Hodges University is a private non-profit university located in Fort Myers, Florida. Founded in 1990 as International College, it was renamed Hodges University in 2007 in recognition of a $12 million donation from local residents Earl and Thelma Hodges. The Hodges University campus in Naples, Florida closed in 2021, with classes and operations consolidated to Fort Myers.
This study examines the determinants of the digital divide and identifies differences in key factors driving digital development between countries in Central Europe and Southeast Asia over the period 2002–2024. The digital gap is understood as disparities in digital infrastructure, digital public service delivery, and human capital that influence broader economic and social development. The analysis integrates institutional quality (government effectiveness, rule of law, and political stability), economic development (log GDP per capita and income inequality), and skills formation inputs (government spending on education and tertiary enrollment) to explain cross-country and temporal variation in digital performance. Given the limited number of countries, the study employs an econometric strategy suitable for small panel data. Dynamic relationships are estimated using bias-corrected least squares dummy variables (LSDVC), while robustness is assessed through static fixed-effects estimation with Driscoll–Kraay standard errors. To address model uncertainty and multicollinearity, Bayesian Model Averaging (BMA) is applied with family constraints to identify the most robust predictors. The results show that income inequality constitutes the main constraint on inclusive digital development in Central Europe, whereas tertiary education expansion is the strongest driver in Southeast Asia. Across both regions, institutional credibility emerges as a universal foundation for sustainable digital progress. The novelty of this study lies in its cross-regional comparative approach using a harmonized digital development index, the combined use of LSDVC and BMA in a small-panel context, and the identification of both universal and region-specific determinants relevant for digital policy design.
This study uses panel data of Taiwan's manufacturing industry from 2010 to 2023 to empirically test the impact mechanism and effect of cross-strait economic integration on Taiwan's regional value chain participation. The study finds that cross-strait economic integration has significantly increased the value chain participation of Taiwan's manufacturing industry, but at the same time has increased Taiwan's dependence on the mainland market. Specifically, for every 1% increase in cross-strait trade intensity, Taiwan's value chain participation index increases by 0.23% and export complexity increases by 0.15%. Heterogeneity analysis shows that the value chain deepening effect of high-tech manufacturing (such as the electronic information industry) is more significant, while traditional manufacturing is mainly manifested in the expansion of participation breadth. The study further finds that after the signing of ECFA, the deepening of cross-strait value chain connections has not significantly improved Taiwan's position in the global value chain, but may lead to a "dependency lock-in" phenomenon. This finding is of great significance for understanding the welfare distribution effect of regional economic integration and its policy implications.
EDOT, PEDOT and their derivatives represent important materials in the field of organic electronics; however, their broader application is frequently hindered by high production costs and synthetic complexity. This review provides a thorough and critical assessment both established and emerging synthetic techniques for EDOT and its derivatives. The discussion focuses on reaction mechanisms, catalytic strategies, functional-group compatibility, starting material costs, and the required number of steps. Herein, the synthetic methodologies are systematically classified based on the nature of their starting materials. Furthermore, the effects of structural functionalization on water solubility, self-doping behavior, and overall device performance in organic electrochemical transistors (OECTs) and bioelectronic systems is also discussed. Moreover, recently developed low-step synthetic strategies employing inexpensive precursors, such as sodium sulfide and 2-chloroacetonitrile, are highlighted as scalable alternatives to conventional multistep routes. Collectively, these developments provide practical insights into the rational design and large-scale production of high-performance EDOT-based semiconductor materials. Finally, this study summarizes recent advancements in PEDOT applications, including flexible electronics, energy storage systems, conductive separation membranes, wearable and neural sensing devices, biointegrated interfaces, and smart actuators.
In the current fast-paced financial environment, strategic decision-making is predicated upon the accurate forecasting as well as risk assessment. This study investigates how machine learning (ML) may be applied to predictive accounting to improve the accuracy of financial projections and evaluate risks. To predict significant financial outcomes like future sales as well as nett profit margin, five regression models such as Linear, Ridge, LASSO, Elastic Nett & Polynomial Regression were employed and assessed using structured financial information from publicly traded firms. In general, Elastic Nett was the best, as it tackled multicollinearity using regularisation and resulting in high predicted accuracy. Feature importance analysis identified nett income, operational expenditures, EBITDA and current revenue as the most critical indications of future.
