Ensuring reliability in B2B services: Fault tolerant inter-organizational workflows

In the age of Business-to-Business (B2B) collaboration, ensuring reliability of workflows underlying inter-organizational business processes is of significant importance. There are, however, quite a few challenges towards achieving seamless operation. Such challenges arise from heterogeneity in infrastructure and coordination mechanism at participant organizations, as well as time and cost associated with recovery from failure. Our research presents foundations for a reliable scheme for recovery from failure of workflow processes spanning through multiple business entities. First, a system model is adapted from the mobile computing literature that serves to establish the requirements to be enforced by each participating organization. In our model, we adopt the Maximal Sequence Path (MSP) approach from Yoo et al. (Lecture Notes in Artificial Intelligence 2132:222–236, 2001), as a means of decomposing workflows into mobile agent-driven processes that communicate via web services at each organization. This decomposition ensures defining logical points within the dynamics of a workflow instance for locating accurate and consistent states of the system for recovery in case of a failure. Then, a set of algorithms for various business scenarios are developed and presented as practical solutions. These algorithms are shown to create checkpoints such that the system is always in a globally consistent state. As such, these algorithms constitute a set of standards that can be incorporated in business process management suites that support reliable inter-organizational collaboration.     

Prediction of suspended sediment concentration from water quality variables

This study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.     

Effect of animation enhanced conceptual change texts on 6th grade students’ understanding of the particulate nature of matter and transformation during phase changes

In this study, the effect of animation enhanced conceptual change texts (CCT–CA) on grade 6 students’ understanding of the particulate nature of matter (PNM) and transformation during the phase changes was investigated. A quasi-experimental design and one control group (CG, N = 25) and one experimental group (EG, N = 26) were used. While the control group taught traditional instruction, the experimental group received CCT–CA instruction. Two different tests, The Particulate Nature of Matter Concept Test (ParNoMaC) and The Transformation of Matter Statement Test (ToMaSaT), were administered as pretest, posttest and delayed test to collect data. Results indicate that while there is no statistically significant difference between groups in pretest, performance of EG students is greater than the CG ones in posttest and delayed test. And also, the EG students are better in remediating their alternative conceptions related to the PNM and transformations during the phase changes. Based on the study, it is concluded that CCT–CA combination may be effective way to improve students’ understanding of basic science and chemical concepts.     

Microstructure evolution of the Sn–Ag–y%Cu interconnect

The lead free Sn–Ag–y%Cu (y = 0.0, 0.5, 1.0 and 2.0) interconnect interfacial microstructures and the microstructure evolution under thermal treatment (isothermal aging, 150 °C/1000 h) were studied in detail by using surface microetching microscopy and cross section microscopy. The corresponding mechanical and reliability behaviors were evaluated by performing shear test and fracture mode analysis before and after the thermal treatment. The results indicate: (i) The interconnects could have different microstructures and intermetallic compound (IMC), depending on the Cu content. The Cu–Sn IMC could have microstructures that were clusters or protrusion-like, Augustine grass leaf-like, scissor-like, tweezers-like, etc. (ii) Ag₃Sn IMCs were not observed at time zero for any interconnect groups, but they occurred after the aging for all groups. The Ag₃Sn IMC could have different microstructures, again depending on Cu content. For low Cu content, the Ag₃Sn IMCs were granules or nodules; for higher Cu content, Ag₃Sn IMCs were plate-like. (iii) The growth of Ag₃Sn plates was promoted by the growth of Cu–Sn IMCs, but indirectly linked to the Cu content. (iv) High Cu content (1.0 wt% and higher) could degrade the mechanical and reliability performances of the LF interconnect by providing a brittle joint, which was mainly achieved through the substantial growth of Cu–Sn IMCs and Ag₃Sn plates.     

The influence of manganese addition on mechanical properties of commercial Al‐4Mg casting alloys

The aim of this study was to investigate the mechanical properties of Al‐Mg‐Si alloys aged to peak hardness with different dispersoid volume fraction. It was found that the tensile strength increases with dispersoid content, for alloys having similar ductility. The effect of an increasingly triaxial stress state on a fracture strain above mentioned alloys were measured using a series of notched tensile specimens whose notch root radius of curvature was changed. The alloy ductility was found to increase with dispersoid content and root radius and to decrease with increased stress triaxiality. The fracture toughness of these alloys was determined as a function of dispersoid content and notch root radius of curvature. It was observed that the fracture toughness increased as the dispersoid content and the notch root radius increased. scanning electron microscope analysis of the fracture surfaces revealed that fracture mechanism was transgranular fracture with dimples formation. It is argued that optimum mechanical properties in these alloys can be achieved at about 0.5 % Mn content.     

Comparison of OR and AI methods in discrete manufacturing using fuzzy logic

Since 1950s the techniques of Operations Research (OR) and Optimization have been utilized to increase the efficiency of the production systems. With the widespread use of computers, it has even become easier to deal with industrial problems. However the complexity of the problems still reveals the difficulty in providing solutions. The use of artificial intelligence (AI) seems to attract the attention of the researcher to overcome to the difficulties. This has already been realized with several successful applications. In this study, the use of AI and OR techniques is compared using fuzzy logic. The progress of manufacturing systems, characteristics of production processes, system managements and system behavior are taken into account. The study is focussed on only discrete manufacturing.     

Stochastic resonance in binary composite hypothesis-testing problems in the Neyman–Pearson framework

Performance of some suboptimal detectors can be enhanced by adding independent noise to their inputs via the stochastic resonance (SR) effect. In this paper, the effects of SR are studied for binary composite hypothesis-testing problems. A Neyman–Pearson framework is considered, and the maximization of detection performance under a constraint on the maximum probability of false-alarm is studied. The detection performance is quantified in terms of the sum, the minimum, and the maximum of the detection probabilities corresponding to possible parameter values under the alternative hypothesis. Sufficient conditions under which detection performance can or cannot be improved are derived for each case. Also, statistical characterization of optimal additive noise is provided, and the resulting false-alarm probabilities and bounds on detection performance are investigated. In addition, optimization theoretic approaches to obtaining the probability distribution of optimal additive noise are discussed. Finally, a detection example is presented to investigate the theoretical results.     

Clean energy investment scenarios using the Bayesian network

Clean energy investment decisions are getting more difficult to make due to public reactions. In order to support the policies in the field, analysis of the positive conditions is needed. This research aims to construct the positive scenarios for nuclear energy and renewable energy investments in the state of Oregon, USA. The Bayesian network technique will be used to create the scenarios. Oregon has a wide range of renewable energies; hence, investment is becoming more complex. Criteria affecting the decisions are taken from the literature, but were reviewed with energy authorities in Oregon in order to define the interactions.     

Feature integration across space, time, and orientation.

The perception of a visual target can be strongly influenced by flanking stimuli. In static displays, performance on the target improves when the distance to the flanking elements increases—presumably because feature pooling and integration vanishes with distance. Here, we studied feature integration with dynamic stimuli. We show that features of single elements presented within a continuous motion stream are integrated largely independent of spatial distance (and orientation). Hence, space-based models of feature integration cannot be extended to dynamic stimuli. We suggest that feature integration is guided by perceptual grouping operations that maintain the identity of perceptual objects over space and time. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Contribution of extracellular polymeric substances from Shewanella sp. HRCR-1 biofilms to U(VI) immobilization

The goal of this study was to quantify the contribution of extracellular polymeric substances (EPS) to U(VI) immobilization by Shewanella sp. HRCR-1. Through comparison of U(VI) immobilization using cells with bound EPS (bEPS) and cells with minimal EPS, we show that (i) bEPS from Shewanella sp. HRCR-1 biofilms contribute significantly to U(VI) immobilization, especially at low initial U(VI) concentrations, through both sorption and reduction; (ii) bEPS can be considered a functional extension of the cells for U(VI) immobilization and they likely play more important roles at lower initial U(VI) concentrations; and (iii) the U(VI) reduction efficiency is dependent upon the initial U(VI) concentration and decreases at lower concentrations. To quantify the relative contributions of sorption and reduction to U(VI) immobilization by EPS fractions, we isolated loosely associated EPS (laEPS) and bEPS from Shewanella sp. HRCR-1 biofilms grown in a hollow fiber membrane biofilm reactor and tested their reactivity with U(VI). We found that, when reduced, the isolated cell-free EPS fractions could reduce U(VI). Polysaccharides in the EPS likely contributed to U(VI) sorption and dominated the reactivity of laEPS, while redox active components (e.g., outer membrane c-type cytochromes), especially in bEPS, possibly facilitated U(VI) reduction.