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The proportion of intense tropical cyclones is expected to increase in a changing climate. However, there is currently no consistent and comprehensive assessment of infectious disease risk following tropical cyclone exposure across countries and over decades. We aimed to explore the tropical cyclone-associated hospitalisation risks and burden for cause-specific infectious diseases on a multi-country scale.

Exposure to floods might increase the risks of adverse birth outcomes. However, the current evidence is scarce, inconsistent, and has knowledge gaps. This study aims to estimate the associations of flood exposure before and during pregnancy with adverse birth outcomes and to identify susceptible exposure windows and effect modifiers.

Wildfire activity is an important source of tropospheric ozone (O3) pollution. However, no study to date has systematically examined the associations of wildfire-related O3exposure with mortality globally.

The acute health effects of short-term (hours to days) exposure to fine particulate matter (PM2·5) have been well documented; however, the global mortality burden attributable to this exposure has not been estimated. We aimed to estimate the global, regional, and urban mortality burden associated with short-term exposure to PM2·5and the spatiotemporal variations in this burden from 2000 to 2019.

Numerical analysis of the optical and electrical properties of GaN-based vertical-cavity surface-emitting lasers (VCSELs) with SiO2-buried structure were performed. The simulation results show that, as the SiO2 layer gets thicker, threshold simply gets lower, but slope efficiency gets initially smaller and then gets larger. The mechanism of these tendencies are explained as, when the SiO2 layer becomes thicker, a better lateral optical confinement can be achieved, while the current confinement becomes worse due to the existence of polarization electric field. Furthermore, since the injection current density is higher for VCSEL with small aperture size, the worse current confinement will result in lower slope efficiency. A proper design of SiO2-buried structure to avoid the effect of leakage current is essentially important to achieve high-efficiency GaN-based VCSELs.

Wildfires are thought to be increasing in severity and frequency as a result of climate change1–5. Air pollution from landscape fires can negatively affect human health4–6, but human exposure to landscape fire-sourced (LFS) air pollution has not been well characterized at the global scale7–23. Here, we estimate global daily LFS outdoor fine particulate matter (PM2.5) and surface ozone concentrations at 0.25° × 0.25° resolution during the period 2000–2019 with the help of machine learning and chemical transport models. We found that overall population-weighted average LFS PM2.5and ozone concentrations were 2.5 µg m−3(6.1% of all-source PM2.5) and 3.2 µg m−3(3.6% of all-source ozone), respectively, in 2010–2019, with a slight increase for PM2.5, but not for ozone, compared with 2000–2009. Central Africa, Southeast Asia, South America and Siberia experienced the highest LFS PM2.5and ozone concentrations. The concentrations of LFS PM2.5and ozone were about four times higher in low-income countries than in high-income countries. During the period 2010–2019, 2.18 billion people were exposed to at least 1 day of substantial LFS air pollution per year, with each person in the world having, on average, 9.9 days of exposure per year. These two metrics increased by 6.8% and 2.1%, respectively, compared with 2000–2009. Overall, we find that the global population is increasingly exposed to LFS air pollution, with socioeconomic disparities.

The global spatiotemporal pattern of mortality risk and burden attributable to tropical cyclones is unclear. We aimed to evaluate the global short-term mortality risk and burden associated with tropical cyclones from 1980 to 2019.

Short-term exposure to ambient PM2·5is a leading contributor to the global burden of diseases and mortality. However, few studies have provided the global spatiotemporal variations of daily PM2·5concentrations over recent decades.

Recent work by Kephart et al.1updates estimates for mortality burden attributable to non-optimal ambient temperatures in Latin America, which helps to understand the climate-related health risks and burden in less-developed areas. Here, we discuss the main findings and focus on methodology that remains controversial in heat health field.

In different periods of time, the real-time reasoning tasks generated by autonomous vehicles are scheduled within the tolerance time, which is an important problem to be solved in autonomous driving. Traditionally, tasks are arranged on the on-board unit (OBU), which results in a long time to complete. Heuristic algorithm is widely used in task scheduling, which often leads to premature convergence. Task scheduling in the edge environment can effectively reduce the completion time of tasks. A workflow scheduling strategy in edge environment is designed. To optimize the completion time of reasoning tasks, this paper proposes a Q-learning algorithm based on simulated annealing (SA-QL). Moreover, this paper comprehensively reflects the performance of SA-RL and PSO algorithm from four aspects. Experimental results show that SA-RL algorithm and PSO algorithm have good performance in feasibility and effectiveness. TD(0) algorithms show better performance of exploration, TD(λ) algorithms show that of convergence.

In the context of climate change and deforestation, Brazil is facing more frequent and unprecedented wildfires. Wildfire-related PM2·5is associated with multiple adverse health outcomes; however, the magnitude of these associations in the Brazilian context is unclear. We aimed to estimate the association between daily exposure to wildfire-related PM2·5and cause-specific hospital admission and attributable health burden in the Brazilian population using a nationwide dataset from 2000 to 2015.

Click-through rate prediction is an important method for online advertising and marketing evaluations. However, for environmental reasons, there is a scarcity and imbalance in the advertising data available. We found that a feature transfer can be applied in a transfer learning method to obtain potential connections from less relevant advertisement data. Considering the complexity and diverse features of advertisement data, a feature transfer cannot allow researchers to discover the relationships among such features within the advertisement data. Therefore, we propose a click-through rate method based on a multi-view feature transfer (MFT). MFT divides the data into common and selected features during the data pretreatment process. It then combines the important feature vectors obtained using a Laplacian matrix with the common features obtained during the pretreatment process to form groups of views. Therefore, combining feature transfer matrix with mutli-view clustering is an innovation of the CTR data prediction process. In our experiments, the MFT model achieved good results. Experiments on a large number of datasets of different sizes and the application of three evaluation indicators show that the MFT method delivers excellent prediction results using the transfer relationships among the characteristics of an advertising dataset, and its performance is better than that of many other advertising click-through rate prediction methods. [ABSTRACT FROM AUTHOR]

BackgroundBoth cold and hot temperature have been associated with the onset of asthma, but it remains largely unknown about the risk of asthma hospitalisation associated with short-term temperature fluctuation or temperature variability (TV).ObjectiveTo explore the association between short-term exposure to TV and asthma hospitalisation in Brazil.MethodsData for asthma hospitalisation and weather conditions were collected from 1816 Brazilian cities between 2000 and 2015. TV was calculated as the SD of all daily minimum and maximum temperatures within 0–7 days prior to current day. A time-stratified case-crossover design was performed to quantify the association between TV and hospitalisation for asthma.ResultsA total of 2 818 911 hospitalisations for asthma were identified during the study period. Each 1°C increase in 0–7 days’ TV exposure was related to a 1.0% (95% CI 0.7% to 1.4%) increase in asthma hospitalisations. The elderly were more vulnerable to TV than other age groups, while region and season appeared to significantly modify the associations. There were 159 305 (95% CI 55 293 to 2 58 054) hospitalisations, US$48.41 million (95% CI US$16.92 to US$78.30 million) inpatient costs at 2015 price and 450.44 thousand inpatient days (95% CI 156.08 to 729.91 thousand days) associated with TV during the study period. The fraction of asthma hospitalisations attributable to TV increased from 5.32% in 2000 to 5.88% in 2015.ConclusionTV was significantly associated with asthma hospitalisation and the corresponding substantial health costs in Brazil. Our findings suggest that preventive measures of asthma should take TV into account.

AbstractNearly 80% of new cases of myopia arise between 9 and 13 years old when puberty development also progresses rapidly. However, little is known about the association between myopia and puberty. We aim to evaluate the association between myopia and menarche, the most important puberty indicator for girls, and to test whether menarche could modify the effects of myopia-related behaviors. The participants came from two consecutive national surveys conducted in 30 provinces in mainland China in 2010 and 2014. We included 102,883 girls (61% had experienced menarche) aged 10–15 years. Risk behaviors for myopia which included sleep duration, homework time, and outdoor activity were measured by self-administrated questionnaire. Myopia was defined according to a validated method, and its relationships with menarche status and behaviors were evaluated by robust Poisson regression models based on generalized estimated equation adjusting for cluster effect of school. We found that postmenarche girls were at 13% (95% confidence interval: 11%–16%) higher risk of myopia than premenarche girls, after adjusting for exact age, urban–rural location, survey year, and four behavioral covariates. Short sleep duration (<7 h/d), long homework time (>1 h/d) and low frequency of weekend outdoor activity tended to be stronger (with higher prevalence ratios associated with myopia) risk factors for myopia in postmenarche girls than in premenarche girls, and their interaction with menarche status was all statistically significant (P< 0.05). Overall, our study suggests that menarche onset may be associated with increased risk of myopia among school-aged girls and could also enhance girls’ sensitivity to myopia-related risk behaviors.

Exposure to temperature variability has been associated with increased risk of mortality and morbidity. We aimed to evaluate whether the association between short-term temperature variability and hospitalisation was affected by local socioeconomic level in Brazil.

Community detection is a challenging issue because most existing methods are not well suited for complex social networks with ambiguous structures. In this paper, we propose a novel community detection method named Stacked Autoencoder-Based Community Detection Method via Ensemble Clustering (CDMEC). This is the first time that we have attempted to apply four different complex network similarity representations to the community detection problem. This work makes up for the insufficiency of the single similarity matrix to describe the similarity relationship between nodes. These similarity representations can fully describe and consider the sufficient local information between nodes in a network topology. Our CDMEC framework combines transfer learning and a stacked autoencoder to obtain an efficient low-dimensional feature representation of complex networks and aggregates multiple inputs through a novel ensemble clustering framework. This novel framework first uses the basic clustering results to construct a consistent matrix, and then it employs the nonnegative matrix factorization (NMF)-based clustering method to detect reliable clustering results from the consistent matrix. The results of various extensive experiments on artificial benchmark networks and real-world networks showed that the proposed CDMEC framework is superior to the existing state-of-the-art community detection methods and has great potential in solving the community detection problems. [ABSTRACT FROM AUTHOR]

Phosphor-in-glass film (PiF) based on sapphire substrate (SS) is a promising laser-driven color converter. Herein, by using an optimized silicon borate glass component, high-performance Y3Al5O12:Ce3+PiFs are successfully prepared on both flat sapphire substrate (FSS) and patterned sapphire substrate (PSS) surfaces. Compared with planar structure of FSS, PSS has a periodic arrangement of conical structures on its surface, which effectively increases the contact area between the PiF and the substrate by approximately 56%, thereby forming a more robust film composite structure. The blue laser light is affected by the structure of PSS and produces a typical diffraction effect, which realizes the dispersion of the convergent beam. This significantly expands the laser spot on PSS-PiF and improves the uniformity of light. The saturation power density of PiF-PSS (6.29 W/mm2) is found to be 91% higher than that of PiF-FSS, while maintaining a high luminous efficiency (220 lm/W) and a low correlated color temperature (4500 K). Finally, the thermal quenching mechanism of PiF with different substrate surface morphologies is compared and analyzed. The present results provide important support for designing the interface structure between SSs and PiF to achieve higher efficiency and higher saturation threshold for warm white light.

Non-optimum temperatures are associated with a considerable mortality burden. However, evidence of temperature with all-cause and cause-specific hospital admissions in tropical countries like Thailand is still limited.

Climate change presents a major public health concern in Australia, marked by unprecedented wildfires, heatwaves, floods, droughts, and the spread of climate-sensitive infectious diseases. Despite these challenges, Australia's response to the climate crisis has been inadequate and subject to change by politics, public sentiment, and global developments. This study illustrates the spatiotemporal patterns of selected climate-related environmental extremes (heatwaves, wildfires, floods, and droughts) across Australia during the past two decades, and summarizes climate adaptation measures and actions that have been taken by the national, state/territory, and local governments. Our findings reveal significant impacts of climate-related environmental extremes on the health and well-being of Australians. While governments have implemented various adaptation strategies, these plans must be further developed to yield concrete actions. Moreover, Indigenous Australians should not be left out in these adaptation efforts. A collaborative, comprehensive approach involving all levels of government is urgently needed to prevent, mitigate, and adapt to the health impacts of climate change.

Socioeconomic development is widely regarded as contributing to improved nutrition in children. We aimed to assess the association between socioeconomic indicators and child and adolescent nutritional status, and the differences in this association between urban and rural areas.

BackgroundHeat exposure has been related to increased morbidity and mortality for several health outcomes. There is little evidence whether this is also true for COPD. This study quantified the relationship between ambient heat and hospitalisation for COPD in the Brazilian population.MethodsData on hospitalisations for COPD and weather conditions were collected from 1642 cities during the 2000–2015 hot seasons. A time-stratified, case-crossover design was used for city-specific analyses, which were then pooled at the regional and national levels using random-effect meta-analyses. Stratified analyses were performed by sex, age group and early/late hot season. Annual change in the association was examined using a random-effect meta-regression model.ResultsThe OR of hospitalisation was 1.05 (95% CI 1.04 to 1.06) for every 5 increase in daily mean temperature at the national level, with the effect estimate stronger in the late hot season compared with the early hot season. The effect was similar in women and in men but was greatest for those aged ≥75 years. The association was stronger in the central west and southeast regions and minimal in the northeast. Assuming a causal relationship, 7.2% of admissions were attributable to heat exposure. There was no significant temporal decline in the impact of ambient heat over the 16-year study period.ConclusionIn Brazil, exposure to ambient heat was positively associated with hospitalisation for COPD, particularly during the late hot season. These data add to the growing evidence base implicating global warming as being an important contributor to the future healthcare burden.

When analyzing graph-structured data, graph neural networks (GNNs) use well-designed propagation mechanism to extract node features. However, these methods only incorporate data from nodes' fixed neighborhoods or whole graph, not accounting for the influence of important nodes outside of the neighborhoods. We offer transferable graph neural networks with deep alignment attention (TGDAA), which is a novel neural network framework that designs aligned attention to assign different weights to related important nodes. In such a scenario, we concurrently remove a number of graph neural network obstacles and make TGDAA accessible for transfer learning issues. Extensive experiments in four aspects: traditional metrics, attention-related methods, varying label rates and ablation study show that the suggested TGDAA performs better than ten state-of-the-art techniques on ten benchmark network datasets. [ABSTRACT FROM AUTHOR]

Delays often occur during some activities in software development projects. Without handling of project delays effectively, many software development projects fail to meet their deadlines. If extra employees with same or similar skills and domain knowledge can be rescheduled for the remaining activities of the delayed projects, it can be possible to reduce or even eliminate existing delays in concurrent software development projects of similar nature. However, it is evident that employee rescheduling may result in delaying other activities, which may lead to the problem of delay propagation. Hence, it is important to investigate how to reduce or even eliminate the delay in one project without impacting other projects. By nature this is an NP-hard problem. Therefore, we propose a novel generic rescheduling strategy based on adaptive ant colony optimization algorithm to provide decision support for software project managers to select appropriate employees to deal with project delays. We have carried out a set of comprehensive experiments to evaluate the performance of the proposed strategy. In addition, three real world software project instances are also utilized to evaluate our strategy. The results show that our strategy is effective, efficient and able to outperform its representative counterparts significantly.

ObjectiveTo evaluate lag-response associations and effect modifications of exposure to floods with risks of all cause, cardiovascular, and respiratory mortality on a global scale.DesignTime series study.Setting761 communities in 35 countries or territories with at least one flood event during the study period.ParticipantsMulti-Country Multi-City Collaborative Research Network database, Australian Cause of Death Unit Record File, New Zealand Integrated Data Infrastructure, and the International Network for the Demographic Evaluation of Populations and their Health Network database.Main outcome measuresThe main outcome was daily counts of deaths. An estimation for the lag-response association between flood and daily mortality risk was modelled, and the relative risks over the lag period were cumulated to calculate overall effects. Attributable fractions of mortality due to floods were further calculated. A quasi-Poisson model with a distributed lag non-linear function was used to examine how daily death risk was associated with flooded days in each community, and then the community specific associations were pooled using random effects multivariate meta-analyses. Flooded days were defined as days from the start date to the end date of flood events.ResultsA total of 47.6 million all cause deaths, 11.1 million cardiovascular deaths, and 4.9 million respiratory deaths were analysed. Over the 761 communities, mortality risks increased and persisted for up to 60 days (50 days for cardiovascular mortality) after a flooded day. The cumulative relative risks for all cause, cardiovascular, and respiratory mortality were 1.021 (95% confidence interval 1.006 to 1.036), 1.026 (1.005 to 1.047), and 1.049 (1.008 to 1.092), respectively. The associations varied across countries or territories and regions. The flood-mortality associations appeared to be modified by climate type and were stronger in low income countries and in populations with a low human development index or high proportion of older people. In communities impacted by flood, up to 0.10% of all cause deaths, 0.18% of cardiovascular deaths, and 0.41% of respiratory deaths were attributed to floods.ConclusionsThis study found that the risks of all cause, cardiovascular, and respiratory mortality increased for up to 60 days after exposure to flood and the associations could vary by local climate type, socioeconomic status, and older age.

Robust evidence is needed for the hazardous effects of outdoor particulate matter (PM) on mortality and morbidity from all types of cancers. To summarize and meta-analyze the association between PM and cancer, published articles reporting associations between outdoor PM exposure and any type of cancer with individual outcome assessment that provided a risk estimate in cohort studies were identified via systematic searches. Of 3,256 records, 47 studies covering 13 cancer sites (30 for lung cancer, 12 for breast cancer, 11 for other cancers) were included in the quantitative evaluation. The pooled relative risks (RRs) for lung cancer incidence or mortality associated with every 10-μg/m3PM2.5or PM10were 1.16 (95% confidence interval [CI], 1.10–1.23; I2 = 81%) or 1.22 (95% CI, 1.02–1.45; I2 = 96%), respectively. Increased but non-significant risks were found for breast cancer. Other cancers were shown to be associated with PM exposure in some studies but not consistently and thus warrant further investigation.

Climate change is increasing the risks of injuries, diseases, and deaths globally. However, the association between ambient temperature and renal diseases has not been fully characterized. This study aimed to quantify the risk and attributable burden for hospitalizations of renal diseases related to ambient temperature.

IMPORTANCE: Few studies have reported the association of economic growth with trends of visual impairment in schoolchildren in China or elsewhere. OBJECTIVES: To describe 30-year trends and patterns in visual impairment in China and to explore the association between visual impairment and economic development. DESIGN, SETTING, AND PARTICIPANTS: In this time series analysis of 7 successive cross-sectional surveys from 1985 to 2014, a total of 1 951 084 schoolchildren aged 7 to 18 years from all provinces and autonomous regions of mainland China, excluding Tibet, were studied. In 1985, the survey was conducted between March to June; In 1991-2014, the surveys were conducted between September and November. Data analysis was performed from April 1, 2018, to January 31, 2019. EXPOSURES: The province-level gross domestic product (GDP) per capita was extracted from the China Statistical Yearbook. MAIN OUTCOMES AND MEASURES: Visual impairment, defined as unaided distance visual acuity of worse than 6/7.5 (20/25 Snellen equivalent), and moderate to severe visual impairment, defined as unaided distance visual acuity of worse than 6/18 (20/63 Snellen equivalent), in the worse eye. RESULTS: A total of 1 951 084 participants (mean [SD] age, 12.6 [3.4] years; 50.5% male) were included in the analysis. Among students aged 7 to 18 years, the prevalence of visual impairment increased from 23.7% (95% CI, 23.6%-23.8%) in 1985 to 35.1% (95% CI, 34.9%-35.3%) in 1995 to 55.0% (95% CI, 54.8%-55.3%) in 2014. In 2014, the prevalence was higher among girls (58.3%; 95% CI, 54.8%-55.3%) vs boys (51.8%; 95% CI, 51.5%-52.1%) (prevalence ratio [PR] girls vs boys, 1.12; 95% CI, 1.11-1.13) and among students living in urban (59.9%; 95% CI, 59.6%-60.2%) vs rural (50.2%; 95% CI, 49.9%-50.5%) areas (PR urban vs rural, 1.17; 95% CI, 1.16-1.18), although a more rapid relative increase in prevalence occurred in rural areas (15.3% in 1985 to 50.2% in 2014) than in urban areas (31.5% in 1985 to 59.9% in 2014). From 1995 to 2014, the GDP in China increased from $1263 to $7594 (in 2014 US$ constant price). After demographic characteristics (cluster effect of school, age, sex, urban vs rural location, and relative socioeconomic status within province) were adjusted for, the regression model revealed that every 100% increase in GDP was associated with a 20% (PR, 1.20; 95% CI, 1.20-1.21) increase in the relative risk of visual impairment and a 27% (PR, 1.27; 95% CI, 1.26-1.27) increase in the relative risk of moderate to severe visual impairment. The association was stronger in male (PR, 1.22; 95% CI, 1.21-1.23 for male [as reference] vs 1.19; 95% CI, 1.18-1.19 for female; P &lt; .001), rural (PR, 1.32; 95% CI, 1.31-1.33 for rural [as reference] vs 1.12; 95% CI, 1.12,-1.33 for urban; P &lt; .001), and younger age groups (PR, 1.27; 95% CI, 1.24-1.29 for 7-9 years of age [as reference] vs 1.34; 95% CI, 1.32-1.36 for 10-12 years of age; P &lt; .001; 1.21; 95% CI, 1.20-1.22 for 13-15 years of age; P &lt; .001; and 1.12; 95% CI, 1.11,-1.13 for 16-18 years of age; P &lt; .001). CONCLUSIONS AND RELEVANCE: The rapid increase of visual impairment prevalence and the association between GDP and visual impairment over time suggest that further exacerbation of childhood visual impairment may occur as economic development continues in China.

Community structure is an important characteristic of complex networks. It determines where important functions of a network are located. Recently, discovering community structure in complex networks has become a hot topic of research. However, the continuous increase in network size has made network structure more complex, and community detection has become extremely difficult in real applications. In particular, the detection results are usually not accurate enough when classical clustering methods are applied to high-dimensional data matrices. In this paper, inspired by the relationship between vertices, we design a novel and effective network adjacency matrix transformation method to describe vertices' similarity in the network topology. On this basis, we propose a framework to extract nonlinear features: community detection with deep transitive autoencoder (CDDTA). This framework can obtain powerful nonlinear features of a real network to make community detection algorithms perform excellently in practice. We further incorporate unsupervised transfer learning into the CDDTA (Transfer-CDDTA) by minimizing the Kullback–Leibler divergence of embedded instances, to discover powerful low-dimensional representations. Finally, we propose a new training strategy and optimization method for our algorithm. Extensive experimental results indicate that our new framework can ensure good performance on both real-world networks and artificial benchmark networks, which outperforms most of the state-of-the-art methods for community detection in social networks. [ABSTRACT FROM AUTHOR]

With the rapid development of online advertising, the click-through rate (CTR) prediction plays an important role in improving the benefits of advertising and user experience. CTR is the most commonly used evaluation indicator of the effects of online advertising. At present, the keys including feature extraction and user click behavior modeling have been taken into consideration by many researchers to design methods for CTR prediction. However, the characteristics of high-dimensional data sparseness and imbalance in advertising data are not fully considered, which results in the insufficient utilization of advertising information. To alleviate the problems of data sparsity and imbalance, this paper proposes a robust integrated locally kernel embedding (RILKE) model to solve data sparseness and incorporate unsupervised transfer learning into RILKE to form an improved model, named robust transfer integrated locally kernel embedding (RTILKE). Through theoretical analysis and empirical experiments, RTILKE can efficiently solve the data-imbalance problem of CTR prediction in online advertising, which effectively improves the prediction performance of advertising responses. [ABSTRACT FROM AUTHOR]