Super high-rise buildings are typically located in bustling urban areas. Due to their considerable height and limited construction space, numerous challenges arise during the construction process. To enhance the safety and efficiency of super high-rise building construction, this paper designs an intelligent construction machine steel truss platform suitable for the core tube structure of such buildings. Numerical analyses of the designed steel truss platform were conducted during the construction, jacking, and self-climbing phases. The analysis results indicate that the strength and stiffness of the intelligent construction machine steel truss platform meet the design standards for steel structures, ensuring the overall safety and reliability of the structure. However, stress and deformation are relatively high at the connections between primary and secondary trusses, particularly in the chords and webs, as well as at the joints between truss beams and columns. To facilitate construction and improve structural safety reserves, technical and structural optimization suggestions are proposed based on the analysis results. This study provides valuable insights for the design of similar intelligent construction machine steel truss platforms in engineering projects.

The Bootloader based on the UDS protocol is the most commonly used for the software update in automobiles. The Bootloader needs to be fully tested to ensure it running Steadily on vehicle. This paper takes the CAN bus as an example, analyzes the relevant protocols of Bootloader, summarizes the testing requirements of Bootloader, and then develops an automated testing system. Moreover, this system has the characteristics of high automation, easy configuration, and strong readability of the test report.

This article mainly discusses the application prospects and suggestions of DeepSeek in theoretical teaching of nursing. DeepSeek, with core technologies such as natural language processing, machine learning, and deep learning, can provide personalized learning plans, real-time feedback, and curriculum generation, bringing innovation to nursing education. The article proposes application suggestions such as using DeepSeek to create customized learning materials, optimizing teaching content and presentation methods, enhancing teaching interactivity, and emphasizes the importance of protecting student privacy and following ethical norms. At the same time, the article also points out strategies that combine traditional teaching methods, such as integrating online and offline teaching and leveraging the leading role of teachers.

The paradigm shifts from a closed system to an always-on and fully connected vehicle leads to a largely increased risk to the automotive in-vehicle domain. Thereby, important automotive-specific protocols, which must be protected from a security point of view. This paper focuses on security aspects of Automotive Ethernet to address security challenges of the DoIP. First, it starts with an overview description of DoIP. Then, based on an exemplary in-vehicle network architecture, diagnostic via automotive ethernet by using DoIP are analyzed under security aspects with the help of Microsoft’s threat model. We identify the assets and attack surface of DoIP End Nodes and DoIP data flow, and risk assessment is carried out for DoIP data flow. Finally, the DoIP Cybersecurity goals and risk treatments are proposed to tackle the identified DoIP attacks.

In the context of the progressive development of integrated communication and sensing technology, indoor motion detection relying on Wi-Fi Channel State Information (CSI) has attracted significant attention within the industry [1][2][3]. Nevertheless, the home environment presents several challenges that impinge on the reliability of CSI. These challenges include interference [4], intricate spatial arrangements, and the instability of device placement. Moreover, motion sensing based on the CSI paths between terminals and routers may encounter problems such as unpredictable effective ranges [5] and difficulties in penetrating walls. To surmount these obstacles, this paper proposes the following technologies: a) adaptive Signal-to-Noise Ratio (SNR) enhancement; b) adversarial sample training; c) a method for refining spatial granularity; and d) a lightweighting approach for edge-side models. These technologies enable the detection of region-specific human activities through Wi-Fi CSI. The experimental findings demonstrate a reduction in false detection rates and the successful implementation of deep-learning models on compact communication equipment.

To enhance the scientific rigor of construction reverse logistics networks, improve the resource utilization rate of construction waste, and mitigate conflicts between corporate profitability and national sustainable development, this study proposes a bi-level optimization model that integrates both global and local optimization. The proposed model incorporates a local optimization module within the framework of global optimization, thereby improving overall network coordination while further enhancing economic and environmental benefits. Analysis demonstrates that the inclusion of local optimization plays a positive role in reducing carbon emissions and alleviating environmental burdens.

To address the challenges of high input dimensionality, high computational costs, and insufficient prediction accuracy in the safety early warning process of hoisting operations, this study proposes a safety early warning model based on Random Forest (RF), Genetic Algorithm (GA), and Support Vector Machine (SVM). First, the RF algorithm is employed to assess the importance of indicators involved in the hoisting safety process, thereby reducing data dimensionality and improving the operational efficiency of the model. Next, the GA is used to optimize the parameters of the SVM to enhance its generalization capability. Finally, an integrated safety early warning model is constructed by combining RF and GA-optimized SVM. Experimental comparisons using randomly selected case data demonstrate that the proposed model offers significant advantages in early warning accuracy. Compared with traditional models, the classification warning accuracy improves by 11%, confirming the feasibility of the model.

The development of cloud computing and big data has promoted the use of cloud servers in machine learning but has also raised concerns about privacy security. To enhance security and efficiency, this paper proposes a multi-key aggregation scheme based on improved Ring Learning With Errors (R-LWE) homomorphic encryption. This method protects the privacy of local model parameters and prevents information leakage through collaborative decryption. Experimental results demonstrate that the proposed scheme can resist collusion attacks, reduce communication overhead, and maintain model accuracy.

A UDS protocol-based ECU Bootloader software architecture adaptable to diverse programming standards is designed and implemented in this study. The proposed architecture adopts a layered design philosophy, comprising five hierarchical levels. The Reprogramming Sequence Manager is responsible for managing the programming process and handling parameters for programming steps. Inter-module communication is realized through standardized RTE interfaces for signal transmission and reception, as well as triggering and monitoring of execution events. The modular layered architecture, combined with functional decoupling design, ensures enhanced software reusability and practical applicability. Experimental results demonstrate that the architecture adapting to different programming sequences and diagnostic service specifications.