M5Si3 (M=Ti, Nb, Mo) based transition-metal silicides for high temperature applications

Transition metal silicides are being considered for future engine turbine components at temperatures up to 1600ºC. Although significant improvement in high temperature strength, room temperature fracture toughness has been realized in the past decade, further improvement in oxidation resistance is needed.;Oxidation mechanism of Ti5Si3-based alloys was investigated. Oxidation behavior of Ti5Si3-based alloy strongly depends on the atmosphere. Presence of Nitrogen alters the oxidation behavior of Ti5Si3 by nucleation and growth of nitride subscale. Ti5Si3.2 and Ti5Si3C0.5 alloys exhibited an excellent oxidation resistance in nitrogen bearing atmosphere due to limited dissolution of nitrogen and increased Si/Ti activity ratio.;MoSi2 coating developed by pack cementation to protect Mo-based Mo-Si-B composites was found to be effective up to 1500ºC. Shifting coating composition to T1+T2+Mo3Si region showed the possibility to extend the coating lifetime above 1500ºC by more than ten times via formation of slow growing Mo3Si or T2 interlayer without sacrificing the oxidation resistance of the coating.;The phase equilibria in the Nb-rich portion of Nb-B system has been evaluated experimentally using metallographic analysis and differential thermal analyzer (DTA). It was shown that Nbss (solid solution) and NbB are the only two primary phases in the 0-40 at.% B composition range, and the eutectic reaction L ↔ Nbss + NbB was determined to occur at 2104+/-5°C by DTA

Whole blueberry protects pancreatic beta-cells in diet-induced obese mouse

Background Blueberry is rich in bioactive substances and possesses powerful antioxidant potential, which can protect against oxidant-induced and inflammatory cell damage and cytotoxicity. The aim of this study was to determine how blueberry affects glucose metabolism and pancreatic β-cell proliferation in high fat diet (HFD)-induced obese mice. Methods Wild type male mice at age of 4 weeks received two different kinds of diets: high-fat diet (HFD) containing 60% fat or modified HFD supplemented with 4% (wt:wt) freeze-dried whole blueberry powder (HFD + B) for 14 weeks. A separate experiment was performed in mice fed with low-fat diet (LFD) containing 10% fat or modified LFD + B supplemented with 4% (wt:wt) freeze-dried whole blueberry powder. The metabolic parameters including blood glucose and insulin levels, glucose and insulin tolerances were measured. Results Blueberry-supplemented diet significantly increased insulin sensitivity and glucose tolerance in HFD + B mice compared to HFD mice. However, no difference was observed in blood glucose and insulin sensitivity between LFD + B and LFD mice. In addition, blueberry increased β-cell survival and prevented HFD-induced β-cell expansion. The most important finding was the observation of presence of small scattered islets in blueberry treated obese mice, which may reflect a potential role of blueberry in regenerating pancreatic β-cells. Conclusions Blueberry-supplemented diet can prevent obesity-induced insulin resistance by improving insulin sensitivity and protecting pancreatic β-cells. Blueberry supplementation has the potential to protect and improve health conditions for both type 1 and type 2 diabetes patients

Machine Vision based Grabbing Objects with Manipulator System Design

In recent years, machine vision technology and robot control technology have attracted lots attention of the researchers. They provide people with fast and efficient services in many fields, which have an increasingly important impact on the modern manufacturing industry and the inspection industry. In this paper, a mechanical vision-based grab control system based on machine vision is developed and analyzed accordingly. This design employs industrial cameras with Gigabit Ethernet ports, six-degree-of-freedom servo drive robots. The Host computer control software is designed on the development platform provided by Microsoft and processed in machine vision image processing. The software has implemented an image processing algorithm. It aims to combine machine vision, robot control and other technologies to achieve precise positioning, recognition and capture of targets. In the end, the proposed method is displayed in the upper computer accordingly

ENVIRONMENTAL SURROUNDINGS AND PERSONAL WELL-BEING IN URBAN CHINA

We examine the relationship between atmospheric pollution, water pollution, traffic congestion, access to parkland and personal well-being using a survey administered across six Chinese cities in 2007. In contrast to existing studies of the determinants of well-being by economists, which have typically employed single item indicators to measure well-being, we use the Personal Well-Being Index (PWI). We also employ the Job Satisfaction Survey (JSS) to measure job satisfaction, which is one of the variables for which we control when examining the relationship between environmental surroundings and personal well-being. Previous research by psychologists has shown the PWI and JSS to have good psychometric properties in western and Chinese samples. A robust finding is that in cities with higher levels of atmospheric pollution and traffic congestion, respondents report lower levels of personal well-being ceteris paribus. We find that a one standard deviation increase in suspended particles or sulphur dioxide emissions is roughly equivalent to a 12-13 percent reduction in average monthly income in the six cities. This result suggests that the personal well-being of China's urban population can be enhanced if China were to pursue a more balanced growth path which curtailed atmospheric pollution.China, Environment, Pollution, Personal Well-Being.

ENVIRONMENTAL SURROUNDINGS AND PERSONAL WELL-BEING IN URBAN CHINA

We examine the relationship between atmospheric pollution, water pollution, traffic congestion, access to parkland and personal well-being using a survey administered across six Chinese cities in 2007. In contrast to existing studies of the determinants of well-being by economists, which have typically employed single item indicators to measure well-being, we use the Personal Well-Being Index (PWI). We also employ the Job Satisfaction Survey (JSS) to measure job satisfaction, which is one of the variables for which we control when examining the relationship between environmental surroundings and personal well-being. Previous research by psychologists has shown the PWI and JSS to have good psychometric properties in western and Chinese samples. A robust finding is that in cities with higher levels of atmospheric pollution and traffic congestion, respondents report lower levels of personal well-being ceteris paribus. Specifically, we find that a one standard deviation increase in suspended particles or sulphur dioxide emissions is roughly equivalent to a 12-13 per cent reduction in average monthly income in the six cities.China, Environment, Pollution, Personal Well-Being.

Reconstruction of tokamak plasma safety factor profile using deep learning

In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile defines the magnetic field line pitch angle, which is the central element in equilibrium reconstruction. The motional Stark effect (MSE) diagnostic has been a standard measurement for the magnetic field line pitch angle in tokamaks that are equipped with neutral beams. However, the MSE data are not always available due to experimental constraints, especially in future devices without neutral beams. Here we develop a deep learning-based surrogate model of the gyrokinetic toroidal code for q profile reconstruction (SGTC-QR) that can reconstruct the q profile with the measurements without MSE to mimic the traditional equilibrium reconstruction with the MSE constraint. The model demonstrates promising performance, and the sub-millisecond inference time is compatible with the real-time plasma control system

PP-MobileSeg: Explore the Fast and Accurate Semantic Segmentation Model on Mobile Devices

The success of transformers in computer vision has led to several attempts to adapt them for mobile devices, but their performance remains unsatisfactory in some real-world applications. To address this issue, we propose PP-MobileSeg, a semantic segmentation model that achieves state-of-the-art performance on mobile devices. PP-MobileSeg comprises three novel parts: the StrideFormer backbone, the Aggregated Attention Module (AAM), and the Valid Interpolate Module (VIM). The four-stage StrideFormer backbone is built with MV3 blocks and strided SEA attention, and it is able to extract rich semantic and detailed features with minimal parameter overhead. The AAM first filters the detailed features through semantic feature ensemble voting and then combines them with semantic features to enhance the semantic information. Furthermore, we proposed VIM to upsample the downsampled feature to the resolution of the input image. It significantly reduces model latency by only interpolating classes present in the final prediction, which is the most significant contributor to overall model latency. Extensive experiments show that PP-MobileSeg achieves a superior tradeoff between accuracy, model size, and latency compared to other methods. On the ADE20K dataset, PP-MobileSeg achieves 1.57% higher accuracy in mIoU than SeaFormer-Base with 32.9% fewer parameters and 42.3% faster acceleration on Qualcomm Snapdragon 855. Source codes are available at https://github.com/PaddlePaddle/PaddleSeg/tree/release/2.8.Comment: 8 pages, 3 figure