Contribution Of Different Factors Research Articles

Revisiting the contribution of different factors in determining the changes in potential evapotranspiration over China.

In this paper, a daily gridded observation data across China from 1961 to 2022 were used to calculate daily potential evapotranspiration (PET). The observed variables included daily temperature, sunshine hours, average wind speed, and average relative humidity. PET was determined using the Penman-Monteith method recommended by the Food and Agriculture Organization (FAO). The long-term trend of PET was investigated in six regions of China during different seasons. To further compressed the influence of various meteorological factors on the PET trend, the contribution of each meteorological element to the long-term trend of PET was analyzed. The results indicate the following: (1) PET reaches its peak during summer which values from 145 to 640 mm, while it is lowest during winter from 21 to 244 mm. (2) The spatial patterns of PET trend changes are relatively similar across the four seasons, characterized by a decrease in the eastern regions and an increase in the western regions. The reduction is most significant during the summer and the range of trend is from -2.04 to 1.48 mm/day, while the increase becomes more pronounced in the winter which trend is from -0.34 to 0.53 mm/day. (3) The contribution of factors varies significantly across different regions. In spring and autumn, RH and U have little difference in contribution from other factors. But tsun is varies different from regions, the contribution value is largest in the northwest and smallest in the northeast. However, during summer, tsun become the most significant contributor in the YZ and SE regions, while in winter, Tm emerges as the most significant contributor to the PET trend in all six subregions. In SW, the contribution from U2 is the smallest in all seasons, with RH and Tm being the two crucial factors determining the PET trend in this region.

Advanced ceramics with integrated structures and functions: Machine learning prediction and experimental verification

Improving the thermal conductivities and bending strengths of ceramic substrates is crucial for their applicability as key components of integrated chips. Herein, we report the latest study in which the thermal conductivity and bending strength of aluminum nitride (AlN) ceramics were systematically predicted using high-precision prediction model approached with a machine learning (ML) method based on extreme gradient enhancement method (XGBoost). The ML model was used to rank the effects of the process parameters, and SHapley Additive exPlanations (SHAP) was employed to quantify the contributions of different factors. Sintering additives were found to have the predominant influence on the thermal conductivity and bending strength; particularly, those with cationic radii within 0.085–0.105 nm enhance both properties. By using Pr2O3 as the sintering additive and adopting the preparation conditions recommended by the ML model, we prepared AlN ceramics with thermal conductivity as high as 195.63 W m−1 K−1 and bending strength of 371.60 ± 9.31 MPa, thus satisfying the application requirement of high thermal conductivity. The proposed model is also applicable to alumina and silicon nitride ceramics. This study provides a practicable and once-for-all strategy to realize the entire process from the ML prediction design to the preparation of ceramic materials exhibiting superior thermal conductivity and high strength.

Factors affecting the place of death in patients with liver cancer in China, 2013–2020: A population-based study

BackgroundDespite the country's substantial liver cancer burden, there is limited research on the factors influencing the place of death (POD) of patients with liver cancer in China. This study aimed to delineate POD distribution among patients with liver cancer, identify the factors associated with hospital deaths, and offer valuable insights for the government to develop healthcare policies. MethodsData from 2013 to 2020 were obtained from the National Mortality Surveillance System (NMSS) of China. This analysis focused on the distribution of POD among individuals who succumbed to liver cancer. Variations in characteristic distributions across different categories were evaluated using a chi-squared test. We also applied a multilevel logistic regression analysis to identify the factors associated with hospital liver cancer deaths. The proportional change in variance was computed to evaluate the contributions of different factors in the model. ResultsFrom 2013 to 2020, the NMSS reported a total of 608,789 liver cancer-related deaths, of which 440,079 (72.29%) died at home, and 158,291 (26.00%) died in the hospital. Home remained the preferred POD among patients with liver cancer. The results demonstrated that female patients, aged between 0 and 14 years, of Han ethnicity, living in urban areas, unmarried, highly educated, and either employed in a professional, staff, or civil servant capacity, or retired patients tended to end their lives in the hospital. ConclusionsIn China, home continues to be the predominant POD for patients with liver cancer, with demographic and socioeconomic factors significantly influencing whether a hospital is their POD. Enhancing healthcare policymakers' understanding of the factors influencing the place of death for patients with liver cancer may assist in creating a more equitable distribution of healthcare resources and providing a variety of choices for minorities with distinct preferences for end-of-life care.

Modeling and forecasting mortality with economic, environmental and lifestyle variables

AbstractTraditional stochastic mortality models tend to extrapolate, to focus on identifying trends in mortality without explaining them. Those that do link mortality with other variables usually limit themselves to GDP. This article presents a novel stochastic mortality model that incorporates a wide range of variables related to economic, environmental and lifestyle factors to predict mortality. The model uses principal components derived from these variables, extending the Niu and Melenberg (Demography 51(5):1755–1773, 2014) model to variables other than GDP, and is applied to 37 countries from the Human Mortality Database. Model fit is superior to the Lee–Carter model for 18 countries. The forecasting accuracy of the proposed model is better than that of the Niu–Melenberg model for half of the countries analyzed under various jump-off years. The model highlights the importance of economic prosperity and healthy lifestyle choices in improving lifespan, while the effect of environmental variables is mixed. By clarifying the specific contributions of different factors and thus making trade-offs explicit, the model is designed to facilitate scenario building and policy planning.

Future projections of compound temperature and precipitation extremes and corresponding population exposure over global land

Extreme climate events are hotspots in global change. However, research on the changes in future compound events and population exposure is still limited. Leveraging from the data of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), this paper aims to analyze the temporal and spatial changes of global compound temperature and precipitation extreme events in the future. We also predict the risk of population exposure and quantify the contribution of different factors to exposure. The results show that: (1) In the next 80 years, compound hot-dry event (CHDE) will increase at a rate of 0.02, 0.03, and 0.08 days per decade under the three scenarios of SSP1–2.6, SSP2–4.5 and SSP5–8.5, respectively. By comparison, compound hot-humidity event (CHHE) shows a downward trend under the three scenarios, with a downward rate of 0.01, 0.02, and 0.11 days per decade, respectively. (2) Under the SSP1–2.6 and SSP2–4.5 scenarios, CHDE and CHHE have two or more mutation points. Under the SSP5–8.5 scenario, CHDE shows a significant upward and CHHE shows a significant downward trend in the middle and late 21st century. These two indices exhibit periodic changes in all three scenarios (3) South Asia, West Asia, and Northeast Africa have higher CHDE values, while regions with higher CHHE values are located in North Asia and Greenland. (4) Climate change is a major factor affecting population exposure. For CHDE, climate, population, and their synergistic effects contribute about 75%, 20%, and 5% to the exposure, respectively. For CHHE, the contributions of these three factors are 85%, 10%, and 5% respectively. These findings provide scientific guidance for the rational formulation of population policies, the effective avoidance of climate disaster risks and the protection of human health.

Spatiotemporal variability and drivers of water microchemistry in the upper Nu-Salween river: With implications for fish habitat conservation

Microchemical maps, also known as “chemoscapes”, hold immense potential for reconstructing fish habitat utilization and guiding conservation efforts. This approach relies on matching the microchemical composition of fish calcified structures (e.g., otoliths) with the surrounding water's microchemistry. However, applying this method faces a major challenge: a clear understanding of the spatiotemporal variability and drivers of water microchemistry, particularly in vast, free-flowing river ecosystems like the Nu-Salween River, Southeast Asia's longest free-flowing river. We analyzed the spatiotemporal variability and influencing factors of water microchemistry (i.e., Na:Ca, Mg:Ca, Mn:Ca, Cu:Ca, Zn:Ca, Se:Ca, Sr:Ca, and Ba:Ca) in the upper Nu-Salween River, based on a two-year sampling. Five elemental ratios (excluding Na:Ca, Mg:Ca, and Zn:Ca) in water demonstrated significant spatiotemporal variability, with Cu:Ca having the largest spatial variation, and Mn:Ca and Sr:Ca showing the greatest temporal variation. More specifically, four elemental ratios (Cu:Ca, Se:Ca, Sr:Ca, and Ba:Ca), exhibited significant variations along the longitudinal gradient, and Mn:Ca, Cu:Ca, Sr:Ca, and Ba:Ca, showed significant differences between mainstreams and tributaries. Temporally, Mn:Ca, Cu:Ca, and Ba:Ca displayed higher values and variations during the wet season, opposing the seasonal patterns of Na:Ca, Mg:Ca, and Sr:Ca. The four-element (Ba:Ca, Sr:Ca, Mg:Ca, and Mn:Ca) forest model showed the highest classification accuracy of 93%. Linear mixed-effects models showed that spatial factors have the largest influence on the variances in water microchemistry (56.36 ± 28.64%). Our study highlights the feasibility and reliability of utilizing microchemistry to reconstruct fish habitat utilization, thereby unveiling promising avenues for a more accurate understanding of fish life history in large rivers characterized by high heterogeneity in water microchemistry. By proportionally accounting for contribution of different factors to water microchemistry variability and relating them to microchemical composition of fish calcified structures, key fish habitats (e.g., spawning grounds) and migratory routes can be more precisely identified and thus protected.

New Model for Predicting Production Capacity of Horizontal Well Volume Fracturing in Tight Reservoirs.

Production prediction is the most important and comprehensive index to measure the effect of oilfield development, and it is also one of the most fundamental problems in oilfield dynamic analysis. However, the recovery prediction is often affected by many factors. Usually, the recovery is predicted by core experiments, numerical simulations, and mathematical models. The main problem is accurately predicting reservoir recovery based on existing data. This paper proposes a comprehensive prediction model for the problem of recovery. First, the correlation coefficients between 14 factors and recovery were calculated based on Pearson, Spearman, gray correlation, variance selection, univariate selection method, and tree model. Second, the weights of the factors were determined using entropy weighting, CRITIC, and hierarchical analysis to clarify the degree of contribution of different factors to the recovery. Finally, a comprehensive evaluation model was established based on the results of the weighting analysis. The results indicate that the correlation coefficient and weight of porosity, permeability, oil saturation, well spacing, cluster spacing, total fluid volume, and horizontal section length are the most relevant to the recovery. The error between the comprehensive evaluation model and the actual results is less than 3%. Therefore, the method can predict the production capacity of the tight reservoirs. The research results of this paper are of guiding significance for improving the recovery of tight reservoirs.

Quantitative Modeling of Financial Contagion: Unraveling Market Dynamics and Bubble Detection Mechanisms

This study explored the complex interplay and potential risk of financial contagion across major financial indices, focusing on the Bucharest Exchange Trading Investment Funds Index (BET-FI), along with global indices like the S&P 500, Nasdaq Composite (IXIC), and Dow Jones Industrial Average (DJIA). Our analysis covered an extensive period from 2012 to 2023, with a particular emphasis on Romania’s financial market. We employed Autoregressive Distributed Lag (ARDL) modeling to examine the interrelations among these indices, treating the BET-FI index as our primary variable. Our research also integrated Exponential Curve Fitting (EXCF) and Generalized Supremum Augmented Dickey–Fuller (GSADF) models to identify and scrutinize potential price bubbles in these indices. We analyzed moments of high volatility and deviations from typical market trends, influenced by diverse factors like government policies, presidential elections, tech sector performance, the COVID-19 pandemic, and geopolitical tensions, specifically the Russia–Ukraine conflict. The ARDL model revealed a stable long-term relationship among the variables, indicating their interconnectedness. Our study also highlights the significance of short-term market shifts leading to long-term equilibrium, as shown in the Error Correction Model (ECM). This suggests the existence of contagion effects, where small, short-term incidents can trigger long-term, domino-like impacts on the financial markets. Furthermore, our variance decomposition examined the evolving contributions of different factors over time, shedding light on their changing interactions and impact. The Cholesky factors demonstrated the interdependence between indices, essential for understanding financial contagion effects. Our research thus uncovered the nuanced dynamics of financial contagion, offering insights into market variations, the effectiveness of our models, and strategies for detecting financial bubbles. This study contributes valuable knowledge to the academic field and offers practical insights for investors in turbulent financial environments.

Response of the mid-latitude ionosphere to the solar eclipse on 25 October 2022: Results of F2–layer vertical sounding

The results of observations of the critical frequency (foF2), ionospheric F2 peak electron density (NmF2) and peak height (hmF2) of the ionosphere over Kharkiv (Ukraine, 49.6° N, 36.3° E) during a partial solar eclipse (SE) on 25 October 2022 are presented. The data analysis showed a clear effect in the variations of foF2 (and, accordingly, NmF2): during the SE, foF2 decreased, and 5 min after the time of maximum eclipse, began to increase to its usual level. From the beginning of the SE, hmF2 decreased from 285 to 255 km, and then it began to grow with a maximum near the time of maximum obscuration. To study longitudinal effects and clarify contributions of different factors to the observed temporal variations of the ionospheric parameters, the results of observations of the ionosphere over Kharkiv were compared with the data obtained by the ionosonde in Pruhonice (Czech Republic, 50.0° N, 14.6° E). The NmF2 electron density decreased due to SE by the factor of 1.46 in Kharkiv, in contrast to 1.33 times in Pruhonice. Comparison of data in universal time (UT) showed a coincidence in the nature and time of fluctuations in both foF2 and hmF2 observed in Kharkiv and Pruhonice on 25 October. Variations in hmF2 caused by SE in Kharkiv and Pruhonice were identical. The SE was accompanied by a plasma flow from the plasmasphere to the ionosphere. The flux density change was about –2 × 1013 m–2s−1 and about 0 for Kharkiv and Pruhonice, respectively. The obtained value of the vertical velocity (tens of meters per second) for Kharkiv is the typical velocity for the mid-latitude F2 layer of the ionosphere. It was confirmed that in addition to typical SE effects, results of observations of the ionosphere over Kharkiv on October 25, 2022 had individual features.

Vector Autoregressive Model of Maize Production in Northern Region of Ghana

Agricultural growth plays a crucial role in the Comprehensive African Agriculture Development Programme (CAADP, 2009) agenda. The program recognizes that increasing agricultural productivity is essential for reducing poverty, meeting food production targets, and lowering production costs and food prices for the impoverished. This study aimed to develop two types of models. The first model employed a vector autoregressive (VAR) approach, which involved regressing the production of maize in one district against the production of maize in other districts at various lags. The second model utilized a VAR framework where the maize production in each district was regressed against the production of maize in other districts and the corresponding climate conditions at different lag periods. The available data spanned from 1968 to 2018 and were recorded on an annual basis. Six climatic variables were included in the analysis. A lag order of 3 was selected for the models. The results of the autocorrelation test using the portmanteau test indicated no serial autocorrelation across all lag periods. The test for normality revealed that the residuals followed a normal distribution. Additionally, there was no evidence of heteroscedasticity in the data. Furthermore, a Granger causality test was conducted on the selected districts to explore causal relationships. Variance decomposition analysis was performed to assess the variance relation in the data and understand the contribution of different factors. Based on adjusted R-squared, mean absolute error (MAE), and root mean squared error (RMSE) values, the models that incorporated climatic variables were found to be the most suitable for forecasting maize production in the selected districts. The VAR model, which captures the interdependencies between the variables, was utilized in this analysis. All variables in the VAR model were treated symmetrically, meaning that their relationships were considered equally important.

Temperate grassland vegetation restoration influenced by grazing exclusion and climate change

Grasslands are one of the most important terrestrial biomes, supporting a wide range of ecological functions and services. Grassland degradation due to overgrazing is a severe issue worldwide, especially in developing regions. However, observations from multiple sources have shown that temperate grasslands in China have significantly increased during the past two decades. It remains controversial what factors have driven the vegetation restoration in this region. In this study, we combined remote-sensing images and field survey datasets to quantify the contributions of different factors to vegetation restoration in six temperate grasslands in northern China. Across the six grasslands, the Normalized Difference Vegetation Index (NDVI) increased by 0.003–0.0319 year−1. The average contributions of grazing exclusion and climate change to the NDVI increase were 49.23 % and 50.77 %, respectively. Precipitation change was the primary climate factor driving vegetation restoration, contributing 50.76 % to the NDVI variance. By contrast, climate warming tended to slow vegetation restoration, and atmospheric CO2 concentration change contributed little to the NDVI increase in the temperate grasslands. These results emphasize the significant contributions of both climate change and human management to grassland vegetation restoration.

Spatial lithological heterogeneity of the deep-sea sediments and its controlling factors in the northern West Caroline Basin, tropical West Pacific

Uncovering the spatial distribution of deep-sea sediment lithologies is crucial for the research on earth system science and the exploration of critical mineral resources. The conventional global deep-sea sediment lithology distribution maps showed that the lithologies were relatively uniform in the global major ocean basins. Furthermore, it was deemed that the deep-sea sediment lithologies were related to the productivity of surface seawater. However, later studies have illustrated that the distribution of deep-sea sediment lithologies exhibited greater variability and heterogeneity than conventional maps and that water depth played a more important role in controlling deep-sea sediment lithologies than productivity. In addition, few studies have focused on the relationship between the heterogeneous deep-sea sediment lithologies and the diverse seafloor topography in the West Pacific. In this study, we provide a high-resolution spatial distribution map of deep-sea sediment lithologies by systematic descriptions of 112 surface samples in the northern West Caroline Basin, tropical West Pacific, with a large range of water depth and a diverse seafloor topography to verify the previous prediction of sediment lithology distribution. Subsequently, based on the results of the determinations of carbonate and biogenic silica contents, the sediment redox-potential tests, and the calculations of multiple variables, we carried out quantitative studies and correlation analyses for these variables to reveal the relative contribution of different factors to the lithological heterogeneity in the northern West Caroline Basin. Our results show that the actual deep-sea sediments have greater heterogeneity in terms of lithological classes and distribution patterns than the earlier predicted lithology maps. Furthermore, our analyses reveal that water depth and seafloor topography are the more important controlling factors on the deep-sea sediment lithology to some extent than productivity and that water depth and topography may exert different effects on the different sediment components. For the calcareous component, water depth is the main controlling factor due to the increased solubility of carbonate with water depth. Carbonate materials can dilute other components in the waters above the carbonate compensation depth, which is around 4500 m below the sea surface in the study area. Meanwhile, the siliceous component may be enriched in the unique negative seafloor topography of the trench and trough with the specific lateral transport mechanism and the redox environment. These findings imply a more complicated actual deep-sea sediment lithology distribution than previously thought given that various water depths and diverse topography in the global oceans, which are significant for understanding deep-sea sedimentary processes and improving the models for predicting global sediment lithology in the deep sea.

Factors Contributing to the Change in Overweight/Obesity Prevalence Among Indian Adults: A multivariate decomposition analysis of data from the National Family Health Surveys.

Concerns over the escalating burden of non-communicable diseases call for the redressal of behavioral risk factors like increased body mass index. Most studies have failed to quantify the contribution of socio-demographic characteristics in a linear trend. The present study aims to estimate the current prevalence of overweight and obesity in Indian adults and the contribution of different socio-demographic factors to the increasing prevalence. We carried out a secondary data analysis of two National Family Health Survey (NFHS) rounds. The final sample includes 558,122 women and 84,477 men from round 4, and 574,099 women and 74,761 men were included from round 5, using a multi-stage stratified random sampling approach. Overweight/obesity was our primary dependent variable. Weighted bivariate analysis was used to ascertain the prevalence, and the adjusted odds ratios were computed to ascertain the potential predictors. The contribution of different factors towards rising burden over two time points was estimated using multivariate decomposition analysis for non-linear response models. Overall weighted prevalence of overweight and obesity in males and females per NFHS-5 was 44.02% and 41.16%, respectively, compared to 37.71% and 36.14% in NFHS-4. Decomposition analyses depict that the proportion of obesity increased by 6.37% and 5.10% points among men and women, respectively, over the two rounds. Compositional differences of participants (endowment) attributed to 16.54 and 49.90% differences, and the difference in coefficient or effect accounted for 83.46 and 50.10%, respectively, of the increase in the prevalence. The most significant factors contributing to increased prevalence were age, improving socio-economic status, smoking, unclean cooking fuel, and diabetes. The incremental rise in such a short period, mainly attributed to the effect of socio-demographic variables, is concerning. Policy interventions should prioritize health advocacy programs and aggressively target behavioral modifications while preparing the health systems to manage the people living with obesity.

Effect of Action Units, Viewpoint and Immersion on Emotion Recognition Using Dynamic Virtual Faces.

Facial affect recognition is a critical skill in human interactions that is often impaired in psychiatric disorders. To address this challenge, tests have been developed to measure and train this skill. Recently, virtual human (VH) and virtual reality (VR) technologies have emerged as novel tools for this purpose. This study investigates the unique contributions of different factors in the communication and perception of emotions conveyed by VHs. Specifically, it examines the effects of the use of action units (AUs) in virtual faces, the positioning of the VH (frontal or mid-profile), and the level of immersion in the VR environment (desktop screen versus immersive VR). Thirty-six healthy subjects participated in each condition. Dynamic virtual faces (DVFs), VHs with facial animations, were used to represent the six basic emotions and the neutral expression. The results highlight the important role of the accurate implementation of AUs in virtual faces for emotion recognition. Furthermore, it is observed that frontal views outperform mid-profile views in both test conditions, while immersive VR shows a slight improvement in emotion recognition. This study provides novel insights into the influence of these factors on emotion perception and advances the understanding and application of these technologies for effective facial emotion recognition training.

Attribution analysis of multi-temporal scale changes of streamflow in the source area of Lancang River with seasonal scale Budyko model

Under the influence of climate change and human activities, the intra-annual distribution characteristics of streamflow have changed, directly affecting the exploitation of water resources and the health of ecosystems. The trend-free pre-whitening Mann-Kendall (TFPW-MK) test method, concentration degree and concentration period, and Bernaola-Galvan (BG) segmentation algorithm were applied to analyze variation trend, intra-annual distribution characteristics, and abrupt year of streamflow. Then, the monthly water storage and monthly actual evaporation of the source area of the Lancang River (SALR) were calculated by the monthly ABCD model. Finally, the contributions of different factors to runoff variability at multiple time scales were quantified using the seasonal-scale Budyko hypothesis approach. The results showed that: (1) The runoff revealed a significant upward trend on the annual scale. Runoff exhibited a significant upward trend in January, October and November, and runoff in other months and seasons exhibited an insignificant upward trend. (2) The intra-annual distribution characteristics of runoff in the SALR showed an obvious “Single-peak type“ distribution, reaching a maximum in July and August. (3) The year of sudden change in streamflow was 2008. (4) The contribution of climate change and human activities to the annual runoff change was 83.3% and 16.7%, respectively. The degree of influence of climate change on runoff change was ranked as spring (96.8%), autumn (85.3%), winter (82.2%) and summer (58.2%). The order of impact of human activity on runoff change was summer (41.8%), winter (17.8%), autumn (14.7%), spring (3.2%).

Quantitative Assessment of the Contribution of Climate and Underlying Surface Change to Multiscale Runoff Variation in the Jinsha River Basin, China

Many studies quantify the impact of climate change and human activities on runoff changes on an annual scale, but few studies have examined this on multiple time scales. This paper quantifies the contribution of different factors to the variability of Jinsha River runoff at multiple time scales (annual, seasonal and monthly). First, the trend analysis of Jinsha River runoff is carried out, and the Mann–Kendall mutation test was then applied to the runoff data for mutation analysis. According to the mutation year, the research period is divided into the base period and the mutation period. By constructing an ABCD hydrological model simulation and monthly scale Budyko model, the contribution rate of human and climate factors to the multitime-scale runoff of Jinsha River is calculated. The results showed that: (1) The sudden year of change in the Jinsha River runoff is 1978, and the Nash coefficients of the ABCD hydrological model in the base period and sudden change period were 0.85 and 0.86, respectively. (2) Climate factors were the dominant factor affecting annual runoff changes (98.62%), while human factors were the secondary factor affecting annual runoff changes (1.38%). (3) The contribution rates of climate factors in spring, summer, autumn, and winter to runoff were 91.68%, 74.08%, 95.30%, and 96.15%, respectively. The contribution rates of human factors in spring, summer, autumn, and winter to runoff were 8.32%, 25.92%, 4.70%, and 3.85%, respectively. (4) The contribution rates of climate factors to runoff in May, June, and July were 95.14%, 102.15%, and 87.79%, respectively. The contribution rates of human factors to runoff in May, June, and July were 4.86%, −2.15%, and 12.21%, respectively.

A Multilevel Decomposition of Time Variation in the Risks of Infant Mortality in Rural Uganda: UDHS 1995–2016

The study assessed the contribution of maternal, child, paternal, household, proximate, and community-level factors to infant mortality risk time variation in rural Uganda between 1995 and 2016. Five rounds of Uganda Demographic and Health Survey data sets were used, and a multilevel mixed-effect logistic regression model was applied to decompose the contribution of different factors to time variation in the risks of infant mortality. All live births that were made five years before the surveys of 1995, 2001, 2006, 2011, and 2016 were considered, with infants who did not survive beyond one year treated as the outcome variable analysis, excluding those who were born less than 12 months before the survey. The fixed part of the model helped us detect the significant variables in determining infant mortality, and yet the random part of the model helped us quantify the amount of time variation in the risks of infant mortality explained by the selected variables. The child-level determinants of infant mortality were sex, birth order, and weight. Among the maternal factors, the study revealed that marital status, access to ANC, use of contraceptives, maternal education level, and preceding birth interval were consistent deterrents of infant mortality, while household size, sanitation, and wealth index remained critical. While controlling for other factors in the rural areas, time variation in the risks of infant mortality was dependent on community factors (such as region, community hygiene, and prenatal care utilization rate), proximate factors (such as access to prenatal care, contraceptives use, place of delivery, and the number of ANC visits), maternal factors (such as marital status, educational level, age, parity, preceding birth interval, desire for pregnancy, and breastfeeding), and endowment. It was observed that the changes in the risks of infant mortality over the period were explained by community (30.7%), proximate (22.7%), maternal (41.0%), and endowment (37.9%). Child-level factors explained 28.2%, and paternal-level education level explained only 30.1%. Remarkably, household-level factors captured 32.3% of the changes in infant mortality. A higher proportion of the explained variation in the risk of infant mortality across communities (PCV) was captured by child, paternal, maternal endowment, and household factors. Interventions to accelerate the reduction in infant mortality should target birth spacing to at least two years, girl child education to at least o level, joint household decision-making in having children, avoiding teenage pregnancies, postnatal care utilization, enforcing at least four ANC visits during pregnancy, improving household sanitation, and increasing access to safe water at household-levels

Comprehensive Error Equation for Fiber Optic Inclinometer

Optical fiber inclinometer technology has been gradually used in various projects. However, the error analysis of this method is decisive for its subsequent development and application. In this study, through three tests, the monitoring length, layout plane, and boundary conditions that may lead to measurement errors are studied in detail and the contribution of different factors to measurement errors is explored. The research results reveal that the measurement errors caused by monitoring length and layout plane have an empirical function relationship with the monitoring length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</i> and the angle θ between the layout plane and the inclined direction. Any changes in the boundary conditions will result in an overall deviation in the calculated deflection curve. After analyzing the characteristics of each error, a comprehensive error equation is proposed. This equation has guiding significance for error correction and technical improvement in optical fiber inclinometer.

Multilayer graphene oxide-based membranes for reverse osmosis water desalination: An atomistically detailed simulation study

Pressure-driven Molecular Dynamics simulations were employed to examine reverse osmosis desalination through graphene-oxide-based multilayered membranes. The effects of functionalization of the graphene-oxide flakes with poly(ethylene imine) branches in water permeability and salt rejection were described in detail. The role of the degree of structural rigidity of the membranes was also explored. A lower degree of rigidity of the membrane resulted in a 6–9 % increase in water permeability depending on the state of functionalization of the flakes. At constant membrane rigidity, functionalization of the membranes’ flakes led to approximately 30 % reduction in water permeability, but the water flux remained 2–3 orders of magnitude higher than that of conventional reverse-osmosis membranes. Moreover, functionalization of the membranes’ flakes resulted in a higher than 20 % enhancement in salt rejection at a pressure difference similar to that in actual reverse osmosis processes. Examination of the swelling behavior of the membranes showed that those based on the functionalized flakes exhibit a tendency to remain structurally coherent with an interlayer separation determined by the presence of the polymer branches. Description of the microscopic mechanisms related to the membranes’ water and ion flux, such as hydrogen bonding and concentration polarization, allowed the assessment of the contribution of different factors involved in desalination, providing new insight towards the fabrication of membranes with improved performance.

The Role of Recent Climate Change in Explaining the Statistical Yield Increase of Maize in Northern Bavaria—A Model Study

Maize yields in many regions of the world have increased significantly since the 1960s. The increase is mainly attributed to technological improvements and climate change. On a regional scale and in recent decades, climate change has altered growth conditions of maize and this, in turn, has influenced changes in yield. In order to analyze the contribution of different factors to yield changes, and to obtain a model setup that could be used for further analyses of yield development, this study systematically investigated the effects of recent climate change, irrigation, cultivar selection and nutrient availability on historical yields in Northern Bavaria. Four sets of simulations were conducted with the mechanistic plant growth model PROMET, during the time period between 1997 and 2020, and the resulting yields were compared to county statistics. In addition, three scenarios were simulated in order to determine yield increase potentials for the highly mechanized agricultural region of Northern Bavaria. The results showed a good agreement with the observed yields (R2 = 0.76), when considering altered nutrient availability, suggesting that an increase in nutrient uptake by plants plays a key role in reproducing yield statistics and has a main contribution to the observed increasing yield trends. Moreover, other factors considered individually, such as recent climate change, irrigation and cultivar selection, could not explain the yield levels and trends shown by the statistics. The scenario simulations demonstrated potential increases in yield due to irrigation and cultivar adaptation. The yield response to irrigation shows a trend, with recent climate change progressing, of 0–25% when irrigating currently grown cultivars and 10–50% when irrigating an adapted cultivar; rainfed cultivar adaptation consistently increased the level of yields by approximately 10%. This study highlights the importance of a dynamic consideration of growth conditions in the course of climate change, rather than static assumptions of model parameters, and emphasizes the importance of the second-order effects of climate change.