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Purpose of Review Increasing wildfire size and severity across the western United States has created an environmental and social crisis that must be approached from a transdisciplinary perspective. Climate change and more than a century of fire exclusion and wildfire suppression have led to contemporary wildfires with more severe environmental impacts and human smoke exposure. Wildfires increase smoke exposure for broad swaths of the US population, though outdoor workers and socially disadvantaged groups with limited adaptive capacity can be disproportionally exposed. Exposure to wildfire smoke is associated with a range of health impacts in children and adults, including exacerbation of existing respiratory diseases such as asthma and chronic obstructive pulmonary disease, worse birth outcomes, and cardiovascular events. Seasonally dry forests in Washington, Oregon, and California can benefit from ecological restoration as a way to adapt forests to climate change and reduce smoke impacts on affected communities. Recent Findings Each wildfire season, large smoke events, and their adverse impacts on human health receive considerable attention from both the public and policymakers. The severity of recent wildfire seasons has state and federal governments outlining budgets and prioritizing policies to combat the worsening crisis. This surging attention provides an opportunity to outline the actions needed now to advance research and practice on conservation, economic, environmental justice, and public health interests, as well as the trade-offs that must be considered. Summary Scientists, planners, foresters and fire managers, fire safety, air quality, and public health practitioners must collaboratively work together. This article is the result of a series of transdisciplinary conversations to find common ground and subsequently provide a holistic view of how forest and fire management intersect with human health through the impacts of smoke and articulate the need for an integrated approach to both planning and practice.

Background Disabilities may play a different role in determining people’s physical activity (PA) and physical inactivity (PI) levels when they go through multiple lifetime transitions (e.g., graduation, marriage) between adolescence and young adulthood. This study investigates how disability severity is associated with changes in PA and PI engagement levels, focusing on adolescence and young adulthood, when the patterns of PA and PI are usually formed. Methods The study employed data from Waves 1 (adolescence) and 4 (young adulthood) of the National Longitudinal Study of Adolescent Health, which covers a total of 15,701 subjects. We first categorized subjects into 4 disability groups: no, minimal, mild, or moderate/severe disability and/or limitation. We then calculated the differences in PA and PI engagement levels between Waves 1 and 4 at the individual level to measure how much the PA and PI levels of individuals changed between adolescence and young adulthood. Finally, we used two separate multinomial logistic regression models for PA and PI to investigate the relationships between disability severity and the changes in PA and PI engagement levels between the two periods after controlling for multiple demographic (age, race, sex) and socioeconomic (household income level, education level) variables. Results We showed that individuals with minimal disabilities were more likely to decrease their PA levels during transitions from adolescence to young adulthood than those without disabilities. Our findings also revealed that individuals with moderate to severe disabilities tended to have higher PI levels than individuals without disabilities when they were young adults. Furthermore, we found that people above the poverty level were more likely to increase their PA levels to a certain degree compared to people in the group below or near the poverty level. Conclusions Our study partially indicates that individuals with disabilities are more vulnerable to unhealthy lifestyles due to a lack of PA engagement and increased PI time compared to people without disabilities. We recommend that health agencies at the state and federal levels allocate more resources for individuals with disabilities to mitigate health disparities between those with and without disabilities.

Cooling technology exploiting the endothermic reaction of fuel has been developed to prevent structural deformation in engines due to air friction and overheating during supersonic flight. Endothermic fuels are liquid hydrocarbon fuels that can absorb heat through endothermic reactions. The initial heat sink capacity of conventional pellet type catalysts for the endothermic reaction cannot be maintained, leading to more rapid deactivation due to coke formation. To maintain the heat sink performance of decomposition catalysts, HZSM-5 catalyst washcoated on metal foam(HZSM-5/metal foam) was used herein. The reactions were carried out in a batch reactor at 673 K at a pressure of 50 bar using methylcyclohexane (MCH) and n-dodecane as reactants. The total MCH conversion achieved with the pellets and HZSM-5/metal foam was same at 93 %. In the case of n-dodecane, the total conversion achieved with the HZSM-5/metal foam (91 %) was superior to that achieved with the pellets (69 %).

Background: Disabilities may play a different role in determining people’s physical activity (PA) and physical inactivity (PI) levels when they go through multiple lifetime transitions (e.g., graduation, marriage) between adolescence and young adulthood. This study investigates how disability severity (i.e., no, minimal, mild, and moderate/severe disability and/or limitation) is associated with changes in PA and PI engagement levels, focusing on adolescence and young adulthood, when the patterns of PA and PI are usually formed. Methods: The study employed data from Waves 1 and 4 of the National Longitudinal Study of Adolescent Health, which covers a total of 15,701 subjects. We first categorized subjects into 4 disability groups: no, minimal, mild, or moderate/severe disability and/or limitation. We then calculated the differences in PA and PI engagement levels between Waves 1 and 4 at the individual level to measure how much the PA and PI levels of individuals changed between adolescence and young adulthood. Finally, we used two separate multinomial logistic regression models for PA and PI to investigate the relationships between disability severity and the changes in PA and PI engagement levels between the two periods after controlling for multiple demographic (age, race, sex) and socioeconomic (income level, education level) variables. Results: We showed that individuals with minimal disabilities were more likely to decrease their PA levels than those without disabilities during transitions from adolescence to young adulthood. Our findings also revealed that individuals with moderate to severe disabilities tended to have higher PI levels than individuals without disabilities when they were young adults. Furthermore, we found that people above the poverty level were more likely to increase their PA levels to a certain degree compared to people in the group below or near the poverty level. Conclusions: Our study partially indicates that individuals with disabilities are more vulnerable to unhealthy lifestyles due to a lack of PA engagement and increased PI time compared to people without disabilities. We recommend that health agencies at the state and federal levels allocate more resources for individuals with disabilities to mitigate health disparities between those with and without disabilities.

Background Farmworkers are at risk of heat-related illness (HRI). We sought to: 1) evaluate the effectiveness of farmworker Spanish/English participatory heat education and a supervisor decision-support mobile application (HEAT intervention) on physiological heat strain; and 2) describe factors associated with HRI symptoms reporting. Methods We conducted a parallel, comparison group intervention study from May–September of 2019 in Central/Eastern Washington State, USA. We used convenience sampling to recruit adult outdoor farmworkers and allocated participating crews to intervention (n = 37 participants) and alternative-training comparison (n = 38 participants) groups. We measured heat strain monthly using heart rate and estimated core body temperature to compute the maximum work-shift physiological strain index (PSImax) and assessed self-reported HRI symptoms using a weekly survey. Multivariable linear mixed effects models were used to assess associations of the HEAT intervention with PSImax, and bivariate mixed models were used to describe factors associated with HRI symptoms reported (0, 1, 2+ symptoms), with random effects for workers. Results We observed larger decreases in PSImax in the intervention versus comparison group for higher work exertion levels (categorized as low, low/medium-low, and high effort), after adjustment for maximum work-shift ambient Heat Index (HImax), but this was not statistically significant (interaction − 0.91 for high versus low/medium-low effort, t = − 1.60, p = 0.11). We observed a higher PSImax with high versus low/medium-low effort (main effect 1.96, t = 3.81, p max with older age (− 0.03, t = − 2.95, p = 0.004), after covariate adjustment. There was no clear relationship between PSImax and the number of HRI symptoms reported. Reporting more symptoms was associated with older age, higher HImax, 10+ years agricultural work, not being an H-2A guest worker, and walking > 3 min to get to the toilet at work. Conclusions Effort level should be addressed in heat management plans, for example through work/rest cycles, rotation, and pacing, in addition to education and other factors that influence heat stress. Both symptoms and indicators of physiological heat strain should be monitored, if possible, during periods of high heat stress to increase the sensitivity of early HRI detection and prevention. Structural barriers to HRI prevention must also be addressed. Trial registration ClinicalTrials.gov Registration Number: NCT04234802, date first posted 21/01/2020.

A broad spectrum of model-derived weather datasets are available in the US. Because each product integrates atmospheric conditions with different model processes, each produces different statistica...

Immigrants may be more vulnerable to coronavirus disease 2019 (COVID-19) than other sub-population groups due to their relatively low socioeconomic status. However, no quantitative studies have examined the relationships between immigrants and COVID-19 health outcomes (confirmed cases and related deaths). We first examined the relationship between total immigrants and COVID-19 health outcomes with spatial Durbin models after controlling for demographic, biophysical and socioeconomic variables. We then repeated the same analysis within multiple subimmigrant groups divided by those with original nativity to examine the differential associations with health outcomes. The result showed that the proportion of all immigrants is negatively associated with the number of confirmed cases and related deaths. At the continent and sub-continent level, we consistently found negative relationships between the number of confirmed cases and the proportion of all sub-immigrant groups. However, we observed mixed associations between the proportion of sub-immigrant groups and the number of deaths. Those counties having a higher prevalence of immigrants from Africa [Eastern Africa: â€"18.6, 95% confidence interval (CI): â€"38.3~â€"2.9; Northern Africa: â€"146.5, 95% CI: â€"285.5~â€"20.1; Middle Africa: â€"622.6, 95% CI: â€"801.4~â€" 464.5] and the Americas (Northern America: â€"90.5, 95% CI: â€" 106.1~â€"73.8; Latin America: â€"6.8, 95% CI: â€"8.1~â€"5.2) mostly had a lower number of deaths, whereas those counties having a higher prevalence of immigrants from Asia (Eastern Asia: 21.0, 95% CI: 7.7~36.2; Western Asia: 42.5, 95% CI: 16.9~68.8; South- Central Asia: 26.6, 95% CI: 15.5~36.9) showed a higher number of deaths. Our results partially support that some immigrants, especially those from Asia, are more vulnerable to COVID-19 than other sub-population groups.

Thermal dehydrogenation of ammonia borane (AB) is one of the simplest methods to generate pure hydrogen for hydrogen power-driven proton exchange membrane fuel cells. For AB thermolysis, it is imperative to develop a highly effective catalyst to initiate the dehydrogenation reaction at a low temperature and eventually achieve enhanced hydrogen production. Here, we introduce a specifically designed AB-SiO2 nanocomposite, which is prepared by simple mixing and pressing processes. Hydrogen evolution during thermolysis of the cotton-structured AB-SiO2 composite is initiated at around 80 °C, and the maximum H2 yield is determined to be 11.2 wt% (2.32 mole equivalents of H2). The yield is found to depend on the size and size distribution of the catalytic SiO2 particles in the hydrogen fuel composite.

Hydrogen release from solid-state ammonia borane (AB, NH3BH3) exhibits a long induction period and requires a relatively high temperature. In the present work, d-mannitol (DM, C6H8(OH)6) was used as an additive to enhance the hydrogen release properties of AB. At proton exchange membrane (PEM) fuel cell operating temperatures, the influence of various operating process parameters (temperature and heating rate) and AB–DM ratio has been investigated. In addition, the composition of gaseous products was analyzed to identify the reaction mechanism using mass spectrometry. In particular, the formation of ammonia, which poisons PEM fuel cells, was quantitatively analyzed. Using solid-state nuclear magnetic resonance, solid products were characterized to better understand the mechanism of AB thermolysis. In the presence of DM, the onset reaction temperature decreased to 80 °C, whereas dehydrogenation kinetics were significantly improved. With DM as an additive, we obtained a high hydrogen yield of 9.1 wt % (1.75...

The heat generated by air friction during the flight of a hypersonic vehicle must be timely removed. For this purpose, a cooling technology based on the endothermic decomposition of the loaded fuel has been developed. In this study, the decomposition of

Background Previous extreme heat and human health studies have investigated associations either over time (e.g. case-crossover or time series analysis) or across geographic areas (e.g. spatial models), which may limit the study scope and regional variation. Our study combines a case-crossover design and spatial analysis to identify: 1) the most vulnerable counties to extreme heat; and 2) demographic and socioeconomic variables that are most strongly and consistently related to heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, acute renal disease, and respiratory disease) across 67 counties in the state of Florida, U. S over 2008–2012. Methods We first used a case-crossover design to examine the effects of air temperature on daily counts of health outcomes. We employed a time-stratified design with a 28-day comparison window. Referent periods were extracted from ±7, ±14, or ± 21 days to address seasonality. The results are expressed as odds ratios, or the change in the likelihood of each health outcome for a unit change in heat exposure. We then spatially examined the case-crossover extreme heat and health odds ratios and county level demographic and socioeconomic variables with multiple linear regression or spatial lag models. Results Results indicated that southwest Florida has the highest risks of cardiovascular disease, dehydration, acute renal disease, and respiratory disease. Results also suggested demographic and socioeconomic variables were significantly associated with the magnitude of heat-related health risk. The counties with larger populations working in farming, fishing, mining, forestry, construction, and extraction tended to have higher risks of dehydration and acute renal disease, whereas counties with larger populations working in installation, maintenance, and repair workers tended to have lower risks of cardiovascular, dehydration, acute renal disease, and respiratory disease. Finally, our results showed that high income counties consistently have lower health risks of dehydration, heat-related illness, acute renal disease, and respiratory disease. Conclusions Our study identified different relationships with demographic/socioeconomic variables for each heat-sensitive health outcome. Results should be incorporated into vulnerability or risk indices for each health outcome.

Occupational heat exposure is associated with substantial morbidity and mortality among outdoor workers. We sought to descriptively evaluate spatiotemporal variability in heat threshold exceedances and describe potential impacts of these exposures for crop and construction workers. We also present general considerations for approaching heat policy-relevant analyses. We analyzed county-level 2011–2020 monthly employment (Bureau of Labor Statistics Quarterly Census of Employment and Wages) and environmental exposure (Parameter-elevation Relationships on Independent Slopes Model (PRISM)) data for Washington State (WA), USA, crop (North American Industry Classification System (NAICS) 111 and 1151) and construction (NAICS 23) sectors. Days exceeding maximum daily temperature thresholds, averaged per county, were linked with employment estimates to generate employment days of exceedances. We found spatiotemporal variability in WA temperature threshold exceedances and crop and construction employment. Maximum temperature exceedances peaked in July and August and were most numerous in Central WA counties. Counties with high employment and/or high numbers of threshold exceedance days, led by Yakima and King Counties, experienced the greatest total employment days of exceedances. Crop employment contributed to the largest proportion of total state-wide employment days of exceedances with Central WA counties experiencing the greatest potential workforce burden of exposure. Considerations from this analysis can help inform decision-making regarding thresholds, timing of provisions for heat rules, and tailoring of best practices in different industries and areas.

People with pre-existing medical conditions, who spend a large proportion of their time indoors, are at risk of emergent morbidities from elevated indoor heat exposures. In this study, indoor heat of structures wherein exposed people received Grady Emergency Services based care in Atlanta, GA, U.S., was measured from May to September 2016.ology: In this case-control study, analyses were conducted to investigate the effect of indoor heat on the odds of 9-1-1 calls for diabetic (n = 90 cases) and separately, for respiratory (n = 126 cases), conditions versus heat-insensitive emergencies (n = 698 controls). Generalized Additive Models considered both linear and non-linear indoor heat and health outcome associations using thin-plate regression splines.Hotter and more humid indoor conditions were non-linearly associated with an increasing likelihood of receiving emergency care for complications of diabetes and severe respiratory distress. Higher heat indices were associated with increased odds of a diabetes (odds ratio for change from 30 to 31 °C: 1.12, 95% CI: 1.08-1.16) or respiratory 9-1-1 medical call versus control (odds ratio for change from 34 to 35 °C: 1.18, 95% CI: 1.09-1.28) call. Both diabetic and respiratory distress patients were more likely to be African-American and/or have comorbidities.In this study, the statistical association of indoor heat exposure with emergency morbidities (diabetic, respiratory) was demonstrated. The study also showcased the value and utility of data gathered by emergency medical dispatch and services from inaccessible private indoor sources (i.e., domiciles) for environmental health.

Current H2-proton exchange membrane fuel cell systems available for commercial applications employ heavy and high-risk physical hydrogen storage containers. However, these compressed or liquefied H2-containing cylinders are only suitable for ground-based electric vehicles, because although highly purified H2 can be stored in a cylinder, it is not compatible with unmanned aerial vehicles (UAVs), which require a lighter and more stable energy source. Here, we introduce a chemical hydrogen storage composite, composed of ammonia borane (AB) as a hydrogen source and various heterogeneous catalysts, to elevate the thermal dehydrogenation rate. Nanoscale SiO2 catalysts with a cotton structure dramatically increase the hydrogen evolution rate on demand, while simultaneously lowering the startup temperature for AB thermolysis. Results show that the dehydrogenation reaction of AB with a cotton-structured SiO2 nanocatalyst composite occurs below 90°C, the reaction time is less than a minute, and the hydrogen generation yield is over 12 wt%, with an activation energy of 63.9 kJ·mol-1.

The purpose of this research is to provide an objective and accurate regional vulnerability index at a finer resolution with the research period from 1983 to 2012 in Korea. To find the spatial patterns and characteristics of regional vulnerability, this research conducted four different types of analyses. First, the most vulnerable regions in terms of demographic, climatological-geographic, socioeconomic, and technological factors were respectively investigated. Second, total vulnerability index combining all the four factors was examined. Next, the most influential factors deciding vulnerability and common spatial patterns of vulnerability were extracted using empirical orthogonal function (EOF) analysis. Lastly, the degree of clustering for each factor was checked using Moran’s I and local indicators of spatial association (LISA). The result found the most vulnerable provinces were Jeolla and Gyeongsang Province, regarding to demographic and climatological- geographic factors, respectively. In the case of socioeconomic factors, the difference between urban and rural areas was larger than the difference between provinces. In addition, the EOF analysis showed that demographic factors would be the most influential factors which explained 32.2 percent of the total variance of data. Lastly, climatological and geographic factors represented the highest degree of clustering (global Moran’s I: 0.51).

Background Some socioeconomically vulnerable groups may experience disproportionately higher risk of extreme heat illness than other groups, but no study has utilized the presence/absence of a social security number (SSN) as a proxy for vulnerable sub-populations. Methods This study focused on the warm season from 2008 to 2012 in Florida, U.S. With a total number of 8,256,171 individual level health outcomes, we devised separate case-crossover models for five heat-sensitive health outcomes (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease), type of health care visit (emergency department (ED) and hospitalization), and patients reporting/not reporting an SSN. Each stratified model also considered potential effect modification by sex, age, or race/ethnicity. Results Mean temperature raised the odds of five heat-sensitive health outcomes with the highest odds ratios (ORs) for heat-related illness. Sex significantly modified heat exposure effects for dehydration ED visits (Males: 1.145, 95 % CI: 1.137–1.153; Females: 1.110, 95 % CI: 1.103–1.117) and hospitalization (Males: 1.116, 95 % CI: 1.110–1.121; Females: 1.100, 95 % CI: 1.095–1.105). Patients not reporting an SSN between 25 and 44 years (1.264, 95 % CI: 1.192–1.340) exhibited significantly higher dehydration ED ORs than those reporting an SSN (1.146, 95 % CI: 1.136–1.157). We also observed significantly higher ORs for cardiovascular disease hospitalization from the no SSN group (SSN: 1.089, 95 % CI: 1.088–1.090; no SSN: 1.100, 95 % CI: 1.091–1.110). Conclusions This paper partially supports the idea that individuals without an SSN could experience higher risks of dehydration (for those 25–45 years), renal disease, and cardiovascular disease than those with an SSN.

Social network services (SNSs) may benefit public health by augmenting surveillance and distributing information to the public. In this study, we collected Twitter data focusing on six different heat-related themes (air conditioning, cooling center, dehydration, electrical outage, energy assistance, and heat) for 182 days from May 7 to November 3, 2014. First, exploratory linear regression associated outdoor heat exposure to the theme-specific tweet counts for five study cities (Los Angeles, New York, Chicago, Houston, and Atlanta). Next, autoregressive integrated moving average (ARIMA) time series models formally associated heat exposure to the combined count of heat and air conditioning tweets while controlling for temporal autocorrelation. Finally, we examined the spatial and temporal distribution of energy assistance and cooling center tweets. The result indicates that the number of tweets in most themes exhibited a significant positive relationship with maximum temperature. The ARIMA model results suggest that each city shows a slightly different relationship between heat exposure and the tweet count. A one-degree change in the temperature correspondingly increased the Box-Cox transformed tweets by 0.09 for Atlanta, 0.07 for Los Angeles, and 0.01 for New York City. The energy assistance and cooling center theme tweets suggest that only a few municipalities used Twitter for public service announcements. The timing of the energy assistance tweets suggests that most jurisdictions provide heating instead of cooling energy assistance.

The purpose of the study was to identify dimensions of benefit sought where consumer cross-border online shoppers seek and to examine the effects of the dimensions on expectancy, perceived performance, disconformity and satisfaction with cross-border online shopping. A total of 258 data were gathered and analyzed using SPSS 22.0. The results of the study identified that six dimensions of benefit sought of cross-border online shopping, which were economic-efficiency, convenience, uniqueness, scarcity, superiority and conformity seeking. There were significant effects of economic-efficiency, scarcity and uniqueness seeking on the expectancy, perceived performance, and satisfaction. In addition, consumers were segmented as four clusters classified using benefit sought of cross-border online shopping. The four clusters were labeled as “high interest type”, “disinterested type”, “unique scarcity type”, and “price-first unfussy type”. The significant differences were found among the clusters in the research constructs of expectancy and disconfirmation theory. The findings of the study suggested practical and managerial implications.

Background Elevated and prolonged exposure to extreme heat is an important cause of excess summertime mortality and morbidity. To protect people from health threats, some governments are currently operating syndromic surveillance systems. However, A lack of resources to support time- and labor- intensive diagnostic and reporting processes make it difficult establishing region-specific surveillance systems. Big data created by social media and web search may improve upon the current syndromic surveillance systems by directly capturing people’s individual and subjective thoughts and feelings during heat waves. This study aims to investigate the relationship between heat-related web searches, social media messages, and heat-related health outcomes. Methods We collected Twitter messages that mentioned “air conditioning (AC)” and “heat” and Google search data that included weather, medical, recreational, and adaptation information from May 7 to November 3, 2014, focusing on the state of Florida, U.S. We separately associated web data against two different sources of health outcomes (emergency department (ED) and hospital admissions) and five disease categories (cardiovascular disease, dehydration, heat-related illness, renal disease, and respiratory disease). Seasonal and subseasonal temporal cycles were controlled using autoregressive moving average-generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) and generalized linear model (GLM). Results The results show that the number of heat-related illness and dehydration cases exhibited a significant positive relationship with web data. Specifically, heat-related illness cases showed positive associations with messages (heat, AC) and web searches (drink, heat stroke, park, swim, and tired). In addition, terms such as park, pool, swim, and water tended to show a consistent positive relationship with dehydration cases. However, we found inconsistent relationships between renal illness and web data. Web data also did not improve the models for cardiovascular and respiratory illness cases. Conclusions Our findings suggest web data created by social medias and search engines could improve the current syndromic surveillance systems. In particular, heat-related illness and dehydration cases were positively related with web data. This paper also shows that activity patterns for reducing heat stress are associated with several health outcomes. Based on the results, we believe web data could benefit both regions without the systems and persistently hot and humid climates where excess heat early warning systems may be less effective. Electronic supplementary material The online version of this article (10.1186/s12940-019-0499-x) contains supplementary material, which is available to authorized users.

Figure S1. Google commercial boundary. Research areas are in dark grey. Figure S2. Pearson correlation coefficients between maximum temperature and web data. We only colored significant Pearson’s correlation coefficients. Maximum temperatures up to three lag days were considered. Figure S3. AIC changes after adding one of web data to the second model (days of week and maximum temperature). Minus (blue) means model improvement. Table S1. Demographic estimate summary of each county in 2014 from the U.S. Census Bureau’s American Community Survey. Table S2. Descriptive summary on weather conditions and AICs. Table S3. Heat-related-illness ED model specifications (***

Cooling centers have played a significant role in reducing the risks of adverse health impacts of extreme heat exposure. However, there have been no comparative studies investigating cooling center preparedness in terms of population coverage, location efficiency, and population coverage disparities among different subpopulation groups. Using a catchment area method with a 0.8 km walking distance, we compared three aspects of cooling center preparedness across twenty-five cities in the U.S. We first calculated the percentage of the population covered by a single cooling center for each city. Then, the extracted values were separately compared to the city’s heat indexes, latitudes, and spatial patterns of cooling centers. Finally, we investigated population coverage disparities among multiple demographics (age, race/ethnicity) and socioeconomic (insurance, poverty) subpopulation groups by comparing the percentage of population coverage between selected subpopulation groups and reference subpopulation groups. Our results showed that cooler cities, higher latitude cities, and cities with dispersed cooling centers tend to be more prepared than warmer cities, lower latitude cities, and cities with clustered cooling centers across the U.S. Moreover, older people (≥65) had 9% lower population coverage than younger people (≤64). Our results suggest that the placement of future cooling centers should consider both the location of other nearby cooling centers and the spatial distribution of subpopulations to maximize population coverage and reduce access disparities among several subpopulations.

V-Cr-Y 합금은 높은 투과도와 선택도를 가진 수소 분리막 재료이다. V-Cr-Y 분리막의 투과속도를 증가시키기 위하여 sputtering을 이용한 V-Cr-Y 박막을 제조하고 그 특성을 연구하였다. V-Cr-Y 성분이 각각 89.8%, 10.0%, 0.2%인 타겟을 이용하여 실리콘웨이퍼 위에 박막을 증착시켰으며, EDS 분석을 통해 박막조성이 타겟조성과 일치함을 확인하였다. 스퍼터링 온도와 출력이 증가할수록 박막의 성장속도와 결정크기가 증가하였으며, 압력이 감소할수록 결정구조가 보다 미세하고 치밀해졌다. 최적 스퍼터링 조건은 교류 고주파(RF), 2mTorr, 300W, 상온이었으며, 이 조건으로 제조한 박막을 열처리 하여 수소분리에 적합한 박막을 얻을 수 있었다.

The statistical relationship between elevation roughness and tornado activity is quantified using a spatial model that controls for the effect of population on the availability of reports. Across a large portion of the central Great Plains the model shows that areas with uniform elevation tend to have more tornadoes on average than areas with variable elevation. The effect amounts to a 2.3% [(1.6%, 3.0%) = 95% credible interval] increase in the rate of a tornado occurrence per meter of decrease in elevation roughness, defined as the highest minus the lowest elevation locally. The effect remains unchanged if the model is fit to the data starting with the year 1995. The effect strengthens for the set of intense tornadoes and is stronger using an alternative definition of roughness. The elevation-roughness effect appears to be strongest over Kansas, but it is statistically significant over a broad domain that extends from Texas to South Dakota. The research is important for developing a local climatological description of tornado occurrence rates across the tornado-prone region of the Great Plains.

Outdoor workers face elevated and prolonged heat exposures and have limited access to air-conditioned spaces. This study’s overarching research aim is to increase knowledge of municipal worker heat exposure and adaptation practices. The study’s sub-objectives are: (1) quantifying exposure misclassification from estimating personal heat exposure from the official weather station; (2) surveying worker’s knowledge and practices to adapt to extreme heat; and (3) relating heat exposure and adaptation practices to self-reported thermal comfort. Participants wore a personal heat exposure sensor over 7 days from June 1st to July 3rd, 2015 in Tallahassee, Florida US. Next, participants confirmed the days that they wore the sensor and reported their daily thermal comfort and heat adaptations. Finally, participants completed an extreme heat knowledge, attitudes, and practices survey. Some participants (37%) experienced hotter and more humid conditions (heat index > 2) than the weather station. The most common heat adaptations were staying hydrated (85%), wearing a hat (46%), and seeking shade (40%). During work hours, higher temperatures increased the odds (odds ratio: 1.21, 95% confidence interval: 1.03–1.41, p = 0.016) of a participant feeling too hot. Shifting work duty indoors made workers to feel more comfortable (odds ratio: 0.28, 95% confidence interval: 0.11–0.70, p = 0.005). In hot and humid climates, everyday, heat exposures continuously challenge the health of outdoor workers.

Epidemiologic studies have indicated that albuminuria is associated with mortality from all causes and cardiovascular disease (CVD), with substantial heterogeneity. We evaluated if the associations of urine albumin creatinine ratio (ACR) with all-cause and CVD mortality differed depending on serum concentrations of persistent organic pollutants (POPs), strong lipophilic chemical mixtures with very long half-lives, which are recently linked to many degenerative diseases. Study subjects were participants of the National Health and Nutrition Examination Survey 1999-2004 who were 60 years or older at baseline (n=1215 and 1067 for organochlorine pesticides (OCPs) and other POPs, respectively). They were followed-up through 2011 (mean follow-up periods: 8.1 and 8.0 years for OCPs and other POPs, respectively). The associations between the ACR and all-cause mortality significantly differed by the serum levels of POPs, especially organochlorine pesticides (OCPs; Pinteraction

This study is carried out in order to bridge the gap to understand the relationships between South Asian and East Asian monsoon systems by comparing the summer (June-September) precipitation of Nepal and South Korea. Summer monsoon precipitation data from Nepal and South Korea during 30 years (1981-2010) are used in this research to investigate the association. NCEP/NCAR reanalysis data are also used to see the nature of large scale phenomena. Statistical applications are used to analyze these data. The analyzed results show that summer monsoon precipitation is higher over Nepal (1513.98 ± 159.29 mm y -1 ) than that of South Korea (907.80 ± 204.71 mm y -1 ) and the wettest period in both the countries is July. However, the coefficient of variation shows that amplitude of interannual variation of summer monsoon over South Korea (22.55%) is larger in comparison to that of Nepal (10.52%). Summer monsoon precipitation of Nepal is found to be significantly correlated to that of South Korea with a correlation coefficient of 0.52 (99% confidence level). Large-scale circulations are studied to further investigate the relationship between the two countries. wind and specific humidity at 850 hPa show a strong westerly from Arabian Sea to BOB and from BOB, wind moves towards Nepal in a northwestward direction during the positive rainfall years. In case of East Asia, strong northward displacement of wind can be observed from Pacific to South Korea and strong anticyclone over the northwestern Pacific Ocean. However, during the negative rainfall years, in the South Asian region we can find weak westerly from the Arabian Sea to BOB, wind is blowing in a southerly direction from Nepal and Bangladesh to BOB.

Removal of toxic heavy metal ions from environmental and biological systems is important, but the use of commercially available heavy metal adsorbents is complicated by the need for specific pretreatment steps. We chose to study human hair treated with perm lotion as a heavy metal adsorbent because it is readily available and contains a large number of sulfur atoms for strong coordination to heavy metal ions. The optimal pH of adsorption by perm lotion-treated human hair was 4.16, which was slightly higher than the isoelectric point (pI) of the hair. The maximum removal ratio at pH 4.16 was 88.5% for a 50 ppm Cu2+ solution, and 96.79% for a 50 ppm Pb2+ solution. Almost 90% of the Pb2+ was removed from a 120 ppm Pb2+ solution. The perm-lotion-treated human hair was a cation-selective adsorbent.

The providers of meteorological information want to know the level of satisfaction of forecast users with their services. To provide better service, meteorological communities of each nation are administering a survey on satisfaction of forecast users. However, most researchers provided these users with simple questionnaires and the respondents had to choose one answer among different satisfaction levels. So, the results of this kind of survey have low explanation power and are difficult to use in developing strategy of forecast service. In this study, instead of cost-loss concept, we applied satisfaction-dissatisfaction concept to the contingency table, which is a useful tool to evaluate value of forecast, and estimated satisfaction value of 24h precipitation forecasts in Shanghai, China and Seoul, Korea. Moreover, not only the individual satisfaction value of forecast but the user group`s satisfaction value was evaluated. As for the result, it is effective to enhance forecast accuracy to improve the satisfaction value of deterministic forecast user group, but in the case of probabilistic forecast, it is important to know the level of dissatisfaction of user group and distribution of probability threshold of forecast users. These results can help meteorological communities to search for a solution which can provide better satisfaction value to forecast users.

G-protein coupled receptors (GPCRs) are involved in a wide variety of physiological processes and are known to be targets for nearly 50% of drugs. The various functions of GPCRs are affected by their cognate ligands which are mainly classified as agonists and antagonists. The purpose of this study is to develop a Bayesian classification model, that can predict a compound as either human GPCR agonist or antagonist. Total 6627 compounds experimentally determined as either GPCR agonists or antagonists covering all the classes of GPCRs were gathered to comprise the dataset. This model distinguishes GPCR agonists from GPCR antagonists by using chemical fingerprint, FCFP_6. The model revealed distinctive structural characteristics between agonistic and antagonistic compounds: in general, 1) GPCR agonists were flexible and had aliphatic amines, and 2) GPCR antagonists had planar groups and aromatic amines. This model showed very good discriminative ability in general, with pretty good discriminant statistics for the training set (accuracy: 90.1%) and a good predictive ability for the test set (accuracy: 89.2%). Also, receiver operating characteristic (ROC) plot showed the area under the curve (AUC) to be 0.957, and Matthew's Correlation Coefficient (MCC) value was 0.803. The quality of our model suggests that it could aid to classify the compounds as either GPCR agonists or antagonists, especially in the early stages of the drug discovery process.

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred site. This method can be applied for qualitative prediction of drug metabolism mediated by CYP1A2 and other CYP450 family enzymes, helping to develop drugs efficiently.

We demonstrate facilitated ion transport in oligomer electrolytes by introducing TiO2 hollow particles of ellipsoidal and spherical shapes. It was found that the TiO2 hollow particles of ellipsoidal shape are much more effective in constructing ionic diffusion paths for the Grotthuss mechanism, resulting in highly enhanced diffusion coefficients of ions such as I(-) and I3(-) in oligomer electrolytes. Compared to the hollow spheres of the TiO2 component, the ellipsoidal hollow particles of the TiO2 component provide 11% larger ionic diffusion coefficients, because of their geometry with a relatively small diffusion resistance. Since the facilitated ion transport can render fast redox reactions at both photo and counter electrodes, the solid state dye-sensitized solar cells employing oligomer electrolytes based on the TiO2 hollow ellipsoids exhibit highly improved photovoltaic performances including highly improved energy-conversion efficiency without destabilizing the cell.

This paper describes a concerted effort to realize an automated robotic service in exhibition environments, characterized by a dense population of people with dynamic, random movements and a complex structure with large-scale dimensions. We describe the developed robotic service framework built based on robot navigation function. Spatial representation of the environment is described, on which spatial path planning, robot localization, and remote monitoring and control of individual robots are developed. As observed from a long-term demonstration, the developed robotic system provided intended robotic services for visitors and obtained satisfactory user feedback, which encourages the adoption of automated robotic services in daily environments.

In an effort to improve drug design and predictions for pharmacokinetics (PK), an empirical model was developed to predict the activation energies (Ea) of cytochrome P450 (CYP450) mediated metabolism. The model, EaMEAD (Activation energy of Metabolism reactions with Effective Atomic Descriptors), predicts the Ea of four major metabolic reactions of the CYP450 enzyme: aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation. To build and validate the empirical model, the E(a) values of the substrates with diverse chemical structures (394 metabolic sites for aliphatic hydroxylation, 27 metabolic sites for N-dealkylation, 9 metabolic sites for O-dealkylation, and 85 metabolic sites for aromatic hydroxylation) were calculated by AM1 molecular orbital (MO). Empirical equations, Quantitative Structure Activity Relationship (QSAR) models, were derived using effective atomic charge, effective atomic polarizability, and bond dipole moments of the substrates as descriptors. EaMEAD is shown to accurately predict Ea with a correlation coefficient (R) of 0.94 and root-mean-square error (RMSE, unit is kcal/mol) of 0.70 for aliphatic hydroxylation, N-dealkylation, and O-dealkylation, and R of 0.83 and RMSE of 0.80 for aromatic hydroxylation, respectively. Physical origin and the role of the effective atomic descriptors of the models are presented in detail. With this model, the Ea of the metabolism can be rapidly predicted without any experimental parameters or time-consuming QM calculation. Regioselectivity prediction with our model is presented in the case of CYP3A4 metabolism. The reliability and ease of use of this model will greatly facilitate early stage PK predictions and rational drug design. Moreover, the model can be applied to develop the Ea prediction model of various types of chemical reactions.

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred si...

Cytochrome P450 3A4 metabolizes nearly 50% of the drugs currently in clinical use with a broad range of substrate specificity. Early prediction of metabolites of xenobiotic compounds is crucial for cost efficient drug discovery and development. We developed a new combined model, MLite, for the prediction of regioselectivity in the cytochrome P450 3A4 mediated metabolism. In the model, the ensemble catalyticphore- based docking method was implemented for the accessibility prediction, and the activation energy estimation method of Korzekwa et al. was used for the reactivity prediction. Four major metabolic reactions, aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation reaction, were included and the reaction data, metabolite information, were collected for 72 well-known substrates. The 47 drug molecules were used as the training set, and the 25 well-known substrates were used as the test set for the ensemble catalyticphore-based docking method. MLite predicted correctly about...

Cytochrome P450 3A4 metabolizes nearly 50% of the drugs currently in clinical use with a broad range of substrate specificity. Early prediction of metabolites of xenobiotic compounds is crucial for cost efficient drug discovery and development. We developed a new combined model, MLite, for the prediction of regioselectivity in the cytochrome P450 3A4 mediated metabolism. In the model, the ensemble catalyticphore- based docking method was implemented for the accessibility prediction, and the activation energy estimation method of Korzekwa et al. was used for the reactivity prediction. Four major metabolic reactions, aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation reaction, were included and the reaction data, metabolite information, were collected for 72 well-known substrates. The 47 drug molecules were used as the training set, and the 25 well-known substrates were used as the test set for the ensemble catalyticphore-based docking method. MLite predicted correctly about 76% of the first two sites in the ranking list of the test set. This predictability is comparable with that of another combined model, MetaSite, and the recently published QSAR model proposed by Sheridan et al. MLite also offers information about binding configurations of the substrate-enzyme complex. This may be useful in drug modification by the structure-based drug design.