Thermal modal analysis of hypersonic composite wing on transient aerodynamic heating.

Considering the influence of thermal stress and material property variations, this study employs the Navier-Stokes equations and Fourier heat conduction law to establish a semi-implicit time-domain numerical analysis method for hypersonic aerothermal-structural coupling. Study the temporal variation pattern of different regions of the composite material wing under aerodynamic heating. Using the obtained transient temperature field of the wing, the thermal modal of the wing at different time points is calculated using the finite element method. Additionally, it conducts an analysis and discussion on the factors influencing the thermal modal. Composites can be effectively utilized as thermal protection materials for aircraft. During the aerodynamic heating process, the leading edge temperature reaches thermal equilibrium first, followed by the trailing edge, and the belly plate experiences a slower thermal response. Temperature rise significantly affects higher-order modes, with the change in material properties during the early stages of heating being the dominant factor. This leads to a faster decrease in natural frequency. As heat conduction progresses, the influencing factors of thermal stresses gradually increase, and the natural frequency decreases slowly or even rises.

Advances in Research on the Activity Evaluation, Mechanism and Structure-Activity Relationships of Natural Antioxidant Peptides.

Antioxidant peptides are a class of biologically active peptides with low molecular weights and stable antioxidant properties that are isolated from proteins. In this review, the progress in research on the activity evaluation, action mechanism, and structure-activity relationships of natural antioxidant peptides are summarized. The methods used to evaluate antioxidant activity are mainly classified into three categories: in vitro chemical, in vitro cellular, and in vivo animal methods. Also, the biological effects produced by these three methods are listed: the scavenging of free radicals, chelation of metal ions, inhibition of lipid peroxidation, inhibition of oxidative enzyme activities, and activation of antioxidant enzymes and non-enzymatic systems. The antioxidant effects of natural peptides primarily consist of the regulation of redox signaling pathways, which includes activation of the Nrf2 pathway and the inhibition of the NF-κB pathway. The structure-activity relationships of the antioxidant peptides are investigated, including the effects of peptide molecular weight, amino acid composition and sequence, and secondary structure on antioxidant activity. In addition, four computer-assisted methods (molecular docking, molecular dynamics simulation, quantum chemical calculations, and the determination of quantitative structure-activity relationships) for analyzing the structure-activity effects of natural peptides are summarized. Thus, this review lays a theoretical foundation for the development of new antioxidants, nutraceuticals, and cosmetics.

Incentive mechanism of multiple green innovation behaviors of equipment manufacturing enterprises: A managers, green coordination groups and employees perspective.

Employees play a pivotal role in the implementing of green development strategies and the attainment of dual-carbon objectives within manufacturing enterprises. Effective motivation of employees, fostering consensus on environmental protection, increased engagement in environmental initiatives, and the cultivation of employee cohesion are all vital for fostering green development within these enterprises. This paper seeks to elucidate the roles of general managers, green coordination groups (GCG), and employees in actualizing green behaviors. Furthermore, it advocates for a double incentive model to be employed in the implementing of green strategies within manufacturing enterprises. The research reveals that multiple factors, including incentive intensity, green capability, effort cost, risk aversion, and green variance, significantly influence the formulation of incentive contracts for green behaviors. The motivation level of the general manager directly impacts the efforts of the GCG, the organization's green climate, the manager's individual efforts, and indirectly influences the motivation and efforts of employees towards green behaviors. Notably, the influence of the organization's green climate on employees surpasses than on the manager, underscoring the imperative for collaboration efforts between the general manager and GCG to instill green behaviors among employees. Hence, it is imperative for the general manager and GCG to collaborate not only on critical aspects of green strategy implementation but also in fostering green behaviors among employees. This collaboration will facilitate the development of a multi-layer incentive mechanism aimed at promoting and facilitating the adoption of green behaviors among employees, thus contributing to the advancement of theory regarding employees' green behaviors and offering practical guidance for effectively realizing dual-carbon targets and achieving high-quality development within enterprises.

EA-Net: Research on skin lesion segmentation method based on U-Net.

Accurate segmentation of skin lesions is a challenging task because the task is highly influenced by factors such as location, shape and scale. In recent years, Convolutional Neural Networks (CNNs) have achieved advanced performance in automated medical image segmentation. However, existing CNNs have problems such as inability to highlight relevant features and preserve local features, which limit their application in clinical decision-making. This paper proposes a CNN with an added attention mechanism (EA-Net) for more accurate medical image segmentation.EA-Net is based on the U-Net network model framework. Specifically, we added a pixel-level attention module (PA) to the encoder section to preserve the local features of the image during downsampling, making the feature maps input to the decoder more relevant to the ground-truth. At the same time, we added a spatial multi-scale attention module (SA) after the decoding process to increase the spatial weight of the feature maps that are more relevant to the ground-truth, thereby reducing the gap between the output results and the ground-truth. We conducted extensive segmentation experiments on skin lesion images from the ISIC 2017 and ISIC 2018 datasets. The results demonstrate that, when compared to U-Net, our proposed EA-Net achieves an average Dice score improvement of 1.94% and 5.38% for skin lesion tissue segmentation on the ISIC 2017 and ISIC 2018 datasets, respectively. The IoU also increases by 2.69% and 8.31%, and the ASSD decreases by 0.3783 pix and 0.5432 pix, indicating superior segmentation performance. EA-Net can achieve better segmentation results when the original image of skin lesions has an obscure boundary and the segmentation area contains interference factors, which proves that the addition of attention mechanism in the encoder and the application of comprehensive attention mechanism can improve the performance of neural network in the field of skin lesions image segmentation.

The Effect of Mushroom Dietary Fiber on the Gut Microbiota and Related Health Benefits: A Review.

Mushroom dietary fiber is a type of bioactive macromolecule derived from the mycelia, fruiting bodies, or sclerotia of edible or medicinal fungi. The use of mushroom dietary fiber as a prebiotic has recently gained significant attention for providing health benefits to the host by promoting the growth of beneficial microorganisms; therefore, mushroom dietary fiber has promising prospects for application in the functional food industry and in drug development. This review summarizes methods for the preparation and modification of mushroom dietary fiber, its degradation and metabolism in the intestine, its impact on the gut microbiota community, and the generation of short-chain fatty acids (SCFAs); this review also systematically summarizes the beneficial effects of mushroom dietary fiber on host health. Overall, this review aims to provide theoretical guidance and a fresh perspective for the prebiotic application of mushroom dietary fiber in the development of new functional foods and drugs.

Generative adversarial networks with adaptive normalization for synthesizing T2-weighted magnetic resonance images from diffusion-weighted images.

Recently, attention has been drawn toward brain imaging technology in the medical field, among which MRI plays a vital role in clinical diagnosis and lesion analysis of brain diseases. Different sequences of MR images provide more comprehensive information and help doctors to make accurate clinical diagnoses. However, their costs are particularly high. For many image-to-image synthesis methods in the medical field, supervised learning-based methods require labeled datasets, which are often difficult to obtain. Therefore, we propose an unsupervised learning-based generative adversarial network with adaptive normalization (AN-GAN) for synthesizing T2-weighted MR images from rapidly scanned diffusion-weighted imaging (DWI) MR images. In contrast to the existing methods, deep semantic information is extracted from the high-frequency information of original sequence images, which are then added to the feature map in deconvolution layers as a modality mask vector. This image fusion operation results in better feature maps and guides the training of GANs. Furthermore, to better preserve semantic information against common normalization layers, we introduce AN, a conditional normalization layer that modulates the activations using the fused feature map. Experimental results show that our method of synthesizing T2 images has a better perceptual quality and better detail than the other state-of-the-art methods.

Accurate identification of key VOCs sources contributing to O3 formation along the Liaodong Bay based on emission inventories and ambient observations.

In order to achieve the precise control of the volatile organic compounds (VOCs) species with high ozone (O3) formation contribution from key sources in Panjin and Yingkou, two coastal industrial cities with severe O3 pollution along the Liaodong Bay, northeast China, the ambient concentrations of 99 VOCs species were measured online at urban-petrochemical (XLT), suburban-industrial (PP), and rural (XRD) sites in July 2019, contemporary monthly anthropogenic VOCs emission inventories were developed. The source contribution of ambient VOCs resolved by positive matrix factorization (PMF) model was comparable with emission inventories, and the location of VOCs sources were speculated by potential source contribution function (PSCF). 17.5 Gg anthropogenic VOCs was emitted in Panjin and Yingkou in July 2019 with potential to form 54.7 Gg-O3 estimated by emission inventories. The average VOC mixing ratios of 47.1, 26.7, and 16.5 ppbv was observed at XLT, PP, and XRD sites, respectively. Petroleum industry (22 %), organic chemical industry (21 %), and mobile vehicle emission (19 %) were identified to be the main sources contributing to O3 formation at XLT site by PMF, while it is organic chemical industry (33 %) and solvent utilization (28 %) contributed the most at PP site. Taking the subdivided source contributions of emission inventories and source locations speculated by PSCF into full consideration, organic raw chemicals manufacturing, structural steel coating, petroleum refining process, petroleum products storage and transport, off-shore vessels, and passenger cars were identified as the key anthropogenic sources. High O3-formation contribution sources, organic chemical industry and solvent utilization were located in the industrial parks at the junction of the two cities and the southeast of Panjin, and petroleum industry distributed in the whole Panjin and offshore areas. These results identify the key VOCs species and sources and speculate the potential geographical location of sources for precisely controlling ground-level O3 along the Liaodong Bay.

Memory Recall: A Simple Neural Network Training Framework Against Catastrophic Forgetting.

It is widely acknowledged that biological intelligence is capable of learning continually without forgetting previously learned skills. Unfortunately, it has been widely observed that many artificial intelligence techniques, especially (deep) neural network (NN)-based ones, suffer from catastrophic forgetting problem, which severely forgets previous tasks when learning a new one. How to train NNs without catastrophic forgetting, which is termed continual learning, is emerging as a frontier topic and attracting considerable research interest. Inspired by memory replay and synaptic consolidation mechanism in brain, in this article, we propose a novel and simple framework termed memory recall (MeRec) for continual learning with deep NNs. In particular, we first analyze the feature stability across tasks in NN and show that NN can yield task stable features in certain layers. Then, based on this observation, we use a memory module to keep the feature statistics (mean and std) for each learned task. Based on the memory and statistics, we show that a simple replay strategy with Gaussian distribution-based feature regeneration can recall and recover the knowledge from previous tasks. Together with the weight regularization, MeRec preserves weights learned from previous tasks. Based on this simple framework, MeRec achieved leading performance with extremely small memory budget (only two feature vectors for each class) for continual learning on CIFAR-10 and CIFAR-100 datasets, with at least 50% accuracy drop reduction after several tasks compared to previous state-of-the-art approaches.

Hydrogen Bonds in Bis(1H-benzimidazole-κN3)cadmium(II) Dibenzoate: Hirshfeld Surface Analysis and AIM Perspective.

The coordination complex bis(1H-benzimidazole-κN3)cadmium(II) dibenzoate has been synthesized and characterized by single crystal diffraction analysis. Cadmium center is six coordinated and formed a distorted octahedron coordinated geometry. The Hirshfeld analysis shows that in the dnorm-surface of the compound, there are dark red spots near the hydrogen-bonds acceptor and donor atoms, while intermolecular interactions result in faint-red spots. The AIM analysis was performed, there exist a BCP in each N(C)-H∙∙∙O hydrogen bond, the bond paths also can be seen, the |V(b)|/G(b) < 1 and the H(b) > 0, the interaction is indicative of being a closed shell. The TG results are consistent with the X-ray diffraction structure.

[Characterization of atmospheric volatile organic compounds in Shenyang, China].

From April, 2008 to July, 2009, 187 atmospheric samples in four different seasons were collected from five sites in Shenyang. The 108 species of VOCs were measured by using the method of pre-concentration-GC-MS. The objectives of this study were to investigate the VOCs pollution level as well as its spatial and temporal distribution, and to identify the main source in Shenyang city. The results showed that the average total mass concentration of VOCs in Shenyang was 371.0 +/- 132.4 microg/m3. The major components were oxygen-containing compounds, halogenated hydrocarbon, alkanes, aromatics and alkenes, which accounted for 57.2%, 20%, 11.4%, 8.5%, and 3.0% of the total mass, respectively. A seasonal variation of VOCs across all the sampling sites was observed, with higher levels in spring and autumn and lower levels in winter and summer. Related with industrial emission, the diurnal variation of the total mean VOCs concentrations at downtown site showed three peaks in winter and two peaks in summer. The VOCs levels in the industrial and downtown areas were higher than those in other areas, with the lowest concentration observed at clean air site with no emission source around. From the results of correlation analysis and concentration ratios analysis, the VOCs pollutants in Shenyang were mainly contributed by automobile exhaust, coal & biofuel combustion, gasoline & solvent evaporation and industrial process.

Current medico-legal death investigation system in China.

Medico-legal death investigation in China has a long history that can be traced back to the Chhin era in the third century BC. We conducted a national study on the current medico-legal death investigation system in China. Our study showed that, at present, medico-legal death investigations in China are conducted by c. 12,000 forensic medical experts mainly within five relatively independent agencies: the police organizations, the prosecutors' offices, the departments/divisions of forensic medicine/science in medical colleges and universities, the Institute of Forensic Science in the Ministry of Justice, and the government or private forensic societies. Owing to China's large population and area, the medico-legal death investigation is largely based on the administrative divisions. There is a wide variation in the scope, extent, and quality of investigations among the agencies and at the different levels of county/district, municipal, and provincial governments. This article gives a general overview of medicolegal death investigation in modern China.

[Experimental research of oil vapor pollution control for gas station with membrane separation technology].

Two kinds of membranes modules, vapor retained glassy membrane based on PEEK hollow fiber membrane modules and vapor permeated rubbery membrane system based on GMT plate-and-frame membrane modules, were used to control the oil vapor pollution during the course of receiving and transferring gasoline in oil station. The efficiencies of the membrane module and the membrane system of them were evaluated and compared respectively in the facilities which were developed by ourselves. It was found that both the two kinds of membranes modules had high efficiency for the separation of VOCs-air mixed gases, and the outlet vapor after treatment all can meet the national standard. When the vapor-enriched gas was returned to the oil tank to simulate the continuously cycle test, the concentration of VOCs in the outlet was also below 25 g x m(-3).