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Purpose: NHHR, the ratio of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol, is a novel lipid marker associated with the risk of heart diseases and various health conditions. However, there is limited evidence regarding the relationship between NHHR and the onset of hypertension and heart diseases. The aim of this study was to investigate the association between NHHR and the new-onset hypertension and heart diseases among the Chinese middle-aged and older general population. Methods: This prospective cohort study utilized data from a nationally representative sample of Chinese residents aged 45 and older, sourced from the China Health and Retirement Longitudinal Study (CHARLS). The primary outcomes of the study were new-onset hypertension and heart diseases. To investigate the relationship between the NHHR and the risk of new-onset hypertension and heart diseases, multivariate logistic regression models and the restricted cubic spline (RCS) method were employed. Additionally, the mediating effects of hypertension on the association between NHHR and heart diseases were assessed using the bootstrap method. Results: A total of 5349 participants were included in the final analysis and three groups of NHHR were identified, including low-stable, medium-stable, and high-stable NHHR. By 2020, 1,631 participants (30.5%) had been newly diagnosed with hypertension, while 1233 (23.1%) developed heart diseases. Compared to those with a low-stable NHHR, individuals in the other two groups showed a significantly increased risk of developing hypertension and heart diseases. The findings remained consistent across various sensitivity analyses. According to the RCS analysis, a partial U-shaped relationship existed between the NHHR and the risk of developing hypertension and heart diseases (P for nonlinear < 0.001). Furthermore, hypertension was found to partially mediate the association between NHHR and heart diseases. Conclusion: The NHHR was closely associated with an increased risk of developing hypertension and heart diseases. In addition, the NHHR partially mediated the development of heart diseases by promoting hypertension progression. In the prevention and treatment of heart diseases, managing both lipid levels and blood pressure is crucial. [ABSTRACT FROM AUTHOR]

Exploring the nonlinear relationship between air pollution and precursor emissions in Qingdao, eastern China is crucial for improving air quality. We simulated 32 emission reduction scenarios based on different volatile organic compound (VOC) and nitrogen oxide (NOx) emission reduction ratios using the Weather Research and Forecasting-Comprehensive Air Quality Model Extensions model. The emission reduction of VOCs was beneficial for reducing fine particulate matter (PM2.5) concentration in January and ozone (O3) concentration in June. However, NOx must be reduced by at least 48% and 70% to decrease PM2.5 and O3 concentrations, respectively, when VOCs are not reduced. The responses of PM2.5 and O3 concentrations to emission reductions from different sources were also evaluated. The reduction in VOC emissions from different sources decreased the PM2.5 concentration in January, and O3 concertation in June, while NOx reduction resulted in an increase. Controlling VOC emissions from industry has a positive effect on improving local PM2.5 and O3, while the emission reductions of NOx from transportation and industry are not conducive to reducing PM2.5 and O3 concentrations. The synergistic emission reduction pathways for NOx and VOCs during PM2.5 and O3 combined pollution were also analyzed. The VOC and NOx emission reductions were beneficial for reducing the comprehensive Air Quality Index (sAQI) values. When the NOx emission reduction was large, the sAQI improvement gradually exceeded that of VOCs. A collaborative optimization path should be adopted that focuses on controlling VOCs first, and further control of combined pollution should depend on the deep reduction of NOx. [ABSTRACT FROM AUTHOR]

In the age of stock planning, urban vitality is a key indication of a city's health and vitality. Using the central city of Tianjin as an example, the study uses multi-source data, such as Weibo check-ins, points of interest, etc., to quantify urban vitality. The Light GBM-SHAP model is chosen to measure the non-linear effects of each indicator on urban vitality in four dimensions: crowd vitality, economic vitality, facility vitality, and environmental vitality. The study also applies spatial visualization and spatial statistical analysis to analyze the vitality of the central city of Tianjin in terms of time and space scales. The findings indicate that: (1) There is clear temporal and geographical variation in the vitality distribution in Tianjin's core urban region. Over time, spring, particularly April, is marked by a surge of vitality brought on by the tourist season and holiday effects; there is a double-peak time in the morning and evening, and the nighttime vitality is particularly strong; and, in terms of space, urban vitality tends to decline from the Heping District outward. (2) Public facility density, living facility density, and building density are the three indicators that most strongly influence urban vitality; each indication has a negligible impact on the temporal dimension of urban vitality. (3) The indicators in Tianjin's core urban region have a substantial impact on urban vitality. Their threshold effect and non-linear influence are evident, and managing the indicators within a suitable range may effectively promote urban vitality. The study's findings might serve as a foundation for Tianjin's core city's urban planning and design. [ABSTRACT FROM AUTHOR]

Key message: We propose an improved exponential equation for the nonlinear dependence of cell measurements on the seasonal cell production in conifer wood. These changes shall simplify the model towards ease in understanding, interpretation and analysis. With methodological development, quantitative parameters of wood cellular structure have featured as dendroclimatic indicators in various studies. But to disentangle climatic impacts in different wood anatomical characteristics, relationships between them have to be described quantitatively and extricated. This study investigates exponential description of the non-linear dependences of conifer wood cell measurements on their seasonal production in cambial zone. Improvement to the equation of exponential model was suggested, simplifying the interpretation and analysis and providing biological meaning to all numerical parameters of the model. This new equation was demonstrated for 630 tree rings from 20 to 40-year-old trees of Siberian spruce (Picea obovata Ledeb.) in Siberia, collected at the experimental plantation, as well as for its subsets from plots of various planting density, demonstrating plausible value of the new proposed numerical parameter of model and dependence of the other parameter on the stand density, probably based on the tree height. Limitation for successful fitting of this exponential model was demonstrated, based on representativity of wide and narrow rings within sample; we proposed possible way to overcome it in certain cases. [ABSTRACT FROM AUTHOR]

The paper analyzes the relationship between urbanization rates, the growing reliance on renewable electricity production, and CO2 emissions per capita. It further examines the economic impact of these urbanization and renewable electricity production variables on GDP per capita using threshold regression and annual data from Jordan covering the period from 1990 to 2022. The objective is to determine whether Jordan can reduce CO2 emissions despite high urbanization rates by increasing the share of renewable energy in electricity production. The fundamental assumption is that the relationship between CO2 emissions and urbanization rates varies according to the share of renewable energy production. Results from the estimated threshold regression model indicate a decline in CO2 emissions per capita during the period from 2004 to 2018, when urbanization rates ranged between 0.78 and 0.91. During this time, the compound growth rate of renewable energy share in electricity production was 24% in regime 2 (2004–2018). In contrast, CO2 emissions per capita increased both before and after this period, suggesting a nonlinear relationship between urbanization rates and CO2 emissions. This nonlinearity implies that Jordan can manage the conflicting goals of the Sustainable Development Goals (SDGs)—sustainable urbanization and low CO2 emissions—by enhancing the share of renewables in electricity production. Additionally, the results demonstrate a positive effect of urbanization rates and renewable energy shares on economic growth. [ABSTRACT FROM AUTHOR]

Contradictory to conventional economic theory, which foresees any increase in the size of government as inflationary, this article provides evidence that the reaction of price levels to changes in the size of government is nonlinear. The price levels do not necessarily increase in response to a rise in the size of the government but only up to a certain threshold or optimal level. Accordingly, this paper utilizes the dynamic panel threshold model to examine the threshold effects of government size (measured as government final consumption expenditure as a proportion of GDP) on inflation using a sample of 10 selected MENA countries from 1980 to 2019. The findings of this study stand out in several ways. First, the results support the nonlinear relationship between government size and inflation in the study area. Second, the government size's estimated threshold level is equivalent to 12.46%. Third, government size negatively impacts inflation in the regime of small governments up to the threshold level. The impact turns positive once the government size goes beyond the threshold level in a regime of large size of government. These findings have ramifications for the conduct of fiscal policy. Policymakers in the MENA region can increase the size of government till it reaches the threshold level without exerting any upward pressure on price levels. [ABSTRACT FROM AUTHOR]

This study addresses the relationship between platelet count and 30-day in-hospital mortality in End-Stage Kidney Disease (ESRD) patients in the intensive care unit (ICU), a topic with limited existing evidence. Utilizing data from the US eICU-CRD v2.0 database (2014–2015), a retrospective cohort study was conducted involving 3700 ICU ESRD patients. We employed binary logistic regression, smooth curve fitting, and subgroup analyses to explore the association between platelet count and 30-day in-hospital mortality. The 30-day in-hospital mortality rate was 13.27% (491/3700), with a median platelet count of 188 × 109/L. After adjusting for covariates, we observed a relationship between platelet count and 30-day in-hospital mortality (OR = 0.98, 95% CI 0.97, 0.99). Subgroup analyses supported these findings. More importantly, a nonlinear association was detected, with an inflection point at 222 × 109/L. The effect sizes (OR) on the left and right sides of the inflection point were 0.94 (0.92, 0.96) and 1.03 (1.00, 1.05), respectively. The most significant finding of this study is the revelation of a nonlinear relationship between baseline platelet count and 30-day in-hospital mortality in ICU patients with ESRD. This discovery explicitly suggests that when ESRD patients are admitted to the ICU, a platelet level closer to 222 × 10⁹/L may predict a lower 30-day in-hospital mortality risk. [ABSTRACT FROM AUTHOR]

Implementing the strongly coupled data assimilation (SCDA) in coupled earth system models remains big challenging, primarily due to accurately estimating the coupled cross background-error covariance. In this work, through simplified two-variable one-dimensional assimilation experiments focusing on the air–sea interactions over the tropical pacific, we aim to clarify that SCDA based on the variance–covariance correlation, such as the ensemble-based SCDA, is limited in handling the inherent nonlinear relations between cross-sphere variables and provides a background matrix containing linear information only. These limitations also lead to the analysis distributions deviating from the truth and miscalculating the strength of rare extreme events. However, free from linear or Gaussian assumptions, the application of the data-driven machine learning (ML) method, such as multilayer perceptron, on SCDA circumvents the expensive matrix operations by avoiding the explicit calculation of background matrix. This strategy presents comprehensively superior performance than the conventional ensemble-based assimilation strategy, particularly in representing the strongly nonlinear relationships between cross-sphere variables and reproducing long-tailed distributions, which help capture the occurrence of small probability events. It is also demonstrated to be cost-effective and has great potential to generate a more accurate initial condition for coupled models, especially in facilitating prediction tasks of the extreme events. [ABSTRACT FROM AUTHOR]

Metabolic dysfunction associated fatty liver disease (MAFLD) is a common cause of liver disease in children and adolescents. The relationship between insulin resistance (IR) and MAFLD in children with short stature remains largely unknown. The present study was to investigate the relationship between the triglyceride–glucose (TyG) index and alanine aminotransferase (ALT) levels in children with short stature. A total of 1754 children with short stature were enrolled. Anthropometric, biochemical and hormonal indexes were collected through physical measurement examinations and laboratory tests. A nonlinear association was found between the TyG index and ALT. The inflection point of the curve was at a TyG index of 8.24. In multivariate piecewise linear regression, only when the TyG index was greater than 8.24 was there a significant positive association between the TyG index and ALT (β 5.75, 95% CI 3.30, 8.19; P < 0.001). However, when the TyG index was less than 8.24, there was no significant association between the TyG index and ALT (β −0.57, 95% CI −1.84, 0.71; P = 0.382). This study demonstrated a nonlinear relationship between TyG index and ALT in children with short stature. This finding suggests that a high TyG index is associated with elevated ALT in children with short stature. [ABSTRACT FROM AUTHOR]

In the context of contemporary urbanization, the significance of diversifying urban public facilities has attracted significant attention. This study examines the relationship between road network and the diversity of urban public facilities in Changsha City. These factors are measured through the Shannon-Weiner Index and Space Syntax method. To provide greater specificity, the study employs Random Forest and Geographically Weighted Regression models to analyze the relationship between road networks and the diversity of urban public facilities. The results identify a nonlinear relationship between these variables. In addition, high accessibility exhibits a stronger association with diversity than accessibility alone, and this correlation is reflected in varying degrees of inconsistency across different study areas. Finally, the cartographic depiction of diversity clusters is overlaid on the road network, demonstrating a significant relationship between the configuration of the road network and diversity patterns. In conclusion, this research emphasizes the robust correlation between the roadway network and the diversity of urban public facilities. It prompts local governments to focus beyond mere equality, with a greater commitment to enhancing the quality of life for residents through diversity. [ABSTRACT FROM AUTHOR]

In light of the integration of digitalization and the energy revolution, digitalization can be integrated into the energy industry to develop energy-saving technologies and improve resource allocation efficiency. On the basis of 2013–2019 Chinese provincial panel data, this paper measures the level of green energy efficiency based on the super-EBM-DEA model and analyzes the linear relationship, nonlinear relationship, and potential mechanism between digitalization and green energy efficiency. The findings indicate that (1) overall, both China's digitalization and green energy efficiency formed a steady upward trajectory during the sample period. Digitalization showed a spatial characteristic of extending and spreading from the eastern region to the central and western regions. Green energy efficiency was characterized by obvious regional heterogeneity. (2) Progress in digitalization has a significant driving effect on green energy efficiency. Subdimensional analysis shows that this driving effect mainly comes from digital development and digital transactions. (3) The impact of digitalization on green energy efficiency presents a threshold effect of economic agglomeration (with a threshold of 0.0257 and a marginally increasing, positive driving trend) and population agglomeration (with a threshold of 4.2750 and a marginally decreasing, positive driving trend). (4) Decomposing changes in green energy efficiency into scale efficiency and pure technical efficiency, this study shows that pure technical efficiency gains due to digitalization are the main driver of green energy efficiency improvements. Finally, some specific policy recommendations are proposed. [ABSTRACT FROM AUTHOR]

China's traditional agriculture has grown quickly but at the cost of excessive pollution and energy consumption. Therefore, low-carbon development in agriculture is crucial to achieving "carbon neutrality" and "carbon peaking." With the development of China's digital economy and the construction of digital villages in recent years, digital technology innovation (DTI) is probably going to a significant role in lowering agricultural carbon emissions (ACEs). Based on Chinese provincial panel data from 2006 to 2021, we analyze the spatial and temporal evolution characteristics of DTI and ACE, explore the impact and the pathways of DTI on ACE using a spatial econometric model, and reveal this impact's heterogeneity and nonlinear character. The findings show that DTI and ACE increased significantly throughout all Chinese provinces. DTI agglomeration is becoming stronger, whereas ACE displays a tendency for discontinuous distribution. DTI is essential in promoting low-carbon development in agriculture, and there are significant spatial spillover effects due to technology spillovers. Heterogeneity analysis indicates that DTI has positive impacts on local agriculture in different regions. However, there is variation in the impact's degree. The mechanism test's findings show how DTI reduces ACE by improving technology and enhancing resource endowment. The relationship between DTI and ACE exhibits an inverted "U" curve, and the level of economic development is the threshold variable that constrains the relationship between the two variables. To achieve a regional balanced low-carbon development in agriculture through DTI, it is important to emphasize the impact of DTI on reducing carbon emissions and to encourage the transfer of mature technology from high-level regions to low-level regions. [ABSTRACT FROM AUTHOR]

California became the first state in the US to require gender diversity on publicly corporate boards. To provide timely evidence on the heated debate, we investigate the impact of board gender diversity on financial performance and how shareholder activism affects the dynamic relationship in the United States. We find that the relation between board gender diversity and firm performance presents an inverted U-shaped nonlinear form. Firm performance increases as the board is more gender-diverse, but performance decreases after the board diversity level reaches a turning point. Furthermore, shareholder activism through proxy proposals enhances the positive effect of diversity and alleviates the negative effect of diversity on firm performance, and the positive effect diminishes after board gender ratios reach an optimal level. Our study captures the dynamic impacts of shareholder activism and board gender diversity on firm performance and provides insights for regulators to make proper decisions in increasing board diversity. [ABSTRACT FROM AUTHOR]

Using a dataset encompassing 228 cities in China spanning from 2005 to 2019, this study explores the nonlinear relationship between air quality and housing prices and devises a strategy that incorporates the instrumental variable and machine learning to address the endogeneity issue. Both traditional models and machine learning models find air pollution affects housing prices in a diminishing manner. The negative impact of air pollution on housing prices decreases when the degree of air pollution intensifies. Such a characteristic is more pronounced in Eastern China and cities with fewer land resource constraints and larger populations. Mechanism analysis also reveals that air pollution could affect residents' perceived air quality and the industrial structure, further contributing to the nonlinear relationship between air quality and housing prices. The further SHapley Additive exPlanations (SHAP) evaluates the importance of air quality in determining housing prices and finds that air quality's contribution outweighs educational and medical resources. The contribution of air quality also shows a distinct regional disparity and has become increasingly important in recent years. The findings refine the benefit assessment accuracy related to air quality improvement. [ABSTRACT FROM AUTHOR]

Background: The common regression models included estimated glomerular filtration rate (eGFR) in the continuous and categorical form for predicting the mortality in COVID-19 inpatients. However, the relationship may be non-linear, and categorizing implies a loss of information. This study aimed to assess the effect of eGFR on admission on death within 30 days among COVID-19 inpatients using flexible and smooth transformations of eGFR and compare the results against the common models. Methods: A retrospective study was conducted on hospitalized COVID-19 patients between April 2019 and July 2019 in Hamadan, Western Iran. The effect of eGFR on the death within 30 days was evaluated using different modeling: categorization, linear, unrestricted cubic spline (USC) with 4 knots, and fractional polynomial (FP). The results adjusted for older age and intensive care unit (ICU) admission. Discrimination power and model performance of the best-fitting model was evaluated using the area under the ROC (AUROC) and Brier score. Results: In total, 2945 patients (median age 61 years; interquartile range 48–73 years) were included, of whom the mortality rate was 9.23%. The relationship between the eGFR and death within 30 days is non-linear, so the degree-2 FP with powers (− 2, − 1) is the best-fitting model. Using the FP model, the risk increased exponentially in eGFR < 45 and then increased linearly and slowly. The AUROC of the FP model involving eGFR, older age, and ICU admission was 0.92 (95% CI 0.90–0.93) with a Brier score of 0.09. Conclusion: There is a non-linear and asymmetric relationship between eGFR and death within 30 days among COVID-19 inpatients. Kidney function can be measured in COCID-19 patients on admission to know better understanding about prognosis of the patients. [ABSTRACT FROM AUTHOR]

Increased globalization in urban areas raise energy consumption that leads to high carbon dioxide discharge and degrade environmental quality. Other economic activities also produce emission; however, a well-established institutional framework can overcome the issues of environmental degradation and minimize the effect of harmful factors on the environment. In this regard, this study investigates the effect of urbanization, energy consumption, and industrialization on carbon dioxide emission by taking into consideration the role of institutional quality in the Belt and Road Initiative (BRI) countries for the period of 2002 to 2019. Employing dynamic panel techniques, the results are in line with theories which show that increased urbanization, energy consumption, industrialization, and economic growth raise carbon dioxide emission and lead to environmental degradation. The study also found that international trade and political stability reduce emission; however, institutional quality as a whole positively affects carbon dioxide emission. The study also found a U-shape relationship between urbanization and carbon dioxide emission. The interaction term between institutional quality and urbanization significantly mitigates carbon dioxide emission and raise environmental sustainability. The findings of this study have considerable policy suggestions for the sample countries. [ABSTRACT FROM AUTHOR]

Continued urbanization requires a deep understanding of how urbanization affects residents' health risks. This study used regression analysis of Chinese provincial-level panel data from 2004 to 2019 and empirically analyzed the nonlinear effects of urbanization on health risks and regional differences using STIRPAT model. Health risks were assessed by the average number of residents' visits to medical facilities and population mortality. We also examined the moderating effect of income and environmental factors. The results show that (1) urbanization increases the average number of residents' visits and reduces population mortality. The positive effect of urbanization in increasing the average number of visits is reinforced by an increase in income level and environmental pollution, whereas the negative effect of urbanization in reducing population mortality is weakened by environmental pollution. (2) Regarding long-term trends, urbanization has an N-shaped relationship with the average number of residents' visits, and a U-shaped relationship with population mortality; (3) Urbanization has an N-shaped relationship with the average number of residents' visits in the eastern, central, and western regions and an inverted N-shaped relationship with population mortality in the eastern region. Urbanization has significant effects on residents' health risks in areas with high levels of infrastructure. According to the results, suggestions are proposed, such as developing new-type urbanization, improving infrastructure, focusing on green urbanization, and promoting national fitness programs. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to quantify the level of new-type urbanization and unravel the spatial and nonlinear effects of new-type urbanization and technological innovation on industrial carbon emissions. Although the impact of traditional urbanization levels on carbon emissions has been widely studied, there is still a huge room for optimization, and the impact of new-type urbanization on carbon emissions has not yet been clarified. Selecting 37 cities in the Yangtze River Delta as a research sample, this paper measures the new-type urbanization based on an evaluation system we build. Consequently, we assess the spatial and nonlinear effects of new-type urbanization and technological innovation on carbon emissions by the spatial Durbin model and non-parameter addictive model, respectively. The results indicate that the new-type urbanization and low-carbon city pilot policy have significant spatial spillover effects on reducing carbon dioxide emissions, while the economic growth plays a positive role in increasing carbon emission. As for nonlinear effects, there is a significant inverted "N"-shaped relationship between the level of new-type urbanization and carbon dioxide emissions, while the nexus between technological innovation and carbon emissions is an inverted "U"-shaped relationship. This paper provides a new perspective for confirming the mechanism of the new-type urbanization on carbon emissions. Meanwhile, these findings are of significance for the relevant authorities in China to develop appropriate policy in carbon dioxide emission reduction. [ABSTRACT FROM AUTHOR]

Improving industrial eco-efficiency is of great significance for building a beautiful China and achieving its carbon peak and neutrality targets. Based on the panel data of 30 provinces in China from 2007 to 2018, this paper uses the super-efficiency SBM model to measure industrial eco-efficiency and empirically tests the influence of green finance on Chinese industrial eco-efficiency from the national and regional levels. The results show that the average level of industrial eco-efficiency in China is relatively stable during the study period with a large space for advancement. Second, there is spatial heterogeneity in Chinese industrial eco-efficiency, showing a gradually decreasing "southeast-northwest" ladder-like distribution. Third, the national-level regression results show that there is a significant "U-shaped" relationship between green financing and industrial eco-efficiency. In addition, the regression results at the regional level indicate that there is regional heterogeneity in the impact of green finance on industrial eco-efficiency. Finally, based on the research conclusions, specific suggestions on how green finance can improve industrial eco-efficiency in China are put forward, including vigorously developing green finance at the macro and micro levels, and exerting the positive effects of green finance in improving industrial eco-efficiency according to the area and the development level of green finance. [ABSTRACT FROM AUTHOR]

Climate variation is a primary driving force influencing the hydrological cycle, resulting in extreme natural disasters such as drought. Investigating the linkage between meteorological and hydrological droughts under climate change is critical for early warning against hydrological drought. In this study, we investigated spatial and temporal characteristics of the drought propagation threshold (PT) in the Han River Basin (HRB) of South Korea. Standardized Precipitation Index (SPI) and Standardized Runoff Index (SRI) were employed to represent meteorological drought and hydrological drought, respectively. Correlation analyses between the SPI at various time scales (1–24 months) and the three-month SRI (SRI-3) were performed to identify the best time scale of SPI corresponding to the SRI-3. To investigate PTs, five general circulation models (GCMs) were selected to compute multi-model ensemble projections under Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. A future period (2021–2099) was sub-divided into two periods such as P2 (2021–2060) and P3 (2061–2099) to estimate temporal variation of PT. Our results indicated that the SPI-4 showed the highest correlation with the SRI-3 in P1, while the SPI-3 best corresponded to the SRI-3 in future periods under both climate change scenarios. Drought characteristics of meteorological and hydrological drought showed increasing trends in P2 and P3 under both climate change scenarios. Spatio-temporal variation in PT was observed throughout the HRB. The PT showed significant decreases in both future periods, while the highest percentage decreases were observed in watersheds located in the northern, mid, and western parts of the HRB in P3 under both climate change scenarios. [ABSTRACT FROM AUTHOR]

Purpose: The preoperative diagnosis of noninvasive fungal rhinosinusitis (NIFRS) is inaccurate, and biomarkers to assist the diagnosis are urgently needed. We aimed to evaluate the relationship between albumin levels and NIFRS to assist in early diagnosis. Methods: Patients with NIFRS and chronic sinusitis were enrolled in this study. Appropriate statistical methods were used to determine whether there was a statistical difference between the groups. Subgroup analysis was performed to investigate the relationship between albumin levels and NIFRS, and a generalised additive model (GAM) was used to perform nonlinear relationships. Results: A total of 620 patients were included, including 240 patients with NIFRS. A close relationship was found between albumin levels and NIFRS (P < 0.0001), and the low albumin group was associated with a higher incidence of NIFRS, which was reduced by 60 and 70% in the middle and high albumin groups, respectively. The subgroup analysis also demonstrated an association between albumin levels and NIFRS, except in patients with an alcohol history (P = 0.0665). Interestingly, a nonlinear relationship is observed according to the adjusted GAM. The inflection point was set at 37.0 g/L. A negative correlation was observed among patients with albumin > 37.0 g/L. When the albumin count was <37.0 g/L, the Y value obviously increased and was saturated at 70%, with no further significant increase. Conclusion: Albumin levels were significantly negatively correlated with the incidence of NIFRS, and the incidence increased markedly among patients with albumin < 37.0 g/L. [ABSTRACT FROM AUTHOR]

Work is reported as a major source of psychological stress. Because of its role in the onset of burnout and its impact on economic and health systems, work stress (WS) has become an issue of great concern. Two categories of physical activity, namely leisure-time and moderate-to-vigorous physical activity (LTPA and MVPA), show promising evidence for reducing WS. Previous findings suggest that LTPA and MVPA allow adults to experience psychological detachment from job demands and to restore their depleted resources at work. However, doses of LTPA and MVPA associated with WS have not been established. This study aimed to address this gap using a cross-sectional, nationally representative sample of 4200 workers. Generalized additive models indicated that one hour a day, on average, spent doing an LTPA of 8.5 metabolic equivalents-hour (METs-hour) was associated with the lowest WS level (p < 0.001, Adjusted R2 = 0.04) while the optimal daily dose of MVPA was around 90 min (p < 0.001, Adjusted R2 = 0.04). A reduction in WS occurs even with low doses of physical activity, aligning with the tenet that getting some physical activity is better than none. This study's findings offer quantitative insights for guiding health policies as well as future longitudinal and experimental studies. [ABSTRACT FROM AUTHOR]

Few studies have estimated the nonlinear association of ambient temperature with the risk of influenza. We therefore applied a time-series analysis to explore the short-term effect of ambient temperature on the incidence of influenza in Wuhan, China. Daily influenza cases were collected from Hubei Provincial Center for Disease Control and Prevention (Hubei CDC) from January 1, 2014, to December 31, 2017. The meteorological and daily pollutant data was obtained from the Hubei Meteorological Service Center and National Air Quality Monitoring Stations, respectively. We used a generalized additive model (GAM) coupled with the distributed lag nonlinear model (DLNM) to explore the exposure-lag-response relationship between the short-term risk of influenza and daily average ambient temperature. Analyses were also performed to assess the extreme cold and hot temperature effects. We observed that the ambient temperature was statistically significant, and the exposure–response curve is approximately S-shaped, with a peak observed at 23.57 ℃. The single-day lag curve showed that extreme hot and cold temperatures were both significantly associated with influenza. The extreme hot temperature has an acute effect on influenza, with the most significant effect observed at lag 0–1. The extreme cold temperature has a relatively smaller effect but lasts longer, with the effect exerted continuously during a lag of 2–4 days. Our study found significant nonlinear and delayed associations between ambient temperature and the incidence of influenza. Our finding contributes to the establishment of an early warning system for airborne infectious diseases. [ABSTRACT FROM AUTHOR]

The relationship between CEO age and corporate risk-taking has remained inconclusive after decades of research. This study contends that CEO may have different risk-taking propensities at different age stages and the relationship could be better captured by nonlinear function. Using a large sample of 6169 firm-year observations of public listed companies in Malaysia, we study the risk-taking behaviour of CEO for the period of 2009 to 2017. We find that CEO age has an inverse U-shaped relationship, such that risk-taking increases with CEO age but reduces beyond certain age threshold. Besides, drawing on socioemotional wealth perspective, we further examine the moderating effect of family CEO. We find that near-retirement family CEO reverses the conservative behaviour at old age and results an inverse S-curved relationship. Findings of this paper yields implication for family business and policymakers seeking to encourage risk-taking behaviour in emerging countries. [ABSTRACT FROM AUTHOR]

A more comprehensive understanding of the impact of the COVID-19 pandemic on changes in pollution could serve us to better deal with the environmental challenges caused by the pandemic. Existing studies mainly focused on the linear impact of the pandemic on the pollutants without considering the impact of other factors. To fill the research gap, the nonlinear relationship between pandemic and pollutants with considering the temperature factor was explored by developing panel threshold regression approach. In the proposed approach, the number of confirmed cases was set as explanatory variable, concentrations of NO2 and PM2.5 were set as explained variables, temperature was used as threshold variable, and other air pollution indicators were used as control variables. The results showed that there is a threshold effect between the changes in confirmed COVID-19 cases and the concentrations of PM2.5 and NO2, confirming the impact of the pandemic on pollutions was nonlinear. The results also show that the negative impact of pandemic on pollution increased when the temperature was rising. This work had theoretical and practical significance. The nonlinear research perspective of this article provided a methodological reference for exploring the relationship between epidemic and pollutant-related variables. Furthermore, this study expanded the scope of application of the threshold panel regression model and enriched the quantitative analysis of epidemics and pollutants. [ABSTRACT FROM AUTHOR]

The sustainable development of China's economy and society has been restricted by energy and environmental issues. With the implementation of the Rise of Central China strategy, Henan, a populous province in China, is facing the threat of an energy shortage as its economy grows steadily and the urbanization process accelerates. Using panel data of 18 cities in Henan Province for the period of 2006–2018, this study investigates the relationship between energy consumption and economic and social development variables based on the extended Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and threshold regression method. The empirical results indicate that economic and social development are significantly positively correlated with total electricity consumption, although there is regional heterogeneity in this relationship. Moreover, there is evidence for a nonlinear relationship between economic and social development and urban energy consumption. These conclusions not only promote the healthy and sustainable economic development of Henan Province but also provide a reference for other provinces and cities that are experiencing rapid development in China. [ABSTRACT FROM AUTHOR]

Vigorously developing high-tech industry has been considered to be an effective way to coordinate economic growth with excessive carbon dioxide (CO2) emissions. However, previous studies have not explored the heterogeneous impacts of high-tech industry on CO2 emissions in regions with different levels of high-tech industry development, and not distinguished the direct and indirect impacts. Based on STIRPAT model, this study investigates the impacts of high-tech industry development on CO2 emissions in China between 2005 and 2016. Adopting the K-medians cluster method, effects in regions with high, middle, and low levels of high-tech industry development are considered. Indirect effects of high-tech industry development on CO2 emissions by affecting industry structure upgrades and economic growth are explored. Empirical results illustrate a positive U-shaped nonlinear link between the level of high-tech industry development and CO2 emissions at the national level and regional (high, middle, and low) level. In terms of indirect impacts, high-tech industry development attenuates the reduction of CO2 emissions due to industry structure upgrades, and promotes economic growth to increase CO2 emissions slightly. The indirect impact intensity gradually decreases as the level of high-tech industry development decreases across three regions. Reasonable implications of our findings are proposed. [ABSTRACT FROM AUTHOR]

The past decade has seen the rapid development of the tourist industry in Southeast Asia. There is increasing concern that tourism is highly affecting CO emissions, but the nature of the relationship is still unclear. The main target of this paper is to investigate the existence of a linear and/or nonlinear relationship between tourism and CO emissions in the five most important countries located in Southeast Asia, using the panel cointegration and pooled mean group techniques. The results indicate that tourism and CO emissions are cointegrated, implying that tourism affects CO emissions in the long run. Our findings support the nonlinear relationship between tourism and emissions as well as economic activities and CO emissions. Accordingly, an inverted U-shaped relationship exists between tourism and emissions confirming the existence of an Environmental Kuznets Curve in the Southeast Asian tourism industry. Furthermore, the empirical results show that economic activities and energy consumption greatly increase emissions. [ABSTRACT FROM AUTHOR]

This paper investigates the housing price-volume nexus based on different levels of liquidity for the US new one-family housing market over the period between January 1963 and November 2009. We mainly analyze the differential responses of trading volumes to housing price changes (denoted as price elasticity), employing a quantile cointegrating approach that allows us to capture housing boom-bust cycles earlier. In addition, we also explore the long-term impacts of monetary policies on housing sales. According to our findings, the price elasticities perform differently across the booms and busts of housing markets. Shifts in price elasticities can transmit signals of oncoming upturns, recovery, and downturns of housing cycles, which possibly relate to the business cycle. On the other hand, a contractionary monetary policy exerts a much effective control over an overheated housing market, but an expansionary monetary policy has a relatively small influence on stimulating a depressed housing market. Our findings offer some important suggestions and policy implications. [ABSTRACT FROM AUTHOR]

The partial correlation is a commonly used measure for assessing the bivariate correlation of two quantitative variables after eliminating the influence of one or more other variables. The partial correlation is generally interpreted as the correlation that would result if the variables to be eliminated were fixed (not allowed to vary and influence the other variables), which is referred to in the statistical literature as conditional correlation. The present paper demonstrates, by means of theoretical derivations and practical examples, that when the assumption of multivariate normality is violated (e.g., as a result of nonlinear relationships among the variables investigated) the usual interpretation of the partial correlation coefficient will be basically incorrect. In extreme cases the value of the partial correlation coefficient may be strongly positive, close to 1, whereas the conditional correlation may have a large negative value. To solve this problem the paper suggests to partial out a certain function (in most cases the square) of the variables whose effects are to be eliminated if nonlinear relationships are likely to occur. [ABSTRACT FROM AUTHOR]

Purpose: To develop a new bionic system from an existing drug dissolution/absorption simulating system (DDASS) to simultaneously predict the release and absorption of enteric-coated formulations. Methods: In accordance with the pH-dependent characteristics of enteric-coated formulations, the modified DDASS was designed to effectively imitate the pH change process of the formulations' transfer from stomach to intestine in vivo. Omeprazole enteric-coated tablets were chosen as the model drug to verify the rationality and feasibility of the modified DDASS. The correlations between USP I system release and beagle dog absorption, as well as between modified DDASS elution/permeation and beagle dog absorption, were investigated by linear and nonlinear regression analyses, respectively. Results: In vitro- in vivo correlation between the modified DDASS elution/permeation method and beagle dog absorption was higher than between the USP I system release and beagle dog absorption in both analytical methods. The ratio of first-order permeation rate constant to first-order release rate constant was consistent with that from modified DDASS. Conclusions: The modified DDASS provided more information than the USP I system did in the evaluation of enteric-coated formulations. The proposed bionic system model could serve as a new method for improving drug effectiveness. [ABSTRACT FROM AUTHOR]

BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W 0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W 0-value usually appeared obviously around the future epicenter 1–3 years before earthquake. It is effective to mid-term prediction. [ABSTRACT FROM AUTHOR]