HfB2-SiC-MoSi2 oxidation resistance coating fabricated through in-situ synthesis for SiC coated C/C composites

A brand new HfB2-SiC-MoSi2 coating was fabricated to protect carbon/carbon (C/C) composites with inner SiC coating from oxidation, which was prepared by in-situ synthesis. In this paper, the C/C substrate with the protection of the HfB2-SiC-MoSi2/SiC coating could resist oxidation in 1773 K air for 408 h. The double coating also presented expected oxidation protection performance at dynamic oxidation environment. In the test process, the surface coating was oxidized to form a self-sealing silicate glass layer containing HfO2 and HfSiO4, which could hinder crack propagation in coating

A framework for the successful implementation of food traceability systems in China

Implementation of food traceability systems in China faces many challenges due to the scale, diversity and complexity of China’s food supply chains. This study aims to identify critical success factors specific to the implementation of traceability systems in China. Twenty-seven critical success factors were identified in the literature. Interviews with managers at four food enterprises in a pre-study helped identify success criteria and five additional critical success factors. These critical success factors were tested through a survey of managers in eighty-three food companies. This study identifies six dimensions for critical success factors: laws, regulations and standards; government support; consumer knowledge and support; effective management and communication; top management and vendor support; and information and system quality

Associations between body composition profile and hypertension in different fatty liver phenotypes

BackgroundIt is currently unclear whether and how the association between body composition and hypertension varies based on the presence and severity of fatty liver disease (FLD).MethodsFLD was diagnosed using ultrasonography among 6,358 participants. The association between body composition and hypertension was analyzed separately in the whole population, as well as in subgroups of non-FLD, mild FLD, and moderate/severe FLD populations, respectively. The mediation effect of FLD in their association was explored.ResultsFat-related anthropometric measurements and lipid metabolism indicators were positively associated with hypertension in both the whole population and the non-FLD subgroup. The strength of this association was slightly reduced in the mild FLD subgroup. Notably, only waist-to-hip ratio and waist-to-height ratio showed significant associations with hypertension in the moderate/severe FLD subgroup. Furthermore, FLD accounted for 17.26% to 38.90% of the association between multiple body composition indicators and the risk of hypertension.ConclusionsThe association between body composition and hypertension becomes gradually weaker as FLD becomes more severe. FLD plays a significant mediating role in their association

Research on the Application of Coal Gasification Slag in Soil Improvement

SEM, particle size analysis, and contaminant content of coarse coal gasification slag (CCGS) produced by Shenhua Xinjiang Chemical Co., Ltd. were measured, respectively, and the physicochemical properties of the soil after improvement using gasification slag were investigated in this paper. The results showed that the slag was porous, the particle size was small and the pollutant content was extremely low. Its pollutants were closely related to the pollutants in the raw coal. The coarse slag had a limited effect on soil particle size and texture improvement; the soil water retention performance increased with the increase of proportion of the slag, while pH and conductivity decreased; the improvement effect on soil SOM and available potassium was remarkable; the larger the proportion of the slag, the stronger the effect on maintaining soil alkali-hydrolyzed nitrogen, ammonium nitrogen, and available phosphorus. However, the effect was small, and increased the ion content, especially the cation in soil, and the sum of the eight soil ions before and after evaporation decreased. The results demonstrated that the CCGS generated by the corporation is feasible for soil improvement, and the study has important reference value for the comprehensive utilization of coal gasification slag

Detection of computer graphics using attention-based dual-branch convolutional neural network from fused color components

With the development of 3D rendering techniques, people can create photorealistic computer graphics (CG) easily with the advanced software, which is of great benefit to the video game and film industries. On the other hand, the abuse of CGs has threatened the integrity and authenticity of digital images. In the last decade, several detection methods of CGs have been proposed successfully. However, existing methods cannot provide reliable detection results for CGs with the small patch size and post-processing operations. To overcome the above-mentioned limitation, we proposed an attention-based dual-branch convolutional neural network (AD-CNN) to extract robust representations from fused color components. In pre-processing, raw RGB components and their blurred version with Gaussian low-pass filter are stacked together in channel-wise as the input for the AD-CNN, which aims to help the network learn more generalized patterns. The proposed AD-CNN starts with a dual-branch structure where two branches work in parallel and have the identical shallow CNN architecture, except that the first convolutional layer in each branch has various kernel sizes to exploit low-level forensics traces in multi-scale. The output features from each branch are jointly optimized by the attention-based fusion module which can assign the asymmetric weights to different branches automatically. Finally, the fused feature is fed into the following fully-connected layers to obtain final detection results. Comparative and self-analysis experiments have demonstrated the better detection capability and robustness of the proposed detection compared with other state-of-the-art methods under various experimental settings, especially for image patch with the small size and post-processing operations.Published versio

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

In this work, we present a processing chain for landscape pattern and ecological security status assessment and prediction based on cellular automata Markov (CA-Markov) and pressure status response pattern (PSRP) models using remotely sensed data (RSD) captured in 1986, 1996, 2006, 2016, and RSD simulated in 2026 over Zhengzhou city, Henan province, China. Three major findings can be withdrawn through the experiments. First, there is a significant changing of landscape type area, especially for building land. The area of building land is up to more than 5%, from 1986 to 2016. Secondly, the heterogeneity of landscape is increasing, and the diversity of landscape is becoming more and more diversifying and complex. Third, the changing trend of ecological security of Zhengzhou city shaped as decreasing and increasing gradually during the last 40 years. While the ecological security status, nowadays, appeared to a good trend by contrast of the previous stages. The predicted results with CA-Markov model show that the level of ecological security is still in moderate and has a trend of moving toward to the center in 2026

A Two-Stage Cascaded Detection Scheme for Double HEVC Compression Based on Temporal Inconsistency

Nowadays, verifying the integrity of digital videos is significant especially for applications about multimedia communication. In video forensics, detection of double compression can be treated as the first step to analyze whether a suspicious video undergoes any tampering operations. In the last decade, numerous detection methods have been proposed to address this issue, but most existing methods design a universal detector which is hard to handle various recompression settings efficiently. In this work, we found that the statistics of different Coding Unit (CU) types have dissimilar properties when original videos are recompressed by the increased and decreased bit rates. It motivates us to propose a two-stage cascaded detection scheme for double HEVC compression based on temporal inconsistency to overcome limitations of existing methods. For a given video, CU information maps are extracted from each short-time video clip using our proposed value mapping strategy. In the first detection stage, a compact feature is extracted based on the distribution of different CU types and Kullback–Leibler divergence between temporally adjacent frames. This detection feature is fed into the Support Vector Machine classifier to identify abnormal frames with the increased bit rate. In the second stage, a shallow convolutional neural network equipped with dense connections is designed carefully to learn robust spatiotemporal representations, which can identify abnormal frames with the decreased bit rate whose forensic traces are less detectable. In experiments, the proposed method can achieve more promising detection accuracy compared with several state-of-the-art methods under various coding parameter settings, especially when the original video is recompressed with a low quality (e.g., more than 8%)