Abstract Prefabricated construction, an innovative approach with energy efficiency, low carbon emissions, lightweight design, and material recyclability, has strong national policy support in China. Precast concrete elements (PCEs) are the most critical component in prefabricated buildings, and the intelligence and automation of concrete spreader equipment determine manufacturing efficiency and product quality. Currently, most spreaders rely on manual or semi - mechanized operations, causing uneven material distribution, high spoilage rates, limited coverage, and high equipment failure rates. By 2024, automated spreaders accounted for 63% of the market share, showing an industrial transition driven by advanced numerical control (NC) systems and robotics. To solve these problems, this study develops a fully intelligent precision concrete spreader for prefabricated components using a Siemens S7 - 200 SMART programmable logic controller (PLC) and SolidWorks 3D modeling. The system integrates a 12 - channel independent hydraulic discharge mechanism, a dual - track traversal structure, a three - level anti - collision protection system, and a PID closed - loop material control algorithm with load cell feedback. The design supports modular manufacturing, easy maintenance, and scalable automation upgrades including machine vision integration for future use.

Abstract To address the low efficiency, high error rate, excessive labor cost and poor safety of traditional manual material sorting, this paper develops a PLC-based automatic control system for material sorting conveyors catering to industrial production demands. Taking Siemens S7-200 SMART PLC as the core controller, the system integrates sensor detection, conveyor driving, sorting execution and human-computer interaction modules to realize automatic identification, transmission and classified sorting of materials with different textures and sizes. This paper systematically presents the system overall scheme, hardware selection, circuit design, software logic and debugging process. Experimental results show that the system features high stability, fast response and high sorting precision. It can replace manual sorting effectively, reduce production and maintenance costs, and improve industrial automation level, possessing favorable application and promotion value.

Abstract Smart inspection of substations should no longer be a single-device test; instead, based on many factors collected from all over the country in recent years, close-loop operations should be carried out. Infrared temperature measurement can be used to find hot spots in the joints and determine the causes of high temperatures, such as phase imbalance in a three-phase system or local heating of bushings; image recognition can read meters, learn the status of a switch, observe appearance defects and foreign objects, etc. Together they are to increase the accuracy, consistency and auditability of the inspection. By enhancing the collection and analysis of data on abnormalities, risks, etc., in substation inspections, it will be possible to strengthen condition-based maintenance and ensure the safe operation of equipment in the future.

Abstract Alternating copolyoxamides are of high interest in high-heat-resistant polyamilies owing to their regular molecular chains, high amide group density, and high intermolecular hydrogen bonds. To cope with the high melt condensation temperature, easy thermal degradation and side reactions, as well as the problem of chain structure regularity in traditional methods, we developed a direct solid-state polymerization approach to prepare alternating copolyoxamide from an equimolar diamine-oxalate precursor. At low melting temperatures and high thermal decomposition temperatures, the ordered form of the precursor is converted into a regular polymer chain structure. The structural formation, thermal stability, and crystallinity of the material were analyzed using FTIR, NMR, DSC, TGA, and XRD. Our results show that direct solid-state polymerization promotes oxalamide bond formation, reduces structural defects in high-temperature polymerization, and produces alternating copolyoxamides with a high melting temperature, high thermal decomposition temperature, and distinct crystallization peaks. We demonstrate that the alternating sequence and oxalammium hydrogen bond network lead to high heat resistance and crystallinity of a material, which can be used to create high-end engineering plastics, electronic packaging materials, and heat-deficient fiber materials.

Abstract The modern cruise industry has this kind of dual challenge—maximizing really tight spatial footprints while still giving passengers a luxurious, human-centric experience. Traditional physical outfitting methods, they're sort of hitting a wall. This paper looks at how we can optimize humanized design and space use in cruise ship interiors by mixing in advanced software and smart electronic tech. Which is interesting. At the macro level—The big picture—We explore how Marine Building Information Modeling (BIM) reclaims void spaces through digital collision detection. And then there's AI-driven analytics that let high-traffic public zones be dynamically modular. it's not just about cramming things in; it's about adapting on the fly. But anyway. Then at the micro level, the cabin level, we examine how Internet of Things (IoT) sensors handle ergonomic HVAC control—You know, making sure the temperature adjusts to how people actually move. Programmable LED arrays are used for circadian psychological comfort, which sounds fancy but it's basically mimicking natural light to keep you sane. And UHD displays act as "Virtual Balconies" to visually expand those cramped interiors. Which is clever, really. The research alhighlights how software interfaces and embedded hardware work together in transformable, modular outfitting. Blending physical marine architecture with digital and electronic innovation—That's the definitive solution for engineering highly efficient, intuitively adaptable, and fundamentally humanized maritime environments. Or we conclude. Maybe that's too tidy, though. But it holds up.

Abstract Aiming at the prominent load fluctuation, insufficient accuracy of traditional forecasting methods and great difficulties in stable power grid regulation under new energy grid-connection, this paper constructs an artificial intelligence load forecasting model and proposes supporting regulation strategies. The Long Short-Term Memory (LSTM) network is adopted to adapt to nonlinear time-series features, the 3σ criterion is applied for data preprocessing, and the Particle Swarm Optimization (PSO) algorithm is introduced to optimize the key parameters of the model so as to improve forecasting accuracy. The comparison results show that the PSO-LSTM model has lower errors and better fitting performance than the single LSTM model. Based on high-precision forecasting results, a coordinated regulation system of source-grid-load-storage as well as a voltage and frequency stability regulation system is established, which can effectively suppress new energy fluctuations and reduce operation risks. It provides technical support for accurate forecasting and safe and stable operation of distribution power grids with high penetration of new energy.

Abstract The popularization of taekwondo in colleges has promoted the development of the non-professional college student taekwondo group, yet this group is plagued by such problems as single application of leg techniques, low scoring rate and poor technical adaptability in actual combat with electronic protective gear. Taking non-professional college student taekwondo athletes as the research object, this paper adopts methods of literature review, practical observation and experimental comparison to analyze the current situation and influencing factors of leg technique application under the electronic protective gear environment, and puts forward optimization strategies and innovative techniques. The research shows that the accuracy requirement of electronic protective gear has made push kicks and their improved techniques replace traditional roundhouse kicks as the core scoring methods, and college student athletes' practical performance of leg techniques is restricted by weak foundation, poor physical fitness, lack of tactical thinking and other issues. The targeted innovative combined leg techniques have been proven to effectively improve the practical scoring rate and technical application efficiency, providing a reference for the optimization of taekwondo teaching and training in colleges and universities.

Abstract The explosive growth of network traffic and increasing complexity of service types have rendered traditional classification and scheduling methods inadequate for meeting efficient transmission demands. By leveraging convolutional neural networks and long short-term memory networks as core architectures, combined with attention mechanisms, this study proposes an intelligent traffic classification model that automatically extracts spatiotemporal traffic features, enabling precise identification of encrypted and complex traffic patterns. A dynamic scheduling mechanism is designed based on classification results to optimize resource allocation and transmission efficiency. Experimental results demonstrate that this approach significantly improves classification accuracy and network throughput, providing robust technical support for intelligent network management and security protection.

Abstract Continuous breakthroughs in waveguide structure design, material composition control and functional integration are transforming specialty optical fibers from conventional optical communication media into enabling technologies covering medicine, industry and defence. This paper systematically reviews the latest application progress of specialty optical fiber technology in two major areas: Medical and industrial. In the medical field, the focus is on the core role of optical fibers in biochemical analyser spectral analysis, holmium laser lithotripsy, endovenous laser ablation (EVLA) for varicose veins, and excimer laser coronary atherectomy (ELCA). In the industrial field, the paper analyses fiberbased precision ranging technology, the application of OPGW power cables in temperature monitoring of converter valves, and recent advances in distributed fiber-optic hydrophone technology. Incorporating the latest achievements in novel fiber technologies such as midinfrared fluoride fibers, multimaterial fibers, and hollowcore fibers, this review aims to provide a systematic reference framework for research and applications of specialty optical fibers in interdisciplinary domains.

Abstract Against the backdrop of digital transformation, the experimental teaching of operating systems is confronted with problems such as the disconnection between traditional models and industrial demands, insufficient student participation, and a single evaluation method. This paper proposes a PBL experimental teaching model empowered by intelligent technology. By integrating three major platforms, namely Tencent Digital Human (dynamic scene construction), VMware Virtualization (hierarchical experimental environment), and Feishu -DeepSeek (intelligent evaluation and feedback), a teaching closed loop of "life-like scene introduction - hierarchical experiment implementation - intelligent evaluation and feedback" is constructed. This model is based on the theoretical framework of "one core, dual drivers, three stages, and four dimensions", with the cultivation of students' systematic abilities as the core. It deeply integrates PBL problem-driven approaches with intelligent technology empowerment, providing a replicable practical paradigm for the teaching reform of operating system courses and having reference value for promoting the digital transformation and upgrading of engineering education.

Abstract Based on the foundation of smart customs construction during the 14th Five-Year Plan period, this paper explores the path for high-quality development of customs informatization in response to new requirements and challenges during the 15th Five-Year Plan period. The study points out that in the future, we should adhere to the core concept of 'Smart Customs, Intelligent Borders, and Smart Connectivity', and systematically promote customs informatization through four key tasks: Building an intelligent digital foundation through technological empowerment, achieving full-process intelligent transformation through business restructuring, creating a port ecosystem community through collaborative sharing, and strengthening national border defenses through security support. Combining local practical cases, the paper proposes phased implementation paths and policy recommendations, aiming to provide reference for the compilation of the 15th Five-Year Plan and future work, and to help build an internationally advanced smart customs system by 2030.

Abstract Earthquakes are severe natural disasters. Accurate analysis and prediction of disaster impacts are vital for disaster prevention and emergency response. Based on domestic and international earthquake data over the past two decades, this paper builds a prediction model with the random forest algorithm. Using descriptive statistics and correlation analysis, key influencing factors are identified. Model performance is verified via actual disaster data. The results show that the random forest algorithm achieves high accuracy and stability in predicting earthquake casualties and economic losses. Compared with traditional statistical methods, it has strong anti-interference ability and good generalization. The model can provide technical support for earthquake early warning, disaster assessment and emergency resource allocation.

Abstract This paper addresses challenges in AI-driven automatic translation and adaptation of English anime series, including insufficient text extraction accuracy, lack of cultural adaptation, and inadequate synchronization between subtitle layout and audio-video playback. A comprehensive system encompassing "visual analysis – translation adaptation – layout and dubbing – rendering detection" is developed. By employing joint model training, dynamic algorithm optimization, and multimodal collaboration techniques, the system achieves precise optimization across key stages such as text recognition, translation adaptation, and audio-video synchronization, supports output in multiple formats and resolutions, and enhances both translation efficiency and quality.

Abstract To address the requirements of industrial robot joint drives for high precision, high load capacity, high smoothness, and compact structure, a design and simulation study of a double-difference cycloidal pinwheel RV reducer was conducted. First, basic parameters such as the number of teeth on the pinwheel and the cycloidal wheel were determined based on the principles of cycloidal pinwheel transmission. Subsequently, models of components such as the cycloidal wheel and pinwheel were created and assembled, and a dynamic model was established in RecurDyn. Simulation results indicate that after the system reaches steady state, the output speed curve remains stable, with an error of 0.21% relative to the theoretical gear ratio. Contact force analysis shows that the maximum contact force occurs between the pinion and cycloid wheel 1. SMISES equivalent stress contour plots reveal that high stresses are primarily concentrated in the meshing contact area between the cycloid wheel and the pinion. The research results indicate that the designed reducer possesses good motion transmission accuracy, meshing stability, and load-carrying capacity. It provides a strong theoretical basis and technical support for the design and application of double-difference cycloidal pinwheel RV reducers, thereby promoting their further development in the field of industrial robot joint drives.

Abstract Intelligent connected vehicles continuously generate vast amounts of vehicle data, driving data, and user behavior data during user's operation. Although users directly contribute to the generation of this data, they are unable to actually access or utilize it due to the relative monopoly held by automakers. Cases such as the Tesla driving data dispute demonstrate that specific scenarios, including accident liability determination, vehicle repairs, and insurance claims are highly dependent on the retrieval and circulation of vehicle data. However, the current regulatory framework relies primarily on the right to know under the Law of the People's Republic of China on the Protection of Consumer Rights and Interests to provide case-by-case remedies, making it difficult to address users' needs for data utilization. The right to data portability stipulated in Article 20 of Regulation (EU) 2016/679 (General Data Protection Regulation, "GDPR") differs from the mere right of access. It can reshape data control relationships in the digital age by promoting data sharing, ensuring the fair distribution of data value, and strengthening the right to informational self-determination. At the institutional level, the right to data portability should not be confined to personal information in the strict sense. Instead, it should take into account the symbiotic nature of smart connected vehicle data and moderately expand its scope of application to include certain data that is directly linked to users and makes an irreplaceable contribution. Drawing on the experience of the Regulation (EU) 2023/2854 (the "EU Data Act"), which has already expanded the scope of data access and transfer for the Internet of Things (IoT) products to include non-personal data, a data portability framework for intelligent connected vehicles can be gradually established through interpretive approaches and industry pilot programs. Supporting rules should be developed to address specific issues such as data acquisition pathways, authorized transfer methods, interface standards, frequency limits, and fee mechanisms. This approach will ensure the protection of user data rights while facilitating the free flow of data.