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Air pollution is a serious challenge in South Korea and worldwide, and negatively impacts human health and mortality rates. To assess air quality and the spatiotemporal characteristics of atmospheric particulate matter (PM), PM concentrations were compared with meteorological conditions and the concentrations of other airborne pollutants over South Korea from 2015 to 2020, using different linear and non-linear models such as linear regression, generalized additive, and multivariable linear regression models. The results showed that meteorological conditions played a significant role in the formation, transportation, and deposition of air pollutants. PM2.5 levels peaked in January, while PM10 levels peaked in April. Both were at their lowest levels in July. Further, PM2.5 was the highest during winter, followed by spring, autumn, and summer, whereas PM10 was the highest in spring followed by winter, autumn, and summer. PM concentrations were negatively correlated with temperature, relative humidity, and precipitation. Wind speed had an inverse relationship with air quality; zonal and vertical wind components were positively and negatively correlated with PM, respectively. Furthermore, CO, black carbon, SO2, and SO4 had a positive relationship with PM. The impact of transboundary air pollution on PM concentration in South Korea was also elucidated using air mass trajectories.

The rapid, accurate, and economical estimation of crop above-ground biomass at the farm scale is crucial for precision agricultural management. The unmanned aerial vehicle (UAV) remote-sensing system has a great application potential with the ability to obtain remote-sensing imagery with high temporal-spatial resolution. To verify the application potential of consumer-grade UAV RGB imagery in estimating maize above-ground biomass, vegetation indices and plant height derived from UAV RGB imagery were adopted. To obtain a more accurate observation, plant height was directly derived from UAV RGB point clouds. To search the optimal estimation method, the estimation performances of the models based on vegetation indices alone, based on plant height alone, and based on both vegetation indices and plant height were compared. The results showed that plant height directly derived from UAV RGB point clouds had a high correlation with ground-truth data with an R2 value of 0.90 and an RMSE value of 0.12 m. The above-ground biomass exponential regression models based on plant height alone had higher correlations for both fresh and dry above-ground biomass with R2 values of 0.77 and 0.76, respectively, compared to the linear regression model (both R2 values were 0.59). The vegetation indices derived from UAV RGB imagery had great potential to estimate maize above-ground biomass with R2 values ranging from 0.63 to 0.73. When estimating the above-ground biomass of maize by using multivariable linear regression based on vegetation indices, a higher correlation was obtained with an R2 value of 0.82. There was no significant improvement of the estimation performance when plant height derived from UAV RGB imagery was added into the multivariable linear regression model based on vegetation indices. When estimating crop above-ground biomass based on UAV RGB remote-sensing system alone, looking for optimized vegetation indices and establishing estimation models with high performance based on advanced algorithms (e.g., machine learning technology) may be a better way.

Recently, auto-encoder-based generative models have been widely used successfully for image processing. However, there are few studies on the realization of continuous input–output mappings for regression problems. Lack of a sufficient amount of training data plagues regression problems, which is also a notable problem in machine learning, which affects its application in the field of materials science. Using variational auto-encoders (VAEs) as generative models for data augmentation, we address the issue of small data size for regression problems. VAEs are popular and powerful auto-encoder-based generative models. Generative auto-encoder models such as VAEs use multilayer neural networks to generate sample data. In this study, we demonstrate the effectiveness of multi-task learning (auto-encoding and regression tasks) relating to regression problems. We conducted experiments on seven benchmark datasets and on one ionic conductivity dataset as an application in materials science. The experimental results show that the multi-task learning for VAEs improved the generalization performance of multivariable linear regression model trained with augmented data.

Background The transmission dynamics and severity of coronavirus disease 2019 (COVID-19) pandemic is different across countries or regions. Differences in governments' policy responses may explain some of these differences. We aimed to compare worldwide government responses to the spread of COVID-19, to examine the relationship between response level, response timing and the epidemic trajectory. Methods Free publicly-accessible data collected by the Coronavirus Government Response Tracker (OxCGRT) were used. Nine sub-indicators reflecting government response from 148 countries were collected systematically from January 1 to May 1, 2020. The sub-indicators were scored and were aggregated into a common Stringency Index (SI, a value between 0 and 100) that reflects the overall stringency of the government's response in a daily basis. Group-based trajectory modelling method was used to identify trajectories of SI. Multivariable linear regression models were used to analyse the association between time to reach a high-level SI and time to the peak number of daily new cases. Results Our results identified four trajectories of response in the spread of COVID-19 based on when the response was initiated: before January 13, from January 13 to February 12, from February 12 to March 11, and the last stage--from March 11 (the day WHO declared a pandemic of COVID-19) on going. Governments' responses were upgraded with further spread of COVID-19 but varied substantially across countries. After the adjustment of SI level, geographical region and initiation stages, each day earlier to a high SI level (SI > 80) from the start of response was associated with 0.44 (standard error: 0.08, P < 0.001, R.sup.2 = 0.65) days earlier to the peak number of daily new case. Also, each day earlier to a high SI level from the date of first reported case was associated with 0.65 (standard error: 0.08, P < 0.001, R.sup.2 = 0.42) days earlier to the peak number of daily new case. Conclusions Early start of a high-level response to COVID-19 is associated with early arrival of the peak number of daily new cases. This may help to reduce the delays in flattening the epidemic curve to the low spread level. Graphic abstract Keywords: COVID-19, Response, Stringency Index, Multivariable linear regression models

Diabetes could impact many ocular tissues. However, the association of the serum aldehydes with diabetes-related eye diseases (DED) remains unclear. Thus, we aimed to examine the above relationship from the general US population of 2013–2014 National Health and Nutrition Examination Survey (NHANES). The multivariable logistic regression and Bayesian kernel machine regression (BKMR) were used to analyze the effect of serum aldehydes on the risk of DED. Pearson’s correlation analysis, the restricted cubic spline (RCS) model, and the linear regression were performed to explore the association between the serum aldehydes and other parameters. The multivariable linear regression was conducted to further underlie the relationship between the serum aldehydes and the glycohemoglobin A1c (HbA1c) in DED participants. Although no significant association was observed between the serum aldehydes and the risk of DED by the multivariable logistic regression and BKMR, the Pearson correlation revealed a positive association between the HbA1c level and the serum level of heptanaldehyde and isopentanaldehyde in DED participants. The RCS model confirmed the above linear correlation. After adjusting for the cofounding factor of smoking, the multivariable linear regression revealed a significant association between the serum level of heptanaldehyde and the HbA1c level in DED participants. Our results suggest that aldehyde exposure did not significantly increase the risk of DED, while heptanaldehyde was the risk factor for increased HbA1c in DED population.

With the continuous expansion of urban scale with dense population and traffic and the gradual improvement of residents’ requirements for environmental quality, the traditional evaluation method relying on acoustic energy is not enough to reflect the feelings of urban crowds about acoustic environment quality. The acoustic environment quality evaluation method based on human subjective perception has gradually become one of the research focuses in the field of environmental noise control. In recent years, various subjective and objective acoustic characteristic parameters have been introduced into the study of acoustic environment assessment in the global literature. However, the extraction of “effective characteristics” from a large number of physical and psychoacoustic characteristics contained in acoustic signals and the creation of a scientific and efficient subjective evaluation model have always been key technical problems in the field of acoustic environment evaluation. Based on subjective human perceptions, the overall acoustic environment quality evaluation of urban open spaces is studied in this paper. Based on the “effective characteristic” parameters and the subjective characteristic proposed in the previous research, including equivalent continuous A-weighted sound pressure level (LA), the difference between median noise and ambient background noise (L50 − L90), Sharpness (Sh), as well as satisfaction (Sat), the multivariable linear regression algorithm is used to further study the intrinsic correlation between the proposed “effective characteristics” and subjective perception. Then, a satisfaction evaluation model of the acoustic environment based on “effective characteristics” is built in this paper. Furthermore, the soundwalk evaluation experiment and the MATLAB numerical simulation experiment are carried out, which verify that the prediction accuracy of the proposed model is more than 92%, the consistency of satisfaction level is more than 88%, as well as the changes in the values of Sh and L50 − L90 have a significant impact on the satisfaction prediction of the proposed model. It shows that the proposed “effective characteristics” more comprehensively describe the quality level of the regional acoustic environment in urban open space compared with a single LA index, and the proposed acoustic environment satisfaction evaluation model based on “effective characteristics” has significant accuracy superiority and regional applicability.

The application of fully recycled aggregate concrete (FRAC) promotes sustainable construction, but its mechanical properties are often unstable due to the high absorption and variability of recycled aggregates. This study investigates the effect of saturation degrees of recycled coarse aggregate (RCA) and recycled fine aggregate (RFA) on FRAC’s mechanical performance and failure mechanisms. Results show that optimal strength is achieved at 70% RCA and 25% RFA saturation. Reducing RFA saturation from 100% to 25% increases compressive strength by 28.8% and tensile strength by 34.6%. RFA saturation has a greater influence than sand ratio or superplasticizer dosage, second only to water–cement ratio. Analysis indicates that excessive saturation leads to pores and microcracks in the interfacial transition zone, weakening bonding. A multiple linear regression model based on recycled aggregate saturation accurately predicts FRAC properties, supporting optimized use of recycled materials and cleaner construction practices.

A successful vaccination implies the induction of effective specific immune responses. We intend to find biomarkers among various immune cell subpopulations, cytokines and antibodies that could be used to predict the levels of specific antibody- and cell-mediated responses after measles–mumps–rubella vaccination. We measured 59 baseline immune status parameters (frequencies of 42 immune cell subsets, levels of 13 cytokines, immunoglobulins) before vaccination and 13 response variables (specific IgA and IgG, antigen-induced IFN-γ production, CD107a expression on CD8+ T lymphocytes, and cellular proliferation levels by CFSE dilution) 6 weeks after vaccination for 19 individuals. Statistically significant Spearman correlations between some baseline parameters and response variables were found for each response variable (p < 0.05). Because of the low number of observations relative to the number of baseline parameters and missing data for some observations, we used three feature selection strategies to select potential predictors of the post-vaccination responses among baseline variables: (a) screening of the variables based on correlation analysis; (b) supervised screening based on the information of changes of baseline variables at day 7; and (c) implicit feature selection using regularization-based sparse regression. We identified optimal multivariate linear regression models for predicting the effectiveness of vaccination against measles–mumps–rubella using the baseline immune status parameters. It turned out that the sufficient number of predictor variables ranges from one to five, depending on the response variable of interest.

With continued global warming, the frequency and severity of heat wave events increased over the past decades, threatening both regional and global food security in the future. There are growing interests to study the impacts of drought on crop. However, studies on the impacts of heat stress on crop photosynthesis and yield are still lacking. To fill this knowledge gap, we used both statistical models and satellite solar-induced chlorophyll fluorescence (SIF) data to assess the impacts of heat stress on wheat yield in a major wheat growing region, the Indo-Gangetic Plains (IGP), India. The statistical model showed that the relationships between different accumulated degree days (ADD) and reported wheat yield were significantly negative. The results confirmed that heat stress affected wheat yield across this region. Building on such information, satellite SIF observations were used to further explore the physiological basis of heat stress impacts on wheat yield. Our results showed that SIF had strong negative correlations with ADDs and was capable of monitoring heat stress. The SIF results also indicated that heat stress caused yield loss by directly impacting the photosynthetic capacity in wheat. Overall, our findings demonstrated that SIF as an effective proxy for photosynthetic activity would improve our understanding of the impacts of heat stress on wheat yield.

Reducing both the energy consumption and CO2 emissions of buildings is nowadays one of the main objectives of society. The use of heating and cooling equipment is among the main causes of energy consumption. Therefore, reducing their consumption guarantees such a goal. In this context, the use of adaptive setpoint temperatures allows such energy consumption to be significantly decreased. However, having reliable data from an external temperature probe is not always possible due to various factors. This research studies the estimation of such temperatures without using external temperature probes. For this purpose, a methodology which consists of collecting data from 10 weather stations of Galicia is carried out, and prediction models (multivariable linear regression (MLR) and multilayer perceptron (MLP)) are applied based on two approaches: (1) using both the setpoint temperature and the mean daily external temperature from the previous day; and (2) using the mean daily external temperature from the previous 7 days. Both prediction models provide adequate performances for approach 1, obtaining accurate results between 1 month (MLR) and 5 months (MLP). However, for approach 2, only the MLP obtained accurate results from the 6th month. This research ensures the continuity of using adaptive setpoint temperatures even in case of possible measurement errors or failures of the external temperature probes.