Assessing the Effects of Personal Characteristics and Context on U.S. House Speakers’ Leadership Styles, 1789-2006

Research on congressional leadership has been dominated in recent decades by contextual interpretations that see leaders’ behavior as best explained by the environment in which they seek to exercise leadership—particularly, the preference homogeneity and size of their party caucus. The role of agency is thus discounted, and leaders’ personal characteristics and leadership styles are underplayed. Focusing specifically on the speakers of the U.S. House of Representatives from the first to the 110th Congress, we construct measures of each speaker’s commitment to comity and leadership assertiveness. We find the scores reliable and then test the extent to which a speaker’s style is the product of both political context and personal characteristics. Regression estimates on speakers’ personal assertiveness scores provide robust support for a context-plus-personal characteristics explanation, whereas estimates of their comity scores show that speakers’ personal backgrounds trump context

Removal of Methylene Blue from aqueous solutions by adsorption on Kaolin: Kinetic and equilibrium studies

International audienceRemoval of Methylene Blue (MB) from aqueous solutions is studied using a raw Algerian kaolin sample as a low-cost adsorbent. The effects of pH, contact time, dye concentration and temperature are all taken into consideration. The adsorption kinetics results are adjusted to best fit the pseudo-second order model. The experimental data are analyzed by Langmuir isotherms, revealing that the maximum adsorption capacity of MB on this kaolin sample equals 52.76 mg/g at T = 25 °C and pH = 6.0. The calculated thermodynamic data demonstrates that adsorption is spontaneous and enhanced at higher temperatures. Desorption studies with water indicate that the adsorbent could successfully retain MB, even after four cycles. From these results, it can be considered that the raw Algerian kaolin sample tested herein is effective in the removal of MB from aqueous solutions and moreover may be used as an alternative to high-cost commercial adsorbents. © 2017 Elsevier B.V

Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques

Machine learning and mathematical modeling techniques have been conducted to model the thin layer drying kinetics of pea pods, under either microwave or conventional air drying,. The effect of nine different microwave output powers (200-1000 W) and five different ventilated oven temperatures (40, 60, 80, 100, and 120 & DEG;C) on drying kinetics was studied. The experimental drying rates were fitted to 11 literature semi-empirical models to determine the kinetic parameters, finding the higher goodness-of-fit for the Midilli et al. model (average R-2 = 0.999 for both drying methods). Moreover, the data were modeled using support vector machine (SVM) for regression which was optimized with dragonfly algorithm (DA) technique. The best result was obtained by Gaussian kernel with the optimal parameters sigma, C, and epsilon values estimated as 0.2871, 78.45, and 0, respectively. The small root mean square error (RMSE = 0.0132) and the high determination coefficient (R-2 = 0.9983) values proved how robust the SVM model is. DA-SVM techniques can reliably be utilized to describe the thin layer drying kinetics of pea pods. It is useful to provide models that can assist in the development of food process control algorithms, and provided insights into complex processes, for the technological design of microwave or convective drying for pea pods preservation. Practical applications Drying of by-products from pea processing industry was investigated as a critical step prior to their valorization. The drying of pea pods has never been investigated before which is the case of the present study whose objective was to study and model the microwave and convective drying kinetics of pea pods. Our research work reported that the Midilli et al. model was the most appropriate to describe the thin layer drying kinetics of pea pods for both drying methods, but mathematical drying models, although a useful tool, remains empirical in nature and product specific. Because of these limitations the new model DA-SVM, developed using artificial intelligence techniques, can reliably be used to describe the nonlinear behavior of pea pods drying. These results could be further used for scale up calculation, which would further allow industrial scale preservation by microwave or convective drying of pea pods