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Objective To evaluate the Health Education Accurately Linking System (HEALS) combined with case-based leaning (CBL)I n nursing teaching ward round in the encephalopathy clinic. Methods Totally 19 registered nurses were recruited. The routing management had been adopted in nursing teaching ward round between January and December. 2021. A modified model based on the HEALS combined with CBL was adopted in the in nursing teaching ward round between January and December. 2021. Practical skills of nurses and their evaluation on nursing teaching ward round were measured. Results Nurses had achieved a higher score of practical skills assessment after implementation of HEALS combined with CBL model (P＜0. 05). The evaluation of enhancing interest, problem solving skills and independent learning in nursing ward rounds based on HEALS combined with CBL was superior to that in routine nursing ward rounts (P＜0. 05). Conclusion The HEALS combined with CBL model has obvious advantages in nursing teaching ward rounds, which can better meet the needs of clinical nursing teaching, effectively cultivate clinical practice and thinking ability, and is conducive to teaching management. (目的 分析健康教育精准连接系统(HEALS)助手联合基于案例学习(CBL)模式在脑病科护理教学查房中的应用效果。方法 选择脑病科19名注册护士作为研究对象。2021年1月—12月实施常规护理查房; 2022年1月—12月护理查房中采用HEALS助手联合CBL模式。比较实施前和实施后护士技能操作成绩, 对比两种模式下护士对护理教学查房效果的评价。结果 实施HEALS助手联合CBL模式后, 护士各项技能操作成绩高于实施前, 差异有统计学意义(P＜0. 05)。在提升兴趣、问题解决能力、自主学习能力方面, 护士对HEALS助手联合CBL模式下护理教学查房的评价优于常规护理查房, 差异有统计学意义(P＜0. 05)。结论 在脑病科护理教学查房中, 应用基于HEALS助手联合CBL模式优势明显, 能更好地满足临床护理教学需求, 有效培养护士临床实践和思维能力, 利于教学管理。)

Objective To investigate the effect of Traditional Chinese Medicine (TCM) ointment rubbing on in the treatment of hypermyotonia after cerebral infarction. Methods Totally 92 patients with hypermyotonia after cerebral infarction were selected and randomly divided into control group (n = 46) and treatment group (n = 46). Both groups were treated with basic medical treatment, acupuncture and moxibustion, physiotherapy, rehabilitation and other routine treatment for cerebral infarction, and the treatment group was treated with TCM ointment rubbing in addition to the comprehensive treatment. The two groups were treated for 2 months, and the modified Ashworth Scale of Muscle Spasticity (MMAS), NIHSS, Fugl-Meyer Assessment (FMA) and Barthel Activity of Daily Living Scale (BI) were compared before and after treatment and between the two groups. Results The score of NIHSS decreased in both treatment group and control group after intervention (P＜0. 05), and score of NIHSS in the treatment group was lower than that in the control group (P＜0. 05). Patients in the treatment group had a lower degree of MMAS compared with those in the control group after intervention(P＜0. 05). The scores of FMA and BI increased in both treatment group and control group after intervention (P＜0. 05), and scores of FMA and BI in the treatment group were higher than those in the control group (P＜0. 05). Conclusion TCM ointment rubbing is potentially effective to relieve the hypermyotonia of patients with cerebral infarction, improve limb function, and improve the activity of daily living. (目的 观察中药膏摩治疗脑梗死后肌张力增高的效果。方法 选取92例脑梗死后肌张力增高患者为研究对象, 随机分成对照组46例和治疗组46例。2组均采用内科基础治疗、针灸、理疗、康复等脑梗死常规治疗, 治疗组在此基础上加用中药膏摩进行治疗。2组均治疗2个月, 比较2组治疗前和治疗后改良Ashworth痉挛评定量表(MMAS)、美国国立卫生研究院脑卒中量表(NIHSS)评分、Fugl-Meyer运动功能(FMA)评分、Barthel日常生活能力指数(BI)。结果 与治疗前相比, 治疗后2组NIHSS评分下降, 差异有统计学意义(P＜0. 05), 且治疗组较对照组下降更明显, 差异有统计学意义(P＜0. 05)。治疗后, 治疗组MMAS评分等级较对照度低, 差异有统计学意义(P＜0. 05)。治疗后, 2组FMA评分和BI较治疗前升高, 差异有统计学意义(P＜0. 05), 且治疗组比对照组升高更明显, 差异具有统计学意义(P＜0. 05)。结论 中药膏摩治疗脑梗死后肌张力增高, 能够显著缓解患者的肌张力, 改善肢体功能, 提高生活能力。)

With the increase of remote sensing image acquisition methods and the number of remote sensing image data, the traditional manual annotation and recognition methods can no longer meet the needs of the present production life. This study explores the use of deep learning techniques to improve the efficiency and accuracy of target detection in remote sensing satellite images, especially for small targets. Traditional target detection methods often face challenges in recognition accuracy and processing speed due to the specificity and complexity of satellite images, such as large size, variable lighting conditions and complex background. Therefore, this paper adopts the YOLOv5 model and introduces the CBAM (Convolutional Block Attention Module) attention mechanism, which significantly improves the detection of small and dense targets. Experimental validation, using the improved YOLOv5 model on the VisDrone2021 dataset, demonstrates that the model improves the mean average percision (mAP) by 1.9% while maintaining realtime performance. This paper provides new ideas for remote sensing image processing, especially for applications in the fields of urban planning and automatic driving, etc. Despite the progress made in this study, the detection of small targets in remote sensing images, the limited classification accuracy and the detection of dynamic targets still need further research. [ABSTRACT FROM AUTHOR]

In the current era of urban construction, smart city management, and digital twinning, three-dimensional reconstruction of urban buildings is particularly important. Traditional methods have limitations in reconstructing complex geometric scenes, while new methods such as Nerf focus on using implicit MLP to represent the geometric space of the model, but suffer from slow training and rendering speeds. To address this issue, this paper proposes the use of 3D Gaussian scatter points for three-dimensional reconstruction of urban buildings, improving training speed and reconstruction quality through optimized and accelerated rendering algorithms. This method demonstrates high efficiency and editability, providing a new solution for urban building reconstruction. [ABSTRACT FROM AUTHOR]

The stability of front-end odometry output is the cornerstone of real-time mapping and high-precision localization. However, in autonomous driving, factors such as sensor types, driving speed, and signal occlusion can impact system performance, particularly causing trajectory drift, reduced accuracy, and decreased reliability during high-speed travel. This study employs a point-by-point update approach for odometry, achieving high-frequency odometry output and mapping updates nearly matching the point sampling rate, effectively eliminating point cloud distortions and providing solid support for tracking high-speed motion and navigation localization. This method emphasizes maintaining registration accuracy and robustness under high-frequency output conditions. Moreover, by utilizing backward propagation to optimize the estimation of posture and position, and ignoring inter-frame processing, this research implements an iterative extended Kalman filter, achieving tight coupling with IMU data. Additionally, the use of the iVox data structure accelerates point cloud searching, enhancing system processing efficiency. Testing in various scenarios has demonstrated that our method exhibits superior performance and robustness across different scene data, showcasing its potential application in the field of autonomous driving. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation. [ABSTRACT FROM AUTHOR]

Urban three-dimensional modeling involves digitizing geographical elements such as urban ground and architecture, profoundly impacting urban management, planning, and development. In recent years, such models have demonstrated various advantages, but traditional challenges in acquiring comprehensive geographical information persist. The emergence of unmanned aerial vehicle (UAV) oblique photography offers a viable solution, enabling cost-effective acquisition of geographical information and facilitating the construction of three-dimensional urban models. However, UAV-based 3D reconstruction encounters certain issues.Conventional 3D reconstruction typically begins with data collection, involving two-dimensional images or point cloud data acquisition using imagery or LiDAR technology. Subsequent steps include data preprocessing, feature extraction and matching, surface model construction, texture mapping, rendering, and generating three-dimensional models. However, traditional methods exhibit limitations and drawbacks, such as inadequate adaptation to complex scenes, increased computational demands for large-scale scenes, and difficulties with dynamic scenes.Deep learning-based 3D reconstruction methods, like MVSNet, employ deep neural networks to infer scene depth information from multiple-view images, yielding high-quality 3D reconstruction results. However, they rely heavily on prior datasets and auxiliary information, limiting generalization.Neural Radiance Fields (NeRF) combine neural networks with volumetric rendering techniques, excelling in reconstructing objects with high precision and detail, handling dynamic scenes, and addressing areas with sparse viewpoints. However, NeRF's input requires sparse point clouds, commonly obtained using COLMAP, which has limitations. To address these limitations, using NeRF with global Structure-from-Motion (SfM) has emerged as a promising solution. This simultaneous processing of the entire dataset estimates camera trajectories and scene structure from shared features and information among multiple images, resulting in more accurate sparse point clouds and significantly improving image quality. Experimental validation using open-source and self-generated datasets demonstrates that this algorithm markedly enhances surface texture quality compared to traditional 3D reconstruction and NeRF with incremental SfM. In summary, this algorithm enhances 3D reconstruction efficacy and exhibits superior robustness, scalability, and accuracy compared to conventional methods. [ABSTRACT FROM AUTHOR]

Panoramic images, prized for their capacity to capture a comprehensive 360-degree field of view, find extensive applications across various domains. However, the challenge arises when attempting to seamlessly stitch together images captured by multiple cameras onto a spherical surface, particularly in cases where the cameras lack concentric alignment. To mitigate this misalignment issue, our paper introduces a novel method that integrates depth information into panoramic image stitching, aiming to enhance registration accuracy and seamline detection. The proposed method encompasses two pivotal steps. Firstly, depth information is employed to rectify the image's placement on the panoramic sphere, facilitating a two-dimensional registration process. This initial step targets the elimination of misalignment problems between images while preserving image clarity. Subsequently, depth information is seamlessly integrated into the smoothing term of the Markov random field energy function to guide seamline detection. Leveraging depth information aids in circumventing foreground obstacles and directs the search through spatially smooth areas, thereby reducing the likelihood of misalignment issues. Experimental results substantiate the effectiveness of employing depth information for image correction on the spherical surface, especially in scenarios where cameras approximate concentric alignment. Furthermore, the integration of depth information into the stitching network construction markedly diminishes misalignment, leading to a notable improvement in panoramic image quality. This signifies a crucial advancement in the domain of panoramic image stitching from an academic perspective. [ABSTRACT FROM AUTHOR]

This paper introduces an enhanced approach to loop closure detection in Simultaneous Localization and Mapping (SLAM) by integrating the Faster-LIO framework with Stable Triangle Descriptors (STD). SLAM, essential in autonomous driving and augmented reality, often encounters cumulative errors affecting mapping accuracy. Traditional detection methods based on sensor data like camera images and LiDAR point clouds struggle with environmental changes that alter scene appearance and geometry. Our approach utilizes LiDAR sensors and STD, exploiting the geometric stability of triangles to maintain robustness against rotational and translational changes. The process involves storing triangle descriptors from key frames in a hash table within the Faster-LIO framework, a voxel-based LiDAR-Inertial Odometry optimized for efficiency and speed. These descriptors are then matched across frames using a voting mechanism to ensure reliable loop closure detection. Validation on the KITTI dataset and a proprietary subterranean parking garage dataset demonstrates that this integration not only enhances loop closure detection but also simplifies computational demands by avoiding complex tree structures. This method shows promise for broader applications in robotics and autonomous systems, with future research focusing on refining the descriptor and expanding its applicability to other sensor modalities. [ABSTRACT FROM AUTHOR]