Fast equilibrium reconstruction by deep learning on EAST tokamak

A deep neural network is developed and trained on magnetic measurements (input) and EFIT poloidal magnetic flux (output) on the EAST tokamak. In optimizing the network architecture, we use automatic optimization in searching for the best hyperparameters, which helps the model generalize better. We compare the inner magnetic surfaces and last-closed-flux surfaces (LCFSs) with those from EFIT. We also calculated the normalized internal inductance, which is completely determined by the poloidal magnetic flux and can further reflect the accuracy of the prediction. The time evolution of the internal inductance in full discharges is compared with that provided by EFIT. All of the comparisons show good agreement, demonstrating the accuracy of the machine learning model, which has the high spatial resolution as the off-line EFIT while still meets the time constraint of real-time control

Proteomic analysis reveals proteins and pathways associated with declined testosterone production in male obese mice after chronic high-altitude exposure

ObjectiveObesity is common in highland areas owing to lifestyle alterations. There are pieces of evidence to suggest that both obesity and hypoxia may promote oxidative stress, leading to hypogonadism in males. These findings indicate an increased risk of hypogonadism in obese males following hypoxia exposure. However, the mechanisms underlying the disease process remain unclear. The current study aims to explore the mechanism of testosterone production dysfunction in obese male mice exposed to a chronic high-altitude hypoxia environment.MethodsAn obese male mouse model was generated by inducing obesity in mice via a high-fat diet for 14 weeks, and the obese mice were then exposed to a high-altitude hypoxia environment for 24 days. Sera and testicular tissues were collected to detect serum lipids, sex hormone level, and testicular oxidative stress indicators. Morphological examination was performed to assess pathological alterations in testicular tissues and suborganelles in leydig cells. Proteomic alterations in testicular tissues were investigated using quantitative proteomics in Obese/Control and Obese-Hypoxia/Obese groups.ResultsThe results showed that chronic high-altitude hypoxia exposure aggravated low testosterone production in obese male mice accompanied by increased testicular oxidative stress and histological damages. In total, 363 and 242 differentially expressed proteins (DEPs) were identified in the two comparison groups, Obese/Control and Obese-Hypoxia/Obese, respectively. Functional enrichment analysis demonstrated that several significant functional terms and pathways related to testosterone production were altered in the two comparison groups. These included cholesterol metabolism, steroid hormone biosynthesis, peroxisome proliferator-activated receptor (PPAR) signaling pathway, oxidative stress responses, as well as retinol metabolism. Finally, 10 representative DEPs were selected for parallel reaction monitoring verification. Among them, StAR, DHCR7, NSDHL, CYP51A1, FDPS, FDX1, CYP11A1, ALDH1A1, and GPX3 were confirmed to be downregulated in the two groups.ConclusionsChronic hypoxia exposure could exacerbate low testosterone production in obese male mice by influencing the expression of key proteins involved in steroid hormone biosynthesis, cholesterol biosynthesis, oxidative stress responses and retinol metabolism

Multiwalled carbon nanotubes co-delivering sorafenib and epidermal growth factor receptor siRNA enhanced tumor-suppressing effect on liver cancer.

OBJECTIVE: This study aimed to investigate the effects of multiwalled carbon nanotubes (MWNTs) co-delivering sorafenib (Sor) and epidermal growth factor receptor (EGFR) siRNA (MWNT/Sor/siRNA) on tumor growth in liver cancer (LC). RESULTS: MWNT/Sor/siRNA was proved to possess increased Sor release, high siRNA stability, and enhanced cellular uptake. In addition, MWNT treatment has few effects on cell proliferation and apoptosis in HepG2 cells; however, MWNT/Sor/siRNA treatment significantly inhibited clone number and induced cell apoptosis, which shows a more favorable antitumor effect than MWNT/Sor and free Sor and free siRNA in HepG2 cells. Moreover MWNT/Sor/siRNA treatment has the most significant antitumor effect CONCLUSIONS: MWNT/Sor/siRNA exhibited a superior antitumor effect METHODS: The MWNT/Sor and MWNT/Sor/siRNA were prepared, and then the morphologies of MWNT/Sor/siRNA were analyzed

Fabrication and properties of zirconia/hydroxyapatite composite scaffold based on digital light processing

Zirconia and hydroxyapatite(HA) are two typical implant materials, which have the advantages of excellent mechanical strength and good biological activity respectively. It was found that composite material had good biocompatibility and mechanical strength compared to the single material. In this paper, the porous scaffolds of ZrO2/HA composite were formed by digital light processing (DLP) technology and their performance were evaluated. Cell experiments showed that the addition of HA had a positive effect on cell proliferation and differentiation. Mechanical tests showed that the composite scaffold with 10 wt% HA had the best compressive capacity due to the pinning and bridging effect of a small amount of HA grains. When scaffolds were immersed in the simulated body fluid (SBF), the compressive strengths of the composite scaffolds decreased within the first 14 days and gradually increased after 14 days. The reason for this phenomenon was the degradation of calcium phosphate components and the deposition of apatite. By the 28th day, the compressive strengths of all the composite scaffolds increased to over 20 MPa, close to that of the zirconia scaffolds during the same period (25 MPa). The compressive strengths of all scaffolds met the requirement of cancellous bone during the entire soaking period, and the composite scaffolds have potential application value in bone repair

Detecting incorrect mask wearing using out-of-distribution detection

Face mask detection has been a significant task since the Covid-19 pandemic began in early 2020. While various researches on mask-face detection techniques up to 2021 are available, only a few have been studied on the three classes (i.e., wearing mask, without mask, and incorrect mask-wearing). This is due to the difficulty in collecting and annotating images of incorrect mask-wearing. As a result, this class in the research has a lower detection accuracy than the other two classes. The objectives of this dissertation are focused on the two-fold: To provide a new dataset of mask faces from Wider Face and Kaggle; To propose a new framework named Out-of-distribution Mask (OOD-Mask) to perform the three-class detection task using only two-class training data. This is achieved by treating the incorrect mask-wearing situation as an anomaly class, leading to a reasonable performance in the absence of training data for the third class.Master of Science (Signal Processing

Research on a Visual Electronic Nose System Based on Spatial Heterodyne Spectrometer

Light absorption gas sensing technology has the characteristics of massive parallelism, cross-sensitivity and extensive responsiveness, which make it suitable for the sensing task of an electronic nose (e-nose). With the performance of hyperspectral resolution, spatial heterodyne spectrometer (SHS) can present absorption spectra of the gas in the form of a two dimensional (2D) interferogram which facilitates the analysis of gases with mature image processing techniques. Therefore, a visual e-nose system based on SHS was proposed. Firstly, a theoretical model of the visual e-nose system was constructed and its visual maps were obtained by an experiment. Then the local binary pattern (LBP) and Gray-Level Co-occurrence Matrix (GLCM) were used for feature extraction. Finally, classification algorithms based on distance similarity (Correlation coefficient (CC); Euclidean distance to centroids (EDC)) were chosen to carry on pattern recognition analysis to verify the feasibility of the visual e-nose system

Machine Learning Techniques and Systems for Mask-Face Detection—Survey and a New OOD-Mask Approach

Mask-face detection has been a significant task since the outbreak of the COVID-19 pandemic in early 2020. While various reviews on mask-face detection techniques up to 2021 are available, little has been reviewed on the distinction between two-class (i.e., wearing mask and without mask) and three-class masking, which includes an additional incorrect-mask-wearing class. Moreover, no formal review has been conducted on the techniques of implementing mask detection models in hardware systems or mobile devices. The objectives of this paper are three-fold. First, we aimed to provide an up-to-date review of recent mask-face detection research in both two-class cases and three-class cases, next, to fill the gap left by existing reviews by providing a formal review of mask-face detection hardware systems; and to propose a new framework named Out-of-distribution Mask (OOD-Mask) to perform the three-class detection task using only two-class training data. This was achieved by treating the incorrect-mask-wearing scenario as an anomaly, leading to reasonable performance in the absence of training data of the third class

Machine learning techniques and systems for mask-face detection—survey and a new OOD-mask approach

Mask-face detection has been a significant task since the outbreak of the COVID-19 pandemic in early 2020. While various reviews on mask-face detection techniques up to 2021 are available, little has been reviewed on the distinction between two-class (i.e., wearing mask and without mask) and three-class masking, which includes an additional incorrect-mask-wearing class. Moreover, no formal review has been conducted on the techniques of implementing mask detection models in hardware systems or mobile devices. The objectives of this paper are three-fold. First, we aimed to provide an up-to-date review of recent mask-face detection research in both two-class cases and three-class cases, next, to fill the gap left by existing reviews by providing a formal review of mask-face detection hardware systems; and to propose a new framework named Out-of-distribution Mask (OOD-Mask) to perform the three-class detection task using only two-class training data. This was achieved by treating the incorrect-mask-wearing scenario as an anomaly, leading to reasonable performance in the absence of training data of the third class.Agency for Science, Technology and Research (A*STAR)Published versionThis work was supported by the Science and Engineering Research Council, Agency of Science, Technology and Research, Singapore, through the National Robotics Program under Grant No. 1922500054