Accurate prediction of US Treasury bond yields is crucial for investment strategies and economic policymaking. This paper explores the application of advanced machine learning techniques, specifically Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) models, in forecasting these yields. By integrating key economic indicators and policy changes, our approach seeks to enhance the precision of yield predictions. Our study demonstrates the superiority of LSTM models over traditional RNNs in capturing the temporal dependencies and complexities inherent in financial data. The inclusion of macroeconomic and policy variables significantly improves the models’ predictive accuracy. This research underscores a pioneering movement for the legacy banking industry to adopt artificial intelligence (AI) in financial market prediction. In addition to considering the conventional economic indicator that drives the fluctuation of the bond market, this paper also optimizes the LSTM to handle situations when rate hike expectations have already been priced-in by market sentiment.

This quasi-experimental study examined the effect of a mechanics course delivered through a Learning Management System (LMS) on the creativity of prospective physics teachers at a teacher training college in Mataram, Indonesia. The study was conducted in the post-pandemic era. Using a pretest-posttest one-group design, the researchers evaluated changes in creativity across three domains: figural, numeric, and verbal. The results showed significant improvements in overall creativity, with the most critical gains observed in the figural domain. Further analysis revealed that fluency was the creative indicator with the most enhancement. In contrast, other indicators displayed varying degrees of improvement. These findings highlight the potential of LMS-based instruction in fostering creativity among future physics educators, particularly in the figural, numeric, and verbal domains. This study adds to the growing body of evidence supporting technology integration into teacher education, especially during times of crisis. Future research should explore more targeted instructional strategies within LMS environments and utilize comprehensive creativity assessment methods further to enhance creative learning experiences for prospective physics teachers.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.

This research presents an innovative perspective on vocational education by merging the Instructional System Design (ISD) model with Innovation in Thailand Vocational Education and Training (InnoTVET) principles. Targeted at nursing students, the course aims to cultivate entrepreneurial skills while connecting vocational training with healthcare policy development. It aligns with global movements in Education for Sustainable Development (ESD), addressing the increasing demand for nurse entrepreneurs who can devise creative healthcare solutions within established policy frameworks. By employing mastery learning techniques alongside design thinking, the course effectively bridges theoretical concepts with practical applications. The mixed-methods study underlines relevant contribution in students’ entrepreneurial mindsets. Results from t-tests reveal the students’ ability to identify opportunities, engage in innovative thinking, and work within policy frameworks. Findings are supported by qualitative data, which demonstrate enhanced confidence, improved problem-solving capacities, and a deeper understanding of healthcare market dynamics. Although expert evaluation of student projects is scheduled for future iterations, the initial outcomes reinforce the course’s success. The course is structured into seven modules spanning 45 hours, featuring active learning components, five business-oriented assignments, and a final innovation project that integrates the curriculum’s core elements. This design ensures students develop both practical expertise and interdisciplinary insights critical to healthcare innovation. The integration of InnoTVET and ISD principles in nursing education sets a precedent for vocational education reform. This example of a successful nursepreneurship initiative provides a scalable model for enhancing vocational programs in diverse fields, fostering innovation and sustainability.

The study aims to explore the impact of examination-oriented education on Chinese English learners and the importance of cultural intelligence in second language acquisition. Through a questionnaire administered to postgraduate students majoring in English in China, the research discovered that the emphasis on test scores and strategies in China’s higher English education system has led to a neglect of cultural backgrounds and cross-cultural communication. The findings underscore the necessity for reforms in English teaching within Chinese higher education to cultivate students’ intercultural intelligence and enhance their readiness for international careers in the era of globalization.

The emergence of the COVID-19 pandemic led to the need to move educational processes to virtual environments and increase the use of digital tools for different teaching uses. This led to a change in the habits of using information and communication technologies (ICT), especially in higher education. This work analyzes the impact of the COVID-19 pandemic on the frequency of use of different ICT tools in a sample of 950 Latin American university professors while focusing on the area of knowledge of the participating professors. To this end, a validated questionnaire has been used, the responses of which have been statistically analyzed. As a result, it has been proven that participants give high ratings to ICT but show insufficient digital competences for its use. The use of ICT tools has increased in all areas after the pandemic but in a diverse way. Differences have been identified in the areas of knowledge regarding the use of ICT for different uses before the pandemic. In this sense, the results suggest that Humanities professors are the ones who least use ICT for didactic purposes. On the other hand, after the pandemic, the use of ICT for communication purposes has been homogenized among the different knowledge areas.