RESUMEN
The challenges in achieving optimal outcomes for wound healing have persisted for decades, prompting ongoing exploration of interventions and management strategies. This study focuses on assessing the potential benefits of implementing a nano-gelatin scaffold for wound healing. Using a rat skin defect model, full-thickness incisional wounds were created on each side of the thoracic-lumbar regions after anesthesia. The wounds were left un-sutured, with one side covered by a gelatin nano-fibrous membrane and the other left uncovered. Wound size changes were measured on days 1, 4, 7, and 14, and on day 14, rats were sacrificed for tissue sample excision, examined with hematoxylin and eosin, and Masson's trichrome stain. Statistical comparisons were performed. The gelatin nanofibers exhibited a smooth surface with a fiber diameter of 260 ± 40 nm and porous structures with proper interconnectivity. Throughout the 14-day experimental period, significant differences in the percentage of wound closure were observed between the groups. Histological scores were higher in the experiment group, indicating less inflammation but dense and well-aligned collagen fiber formation. A preliminary clinical trial on diabetic ulcers also demonstrated promising results. This study highlights the potential of the nano-collagen fibrous membrane to reduce inflammatory infiltration and enhance fibroblast differentiation into myofibroblasts during the early stages of cutaneous wound healing. The nano-fibrous collagen membrane emerges as a promising candidate for promoting wound healing, with considerable potential for future therapeutic applications.
RESUMEN
Global warming and pollution could lead to the destruction of marine habitats and loss of species. The anomalous behavior of underwater creatures can be used as a biometer for assessing the health status of our ocean. Advances in behavior recognition have been driven by the active application of deep learning methods, yet many of them render superior accuracy at the cost of high computational complexity and slow inference. This paper presents a real-time anomalous behavior recognition approach that incorporates a lightweight deep learning model (Lite3D), object detection, and multitarget tracking. Lite3D is characterized in threefold: (1) image frames contain only regions of interest (ROI) generated by an object detector; (2) no fully connected layers are needed, the prediction head itself is a flatten layer of 1 × [Formula: see text] @ 1× 1, [Formula: see text]= number of categories; (3) all the convolution kernels are 3D, except the first layer degenerated to 2D. Through the tracking, a sequence of ROI-only frames is subjected to 3D convolutions for stacked feature extraction. Compared to other 3D models, Lite3D is 50 times smaller in size and 57 times lighter in terms of trainable parameters and can achieve 99% of F1-score. Lite3D is ideal for mounting on ROV or AUV to perform real-time edge computing.
RESUMEN
Recently, an upsurge of deep learning has provided a new direction for the field of computer vision and visual tracking. However, expensive offline training time and the large number of images required by deep learning have greatly hindered progress. This paper aims to further improve the computational performance of CNT which is reported to deliver 5 fps performance in visual tracking, we propose a method called Fast-CNT which differs from CNT in three aspects: firstly, an adaptive k value (rather than a constant 100) is determined for an input video; secondly, background filters used in CNT are omitted in this work to save computation time without affecting performance; thirdly, SURF feature points are used in conjunction with the particle filter to address the drift problem in CNT. Extensive experimental results on land and undersea video sequences show that Fast-CNT outperforms CNT by 2~10 times in terms of computational efficiency.
RESUMEN
The paper demonstrates a following robot with omni-directional wheels, which is able to take action to avoid obstacles. The robot design is based on both fuzzy and extension theory. Fuzzy theory was applied to tune the PMW signal of the motor revolution, and correct path deviation issues encountered when the robot is moving. Extension theory was used to build a robot obstacle-avoidance model. Various mobile models were developed to handle different types of obstacles. The ultrasonic distance sensors mounted on the robot were used to estimate the distance to obstacles. If an obstacle is encountered, the correlation function is evaluated and the robot avoids the obstacle autonomously using the most appropriate mode. The effectiveness of the proposed approach was verified through several tracking experiments, which demonstrates the feasibility of a fuzzy path tracker as well as the extensible collision avoidance system.