Impact of military type footwear and load carrying workload on postural stability

Postural stability has been shown to be impacted by footwear and task performed. This study analysed the impact of two military footwear, standard boot (STB) and minimalist boot (MTB) on postural stability, before (PRE) and after (POST) a load carriage task. Sixteen participants were tested for postural stability using sensory organisation and motor control tests on Neurocom Equitest?. Postural sway, equilibrium scores and postural latencies were analysed using a two-factor repeated measures ANOVA: boot type (STB-MTB)?×?time (PRE-POST) load carriage task. Significantly greater postural sway variables, lower balance scores and slower postural latencies were seen in STB and POST load carriage conditions (p?<?.05). The results suggest that MTB exhibited greater balance compared to STB in balance conditions that rely on somatosensory feedback and that balance is lowered after a load carriage task. Decrements in postural stability could be attributed to boot design characteristics and muscular exertion due to the load carriage task.

Practitioner Summary: Maintaining optimal postural stability is crucial in military. Impact of military footwear types and load carriage task on postural stability are addressed. Findings provide footwear design and physical exertion implications on postural stability leading to potential interventions that reduce postural stability decrements; thereby, reducing potential falls and fall related injuries.     

基于组合双向拍卖的云资源调度方法

针对跨数据中心的资源调度问题，提出了一种基于组合双向拍卖（PCDA）的资源调度方案。首先，将云资源拍卖分为三个部分：云用户代理报价、云资源提供商要价、拍卖代理组织拍卖；其次，在定义用户的优先级及任务紧迫度的基础上，在拍卖过程中估算每一个工作发生的服务等级协议（SLA）违规并以此计算云提供商的收益，同时每轮竞拍允许成交多项交易；最终达到根据用户等级合理分配云资源调度的效果。仿真实验结果表明该算法保证了竞拍成功率，与传统一次拍卖成交一项的组合双向拍卖方案相比，PCDA在竞拍时间段产生的能耗降低了35.00%，拍卖云提供商的利润提高了约38.84%。     

Couple-based particle swarm optimization for short-term hydrothermal scheduling

A novel couple-based particle swarm optimization (CPSO) is presented in this paper, and applied to solve the short-term hydrothermal scheduling (STHS) problem. In CPSO, three improvements are proposed compared to the canonical particle swarm optimization, aimed at overcoming the premature convergence problem. Dynamic particle couples, a unique sub-group structure in maintaining population diversity, is adopted as the population topology, in which every two particles compose a particle couple randomly in each iteration. Based on this topology, an intersectional learning strategy using the partner learning information of last iteration is employed in every particle couple, which can automatically reveal useful history information and reduce the overly rapid evolution speed. Meanwhile, the coefficients of each particle in a particle couple are set as distinct so that the particle movement patterns can be described and controlled more precisely. In order to demonstrate the effectiveness of our proposed CPSO, the algorithm is firstly tested with four multimodal benchmark functions, and then applied to solve an engineering multimodal problem known as STHS, in which two typical test systems with four different cases are tested, and the results are compared with those of other evolutionary methods published in the literature.     

Research on Circular Area Search algorithm of multi-robot service based on SOA cloud platform

Considering autonomous mobile robots with a variety of specific functions as a kind of service, when there are many types and quantities of services and the density of regional services is large, proposing an algorithm of Circular Area Search (CAS) because of the problem of multi-robot service scheduling in various areas. Firstly, Django is used as the web framework to build the Service-Oriented Architecture (SOA) multi-robot service cloud platform, which is the basic platform for multi-service combination. Then, the service type, the latitude and longitude and the scoring parameters of the service are selected as the service search metrics to design the CAS algorithm that based on the existing service information registered in MySQL and the Gaode Map for screening optimal service, and then providing the service applicant with the best service. Finally, the service applicant applies for the self-driving tour service as an example to perform performance simulation test on the proposed CAS algorithm. The results show that the CAS algorithm of the multi-robot service cloud platform proposed in this paper is practical compared to the global search. And compared with the Greedy Algorithm experiment, the service search time is reduced about 58% compared with the Greedy Algorithm, which verifies the efficiency of CAS algorithm.     

基于改进人工蜂群算法的多目标绿色柔性作业车间调度研究

针对多目标绿色柔性作业车间调度问题（MGFJSP）的特点，提出从碳排放量、噪声和废弃物这3个指标来综合评定环境污染程度，建立了以最小化最大完成时间和环境污染程度为优化目标的MGFJSP模型，并提出了一种改进的人工蜂群算法来求解该模型。算法的具体改进包括：设计了一种三维向量的编码和对应解码方案，在跟随蜂搜索阶段引入一种有效的动态邻域搜索操作来提高算法的局部搜索能力，在侦查蜂阶段提出产生新食物源的策略用于增加种群的多样性。最后进行了实验研究与算法对比，以验证所建模型和所提算法的有效性。     

模型驱动的大数据流水线框架PiFlow

复杂流程的大数据处理多依托于流水线系统，但大数据处理的流水线系统在易用性、功能复用性、扩展性以及处理性能等方面存在不足。针对上述问题，为提高大数据处理环境的构建与开发效率,优化处理流程，提出了一种模型驱动的大数据流水线框架PiFlow。首先，将大数据处理过程抽象为有向无环图；然后，开发一系列组件用于构建数据处理流水线，并设计了流水线任务执行机制。同时，为规范和简化流水线框架的描述，设计了基于模型驱动的大数据流水线描述语言——PiFlowDL，该语言以模块化、层次化的方式对大数据处理任务进行描述。PiFlow以所见即所得(WYSIWYG)的方式配置流水线，集成了状态监控、模板配置、组件集成等功能，与Apache NiFi相比有2~7倍的性能提升。     

Reality-based interaction affecting mental workload in virtual reality mental arithmetic training

ABSTRACT

The concept of digital game-based learning (DGBL) evolves rapidly together with technological enhancements of virtual reality (VR) and smart phones. However, the mental workload (MWL) that VR-training applications demand and motivational qualities originating from user experience (UX) should be identified in order to create effective and enjoyable training/learning challenges that fit with individual users’ capabilities. This study examined the effects of reality-based interaction (RBI) and VR on measures of student motivation and MWL, in a mental arithmetic game for secondary school pupils. In a randomised controlled trial with sixty school children, a mental arithmetic game was tested with three different interaction and two different presentation methods – VR RBI, VR head-mounted-display tapping and tablet flick-gesture. Results found a significant effect of RBI on MWL but no differences in enjoyment of training were found between VR-experience and tablet training-experience. In fact, adding the gaming-context to the mental arithmetic task created an enjoyable, motivating experience regardless of presentation or interaction-style.     

A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility

The traditional flexible job shop scheduling problem (FJSP) considers machine flexibility but not worker flexibility. Given the influence and potential of human factors in improving production efficiency and decreasing the cost in practical production systems, we propose a mathematical model of an extended FJSP with worker flexibility (FJSPW). A hybrid artificial bee colony algorithm (HABCA) is presented to solve the proposed FJSPW. For the HABCA, effective encoding, decoding, crossover and mutation operators are designed, and a new effective local search method is developed to improve the speed and exploitation ability of the algorithm. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the HABCA. Extensive computational experiments carried out to compare the HABCA with some well-performing algorithms from the literature confirm that the proposed HABCA is more effective than these algorithms, especially on large-scale FJSPW instances.