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Surface hardness is an important index for evaluating the quality of surface processing. Ultrasonic rolling and extrusion technology plays a very significant role in surface strengthening. Taking 42 CrMo bearing steel as the object,the range analysis was carried out on the orthogonal experiment results of ultrasonic rolling extrusion,and the significance of process parameters on surface hardness was obtained. The reliability and accuracy of BP neural network model and stepwise regression model were compared and analyzed in sections by using k-fold cross validation method. This process fully considers the fitting ability and prediction ability of the model. The results show that the validation error range and average error of the stepwise regression model are smaller,and the prediction accuracy of the model is higher. Finally,the established prediction model of surface hardness has strong overall and coefficient significance,which can be applied to the optimization and improvement of surface quality in ultrasonic rolling extrusion.

Iran and Iraq face a water shortage crisis especially in the recent decade. Hence, the regions with high potential water resources are highly important to these countries. Precipitation sampling for 18O and 2H analyses was conducted in 10 stations in the west of Iran and 2 stations in the east of Iraq during this study. Studying precipitation and its moisture sources using the backward trajectories of the HYSPLIT model, developing seasonal meteoric water lines (MWLs), and simulating the spatial variations of stable isotopes in precipitation using stepwise regression yielded valuable results regarding the hydrometeorology of the study region. The precipitation moisture sources in the study region were determined using the backward trajectories of the HYSPLIT model. These backward trajectories demonstrated the domination of various moisture sources including the Mediterranean Sea, the Black Sea, the Red Sea, and also the Persian Gulf in different seasons as reflected in the seasonal MWLs. In addition to moisture sources, the effects of meteorological parameters (temperature and precipitation amount) on the stable isotope content of precipitation also showed a notable correlation (with R2 = 0.58 and 0.46, respectively). Surface water resources were also plotted on the developed seasonal MWLs, while some samples deviated from seasonal MWLs due to the evaporation effect.

The insulated gate bipolar transistor (IGBT), one of the most vulnerable component, is one of the most precious central component in the converter interior. High junction temperature will lead to device failure, which is the main reason of failure of power electronic system. Therefore, on-line high precision measurement of IGBT module junction temperature is the basis of life prediction and reliability evaluation of high-power power conversion equipment. In this paper, the principle of IGBT junction temperature extraction and the latest development of related technologies are summarized. In particular, the working principle and shortcomings of temperature sensitive electrical parameter (TSEP) method are summarized. The change of junction temperature will affect the inter-electrode capacitance in the internal structure of IGBT, which will cause the change of temperature sensitive electrical parameters. The single temperature sensitive electrical parameter method is easily affected by IGBT structure and inter-electrode capacitance. This paper presents an algorithm for high precision on-line detection of IGBT junction temperature. The parameter types are optimized by stepwise regression and the model is established accordingly. In this paper, IGBT: FF50R12RT4 is used as the experimental equipment. By comparing the junction temperature model established based on multiple linear stepwise regression algorithm with the junction temperature model based on traditional temperature sensitive electrical parameters, it is proved that the algorithm has better fitting degree and precision, and the algorithm can be used for high precision online extraction of junction temperature.

The major objective of this paper is to build a cost prediction model for general aviation aircraft using artificial neural network (ANN) and principle component analysis (PCA) methods. A total number of 22 samples of general aviation aircraft collected from the literature are utilized to train and test the model. In the PCA, eigenvalues of PC1 and PC2 are 6.987 and 1.529, respectively, indicating that they have the strongest interpretation of the original variable information and are retained as cost influencing variables to train the ANN model. The pure multiple linear regression (MLR), stepwise regression (SR) and ANN models are built respectively for comparison. The comparative results reveal that the ANN method has better estimation effect than MLR and SR models in case of multi-collinearity of data. Combined with PCA, the ANN model is optimized, with MAPE, MAE, R and RMSE values of training and testing samples to be 0.009 and 0.015, 1.222 and 3, 0.9999 and 0.9994, 1.667 and 3.416, respectively. Finally, a more accurate and practical prediction model is developed. More importantly, this research can provide an important reference for general aviation aircraft companies in term of product cost planning and corporate sales strategy.

The accurate estimation of forest biomass is very significant to the study of regional ecosystems. China's National Forest Continuous Inventory data and Landsat 8 data were used to establish a linear regression (LR) model for the estimation of forest aboveground biomass (AGB) based on forest type. The original results of the LR yielded upon conducting AGB estimation were insufficient. Therefore, an interpolation map of the residuals of the observed and predicted AGB was used to correct the error of the original LR model. There is a highly positive result of the accuracy of the corrected AGB map. First, the AGB estimation based on forest type could effectively improve its accuracy. For example, significant improvements were made in the estimations of broadleaf, coniferous, and mixed forests compared to that of the total vegetation conducted using the original LR model. Second, semivariance analysis should be conducted before spatial interpolation using the Kriging method to determine optimal semivariogram models and parameters. The optimal semivariogram model for broadleaf and total forests was the exponential model, while that for the coniferous and mixed forests was the spherical model. Third, combining Kriging interpolation predicted the AGB map effectively and reduced the under- and overestimation of AGB, although it did not fully eliminate this limitation. The R2 values of broadleaf, coniferous, and mixed forests were improved to 0.897, 0.856, and 0.826, respectively. Overall, the methods used in this study provide an effective approach towards improving the accuracy of AGB estimations by reducing under- and overestimation based on remote sensing data and increasing the ability to monitor the forest ecosystem.

Epigenetic clock, a highly accurate age estimator based on DNA methylation (DNAm) level, is the basis for predicting mortality/morbidity and elucidating the molecular mechanism of aging, which is of great significance in forensics, justice, and social life. Herein, we integrated machine learning (ML) algorithms to construct blood epigenetic clock in Southern Han Chinese (CHS) for chronological age prediction. The correlation coefficient (r) meta-analyses of 7,084 individuals were firstly implemented to select five genes (ELOVL2, C1orf132, TRIM59, FHL2, and KLF14) from a candidate set of nine age-associated DNAm biomarkers. The DNAm-based profiles of the CHS cohort (240 blood samples differing in age from 1 to 81 years) were generated by the bisulfite targeted amplicon pyrosequencing (BTA-pseq) from 34 cytosine-phosphate-guanine sites (CpGs) of five selected genes, revealing that the methylation levels at different CpGs exhibit population specificity. Furthermore, we established and evaluated four chronological age prediction models using distinct ML algorithms: stepwise regression (SR), support vector regression (SVR-eps and SVR-nu), and random forest regression (RFR). The median absolute deviation (MAD) values increased with chronological age, especially in the 61–81 age category. No apparent gender effect was found in different ML models of the CHS cohort (all p > 0.05). The MAD values were 2.97, 2.22, 2.19, and 1.29 years for SR, SVR-eps, SVR-nu, and RFR in the CHS cohort, respectively. Eventually, compared to the MAD range of the meta cohort (2.53–5.07 years), a promising RFR model (ntree = 500 and mtry = 8) was optimized with an MAD of 1.15 years in the 1–60 age categories of the CHS cohort, which could be regarded as a robust epigenetic clock in blood for age-related issues.

Nighttime PM2.5 detection by remote sensing can expand understanding of PM2.5 spatiotemporal patterns due to wider coverage compared to ground monitors and by supplementing traditional daytime detection. However, using remote sensing data to invert PM2.5 at night is still challenging. Compared with daytime detection, which operates on sunlight, nighttime detection operates on much weaker moonlight and artificial light sources, complicating signal extraction. Moreover, as the attempts to sense PM2.5 remotely using satellite data are relatively recent, the existing nighttime models are still not mature, overlooking many important factors such as stray light, seasonality in meteorological effects, and observation angle. This paper attempts to improve the accuracy of nighttime PM2.5 detection by proposing an inversion model that takes these factors into consideration. The Visible Infrared Imaging Radiometer Suite/Day/Night Band (VIIRS/DNB) on board the polar-orbiting Suomi National Polar-orbiting Partnership (Suomi NPP) and National Oceanic Atmospheric Administration-20 (NOAA-20) was used to establish a nighttime PM2.5 inversion model in the Beijing area from 1 March 2018 to 28 February 2019. The model was designed by first studying the effects of these factors through a stepwise regression, then building a multivariate regression model to compensate for these effects. The results showed that the impact of satellite viewing zenith angle (VZA) was strongest, followed by seasonality and moonlight. Total accuracy was measured using correlation coefficient (R) compared to ground measurements, achieving 0.87 over the urban area and 0.74 over the suburbs. Specifically, the proposed method works efficiently at subsatellite points, which in this case correspond to VZA from 0 and 5°. In spring, summer, autumn, and winter, the R reached 0.95, 0.93, 0.94, and 0.97 at subsatellite points in the urban area, while it was 0.88, 0.82, 0.85, and 0.77 in the suburbs.

As a crucial component of the financial sector, banks play an intermediary role in creating and providing financial services to customers. Therefore, the evaluation of banking sector activity is important for stakeholders and managers. This paper investigates the key criteria in analyzing bank performance and efficiency and the relative performance of Turkish banks in terms of the pre-determined criteria during 2008–2018. This study aims to introduce a robust and easy-to-calculate mathematical model for estimating bank performance using stepwise regression and TOPSIS methods. The TOPSIS ranking of banks from the best to the worst allows establishing that the bank with the highest mean score is Akbank (AB), while Ziraat Bank (ZB) and Garanti Bank (GB) follow AB over the period. The results of the stepwise regression analysis show that managing non-performing loans and expenses (both personnel and interest expenses) are critical to high performance in the banking sector.

An assortment of evolutionary strategies (EvSs) is put forward for guiding interested countries toward the maximization of their environmental sustainability (ES), as demonstrated on the "landmark" Environmental Sustainability Index (ESI) 2005. The EvSs employ: (a) as inputs/chromosomes, the sets of ESI constructs which have been established as significant as well as sufficient in determining country ES; (b) as output, the singleton ESI 2005 scores which express the levels of ES attained by the participating countries; (c) as fitness function, the most accurate first degree (according to the primary literature) polynomials linking (a) and (b); and (d) realistic quasi-monotonic ES improvements, implemented via the application of exclusively positive mutations to the selected genes of each next-generation chromosome and the subsequent changes made to the remaining genes, with the sign and magnitude of each change being determined by the cross-correlation (CC) coefficients between the set of mutated genes and each remaining gene of the new chromosome. The created EvSs cover all the direct and stepwise approximations linking constructs and scores, thus offering alternative paths toward attaining maximal ES. Future research shall focus upon the selection of the EvS which affords maximal ES improvement at each generation. [ABSTRACT FROM AUTHOR]

The current work was carried out at Agronomy Department Farm, Faculty of Agriculture, Assiut University, Egypt, to study the direct and indirect effects and stepwise regression of yield attributes on forage and seed yields of ten alfalfa genotypes. Two experiments were carried out as experiment I (2017-2019) and experiment II (2018-2020). The experiments were laid out in a split block design with three replications. Five sowing dates at 10th October (D1), 10th November (D2), 10th December (D3), 20th March (D4) and 20th April (D5) were used. Fifteen cuts were taken from each sowing date in two years, while, seed yield was taken in the second year which take in the first week of March, April and May for the studied sowing date. The obtained results of path analysis, show that, fresh forage yield has the greatest influence on protein forage yield in each sowing date. However, its indirect effects were negative via mean dry matter percentage or negligible via protein percentage in both experiments. Also, the number of pods/plant and number of seeds/pod contributed to most direct effect on seed yield/plant for each sowing dates. Meantime, the results of stepwise regression analysis showed that, the model no.2 which included two independent traits i.e. dry forage yield and protein percentage resembled the fit model for protein yield. Also, the model no.2 which possessed two independent traits i.e. number of seeds/plant and 1000-seed weight has strong contribution to seed yield/plant. [ABSTRACT FROM AUTHOR]

This experiment was conducted at the Field Crops Institute, Agricultural Experimental Station of Setif (ITGC-AES), Eastern semi-arid areas of Algeria, during two successive cropping seasons, 2010/11 and 2011/12. The aim of the study was to evaluate the association of yield and yield-related traits and determine the direct and indirect effects of yield-related traits on grain yield. The plant materials consisted of 330 F3 and 174 F4 durum wheat lines along with their four parents and one control cultivar, which were evaluated under rainfed conditions in a semi-arid region. Data on nine agronomic traits were recorded. Sufficient genetic variability was observed among wheat traits as indicated by the minimum and maximum mean values and confirmed by the phenotypic and genotypic coefficients of variation that took intermediate and high estimates for most of the traits evaluated both in F3 and F4 generations. A high heritability (>60%) was observed for almost all the traits studied indicating the involvement of the additive action of genes in their genetic determinism. Results of stepwise regression and path analysis showed that biological yield, harvest index and number of spikes were the most determinant components of grain yield, exhibiting high positive direct effects (0.697, 0.683 and 0.293 in F3 vs 0.695, 0.205 and 0.560 in F4, respectively) coupled with positive and significant correlations (r=0.696*, r=0.778* and r=0.127* in F3 vs r=0.686*, r=0.628* and r=0.491* in F4, respectively) with this trait. These three yield-contributing traits can be considered as suitable indirect selection criteria to improve grain yield in the subsequent generation of the wheat breeding program. [ABSTRACT FROM AUTHOR]

This paper presents findings from a research study focused on calibrating and improving the robustness of a permanent deformation model for unbound aggregate base/subbase materials, recently developed at the University of Illinois and referred to as the UIUC rutting model. The model provides estimates for permanent strains as a function of load cycles, applied deviator stress, and the ratio of mobilized shear stress to strength. Sixteen aggregate materials were tested for permanent deformation both at their source gradations, and an engineered gradation. Based on multiple linear regression analyses, the UIUC rutting model parameters were determined to estimate the laboratory-measured permanent strains with high accuracy. Constrained and stepwise regression statistical approaches were employed to reduce variability in the regression model parameters and express the model parameters as function of material properties such as shear strength, morphological shape properties, gradation, and compaction characteristics. Results from stepwise analyses indicated that the most accurate predictions could be achieved by considering particle shape properties. Finally, a framework was established to predict unbound aggregate permanent deformation trends of flexible pavement base/subbase layers using the UIUC model parameter equations obtained from stepwise regression, along with the physical/mechanical properties of the aggregate materials determined from standard laboratory tests. [ABSTRACT FROM AUTHOR]

Copyright of Information & Control is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Practical Pharmacy & Clinical Remedies is the property of Editorial Department of Practical Pharmacy & Clinical Remedies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives：With the acceleration of urbanization and the rapid expansion of urban population, the population data on the street scale plays an important role in urban economic, social, resource and environmental development. Therefore, estimating population on urban street scales with high‐resolution remote sensing images is of great theoretical and practical value for promoting sustainable urban development. Methods：This paper uses the building information in remote sensing images and census data to analyze the correlation between street building information and population data, and the city street scale population estimation models based on building information are proposed. The best estimation model for the number and geometric characteristics of buildings and population is established based on the multivariate stepwise regression method and the Akaike information criterion that determine the significant feature variables of the building.Results：The Experiments show that the population estimation model proposed in this paper can estimate the population of street scale with high accuracy. On the one hand, the floor data can be directly applied to the estimation model. On the other hand, the floor data is also the basis for classifying street buildings. Dividing street buildings into low‐rise, middle‐rise and high‐rise buildings according to the number of floors can significantly improve the population estimation effect of the model. Conclusions：The population estimation model proposed in this paper can estimate the population of street scale with high accuracy. This paper uses street‐scale data in administrative divisions, but the estimation range of the proposed model is theoretically also valid in population estimation across street areas, which needs to be verified in subsequent studies. [ABSTRACT FROM AUTHOR]

The total output value of mutton in Northwestern China has accounted for more than 60% of the total output value of animal husbandr}' over the years. It can be seen that the mutton industry in Northwest China not only plays a pivotal role in animal husbandry, but also plays an important role in Chinese agriculture. In this study, based on cost accounting theory, income-related theories and total factor productivity theory, using basic knowledge of statistics and economics, drawing on existing research results at home and abroad, and adopting a combination of qualitative analysis and quantitative analysis of SAS multiple stepwise regression, the changing trends of cost-benefit of mutton sheep breeding in Northwest agricultural and pastoral areas and influencing factors of production costs and production efficiency were investigated, aiming to provide reference for saving mutton sheep feeding material resources, reducing mutton sheep breeding costs, and improving mutton sheep breeding benefits. [ABSTRACT FROM AUTHOR]

Copyright of Journal of International Social Research is the property of Journal of International Social Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the present study, the objective was to evaluate the effects of barley kernel size, grind level, and extract solvent on the antioxidant activities associated with total phenolic content and phenolic acid compositions. Three barley varieties (Kindred, Azure, and Tradition), were used and the results showed that with the exception of superoxide anion radical scavenging activity, thin kernel size fraction (2.0 mm) showed much higher antioxidant activities and total phenolic content than those in the 2.4 mm and 2.8 mm fractions when averaged across barley cultivar, grind level, and extract solvent. A similar trend was found for individual phenolic acid compositions among kernel sizes. Simple correlation analysis revealed that total phenolic content showed strong correlation with DPPH· scavenging activity, ABTS + scavenging activity, and reducing power. Generally, fine grind and 80% acetone extract showed averaged highest values in antioxidant activities, total phenolic content, and individual phenolic acid compositions. Stepwise linear regression showed that extract solvent was the most important factor for DPPH· scavenging activity, ABTS + scavenging activity, reducing power, and iron chelating activity. The sum of syringic acid and caffeic acid exhibited a dominant role in explaining the major variation in antioxidant activities except for superoxide anion radical scavenging activity. [ABSTRACT FROM AUTHOR]

Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management. [ABSTRACT FROM AUTHOR]

Monitoring the aboveground biomass (AGB) of maize is essential for improving site-specific nutrient management and predicting yield to ensure food safety. A low-altitude unmanned aerial vehicle (UAV) was employed to acquire hyperspectral imagery of the maize canopy at three growth stages (V6, R1, R3) to estimate the maize AGB. Five maize nitrogen (N) rate experiments were conducted in Lishu County, Jilin Province, Northeastern China, to create different biomass conditions. Combined with crop height data obtained from the field measurements, 30 narrowband vegetation indices (VIs) were extracted using surface reflectance data from the hyperspectral imagery. Stepwise regression, random forest (RF) regression and XGBoost regression models were used to predict the fresh and dry AGB of the 2019 growth season. The study revealed that (1) crop height explained the most variability (60–70%) in the maize dry and fresh AGB estimation of the V6 growth stage, while different VIs exhibited various importance for AGB estimation at other maize growth stages; (2) XGBoost regression models demonstrated high prediction accuracy in both fresh and dry AGB estimation, compared with stepwise regression and RF models. (3) XGBoost models also presented high prediction accuracy at each single-growth stage and the whole-growth stage, with the highest accuracy for the dry AGB at V6 growth stage (R2 = 0.81, RMSE = 0.27 t/ha). This study demonstrated the capability of UAV-based hyperspectral imagery for estimating maize AGB at the field scale, which can be used to assist precision agriculture.

For wet disk clutches, the energy input is strongly influenced by its friction behavior. However, the friction behavior cannot be simulated and therefore is mostly derived from experimental data for specific clutch systems. This paper presents a new approach for the identification and validation of linear friction models using analysis of variance (ANOVA) and stepwise regression. Therefore, we use experimental data of three different friction systems with paper- and carbon-based friction lining. The designed experiments support an efficient parameter-based analysis of the friction behavior. The obtained models can be used as an input for thermal simulations, for example, but can also support a better understanding of the main influencing factors and are applicable to various friction systems. For validation, the obtained models are applied to measured data. A good correspondence between the simulated and measured friction behavior can be shown for speeds in the investigated operating range. The presented procedure can be easily adapted, for different factors and operation modes, as well as other applications.

In this paper, we presented a methodology for efficient and accurate modeling of water losses in hydraulic tunnels under inside internal water pressure, based on multiple linear regression (MLR). The methodology encompasses all steps needed to obtain an adequate mathematical relation between total water losses and relevant measurements in the tunnel, such as reservoir water level, piezometric levels, concrete and water temperatures, size of cracks, etc. Once the data are preprocessed and input variables were chosen, correlation analysis and PCA (principal component analysis) reduction were performed in order to obtain the pool of regression functions. Through an iterative process, according to stepwise regression principles, the most adequate MLR model in terms of accuracy and complexity was chosen. The methodology presented has been validated in modeling water losses in the hydraulic tunnel under the pressure of PSHPP “Bajina Bašta” in the Republic of Serbia. The obtained results have shown significantly better accuracy compared to the results published by other authors, proving that the developed model can be used as a powerful tool in future analyses of tunnel losses and remediation planning.

Based on the road surface (bridge deck) temperature, relative humidity, air temperature, wind speed and precipitation observed at two road surface meteorological stations and two bridge deck meteorological stations, as well as subsurface temperature at different depths observed at Hefei meteorological station, the independent variables are selected to establish the relationship between these factors and road surface temperature, using random forest and stepwise regression. The performance of these two methods was compared, and the importance of each factor was analyzed. Results show that the road surface (bridge deck) temperature linearly correlates with air temperature. In the case of low air temperature conditions (air temperature ≤ 8 °C), the road surface temperature is mainly higher than air temperature observed at the same station, and the bridge deck temperature is mainly lower than air temperature. In the retrieving of road surface temperature and bridge deck temperature, the random forest algorithm has lower mean absolute error (MAE) and root mean square error (RMSE) than the stepwise regression algorithm, especially in the retrieving of road surface temperature. MAE of road surface temperature retrieved by random forest on two bridge deck stations is reduced by 0.19 °C and 0.26 °C compared with the stepwise regression, and RMSE is reduced by 0.33 °C and 0.49 °C, respectively. The bias in the retrievals can be originated from the model itself and the error in the observations. Among the factors in the random forest model, air temperature is the most important. Meanwhile, there are differences in the importance of each factor in the retrieval of road surface temperature and bridge deck temperature. The subsurface temperature is more important in retrieving road surface temperature, while humidity and wind speed are generally more important to bridge deck temperature. It should be noted that due to the limitation of the observations, this study did not consider the net radiative flux, and the influence of net radiative flux on bridge deck and road surface temperature may be different.

Purpose: Gait speed is related to physical function in older adults. This cross-sectional study examined the best predictors of maximal gait speed (MGS) among physical abilities, and general factors in healthy, rural community-dwelling older adults. Methods: MGS, muscle strength, and postural sway were measured in 55 community-dwelling participants (age, 72.1 ± 6.8, range 61–87 years; 72.7% women). Two stepwise regressions were used to find MGS predictors in two models: physical abilities and global. Results: Strength of knee extensors with 60° of knee flexion (KStrength60°) and maximal distance in the anterior-posterior direction with eyes closed explained 50.2% of MGS variance (p < .05) in the physical abilities model. KStrength60°, age, and level of physical activity explained 63.9% of MGS variance (p < .05) in the global model. Conclusions: Regardless of the model, KStrength60° was the best predictor of MGS in rural female older adults. Future research should examine the generalization of these findings to rural male older adults. [ABSTRACT FROM AUTHOR]

The response to selection in any crop improvement program depends on the degree of variance and heritability. The objective of the current study was to explain variance and heritability components in Indian mustard Brassica juncea (L). Czern & Coss to recognize promising genotypes for effective breeding. Two hundred and eighty-nine diverse accessions of Indian mustard belonging to four continents were analyzed for yield and yield-related traits (20 traits) over two seasons (2017–2018 and 2018–2019) using an alpha lattice design. The genetic variance was found to be significant (P ≤ 0.01) for the individual and under pooled analysis for all of the evaluated traits, demonstrating the presence of significant genetic variability in the diversity panel, which bids greater opportunities for utilizing these traits in future breeding programs. High heritability combined with high genetic advance as percent of mean and genotypic coefficient of variation was observed for flowering traits, plant height traits, seed size, and seed yield/plant; hence, a better genetic gain is expected upon the selection of these traits over subsequent generations. Both correlation and stepwise regression analysis indicated that the main shoot length, biological yield, total seed yield, plant height up to the first primary branch, seed size, total siliqua count, days to flowering initiation, plant height at maturity, siliquae on the main shoot, main shoot length, and siliqua length were the most significant contributory traits for seed yield/plant. Also, promising genotypes were identified among the diversity panel, which can be utilized as a donor to improve Indian mustard further. These results indicated a greater scope for improving seed yield per plant directly through a selection of genotypes having the parsimonious combination of these nine traits.

In this study, spatial-temporal variation of dust deposition rate in the western and eastern half of Tehran and its climatic parameters affecting were studied. At 34 points in the city, dust samples were collected by glass traps from the roof, for twelve months, and the climatic data were obtained and analyzed from relevant organizations. The highest deposition rate is in the western half of the city and its total amount has varied from 54.52 to 121.21 g/m²/y. In both halves of the city, summer has the highest dust deposition rate and its central areas have the highest amount. There were significant positive correlations between dust deposition rate with temperature and medium wind speed, and there were significant negative correlations between dust deposition rate with rainfall and relative humidity in all months, which justifies the high dust deposition rate in the dry seasons of the year. The results of stepwise regression showed that rainfall was the most important factor affecting the dust deposition rate in both halves of the city. The city of Tehran has a special geographical location the presence of mountains like a barrier has prevented dust from leaving the city and the air inlet corridor of Tehran has faced problems due to the expansion of building construction and high-rise building. Other factors affecting the rate of dust deposition in this city, in addition to the distance from the main source of dust production, atmospheric parameters can be mentioned the existence of barren lands around the city, vegetation cover, construction operations, and traffic. [ABSTRACT FROM AUTHOR]

This study was conducted to evaluate the relationships among some of the agronomic and physiological traits of twelve Thymus daenensis subsp. daenensis ecotypes under normal and salt (120 mM NaCl) stress conditions in a factorial experiment using randomized complete block design with three replications. Shoot dry weight under both conditions had significant positive correlation with chlorophyll a and b, potassium, relative water content, canopy area, leaf length and number of stems in plant and had significant negative correlation with sodium to potassium ratio and ion leakage. According to stepwise regression results, canopy area and carotenoid under normal condition and leaf length and number of stems in plant under salt stress condition were entered to the model. The result of path analysis showed that canopy area had the most positive direct effect on shoot dry weight under normal condition and leaf length and number of stems in plant had positive effects on shoot dry weight under salt stress condition. [ABSTRACT FROM AUTHOR]

As stock exchange has a random and non-linear nature, predicting its related indexes with minimal error has been a major challenge for researchers and investors. In this paper, by reviewing the literature, the authors extracted used the representative indicators of different technical analysis and statistical models to pre-process data. It was followed by selecting the best predictor variables for artificial neural network model, which predicts Tehran's stock exchange index. The methods used to identify and select effective factors in the exchange index were stepwise regression and panel data models. Then, the multi-layer artificial neural network based on Levenberg–Marquardt and imperialist competitive algorithms were used to predict the intended index. Finally, the proposed model was tested on the top fifty companies in Tehran's Stock Exchange and its efficiency was evaluated. [ABSTRACT FROM AUTHOR]

The response to selection in any crop improvement program depends on the degree of variance and heritability. The objective of the current study was to explain variance and heritability components in Indian mustard Brassica juncea (L). Czern & Coss to recognize promising genotypes for effective breeding. Two hundred and eighty-nine diverse accessions of Indian mustard belonging to four continents were analyzed for yield and yield-related traits (20 traits) over two seasons (2017–2018 and 2018–2019) using an alpha lattice design. The genetic variance was found to be significant (P ≤ 0.01) for the individual and under pooled analysis for all of the evaluated traits, demonstrating the presence of significant genetic variability in the diversity panel, which bids greater opportunities for utilizing these traits in future breeding programs. High heritability combined with high genetic advance as percent of mean and genotypic coefficient of variation was observed for flowering traits, plant height traits, seed size, and seed yield/plant; hence, a better genetic gain is expected upon the selection of these traits over subsequent generations. Both correlation and stepwise regression analysis indicated that the main shoot length, biological yield, total seed yield, plant height up to the first primary branch, seed size, total siliqua count, days to flowering initiation, plant height at maturity, siliquae on the main shoot, main shoot length, and siliqua length were the most significant contributory traits for seed yield/plant. Also, promising genotypes were identified among the diversity panel, which can be utilized as a donor to improve Indian mustard further. These results indicated a greater scope for improving seed yield per plant directly through a selection of genotypes having the parsimonious combination of these nine traits. [ABSTRACT FROM AUTHOR]

This study estimated the severity of Nilahue-Barahona and Las Máquinas wildfires, occurred in central Chile during the summer of 2016-17, by an empirical-statistical modelling based on pre- and post-fire arithmetic differences of spectral indices sensitive to vigor, turgor and calcination states of vegetation. By doing this, map of damages were created to aid the efficient management and ecological restoration of disturbed forestry ecosystems. The index differences were calculated from Sentinel-2 satellite images, acquired anually in the summers spanning from 2016 to 2019. The resulting nine index-derived differences were used as predictors of burn severity, field-measured during the summer of 2019 using the CBI (composite burn index) method, into a linear stepwise regression that allowed for selecting those with the highest predictability. CBI yielded low correlations as its calculation includes low vegetation strata largely recovered at the time of the field data collection. However, when overstory field data were used alone, correlations increased (70 % of the data = 0.80, P < 0.05). This was because this stratum was still appreciably damaged during the field campaign, along with its best representation from the image planimetry. The burn severity of both wildfires was mapped using the overstory data as regressand in a model based on NDWIex-ante-2019, NDWIex-ante-2018, NBRex-ante-2018 and NBRex-ante-2017 differences (R² ad = 0.77, RMSE = 0.35). [ABSTRACT FROM AUTHOR]

Copyright of Menoufia Journal of Plant Production is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forest Research is the property of Forest Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Forest Research is the property of Forest Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study examined if the Nigerian agricultural output has spurred economic growth and the best fit agricultural financing gap model for growing the economy. The study explored the dynamics of different technicality approach that stepwise regression has to offer. From the seven baskets of predictors – agricultural guaranteed finance to oil palm, cocoa, groundnuts, fishery, poultry, cattle, roots and tubers – the step fitted three predictors: roots and tubers, cocoa and poultry based on “a b” parameter with the highest “t-stats” and significant p-value and subsequently executed the model using stepwise regression analysis with the help of Statistical Package for Social Sciences (SPSS) version 23. The dataset covers a thirty-six year period from 1981 to 2017. The source of the data is from the Central Bank of Nigeria 2018 statistical bulletin. The findings showed that individually, root and tubers has the most contributory impact on economic growth with 81 percent. Jointly followed is cocoa at 87 percent and poultry at 90 percent. The study thus recommends a comparative cost advantage to financing agriculture with the most impactful contribution to economic growth based on the model.

In Poland, institutions that perform public tasks in the field of the labour market are state employment services, including District Labour Offices (PUPs). They try to restore the unemployed to the open labour market using active forms of their activation. The activities of PUPs in the field of customer activation are subject to annual evaluation. According to the “Act on Employment Promotion and on Labour Market Institutions”, four indicators were selected for the evaluation of the functioning of labour offices in 2015–2017. Among them, the employment effectiveness index is very important, as its level allows us to assess to what extent active forms of activation of the unemployed implemented by PUPs contribute to the return of the unemployed to employment. This assessment is so important that in Poland as well as in other countries research has been conducted on both active forms of activation of the unemployed and employment policies. The aim of the research whose results are presented in the article was to identify factors influencing the employment effectiveness index achieved by District Labour Offices (PUPs) operating in Poland in 2016. Selected statistical methods were used, including correlation and regression analysis as well as multidimensional correspondence analysis. It turned out that the methods applied did not make it possible to unambiguously identify factors which had significantly affected the employment effectiveness index calculated on the basis of all forms of activation. It may turn out that clearer relations could be identified if this indicator was considered separately for particular forms of activation.

To examine potential relationships between meteorological variables and forest fires in Serbia, daily temperature, precipitation, relative humidity and wind speed data for 15 meteorological stations across Serbia were used to construct fire indices. The daily values of the Ångström and Nesterov indices were calculated for the period 2000–2017. A high number of forest fires occurred in 2007 and 2012 in Serbia, during a period of extremely high air temperatures in 2007, followed by the longest heat wave and the worst drought in 2012.

Reforestation is a primary factor in protecting upland forests providing economic sustenance for livelihood and keeping watersheds intact. In this study, we evaluated the importance of several direct and indirect drivers that can influence people’s decision for reforestation. Acquiring data from Cao Phong district of Vietnam, we utilized Bayesian Network (BN) to analyze how household characteristics, socio-economic status, biophysical environment, institutional support, and farm characteristics influenced reforestation decisions of local people. BN allowed us to identify 1) the main drivers that affect landholders ‘planted forest area, 2) how the key drivers affect among themselves, and 3) what causes constraints in tree planting. We surveyed 100 households for potential drivers, identified significant drivers by using bivariate analysis and stepwise linear regression, and created a BN to predict scenarios with different household’s perception regarding the planted forest area. The results revealed five direct drivers (attitude of household to tree planting, sources of investment capital for planting practice, land area, distance from household to market, experience of participating in forestry program) and seven indirect drivers (information about forestry program, incentives supported for tree planters, land tenure, accessibility to plantation forest, rotation length of planting trees, forest area, household income) that significantly influenced farmers’ reforestation decisions. Constraints in planting trees were due to the difficulties in protecting property from mortality and unreliable profit. Our results can assist design efficient forestry programs in Vietnam and in other comparable areas.

This study aims to study the estimation of production cost functions as it reflects the relationship between total costs and actual production. The main economic indicators that reflect the extent to which the agricultural production units achieved economic efficiency in their use of the productive resources involved in the production process as well as the extent to which they are achieved These units of maximizing the profits of the agricultural product, as well as the study of production costs and the associated indicators are important both on the productivity unit or at the national level, which can be used in the design and analysis of price policies and help the user Farmers in their productive decision-making, which would encourage producers to continue production or stop production. As well as to determine the volume of production that maximizes profits and thus achieve economic efficiency of the use of productive elements.

Wild pears are an important rootstock for different pear cultivars and considering it is nearly tolerant to drought stress it is necessary to use different treatments to increase its tolerance to drought stress. Electrolytes leakage is an important and effective factor in response to drought stress and related traits. For this purpose, a factorial experiment was designed in a randomized complete blocks design with three replications to identify the traits affecting electrolytes leakage. These traits were studied in 12 treatments including different levels of drought stress and different concentrations of brassinosteroids on wild pears in spring and summer of 2017 in greenhouse conditions. The result showed a positive significant correlation between proline content (0.83**), malondialdehyde (0.98**), hydrogen peroxide and electrolyte leakage. The correlation for dry weight of root, leaf and stem, height growth, chlorophyll a, b and carotenoid was significantly negative. Stepwise regression analysis indicated that chlorophyll a, leaf dry weight, height growth and hydrogen peroxide were the most important effective traits on electrolytes leakage and showed 99% of total variance. Based on the path analysis results, the traits of electrolytes leakage and height growth and chlorophyll a showed the maximum positive direct and significant at p

Data analysis was conducted to investigate the causes of the difference in creep strength between the heats of Gr. 91 steel. Creep rupture data were divided into multiple regions and fitted with an exponential degradation model. The Larson-Miller constant of each heat obtained by the fitting was used as the objective variable, and chemical concentrations of important elements, hardness and grain size were used as explanatory variables. The stepwise method was used for the data analysis. As a result of the data analysis, it is found that hardness in region H, grain size in region G, and Cr and C concentrations in regions M and L are the causes of the difference in creep strength between the heats.

A child health experiment was designed to achieve the maximum positive variables through the screening of different disease variables, using the Plackett-Burman design. Eleven variables of the children disease were identified: Acute Bronchial Meningitis, Respiratory tract infection, chronic liver disease, Congestive cardiac failure, upper respiratory tract infection, Urinary tract infection, Ante-partum Haemorrhage, Post-partum Haemorrhage, Sickle cell disease, Neonatal Jandice, and Benigu prostal hyperplasia. The selected variables were evaluated through statistical analysis, based on their significance, coefficient value and standard effect plot. The results suggested that three variables, namely, upper respiratory tract infection, urinary tract infection, Ante-Partum Haemorrhage and the combination of urinary tract infection/Ante-Partum Haemorrhage had influence with high confidence levels, while the remaining eight variables did not show significant effect on the children age. The coefficient of determination value R2 (63.42%) also showed the model used for prediction to be significant (p less than 0.05). The plot for the standard effect for each component and its traits provided accurate data by which to select well-suited variables and further statistical optimization of medium and process parameters was explored using stepwise regression methodology. [ABSTRACT FROM AUTHOR]

The aim of this paper is to assess whether three well-known commodity-specific variables (basis, hedging pressure, and momentum) may improve the predictive power for commodity futures returns of models otherwise based on macroeconomic factors. We compute recursive, out-of-sample forecasts for the monthly returns of fifteen commodity futures, when estimation is based on a stepwise model selection approach under a probability-weighted regime-switching regression that identifies different volatility regimes. We systematically compare these forecasts with those produced by a simple AR(1) model that we use as a benchmark and we find that the inclusion of commodity-specific factors does not improve the forecasting power. We perform a back-testing exercise of a mean–variance investment strategy that exploits any predictability of the conditional risk premium of commodities, stocks, and bond returns, also consider transaction costs caused by portfolio rebalancing. The risk-adjusted performance of this strategy does not allow us to conclude that any forecasting approach outperforms the others. However, there is evidence that investment strategies based on commodity-specific predictors outperform the remaining strategies in the high-volatility state. [ABSTRACT FROM AUTHOR]

The understanding of the behavior of a wind turbine is difficult due to changes in weather conditions. To obtain the response of a wind turbine influenced by changes in both wind speed and its direction, using the meteorological station data is often preferred to using the real turbine data. Furthermore, simulated data can be easily extrapolated to varied turbine hub heights. In order to estimate the most effective power output in this study, a wind turbine simulation was developed. The simulation depends on the real meteorological data. For the purpose, three modeling techniques, namely Multi-Nonlinear Regression (MNLR), Adaptive Neuro-Fuzzy Inference System (ANFIS), and support vector machines (SVM) were used. In SVM learning process, polynomial and radial basis kernel functions were used. Models were compared to wind turbine measurement values in the same region for similar data. MNLR was used to determine quantify the strength of the relationship between parameters and to eliminate the ineffective parameters. Efficient parameters preferred for training and testing phases of the SVM and ANFIS. The Subtractive Clustering and Grid Partitioning methods were used to identify the inference parameters of ANFIS. According to performance evaluations, MNLR-ANFIS modeling based on Subtractive Clustering gave better results than Grid Partitioning. The results showed that proposed collaborative model could be applied to wind power estimation problems. [ABSTRACT FROM AUTHOR]