Inspection and Evaluation of a Bridge Deck Partially Reinforced With GFRP Rebars

The corrosion of steel can be a significant problem in bridge decks in which the reinforcing and prestressing steel are accessible to deicing salts and combinations of moisture, temperature and chlorides through cracks, leading to concrete deterioration and loss of serviceability. Fiber Reinforced Polymer (FRP) rebars have emerged as one alternative to steel reinforcement in corrosive environments. The objective of this study is to evaluate the cracks formed on a bridge deck that is partially reinforced with glass fiber reinforced polymer (GFRP) rebars. The bridge constructed in 1997 is in Bourbon County, KY, on US460 over the Rogers\u27 Creek. Its deck is partially reinforced with GFRP rebars in place of epoxy coated steel rebars. The bridge has been monitored for cracks over a period of two years from June 1998 to July 2000. The maximum measured crack width of 0.013 in (0.3 mm) in the GFRP reinforced section meets the maximum allowed by ACI (Section 10.6) and AASHTO (Section 8.16.8.4) specifications in steel reinforced structures for exterior exposure

Digging into acceptor splice site prediction : an iterative feature selection approach

Feature selection techniques are often used to reduce data dimensionality, increase classification performance, and gain insight into the processes that generated the data. In this paper, we describe an iterative procedure of feature selection and feature construction steps, improving the classification of acceptor splice sites, an important subtask of gene prediction. We show that acceptor prediction can benefit from feature selection, and describe how feature selection techniques can be used to gain new insights in the classification of acceptor sites. This is illustrated by the identification of a new, biologically motivated feature: the AG-scanning feature. The results described in this paper contribute both to the domain of gene prediction, and to research in feature selection techniques, describing a new wrapper based feature weighting method that aids in knowledge discovery when dealing with complex datasets

The International Space Station Solar Alpha Rotary Joint Anomaly Investigation

The Solar Alpha Rotary Joint (SARJ) is a single-axis pointing mechanism used to orient the solar power generating arrays relative to the sun for the International Space Station (ISS). Approximately 83 days after its on-orbit installation, one of the two SARJ mechanisms aboard the ISS began to exhibit high drive motor current draw. Increased structural vibrations near the joint were also observed. Subsequent inspections via Extravehicular Activity (EVA) discovered that the nitrided case hardened steel bearing race on the outboard side of the joint had extensive damage to one of its three rolling surfaces. A far-reaching investigation of the anomaly was undertaken. The investigation included metallurgical inspections, coupon tests, traction kinematics tests, detailed bearing measurements, and thermal and structural analyses. The results of the investigation showed that anomaly had most probably been caused by high bearing edge stresses that resulted from inadequate lubrication of the rolling contact. The profile of the roller bearings and the metallurgical properties of the race ring were also found to be significant contributing factors. To mitigate the impact of the damage astronauts cleaned and lubricated the race ring surface with grease. This corrective action led to significantly improved performance of the mechanism both in terms of drive motor current and induced structural vibration

Neuroevolutionary reinforcement learning for generalized control of simulated helicopters

This article presents an extended case study in the application of neuroevolution to generalized simulated helicopter hovering, an important challenge problem for reinforcement learning. While neuroevolution is well suited to coping with the domain’s complex transition dynamics and high-dimensional state and action spaces, the need to explore efficiently and learn on-line poses unusual challenges. We propose and evaluate several methods for three increasingly challenging variations of the task, including the method that won first place in the 2008 Reinforcement Learning Competition. The results demonstrate that (1) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering, (2) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models, (3) due to the difficulty of learning reliable models, model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and (4) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms

Neither participation nor revolution: the strategy of the Moroccan Jamiat al-Adl wal-Ihsan

Scholars and students of Islamist movements are divided over the issue of Islamists' commitment to democracy and a number of studies have attempted to discover the true nature of Islamist parties. This paper rejects this approach and argues that the behaviour of Islamist parties can be better understood through an analysis of the constraints and opportunities that their surrounding environment provides. Specifically, the paper aims at explaining the choice of the Moroccan Jamiat al-Adl wal-Ihsan neither to participate in institutional politics nor to undertake violent actions to transform the regime. This is done through an examination of its relations with the other political actors. The paper argues that Jamiat al-Adl wal-Ihsan's behaviour is as much the product of rational thinking as it is of ideology and provides evidence to support this claim. Such findings are important not only in the Moroccan context, but contribute to a growing literature claiming that Islamist movements should be treated as rational political actors operating under 'environmental' constraints and opportunities

Highly-accurate 5-axis flank CNC machining with conical tools

A new method for $5$-axis flank computer numerically controlled (CNC) machining using a predefined set of tappered ball-end-mill tools (aka conical) cutters is proposed. The space of lines that admit tangential motion of an associated truncated cone along a general, doubly curved, free-form surface is explored. These lines serve as discrete positions of conical axes in 3D space. Spline surface fitting is used to generate a ruled surface that represents a single continuous sweep of a rigid conical milling tool. An optimization based approach is then applied to globally minimize the error between the design surface and the conical envelope. Our computer simulation are validated with physical experiments on two benchmark industrial datasets, reducing significantly the execution times while preserving or even reducing the milling error when compared to the state-of-the-art industrial software

The impact of large scale licensing examinations in highly developed countries: a systematic review

BACKGROUND: To investigate the existing evidence base for the validity of large-scale licensing examinations including their impact. METHODS: Systematic review against a validity framework exploring: Embase (Ovid Medline); Medline (EBSCO); PubMed; Wiley Online; ScienceDirect; and PsychINFO from 2005 to April 2015. All papers were included when they discussed national or large regional (State level) examinations for clinical professionals, linked to examinations in early careers or near the point of graduation, and where success was required to subsequently be able to practice. Using a standardized data extraction form, two independent reviewers extracted study characteristics, with the rest of the team resolving any disagreement. A validity framework was used as developed by the American Educational Research Association, American Psychological Association, and National Council on Measurement in Education to evaluate each paper’s evidence to support or refute the validity of national licensing examinations. RESULTS: 24 published articles provided evidence of validity across the five domains of the validity framework. Most papers (n = 22) provided evidence of national licensing examinations relationships to other variables and their consequential validity. Overall there was evidence that those who do well on earlier or on subsequent examinations also do well on national testing. There is a correlation between NLE performance and some patient outcomes and rates of complaints, but no causal evidence has been established. CONCLUSIONS: The debate around licensure examinations is strong on opinion but weak on validity evidence. This is especially true of the wider claims that licensure examinations improve patient safety and practitioner competence