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Visual tracking is an essential building block for target tracking and capture of the underwater vehicles. On the basis of remotely autonomous control architecture, this paper has proposed an improved kernelized correlation filter (KCF) tracker and a novel fuzzy controller. The model is trained to learn an online correlation filter from a plenty of positive and negative training samples. In order to overcome the influence from occlusion, the improved KCF tracker has been designed with an added self-discrimination mechanism based on system confidence uncertainty. The novel fuzzy logic tracking controller can automatically generate and optimize fuzzy rules. Through Q-learning algorithm, the fuzzy rules are acquired through the estimating value of each state action pairs. An S surface based fitness function has been designed for the improvement of learning based particle swarm optimization. Tank and channel experiments have been carried out to verify the proposed tracker and controller through pipe tracking and target grasp on the basis of designed open frame underwater vehicle.
Written by eminent international judges, scholars and practitioners, this book offers a timely study of China's role in international dispute resolution in the context of the construction of the 'Belt and Road Initiative' (BRI). It provides in-depth analysis of the law and practice in the fields of international trade, commerce, investment and international law of the sea, as they relate to the BRI construction. It is the first comprehensive assessment of China's policy and practice in international dispute resolution, in general and in individual fields, in the context of the BRI construction. This book will be an indispensable reading for scholars and practitioners with interest in China and international dispute resolution. It also constitutes an invaluable reference for anyone interested in the changing international law and order, in which China is playing an increasingly significant role, particularly through the BRI construction.
The high overall plant-based diet index (PDI) is considered to protect against type 2 diabetes in the general population. However, whether the PDI affects gestational diabetes mellitus (GDM) risk among pregnant women is still unclear. We evaluated the association between PDI and GDM risk based on a Chinese large prospective cohort – the Tongji Maternal and Child Health Cohort. Dietary data were collected at 13–28 weeks of pregnancy by a validated semi-quantitative FFQ. The PDI was obtained by assigning plant food groups positive scores while assigning animal food groups reverse scores. GDM was diagnosed by a 75 g 2-h oral glucose tolerance test at 24–28 weeks of gestation. Logistic regression models were fitted to estimate OR of GDM, with associated 95 % CI, comparing women in different PDI quartiles. Among the total 2099 participants, 169 (8·1 %) were diagnosed with GDM. The PDI ranged from 21·0 to 52·0 with a median of 36·0 (interquartile range (IQR) 33·0–39·0). After adjusting for social-demographic characteristics and lifestyle factors etc., the participants with the highest quartile of PDI were associated with 57 % reduced odds of GDM compared with women in the lowest quartile of PDI (adjusted OR 0·43; 95 % CI 0·24, 0·77; Pfor trend = 0·005). An IQR increment in PDI was associated with 29 % decreased odds of GDM (adjusted OR 0·71; 95 % CI 0·56, 0·90). Findings suggest that adopting a plant-based diet during pregnancy could reduce GDM risk among Chinese women, which may be valuable for dietary counselling during pregnancy.
This study aimed to explore the impacts of COVID-19 outbreak on mental health status in general population in different affected areas in China.
This was a comparative study including two groups of participants: (1) general population in an online survey in Ya'an and Jingzhou cities during the COVID-19 outbreak from 10–20 February 2020; and (2) matching general population selected from the mental health survey in Ya'an in 2019 (from January to May 2019). General Health Questionnaire (GHQ-12), Self-rating Anxiety Scale (SAS), and Self-rating Depression Scale (SDS) were used.
There were 1775 participants (Ya'an in 2019 and 2020: 537 respectively; Jingzhou in 2020: 701). Participants in Ya'an had a significantly higher rate of general health problems (GHQ scores ⩾3) in 2020 (14.7%) than in 2019 (5.2%) (p < 0.001). Compared with Ya'an (8.0%), participants in Jingzhou in 2020 had a significantly higher rate of anxiety (SAS scores ⩾50, 24.1%) (p < 0.001). Participants in Ya'an in 2020 had a significantly higher rate of depression (SDS scores ⩾53, 55.3%) than in Jingzhou (16.3%) (p < 0.001). The risk factors of anxiety symptoms included female, number of family members (⩾6 persons), and frequent outdoor activities. The risk factors of depression symptoms included participants in Ya'an and uptake self-protective measures.
The prevalence of psychological symptoms has increased sharply in general population during the COVID-19 outbreak. People in COVID-19 severely affected areas may have higher scores of GHQ and anxiety symptoms. Culture-specific and individual-based psychosocial interventions should be developed for those in need during the COVID-19 outbreak.
The accurate prediction of turbulent mixing induced by Rayleigh–Taylor (R–T), Richtmyer–Meshkov (R–M) and Kelvin–Helmholtz (K–H) instabilities is very important in understanding natural phenomena and improving engineering applications. In applications, the prediction of mixing with the Reynolds-averaged Navier–Stokes (RANS) equation remains the most widely used method. The RANS method involves two aspects, i.e. physical modelling and model coefficients. Generally, the latter is determined empirically; thus, there is a lack of universality. In this paper, inspired by the well-known Reynolds decomposition, we propose a methodology to determine the model coefficients with the following three steps: (i) preset a set of analytical RANS solutions by fully using the knowledge of mixing evolutions; (ii) simplify the differential RANS equations to algebraic equations by imposing the preset solutions to RANS equations; (iii) solve the algebraic equations approximately to give the values of the entire model coefficients. The specific application of this methodology in the widely used K–L mixing model shows that, using the same set of model coefficients determined from the current methodology, the K–L model successfully predicts the mixing evolutions in terms of different physical quantities (e.g. temporal scalings and spatial profiles), density ratios and problems (e.g. R–T, R–M, K–H and reshocked R–M mixings). It is possible to extend this methodology to other turbulence models characterised with self-similar evolutions, such as K-
Southern China is affected by multi-stage tectonic activities, with strong fold deformation, complex fault systems and poor shale gas preservation conditions. Here, we used shale samples from the lower Silurian Longmaxi shale in the complex tectonic area of Southern China, to study the relationship between differential structural deformation, and pore structure and adsorption capacity. According to the deformation mechanism of the shale, it is further divided into brittle-slip rheological deformation (BD) and ductile-slip rheological deformation (DD). The results show that all micro-fractures can be observed under scanning electron microscopy in deformed shale samples, but in shale samples with different types of rheological deformation, the micro-fractures have large differences in scale, fracture length and lateral connectivity. The micro-fractures developed in DD shales are small in scale and short in fracture length, but have strong local connectivity. In contrast, brittle minerals are more developed in BD shales, and interlayer shearing has formed micro-fractures with large fracture length and good lateral connectivity, which is beneficial for later fracturing. In these two types of deformed shales, pores in organic matter are rare, and sporadic organic pores have small pore size and poor connectivity. The total pore volume (1.8–2.4 × 10−2 cm3 g–1) of BD shale samples is higher than that of DD shale samples (0.8–1.6 × 10−2 cm3 g–1). There is a positive correlation between total pore volume and quartz content. In addition, the specific surface area (12–18 m2 g–1) of DD shale samples is larger than that of BD shale samples (6–12 m2 g–1).
A novel g-C3N4 nanoparticle@porous g-C3N4 (CNNP@PCN) composite has been successfully fabricated by loading g-C3N4 nanoparticles on the porous g-C3N4 matrix via a simply electrostatic self-assembly method. The composition, morphological structure, optical property, and photocatalytic performance of the composite were evaluated by various measurements, including XRD, SEM, TEM, Zeta potential, DRS, PL, FTIR, and XPS. The results prove that the nanolization of g-C3N4 leads to an apparent blueshift of the absorption edge, and the energy band gap is increased from 2.84 eV of porous g-C3N4 to 3.40 eV of g-C3N4 nanoparticle (Fig. 6). Moreover, the valence band position of the g-C3N4 nanoparticle is about 0.7 eV lower than that of porous g-C3N4. Therefore, the photo-generated holes and electrons in porous g-C3N4 can transfer to the conduction band of g-C3N4 nanoparticle, thereby obtaining higher separation efficiency of photo-generated carriers as well as longer carrier lifetime. Under visible-light irradiation, 6CNNP@PCN exhibits the highest photocatalytic performance (Fig. 8) on MB, which is approximately 3.4 times as that of bulk g-C3N4.
The quality of the polymer raw material used in plastic processing methods is an important characteristic because it is one of the main factors in producing quality products. Therefore, the characterization of polymeric pellets in the polymer processing industry is very important to avoid using inferior materials. In general, differences in the interiors of polymeric pellets reflect differences in their densities. In this study, a high-sensitivity magnetic levitation method was used to characterize the polymeric pellets in four different occasions. The device used has a high sensitivity that can distinguish minute differences as small as of 0.0041 g/cm3 in density between different samples. In addition, the method can obtain a sample's density without knowing the weight and volume of the sample. This method can be used to characterize materials by testing only a single pellet, which is very useful for polymeric pellet characterization.