Zefeng Wang

During the start stage of ship welding, obtaining the three-dimensional coordinates of welding target points is affected by confined installation space, surface reflection, and deployment constraints. This paper proposes a low-complexity point-wise three-dimensional localization method based on two-dimensional visual planar guidance and one-dimensional point-laser distance constraints. A direct computation model of the laser incident point in the robot base coordinate system is established from the tool center point pose, the extrinsic parameters of the point-laser module, and real-time ranging data, enabling target-point coordinate estimation without dense three-dimensional reconstruction. A dual-stage stabilization strategy is introduced by combining ranging-level filtering, spatial coordinate smoothing, and outlier suppression. Image error-based visual closed-loop alignment is further used as a pre-measurement step to ensure that the point laser acts on the target region. Experimental results show that, after workplane-level extrinsic correction, independent validation points achieve a mean three-dimensional Euclidean error of 1.54 mm with a standard deviation of 0.28 mm. The average planar error in closed-loop alignment experiments is 1.124 mm. Passive binocular depth measurement on the current platform still yields an RMSE of 6.16 mm after linear correction. A simulated fillet-weld task verifies the feasibility of the complete perception-to-execution workflow. The proposed method provides a low-complexity coordinate acquisition route for discrete welding target points before arc ignition.

Gut plasmids show heightened sensitivity to gut microenvironmental changes compared to their bacterial hosts. To explore their significance in colorectal cancer (CRC), we analyzed metagenomic data from 863 participants (312 CRC, 387 high-risk, 164 low-risk). Plasmid and bacterial profiles were characterized, along with trace elements and metabolites. Differential analysis, functional gene assessment (ARG, MGE, MRG, VFGB), random forest modeling, and structural equation modeling (SEM) were applied. In terms of overall abundance, plasmids in both the high-risk and CRC groups exhibited a decreasing trend. Gut plasmids significantly influenced the functional genes (ARG, MGE, MRG, VFGB) of their bacterial hosts. Six key bacterial hosts (Enterobacterales, Bucrkholderiales, Hyphomicrobiales, Lactobacillales, Bacteroidales, Campylobacterales) and 12 plasmid markers were identified. The plasmid-based model effectively predicted CRC risk. SEM revealed that trace elements (e.g., Ni), metabolites (e.g., 5-Hydroxytryptophol), and host bacteria (e.g., Campylobacterales, Enterobacterales) predominantly exerted negative effects on most plasmids, whereas Ni exhibited a positive influence on plasmids NZ_CP013564.1, NZ_CP024312.1, and NZ_CP48284.1. We characterized the composition of gut plasmids and their bacterial hosts, explored the impacts of gut plasmids on bacterial functionality, and mapped multi-omics interaction networks linking plasmids, hosts, and metabolic features. The alternative text for this image may have been generated using AI.

Emergency rescue robots are currently widely used in disaster relief scenarios such as earthquake response, mine collapses, and industrial explosions. Due to environmental challenges such as terrain variability and weak GPS signals, robots experience reduced localization accuracy and prolonged path planning, failing to meet the demands of emergency rescue operations. To address these issues, this study employs the Ackermann quick-response emergency mobile robot as the experimental platform. Initially, the application of the UKF algorithm significantly enhanced the state estimation accuracy of the robot. Combined with the AMCL algorithm, it achieved high-precision localization with an average accuracy error less than ±0.036 m. Subsequently, the DAC-A* algorithm was proposed to address the issues with the traditional A* algorithm, such as excessive turning points and large turning angles, which delay the robot's arrival at the rescue site. Simulations conducted in Matlab demonstrate that compared to the traditional A* algorithm, the DAC-A* algorithm reduces the time to reach the rescue destination by nearly 40%, substantially improving rescue efficiency. Lastly, this research provides reliable technical support for disaster response, bearing significant practical significance and application value. Future enhancements through further algorithm optimization and exploration of more application scenarios could better meet the real needs of emergency rescue.

In response to the severe impact of work-related musculoskeletal disorders (WMSDs), this study proposes an advanced motion rehabilitation solution. By integrating inertial measurement unit (IMU) sensors and MediaPipe skeletal point technology, we aim to deliver personalized and precise motion assessment and rehabilitation guidance for WMSDs patients. Capturing and analyzing the shoulder and hip movements of 130 volunteers, we constructed a multimodal dataset with 30[ ]600 sets of data. A hybrid model combining one-dimensional convolutional neural networks (1D-CNN) and gated recurrent units (GRUs) was employed, achieving 97% accuracy in action recognition through dynamic dropout and L2 regularization. Additionally, an automatic scoring mechanism based on deep learning and Chinese limb motor function standards was introduced to enhance motion rehabilitation. The innovative application of multimodal fusion, deep learning scoring, and an efficient hybrid model not only improves scoring efficiency and consistency but also contributes new technologies and methods to the field of motion rehabilitation for WMSDs. Future work will focus on improving data collection and processing techniques, exploring new model structures and algorithms, and developing customized rehabilitation plans to meet individual needs.

Background: Cancer pain affects 60%–80% of patients with advanced cancer, and approximately 30% of patients experience inadequate pain control. Although the World Health Organization (WHO) three‐step analgesic ladder has substantially improved pain management, inadequate analgesia, opioid‐related adverse effects, and refractory pain continue to pose significant clinical challenges. Objective: This narrative review critically evaluates recent advances in cancer pain management, including novel analgesics, optimized opioid formulations, nanomedicine, invasive interventions (intrathecal drug delivery, neuromodulation, and neurolysis), and complementary integrative therapies, aiming to provide up‐to‐date insights for clinicians and researchers. Results: Novel analgesics targeting μ‐opioid receptor bias, ion channels, and multitarget strategies have shown preclinical promise. Improved opioid formulations and nanomedicine‐based approaches may enhance drug delivery and reduce toxicity. Fourth‐step interventions, including intrathecal drug delivery, neuromodulation, and neurolysis, provide targeted options for selected patients with refractory cancer pain. Integrative therapies, including mind–body interventions, acupuncture, massage, music therapy, and game‐based approaches, may improve symptom burden and quality of life, although evidence for direct analgesic effects remains limited. Conclusion: Cancer pain management is evolving toward a multimodal and increasingly personalized framework. Despite substantial progress, many emerging therapies lack robust cancer‐specific validation. High‐quality clinical trials, standardized treatment protocols, and improved translational strategies are needed to establish evidence‐based precision cancer pain management.