Human Capital Investment and the Gender Division of Labor

We use a model of human capital investment and activity choice to explain facts describing gender differentials in the levels and returns to human capital investments. These include the higher return to and level of schooling, the small effect of healthiness on wages, and the large effect of healthiness on schooling for females relative to males. The model incorporates gender differences in the level and responsiveness of brawn to nutrition in a Roy-economy setting in which activities reward skill and brawn differentially. Empirical evidence from rural Bangladesh provides support for the model and the importance of the distribution of brawn.brawn, health, schooling, gender

Excess molar enthalpies of binary and ternary systems involving hydrocarbons and ethers

In modern separation design, an important part of many phase-equilibrium calculations is the mathematical representation of pure-component and mixture enthalpies. Mixture enthalpy data are important not only for determination of heat loads, but also for the design of distillation units. Further, mixture enthalpy data, when available, are useful for extending vapor-liquid equilibria to higher (or lower) temperatures, through the use of the Gibbs-Helmholtz equation. In this connection excess molar enthalpies for several binary and ternary mixtures involving ethers and hydrocarbons have been measured at the temperature 298.15 K and atmospheric pressure, over the whole mole fraction range. Values of the excess molar enthalpies were measured by means of a modified flow microcalorimeter (LKB 10700-1) and the systems show endothermic behavior. The Redlich-Kister equation has been used to correlate experimental excess molar enthalpy data of binary mixtures. Smooth representations of the excess molar enthalpy values of ternary mixtures are accomplished by means of the Tsao-Smith equation with an added ternary contribution term and are used to construct excess enthalpy contours on Roozeboom diagrams. The values of the standard deviations indicate good agreement between experimental results and those calculated from the smoothing equations. The experimental excess enthalpy data are also correlated and predicted by means of solution theories (Flory theory and Liebermann-Fried model) for binary and ternary mixtures, respectively. These solution theories correlate the binary heats of mixing data with reasonable accuracy. The prediction of ternary excess molar enthalpy by means of the solution theories are also presented on Roozeboom diagrams. The predictions of ternary excess enthalpy data by means of these theories are reasonably reliable

Magnetization reversal and anomalous coercive field temperature dependence in MnAs epilayers grown on GaAs(100) and GaAs(111)B

The magnetic properties of MnAs epilayers have been investigated for two different substrate orientations: GaAs(100) and GaAs(111). We have analyzed the magnetization reversal under magnetic field at low temperatures, determining the anisotropy of the films. The results, based on the shape of the magnetization loops, suggest a domain movement mechanism for both types of samples. The temperature dependence of the coercivity of the films has been also examined, displaying a generic anomalous reentrant behavior at T$>$200 K. This feature is independent of the substrate orientation and films thickness and may be associated to the appearance of new pinning centers due to the nucleation of the $\beta$-phase at high temperatures.Comment: 9 pages, 7 figure

Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors’ classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively

Quantifying the metabolic capabilities of engineered Zymomonas mobilis using linear programming analysis

BACKGROUND: The need for discovery of alternative, renewable, environmentally friendly energy sources and the development of cost-efficient, "clean" methods for their conversion into higher fuels becomes imperative. Ethanol, whose significance as fuel has dramatically increased in the last decade, can be produced from hexoses and pentoses through microbial fermentation. Importantly, plant biomass, if appropriately and effectively decomposed, is a potential inexpensive and highly renewable source of the hexose and pentose mixture. Recently, the engineered (to also catabolize pentoses) anaerobic bacterium Zymomonas mobilis has been widely discussed among the most promising microorganisms for the microbial production of ethanol fuel. However, Z. mobilis genome having been fully sequenced in 2005, there is still a small number of published studies of its in vivo physiology and limited use of the metabolic engineering experimental and computational toolboxes to understand its metabolic pathway interconnectivity and regulation towards the optimization of its hexose and pentose fermentation into ethanol. RESULTS: In this paper, we reconstructed the metabolic network of the engineered Z. mobilis to a level that it could be modelled using the metabolic engineering methodologies. We then used linear programming (LP) analysis and identified the Z. mobilis metabolic boundaries with respect to various biological objectives, these boundaries being determined only by Z. mobilis network's stoichiometric connectivity. This study revealed the essential for bacterial growth reactions and elucidated the association between the metabolic pathways, especially regarding main product and byproduct formation. More specifically, the study indicated that ethanol and biomass production depend directly on anaerobic respiration stoichiometry and activity. Thus, enhanced understanding and improved means for analyzing anaerobic respiration and redox potential in vivo are needed to yield further conclusions for potential genetic targets that may lead to optimized Z. mobilis strains. CONCLUSION: Applying LP to study the Z. mobilis physiology enabled the identification of the main factors influencing the accomplishment of certain biological objectives due to metabolic network connectivity only. This first-level metabolic analysis model forms the basis for the incorporation of more complex regulatory mechanisms and the formation of more realistic models for the accurate simulation of the in vivo Z. mobilis physiology