Quantification of the inherent uncertainty in the relaxation modulus and creep compliance of asphalt mixes

Advanced material characterization of asphalt concrete is essential for realistic and accurate performance prediction of flexible pavements. However, such characterization requires rigorous testing regimes that involve mechanical testing of a large number of laboratory samples at various conditions and set-ups. Advanced measurement instrumentation in addition to meticulous and accurate data analysis and analytical representation are also of high importance. Such steps as well as the heterogeneous nature of asphalt concrete (AC) constitute major factors of inherent variability. Thus, it is imperative to model and quantify the variability of the needed asphalt material’s properties, mainly the linear viscoelastic response functions such as: relaxation modulus, \(E(t)\), and creep compliance, \(D(t)\). The objective of this paper is to characterize the inherent uncertainty of both \(E(t)\) and \(D(t)\) over the time domain of their master curves. This is achieved through a probabilistic framework using Monte Carlo simulations and First Order approximations, utilizing \(E^{*}\) data for six AC mixes with at least eight replicates per mix. The study shows that the inherent variability, presented by the coefficient of variation (COV), in \(E(t)\) and \(D(t)\) is low at small reduced times, and increases with the increase in reduced time. At small reduced times, the COV in \(E(t)\) and \(D(t)\) are similar in magnitude; however, differences become significant at large reduced times. Additionally, the probability distributions and COVs of \(E(t)\) and \(D(t)\) are mix dependent. Finally, a case study is considered in which the inherent uncertainty in \(D(t)\) is forward propagated to assess the effect of variability on the predicted number of cycles to fatigue failure of an asphalt mix.     

ProbExplorer: Uncertainty‐guided Exploration and Editing of Probabilistic Medical Image Segmentation

In this paper, we develop an interactive analysis and visualization tool for probabilistic segmentation results in medical imaging. We provide a systematic approach to analyze, interact and highlight regions of segmentation uncertainty. We introduce a set of visual analysis widgets integrating different approaches to analyze multivariate probabilistic field data with direct volume rendering. We demonstrate the user's ability to identify suspicious regions (e.g. tumors) and correct the misclassification results using a novel uncertainty‐based segmentation editing technique. We evaluate our system and demonstrate its usefulness in the context of static and time‐varying medical imaging datasets.     

Outbound logistics exception monitoring: A multi-perspective ontologies’ approach with intelligent agents

Logistics consists of a complex network of organizations and processes where exception monitoring is critical for the success of logistics service. In order to detect exceptions effectively, exception monitoring requires proper understanding of the possible exceptions. However, the extant exception monitoring approaches or systems still lack sufficient emphasis in exceptions understanding. This paper presents a novel outbound logistics exception monitoring approach by incorporating multi-perspective ontologies and intelligent agents. Specially, the multi-perspective ontologies, involving static ontology, social ontology and dynamic ontology, are firstly employed to develop the taxonomy of the logistics exception, to reflect the situation dependencies of logistics exception and to represent the dynamic nature of business processes. From this point forwards, an outbound exception monitoring system is designed by introducing multi-intelligent agents, which can ensure autonomous, flexible, and collaborative exception monitor in logistics service. Finally, the presented approach and designed system are exhibited through a case study of two ubiquitous logistics exceptions, which indicates that the proposed multi-perspective ontologies provide better understanding of exceptions thereby enabling the designed outbound exception monitoring system to perform well.     

Measuring research performance of Iraqi universities using Scopus data

Scientometrics - This study investigates the research performance of the Iraqi public and private universities using the Scopus citation database. The investigation consists of three stages. The...     

Brazil’s new national policy on solid waste: challenges and opportunities

Brazil, one of the world’s largest developing countries, has recently introduced a new solid waste management regulatory policy. This new regulatory policy will have implications for a wide variety of stakeholders and sets the stage for opportunities and lessons to be learned. These issues are discussed in this article.     

Analysing green supply chain management practices in Brazil’s electrical/electronics industry using interpretive structural modelling

Industries need to adopt the environmental management concepts in the traditional supply chain management. The green supply chain management (GSCM) is an established concept to ensure environment-friendly activities in industry. This paper identifies the relationship of driving and dependence that exists between GSCM practices with regard to their adoption within Brazilian electrical/electronic industry with the help of interpretive structural modelling (ISM). From the results, we infer that cooperation with customers for eco-design practice is driving other practices, and this practice acts as a vital role among other practices. Commitment to GSCM from senior managers and cooperation with customers for cleaner production occupy the highest level.     

A Virtual Environment for the Design and Simulated Construction of Prefabricated Buildings

The construction industry has acknowledged that its current working practices are in need of substantial improvements in quality and efficiency and has identified that computer modelling techniques and the use of prefabricated components can help reduce times, costs, and minimise defects and problems of on-site construction. This paper describes a virtual environment to support the design and construction processes of buildings from prefabricated components and the simulation of their construction sequence according to a project schedule. The design environment can import a library of 3-D models of prefabricated modules that can be used to interactively design a building. Using Microsoft Project, the construction schedule of the designed building can be altered, with this information feeding back to the construction simulation environment. Within this environment the order of construction can be visualised using virtual machines. Novel aspects of the system are that it provides a single 3-D environment where the user can construct their design with minimal user interaction through automatic constraint recognition and view the real-time simulation of the construction process within the environment. This takes this area of research a step forward from other systems that only allow the planner to view the construction at certain stages, and do not provide an animated view of the construction process.     

The impact of print exposure quality and inference construction on syllogistic reasoning.

This study extended the work of S. Siddiqui, R. F. West, and K. E. Stanovich (1998), who studied the link between general print exposure and syllogistic reasoning. It was hypothesized that exposure to certain text structures that contain well-delineated logical forms, such as popularized scientific texts, would be a better predictor of deductive reasoning skill than general print exposure, which is not sensitive to the quality of an individual's reading activity. Furthermore, it was predicted that the ability to generate explanatory bridging inferences while reading would also be predictive of syllogistic reasoning. Undergraduate students (N = 112) were tested for vocabulary, nonverbal cognitive ability, exposure to general print, exposure to popularized scientific literature, and the ability to comprehend texts distinguished by the number of inferences that must be generated to support comprehension. Hierarchical multiple regression analyses showed that a combined measure of exposure to general and scientific literature was a significant predictor of syllogistic reasoning ability. Additionally, the ability to comprehend high-inference-load texts was related to solving syllogisms that were inconsistent with world knowledge, indicating an overlap in deductive reasoning skill and text comprehension processes. (PsycINFO Database Record (c) 2010 APA, all rights reserved)