High-Resolution Estimates of N2O Emissions from Inland Waters and Wetlands in China.

Inland waters (rivers, lakes, and reservoirs) and wetlands (marshes and coastal wetlands) represent large and continuous sources of nitrous oxide (N2O) emissions, in view of adequate biomass and anaerobic conditions. Considerable uncertainties remain in quantifying spatially explicit N2O emissions from aquatic systems, attributable to the limitations of models and a lack of comprehensive data sets. Herein, we conducted a synthesis of 1659 observations of N2O emission rates to determine the major environmental drivers across five aquatic systems. A framework for spatially explicit estimates of N2O emissions in China was established, employing a data-driven approach that upscaled from site-specific N2O fluxes to robust multiple-regression models. Results revealed the effectiveness of models incorporating soil organic carbon and water content for marshes and coastal wetlands, as well as water nitrate concentration and dissolved organic carbon for lakes, rivers, and reservoirs for predicting emissions. Total national N2O emissions from inland waters and wetlands were 1.02 × 105 t N2O yr-1, with contributions from marshes (36.33%), rivers (27.77%), lakes (25.27%), reservoirs (6.47%), and coastal wetlands (4.16%). Spatially, larger emissions occurred in the Songliao River Basin and Continental River Basin, primarily due to their substantial terrestrial biomass. This study offers a vital national inventory of N2O emissions from inland waters and wetlands in China, providing paradigms for the inventorying work in other countries and insights to formulate effective mitigation strategies for climate change.

Effects of country success on COVID-19 cumulative cases and excess deaths in 169 countries.

COVID-19 has caused over 260 million confirmed cases and over 5 million deaths globally. The results of statistical and multiple criteria analyses on the success of 169 countries and on COVID-19 cumulative cases and excess deaths show that the prosperity of a country relates directly to the consequences due to the pandemic. The topic of this article is the Country Success and COVID-19 (CSC) Map of the World. As a country's success grows, this map shows how cumulative cases of COVID-19 increase; at the same time, excess deaths decrease. The indicators in the system of criteria regarding country success and sustainability are interrelated. Conditional country successes remain quite similar, despite changes to the numbers of countries and their indicators. Likewise, the seven clusters of countries under consideration group together independently of which system of indicators had been applied for their analysis. The 2020 Inglehart-Welzel Cultural Map of the World, which is grounded on surveys, and the CSC Map, which is grounded on statistical indicators, have axes that correlate with one another significantly. The CSC Map Model explains over 63% of the dispersions pertinent to COVID-19 cumulative cases, over 52% of COVID-19 excess deaths, and over 95% of country success variables. The layout of the clusters on the CSC Map changes little over time. Upon performance of the correlation analysis, it was established that strong and statistically significant relationships exist between 169 countries success and sustainability linked with their current air quality score (r = 0.602, p < 0.01) and the environmental performance index (EPI) score (r = 0.931, p < 0.01). The results obtained show that when a country's EPI score and current air quality improve by 1%, excess deaths decrease, respectively, by 2.33 and 1.55%. Global integrated analysis on country successes, COVID-19 cumulative cases, and excess deaths comprise this study.

Outpatient clinic waiting time, provider communication styles and satisfaction with healthcare in India.

Purpose - The purpose of this paper is to evaluate the impact of extended waiting time on patients' perceptions of provider communication skills and in-clinic satisfaction, in three major cities in India. Design/methodology/approach - In total, 625 patients were interviewed. The multivariate general linear model was used to determine the causality and relationship between the independent and the dependent variable. A moderation analysis was also conducted to assess waiting time role as a potential moderator in doctor-patient communication. Findings - Results show that patients with higher waiting time were less satisfied with health care quality. Male patients and patients of male providers were more affected by extended waiting time than female patients and patients of female providers. The advanced regression analysis, however, suggests weak support for waiting time and its effect on overall satisfaction with clinic quality. Waiting time did not moderate the relationship between satisfaction with dominant communication style, and overall satisfaction at the outpatient clinic. Research limitations/implications - A cross-sectional study does not easily lend itself to explaining causality with certainty. Thus, sophisticated techniques, such as structural equation modelling may also be utilized to assess the influence of extended waiting time on satisfaction with healthcare at outpatient clinics. Practical implications - Findings are relevant for providers as the onus is on them to ensure patient satisfaction. They should initiate a workable waiting time assessment model at the operational level. Originality/value - There has been a relatively lesser focus on patient waiting time in patient-provider satisfaction studies. In India, this aspect is still vastly unexplored especially in the context of outpatient clinics. Gender wise pattern of patient satisfaction and waiting time is also missing in most studies.

Construction of prediction models for growth traits of soybean cultivars based on phenotyping in diverse genotype and environment combinations.

As soybean cultivars are adapted to a relatively narrow range of latitude, the effects of climate changes are estimated to be severe. To address this issue, it is important to improve our understanding of the effects of climate change by applying the simulation model including both genetic and environmental factors with their interactions (G×E). To achieve this goal, we conducted the field experiments for soybean core collections using multiple sowing times in multi-latitudinal fields. Sowing time shifts altered the flowering time (FT) and growth phenotypes, and resulted in increasing the combinations of genotypes and environments. Genome-wide association studies for the obtained phenotypes revealed the effects of field and sowing time to the significance of detected alleles, indicating the presence of G×E. By using accumulated phenotypic and environmental data in 2018 and 2019, we constructed multiple regression models for FT and growth pattern. Applicability of the constructed models was evaluated by the field experiments in 2020 including a novel field, and high correlation between the predicted and measured values was observed, suggesting the robustness of the models. The models presented here would allow us to predict the phenotype of the core collections in a given environment.

Development of Models to Estimate Total Soil Carbon across Different Croplands at a Regional Scale Using RGB Photography.

A quick, accurate and cost-effective method for estimating total soil carbon is necessary for monitoring its levels due to its environmentally and agronomically irreplaceable importance. There are several impediments to both laboratory analysis and spectroscopic sensor technology because the former is both expensive and time-consuming whereas the initial cost of the latter is too high for farmers to afford. RGB photography obtained from digital cameras could be used to quickly and cheaply estimate the total carbon (TC) content of the soil. In this study, we developed models to predict soil TC contents across different cropland types including paddy, upland and orchard fields as well as the TC content of the soil combined from all the aforementioned cropland types on a regional scale. Soil colour measurements were made on samples from the Chungcheongnam-do province of South Korea. The soil TC content ranged from 0.045% to 6.297%. Modelling was performed using multiple linear regression considering the soil moisture levels and illuminance. The best soil TC prediction model came from the upland soil and gave training and validation r2 values of 0.536 and 0.591 with RMSE values of 0.712% and 0.441%, respectively. However, the most accurate equation is the one that produces the lowest RMSE value. Hence, although the model for the upland soil was the most stable of all, the paddy soil model which gave training and validation r2 values of 0.531 and 0.554 with RMSE values of 0.240% and 0.199%, respectively, was selected as the best soil TC prediction equation of all due to its comparatively high r2 value and the lowest RMSE of all equations.

Impacts from Economic Development and Environmental Factors on Life Expectancy: A Comparative Study Based on Data from Both Developed and Developing Countries from 2004 to 2016.

Both economic development level and environmental factors have significant impacts on life expectancy at birth (LE). This paper takes LE as the research object and selects nine economic and environmental indicators with various impacts on LE. Based on a dataset of economic and environmental indicators of 20 countries from 2004 to 2016, our research uses the Pearson Correlation Coefficient to evaluate the correlation coefficients between the indicators, and we use multiple regression models to measure the impact of each indicator on LE. Based on the results from models and calculations, this study conducts a comparative analysis of the influencing mechanisms of different indicators on LE in both developed and developing countries, with conclusions as follow: (1) GDP per capita and the percentage of forest area to land area have a positive impact on LE in developed countries; however, they have a negative impact on LE in developing countries. Total public expenditure on education as a percentage of GDP and fertilizer consumption have a negative impact on LE in developed countries; however, they have a positive impact on LE in developing countries. Gini coefficient and average annual exposure to PM2.5 have no significant effect on LE in developed countries; however, they have a negative impact on LE in developing countries. Current healthcare expenditures per capita have a negative impact on LE in developed countries, and there is no significant impact on LE in developing countries. (2) The urbanization rate has a significant positive impact on LE in both developed countries and developing countries. Carbon dioxide emissions have a negative impact on LE in both developed and developing countries. (3) In developed countries, GDP per capita has the greatest positive impact on LE, while fertilizer consumption has the greatest negative impact on LE. In developing countries, the urbanization rate has the greatest positive impact on LE, while the Gini coefficient has the greatest negative impact on LE. To improve and prolong LE, it is suggested that countries should prioritize increasing GDP per capita and urbanization level. At the same time, countries should also work on reducing the Gini coefficient and formulating appropriate healthcare and education policies. On the other hand, countries should balance between economic development and environmental protection, putting the emphasis more on environmental protection, reducing environmental pollution, and improving the environment's ability of self-purification.

Discrimination of idiopathic Parkinson's disease and vascular parkinsonism based on gait time series and the levodopa effect.

Idiopathic Parkinson's disease (IPD) and vascular parkinsonism (VaP) present highly overlapping phenotypes, making it challenging to distinguish between these two parkinsonian syndromes. Recent evidence suggests that gait assessment and response to levodopa medication may assist in the objective evaluation of clinical differences. In this paper, we propose a new approach for gait pattern differentiation that uses convolutional neural networks (CNNs) based on gait time series with and without the influence of levodopa medication. Wearable sensors positioned on both feet were used to acquire gait data from 14 VaP patients, 15 IPD patients, and 34 healthy subjects. An individual's gait features are affected by physical characteristics, including age, height, weight, sex, and walking speed or stride length. Therefore, to reduce bias due to intersubject variations, a multiple regression normalization approach was used to obtain gait data. Recursive feature elimination using the linear support vector machine, lasso, and random forest were applied to infer the optimal feature subset that led to the best results. CNNs were implemented by means of various hyperparameters and feature subsets. The best CNN classifiers achieved accuracies of 79.33%±6.46, 82.33%±10.62, and 86.00%±7.12 without (off state), with (on state), and with the simultaneous consideration of the effect of levodopa medication (off/on state), respectively. The response to levodopa medication improved classification performance. Based on gait time series and response to medication, the proposed approach differentiates between IPD and VaP gait patterns and reveals a high accuracy rate, which might prove useful when distinguishing other diseases related to movement disorders.

Stature estimation from different combinations of foot measurements using linear and multiple regression analysis in a North Indian male population.

Establishing the identity of the deceased is the most important task for forensic anthropologists in forensic case-work involving unidentified human remains. In such cases, forensic anthropologists examine the remains to derive the biological profile of the deceased i.e. estimation of age, sex, stature, and ethnicity to narrow down the search of the missing. Dismembered remains are recovered in mass disasters such as train mishaps, airplane crashes, earthquakes, and terrorists' attacks or in homicidal cases where perpetrator intentionally mutilates the dead body to conceal the identity of the victim. Stature estimation is considered as one of the most important tasks when a mutilated foot is recovered in process of narrowing down the pool of possible suspects/victims. Allometry is the underlying principle for estimation of stature from foot dimensions. It has been learnt from the published literature that multiple regression models including more than one factor enhances the estimation accuracies. Among the various foot dimensions, foot length is the most frequent parameter used in the estimation of stature in forensic literature. In the present study, an attempt has been made to standardize the stature estimation models from various possible combinations of foot dimensions. For this purpose, 388 Jatt Sikh males aged between 18 and 30 years were recruited from various villages of Ludhiana district of Punjab State in Northern India. Stature, five foot length measurements, and two foot breadth measurements were taken on each subject. Linear and multiple regression models were derived for the estimation of stature from various foot measurements. The highest coefficient of determination and estimation accuracy (the least standard error of estimation S.E.E) was observed from T1 (R2 = 0.397, S.E.E = 4.7109) when a single foot dimension was included in the model, (R2 = 0.416, S.E.E = 4.6425) from (T1, T3) when two-foot lengths were taken, (R2 = 0.418, S.E.E = 4.6426) from (T1, T3, T4) when three-foot lengths were included, (R2 = 0.418, S.E.E = 4.6473) from (T1, T3, T4, T5) when four-foot lengths were included, and (R2 = 0.418, S.E.E = 4.6531) when all the five foot lengths (T1, T2, T3, T4, T5) were included in the regression model. It has been concluded that multiple regression models provide more accurate results than linear regression models. However, inclusion of a factor having a weak correlation with stature in the regression model, decreases the accuracy of the model.

Scarce evidence of ozone effect on recent health and productivity of alpine forests-a case study in Trentino, N. Italy.

We investigated the significance of tropospheric ozone as a factor explaining recent tree health (in terms of defoliation) and productivity (in terms of basal area increment, BAI) in 15 ICP Forests level I and one level II plots in alpine forests in Trentino (N. Italy). Mean daily ozone summer concentrations varied between 30 and 72 parts per billion (ppb) leading to large exceedance of concentration-based critical levels set to protect forest trees. Phytoxic ozone dose (POD0) estimated at the level II plot over the period 1996-2009 was 31-61 mmol m-2 projected leaf area (PLA). The role of ozone was investigated taking into account other site and environmental factors. Simple linear regression, multiple linear regression (MLR, to study mean periodical defoliation and mean periodical BAI), and linear mixed models (LMM, to study annual defoliation data) were used. Our findings suggest that-regardless of the metric adopted-tropospheric ozone is not a significant factor in explaining recent status and trends of defoliation and BAI in the alpine region examined. Both defoliation and BAI are in turn driven by biotic/abiotic damage, nutritional status, DBH (assumed as a proxy for age), and site characteristics. These results contrast with available ozone-growth dose response relationships (DRRs) and other observational studies. This may be due to a variety of concurrent reasons: (i) DRRs developed for individual saplings under controlled condition are not necessarily valid for population of mature trees into real forest ecosystems; (ii) some observational studies may have suffered from biased design; and (iii) since alpine forests have been exposed to high ozone levels (and other oxidative stress) over decades, possible acclimation mechanisms cannot be excluded.

Integrating Map Algebra and Statistical Modeling for Spatio- Temporal Analysis of Monthly Mean Daily Incident Photosynthetically Active Radiation (PAR) over a Complex Terrain.

This study aims at quantifying spatio-temporal dynamics of monthly mean dailyincident photosynthetically active radiation (PAR) over a vast and complex terrain such asTurkey. The spatial interpolation method of universal kriging, and the combination ofmultiple linear regression (MLR) models and map algebra techniques were implemented togenerate surface maps of PAR with a grid resolution of 500 x 500 m as a function of fivegeographical and 14 climatic variables. Performance of the geostatistical and MLR modelswas compared using mean prediction error (MPE), root-mean-square prediction error(RMSPE), average standard prediction error (ASE), mean standardized prediction error(MSPE), root-mean-square standardized prediction error (RMSSPE), and adjustedcoefficient of determination (R²adj.). The best-fit MLR- and universal kriging-generatedmodels of monthly mean daily PAR were validated against an independent 37-year observeddataset of 35 climate stations derived from 160 stations across Turkey by the Jackknifingmethod. The spatial variability patterns of monthly mean daily incident PAR were moreaccurately reflected in the surface maps created by the MLR-based models than in thosecreated by the universal kriging method, in particular, for spring (May) and autumn(November). The MLR-based spatial interpolation algorithms of PAR described in thisstudy indicated the significance of the multifactor approach to understanding and mappingspatio-temporal dynamics of PAR for a complex terrain over meso-scales.

Effects of Wind Turbine Noise on Self-Reported and Objective Measures of Sleep.

STUDY OBJECTIVES: To investigate the association between self-reported and objective measures of sleep and wind turbine noise (WTN) exposure. METHODS: The Community Noise and Health Study, a cross-sectional epidemiological study, included an in-house computer-assisted interview and sleep pattern monitoring over a 7 d period. Outdoor WTN levels were calculated following international standards for conditions that typically approximate the highest long-term average levels at each dwelling. Study data were collected between May and September 2013 from adults, aged 18-79 y (606 males, 632 females) randomly selected from each household and living between 0.25 and 11.22 kilometers from operational wind turbines in two Canadian provinces. Self-reported sleep quality over the past 30 d was assessed using the Pittsburgh Sleep Quality Index. Additional questions assessed the prevalence of diagnosed sleep disorders and the magnitude of sleep disturbance over the previous year. Objective measures for sleep latency, sleep efficiency, total sleep time, rate of awakening bouts, and wake duration after sleep onset were recorded using the wrist worn Actiwatch2® from a subsample of 654 participants (289 males, 365 females) for a total of 3,772 sleep nights. RESULTS: Participant response rate for the interview was 78.9%. Outdoor WTN levels reached 46 dB(A) with an arithmetic mean of 35.6 and a standard deviation of 7.4. Self-reported and objectively measured sleep outcomes consistently revealed no apparent pattern or statistically significant relationship to WTN levels. However, sleep was significantly influenced by other factors, including, but not limited to, the use of sleep medication, other health conditions (including sleep disorders), caffeine consumption, and annoyance with blinking lights on wind turbines. CONCLUSIONS: Study results do not support an association between exposure to outdoor WTN up to 46 dB(A) and an increase in the prevalence of disturbed sleep. Conclusions are based on WTN levels averaged over 1 y and, in some cases, may be strengthened with an analysis that examines sleep quality in relation to WTN levels calculated during the precise sleep period time.