Abstract This work explores the integration of immersive virtual reality (VR) technologies and data mining to enhance talent development in education. It highlights the limitations of traditional teaching models overemphasis on theory, lack of personalization, and disorganized resources and proposes an immersive learning platform built using Unity 3D and 3ds Max, compatible with the Oculus DK2 VR headset. The platform simulates realistic environments (e.g., virtual... This work explores the integration of immersive virtual reality (VR) technologies and data mining to enhance talent development in education. It highlights the limitations of traditional teaching models overemphasis on theory, lack of personalization, and disorganized resources and proposes an immersive learning platform built using Unity 3D and 3ds Max, compatible with the Oculus DK2 VR headset. The platform simulates realistic environments (e.g., virtual campuses) to foster deep engagement through interactivity, spatial realism, and intuitive navigation. A key innovation is the incorporation of data mining techniques, particularly artificial neural networks (using the perceptron rule), to process user interaction data, optimize system performance, and tailor learning experiences. Results show significant improvements post implementation: response time decreased from 2.3s to 0.78s, scene count doubled, and visual detail quality improved. Collision detection and dual mode roaming (automatic/manual) further enhance immersion and usability. The study concludes that combining immersive VR with targeted data mining creates a more effective, personalized, and scalable environment for cultivating innovative, interdisciplinary talents. However, challenges remain in reducing data redundancy and improving system speed. The platform demonstrates strong portability across devices and offers a replicable model for future VR-based educational systems. Read More Read Less

Abstract Customer value evaluation is a critical task for new energy vehicle enterprises seeking to optimize resource allocation, improve customer relationship management, and enhance long term profitability under increasingly competitive market conditions. Traditional customer value models often rely on static parameters and heuristic weighting schemes, which struggle to scale effectively under large customer volumes and high dimensional data. To address these limitations, this paper proposes... Customer value evaluation is a critical task for new energy vehicle enterprises seeking to optimize resource allocation, improve customer relationship management, and enhance long term profitability under increasingly competitive market conditions. Traditional customer value models often rely on static parameters and heuristic weighting schemes, which struggle to scale effectively under large customer volumes and high dimensional data. To address these limitations, this paper proposes a customer value evaluation model based on an improved genetic algorithm (GA). Building on the classical GA framework, an adaptive genetic algorithm is introduced in which crossover and mutation probabilities are dynamically adjusted according to population fitness characteristics, thereby enhancing global search capability and convergence stability. The proposed model encodes customer characteristic parameters as chromosomes and employs a fitnessdriven evolutionary process to identify optimal parameter combinations for customer value assessment. Simulation experiments conducted on a representative new energy vehicle enterprise dataset demonstrate that the improved genetic algorithm converges faster and achieves higher accuracy than the traditional GA. The results confirm that the proposed approach effectively improves the precision and robustness of customer value evaluation, providing a scalable and intelligent decision support tool for enterprise customer management. Read More Read Less

Abstract This article presents an improved genetic algorithm (DGA) to optimize teaching resource allocation particularly classroom assignments for chemistry courses. Traditional scheduling methods are often inefficient and inflexible, particularly for lab based disciplines that require spatial continuity. The proposed DGA uses decimal encoding (rather than binary) to reduce chromosome length and avoid the Hamming cliff problem, thereby enhancing computational efficiency and accelerating convergence. The fitness... This article presents an improved genetic algorithm (DGA) to optimize teaching resource allocation particularly classroom assignments for chemistry courses. Traditional scheduling methods are often inefficient and inflexible, particularly for lab based disciplines that require spatial continuity. The proposed DGA uses decimal encoding (rather than binary) to reduce chromosome length and avoid the Hamming cliff problem, thereby enhancing computational efficiency and accelerating convergence. The fitness function prioritizes minimizing classroom changes between consecutive periods by evaluating teaching building, floor, and location proximity. Genetic operators including elite retention, improved single point crossover, and mutation are tailored to reinforce classroom continuity while maintaining population diversity. Comparative experiments with a standard genetic algorithm (SGA) show that DGA achieves higher fitness values and faster execution times. In real world testing with undergraduate chemistry classes, students in the DGA-optimized group (Group A) outperformed the control group (Group B) in both average scores and performance consistency. Statistical analysis, including t-tests and Cohen's d effect sizes, confirms the educational significance of the improvements, especially in Chemistry (1), where a medium to large effect (d ~ 0.65) was observed. The study concludes that the improved algorithm effectively enhances scheduling efficiency, instructional continuity, and student learning outcomes, with potential for broader application across educational contexts. Read More Read Less