Fast-track program vs traditional care in surgery for gastric cancer

AIM:To systematically review the evidence for the effectiveness of fast-track program vs traditional care in laparoscopic or open surgery for gastric cancer.METHODS:PubMed,Embase and the Cochrane library databases were electronically searched for published studies between January 1995 and April 2013,and only randomized trials were included.The references of relevant studies were manually searched for further studies that may have been missed.Search terms included"gastric cancer","fast track"and"enhanced recovery".Five outcome variables were considered most suitable for analysis:postoperative hospital stay,medical cost,duration to first flatus,C-reactive protein(CRP)level and complications.Postoperative hospital stay was calculated from the date of operation to the date of discharge.Fixed effects model was used for meta-analysis.RESULTS:Compared with traditional care,fasttrack program could significantly decrease the postoperative hospital stay[weighted mean difference(WMD)=-1.19,95%CI:-1.79--0.60,P=0.0001,fixed model],duration to first flatus(WMD=-6.82,95%CI:-11.51--2.13,P=0.004),medical costs(WMD=-2590,95%CI:-4054--1126,P=0.001),and the level of CRP(WMD=-17.78,95%CI:-32.22--3.35,P=0.0001)in laparoscopic surgery for gastric cancer.In open surgery for gastric cancer,fast-track program could also significantly decrease the postoperative hospital stay(WMD=-1.99,95%CI:-2.09--1.89,P=0.0001),duration to first flatus(WMD=-12.0,95%CI:-18.89--5.11,P=0.001),medical cost(WMD=-3674,95%CI:-5025--2323,P=0.0001),and the level of CRP(WMD=-27.34,95%CI:-35.42--19.26,P=0.0001).Furthermore,fast-track program did not significantly increase the incidence of complication(RR=1.39,95%CI:0.77-2.51,P=0.27,for laparoscopic surgery;and RR=1.52,95%CI:0.90-2.56,P=0.12,for open surgery).CONCLUSION:Our overall results suggested that compared with traditional care,fast-track program could result in shorter postoperative hospital stay,less medical costs,and lower level of CRP,with no more complications occurring in both laparoscopic and open surgery for gastric cancer.

Optimal set integer programming algorithm for multiple maneuvering targets tracking in clutter

The aim of this paper is to solve the problems of multitarget tracking in clutter. Firstly, the data association of measurement-to-target is formulated as an integer programming problem. Through using the linear programming （LP） based branchand-bound method and adjusting the constraint conditions, an optimal set integer programming （OSIP） algorithm is then proposed for tracking multiple non-maneuvering targets in clutter. For the case of maneuvering targets, this paper introduces the OSIP algorithm into the filtering step of the interacting multiple model （IMM） algorithm resulting in the IMM based on OSIP algorithm. Extensive Monte Carlo simulations show that the presented algorithms can obtain superior estimations even in the case of high density noises.

Using approximate dynamic programming for multi-ESM scheduling to track ground moving targets

This paper researches the adaptive scheduling problem of multiple electronic support measures(multi-ESM) in a ground moving radar targets tracking application. It is a sequential decision-making problem in uncertain environment. For adaptive selection of appropriate ESMs, we generalize an approximate dynamic programming(ADP) framework to the dynamic case. We define the environment model and agent model, respectively. To handle the partially observable challenge, we apply the unsented Kalman filter(UKF) algorithm for belief state estimation. To reduce the computational burden, a simulation-based approach rollout with a redesigned base policy is proposed to approximate the long-term cumulative reward. Meanwhile, Monte Carlo sampling is combined into the rollout to estimate the expectation of the rewards. The experiments indicate that our method outperforms other strategies due to its better performance in larger-scale problems.

Analysis of Nuclear Track Parameters of CN-85 Detector Irradiated to Thermal Neutrons by Using MATLAB Program

CN-85 detector which covered with boric acid H3Bo3 pellete has been irradiated by thermal neutrons from (241Am-9Be) source with activity 12 Ci and neutron flux 105 n. cm-2. s-1. The irradiation times-TD for detector were 4 h, 8 h, 16 h and 24 h. The track detector has been etched with sodium hydroxide. After chemical etching of the irradiated CN-85 detector, the images have been taken from a digital camera connected to the optical microscope. Image processing for the output images has been performed using MATALB program, and these images were analyzed and we had found the following relations: a) The relation between summation of opened track or surface density for tracks (intensity-IT) varies with radius of opening (track radius-RT). b) The relation between the tracks number-NT varies with the tracks diameter-DT (in micrometer) and tracks area-AT. That analysis of image processing was obtained, and the track intensity-IT was decreased with increase of track radius-RT at all of the irradiation time-TD. And the track intensity-IT was increased with increasing irradiation time-TD (h) for different track radius-RT (0.4225, 0.845, 1.2675 and 1.69 μm). The study indicates the possibility of using the analysis of image processing to CN-85 detector for classification of α-particle emitters through limitation of radius of track-RT, in addition to the contribution of these techniques in preparation of nano-filters and nono-membrane in nanotechnology fields.

基于功率谱流形的信息几何DP-TBD算法

针对复杂杂波背景下目标信杂比起伏而导致的漏检问题,本文结合信息几何检测器的性能优势与动态规划检测前跟踪(Dynamic Programming Track Before Detect,DP-TBD)的多帧信息积累能力,提出了基于功率谱流形的信息几何DP-TBD算法.该算法利用功率谱流形与矩阵流形对偶关系,设计了功率谱信息几何检测器,将信息几何检测器的计算复杂度降低了近两个数量级.通过实测数据实验验证,功率谱DP-TBD算法可实现与矩阵DP-TBD算法相近的检测性能,并将运算时间降低为矩阵DP-TBD算法的3%~8%.此外,相较于信息几何检测器,功率谱DP-TBD可将检测信杂比(Signal-to-Clutter Ratio,SCR)提高2~3 dB.

基于航迹消除与策略迭代的无人机集群区域目标搜索方法

无人机集群区域搜索在军事领域以及民用领域的搜救、巡逻、监测、环境勘测等方面有着广泛的应用,但如何保证不同场景下无人机集群搜索方法的效率问题依然是个难题。为了更好地解决搜索目标先验信息已知的无障碍区域内多无人机集群搜索航迹规划问题,提高无人机集群搜索效率,本文根据目标区域热度以及传感器探测概率等先验信息,提出了一种基于无人机航迹消除策略的概率计算方法,并在此基础上结合策略迭代算法动态规划无人机航迹,找到单个无人机航迹覆盖率最优策略;进而通过适当组合顺序实现无人机集群区域目标搜索整体覆盖率最优;最后,通过仿真计算验证了算法的有效性。

高速铁路区间完全中断场景双向到发线运用优化

针对高速铁路区间完全中断场景下的列车运行调整问题,提出了双向到发线运用优化策略。从宏观角度将列车运行过程抽象为由事件和活动构成的网络,对提出的双向到发线运用策略进行建模,建立高速铁路区间完全中断场景下列车运行调整混合整数线性规划(MILP)模型。根据现场实时性要求,采用自编算法与商业优化软件Gurobi相结合的方式对模型进行求解。运用模型和算法对构建的9个现场中断案例进行算例测试。结果表明,双向到发线运用优化策略可以有效减少中断事件对列车运行的影响,相比于目前到发线运用策略,最多可减少10%的列车延误时间。

乳腺癌患者化疗相关不良反应可视化评估管理程序的研发及可用性评价

目的 研发基于知识和数据可视化的乳腺癌患者化疗相关不良反应评估管理程序,并评价其可用性。方法 组建多学科研发团队,采用专家工作组法构建乳腺癌患者化疗相关不良反应可视化评估管理程序研发框架,基于敏捷软件开发原则研发可视化评估管理程序,选取13例乳腺癌患者基于眼动追踪技术进行可用性评价。结果 基于研发框架研发了乳腺癌患者化疗相关不良反应可视化评估管理程序,基于眼动追踪技术发现可用性问题5项并进行了程序优化,系统可用性量表平均得分为(76.35±13.17)分。结论 研发的乳腺癌化疗患者不良反应可视化评估及管理程序可用性良好,适用于乳腺癌患者不良反应的评估及管理。

无人驾驶混合动力汽车轨迹跟踪节能控制融合研究

为进一步提高无人驾驶混合动力汽车轨迹跟踪精度和能耗经济性,提出了一种轨迹跟踪节能控制融合策略。首先,建立车辆运动学模型,采用模型预测控制策略对车辆进行轨迹跟踪控制;在此基础上,以速度为交互变量,提出了一种三阶段动态规划节能控制策略,在线优化最优经济性函数,以降低整车能耗总成本;最后,选择相互独立的纯跟踪轨迹跟踪算法与功率跟随节能控制策略进行比较。结果表明,所提出的轨迹跟踪节能控制融合策略提高了轨迹跟踪效果,降低了整车能耗总成本,轨迹跟踪精度提高了70.47%,纯电动和混合驱动模式下能耗总成本分别下降了4.52%和25.10%。

凸近似避障及采样区优化的智能车辆轨迹规划

针对结构化道路下作匀速运动的智能车辆避障轨迹规划问题,提出一种基于凸近似避障原理及采样区域优化的智能车辆轨迹规划方法。引入凸近似避障原理,得到轨迹可行域范围;将采样区域分为静态采样区、动态采样区两部分,并根据障碍物运动状态,另外划分动态、静态障碍物采样区;采用“动态规划(DP)+二次规划(QP)”思想求解轨迹:利用五次多项式对采样点依次连接,建立动态规划代价函数并筛选得到粗略轨迹;通过二次规划及约束条件的构造,对粗略轨迹进行平滑,最终得到最优轨迹。仿真结果表明:对于静态、低速、动态三种障碍物,该车能够有效地得到平滑轨迹并避开障碍物。

The training process： Planning for strength-power training in track and field. Part 2： Practical and applied aspects

在制定田径运动员力量—爆发力训练方案前,首先需要理解训练原则与训练理论。训练原则包括超负荷原则、多样性原则及专项性原则。其中每个原则都应纳入适当的训练体系中。从概念上讲,训练分期围绕训练原则,并为训练计划和方案提供有利条件,使之能够允许训练变量(如训练方法选择、训练强度和训练量)的逻辑整合和控制。运动员的适应和发展与教练员能否制定高效训练方案的能力密不可分。该能力包括:对运动如何影响生理适应和运动表现的理解(例如最大力量、力量发展和爆发力等);如何优化训练效果,确保训练能使运动潜能最大化地转变为运动表现;以及如何在适当水平(宏观、中观、微观)的变化下实施训练中的疲劳管理和运动表现的优化。

Optimal Fixed-Point Tracking Control for Discrete-Time Nonlinear Systems via ADP

Based on adaptive dynamic programming(ADP),the fixed-point tracking control problem is solved by a value iteration(VI) algorithm. First, a class of discrete-time(DT)nonlinear system with disturbance is considered. Second, the convergence of a VI algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value,and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks(NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method.

Multi-view video color correction using dynamic programming

Color inconsistency between views is an important problem to be solved in multi-view video systems. A multi-view video color correction method using dynamic programming is proposed. Three-dimensional histograms are constructed with sequential conditional probability in HSI color space. Then, dynamic programming is used to seek the best color mapping relation with the minimum cost path between target image histogram and source image histogram. Finally, video tracking technique is performed to correct multi-view video. Experimental results show that the proposed method can obtain better subjective and objective performance in color correction.

Optimal Tracking Control for a Class of Unknown Discrete-time Systems with Actuator Saturation via Data-based ADP Algorithm

为有致动器浸透和未知动力学的分离时间的系统的一个班的一个新奇最佳的追踪控制方法在这份报纸被建议。计划基于反复的适应动态编程(自动数据处理) 算法。以便实现控制计划，一个 data-based 标识符首先为未知系统动力学被构造。由介绍 M 网络，稳定的控制的明确的公式被完成。以便消除致动器浸透的效果， nonquadratic 表演功能被介绍，然后一个反复的自动数据处理算法被建立与集中分析完成最佳的追踪控制解决方案。为实现最佳的控制方法，神经网络被用来建立 data-based 标识符，计算性能索引功能，近似最佳的控制政策并且分别地解决稳定的控制。模拟例子被提供验证介绍最佳的追踪的控制计划的有效性。

Off-policy integral reinforcement learning optimal tracking control for continuous-time chaotic systems

This paper estimates an off-policy integral reinforcement learning（IRL） algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton–Jacobi–Bellman（HJB） equation, an off-policy IRL algorithm is proposed.It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method.

Approximation-error-ADP-based optimal tracking control for chaotic systems with convergence proof

In this paper, an optimal tracking control scheme is proposed for a class of discrete-time chaotic systems using the approximation-error-based adaptive dynamic programming （ADP） algorithm. Via the system transformation, the optimal tracking problem is transformed into an optimal regulation problem, and then the novel optimal tracking control method is proposed. It is shown that for the iterative ADP algorithm with finite approximation error, the iterative performance index functions can converge to a finite neighborhood of the greatest lower bound of all performance index functions under some convergence conditions. Two examples are given to demonstrate the validity of the proposed optimal tracking control scheme for chaotic systems.

基于深度知识追踪的个性化推荐编程实训系统建设与教学实践

在软件设计类实验课程中,过高的生师比使学生缺乏充分的教师个别指导。针对此问题,文章研究了基于深度知识追踪的个性化推荐学习方法,并应用于上机编程实训环节教学。该方法采用融合注意力机制的动态键-值记忆网络,对学生的历史编程实训答题记录进行建模;然后通过双向门控循环单元神经网络追踪学生编程能力水平随时间的变化;最后根据由多种学习属性构成的推荐度来生成个性化推荐方案。运用该方法建设的个性化推荐编程实训系统,能够为学生个体生成个性化的编程实训指导方案,支持学生独立自主地进行编程实训,尤其适用于大规模在线编程教育场景。

一种基于低门限CFAR的检测前跟踪算法

相比于先检测后跟踪的方法,检测前跟踪在低信噪比环境中有更加优秀的效果。本文提出一种适用于应用在雷达系统中的检测前跟踪方法,利用低门限CFAR与DP-TBD级联的方法,再经过检测门限判决,解决了TBD检测在雷达系统中的时间牺牲问题,并优化了DP-TBD的算法,降低复杂度,给出了低门限CFAR的门限取值方法,仿真结果表明,本文提出的算法在4帧积累时比传统的CFAR检测信噪比改善了约4.5dB。