Parametrical optimization of laser surface alloyed NiTi shape memory alloy with Co and Nb by the Taguchi method

Different high-purity metal powders were successfully alloyed on to a nickel titanium (NiTi) shape memory alloy (SMA) with a 3 kW carbon dioxide (CO2) laser system. In order to produce an alloyed layer with complete penetration and acceptable composition profile, the Taguchi approach was used as a statistical technique for optimizing selected laser processing parameters. A systematic study of laser power, scanning velocity, and pre-paste powder thickness was conducted. The signal-to-noise ratios (S/N) for each control factor were calculated in order to assess the deviation from the average response. Analysis of variance (ANOVA) was carried out to understand the significance of process variables affecting the process effects. The Taguchi method was able to determine the laser process parameters for the laser surface alloying technique with high statistical accuracy and yield a laser surface alloying technique capable of achieving a desirable dilution ratio. Energy dispersive spectrometry consistently showed that the per cent by weight of Ni was reduced by 45 per cent as compared with untreated NiTi SMA when the Taguchi-determined laser processing parameters were employed, thus verifying the laser's processing parameters as optimum

A Kidney Biopsy Simulation Training Program: First Year\u27s Results

BACKGROUND: Nephrology attracts fewer medical graduates despite the growing care and workforce demand. Interventional Nephrology could re-foster interest in this subspecialty. Percutaneous kidney biopsy (PKB) is the most common procedure and should be adequately taught through simulation training according to ACGME requirements. We initiated a PKB simulation training program and we designed a two-year study in order to examine its effect on the confidence level, the procedural competence and the satisfaction with this training of Nephrology fellows compared to historical controls (fellows trained on PKBs before the initiation of the program). METHODS: All fellows were consented and trained at UNM’s simulation center (BATCAVE) with a renal biopsy ultrasound training model (CAE Healthcare Blue PhantomTM). Participants demographics and previous PKB experience was collected. Pre-assigned readings, online videos and hands-on practice on the simulation model were utilized as educational strategies. Performance of the trainee during each one-hour session was graded by the use of an evaluation form specifically designed for PKBs. Pre-and post-simulation surveys evaluated the participants’ confidence level quantitatively. All participants completed the satisfaction with PKB simulation experience scale (PKB-SSE). RESULTS: All three 1st and 2nd year current renal fellows completed the simulation training. The following table summarizes the basic information acquired from their training. Overall, the program enhanced the confidence level of fellows without previous experience on performing PKBs. All fellows expressed a high level of satisfaction from their participation in this training. CONCLUSIONS: PKB simulation training may improve trainees’ confidence level especially for those without prior experience as well as their satisfaction with the training. The procedural competence of the trainees on PKBs will be evaluated during the second year of their fellowship and will be compared to the procedural competence of historical controls

Sequential heuristic optimisation of a real offshore wind farm site considering turbine placement and cable layout

Competition within the energy generation industry provides an incentive for developers to build offshore wind farms with a low levelised cost of energy. Therefore, there is a need for design optimisation to reduce costs and increase energy capture. A sequential approach to optimise turbine placement and cable layout is presented, using a heuristic k-opt algorithm and mixed-integer linear programming respectively. Energy storage is considered as a means to further improve the cable selection process. A case study is carried out on the Lillgrund offshore wind farm and the resulting layout improves energy capture by 6%. Cable costs are increased but the electrical losses are reduced such that there is an overall saving over the project lifetime of 20%. Energy storage as a means to peak shave the power seen by a cable in order to reduce electrical losses or de-rate a cable section was found to be impractically large and not profitable. Future work will consider secondary revenue streams to remedy this

Longitudinal changes in prospective memory and their clinical correlates at 1-year follow-up in first-episode schizophrenia

This study aimed to investigate prospective memory (PM) and the association with clinical factors at 1-year follow-up in first-episode schizophrenia (FES). Thirty-two FES patients recruited from a university-affiliated psychiatric hospital in Beijing and 17 healthy community controls (HCs) were included. Time- and event-based PM (TBPM and EBPM) performances were measured with the Chinese version of the Cambridge Prospective Memory Test (CCAMPROMPT) at baseline and at one-year follow-up. A number of other neurocognitive tests were also administered. Remission was determined at the endpoint according to the PANSS score _ 3 for selected items. Repeated measures analysis of variance revealed a significant interaction between time (baseline vs. endpoint) and group (FES vs. HCs) for EBPM (F(1, 44) = 8.8, p = 0.005) and for all neurocognitive components. Paired samples ttests showed significant improvement in EBPM in FES (13.1±3.7 vs. 10.3±4.8; t = 3.065, p = 0.004), compared to HCs (15.7±3.6 vs. 16.5±2.3; t = -1.248, p = 0.230). A remission rate of 59.4% was found in the FES group. Analysis of covariance revealed that remitters performed significantly better on EBPM (14.9±2.6 vs. 10.4±3.6; F(1, 25) = 12.2, p = 0.002) than non-remitters at study endpoint. The association between EBPM and 12-month clinical improvement in FES suggests that EBPM may be a potential neurocognitive marker for the effectiveness of standard pharmacotherapy. Furthermore, the findings also imply that PM may not be strictly a trait-related endophenotype as indicated in previous studies

Image Retrieval using Multi-scale CNN Features Pooling

In this paper, we address the problem of image retrieval by learning images representation based on the activations of a Convolutional Neural Network. We present an end-to-end trainable network architecture that exploits a novel multi-scale local pooling based on NetVLAD and a triplet mining procedure based on samples difficulty to obtain an effective image representation. Extensive experiments show that our approach is able to reach state-of-the-art results on three standard datasets.Comment: Accepted at ICMR 202

A adaptação curricular e a construção de materiais pedagógicos

Este relatório tem como objeto de estudo a importância dos materiais adaptados para crianças com NEE (necessidades educativas especiais), assim como, tentar compreender e verificar o porquê dos mesmos não serem sistematicamente usados. Visitados autores de referência e, explicadas e reflectidas as atividades realizadas e a construção dos materiais adaptados para trabalhar determinados temas e conteúdos do programa do 1ºCiclo procurei encontrar resposta para as perguntas que balizaram este estudo, procurando sempre a contextualização do mesmo. Durante o estágio, local onde escolhi a problemática para a realização deste relatório, foquei-me essencialmente numa criança com NEE, à qual era diagnosticada uma paralisia cerebral. Foi com esta criança que trabalhei a partir dos materiais adaptados que construí ao longo do estágio. A metodologia escolhida é de cariz qualitativo tendo usado como instrumento de recolha de dados a observação participante que foram registadas na forma de notas de campo. A análise dos dados permitiu-me perceber a importância do uso desses materiais num trabalho quer de diferenciação pedagógica quer de inclusão na sala de aula de um aluno com NEE

MutDB: update on development of tools for the biochemical analysis of genetic variation

Understanding how genetic variation affects the molecular function of gene products is an emergent area of bioinformatic research. Here, we present updates to MutDB (http://www.mutdb.org), a tool aiming to aid bioinformatic studies by integrating publicly available databases of human genetic variation with molecular features and clinical phenotype data. MutDB, first developed in 2002, integrates annotated SNPs in dbSNP and amino acid substitutions in Swiss-Prot with protein structural information, links to scores that predict functional disruption and other useful annotations. Though these functional annotations are mainly focused on nonsynonymous SNPs, some information on other SNP types included in dbSNP is also provided. Additionally, we have developed a new functionality that facilitates KEGG pathway visualization of genes containing SNPs and a SNP query tool for visualizing and exporting sets of SNPs that share selected features based on certain filters

Identification of deleterious non-synonymous single nucleotide polymorphisms using sequence-derived information

<p>Abstract</p> <p>Background</p> <p>As the number of non-synonymous single nucleotide polymorphisms (nsSNPs), also known as single amino acid polymorphisms (SAPs), increases rapidly, computational methods that can distinguish disease-causing SAPs from neutral SAPs are needed. Many methods have been developed to distinguish disease-causing SAPs based on both structural and sequence features of the mutation point. One limitation of these methods is that they are not applicable to the cases where protein structures are not available. In this study, we explore the feasibility of classifying SAPs into disease-causing and neutral mutations using only information derived from protein sequence.</p> <p>Results</p> <p>We compiled a set of 686 features that were derived from protein sequence. For each feature, the distance between the wild-type residue and mutant-type residue was computed. Then a greedy approach was used to select the features that were useful for the classification of SAPs. 10 features were selected. Using the selected features, a decision tree method can achieve 82.6% overall accuracy with 0.607 Matthews Correlation Coefficient (MCC) in cross-validation. When tested on an independent set that was not seen by the method during the training and feature selection, the decision tree method achieves 82.6% overall accuracy with 0.604 MCC. We also evaluated the proposed method on all SAPs obtained from the Swiss-Prot, the method achieves 0.42 MCC with 73.2% overall accuracy. This method allows users to make reliable predictions when protein structures are not available. Different from previous studies, in which only a small set of features were arbitrarily chosen and considered, here we used an automated method to systematically discover useful features from a large set of features well-annotated in public databases.</p> <p>Conclusion</p> <p>The proposed method is a useful tool for the classification of SAPs, especially, when the structure of the protein is not available.</p

Conductance anomalies and the extended Anderson model for nearly perfect quantum wires

Anomalies near the conductance threshold of nearly perfect semiconductor quantum wires are explained in terms of singlet and triplet resonances of conduction electrons with a single weakly-bound electron in the wire. This is shown to be a universal effect for a wide range of situations in which the effective single-electron confinement is weak. The robustness of this generic behavior is investigated numerically for a wide range of shapes and sizes of cylindrical wires with a bulge. The dependence on gate voltage, source-drain voltage and magnetic field is discussed within the framework of an extended Hubbard model. This model is mapped onto an extended Anderson model, which in the limit of low temperatures is expected to lead to Kondo resonance physics and pronounced many-body effects