Hunan Software Vocational Institute

This study focuses on the application and effect of Mindfulness-Based Interventions (MBI) in the prevention of recurrence of Substance Use Disorders (SUD). Firstly, by systematically sorting out the core theories and mechanisms of mindfulness therapy, clarify its role paths in cognitive awareness, emotion regulation, and impulse control; Subsequently, combined with existing intervention studies and empirical data, the improvement effects of the mindfulness training program on the recurrence rate, physiological stress indicators, and mental health level were analyzed. The study adopted a randomized controlled design. The experimental group that received eight weeks of mindfulness training was compared with the control group that received conventional withdrawal treatment. Data from the MAAS Mindfulness Awareness Scale, the BSCS Self-control Scale, and survival analysis were collected. The results showed that the risk of recurrence in the experimental group was significantly reduced, the ability of mindfulness awareness and self-control was continuously enhanced, and there was a good maintenance effect during the three-month follow-up. The discussion section further explores how mindfulness intervention can reshape the cognitive response to material craving by enhancing present awareness and non-evaluative attitudes, and puts forward improvement suggestions in response to the sample limitations and implementation difficulties of existing research. The research conclusion provides a theoretical basis and practical path for incorporating MBI into the comprehensive intervention system of SUD, and at the same time points out the direction for future multi-center and large-sample longitudinal follow-up studies.

In laser beam oscillation welding systems, circular beam scanning is the most widely applied technique. This paper addresses the issues of high cost associated with galvanometer-based beam oscillation methods and the inconvenient adjustment of wedge prism rotation approaches, proposing a cost-effective and easily adjustable beam oscillation method utilizing off-axis rotation of the focusing lens. By rotating the focusing lens off-axis, an offset distance <i>R</i> is created between the optical axis and the lens rotational axis, thereby generating a circular scanning spot with radius <i>R</i>_ring . The theoretical analysis explores the impact of off-axis lens rotation on beam focusing characteristics. An experimental setup was designed to conduct welding tests on automotive SUS301L-HT stainless steel tubes. Results demonstrate that the weld strength of all samples exceeded 92% of the base material strength, with weld consistency improved by 15% compared to the wedge prism rotation method.

The widespread use of electric bikes has made rider safety a crucial issue in intelligent transportation research. To address the limitations of existing methods in terms of accuracy and robustness in electric bike license plate recognition, helmet detection, and rider detection tasks, this paper proposes a novel electric bike safety detection method, F-YOLOv8, which integrates license plate region recognition. Based on the YOLOv8 architecture, this method incorporates a feature pyramid network (FPN) to optimize multi-scale object detection capabilities, while introducing an attention mechanism to enhance the extraction of key features. The study constructs a high-quality dataset covering various riding scenarios and violations, including image data collected in Guangzhou and Foshan, Guangdong Province, annotated using LabelImg.The label types include Ebike (rider), plate (license plate), gzplace (license plate region), helmet (wearing a helmet), nohelmet (not wearing a helmet), and manned (carrying a passenger). Experimental results demonstrate significant improvements across all metrics: the precision on Ebike reached 96.1%, on Plate reached 92.9%, on Helmet reached 84.6%, and on Manned tasks reached 67.9%. This study provides an efficient and reliable detection method for enhancing electric bike riding safety management, laying a technical foundation for the development of intelligent transportation systems.

Abstract Recent advances in Semantic IoT data integration have highlighted the importance of multimodal fusion in emotion recognition systems. Human emotions, formed through innate learning and communication, are often revealed through speech and facial expressions. In response, this study proposes a hidden Markov model‐based multimodal fusion emotion detection system, combining speech recognition with facial expressions to enhance emotion recognition rates. The integration of such emotion recognition systems with Semantic IoT data can offer unprecedented insights into human behavior and sentiment analysis, contributing to the advancement of data integration techniques in the context of the Internet of Things. Experimental findings indicate that in single‐modal emotion detection, speech recognition achieves a 76% accuracy rate, while facial expression recognition achieves 78%. However, when state information fusion is applied, the recognition rate increases to 95%, surpassing the national average by 19% and 17% for speech and facial expressions, respectively. This demonstrates the effectiveness of multimodal fusion in emotion recognition, leading to higher recognition rates and reduced workload compared to single‐modal approaches.

No matter the traditional Ming-style furniture is made of exquisite materials, elegant, and simple, or the literati philosophy, connotation, and literati spiritual outlook, values, world outlook, etc. reflected behind it, all have left us with excellent cultural heritage. As a fast-developing technology, virtual reality has a bright future. This paper mainly studies the Ming-style furniture display based on immersive 5G virtual reality technology. Ming-style furniture displays design prototype system development based on user experience. The prototype of the guided design drawings is completed, the Ming-style furniture and display space is modeled in Unity3d, interactive elements are added, and finally it is realized that users can roam in the scene from a first-person perspective and the furniture in the scene, color, material, etc., as well as the detailed display of the items in the scene, the control of the lights in the scene, the control of the sound, and so on are changed. After the establishment of the system, a certain user evaluation is performed on the established system. Questionnaire surveys, focus group interviews, and other methods are used to conduct very detailed investigations and usability tests on users. Then, the test results are realized and the system is optimized to a certain extent. Finally, the user experience of the system design is improved. The experimental survey results show that the average user experience score is 43.7 points and the rating is in the “good” range, which basically meets the user experience. This research plays an important role in improving user experience and at the same time enriches the research in the field of domestic furniture virtual display and has certain significance for the future development of furniture companies.

With the rapid development of information technology, industry and service industries have achieved rapid development in recent years. Then, looking at the development of agriculture, the popularity of informatization lags far behind industry and service industries, directly hindering the digital development of agriculture. Starting from the current agricultural machinery driving operation scene, this paper carried out a simplified research on the traditional agricultural machinery driving operation method through the agricultural machinery kinematics model, and based on the related theory of deep reinforcement learning to study the agricultural machinery path tracking in the agricultural operation scene, it carried out the controller design, built the agricultural machinery autonomous path tracking framework operating mechanism under deep reinforcement learning, and further researched through experimental design and found that the agricultural machinery autonomous path tracking control can achieve better automatic control after empirical learning. I-DQN algorithm enables agricultural robots to adapt to the environment faster when performing path tracking, which improves the performance of path tracking. It has important guiding significance for further promoting the automatic navigation and control of agricultural machinery to realize the efficient operation of agricultural mechanization.

Action recognition in Taekwondo competitions and training is an important task, which can provide a very valuable reference factor for technicians, athletes, and coaches. We propose a graph convolution framework with part of the perception structure to recognize, decompose, and analyze Taekwondo actions. Taking advantage of the long short-term memory of a part of the perception structure, the recognized Taekwondo actions are marked in time series, and then features are extracted from the graph convolution level to obtain the spatial and temporal associations between joints. Predict the action category and perform score matching based on the manual tag database. Finally, it is verified on our self-made Taekwondo competition data set. Our method has an average accuracy of 90% in action recognition, and an average action score matching rate of 74.6%. The accuracy of action recognition is high, which provides great assistance to Taekwondo e training and competitions.

To investigate primary breakup close to an injector, this paper presents both experimental and numerical research on high-pressure common-rail diesel injection. We propose a new method named SD-ELSA model to realize automatically identifying droplet features for high-pressure diesel spray based on the classic ELSA (Eulerian Lagrangian Spray Atomization) model; this method is suitable for varied injection operation conditions. The SD-ELSA first identifies the liquid bulk due to breakup of the continuous phase in near field, and then converts the Eulerian liquid bulk into Lagrangian particles to complete the calculation of the total spray atomization. The SD-ELSA model adopts two key criteria, i.e., the sphericity (S) and the particle diameter (D); the qualified liquid mass is transformed into Lagrangian particle, realizing the coupling of the Eulerian–Lagrangian model. The SD-ELSA model illustrates the total diesel spray atomization process from the breakup liquid column to the droplets.

Abstract Aiming at the problem that the traditional transportation route planning method can adaptively allocate a small number of transportation points, which leads to the long time required for the final route planning, a recurrent neural network-based e-commerce logistics transportation route planning method is studied. Use recursive neural network to extract e-commerce logistics characteristics, calculate the adaptive probability parameters shown by individual transportation individuals, combine gene blocks to control adaptively processed transportation points, set transportation route neighborhood search mechanisms, and build transportation route cost constraint numerical relationships. Combining the congestion parameters generated when the vehicle is running, set the route planning plan, and finally complete the research on the transportation route planning method. After preparing the e-commerce logistics transportation data, simulate the planning of the e-commerce logistics transportation route, apply two traditional transportation route planning methods and the designed route planning method to experiment, and the results show: the planning time required for the designed route planning method The shortest.KeywordsRecurrent neural networkE-commerce logistics transportation routePlanning methodTransportation point

Abstract Since the report of the 19th National Congress of the Communist Party of China proposed the implementation of the rural revitalization strategy, the rural economy has developed rapidly, the effect of industrial agglomeration has been further deepened, user consumption levels and procurement needs have continued to increase, and logistics activities have increased sharply. The logistics distribution is a key link in the logistics service supply chain, and the study of logistics distribution path planning is of great significance to the development of rural revitalization. This paper aims to study the application of swarm intelligence algorithm in logistics distribution route planning under the background of rural revitalization. This article first expounds the problems and reasons that hinder rural logistics distribution, puts forward the form and optimization goal of logistics distribution path planning, and explains its definition, classification and solution methods respectively. This paper proposes the Intelligent Ant Colony Algorithm (IACA) to solve the logistics distribution path planning and solve the problem of multiple distribution centers. Finally, the algorithm is compared with the Cat Group Algorithm (CGA) for simulation experiments to verify the effectiveness of the algorithm. The experimental results show that when the number of distribution centers is 14, the optimal number of iterations for CGA and IACA is 41 and 30, respectively. When the number of distribution centers is 48, the optimal number of iterations for CGA and IACA is 202, respectively. 100 times, it shows that the algorithm proposed in this paper can be well used in the path planning of logistics distribution, improve the efficiency of distribution, and reduce the cost of logistics distribution.KeywordsRural revitalizationIntelligent algorithmLogistics distributionRoute planning