Search

ObjectiveTo discuss the characteristics of psychological assistance hotline calls and operators' coping strategies of before and after the COVID-19 outbreak， in order to further improve the assistance ability of the psychological crisis hotline.MethodsA retrospective study was conducted on the demographics characteristics， call problems， coping strategies， and call time trends recorded by Changchun psychological assistance hotline information registration platform before the epidemic in Changchun City （January 20， 2019-April 20， 2019） and during the epidemic period （January 20， 2020-April 20， 2020）.ResultsThe differences between gender， age， marital status， location， and occupation type before and during the epidemic were statistically significant （χ2=11.205， 234.240， 152.083， 265.458， 353.385， P

Due to the complex background, small and dense targets, and large scale continuous changes in remote sensing images, universal object detectors are difficult to adapt well, resulting in poor detection performance. To address the above issues, a multi-branch feature mapping based remote sensing image object detection algorithm is proposed based on the YOLOv5s model. Firstly, a RepVGG module combined with gated channel transformation is designed using structural reparameterization technology. Its series structure is used to replace the C3 module of the original backbone network, aggregating global contextual information and enhancing feature expression and extraction capabilities. Secondly, the adaptive exponential weighted pooling method and the sampling method of inverse process reconstruction feature fusion network are used to maximize the retention of feature information and improve the detection performance of smaller targets. Finally, the combination of EIoU and Focal Loss is introduced as the loss function of the model to optimize the regression rate of the prediction box and the loss weight distribution of difficult and easy samples, further improving the positioning accuracy. The experimental results on the DIOR and NWPU VHR-10 datasets show that the average accuracy of the proposed algorithm reaches 92.2% and 92.5%, respectively, which is 3.5 percentage points and 5.6 percentage points higher than YOLOv5s, achieving better detection performance. At the same time, the real-time performance also meets the requirements of remote sensing image object detection in actual scenes.

Considering the characteristics of remote sensing land cover images with a wide variety of types and complex object edges as well as the limited receptive field of local convolutions in existing segmentation networks resulting in inadequate utilization of contextual information, leading to issues such as blurred object edges and low segmentation accuracy, this paper proposes a remote sensing land cover segmentation algorithm based on the UNet3+ network architecture. Firstly, during the decoding process, a similarity-aware point affiliation operator is introduced as an upsampling method. This operator aggregates multiple proposals from the feature pyramid to enhance the segmentation capability for object boundary details. Secondly, in the encoding process, a selective kernel module is introduced to optimize the downsampling approach. This module enables neurons to achieve an adaptive receptive field size, facilitating the acquisition of multi-scale information from target features and precise capture of valuable detailed semantic information. Finally, in the skip-connection phase, a dual multi-scale attention module is added to perform weighted fusion of features from different scales, enabling the model to better focus on both local details and global contextual information. Experimental results on the WHDLD and ISPRS Potsdam datasets demonstrate that the proposed algorithm achieves mean intersection over union (MIoU) improvements of 64.4% and 75.4% respectively, compared to baseline models, the improvement is about 2.6 percentage points and 3.2 percentage points respectively. This also validates the effectiveness of the proposed algorithm in addressing the issue of blurry segmentation edges. [ABSTRACT FROM AUTHOR]

Electrochemical water splitting is considered to be a green and flexible strategy for the mass production of hydrogen fuel, while the high cost and insufficent activity of current cathode catalysts severely suffocate the widespread thriving of hydrogen economy. Herein, we present a bottom‐up assembly strategy to the controllable construction of 2D/2D heterojunctions built from cobalt‐iron selenide nanolamellas and Ti3C2Tx MXene nanosheets. This unique architectural design gives the resulting CoyFe1‐ySe2/Ti3C2Tx catalysts a series of interesting structural advantages, such as 2D/2D heterostructure, large active surface areas, modulated electronic structure, uniform CoyFe1‐ySe2 dispersion, and good electron conductivity, thereby leading to strong synergistic coupling effects. As a consequence, the optimized Co0.7Fe0.3Se2/Ti3C2Tx electrocatalyst with an appropriate Co/Fe ratio possesses unusual hydrogen evolution properties in terms of a low overpotential of 69 mV at 10 mA cm−2, a small Tafel slope of 51 mV dec−1 and reliable long‐term durability, which are more competitive than those of bare Ti3C2Tx, FeSe2 and CoSe catalysts. [ABSTRACT FROM AUTHOR]

Listeria monocytogenes is a serious pathogen and can exacerbate harmful effects through the formation of biofilm. Inhibition of or reduction in L. monocytogenes biofilm is a promising strategy to control L. monocytogenes in the food industry. In our previous study, it was found that plantaricin Q7 produced by Lactiplantibacillus plantarum Q7 could inhibit and reduce L. monocytogenes biofilm, but the specific mechanism remains unclear. In this study, the inhibitive and reduced activity of plantaricin Q7 on L. monocytogenes biofilm was investigated by metabolomics. The results showed that plantaricin Q7 inhibited the synthesis of L. monocytogenes biofilm mainly through purine metabolism and glycerol phospholipid metabolism, and the key differential metabolites included acetylcholine and hypoxanthine with a decrease in abundance from 5.80 to 4.85. In addition, plantaricin Q7 reduced the formed L. monocytogenes biofilm by purine metabolism and arginine biosynthesis, and the main differential metabolites were N-acetylglutamate and D-ribose-1-phosphate with a decrease in abundance from 6.21 to 4.73. It was the first report that purine metabolism and amino acid metabolism were the common metabolic pathway for plantaricin Q7 to inhibit and reduce L. monocytogenes biofilm, which could be potential targets to control L. monocytogenes biofilm. A putative metabolic pathway for L. monocytogenes biofilm inhibition and reduction by plantaricin Q7 was proposed. These findings provided a novel strategy to control L. monocytogenes biofilm in food processing. [ABSTRACT FROM AUTHOR]

Recent study has shown that deep feature matching methods in metric learning, combined with large-scale and diverse training data, can significantly enhance the generalization ability of person re-identification. However, many existing methods generate large memory and computational costs, such as classification parameters or class memory learning. To address these issues, a new generalization person re-identification method based on correlation graph sampler(CGS) is proposed. The basic idea of CGS is to construct a nearest neighbor relationship graph for all classes using local sensitive Hashing (LSH) and feature metrics at the beginning of training. This ensures that each small batch of training samples is composed of randomly selected base classes and near-neighboring classes that are similar to the base classes to provide informative and challenging learning examples and improve the discriminative learning ability of person re-identification models. The sampling principle of CGS is influenced by the quality of features extracted by the backbone network, and therefore, the sampling ability of CGS can be enhanced with the training of the backbone network and has learnability. Through cross-evaluation of this method on large-scale datasets(including CUHK03, Market-1501, and MSMT17), extensive experimental results demonstrate the effectiveness of this method and showcase its potential in person re-identification applications. [ABSTRACT FROM AUTHOR]

In this work, we have fabricated a novel Fenton-like ferrihydrite/MoS2 (Fh/MoS2) composite and verified that the introduction of a small amount of iron on the surface of MoS2 can directly promote the exposure of Mo4+, finally enhancing the catalytic activity of the catalyst. Even though the content of iron element is only 1.19% in the composite, the reaction rate constant of Fh/MoS2 system for the degradation of environmental pollutants, such as organic dyes, antibiotic, and ionic liquid, is all much better than that of pure MoS2 system, which is attributed to much more generation of reactive oxygen species derived from synergistic effect of Fe3+/Fe2+ and Mo4+/Mo6+ redox cycles. The results of XPS and low-temperature EPR confirm that the exposure amount of Mo4+ active sites of 10% Fh/MoS2 is greatly increased, which is conducive to the conversion of Fe3+ to Fe2+ in the reaction process, thus effectively promoting the activation of H2O2. [ABSTRACT FROM AUTHOR]

Antler ossified tissue has been widely used for the extraction of bioactive peptides. In this study, collagen was prepared from antler ossified tissue via acetic acid and pepsin. Five different proteases were used to hydrolyze the collagen and the hydrolysate treated by neutrase (collagen peptide named ACP) showed the highest DPPH radical clearance rate. The extraction process of ACP was optimized by response surface methodology, and the optimal conditions were as follows: a temperature of 52 °C, a pH of 6.1, and an enzyme concentration of 3200 U/g, which resulted in the maximum DPPH clearance rate of 74.41 ± 0.48%. The peptides (ACP-3) with the strongest antioxidant activity were obtained after isolation and purification, and its DPPH free radical clearance rate was 90.58 ± 1.27%; at the same time, it exhibited good scavenging activity for ABTS, hydroxyl radical, and superoxide anion radical. The study investigated the protective effect of ACP-3 on oxidative damage in HaCaT cells. The findings revealed that all groups that received ACP-3 pretreatment exhibited increased activities of SOD, GSH-Px, and CAT compared to the model group. Furthermore, ACP-3 pretreatment reduced the levels of ROS and MDA in HaCaT cells subjected to H2O2-induced oxidative damage. These results suggest that collagen peptides derived from deer antler ossified tissue can effectively mitigate the oxidative damage caused by H2O2 in HaCaT cells, thereby providing a foundation for the utilization of collagen peptides in pharmaceuticals and cosmetics. [ABSTRACT FROM AUTHOR]