Effects of Capping on the (Ga,Mn)As Magnetic Depth Profile

Annealing can increase the Curie temperature and net magnetization in uncapped (Ga,Mn)As films, effects that are suppressed when the films are capped with GaAs. Previous polarized neutron reflectometry (PNR) studies of uncapped (Ga,Mn)As revealed a pronounced magnetization gradient that was reduced after annealing. We have extended this study to (Ga,Mn)As capped with GaAs. We observe no increase in Curie temperature or net magnetization upon annealing. Furthermore, PNR measurements indicate that annealing produces minimal differences in the depth-dependent magnetization, as both as-grown and annealed films feature a significant magnetization gradient. These results suggest that the GaAs cap inhibits redistribution of interstitial Mn impurities during annealing.Comment: 12 pages, 3 figures, submitted to Applied Physics Letter

Cluster analysis of wind turbine alarms for characterising and classifying stoppages

Turbine alarm systems can give useful information to remote technicians on the cause of a fault or stoppage. However, alarms are generally generated at much too high a rate to gain meaningful insight from on their own, so generally require extensive domain knowledge to interpret. By grouping together commonly occurring alarm sequences, the burden of analysis can be reduced. Instead of analysing many individual alarms that occur during a stoppage, the stoppage can be linked to a commonly occurring sequence of alarms. Hence, maintenance technicians can be given information about the shared characteristics or root causes of stoppages where that particular alarm sequence appeared in the past. This research presents a methodology to identify relevant alarms from specific turbine assemblies and group together similar alarm sequences as they appear during stoppages. Batches of sequences associated with 456 different stoppages are created, and features are extracted from these batches representing the order the alarms appeared in. The batches are grouped together using clustering techniques, and evaluated using silhouette analysis and manual inspection. Results show that almost half of all stoppages can be attributed to one of 15 different clusters of alarm sequences

c-axis Josephson Tunnelling in Twinned and Untwinned YBCO-Pb Junctions

Within a microscopic two band model of planes and chains with a pairing potential in the planes and off diagonal pairing between planes and chains we find that the chains make the largest contribution to the Josephson tunnelling current and that through them the d-wave part of the gap contributes to the current. This is contrary to the usual assumption that for a d-wave tetragonal superconductor the c-axis Josephson current for incoherent tunnelling into an s-wave superconductor is zero while that of a d-wave orthorhombic superconductor with a small s-wave component to its gap it is small but non-zero. Nevertheless it has been argued that the effect of twins in YBCO would lead to cancellation between pairs of twins and so the observation of a current in c-axis YBCO-Pb experiments is evidence against a d-wave type order parameter. We argue that both theory and experiment give evidence that the two twin orientations are not necessarily equally abundant and that the ratio of tunnelling currents in twinned and untwinned materials should be related to the relative abundance of the two twin orientations.Comment: 6 pages, RevTeX 3.0, 15 PostScript figur

Automatically identifying and predicting unplanned wind turbine stoppages using SCADA and alarms system data: case study and results

Using 10-minute wind turbine SCADA data for fault prediction offers an attractive way of gaining additional prognostic capabilities without needing to invest in extra hardware. To use these data-driven methods effectively, the historical SCADA data must be labelled with the periods when the turbine was in faulty operation as well the sub-system the fault was attributed to. Manually identifying faults using maintenance logs can be effective, but is also highly time consuming and tedious due to the disparate nature of these logs across manufacturers, operators and even individual maintenance events. Turbine alarm systems can help to identify these periods, but the sheer volume of alarms and false positives generated makes analysing them on an individual basis ineffective. In this work, we present a new method for automatically identifying historical stoppages on the turbine using SCADA and alarms data. Each stoppage is associated with either a fault in one of the turbine's sub-systems, a routine maintenance activity, a grid-related event or a number of other categories. This is then checked against maintenance logs for accuracy and the labelled data fed into a classifier for predicting when these stoppages will occur. Results show that the automated labelling process correctly identifies each type of stoppage, and can be effectively used for SCADA-based prediction of turbine fault

Cmah-dystrophin deficient mdx mice display an accelerated cardiac phenotype that is improved following peptide-PMO exon skipping treatment

Duchenne muscular dystrophy (DMD) is caused by loss of dystrophin protein, leading to progressive muscle weakness and premature death due to respiratory and/or cardiac complications. Cardiac involvement is characterized by progressive dilated cardiomyopathy, decreased fractional shortening and metabolic dysfunction involving reduced metabolism of fatty acids—the major cardiac metabolic substrate. Several mouse models have been developed to study molecular and pathological consequences of dystrophin deficiency, but do not recapitulate all aspects of human disease pathology and exhibit a mild cardiac phenotype. Here we demonstrate that Cmah (cytidine monophosphate-sialic acid hydroxylase)-deficient mdx mice (Cmah−/−;mdx) have an accelerated cardiac phenotype compared to the established mdx model. Cmah−/−;mdx mice display earlier functional deterioration, specifically a reduction in right ventricle (RV) ejection fraction and stroke volume (SV) at 12 weeks of age and decreased left ventricle diastolic volume with subsequent reduced SV compared to mdx mice by 24 weeks. They further show earlier elevation of cardiac damage markers for fibrosis (Ctgf), oxidative damage (Nox4) and haemodynamic load (Nppa). Cardiac metabolic substrate requirement was assessed using hyperpolarized magnetic resonance spectroscopy indicating increased in vivo glycolytic flux in Cmah−/−;mdx mice. Early upregulation of mitochondrial genes (Ucp3 and Cpt1) and downregulation of key glycolytic genes (Pdk1, Pdk4, Ppara), also denote disturbed cardiac metabolism and shift towards glucose utilization in Cmah−/−;mdx mice. Moreover, we show long-term treatment with peptide-conjugated exon skipping antisense oligonucleotides (20-week regimen), resulted in 20% cardiac dystrophin protein restoration and significantly improved RV cardiac function. Therefore, Cmah−/−;mdx mice represent an appropriate model for evaluating cardiac benefit of novel DMD therapeutics

Orthorhombically Mixed s and dx2−y2_{x^2-y^2} Wave Superconductivity and Josephson Tunneling

The effect of orthorhombicity on Josephson tunneling in high T$_c$ superconductors such as YBCO is studied for both single crystals and highly twinned crystals. It is shown that experiments on highly twinned crystals experimentally determine the symmetry of the superconducting twin boundaries (which can be either even or odd with respect to a reflection in the twinning plane). Conversely, Josephson experiments on highly twinned crystals can not experimentally determine whether the superconductivity is predominantly $s$-wave or predominantly $d$-wave. The direct experimental determination of the order-parameter symmetry by Josephson tunneling in YBCO thus comes from the relatively few experiments which have been carried out on untwinned single crystals.Comment: 5 pages, RevTeX file, 1 figure available on request ([email protected]

Issues with data quality for wind turbine condition monitoring and reliability analyses

In order to remain competitive, wind turbines must be reliable machines with efficient and effective maintenance strategies. However, thus far, wind turbine reliability information has been closely guarded by the original equipment manufacturers (OEMs), and turbine reliability studies often rely on data that are not always in a usable or consistent format. In addition, issues with turbine maintenance logs and alarm system data can make it hard to identify historical periods of faulty operation. This means that building new and effective data-driven condition monitoring techniques and methods can be challenging, especially those that rely on supervisory control and data acquisition (SCADA) system data. Such data are rarely standardised, resulting in challenges for researchers in contextualising these data. This work aims to summarise some of the issues seen in previous studies, highlighting the common problems seen by researchers working in the areas of condition monitoring and reliability analysis. Standards and policy initiatives that aim to alleviate some of these problems are given, and a summary of their recommendations is presented. The main finding from this work is that industry would benefit hugely from unified standards for turbine taxonomies, alarm codes, SCADA operational data and maintenance and fault reporting

From M&V to M&T: An artificial intelligence-based framework for real-time performance verification of demand-side energy savings

The European Union's Energy Efficiency Directive is placing an increased focus on the measurement and verification (M&V) of demand side energy savings. The objective of M&V is to quantify energy savings with minimum uncertainty. M&V is currently undergoing a transition to practices, known as M&V 2.0, that employ automated advanced analytics to verify performance. This offers the opportunity to effectively manage the transition from short-term M&V to long-term monitoring and targeting (M&T) in industrial facilities. The original contribution of this paper consists of a novel, robust and technology agnostic framework that not only satisfies the requirements of M&V 2.0, but also bridges the gap between M&V and M&T by ensuring persistence of savings. The approach features a unique machine learning-based energy modelling methodology, model deployment and an exception reporting system that ensures early identification of performance degradation. A case study demonstrates the effectiveness of the approach. Savings from a real-world project are found to be 177,962 +/- 12,334 kWh with a 90% confidence interval. The uncertainty associated with the savings is 8.6% of the allowable uncertainty, thus highlighting the viability of the framework as a reliable and effective tool

A robust prescriptive framework and performance metric for diagnosing and predicting wind turbine faults based on SCADA and alarms data with case study

Using 10-minute wind turbine supervisory control and data acquisition (SCADA) system data to predict faults can be an attractive way of working toward a predictive maintenance strategy without needing to invest in extra hardware. Classification methods have been shown to be effective in this regard, but there have been some common issues in their application within the literature. To use these data-driven methods effectively, historical SCADA data must be accurately labelled with the periods when turbines were down due to faults, as well as with the reason for the fault. This can be manually achieved using maintenance logs, but can be highly tedious and time-consuming due to the often unstructured format in which this information is stored. Alarm systems can also help, but the sheer volume of alarms and false positives generated complicate efforts. Furthermore, a way to implement and evaluate the field deployed system beyond simple classification metrics is needed. In this work, we present a prescribed and reproducible framework for: (i) automatically identifying periods of faulty operation using rules applied to the turbine alarm system; (ii) using this information to perform classification which avoids some of the common pitfalls observed in literature; and (iii) generating alerts based on a sliding window metric to evaluate the performance of the system in a real-world scenario. The framework was applied to a dataset from an operating wind farm and the results show that the system can automatically and accurately label historical stoppages from the alarms data. For fault prediction, classification scores are quite low, with precision of 0.16 and recall of 0.49, but it is envisaged that this can be greatly improved with more training data. Nonetheless, the sliding window metric compensates for the low raw classification scores and shows that 71% of faults can be predicted with an average of 30 h notice, with false alarms being active for 122 h of the year. By adjusting some of the parameters of the fault prediction alerts, the duration of false alarms can be drastically reduced to 2 h, but this also reduces the number of predicted faults to 8%