Analysis of case histories on deep excavations in marine clay

Two case histories on deep excavation of marine clay are used to study the use of a decision-making tool based on a new deign method called the Mobilized Strength Design (MSD) method which allows the designer to use a simple method of predicting ground displacements during deep excavation. This application can approximately satisfy both safety and serviceability requirements by predicting stresses and displacements under working conditions by introducing the concept of "Mobilizable soil strength". The new method accommodates a number of features which are important to design of underground construction between retaining walls, including different deformation mechanism in different stages of excavation. The influence of wall depth, wall flexibility and stratified ground are the major focus of this paper. These developments should make it possible for a design engineer to take informed decisions on the influence of wall stiffness, or on the need for a jet-grouted base slab, for example, without having to conduct project-specific Finite Element Analysis.

Emergency department crowding: prioritising quantified crowding measures using a Delphi study.

AimsEmergency department (ED) crowding has been associated with a number of negative health outcomes, including unnecessary deaths, increased waiting times and a decrease in care quality. Despite the seriousness of this issue, there is little agreement on appropriate crowding measures to assess crowding effects on ED operations. The objective of this study was to prioritise a list of quantified crowding measures that would assess the current state of a department.MethodsA three round Delphi study was conducted via email and an Internet based survey tool. The panel consisted of 40 professionals who had exposure to and expertise in crowding. Participants submitted quantified crowding measures which, through three rounds, were evaluated and ranked to assess participant agreement for inclusion.ResultsThe panel identified 27 measures of which eight (29.6%) reached consensus at the end of the study. These measures comprised: (1) ability of ambulances to offload; (2) patients who leave without being seen or treated; (3) time until triage; (4) ED occupancy rate; (5) patients' total length of stay in the ED; (6) time to see a physician; (7) ED boarding time; and (8) number of patients boarding in the ED.ConclusionsThis study resulted in the identification of eight quantified crowding measures, which present a comprehensive view of how crowding is affecting ED operations, and highlighted areas of concern. These quantified measures have the potential to make a considerable contribution to decision making by ED management and to provide a basis for learning across different departments.

Bayesian Active Learning for Classification and Preference Learning

Information theoretic active learning has been widely studied for probabilistic models. For simple regression an optimal myopic policy is easily tractable. However, for other tasks and with more complex models, such as classification with nonparametric models, the optimal solution is harder to compute. Current approaches make approximations to achieve tractability. We propose an approach that expresses information gain in terms of predictive entropies, and apply this method to the Gaussian Process Classifier (GPC). Our approach makes minimal approximations to the full information theoretic objective. Our experimental performance compares favourably to many popular active learning algorithms, and has equal or lower computational complexity. We compare well to decision theoretic approaches also, which are privy to more information and require much more computational time. Secondly, by developing further a reformulation of binary preference learning to a classification problem, we extend our algorithm to Gaussian Process preference learning.

The decay of Saffman and batchelor turbulence subject to rotation, stratification or an imposed magnetic field

We consider unforced, statistically-axisymmetric turbulence evolving in the presence of a background rotation, an imposed stratification, or a uniform magnetic field. We focus on two canonical cases: Saffman turbulence, in which E(κ → 0) ∼ κ 2, and Batchelor turbulence, in which E(κ → 0) ∼ κ 4. It has recently been shown that, provided the large scales evolve in a self-similar manner, then u ⊥ 2ℓ ⊥ 2ℓ // = constant in Saffman turbulence and u ⊥ 2ℓ ⊥ 4ℓ // = constant in Batchelor turbulence (Davidson, 2009, 2010). Here the subscripts ⊥ and // indicate directions perpendicular and parallel to the axis of symmetry, and ℓ ⊥, ℓ //, and u ⊥ are suitably defined integral scales. These constraints on the integral scales allow us to make simple, testable predictions for the temporal evolution of ℓ ⊥, ℓ //, and u ⊥ in rotating, stratified and MHD turbulence.

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

Travel behaviour applied in freight transportation using intelligent products

The route planning problem for an order in freight transportation involves the selection of the best route for its transportation given a set of options that the network can offer. In its adaptive (or dynamic) version, the problem deals with the planning of a new route for an order while it is actually in transit typically because part or all of its pre-selected route is blocked or disrupted. In the intelligent product approach we are proposing, an order would be capable of identifying and evaluating such new routes in an automated manner and choosing the most preferable one without the intervention of humans. Because such approaches seek to mirror (and then automate) human decision making, in this paper we seek to identify new ways for dynamic route planning in industrial logistics inspired by the way people make similar decisions about their journey when they travel in multi-modal networks. We propose a new simulation game as a methodological tool for capturing their travel behaviour and we use it in this study. The results show that a simulation game can be used for capturing strategies and tactics of travellers and that intelligent products can provide a proper platform for the usage of such strategies in freight logistics. © 2012 IEEE.