Liquid yield from juniper and mesquite bio‐fuel gasification

Mesquite (Prosopis glandulosa) and redberry juniper (Juniperus pinchotii) are two woody species which dominate uncultivated lands in the south central Great Plains, USA (Texas, Oklahoma) and may have potential for bioenergy utilization. In this study, these two biomasses were gasified in an adiabatic, updraft fixed‐bed gasifier using air as medium. The products include low heating value gas and liquid tar yield. The effects of fuel moisture content and the equivalence ratio (ER) on liquid yields including heavy tar and light tar and higher heating value (HHV) of the producer gas were investigated. It was found that updraft gasifiers produce a large amount of tar, in a range of 100 g/Nm³ of gas produced. As the equivalence ratio (ER) increased (i.e. more rich char conditions), peak temperature (T_peak) within the bed decreased due to the lesser partial oxidization. The liquid yield showed peak value with change in moisture content between 6 and 24%. Heavy tar is a very high quality fuel with a higher heating value of over 29 MJ/kg on a dry, ash‐free (DAF) basis which is a slightly higher than that of ethanol. Copyright © 2015 John Wiley & Sons, Ltd.     

Finite-point method for efficient timing characterization of sequential elements

Timing characterization of sequential elements, such as latches and flip-flops, is one of the critical steps for timing closure in the pipelined design. Traditional characterization of setup and hold time constraints is computationally intensive, due to the demand on high accuracy in monitoring the operation failure. To improve the efficiency, this work proposes a finite-point based method for the characterization of setup and hold time constraints. The finite-point method identifies several critical data points in the non-linear curve of timing characteristics, and abstracts the essential setup/hold information from them. Moreover, compact models are derived for each point, further reducing the computation cost. The proposed method is general for all sequential elements in the standard cell library. It is comprehensively validated using benchmark circuits at 45 nm node. Experimental results demonstrate approximately 25× reduction in characterization time, with the prediction error in setup and hold time within 9% of FO₄ nominal delay, as compared to that of SPICE simulation results.     

Duplex real-time PCR assay for the simultaneous determination of the roe deer (Capreolus capreolus) and deer (sum of fallow deer,red deer and sika deer) content in game meat products

Due to the high price of game meat, food producers may be tempted to adulterate their products with cheaper meat. This paper presents a duplex real-time PCR assay which allows the simultaneous determination of the content of roe deer (Capreolus capreolus) and deer* (the sum of fallow deer (Dama dama), red deer (Cervus elaphus) and sika deer (Cervus nippon)) in food products to detect food adulteration. Relative quantification is carried out by using a reference (“all meat”) PCR assay based on the myostatin gene. The quantification approach was validated by analyzing binary meat mixtures with pork, “all game” meat mixtures containing each of the four game species in pork and a model game sausage. Compared to singleplex assays the duplex assay is time and cost saving. Thus, it is highly applicable to routine analysis in order to verify the authenticity of game meat products.     

On oscillation reduction in feedback control for processes with an uncertain dead time and internal–external disturbances

This paper aims to find a practical solution to reduce oscillation on the Smith Predictor (SP) based design with the dead time (DT) uncertainty, making it less sensitive to DT change and more effective in disturbance rejection. First, a conditional feedback mechanism is introduced in SP to reduce the amount of oscillation caused by the model inaccuracies in the DT parameter. Then, to address the oscillation caused by the phase lag in traditional PI controller and uncertain dynamics, this conditional SP is combined with active disturbance rejection control (ADRC), assisted by the knowledge of process dynamics. A practical tuning method is provided for the practicing engineers. The proposed approach is validated in extensive simulation studies with different types of plants and in frequency domain analysis. The simulation results show significant improvements in performance robustness and transient response.     

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.