Assimilation and Educational Achievement: The Case of Coptic Orthodox Egyptian Immigrants in Texas

Scholarly debate swirls around how well children of immigrants are faring and what factors promote their educational success. While prior research has found that assimilation into the mainstream society is the key to educational success among immigrants, other studies have found that high involvement in one’s ethnic community contributes to better educational outcomes. The current study focuses on the assimilation of a unique sample of Coptic Orthodox Egyptian immigrants in Texas (N= 106). Specifically, this study examines the influence of two predictors—parental religious involvement and parental school involvement—on children’s educational achievement. Results show that the majority of Coptic Orthodox immigrant parents intensively participate in their ethnic church/community, yet their religious participation does not have a statistically significant influence on their children’s educational achievement. On the contrary, parental school involvement, as a form of assimilation into the mainstream society, significantly and positively contributes to their children’s educational achievement. These findings suggest that Coptic Orthodox immigrants follow upward mobility assimilation to the American mainstream society

A modified generalized projective Riccati equation method

A modification of the generalized projective Riccati equation method is proposed to treat some nonlinear evolution equations and obtain their exact solutions. Some known methods are obtained as special cases of the proposed method. In addition, the method is implemented to find new exact solutions for the well-known Dreinfelds-Sokolov-Wilson system of nonlinear partial differential equations

An investigation into problems facing small-to-medium sized enterprises in achieving growth in the Eastern Cape : enhancing the strategy for developing small 'growth potential' firms in the Eastern Cape

As the world economy continues to move towards increased integration, some of the greatest opportunities for Small-to-Medium Sized Enterprises (SMEs) will derive from their ability to participate in the global marketplace. It is generally accepted that SMEs are becoming increasingly important in terms of employment, wealth creation, and the development of innovation. However, there are considerable doubts about the quality of management in this sector with policy-makers suggesting that there are particular weaknesses in innovation, a lack of financial acumen, marketing, entrepreneurial flair, practical knowledge, and human resource management. As a result, many firms do not reach their full potential and fail to grow. According to organisational life cycle models, the introductory phase is particularly important since it is generally known and accepted that there is a high mortality rate of SMEs within the first two years. Given this high failure rate, it becomes vital to research the factors/characteristics/management abilities that are required to enable the SME to survive and indeed progress to the growth phase of the organizational life cycle. This research seeks to investigate the internal and external factors that are consistent in the success of SMEs who have reached the growth phase. A significant contribution to the enhancement of the growth potential of a firm will be made through the understanding of these factors

Tree-Based Approaches for Predicting Financial Performance

The lending industry commonly relied on assessing borrowers’ repayment performance to make lending decisions. This is to safeguard their assets and maintain their profitability. With the rise of Artificial Intelligence, lenders resorted to Machine Learning (ML) algorithms to solve this problem. In this study, the novelty introduced is applying ML’s Tree-based methods to a large dataset and accurately predicting financial repayment performance without using any repayment history, which was utilized in all literature reviewed. Instead, the attributes used were demographics and psychographics of applicants, only. The study’s proprietary US-based dataset comprises an anonymous population whose owner does not wish to be disclosed and it contains the information of about half a million beneficiaries with a very balanced bimodal binary target distribution. An Area Under the Curve of Receiver Characteristic Operator (ROC-AUC) of 85% was achieved with a binary classification target using CatBoost API. The study also experimented with a given tri-class target. Furthermore, this research used ML to gain insight into which attributes contribute the most to the repayment prediction. The study also tested whether similar results can be achieved with fewer attributes for the sake of the practicality of application by the data owner. The best model was applied to one of the biggest publicly available financial datasets for verification. The original research of said dataset had an accuracy score of 82%, this study achieved 79% using 5-fold Cross-Validation (CV). This result was achieved with Tree-Based models with a complexity of O(log n) compared to O(2n) in the original research, which is a significant efficiency enhancement

Global Stability of Generalized Within-host Chikungunya Virus Dynamics Models

This paper proposes two models of a general nonlinear within-host Chikungunya virus (CHIKV) dynamics. The production, incidence, proliferation and removal rates of all compartments are modeled by general nonlinear functions that satisfy a set of reasonable conditions. The second model takes into consideration two forms of infected host cells: (i) latently infected cells which do not produce the CHIKV, (ii) actively infected cells which generate the CHIKV particles. We show that all the solutions of the models are nonnegative and bounded. The global stability of the steady states of the models is proven by applying Lyapunov method and LaSalle’s invariance principle. We perform numerical simulations to complement the obtained theoretical results

Quantifying nanoscale charge density features of contact-charged surfaces with an FEM/KPFM-hybrid approach

Kelvin probe force microscopy (KPFM) is a powerful tool for studying contact electrification at the nanoscale, but converting KPFM voltage maps to charge density maps is non-trivial due to long-range forces and complex system geometry. Here we present a strategy using finite element method (FEM) simulations to determine the Green's function of the KPFM probe/insulator/ground system, which allows us to quantitatively extract surface charge. Testing our approach with synthetic data, we find that accounting for the AFM tip, cone and cantilever are necessary to recover a known input, and that commonly applied heuristics and approximations lead to gross miscalculation. Applying it to experimental data, we demonstrate its capacity to extract realistic surface charge densities and fine details from contact charged surfaces. Our method gives a straightforward recipe to convert qualitative KPFM voltage data into quantitative charge data over a range of experimental conditions, enabling quantitative contact electrification experiments at the nanoscale