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This study presents an advanced conveyor belt inspection system that integrates laser scanning technology with deep learning to achieve high-precision micro-damage detection. The system is designed to overcome two major challenges in industrial inspection: ensuring reliable operation under harsh environmental conditions such as dust, vibration, and low illumination, and enabling the early identification of subtle belt defects that are often overlooked by conventional approaches. To this end, an innovative hardware platform was developed, combining laser-based illumination with high-speed imaging to enhance defect visibility. On the algorithmic side, an improved You Only Look Once (YOLO)v7 model was proposed, incorporating four enhancements-funnel rectified linear unit (F-ReLU) activation, spatial pyramid pooling fast cross stage partial convolution (SPPFCSPC) module, efficient intersection over union (EIoU) loss function, and squeeze-and-excitation (SE-Net) attention mechanism. A comprehensive dataset was constructed from both laboratory test benches and field-collected samples in a coal coking plant, ensuring robustness across diverse operating conditions. Experimental results demonstrate that the improved YOLOv7 achieves a mean average precision (mAP@0.5) of 96.6%, significantly surpassing the baseline YOLOv7 (90.7%) and outperforming recent detectors such as DETR and RT-DETR in both accuracy and efficiency. Moreover, long-term reliability tests, including 72-hour continuous operation and low-light industrial deployment, validated the system’s stability and adaptability. These contributions highlight not only the technical novelty of combining laser-enhanced imaging with deep learning, but also the practical value for predictive maintenance, safe production, and sustainable operation. This work offers a robust and scalable inspection framework, advancing the digitalization and intelligent automation of conveyor systems in line with Industry 4.0 principles.

This paper presents the design, implementation, and testing of an advanced conveyor belt surface monitoring system, specifically engineered for harsh and complex industrial environments. The system integrates multiple cutting-edge technologies, including programmable logic controllers (PLC), laser scanning, industrial-grade cameras, and deep learning algorithms, particularly YOLOv7, to achieve real-time, high-precision monitoring of conveyor belt conditions. Key innovations include optimized detection location based on failure modes, advanced PLC integration for seamless automation, and intelligent dust-proof features to maintain accuracy in challenging conditions. Through strategic placement of detection devices and multi-mode control strategies (local, remote, and automatic), the system offers unparalleled adaptability and responsiveness. The system leverages robust data management for trend analysis and predictive maintenance, enhancing operational efficiency. The hardware architecture comprises PLC-based control systems, high-resolution industrial cameras, and laser emitters, while the software features a two-tier structure combining human-machine interaction (HMI) with real-time data processing capabilities. Experimental results show that the system is highly effective in detecting common belt defects such as foreign objects, tears, and shallow scratches, ensuring optimal operational efficiency and minimizing downtime. The system’s scalability, robust data management, and adaptability to low-light and dusty conditions make it ideal for deployment in large-scale industrial operations, where continuous monitoring and early fault detection are critical to maintaining productivity and safety.

The tendon-bone junction (TBJ), a critical transitional zone where tendons and bones connect, is particularly prone to injury due to the forces from muscle contractions and skeletal movements. Once tendon-bone injuries occur, the complex original tissue structure is difficult to restore, increasing the risk of re-tear. In this study, we initially established a rabbit model of tendon-bone injury and treated it using either interference screw or suture anchor. Biomechanical testing demonstrated the maximum tension and strength of TBJ with interference screw fixation were superior. However, histologic and immunohistochemical results showed more tissue regeneration and expression of cartilage markers at the site of injury with suture anchor fixation. Moreover, Gelatin Methacryloyl encapsulated with exosomes from mesenchymal stem cell (GelMA-exosomes) were prepared, showing a consistent and stable exosome release characteristic. The combined application of GelMA-exosomes with either interference screws or suture anchors further enhanced the healing of tendon-bone injuries, which may be achieved by promoting cellular proliferation as well as regulating the decreased expression of local pro-inflammatory factors IL-1β, IL-6 and TNF-α and increased expression of anti-inflammatory factors IL-10 and TGF-β. This provides a viable therapeutic strategy to enhance tendon-bone healing.

Glycogen storage disease type 1b (GSD1b) is a rare genetic disorder, resulting from mutations in the SLC37A4 gene located on chromosome 11q23.3. Although the SLC37A4 gene has been identified as the pathogenic gene for GSD1b, the complete variant spectrum of this gene remains to be fully elucidated. In this study, we present three patients diagnosed with GSD1b through genetic testing. We detected five variants of the SLC37A4 gene in these three patients, with three of these mutations (p. L382Pfs*15, p. G117fs*28, and p. T312Sfs*13) being novel variants not previously reported in the literature. We also present a literature review and general overview of the currently reported SLC37A4 gene variants. Our study expands the mutation spectrum of SLC37A4, which may help enable genetic testing to facilitate prompt diagnosis, appropriate intervention, and genetic counseling for affected families.

With the continuous development of science and technology, carbon steel is widely used in production and life. Especially at the practical application level, in the engineering application, the analysis of the mechanical properties of carbon steel materials has become increasingly prominent. The tensile test is a mechanical property test in which a standard tensile specimen is pulled to fracture at a specified tensile speed under the continuous action of static axial tensile force. The force and elongation are continuously recorded during the tensile process so as to obtain its strength criterion and plasticity criterion. In this paper, the characteristics of mechanical properties of the high, medium, and low carbon steels under uniaxial static tension are discussed. The reasons for the different mechanical properties of steels with different carbon content are explained from a microscopic perspective. The study provides some basic assistance to those who need relevant information.

As the technology for offshore wireless transmission and collaborative innovation in unmanned ships continues to mature, research has been gradually carried out in various countries on methods of compressing and transmitting perceptual video while driving ships remotely. High Efficiency Video Coding (H.265/HEVC) has played an extremely important role in the field of Unmanned Aerial Vehicle (UAV) and autopilot, and as one of the most advanced coding schemes, its performance in compressing visual sensor video is excellent. According to the characteristics of shipborne vision sensor video (SVSV), optimizing the coding aspects with high computational complexity is one of the important methods to improve the video compression performance. Therefore, an efficient video coding technique is proposed to improve the efficiency of SVSV compression. In order to optimize the compression performance of SVSV, an intra-frame coding delay optimization algorithm that works in the intra-frame predictive coding (PC) session by predicting the Coding Unit (CU) division structure in advance is proposed in combination with deep learning methods. The experimental results show that the total compression time of the algorithm is reduced by about 45.49% on average compared with the official testbed HM16.17 for efficient video coding, while the Bjøntegaard Delta Bit Rate (BD-BR) increased by an average of 1.92%, and the Peak Signal-to-Noise Ratio (BD-PSNR) decreased by an average of 0.14 dB.

This paper mainly studies and designs the control system of a four-degree-of-freedom manipulator. As a motion control system, the control system is mainly oriented to the bottom, striving to develop an industrial manipulator system with high stability, strong reliability, and accurate positioning. Firstly, according to the control requirements of the mechanical arm system, a single CPU system control scheme is designed as a whole, that is, to control the steering gear rotation by controlling the duty cycle of the PWM wave output by the central controller, to achieve the position control of each joint. In terms of hardware, the main components are ARM microprocessor STM32F103ZET6, MG995 steering gear, MG945 steering gear, ultrasonic sensor, and power module. The joint motion control system is built by building a hardware platform and designing a software control program. In software design, the control program is divided into an initialization module and an operation module according to the modular design idea, and the program of each module is designed separately.

In the industrial production process, people need to detect and control the temperature in various types of heating furnaces. The equipment selected for the water temperature control system designed in this paper is a single-chip microcomputer. The single-chip microcomputer has the characteristics of low power consumption, high performance, good reliability, and ease of production. Moreover, the single-chip microcomputer is more convenient, simple, and flexible in step control of temperature, which can improve the technical indicators of the controlled temperature, thus greatly improving the reliability of the product. In the design process, firstly, the hardware design is carried out. Secondly, the software design is carried out. And finally, comprehensive debugging is carried out to realize the constant temperature intelligent control of water temperature.

Falling objects from façades often lead to serious accidents, which has become a technical problem to be solved urgently. This paper established a database of potential safety hazard of falling objects from façades via the statistics of detection façades in Shanghai. Some detailed insufficiencies and swelling defects were analyzed. A risk assessment system of falling objects from façades was established using the Fault Tree Analysis (FTA) method. The weight coefficient was determined by the Analytic Hierarchy Process (AHP). The beta distribution was used to fit the probability distribution of the occurrence probability of the elementary risk event. Based on the Monte Carlo model, the risk of falling objects from façades was assessed. A probability distribution of the risk probability of falling objects from façades and the importance of elementary risk factors were obtained. Some risk control measures of falling objects from façades were proposed.

The High Efficiency Video Coding Standard (HEVC) is one of the most advanced coding schemes at present, and its excellent coding performance is highly suitable for application in the navigation field with limited bandwidth. In recent years, the development of emerging technologies such as screen sharing and remote control has promoted the process of realizing the virtual driving of unmanned ships. In order to improve the transmission and coding efficiency during screen sharing, HEVC proposes a new extension scheme for screen content coding (HEVC-SCC), which is based on the original coding framework. SCC has improved the performance of compressing computer graphics content and video by adding new coding tools, but the complexity of the algorithm has also increased. At present, there is no delay in the compression optimization method designed for radar digital video in the field of navigation. Therefore, our paper starts from the perspective of increasing the speed of encoded radar video, and takes reducing the computational complexity of the rate distortion cost (RD-cost) as the goal of optimization. By analyzing the characteristics of shipborne radar digital video, a fast encoding algorithm for shipborne radar digital video based on deep learning is proposed. Firstly, a coding tree unit (CTU) division depth interval dataset of shipborne radar images was established. Secondly, in order to avoid erroneously skipping of the intra block copy (IBC)/palette mode (PLT) in the coding unit (CU) division search process, we designed a method to divide the depth interval by predicting the CTU in advance and limiting the CU rate distortion cost to be outside the traversal calculation depth interval, which effectively reduced the compression time. The effect of radar transmission and display shows that, within the acceptable range of Bjøntegaard Delta Bit Rate (BD-BR) and Bjøntegaard Delta Peak Signal to Noise Rate (BD-PSNR) attenuation, the algorithm proposed in this paper reduces the coding time by about 39.84%, on average, compared to SCM8.7.