Two-agent two-machine flowshop scheduling with learning effects to minimize the total completion time

We study a two-agent scheduling problem in a two-machine permutation flowshop with learning effects. The objective is to minimize the total completion time of the jobs from one agent, given that the maximum tardiness of the jobs from the other agent cannot exceed a bound. We provide a branch-and-bound algorithm for the problem. In addition, we present several genetic algorithms to obtain near-optimal solutions. Computational results indicate that the algorithms perform well in either solving the problem or efficiently generating near-optimal solutions.     

A Method for Systematic Assessment of Intrinsically Disordered Protein Regions by NMR

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an “indirect/reflected” detection system for evaluating the physicochemical properties of IDPs using nuclear magnetic resonance (NMR). This approach employs a “chimeric membrane protein”-based method using the thermostable membrane protein PH0471. This protein contains two domains, a transmembrane helical region and a C-terminal OB (oligonucleotide/oligosaccharide binding)-fold domain (named NfeDC domain), connected by a flexible linker. NMR signals of the OB-fold domain of detergent-solubilized PH0471 are observed because of the flexibility of the linker region. In this study, the linker region was substituted with target IDPs. Fifty-three candidates were selected using the prediction tool POODLE and 35 expression vectors were constructed. Subsequently, we obtained ¹⁵N-labeled chimeric PH0471 proteins with 25 IDPs as linkers. The NMR spectra allowed us to classify IDPs into three categories: flexible, moderately flexible, and inflexible. The inflexible IDPs contain membrane-associating or aggregation-prone sequences. This is the first attempt to use an indirect/reflected NMR method to evaluate IDPs and can verify the predictions derived from our computational tools.     

Evaluation of ergonomic approach and musculoskeletal disorders in two different organizations in a truck assembly plant

The aim of this study was to assess the ergonomic physical exposure, organizational and psychosocial factors in a truck assembly plant for two different cycle times (11 min and 8 min). A self-reported questionnaire was applied to evaluate subjective physical exposure, organizational and psychosocial factors by operators in two organization of an assembly process. The initial cycle time was 11 min (system A) and the new was 8 min (system B). The same work and assembly tasks had to be completed in both systems. However, the organization and distribution of the tasks and workstations were reorganized. The results of the questionnaire showed that subjective estimation by the operators regarding ergonomic risk factors was better in the new organization and self-reported WR-MSDs symptoms were fewer. However, exposure to risk factors and WR-MSDs symptoms was not statistically different between two cycle times. The findings provide better understanding of how organizational changes can modify ergonomic exposure in manufacturing assembly industries. Effective interventions are thus not only engineering solutions but also organizational and administrative adaptations.     

Toward the adaptation of component‐based architectures by model transformation: behind smart user interfaces

Graphical user interfaces are not always developed for remaining static. There are GUIs with the need of implementing some variability mechanisms. Component‐based GUIs are an ideal target for incorporating this kind of operations, because they can adapt their functionality at run‐time when their structure is updated by adding or removing components or by modifying the relationships between them. Mashup user interfaces are a good example of this type of GUI, and they allow to combine services through the assembly of graphical components. We intend to adapt component‐based user interfaces for obtaining smart user interfaces. With this goal, our proposal attempts to adapt abstract component‐based architectures by using model transformation. Our aim is to generate at run‐time a dynamic model transformation, because the rules describing their behavior are not pre‐set but are selected from a repository depending on the context. The proposal describes an adaptation schema based on model transformation providing a solution to this dynamic transformation. Context information is processed to select at run‐time a rule subset from a repository. Selected rules are used to generate, through a higher‐order transformation, the dynamic model transformation. This approach has been tested through a case study which applies different repositories to the same architecture and context. Moreover, a web tool has been developed for validation and demonstration of its applicability. The novelty of our proposal arises from the adaptation schema that creates a non pre‐set transformation, which enables the dynamic adaptation of component‐based architectures. Copyright © 2014 Copyright © 2014 John Wiley & Sons, Ltd.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

A prediction-based dynamic file assignment strategy for parallel file systems

Nowadays, the rapid development of the internet calls for a high performance file system, and a lot of efforts have already been devoted to the issue of assigning nonpartitioned files in a parallel file system with the aim of pursuing a prompt response to requests. Yet most of the existing strategies still fail to bring about an optimal performance on system mean response time metrics, and new strategies which can achieve better performance in terms of mean response time become indispensable for parallel file systems. This paper, while addressing the issue of assigning nonpartitioned files in parallel file systems where the file accesses exhibit Poisson arrival rates and fixed service times, presents an on-line file assignment strategy, named prediction-based dynamic file assignment (PDFA), to minimize the mean response time among disks under different workload conditions, and a comparison of the PDFA with the well-known file assignment algorithms, such as HP and SOR. Comprehensive experimental results show that PDFA is able to improve the performance consistently in terms of mean response time among all algorithms for comparison.     

基于DSP的电子行走辅助系统的设计与实现

为帮助视障者独立行走，基于DSP设计一套电子行走辅助系统。介绍系统的功能和基本结构，详细描述各模块的硬件设计和系统软件工作流程。该系统以实时图像处理为基础，能够完全脱离 PC机实现视频图像的采集、处理和语音提示等功能，帮助视障碍者独立行走。该系统具有小巧、便携、使用方便的特点，与传统助盲设备相比提高了导盲精度，与大型电子盲道或全球定位系统相比节省了成本，实验验证了其有效性。     

控规"心结"之解

法定的控规在执行过程中屡遭调整,困扰着规划师.这是由于编制控规的"时间差"、"批零差"、"计划/市场契合"等问题导致.依据法理,创新编制控规的新机制,发挥政府主导,各利益相关者多方参与是解因之道.     

裂解态正癸烷点火延迟时间的理论研究（英）

在先进飞行器发动机中,吸热碳氢燃料在进入燃烧室之前会发生热裂解反应,生成未反应燃料和小分子裂解产物的混合物(称为裂解态燃料)。本工作研究了在1300~1800 K、0.1~3.0 MPa和当量比为1.0的条件下,不同的裂解转化率、裂解压力、点火压力和自由基对正癸烷裂解着火特性的影响。通过采用一种精确的组合机理,从理论上计算了流动反应器中3.0和5.0 MPa下正癸烷裂解组分,与文献中的实验结果吻合较好。结果表明,正癸烷在3 MPa和5 MPa下裂解的出口转化率分别为46.2%和58.8%,裂解产物分布一致,但乙烯的含量随着压力的升高明显的降低,而烷烃含量随着压力的增大而增加。尽管自由基总体含量很低,但在3 MPa条件下裂解产物中的自由基浓度依然高于5 MPa条件下。对于点火延迟时间的计算结果则表明,裂解态正癸烷的点火延迟时间随着转化率的增大而延长,且在5 MPa下随着转化率的变化更明显。相同转化率下,5 MPa下的裂解态正癸烷的点火延迟时间比3 MPa下更短。此外,与无自由基的裂解正癸烷相比,裂解正癸烷中自由基的存在可以加速着火过程,转化率小于40%时,着火延迟时间缩短15%以上。     

Quantile regression on the nonlinear relationship between land use and trip time

This study conceptually confirms and empirically tests the potential that the significance and magnitude of the compact land use–trip time relationship differ by the degree of compactness and trip time. Based on a quantile regression of about 25,000 commuters in Seoul, Korea, the empirical analysis suggests a significant level for each neighbourhood land use variable and a magnitude change within the level. Weekend trip and school densities are significant for shorter trip time commuters, population density is significant for longer trip time commuters, and pedestrian density is significant for all commuters. Wherever significant, the land use variables exert stronger effects as their values increase.