Study of the quasi-two-body decays B^{0}_{s} \rightarrow \psi(3770)(\psi(3686))\pi^+\pi^- with perturbative QCD approach

In this note, we study the contributions from the S-wave resonances, f_{0}(980) and f_{0}(1500), to the B^{0}_{s}\rightarrow \psi(3770)\pi^ {+}\pi^{-} decay by introducing the S-wave \pi\pi distribution amplitudes within the framework of the perturbative QCD approach. Both resonant and nonresonant contributions are contained in the scalar form factor in the S-wave distribution amplitude \Phi^S_{\pi\pi}. Since the vector charmonium meson \psi(3770) is a S-D wave mixed state, we calculated the branching ratios of S-wave and D-wave respectively, and the results indicate that f_{0}(980) is the main contribution of the considered decay, and the branching ratio of the \psi(2S) mode is in good agreement with the experimental data. We also take the S-D mixed effect into the B^{0}_{s}\rightarrow \psi(3686)\pi^ {+}\pi^{-} decay. Our calculations show that the branching ratio of B^{0}_{s}\rightarrow \psi(3770)(\psi(3686))\pi^ {+}\pi^{-} can be at the order of 10^{-5}, which can be tested by the running LHC-b experiments.Comment: 10 pages, 3 figure

Monitoring Aging of Power Semiconductor Devices Based on Case Temperature

The aging of an electronic component in an electronic power converter can be monitored based on two or more case temperature measurements. A power electronic device is enclosed in a package having a baseplate, in which the power electronic device generates heat during operation and the baseplate transfers heat to a heat dissipating device or a cooling device. Sensors measure temperatures at first and second locations on a surface of the baseplate. A data processor calculates a value for a first parameter based on the temperatures at the first and second locations, in which the first parameter is indicative of an aging process of the power electronic device, and generates a first signal based on mined threshold. The data processor calculates a value for a second parameter based on the first parameter value, a predetermined look-up table, and the temperatures at the first and second locations, in which the second parameter is indicative of another aging process of the semiconductor switching devices, and generates a second signal based on a comparison of the calculated value and a second predetermined threshold

Context-Transformer: Tackling Object Confusion for Few-Shot Detection

Few-shot object detection is a challenging but realistic scenario, where only a few annotated training images are available for training detectors. A popular approach to handle this problem is transfer learning, i.e., fine-tuning a detector pretrained on a source-domain benchmark. However, such transferred detector often fails to recognize new objects in the target domain, due to low data diversity of training samples. To tackle this problem, we propose a novel Context-Transformer within a concise deep transfer framework. Specifically, Context-Transformer can effectively leverage source-domain object knowledge as guidance, and automatically exploit contexts from only a few training images in the target domain. Subsequently, it can adaptively integrate these relational clues to enhance the discriminative power of detector, in order to reduce object confusion in few-shot scenarios. Moreover, Context-Transformer is flexibly embedded in the popular SSD-style detectors, which makes it a plug-and-play module for end-to-end few-shot learning. Finally, we evaluate Context-Transformer on the challenging settings of few-shot detection and incremental few-shot detection. The experimental results show that, our framework outperforms the recent state-of-the-art approaches.Comment: Accepted by AAAI-202

Effects of pretreatment on flavor of peanut oil with cold-pressed process

The volatile flavor compounds from cold-pressed peanut oils which were pretreated by pulsed electric field (PEF), microwave (MW) and ultrasonic wave (UW) respectively were concentrated by HS-SPME and analyzed by GC-MS. The types and relative contents of aldehydes, ketones, pyrazines and other volatile flavor substances in peanut oil were studied. The results indicated that a total of 97 volatile flavor substances were identified from the tested samples. And the cold-pressed peanut oil prepared by different pretreatment methods had different volatile flavor substances. In PEF-pretreated-cold-pressed peanut oil, acetoin was the characteristic flavor compound which has a pleasant aroma of butter. Among the volatile flavor substances of MW-pretreated-cold-pressed peanut oil, pyrazines and pyrroles have nutty and roasty flavor, and this type of cold-pressed peanut oil had flavor characteristics similar to those of hot-pressed peanut oil. The volatile flavor substances of UW-pretreated-cold-pressed peanut oil were mainly acids, showing a smell of oleic acid. Hence, there were significant differences in the effect of pretreatment on volatile flavor substances of cold-pressed peanut oil, and different flavors of cold-pressed peanut oil can be obtained by changing the pretreatment method

Effects of L-carnitine against oxidative stress in human hepatocytes: involvement of peroxisome proliferator-activated receptor alpha

<p>Abstract</p> <p>Background</p> <p>Excessive oxidative stress and lipid peroxidation have been demonstrated to play important roles in the production of liver damage. L-carnitine is a natural substance and acts as a carrier for fatty acids across the inner mitochondrial membrane for subsequent beta-oxidation. It is also an antioxidant that reduces metabolic stress in the cells. Recent years L-carnitine has been proposed for treatment of various kinds of disease, including liver injury. This study was conducted to evaluate the protective effect of L-carnitine against hydrogen peroxide (H₂O₂)-induced cytotoxicity in a normal human hepatocyte cell line, HL7702.</p> <p>Methods</p> <p>We analyzed cytotoxicity using MTT assay and lactate dehydrogenase (LDH) release. Antioxidant activity and lipid peroxidation were estimated by reactive oxygen species (ROS) levels, activities and protein expressions of superoxide dismutase (SOD) and catalase (CAT), and malondialdehyde (MDA) formation. Expressions of peroxisome proliferator-activated receptor (PPAR)-alpha and its target genes were evaluated by RT-PCR or western blotting. The role of PPAR-alpha in L-carnitine-enhanced expression of SOD and CAT was also explored. Statistical analysis was performed by a one-way analysis of variance, and its significance was assessed by Dennett's post-hoc test.</p> <p>Results</p> <p>The results showed that L-carnitine protected HL7702 cells against cytotoxity induced by H₂O₂. This protection was related to the scavenging of ROS, the promotion of SOD and CAT activity and expression, and the prevention of lipid peroxidation in cultured HL7702 cells. The decreased expressions of PPAR-alpha, carnitine palmitoyl transferase 1 (CPT1) and acyl-CoA oxidase (ACOX) induced by H₂O₂can be attenuated by L-carnitine. Besides, we also found that the promotion of SOD and CAT protein expression induced by L-carnitine was blocked by PPAR-alpha inhibitor MK886.</p> <p>Conclusions</p> <p>Taken together, our findings suggest that L-carnitine could protect HL7702 cells against oxidative stress through the antioxidative effect and the regulation of PPAR-alpha also play an important part in the protective effect.</p

A Hierarchical Fused Quantum Fuzzy Neural Network for Image Classification

Neural network is a powerful learning paradigm for data feature learning in the era of big data. However, most neural network models are deterministic models that ignore the uncertainty of data. Fuzzy neural networks are proposed to address this problem. FDNN is a hierarchical deep neural network that derives information from both fuzzy and neural representations, the representations are then fused to form representation to be classified. FDNN perform well on uncertain data classification tasks. In this paper, we proposed a novel hierarchical fused quantum fuzzy neural network (HQFNN). Different from classical FDNN, HQFNN uses quantum neural networks to learn fuzzy membership functions in fuzzy neural network. We conducted simulated experiment on two types of datasets (Dirty-MNIST and 15-Scene), the results show that the proposed model can outperform several existing methods. In addition, we demonstrate the robustness of the proposed quantum circuit

Fast Generation of High-Fidelity Mechanical Non-Gaussian States via Additional Amplifier and Photon Subtraction

Non-Gaussian states (NGSs) with higher-order correlation properties have wide-range applications in quantum information processing. However, the preparation of such states with high quality still faces practical challenges. Here, we propose a protocol to rapidly generate two types of mechanical NGSs, Schr\"{o}dinger cat states and Fock states, in dissipative optomechanical systems, even when the cooperativity is smaller than one ($g^2/\kappa\gamma<1$). In contrast to the usual scheme of directly applying non-Gaussian operations on the entangled optical mode, we show that an additional phase-sensitive amplifier can accelerate the generation and also precisely control the type of NGSs. Then, a principally deterministic multi-photon subtraction induced by the Rydberg-blockade effect is adopted to produce large-sized NGSs. The protocol can be implemented with state-of-the-art experimental systems with close to unit fidelity. Moreover, it can also be extended to generate a four-component cat state and provide new possibilities for future quantum applications of NGSs.Comment: 7 pages, 4 figure