TPC-W e-commerce benchmark evaluation

Correctly interpreting benchmark results requires a basic knowledge of the synthetic workload the benchmark uses to determine how well it represents diverse e-commerce applications' real-world workloads. Factors that influence these results include the characteristics of the system under test, the procedures used to execute the tests, and the performance metrics the benchmark generates. TPC-W performs server evaluation in a controlled Internet e-commerce environment that simulates the activities of a business-oriented transactional Web server. The authors used experimental results from their TPC-W implementation to assess the benchmark's behavior, including its granularity and sensitivity to changes in workload and system parameters.     

Coordinated scheduling of production and delivery from multiple plants

This paper deals with the scheduling of orders and vehicle assignment for production and distribution planning in a scenario of no-wait, immediate delivery to the customer site. We first describe the problem and then present an integer programming model that maximizes the weighted value of the orders served. We consider a special case of the problem which can be solved in polinomial time by a minimum cost flow algorithm. Based on this approach we develop a heuristic procedure for the general case. Comparisons with an exact graph-based method attest that our heuristic produces good-quality solutions in short running times.     

Fracture mechanisms and failure processes at stiffener run-outs in polymer matrix composite stiffened elements

This paper describes the fractographic analyses of three stringer run-out designs which had been loaded to failure in tension. The main aims of the investigation were to deduce the failure processes in the elements, and to characterise the effect of local geometry of the stringer run-out on the failure process. The analysis showed that the critical failure mechanism in the elements was the development of +45° ply splitting at the skin surface, initially under mode I dominated intralaminar fracture. However, as these splits grew beneath the stringer foot, the mode II component increased. This led to mixed-mode delamination growth, extending parallel to the +45° ply, at the skin/adhesive interface. Subsequently, the delamination migrated through the skin via ply splits, ultimately reaching the interface between the second and third (−45°/0°) plies, in which it remained until catastrophic failure.

Extending and tapering the stringer foot led to shifting of the site of the +45° ply splitting; this was attributed to in-plane tensile stresses in the skin being inhibited in the modified designs. The reduced out-of-plane support on the stiffener foot in the modified designs led to an increase in the mode I component at the delamination from the stringer tip. It also led to an increase in the degree of multi-plane delamination growth. Based on the fractographic observations, recommendations for modelling the elements were suggested.     

Development of an Active Thermoplastic Film with Oxygen Scavengers Made of Activated Carbon and Sodium Erythorbate

An active thermoplastic film made of low‐density polyethylene (LDPE) filled with oxygen scavengers made of powdered activated carbon (PAC) impregnated with sodium erythorbate (SE) was developed for packaging applications. Initial tests indicated that the impregnation of PAC with SE enhanced the heat resistance of SE, thereby allowing processing at temperatures typical of LDPE manufacturing. Subsequently, LDPE films with PAC/SE particles were manufactured in coupons that represented a typical juice package, and experiments indicated that these films absorbed 3.57 mg of oxygen in 11 days. This amount corresponded to 80% the concentration of oxygen in the headspace of the package. Furthermore, findings indicated that active particles alone have 10 times higher oxygen absorption capacity than the active LDPE film. Finally, the physical properties of the film were characterized by microscopy where oxygen scavengers showed a good dispersion within the matrix. However, 20 wt.% of these active particles decreased tensile strength of the film by 53%. Copyright © 2014 John Wiley & Sons, Ltd.     

The State‐of‐the‐Art in Predictive Visual Analytics

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.     

A diagnoser algorithm for anomaly detection in DEDS under partial and unreliable observations: characterization and inclusion in sensor configuration optimization

Complex engineering systems have to be carefully monitored to meet demanding performance requirements, including detecting anomalies in their operations. There are two major monitoring challenges for these systems. The first challenge is that information collected from the monitored system is often partial and/or unreliable, in the sense that some occurred events may not be reported and/or may be reported incorrectly (e.g., reported as another event). The second is that anomalies often consist of sequences of event patterns separated in space and time. This paper introduces and analyzes a diagnoser algorithm that meets these challenges for detecting and counting occurrences of anomalies in engineering systems. The proposed diagnoser algorithm assumes that models are available for characterizing plant operations (via stochastic automata) and sensors (via probabilistic mappings) used for reporting partial and unreliable information. Methods for analyzing the effects of model uncertainties on the diagnoser performance are also discussed. In order to select configurations that reduce sensor costs, while satisfying diagnoser performance requirements, a sensor configuration selection algorithm developed in previous work is then extended for the proposed diagnoser algorithm. The proposed algorithms and methods are then applied to a multi-unit-operation system, which is derived from an actual facility application. Results show that the proposed diagnoser algorithm is able to detect and count occurrences of anomalies accurately and that its performance is robust to model uncertainties. Furthermore, the sensor configuration selection algorithm is able to suggest optimal sensor configurations with significantly reduced costs, while still yielding acceptable performance for counting the occurrences of anomalies.     

Pretreatment predictors of dropout from cognitive behavioral therapy for PTSD in Iraq and Afghanistan war veterans.

Cognitive–behavioral therapies (CBTs) can be effective treatments for posttraumatic stress disorder (PTSD) but their effectiveness is limited by high rates of premature dropout. Few studies have compared pretreatment characteristics of treatment completers and dropouts, and only one has examined these factors in Operations Iraqi Freedom and Enduring Freedom (OIF/OEF) Veterans. This study analyzed archival clinical data from 117 OEF/OIF Veterans evaluated and treated through a Veterans Affairs PTSD clinic. High numbers dropped out of treatment (68%). Treatment dropouts (n = 79) and completers (n = 38) differed significantly on Minnesota Multiphasic Personality Inventory-2 (MMPI-2) scales, PTSD symptom severity, and age. Regression analyses identified one MMPI-2 scale, TRT (negative treatment indicators), and age as unique but modest predictors of dropout. (PsycINFO Database Record (c) 2011 APA, all rights reserved)     

Phase transition characteristics and dielectric properties of rare-earth (La,Pr, Nd,Gd) doped Ba(Zr0.09Ti0.91)O3 ceramics

A-site deficient rare-earth doped barium zirconate titanate (BZT) ceramics (Ba_1−yLn_2y/3)Zr_0.09Ti_0.91O₃ (Ln = La, Pr, Nd, Gd) are obtained by a modified solid-state reaction method. Perovskite-like single-phase compounds were confirmed from X-ray diffraction data. Morphological analysis on sintered samples shows that the addition of rare-earth ions inhibits the growth of the grain and remarkably changes the grain morphology. The effect of rare-earth addition to BZT on phase transition and dielectric properties is analyzed. A dramatic fall in the transition temperature occurs when BZT ceramic is doped with rare-earths. Moreover, diffusivity degree of the phase transition increases and a relaxor-type behaviour is induced due to both the increment of the lanthanide content and the increase of the ionic radius of the dopant element. High values of dielectric tunability are obtained for lanthanum doped BZT. A direct relation between transition temperature and tunability is discussed. Conclusively, low permittivity and high tunability materials can be obtained by the adequate substitution of rare-earths into BZT ceramics.     

Electric distribution network reconfiguration based on a fuzzy multi-criteria decision making algorithm

Topological reconfiguration is an important tool for the planning and operation of electric power distribution systems. The amount of time that an algorithm spends to obtain an alternative topological status for the system is not a primary concern, as the main goals of distribution operation planning are the reduction of power losses, the enhancement of the voltage profile, and the increase of reliability levels. The utility can use multiple criteria regarding the observation of regulation policies and public awareness to drive the topological reconfiguration. Several researchers are looking for new optimization methods, as the complexity of this combinatorial issue is high in large systems and the classic optimization methods are failing to address the problem reasonably. Therefore, a new fuzzy multi-criteria decision making algorithm for the proper processing of the information sources available at the utilities in the context of distribution network reconfiguration is proposed. The algorithm is evaluated through the use of a proof-of-concept implementation in a set of case studies based on actual distribution systems.