A Method to Identify and Analyze Biological Programs through Automated Reasoning.

Predictive biology is elusive because rigorous, data-constrained, mechanistic models of complex biological systems are difficult to derive and validate. Current approaches tend to construct and examine static interaction network models, which are descriptively rich but often lack explanatory and predictive power, or dynamic models that can be simulated to reproduce known behavior. However, in such approaches implicit assumptions are introduced as typically only one mechanism is considered, and exhaustively investigating all scenarios is impractical using simulation. To address these limitations, we present a methodology based on automated formal reasoning, which permits the synthesis and analysis of the complete set of logical models consistent with experimental observations. We test hypotheses against all candidate models, and remove the need for simulation by characterizing and simultaneously analyzing all mechanistic explanations of observed behavior. Our methodology transforms knowledge of complex biological processes from sets of possible interactions and experimental observations to precise, predictive biological programs governing cell function

Toward Cultural Oncology: The Evolutionary Information Dynamics of Cancer

'Racial' disparities among cancers, particularly of the breast and prostate, are something of a mystery. For the US, in the face of slavery and its sequelae, centuries of interbreeding have greatly leavened genetic differences between 'Blacks' and 'whites', but marked contrasts in disease prevalence and progression persist. 'Adjustment' for socioeconomic status and lifestyle, while statistically accounting for much of the variance in breast cancer, only begs the question of ultimate causality. Here we propose a more basic biological explanation that extends the theory of immune cognition to include elaborate tumor control mechanisms constituting the principal selection pressure acting on pathologically mutating cell clones. The interplay between them occurs in the context of an embedding, highly structured, system of culturally specific psychosocial stress which we find is able to literally write an image of itself onto disease progression. The dynamics are analogous to punctuated equilibrium in simple evolutionary proces

Optimizing personal computer configurations with heuristic-based search methods

Given the diversity and limited compatibility for personal computer hardware, obtaining an (sub-)optimal configuration for different usage restricted to some budget limits and other possible criteria can be challenging. In this paper, we firstly formulated these common configuration problems as discrete optimization problems to flexibly add in or modify users' requirements. More interestingly, we proposed two intelligent optimizers: a simple-yet-powerful beam search method and a min-conflict heuristic-based micro-genetic algorithm (MGA) to solve this real-life optimization problem. The heuristic-based MGA consistently outperformed the beam search and branch-and-bound method in most test cases. Furthermore, our work opens up exciting directions for investigation.postprin

A concept for actuating and controlling a leg of a novel walking parallel kinematic machine tool

The scope of this paper is to present a novel method of actuating the legs of a walking parallel kinematic machine tool (WalkingHex) such that the upper spherical joint can be actively driven while walking and remain a free, passive joint while performing machining operations. Different concepts for the number of Degrees of Freedom (DoF) and methods for actuating the chosen concept are presented, leading to a description of a three-wire actuated spherical joint arrangement. The inverse kinematics for the actuation mechanism is defined and a control methodology that accounts for the redundantly actuated nature of the mechanism is explored. It is demonstrated that a prototype of the system is capable of achieving a motion position accuracy within 5.64% RMS. Utilising the concept presented in this paper, it is possible to develop a walking robot that is capable of manoeuvring into location and performing precision machining or inspection operations

Modelling and optimising micro-nozzle resin injection repair of impacted composites using CFD

Resin injection repair is identified to have a gap of knowledge and rigour in the modelling and execution of the process. We outline the strategy of our proposed predictive modelling strategy of ‘reconstruction-simulation-injection’ to simulate real cases to improve repair outcomes. We model the damage zone using Darcy’s law and determine permeability using two methods applied on the Kozeny-Carman equation. We then discuss how we evaluate porosity and detail two proposed methods on reconstructing the porosity field. We verify the model through simulation, and demonstrate verification using a novel comprehensive 2D porosity liquid-ideal gas phase flow model after deriving the analytical solution, which is a contribution of our work. Next, we apply the now-established model to reconstruct real damage cases using the two methods and compare them. We also calibrate the permeability parameter for the model by comparison to a simulation accuracy index, and also calibrate an ultrasonic scanning parameter to minimise reconstruction artefacts as well as the sensitivity of the reconstructed geometry characteristics to scan parameter variations. Then, we validate the model by simulating real repair cases and comparing them to the experimental outcomes, achieving simulation accuracy indices of about 70% or more. We demonstrate the application of the resin injection model by applying resin injection in a proof-of-concept simulation and use it for a case study, and examine the importance of hole configuration, vacuum usage as well as resin flow behaviour between inlet and outlet holes in the context of a given damage area geometry. It is important to maximise the total length of resin flow paths available, through carefully placing inlet and outlet holes, to allow resin to infiltrate the damage zone as much as possible. Vacuum increases the minimum achievable filling, and it is still invariably better to use vacuum with an optimal hole placement, instead of one or the other. In a second case study, we improve the predicted outcome by the model after intentionally changing the hole configuration to maximise resin infiltration, demonstrating that filling can be improved by placing holes intelligently (e.g. by using gathered information on the damage area, together with knowledge of how resin would flow). Using this, we conduct an optimisation study of the resin injection model by first setting up the optimisation strategy and carefully determining the methodology. The optimisation procedure is verified by using one and two degree-of-freedom optimisation cases, with known optima. Then, the optimisation strategy is applied to reconstructed repair cases to demonstrate and assess the efficacy of the optimisation procedure, with average reductions in unfilled volumes of approximately 28% compared to initial configurations.Open Acces

Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]

Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing

Reliability of Erasure Coded Storage Systems: A Geometric Approach

We consider the probability of data loss, or equivalently, the reliability function for an erasure coded distributed data storage system under worst case conditions. Data loss in an erasure coded system depends on probability distributions for the disk repair duration and the disk failure duration. In previous works, the data loss probability of such systems has been studied under the assumption of exponentially distributed disk failure and disk repair durations, using well-known analytic methods from the theory of Markov processes. These methods lead to an estimate of the integral of the reliability function. Here, we address the problem of directly calculating the data loss probability for general repair and failure duration distributions. A closed limiting form is developed for the probability of data loss and it is shown that the probability of the event that a repair duration exceeds a failure duration is sufficient for characterizing the data loss probability. For the case of constant repair duration, we develop an expression for the conditional data loss probability given the number of failures experienced by a each node in a given time window. We do so by developing a geometric approach that relies on the computation of volumes of a family of polytopes that are related to the code. An exact calculation is provided and an upper bound on the data loss probability is obtained by posing the problem as a set avoidance problem. Theoretical calculations are compared to simulation results.Comment: 28 pages. 8 figures. Presented in part at IEEE International Conference on BigData 2013, Santa Clara, CA, Oct. 2013 and to be presented in part at 2014 IEEE Information Theory Workshop, Tasmania, Australia, Nov. 2014. New analysis added May 2015. Further Update Aug. 201

A knowledge-based geometry repair system for robust parametric CAD models

In modern multi-objective design optimization (MDO) an effective geometry engine is becoming an essential tool and its performance has a significant impact on the entire MDO process. Building a parametric geometry requires difficult compromises between the conflicting goals of robustness and flexibility. This article presents a method of improving the robustness of parametric geometry models by capturing and modeling engineering knowledge with a support vector regression surrogate, and deploying it automatically for the search of a more robust design alternative while trying to maintain the original design intent. Design engineers are given the opportunity to choose from a range of optimized designs that balance the ‘health’ of the repaired geometry and the original design intent. The prototype system is tested on a 2D intake design repair example and shows the potential to reduce the reliance on human design experts in the conceptual design phase and improve the stability of the optimization cycle. It also helps speed up the design process by reducing the time and computational power that could be wasted on flawed geometries or frequent human intervention

Uncertainty Propagation and Feature Selection for Loss Estimation in Performance-based Earthquake Engineering

This report presents a new methodology, called moment matching, of propagating the uncertainties in estimating repair costs of a building due to future earthquake excitation, which is required, for example, when assessing a design in performance-based earthquake engineering. Besides excitation uncertainties, other uncertain model variables are considered, including uncertainties in the structural model parameters and in the capacity and repair costs of structural and non-structural components. Using the first few moments of these uncertain variables, moment matching requires only a few well-chosen point estimates to propagate the uncertainties to estimate the first few moments of the repair costs with high accuracy. Furthermore, the use of moment matching to estimate the exceedance probability of the repair costs is also addressed. These examples illustrate that the moment-matching approach is quite general; for example, it can be applied to any decision variable in performance-based earthquake engineering. Two buildings are chosen as illustrative examples to demonstrate the use of moment matching, a hypothetical three-story shear building and a real seven-story hotel building. For these two examples, the assembly-based vulnerability approach is employed when calculating repair costs. It is shown that the moment-matching technique is much more accurate than the well-known First-Order-Second-Moment approach when propagating the first two moments, while the resulting computational cost is of the same order. The repair-cost moments and exceedance probability estimated by the moment-matching technique are also compared with those by Monte Carlo simulation. It is concluded that as long as the order of the moment matching is sufficient, the comparison is satisfactory. Furthermore, the amount of computation for moment matching scales only linearly with the number of uncertain input variables. Last but not least, a procedure for feature selection is presented and illustrated for the second example. The conclusion is that the most important uncertain input variables among the many influencing the uncertainty in future repair costs are, in order of importance, ground-motion spectral acceleration, component capacity, ground-motion details and unit repair costs