Microwave plasma chemical vapour deposition diamond nucleation on ferrous substrates with Ti and Cr interlayers

Diamond-coated steel is considered an important issue in synthetic diamond technology due to the great economical importance of enhancing the wear resistance and surface hardness of commercial Fe-based alloys. However, direct diamond coating by chemical vapour deposition (CVD) is rather problematic-adhesion and growth are seriously affected. The use of interlayers is a common approach to minimise these problems. This work reports an investigation on the establishment of good nucleation and growth conditions of diamond films by microwave plasma CVD (MPCVD) on ferrous substrates coated with Ti and Cr interlayers. Commercial grade ferrous substrates were pre-coated with commercial interlayers by sputtering (Ti, Cr) and electroplating (Cr) techniques. Steel substrates led to better results than iron cast substrates. The best films were obtained on Ti pre-coated steel substrate. The results on Cr interlayers pointed to the advantage of electroplating over the physical vapour deposition (PVD) sputtering. From the two selected parameter sets for diamond deposition, the one using lower power level conducted to the best results. Initial roughness and growth parameters were found to counteract on the uniformity of the diamond films. The morphology was studied by scanning electron microscopy (SEM), the roughness was estimated by profilometry, while diamond quality and stress state were evaluated by mu-Raman spectroscopy. (C) 2002 Elsevier Science B.V. All rights reserved

The relationship between depression and risk of violence in portuguese community-dwelling older people

Background: Mental disorders are highly prevalent in older people, being depression a predominant disorder. Evidence points to a possible relationship between depression and violence against older people. Nonetheless, the role of the depressive symptomology severity in the risk of violence against older people remains unclear. Thus, this study’s main objective was to analyze the relationship between geriatric depressive symptomatology and the risk of violence against older people. Methods: This exploratory study involved 502 community-dwelling older persons aged 65 to 96 years (73.3 ± 6.5). Measures were performed using the Geriatric Depression Scale and the Risk Assessment of Violence against the Non- Institutionalized Elderly scale. Results: One hundred nineteen older people (23.7%) had mild/moderate depressive symptomology, and twenty-six (5.2%) had severe depressive symptomology. There were significant relationships between the severity of depressive symptomatology and the risk of violence (p < 0.05). The presence of depressive symptomatology increased the likelihood of being victims of violence, particularly among women (odds ratio: 2–8, p < 0.05). Conclusions: The severity of depressive symptomatology plays an essential role in the risk of violence against community-dwelling older people. Moreover, it was found that older persons with depression symptomatology were at higher risk of being victims of violence. Our study findings support the need for protective measures within mental health national or regional policies to prevent depression and violence against community-dwelling older people

Decoding machine learning benchmarks

Despite the availability of benchmark machine learning (ML) repositories (e.g., UCI, OpenML), there is no standard evaluation strategy yet capable of pointing out which is the best set of datasets to serve as gold standard to test different ML algorithms. In recent studies, Item Response Theory (IRT) has emerged as a new approach to elucidate what should be a good ML benchmark. This work applied IRT to explore the well-known OpenML-CC18 benchmark to identify how suitable it is on the evaluation of classifiers. Several classifiers ranging from classical to ensembles ones were evaluated using IRT models, which could simultaneously estimate dataset difficulty and classifiers' ability. The Glicko-2 rating system was applied on the top of IRT to summarize the innate ability and aptitude of classifiers. It was observed that not all datasets from OpenML-CC18 are really useful to evaluate classifiers. Most datasets evaluated in this work (84%) contain easy instances in general (e.g., around 10% of difficult instances only). Also, 80% of the instances in half of this benchmark are very discriminating ones, which can be of great use for pairwise algorithm comparison, but not useful to push classifiers abilities. This paper presents this new evaluation methodology based on IRT as well as the tool decodIRT, developed to guide IRT estimation over ML benchmarks.Comment: Paper published at the BRACIS 2020 conference, 15 pages, 4 figure

How to combine visual features with tags to improve movie recommendation accuracy?

Previous works have shown the effectiveness of using stylistic visual features, indicative of the movie style, in content-based movie recommendation. However, they have mainly focused on a particular recommendation scenario, i.e., when a new movie is added to the catalogue and no information is available for that movie (New Item scenario). However, the stylistic visual features can be also used when other sources of information is available (Existing Item scenario). In this work, we address the second scenario and propose a hybrid technique that exploits not only the typical content available for the movies (e.g., tags), but also the stylistic visual content extracted form the movie files and fuse them by applying a fusion method called Canonical Correlation Analysis (CCA). Our experiments on a large catalogue of 13K movies have shown very promising results which indicates a considerable improvement of the recommendation quality by using a proper fusion of the stylistic visual features with other type of features