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Proton is an ideal charge carrier for rechargeable batteries due to its small ionic radius, ultrafast diffusion kinetics and wide availability. However, in commonly used acid electrolytes, the co-interaction of polarized water and proton (namely hydronium) with electrode materials often causes electrode structural distortions. The hydronium adsorption on electrode surfaces also facilitates hydrogen evolution as an unwanted side reaction. Here, a “water-in-sugar” electrolyte with high concentration of glucose dissolved in acid to enable the naked proton intercalation, as well as an extended 3.9 V working potential window, is shown. A glucose-derived organic thin film is formed on electrode surface upon cycling. Molecular dynamics simulations reveal the significant decrease of free water in bulk electrolytes, while density functional theory calculations indicate that glucose preferentially binds to the electrode surface which can inhibit water adsorption. The scarcity of free water and the protective organic film work in synergy to suppress water interactions with the electrode surface, which enables the naked proton (de)intercalation. The “water-in-sugar” electrolyte significantly enhances a MoO3 electrode for stable cycling over 100 000 times. This facile electrolyte approach opens new avenues to aqueous electrochemistry and energy storage devices.

AbstractThe purpose of this study was to examine the changes in severity of anxiety and depression symptoms, stress and sleeping quality after three months of mass quarantine for COVID-19 among undergraduate fresh students compared to their pre-COVID-19 measures. We used participants from the Chinese Undergraduate Cohort (CUC), a national prospective longitudinal study to examine the changes in anxiety and depression symptoms severity, stress and sleep quality after being under mass quarantine for three months. Wilcoxon matched pair signed-rank test was used to compare the lifestyle indicators. Severity of anxiety, depression symptoms, stress and sleep quality were compared with Wilcoxon signed-rank test. We used generalized estimating equation (GEE) to further quantify the change in mental health indicators and sleep quality after the COVID-19 mass quarantine compared to baseline. This study found that there was no deterioration in mental health status among Chinese new undergraduate students in 2020 after COVID-19 mass quarantine compared with the baseline measures in 2019. There was an improvement in sleep quality and anxiety symptoms. After adjusting for age, sex, exercise habit, time spent on mobile gadgets, and time spent outdoors, year 2020 was significantly associated with severity of depression symptoms in males (OR:1.52. 95%CI:1.05–2.20, p-value = 0.027). Year 2020 was significantly associated with the improvement of sleeping quality in total (OR:0.45, 95%CI:0.38–0.52, p < 0.001) and in all the subgroups. This longitudinal study found no deterioration in mental health status among Chinese new undergraduate students after three months of mass quarantine for COVID-19.

Background: As COVID-19 vaccination programs are being implemented widely, it is important to examine the attitudes of adolescents toward the COVID-19 vaccine and its uptake. The aim of this study was to examine the acceptance of and attitudes toward COVID-19 vaccines, and their associated factors among adolescents in China. Methods: This was a cross-sectional, observational study conducted between November 27, 2020 and March 12, 2021 using snowball sampling method. Basic sociodemographic characteristics, health-related information, severity of depressive and anxiety symptoms, and attitudes and behavior toward COVID-19 vaccines were assessed. Results: Overall, 1,057 adolescents participated in this study, yielding a response rate of 89.3%. There were 799 (75.59%) [95% Confidence Interval (CI) 73.00-78.18%] adolescents who would accept future COVID-19 vaccination. Binary logistic regression analysis revealed that adolescents who previously heard about COVID-19 vaccines (P = 0.001, odds ratio (OR) = 1.90, 95%CI:1.32-2.74), who thought that COVID-19 vaccines could protect them from COVID-19 infection (P = 0.002, OR = 2.93, 95%CI: 1.49-5.70), and those who encouraged their family members and friends to get vaccinated (P < 0.001, OR = 12.19, 95%CI: 6.78-21.92) and who believed that vaccines are safe (P = 0.012, OR = 3.94, 95%CI: 1.36-11.44) were more likely to accept future COVID-19 vaccination. In addition, younger adolescents (P = 0.003, OR = 0.93, 95%CI: 0.89-0.98) were more likely to accept future COVID-19 vaccines than older adolescents. Conclusions: In conclusion, Chinese adolescents appeared to have positive attitudes toward COVID-19 vaccines. It is important to increase public confidence and knowledge regarding the efficacy and safety of COVID-19 vaccines to maximize the success of vaccination programs.

This study was conducted to determine the effects of supplemental dietary benzoic acid on production performance, egg quality, intestinal morphology, and intestinal microbiota of laying hens. A total of seven hundred twenty 45-wk-old Lohman pink-shell laying hens were randomly allocated to 3 dietary treatments: control (CON), diet supplemented with 1,000 mg/kg benzoic acid (BA1), and 2,000 mg/kg benzoic acid (BA2). Each treatment included 10 replicates of 24 hens; laying hens were monitored for 16 wk. Overall, the results indicate that benzoic acid supplementation had no effect on laying rate, feed intake, feed conversion ratio, and breaking rate; however, a decrease in egg weight (P < 0.01) was observed in the BA2 group. Albumen height and Haugh unit (HU) were also linearly increased in the BA1 and BA2 groups (linear effect, P < 0.05). An increase in duodenum villus height (V) (quadratic effect, P = 0.041) and crypt depth (C) (linear effect, P = 0.012) was observed in the BA2 group, whereas an increased jejunum C and decreased V/C (quadratic effect, P < 0.05) in the BA1 group. Moreover, an increase in ileum V and C (quadratic effect, P < 0.05) was observed in the BA1 group. Microbial richness and diversity were reduced in the BA2 group (P < 0.01). An increase in the abundance of Clostridia (class), Clostridiales (order), Ruminococcaceae (family), and Lachnospiraceae (family) was noted in the BA1 group, whereas an enrichment of Bacteroides caecicola (species) was observed in the BA2 group. The HU positively correlated with genus Sphaerochaeta and Enorma (r = 0.56, 0.56; P < 0.05) but negatively correlated with Romboutsia, Subdoligranulum, Helicobacter, and Mucispirillum (r = -0.58, -0.49, -0.48; -0.70; P < 0.05). In conclusion, dietary supplementation with benzoic acid had no effect on production performance, but it significantly improved egg quality. In addition, 1,000 mg/kg benzoic acid positively modulated intestinal health by improving intestinal morphology and

Background: Vaccination is an important preventative measure against the coronavirus disease 19 (COVID-19) pandemic. To implement vaccination and immunization programs effectively, it is essential to investigate public attitudes toward COVID-19 vaccines. This study examined the attitudes of Chinese college students toward COVID-19 vaccines and their associated factors. Methods: A cross-sectional study was conducted in college students nationwide from December 27, 2020 to January 18, 2021. Attitudes toward COVID-19 vaccines and acceptance of future vaccination programs were assessed. Results: Totally, 2,881 college students participated in this survey; of them, 76.3% (95% CI: 74.8% - 77.9%) were willing to accept a COVID-19 vaccine in the future. Multiple logistic analysis revealed that students living in urban (OR=1.409, 95% CI: 1.152 - 1.724, p=0.001) and those studying health-related courses (OR=1.581, 95% CI: 1.291 - 1.935, p<0.001) were more likely to have a positive attitude toward COVID-19 vaccines. In addition, those who were worried about being infected with COVID-19 (very much vs no, OR=1.690, 95% CI: 1.212-2.356, p=0.002), heard previously about COVID-19 vaccines (OR=1.659, 95% CI: 1.268-2.170, p<0.001), believed that vaccines are safe (Yes vs No, OR=3.570, 95% CI: 1.825-6.980), thought that vaccines can protect people from being infected with COVID-19 (Yes vs No, OR=1.957, 95% CI: 1.286-2.979, p=0.002), and had encouraged their family and friends to have a vaccine (Yes vs No, OR=17.745, 95% CI: 12.271-25.660, p<0.001) had higher acceptance of COVID-19 vaccination. Conclusions: A high rate of acceptance of COVID-19 vaccines was found among Chinese college students. However, vaccine uptake may be reduced by concerns about vaccine safety and efficacy. Alleviating these concerns and enhancing public confidence in vaccines are crucial for future immunization programs against the COVID-19 pandemic.

Children are increasingly using wearables with physical activity tracking features. Although research has designed and evaluated novel features for supporting parent-child collaboration with these wearables, less is known about how families naturally adopt and use these technologies in their everyday life. We conducted interviews with 17 families who have naturally adopted child-owned wearables to understand how they use wearables individually and collaboratively. Parents are primarily motivated to use child-owned wearables for children's long-term health and wellbeing, whereas children mostly seek out entertainment and feeling accomplished through reaching goals. Children are often unable to interpret or contextualize the measures that wearables record, while parents do not regularly track these measures and focus on deviations from their children's routines. We discuss opportunities for making naturally-occurring family moments educational to positively contribute to children's conceptual understanding of health, such as developing age-appropriate trackable metrics for shared goal-setting and data refection.

Miniaturized light sources are important component for the development of on-chip optical networks. In this work, InGaAs/InP single quantum well (QW) nanowire array light emitting diodes (LEDs) with high uniformity have been demonstrated at telecommunication wavelength with high-speed modulation towards GHz-level operation.

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper.

Unsupervised Domain Adaptation aims to learn a classifier for an unlabeled target domain by transferring knowledge from a labeled source domain. Most existing approaches learn domain-invariant features by adapting the entire information of each image. However, forcing adaptation of domain-specific components can undermine the effectiveness of learned features. We propose a novel architecture called Deep Ladder Reconstruction-Classification Network (DLaReC) which is designed to learn cross-domain shared contents by suppressing domain-specific variations. The DLaReC adopts an encoder with cross-domain sharing and a target-domain reconstruction decoder. The encoder and decoder are connected with residual shortcuts at each intermediate layer. By this means, the domain-specific components are directly fed to the decoder for reconstruction, relieving the pressure to learn domain-specific variations at later layers of the shared encoder. Therefore, DLaReC allows the encoder to focus on learning cross-domain shared representations and ignore domain-specific variations. DLaReC is implemented by jointly learning three tasks: supervised classification of the source domain, unsupervised reconstruction of the target domain and cross-domain shared representation adaptation. Extensive experiments on Digit, Office31, ImageCLEF-DA and Office-Home datasets demonstrate the DLaReC outperforms state-of-the-art methods on the whole. The average accuracy on the Digit datasets, for instance, is improved from 95.6% to 96.9%. In addition, the result on Amazon → Webcam obtains significant improvement, i.e., from 91.1% to 94.7%.

A better understanding of the water and energy cycles at climate scale in the Third Pole Environment is essential for assessing and understanding the causes of changes in the cryosphere and hydrosphere in relation to changes of plateau atmosphere in the Asian monsoon system and for predicting the possible changes in water resources in South and East Asia. This paper reports the following results: (1) A platform of in situ observation stations is briefly described for quantifying the interactions in hydrosphere-pedosphere-atmosphere-cryosphere-biosphere over the Tibetan Plateau. (2) A multiyear in situ L-Band microwave radiometry of land surface processes is used to develop a new microwave radiative transfer modeling system. This new system improves the modeling of brightness temperature in both horizontal and vertical polarization. (3) A multiyear (2001–2018) monthly terrestrial actual evapotranspiration and its spatial distribution on the Tibetan Plateau is generated using the surface energy balance system (SEBS) forced by a combination of meteorological and satellite data. (4) A comparison of four large scale soil moisture products to in situ measurements is presented. (5) The trajectory of water vapor transport in the canyon area of Southeast Tibet in different seasons is analyzed, and (6) the vertical water vapor exchange between the upper troposphere and the lower stratosphere in different seasons is presented.

In network theory depression is conceptualized as a complex network of individual symptoms that influence each other, and central symptoms in the network have the greatest impact on other symptoms. Clinical features of depression are largely determined by sociocultural context. No previous study examined the network structure of depressive symptoms in Hong Kong residents. The aim of this study was to characterize the depressive symptom network structure in a community adult sample in Hong Kong during the COVID-19 pandemic. A total of 11,072 participants were recruited between 24 March and 20 April 2020. Depressive symptoms were measured using the Patient Health Questionnaire-9. The network structure of depressive symptoms was characterized, and indices of “strength”, “betweenness”, and “closeness” were used to identify symptoms central to the network. Network stability was examined using a case-dropping bootstrap procedure. Guilt, Sad Mood, and Energy symptoms had the highest centrality values. In contrast, Concentration, Suicide, and Sleep had lower centrality values. There were no significant differences in network global strength (p = 0.259), distribution of edge weights (p = 0.73) and individual edge weights (all p values > 0.05 after Holm–Bonferroni corrections) between males and females. Guilt, Sad Mood, and Energy symptoms were central in the depressive symptom network. These central symptoms may be targets for focused treatments and future psychological and neurobiological research to gain novel insight into depression.

The proliferation of smart devices has led to a huge amount of data streaming in the Internet of Things (IoT). However, the resource-limited devices cannot satisfy the demands of computing-in-tensive but delay-sensitive applications. The data delivery among devices may be tampered with by malicious users. These pose new challenges to provide secure and intelligent services in IoT. Blockchain and reinforcement learning (RL) are promising techniques for establishing a secure environment and intelligent resource management. In this article, we introduce a novel software defined networking (SDN)-enabled architecture for edge-cloud orchestrated computing to support secure and intelligent services in IoT. We first introduce the SDN-enabled architecture by integrating cloud computing, edge computing, and IoT networks. Then we provide several applications of SDN-enabled architecture in edge-cloud orchestrated computing. Next, we propose the blockchain and RL envisioned solutions to implement secure and intelligent services in IoT. Moreover, a case study of blockchain- and RL-enabled secure and intelligent computing offloading is presented to validate its effectiveness. We finally provide our conclusion and discuss several promising research directions.

‘Aerodynamic resistance’ (hereafter ra) is a preeminent variable in the modelling of evapotranspiration (ET), and its accurate quantification plays a critical role in determining the performance and consistency of thermal remote sensing-based surface energy balance (SEB) models for estimating ET at local to regional scales. Atmospheric stability links ra with land surface temperature (LST) and the representation of their interactions in the SEB models determines the accuracy of ET estimates.The present study investigates the influence of ra and its relation to LST uncertainties on the performance of three structurally different SEB models by combining nine OzFlux eddy covariance datasets from 2011 to 2019 from sites of different aridity in Australia with MODIS Terra and Aqua LST and leaf area index (LAI) products. Simulations of the latent heat flux (LE, energy equivalent of ET in W/m2) from the SPARSE (Soil Plant Atmosphere and Remote Sensing Evapotranspiration), SEBS (Surface Energy Balance System) and STIC (Surface Temperature Initiated Closure) models forced with MODIS LST, LAI, and in-situ meteorological datasets were evaluated using observed flux data across water-limited (semi-arid and arid) and radiation-limited (mesic) ecosystems.Our results revealed that the three models tend to overestimate instantaneous LE in the water-limited shrubland, woodland and grassland ecosystems by up to 60% on average, which was caused by an underestimation of the sensible heat flux (H). LE overestimation was associated with discrepancies in ra retrievals under conditions of high atmospheric instability, during which errors in LST (expressed as the difference betwee

Infectious disease presents great hazards to public health, due to their high infectivities and potential lethalities. One of the effective methods to hinder the spread of infectious disease is vaccination. However, due to the limitation of resource and the medical budget, vaccinating all people is not feasible in practice. Besides, the vaccinating effects are difficult to be timely observed through traditional ways, such as outpatient services. To tackle above problem, we propose an e-healthcare mobile social internet of things (MSIoTs) based targeted vaccination scheme to fast contain the spread of the infectious disease. Specifically, we first develop an e-healthcare MSIoT architecture by integrating the e-healthcare system and MSIoTs, whereby the spread status of the infectious disease is timely collected. Furthermore, a graph coloring and spreading centrality-based optional candidate searching algorithm is devised to hunt for the candidates that are powerfully capable of preventing infectious disease. Especially, in order to reduce the vaccination cost, we design an optimal vaccinated target selection algorithm to choose a minimum number of targets whose locations are differentially distributed. Extensive simulations demonstrate that the proposed scheme can effectively prevent infectious disease as compared to conventional schemes.

Although a few studies have reported the effects of several polymorphisms on major adverse cardiovascular events (MACE) in patients with acute coronary syndromes (ACS) and those undergoing percutaneous coronary intervention (PCI), these genotypes account for only a small fraction of the variation and evidence is insufficient. This study aims to identify new genetic variants associated with MACE end point during the 18-month follow-up period by a two-stage large-scale sequencing data, including high-depth whole exome sequencing of 168 patients in the discovery cohort and high-depth targeted sequencing of 1793 patients in the replication cohort. We discovered eight new genotypes and their genes associated with MACE in patients with ACS, including MYOM2 (rs17064642), WDR24 (rs11640115), NECAB1 (rs74569896), EFR3A (rs4736529), AGAP3 (rs75750968), ZDHHC3 (rs3749187), ECHS1 (rs140410716), and KRTAP10-4 (rs201441480). Notably, the expressions of MYOM2 and ECHS1 are downregulated in both animal models and patients with phenotypes related to MACE. Importantly, we developed the first superior classifier for predicting 18-month MACE and achieved high predictive performance (AUC ranged between 0.92 and 0.94 for three machine-learning methods). Our findings shed light on the pathogenesis of cardiovascular outcomes and may help the clinician to make a decision on the therapeutic intervention for ACS patients.