Meeting the Hospital Oxygen Demand with a Decentralized Autonomous PV System: Effect of PV Tracking Systems

When it comes to supplying oxygen, current standard hospitals in Iran have proven inadequate in the face of the COVID-19 pandemic, particularly during infection peaks. Power disruptions drastically reduce the oxygen pressure in hospitals, putting patients' health at risk. The present study is the first to attempt to power an oxygen concentrator with a solar-energy-based system. The HOMER 2.81 package was used for technical–economic–environmental–energy analysis. The most notable aspects of this work include evaluating different available solar trackers, using up-to-date equipment price data and up-to-date inflation rate, considering the temperature effects on solar cell performance, sensitivity analysis for the best scenario, considering pollution penalties, and using a three-time tariff system with price incentives for renewable power. The study has been carried out at Hajar Hospital, Shahrekord, Chaharmahal and Bakhtiari Province, Iran. The study showed that, by supplying 60% of the power demand, the dual-axis solar tracking system offered the highest annual power output (47,478 kWh). Furthermore, generating power at—$0.008/kWh due to selling power to the grid, the vertical-axis tracker was found to be the most economical design. Comparing the configuration with a vertical-axis tracker with the conventional scenario (relying on the power distribution grid), the investment is estimated to be recovered in three years with $234,300 in savings by the end of the 25th year. In the best economic scenario, 6137 kg CO2 is produced, and the analysis revealed the negative impact of a temperature rise on the performance and solar power output.

Room-temperature-storable chemiluminescence freeze-drying mixes for detection of SARS-CoV-2 neutralizing antibody

As the vaccine was successfully developed, the spread of the epidemic (COVID-19) was effectively controlled. But there are still thousands of people affected COVID-19 after being vaccinated. Neutralizing activity has become a critical method for quantifying neutralizing antibody against SARS-CoV-2. However, limited to the strict conditions of cold chain transportation, the neutralizing activity test has not been widely promoted. In this study, a room-temperature-storable chemiluminescence freeze-drying mixes for SARS-CoV-2 neutralizing antibody detection was developed to decrease the cost of lyophilization step for promoting its application in third world countries. Several freeze concentrated solutions were used to protect the antigen bioactivity. The mixes can be stored at room temperature over 12 months and still exhibited great accuracy and precision. Thus, the proposed room-temperature-storable chemiluminescence freeze-drying mixes offers a cheap and stable storage method for SARS-CoV-2 neutralizing antibody detection and shows a great potential for promoting the neutralizing activity test.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran.

.In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

Inferred vehicular emissions at a near-road site: Impacts of COVID-19 restrictions, traffic patterns, and ambient air temperature.

Vehicles are a major source of anthropogenic emissions of carbon monoxide (CO), nitrogen oxides (NOx), and black carbon (BC). CO and NOx are known to be harmful to human health and contribute to ozone formation, while BC absorbs solar radiation that contributes to global warming and also has negative impacts on human health and visibility. Travel restrictions implemented during the COVID-19 pandemic provide researchers the opportunity to study the impact of large, on-road traffic reductions on local air quality. Traffic counts collected along Interstate-95, a major eight-lane highway in Maryland (US), reveal a 60% decrease in passenger car totals and an 8.6% (combination-unit) and 21% (single-unit) decrease in truck traffic counts in April 2020 relative to prior Aprils. The decrease in total on-road vehicles led to the near-elimination in stop-and-go traffic and a 14% increase in the mean vehicle speed during April 2020. Ambient near-road (NR) BC, CO, NOx, and carbon dioxide (CO2) measurements were used to determine vehicular emission ratios (ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO, ΔNOx/ΔCO2, and ΔCO/ΔCO2), with each ratio defined as the slope value of a linear regression performed on the concentrations of two pollutants within an hour. A decrease of up to a factor of two in ΔBC/ΔCO, ΔBC/ΔCO2, ΔNOx/ΔCO2, and in the fraction of on-road diesel vehicles from weekdays to weekends shows diesel vehicles to be the dominant source of BC and NOx emissions at this NR site. We estimate up to a 70% reduction in BC emissions in April 2020 compared to earlier years, and attribute much of this to lower diesel BC emissions resulting from improvements in traffic flow and fewer instances of acceleration and braking. Future efforts to reduce vehicular BC emissions should focus on improving traffic flow or turbocharger lag within diesel engines. Inferred BC emissions from the NR site also depend on ambient temperature, with an increase of 54% in ΔBC/ΔCO from -5 to 20 °C during the cold season, similar to previous studies that reported increasing BC emissions with rising temperature. The default setting of MOVES3, the current version of the mobile emission model used by the US EPA, does not adjust hot-running BC emissions for ambient temperature. Future work will focus on improving the accuracy of mobile emissions in air quality modeling by incorporating the effects of temperature and traffic flow in the system used to generate mobile emissions input for commonly used air quality models.

A novel infrared forehead thermometer with ambient temperature compensation for body temperature measurement in low-temperature environment

Body temperature screening and measurement using infrared forehead thermometer (IFT), a non-contact thermometer, is an important method to prevent the spread of COVID-19 at present. However, low accuracy and unreliability of current IFT due to ambient temperature effect prevent it application in most of low-temperature environment. The aim of this study was to measure the body temperature accurately using IFT in low-temperature environment. A novel IFT with broad working temperature range and ambient temperature compensation was designed and fabricated, and the performance was evaluated. Also an ambient temperature compensation method based on Bluetooth module was introduced to improve the accuracy of body temperature measurement for the first time. The experiment results demonstrated that the laboratory indication error and repeatability in test mode of this developed IFT were all below 0.2℃ in ambient temperature range of (3~35) ℃. While the extended uncertainty for laboratory indication error was less than 0.1℃ (k=2). Compared with the contact electronic clinical thermometer, the difference of body temperature was improved within the scope of (-0.3~+0.3)℃ in low-temperature measurement environment. All the results showed that the IFT fabricated in this paper is sufficient and competent for body temperature screening and clinical body temperature measurement in most of low-temperature environment. © 2022 SPIE.

Effects of Dust Event Days on Influenza: Evidence from Arid Environments in Lanzhou

Airborne aerosol is believed to be an important pathway for infectious disease transmissions like COVID-19 and influenza. However, the effects of dust event days on influenza have been rarely explored, particularly in arid environments. This study explores the effects of ambient particulate matter (PM) and dust events on laboratory-confirmed influenza in a semi-arid city. A descriptive analysis of daily laboratory-confirmed influenza (influenza) cases, PM (PM10 and PM2.5), meteorological parameters, and dust events were conducted from 2014 to 2019 in Lanzhou, China. The case-crossover design combined with conditional Poisson regression models was used to estimate the lagging effects of PM and dust events on influenza. In addition, a hierarchical model was used to quantitatively evaluate the interactive effect of PM with ambient temperature and absolute humidity on influenza. We found that PM and dust events had a significant effect on influenza. The effects of PM10 and PM2.5 on influenza became stronger as the cumulative lag days increased. The greatest estimated relative risks (RRs) were 1.018 (1.011,1.024) and 1.061 (1.034,1.087), respectively. Compared with the non-dust days, the effects of dust events with duration ≥ 1 day and with duration ≥ 2 days on influenza were the strongest at lag0 day, with the estimated RRs of 1.245 (95% CI: 1.061–1.463) and 1.483 (95% CI: 1.232–1.784), respectively. Subgroup analysis showed that pre-school children and school-aged children were more sensitive to PM and dust events exposure. Besides, we also found that low humidity and temperature had an interaction with PM to aggravate the risk of influenza. In summary, ambient PM and dust events exposure may increase the risk of influenza, and the risk of influenza increases with the dust events duration. Therefore, more efforts from the government as well as individuals should be strengthened to reduce the effect of PM on influenza, particularly in cold and dry weather.

The Relative Roles of Ambient Temperature and Mobility Patterns in Shaping the Transmission Heterogeneity of SARS-CoV-2 in Japan.

We assess the effects of ambient temperature and mobility patterns on the transmissibility of COVID-19 during the epidemiological years of the pandemic in Japan. The prefecture-specific daily time-series of confirmed coronavirus disease 2019 (COVID-19) cases, meteorological variables, levels of retail and recreation mobility (e.g., activities, going to restaurants, cafes, and shopping centers), and the number of vaccinations were collected for six prefectures in Japan from 1 May 2020 to 31 March 2022. We combined standard time-series generalized additive models (GAMs) with a distributed lag non-linear model (DLNM) to determine the exposure-lag-response association between the time-varying effective reproductive number (Rt), ambient temperature, and retail and recreation mobility, while controlling for a wide range of potential confounders. Utilizing a statistical model, the first distribution of the mean ambient temperature (i.e., -4.9 °C) was associated with an 11.6% (95% confidence interval [CI]: 5.9-17.7%) increase in Rt compared to the optimum ambient temperature (i.e., 18.5 °C). A retail and recreation mobility of 10.0% (99th percentile) was associated with a 19.6% (95% CI: 12.6-27.1%) increase in Rt over the optimal level (i.e., -16.0%). Our findings provide a better understanding of how ambient temperature and mobility patterns shape severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. These findings provide valuable epidemiological insights for public health policies in controlling disease transmission.

Asthma mortality attributable to ambient temperatures: A case-crossover study in China.

BACKGROUND: Whether ambient temperature exposure contributes to death from asthma remains unknown to date. We therefore conducted a case-crossover study in China to quantitatively evaluate the association and burden of ambient temperature exposure on asthma mortality. METHODS: Using data from the National Mortality Surveillance System in China, we conducted a time-stratified case-crossover study of 15 888 individuals who lived in Hubei and Jiangsu province, China and died from asthma as the underlying cause in 2015-2019. Individual-level exposures to air temperature and apparent temperature on the date of death and 21 days prior were assessed based on each subject's residential address. Distributed lag nonlinear models based on conditional logistic regression were used to quantify exposure-response associations and calculate fraction and number of deaths attributable to non-optimum ambient temperatures. RESULTS: We observed a reverse J-shaped association between air temperature and risk of asthma mortality, with a minimum mortality temperature of 21.3 °C. Non-optimum ambient temperature is responsible for substantial excess mortality from asthma. In total, 26.3% of asthma mortality were attributable to non-optimum temperatures, with moderate cold, moderate hot, extreme cold and extreme hot responsible for 21.7%, 2.4%, 2.1% and 0.9% of asthma mortality, respectively. The total attributable fraction and number was significantly higher among adults aged less than 80 years in hot temperature. CONCLUSIONS: Exposure to non-optimum ambient temperature, especially moderate cold temperature, was responsible for substantial excess mortality from asthma. These findings have important implications for planning of public-health interventions to minimize the adverse respiratory damage from non-optimum ambient temperature.

Phase-Change Material Design for Thermoelectric Generator-Assisted Building Integrated Photovoltaic

As the coronavirus pandemic has brought about global economic recession and reduction in greenhouse gas emissions, energy efficient building retrofitting has become a comprehensive solution to increase the employment rate and reduce the energy consumption of buildings. This situation requires more energy-efficient integrated generation systems. In this study, an integrated generation system is proposedfor building integrated photovoltaic, thermoelectric generator, and phase change material as an enhanced generation system for buildings. In the proposed system, the phase change material absorbs solar radiation as latent heat within the melting temperature, increasing the photovoltaic conversion efficiency. Additionally, the thermoelectric generator harvests additional electricity as the temperature difference is maintained during the phase change. The total generated energy of the proposed system highly depends on the melting temperature and thickness of the phase change material. Therefore, the appropriate melting temperature and thickness design conditions of the phase change material were derived with the following simulations based on transient energy balance equations in 12 daily profiles. As a result, the optimal melting temperature increased by 5.4°F (3.6°C) and 1.9°F (1.04°C) with an insolation increase of 317 Btu/ft2 (1000 Wh/m2) and a 1.8°F (1°C) increase in ambient temperature, respectively. In addition, the optimal thickness increased by 0.04 in (2.5 mm) with an insolation increase of 317 Btu/ft2 (1000 Wh/m2).

Effects of Agrivoltaic Systems on the Surrounding Rooftop Microclimate

Agrivoltaic systems have the potential to maximize the usefulness of spaces in building rooftops. Urban farming systems improve the microclimatic conditions, which are beneficial to solar photovoltaic (PV) systems, as they lower the operating temperatures, resulting in a higher operating efficiency. Microclimate simulations by means of ENVI-met simulation showed that between 0800 h and 1800 h, PV temperatures in the plot that has crops below the PV system were on average lower by 2.83 °C and 0.71 °C as compared without crops on a typical sunny and cloudy day, respectively. Hence, we may see PV efficiency performance improvement of 1.13–1.42% and 0.28–0.35% on a sunny day and cloudy day, respectively. Data collected from a physical prototype of an agrivoltaic system suggested that evaporative cooling was responsible for the reduction in ambient temperatures. The presence of crops growing underneath the PV canopy resulted in the agrivoltaic prototype generating between 3.05 and 3.2% more energy over the day as compared to a control system with no crops underneath.

IoT-Based Remote Health Monitoring System Employing Smart Sensors for Asthma Patients during COVID-19 Pandemic

COVID-19 and asthma are respiratory diseases that can be life-threatening in uncontrolled circumstances and require continuous monitoring. A poverty-stricken South Asian country like Bangladesh has been bearing the brunt of the COVID-19 pandemic since its beginning. The majority of the country’s population resides in rural areas, where proper healthcare is difficult to access. This emphasizes the necessity of telemedicine, implementing the concept of the Internet of Things (IoT), which is still under development in Bangladesh. This paper demonstrates how the current challenges in the healthcare system are resolvable through the design of a remote health and environment monitoring system, specifically for asthma patients who are at an increased risk of COVID-19. Since on-time treatment is essential, this system will allow doctors and medical staff to receive patient information in real time and deliver their services immediately to the patient regardless of their location. The proposed system consists of various sensors collecting heart rate, body temperature, ambient temperature, humidity, and air quality data and processing them through the Arduino Microcontroller. It is integrated with a mobile application. All this data is sent to the mobile application via a Bluetooth module and updated every few seconds so that the medical staff can instantly track patients’ conditions and emergencies. The developed prototype is portable and easily usable by anyone. The system has been applied to five people of different ages and medical histories over a particular period. Upon analyzing all their data, it became clear which participants were particularly vulnerable to health deterioration and needed constant observation. Through this research, awareness about asthmatic symptoms will improve and help prevent their severity through effective treatment anytime, anywhere.

Self-powered wireless sensing system driven by daily ambient temperature energy harvesting

This work demonstrates a self-powered wireless IoT sensing system driven by daily ambient temperature energy harvesting. A novel approach using a thermoelectric generator (TEG) which harvests thermal energy from daily ambient temperature fluctuations into electricity as a power source for wireless IoT devices is proposed and investigated. A harvested temperature difference across the TEG is created by employing a phase change material (PCM) which allows the temperature value to be maintained near the melting point at the phase transition during the ambient temperature fluctuations. Experimental results indicated that the harvested temperature differences are 10 degrees C and 5 degrees C for 300 ml and 30 ml of PCM, respectively, during the ambient temperature variations from 5 degrees C to 25 degrees C. By utilizing the ambient temperature fluctuations between day and night times, an average maximum output power of TEG measured for 3 days is 340 mu W and DC-DC electronic conversion efficiency is 28.3%. The total storable energy of approximately 1.46 J for a day is estimated. A capacitor as an energy storage unit could be fully charged and its usable energy is calculated at 0.686 J. The current consumption of 2.1 mu A for every transmit cycle is estimated. A self-powered wireless IoT sensing system driven by realistic ambient temperature variations able to sense the ambient temperature as well as the ambient humidity and transmit to a computer via RF communication is demonstrated successfully.

Heat Load Profiles in Industry and the Tertiary Sector: Correlation with Electricity Consumption and Ex Post Modeling

The accurate prediction of heat load profiles with a daily resolution is required for a broad range of applications, such as potential studies, design, or predictive operation of heating systems. If the heat demand of a consumer mainly originates from (production) processes independent of the ambient temperature, existing load profile prediction methods fail. To close this gap, this study develops two ex post machine learning models for the prediction of the heat demand with a daily resolution. The selected input features are commonly available to each consumer connected to public natural gas and electricity grids or operating an energy monitoring system: Ambient temperature, weekday, electricity consumption, and heat consumption of the last seven days directly before the predicted day. The study’s database covers electricity and natural gas consumption load profiles from 82 German consumers over a period of two years. Electricity and heat consumption correlate strongly with individual patterns for many consumers. Both shallow and deep learning algorithms from the Python libraries Scikit-Learn and Keras are evaluated. A Long Short-Term Memory (LSTM) model achieves the best results (the median of R2 is 0.94). The ex post model architecture makes the model suitable for anomaly detection in energy monitoring systems.

Ambient temperature and Covid-19 transmission: An evidence from a region of Iran based on weather station and satellite data.

BACKGROUND: The SARS-CoV-2 virus pandemic is primarily transmitted by direct contact between infected and uninfected people, though, there are still many unknown factors influencing the survival and transmission of the virus. Air temperature is one of the main susceptible factors. This study aimed to explore the impact of air and land surface temperatures on Covid-19 transmission in a region of Iran. METHOD: Daily Land Surface Temperature (LST) measured by satellite and Air Temperature measured by weather station were used as the predictors of Covid-19 transmission. The data were obtained from February 2020 to April 2021. Spatio-temporal kriging was used in order to predict LST in some days in which no image was recorded by the satellite. The validity of the predicted values was assessed by Bland-Altman technique. The impact of the predictors was analyzed by Distributed Lag Non-linear Model (DLNM). In addition to main effect of temperature, its linear as well as non-linear interaction effect with relative humidity were considered using Generalized Additive Model (GAM) and a bivariate response surface model. Sensitivity analyses were done to select models' parameters, autocorrelation model and function of associations. RESULTS: The dose-response curve revealed that the impact of both predictors was not obvious, though, the risk of transmission tended to be positive due to low values of temperatures. Although the linear interaction effect was not statistically significant, but joint patterns showed that the impact of both LST and AT tended to be different when humidity values were changed. CONCLUSION: However the findings suggested that both LST and AT were not statistically important predictors, but they tended to predict the Covid-19 transmission in some lags. Because of local based evidence, the wide confidence intervals and then non-significant values should be cautiously interpreted.

The sensitivity and specificity analyses of ambient temperature and population size on the transmission rate of the novel coronavirus (COVID-19) in different provinces of Iran.

On 10 April 2020, Iran reported 68,192 COVID-19 cumulative cases including 4232 death and 35,465 recovery cases. Numerous factors could influence the transmission rate and survival of coronavirus. On this basis and according to the latest epidemiological researches, both ambient temperature (AT) and population size (PS) can be considered as significant transmissibility factors for coronavirus. The analysis of receiver operating characteristics (ROC) allows measuring the performance of a classification model using the confusion matrix. This study intends to investigate the sensitivity of AT and PS on the transmission rate of the novel coronavirus in different provinces of Iran. For this purpose, the information of each province of Iran including the annual average of AT and the number of healthy and diseased cases are categorized. Subsequently, the sensitivity and specificity analyses of both AT and PS factors are performed. The obtained results confirm that AT and PS have low sensibility and high sensitivity, respectively. Thus, there is no scientific reason to confirm that the number of COVID-19 cases in warmer climates is less than that of moderate or cold climates. Therefore, it is recommended that the cities/provinces with a population of over 1.7 million people have stricter inspections and more precise controls as their management policy.

Near-physiological-temperature serial crystallography reveals conformations of SARS-CoV-2 main protease active site for improved drug repurposing.

The COVID-19 pandemic has resulted in 198 million reported infections and more than 4 million deaths as of July 2021 (covid19.who.int). Research to identify effective therapies for COVID-19 includes: (1) designing a vaccine as future protection; (2) de novo drug discovery; and (3) identifying existing drugs to repurpose them as effective and immediate treatments. To assist in drug repurposing and design, we determine two apo structures of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease at ambient temperature by serial femtosecond X-ray crystallography. We employ detailed molecular simulations of selected known main protease inhibitors with the structures and compare binding modes and energies. The combined structural and molecular modeling studies not only reveal the dynamics of small molecules targeting the main protease but also provide invaluable opportunities for drug repurposing and structure-based drug design strategies against SARS-CoV-2.

Transmission of SARS-CoV-2 virus and ambient temperature: a critical review.

The coronavirus disease 2019 (COVID-19) pandemic has brought unprecedented public health, and social and economic challenges. It remains unclear whether seasonal changes in ambient temperature will alter spreading trajectory of the COVID-19 epidemic. The probable mechanism on this is still lacking. This review summarizes the most recent research data on the effect of ambient temperature on the COVID-19 epidemic characteristic. The available data suggest that (i) mesophilic traits of viruses are different due to their molecular composition; (ii) increasing ambient temperature decreases the persistence of some viruses in aquatic media; (iii) a 1°C increase in the average monthly minimum ambient temperatures (AMMAT) was related to a 0.72% fewer mammalian individuals that would be infected by coronavirus; (iv) proportion of zoonotic viruses of mammals including humans is probably related to their body temperature difference; (v) seasonal divergence between the northern and southern hemispheres may be a significant driver in determining a waved trajectory in the next 2 years. Further research is needed to understand its effects and mechanisms of global temperature change so that effective strategies can be adopted to curb its natural effects. This paper mainly explores possible scientific hypothesis and evidences that local communities and authorities should consider to find optimal solutions that can limit the transmission of SARS-CoV-2 virus.

Physical distancing implementation, ambient temperature and Covid-19 containment: An observational study in the United States.

Governments may relax physical distancing interventions for coronavirus disease 2019 (Covid-19) containment in warm seasons/areas to prevent economic contractions. However, it is not clear whether higher temperature may offset the transmission risk posed by this relaxation. This study aims to investigate the associations of the effective reproductive number (Rt) of Covid-19 with ambient temperature and the implementation of physical distancing interventions in the United States (US). This study included 50 states and one territory of the US with 4,532,650 confirmed cases between 29 January and 31 July 2020. We used an interrupted time-series model with a state-level random intercept for data analysis. An interaction term of 'physical distancing×temperature' was included to examine their interactions. Stratified analyses by temperature and physical distancing implementation were also performed to analyse the modifying effects. The overall median (interquartile range) Rt was 1.2 (1.0-2.3). The implementation of physical distancing was associated with a 12% decrease in the risk of Rt (relative risk [RR]: 0.88, 95% confident interval [CI]: 0.86-0.89), and each 5 °C increase in temperature was associated with a 2% decrease (RR: 0.98, 95%CI: 0.97-0.98). We observed a statistically significant interaction between temperature and physical distancing implementation, but all the RRs were small (close to one). The containing effects of high temperature were attenuated by 5.1% when physical distancing was implemented. The association of COVID-19 Rt with physical distancing implementation was more stable (0.88 vs. 0.89 in days when temperature was low and high, respectively). Increased temperature did not offset the risk of Covid-19 Rt posed by the relaxation of physical distancing implementation. Our study does not recommend relaxing the implementation of physical distancing interventions in warm seasons/areas.

Systematic review of ambient temperature exposure during pregnancy and stillbirth: Methods and evidence.

BACKGROUND: Associations between ambient temperature exposure during pregnancy and stillbirth have been reviewed and described in the literature. However, there is no existing review of environmental and epidemiologic methods applied to measure stillbirths resulting from exposure to ambient temperatures during pregnancy. The objective of this study is to systematically review published methods, data sources, and data linkage practices to characterize associations between ambient temperature and stillbirth to inform stillbirth prevention and risk management strategies. METHODS: A systematic review of published studies that assess the association between ambient temperature exposure during pregnancy using any measures or approach and stillbirth was undertaken in Cochrane Library, PubMed, Medline, Scopus, Embase, and Web of Science of studies (2000-2020, inclusive). Selection of studies were assessed by pre-specified eligibility criteria and documented using PRISMA. Citations were managed using EndNote X8 whilst selection, reviewing, and data extraction were performed using Covidence. The screening, selection, and data extraction process consisted of two blind, independent reviews followed by a tertiary independent review. An adapted Critical Appraisal Skills Program (CASP) checklist was used to assess quality and bias. The main findings and characteristics of all studies was extracted and summarized. Where appropriate, a meta-analysis will be performed for measures of association. RESULTS: Among 538 original records, 12 eligible articles were identified that analysed associations between ambient temperature exposure and stillbirth for 42,848 stillbirths among 3.4 million births across seven countries. Varied definitions of stillbirth were reported based on gestational age, birthweight, both, or neither. The overall rate of stillbirth ranged from 1.9 to 38.4 per 1000 among six high-income countries and one low-middle-income country. All study designs were retrospective and included ten cohort studies, three case-crossover studies, and two additional case-control subgroup analysis. Exposure data for ambient temperature was mostly derived from standard municipal or country-level monitors based on weather stations (66.6%) or a forecasting model (16.7%); otherwise, not reported (16.7%). Results were not statistically pooled for a meta-analysis due to heterogeneity of methods and models among included studies. All studies reported associations of increased risk of stillbirth with ambient temperature exposures throughout pregnancy, particularly in late pregnancy. One study estimates 17-19% (PAR) of stillbirths are potentially attributable to chronic exposure to hot and cold ambient temperatures during pregnancy. Overall, risk of stillbirth was observed to increase below 15 °C and above 23.4 °C, where highest risk is above 29.4 °C. CONCLUSION: Exposure to hot and cold temperatures during pregnancy may increase the risk of stillbirth, although a clear causative mechanism remains unknown. Despite lack of causal evidence, existing evidence across diverse settings observed similar effects of increased risk of stillbirth using a variety of statistical and methodological approaches for exposure assessments, exposure windows, and data linkage. Managing exposure to ambient temperatures during pregnancy could potentially decrease risk of stillbirth, particularly among women in low-resource settings where access to safe antenatal and obstetric care is challenging. To fully understand the effects or dose-response relationship of maternal exposure to ambient temperatures and stillbirth, future studies should focus on biological mechanisms and contributing factors in addition to improving measurement of ambient temperature exposure.

A Numerical Analysis of the Cooling Performance of a Hybrid Personal Cooling System (HPCS): Effects of Ambient Temperature and Relative Humidity.

Hybrid personal cooling systems (HPCS) incorporated with ventilation fans and phase change materials (PCMs) have shown its superior capability for mitigating workers' heat strain while performing heavy labor work in hot environments. In a previous study, the effects of thermal resistance of insulation pads, and latent heat and melting temperature of PCMs on the HPCS's thermal performance have been investigated. In addition to the aforementioned factors, environmental conditions, i.e., ambient temperature and relative humidity, also significantly affect the thermal performance of the HPCS. In this paper, a numerical parametric study was performed to investigate the effects of the environmental temperature and relative humidity (RH) on the thermal management of the HPCS. Five levels of air temperature under RH = 50% (i.e., 32, 34, 36, 38 and 40 °C) and four levels of environmental RH at two ambient temperatures of 36 and 40 °C were selected (i.e., RH = 30, 50, 70 and 90%) for the numerical analysis. Results show that high environmental temperatures could accelerate the PCM melting process and thereby weaken the cooling performance of HPCS. In the moderately hot environment (36 °C), HPCS presented good cooling performance with the maximum core temperature at around 37.5 °C during excise when the ambient RH ≤ 70%, whereas good cooling performance could be only seen under RH ≤ 50% in the extremely hot environment (40 °C). Thus, it may be concluded that the maximum environmental RH under which the HPCS exhibiting good cooling performance decreases with an increase in the environmental temperature.