To Compare Study on the the Cognition,Attitude and Behavior of Sexually Transmitted Diseases in Medical Students and Normal Students

目的比较医学生和师范生对性传播疾病STD防治知识的掌握情况以及对性行为态度及性行为的差异,为健康教育工作提供依据。方法自行设计性传播疾病KAP调查问卷,采用整群抽样的方法,对牡丹江师范学院和牡丹江医学院注册在读的学生进行问卷调查。结果医学生对STD相关知识的了解程度普遍高于师范生;医学生和师范生知晓性病的三大传播途径(性交、输血或血制品、孕妇传给婴儿)的比例差别无统计学意义(P>0.05),但在某些传播、非传播途径和预防性传播疾病知晓情况上,差异具有统计学意义(P0.05),but there were significant differences in the awareness of some transmission,non transmission routes andPrevention(P<0.05). CONCLUSION according to the needs of students in science students, the knowledge of sexual behavior andsexual behavior should be carried out to prevent sexually transmitted diseases

Expression and test of the neutralization Fab antibody against infectious bursal disease virus

为表达抗鸡传染性法氏囊病病毒(IbdV)抗体fAb并检测其中和活性,本研究将抗IbdV抗体的轻链(l)和重链片段(fd)基因分别克隆于P ET-27b(+)载体中,并转化于大肠杆菌rOSETTA(dE3)进行诱导表达。将l和fd片段包涵体蛋白变性后等量混合于复性液中,制备fAb并对其进行活性鉴定。结果显示l和fd蛋白相对分子质量大小分别为25 ku和28 ku。WESTErn blOT和ElISA检测结果表明,获得的抗体fAb大小约为50 ku,并且与VP2蛋白和不同病毒株均具有特异性结合能力。体外中和试验结果表明,获得的IbdV抗体fAb具有中和活性,可以有效阻断IbdV(b87株)对鸡胚成纤维细胞(df1)的感染。本实验获得的IbdV抗体fAb有望成为治疗Ibd的候选生物制剂,为研制治疗Ibd抗体制剂奠定了基础。To express the neutralizing Fab antibody against infectious bursal disease virus(IBDV),the gene of light chain(L)or heavy chain fragment(Fd) against IBDV was cloned into the prokaryotic expression plasmid,respectively,and then the recombinant L or Fd was expressed in E.coli Rosetta(DE3),respectively,and purified through sole denaturation and co-renaturation of inclusion body.Western blot results showed that the Fab was approximately 50 ku.ELISA results showed that the Fab exhibited binding ability and specify to VP2 for different IBDV strains.The results of neutralization test in vitro showed that the Fab exhibited neutralizing activity to IBDV-B87 strainin chicken embryo fibroblast(DF1) cells.The Fab antibody prepared in this study is expected to become a candidate drug for treatment of IBD,which laid the foundation for the treatment of IBD.黑龙江省应用技术研究与开发计划项目(GC13C104

Energy stochastic optimization scheduling for micro-grid based on photovoltaic forecasting

可再生能源的间歇性和负荷的随机性对微电网能源管理系统(EMS)产生了巨大的挑战。在随机环境下的能源优化调度问题在微电网的研究中具有重要意义。以微电网中光伏发电系统的功率预测为基础,将光伏预测误差当做随机变量,建立了一种基于期望模型的能源随机优化调度模型。用Monte Carlo模拟方法生成了光伏发电预测误差的情景集,应用粒子群优化算法来解决随机优化调度模型。通过与确定性模型产生的调度方案相对比,证明了随机优化调度模型更加有效。</p

Dynamic Energy Management of a Microgrid using Approximate Dynamic Programming and Deep Recurrent Neural Network Learning

This paper focuses on economical operation of a microgrid (MG) in real-time. A novel dynamic energy management system (EMS) is developed to incorporate efficient management of energy storage system (ESS) into MG real-time dispatch while considering power flow constraints and uncertainties in load, renewable generation and real-time electricity price. The developed dynamic energy management mechanism does not require long-term forecast and optimization or distribution knowledge of the uncertainty, but can still optimize the long-term operational costs of MGs. First, the real-time scheduling problem is modeled as a finite-horizon Markov decision process (MDP) over a day. Then, approximate dynamic programming (ADP) and deep recurrent neural network (RNN) learning are employed to derive a near optimal real-time scheduling policy. Last, using real power grid data from California Independent System Operator (CAISO), a detailed simulation study is carried out to validate the effectiveness of the proposed method.</p

Multi-inverter AC micro-grid distributed economical automatic power generation control algorithm

本发明涉及多逆变器型交流微电网分布式经济性自动发电控制算法，包括以下步骤；为微电网中每个逆变器接口电源分配一个智能体以完成通信与数据计算；基于N‑1规则，设计各智能体间的通信拓扑；分布式识别微电网运行状态；基于识别的微电网运行状态进行算法初始化；通过各智能体之间数据交互，分布式地得到各电源发电量参考值；将发电量参考值下达到底层下垂控制器，实现微电网分布式经济性自动发电控制。本发明能够将大时间尺度的经济调度与小时间尺度的自动发电控制紧密组合为经济性自动发电控制，使基于下垂控制的多逆变器型微电网实时经济运行；并且算法可以完全分布式实施，具有较强的鲁棒性。</p

Residential Energy Management with Deep Reinforcement Learning

A smart home with battery energy storage can take part in the demand response program. With proper energy management, consumers can purchase more energy at off-peak hours than at on-peak hours, which can reduce the electricity costs and help to balance the electricity demand and supply. However, it is hard to determine an optimal energy management strategy because of the uncertainty of the electricity consumption and the real-time electricity price. In this paper, a deep reinforcement learning based approach has been proposed to solve this residential energy management problem. The proposed approach does not require any knowledge about the uncertainty and can directly learn the optimal energy management strategy based on reinforcement learning. Simulation results demonstrate the effectiveness of the proposed approach

Residential Demand Side Response Considering Energy Storages and Renewable Energy Sourced Generators

传统居民侧需求响应策略很少考虑储能和新能源发电环境，难以适应新的发展趋势和遇到的新挑战，如新能源发电的随机性对需求响应策略带来风险成本，电池储能系统的缓冲效果增加了模型求解的复杂度。针对上述问题，研究了含储能及新能源发电的居民侧需求响应策略。考虑了新能源发电随机性和电池缓冲作用，建立了居民侧两阶段随机规划需求响应模型以降低预测误差对需求响应策略带来的风险成本。此外，在模型精度和计算复杂度之间进行了折中，使模型不依赖于复杂的智能算法即可快速求解。通过与不考虑随机性的需求响应策略对比，验证了本文提出的需求响应策略的有效性

Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning

Driven by the recent advances in electric vehicle (EV) technologies, EVs have become important for smart grid economy. When EVs participate in demand response program which has real-time pricing signals, the charging cost can be greatly reduced by taking full advantage of these pricing signals. However, it is challenging to determine an optimal charging strategy due to the existence of randomness in traffic conditions, user&#39;s commuting behavior, and the pricing process of the utility. Conventional model-based approaches require a model of forecast on the uncertainty and optimization for the scheduling process. In this paper, we formulate this scheduling problem as a Markov Decision Process (MDP) with unknown transition probability. A model-free approach based on deep reinforcement learning is proposed to determine the optimal strategy for this problem. The proposed approach can adaptively learn the transition probability and does not require any system model information. The architecture of the proposed approach contains two networks: a representation network to extract discriminative features from the electricity prices and a Q network to approximate the optimal action-value function. Numerous experimental results demonstrate the effectiveness of the proposed approach.</p

An Integrative DR Study for Optimal Home Energy Management Based on Approximate Dynamic Programming

This paper presents an integrative demand response (DR) mechanism for energy management of appliances, an energy storage system and an electric vehicle (EV) within a home. The paper considers vehicle-to-home (V2H) and vehicle-to-grid (V2G) functions for energy management of EVs and the degradation cost of the EV battery caused by the V2H/V2G operation in developing the proposed DR mechanism. An efficient optimization algorithm is developed based on approximate dynamic programming, which overcomes the challenges of solving high dimensional optimization problems for the integrative home energy system. To investigate how the participation of different home appliances affects the DR efficiency, several DR scenarios are designed. Then, a detailed simulation study is conducted to investigate and compare home energy management efficiency under different scenarios.</p

A genetic algorithm-based hybrid optimization approach for microgrid energy management

This paper proposes a novel Meta-heuristic based hybrid optimization method for Microgrid energy management system. First, microgrid energy management problem is modeled as a mixed integer nonlinear programming with the consideration of quadratic fuel cost of distributed generators and their startup/shut-down states. In order to obtain a favorable solution, a hybrid solution procedure combined quadratic programming and genetic algorithm is proposed to solve the problem. Then, the proposed method is verified via numerical simulation. Through the comparison of optimization result with IBM ILOG CPLEX Optimizer, simulation shows that the proposed algorithm has the advantage of finding better scheduling solution which leads to less operating cost