遵循“Crash plan”查体顺序在多发性创伤临床教学中的应用

多发性创伤是骨科的常见急、危、重症,并发症多,病死率和致残率较高,更容易出现骨折漏诊,导致多发性创伤的早期诊断存在较多的困难。常规系统的查体方法虽然全面,但对于实习学生而言在急重症患者面前难以有效掌握,所以急需一些实用、快速、简便、易于记忆的骨科查体方法。而"Crash plan"(打碎计划)可以作为一种快速、易学的查体系统,有助于实习同学快速、全面掌握临床查体要点,从一定程度上防止多发性创伤漏诊的发生几率。通过临床实践,发现"Crash plan"查体方法能够很好地促进实习同学对骨科系统查体的快速掌握,做到学习有兴趣,查体快速不易遗漏,对促进实习同学角色转变具有很好的引导作用。

Teaching Reform and Research on Art Design Majors in Higher Vocational Colleges under the Background of “Double High Plan”

Training talents for the society is the responsibility of colleges and universities.The society needs applied and innovative art design majors.In order to cultivate talents needed by society and keep up with the development plan of the Ministry of Education,higher vocational colleges need to reform.This paper adopts the method of theoretical analysis to elaborate from the four aspects of focusing equally on science and education,promote learning by competition;integrating industry and education,nurturing talents together;keeping the mission in mind while serving students;and finding the right positioning,giving full play to the advantages.

Research on Teaching Innovation Team Construction with“Double-Qualified”Teachers Under the Background of the“Double-High Plan”

With the continuous development of education in China,various vocational colleges have responded to the“Double-High Plan”and are actively building teaching innovation teams with“double-qualified teachers.”By improving the professional level of teachers and increasing practical opportunities,vocational colleges and universities can nurture high-level teachers and improve their teaching level so as to meet the demand for high-level technical skills training in the new era.This paper discusses the significance of constructing a“double-qualified”education model under the background of the“Double-High Plan,”the requirements of the“double-qualified”education model for teachers,the problems in constructing a teaching innovation team with“double-qualified”teachers,and the countermeasures for these problems.

Thoughts on the Management of Personnel Files in Higher Vocational Colleges in the Context of“Double High Plan”

Starting from the background of the"Double High Plan"in conjunction with the role of personnel file management in advancing the"Double High Plan",this paper analyzes the current status of personnel file management in higher vocational colleges and the necessity of personnel management in higher vocational colleges,and explores the effective measures to strengthen the informatization of personnel files management to make it more reasonable,standardized and informatized.

Mr.Zhangshou Interviewed the China Area Manager of “Asia Investment Plan”of Europe Union

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Remarkable achievements in rice breeding was acquired in the China's “Eighth Five-year plan”

During the China’"Eighth Five-year Plan", the Chinese rice breeders developed hundreds of new rice varieties(combinations) which have higher yield, better rice quality or higher resistance. Among them, 83 new varieties (combinations) were released in the national or provincial levels. In addition, many new germplasms and excellence breeding materials were obtained which is rery useful for the rice breeding in the future.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Motion Planning for Autonomous Driving with Real Traffic Data Validation

Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.

基于Plant Simulation的压气机叶片型线加工产线分析与优化

运用专业仿真软件Plant Simulation,根据压气机叶片型线机械加工工艺特点和物料运行流程建立生产线仿真模型,从产能、设备利用率及产线瓶颈等多方面进行仿真分析与优化。结果表明:Plant Simulation仿真平台的仿真可以找出规划设计方案中存在的问题并验证方案的合理性。该仿真结果为型线机械加工产线的优化设计提供了可靠依据,达到了节约投资成本和缩短设计周期的目的。

C-PLAN指数作为免疫检查点抑制剂治疗晚期食管癌预后指标的临床研究

目的评估C反应蛋白(CRP)、体力状况评分(PS)、乳酸脱氢酶(LDH)、白蛋白(ALB)及衍生中性粒细胞与淋巴细胞比值(dNLR)综合(C-PLAN)指数在接受免疫检查点抑制剂(ICI)治疗的晚期食管癌患者中的预后价值。方法收集扬州大学附属医院接受ICI治疗的147例晚期食管癌患者首次免疫治疗前的血液学指标。对CRP、PS、LDH、ALB及dNLR进行评分并相加得到C-PLAN指数。采用卡方检验分析C-PLAN指数与临床病理特征的相关性;采用Kaplan-Meier生存曲线分析C-PLAN指数对患者总生存期(OS)与无进展生存期(PFS)的影响;采用单因素和多因素Cox风险比例回归模型分析C-PLAN指数是否为影响预后的独立因素。结果147例晚期食管癌患者根据C-PLAN指数分为低风险组(<2分,n=46)和高风险组(≥2分,n=101)。C-PLAN指数与年龄、性别、PS评分、吸烟、临床分期、体质量指数、病理类型、治疗策略和是否手术均无相关性(P>0.05)。低风险组PFS和OS优于高风险组,差异有统计学意义(P<0.001)。在单因素Cox回归分析中,C-PLAN指数是PFS(P<0.001)和OS(P=0.002)的影响因素。在多因素Cox分析中,C-PLAN指数是影响PFS(P=0.001)和OS(P=0.006)的独立预后因素。结论C-PLAN指数可以作为预测接受ICI治疗的晚期食管癌患者预后的可靠临床指标。

Optimal search path planning of UUV in battlefeld ambush scene

Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.

Efficient Route Planning for Real-Time Demand-Responsive Transit

Demand Responsive Transit (DRT) responds to the dynamic users’ requests without any fixed routes and timetablesand determines the stop and the start according to the demands. This study explores the optimization of dynamicvehicle scheduling and real-time route planning in urban public transportation systems, with a focus on busservices. It addresses the limitations of current shared mobility routing algorithms, which are primarily designedfor simpler, single origin/destination scenarios, and do not meet the complex demands of bus transit systems. Theresearch introduces an route planning algorithm designed to dynamically accommodate passenger travel needsand enable real-time route modifications. Unlike traditional methods, this algorithm leverages a queue-based,multi-objective heuristic A∗ approach, offering a solution to the inflexibility and limited coverage of suburbanbus routes. Also, this study conducts a comparative analysis of the proposed algorithm with solutions based onGenetic Algorithm (GA) and Ant Colony Optimization Algorithm (ACO), focusing on calculation time, routelength, passenger waiting time, boarding time, and detour rate. The findings demonstrate that the proposedalgorithmsignificantly enhances route planning speed, achieving an 80–100-fold increase in efficiency over existingmodels, thereby supporting the real-time demands of Demand-Responsive Transportation (DRT) systems. Thestudy concludes that this algorithm not only optimizes route planning in bus transit but also presents a scalablesolution for improving urban mobility.

Safe Motion Planning and Control Framework for Automated Vehicles with Zonotopic TRMPC

Model mismatches can cause multi-dimensional uncertainties for the receding horizon control strategies of automated vehicles(AVs).The uncertainties may lead to potentially hazardous behaviors when the AV tracks ideal trajectories that are individually optimized by the AV's planning layer.To address this issue,this study proposes a safe motion planning and control(SMPAC)framework for AVs.For the control layer,a dynamic model including multi-dimensional uncertainties is established.A zonotopic tube-based robust model predictive control scheme is proposed to constrain the uncertain system in a bounded minimum robust positive invariant set.A flexible tube with varying cross-sections is constructed to reduce the controller conservatism.For the planning layer,a concept of safety sets,representing the geometric boundaries of the ego vehicle and obstacles under uncertainties,is proposed.The safety sets provide the basis for the subsequent evaluation and ranking of the generated trajectories.An efficient collision avoidance algorithm decides the desired trajectory through the intersection detection of the safety sets between the ego vehicle and obstacles.A numerical simulation and hardware-in-the-loop experiment validate the effectiveness and real-time performance of the SMPAC.The result of two driving scenarios indicates that the SMPAC can guarantee the safety of automated driving under multi-dimensional uncertainties.

Heuristic Expanding Disconnected Graph:A Rapid Path Planning Method for Mobile Robots

Existing mobile robots mostly use graph search algorithms for path planning,which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity.Due to the limitations of the neighborhood search strategy,the robots could hardly obtain the most optimal global path.A global path planning algorithm,denoted as EDG*,is proposed by expanding nodes using a well-designed expanding disconnected graph operator(EDG)in this paper.Firstly,all obstacles are marked and their corners are located through the map pre-processing.Then,the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes.Finally,the EDG*heuristic iterative algorithm is proposed.It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset.Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM.The result shows that the proposed EDG*reduced the planning time by more than 90%and total length of paths reduced by more than 4.6%.Compared to A*,Dijkstra and JPS,EDG*does not show an exponential explosion effect in map size.The EDG*showed better performance in terms of path smoothness,and collision avoidance.This shows that the EDG*algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.

A Planning Method for Operational Test of UAV Swarm Based on Mission Reliability

The unmanned aerial vehicle(UAV)swarm plays an increasingly important role in the modern battlefield,and the UAV swarm operational test is a vital means to validate the combat effectiveness of the UAV swarm.Due to the high cost and long duration of operational tests,it is essential to plan the test in advance.To solve the problem of planning UAV swarm operational test,this study considers the multi-stage feature of a UAV swarm mission,composed of launch,flight and combat stages,and proposes a method to find test plans that can maximize mission reliability.Therefore,a multi-stage mission reliability model for a UAV swarm is proposed to ensure successful implementation of the mission.A multi-objective integer optimization method that considers both mission reliability and cost is then formulated to obtain the optimal test plans.This study first constructs a mission reliability model for the UAV swarm in the combat stage.Then,the launch stage and flight stage are integrated to develop a complete PMS(Phased Mission Systems)reliability model.Finally,the Binary Decision Diagrams(BDD)and Multi Objective Quantum Particle Swarm Optimization(MOQPSO)methods are proposed to solve the model.The optimal plans considering both reliability and cost are obtained.The proposed model supports the planning of UAV swarm operational tests and represents a meaningful exploration of UAV swarm test planning.

Influence of a diet meal plan on pepsinogen I and II,gastrin-17,and nutritional status in gastric ulcer patients

BACKGROUND Gastric ulcers(GUs)have a high risk of clinical morbidity and recurrence,and further exploration is needed for the prevention,diagnosis,and treatment of the disease.AIM To investigated the effects of a diet plan on pepsinogen(PG)I,PG II,gastrin-17(G-17)levels and nutritional status in patients with GUs.METHODS A total of 100 patients with GUs treated between May 2022 and May 2023 were enrolled,with 47 patients in the control group receiving routine nursing and 53 patients in the experimental group receiving dietary nursing intervention based on a diet plan.The study compared the two groups in terms of nursing efficacy,adverse events(vomiting,acid reflux,and celialgia),time to symptom improvement(burning sensation,acid reflux,and celialgia),gastric function(PG I,PG II,and G-17 levels),and nutritional status[prealbumin(PA)and albumin(ALB)levels].RESULTS The experimental group showed a markedly higher total effective rate of nursing,a significantly lower incidence of adverse events,and a shorter time to symptom improvement than the control group.Additionally,the experimental group’s post-intervention PG I,PG II,and G-17 levels were significantly lower than preintervention or control group levels,whereas PA and ALB levels were significantly higher.CONCLUSION The diet plan significantly reduced PG I,PG II,and G-17 levels in patients with GUs and significantly improved their nutritional status.

Ground threat prediction-based path planning of unmanned autonomous helicopter using hybrid enhanced artificial bee colony algorithm

Unmanned autonomous helicopter(UAH)path planning problem is an important component of the UAH mission planning system.Aiming to reduce the influence of non-complete ground threat information on UAH path planning,a ground threat prediction-based path planning method is proposed based on artificial bee colony(ABC)algorithm by collaborative thinking strategy.Firstly,a dynamic threat distribution probability model is developed based on the characteristics of typical ground threats.The dynamic no-fly zone of the UAH is simulated and established by calculating the distribution probability of ground threats in real time.Then,a dynamic path planning method for UAH is designed in complex environment based on the real-time prediction of ground threats.By adding the collision warning mechanism to the path planning model,the flight path could be dynamically adjusted according to changing no-fly zones.Furthermore,a hybrid enhanced ABC algorithm is proposed based on collaborative thinking strategy.The proposed algorithm applies the leader-member thinking mechanism to guide the direction of population evolution,and reduces the negative impact of local optimal solutions caused by collaborative learning update strategy,which makes the optimization performance of ABC algorithm more controllable and efficient.Finally,simulation results verify the feasibility and effectiveness of the proposed ground threat prediction path planning method.

Stochastic programming based coordinated expansion planning of generation,transmission,demand side resources,and energy storage considering the DC transmission system

With the increasing penetration of wind and solar energies,the accompanying uncertainty that propagates in the system places higher requirements on the expansion planning of power systems.A source-grid-load-storage coordinated expansion planning model based on stochastic programming was proposed to suppress the impact of wind and solar energy fluctuations.Multiple types of system components,including demand response service entities,converter stations,DC transmission systems,cascade hydropower stations,and other traditional components,have been extensively modeled.Moreover,energy storage systems are considered to improve the accommodation level of renewable energy and alleviate the influence of intermittence.Demand-response service entities from the load side are used to reduce and move the demand during peak load periods.The uncertainties in wind,solar energy,and loads were simulated using stochastic programming.Finally,the effectiveness of the proposed model is verified through numerical simulations.

A Reverse Path Planning Approach for Enhanced Performance of Multi-Degree-of-Freedom Industrial Manipulators

In the domain of autonomous industrial manipulators,precise positioning and appropriate posture selection in path planning are pivotal for tasks involving obstacle avoidance,such as handling,heat sealing,and stacking.While Multi-Degree-of-Freedom(MDOF)manipulators offer kinematic redundancy,aiding in the derivation of optimal inverse kinematic solutions to meet position and posture requisites,their path planning entails intricate multiobjective optimization,encompassing path,posture,and joint motion optimization.Achieving satisfactory results in practical scenarios remains challenging.In response,this study introduces a novel Reverse Path Planning(RPP)methodology tailored for industrial manipulators.The approach commences by conceptualizing the manipulator’s end-effector as an agent within a reinforcement learning(RL)framework,wherein the state space,action set,and reward function are precisely defined to expedite the search for an initial collision-free path.To enhance convergence speed,the Q-learning algorithm in RL is augmented with Dyna-Q.Additionally,we formulate the cylindrical bounding box of the manipulator based on its Denavit-Hartenberg(DH)parameters and propose a swift collision detection technique.Furthermore,the motion performance of the end-effector is refined through a bidirectional search,and joint weighting coefficients are introduced to mitigate motion in high-power joints.The efficacy of the proposed RPP methodology is rigorously examined through extensive simulations conducted on a six-degree-of-freedom(6-DOF)manipulator encountering two distinct obstacle configurations and target positions.Experimental results substantiate that the RPP method adeptly orchestrates the computation of the shortest collision-free path while adhering to specific posture constraints at the target point.Moreover,itminimizes both posture angle deviations and joint motion,showcasing its prowess in enhancing the operational performance of MDOF industrial manipulators.