An efficient hybridized genetic algorithm architecture for the flexible job shop scheduling problem

The work presented in this paper proposes hybridized genetic algorithm architecture for the Flexible Job Shop Scheduling Problem (FJSP). The efficiency of the genetic algorithm is enhanced by integrating it with an initial population generation algorithm and a local search method. The usefulness of the proposed methodology is illustrated with the aid of an extensive computational study on 184 benchmark problems with the objective of minimizing the makespan. Results highlight the ability of the proposed algorithm to first obtain optimal or near-optimal solutions, and second to outperform or produce comparable results with these obtained by other best-known approaches in literature.     

Numerical modelling of blood clot extraction by aspiration thrombectomy. Evaluation of aspiration catheter geometry

Aspiration thrombectomy is one of the most effective systems for blood clot removal and vessel recanalization. We present the results of a study involving the modelling and extraction of blood clots in the arteries of the human body using the following computer tools: Bond-Graph methodology for the fluid domain and Multi-Body Simulation for the mechanical domain. The modelling for the mechanical domain focuses on the clot and the distal end section of an aspiration device. Our final model considers an elastic characterization of the blood clot with progressive detachment from the vessel wall. We conclude that the results of such modelling could potentially improve the effectiveness of blood clot removal by reducing the risk of clot fragmentation. Such modelling could also potentially provide an adjunct technique in improving recanalization of arteries over a range of given parameters (mechanical properties of the vessel, mechanical properties of the blood clot, blood clot length, suction pressure, catheter – clot distance, catheter shape, catheter diameter and vessel occlusion).     

What to expect when you are consolidating: effective prediction models of application performance on multicores

Consolidation of multiple applications with diverse and changing resource requirements is common in multicore systems as hardware resources are abundant. As opportunities for better system usage become ample, so are opportunities to degrade individual application performances due to unregulated performance interference between applications and system resources. Can we predict a performance region within which application performance is expected to lie under different consolidations? Alternatively, can we maximize resource utilization while maintaining individual application performance targets? In this work we provide a methodology that offers answers to the above difficult questions by constructing a queueing-theory based tool that can be used to accurately predict application scalability on multicores. The tool can also provide the optimal consolidation suggestions to maximize system resource utilization while meeting application performance targets. The proposed methodology is based on asymptotic analysis that can quickly provide a range of performance values that the user should expect under various consolidation scenarios. In addition, when more accurate performance forecasting is needed, the methodology can provide more accurate predictions using approximate mean value analysis. The methodology is light-weight as it relies on capturing application resource demands using standard system monitoring, via non-intrusive low-level measurements. We evaluate our approach on an IBM Power7 system using the DaCapo and SPECjvm2008 benchmark suites. From 900 different consolidations of application instances, our tool accurately predicts the average iteration time of collocated applications with an average error below 9 per cent. Experimental and analytical results are in excellent agreement, confirming the robustness of the proposed methodology in suggesting the best consolidations that meet given performance objectives of individual applications while maximizing system resource utilization.     

Deadlock-free scheduling of an automated manufacturing system using an enhanced colored time resource Petri-net model-based evolutionary endosymbiotic learning automata approach

Deadlock-free scheduling of parts is vital for increasing the utilization of an Automated Manufacturing System (AMS). An existing literature survey has identified the role of an effective modeling methodology for AMS in ensuring the appropriate scheduling of the parts on the available resources. In this paper, a new modeling methodology termed as Extended Color Time Net of Set of Simple Sequential Process with Resources (ECTS³PR) has been presented that efficiently handles dynamic behavior of the manufacturing system. The model is subsequently utilized to obtain a deadlock-free schedule with minimized makespan using a new Evolutionary Endosymbiotic Learning Automata (EELA) algorithm. The ECTS³PR model, which can easily handle various relations and structural interactions, proves to be very helpful in measuring and managing system performances. The novel algorithm EELA has the merits of both endosymbiotic systems and learning automata. The proposed algorithm performs better than various benchmark strategies available in the literature. Extensive experiments have been performed to examine the effectiveness of the proposed methodology, and the results obtained over different data sets of varying dimensions authenticate the performance claim. Superiority of the proposed approach has been validated by defining a new performance index termed as the ‘makespan index’ (MI), whereas the ANOVA analysis reveals the robustness of the algorithm.     

A biologically inspired hierarchical goal directed navigation model

We propose an extended version of our previous goal directed navigation model based on forward planning of trajectories in a network of head direction cells, persistent spiking cells, grid cells, and place cells. In our original work the animat incrementally creates a place cell map by random exploration of a novel environment. After the exploration phase, the animat decides on its next movement direction towards a goal by probing linear look-ahead trajectories in several candidate directions while stationary and picking the one activating place cells representing the goal location. In this work we present several improvements over our previous model. We improve the range of linear look-ahead probes significantly by imposing a hierarchical structure on the place cell map consistent with the experimental findings of differences in the firing field size and spacing of grid cells recorded at different positions along the dorsal to ventral axis of entorhinal cortex. The new model represents the environment at different scales by populations of simulated hippocampal place cells with different firing field sizes. Among other advantages this model allows simultaneous constant duration linear look-ahead probes at different scales while significantly extending each probe range. The extension of the linear look-ahead probe range while keeping its duration constant also limits the degrading effects of noise accumulation in the network. We show the extended model’s performance using an animat in a large open field environment.     

Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy

This study presents the development of a multi-criteria control methodology for flexible manufacturing systems (FMSs). The control methodology is based on a two-tier decision making mechanism. The first tier is designed to select a dominant decision criterion and a relevant scheduling rule set using a rule-based algorithm. In the second tier, using a look-ahead multi-pass simulation, a scheduling rule that best advances the selected criterion is determined. The decision making mechanism was integrated with the shop floor control module that comprises a real-time simulation model at the top control level and RapidCIM methodology at the low equipment control level. A factorial experiment was designed to analyze and evaluate the two-tier decision making mechanism and the effects that the main design parameters have on the system’s performance. Next, the proposed control methodology was compared to a selected group of scheduling rules/policies using DEA. The results demonstrated the superiority of the suggested control methodology as well as its capacity to cope with a fast changing environment.     

Planning Steps Forward in Development: In Girls Earlier than in Boys

The development of planning ability in children initially aged four and five was examined longitudinally with a retest-interval of 12 months using the Tower of London task. As expected, problems to solve straightforward without mental look-ahead were mastered by most, even the youngest children. Problems demanding look-ahead were more difficult and accuracy improved significantly with age and over time. This development was strongly moderated by sex: In contrast to coeval boys, four year old girls showed an impressive performance enhancement at age five, reaching the performance of six year olds, whereas four year old boys lagged behind and caught up with girls at the age of six, the typical age of school enrollment. This sex-specific development of planning was clearly separated from overall intelligence: young boys showed a steeper increase in raw intelligence scores than girls, whereas in the older groups scores developed similarly. The observed sex differences in planning development are evident even within a narrow time window of twelve months and may relate to differences in maturational trajectories for girls and boys in dorsolateral prefrontal cortex.     

Energy-aware task scheduling in heterogeneous computing environments

Efficient application scheduling is critical for achieving high performance in heterogeneous computing (HC) environments. Because of such importance, there are many researches on this problem and various algorithms have been proposed. Duplication-based algorithms are one kind of well known algorithms to solve scheduling problems, which achieve high performance on minimizing the overall completion time (makespan) of applications. However, they pursuit of the shortest makespan overly by duplicating some tasks redundantly, which leads to a large amount of energy consumption and resource waste. With the growing advocacy for green computing systems, energy conservation has been an important issue and gained a particular interest. An existing technique to reduce energy consumption of an application is dynamic voltage/frequency scaling (DVFS), whose efficiency is affected by the overhead of time and energy caused by voltage scaling. In this paper, we propose a new energy-aware scheduling algorithm with reduced task duplication called Energy-Aware Scheduling by Minimizing Duplication (EAMD), which takes the energy consumption as well as the makespan of an application into consideration. It adopts a subtle energy-aware method to search and delete redundant task copies in the schedules generated by duplication-based algorithms, and it is easier to operate than DVFS, and produces no extra time and energy consumption. This algorithm not only consumes less energy but also maintains good performance in terms of makespan compared with duplication-based algorithms. Two kinds of DAGs, i.e., randomly generated graphs and two real-world application graphs, are tested in our experiments. Experimental results show that EAMD can save up to 15.59 % energy consumption for HLD and HCPFD, two classic duplication-based algorithms. Several factors affecting the performance are also analyzed in the paper.     

Hierarchical genetic-based grid scheduling with energy optimization

An optimization of power and energy consumptions is the important concern for a design of modern-day and future computing and communication systems. Various techniques and high performance technologies have been investigated and developed for an efficient management of such systems. All these technologies should be able to provide good performance and to cope under an increased workload demand in the dynamic environments such as Computational Grids (CGs), clusters and clouds. In this paper we approach the independent batch scheduling in CG as a bi-objective minimization problem with makespan and energy consumption as the scheduling criteria. We use the Dynamic Voltage Scaling (DVS) methodology for scaling and possible reduction of cumulative power energy utilized by the system resources. We develop two implementations of Hierarchical Genetic Strategy-based grid scheduler (Green-HGS-Sched) with elitist and struggle replacement mechanisms. The proposed algorithms were empirically evaluated versus single-population Genetic Algorithms (GAs) and Island GA models for four CG size scenarios in static and dynamic modes. The simulation results show that proposed scheduling methodologies fairly reduce the energy usage and can be easily adapted to the dynamically changing grid states and various scheduling scenarios.     

Schistosome population genetic structure: when clumping worms is not just splitting hairs

Schistosomiasis is a major public health problem, affecting over 200 million people worldwide. Although Schistosoma mansoni has been studied rigorously in an attempt to provide a vaccine based on a number of candidate antigens, there has been a lack of complementary effort to determine the range and distribution of variation in representative molecules throughout natural populations. Here, Jason Curtis and Dennis Minchella highlight current (and suggest future) research efforts aimed at assessing genetic variation in schistosome populations, and call for a more robust consideration of schistosome population genetics.     

城市中的花粉致敏植物及其影响因素

大量致敏花粉飘散于空气中会引发花粉症,严重危害人体健康。由于不当的城市绿地建设使得花粉致敏植物在城市中大量聚集,加之城市热岛效应和交通污染等多方面的因素,使得城市中花粉症患者数量激增。综述了花粉致敏植物的种类、物候特征和影响因素提出了控制花粉致敏植物的建议;总结了目前花粉致敏植物研究中应进一步关注的问题,旨在为改善城市空气质量,建设合理绿化环境提供科学依据。     

A DSS for job scheduling under process interruptions

The primary objective of this research is to solve the job-shop scheduling problems (JSSPs), by minimizing the makespan, with and without process interruptions. In this paper, we first developed a genetic algorithm for solving JSSPs, and then improved the algorithm by integrating it with two simple priority rules and a hybrid rule. The performance of the developed algorithm was tested by solving 40 benchmark problems and comparing their results with that of a number of well-known algorithms. In addition, we have studied the job-shop scheduling under process interruptions such as machine unavailability and breakdown. For convenience of implementation, we have developed a decision support system (DSS). In the DSS, we built a graphical user interface for user friendly data inputs, model choices, and output generation. An overview of the DSS and an analysis of the experimental results are provided. The incorporation of priority rules, and a hybrid rule, not only improves the solutions but also reduces the overall computational time. The experimental results show that when the machine unavailability information is known in advance, the effect on the schedule is very little compared to the sudden machine breakdown scenario.     

Impact of Routing Flexibility on the Performance of an FMS—A Simulation Study

The evolving manufacturing environment is characterized by a drive toward increasing flexibility. One possible manifestation of flexibility within an FMS is in the form of routing flexibility. Providing this typically is an expensive proposition, and system designers therefore aim to provide only the required levels commensurate with a given set of operating conditions. This paper presents a framework based on a Taguchi experimental design for studying the nature of the impact of varying levels of routing flexibility on the performance of an FMS. Simulation results indicate that increases in routing flexibility, when made available at the cost of an associated penalty on operation processing time, is not always beneficial. There is an optimal flexibility level, beyond which system performance deteriorates, as judged by the makespan measure of performance. It is suggested that the proposed methodology can be used in practice for not only setting priorities on specific design and control factors but also for highlighting likely factor level combinations that could yield near-optimal shop performance.     

Focus: Addiction: Methodological and Conceptual Limitations in Exercise Addiction Research

The aim of this brief analytical review is to highlight and disentangle research dilemmas in the field of exercise addiction. Research examining exercise addiction is primarily based on self-reports, obtained by questionnaires (incorporating psychometrically validated instruments), and interviews, which provide a range of risk scores rather than diagnosis. Survey methodology indicates that the prevalence of risk for exercise addiction is approximately 3 percent among the exercising population. Several studies have reported a substantially greater prevalence of risk for exercise addiction in elite athletes compared to those who exercise for leisure. However, elite athletes may assign a different interpretation to the assessment tools than leisure exercisers. The present paper examines the: 1) discrepancies in the classification of exercise addiction; 2) inconsistent reporting of exercise addiction prevalence; and 3) varied interpretation of exercise addiction diagnostic tools. It is concluded that there is the need for consistent terminology, to follow-up results derived from exercise addiction instruments with interviews, and to follow a theory-driven rationale in this area of research.     

A comparison of the variation in Indian populations of pigeonpea cyst nematode, Heterodera cajani revealed by morphometric and AFLP analysis

The cyst nematode Heterodera cajani is one of the major endemic diseases of pigeonpea, an important legume for food security and protein nutrition in India. It occurs in several pulse crops grown over a range of Indian agro climatic conditions but the extent of its intraspecific variation is inadequately defined. In view of this, 11 populations of Heterodera cajani were analyzed using morphometrics and the results correlated with those obtained from an AFLP approach using 24 primer pair combinations that amplified a total of 1278 AFLP markers. The cluster solution from this binary data indicated similarities for five populations that differed from those suggested by morphometrics. The differences obtained could not be related to geographic distance between populations. The data suggests that recent and long distance dispersal has occurred whose causes need to be defined to restrict further field introductions. Four AFLP primer pairs clustered the populations similarly to that generated using all 24 primer pairs. This simplified approach may provide a rapid basis for discriminating populations for their future management and help to check further distribution in agricultural trade. It may also have potential to determine differences in populations that relate to host range or virulence to resistance genes.     

蛋白质结构确定领域中的几种矩阵填充算法的对比评估

目的 目前,如何从核磁共振(nuclear magnetic resonance,NMR)光谱实验中准确地确定蛋白质的三维结构是生物物理学中的一个热门课题,因为蛋白质是生物体的重要组成成分,了解蛋白质的空间结构对研究其功能至关重要,然而由于实验数据的严重缺乏使其成为一个很大的挑战。方法 在本文中,通过恢复距离矩阵的矩阵填充(matrix completion,MC)算法来解决蛋白质结构确定问题。首先,初始距离矩阵模型被建立,由于实验数据的缺乏,此时的初始距离矩阵为不完整矩阵,随后通过MC算法恢复初始距离矩阵的缺失数据,从而获得整个蛋白质三维结构。为了进一步测试算法的性能,本文选取了4种不同拓扑结构的蛋白质和6种现有的MC算法进行了测试,探究了算法在不同的采样率以及不同程度噪声的情况下算法的恢复效果。结果 通过分析均方根偏差(root-mean-square deviation,RMSD)和计算时间这两个重要指标的平均值及标准差评估了算法的性能,结果显示当采样率和噪声因子控制在一定范围内时,RMSD值和标准差都能达到很小的值。另外本文更加具体地比较了不同算法的特点和优势,在精确采样情况下...     

Multi-Image Colocalization and Its Statistical Significance

Accurately localizing molecules within the cell is one of main tasks of modern biology, and colocalization analysis is one of its principal and most often used tools. Despite this popularity, interpretation is often uncertain because colocalization between two or more images is rarely analyzed to determine whether the observed values could have occurred by chance. To address this, we have developed a robust methodology, based on Monte Carlo randomization, to measure the statistical significance of a colocalization. The method works with voxel-based, intensity-based, object-based, and nearest-neighbor metrics. We extend all of these to measure colocalization in images with three colors. We also introduce three new metrics; blob colocalization, where the blob consists of a local maximum surrounded by a three-dimensional group of voxels; cluster diameter, to measure the clustering of fluorophores in three or more images; and the intercluster distance to measure the distance between these clusters. The robustness of these metrics was tested by varying the image thresholds over a broad range, which produced no change in the statistical significance of the colocalizations. A comparison of blob colocalization with voxel and Manders colocalization metrics shows that the different measures produce consistent results with similar values for significance and nonsignificance. Using our methodology, we are able to determine not only whether the labeled molecules colocalize with a probability greater than chance, but also whether they are sequestrated into different compartments. The program, written in C++, is freely available as source, as well as in a Linux version.     

A Population Estimate for Golden Eagles in the Western United States

ABSTRACT Researchers have suggested golden eagle (Aquila chrysaetos) populations may be declining in portions of their range. However, there are few baseline data describing golden eagle populations across their range in the western United States. We used aerial line transect distance methodology with a double-observer modification to estimate golden eagle population numbers in 4 bird conservation regions of the western United States. We conducted surveys from 16 August to 8 September 2003, after most golden eagles had fledged and before fall migration. The goal of our sampling strategy was to provide >80% power (α = 0.1) to detect an annual rate of total population change >3% per year over a 20-year period. We observed 172 golden eagles across 148 transects and estimated 27,392 golden eagles (90% CI: 21,352-35,140) occurred in the study area during the late summer and early fall of 2003. Following the surveys, we used Monte Carlo simulation to determine the statistical power to detect trends in the golden eagle populations if yearly surveys were continued over a 20-year monitoring period. The simulation indicated the desired power could be achieved under the current methodology and sample size. The methods utilized in this study can be implemented for other raptor species when population estimates that include nonbreeding members of a population are needed. The results of this study can be utilized by professionals to help manage golden eagle populations and to develop conservation strategies.     

An Artificial Neural Network Estimation of Gait Balance Control in the Elderly Using Clinical Evaluations

The use of motion analysis to assess balance is essential for determining the underlying mechanisms of falls during dynamic activities. Clinicians evaluate patients using clinical examinations of static balance control, gait performance, cognition, and neuromuscular ability. Mapping these data to measures of dynamic balance control, and the subsequent categorization and identification of community dwelling elderly fallers at risk of falls in a quick and inexpensive manner is needed. The purpose of this study was to demonstrate that given clinical measures, an artificial neural network (ANN) could determine dynamic balance control, as defined by the interaction of the center of mass (CoM) with the base of support (BoS), during gait. Fifty-six elderly adults were included in this study. Using a feed-forward neural network with back propagation, combinations of five functional domains, the number of hidden layers and error goals were evaluated to determine the best parameters to assess dynamic balance control. Functional domain input parameters included subject characteristics, clinical examinations, cognitive performance, muscle strength, and clinical balance performance. The use of these functional domains demonstrated the ability to quickly converge to a solution, with the network learning the mapping within 5 epochs, when using up to 30 hidden nodes and an error goal of 0.001. The ability to correctly identify the interaction of the CoM with BoS demonstrated correlation values up to 0.89 (P<.001). On average, using all clinical measures, the ANN was able to estimate the dynamic CoM to BoS distance to within 1 cm and BoS area to within 75 cm². Our results demonstrated that an ANN could be trained to map clinical variables to biomechanical measures of gait balance control. A neural network could provide physicians and patients with a cost effective means to identify dynamic balance issues and possible risk of falls from routinely collected clinical examinations.     

Optimising a coal rail network under capacity constraints

This research deals with an innovative methodology for optimising the coal train scheduling problem. Based on our previously published work, generic solution techniques are developed by utilising a ??toolbox?? of standard well-solved standard scheduling problems. According to our analysis, the coal train scheduling problem can be basically modelled a Blocking Parallel-Machine Job-Shop Scheduling (BPMJSS) problem with some minor constraints. To construct the feasible train schedules, an innovative constructive algorithm called the SLEK algorithm is proposed. To optimise the train schedule, a three-stage hybrid algorithm called the SLEK-BIH-TS algorithm is developed based on the definition of a sophisticated neighbourhood structure under the mechanism of the Best-Insertion-Heuristic (BIH) algorithm and Tabu Search (TS) metaheuristic algorithm. A case study is performed for optimising a complex real-world coal rail system in Australia. A method to calculate the lower bound of the makespan is proposed to evaluate results. The results indicate that the proposed methodology is promising to find the optimal or near-optimal feasible train timetables of a coal rail system under network and terminal capacity constraints.