Mean Regression Coefficient Research Articles

Machine Learning for the Genomic Prediction of Growth Traits in a Composite Beef Cattle Population.

The adoption of genomic selection is prevalent across various plant and livestock species, yet existing models for predicting genomic breeding values often remain suboptimal. Machine learning models present a promising avenue to enhance prediction accuracy due to their ability to accommodate both linear and non-linear relationships. In this study, we evaluated four machine learning models-Random Forest, Support Vector Machine, Convolutional Neural Networks, and Multi-Layer Perceptrons-for predicting genomic values related to birth weight (BW), weaning weight (WW), and yearling weight (YW), and compared them with other conventional models-GBLUP (Genomic Best Linear Unbiased Prediction), Bayes A, and Bayes B. The results demonstrated that the GBLUP model achieved the highest prediction accuracy for both BW and YW, whereas the Random Forest model exhibited a superior prediction accuracy for WW. Furthermore, GBLUP outperformed the other models in terms of model fit, as evidenced by the lower mean square error values and regression coefficients of the corrected phenotypes on predicted values. Overall, the GBLUP model delivered a superior prediction accuracy and model fit compared to the machine learning models tested.

A Deep Recurrent Neural Network for Predicting Subject-specific Facial Soft Tissue Interaction

Predicting biological soft tissue interaction is of great interest for developing computer-aided decision systems. This study aims to develop and evaluate a novel deep-learning approach based on the recurrent neural network for predicting facial soft tissue impact with a rubber ball. A computational workflow was established including a subject-specific finite element model of the facial soft tissue under interaction with the rubber ball. A series of simulations under different ball velocities was performed to build the learning database. We implemented a long-short-term memory (LSTM) model and then evaluated its performance using root mean square error (RMSE) and regression coefficient metrics. The obtained results showed a RMSE of 3.13 mm and a Pearson correlation coefficient of 0.98 for soft tissue displacement prediction. A RMSE of 0.001 MPa and a Pearson correlation coefficient of 0.94 was also obtained for soft tissue von Mises stress prediction. The present study showed the robustness and accuracy of the recurrent neural network for predicting complex soft tissue interaction behaviours. Our findings open new avenues for deploying novel deep learning workflow for human-facial softtissue interaction. As perspective, this workflow will be integrated into our interactive facial analysis and rehabilitation system.

The Effect of Strategic Containment on Organizational Brilliance : An Analytical Research in Ur State Company

The current research aims to demonstrate the relationship and impact between strategic containment and its dimensions (adopting alternatives, facilitating adaptability, synthesizing information, and implementing the chosen strategy) on organizational brilliance and its dimensions (brilliance in leadership, brilliance in service and innovation, and brilliance in Knowledge) in the Ur State Company. The research problem question was whether top management in the organization practices the strategic containment process or not, and does it affect organizational brilliance? The sample of the research consisted of middle management managers (120) participants. To analyze data and information and test hypotheses, the research used many statistical methods such as standard deviation, coefficient of variation, arithmetic mean, and simple regression coefficient by the statistical program (SPSS V26). The most obvious result of the research is that strategic containment and its dimensions totally and individually affect the variable organizational brilliance directly. Also, there is a lack of clarity in explaining the details of ongoing innovations and how to achieve outstanding performance, which negatively affects employees’ understanding, and then the researched company cannot achieve organizational brilliance. Research type: Research paper

Allometric equations for estimating aboveground biomass carbon in five tree species grown in an intercropping agroforestry system in southern Ontario, Canada

Allometric equations were developed for estimating aboveground biomass carbon (AGBC) in five tree species grown in a tree-based intercropping system at the University of Guelph Agroforestry Research Station, Guelph, Ontario, Canada. A total of 66 representative trees from five species: red oak (Quercus rubra) [n = 12], black walnut (Juglans nigra) [n = 16], black locust (Robinia pseudoacacia) [n = 10], white ash (Fraxinus americana) [n = 15], Norway spruce (Picea abies) [n = 13] were selected, harvested and their aboveground biomass and carbon content were quantified. Three commonly used allometric models were used to develop predictive equations. Regression models were developed and parameterized for each tree species and the best are presented based on information criteria (AIC, AICc, and BIC), mean absolute percentage error (MAPE), over/under estimation (MOUE), root mean square error (RMSE), R2, and regression coefficients (a, b) of the observed/predicted (OP) linear regression analysis. All equations with diameter at breast height (D) only and D and tree height (H) as the predictor variables fitted the AGBC data well, with R2 > 97% and RMSE < 40. However, a power model using D as the only predictor is recommended as the best model for black walnut, black locust, white ash, and Norway spruce. The models presented are the best fitted allometric equations for the indicated species and are recommended for these species, growing on similar soils under the same temperate conditions at densities of < 125 tree per hectare.

Analysis of traffic noise spatial distribution characteristics and influencing factors in high-density cities

Urban spatial structure factors can have considerable effects on traffic noise distribution. This study aimed to describe the spatial distribution characteristics of traffic noise and influencing factors in high-density cities. Fifteen indexes were selected from the three aspects of building layout, road organization and land use to analyse the influence of urban spatial structure factors on the noise spatial distribution. This paper selected the central area of Tianjin as the study area. The noise level was generated by the noise simulation software CadnaA, and the numerical statistics were determined by ArcGIS. The correlation between various urban indexes and the spatial distribution of traffic noise were determined with correlation analysis, and multiscale geographically weighted regression (MGWR) analysis was used to determine the degree and scope of influence of various urban indexes on the spatial distribution of traffic noise. The results indicate that there are significant differences in the spatial agglomeration forms of high, medium, and low noise areas among research units, and the spatial agglomeration of low noise areas in each unit is stronger. The building layout indexes, except for the average building height (ABH), are significantly negatively correlated with the noise levels in low noise areas (L70–L90). The road length index (RLI), road area density (RAD), road landscape shape index (RLSI), trunk road length index (TRLI), proportion of traffic land (PT), and degree of land mixing (DLM) are significantly positively correlated with the noise levels of all areas within the unit (L10–L90). Among the three aspects of building layout, road organization, and land use, the architectural landscape shape index (ALSI), RAD, and DLM have the greatest influence on the average spatial sound pressure level (Lavg), with mean regression coefficients of −0.254, 0.520, and 0.174, respectively. The ground space index (GSI), ALSI, proportion of residential land (PR) and PT are the global factors influencing Lavg.

Influence of urbanization on schistosomiasis infection risk in Anhui Province based on sixteen year's longitudinal surveillance data: a spatio-temporal modelling study

BackgroundUrbanization greatly affects the natural and social environment of human existence and may have a multifactoral impact on parasitic diseases. Schistosomiasis, a common parasitic disease transmitted by the snail Oncomelania hupensis, is mainly found in areas with population aggregations along rivers and lakes where snails live. Previous studies have suggested that factors related to urbanization may influence the infection risk of schistosomiasis, but this association remains unclear. This study aimed to analyse the effect of urbanization on schistosomiasis infection risk from a spatial and temporal perspective in the endemic areas along the Yangtze River Basin in China.MethodsCounty-level schistosomiasis surveillance data and natural environmental factor data covering the whole Anhui Province were collected. The urbanization level was characterized based on night-time light data from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and the National Polar-Orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The geographically and temporally weighted regression model (GTWR) was used to quantify the influence of urbanization on schistosomiasis infection risk with the other potential risk factors controlled. The regression coefficient of urbanization was tested for significance (α = 0.05), and the influence of urbanization on schistosomiasis infection risk was analysed over time and across space based on significant regression coefficients. Variables studied included climate, soil, vegetation, hydrology and topography.ResultsThe mean regression coefficient for urbanization (0.167) is second only to the leached soil area (0.300), which shows that the urbanization is the most important influence factors for schistosomiasis infection risk besides leached soil area. The other important variables are distance to the nearest water source (0.165), mean minimum temperature (0.130), broadleaf forest area (0.105), amount of precipitation (0.073), surface temperature (0.066), soil bulk density (0.037) and grassland area (0.031). The influence of urbanization on schistosomiasis infection risk showed a decreasing trend year by year. During the study period, the significant coefficient of urbanization level increased from − 0.205 to − 0.131.ConclusionsThe influence of urbanization on schistosomiasis infection has spatio-temporal heterogeneous. The urbanization does reduce the risk of schistosomiasis infection to some extend, but the strength of this influence decreases with increasing urbanization. Additionally, the effect of urbanization on schistosomiasis infection risk was greater than previous reported natural environmental factors. This study provides scientific basis for understanding the influence of urbanization on schistosomiasis, and also provides the feasible research methods for other similar studies to answer the issue about the impact of urbanization on disease risk.

Assessing the performance of the Gaussian Process Regression algorithm to fill gaps in the time-series of daily actual evapotranspiration of different crops in temperate and continental zones using ground and remotely sensed data

The knowledge of crop evapotranspiration is crucial for several hydrological processes, including those related to the management of agricultural water sources. In particular, the estimations of actual evapotranspiration fluxes within fields are essential to managing irrigation strategies to save water and preserve water resources. Among the indirect methods to estimate actual evapotranspiration, ETa, the eddy covariance (EC) method allows to acquire continuous measurement of latent heat flux (LE). However, the time series of EC measurements are sometimes characterized by a lack of data due to the sensors' malfunctions. At this aim, Machine Learning (ML) techniques could represent a powerful tool to fill possible gaps in the time series. In this paper, the ML technique was applied using the Gaussian Process Regression (GPR) algorithm to fill gaps in daily actual evapotranspiration. The technique was tested in six different plots, two in Italy, three in the United States of America, and one in Canada, with different crops and climatic conditions in order to consider the suitability of the ML model in various contexts. For each site, the climate variables were not the same, therefore, the performance of the method was investigated on the basis of the available information. Initially, a comparison of ground and reanalysis data, where both databases were available, and between two different satellite products, when both databases were available, have been conducted. Then, the GPR model was tested. The mean and the covariance functions were set by considering a database of climate variables, soil water status measurements, and remotely sensed vegetation indices. Then, five different combinations of variables were analyzed to verify the suitability of the ML approach when limited input data are available or when the weather variables are replaced with reanalysis data. Cross-validation was used to assess the performance of the procedure. The model performances were assessed based on the statistical indicators: Root Mean Square Error (RMSE), coefficient of determination (R2), Mean Absolute Error (MAE), regression coefficient (b), and Nash-Sutcliffe efficiency coefficient (NSE). The quite high Nash Sutcliffe Efficiency (NSE) coefficient, and the root mean square error (RMSE) low values confirm the suitability of the proposed algorithm.

Mapping stunted children in Ethiopia using two decades of data between 2000 and 2019. A geospatial analysis through the Bayesian approach

BackgroundChildhood stunting is a major public health problem globally, resulting in poor cognition and educational performance, low adult wages, low productivity, and an increased risk of nutrition-related chronic diseases in adulthood life. Accurate and reliable data on the prevalence of stunting over time with a sub-national estimate are scarce in Ethiopia.ObjectiveOur objective was to investigate the spatiotemporal distributions and ecological level drivers of stunting among under-five children over time in Ethiopia.MethodsA geospatial analysis using the Bayesian framework was employed to map the spatial variations of stunting among children aged less than five years. The data for the primary outcome were obtained from the Ethiopian Demographic and Health Surveys (2000–2019) and covariates data were accessed from different publicly available credible sources. The spatial binomial regression model was fitted to identify drivers of child stunting using the Bayesian approach.ResultThe national prevalence of stunting was 47.9 in 2000, 43.3 in 2005, 37.3 in 2011, 36.6 in 2016, and 35.9 in 2019, with a total reduction rate of 25%. Substantial spatial clustering of stunting was observed in the Northern (Tigray), Northcentral (Amhara), and Northwestern (Amhara) parts of Ethiopia. Temperature (mean regression coefficient (β): −0.19; 95% credible interval (95% CrI): −0.25, −0.12) and population density (β: −0.012; 95% CrI: −0.016, −0.009) were negatively associated with stunting, whereas travel time to the nearest cities (β: 0.12; 95% CrI: 0.064, 0.17) was positively associated with child stunting in Ethiopia.ConclusionThe prevalence of stunting varied substantially at subnational and local levels over time. Clustering of stunted children were observed in the Northern parts of Ethiopia. Temperature, population density and travel time to the nearest cities were identified as the drivers of stunting in children. Improving community awareness of child nutrition through community health extension programs should be strengthened.

Artificial neural network simulation and development of a predictive model to anticipate performance of a hybrid plant combined with PVT solar system

This article uses the neural network method and numerical data for predicting the performance of an integrated energy system. The studied system is an integrated energy system consisting of a gas turbine with an air preheater and subsystems such as the Kalina cycle, supercritical carbon oxide cycle and hydrogen production. In this unit, a photovoltaic thermal (PVT) module has been used to generate electric power, as well as heating and starting the organic Rankine cycle. In addition, a Proton exchange membrane (PEM) electrolyzer to produce hydrogen is integrated. To propose a model that can predict the behavior of the integrated system by changing the parameters, the important outputs of the system such as energy efficiency, exergy efficiency, exergy destruction rate and net output power have been extracted in the form of functions according to the input parameters namely T11, P11, P18, Rp. The results show that the suggested functions of four outputs have the acceptable value for mean square error and regression coefficient which show high accuracy. The influence of each variable on each other and their interaction on the responses are drawn and adequately explained using three-dimensional response surface diagrams. The analysis represented that the amount of exergy destruction in the high-pressure ratio is at its maximum value with the increase in the temperature of point 4. In addition, it is at its maximum value in the high-pressure ratio with different changes in the pressure of point 11. Generally, the p-values for predicted values less than 0.001 represent that the model is statistically considerable at the 99 % confidence level. The p-values for suggested functions show a high level of confidence.

Experimental investigation and machine learning modeling of heat transfer characteristics for water based nanofluids containing magnetic Fe3O4 nanoparticles

The convective heat transfer coefficients of Fe3O4 magnetic nanofluids in both laminar and turbulent flow states inside a pipe were determined as base values and adequately simulated using machine learning techniques. Three selected machine learning methods for the simulations were Multiple Linear Regression Analysis (MLR), Radial Basis Function-Backpropagation (RBF-BP), and Least Squares-Support Vector Machines (LS-SVM). Initially, MLR was employed to fit the polynomial equation, followed by the selection of the best RBF-BP model with 6 hidden layer neurons using the grid search cross-validation method. For the LS-SVM model, a kernel function of 0.3 and a regularization parameter of 100 were used. By conducting a detailed comparison of the numerical patterns of the accuracy evaluation parameters, the RBF-BP and LS-SVM models were evaluated, with the LS-SVM model demonstrating superiority. The main parameters in the Reynolds number-mass fraction simulation were the mean square error (MSE) and regression coefficient (R2), which obtained specific values of MSE= 0.34 and R2 = 0.99994 under laminar flow conditions. Similarly, in the Reynolds number-magnetic field strength simplification, the best values for laminar flow conditions were MSE= 3.85 and R2 = 0.99993. The validity and accuracy of the model predictions were further demonstrated through visual comparisons of simulated and experimental values using Three-Dimensional smoothed surface plots. The groundbreaking discoveries hold the potential to catalyze advancements and foster significant progress in the fields of machine learning and nanotechnology. As a valuable resource, these results play a crucial role in propelling further research and development efforts, thus making noteworthy contributions to the continuous growth and innovation in these cutting-edge disciplines.

Stability analysis of yield, yield attributes and essential oil content in fennel (Foeniculum vulgare Mill.) evaluated under a long-term organic production system

Eight varieties of fennel (Foeniculum vulgare Mill.) were evaluated under field trial for their stability of yield, yield attributes and essential oil content under the organic production system in six consecutive years from 2016 to 2021. Mean square due to environment + (variety × environment) was significant for all the traits studied indicating the existence of variety × environment interaction. Based on the mean performance, regression coefficient and deviation from regression values, it was found that stability of yield and yield components are imparted in the varieties, GF-12 and AF-1 across the years through the stable performance of characters and like numbers of primary and secondary branches, number of umbels and umbellate and seed yield. However, variety RF-101 for essential oil content can be considered as most suitable, stable and adopted to organic production system compared to other varieties. Correlation analysis revealed highly positive relationship in plant height, number of primary branches, number of umbels and umbellate per plant and seed yield. Based on the findings, fennel growers are apprised to select stable high-yielding fennel varieties for the organic production systems in semi-arid regions of India. Along with their use in hybridization programmes to converge the stability characteristics of seed yield for the development of a stable variety adapted to a wider range of environments under organic production systems.

Morphometric Indices and Parasitic Incidence of Synodontis nigrita from Lower River Benue, Makurdi, Nigeria

The study was carried out to determine the morphometric indices and parasitic incidence of 160 fresh samples of Synodontis nigrita comprising of 80 samples each of female and male sexes. Fish samples were obtained from the catches of local fishermen along Lower River Benue at Wadata fish landing site of Benue State. Length–weight relationship was analyzed using the equation W=aLb. The condition factor of the fish samples was determined using the equation, K=100W/L3. Samples of Synodontis nigrita were later subjected to parasitological examination using standard parasitological method. The mean total length, body weight, condition factor, regression and correlation coefficients were higher for un-infested female samples of Synodontis nigrita than the infested female. Similarly, the mean total length body weight, condition factor, regression and correlation coefficients of un-infested male samples of Synodontis nigrita were higher than the infested males. A total of three hundred and fifty seven (357) parasites were recovered from 76(47.50%) infested samples. Out of the 357 parasites, 251 were recovered from 49(61.3%) infested female samples, 106 parasites were recovered from 27(33.8%) infested male samples. Among the infested parts of the female samples, the intestine accounted for the highest number/percentage (116/46.4%) parasite load, the least 2(0.8%) was recorded for the skin. Between the external parts (gill and skin) of the female fish samples, while the gill recorded the higher number of parasite load 28(12.8%), skin recorded 2(0.8%). Similarly, among the infested parts of the male samples, the intestine accounted for the highest number/percentage (65/61.3%) parasite load, the least 9(8.5%) was recorded for the gill. Between the external parts (gill and skin), the gill recorded 9(8.5%) number/percentage parasite load, no parasite was recovered from the skin. Prevalence (61.25), number of parasites (251) and intensity (5.10) of parasite infection were higher for female samples compared to the male with the prevalence, and intensity of parasite infection of 33.80, 106 and 3.90, respectively. However, the chi square value showed that there was no significant difference (p&gt;0.05) in the prevalence, number of parasites and intensity of parasite infection between the sexes of Synodontis nigrita. Variation in the prevalence and intensity of parasite infection of female and male Synodontis nigrita based on size groups existed. However, it was generally observed that longer and heavier sized fish of both sexes were more infested with higher number of parasites than the smaller sized samples of both sexes.

Exploring spatial and environmental heterogeneity affecting energy consumption in commercial buildings using machine learning

Building energy consumption (BEC) is a critical indicator for promoting sustainable building development. However, previous studies have neglected the spatial heterogeneity of the urban environment and its landscape configuration on BEC impacts. This paper aims to identify the factors influencing commercial building energy consumption in Singapore, including building basic information, Green Mark certification, usage behaviours, and urban environment. We employed geographically weighted regression models to analyse influencing factors, K-means to classify clusters, and artificial neural networks for prediction. Our approach yielded a significant improvement in the fitting effect (by 38%) compared to traditional regression algorithms. Our findings showed a strong correlation between BEC and geographical information and economic development. Building basic information was the most influential aspect, with building coverage area being the most dominant driving force with positive impacts, reaching a mean regression coefficient of 0.423. Land surface temperature had the most potent negative effect on BEC among urban factors, while water area ratio and gross plot ratio had a strong non-stationary influence on BEC. Our study provides insights into the evolving and heterogeneous nature of the urban environment, supporting decision-making for more sustainable metropolitan development that benefits both people and nature.

Validation with single-step SNPBLUP shows that evaluations can continue using a single mean of genotyped individuals, even with multiple breeds

BackgroundIn genomic prediction, it is common to centre the genotypes of single nucleotide polymorphisms based on the allele frequencies in the current population, rather than those in the base generation. The mean breeding value of non-genotyped animals is conditional on the mean performance of genotyped relatives, but can be corrected by fitting the mean performance of genotyped individuals as a fixed regression. The associated covariate vector has been referred to as a ‘J-factor’, which if fitted as a fixed effect can improve the accuracy and dispersion bias of sire genomic estimated breeding values (GEBV). To date, this has only been performed on populations with a single breed. Here, we investigated whether there was any benefit in fitting a separate J-factor for each breed in a three-way crossbred population, and in using pedigree-based expected or genome-based estimated breed fractions to define the J-factors.ResultsFor body weight at 7 days, dispersion bias decreased when fitting multiple J-factors, but only with a low proportion of genotyped individuals with selective genotyping. On average, the mean regression coefficients of validation records on those of GEBV increased with one J-factor compared to none, and further increased with multiple J-factors. However, for body weight at 35 days this was not observed. The accuracy of GEBV remained unchanged regardless of the J-factor method used. Differences between the J-factor methods were limited with correlations approaching 1 for the estimated covariate vector, the estimated coefficients of the regression on the J-factors, and the GEBV.ConclusionsBased on our results and in the particular design analysed here, i.e. all the animals with phenotype are of the same type of crossbreds, fitting a single J-factor should be sufficient, to reduce dispersion bias. Fitting multiple J-factors may reduce dispersion bias further but this depends on the trait and genotyping rate. For the crossbred population analysed, fitting multiple J-factors has no adverse consequences and if this is done, it does not matter if the breed fractions used are based on the pedigree-expectation or the genomic estimates. Finally, when GEBV are estimated from crossbred data, any observed bias can potentially be reduced by including a straightforward regression on actual breed proportions.

Machine learning interpretable-prediction models to evaluate the slump and strength of fly ash-based geopolymer

This study used three artificial intelligence-based algorithms – adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs), and gene expression programming (GEP) – to develop empirical models for predicting the compressive strength (CS) and slump values of fly ash-based geopolymer concrete. A database of 245 CS and 108 slump values were established from the published literature, where 17 significant parameters were chosen as input variables for the development of models. The algorithms were trained and tested using statistical measures including Nash-Sutcliffe efficiency, root-squared error, root-mean-square error, relative-root-mean-square error, mean absolute error, correlation coefficient, and regression coefficient. The comparison results showed that the GEP model was superior to the ANFIS and ANN models in terms of R-value, R2, and RMSE for both CS and slump prediction. The R-value for the CS models was 0.94 (GEP), 0.92 (ANFIS), and 0.91 (ANN), while for the slump it was 0.96 (GEP), 0.91 (ANFIS), and 0.90 (ANN). Moreover, the performance index factor values for slump and CS were found 0.03 and 0.029 for GEP-models and 0.036, 0.030 for ANFIS-models and 0.035 and 0.034 for ANN-models respectively. The sensitivity and parametric analysis were also performed for GEP-developed model. Results demonstrate that the GEP model generates more accurate prediction for the slump and CS of fly ash-based geopolymer after being rigorously trained and its hyperparameters optimized.

Tuberculosis in the Russian Federation: Prognosis and Epidemiological Models in a Situation After the COVID-19 Pandemic

AimBecause of the COVID-19 pandemic, many support programs for tuberculosis (TB) patients have been discontinued and TB mass screening activities decreased worldwide, resulting in a decrease in new case detection and an increase in TB deaths (WHO, WHO global lists of high burden countries for TB, multidrug/rifampicin-resistant TB (MDR/RR-TB) and TB/HIV, 2021–2025, 2021). The study aimed to assess changes in epidemiological indicators of tuberculosis in the Russian Federation and to simulate these indicators in the post-COVID-19 period.Materials and MethodsThe main epidemiological indicators of tuberculosis were analyzed with the use of government statistical data for the period from 2009 to 2021. Further mathematical modeling of epidemiological indicators for the coming years was carried out, taking into account the TB screening by chest X-ray. Statistical analysis was carried out using the software environment R (v.3.5.1) for statistical computing and the commercial software Statistical Package for the Social Sciences (SPSS Statistics for Windows, version 24.0, IBM Corp., 2016). Time series forecasting was performed using the programming language for statistical calculations R, version 4.1.2 and the bsts package, version 0.9.8.Study ResultsThe study has found that the mean regression coefficient of a single predictor differs in the model for TB incidence and mortality (0.0098 and 0.0002, respectively). Forecast of overall incidence, the incidence of children and the forecast for mortality using the basic scenario (screening 75–78%) for the period from 2022 to 2026 was characterized by a mean decrease rate of 23.1%, 15.6% and 6.0% per year, respectively. A conservative scenario (screening 47–63%) of overall incidence indicates that the incidence of children and the forecast for mortality will continue to decrease with a mean decrease rate of 23.2%, 15.6% and 6.0% per year, respectively. Comparable data were obtained from the forecast of overall incidence, the incidence of children and the forecast for mortality using the optimistic scenario (screening 82–89%) with a mean decrease rate of 22.9%, 15.4% and 6.0% per year, respectively.ConclusionsIt has been proven that the significance of screening with chest X-ray as a predictor of mortality is minimal. However, TB screening at least 60% of the population (chest X-ray in adults and immunological tests in children) have provided relationship between the TB screening rate and TB mortality rate (TB mortality rate increases with an increase in the population coverage and, conversely, decreases with a decrease in the population coverage).

Tuberculosis prevalence in children in the Northwestern Federal District of Russia before and after COVID-19 pandemic: prognosis and epidemiological models

Aim. To assess changes in the epidemic indicators of tuberculosis infection (TB) in children in the Northwestern Federal District of Russia before and after the COVID-19 pandemic based on mathematical modeling and forecasting.Materials and methods. The main epidemiological indicators of TB were analyzed using the official statistical data for 2009–2021. A mathematical forecasting of epidemiological indicators was performed based on chest X-ray screening for TB. A statistical analysis was carried out using the software environment R (v.3.5.1) and the commercial software Statistical Package for Social Sciences (SPSS Statistics for Windows, version 24.0, IBM Corp., 2016). Time series forecasting was performed using the programming language of statistical calculations R, version 4.1.2 and the bsts package, version 0.9.8. Results. The mean regression coefficient of a single predictor was found to differ in a model for TB morbidity in children is 0.0098. X-ray screening for TB was established to be a significant mortality predictor in children. At least 60% of the population should undergo TB screening in order for TB prevalence to be controlled in a country with a population above 140 million people.Conclusions. The conducted study revealed a positive correlation between the incidence of tuberculosis in children in Russia and TB screening in at least 60% of the population. Under the current TB screening system in Russia, the epidemic TB situation will continue to improve, despite COVID-19 restrictions. At the same time, in the Northwestern Federal District of Russia, preventive TB screening can be considered sufficient only in the Kaliningrad, Murmansk, and Pskov Oblasts.

DETERMINATION OF WALL HEAT FLUX BASED ON BUBBLE DEPARTURE AND LIFT-OFF DIAMETERS FOR VARYING PRESSURE AND FLOW VELOCITY CONDITIONS

In this study, the bubble departure and lift-off (BDL) boiling model has been improved by implementing the subcooling suppression factor and flow-related parameters, with the aim of developing an accurate heat transfer model for various engineering applications. The BDL model has been exploited using a MATLAB program to determine the variation of wall heat flux that controls the boiling curve. Three different parameters, namely, bulk flow velocity, operating pressure, and bulk flow temperature have been considered in this study. Experimental conditions were simulated as per Chen's BDL model algorithm, and the resulting wall heat flux was compared with the measured values. Additionally, different boiling curves for bubble departure and lift diameters were generated and their effect on varying operating conditions was studied. The resultant boiling curves were compared with the experimental data by means of mean absolute error (MAE) and regression coefficient (&lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt;) values. In general, with a reasonable margin of error, i.e., MAE &amp;#61; 3.35&amp;#37;, and &lt;i&gt;R&lt;/i&gt;&lt;sup&gt;2&lt;/sup&gt; &amp;#61; 0.99, the algorithm performed well in comparison with the experimental data. The bubble departure and lift diameters were also correlated with the experiment and the predictions were satisfactorily close.

Mapping wasted children using data from the Ethiopia Demographic and Health Surveys between 2000 and 2019: A bayesian geospatial analysis

ObjectivesUndernutrition, particularly wasting, is continuing to be a major challenge in developing countries like Ethiopia. However, data on the geographic variations over time are limited. We aimed to investigate the spatiotemporal variation of wasting in Ethiopia using two decades of Ethiopia Demographic and Health Surveys data, from 2000 to 2019. MethodsTrend and geospatial regression analysis using a bayesian framework were used to predict wasting in Ethiopia among children aged <5 y. The primary outcomes (wasting) were obtained from the Ethiopia Demographic and Health Surveys conducted between 2000 and 2019. Covariates were accessed from different publicly available credible sources at a high resolution. The spatial binomial regression model through the bayesian framework was fitted to identify drivers of wasting among children in Ethiopia. ResultsThe overall national prevalence of wasting among children aged <5 y in Ethiopia was 11.9% in 2000, 11.1% in 2005, 10.2% in 2011, 12.3% in 2016, and 9.4% in 2019, with substantial spatial variation across subnational and local levels over time. Spatial clustering of wasting was observed in eastern Ethiopia (Afar and Somali regions). Altitude (mean regression coefficient = –0.38; 95% credible interval, –0.69 to –0.07) and population density (mean regression coefficient = –0.02; 95% credible interval, –0.03 to –0.01) were negatively associated with wasting, whereas distance to health facilities (mean regression coefficient = 0.13; 95% credible interval, 0.03–0.23) was positively associated with wasting. ConclusionsThe reduction in the national prevalence of wasting among children was not as expected. Spatial clustering of wasting was observed in the northern, northeastern, eastern, and western parts of Ethiopia. Spatial clustering of wasting was associated with altitude, precipitation, population density, distance to health facilities, travel time to the nearest cities, and distance to a water body. Early screening and treatment of wasted children should be strengthened. Furthermore, outreach community awareness, especially in rural parts of the country, should be recommended through community health extension workers.

Prediction models for marshall mix parameters using bio-inspired genetic programming and deep machine learning approaches: A comparative study

This research study utilizes four machine learning techniques, i.e., Multi Expression programming (MEP), Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Ensemble Decision Tree Bagging (DT-Bagging) for the development of new and advanced models for prediction of Marshall Stability (MS), and Marshall Flow (MF) of asphalt mixes. A comprehensive and detailed database of 343 data points was established for both MS and MF. The predicting variables were chosen among the four most influential, and easy-to-determine parameters. The models were trained, tested, validated, and the outcomes of the newly developed models were compared with actual outcomes. The root squared error (RSE), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root mean square error (RMSE), relative root mean square error (RRMSE), regression coefficient (R2), and correlation coefficient (R), were all used to evaluate the performance of models. The sensitivity analysis (SA) revealed that in the case of MS, the rising order of input significance was bulk specific gravity of compacted aggregate, Gmb (38.56 %) > Percentage of Aggregates, Ps (19.84 %) > Bulk Specific Gravity of Aggregate, Gsb (19.43 %) > maximum specific gravity paving mix, Gmm (7.62 %), while in case of MF the order followed was: Ps (36.93 %) > Gsb (14.11 %) > Gmb (10.85 %) > Gmm (10.19 %). The outcomes of parametric analysis (PA) consistency of results in relation to previous research findings. The DT-Bagging model outperformed all other models with values of 0.971 and 0.980 (R), 16.88 and 0.24 (MAE), 28.27 and 0.36 (RMSE), 0.069 and 0.041 (RSE), 0.020 and 0.032 (RRMSE), 0.010 and 0.016 (PI), 0.931 and 0.959 (NSE), for MS and MF, respectively. The results of the comparison analysis showed that ANN, ANFIS, MEP, and DT-Bagging are all effective and reliable approaches for the estimation of MS and MF. The MEP-derived mathematical expressions represent the novelty of MEP and are relatively simple and reliable. Roverall values for MS and MF were in the order of DT-Bagging >MEP >ANFIS >ANN with all values exceeding the permitted range of 0.80 for both MS and MF. Hence, all the modeling approaches showed higher performance, possessed high generalization and predication capabilities, and assess the relative significance of input parameters in the prediction of MS and MF. Hence, the findings of this research study would assist in the safer, faster, and sustainable prediction of MS and MF, from the standpoint of resources and time required to perform the Marshall tests.