This paper presents a comprehensive Holographic Garden Landscape Simulation System (HGLSS) that combines L-system-based plant generation, binocular stereo vision reconstruction, and fractal modeling in response to growing demands for ecological sustainability and immersive design technologies. The majority of 3D garden software available today lacks immersive, real-time visual feedback, and traditional 2D design techniques are restricted in their ability to convey spatial reality and interactivity. We suggest a multi-module system architecture that consists of a dynamic resource library and a model scene design engine in order to overcome these difficulties. The system uses particle systems, and stochastic fractal algorithms to create natural features like trees, clouds, water, and terrain. It also uses binocular stereo vision to recreate the terrain. Realtime rendering using the OSG and Unity3D improves simulation realism. Hierarchical analysis-based performance evaluation identifies five important metrics: usability, extensibility, functionality, authenticity, and interaction. Of these, interactivity has the highest weight (0.583). The system’s ability to accomplish 1:1 architectural-terrain restoration and high-fidelity modeling of intricate elements like spruce trees and stone formations is demonstrated by its practical deployment in a recently created ecological area. The system’s ability to increase design accuracy, artistic realism, and user happiness is confirmed by the evaluation’s mean design quality score of 94.7 across ten technical and perceptual parameters.
This research conducts an in-depth investigation into the fundamental components of innovation competence and constructs a linear spatial model to map out the structure of innovation and entrepreneurial capabilities. By dissecting the internal mechanisms of innovative thinking and action, the study establishes a multi-objective optimize work aimed at maximizing the effectiveness and fairness of educational resource distribution. Utilizing a grey relational analysis algorithm, the study executes a series of simulation experiments to validate the model’s performance. The findings reveal a notable enhancement in key metrics: efficiency in educational resource usage improves by 18.72%, while allocation fairness increases by 20.98%, indicating a shift toward more balanced development. Additionally, the correlation index with the ideal entrepreneurship benchmark reaches 0.3177, underscoring the strategy’s ability to support innovation and entrepreneurship training through optimized resource deployment.
This study introduces a visual communication framework designed specifically for animated character representation in virtual reality environments. The framework combines Sobel-based edge extraction, Hilditch skeletonization, motion capture integration, and adaptive color optimization to improve image clarity, motion fluidity, and overall visual appeal. Through a series of ablation studies, the contribution of each component is quantitatively evaluated, and the overall system achieves a recognition rate of 86.72%. Furthermore, a user evaluation with 30 participants demonstrates significant improvements in perceived motion realism and design coherence compared to a conventional system. These findings highlight the system’s potential for immersive content design and interactive virtual media applications.
This paper improves the fire detection method for distribution systems based on RBI theory, establishes a reliability model for fire risk in distribution systems, and uses the risk assessment results to formulate a fire detection plan suitable for the evaluation object. The probability of a fire accident occurring in a distribution system line is expressed using five parameters. Based on the classification standards for failure probability levels, the probability level of a fire occurring in a distribution system line is determined. The severity of fire consequences in distribution system lines is summarized using six parameters. Based on the classification criteria for consequence levels, the consequence level of fire accidents in distribution system lines is determined. Finally, the fire risk reliability of the distribution system is assessed using the fire risk matrix. Taking a certain substation as an example, an RBI assessment of its fire risk was conducted, identifying three high-risk projects. Corresponding fire prevention and control measures were proposed for rectification.
This paper employs traditional acoustic features such as fundamental frequency, frequency perturbation, amplitude perturbation, Mel-frequency cepstral coefficients (MFCCs), and linear predictive cepstral coefficients (LPCCs) to study the identification of vocal characteristics in modern pop singing. By combining dynamic vocal fold image sequences, the vocal fold vibration components are extracted. Through the selection of key points on the vocal folds and the extraction of feature parameters, the dynamic analysis of vocal fold vibration characteristics under different singing techniques is conducted. There are significant differences in vocal fold spectra under different singing techniques. For example, in the growling singing state, there are more sound impurities and poorer tone purity, while in the normal chest voice technique, overtones are strong and account for a larger proportion. The glottal area change index under the normal chest voice technique ranges from 0.379 to 0.437, significantly lower than that of the growling and breathy voice techniques, but higher than that of the uniformly distributed vibration state. Additionally, the average vibration rate at three key vocal fold points is 3500–4500, with an amplitude of 5–11, showing significant differences from growling, localized vibration, and breathy voice techniques. The vocal fold vibration characteristics of modern pop vocal techniques exhibit high diversity and individuality, pointing to new research directions in music science.
Against the backdrop of rapid development in the global digital economy, agricultural products cross-border e-commerce has emerged as a crucial channel for promoting agricultural transformation, upgrading, and increasing farmers’ income. However, the structural shortage of marketing talent and insufficient supply in the farmer training system have increasingly become major constraints to the sector’s sustainable development. This paper systematically analyzes the competency characteristics, growth challenges, and practical demands of marketing talent in cross-border e-commerce for agricultural products. It identifies key supply-side shortcomings such as outdated training content, fragmented resource allocation, and overly uniform teaching models. In particular, the current training system fails to integrate digital skills, cross-cultural communication, and international market strategies, which are essential for success in the global e-commerce landscape. Furthermore, it proposes reform paths from the perspectives of industry chain integration, platform collaboration, competency certification, and incentive mechanisms. By fostering collaboration between various stakeholders, including e-commerce platforms, agricultural enterprises, and educational institutions, the paper suggests the establishment of a more coordinated and sustainable training system. The study emphasizes that the training system should align with industrial strategies, forming a long-term mechanism combining institutional support and internal motivation. The paper also highlights the importance of developing a comprehensive policy framework that supports talent cultivation, ensuring both immediate results and long-term success in the global marketplace. It argues that building a new farmer training system oriented by demand, grounded in practice, and aimed at long-term growth is essential for promoting the global reach of agricultural product brands and achieving rural revitalization.
With the launching and advancement of the “Belt and Road” initiative, international trade exchanges have grown increasingly close, and the role of Business English major in global communication has become more prominent, presenting both opportunities for disciplinary development and new challenges in talent cultivation. Against the background of New Liberal Arts, higher demands are placed on cultivating interdisciplinary competencies and digital capabilities. Graduation theses, as a pivotal component of talent development, face transformative requirements. Current Business English thesis programs in application-oriented universities confront issues including homogenized research topics, insufficient analytical skills, and academic misconduct. This paper takes on a qualitative approach, utilizes descriptive statistics to identify root causes and proposes strategies through six dimensions: authentic topic selection, deepened integration of theory-practice, standardized language expression, obedience to academic norms, improvement of thesis process management and close connection to the new trend. It further establishes innovative collaborative education models featuring “dual-track topic selection, dual-mentor guidance, and diversified evaluation” to explore thesis reform pathways aligned with New Liberal Arts principles. The proposed approaches tend to transform graduation theses into essential vehicles for developing students’ comprehensive competencies, thereby fostering well-rounded Business English professionals.
In circuit design work, parameter optimization is an inevitable issue, especially in analog circuit design, which requires a high level of experience from designers. In traditional parameter optimization processes, designers may also rely on optimization algorithms to find optimal solutions. This paper uses reinforcement learning algorithms to find optimal strategies, exploring two functions under model-free reinforcement learning algorithms: the value function and the policy function. These functions are estimated using recursive forms and policy gradients. Using the Y parameter to extract equivalent circuit parameters in RF circuits, a frequency AI model is established to optimize the parameters of RF circuits. The optimization effect is verified through metrics such as gain and frequency, and the final optimized results of the RF circuit are calculated. The distribution of the receptive field in the value function method model tends toward a Gaussian distribution, exhibiting sparsity, with weight values primarily distributed at both ends of 0, and the frequency approaching 120. This paper proposes three optimization schemes for parameter tuning, with the optimal solution coordinates for Schemes 1 to 3 being [3.671, 0.749], [3.726, 0.834], and [3.847, 0.578], respectively. After optimization, the static power consumption of the RF circuit was reduced by over 54% compared to before optimization, and the circuit cost was reduced by over 40%, indicating that the method proposed in this paper has good optimization effects.
Due to its unique historical evolution, the painting techniques of the Lingnan region in modern times have been influenced to a certain extent by Western culture. This paper obtained approximately 3,000 images of modern Lingnan landscape paintings and Western landscape paintings through on-site interviews and online collection. The images were rotated, cropped, and scaled to enhance the data representation of the paintings and complete the preprocessing of the research data. In terms of painting image classification, the optimal feature subset was selected based on the CGO optimization algorithm, and the cross-task feature fusion module was used to fuse painting image features. This enabled the construction of a multi-faceted artistic painting classification model, achieving the classification task of different painting types under a unified framework. In terms of painting image emotional classification, self-learning and knowledge transfer techniques based on sparse autoencoders were introduced as methods for painting image emotional semantic analysis and unsupervised feature learning. Combining the image features of modern and contemporary Lingnan landscape paintings and Western landscape paintings, we propose a painting image emotional classification system framework comprising three major modules: source domain local feature learning, target domain global feature extraction, and image emotional classification. This framework is used to construct a painting emotional classification model. The designed painting emotion classification model not only demonstrates emotion classification accuracy significantly higher than similar models (>0.730) but also achieves a classification performance standard deviation <0.010 after oversampling strategies, demonstrating excellent robustness. This provides a robust and effective technical foundation for analyzing the artistic ambiance of painting images.
This paper addresses the need for English speaking training in a digital education environment by designing an intelligent English speaking training system based on deep learning. The system employs a semantic understanding model that integrates role information and historical dialogue context, utilizing a BERT-BiLSTM-CRF joint framework to achieve intent recognition and slot value filling. In the speech preprocessing stage, the system innovatively applies spectral entropy-based endpoint detection (VAD) to optimize the processing of low-energy speech signals, and combines pre-emphasis and Hamming window framing techniques to enhance recognition robustness. On the ATIS dataset, the system achieves an intent recognition accuracy of 99.19% and a slot filling accuracy of 97.24%, representing improvements of 0.7% and 1.1% over the best baseline, respectively. System performance testing shows that in real teaching interactions, the average response latency is 1024.2 ms, with 98% speech recognition accuracy, 98% task completion rate, and 92% pronunciation correction rate. In educational empirical studies, students’ oral English scores significantly improved from 71.06 ± 15.99 points to 88.31 ± 8.54 points (+24.25%), the failure rate decreased from 24.51% to 0%, and the excellent rate (>90 points) increased from 16.67% to 48.04%. The learning attitude questionnaire showed that the number of students who fully agreed with “fluent English speaking” increased from 41 to 80 (+95.1%), and the willingness to persist in daily training increased by 252.4% (from 21 to 74 students). The study indicates that the system effectively enhances oral training efficiency through deep semantic understanding and multimodal interaction design, providing technical support for digital English teaching.