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Laser sensing and vision sensing smart canes can improve the convenience of travel for the visually impaired, but for the present, most of the system functions of laser sensing and vision sensing smart canes are still defective. Guide equipment and smart blind canes are introduced and classified first, and the smart blind canes based on vision sensing, laser sensing and laser vision sensing are investigated, respectively, and the research status of laser vision sensing smart blind canes is sorted out. The advantages and disadvantages of various laser vision sensing smart blind canes are summarized, especially the research development of laser vision fusion as the core of new smart canes. The future development prospects of laser vision sensing smart blind cane are overviewed, to boost the development of laser vision sensing smart blind cane, to provide safe and efficient travel guarantee for the visually impaired.

In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on a smart cane. The intelligent guide system relies on 2D LiDAR, an RGB-D camera, IMU, GPS, Jetson nano B01, STM32, and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on the cartographer algorithm, thus achieving simultaneous localization and mapping (SLAM). At the same time, through the improved YOLOv5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving, and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved YOLOv5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the omnidirectional wheels at the bottom of the smart cane and provide the smart cane with a self-leading blind guide function, like a “guide dog”, which can effectively guide the visually impaired to avoid obstacles and reach their predetermined destination, and can quickly and effectively identify the obstacles on the way out. The mapping and positioning accuracy of the system’s laser SLAM is 1 m ± 7 cm, and the laser SLAM speed of this system is 25~31 FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor environments. The improved YOLOv5 helps to identify 86 types of objects. The recognition rates for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25–26 FPS.

In recent years, simultaneous localization and mapping with the fusion of LiDAR and vision fusion has gained extensive attention in the field of autonomous navigation and environment sensing. However, its limitations in feature-scarce (low-texture, repetitive structure) environmental scenarios and dynamic environments have prompted researchers to investigate the use of combining LiDAR with other sensors, particularly the effective fusion with vision sensors. This technique has proven to be highly effective in handling a variety of situations by fusing deep learning with adaptive algorithms. LiDAR excels in complex environments, with its ability to acquire high-precision 3D spatial information, especially when dealing with complex and dynamic environments with high reliability. This paper analyzes the research status, including the main research results and findings, of the early single-sensor SLAM technology and the current stage of LiDAR and vision fusion SLAM. Specific solutions for current problems (complexity of data fusion, computational burden and real-time performance, multi-scenario data processing, etc.) are examined by categorizing and summarizing the body of the extant literature and, at the same time, discussing the trends and limitations of the current research by categorizing and summarizing the existing literature, as well as looks forward to the future research directions, including multi-sensor fusion, optimization of algorithms, improvement of real-time performance, and expansion of application scenarios. This review aims to provide guidelines and insights for the development of SLAM technology for LiDAR and vision fusion, with a view to providing a reference for further SLAM technology research.

Fiber Bragg gratings (FBGs) are widely used in stress and temperature sensing due to their small size, light weight, high resistance to high temperatures, corrosion, electromagnetic interference, and low cost. In recent years, various structural enhancements and sensitization to FBGs have been explored to improve the performance of ocean temperature and depth sensors, thereby enhancing the accuracy and detection range of ocean temperature and depth data. This paper reviews advancements in temperature, pressure, and dual-parameter enhancement techniques for FBG-based sensors. Additionally, the advantages and disadvantages of each method are compared and analyzed, providing new directions for the application of FBG sensors in marine exploration.

Astragalus membranaceus ( A. membranaceus ) belongs to the genus Astragalus within the family Leguminosae. Astragalus membranaceus var. mongholicus ( A. mongholicus ) is a variety of A. membranaceus . Its dried root is a Chinese herbal medicine, also known as “Huang Qi”. Sequencing technology has made a large amount of high-quality transcriptomics data for A. mongholicus and A. membranaceus publicly available. Their whole genomes were sequenced in 2022 and 2024, respectively. Despite the increasing availability of genomic and transcriptomic data, a reliable platform for comparing and mining gene function information remains unavailable. To fill this gap, this study aimed to develop AmDB ( http://bioinformatics.nwnu.edu.cn/AmDB/ ), a functional genomics database of A. membranaceus . First, functional and structural annotations were performed for 27,245 genes based on genomic data, using algorithms and software tools. Second, 62 transcriptome samples were collected from the SRA database. A co-expression network was constructed using the Pearson correlation coefficient and the Mutual Rank method, containing 224,615 positive and 116,806 negative gene pairs, covering 94.87% of the coding genes. The network analysis revealed that the phenylalanine ammonia-lyase function was closely related to phenylpropanoid metabolism, and the positively co-expressed genes in the network exhibited similar expression patterns. These findings validated the reliability of the network. Finally, AmDB implemented easy-to-use bioinformatics tools, including gene set enrichment analysis, BLAST, sequence extraction, orthologue identification, JBrowse, and expression data extraction.

In this paper, a pressure sensor based on a metal diaphragm and lever structure is designed, the sensing principle and mechanical structure of this sensor are analyzed and simulated, and its sensitization effectiveness and temperature compensation are verified. The maximum deflections of metal diaphragms of different sizes and materials were compared, and it was found that the square beryllium bronze diaphragm with a thickness of 1 mm and a side length of 20 mm had good elastic properties. The influence of the FBG in different positions of the lever on the center wavelength is analyzed. The sensitivity of the bare FBG is markedly improved under the influence of the two structures of the square elastic diaphragm and the lever, with a typical pressure sensitivity of 3.35 nm/MPa at 3 mm to the left of the lever center. The purpose of temperature compensation is achieved by adding another FBG that measures the temperature, and the sensing sensitivity can be tuned by adjusting the position of the FBG. It can meet the detection needs of a small range and high sensitivity.

The electrical sensing elements used in the traditional XBT (Expendable Bathythermograph) have problems such as low sensitivity and slow response time, and it is difficult to overcome the complex marine environment using the time–depth formula. In this paper, an ocean temperature depth sensor based on brass diaphragm and liquid filling is designed. The stress response time of FBGs with different lengths and the heat transfer time of different liquid materials are compared, and it is found that a fast response of 51 ms can be obtained by using GaInSn liquid for temperature sensing. The center deflection changes of brass diaphragms with different radii are analyzed, and the brass diaphragms with radius and thickness of 10 mm and 1 mm are selected, which still have good elastic properties under the pressure of 5 MPa. The influence of the inner metal shell section radius on the temperature and depth sensitivity is analyzed. When the final section radius is 3 mm, the temperature sensitivity of the sensor is 1.065 nm/°C, the pressure sensitivity is 1.245 nm/MPa, and the response time of temperature and depth is relatively close. Compared with the traditional temperature and depth sensors using empirical formulas for calculation, the data accuracy is improved, and a wide range of sensitivity can be tuned by adjusting the size of the internal metal shell, which can meet the needs of ocean temperature and depth data detection with high sensitivity and fast response time.

Exogenous contaminants in traditional Chinese medicine (TCM), including pesticide residues, heavy metals, mycotoxins, and sulfur dioxide residues, pose significant risks to human health and environmental safety. Conventional detection methods are limited by insufficient sensitivity, complex sample preparation, and challenges in multi-residue analysis, compromising accuracy and efficiency. To address these critical bottlenecks—particularly the escalating regulatory demands and trade barriers due to contamination incidents—this review establishes the first integrated ‘dual track’ quality control framework for TCM contaminants. We propose a novel risk stratified strategy synergizing laboratory grade accuracy with field deployable screening, overcoming the sensitivity portability trade-off. This work provides a roadmap for establishing globally harmonized standards. Future research should prioritize high-throughput methods, intelligent analytics, and green detection technologies. Integrating AI-driven automation with data traceability could establish unified systems for contaminant detection and degradation, enhancing TCM quality control and global competitiveness.

With the development of unmanned combat air vehicles (UCAVs) and artificial intelligence (AI), within visual range (WVR) air combat confrontations utilizing intelligent UCAVs are expected to be widely used in future air combats. As controlling highly dynamic and uncertain WVR air combats from the ground stations of the UCAV is not feasible, it is necessary to develop an algorithm that can generate highly intelligent air combat strategies in order to enable UCAV to independently complete air combat missions. In this paper, a 1-vs.-1 WVR air combat strategy generation algorithm is proposed using the multi-agent deep deterministic policy gradient (MADDPG). A 1-vs.-1 WVR air combat is modeled as a two-player zero-sum Markov game (ZSMG). A method for predicting the position of the target is introduced into the model in order to enable the UCAV to predict the target’s actions and position. Moreover, to ensure that the UCAV is not limited by the constraints of the basic fighter maneuver (BFM) library, the action space is considered to be a continuous one. At the same time, a potential-based reward shaping method is proposed in order to improve the efficiency of the air combat strategy generation algorithm. Finally, the efficiency of the air combat strategy generation algorithm and the intelligence level of the resulting strategy is verified through simulation experiments. The results show that an air combat strategy using target position prediction is superior to the one that does not use target position prediction.

The competence enhancement of college students is the overarching objective of curriculum construction and reform, which is crucial for the construction of first-class majors and disciplines. Principles of Genetic Engineering is an important professional course for the students of Biotechnology and Biomedical Engineering that are the national first-class construction majors at Guizhou Medical University in China. To increase the personalized self-directed and mutual learning opportunities for undergraduate and graduate students in biomedical science and engineering, an innovative teaching was designed to implement a vivid and interactive learning mode based on online and offline platform, smart teaching tools and scientific research achievements. After multiple rounds of teaching practice, continuous improvement and iterative updates, a 'three-stage and three-guidance' learning model was established to integrate pre-class, in-class, and post-class stages under the guidance of self-learning, question and research. In result, this curriculum was identified as the provincial 'Golden Course' and the average overall satisfaction rate for students reached 96.88%. Totally, this pattern can motivate students' intrinsic passion and interests in learning and enhance their scientific research thinking, innovation ability, teamwork and comprehensive skills in solving practical problems, supporting that the effective curriculum reform can promote the cultivation of high-quality talents and support the construction of first-class majors and disciplines.