The Utility Model Relates to a Training Platform for Neurosurgery Multi-Purpose Medical Nursing Worker

With the development of medical level, specialized, refined and multidisciplinary collaborative therapy is the requirement and main development direction of neurosurgery. With the improvement of people’s medical awareness, people cannot meet the simple surgical treatment, and there is a great demand for nursing treatment. At the same time, the demand for efficiency and convenience of medical nursing practice teaching continues to improve, and the multi-functional medical nursing training innovation platform has been paid more and more attention. The rapid development of material technology as well as digitalization has brought about a huge change, and we have created a multifunctional, spatially efficient and easy-to-transfer information platform for medical care training. A base box, placement drawer, platform board and display are used as the base module and the base module is filled with specific functional components. Lifting and lowering using motors and spiral base, moving using universal wheels. These devices together constitute the training platform. A survey of students and teachers was conducted through a questionnaire, and they all gave very good feedback that the multi-functional platform was very practical and useful. This platform effectively solves the drawbacks of the original training platform which is time-consuming, laborious and inconvenient, and is worthy of further promotion and research.

Impact of a new teaching model on the fine cosmetic suturing operation and quantitative assessment of the training effect on plastic surgeons

Background Traditional lecture-based teaching(TLT)has long been the primary method of teaching plastic suturing techniques and even surgical education.It has been challenging to adapt this approach to fit the educational objectives of plastic surgery,which is a very practical science.Additionally,it is mainly teacher-led,and the course content is teacher-driven,which has disadvantages such as difficulty in motivating students and disconnection from clinical practice.Therefore,we developed a video point-to-point teaching(VPT)method and teamwork-based teaching(TBT)to study the effect of the new teaching model on fine cosmetic suturing operation(FCSO)and training outcomes for plastic surgeons.Methods We selected 30 junior doctors from the Department of Plastic and Reconstructive Surgery of the Chinese PLA General Hospital.All trainees were randomly assigned to three groups:TLT,VPT,and TBT.All trainees had their performances photographed,and a senior attending physician was appointed as a rater.We rated the process and results of FCSO according to a uniform rubric following the double-blind principle to compare the effects of different teaching modes on the trainees’FCSO and differences in training outcomes.Results There was no significant effect of video recording on trainees’FCSO(P>0.05).The total scores of the first suturing in the three groups were as follows:TLT group(13.18​±​1.66),VPT group(13.63​±​1.97),and TBT group(13.50​±​2.26),with no significant difference among the groups(P>0.05),indicating that the starting level of the trainees in the three groups was basically the same.There was no significant difference(P>0.05)between the VPT(20.30​±​2.17)and TBT(20.38​±​2.29)groups,but both of these groups were significantly better than the TLT group(16.43​±​1.86,P<0.01).Conclusion The TBT and VPT methods are significantly better than TLT.However,the TBT method is more economical and optimal for teachers and better utilizes students’initiative in learning and operation,which improves the teaching level and training efficiency.

DPAL-BERT:A Faster and Lighter Question Answering Model

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering systems.However,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced efficiency.This study aims to minimize the inference time of such models while maintaining computational performance.It also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and DPAL-BERTC.This research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge transfer.The experimental results showed that the distilled models greatly outperform models trained from scratch.In addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the original.This demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

Sampling Methods for Efficient Training of Graph Convolutional Networks:A Survey

Graph convolutional networks(GCNs)have received significant attention from various research fields due to the excellent performance in learning graph representations.Although GCN performs well compared with other methods,it still faces challenges.Training a GCN model for large-scale graphs in a conventional way requires high computation and storage costs.Therefore,motivated by an urgent need in terms of efficiency and scalability in training GCN,sampling methods have been proposed and achieved a significant effect.In this paper,we categorize sampling methods based on the sampling mechanisms and provide a comprehensive survey of sampling methods for efficient training of GCN.To highlight the characteristics and differences of sampling methods,we present a detailed comparison within each category and further give an overall comparative analysis for the sampling methods in all categories.Finally,we discuss some challenges and future research directions of the sampling methods.

A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory

Reinforcement learning(RL) algorithms have been demonstrated to solve a variety of continuous control tasks. However,the training efficiency and performance of such methods limit further applications. In this paper, we propose an off-policy heterogeneous actor-critic(HAC) algorithm, which contains soft Q-function and ordinary Q-function. The soft Q-function encourages the exploration of a Gaussian policy, and the ordinary Q-function optimizes the mean of the Gaussian policy to improve the training efficiency. Experience replay memory is another vital component of off-policy RL methods. We propose a new sampling technique that emphasizes recently experienced transitions to boost the policy training. Besides, we integrate HAC with hindsight experience replay(HER) to deal with sparse reward tasks, which are common in the robotic manipulation domain. Finally, we evaluate our methods on a series of continuous control benchmark tasks and robotic manipulation tasks. The experimental results show that our method outperforms prior state-of-the-art methods in terms of training efficiency and performance, which validates the effectiveness of our method.