Pseudo-spherical submanifolds with 1-type pseudo-spherical Gauss map

In this work, we study the pseudo-Riemannian submanifolds of a pseudo-sphere with 1-type pseudo-spherical Gauss map. First, we classify the Lorentzian surfaces in a 4-dimensional pseudo-sphere $\mathbb{S}^4_s(1)$ with index s, $s=1, 2$, and having harmonic pseudo-spherical Gauss map. Then we give a characterization theorem for pseudo-Riemannian submanifolds of a pseudo-sphere $\mathbb{S}^{m-1}_s(1)\subset\mathbb{E}^m_s$ with 1-type pseudo-spherical Gauss map, and we classify spacelike surfaces and Lorentzian surfaces in the de Sitter space $\mathbb{S}^4_1(1)\subset\mathbb{E}^5_1$ with 1-type pseudo-spherical Gauss map. Finally, according to the causal character of the mean curvature vector we obtain the classification of submanifolds of a pseudo-sphere having 1-type pseudo-spherical Gauss map with nonzero constant component in its spectral decomposition

A critical assessment of imbalanced class distribution problem: the case of predicting freshmen student attrition

Predicting student attrition is an intriguing yet challenging problem for any academic institution. Class-imbalanced data is a common in the field of student retention, mainly because a lot of students register but fewer students drop out. Classification techniques for imbalanced dataset can yield deceivingly high prediction accuracy where the overall predictive accuracy is usually driven by the majority class at the expense of having very poor performance on the crucial minority class. In this study, we compared different data balancing techniques to improve the predictive accuracy in minority class while maintaining satisfactory overall classification performance. Specifically, we tested three balancing techniques—oversampling, under-sampling and synthetic minority over-sampling (SMOTE)—along with four popular classification methods—logistic regression, decision trees, neuron networks and support vector machines. We used a large and feature rich institutional student data (between the years 2005 and 2011) to assess the efficacy of both balancing techniques as well as prediction methods. The results indicated that the support vector machine combined with SMOTE data-balancing technique achieved the best classification performance with a 90.24% overall accuracy on the 10-fold holdout sample. All three data-balancing techniques improved the prediction accuracy for the minority class. Applying sensitivity analyses on developed models, we also identified the most important variables for accurate prediction of student attrition. Application of these models has the potential to accurately predict at-risk students and help reduce student dropout rates

Assessment of Optimum Renewable Energy System for the Somalia–Turkish Training and Research Hospital in Mogadishu

Somalia–Turkish Training and Research Hospital in Mogadishu, is only powered by diesel generator currently. In this paper, the energy demand of this hospital is utilized by determining the optimum hybrid renewable energy generating system. By HOMER, a sensitivity analysis has been made with emphasis on three significant variables such as average wind speed, present diesel price, and solar radiation. From the results, it can be said that an optimum system is the standalone wind-diesel-battery storage Hybrid Renewable Energy System (HRES) with the configuration of 1,000 kW wind turbine, 350 kW diesel generator, 250 kW power converters, and 300 batteries. Additionally, the net present cost of the optimum system is calculated to be $5,056,700 and its cost of energy is estimated to be 0.191$/kWh. The present cost of energy for Somalia is 0.5 $/kWh. This shows that the energy cost for the proposed HRES is cheaper than the conventional one. Lastly, according to the results, it is clear that the wind–diesel–battery storage HRES seems more environment friendly than other HRESs