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In this paper, we explore the use of opinion dynamics models to examine changes in opinions of lifelong learning, and to identify groups of people for targeted intervention to improve their reception to lifelong learning. This method has a multitude of potential applications related to clustering and segmentation. Singapore's initiative for lifelong learning, SkillsFuture, serves as a case study for the application of the technique. We first found a positive correlation between how motivated people are to learn and a negative correlation between how much people know about SkillsFuture opportunities, with respect to their reception to SkillsFuture courses. Then, we used an opinion dynamics model to model the evolution of opinions to produce clusters of people whose opinions will converge. The clusters were then grouped into three groups: those with the largest increase, largest decrease and a middle group. Further examining the covariates of the people in each cluster (e.g., age, last drawn salary, etc.), we propose organizing sharing sessions for the group with the largest increase, intervention for the group with the largest decrease, and gathering more information about the motivations of the middle group. [ABSTRACT FROM AUTHOR]

Ambient air pollution is a risk factor for both acute and chronic diseases and poses serious health threats to the world population. We aim to study the relationship between air pollution and all-cause mortality in the context of a city-state exposed to the Southeast Asian haze problem. The primary exposure was ambient air pollution, as measured by the Pollutants Standards Index (PSI). The outcome of interest was all-cause mortality from 2010–2015. A time-stratified case-crossover design was performed. A conditional Poisson regression model, including environmental variables such as PSI, temperature, wind speed, and rainfall, was fitted to the daily count of deaths to estimate the incidence rate ratio (IRR) of mortality per unit increase in PSI, accounting for overdispersion and autocorrelation. To account for intermediate exposure effects (maximum lag of 10 days), a distributed lag non-linear model was used. There were 105,504 deaths during the study period. Increment in PSI was significantly associated with an increased risk of mortality. The adjusted IRR of mortality per the 10-unit increase in PSI was 1.01 (95%CI = 1.00–1.01). The lag effect was stronger when PSI was in the unhealthy range compared to the good and moderate ranges. At lag = 7 days, PSI appeared to have an adverse effect on mortality, although the effect was not significant. These findings provide evidence on the general health hazard of exposure to air pollution and can potentially guide public health policies in the region. [ABSTRACT FROM AUTHOR]

Introduction: Ambient air pollution is a risk factor for a diverse range of acute and chronic diseases worldwide, including cardiac arrests and respiratory illnesses. This study aims to investigate the relationship between ambient air pollution and all-cause mortality in a city (Singapore) exposed to the Southeast Asian haze problem. Findings will assist in quantifying the healthcare-related economic burden due to airborne pollutants and contribute to our understanding of environmental roles in urban mortality. Hypothesis: It is hypothesized that exposure to increased Pollutant Standards Index (PSI) is associated with increases in all-cause mortality. Methods: A time-stratified case-crossover design was performed with daily all-cause mortality counts in 2010-2015. Examined data were compared against control days of negligible exposure. A conditional Poisson regression model that included potential confounders such as daily ambient temperature, rainfall, and wind speed was fitted to the daily death counts. All models were adjusted for over-dispersion and autocorrelation. PSI was categorized based on National Environment Agency's classification (0-50=Good, 51-100=Moderate, >100=Unhealthy), whereas wind speed, temperature, and rainfall were by tertiles. Results: There were 105504 deaths during the study period. Moderate (Risk ratio/IRR=1.05, 95% CI=1.03-1.07) and unhealthy (Risk Ratio/IRR=1.08, 95% CI=1.03-1.14) PSI levels show significant association with all-cause mortality. Unhealthy temperatures (above 28.5 °C) also show significant association at a Risk Ratio/IRR=1.04, 95% CI=1.02-1.06. Each increment of 30 units in PSI on the same day and previous 1-5 days was significantly associated with 2.51%-3.40% excess risk of mortality (p<0.001). Each increment of 1°C in temperature exhibited health implications in lag day 0-2 with 0.95%-2.00% excess risk of mortality (p<0.05). Conclusions: We found significant short-intermediate term association between ambient air pollution and temperature on all-cause mortality after adjusting for other meteorological indicators. These findings substantiate the health hazards of air pollutant exposure, presenting an argument for concerted efforts to tackle transboundary haze. [ABSTRACT FROM AUTHOR]

To reduce the pace of climate change and achieve the goals set in Paris Agreement by 2030, Association of Southeast Asian Nations (ASEAN) countries have started to prioritize sustainability as one of their top agendas. Numerous studies have demonstrated that one of the most important issues that must be addressed to halt climate change is the urban heat island (UHI). Given the different mitigation strategies available, the focus of our study here is to assess the influence of green spaces and Green Mark commercial buildings on Singapore's temperature distribution using non-exhaustive factors related to energy consumption and efficiency. Additionally, this paper examines the effectiveness of green spaces and commercial buildings in reducing the rate of temperature change. This study uses ArcGIS software to map data, perform spatial analysis through cloud-based mapping, and produce visual representations with geographic information systems (GIS) to promote greater insight on the formulation of goals and policy making for strategic management. In comparison to non-commercial districts, our findings show that commercial districts have the lowest percentage of temperature change, an estimated 1.6 percent, due to a high concentration of green spaces and Green Mark commercial buildings. Our research also helps to close the research gaps in determining the efficacy of Green Mark commercial buildings, skyrise greeneries, gardens, and national parks. It also helps to minimize the bottleneck of expensive building costs and environmental damage that would have occurred from a design flaw found too late in the urban planning and construction process.

The accurate prediction of ambulance demand provides great value to emergency service providers and people living within a city. It supports the rational and dynamic allocation of ambulances and hospital staffing, and ensures patients have timely access to such resources. However, this task has been challenging due to complex multi-nature dependencies and nonlinear dynamics within ambulance demand, such as spatial characteristics involving the region of the city at which the demand is estimated, short and long-term historical demands, as well as the demographics of a region. Machine learning techniques are thus useful to quantify these characteristics of ambulance demand. However, there is generally a lack of studies that use machine learning tools for a comprehensive modeling of the important demand dependencies to predict ambulance demands. In this paper, an original and novel approach that leverages machine learning tools and extraction of features based on the multi-nature insights of ambulance demands is proposed. We experimentally evaluate the performance of next-day demand prediction across several state-of-the-art machine learning techniques and ambulance demand prediction methods, using real-world ambulatory and demographical datasets obtained from Singapore. We also provide an analysis of this ambulatory dataset and demonstrate the accuracy in modeling dependencies of different natures using various machine learning techniques.

Background Prior studies have demonstrated the association of air pollution with cardiovascular deaths. Singapore experiences seasonal transboundary haze. We investigated the association between air pollution and acute myocardial infarction ( AMI ) incidence in Singapore. Methods and Results We performed a time-stratified case-crossover study on all AMI cases in the Singapore Myocardial Infarction Registry (2010-2015). Exposure on days where AMI occurred (case days) were compared with the exposure on days where AMI did not occur (control days). Control days were chosen on the same day of the week earlier and later in the same month and year. We fitted conditional Poisson regression models to daily AMI incidence to include confounders such as ambient temperature, rainfall, wind-speed, and Pollutant Standards Index. We assessed relationships between AMI incidence and Pollutant Standards Index in the entire cohort and subgroups of individual-level characteristics. There were 53 948 cases. Each 30-unit increase in Pollutant Standards Index was association with AMI incidence (incidence risk ratio [ IRR ] 1.04, 95% CI 1.03-1.06). In the subgroup of ST -segment-elevation myocardial infarction the IRR was 1.00, 95% CI 0.98 to 1.03, while for non-ST-segment-elevation myocardial infarction, the IRR was 1.08, 95% CI 1.05 to 1.10. Subgroup analyses showed generally significant. Moderate/unhealthy Pollutant Standards Index showed association with AMI occurrence with IRR 1.08, 95% CI 1.05 to 1.11 and IRR 1.09, 95% CI 1.01 to 1.18, respectively. Excess risk remained elevated through the day of exposure and for >2 years after. Conclusions We found an effect of short-term air pollution on AMI incidence, especially non-ST-segment-elevation myocardial infarction and inpatient AMI . These findings have public health implications for primary prevention and emergency health services during haze.