This study examines the factors influencing e-government adoption in the Tangerang city government from 2010 to 2022. We gathered statistics from multiple sources to reduce joint source prejudice, resulting in a preliminary illustration of 1670 annotations from 333 regions or cities. These regions included major urban centers such as Jakarta, Surabaya, Bandung, Medan, Makassar, and Denpasar, as well as other significant municipalities across Indonesia. After removing anomalous values, we retained a final illustration of 1656 annotations. Results indicate that higher-quality digital infrastructure significantly boosts e-government adoption, underscoring the necessity for resilient digital platforms. Contrary to expectations, increased budget allocation for digital initiatives negatively correlates with adoption levels, suggesting the need for efficient spending policies. IT training for staff showed mixed results, highlighting the importance of identifying optimal training environments. The study also finds that policy adaptability and organizational complexity moderate the relationships between digital infrastructure, budget, IT training, and e-government adoption. These findings emphasize the importance of a holistic approach integrating technological, organizational, and policy aspects to enhance e-government implementation. The insights provided are valuable for policymakers and practitioners aiming to improve digital governance and service delivery. This study reveals the unexpected negative correlation between budget allocation and e-government adoption and introduces policy adaptability and organizational complexity as critical moderating factors, offering new insights for optimizing digital governance.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

In 2015, the newly built undergraduate colleges have accounted for half of the ordinary undergraduate colleges. Through the investigation, it is concluded that the newly built undergraduate colleges in Sichuan have the following commonalities in the transformation: the school positioning of "application-oriented"; The embodiment of the new university spirit of "serving local construction"; The talent training goal of "innovative and composite applied talents"; Flexible personnel training curriculum system.

Intelligent toy design and development talents need to master certain electronic intelligent control, arts and crafts design, product modeling design and other skills. There is a shortage of intelligent toy designers in our country, and toy enterprises are in urgent need of professional and technical personnel engaged in toy product modeling and functional design. Therefore, it is urgent to cultivate intelligent toy design and development talents. This paper explores the necessity of cross-professional training of intelligent toy design and development talents, relies on teachers' scientific research and enterprise projects, etc., takes graduation projects as a breakthrough, pushes back the talent training curriculum system, and proposes an cross-professional collaborative training model. Through cross-professional combination training intelligent toy design talents, so that they have the design thinking of toy designers and a certain degree of electronic engineer design thinking, can better adapt to the rapid development of modern toy design industry, enterprises changing new requirements.