Success Factors of Agribusiness Digital Marketplaces

Immunohistochemical Identification and Image Analysis Quantification of Oestrogen and Progesterone Receptors in Canine and Feline Meningioma

Current observational evidence indicates that maternal smoking during pregnancy is associated with reduced birthweight in offspring. However, less is known about the effect of smokeless tobacco on birthweight and about the possible mechanisms involved in this relationship. This paper studies the effect of Swedish smokeless tobacco (snus) on offspring birthweight comparing the results obtained from a conventional linear regression analysis and from a quasi-experimental sibling design using a multilevel linear regression analysis. From the Swedish Medical Birth Register, we investigated 604,804 singletons born between 2002 and 2010. From them, we isolated 8,861 siblings from 4,104 mothers with discrepant snus-use habits (i.e., women who had at least one pregnancy during which they used snus and at least one other pregnancy in which they did not). The conventional analysis shows that continuous snus use throughout the pregnancy reduces birthweight in 47 g while quitting or relapsing snus has a minor and statistically non-significant effect (−6 g and −4 g, respectively). However, using a sibling analysis the effect observed for mothers who continue to use snus during pregnancy is less intense than that observed with previous conventional analyses (−20 g), and this effect is not statistically significant. Sibling analysis shows that quitting or relapsing snus use after the first trimester slightly reduces birthweight (14 g).However, this small change is not statistically significant. The sibling analysis provides strong causal evidence indicating that exposure to snus during pregnancy has a minor effect on birthweight reduction. Our findings provide a new piece of causal evidence concerning the effect of tobacco on birthweight and support the hypothesis that the harmful effect of smoking on birthweight is not mainly due to nicotine.

This present work uses artificial neural networks (ANNs) to examine the association between various dimensions of coaching leadership and turnover Intention. The coaching leadership data were collected from 194 employees across multiple schools in Korea. The ANN models are capable of higher predictive accuracy than conventional linear regression analysis. An individual ANN software was developed to predict and evaluate the relative importance of input variables on turnover intention. Furthermore, we identified the nonlinear relationship by performing a sensitivity analysis on the model. Based on the results, we concluded that coaching leadership strongly affects teachers’ attitudes toward not leaving their school. The graphical illustration of results provided strong evidence of nonlinear and complexity, suggesting that ANN models can recognize the relationship between coaching leadership dimensions with turnover Intention.

The objective of this study is to establish the relationship between Internship Experience and Future Career Prospects among Business Students at Mbarara University of Science and Technology. Using a quantitative approach and a cross-sectional survey design, data was gathered from 100 alumni. Using an open-ended questionnaire, Mbarara University of Science and Technology business students' internship experiences and future career prospects were surveyed quantitatively. In addition, a conventional linear regression analysis was performed. The study's findings demonstrated that among the Mbarara University of Science and Technology's business students, internship experience had a strong positive and significant influence on their future career prospects. The study relates to the ongoing discussion about how business students' internship experience influences their chances for future employment.

The demand for rail freight transportation is a continuously changing process over space and time and is affected by many quantitative and qualitative factors. In order to develop a more rational transport planning process to be followed by railway organizations, there is a need to accurately forecast freight demand under a dynamic and uncertain environment. In conventional linear regression analysis, the deviations between the observed and the estimated values are supposed to be due to observation errors. In this paper, taking a different perspective, these deviations are regarded as the fuzziness of the system's structure. The details of fuzzy linear regression method are put forward and discussed in the paper. Based on an analyzes of the characteristics of the rail transportation problem, the proposed model was successfully applied to a real example from China. The results of that application are also presented here.

PurposeFor successful prevention and intervention, it is important to unravel the complex constellation of factors that affect neurocognitive functioning after pediatric intensive care unit (PICU) admission. This study aims (1) to elucidate the potential relevance of patient and PICU-related characteristics for long-term adverse neurocognitive outcome after PICU admission for bronchiolitis, and (2) to perform a preliminary exploration of the potential of machine learning as compared to linear regression to improve neurocognitive outcome prediction in a relatively small sample of children after PICU admission.MethodsThis cross-sectional observational study investigated 65 children aged 6–12 years with previous PICU admission for bronchiolitis (age ≤ 1 year). They were compared to demographically comparable healthy peers (n = 76) on neurocognitive functioning. Patient and PICU-related characteristics used for the prediction models were as follows: demographic characteristics, perinatal and disease parameters, laboratory results, and intervention characteristics, including hourly validated mechanical ventilation parameters. Neurocognitive outcome was measured by intelligence and computerized neurocognitive testing. Prediction models were developed for each of the neurocognitive outcomes using Regression Trees, k-Nearest Neighbors, and conventional linear regression analysis.ResultsThe patient group had lower intelligence than the control group (p < .001, d = −0.59) and poorer performance in neurocognitive functions, i.e., speed and attention (p = .03, d = −0.41) and verbal memory (p < .001, d = −0.60). Lower intelligence was predicted by lower birth weight and lower socioeconomic status (R2 = 25.9%). Poorer performance on the speed and attention domain was predicted by younger age at follow-up (R2 = 53.5%). Poorer verbal memory was predicted by lower birth weight, younger age at follow-up, and greater exposure to acidotic events (R2 = 50.6%). The machine learning models did not reveal added value in terms of model performance as compared to linear regression.Conclusion: The findings of this study suggest that in children with previous PICU admission for bronchiolitis, (1) lower birth weight, younger age at follow-up, and lower socioeconomic status are associated with poorer neurocognitive outcome; and (2) greater exposure to acidotic events during PICU admission is associated with poorer verbal memory outcome. The findings of this study provide no evidence for the added value of machine learning models as compared to linear regression analysis in the prediction of long-term neurocognitive outcome in a relatively small sample of children.What is Known:• Adverse neurocognitive outcomes are described in PICU survivors, which are known to interfere with development in other major domains of functioning, such as mental health, academic achievement, and socioeconomic success, highlighting neurocognition as an important outcome after PICU admission.• Machine learning is a rapidly growing field of artificial intelligence that is increasingly applied in health care settings, with great potential to capture the complexity of outcome prediction.What is New:• This study shows that lower birth weight, lower socioeconomic status, and greater exposure to acidotic events during PICU admission for bronchiolitis are associated with poorer long-term neurocognitive outcome after PICU admission. Results provide no evidence for the added value of machine learning models in a relatively small sample of children.• As bronchiolitis seldom manifests neurologically, the relation between acidotic events and neurocognitive outcome may reflect either potentially harmful effects of acidosis itself or related processes such as hypercapnia or hypoxic and/or ischemic events during PICU admission. This study further highlights the importance of structured follow-up to monitor long-term outcome of children after PICU admission.

In this paper, X-ray images of the stomach are recognized by using the revised GMDH (Group Method of Data Handling)-type neural network algorithm. The revised GMDH-type neural networks can automatically organize themselves by using the heuristic self-organization method, which was developed in the GMDH algorithm and is very similar to the Genetic Algorithm. The structural parameters such as the useful input variables, the number of the neurons in the hidden layers, optimum neuron architectures in the hidden layers and the number of feedback loop calculations are automatically selected so as to minimize an error criterion defined as AIC (Akaike’s information criterion). AIC can not be used in the conventional multi-layered neural networks using the back propagation method but AIC can be used in the GMDH-type neural network algorithm because the GMDH-type neural networks use the conventional linear regression analysis in order to estimate the connection weights. In this paper, it is shown that this neural network algorithm is very useful in the recognition of X-ray images.

In previous studies, researchers estimated short-term relationships between financial credits and health outcomes using conventional regression analyses, but they did not account for time-varying confounders affected by prior treatment (CAPTs) or the credits' cumulative impacts over time. In this study, we examined the association between total number of years of receiving New Zealand's Family Tax Credit (FTC) and self-rated health (SRH) in 6,900 working-age parents using 7 waves of New Zealand longitudinal data (2002-2009). We conducted conventional linear regression analyses, both unadjusted and adjusted for time-invariant and time-varying confounders measured at baseline, and fitted marginal structural models (MSMs) that more fully adjusted for confounders, including CAPTs. Of all participants, 5.1%-6.8% received the FTC for 1-3 years and 1.8%-3.6% for 4-7 years. In unadjusted and adjusted conventional regression analyses, each additional year of receiving the FTC was associated with 0.033 (95% confidence interval (CI): -0.047, -0.019) and 0.026 (95% CI: -0.041, -0.010) units worse SRH (on a 5-unit scale). In the MSMs, the average causal treatment effect also reflected a small decrease in SRH (unstabilized weights: β = -0.039 unit, 95% CI: -0.058, -0.020; stabilized weights: β = -0.031 unit, 95% CI: -0.050, -0.007). Cumulatively receiving the FTC marginally reduced SRH. Conventional regression analyses and MSMs produced similar estimates, suggesting little bias from CAPTs.

BackgroundThis study explains how to compare the relative bias of instrumental variable and conventional regression using negative controls and bias plots. Conventional observational analyses such as multivariable adjusted regression depend...

Topicality. The modern development of field hockey requires a more detailed analysis of individual aspects of the game, the study of quantitative and qualitative indicators of technical and tactical actions performed in different game zones of the hockey field is relevant. The purpose of the study is to determine qualitative and quantitative model indicators of technical and tactical competitive actions in different zones of the playing field of highly qualified field hockey players. Material and methods. The following methods of scientific research were used to implement the research goal: analysis of literary sources, pedagogical observation and analysis of competitive activity, methods of mathematical statistics. Technical and tactical actions (TTA) in field hockey were studied in the process of observing the games of highly qualified teams during the World League, World Universiade, and Euroleague competitions. Results. According to the conclusions of our research, the quantitative indicators of the protection zone "A" should be greater than 40 TTA, the qualitative indicators should be higher than 88%, zone "B" respectively 54 TTA and 82 %; in the zone of action of halfbacks "V", the number of performed actions must be higher than 48 TTA with an efficiency of at least 79 %; in zone "G" the quantitative indicator should exceed 38 TTA, their efficiency is higher than 68 %. According to the direction of the attack (from the left side, from the center, from the right side), the quantitative indicators should be distributed almost evenly and fluctuate within 50-70 TTA, the average efficiency indicator along these lines should not be lower than78 %. In general, the team's game should be characterized by the following indicators of technical and tactical actions: their sum should be at least 686 TTA, the efficiency should be higher than 78 %. Conclusions. The method of researching competitive activity by zones and lines of attack is quite informative, and therefore can be an effective tool for improving the educational and training process of qualified field hockey players.

With the advent of advanced geographical informational systems (GIS) and remote sensing technologies in recent years, topographic (elevation, slope, and aspect) and vegetation attributes are routinely available from digital elevation models (DEMs) and normalized difference vegetation index (NDVI) at different spatial (watershed, regional) scales. This study explores the use of topographic and vegetation attributes in addition to soil attributes to develop pedotransfer functions (PTFs) for estimating soil saturated hydraulic conductivity in the rangeland of central Zagros. We investigated the use of artificial neural networks (ANNs) in estimating soil saturated hydraulic conductivity from measured particle size distribution, bulk density, topographic attributes, normalized difference vegetation index (NDVI), soil organic carbon (SOC), and CaCo3 in topsoil and subsoil horizon. Three neural networks structures were used and compared with conventional multiple linear regression analysis. The performances of the models were evaluated using spearman’s correlation coefficient (r) based on the observed and the estimated values and normalized mean square error (NMSE). Topographic and vegetation attributes were found to be the most sensitive variables to estimate soil saturated hydraulic conductivity in the rangeland of central Zagros. Improvements were achieved with neural network (r=0.87) models compared with the conventional multiple linear regression (MLR) model (r=0.69).

Determining the role of gut microbial communities in aging-related phenotypes, including weight loss, is an emerging gerontology research priority. Gut microbiome datasets comprise relative abundances of microbial taxa that necessarily sum to 1; analysis ignoring this feature may produce misleading results. Using data from the Osteoporotic Fractures in Men (MrOS) study (n = 530; mean [SD] age = 84.3 [4.1] years), we assessed 163 genera from stool samples and body weight. We compared conventional analysis, which does not address the sum-to-1 constraint, to compositional analysis, which does. Specifically, we compared elastic net regression (for variable selection) and conventional Bayesian linear regression (BLR) and network analysis to compositional BLR and network analysis; adjusting for past weight, height, and other covariates. Conventional BLR identified Roseburia and Dialister (higher weight) and Coprococcus-1 (lower weight) after multiple comparisons adjustment (p < .0125); plus Sutterella and Ruminococcus-1 (p < .05). No conventional network module was associated with weight. Using compositional BLR, Coprococcus-2 and Acidaminococcus were most strongly associated with higher adjusted weight; Coprococcus-1 and Ruminococcus-1 were most strongly associated with lower adjusted weight (p < .05), but nonsignificant after multiple comparisons adjustment. Two compositional network modules with respective hub taxa Blautia and Faecalibacterium were associated with adjusted weight (p < .01). Findings depended on analytical workflow. Compositional analysis is advocated to appropriately handle the sum-to-1 constraint.

Gut microbiome datasets comprise microbial taxa relative abundances that necessarily sum to 1; analysis ignoring this feature may produce misleading results. We assessed 163 genera from the first batch of Microbiome Ancillary Study (n=530) stool samples and examined associations between microbiota and body weight. We compared conventional Bayesian linear regression (BLR) and network analysis to their compositional counterparts, adjusting for past weight and other covariates. Conventional BLR identified Roseburia and Dialister (positive association) and Coprococcus-1 (negative association) after multiple comparisons adjustment(P<.0125). No conventional network module was associated with weight. Using compositional BLR, men with higher Coprococcus-2 and Acidaminococcus had higher weight, whereas men with higher Coprococcus-1 and Ruminococcus-1 had lower weight (P<.05), but findings were non-significant after multiple comparisons adjustment. Two compositional network modules with respective hub taxa Blautia and Faecalibacterium were associated with weight(P<.01). Findings depended on analytical workflow; compositional analysis is advocated to appropriately handle the sum-to-1 constraint.

Introduction The permeability of a porous rock to a saturating fluid is determined by the geometry of the rock pore system and not by the physical properties of the fluid. This general statement assumesthe absence of a chemical reaction between the rock and fluid anda single homogeneous fluid phase. If more than one fluid is present, permeability to any fluid depends not only on the geometry of the permeability to any fluid depends not only on the geometry of the rock pore system but also on the fraction and distribution of each fluid phase, the interfacial tensions, the saturation history, and possibly other factors. Although direct prediction of relative permeability from theoretical considerations is a worthwhile objective, the most successful techniques for making these predictions are essentially empirical. Rather than predictions are essentially empirical. Rather than attempting a theoretical solution to the problem, we have used an entirely empirical approach. In our study a rather extensive set of relative permeability data was compiled, and conventional stepwise linear regression analysis techniques were used to develop prediction equations from the laboratory data. This procedure is designed to produce a satisfactory fit of the data with a minimum of terms in the equation; it is not intended to provide the best possible data fit. Development of Empirical Equations The data used as a basis for the study were derived from oil and gas fields in the continental U.S., Alaska. Canada, Libya, Iran, Argentina, and the United Arab Republic. All the laboratory tests were made at room temperature and atmospheric pressure. We made no attempt to group the data according to laboratory techniques used in measuring relative permeability since this information was not available for many of the data sets. Each set of relative permeability data was classified as either carbonate or noncarbonate mation was insufficient for more detailed lithologic characterization. In addition to identifying data sets as carbonate or noncarbonate, rough wettability classifications were made according to three arbitrary criteria:The rock was considered water-wet if kro at high oil saturations in an oil/water system greatly exceeded kro in a gas/oil system at the same oil saturations, provided that krg in a gas/oil system greatly exceeded krw in an oil/water system at or near residual oil saturation after waterflooding.The rock was considered oil-wet when kro in the oil/water system was approximately equal to kro in the gas/oil system, provided that krg in the gas/oil system was approximately equal krw in the oil/water system.The rock was considered of intermediate wettability when it did not meet clearly either the water-wet or the oil-wet classification criteria. After the data sets had been classified according to lithology and wettability, stepwise linear regression analysis was used to develop equations that would approximate the measured relative permeabilities from such factors as fluid saturations, permeability, and porosity. The equations developed in this study are porosity. The equations developed in this study are presented in Appendix A. presented in Appendix A. The "goodness of fit" of each equation was determined according to the statistical concept of R2 (the coefficient of multiple determination), which indicates the amount of variation about the mean that the model accounts for. A low value of R2 indicates an inadequate data fit and suggests that additional variables, higher order terms, or cross-products of the independent variables are needed. An F test was employed to eliminate insignificant variables from the regression equations. JPT P. 2905

Drilling and blasting method has been used for many years in underground excavations and still is very popular because of its many advantages. Blast performance is ordinarily measured by specific charge and by explosive consumption of broken rock. The empirical models are available for estimation of specific charge and different sets of parameters. This paper presents the possibility of applying artificial neural networks (ANNs) to estimate the specific charge in various conditions of tunnel blasting. Among available existing parameters in the literature, some of the most influencing parameters are selected. After running different models, P wave, rock-quality designation (RQD), tunnel area, maximum depth of the hole, and coupling ratio (charge-to-hole diameter) are selected to estimate specific charge of tunnel blasting under various conditions. Also, conventional multi variable linear regression analysis (MVLRA) is applied to estimate specific charge. The results show that the accuracy of ANN is more than the MVLRA-based models.