International Journal of Advanced AI Applications

The Design and Implementation of a Robotic Arm Digital Twin System Based on ESP32

Li Zheng — 2025-08-23

This paper designs and implements an ESP32-based digital twin system for a robotic arm. The system innovatively utilizes digital twin technology to achieve real-time monitoring, remote control, and virtual-real synchronization of the robotic arm through bidirectional mapping and real-time interaction between the virtual and real worlds. It aims to address the issues of complex operation, high safety risks, and expensive costs associated with traditional industrial robot teaching. The hardware component of the system is designed based on a 1:7 scaled-down version of the ABB IRB-460 robotic arm, with non-standard parts manufactured using 3D printing technology. ESP32 is employed as the control core, replacing the traditional high-cost industrial robot system control cabinet. In the software component, this paper not only constructs a virtual robotic arm model and realizes three-dimensional visualization through Unity3D but also specifically develops an APP-end teaching pendant. This teaching pendant not only supports users in controlling the robotic arm through a UI control panel but also introduces ChatGPT technology to enable remote control of the robotic arm via voice commands. Experimental results demonstrate that the system possesses high virtual-real consistency and operational stability, effectively lowering the learning threshold and significantly enhancing students' understanding and practical abilities in industrial robot operations. It provides an efficient and innovative solution for industrial robot teaching.

Intelligent Recommendation and Application of Mine Exploration Route Based on Django-Ant Colony Optimization Algorithm

Jie Tian — 2025-08-23

This paper presents a novel mine exploration route recommendation system that combines the Django web framework with the Ant Colony Optimization (ACO) algorithm. The system is designed to enhance the efficiency and accuracy of route planning for mine exploration personnel. By leveraging the pheromone update and path selection mechanisms characteristic of ant foraging behaviour, the ACO algorithm achieves a balance between global coverage and local optimization. This ensures that the recommended routes not only meet the specific requirements of exploration tasks but also minimize unnecessary detours, thereby improving overall exploration efficiency. Data acquisition for the system is supported by web crawling technology, employing tools such as request and BeautifulSoup to gather publicly available geological data from mining areas. This enriches the system's data resources and strengthens the foundation for providing precise route recommendations. The system's interface is crafted to be user-friendly, with a well-organized layout that centres on the needs of exploration personnel. This design enables users to quickly familiarize themselves with the system and concentrate on their exploration tasks, ultimately boosting the effectiveness and quality of mining area exploration work.

AI-Powered Personalized English Teaching for Primary Students: Strategies and Outcomes

Chengwei Peng — 2025-08-20

Personalised teaching has emerged as a transformative approach in modern English language instruction, with AI technologies playing an increasingly pivotal role. The present study investigates the application of AI in the development of personalised English teaching strategies for primary students, thus addressing the growing need for adaptive and individualised learning solutions. The present study employs a mixed-methods approach, combining data analysis from AI-powered platforms with qualitative insights from interviews with educators. The findings of the study demonstrate that AI-driven personalisation significantly improves student engagement, language acquisition, and cognitive development in English learning. These findings contribute to both theoretical advancements in language education and practical applications of AI in pedagogical settings. The study concludes with the presentation of evidence-based recommendations for the optimisation of AI integration in personalised English instruction. These recommendations are twofold, namely that educational equity should be AI-enabled and that student welfare should be safeguarded. This research is of particular significance for the advancement of modern language teaching methodologies in the era of intelligent education.

Synergizing Gradient Boosting and Random Forest: An Interpretable Dual-Model Framework to Unveil Key Stressors and Mechanisms in Adolescent Educational Mental Health

Fei GU — 2025-08-09

Adolescent educational stress, shaped by intricate interactions among psychological, physiological, and environmental factors, poses a substantial challenge to educational mental health. Traditional assessment methods, however, struggle to capture these dynamic relationships, thereby limiting the effectiveness of interventions. To address this gap, the present study introduces an interpretable dual-model framework integrating Gradient Boosting Machine (GBM) and Random Forest (RF). Leveraging data from 1,000 adolescents, this framework identifies key stressors and their underlying mechanisms through hyperparameter optimization and multi-modal validation (Spearman correlations, SHapley Additive exPlanations [SHAP] analysis, and feature importance rankings). The framework achieved high predictive accuracy (R² > 0.80, MAE < 0.15). Key findings include that self-esteem emerges as the dominant stress predictor (ΔR² ≈ 0.13), followed by academic performance (ΔR² ≈ 0.11). SHAP visualizations further revealed nonlinear threshold effects (e.g., those related to academic performance) and anxiety-mediated pathways. Additionally, model comparisons indicated that RF exhibited superior noise robustness (MAE = 0.135 versus GBM’s 0.146), whereas GBM better captured linear relationships in physiological variables. By leveraging feature importance rankings, the framework enables targeted stress interventions, thus optimizing resource allocation in educational mental health.

Challenges and optimization ideas in low-power design for Internet of Things devices

Jiulong Zhang — 2025-08-11

This paper presents a systematic low-power optimization framework for Internet of Things (IoT) devices, aiming to address persistent energy consumption challenges in long-term deployments. By constructing a modular simulation model with four evaluation metrics—average power, response time, battery life, and task success rate—we compare baseline, hardware-only, and collaborative software-hardware strategies. Results show the proposed approach reduces average power to 7.9mW, extends battery life to 158.6 hours, and achieves a 97.2% task completion rate. Field deployment confirms its adaptability and reliability. The framework provides a practical and scalable solution for low-power IoT design.