Explore the vast range of titles available.

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.

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 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 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

Understanding the complex mechanisms of climate change and its environmental consequences requires the collection and subsequent analysis of geospatial data from observations and numerical modeling. Multivariable linear regression and mixed-effects models were used to estimate daily surface fine particulate matter (PM2.5) levels in the megacity of Pakistan. The main parameters for the multivariable linear regression model were the 10-km-resolution satellite aerosol optical depth (AOD) and daily averaged meteorological parameters from ground monitoring (temperature, dew point, relative humidity, wind speed, wind direction, and planetary boundary layer height). Ground-based PM2.5 was measured in two stations in the city, Korangi (industrial/residential) and Tibet Center (commercial/residential). The initial linear regression model was modified using a stepwise selection procedure and adding interaction parameters. Finally, the modified model showed a strong correlation between the PM2.5–satellite AOD and other meteorological parameters (R2 = 0.88–0.92 and p-value = 10−7 depending on the season and station). The mixed-effect technique improved the model performance by increasing the R2 values to 0.99 and 0.93 for the Korangi and Tibet Center sites, respectively. Cross-validation methods were used to confirm the reliability of the model to predict PM2.5 after 10 years.

Both magnetic Barkhausen noise (MBN) and tangential magnetic field (TMF) strength can be applied in the quantitative prediction of surface hardness of ferromagnetic specimens. The prediction accuracy depends on the selected model and the input parameters of the model. In this study, the relationship between the surface hardness of 12CrMoV steel plate and the measured MBN and TMF signals is investigated with multivariable linear regression (MLR) model and BP neural network technique. A comparative study between the MLR and BP model is conducted. The external validation results show that the BP model utilizing four MBN features as the input nodes has a smaller average prediction error (3.7%) than that of the MLR model (13.2%). Features extracted from the MBN and TMF signals are combined together as the input parameters of the BP model in order to achieve high accuracy. After adding two more TMF features into the input nodes of the BP network, the external validation results suggest that the average prediction error is decreased from 3.7 to 3.5%.

Objective: To assess the association between lipid metabolism and fetal fraction, which is a critical factor in ensuring a highly accurate non-invasive prenatal testing (NIPT), and on the rate of screen failures or “no calls” in NIPT. Methods: A total of 4,514 pregnant women at 12–26 weeks of gestation underwent NIPT sequencing and serum lipid measurements. Univariate analysis and multivariate regression models were used to evaluate the associations of serum lipid concentrations with the fetal fraction and the rate of screen failures. Results: The fetal fraction decreased with increased low-density lipoprotein cholesterol and triglyceride (TG) levels, which were significant factors (standardized coefficient: −0.11). Conversely, high-density lipoprotein cholesterol and the interval between the two tests were positively correlated with the fetal fraction. The median fetal fraction was 10.88% (interquartile range, 8.28–13.89%) and this decreased with TG from 11.56% at ≤1.10 mmol/L to 9.51% at >2.30 mmol/L. Meanwhile, multivariate logistic regression analysis revealed that increased TG levels were independently associated with the risk of screen failures. The rate of screen failures showed an increase with TG levels from 1.20% at ≤1.70 mmol/L to 2.41% at >2.30 mmol/L. Conclusions: The fetal fraction and the rate of screen failures in NIPT are affected by TG levels. Meanwhile, in pregnant women with high TG levels, delaying the time between NIPT blood collections can significantly increase the fetal fraction.

Rural housing abandonment (RHA) is a negative process associated with urbanization that is influenced by many factors, such as natural, social-economic and system management factors. This paper conducted a micro-level analysis of the influencing factors of spatial distribution in the process of RHA through an empirical study on Heshun County on the Loess Plateau of China. The kernel density estimation and spatial autocorrelation analysis showed that the research area has spatial agglomeration of severe RHA. The results of the ordinary least squares (OLS) model reflected that influencing factors of RHA include road distance, housing and village area ratio, per household housing area, per capita income in villages, county town distance, slope, elevation, per capita arable land, arable land quality grade and arable land and housing area ratio. The first four factors were negatively correlated with RHA, and the last six were positively correlated. Multivariable linear regression analysis showed that the arable land and housing area ratio and the housing and village area ratio are the main influencing factors for the formation of housing abandonment. That is, a lower arable land and housing area ratio and a larger housing and village area ratio can reduce the risk of RHA.

ObjectiveTo determine the effects of alanine transaminase (ALT) levels on the screening failure rates or “no calls” due to low fetal fraction (FF) to obtain a result in non-invasive prenatal screening (NIPS).MethodsNIPS by sequencing and liver enzyme measurements were performed in 7,910 pregnancies at 12–26 weeks of gestation. Univariate and multivariable regression models were used to evaluate the significant predictors of screening failure rates among maternal characteristics and relevant laboratory parameters.ResultsOf the 7,910 pregnancies that met the inclusion criteria, 134 (1.69%) had “no calls.” Multiple logistic regression analysis demonstrated that increased body mass index, ALT, prealbumin, albumin levels, and in vitro fertilization (IVF) conception rates were independently associated with screening failures. The test failure rate was higher (4.34 vs. 1.41%; P < 0.001) in IVF pregnancies relative to those with spontaneous conceptions. Meanwhile, the screening failure rates increased with increasing ALT levels from 1.05% at ≤10 U/L to 3.73% at >40 U/L. In particular, IVF pregnancies with an ALT level of >40 U/L had a higher test failure rate (9.52%). Compared with that for an ALT level of ≤10 U/L, the adjusted odds ratio of “no calls” for ALT levels of 10–20, 21–40, and >40 U/L was 1.204 [95% confidence interval (CI), 0.709–2.045], 1.529 (95% CI, 0.865–2.702), and 2.764 (95% CI, 1.500–5.093) ( P trend < 0.001), respectively.ConclusionsIncreased ALT and IVF conceptions were associated with a higher screening failure rates in NIPS. Therefore, a feasible strategy to adjust these factors to reduce the probability of “no calls” due to low FF would be of great clinical significance.

In this essay, we analyze possible influencing factors which relate to population agingusing SPSS.In accord with the multivariable linear regression model, we conclude that the health expenditure of government and society along with population density have a significant correlation with population aging. Moreover, according to Principal Component Analysis (PCA), the result indicates that such factors as per capita GDP, citizen consumption and so forth have a prominent influence on population aging, and also analyze different influencing degrees of different factors during different periods.

Composite structures are known for their ability to be tailored according to specific operating requisites. Therefore, when modelling these types of structures or components, it is important to account for their response variability, which is mainly due to significant parametric uncertainty compared to traditional materials. The possibility of manufacturing a material according to certain needs provides greater flexibility in design but it also introduces additional sources of uncertainty. Regardless of the origin of the material and/or geometrical variabilities, they will influence the structural responses. Therefore, it is important to anticipate and quantify these uncertainties as much as possible. With the present work, we intend to assess the influence of uncertain material and geometrical parameters on the responses of composite structures. Behind this characterization, linear static and free vibration analyses are performed considering that several material properties, the thickness of each layer and the fibre orientation angles are deemed to be uncertain. In this study, multivariable linear regression models are used to model the maximum transverse deflection and fundamental frequency for a given set of plates, aiming at characterizing the contribution of each modelling parameter to the explanation of the response variability. A set of simulations and numerical results are presented and discussed.

In this study, an experimental system entailing ciprofloxacin hydrochloride (CIP) removal from aqueous solution is modeled by using artificial neural networks (ANNs). For modeling of CIP removal from aqueous solution using bentonite and activated carbon, we utilized the combination of output-dependent data scaling (ODDS) with ANN, and the combination of ODDS with multivariable linear regression model (MVLR). The ANN model normalized via ODDS performs better in comparison with the ANN model scaled via standard normalization. Four distinct hybrid models, ANN with standard normalization, ANN with ODDS, MVLR with standard normalization, and MVLR with ODDS, were also applied. We observed that ANN and MVLR estimations’ consistency, accuracy ratios and model performances increase as a result of pre-processing with ODDS.