whu-nercms at trecvid2021:instance search task

We will make a brief introduction of the experimental methods and results of the WHU-NERCMS in the TRECVID2021 in the paper. This year we participate in the automatic and interactive tasks of Instance Search (INS). For the automatic task, the retrieval target is divided into two parts, person retrieval, and action retrieval. We adopt a two-stage method including face detection and face recognition for person retrieval and two kinds of action detection methods consisting of three frame-based human-object interaction detection methods and two video-based general action detection methods for action retrieval. After that, the person retrieval results and action retrieval results are fused to initialize the result ranking lists. In addition, we make attempts to use complementary methods to further improve search performance. For interactive tasks, we test two different interaction strategies on the fusion results. We submit 4 runs for automatic and interactive tasks respectively. The introduction of each run is shown in Table 1. The official evaluations show that the proposed strategies rank 1st in both automatic and interactive tracks.Comment: 9 pages, 4 figure

Strain-restricted transfer of ferromagnetic electrodes for constructing reproducibly superior-quality spintronic devices

Spintronic device is the fundamental platform for spin-related academic and practical studies. However, conventional techniques with energetic deposition or boorish transfer of ferromagnetic metal inevitably introduce uncontrollable damage and undesired contamination in various spin-transport-channel materials, leading to partially attenuated and widely distributed spintronic device performances. These issues will eventually confuse the conclusions of academic studies and limit the practical applications of spintronics. Here we propose a polymer-assistant strain-restricted transfer technique that allows perfectly transferring the pre-patterned ferromagnetic electrodes onto channel materials without any damage and change on the properties of magnetism, interface, and channel. This technique is found productive for pursuing superior-quality spintronic devices with high controllability and reproducibility. It can also apply to various-kind (organic, inorganic, organic-inorganic hybrid, or carbon-based) and diverse-morphology (smooth, rough, even discontinuous) channel materials. This technique can be very useful for reliable device construction and will facilitate the technological transition of spintronic study

Effects of heating rate and sintering temperature on 316L stainless steel powders sintered under multi-physical field coupling

This article presents an approach to fabricate microcomponents using multiphysical field sintering technique. In this study, 316 L stainless steel powders were sintered at different heating rates and sintering temperatures to produce cylindrical compacts with diameters of 1.0 mm and heights of 1.0 mm. The effects of heating rates and sintering temperatures on sintering densification were studied. It shows that both heating rate and sintering temperature directly affect the densification of sintered compacts, and that the electric field not only provides Joule heat for a compact, but also makes a great contribution to atom diffusion migration, which leads to compact densification. Multiphysical field coupling combines the coupling of stress field, temperature field, and electric field, and these fields work reciprocally rather than independently. Sintered compact with relative density of 99.20% is fabricated at low sintering temperature of 900°C, short sintering of 6 min

3,3′-Diindolylmethane protects cardiomyocytes from LPS-induced inflammatory response and apoptosis

Abstract Background 3,3′-Diindolylmethane (DIM) has been extensively studied as a potential therapeutic drug with free radical scavenging, antioxidant and anti-angiogenic effects. However, whether DIM has similar effects on cardiomyocytes remains unknown. Here we evaluated DIM’s influence on inflammation and apoptosis of H9C2 cardiomyocytes induced by LPS and to explore the possible mechanism of the effects. Methods H9C2 cells were incubated with DIM (10, 20 and 30 μM) with or without LPS for 24 h. The cytotoxicity of DIM was detected by CCK-8. The levels of tumour necrosis factor (TNF)-α and interleukin (IL)-6 were then measured using RT-qPCR and ELISA. Cell apoptosis rate and reactive oxygen species (ROS) content after DIM treatment were measured by flow cytometry. Expressions of NFκB, P-NFκB, IκBa, P-IκBa, Bax and Bcl-2 after DIM treatment were detected by western blot. The rate of NFκB nuclear translocation after DIM treatment was determined by immunocytochemical analysis. Results LPS stimulation promoted TNF-α and IL-6 mRNA expression. After treatment with various concentrations of DIM (10, 20 and 30 μM), TNF-α and IL-6 mRNA expression was clearly impaired, especially in the LPS + DIM30(μM) group. ELISA was used to measure TNF-α and IL-6 concentrations in cellular supernatant, and the result was verified to be consistent with RT-qPCR. Additionally, DIM treatment significantly blocked LPS-induced oxidative stress and inhibited LPS-induced apoptosis in H9C2 cardiomyocytes according to the results detected by flow cytometry. Moreover, compared with LPS alone, DIM significantly inhibited the LPS-induced phosphorylation of NFκB (p-NFκB) and Bax expression and increased Bcl-2 expression. Conclusions DIM may have a protective effect for H9C2 cardiomyocytes against LPS-induced inflammatory response and apoptosis. DIM may be a new insight into the treatment of septic cardiomyopathy

Systemic risk prediction based on Savitzky-Golay smoothing and temporal convolutional networks

Based on the data from January 2007 to December 2021, this paper selects 14 representatives from four levels of the extreme risk of financial institutions, the contagion effect between financial systems, volatility and instability of financial markets, liquidity, and credit risk systemic risk. By constructing a Savitzky-Golay-TCN deep convolutional neural network, the systemic risk indicators of China's financial market are predicted, and their accuracy and reliability are analyzed. The research found that: 1) Savitzky-Golay-TCN deep convolutional neural network has a strong generalization ability, and the prediction effect on all indices is stable. 2) Compared with the three control models (time-series convolutional network (TCN), convolutional neural network (CNN), and long short-term memory (LSTM)), the Savitzky-Golay-TCN deep convolutional neural network has excellent prediction accuracy, and its average prediction accuracy for all indices has increased. 3) Savitzky-Golay-TCN deep convolutional neural network can better monitor financial market changes and effectively predict systemic risk

Investigation on Effective Thermal Conductivity of Fibrous Porous Materials as Vacuum Insulation Panels’ Core Using Lattice Boltzmann Method

Vacuum Insulation Panels (VIPs) provide significant adiabatic performance for heat/cooling systems to reduce energy consumption. The application of fibrous porous material (FPM) as the ideal core of VIPs has gained global attention in recent decades. The microstructure and physical properties of FPMs, filled as novel VIPs’ core material, and holding superior thermal performance, affected effective thermal conductivity (ETC) greatly. Aiming to deeply understand heat transfer mechanisms, a holistic simulation method that combined with a developed 3D FPM structure generation method and a D3Q15-Lattice Boltzmann method (LBM) is proposed to simulate the heat transfer in FPM and to illuminate the influence factors of ETC on the microstructure of FPM in a vacuum. The improved and modified mesoscopic 3D fibrous random micro-structure generation approach involved five structural parameters: generation probability of nucleus growth, fiber length, diameter, coincidence rate, and orientation angle. The calculation model of ETC is established, and the discrete velocity, distribution, evolution, and boundary conditions of D3Q15-LBM are invested in detail. The model is validated with influences of different microstructure parameters. It indicated that FPM with finer diameter, smaller average pore size, and bigger orientation angle easily gain the lower ETC in a vacuum. The ETC was also affected by the orientation angles of fibers. The more the heat transfer direction is inconsistent with the length direction of the fiber, the better the adiabatic performance is. The reliability of the model is verified by comparison, and this work is a reference to optimize the fibrous core of VIPs

Domain knowledge-guided interpretive machine learning: formula discovery for the oxidation behavior of ferritic-martensitic steels in supercritical water

A general formula with high generalization and accurate prediction power is highly desirable for science, technology and engineering. In addition to human beings, artificial intelligence algorithms show great promise for the discovery of formulas. In this study, we propose a domain knowledge-guided interpretive machine learning strategy and demonstrate it by studying the oxidation behavior of ferritic-martensitic steels in supercritical water. The oxidation Cr equivalent is, for the first time, proposed in the present work to represent all contributions of alloying elements to oxidation, derived by our domain knowledge and interpretive machine learning algorithms. An open-source tree classifier for linear regression algorithm is also, for the first time, developed to materialize the formula with collected data. This algorithm effectively captures the linear correlation between compositions, testing environments and oxidation behaviors from the data. The sure independence screening and sparsifying operator algorithm finally assembles the information derived from the tree classifier for linear regression algorithm, resulting in a general formula. The general formula with the determined parameters has the power to predict, quantitatively and accurately, the oxidation behavior of ferritic-martensitic steels with multiple alloying elements exposed to various supercritical water environments, thereby providing guidance for the design of anti-oxidation steels and hence promoting the development of power plants with improved safety. The present work demonstrates the power of domain knowledge-guided interpretive machine learning with respect to the data-driven discovery of physics-informed formulas and the acceleration of materials informatics development

A Study on Impact Force Detection Method Based on Piezoelectric Sensing

Impact force refers to a transient phenomenon with a very short-acting time, but a large impulse. Therefore, the detection of impact vibration is critical for the reliability, stability, and overall life of mechanical parts. Accordingly, this paper proposes a method to indirectly characterize the impact force by using an impact stress wave. The LS-DYNA software is utilized to establish the model of a ball impacting the steel plate, and the impact force of the ball and the impact response of the detection point are obtained through explicit dynamic finite element analysis. In addition, on this basis, a correspondence between the impact force and the impact response is established, and finally, an experimental platform for impact force detection is built for experimental testing. The results obtained by the finite element method are in good agreement with the experimental measurement results, and it can be inferred that the detected piezoelectric signal can be used to characterize the impact force. The method proposed herein can guide the impact resistance design and safety assessment of structures in actual engineering applications

Physics-Based Electrothermal Stress Evaluation Approach of IGBT Modules Combined With Artificial Neural Network Model

Due to the disparate timescale behavior in the electrical and thermal aspects, achieving a balance between simulation efficiency and accuracy in electrothermal analysis of insulated gate bipolar transistor (IGBT) modules has been a challenging task. A physical-based electrothermal stress evaluation approach combining with artificial neural network (ANN) model is proposed in this article, which significantly improves performance in circuit simulation. The training data for ANN models are derived from the Hefner physical model, a well-established model integrated in Saber. By re-expressing the Hefner model using MATLAB scripts, high-precision data can be efficiently obtained. Double-pulse experiments show that the switching transient characterized by the Hefner model have high precision, with an error within 5% compared to the experimental data. Additionally, the transient behavior of IGBT devices is further described by a two-layer feed-forward ANN, trained using datasets obtained by varying parasitic or operating parameters in the re-expressed Hefner model. Combining the physical model with the ANN models, the proposed approach can simulate not only transient electrical behavior but also long-term thermal behavior with accurate switching energy. This approach has been implemented in MATLAB/Simulink and verified with Saber for system-level circuit simulation. The electrothermal stress evaluation results show that the simulation efficiency is significantly improved (180 times faster than Saber under the simulation settings in this article), while maintaining high precision, and the error is within 2.5%. Experimental results also validate the accuracy of proposed model in predicting the voltage and current stress, with a maximum error of 1.5%

The Out-of-Plane Compression Behavior of Cross-Ply AS4/PEEK Thermoplastic Composite Laminates at High Strain Rates

The dynamic mechanical behavior of thermoplastic composites over a wide range of strain rates has become an important research topic for extreme environmental survivability in the fields of military protection, aircraft safety, and aerospace engineering. However, the dynamic compression response in the out-of-plane direction, which is one of the most important loading conditions resulting in the damage of composite materials, has not been investigated thoroughly when compared to in-plane compression and tensile behavior under high strain rates. Thus, we used split Hopkinson pressure bar (SHPB) tests to conduct the out-of-plane compression test of cross-ply carbon fiber-reinforced polyetheretherketone (AS4/PEEK) composite laminates. Afterward, the damage mechanism under different strain rates was characterized by the macrostructure morphologies and scanning electron microscope micrographs. Two major cases of the incomplete failure condition and complete failure condition were discussed. Dynamic stress-strain curves expound the strain rates dependencies of elastic modulus, failure strength, and failure strain. An obvious spring-back process could be observed under incomplete failure tests. For the complete failure tests, secondary loading could be observed by reconstructing and comparing the dynamic response history. Lastly, various failure modes that occurred in different loading strain rates illustrate that the damage mechanism also shows obvious strain rate sensitivity