This study investigates the role of Artificial Intelligence (AI) in promoting a sustainable environment within the G-7 countries by testing the Load Capacity Curve (LCC) hypothesis. Additionally, it examines the effects of financial development, the digital economy, and urbanization on the load capacity factor using data from 2010 to 2022. The research employs cross-sectional dependence and slope homogeneity tests, revealing issues of cross-sectional dependence and heterogeneity. Panel unit root tests, both first and second generation, confirm that the variables are free from unit root problems. Furthermore, panel cointegration tests indicate that the variables are cointegrated in the long run. To assess the impact of the explanatory variables on the load capacity factor, the study utilizes the Method of Moments Quantile Regression (MMQR). The findings reveal a U-shaped relationship between income and the load capacity factor, supporting the LCC hypothesis in the G-7 region. The results also indicate that AI innovation and financial development have a significant positive correlation with the load capacity factor. In contrast, the digital economy and urbanization are found to significantly reduce the load capacity factor. Robustness checks, including the Driscoll-Kraay standard error, Augmented Mean Group, and Common Correlated Effect Mean Group estimation approaches, validate the findings obtained from the MMQR method. Moreover, the Dumitrescu-Hurlin (D-H) causality assessment is utilized to explore the causal connections between variables. The results reveal a unidirectional causal relationship between income and the load capacity factor. Additionally, bidirectional causal relationships are the remaining explanatory variables and load capacity factors. 

Leveraging AI for Promoting Sustainable Environments in G-7: The Impact of Financial Development and Digital Economy via MMQR Approach

Desert farmland provides food for desert areas, but water is the main limiting factor of this region, thus desert farmland has an extremely fragile ecological environment. This study investigated the temporal and spatial variations of vegetation NDVI (Normalized Difference Vegetation Index) in the Ulan Buh Desert, China, from 1990 to 2022, using long-term Landsat satellite data obtained from the Google Earth Engine platform and local statistical data. The results showed that from 1990 to 2022, the NDVI exhibited relatively small fluctuations and a steady increase. Furthermore, the study analyzed the impact of climate factors, namely precipitation and temperature, on NDVI, and collected the groundwater lever changes under irrigation and farmland development. The results demonstrated a positive correlation between NDVI and both precipitation and temperature from 1990 to 2006. The study area experienced an overall trend of increasing humidity. Specifically, from 1990 to 2006, significant positive correlations with precipitation and temperature were observed in 4.4% and 5.5% of the region, respectively. From 2007 to 2022, significant positive correlations were observed in 5.4% and 72.8% of the region for precipitation and temperature, respectively. These findings suggest that temperature has become increasingly influential on vegetation NDVI, while the impact of precipitation remains relatively stable. Moreover, the study assessed the impact of human activities on vegetation NDVI. The results revealed that from 1990 to 2006, human activities contributed to 43.1% of the promotion of local vegetation NDVI, which increased to 90.9% from 2007 to 2022. This study provides valuable insights into the dynamics of vegetation in the Ulan Buh Desert and its response to climatic changes and human activities. The findings highlight the significance of climate conditions and human interventions in shaping the vegetation dynamics in the region, offering essential information for ecological restoration and conservation efforts.

Study of Changes in the Ulan Buh Desert under the Dual Impacts of Desert Farmland Development and Climate Change

Beam pumping units play a key role in oil extraction. There is an increasing demand for optimal oil-extracting performance as operational environments are becoming more challenging and complex. Therefore, it is vital to create an acceptable mathematical model and develop robust control mechanisms. In the past decades, researchers have achieved fruitful results regarding modeling and control methods in this field. This article provides a comprehensive review of the research on modeling and control methods for beam pumping units, including mathematical models based on differential equations and boundary conditions, as well as the control strategies designed. Finally, future perspectives and recommendations are presented. 

Modeling and Control for Beam Pumping Units: An Overview

Knowledge Management (KM) is crucial since it is used for managerial decisions that affect the organization’s success. To improve the quality of KM, there is an innovation that implements Artificial Intelligence (AI) for the KM process. AI is a machine learning tool that can execute human tasks, adapt to new inputs, and learn from experience. This study aims to investigate the growth of AI in KM and evaluate how AI can be applied in KM to manage information and knowledge. The study used a systematic literature review method with PRISMA and data sources from Scopus. A total of 30 articles were examined in the review analysis. The research results found that the implementation of AI in KM was already conducted on various continents and most of the previous studies discussed this topic in the General field. Furthermore, the review discovered that AI can be applied to fundamental knowledge management process, decision-making, knowledge forecasting, and knowledge exchanges. This research also indicates that the implementation of AI in KM is growing and the topic of AI research in KM continues to develop. This study provides insight into prospects for innovation and improvement by offering evaluations for the future development of AI in KM.

The Implementation of Artificial Intelligence in Knowledge Management: A Systematic Literature Review

In recent years, the demand for intelligent and interactive online education systems has grown significantly, driven by the need for personalized learning experiences and effective student engagement. This study proposes a novel approach that integrates the Dwarf Mongoose Optimization (DMO) algorithm with a Gated Recurrent Unit (GRU) neural network to develop an AI-powered interactive online learning model. The proposed DMO-GRU framework leverages the optimization capability of DMO for feature selection and parameter tuning, while GRU effectively captures temporal learning patterns in student data. A comprehensive literature review was conducted using databases such as IEEE Xplore, ACM, and Google Scholar, focusing on studies from the last decade. The model was evaluated through experimental analysis using key regression and classification metrics, including Mean Absolute Error, Root Mean Square Error, R2 Score, Accuracy, Precision, Recall, Sensitivity, Specificity, and F1-Score with training time. Results indicate that the DMO-GRU outperforms traditional models such as Linear Regression, Random Forest, SVR, and XGBoost, offering higher prediction accuracy and better identification of student learning outcomes. The system also supports interactive modes such as audio, video, and one-to-one sessions, contributing to improved learner engagement. This study demonstrates the potential of AI-driven optimization techniques to enhance the effectiveness, adaptability, and personalization of online education platforms. 

Interactive online learning method for students based on artificial intelligence

Retracted: Design of Artistic Creation Style Extraction Model Based on Color Feature Data

https://downloads.hindawi.com/journals/mpe/2023/9874596.pdf

Retracted: Automatic Classification of Basic Nursing Teaching Resources Based on the Fusion of Multiple Neural Networks

Retracted: Design of an Intelligent Virtual Classroom Platform for Ideological and Political Education Based on the Mobile Terminal APP Mode of the Internet of Things

Retracted: An Analysis of the Driving Factors of the Desertification in the Desert-Oasis Transition Zone Utilizing the Perspective of the Marx’s Concept of Nature: A Case Study of the Ulan Buh Desert

Retracted: Analysis of the Shortest Path in Spherical Fuzzy Networks Using the Novel Dijkstra Algorithm

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