Zhejiang DongFang Vocational and Technical College

Aiming to facilitate the high-value repurposing of industrial steel slag, this investigation synthesized basalt fiber-reinforced steel-slag foamed concrete (BFSFC) incorporating fiber volume fractions of 0%, 0.15%, 0.30%, and 0.45%. The experimental study assessed compressive and flexural capacities alongside resistance to freeze-thaw cycling, while micro-void was reconstructed via X-ray CT and corroborated by SEM imaging. Grey Relational Analysis (GRA) was subsequently utilized to correlate pore metrics with strength degradation under freezing conditions. Empirical findings identify 0.30% as the critical fiber volume fraction; this optimal mix yielded a 28-day compressive strength of 1.322MPa (a 12.03% increment over the reference) and elevated flexural strength to 0.517MPa (an increase of approximately 64%). Following 15 freeze-thaw iterations, the BFSFC2 specimen maintained a mass loss below 5% and restricted strength deterioration to 8.69%, a sharp contrast to the 31.14% decay observed in the control group. Microscopic analysis attributes this stability to a fiber-induced reduction in pore network complexity and morphological refinement. Furthermore, GRA confirms that fiber dosage exhibits the strongest correlation with fractal dimension (0.991), whereas frost-induced strength decline is primarily governed by fractal dimension (0.805) and large pore size (> 200μm, 0.743), highlighting the dominance of pore network complexity and large pore-defects in freezing behavior. Conclusively, BFSFC2 provides a balanced improvement in mechanical performance and freeze-thaw resistance, supporting a microstructure-based optimization route for producing lightweight foamed concrete using steel slag for cold regions.

The study investigated the effectiveness of using the engineering design process (EDP) to enhance student learning outcomes in a physics course. A mixed-methods approach was used in this study. A quasi-experimental pretest-posttest control group (CG) design was applied for the quantitative phase, while a semi-structured interview was employed for the qualitative phase. The study sample consisted of two classes randomly assigned from the Department of Mechanical Engineering at a technical college, which served as the experimental group (EG) and the other as the CG. Before the intervention, the pretest was administered to both groups. The EDP was implemented in the EG during the intervention, whereas the CG was taught using conventional teaching methods. After the intervention, the posttest was administered to both groups, and a semi-structured interview was conducted with eight students in the EG. The quantitative data were analyzed using independent-samples Mann–Whitney U-tests. The qualitative data were analyzed using thematic analysis. The results showed a statistically significant difference in mean scores between the EG and the CG, indicating that learning physics through the EDP model improved students' physics learning outcomes in the EG. Furthermore, the qualitative analysis revealed that students in the EG perceived EDP as an effective and contextually appropriate approach for learning physics within an engineering context.

Against the backdrop of the global digital wave and the upgrading of intelligent manufacturing, private enterprises in Wenzhou, as an important force in China's private economy, are facing multiple challenges such as technological transformation in traditional manufacturing and commercial circulation, intelligent industrial development, and intensified market competition. Taking "Digital and Intelligent Empowerment · Wenzhou Smart Manufacturing" as the theme, this paper deeply explores the current situation, bottlenecks, paths, and guarantee systems for the digital transformation of private enterprises in Wenzhou from three dimensions: enterprise endogenous motivation, government policy empowerment, and social ecology co-construction. It aims to provide theoretical reference and practical guidance for Wenzhou to build the "third pole of the province" and achieve high-quality development of the private economy.

Chronic pain affects over 30% of the global population, yet non-pharmacological interventions with clear neurophysiological mechanisms remain limited. While virtual reality (VR) and music therapy independently show promise in pain management, the neural oscillatory underpinnings of rhythm-synchronized audiomotor integration in VR therapy remain poorly understood. This study aimed to investigate whether music-synchronized virtual reality (MSVR) enhances analgesia through distinct neural mechanisms compared to conventional distraction-based VR or non-immersive interventions. 90 healthy adults (45 female, 22 ± 2 years) were randomized to a single session of: (1) MSVR (rhythm-synchronized visuomotor tasks with music), (2) conventional VR (identical tasks with white noise), or (3) non-immersive 2D control. Pressure pain thresholds (PPT) were measured at five movement-generating muscle sites. Conditioned pain modulation (CPM) efficiency was assessed using PPT and cold-water stimulation. 64-channel EEG recorded theta, alpha, beta, and gamma oscillations. Outcomes included PPT changes, CPM efficiency, cold pain intensity/unpleasantness, and neural spectral power. MSVR significantly increased PPTs across all sites (15-25% vs. control, P < 0.001; superior to VR in upper limbs, P < 0.05) and enhanced CPM efficiency by 18% (vs. control, P = 0.010; vs. VR, P = 0.046). Cold pain intensity decreased by 22% with MSVR (P < 0.05 vs. both groups). MSVR uniquely enhanced parietal alpha oscillations during and post-intervention (P = 0.001). MSVR also induced greater immersion and realism than VR (P < 0.001). MSVR significantly enhances endogenous pain inhibition and elevates pain thresholds more effectively than conventional VR or 2D interventions, primarily through rhythmic audiomotor integration modulating parietal alpha-oscillation modulation. These findings suggest MSVR as a scalable digital therapeutic strategy for pain management.

This paper presents an innovative adaptive control system for collaborative sorting robotic arms achieving three key technical breakthroughs: (1) a multimodal sensor fusion algorithm integrating vision, force, and position sensors with dynamic reliability weighting achieving 98.7% sorting accuracy, (2) a distributed edge computing architecture enabling 3.2ms average response time through local processing optimization, and (3) adaptive control mechanisms with online learning capabilities delivering 847 items/hour throughput capacity. The system combines advanced fusion algorithms with machine learning techniques to optimize performance under varying operational conditions including payload changes, environmental disturbances, and collaborative coordination requirements. Experimental validation demonstrates 15% improvement in accuracy over commercial systems, 60% reduction in communication latency compared to centralized architectures, and robust operation with maintained performance above 85% during sensor failures. The research contributes to intelligent manufacturing advancement by providing a scalable framework for collaborative robotic sorting applications that adapts to dynamic production requirements while maintaining optimal performance metrics in Industry 4.0 implementations.

In vocational education, big data analysis of student behaviour has problems such as single data dimension, failure to effectively identify differences between student groups, and overly simple data analysis methods. Understanding student behavior in vocational education is essential for tailoring interventions that enhance learning outcomes, and leveraging big data enables more nuanced, data-driven insights that support precision education strategies. This paper applies the K-means clustering algorithm to deeply mine big data student behaviour patterns in vocational education, providing more precise and personalized data analysis tools and decision support for vocational education. The vocational education dataset, focusing on student behaviour data, is preprocessed through data cleaning, removal of invalid entries, and format unification. This is followed by behavioural analysis and key feature selection for clustering. The appropriate number of clusters is set, and the Euclidean distance is used to measure the similarity between each student and the centre point to complete the division of student behaviour patterns. The K-means clustering results are deeply analyzed to identify the behavioural characteristics of different student groups, extract valuable information, and analyze learning tendencies and participation, providing a basis for personalized education intervention. The experimental results show that the silhouette coefficient of K-means in 5 samples is between 0.78 and 0.92, and the variance ratio is between 0.82 and 0.91, which can achieve better clustering effects.

Internal discharge and mechanism defects are common defects in gas insulated switchgear (GIS) equipment. Here, X-ray digital radiography technology was used to detect defects in several faulty GIS equipment in power grid. Firstly, the X-ray detection results showed that metal debris were found in the faulty circuit breaker compartment. Through inspection of the equipment compartment, it was confirmed that there were a large amount of metal erosion, discharge dust, metal slag, etc. inside, indicating the occurrence of multi position discharge. Then, X ray was used to detect that the distance between the moving and static contacts of the faulty circuit breaker had increased. After opening the compartment for inspection, it was found that the bolts between the fixing mechanism and the chassis were loose. Subsequently, the position of the moving and static contacts of a faulty GIS equipment isolation switches was detected, and it was found that the moving contacts exceeded the shielding cover when in the opening position. Finally, X-ray was first used to inspect the mechanism side guide ring of the circuit breaker, and it was found that the guide ring was loose. This work further validates the efficiency and accuracy of X-ray detection for discharge defects and active mechanism defects in GIS equipment. (Abstract)

Direct numerical simulation of the shock wave/turbulent boundary layer interaction on a compression ramp and curved compression ramps with different radii of the curvature at the Mach number Ma=5.0 and Reynolds number Re=16 800/mm is performed, and the purpose of the study is to investigate the impact of different radii of the curvature on the development of the flow. The flow structure and turbulence properties are analyzed. As the curved angle radius increases, the range of flow deceleration and the impact of the shock wave interaction on the turbulent boundary layer gradually decrease, and the peak value of turbulent pulsation amplification in the interaction zone becomes smaller. Mean skin friction decomposition is carried in upstream undisturbed region and reattachment region. The skin friction coefficient in the upstream is primarily composed of the viscous dissipation term Cf,V and the turbulent kinetic energy production term Cf,T. While in the reattachment zone, it is mainly balanced by the term Cf,T and the spatial growth term Cf,G. Bidimensional empirical mode decomposition is applied to further study the contribution of the turbulent motion at different scales to Cf,T, and the result shows that for the compression ramp, Cf,T is mainly contributed by the large-scale vortex structure generated in the interaction zone, while for the curved compression ramp, it is mainly contributed by the rapid amplification of turbulent pulsations caused by the shock wave interaction. This study is not only a new parametric study of the shock wave/turbulent boundary interaction but also provides a reference for the aerodynamic design of hypersonic vehicles.