point cloud reflectivity and color. This paper proposes a robust and automatic method to extract building facade maps. First, an improved 3D Hough transform is proposed by adding shift vote and 3D convolution of the accumulator to improve computational efficiency and reduce peak fuzziness and dependence on the step selection. These modifications make the extraction of potential planes fast and accurate. Second, the coplane and vertical plane constraints are introduced to eliminate pseudoplanes and nonbuilding facades. Then, we propose a strategy to refine the potential facade and to achieve the accurate calibration and division of the adjacent facade boundaries by clustering the refined point clouds of the facade. This process solves the problem where adjoining surfaces are merged into the same surface in the traditional method. Finally, the extracted facade point clouds are converted into feature images. Doors, windows, and building edges are accurately extracted via deep learning and digital image processing techniques, which combine to achieve accurate extraction of building facades. The proposed method was tested on the MLS and TLS point cloud datasets, which were collected from different cities with different building styles. Experimental results confirm that the proposed method decreases computational burden, improves efficiency, and achieves the accurate differentiation of adjacent facade boundaries with higher accuracy compared with the traditional method, verifying the robustness of the method. Additionally, the proposed method uses only point cloud geometry information, effectively reducing data requirements and acquisition costs. [ABSTRACT FROM AUTHOR]

Terrain feature points, such as the peaks and saddles, are the basic framework of surface topography and its undulations, which significantly affect the spatial distribution of surface topography. In the past, terrain feature points were extracted separately for each type, while the internal connections between the terrain feature points were ignored. Therefore, this work proposes an integrated algorithm for extracting terrain feature-point clusters, including the peaks, saddles and runoff nodes, based on the DEM data. This method includes two main processes: positive terrain-constrained ridgeline extraction and terrain feature-point cluster extraction. Firstly, a threshold determination method of flow accumulation in the hydrological analysis is proposed by combining morphological characteristics with runoff simulation, and the ridgelines are extracted based on this threshold. Subsequently, the peaks and their control areas are extracted by space segmentation. Meanwhile, the saddles and runoff nodes are obtained by spatial intersection. Finally, the integrated terrain feature-point clusters are obtained by merging the three extracted terrain feature points. This method was experimented with in the six typical sample areas in Shaanxi Province and verified its results by contour lines and optical images. It shows that the spatial positions of the extracted terrain feature clusters are accurate, and the coupling relationships are great. Finally, the experiments show that the statistical attributes of point clusters and their spatial distribution trends have an obvious correlation with geomorphic types and geomorphic zoning, which can provide an important reference for geomorphic zoning and mapping. [ABSTRACT FROM AUTHOR]

The role of needle-based confocal laser endomicroscopy (nCLE) in the diagnosis of pancreatic cystic lesions (PCLs) remains controversial. This study aimed to systematically evaluate the diagnostic accuracy and adverse effects of nCLE in the detection of pathological subtypes in patients with PCLs. We conducted a comprehensive literature search using MEDLINE, EMBASE, and Cochrane for identifying studies that reported the use of nCLE for PCLs diagnosis (dated prior to 10 October 2020). Studies with a sample size >10 were included. We used the QUADAS-2 criteria for quality evaluation. We first extracted the diagnostic rates and the information on adverse events (AEs) from the studies; then used STATA15.0 to calculate the variables, draw forest plots and summary receiver operating characteristic (SROC) curves; and finally, we completed subgroup analyses to explore the heterogeneity. Overall, 299 article titles were identified after an initial search, and ten studies with 547 individuals with PCLs were included in the analysis. The pooled sensitivity, specificity, positive likelihood ratio, and pooled negative likelihood ratio of nCLE in detecting gastric disorders were 90%, 96%, 20.4, and 0.11, respectively. The pooled sensitivity and specificity showed a substantial heterogeneity. An ROC curve was constructed with an area under the curve (AUC) of 0.94. The overall AEs rate of pancreatitis was 2.7%. We showed that nCLE had a relatively high sensitivity and specificity for diagnosing PCLs with a relatively low rate of AEs occurring. We suggest that nCLE has good diagnostic accuracy for PCLs. [ABSTRACT FROM AUTHOR]

Two‐dimensional perovskites have attracted substantial attention for solar cell applications because of their higher stability as compared to their 3D analogs. To achieve efficient charge transport in thin‐film devices, obtaining high crystalline perovskite crystals perpendicularly aligned to the substrate is of great importance. This article reports the scalable printing of high‐quality Dion–Jacobson (DJ) perovskite thin films via tailoring crystallization kinetics. Introducing a small amount of 1‐methyl‐2‐pyrrolidinone to the conventional N,N‐dimethylformamide:dimethyl sulfoxide‐based precursor, the strong coordination with ammonium spacers enables a notably retarded crystallization, which results in perovskite films with distinctly enhanced crystallinity, highly vertical orientation, and graded phase distribution. Accordingly, efficient charge generation and ultrafast interphase charge transfer are realized. The champion DJ perovskite device delivers a high current density of 17.10 mA cm–2, an impressive open‐circuit voltage of 1.21 V, leading to a stabilized efficiency of 16.19%. In addition, the devices processed from the ternary solvent exhibit remarkably improved stability under stimuli with light, heat, and humidity, benefiting from their superb phase stability. This work demonstrates an important advancement in scalable deposition of DJ perovskite thin films for efficient and stable photovoltaic devices. [ABSTRACT FROM AUTHOR]

Background and Objectives: Up till now, there are still controversies about the specific indication of endoscopic resection for small gastric subepithelial tumors (gSETs) originating from muscularis propria. We aimed to investigate the safety of endoscopic resection and postoperative pathology analysis.Method: The patients with primary small gSETs originating from muscularis propria, treated by endoscopic resection in the endoscopic center of Shengjing Hospital between January, 2011 and September, 2019 were enrolled. The complete resection rate, adverse events and clinicopathological features were recorded.Result: A total of 936 patients with 972 gastric SETs ≤ 2 cm originating from muscularis propria were included in our study. All the lesions were successfully treated by endoscopic resection. Nearly half of lesions were proved to be gastrointestinal stromal tumor (GIST) [n = 411 (42.3%)] according to postoperative pathology. All the objects were further subdivided into 2 groups, ≤ 1 cm, > 1 and ≤ 2 cm gSETs. The risk of gastric GIST of intermediate/high risk in the group (> 1 and ≤ 2 cm gSETs) is 8.41 times as that of gastric GIST in the group (the size of gastric ≤ 1 cm gSETs) (P < 0.05).Conclusion: Endoscopic resection is a safe and effective treatment for small gSETs. gSETs (1-2 cm) is more risky than gSETs (≤ 1 cm) and should be resected. This should be evaluated by further studies. [ABSTRACT FROM AUTHOR]

We systematically evaluated the difference in diagnostic accuracy, check time, and adverse effects between magnetically controlled capsule gastroscopy (MCCG) and conventional gastroscopy in patients with gastric disorders. PubMed, Embase, Cochrane, CKNI and Wanfang Data were searched for studies dated prior to 30 January 2020. We used the QUADAS criteria for quality evaluation. We extracted the diagnostic rate, check time, and adverse events from the studies, then used STATA15.0 to calculate variables (sensitivity, specificity, positive likelihood ratio [LR+], negative likelihood ratio [LR−] and diagnostic odds ratio), draw forest plots and SROC curves, and completed sensitivity analysis. A total of 278 titles were identified after an initial search and nine studies with 1,146 individuals were included in the analysis. The pooled sensitivity, specificity, LR+ and LR− of MCCG in detecting gastric disorders were 90%, 92%, 10.6, and 0.11, respectively. A ROC curve was drawn with Q = 0.9060 and AUG = 0.96. The diagnostic odds ratio was 93. No significant difference in diagnosis accuracy was demonstrated between MCCG and conventional gastroscopy. The MCCG had a longer check time than conventional gastroscopy and the adverse events occurrence rate was lower. MCCG is a convenient and reliable examination method for gastric diseases. [ABSTRACT FROM AUTHOR]

The NS-K-PC host with unique microporous structure and uniform doping of N/S heteroatoms ensures the excellent cycling performance of NS-K-PC/Se cathode with carbonate-based electrolyte. [Display omitted] Selenium cathode attracts great attention due to its high theoretical volumetric capacity and better electrical conductivity than sulfur cathode. Herein, N/S co-doped microporous carbon (NS-K-PC) is designed and prepared as Se host by a spray drying process of the poly(styrenesulfonic acid)-melamine salt solution followed by carbonization and activation process. The as-prepared NS-K-PC shows a very high micropore contribution of 94.8% in the total surface area, and a total N/S heteroatom doping level of 2.5 wt% in the carbon matrix. The NS-K-PC/Se cathode delivers a high reversible capacity of 499.2 mA h g−1 at 0.1C, superior rate capacity of 324 mA h g−1 at 8C, and great cycling stability with a capacity decay of 0.081% per cycle over 500 cycles at 1C. Additionally, a comparative study demonstrates that NS-K-PC/Se cathode with the carbonate-based electrolytes exhibit better cycling stability than those with ether-based electrolytes primarily resulted from a direct solid–solid conversion of Se to Li 2 Se bypassing the formation of soluble polyselenides. [ABSTRACT FROM AUTHOR]

Background: Autism spectrum disorders (ASD) imply a spectrum of symptoms rather than a single phenotype. ASD could affect brain connectivity at different degree based on the severity of the symptom. Given their excellent learning capability, graph neural networks (GNN) methods have recently been used to uncover functional connectivity patterns and biological mechanisms in neuropsychiatric disorders, such as ASD. However, there remain challenges to develop an accurate GNN learning model and understand how specific decisions of these graph models are made in brain network analysis. Results: In this paper, we propose a graph attention network based learning and interpreting method, namely GAT-LI, which learns to classify functional brain networks of ASD individuals versus healthy controls (HC), and interprets the learned graph model with feature importance. Specifically, GAT-LI includes a graph learning stage and an interpreting stage. First, in the graph learning stage, a new graph attention network model, namely GAT2, uses graph attention layers to learn the node representation, and a novel attention pooling layer to obtain the graph representation for functional brain network classification. We experimentally compared GAT2 model's performance on the ABIDE I database from 1035 subjects against the classification performances of other well-known models, and the results showed that the GAT2 model achieved the best classification performance. We experimentally compared the influence of different construction methods of brain networks in GAT2 model. We also used a larger synthetic graph dataset with 4000 samples to validate the utility and power of GAT2 model. Second, in the interpreting stage, we used GNNExplainer to interpret learned GAT2 model with feature importance. We experimentally compared GNNExplainer with two well-known interpretation methods including Saliency Map and DeepLIFT to interpret the learned model, and the results showed GNNExplainer achieved the best interpretation performance. We further used the interpretation method to identify the features that contributed most in classifying ASD versus HC. Conclusion: We propose a two-stage learning and interpreting method GAT-LI to classify functional brain networks and interpret the feature importance in the graph model. The method should also be useful in the classification and interpretation tasks for graph data from other biomedical scenarios. [ABSTRACT FROM AUTHOR]

[Display omitted] • Constructing high-conductivity freestanding 3D sulfur host for Li-S batteries. • Realizing efficient anchoring and accelerated conversion of polysulfides. • The cell exhibits excellent rate capability and cycling stability. Highly conductive freestanding cathode, featuring with a unique aerogel configuration with sulfur deposited on low-defect graphene and subsequent encapsulation in poly(3,4-ethylene-dioxythiophene):poly(styrenesulfonate), is designed and synthesized for lithium-sulfur batteries. The freestanding cathode exhibits an ultrahigh conductivity for fast ions/electrons transportation and realizes effective immobilization of polysulfides, thereby enhancing lithium storage performance. This research provides a potential for use in high-performance flexible lithium-sulfur batteries. [ABSTRACT FROM AUTHOR]

• 24,918 Landsat images focused on the lower Yellow River were used to monitor SSC during the period 1997–2020. • The proposed HGAR method significantly outperforms other retrieval methods in terms of retrieval accuracy. • HGAR is a reliable method for SSC retrieval and can be used for long-term monitoring of SSCs. Monitoring suspended sediment concentrations (SSCs) of channel waters is essential for evaluating important issues such as erosion, sediment transport and deposition, reservoir siltation, and water pollution throughout watersheds. Remote sensing technology provides a new approach to large-scale SSC monitoring, but the accuracy of existing retrieval methods based on remote sensing technology still needs to be improved for complex environments. This study proposes a new hyperparameter and globally adaptive retrieval (HGAR) method for river SSC retrieval that combines a remote sensing technique and machine learning methods. The HGAR method uses the light gradient-boosting machine method as the basic model for SSC retrieval, and the tree-structured Parzen estimator method, coefficient of determination, and residual prediction deviation are combined to optimize the basic model's hyperparameters. The experiment was conducted in the lower Yellow River, where 24,918 Landsat images from 1997 to 2020 were acquired using Google Earth Engine, and 1,772 monthly cloud-free images were obtained in the end. The sequential backward floating selection method was adopted to determine the optimal modeling features. Two regression and three machine learning methods were compared with the proposed method. The results demonstrate that the proposed HGAR method significantly outperforms other retrieval methods in terms of retrieval accuracy (as evidenced by a correlation coefficient of 0.626 and a Nash-Sutcliffe efficiency coefficient of 0.613), generalizability, and robustness. Finally, the model explanation results show that the HGAR method can successfully capture the key information for SSC retrieval and has higher flexibility than traditional statistical models. In addition, we found that the overall fit and the fit of local extreme values are inconsistent in the lower Yellow River, and the proposed improved accuracy indicator balances the two aspects well. In conclusion, the proposed HGAR method can be used effectively to estimate channel SSC and is expected to provide technical support for long-term and large-scale monitoring of the channel surface SSC. [ABSTRACT FROM AUTHOR]

Rice stripe virus (RSV) is one of the most destructive viral diseases affecting rice production. However, so far, only one RSV resistance gene has been cloned, the molecular mechanisms underlying host-RSV interaction are still poorly understood. Here, we show that increasing levels or signaling of brassinosteroids (BR) and jasmonic acid (JA) can significantly enhance the resistance against RSV. On the contrary, plants impaired in BR or JA signaling are more susceptible to RSV. Moreover, the enhancement of RSV resistance conferred by BR is impaired in OsMYC2 (a key positive regulator of JA response) knockout plants, suggesting that BR-mediated RSV resistance requires active JA pathway. In addition, we found that RSV infection suppresses the endogenous BR levels to increase the accumulation of OsGSK2, a key negative regulator of BR signaling. OsGSK2 physically interacts with OsMYC2, resulting in the degradation of OsMYC2 by phosphorylation and reduces JA-mediated defense to facilitate virus infection. These findings not only reveal a novel molecular mechanism mediating the crosstalk between BR and JA in response to virus infection and deepen our understanding about the interaction of virus and plants, but also suggest new effective means of breeding RSV resistant crops using genetic engineering. Author summary: Brassinosteroids (BR) and jasmonic acid (JA) play critical roles in responding to various stresses. However, the roles of BR and JA, particularly, the crosstalk between these two phytohormones in viral resistance is still very limited. In this work, we found that both BR and JA positively regulate RSV resistance, and JA pathway is necessary for BR-mediated RSV resistance in rice. RSV infection significantly inhibits the BR signaling pathway and increases the accumulation of OsGSK2. OsGSK2 interacts with and phosphorylates OsMYC2, resulting in the degradation of OsMYC2 and suppression of the JA-mediated RSV resistance response to facilitate virus infection. These findings revealed the molecular mechanism of crosstalk between the BR and JA in response to virus infection and deepen our understanding about the mechanism of RSV resistance. [ABSTRACT FROM AUTHOR]

Endoscopic ultrasound-guided gastroenterostomy (EUS-GE) is an emerging procedure for gastric outlet obstruction (GOO) as an alternative to endoscopic stent placement in the duodenum or surgery; however, it is technically challenging. This study aimed to evaluate the safety, success rate, and adverse events rate associated with retrieval anchor-assisted EUS-GE. Data from patients who underwent retrieval anchor-assisted EUS-GE for malignant and benign GOO were retrospectively analyzed. Patients' clinical and demographic characteristics, procedure time, and success and adverse event rates were recorded. A total of 10 patients (6 females; mean age 63.2 ± 5.8 years) were included in our study. Nine cases were malignant and one case was benign GOO. Nine patients received retrievable anchor-assisted EUS-GE for GOO. One patient received retrievable anchor-assisted EUS-GE and concurrent EUS-guided hepatogastrostomy due to the biliary obstruction. There were no complications during any of the procedures. The rate of technical and clinical success was 100%. EUS-GE is a safe and effective procedure for GOO. The retrieval anchor can make EUS-GE easier to perform successfully. [ABSTRACT FROM AUTHOR]

Deep neural networks have recently been applied to the study of brain disorders such as autism spectrum disorder (ASD) with great success. However, the internal logics of these networks are difficult to interpret, especially with regard to how specific network architecture decisions are made. In this paper, we study an interpretable neural network model as a method to identify ASD participants from functional magnetic resonance imaging (fMRI) data and interpret results of the model in a precise and consistent manner. First, we propose an interpretable fully connected neural network (FCNN) to classify two groups, ASD versus healthy controls (HC), based on input data from resting-state functional connectivity (rsFC) between regions of interests (ROIs). The proposed FCNN model is a piecewise linear neural network (PLNN) which uses piecewise linear function LeakyReLU as its activation function. We experimentally compared the FCNN model against widely used classification models including support vector machine (SVM), random forest, and two new classes of deep neural network models in a large dataset containing 871 subjects from ABIDE I database. The results show the proposed FCNN model achieves the highest classification accuracy. Second, we further propose an interpreting method which could explain the trained model precisely with a precise linear formula for each input sample and decision features which contributed most to the classification of ASD versus HC participants in the model. We also discuss the implications of our proposed approach for fMRI data classification and interpretation. [ABSTRACT FROM AUTHOR]

As perovskite solar cells (PSCs) are highly efficient, demonstration of high‐performance printed devices becomes important. 2D/3D heterostructures have recently emerged as an attractive way to relieving the film inhomogeneity and instability in perovskite devices. In this work, a 2D/3D ensemble with 2D perovskites self‐assembled atop 3D methylammonium lead triiodide (MAPbI3) via a one‐step printing process is shown. A clean and flat interface is observed in the 2D/3D bilayer heterostructure for the first time. The 2D perovskite capping layer significantly suppresses nonradiative charge recombination, resulting in a marked increase in open‐circuit voltage (VOC) of the devices by up to 100 mV. An ultrahigh VOC of 1.20 V is achieved for MAPbI3 PSCs, corresponding to 91% of the Shockley–Queisser limit. Moreover, notable enhancement in light, thermal, and moisture stability is obtained as a result of the protective barrier of the 2D perovskites. These results suggest a viable approach for scalable fabrication of highly efficient perovskite solar cells with enhanced environmental stability. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to assess the long-term behavior of the over-the-scope clip (OTSC) after gastric application. Material and methods: A database of all patients who had OTSC placement with regular follow-up at a tertiary hospital between November 2013 and September 2016 was retrospectively analyzed. The incidence, time, and related adverse events of clip attachment and detachment were recorded. The relationship between the position of the clip and detachment was assessed by Fisher's exact test. Results: Seventy-two patients who had gastric OTSC placement due to iatrogenic perforations by endoscopic interventions were included in the study. The overall incidence of clip detachment was 27.8% (20/72). Fisher's exact test confirmed a significant association between clip detachment rate and clip position (p =.005): cardia 50% (3/6); gastric fundus 13.2% (5/38); gastric body 75% (9/16); and gastric antrum 25% (3/12). Except for one patient (5%; 1/20) who experienced minor gastric hemorrhage, there were no complications related to spontaneous clip detachment. There was no delayed bleeding, perforation, or obstruction due to clips staying in place in the long-term. Conclusions: Long-term gastric clip attachment appears to be safe even if spontaneous detachment occurs. [ABSTRACT FROM AUTHOR]

Background and Aims: Endoscopic full-thickness resection (EFTR) is difficult to perform in a retroflexed fashion in the gastric fundus. The present study aims at exploring whether direct EFTR can be a simple, effective and safe procedure to treat intraluminal-growth submucosal tumors originating from the muscularis propria.Methods: The patients with intraluminal-growth submucosal tumors originating from the muscularis propria in gastric fundus treated by direct EFTR between 01 January 2017 and 01 September 2018 were retrospectively reviewed. In addition, we analyzed the patients with intraluminal-growth submucosal tumors originating from the muscularis propria in gastric fundus treated by traditional EFTR. The differences in tumor resection time, cost-effectiveness, and complication rate were evaluated.Results: Forty patients were enrolled in the present study, 20 patients of which were in the direct EFTR group and 20 patients of which were in the traditional EFTR group. En-bloc resections of gastric tumors were successfully performed in all 40 cases. There was no significant difference in the average tumor size of the two groups (24.3 ± 2.9 mm in direct EFTR group verus 24.0 ± 2.6 mm in the traditional group, p = 0.731), but significant difference existed in the operative time between two groups (35.0 ± 8.2 min in direct EFTR group verus 130.6 ± 51.9 min in the traditional group, p<0.05). No complications, such as postoperative bleeding and perforation, occurred in any groups.Conclusions: Direct EFTR is a safe, simple and cost-effective procedure for SMTs with an intraluminal growth pattern originating from the muscularis propria layer in the gastric fundus. [ABSTRACT FROM AUTHOR]

Deep learning models have been successfully applied to the analysis of various functional MRI data. Convolutional neural networks (CNN), a class of deep neural networks, have been found to excel at extracting local meaningful features based on their shared-weights architecture and space invariance characteristics. In this study, we propose M2D CNN, a novel multichannel 2D CNN model, to classify 3D fMRI data. The model uses sliced 2D fMRI data as input and integrates multichannel information learned from 2D CNN networks. We experimentally compared the proposed M2D CNN against several widely used models including SVM, 1D CNN, 2D CNN, 3D CNN, and 3D separable CNN with respect to their performance in classifying task-based fMRI data. We tested M2D CNN against six models as benchmarks to classify a large number of time-series whole-brain imaging data based on a motor task in the Human Connectome Project (HCP). The results of our experiments demonstrate the following: (i) convolution operations in the CNN models are advantageous for high-dimensional whole-brain imaging data classification, as all CNN models outperform SVM; (ii) 3D CNN models achieve higher accuracy than 2D CNN and 1D CNN model, but 3D CNN models are computationally costly as any extra dimension is added in the input; (iii) the M2D CNN model proposed in this study achieves the highest accuracy and alleviates data overfitting given its smaller number of parameters as compared with 3D CNN. [ABSTRACT FROM AUTHOR]

Selenium–sulfur solid solutions show unique advantages beyond sulfur and selenium as cathode materials for lithium batteries. Herein, we propose a dual-confined Se x S y -based cathode with a three-dimensional (3D) network structure where Se 0.4 S 0.6 is first encapsulated in double-layered hollow carbon spheres (DLHCs) and embedded by 3D graphene-like material (3DG) shells. The dissolution of polysulfides/ polyselenides in the cathode is effectively inhibited through dual confinement from DLHCs and 3DG as well as chemical binding between sulfur and selenium. Benefiting from the indispensable advantages of Se 0.4 S 0.6 and the uniquely designed host architecture, the 3DG-DLHC-Se 0.4 S 0.6 cathode delivers a high reversible capacity of 627 mAh g−1 at 0.2 A g−1 after 200 cycles, good rate capability of 533 mAh g−1 at 2 A g−1, and outstanding long cycling stability over 500 cycles with Coulombic efficiency almost reaching 100%. Image 1 • The dual-confined Se x S y -based cathode with a 3D network structure is synthesized. • The Se 0.4 S 0.6 is uniformly trapped inside the 3D interconnected conducting network. • The 3DG-DLHC-Se 0.4 S 0.6 exhibits excellent electrochemical performance. • The rationally designed Se x S y -based material is a promising cathode candidate. [ABSTRACT FROM AUTHOR]

Grain size and uniformity index of fragments produced by tunnel boring machine (TBM) reflect tunneling states. Traditional manual fragment identification methods will reduce tunneling safety and efficiency. Therefore, an automatic fragment classification method is significant. However, the fragments classification accuracy of existing deep learning methods will decrease under different tunnel projects with various kinds of fragments. In this paper, a rock fragment classification method is proposed based on convolutional neural network (CNN). To distinguish fragment and background with very similar grey level in the image, contrast limited adaptive histogram equalization is used to enhance fragment edge features. In addition, image regeneration is used in data preparation process to balance data set. During hyperparameter tuning in model training, this research applies Bayesian optimization to acquire optimum model. Experiments show that proposed CNN model can acquire an accuracy of 91.88% in classifying various types of fragments, improving 39.21%, 11.64%, 13.64% and 9.45%, respectively, compared with LeNet, ResNet, VGG and AlexNet. Batch normalization, DropBlock and global average pooling skills are used to alleviate overfitting of CNN model. Based on proposed model pre-trained on a single project data set and a small amount of new data, the migrated model can achieve 89.86% accuracy on a new tunnel data set. The experiment results demonstrate a great generalization ability of proposed model dealing with various kinds of fragments. Highlights: Developing a fragments image-based deep learning architecture to classify rock fragments produced by tunnel boring machine. Trained by Bayesian optimization, proposed method is tested on rock fragment images collected from tunnel projects and acquire better accuracy, precision and recall on the test set than the LeNet, ResNet, VGG and AlexNet based fragment classification models. By integrating the deep learning theories to alleviate model overfitting, proposed model has higher accuracy than existing image classification models when dealing with rock fragments under different tunnel projects. [ABSTRACT FROM AUTHOR]

Background: Magnetically controlled capsule endoscopy (MCE) has emerged as a feasible and efficient diagnostic modality for gastric diseases. In a special group of patients, MCE may have an advantage over standard gastroscopy. Objective: This study aimed to evaluate the clinical utility of MCE in high-risk patients for standard gastroscopy. Methods: In this study, patients with high-risk factors for standard gastroscopy were examined by MCE between 1 January 2017 and 31 December 2017. The examination time, results, adverse events and clinical outcome were recorded. Results: Forty-two patients with high-risk factors for standard gastroscopy were enrolled in the present study. All patients successfully underwent MCE without any adverse events. Based on the findings from MCE, the patients were successfully treated. Conclusions: For patients with high-risk factors for standard gastroscopy, MCE could be the perfect alternative examination for gastric diseases. We recommend MCE as the first-line examination for high-risk patients, but its utility should be confirmed in further clinical studies. [ABSTRACT FROM AUTHOR]

Ensuring scalability of recommender systems without sacrificing the quality of the recommendations produced, presents significant challenges, especially in the large-scale, real-world setting of mobile ad targeting. In this paper, we propose MobRec, a novel two-stage user similarity based approach to recommendation which combines information provided by slowly-changing features of the mobile context and implicit user feedback indicative of user preferences. MobRec uses the contextual features to cluster, during an off-line stage, users that share similar patterns of mobile behavior. In the online stage, MobRec focuses on the cluster consisting of users that are most similar to the target user in terms of their contextual features as well as implicit feedback. MobRec also employs a novel strategy for robust estimation of user preferences from noisy clicks. Results of experiments using a large-scale real-world mobile advertising dataset demonstrate that MobRec outperforms the state-of-the-art neighborhood-based as well as latent factor-based recommender systems, in terms of both scalability and the quality of the recommendations. [ABSTRACT FROM AUTHOR]

Selenium disulfide (SeS 2 ) exhibits attractive advantages beyond naked selenium and sulfur as an electroactive material for lithium storage. Herein, a three-dimensional (3D) interconnected, sulfur-doped mesoporous carbon (ISMC) with a sulfur content of 9.3 wt% has been prepared by a simple in-situ constructing strategy to confine SeS 2 for lithium batteries. The sulfur doping enhances the conductivity of the carbon matrix and inhibits the diffusion of polysulfides and polyselenides, while the 3D mesoporous network facilitates fast electron and ion transport. Benefiting from the synergy between sulfur doping and 3D network architecture, the SeS 2 @ISMC composite displays a high reversible capacity of 486 mAh g −1 after 200 cycles at 0.5 A g −1 and outstanding rate capability of 465 mAh g −1 at 4 A g −1 . [ABSTRACT FROM AUTHOR]

The silicon oxycarbonitride with ultrafine CoS (CoS@SiOCN) spherical composite as anode material for lithium-ion batteries is synthesized through the pyrolysis of the polymerized product of Co(II) with Schiff-base ligand of aminoethyl-aminopropyl-trimethoxy silane and salicylaldehyde and the simultaneous in-situ formation of CoS. Owing to the molecular level complexation of Co(II) and the subsequent in-situ sulfidation, the generated ultrafine CoS is well-dispersed in SiOCN sphere. Compared with the Co@SiOCN and SiOCN anodes, the CoS@SiOCN anode displays the highest initial reversible capacity of 834 mAh g−1 with an initial Coulombic efficiency of 68.5% at 200 mA g−1. At 1000 mA g−1, the CoS@SiOCN anode still exhibits outstanding cycling performance, delivering a reversible capacity of 536 mAh g−1 after 800 cycles with a retention of 80.4%. The full cell with pre-lithiated CoS@SiOCN anode and LiFePO 4 cathode delivers an initial discharge capacity of 146 mAh g−1 at 0.2 C and maintains 115 mAh g−1 at 0.5 C after 50 cycles. The outstanding electrochemical performances can be attributed to the structure of SiOCN with the active SiO x C 4- x (0 ≤ x ≤ 4) nanodomain and free carbon conductive network, and well-dispersed CoS. The electrochemical analysis indicates both CoS and in-situ formed Co nanoparticles at the high redox voltage can contribute to the electrochemical properties. • The synthesis strategy of complexation-assisted polymerization is developed. • A novel Co(II) complex with Schiff-base ligand containing silicon is prepared. • Ultrafine CoS is well dispersed in SiOCN matrix during in-situ conversion process. • CoS facilitates the reaction kinetics within SiOCN during lithiation/delithiation. [ABSTRACT FROM AUTHOR]

The shuttle effect of polysulfides and sluggish reaction kinetics have been the main obstacles for the practical applications of lithium-sulfur (Li-S) batteries. Herein, we report a rationally designed sulfur cathode (DLHC/S@MnO 2 -ACNT) based on double-layered hollow carbon sphere (DLHC) decorated with manganese dioxide (MnO 2) shielding layer and carbon nanotube conductive network (ACNT). The Ultramicrotome technique demonstrates that S and MnO 2 nanosheets are mainly located into the interlayer of the DLHC. In the elaborate nanostructured protocol, the interlayer MnO 2 nanosheets catalyze the conversion of polysulfides and the surface MnO 2 nanosheets effectively adsorb polysulfides to suppress the shuttle effect. Additionally, the ACNT conductive network facilitates electrons/ions transport. Benefiting from the hierarchical confinement, Li-S batteries with DLHC/S@MnO 2 -ACNT composite cathodes exhibit a high reversible capacity of 1051 mAh g−1 at 0.5 C and a remarkable cycling stability over 500 cycles at 1 C. Even at a high sulfur loading of 4 mg cm−2, the cell delivers a superior areal capacity of 4.47 mAh cm−2. This work provides a new route to the design of hierarchical confinement sulfur hosts for high-performance Li-S batteries. [Display omitted] [ABSTRACT FROM AUTHOR]

The rapid development of 5G network-connected Internet of Things (IoT) has attracted great attention of academia and industry with a huge demand for IoT application task processing, which is usually delay-sensitive with the constraints of resource limitation and cost budget. Facing the challenges of massive computing tasks, various users' requirements and resource limitations, this paper formulates a multi-objective optimization problem for computation offloading in a collaborative edge-cloud computing paradigm for handling various types of IoT tasks, aiming to provide efficient computing service to multiple IoT users. With the aim of minimizing task processing delay, mobile device energy consumption and economic cost, a discrete multi-objective Invasive Tumor Growth Optimization algorithm based on Invasive Tumor Growth Optimization is proposed to obtain diverse Pareto-optimal solutions, employing four types of cells with different search strategies to improve the search efficiency, convergence and diversity. Simulation results under complex scenarios show that the proposed algorithm can effectively solve the modeled computation offloading optimization problem of various task types and task scales and can also be well applied to a variety of different computing platforms. Compared with state-of-the-art algorithms, our proposed algorithm achieves better convergence and diversity in three objectives, which implies superior performance, scalability and applicability. [ABSTRACT FROM AUTHOR]

Optical flow estimation has been concerned with biological plausibility since the optical flow was drawn from psychology. In this Letter, optical flow estimation combining with objects edge features is proposed. Inspired of functional division and collaboration of edge detection in primary visual cortex and motion information acquisition in a motion-selective area in the human visual cortex, a neural sub-network is introduced to extract motion edge information as primary visual cortex does, and the other neural sub-network is utilised to perceive motion information as a motion-selective area in visual cortex does. The inference sub-network in the authors' model used augmentation features composed of objects, edge features and general features as inputs to predict the optical flow. In experiments on large displacements benchmark datasets, their approach showed promising results to improve the performance of optical flow comparing with the other models based on the visuomotor perception of visual cortex processing mechanism. [ABSTRACT FROM AUTHOR]

Optical flow estimation has been concerned with biological plausibility since the optical flow was drawn from psychology. In this Letter, optical flow estimation combining with objects edge features is proposed. Inspired of functional division and collaboration of edge detection in primary visual cortex and motion information acquisition in a motion‐selective area in the human visual cortex, a neural sub‐network is introduced to extract motion edge information as primary visual cortex does, and the other neural sub‐network is utilised to perceive motion information as a motion‐selective area in visual cortex does. The inference sub‐network in the authors' model used augmentation features composed of objects, edge features and general features as inputs to predict the optical flow. In experiments on large displacements benchmark datasets, their approach showed promising results to improve the performance of optical flow comparing with the other models based on the visuomotor perception of visual cortex processing mechanism. [ABSTRACT FROM AUTHOR]

CoOOH and Co(OH) 2 have great potential in lithium-ion battery anodes due to their high theoretical specific capacities. But they suffer from large volume change during the cycle and hence display fast capacity fading. Carbon coated ultrathin two-dimensional porous nanostructure engineering can be conducive to improving cycling stability of both CoOOH and Co(OH) 2 , but it is a huge challenge. Herein, a tetraethyl orthosilicate (TEOS) assisted synthesis of carbon coated ultrathin porous CoOOH/Co(OH) 2 hybrid nanosheets (COH@C NSs) has been proposed and achieved successfully. This original idea using the TEOS as the reactant not only constructs the ultrathin two-dimensional nanosheets as a structure directing reagent but also forms a porous structure as a precursor of SiO 2 pore templates. As a result, the COH@C NSs integrate the advantages of ultrathin two-dimensional nanosheets, porous structure and carbon coating, and hence have significantly improved structural stability and electrochemical kinetics during the cycle, delivering 845.3 mA h g−1 in 350th cycle at 200 mA g−1 and 696.2 mA h g−1 in 400th cycle at 1000 mA g−1. This work gives a unique insight for engineering ultrathin two-dimensional porous nanostructure of high-capacity CoOOH/Co(OH) 2 hybrid electrode. • Ultrathin CoOOH/Co(OH) 2 hybrid was synthesized by a original TEOS assisted strategy. • The ultrathin nanostructure enhanced reaction kinetics and structure stability of the hybrid. • The as-prepared hybrid nanosheets displayed outstanding performance in lithium storage. • 845 and 696 mA h g−1 were retained at 400th and 420th cycle at 200 and even 1000 mA g−1, respectively. [ABSTRACT FROM AUTHOR]

Because the cost of refined sugar substrate and limit of worldwide food availability, lignocellulosic materials are attractive for use in lactic acid (LA) production. In this study, we found Lactobacillus pentosus strain FL0421 produced LA with high yields (0.52–0.82 g/g stover) from five NaOH-pretreated and washed agro stovers through simultaneous saccharification and fermentation (SSF). We developed a fed-batch SSF process at 37 °C and pH 6.0 using the cellulase of 30 FPU/g stover and 10 g/L yeast extract in a 5-L bioreactor to produce LA from 14% (w/w) NaOH-pretreated and washed corn stover under non-sterile condition. The LA-titer, yield and productivity reached 92.30 g/L, 0.66 g/g stover and 1.92 g/L/h, respectively; and acetic acid titer and yield reached 34.27 g/L and 0.24 g/g stover. This study presented a feasible process for LA production from agro stovers and provided a candidate strain for genetic engineering for high-titer and -yield lignocellulosic LA production. [ABSTRACT FROM AUTHOR]

Because the cost of refined sugar substrate and limit of worldwide food availability, lignocellulosic materials are attractive for use in lactic acid (LA) production. In this study, we found Lactobacillus pentosus strain FL0421 produced LA with high yields (0.52–0.82 g/g stover) from five NaOH-pretreated and washed agro stovers through simultaneous saccharification and fermentation (SSF). We developed a fed-batch SSF process at 37 °C and pH 6.0 using the cellulase of 30 FPU/g stover and 10 g/L yeast extract in a 5-L bioreactor to produce LA from 14% (w/w) NaOH-pretreated and washed corn stover under non-sterile condition. The LA-titer, yield and productivity reached 92.30 g/L, 0.66 g/g stover and 1.92 g/L/h, respectively; and acetic acid titer and yield reached 34.27 g/L and 0.24 g/g stover. This study presented a feasible process for LA production from agro stovers and provided a candidate strain for genetic engineering for high-titer and -yield lignocellulosic LA production. [ABSTRACT FROM AUTHOR]

Se as an active material attracts tremendous attention because of its high volumetric capacity and better conductivity than S for rechargeable batteries. Herein, a carbon nanofiber membrane (PM-CNF) is designed and prepared as free-standing Se host by electrospinning the solution of polyacrylonitrile (PAN) and poly(styrenesulfonic acid)-melamine salt, followed by carbonization and activation process. PM-CNF shows super flexibility and high N-doping level of 8.92 % (pyrrolic-N: 2.15 % and pyridinic-N: 3.07 %), which is beneficial for ions diffusion and electrons transformation. The free-standing PM-CNF/Se cathode delivers a high reversible capacity of 570 mAh g−1 at 0.5 C, superior rate capacity of 375 mAh g−1 at 8 C, and stabile cycling over 1800 cycles. The excellent electrochemical performance can be ascribed to high Li-ion diffusion coefficient (2.99 × 10−9 cm2 s−1) and capacitance-contributed ratio (77.1 % at 0.1 mV s−1). The analysis of the charge/discharge process reveals that no polyselenide intermediates are observed during the first cycling process and thus the inhibited shuttle effect of polyselenides over cycling, which primarily contributes to the long cycling stability. [Display omitted] • PSSH-Melamine salt improves flexibility and N-doping level of carbonized PAN membrane. • PM-CNF/Se cathode exhibits outstanding electrochemical performances. • The free-standing cathode shows favorable kinetics. • No polyselenide intermediates are observed. [ABSTRACT FROM AUTHOR]

[Display omitted] • One-step in-situ approach to synthesize N/F co-doped hollow micro/mesoporous carbons. • Realizing efficient anchoring and accelerated conversion of polysulfides. • Opening up new horizons to design functional hollow-structured materials. Heteroatom-doped hollow carbon nanostructures are one of the most promising sulfur hosts for high-performance lithium-sulfur (Li-S) batteries. However, finding a facile and controllable synthesis and enhancing their immobilization effect on polysulfides have been challenging. Herein, we propose a one-step in-situ reaction approach to synthesize N/F dual-doped hollow micro/mesoporous carbon nanospheres (NFHCs) as multifunctional sulfur host, which integrates the physical confinement of hollow porous structure and chemical adsorption/catalytic ability of N/F active sites to realize strong anchoring and accelerated conversion of polysulfides. This sulfur cathode based on NFHCs host with a high sulfur content of 80 wt% enables a superior high-rate capacity of 689 mAh g−1 at 3C, accompanied by outstanding cycle life over 500 cycles at 2C with a capacity decay of only 0.05% per cycle and nearly 100% Coulombic efficiency. This work opens up new horizons to design nanostructured functional hollow carbons for advanced Li-S batteries. [ABSTRACT FROM AUTHOR]

Adding self-healing microcapsules into the asphalt binder seems to be an effective way to autonomously repair the micro-cracks in asphalt concrete, slow fatigue cracks growth rate, restore original mechanical properties, and further enlarge the fatigue life. Size including the diameter of the capsules and the thickness of the shell wall is one key parameter significantly determining the properties of microcapsules. Eleven microcapsule samples fabricated under different stirring rates with different core/shell thickness ratio are prepared. An optimal set of parameters suitable for introduction into asphalt is studied based on the microscope observation, component identification, and thermogravimetric estimation. The self-healing capability of the selected microcapsules is further evaluated based on the fatigue life recovery test. From testing results, it is shown that microcapsules fabricated under the 800 rpm stirring speed with 1:1 core/shell thickness ratio have a much more satisfactory size and shell structure, and present superior capability to improve the healing behavior of asphalt. [ABSTRACT FROM AUTHOR]

Lactic acid (LA) is an important chemical with various industrial applications. Non-food feedstock is commercially attractive for use in LA production; however, efficient LA fermentation from lignocellulosic biomass resulting in both high yield and titer faces technical obstacles. In this study, the thermophilic bacterium Bacillus coagulans LA204 demonstrated considerable ability to ferment glucose, xylose, and cellobiose to LA. Importantly, LA204 produces LA from several NaOH-pretreated agro stovers, with remarkably high yields through simultaneous saccharification and fermentation (SSF). A fed-batch SSF process conducted at 50 °C and pH 6.0, using a cellulase concentration of 30 FPU (filter paper unit)/g stover and 10 g/L yeast extract in a 5-L bioreactor, was developed to produce LA from 14.4% (w/w) NaOH-pretreated non-sterile corn stover. LA titer, yield, and average productivity reached 97.59 g/L, 0.68 g/g stover, and 1.63 g/L/h, respectively. This study presents a feasible process for lignocellulosic LA production from abundant agro stovers. [ABSTRACT FROM AUTHOR]

The polymeric nanocomposites (PANI/AD-LDH) were prepared by in situ polymerization based on polyaniline (PANI) and decavanadate-intercalated and γ-aminopropyltriethoxysilane (APTS)-grafted ZnAl-layered double hydroxide (AD-LDH). FTIR and XRD studies confirm the grafting of APTS with decavanadate-intercalated LDH (D-LDH). The extent of grafting (wt%) has also been estimated on the basis of the residue left in nitrogen atmosphere at 800 °C in TGA. SEM and XPS studies show the partial exfoliation of grafted LDH in the PANI matrix and the interfacial interaction between PANI and grafted LDH, respectively. The grafted intercalated layered double hydroxide in reinforcing the properties of the PANI nanocomposites has also been investigated by open circuit potential (OCP), tafel polarization curves (TAF), electrochemical impendence spectroscopy (EIS), salt spray test and TGA-DTA. The experimental results indicate that the PANI/AD-LDH has a higher thermal stability and anticorrosion properties relative to the PANI. [ABSTRACT FROM AUTHOR]

This paper reviews and evaluates several state-of-the-art feature description algorithms. The components of each feature description method are analyzed and their applications in dealing with specific challenges are identified. In the paper, we compare state-of-the-art feature description methods including the SIFT, DAISY, HRI-CSLTP, LIOP, MROGH and MRRID with specific measurement regulation. The quantitative comparative results demonstrate these algorithms’ applications in different scenes, which provide a certain guide for designing novel feature description algorithms. [ABSTRACT FROM AUTHOR]

• Intermetallic Al 11 Ce 3 phases are grown in a faceted mode. • Mg addition promotes the formation of divorced eutectics during casting. • Lamellar and rod-like eutectics prevail in Al-Ce alloys upon rapid solidification. • The transition from a hypereutectic to a eutectic microstructure is enabled. [Display omitted] Al-Ce alloys emerge as promising heat-resist aluminum alloys with service temperatures above 250 °C. Here we report the solidification behavior and the microstructure diversity of Al-Ce alloys, aiming to bridge the gap between solidification condition and microstructures. Both conventional casting and arc surface rapid melting (ASRM) are employed to generate varied solidification conditions. Owing to the large entropy of fusion, intermetallic Al 11 Ce 3 phases are grown in a faceted mode with a strong growth anisotropy, and Chinese-script Al 11 Ce 3 /α-Al eutectics are produced upon conventional casting. The addition of Mg restrains the coupled growth of eutectics and promotes the generation of divorced eutectics in cast Al-Ce alloys. Upon rapid solidification during ASRM, regular lamellar and rod-like eutectics prevail in Al-Ce binary alloys, and fishbone eutectics dominate the Al-Ce-Mg ternary alloys. Also, the transition from a hypereutectic microstructure to a fully eutectic microstructure is triggered by ASRM. The interplay of intermetallic Al 11 Ce 3 and Al 11 Ce 3 /α-Al eutectics is discussed and the formation of refined regular eutectics is highlighted. [ABSTRACT FROM AUTHOR]

Abstract: The polymer–clay nanocomposites (PANI/AD-LDH) were prepared by chemical grafting based on polyaniline (PANI) and corrosion inhibitor (decavanadate)-intercalated and γ-aminopropyltriethoxysilane (APTS)-modified Zn–Al layered double hydroxide (AD-LDH). The chemical grafting of PANI onto decavanadate-intercalated LDH (D-LDH) was confirmed by Fourier transformation infrared (FTIR) spectroscopy. The interlayer and surface changes of the clay in the prepared nanocomposite materials and the grafted nanoparticles were studied by X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy (SEM). The corrosion resistance behavior of PANI/AD-LDH/epoxy coating was investigated by open circuit potential (OCP), potentiodynamic polarization and electrochemical impendence spectroscopy (EIS) techniques in 3.5% NaCl solution and also compared with that of PANI and D-LDH. The results indicated that the PANI/AD-LDH coating offered better protection against corrosion than the PANI and D-LDH coating did. [Copyright &y& Elsevier]

Abstract: A high precision localization scheme for profiled extended objects based-on Generalized Hough Transform (GHT) is proposed. The proposed localization scheme utilizes the edge points’ gratitude orientations of profiled extended objects to build an R-table in frame t-1. When frame t is coming, we search the R-table according to the pre-computed edge points’ gratitude orientations of profiled extended objects in current frame, generating an accumulator array. The coordination corresponding to the accumulator's peak value is regarded as coarse localization point in current frame. Then, a relaxation iteration method is proposed to refine the coarse localization point to obtain a sub-pixel localization point. Numerical experiments show that the localization scheme has following characteristics: high-precision and stability. Furthermore, we present the results of our work which involves investigation of the performance evaluation for the proposed localization scheme. [Copyright &y& Elsevier]

• Constructing dual-confined catalytic nanoreactor as cathode host for Li-SeS 2 batteries. • Realizing efficient anchoring and fast conversion of polysulfides/polyselenides. • The cell exhibits excellent electrochemical performance. [Display omitted] A dual-confined nanostructured reactor, featured with the small-sized platinum nanoparticles embedded in dendritic mesoporous silica nanospheres with a hierarchical porous graphene-like carbon protection shell, is designed and synthesized as an efficient SeS 2 host for high-performance lithium-selenium/sulfur batteries. The rationally designed host framework, which combines the merits of strong chemical interaction effect, abundant catalytic active sites, and excellent conducting network, can realize efficient anchoring and fast conversion of lithium polysulfides/polyselenides during the electrochemical processes. Thus, this SeS 2 -based cathode shows a high initial capacity of 1013 mAh g−1 at 0.2 A g−1 and an outstanding cycling stability, with a capacity of 426 mAh g−1 over 500 cycles at 2 A g−1, corresponding to a low capacity decay of 0.07% per cycle. This study demonstrates a novel and efficient approach to overcome the hurdles of current lithium-sulfur systems for stable rechargeable batteries. [ABSTRACT FROM AUTHOR]

• A Triplet network framework with attentive temporal pooling is proposed, which is the first trial to assess the quality of simulation for droplet respiratory propagation automatically. • A regularization constraint for triplet loss boosts the performance. • The proposed approach has generalization for other applications such as 3D object retrieval. Respiratory droplet propagation has been extensively explored with simulation and experimental methods. However, there still exists a huge gap between these methods, making automatic assessment of simulation quality quantitatively being a challenge. To address above problem, in this work, a triplet neural network framework with multi-scale CNN-BiLSTM network is developed. Firstly, Conditional Variational Auto-Encoder (CVAE) is utilized to generate multi-view simulations. Secondly, YOLOv3 is adopted to extract droplet regions of real image and simulation results. Then, a multi-scale CNN-BiLSTM network with attentive temporal pooling is designed to extract and aggregate temporal information across consecutive frames. Finally, all above networks are constructed into a triplet structure with triplet loss, and a regularization constraint being denoted as reconstruction term and prediction term is proposed. To demonstrate the performance of our approach, a new dataset is established including real sequences of cough droplets and simulation results. We validate the effectiveness and feasibility of our proposed framework using our dataset and two benchmarks, the PSB dataset and the ETH dataset, for 3D object retrieval. Our approach outperforms state-of-the-arts on our dataset and achieves comparative performance on PSB and ETH for 3D object retrieval, given quantitative quality assessment of simulation for droplet respiratory propagation automatically. [ABSTRACT FROM AUTHOR]

Abstract: An iopromide (IOPr)-degrading bacterium was isolated from activated sludge of a wastewater treatment plant in Shanghai. Based on its morphology, physiological-biochemical characteristics and a phylogenetic analysis of its 16S rRNA sequence, the bacterium was identified and named as Pseudomonas sp. I-24. The optimum condition for degrading IOPr was at 30°C and pH 7.0. After 5days, strain I-24 could degrade 30mg/L IOPr by 99% in a basal salts medium with a 5% (V/V) inoculum and 200mg/L starch as the primary substrate. When applied to an Anaerobic-Anoxic/Aerobic (A2/O) process, with the coexistence of other bacteria, the strain I-24 got lower (61.3%) IOPr removal, but in two A2/O systems (with and without I-24 inoculation), the CODcr removal were both approximately 95%. The trial dosed with strain I-24 showed better IOPr removal than the un-dosed one. I-24 sustained its abundance in the A2/O system during the experiment. [Copyright &y& Elsevier]

A phase-field model was revised to study the abnormal growth of Goss grains during the annealing process in Fe-3%Si steels, in which the interaction between the second-phase particles and Goss grain boundaries (GBs) was considered. The results indicate that the abnormal growth of Goss grains occurs due to the different dissolvability of the particles at Goss GBs compared with the other GBs. Moreover, the degree of abnormal growth increases first and then decreases with an increasing particle content. Meanwhile, the size advantage of Goss grain can further promote the degree of abnormal growth. Two types of island grains were found according to the simulated results, which is consistent with the experimental observations. A proper GB dissolvability of particles is the key factor for the formation of isolated island grains, and a higher local particle density at GBs is the main reason for the appearance of serial island grains. These findings can provide guidance for the desired texture control in silicon steels. [ABSTRACT FROM AUTHOR]

This paper proposes a picowatt voltage reference for Internet-of-Things applications, which require fast response speed and high insensitivity to variations of the process, supply voltage, and temperature (PVT). By employing a novel topology with a startup circuit and an effective PVT compensation method, the proposed voltage reference can achieve both high PVT stability and shorten the setup time. Based on a 0.18 μm standard CMOS process, post-layout simulation results show that the proposed design achieves a mean reference voltage of 755.6 mV with a variation coefficient of 0.11 % without trimming. The line regulation is 13.2 ppm/V over a supply voltage from 1 V to 1.8 V at 27 °C. The average temperature coefficient is about 26 ppm/°C in a temperature range from −20 °C to 120 °C. The 1 % settling time of the output reference voltage is about 274 μs. [ABSTRACT FROM AUTHOR]