Properties, Preparation, and Application of Carbon Nanotubes

Carbon nanotubes (CNTs), due to their unique one-dimensional nanostructures and extraordinary physicochemical properties, have been a research hotspot in the field of materials science and nanotechnology. Their extremely high strength, good electrical conductivity, and excellent thermal conductivity make CNTs promising for a wide range of applications in many fields. In the medical field, CNTs are used in drug delivery, tumor treatment, and bioimaging due to their excellent biocompatibility and tunable surface properties. Especially in tumor treatment, CNTs can be used as drug carriers to deliver anti-tumor drugs directly to the lesion site and reduce damage to normal tissues. In the energy sector, the application of CNTs is mainly focused on energy storage devices and photovoltaic cells. Their high conductivity and large specific surface area make them ideal electrode materials, especially in supercapacitors and lithium-ion batteries. In addition, CNTs can also improve the photoelectric conversion efficiency of photovoltaic cells and promote the development of renewable energy technologies. In the future, the research of CNTs will continue to deepen, not only to find more efficient and economical applications in existing application fields, but also to explore new application fields, such as applications in environmental monitoring and biosensors. With the continuous progress of preparation technology and the reduction of cost, CNTs are expected to be commercialized in more fields and make greater contributions to the development of human society

Open Compound Domain Adaptation with Object Style Compensation for Semantic Segmentation

Many methods of semantic image segmentation have borrowed the success of open compound domain adaptation. They minimize the style gap between the images of source and target domains, more easily predicting the accurate pseudo annotations for target domain's images that train segmentation network. The existing methods globally adapt the scene style of the images, whereas the object styles of different categories or instances are adapted improperly. This paper proposes the Object Style Compensation, where we construct the Object-Level Discrepancy Memory with multiple sets of discrepancy features. The discrepancy features in a set capture the style changes of the same category's object instances adapted from target to source domains. We learn the discrepancy features from the images of source and target domains, storing the discrepancy features in memory. With this memory, we select appropriate discrepancy features for compensating the style information of the object instances of various categories, adapting the object styles to a unified style of source domain. Our method enables a more accurate computation of the pseudo annotations for target domain's images, thus yielding state-of-the-art results on different datasets.Comment: Accepted by NeurlPS202

Simplifying the Formal Verification of Safety Requirements in Zone Controllers through Problem Frames and Constraints based Projection

Formal methods have been applied widely to verifying the safety requirements of Communication-Based Train Control (CBTC) systems, while the problem situations could be much simplified. In industrial practices of CBTC systems, however, huge complexity arises, which renders those methods nearly impossible to apply. In this paper, we aim to reduce the state space of formal verification problems in Zone Controller, a sub-system of a typical CBTC. We achieve the simplification goal by reducing the total number of device variables. To do this, two projection methods are proposed based on Problem Frames and constraints, respectively. The Problem Frames based method decomposes the system according to sub-properties through functional decomposition, whilst the constraints based projection method removes redundant variables. Our industrial case study demonstrates the feasibility though an evaluation, confirming that these two methods are effective in reducing the state spaces of complex verification problems in this application domain

Genetic factors associated with patient-specific warfarin dose in ethnic Indonesians

<p>Abstract</p> <p>Background</p> <p><it>CYP2C9 </it>and <it>VKORC1 </it>are two major genetic factors associated with inter-individual variability in warfarin dose. Additionally, genes in the warfarin metabolism pathway have also been associated with dose variance. We analyzed Single Nucleotide Polymorphisms (SNPs) in these genes to identify genetic factors that might confer warfarin sensitivity in Indonesian patients.</p> <p>Methods</p> <p>Direct sequencing method was used to identify SNPs in <it>CYP2C9, VKORC1, CYP4F2, EPHX1, PROC </it>and <it>GGCX </it>genes in warfarin-treated patients. Multiple linear regressions were performed to model the relationship warfarin daily dose requirement with genetic and non-genetic variables measured and used to develop a novel algorithm for warfarin dosing.</p> <p>Results</p> <p>From the 40 SNPs analyzed, <it>CYP2C9 </it>rs17847036 and <it>VKORC1 </it>rs9923231 showed significant association with warfarin sensitivity. In our study population, no significant correlation could be detected between <it>CYP2C9*3, CYP2C9C</it>-65 (rs9332127), <it>CYP4F2 </it>rs2108622, <it>GGCX </it>rs12714145, <it>EPHX1 </it>rs4653436 and <it>PROC </it>rs1799809 with warfarin sensitivity.</p> <p>Conclusions</p> <p><it>VKORC1 </it>rs9923231 AA and <it>CYP2C9 </it>rs17847036 GG genotypes were associated with low dosage requirements of most patients (2.05 ± 0.77 mg/day and 2.09 ± 0.70 mg/day, respectively). <it>CYP2C9 </it>and <it>VKORC1 </it>genetic variants as well as non-genetic factors such as age, body weight and body height account for 15.4% of variance in warfarin dose among our study population. Additional analysis of this combination could allow for personalized warfarin treatment in ethnic Indonesians.</p

GIS Partial Discharge Pattern Recognition Based on a Novel Convolutional Neural Networks and Long Short-Term Memory

Distinguishing the types of partial discharge (PD) caused by different insulation defects in gas-insulated switchgear (GIS) is a great challenge in the power industry, and improving the recognition accuracy of the relevant models is one of the key problems. In this paper, a convolutional neural network and long short-term memory (CNN-LSTM) model is proposed, which can effectively extract and utilize the spatiotemporal characteristics of PD input signals. First, the spatial characteristics of higher-level PD signals can be obtained through the CNN network, but because CNN is a deep feedforward neural network, it does not have the ability to process time-series data. The PD voltage signal is related to the time dimension, so LSTM saves and analyzes the previous voltage signal information, realizes the modeling of the time dependence of the data, and improves the accuracy of the PD signal pattern recognition. Finally, the pattern recognition results based on CNN-LSTM are given and compared with those based on other traditional analysis methods. The results show that the pattern recognition rate of this method is the highest, with an average of 97.9%, and its overall accuracy is better than that of other traditional analysis methods. The CNN-LSTM model provides a reliable reference for GIS PD diagnosis

Effects of exercise training on systolic and diastolic function of mice with diabetic cardiomyopathy

Objective: This study aims to evaluate the effects of exercise training on heart function of mice with diabetic cardiomyopathy. Materials and Methods: Twenty-four healthy C57 mice were randomly divided into three groups high-fat exercise group (n = 8), high-fat control group (n = 8), and low-fat control group (n = 8). High-fat groups were fed with a high-fat diet for 16 weeks, and the high-fat exercise group was subjected to aerobic treadmill exercise and resistance exercise for 8 weeks. After 24 weeks, the cardiac structure and function of the three groups were detected, and the indexes of the mouse heart were analyzed and compared. Results: The high-fat control group maintained hyperglycemia. The results of echocardiography showed that left ventricular eject fraction in the high-fat exercise group and the low-fat control group was significantly higher than that of the high-fat control group (68.99% ± 2.04% vs. 60.41% ± 2.31%, 66.16% ± 2.12% vs. 60.41% ± 2.31%, P 0.05), but there was no significant difference. Conclusion: The above data indicated that 8 weeks of exercise training can improve the heart function of mice with diabetic cardiomyopathy, especially diastolic heart function. Left ventricular systolic function had some trend to improve, but there is no statistical difference. Exercise intervention may promote the rehabilitation of diabetic cardiomyopathy

Partial Discharge Pattern Recognition of Gas-Insulated Switchgear via a Light-Scale Convolutional Neural Network

Partial discharge (PD) is one of the major form expressions of gas-insulated switchgear (GIS) insulation defects. Because PD will accelerate equipment aging, online monitoring and fault diagnosis plays a significant role in ensuring safe and reliable operation of the power system. Owing to feature engineering or vanishing gradients, however, existing pattern recognition methods for GIS PD are complex and inefficient. To improve recognition accuracy, a novel GIS PD pattern recognition method based on a light-scale convolutional neural network (LCNN) without artificial feature engineering is proposed. Firstly, GIS PD data are obtained through experiments and finite-difference time-domain simulations. Secondly, data enhancement is reinforced by a conditional variation auto-encoder. Thirdly, the LCNN structure is applied for GIS PD pattern recognition while the deconvolution neural network is used for model visualization. The recognition accuracy of the LCNN was 98.13%. Compared with traditional machine learning and other deep convolutional neural networks, the proposed method can effectively improve recognition accuracy and shorten calculation time, thus making it much more suitable for the ubiquitous-power Internet of Things and big data

Using the parietal branch of superficial temporal vessels: A good approach to total ear replantation

Although ear reconstruction is a mature procedure, emergency microsurgical replantation has still been regarded as the optimal treatment for ear amputation due to its cost-effectiveness and aesthetically pleasing results. Successful microsurgical ear replantation is rare because of the difficulty in identifying suitable vessels for anastomosis. We describe two cases of total ear microsurgical replants using the parietal branch of the superficial temporal vessels (STV) as the recipient vessels. The STV parietal branch was dissected up to a sufficient length after thorough debridement, and the amputated ears were revascularized using end-to-end anastomosis. Our experience shows that the parietal branch of the STV is an ideal recipient vessel option for total ear replantation

Internal hernia through hepatic falciform ligament iatrogenic defect in a neonate: A case report and review of the literature

Internal hernia through an iatrogenic defect in the hepatic falciform ligament and acquired jejunal atresia in a 8-day-old neonate was reported. The PubMed, MEDLINE, CNKI, Wanfang and Weipu databases were searched The literature about the hepatic falciform ligament iatrogenic defect causing internal hernia was analysed. Ten other cases were collected from the world literature. Herniated intestinal necrosis was found in four cases. All cases were recovered uneventfully after operation. Internal herniation through an iatrogenic defect in the hepatic falciform ligament is extremely rare. However, the case reports are increasing, especially in the era of laparoscopic surgery. Adequate closure or open the defect is essential to prevent internal hernia occurrence