SURFACTANTS DERIVED FROM 2-HYDROXY-4-(METHYLTHIO)BUTYRIC ACID: PHASE BEHAVIOR, INTERFACIAL ACTIVITY, MICROEMULSIONS AND MORE

Surfactants derived from 2-hydroxy-4-(methylthio) butyric acid were investigated. Anionic surfactants derived from this molecule have excellent water solubility, hardness tolerance and surface activity. Nonionic surfactants based on this molecule presented excellent phase behavior in water, high surface activity, good foaming/wetting ability, and good laundry performance. The headgroup hydrophilicity of the nonionic surfactants were enhanced with oligomeric sulfoxide ester units or extra ethylene oxide units, and their water solubility and surface activity were improved from the monomer sulfoxide esters, while maintaining good foaming, wetting and laundry performances. The application of these surfactant classes was further explored; a potential foam- breaker class and a promising emulsifier family were found

Structure and Morphology Characteristics of Fullerene C 60

Fullerene C60 nanotubes (FNTs) were prepared via liquid-liquid interfacial precipitation using N-methyl-2-pyrrolidone (NMP) as solvent and isopropyl alcohol (IPA) as precipitation agent at 8°C. C60-saturated NMP solutions were exposed to visible light to promote the growth of FNTs. Scanning electron microscopy revealed that fibers prepared in the NMP/IPA system show three different morphologies. On the basis of the different morphologies of fullerene C60 nanofibers (FNFs), a possible growth mechanism to describe the formation process of FNTs is proposed

Bilateral U‐Net semantic segmentation with spatial attention mechanism

Abstract Aiming at the problem that the existing models have a poor segmentation effect on imbalanced data sets with small‐scale samples, a bilateral U‐Net network model with a spatial attention mechanism is designed. The model uses the lightweight MobileNetV2 as the backbone network for feature hierarchical extraction and proposes an Attentive Pyramid Spatial Attention (APSA) module compared to the Attenuated Spatial Pyramid module, which can increase the receptive field and enhance the information, and finally adds the context fusion prediction branch that fuses high‐semantic and low‐semantic prediction results, and the model effectively improves the segmentation accuracy of small data sets. The experimental results on the CamVid data set show that compared with some existing semantic segmentation networks, the algorithm has a better segmentation effect and segmentation accuracy, and its mIOU reaches 75.85%. Moreover, to verify the generality of the model and the effectiveness of the APSA module, experiments were conducted on the VOC 2012 data set, and the APSA module improved mIOU by about 12.2%

The Association between Physical Activity, Self-Compassion, and Mental Well-Being after COVID-19: In the Exercise and Self-Esteem Model Revised with Self-Compassion (EXSEM-SC) Perspective

During the great life-altering challenges brought by Coronavirus 2019, school closures and lack of access to exercise and social interactions may have increased students’ negative emotions. The current research acts as a follow-up study to the development of the EXSEM-SC, using the Repeated Measures Panel Analysis Framework (RMPAF) to examine the stability of the model in revealing the relationship between physical activity, self-compassion, and mental well-being among Hong Kong adolescents. It is also aimed at examining the changes in physical activity, self-compassion, and mental well-being among Hong Kong adolescents between, before, and after the peak of COVID-19 using the EXSEM-SC Model. The RMPAF has involved 572 (60% Female, Mage = 13.63, SD =1.31) Hong Kong secondary school students. Furthermore, using the abductive qualitative approach, a total of 25 (Mage = 14.84, SD = 1.40) students were involved in the in-depth interviews to further investigate the relationships within the EXSEM-SC. The quantitative results showed that the relationship between physical activity and self-compassion could be demonstrated by the EXSEM-SC, with a satisfactory goodness-of-fit index in the SEMs, as well as satisfying model construct consistency. Moreover, it showed no significant differences in the level of physical activity, self-compassion, and mental well-being during and after the peak of COVID-19. The qualitative results demonstrated two new categories within the EXSEM-SC variables, which are personality traits and injuries experiences. With the stability of the EXSEM-SC model among adolescents, it is expected that the physical activity intervention, which is based on the EXSEM-SC model, could also aim at easing Hong Kong adolescent’s mental health issues. In addition, in terms of generating a long-term impact among students, the physical activity and self-compassionate intervention should be promoted among schools. However, the quantitative properties of the two new categories in the qualitative outcomes should be involved in future investigation

A consistency evaluation of signal-to-noise ratio in the quality assessment of human brain magnetic resonance images

Abstract Background Quality assessment of medical images is highly related to the quality assurance, image interpretation and decision making. As to magnetic resonance (MR) images, signal-to-noise ratio (SNR) is routinely used as a quality indicator, while little knowledge is known of its consistency regarding different observers. Methods In total, 192, 88, 76 and 55 brain images are acquired using T2 *, T1, T2 and contrast-enhanced T1 (T1C) weighted MR imaging sequences, respectively. To each imaging protocol, the consistency of SNR measurement is verified between and within two observers, and white matter (WM) and cerebral spinal fluid (CSF) are alternately used as the tissue region of interest (TOI) for SNR measurement. The procedure is repeated on another day within 30 days. At first, overlapped voxels in TOIs are quantified with Dice index. Then, test-retest reliability is assessed in terms of intra-class correlation coefficient (ICC). After that, four models (BIQI, BLIINDS-II, BRISQUE and NIQE) primarily used for the quality assessment of natural images are borrowed to predict the quality of MR images. And in the end, the correlation between SNR values and predicted results is analyzed. Results To the same TOI in each MR imaging sequence, less than 6% voxels are overlapped between manual delineations. In the quality estimation of MR images, statistical analysis indicates no significant difference between observers (Wilcoxon rank sum test, p w  ≥ 0.11; paired-sample t test, p p  ≥ 0.26), and good to very good intra- and inter-observer reliability are found (ICC, p icc  ≥ 0.74). Furthermore, Pearson correlation coefficient (r p ) suggests that SNRwm correlates strongly with BIQI, BLIINDS-II and BRISQUE in T2 * (r p  ≥ 0.78), BRISQUE and NIQE in T1 (r p  ≥ 0.77), BLIINDS-II in T2 (r p  ≥ 0.68) and BRISQUE and NIQE in T1C (r p  ≥ 0.62) weighted MR images, while SNRcsf correlates strongly with BLIINDS-II in T2 * (r p  ≥ 0.63) and in T2 (r p  ≥ 0.64) weighted MR images. Conclusions The consistency of SNR measurement is validated regarding various observers and MR imaging protocols. When SNR measurement performs as the quality indicator of MR images, BRISQUE and BLIINDS-II can be conditionally used for the automated quality estimation of human brain MR images

Characteristics of Chemical Accidents and Risk Assessment Method for Petrochemical Enterprises Based on Improved FBN

Refining and chemical integration is the major trend in the development of the world petrochemical industry, showing intensive and large-scale development. The accident risks caused by this integration are complex and diverse, and pose new challenges to petrochemical industry safety. In order to clarify the characteristics of the accident and the risk root contained in the production process of the enterprise, avoid the risk reasonably and improve the overall safety level of the petrochemical industry, in this paper, 159 accident cases of dangerous chemicals in China from 2017–2021 were statistically analyzed. A Bayesian network (BN)-based risk analysis model was proposed to clarify the characteristics and root causes of accident risks in large refining enterprises. The prior probability parameter in the Bayesian network was replaced by the comprehensive weight, which combined subjective and objective weights. A hybrid method of fuzzy set theory and a noisy-OR gate model was employed to eliminate the problem of the conditional probability parameters being difficult to obtain and the evaluation results not being accurate in traditional BN networks. Finally, the feasibility of the methods was verified by a case study of a petrochemical enterprise in Zhoushan. The results indicated that leakage, fire and explosion were the main types of accidents in petrochemical enterprises. The human factor was the main influencing factors of the top six most critical risk root causes in the enterprise. The coupling risk has a relatively large impact on enterprise security. The research results are in line with reality and can provide a reference for the safety risk management and control of petrochemical enterprises