Prediction of Compressive Strength of Fly Ash Based Concrete Using Individual and Ensemble Algorithm

Machine learning techniques are widely used algorithms for predicting the mechanical properties of concrete. This study is based on the comparison of algorithms between individuals and ensemble approaches, such as bagging. Optimization for bagging is done by making 20 sub-models to depict the accurate one. Variables like cement content, fine and coarse aggregate, water, binder-to-water ratio, fly-ash, and superplasticizer are used for modeling. Model performance is evaluated by various statistical indicators like mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). Individual algorithms show a moderate bias result. However, the ensemble model gives a better result with R2 = 0.911 compared to the decision tree (DT) and gene expression programming (GEP). K-fold cross-validation confirms the model’s accuracy and is done by R2, MAE, MSE, and RMSE. Statistical checks reveal that the decision tree with ensemble provides 25%, 121%, and 49% enhancement for errors like MAE, MSE, and RMSE between the target and outcome response.

Examining the Educational and Employment Outcomes of Reverse Credit Transfer

Reverse credit transfer (RCT) is an emerging policy designed to award associate’s degrees to students who transfer from 2-year to 4-year colleges after transfer. The purpose of this study is to estimate the impact of RCT degree receipt on students’ university and labor outcomes using data from Texas, where the legislature passed RCT policy in 2011. We find that posttransfer associate’s degree recipients are significantly more likely to persist and attain in universities compared to their peers who were eligible for RCT but did not receive the degree, and these benefits are often larger for students from populations historically marginalized from higher education. However, these estimates are suggestive given the potential of self-selection biasing the estimates upward, and the results are sensitive to moderate bias from unobserved variables. We find limited evidence of additional benefit of these associate’s degrees, which are largely academic and transfer-oriented degrees, on labor outcomes.

Mothers and Wives

This chapter demonstrates links between the presumed moderate bias and pacifism of women with the emphasis on women’s roles as mothers and wives. Focusing on how women’s engagement is filtered through mother work, ideas of world preservation presume the home as a site of tranquility, with women naturally located there. Caring is established as counter to radical violent action, dovetailing with moderation narratives, yet premising antiradicalization programs on ideas that wives and mothers are pro-state or nonextreme is questionable. The chapter concludes by arguing that debates over women’s appropriate roles are the hidden battleground over which both counter-radicalization and radical groups operate. Radical groups present a narrative about the failures of Western society and feminism to protect women, and a consequent emasculation of men. Counter-radicalization efforts presuming that Western society and feminism have benefited all women equally fail to appreciate the difficulties that young Muslim women face negotiating complex identities under conditions of discrimination, poverty, and Islamophobia.

Assessing the Quality of Shortwave and Longwave Irradiance Observations over the Ocean: One Year of High-Time-Resolution Measurements at the Lampedusa Oceanographic Observatory

Measurements of downwelling shortwave (SW) and longwave (LW) irradiance were carried out on an oceanographic buoy close to the island of Lampedusa (Italy), in the central Mediterranean Sea. Irradiance measurements on the buoy were acquired at high time resolution together with the radiometer pitch and roll angles. The measurements carried out during 2016 have been compared with ground-based observations made at the Lampedusa Atmospheric Observatory, about 15 km northeast of the buoy. The radiometers were compared before and after deployment on the buoy and are traceable to the World Radiometric Reference scale. The SW measurements were corrected for the thermal offset. A small bias (measurements over the sea are smaller than on land) of about −2 W m−2 is found in the daily mean SW, and a moderate bias of +6.2 W m−2 (irradiance over the sea is larger than on land) is found in the LW. Similar biases are found when instantaneous measurements obtained with horizontal radiometers, clean domes, and cloud-free conditions are selected, suggesting that impacts of the moving platform and poor dome cleaning are minor at this site. The effect of the mean tilt angle was also investigated. Deviations in the hourly mean SW irradiance are on the order of 20% for a mean offset of 4° with respect to the solar zenith angle; the effect of tilt angle on LW irradiance appears to be negligible. Radiative transfer calculations show that the observed biases may be ascribed to the differences in the instrument altitude (through radiation absorption, scattering, and emission by the atmospheric constituents in the lowest atmospheric layers) and in the SW surface albedo.

Response of snowpack to +2°C global warming in Hokkaido, Japan

The response of snowpack to a +2°C global warming relative to the present climate was estimated in Hokkaido, Japan, using a physical snowpack model driven by dynamically downscaled (DDS) data, after model evaluation. The evaluation revealed that the snowpack model successfully reproduced the height of snow cover (HS), snow water equivalent (SWE) and snow-covered days (SCDs), but had a moderate bias in the thickness ratios of melt form (MF) and hoar category (HC). The DDS-forced simulation predicted that the seasonal-maximum HS and SWE would decrease by 30–40% in the southwestern and eastern parts of Hokkaido due to a large decrease in snowfall during the accumulation period, and that the HS and SWE in the north would decrease, albeit not significantly due to uncertain atmospheric forcing. The number of SCDs in Hokkaido was predicted to decline by ~30 d. Additionally, ~50% of snowpack thickness during a season would be MF in most areas, whereas HC would be <50% all over Hokkaido.

Measuring the Impacts of Teachers: Comment

Chetty, Friedman, and Rockoff (2014a, b) study value-added (VA) measures of teacher effectiveness. CFR (2014a) exploits teacher switching as a quasi-experiment, concluding that student sorting creates negligible bias in VA scores. CFR (2014b) finds VA scores are useful proxies for teachers' effects on students' long-run outcomes. I successfully reproduce each in North Carolina data. But I find that the quasi-experiment is invalid, as teacher switching is correlated with changes in student preparedness. Adjusting for this, I find moderate bias in VA scores, perhaps 10–35 percent as large, in variance terms, as teachers' causal effects. Long-run results are sensitive to controls and cannot support strong conclusions. (JEL H75, I21, J45)

Validity of WebCAAFE questionnaire for assessment of schoolchildren’s dietary compliance with Brazilian Food Guidelines

ObjectiveTo estimate reporting bias of WebCAAFE, a web-based questionnaire for the assessment of food intake (recall of frequency of intake of thirty-two food items the day before) and physical activity in schoolchildren.DesignCross-sectional study. Self-reported food intake on WebCAAFE was compared with direct observation of school meals in five public schools. Additional data included school grade, sex, BMI, socio-economic status and access to Internet at home. Poisson regression was used to calculate the reporting bias (WebCAAFE v. direct observation) and the sample size necessary to detect a statistically significant difference between WebCAAFE reports and at least 75 % compliance with the recommendations for a healthy diet.SettingIntentional sample of five elementary public schools in Florianopolis, Brazil.SubjectsSchoolchildren (n 629) from 2nd to 5th grades.ResultsModerate bias magnitude was found for most food groups of interest. Frequency of consumption was not related to the bias. Sample sizes necessary to detect the compliance with dietary recommendations varied between four and seventy-four individuals for the different groups investigated.ConclusionsAfter adjusting for moderate bias, WebCAAFE may be used as a food questionnaire for evaluation of schoolchildren’s food compliance on a group level, even with a relatively small sample size.

Explicit and implicit attitudes toward lung cancer (LC) relative to breast cancer (BC).

8017 Background: Emerging research suggests that LC may be associated with greater levels of stigma, shame and hopelessness compared to other cancers. This study measured explicit, conscious attitudes (EAs) and used the Implicit Association Test (IAT) to assess implicit, unconscious attitudes (IAs) of LC relative to BC. Methods: To assess EAs, participants (Ps), [people with cancer (n=243), caregivers (n=677), healthcare providers (HCPs, n=142), and the general public (n=864)] were asked to rate their agreement, on a six-point scale, with statements about how people with LC and BC “do feel” (descriptive attitudes) or “ought to feel” (normative attitudes) about their disease. IAs were measured with three IATs that used LC or BC images with words representing good/bad; hope/despair; or suitable/shameful. An IAT D score indicated the strength of bias against LC relative to BC: >0.65 = strong bias; 0.35-0.65 = moderate bias; 0.15-0.35 = slight bias; -0.15 -+0.15 = no bias, and < -0.15 indicated bias against BC. Results: EAs and IAs were substantially more negative towards LC. Most Ps provided more negative ratings for LC than BC for both descriptive (70%vs.8%) and normative statements (56% vs. 3%). Ps had strong negative IAs towards LC compared to BC (bad: 74% vs. 10%; despair: 75% vs. 9%; shame: 67% vs. 17%). These trends were consistent across caregivers, patients, HCPs, and the public. EAs and IAs were uncorrelated. Conclusions: Ps had greater explicit and implicit negative bias against LC compared to BC. [Table: see text]