Contribution Of Different Factors Research Articles

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.

What matters more, biomass accumulation or allocation, in yield and water productivity improvement for winter wheat during the past two decades？

Continuous increases in crop production and water productivity (WP) are important for agriculture that is facing fresh-water shortages globally. Biomass production and the allocation of biomass to grains are two critical factors influencing crop production and WP. A long-term field experiment was conducted at the Luancheng Agro-Eco-Experimental station in the North China Plain (NCP) for twenty winter wheat growing seasons (2002–2022) under six irrigation treatments to examine the contributions of different factors to yield and WP improvement. The six irrigation treatments were classified into four levels of water supply, i.e., adequate water supply (AW), light water deficit (LD), moderate water supply (MD), and serious water deficit (SD). The results showed that the average yield of winter wheat for all the treatments increased at a rate of 79.1 kg/ha/yr, while the corresponding seasonal evapotranspiration (ET) increased by 2.0 mm/yr, resulting in an improvement in WP at a rate of 0.012 kg/m3/yr over the past 20 years. The maximum dry matter accumulation and the highest yield improvement were observed for the AW treatment, which increased by 119.0 kg/ha/yr. On average, the highest WP was achieved at 1.84 kg/m3 under MD conditions during the past two decades. Under various water supply conditions, the leaf photosynthesis rate and leaf area of a single plant were relatively stable. The plant density showed an increasing trend, and the anthesis date gradually advanced with cultivar renewal, which contributed to the increased biomass accumulation during reproductive stages. The biomass allocation had less of an effect on the final yield in recent years since the harvest index (HI) has nearly approached the upper limit, and the increase in biomass accumulation contributed more to a sustained increase in the yield and WP of winter wheat. Breeding cultivars that could tolerate high density and be less sensitive to water stress would benefit biomass production to improve the grain yield of winter wheat in future.

HPR27 Socioeconomic Inequalities in out of Pocket and Catastrophic Health Expenditures Among the Households in Pakistan

The analysis of health inequality is essential for providing the evidence on who is being left behind and informs equity-oriented policies, programs, and practices. Therefore, socioeconomic inequalities in Out-of-Pocket Health Expenditures (OOPHEs), Catastrophic Health Expenditures (CHEs) and contribution of different factors in inequalities are assessed in this study.

Wear Resistance Prediction of AlCoCrFeNi-X (Ti, Cu) High-Entropy Alloy Coatings Based on Machine Learning

In order to save the time and cost of friction and wear experiments, the coating composition (different contents of Al, Ti, and Cu elements), ratio of hardness and elastic modulus (H3/E2), vacuum heat treatment (VHT) temperature, and wear form were used as input variables, and the wear rates of high-entropy alloy (HEA) coatings were used as output variables. The dataset was entirely obtained by experiment. Four machine learning algorithms (classification and regression tree (CART), random forest (RF), gradient boosting decision tree (GBDT), and adaptive boosting (AdaBoost)) were used to predict the wear resistance of HEA coatings based on a small amount of data. The results show that except for the GBDT model, the other three models had good performance. Because of the small amount of data, the CART model demonstrated the best prediction performance and can provide guidance for predicting the wear resistance of AlCoCrFeNi-X (Ti, Cu) HEA coatings for drilling equipment. Furthermore, the contribution of different factors to the wear rate of AlCoCrFeNi-X (Ti, Cu) HEA coatings was obtained. Al content had the greatest influence on wear rate, followed by H3/E2, wear form, and VHT temperature.

Characterization of water−soluble brown carbon in atmospheric fine particles over Xi'an, China: Implication of aqueous brown carbon formation from biomass burning

Brown carbon (BrC) aerosols can affect not only the climate but also human health, however, the light absorption, chemical compositions, and formation mechanisms of BrC are still uncertain, which leads to uncertainties in the accurate estimation of its climate and health impacts. In this study, highly time − resolved brown carbon (BrC) in fine particles was investigated in Xi'an using offline aerosol mass spectrometer analysis. The light absorption coefficient (babs365) and mass absorption efficiency (MAE365) at 365 nm of water−soluble organic aerosol (WSOA) generally increased with oxygen−to−carbon (O/C) ratios, indicating that oxidized OA could have more impacts on BrC light absorption. Meanwhile, the light absorption appeared to increase generally with the increases of nitrogen−to−carbon (N/C) ratios and water−soluble organic nitrogen; strong correlations (R of 0.76 for CxHyNp+ and R of 0.78 for CxHyOzNp+) between babs365 and the N − containing organic ion families were observed, suggesting that the N − containing compounds are the effective BrC chromophores. babs365 correlated relatively well with BBOA (r of 0.74) and OOA (R of 0.57), but weakly correlated with CCOA (R of 0.33), indicating that BrC in Xi'an was likely to be associated with biomass burning and secondary sources. A multiple linear regression model was applied to apportion babs365 to contributions of different factors resolved from positive matrix factorization on water-soluble organic aerosols (OA) and obtained MAE365 values of different OA factors. We found that biomass-burning organic aerosol (BBOA) dominated the babs365 (48.3 %), followed by oxidized organic aerosol (OOA, 33.6 %) and coal combustion organic aerosol (CCOA, 18.1 %). We further observed that nitrogen−containing organic matter (i.e., CxHyNp+ and CxHyOzNp+) increased with the increase of OOA/WSOA and the decrease of BBOA/WSOA, especially under high ALWC conditions. Our work offered proper observation evidence that BBOA is oxidized through the aqueous formation to produce BrC in Xi'an, China.

Vegetation Greening Enhanced the Regional Terrestrial Carbon Uptake in the Dongting Lake Basin of China

Terrestrial ecosystem carbon uptake is essential to achieving a regional carbon neutrality strategy, particularly in subtropical humid areas with dense vegetation. Due to the complex spatial and temporal heterogeneity of the carbon uptake of ecosystems, it is difficult to quantify the influence and contribution of different factors. With the aid of multisource remote sensing data, the spatiotemporal characteristics of carbon uptake and the impact of vegetation change were explored in the Dongting Lake Basin from 2001 to 2020. Based on the Conversion of Land Use and its Effects at Small regional extent (CLUE-S) model, we simulated the land use of the basin under different development scenarios in 2030. Our results showed that the basin has demonstrated a significant greening trend in the last 20 years, with a multiyear average NDVI (normalized difference vegetation index) of 0.60 and an increasing trend (y = 0.0048x − 9.069, R2 = 0.89, p &lt; 0.01). In this context, the multiyear mean of net ecosystem productivity (NEP) was 314.95 g C·m−2·a−1 and also showed a significant increasing trend (y = 1.8915x + 295.09, R2 = 0.23, p &lt; 0.05). Moreover, though the future carbon uptake might decrease because of the enhanced anthropogenic activities, the ecological conservation scenario might mitigate the reduction by 0.05 × 107 t. In conclusion, the greening trend enhanced the ecosystem carbon uptake in the Dongting lake basin. Considering the representativeness of the Dongting Lake Basin, the results of our study would provide useful clues for understanding the trend and pattern of terrestrial carbon uptake and for guiding the carbon neutrality strategy in the subtropical humid area.

Investigating Factors Contributing to Phytoplankton Biomass Declines in the Lower Sacramento River

Phytoplankton subsidies from river inputs and wetland habitats can be important food sources for pelagic organisms in the Sacramento–San Joaquin Delta (Delta). However, while the Sacramento River is a key contributor of water to the Delta, providing 80% of the mean annual inflow, the river is only a minor source of phytoplankton to the system. The reason for low phytoplankton biomass in the Sacramento River is not well understood but appears to be associated with a 65- km stretch of the lower river where chlorophyll-a (Chl-a) concentrations can decline by as much as 90%. We conducted two surveys along the lower Sacramento River, in spring and fall of 2016, to investigate the relative contributions of different factors potentially driving this Chl-a decline. Our study evaluated the change in Chl-a concentrations as a result of dilution from tributaries, light availability, nutrient concentrations, nutrient uptake, phytoplankton productivity, zooplankton grazing, and clam grazing. Chl-a concentration decreased from 14 µg L–1 to 1.8 µg L–1 in the spring and from 4.0 µg L–1 to 1.2 µg L–1 in the fall. Dilutions from the Feather River and American River contributed to 39% and 11% of Chl-a decline, respectively, during the spring. Average water depths roughly doubled downstream of the American River confluence, reducing water column light availability and lowering productivity. Zooplankton and clam grazing rates were generally low. Using a mass balance analysis, the measured variables explained 76% of the observed decline in Chl-a in the spring, suggesting additional losses from unidentified factors. We found that phytoplankton biomass is regulated by multiple potential factors in the lower Sacramento River, emphasizing the need for practitioners of restoration and management programs to evaluate multiple potential factors when attempting to enhance phytoplankton production in the Delta, or other large river systems.

Short stature related to Growth Hormone Insensitivity (GHI) in childhood.

Linear growth during childhood is the result of the synergic contribution of different factors. The best growth determinant system during each period of life is represented by the growth hormone-insulin-like growth factor axis (GH-IGF), even if several other factors are involved in normal growth. Within the broad spectrum of growth disorders, an increased importance has been placed on growth hormone insensitivity (GHI). GHI was reported for the first time by Laron as a syndrome characterized by short stature due to GH receptor (GHR) mutation. To date, it is recognized that GHI represents a wide diagnostic category, including a broad spectrum of defects. The peculiar characteristic of GHI is the low IGF-1 levels associated with normal or elevated GH levels and the lack of IGF-1 response after GH administration. Recombinant IGF-1 preparations may be used in the treatment of these patients.

Are pediatricians responsible for maintaining high MMR vaccination coverage? Nationwide survey on parental knowledge and attitudes towards MMR vaccine in Serbia.

To assess parental knowledge and attitudes related to MMR vaccination and to determine factors associated with parental decision whether to vaccinate their child with MMR vaccine in Serbian population. The selection of participants was performed using multi-phase sampling. Seventeen out of the total 160 public health centers on the territory of Republic of Serbia were randomly selected. All parents of children up to the age of 7 who visited the pediatrician at the public health centers from June to August 2017 were recruited. Parents filled in an anonymous questionnaire regarding their knowledge, attitudes and practices in immunization with MMR vaccine. The relative contribution of different factors was explored by univariable and multivariable logistic regression analysis. The majority of parents were female (75.2%), with mean age of 34.3 ± 5.7 years, and the average age of children was 4.7 ± 2.4 years, 53.7% of them were girls. In the multivariable model, getting information on vaccination from a pediatrician was associated with 7.5 fold increased probability to vaccinate child with MMR vaccine (OR = 7.52; 95% CI 2.73-20.74; p<0.001), while previous vaccination of the child increased this chance two times (OR = 2.07; 95% CI 1.01-4.27; p = 0.048), and having two children was associated with 84% increase in chance of vaccinating child with MMR vaccine compared to having one child or three or more children (OR = 1.84; 95% CI 1.03-3.29; p = 0.040). Our study emphasized the key role of pediatricians in the formation of parental attitude on MMR vaccination of their child.

Modeling of laser-assisted micro-milling

The difficulties in the mechanical micro-machining process regarding the tool deflection, size effect and tool wear are much more pronounced than those in mechanical macro-machining. However, the role of mechanical micro-machining cannot be overestimated as an alternative against the non-traditional machining methods in manufacturing complex micro-parts with high dimensional and geometrical accuracies. Laser-Assisted Machining (LAM) can be used as a promising method by reducing the cutting forces and providing the possibility of increasing the Material Removal Rate (MRR) to enhance the productivity of mechanical micro-machining of difficult-to-cut material. Unlike common LAM, the parts are structured before machining using the ultra-short pulsed laser, followed by removing some parts of the material and consequently changing uncut chip thicknesses at the machining process. A model has been developed to understand how the structuring of the parts before micro-milling affects the uncut chip thicknesses at the micro-milling process. The model includes a variety of input parameters in terms of structure, tool, and process parameters to investigate this novel LAM comprehensively. Based on model results, the uncut chip thicknesses are reduced through the structuring of the part. The structure density was found as the most crucial parameter in uncut chip thickness reduction. The influence of structure density on cutting force reduction was also studied in the experiment. Workpieces made of titanium alloy (Ti6Al4V) were structured using a pico-second laser at different structure densities. The structured parts were machined at two feeds of 10 and 50 µm/tooth with a 35 m/min constant cutting speed. Structure density's role in reducing cutting force was also considerable. In addition, the contribution of different factors affecting cutting force reduction in this novel LAM process is discussed.

Leveraging scaffold information to predict protein-ligand binding affinity with an empirical graph neural network.

Protein-ligand binding affinity prediction is an important task in structural bioinformatics for drug discovery and design. Although various scoring functions (SFs) have been proposed, it remains challenging to accurately evaluate the binding affinity of a protein-ligand complex with the known bound structure because of the potential preference of scoring system. In recent years, deep learning (DL) techniques have been applied to SFs without sophisticated feature engineering. Nevertheless, existing methods cannot model the differential contribution of atoms in various regions of proteins, and the relationship between atom properties and intermolecular distance is also not fully explored. We propose a novel empirical graph neural network for accurate protein-ligand binding affinity prediction (EGNA). Graphs of protein, ligand and their interactions are constructed based on different regions of each bound complex. Proteins and ligands are effectively represented by graph convolutional layers, enabling the EGNA to capture interaction patterns precisely by simulating empirical SFs. The contributions of different factors on binding affinity can thus be transparently investigated. EGNA is compared with the state-of-the-art machine learning-based SFs on two widely used benchmark data sets. The results demonstrate the superiority of EGNA and its good generalization capability.