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The 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

In Named Data Networking (NDN), the processing of the packet is based on the data rather than the communication endpoint. It's very suitable for the core demand of content sharing on the internet today. However, there are still some problems to be dealt with, including mobility support of the producer. By analyzing existing solutions, we put forward a solution finding MCS (Mobile Content Source) passively by HR(Home Repository) and leading interest packets to find MCS actively in the local scope at the same time to speed up to search MCS and to support NDN producer mobility. Triangle routing problem in single HR and low efficiency problem can be based on this solution. Experimental results show that our scheme does better in reducing delay with low handoff cost than other typical solutions.

Background In the context of global warming, studies have turned to assess the temporal trend of the association between temperature and health outcomes, which can be used to reflect whether human beings have adapted to the local temperature. However, most studies have only focused on hot temperature and mortality. We aim to investigate the temporal variations in the association between ambient temperature and hospitalisations for cardiovascular diseases in Queensland, Australia from 1995 to 2016. Methods and findings We obtained data on 1,855,717 cardiovascular hospitalisations (mean age: 65.9 years, 42.7% female) from all 443 postal areas in Queensland, Australia between January 1, 1995 and December 31, 2016. Grid-level meteorological data were downloaded from scientific information for landowners. We used a time-stratified case-crossover design fitted with a conditional quasi-Poisson regression model and time-varying distributed lag nonlinear model (DLNM) to evaluate the association between temperature and cardiovascular hospitalisations and the temporal trends of the associations. Stratified analyses were performed in different age, sex, and climate zones. In all groups, relative risks (RRs) of cardiovascular hospitalisations associated with high temperatures (heat effects) increased, but cold effects showed a decreasing trend from 1995 to 2016. The increasing magnitude of heat effects was larger (p = 0.002) in men than in women and larger (p < 0.001) in people aged ≤69 years than in those aged ≥70 years. There was no apparent difference amongst different climate zones. The study was limited by the switch from ICD-9 to ICD-10 coding systems, by being unable to separate first-time hospitalisation from repeated hospitalisations, and possibly by confounding by air pollution or by influenza infections. Conclusion The impacts of cold temperatures on cardiovascular hospitalisations have decreased, but the impacts of high temperatures have increased in Queensland, Australia. The findings highlight that Queensland people have adapted to the impacts of cold temperatures, but not high temperatures. The burden of cardiovascular hospitalisations due to high temperatures is likely to increase in the context of global warming., Peng Lu and colleagues investigate associations over time between ambient temperatures and hospitalizations for cardiovascular diseases., Author summary Why was this study done? Studies have reported temporal decline in the association between hot temperature and cardiovascular mortality, which indicates that people may have adapted to the warming climate. However, little is known about the change in the association between ambient temperature and cardiovascular hospitalisation in recent decades. What did the researchers do and find? Using 1.9 million cardiovascular hospitalisations during 1995–2016 in Queensland, Australia, we developed a model to evaluate the association between temperature and cardiovascular hospitalisations and their trend over time. We found that the impact of cold temperature on cardiovascular hospitalisation decreased, but the impact of high temperature on cardiovascular hospitalisations increased from 1995 to 2016. The results were robust amongst different subgroups. The increasing heat effect was more serious for men than women and for people ≤69 years than people ≥70 years. What do these findings mean? Given the increased associations between hot temperature and cardiovascular hospitalisation, the acceleration of global warming, and an aging population, we highlight a worrying trend that the burden of cardiovascular healthcare facilities is to increase over time on high-temperature days. Heat warning systems should focus more on men and people ≤69 years old, since heat effects on these groups increased more noticeably than for women and people ≥70 years old, respectively.

Background Heat exposure is a risk factor for urologic diseases. However, there are limited existing studies that have examined the relationship between high temperatures and urologic disease. The aim of this study was to examine the associations between heat exposure and hospitalizations for urologic diseases in Queensland, Australia, during the hot seasons of 1995–2016 and to quantify the attributable risks. Methods We obtained 238 427 hospitalized cases with urologic diseases from Queensland Health between 1 December 1995 and 31 December 2016. Meteorological data were collected from the Scientific Information for Land Owners—a publicly accessible database of Australian climate data that provides daily data sets for a range of climate variables. A time-stratified, case-crossover design fitted with the conditional quasi-Poisson regression model was used to estimate the associations between temperature and hospitalizations for urologic diseases at the postcode level during each hot season (December–March). Attributable rates of hospitalizations for urologic disease due to heat exposure were calculated. Stratified analyses were performed by age, sex, climate zone, socio-economic factors and cause-specific urologic diseases. Results We found that a 1°C increase in temperature was associated with a 3.3% [95% confidence interval (CI): 2.9%, 3.7%] increase in hospitalization for the selected urologic diseases during the hot season. Hospitalizations for renal failure showed the strongest increase 5.88% (95% CI: 5.25%, 6.51%) among the specific causes of hospital admissions considered. Males and the elderly (≥60 years old) showed stronger associations with heat exposure than females and younger groups. The sex- and age-specific associations with heat exposure were similar across specific causes of urologic diseases. Overall, nearly one-fifth of hospitalizations for urologic diseases were attributable to heat exposure in Queensland. Conclusions Heat exposure is associated with increased hospitalizations for urologic disease in Queensland during the hot season. This finding reinforces the pressing need for dedicated public health-promotion campaigns that target susceptible populations, especially for those more predisposed to renal failure. Given that short-term climate projections identify an increase in the frequency, duration and intensity of heatwaves, this public health advisory will be of increasing urgency in coming years.

To examine the associations between ambient temperature and hospitalizations for acute kidney injury (AKI) in Queensland, Australia, 1995–2016. Data were collected on a total of 34 379 hospitalizations for AKI from Queensland between 1 January 1995 and 31 December 2016. Meteorological data were downloaded from the Queensland Government’s Department of Environment and Science. We assessed the temperature-AKI relationship using a time-stratified case-crossover design fitted with conditional quasi-Poisson regression model and time-varying distributed lag non-linear model. Stratified analyses were performed by age, sex, climate zone and socioeconomic group. Both cold and hot temperatures were associated with hospitalizations for AKI. There were stronger temperature-AKI associations among women than men. Cold effects were only positive in the ⩾70 years age group. Hot effects were stronger in the ⩽59 years age group than in the >60 years age group. In different climate zone areas, cold effects decreased with increasing local mean temperatures, while hot effects increased. In different socio-economic status groups, hot effects were stronger in the poor areas than the affluent areas. From 1995 to 2016, the magnitude of associations between cold temperature and hospitalizations for AKI decreased, while the hot effect increased. The associations between hot temperature and hospitalizations for AKI become stronger, while the magnitude of cold effect decreased from 1995 to 2016. This trend may accelerate over the coming decades, which warrants further research. More attention is needed toward susceptible population including women, people ⩾70 years, and the people living in hot climate zones and in low socioeconomic status areas.

Background The proportion of asthma patients with mild to moderate exacerbations is far greater than the number who experience episodes that are severe enough to require emergency room visits or hospital admission. However the routinely collected data from hospitals is absent in the past. Objective To evaluate associations between short-term exposures to air pollutants and hospital outpatient visits for asthma in China. Methods We obtained data for 143,057 asthma outpatient visits from the largest hospitals in 17 Chinese cities, between Jan 01 2013 and Dec 31 2015. We used daily concentrations of air pollutants measured by the China National Environmental Monitoring Centre. We used a time-stratified case-crossover design, and fitted conditional logistic regression models to determine the associations. Results Particulate matter ≤10μm in diameter (PM10) and nitrogen dioxide (NO2) were associated with increased risks of hospital outpatient visits for asthma on the same day, while the effects were delayed for particulate matter ≤2.5μm in diameter (PM2.5) and sulphur dioxide (SO2). For the cumulative effect model at lag05 days, 10 μg/m3 increase in air pollutants concentrations were correlated with hospital outpatient visits for asthma with odds ratios (ORs) and 95% confidence intervals 1.004 (1.000-1.008) for PM2.5, 1.005 (1.002-1.008) for PM10, 1.030 (1.021-1.040) for NO2, and 1.015 (1.008-1.021) for SO2. Almost one in nine (10.9%; 7.7, 13.9%) hospital outpatient visits for asthma were attributable to NO2. Conclusion Short-term exposures to PM2.5, PM10, NO2 and SO2 were associated with hospital outpatient visits for asthma in China.

Software defect prediction technology plays an important role in ensuring software quality. The traditional software defect prediction model can only perform “shallow learning” and cannot perform deep mining of data features. Aiming at this problem, we use the stacked denoising auto-encoder (SDAE) to superimpose into deep neural network. First, the deep network model was built through the stacked layers of denoising auto-encoder (DAE), then the unsupervised method was used to train each layer in turn with noised input for more robust expression, characteristics were learnt supervised by back propagation (BP) neural network and the whole net was optimized by using error back propagation. Simulation experiments prove that the prediction accuracy of our SDAE model is significantly improved compared with the traditional SVM and KNN prediction model.

With the development of information technology, the causes of software-intensive system failures become more complicated. This paper analyzes the correlation of various fault factors of software-intensive equipment and uses deep learning model to do fault prediction in complex electronic information system. Experimental results show that neural network model based on feature interaction can get better effect than some other methods.

In recent years, more and more researches are focusing on the field of disaster emergency management, especially in emergency resource dispatching. In a disaster scenario, a control center needs to obtain a large amount of real-time information at the scene to make decisions timely and accurately. Traditional Cloud Computing has many shortcomings in terms of delay and bandwidth, and Edge Computing (EC) will be more suitable for this scenario. We apply EC to the emergency rescue to cope with the problem of latency needs. First, we propose an edge-based emergency rescue architecture that consists of three layers: Cloud Layer, Edge Layer, and Resource Layer. Based on this, we give a resource scheduling model that requires the collaboration of Cloud and Edge Layers. The Cloud Layer gives a partition for these tasks, and all sub-tasks are assigning to the edge servers to get a locally optimal solution. Finally, these solutions from different edge servers are summarized to the Cloud Layer to get a globally optimal solution. We compare our algorithm with CS-GA and VRP. The simulation results show that RSE has good performance in scheduling time and cost.

Aim. To examine the temporal variations of the association between ambient temperature and mortality for cardiovascular diseases in Queensland, Australia between 1997 and 2013. Methods. We obtained 147 238 cardiovascular deaths data from Queensland Health between 1 January 1997 and 31 December 2013. Time-stratified case-crossover design was fitted via the conditional quasi-Poisson regression with time-varying distributed lag non-linear model to estimate the associations between temperature and cardiovascular mortality. Stratified analyses were performed by age, sex, climate zone, and socioeconomic status. Results. We found a substantial decrease in the cold effect, while there was no significant change in the heat effect. Results of subgroup analyses showed an increasing trend for heat effects in men, people ⩽84 years old, those living in low and middle socioeconomic areas and those living in hot climate areas. There was a decreasing trend for the magnitude of associations between temperature (both cold and hot temperatures) and cardiovascular mortality in people ⩾85 years old and in areas of high socioeconomic status. Conclusions. The associations between cold temperature and cardiovascular mortality decreased in Queensland, Australia between 1997 and 2013, but no declines were observed for hot temperatures. Men, people ⩽84 years old, people living in low and middle socioeconomic score areas and people living in hot climate areas demonstrated increased susceptibility to hot temperatures. Our findings suggest a need for heat awareness health promotion campaigns to enhance adaptation to a warming climate among vulnerable population.

Aim To determine the associations between outdoor air pollution and hospital outpatient visits for mental disorders in China. Methods We obtained data of 111,842 hospital outpatient visits for mental disorders from the largest hospitals of 13 cities, China, between January 01, 2013 and December 31, 2015. We collected air pollutant data including particulate matter ≤2.5 µm in diameter (PM2.5), particulate matter ≤10 µm in diameter (PM10), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2) from China National Environmental Monitoring Centre during the same period. We conducted a time-stratified case-crossover design with conditional logistic regression models to determine the associations. Results A 10 µg/m3 increase in PM2.5, PM10, NO2 and SO2 was associated with a significant increase in hospital outpatient visits for mental disorders on the current day. When stratified by age, sex and season, the effects of PM2.5 and NO2 were robust among different subgroups at lag05 days. PM10 showed positive associations in males, in cold season, and in depression patients. SO2 showed positive associations in males, in cold season, and in anxiety patients. O3 showed positive associations in females, in warm season, and in depression patients. Nearly one sixth hospital outpatient visits for mental disorders can be attributable to NO2. Conclusions Short-term increase in PM2.5, PM10, NO2, SO2 and O3 concentrations was significantly associated with exacerbation of mental disorders in China as indicated by increases in hospital outpatient visits. NO2 had more serious health threat than other pollutants in terms of mental disorders. Our findings strongly suggest a need for more strict emission control regulations to protect mental health from air pollution.

Background Conjunctivitis has hazardous effects on patients’ quality of life through influencing school performance, work productivity, and daily activities such as driving. However, limited evidence is available on the contributory role of air pollution on conjunctivitis, particularly in China. Methods We obtained data of 81,351 conjunctivitis outpatients from the largest comprehensive hospitals of four cities, China, between Jan 1, 2013 and Dec 31, 2014. Data on air pollutants, including particulate matter ≤2.5 μm in diameter (PM2.5), particulate matter ≤10 μm in diameter (PM10), nitrogen dioxide (NO2), ozone (O3) and sulphur dioxide (SO2) were collected from China National Environmental Monitoring Centre. Conjunctivitis outpatient visits were linked with air pollution concentrations by the visiting dates. A time-stratified case-crossover design with conditional logistic regression model was used to examine the effect of short-term exposure to air pollution on conjunctivitis outpatient visits. Results We found that the associations between air pollutants (per 10 μg/m3 increase) and hospital outpatient visits for asthma were [odds ratios (ORs) and 95% confidence intervals]: 1.004(1.002–1.007) for PM2.5, 1.004 (1.002–1.005) for PM10, 1.012(1.005–1.020) for NO2, 1.006 (1.001–1.011) for SO2, and 1.007 (1.003–1.010) for O3, respectively at lag0 day. Outpatients aged 35–64 years showed significant associations with exposure to PM2.5 (1.005, 1.001–1.010), PM10 (1.005, 1.002–1.008), NO2 (1.014, 1.003–1.026), and O3 (1.005, 1.000–1.011), while those aged 15–34 years showed significant associations with exposure to O3 (1.010, 1.004–1.017). Conclusions Short-term exposure to air pollution has impacts on outpatient visits for conjunctivitis in China. This study suggests that improving air quality could protect eye health.