The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.

This paper carries out an analysis and reflection on how technoscience reaches Geography through Geographic Information Technologies, how it impacts the production of geographic knowledge and how it derives in the possibility of digital experimentation in the discipline in an environment called geo-digital reality. It is shown that advances in GIT have allowed overcoming old limitations, enriching more and more the observations made by Geography, and it is also highlighted the promising future of digital experimentation in Geography through all the possibilities offered by current technological developments.

Fe3+-doped nano-TiO2 powders were prepared by sol-gel method. The photocatalytic activity of Fe3+-doped TiO2 nanoparticles was studied by using UV lamp as light source and methylene blue as degradation target. The photocatalytic activity of Fe3+-doped TiO2 was studied by degradation of 4L methylene blue solution with initial concentration of 10mg · L - 1. The results show that the photocatalytic activity of TiO2 can be improved by the addition of Fe3+. When the molar ratio of Fe3+ is 0.5-1%, the calcination temperature is 500 ℃. The photocatalytic degradation of methylene blue is the best.

In the era of rapid information technology development, artificial intelligence (AI) and virtual reality (VR) technologies have gradually infiltrated the field of university English teaching, brought significant applications and impacted to English language learning in listening, speaking, writing, translation, and personalized learning. AI plays a vital role as an auxiliary teaching method in university English instruction, and the integration of VR technology further enhances teaching efficiency. This research will propose relevant recommendations to provide theoretical references for university English education in the age of AI, while also offering insights and guidance to educators in the education industry during the informatization reform of education.

Based on the population change data of 2005–2009, 2010–2014, 2015–2019 and 2005–2019, the shrinking cities in Northeast China are determined to analyze their spatial distribution pattern. And the influencing factors and effects of shrinking cities in Northeast China are explored by using multiple linear regression method and random forest regression method. The results show that: 1) In space, the shrinking cities in Northeast China are mainly distributed in the “land edge” areas represented by Changbai Mountain, Sanjiang Plain, Xiaoxing’an Mountain and Daxing’an Mountain. In terms of time, the contraction center shows an obvious trend of moving northward, while the opposite expansion center shows a trend of moving southward, and the shrinking cities gather further; 2) in the study of influencing factors, the results of multiple linear regression and random forest regression show that socio-economic factors play a major role in the formation of shrinking cities; 3) the precision of random forest regression is higher than that of multiple linear regression. The results show that per capita GDP has the greatest impact on the contraction intensity, followed by the unemployment rate, science and education expenses and the average wage of on-the-job workers. Among the four influencing factors, only the unemployment rate promotes the contraction, and the other three influencing factors inhibit the formation of shrinking cities to various degrees.

The COVID-19 epidemic caused unexpected complications, complexities and challenges in higher educational institutions (HEIs). In order to promote and strengthen the role of women leadership, this study aimed to clarify the unique challenges faced by female leaders at Saudi HEIs during the epidemic, find possible solutions to these challenges, and provide policy as well as management implications. A systematic literature review (SLR) was conducted, examining 27 records (i.e., research papers, articles and conference studies). The data were qualitatively analysed and categorized based on themes like challenges faced, opportunities recognized, and solutions proposed. Findings highlighted women leaders in Saudi HEIs grappled with multiple challenges, including technological barriers, cultural constraints, and increased workloads. Merging challenges with solvable strategies offers a forward-looking perspective, advocating for systemic changes that can shape a resilient and inclusive future for HEIs in Saudi Arabia.