Trajectory Control of Electro Hydraulic Excavator using Fuzzy Self Tuning Algorithm with Neural Network

This paper presents the trajectory control of a 2DOF mini electro-hydraulic excavator by using fuzzy self tuning with neural network algorithm. First, the mathematical model is derived for the 2DOF mini electro-hydraulic excavator. The fuzzy PID and fuzzy self tuning with neural network are designed for circle trajectory following. Its two links are driven by an electric motor controlled pump system. The experimental results demonstrated that the proposed controllers have better control performance than the conventional controller.

DONUTS: A Science Frame Autoguiding Algorithm with Sub-Pixel Precision, Capable of Guiding on Defocused Stars

We present the DONUTS autoguiding algorithm, designed to fix stellar positions at the sub-pixel level for high-cadence time-series photometry, and also capable of autoguiding on defocused stars. DONUTS was designed to calculate guide corrections from a series of science images and recentre telescope pointing between each exposure. The algorithm has the unique ability of calculating guide corrections from undersampled to heavily defocused point spread functions. We present the case for why such an algorithm is important for high precision photometry and give our results from off and on-sky testing. We discuss the limitations of DONUTS and the facilities where it soon will be deployed.

Pigmented paravenous chorioretinal atrophy is associated with a mutation within the crumbs homolog 1 (CRB1) gene

Pigmented paravenous chorioretinal atrophy (PPCRA) is an unusual retinal degeneration characterized by accumulation of pigmentation along retinal veins. The purpose of this study was to describe the phenotype of a family with PPCRA, determine the mode of inheritance, and identify the causal mutation.

Non-convex dynamic economic/environmental dispatch with plug-in electric vehicle loads

Electric vehicles are a key prospect for future transportation. A large penetration of electric vehicles has the potential to reduce the global fossil fuel consumption and hence the greenhouse gas emissions and air pollution. However, the additional stochastic loads imposed by plug-in electric vehicles will possibly introduce significant changes to existing load profiles. In his paper, electric vehicles loads are integrated into an 5-unit system using a non-convex dynamic dispatch model. The actual infrastructure characteristics including valve-point effects, load balance constrains and transmission loss have been included in the model. Multiple load profiles are comparatively studied and compared in terms of economic and environmental impacts in order o identify patterns to charge properly. The study as expected shows ha off-peak charging is the best scenario with respect to using less fuels and producing less emissions.

A review of dynamic Bayesian network techniques with applications in healthcare risk modelling

Coping with an ageing population is a major concern for healthcare organisations around the world. The average cost of hospital care is higher than social care for older and terminally ill patients. Moreover, the average cost of social care increases with the age of the patient. Therefore, it is important to make efficient and fair capacity planning which also incorporates patient centred outcomes. Predictive models can provide predictions which their accuracy can be understood and quantified. Predictive modelling can help patients and carers to get the appropriate support services, and allow clinical decision-makers to improve care quality and reduce the cost of inappropriate hospital and Accident and Emergency admissions. The aim of this study is to provide a review of modelling techniques and frameworks for predictive risk modelling of patients in hospital, based on routinely collected data such as the Hospital Episode Statistics database. A number of sub-problems can be considered such as Length-of-Stay and End-of-Life predictive modelling. The methodologies in the literature are mainly focused on addressing the problems using regression methods and Markov models, and the majority lack generalisability. In some cases, the robustness, accuracy and re-usability of predictive risk models have been shown to be improved using Machine Learning methods. Dynamic Bayesian Network techniques can represent complex correlations models and include small probabilities into the solution. The main focus of this study is to provide a review of major time-varying Dynamic Bayesian Network techniques with applications in healthcare predictive risk modelling.

A biased random-key genetic algorithm with forward-backward improvement for the resource constrained project scheduling problem

This paper presents a biased random-key genetic algorithm for the resource constrained project scheduling problem. The chromosome representation of the problem is based on random keys. Active schedules are constructed using a priority-rule heuristic in which the priorities of the activities are defined by the genetic algorithm. A forward-backward improvement procedure is applied to all solutions. The chromosomes supplied by the genetic algorithm are adjusted to reflect the solutions obtained by the improvement procedure. The heuristic is tested on a set of standard problems taken from the literature and compared with other approaches. The computational results validate the effectiveness of the proposed algorithm.

Postpacing abnormal repolarization in catecholaminergic polymorphic ventricular tachycardia associated with a mutation in the cardiac ryanodine receptor gene.

BACKGROUND Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an arrhythmogenic disease for which electrophysiological studies (EPS) have shown to be of limited value.OBJECTIVE This study presents a CPVT family in which marked postpacing repolarization abnormalities during EPS were the only consistent phenotypic manifestation of ryanodine receptor (RyR2) mutation carriers.METHODS The study was prompted by the observation of transient marked QT prolongation preceding initiation of ventricular fibrillation during atrial fibrillation in a boy with a family history of sudden cardiac death (SCD). Family members underwent exercise and pharmacologic electrocardiographic testing with epinephrine, adenosine, and flecainide. Noninvasive clinical test results were normal in 10 patients evaluated, except for both epinephrine- and exercise-induced ventricular arrhythmias in 1. EPS included bursts of ventricular pacing and programmed ventricular extrastimulation reproducing short-long sequences. Genetic screening involved direct sequencing of genes involved in long QT syndrome as well as RyR2.RESULTS Six patients demonstrated a marked increase in QT interval only in the first beat after cessation of ventricular pacing and/or extrastimulation. All 6 patients were found to have a heterozygous missense mutation (M4109R) in RyR2. Two of them, presenting with aborted SCD, also had a second missense mutation (I406T- RyR2). Four family members without RyR2 mutations did not display prominent postpacing QT changes.CONCLUSION M4109R- RyR2 is associated with a high incidence of SCD. The contribution of I406T to the clinical phenotype is unclear. In contrast to exercise testing, marked postpacing repolarization changes in a single beat accurately predicted carriers of M4109R- RyR2 in this family.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

Comparison of Computerized Dynamic Posturography (CDP) results with subjective visual vertical (SVV) test in patients with and without vestibular dysfunction: effects of horizontal headshaking

The goal of the study was to identify what effect headshaking in the horizontal plane has on Computerized Dynamic Posturography results in normals and patients with unilateral vestibular dysfunction. Additionally, the results were compared to results of the dynamic subjective visual vertical test.

An isentropic flow algorithm with body fitted meshes

An efficient algorithm based on flux difference splitting is presented for the solution of the three-dimensional equations of isentropic flow in a generalised coordinate system, and with a general convex gas law. The scheme is based on solving linearised Riemann problems approximately and in more than one dimension incorporates operator splitting. The algorithm requires only one function evaluation of the gas law in each computational cell. The scheme has good shock capturing properties and the advantage of using body-fitted meshes. Numerical results are shown for Mach 3 flow of air past a circular cylinder. Furthermore, the algorithm also applies to shallow water flows by employing the familiar gas dynamics analogy.