Metallurgy and properties of plasma spray formed materials

Understanding the fundamental metallurgy of vacuum plasma spray formed materials is the key to enhancing and developing full material properties. Investigations have shown that the microstructure of plasma sprayed materials must evolve from a powder splat morphology to a recrystallized grain structure to assure high strength and ductility. A fully, or near fully, dense material that exhibits a powder splat morphology will perform as a brittle material compared to a recrystallized grain structure for the same amount of porosity. Metallurgy and material properties of nickel, iron, and copper base alloys will be presented and correlated to microstructure

Effect of Particle Orientation on the Elastic Anisotropy of Al/SiCp Metal Matrix Composites

Metal matrix composites (MMCs) are promising new materials for structural applications because of their high specific stiffness and strength, and high temperature stability. Of particular interest are the discontinuous silicon carbide (SiC) reinforced aluminum metal matrix composites. The improved mechanical properties are governed by the properties of the constituent phases, as well as the SiC particle characteristics such as shape, aspect ratio and orientation. The particle characteristics have a major effect on the anisotropic properties of these composites. The overall properties also depend on the manufacturing process of these composites since it determines the orientation of the particles and may produce internal defects such as porosity and intermetallic compounds [l]. Thus it is important to experimentally characterize the effective elastic properties and to theoretically predict them from the knowledge of the constituent properties and the microstructures

Status of ENDF/B-V fission yields

The features of ENDF/B-V with respect to fission yields are described. Yields for approximately 1100 nuclides are given for each of 11 fissioning nuclides at one or more fission-neutron energies, plus the yield uncertainties. Selected chain yields and uncertainties are illustrated. Aspects of the evaluation process are discussed. 4 figures, 3 tables. (RWR

Critical Review of Theoretical Models for Anomalous Effects (Cold Fusion) in Deuterated Metals

We briefly summarize the reported anomalous effects in deuterated metals at ambient temperature, commonly known as "Cold Fusion" (CF), with an emphasis on important experiments as well as the theoretical basis for the opposition to interpreting them as cold fusion. Then we critically examine more than 25 theoretical models for CF, including unusual nuclear and exotic chemical hypotheses. We conclude that they do not explain the data.Comment: 51 pages, 4 Figure

Parental experience of an early developmental surveillance programme for autism within Australian general practice: A qualitative study

Objectives Implementing support and services early in the life course has been shown to promote positive developmental outcomes for children at high likelihood of developmental conditions including autism. This study examined parents'/caregivers' experiences and perceptions about a digital developmental surveillance pathway for autism, the autism surveillance pathway (ASP), and usual care, the surveillance as usual (SaU) pathway, in the primary healthcare general practice setting. Design This qualitative study involves using a convenience selection process of the full sample of parents/caregivers that participated in the main programme, 'General Practice Surveillance for Autism', a cluster-randomised controlled trial study. All interviews were audio-recorded, transcribed and coded using NVivo V.12 software. An inductive thematic interpretive approach was adopted and data were analysed thematically. Participants Twelve parents/caregivers of children with or without a developmental condition/autism (who participated in the main programme) in South Western Sydney and Melbourne were interviewed. Settings All interviews were completed over the phone. Results There were seven major themes and 20 subthemes that included positive experiences, such as pre-existing patient-doctor relationships and their perceptions on the importance of knowing and accessing early support/services. Barriers or challenges experienced while using the SaU pathway included long waiting periods, poor communication and lack of action plans, complexity associated with navigating the healthcare system and lack of understanding by general practitioners (GPs). Common suggestions for improvement included greater awareness/education for parents/carers and the availability of accessible resources on child development for parents/caregivers. Conclusion The findings support the use of digital screening tools for developmental surveillance, including for autism, using opportunistic contacts in the general practice setting. Trial registration number ANZCTR (ACTRN12619001200178)

Come to the dark side! The role of functional traits in shaping dark diversity patterns of south-eastern European hoverflies

1. Dark diversity represents the set of species that can potentially inhabit a given area under particular ecological conditions, but are currently 'missing' from a site. This concept allows characterisation of the mechanisms determining why species are sometimes absent from an area that seems ecologically suitable for them. 2. The aim of this study was to determine the dark diversity of hoverflies in south-eastern Europe and to discuss the role of different functional traits that might increase the likelihood of species contributing to dark diversity. Based on expert opinion, the Syrph the Net database and known occurrences of species, the study estimated species pools, and observed and dark diversities within each of 11 defined vegetation types for 564 hoverfly species registered in south-eastern Europe. To detect the most important functional traits contributing to species being in dark diversity across different vegetation types, a random forest algorithm and respective statistics for variable importance were used. 3. The highest dark diversity was found for southwest Balkan sub-Mediterranean mixed oak forest type, whereas the lowest was in Mediterranean mixed forest type. Three larval feeding modes (saproxylic, and phytophagous on bulbs or roots) were found to be most important for determining the probability of a species contributing to hoverfly dark diversity, based on univariate correlations and random forest analysis. 4. This study shows that studying dark diversity might provide important insights into what drives community assembly in south-eastern European hoverflies, especially its missing components, and contributes to more precise conservation prioritisation of both hoverfly species and their habitats.Peer reviewe

Synthesis and characterization of hybrid organic-inorganic materials based on sulphonated polyamideimide and silica

The preparation of hybrid organic–inorganic membrane materials based on a sulphonated polyamideimide resin and silica filler has been studied. The method allows the sol–gel process to proceed in the presence of a high molecular weight polyamideimide, resulting in well dispersed silica nanoparticles (<50 nm) within the polymer matrix with chemical bonding between the organic and inorganic phases. Tetraethoxysilane (TEOS) was used as the silica precursor and the organosilicate networks were bonded to the polymer matrix via a coupling agent aminopropyltriethoxysilane (APTrEOS). The structure and properties of these hybrid materials were characterized via a range of techniques including FTIR, TGA, DSC, SEM and contact angle analysis. It was found that the compatibility between organic and inorganic phases has been greatly enhanced by the incorporation of APTrEOS. The thermal stability and hydrophilic properties of hybrid materials have also been significantly improved

Using machine-learning approach to distinguish patients with methamphetamine dependence from healthy subjects in a virtual reality environment

Background: The aim of this study was to evaluate whether machine learning (ML) can be used to distinguish patients with methamphetamine dependence from healthy controls by using their surface electroencephalography (EEG) and galvanic skin response (GSR) in a drug-simulated virtual reality (VR) environment. Methods: A total of 333 participants with methamphetamine (METH) dependence and 332 healthy control subjects were recruited between January 2018 and January 2019. EEG (five electrodes) and GSR signals were collected under four VR environments: one neutral scenario and three METH-simulated scenarios. Three ML classification techniques were evaluated: random forest (RF), support vector machine (SVM), and logistic regression (LR). Results: The MANOVA showed no interaction effects among the two subject groups and the 4 VR scenarios. Taking patient groups as the main effect, the METH user group had significantly lower GSR, lower EEG power in delta (p < .001), and alpha bands (p < .001) than healthy subjects. The EEG power of beta band (p < .001) and gamma band (p < .001) was significantly higher in METH group than the control group. Taking the VR scenarios (Neutral versus METH‐VR) as the main effects, the GSR, EEG power in delta, theta, and alpha bands in neutral scenario were significantly higher than in the METH‐VR scenario (p < .001). The LR algorithm showed the highest specificity and sensitivity in distinguishing methamphetamine‐dependent patients from healthy controls. Conclusion: The study shows the potential of using machine learning to distinguish methamphetamine-dependent patients from healthy subjects by using EEG and GSR data. The LR algorithm shows the best performance comparing with SVM and RF algorithm

Asian-Pacific consensus statement on the management of chronic hepatitis B: a 2008 update

Large amounts of new data on the natural history and treatment of chronic hepatitis B virus (HBV) infection have become available since 2005. These include long-term follow-up studies in large community-based cohorts or asymptomatic subjects with chronic HBV infection, further studies on the role of HBV genotype/naturally occurring HBV mutations, treatment of drug resistance and new therapies. In addition, Pegylated interferon α2a, entecavir and telbivudine have been approved globally. To update HBV management guidelines, relevant new data were reviewed and assessed by experts from the region, and the significance of the reported findings were discussed and debated. The earlier “Asian-Pacific consensus statement on the management of chronic hepatitis B” was revised accordingly. The key terms used in the statement were also defined. The new guidelines include general management, special indications for liver biopsy in patients with persistently normal alanine aminotransferase, time to start or stop drug therapy, choice of drug to initiate therapy, when and how to monitor the patients during and after stopping drug therapy. Recommendations on the therapy of patients in special circumstances, including women in childbearing age, patients with antiviral drug resistance, concurrent viral infection, hepatic decompensation, patients receiving immune-suppressive medications or chemotherapy and patients in the setting of liver transplantation, are also included

Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy

Background: Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective: The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods: We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results: Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P.05). Conclusions: Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance