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BackgroundThe COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. ObjectiveThe aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. MethodsWe used a web crawler tool and a set of predefined search terms (New Coronavirus Pneumonia, New Coronavirus, and COVID-19) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. ResultsBased on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. ConclusionsConcerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

COVID-19, reduced funding and a shortage of healthcare workers has led to growing international concern about patient violence towards medical staff in medical settings. As the number of reported physical and verbal assaults increases, many medical staff are considering leaving their positions due to the resulting impact on their mental and physical wellbeing, creating a critical need to understand the causes for violence towards medical staff working on the front line. This study aims to examine the causes for patient violence towards medical staff in China during the COVID-19 pandemic. A case library was created containing twenty reported incidents of patient violence towards medical staff during the pandemic in China. Based on the Triadic Reciprocal Determinism (TRD) theory, we identify the personal, environmental, and behavioral factors, that cause incidents of violence towards medical staff. The outcome was set as 'Medical Staff Casualties', referring to whether, due to the violence experienced, the medical staff member was injured or died, or only experienced threatening or insulting behavior. Data was analyzed using Qualitative Comparative Analysis (QCA) to clarify the relationship between the different conditions and their relationship with the outcome. The study's results reveal that Relationship Closeness is a necessary condition for patient violence in the presence of outcome. Secondly, four distinct types of causes for patient violence towards medical staff were identified: Strong Relationship Oriented Violence, Healthcare Resources and Services Mismatched Violence, Violence caused by Ineffective Patient-Physician Communication, and Ineffective Communication Superimposed Low Patient Compliance Violence. Scientific guidance is provided for the creation of measures to prevent future violence towards medical staff from occurring. Strict precautions should be taken for preventing violence to protect a healthy society and harmonious medical environment, emphasizing the need for joint governance of multiple participants., Author(s): Mengmeng Yin [sup.1] , Wei Zhang [sup.1] , Richard Evans [sup.2] , Chengyan Zhu [sup.3] , Longwen Wang [sup.4] , Jun Song [sup.5] Author Affiliations: (1) https://ror.org/00p991c53, grid.33199.31, 0000 [...]

Since 2015, there have been numerous reports, mostly unsubstantiated, of teen suicides associated with an online contest - the Blue Whale Challenge (BWC) in Russia, Europe and India. Recently, reports emerged of possible BWC cases in China. 7 Cases were selected from Chinese media reports by online searching. Multiple sources of information (e.g., published reports, social media entries) were searched and examined for detailed information about cases, to collect information on pre-game situations, game activities, and post-game conditions. Thematic analysis was used to determine themes in BWC victim antecedents, behaviors, and consequences. Two of seven cases were female. Ages ranged 11-19 years (M = 15.57, SD = 2.94). Thematic analysis of the seven Chinese cases revealed a predisposition phase (low mood, interpersonal problems, poor school performance) followed by a five-stage process of BWC involvement: 1) contact with a death-oriented game (BWC); 2) acceptance of the game's rules and the escalating challenges; 3) BWC incidents conclusion - a suicide attempt/completion; 4) discovery by others and/or game rejection, followed by 5) personal recovery. Analyses revealed interventions are required at each victim stage, and the necessity for increased online efforts. As most adolescent cases showed school problems (e.g., poor school performance, absenteeism), we recommend increased efforts at primary to tertiary schools to assess for and address personal difficulties of students demonstrating poor school performance. Results call for a new wave of revised suicide prevention methods led by digital natives but in collaboration with government, media and communities to address unique contemporary risks., Author(s): ChengYan Zhu [sup.1] , Keith Harris [sup.2] [sup.3] , Wei Zhang [sup.4] , Ksenia Tsyganova [sup.5] , Menghan Li [sup.6] , Runxi Zeng [sup.6] Author Affiliations: (1) grid.49470.3e, 0000 [...]

The moisture content of fabrics directly affects the physical properties of fabrics, it is important to accurately detect the moisture content of fabrics. To verify the feasibility of hyperspectral imaging technology for detecting the moisture content of cotton fabrics and thus improve the accuracy of moisture content detection, 100 groups of hyperspectral images of cotton fabrics with different thicknesses and moisture contents were acquired, the characteristics of the spectral curves of cotton fabrics were elaborated, and the effects of thickness and moisture content on the spectral curves of cotton fabrics were analyzed. Based on the research above, 1190, 1450 and 1940 nm bands were determined as the characteristic bands, the content gradient method was selected to divide the training set and sample set, PLS (Partial Least Squares) prediction models of the water content with MSC (Multiplicative Scatter Correction), SNV (Standard Normal Variate) and the first order derivative spectral pretreatment methods were established respectively, and the accuracy of the established three models was compared by correlation coefficient and standard error finally. The following conclusions were drawn: the change of water content in cotton fabric not only affects the overall reflectance of the cotton fabric spectral curve but also changes the position and peak of the characteristic peaks; with the increase of water content, the proportion of pure water in the mixed spectra of cotton fabric containing water increases, the position of the characteristic peaks is shifted and the peak increases; for cotton fabrics, the final spectral curves is the overlap of the water NIR (Near Infrared) and cotton absorption band at the position; the correlation coefficients of the training set and the test set of the established PLS-First Order Derivative model reached 0.98239 and 0.97637, the standard errors are 0.0003 and 0.00041, which proves the feasible of the method of utilizing hyperspectral imaging system with the prediction model for quantitative moisture content inspection of cotton fabrics. [ABSTRACT FROM AUTHOR]

Water spot detection in fabrics is conventionally reliant on direct visual assessment, a practice ingrained in our daily lives and textile inspection field. While valuable, this method's accuracy can be significantly affected by factors like fabric color and patterns resulting from additive textile dyes. In response, we present a detection method employing near-infrared (NIR) hyperspectral imaging, effectively reducing the influence of textile dyes and highlighting water spots. Initially, we acquired NIR hyperspectral images of fabric samples post-dyeing and moisture content conditioning. Remarkably, we observed that typical textile dyes minimally affected the NIR reflectance within the wavelength range of 1265–1626 nm, with a discernible decrease correlated to higher fabric moisture content. Subsequently, we captured NIR hyperspectral images of nine fabrics exhibiting varied colors and water spot characteristics. These images were then transformed into grayscale representations. Among them, the NIR images at 1459 nm emerged as preferred feature images for water spot detection, determined through analyses of contrast, entropy, and principal component images. While conducting a spray-rating evaluation of water spots in water-sprayed samples, comparing them with their respective feature images; the results affirm the efficacy of our developed feature images in facilitating visual identification and analysis of water spots in diverse fabrics, ultimately enhancing the accuracy of fabric water-repellency evaluation. [ABSTRACT FROM AUTHOR]

To enhance the flexural properties of glass fiber‐reinforced vinyl ester resin (GF‐VER) composites, this study focused on the influence of the duration of surface treatment on the flexural properties using polydopamine (PDA) and nanosilicon dioxide (nano‐SiO2). The GF‐VER composites were prepared via hot pressing. Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were utilized to characterize the microstructural and chemical attributes of the GF and GF‐VER composites. The mechanical properties of the GF‐VER composites were characterized using the flexural strength test. Furthermore, thermogravimetry was employed to investigate the thermal stability of the GF and the content of PDA nanoparticles on the GF. The results demonstrated that upon extending the treatment period, a greater quantity of PDA‐SiO2 nanoparticles adhered to the surface of the GF. The analysis of these results established that the optimal treatment duration is 12 h. This duration facilitated an enhanced adhesion between GF and VER owing to PDA‐SiO2, resulting in an optimal flexural strength of 562.8 MPa and signifying a 8.86% increase compared with the flexural strength of the untreated GF‐VER composites. Overall, the findings indicate that PDA‐SiO2 treatment greatly improves the flexural strength of GF‐VER composites. Highlights: The method for modifying the GF surface was simple and mild.Silica nanoparticles were grafted onto GFs using dopamine as a reaction platform.The effect of the duration of PDA‐SiO2 treatment on the flexural strength of GF‐VER composites was examined.SEM revealed an improved GF‐VER interface.The flexural strength of the GF‐VER composites improved by 8.86%. [ABSTRACT FROM AUTHOR]

In this study, a novel multifunctional woven silk fabric characterized by quick-drying and bacteriostasis was developed through the addition of modified polyester filaments. The effect of fabric structure and filament proportion on the properties of multifunctional silk fabrics was investigated to determine the optimal weaving parameters for optimizing the overall fabric manufacturing process. Moreover, the fabric's basic wearing properties, hygroscopic performance (e.g. wicking height, drip diffusion, and permeability), and antibacterial performance against Escherichia coli and Staphylococcus aureus were examined. Subsequently, the optimum parameters and weave structures were analyzed and evaluated using AHC (Analytic Hierarchy Process)-TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method. The novel fabric exhibits great wearability, antibacterial properties, dyeing properties, and hand feeling, and it shows great commercial potential and takes on a vital significance in future silk product development. [ABSTRACT FROM AUTHOR]

The purpose of this study is to use acoustic emission (AE) technology to explore the changes in the interface and mechanical properties of GF/VER composite materials after being treated with NaOH and to analyze the optimal modification conditions and damage propagation process. The results showed that the GF surface became rougher, and the number of reactive groups increased after treating the GF with a NaOH solution. This treatment enhanced the interfacial adhesion between the GF and VER, which increased the interfacial shear strength by 25.31% for monofilament draw specimens and 27.48% for fiber bundle draw specimens compared to those before the GF was modified. When the modification conditions were a NaOH solution concentration of 2 mol/L and a treatment time of 48 h, the flexural strength of the GF/VER composites reached a peak value of 346.72 MPa, which was enhanced by 20.96% compared with before the GF was modified. The process of damage fracture can be classified into six types: matrix cracking, interface debonding, fiber pullout, fiber relaxation, matrix delamination, and fiber breakage, and the frequency ranges of these failure mechanisms are 0~100 kHz, 100~250 kHz, 250~380 kHz, 380~450 kHz, 450~600 kHz, and 600 kHz and above, respectively. This paper elucidates the fracture process of GF/VER composites in three-point bending. It establishes the relationship between the AE signal and the interfacial and force properties of GF/VER composites, realizing the classification of the damage process and characterizing the mechanism. The frequency ranges of damage types and failure mechanisms found in this study offer important guidance for the design and improvement of composite materials. These results are of great significance for enhancing the interfacial properties of composites, assessing the damage and fracture behaviors, and implementing health monitoring. [ABSTRACT FROM AUTHOR]

In the textile industry, distinguishing between wool and cashmere can be a challenging task. Extensive research based on the microscopic images of the two has achieved very good results. However, the microscopic slide preparation process required for this approach is time-consuming and labor-intensive, limiting its practical application. To address this challenge, this paper proposes a new method that integrates artificial neural networks and hyperspectral imaging technology. The novelty of this approach lies in the fact that it does not require sample preparation, and is more simple, fast, and nondestructive. Firstly, a total of 225 wool samples and 160 cashmere samples were selected from the acquired hyperspectral images. The spectral curves (range 900–2500 nm) of these samples were extracted using the Region of Interest (ROI) tool in the ENVI software, and their characteristics were analyzed. Subsequently, due to the similarities and strong correlation between their spectral curves, Principal Component Analysis (PCA) was employed to reduce the dimensionality of the data. A single-layer neural network and a multi-layer neural network were developed using the LR (Logistic Regression) and MLP (Multilayer Perceptron) models, respectively, with a training-to-validation set ratio of 7:3. The model trained with LR achieved an accuracy of 90.3% on the training set and 81.0% on the validation set, suggesting underfitting. The MLP model performed best with five principal components, attaining a training set accuracy of 94.1% and a validation set accuracy of 92.2%. Precision, recall, and F 1-score were used to evaluate the two models, and comparison of the classification performance of the two models revealed that the MLP significantly outperformed the LR model. Therefore, the application of hyperspectral imaging technology enables rapid and non-destructive identification of wool and cashmere. [ABSTRACT FROM AUTHOR]

Color prediction and color management for digital printed fabrics remain a challenging task. Accurate prediction of color appearances of digital printed fabrics would enable designers and manufacturers to better fulfill their design requirements and creative visions. We propose a color prediction method for silk digital printing utilizing a Pix2Pix Generative Adversarial Network (GAN) framework. This method aims to generate predicted images that possess the same stylistic and color characteristics as their actual fabrics after production. To develop and validate the method, color data and images are collected and processed from 5252 sets of paired original Pantone TPX color card and actual print sample fabrics. The results of this study demonstrate that the method can predict the colors of silk digital print samples while effectively reproducing the effects of inkjet printing in silk fabrics including silk crepe satin and silk twill. The method exhibits high prediction accuracy to an average CIEDE2000 value of 2.372 for silk crepe satin and 1.846 for silk twill. The findings of this research not only enhance the efficiency and accuracy of color management in fabric digital printing technology but also contribute to the exploration and development of high-fidelity color prediction techniques within the textile industry. [ABSTRACT FROM AUTHOR]