优化煎服方法对中药汤剂药效的影响分析

分析中药汤剂优化煎服方法对药效的影响。方法 选取100例服用中药汤剂患者,分为两组,各50例。结果 优化煎服方法组患者的中医症候积分、心理状态评分、SIP评分、PSQI评分、生活质量评分、不良反应发生率、复发率均低于自来水直接煎服组,总有效率、治疗满意度均高于自来水直接煎服组(P<0.05)。结论 中药汤剂优化煎服方法更能增强药效。

优化服务促全民创业

江西省农村信用联社积极开展"创业服务年"系列活动,全省各级农村信用社(农商银行、合作银行)充分发挥农村信用社金融主力军的作用,积极主动地融入区域经济发展之中,切实提升服务水平、转变工作作风、优化服务环境、提升服务举措,加大信贷投放,为推进江西科学发展、进位赶超、绿色崛起,促进地方经济的发展作出了积极的贡献。

Improved ant colony optimization for multi-depot heterogeneous vehicle routing problem with soft time windows

Considering that the vehicle routing problem （VRP） with many extended features is widely used in actual life, such as multi-depot, heterogeneous types of vehicles, customer service priority and time windows etc., a mathematical model for multi-depot heterogeneous vehicle routing problem with soft time windows （MDHVRPSTW） is established. An improved ant colony optimization （IACO） is proposed for solving this model. First, MDHVRPSTW is transferred into different groups according to the nearest principle, and then the initial route is constructed by the scanning algorithm （SA）. Secondly, genetic operators are introduced, and crossover probability and mutation probability are adaptively adjusted in order to improve the global search ability of the algorithm. Moreover, the smooth mechanism is used to improve the performance of the ant colony optimization （ACO）. Finally, the 3-opt strategy is used to improve the local search ability. The proposed IACO was tested on three new instances that were generated randomly. The experimental results show that IACO is superior to the other three existing algorithms in terms of convergence speed and solution quality. Thus, the proposed method is effective and feasible, and the proposed model is meaningful.

A Novel Vertical Handoff Algorithm with Dynamic Weights in Wireless Heterogeneous Networks

In order to enhance the quality of vertical handoff in an overlay wireless network, multiple attributes are taken into account when optimizing the vertical handoff decision including user-based and network-based QoS factors. In this paper, we develop a novel vertical handoff algorithm in an integrated 3G cellular and Wireless LAN networks. The proposed algorithm can adjust the weight of each QoS attribute dynamically as the networks change, trace the network condition and choose the optimal access point at transient regions. Simulation results show that this algorithm is able to provide accurate handoff decision, resulting in small unnecessary handoff numbers, good performance of throughput and handoff delay in heterogeneous environments.

A Greedy Algorithm for Task Offloading in Mobile Edge Computing System

Mobile edge computing （MEC） is a novel technique that can reduce mobiles＇ com- putational burden by tasks offioading, which emerges as a promising paradigm to provide computing capabilities in close proximity to mobile users. In this paper, we will study the scenario where multiple mobiles upload tasks to a MEC server in a sing cell, and allocating the limited server resources and wireless chan- nels between mobiles becomes a challenge. We formulate the optimization problem for the energy saved on mobiles with the tasks being dividable, and utilize a greedy choice to solve the problem. A Select Maximum Saved Energy First （SMSEF） algorithm is proposed to realize the solving process. We examined the saved energy at different number of nodes and channels, and the results show that the proposed scheme can effectively help mobiles to save energy in the MEC system.

An Improved Genetic Algorithm for Allocation Optimization of Distribution Centers

This paper introduced an integrated allocation model for distribution centers (DCs). The facility cost, inventory cost, transportation cost and service quality were considered in the model. An improved genetic algorithm (IGA) was proposed to solve the problem. The improvement of IGA is based on the idea of adjusting crossover probability and mutation probability. The IGA is supplied by heuristic rules too. The simulation results show that the IGA is better than the standard GA(SGA) in search efficiency and equality.

DPTSV:A Dynamic Priority Task Scheduling Strategy for TSS Deadlock Based on Value Evaluation

This paper analyzes the threat of TCG Software Stack(TSS)/TCM Service Module(TSM) deadlock in multi-user environment such as cloud and discusses its causes and mechanism.In addition,this paper puts forward a dynamic priority task scheduling strategy based on value evaluation to handle this threat.The strategy is based on the implementation features of trusted hardware and establishes a multi-level ready queue.In this strategy,an algorithm for real-time value computing is also designed,and it can adjust the production curves of the real time value by setting parameters in different environment,thus enhancing its adaptability,which is followed by scheduling and algorithm description.This paper also implements the algorithm and carries out its performance optimization.Due to the experiment result from Intel NUC,it is shown that TSS based on advanced DPTSV is able to solve the problem of deadlock with no negative influence on performance and security in multi-user environment.

Optimizing bus services with variable directional and temporal demand using genetic algorithm

As a major mode choice of commuters for daily travel, bus transit plays an important role in many urban and metropolitan areas. This work proposes a mathematical model to optimize bus service by minimizing total cost and considering a temporally and directionally variable demand. An integrated bus service, consisting of all-stop and stop-skipping services is proposed and optimized subject to directional frequency conservation, capacity and operable fleet size constraints. Since the research problem is a combinatorial optimization problem, a genetic algorithm is developed to search for the optimal result in a large solution space. The model was successfully implemented on a bus transit route in the City of Chengdu, China, and the optimal solution was proved to be better than the original operation in terms of total cost. The sensitivity of model parameters to some key attributes/variables is analyzed and discussed to explore further the potential of accruing additional benefits or avoiding some of the drawbacks of stop-skipping services.

Optimization on bicriterion policies for M/G/1 system with second optional service

We compare the optimal operating cost of the two bicriterion policies, <p,T> and <p,N>, for an M/G/1 queueing system with second optional service, in which the length of the vacation period is randomly controlled either by the number of arrivals during the idle period or by a timer. After all the customers are served in the queue exhaustively, the server immediately takes a vacation and may operate <p,T> policy or <p,N> policy. For the two bicriterion policies, the total average cost function per unit time is developed to search the optimal stationary operating policies at a minimum cost. Based upon the optimal cost the explicit forms for joint optimum threshold values of (p,T) and (p,N) are obtained.

Establishment and application of service mapping model for proactive remanufacturing impeller

Since the complex impeller structure and the difficult remanufacturing process may easily cause advance remanufacturing or excessive use,an optimized design method of impeller and service mapping model was presented for its proactive remanufacturing with setting up to explore the best remanufacturing time point in this work.Considering a certain model of long distance pipeline compressor impeller with the Basquin equation and the design method of impeller,the mathematical relationship between the changes of structure and life of the impeller was established.And the service mapping model between the structure and life was set up and simulated by ANSYS software.Thus,the service mapping model was applied to feedback the original design for proactive remanufacturing.In this work,the best proactive remanufacturing time point of impeller was analyzed with the service mapping model,and the structural parameter values could be optimized at this time point.Meanwhile,in the results of this simulation,it proves that the impeller under this optimization performance could satisfy the impeller operating demands.Therefore,comparing with the traditional optimization design method,the remanufacturing optimized design based on the service mapping model is feasible in proactive remanufacturing for sustainable development.

Service optimization in programmable cloud network

With the rapid development in cloud data centers and cloud service customers,the demand for high quality cloud service has been grown rapidly.To face this reality,this paper focuses on service optimization issues in cloud computing environment.First,a service-oriented architecture is proposed and programmable network facilities are utilized in it to optimize specific cloud services.Then various cloud services are categorized into two subcategories;static services and dynamic services.Furthermore,the concepts of cloud service quality and cloud resource idle rate are defined,and the aforementioned concepts have also been taken into consideration as parameters in the service optimization algorithm to improve the cloud service quality and optimize system workload simultaneously.Numerical simulations are conducted to verify the effectiveness of the proposed algorithm in balancing the workload of all servers.

A hybrid discrete particle swarm optimization-genetic algorithm for multi-task scheduling problem in service oriented manufacturing systems

To cope with the task scheduling problem under multi-task and transportation consideration in large-scale service oriented manufacturing systems(SOMS), a service allocation optimization mathematical model was established, and then a hybrid discrete particle swarm optimization-genetic algorithm(HDPSOGA) was proposed. In SOMS, each resource involved in the whole life cycle of a product, whether it is provided by a piece of software or a hardware device, is encapsulated into a service. So, the transportation during production of a task should be taken into account because the hard-services selected are possibly provided by various providers in different areas. In the service allocation optimization mathematical model, multi-task and transportation were considered simultaneously. In the proposed HDPSOGA algorithm, integer coding method was applied to establish the mapping between the particle location matrix and the service allocation scheme. The position updating process was performed according to the cognition part, the social part, and the previous velocity and position while introducing the crossover and mutation idea of genetic algorithm to fit the discrete space. Finally, related simulation experiments were carried out to compare with other two previous algorithms. The results indicate the effectiveness and efficiency of the proposed hybrid algorithm.

Modeling and Optimization of Bandwidth Supply Performance for Cloud-Assisted Video Systems under Flash Crowd

The large-scale and sudden video content access such as flash crowds results in huge bandwidth demand,which severely influence user quality of experience and quality of service of video systems.In this paper,we firstly discuss the main reason of generation of flash crowds for video streaming services and analyze key factor for balance recovery between supply and demand of upload bandwidth.We construct two models:bandwidth supply capacity model of video systems and bandwidth demand model of users,which measures usage amount of bandwidth of the cloud.Based on the built models,we further employ a community-based cooperative caching strategy of video resources to promote supply capacity of upload bandwidth of video systems.Extensive tests show how the proposed cooperative caching strategy achieves much better performance results in comparison with original solution.

Research of University Educational Administration System Bottlenecks and Overall Optimization Scheme

This paper analyzes of the bottleneck to influence the running efficiency of educational administration system in universities and colleges,and from the system deployment architecture, server configuration, database SQL statement optimization and other aspects, it puts forwards the optimization scheme.

Movement-assisted service composition optimization in dynamic networks

The mobility of service providers brings new features into the research of dynamic network based service composition.From an optimistic perspective,the mobility of services could benefit the optimization of service composition,if properly handled.Therefore,the impacts of node mobility on the dynamic network based service composition are investigated.Then,a movement-assisted optimization method,namely MASCO,is proposed to improve the performance of the composited services by minimizing the length of data stream and the hop-counts of the service routes in the underlying networks.The correctness and efficiency of the proposed method are then verified through theoretical analysis and computer simulations.

A Research on QFD-＂House of Brand＂ in Fashion Industry

Fashion continues to be an area that reflects the increasing popularity of different theoretical and practical approaches of researchers from different disciplines. Due to the rapid cycles of fashion, sustainable products and processes in terms of technical creativity and innovative approach are needed. In this context, fashion marketing emerges as an integrative marketing application with its both technical and social aspects by taking potential customers center that symbolize rapid change and creativity of fashion products. In this study, QFD-House of Brand was designed with Quality Function Deployment （QFD） approach. In this context, the experts （focus group） in Fashion Industry evaluated the selected fashion brand and its selected products by prioritizing the criteria. These evaluations were used in QFD-House of Brand. Thus, technical and social aspects of fashion industry were analysed together. By this technique, QFD-House of Brand established in relation to products and brand was interpreted and optimization suggestions were presented in accordance with the findings of the research. In addition, suggestions on sustainability and optimization of brand equity in fashion industry were provided.

The Optimization of Radio over Fiber Link by Proper Link Gain Control

we demonstrate the adjustability of optimal input power(OIP) of the radio over fiber(RoF) link by proper link gain control in the central unit(CU) and remote antenna unit(RAU).The experiment results show that the reading and writing distance(RWD)of the radio frequency identification(RFID)service and the throughput of the WiFi service have a max increase of 3cm and 6.975Mbit/s respectively when the OIP value equals to output power of commercial products,compared with OIP value with 5-dBm right/left shift to the output power.

A Stochastic Model for Optimizing the Patching Time of Software Bugs

New bugs and vulnerabilities are discovered and reported from time to time even after software products are released. One of the common ways to handle these bugs is to patch the software. In this paper, the authors propose a stochastic model for optimizing the patching time for software bugs and vulnerabilities. The optimal patching time can be computed in the patching script development and operational costs in fix. The authors present two case studies using the Nimda worm vulnerability in Microsoft Internet Information Services web server and the bug report of the Debian project. These studies indicate that the patch applications are later than their optimal fix time.

QOS-BASED MULTICAST ROUTING OPTIMIZATION ALGORITHMS FOR INTERNET

Most of the multimedia applications require strict Quality-of-Service (QoS) guarantee during the communication between a single source and multiple destinations. The paper mainly presents a QoS Multicast Routing algorithms based on Genetic Algorithm (QMRGA). Simulation results demonstrate that the algorithm is capable of discovering a set of QoS-based near optimized, non-dominated multicast routes within a few iterations, even for the networks environment with uncertain parameters.

Enhancing customers prevention efforts:An incentive feedback mechanism design

To improve the inefficient prevention caused by customers unwillingness to adopt prevention strategies in health management,an incentive feedback mechanism that is based on game theory and contract design theory is introduced.The conditions for making customers and health maintenance organizations(HMOs)willing to participate in the proposed mechanism are given.A dual nonlinear programming model is used to identify the optimal prevention effort of customers and the pricing strategy of HMOs.Results show that to generate increased benefits,HMOs need to consider cost sharing when customers are not familiar with the proposed health services.When health services are gradually accepted,the cost sharing factor can be gradually reduced.Simulation shows that under random circumstances in which the market reaches a certain size,the proposed method exhibits a positive network externality.Motivated by network externality,HMOs only need to make their customers understand that the larger the number of participants,the greater the utility of each person.Such customers may then spontaneously invite others to purchase insurance.