A Multi-Scale Target Detection Method Using an Improved Faster Region Convolutional Neural Network Based on Enhanced Backbone and Optimized Mechanisms.

Currently, existing deep learning methods exhibit many limitations in multi-target detection, such as low accuracy and high rates of false detection and missed detections. This paper proposes an improved Faster R-CNN algorithm, aiming to enhance the algorithm's capability in detecting multi-scale targets. This algorithm has three improvements based on Faster R-CNN. Firstly, the new algorithm uses the ResNet101 network for feature extraction of the detection image, which achieves stronger feature extraction capabilities. Secondly, the new algorithm integrates Online Hard Example Mining (OHEM), Soft non-maximum suppression (Soft-NMS), and Distance Intersection Over Union (DIOU) modules, which improves the positive and negative sample imbalance and the problem of small targets being easily missed during model training. Finally, the Region Proposal Network (RPN) is simplified to achieve a faster detection speed and a lower miss rate. The multi-scale training (MST) strategy is also used to train the improved Faster R-CNN to achieve a balance between detection accuracy and efficiency. Compared to the other detection models, the improved Faster R-CNN demonstrates significant advantages in terms of mAP@0.5, F1-score, and Log average miss rate (LAMR). The model proposed in this paper provides valuable insights and inspiration for many fields, such as smart agriculture, medical diagnosis, and face recognition.

[Exploration of relationship between postoperative serum caveolin-1 contents and delayed healing of tibial fracture patients].

OBJECTIVE: To explore relationship between postoperative serum caveolin-1 contents after open reduction and internal fixation and delayed healing of tibial fracture patients. METHODS: From April 2018 to June 2020, 134 tibial fracture patients underwent open reduction and internal fixation were included, and divided into delayed healing group and normal healing group according to fracture healing condition. Contents of serum caveolin-1 protein before operation, 1, 4, 8, 12 weeks after operation between two groups were compared. Influencing factors of delayed healing was analyzed by Logistic regression model, and predictive value of serum caveolin-1 protein on delayed healing was analyzed by receiver operating characteristic(ROC) curve. RESULTS: Fracture healing was evaluated at 4 months after fracture, 93 patients healed well and 41 patients delayed heal. At 1, 4, 8 and 12 weeks, content of serum caveolin-1 protein in delayed healing group was lower than that of normal healing group(P<0.05). There were statistical difference in smoking, diabetes, open fracture and Gutlio Ⅲ fracture between delayed healing group and normal healing group(P<0.05). Smoking, diabetes, open fracture, Gustlio Ⅲ fracture, decrease of serum caveolin-1 protein at 4 and 8 weeks after operation were risk factor of delayed healing by Logistic analysis(P<0.05). By ROC curve analysis, content of serum caveolin-1 protein had predictive value for delayed fracture, the best cut-off values were 12.45 ng/ml and 12.52 ng/ml respectively, corresponding sensitivity were 45.34%, 43.90%, and specificity were 80.65% and 87.10% (P<0.05). CONCLUSION: Decrease of serum caveolin-1 protein content at 4 and 8 weeks after open reduction and internal fixation for tibia fracture patients were related with delayed healing. Detection of serum caveolin-1 protein content at 4 and 8 weeks after operation has predictive value for delayed healing.

Multimaterial 3D Printing of Highly Stretchable Silicone Elastomers.

3D printing of silicone elastomers with the direct ink writing (DIW) process has demonstrated great potential in areas as diverse as flexible electronics, medical devices, and soft robotics. However, most of current silicones are not printable because of their low viscosity and long curing time. The lack of systematic research on materials, devices, and processes during printing makes it a huge challenge to apply the DIW process more deeply and widely. In this report, aiming at the dilemmas in materials, devices, and processes, we proposed a comprehensive guide for printing highly stretchable silicone. Specifically, to improve the printability of silicone elastomers, nanosilica was added as a rheology modifier without sacrificing any stretching ability. To effectively control print speed and accuracy, a theoretical model was built and verified. With this strategy, silicone elastomers with different mechanical properties can all be printed and can realize infinite time and high speed printing (>25 mm/s) while maintaining accuracy. Here, super-stretchable silicone that can be stretched to 2000% was printed for the first time, and complex structures can be printed with high quality. For further demonstration, prosthetic nose, data glove capable of detecting fingers' movement, and artificial muscle that can lift objects were printed directly. We believe that this work could provide a guide for further work using the DIW process to print soft matters in a wide range of application scenarios.