Low strain, long life creep fatigue of AF2-1DA and INCO 718

Two aircraft turbine disk alloys, GATORIZED AF2-DA and INCO 718 were evaluated for their low strain long life creep-fatigue behavior. Static (tensile and creep rupture) and cyclic properties of both alloys were characterized. The cntrolled strain LCF tests were conducted at 760 C (1400 F) and 649 C (1200 F) for AF2-1DA and INCO 718, respectively. Hold times were varied for tensile, compressive and tensile/compressive strain dwell (relaxation) tests. Stress (creep) hold behavior of AF2-1DA was also evaluated. Generally, INCO 718 exhibited more pronounced reduction in cyclic life due to hold than AF2-1DA. The percent reduction in life for both alloys for strain dwell tests was greater at low strain ranges (longer life regime). Changing hold time from 0 to 0.5, 2.0 and 15.0 min. resulted in corresponding reductions in life. The continuous cycle and cyclic/dwell initiation failure mechanism was predominantly transgranular for AF2-1DA and intergranular for INCO 718

The role of DNA methylation in human pancreatic neuroendocrine tumours

Pancreatic neuroendocrine tumours (PNETs) are the second most common pancreatic tumour. However, relatively little is known about their tumourigenic drivers, other than mutations involving the multiple endocrine neoplasia 1 (MEN1), ATRX chromatin remodeler, and death domain-associated protein genes, which are found in ~40% of sporadic PNETs. PNETs have a low mutational burden, thereby suggesting that other factors likely contribute to their development, including epigenetic regulators. One such epigenetic process, DNA methylation, silences gene transcription via 5’methylcytosine (5mC), and this is usually facilitated by DNA methyltransferase enzymes at CpG-rich areas around gene promoters. However, 5’hydroxymethylcytosine, which is the first epigenetic mark during cytosine demethylation, and opposes the function of 5mC, is associated with gene transcription, although the significance of this remains unknown, as it is indistinguishable from 5mC when conventional bisulfite conversion techniques are solely used. Advances in array-based technologies have facilitated the investigation of PNET methylomes and enabled PNETs to be clustered by methylome signatures, which has assisted in prognosis and discovery of new aberrantly regulated genes contributing to tumourigenesis. This review will discuss the biology of DNA methylation, its role in PNET development, and impact on prognostication and discovery of epigenome-targeted therapies

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

Quasi-Steady Analytical Model Benchmark of an Impulse Turbine for Wave Energy Extraction

This work presents a mean line analysis for the prediction of the performance and aerodynamic loss of axial flow impulse turbine wave energy extraction, which can be easily incorporated into the turbine design program. The model is based on the momentum principle and the well-known Euler turbine equation. Predictions of torque, pressure drop, and turbine efficiency showed favorable agreement with experimental results. The variation of the flow incidence and exit angles with the flow coefficient has been reported for the first time in the field of wave energy extraction. Furthermore, an optimum range of upstream guide vanes setting up angle was determined, which optimized the impulse turbine performance prediction under movable guide vanes working condition

Design and simulation studies of the novel beam arrival monitor pickup at Daresbury Laboratory

We present the novel beam arrival monitor pickup design currently under construction at Daresbury Laboratory, Warrington, UK. The pickup consists of four flat electrodes in a transverse gap. CST Particle Studio simulations have been undertaken for the new pickup design as well as a pickup design from DESY, which is used as a reference for comparison. Simulation results have highlighted two advantages of the new pickup design over the DESY design; the signal bandwidth is 25 GHZ, which is half that of the DESY design and the response slope is a factor of 1.6 greater. We discuss optimisation studies of the design parameters in order to maximise the response slope for bandwidths up to 50 GHz and present the final design of the pickup

Bayesian Learning-Based Adaptive Control for Safety Critical Systems

Deep learning has enjoyed much recent success, and applying state-of-the-art model learning methods to controls is an exciting prospect. However, there is a strong reluctance to use these methods on safety-critical systems, which have constraints on safety, stability, and real-time performance. We propose a framework which satisfies these constraints while allowing the use of deep neural networks for learning model uncertainties. Central to our method is the use of Bayesian model learning, which provides an avenue for maintaining appropriate degrees of caution in the face of the unknown. In the proposed approach, we develop an adaptive control framework leveraging the theory of stochastic CLFs (Control Lyapunov Functions) and stochastic CBFs (Control Barrier Functions) along with tractable Bayesian model learning via Gaussian Processes or Bayesian neural networks. Under reasonable assumptions, we guarantee stability and safety while adapting to unknown dynamics with probability 1. We demonstrate this architecture for high-speed terrestrial mobility targeting potential applications in safety-critical high-speed Mars rover missions.Comment: Corrected an error in section II, where previously the problem was introduced in a non-stochastic setting and wrongly assumed the solution to an ODE with Gaussian distributed parametric uncertainty was equivalent to an SDE with a learned diffusion term. See Lew, T et al. "On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation

Temporal Logic Control of POMDPs via Label-based Stochastic Simulation Relations

The synthesis of controllers guaranteeing linear temporal logic specifications on partially observable Markov decision processes (POMDP) via their belief models causes computational issues due to the continuous spaces. In this work, we construct a finite-state abstraction on which a control policy is synthesized and refined back to the original belief model. We introduce a new notion of label-based approximate stochastic simulation to quantify the deviation between belief models. We develop a robust synthesis methodology that yields a lower bound on the satisfaction probability, by compensating for deviations a priori, and that utilizes a less conservative control refinement

A note on exploration of IoT generated big data using semantics

yesWelcome to this special issue of the Future Generation Computer Systems (FGCS) journal. The special issue compiles seven technical contributions that significantly advance the state-of-the-art in exploration of Internet of Things (IoT) generated big data using semantic web techniques and technologies

Intracellular sodium elevation reprograms cardiac metabolism

Intracellular Na elevation in the heart is a hallmark of pathologies where both acute and chronic metabolic remodelling occurs. Here, we assess whether acute (75 μM ouabain 100 nM blebbistatin) or chronic myocardial Nai load (PLM3SA mouse) are causally linked to metabolic remodelling and whether the failing heart shares a common Na-mediated metabolic ‘fingerprint’. Control (PLMWT), transgenic (PLM3SA), ouabain-treated and hypertrophied Langendorff-perfused mouse hearts are studied by 23Na, 31P, 13C NMR followed by 1H-NMR metabolomic profiling. Elevated Nai leads to common adaptive metabolic alterations preceding energetic impairment: a switch from fatty acid to carbohydrate metabolism and changes in steady-state metabolite concentrations (glycolytic, anaplerotic, Krebs cycle intermediates). Inhibition of mitochondrial Na/Ca exchanger by CGP37157 ameliorates the metabolic changes. In silico modelling indicates altered metabolic fluxes (Krebs cycle, fatty acid, carbohydrate, amino acid metabolism). Prevention of Nai overload or inhibition of Na/Camito may be a new approach to ameliorate metabolic dysregulation in heart failure