The use of telephone befriending in low level support for socially isolated older people--an evaluation.

There is increasing policy recognition that the alleviation of social isolation and loneliness in older people should be prioritised. Recently, technology, such as telephone networks and the Internet, has received attention in supporting isolated and lonely older people. Despite lack of evidence, telephone befriending has been considered an effective low-level method to decrease loneliness among older people. This study evaluated the impact of a national befriending scheme for isolated and/or lonely older people, involving eight project sites across the UK 2007-2008. The purpose was to assess the impact of different models of telephone-based befriending services on older people's health and well-being. A mixed methods approach was used. This paper reports on the findings from 40 in-depth interviews with older service recipients. The most important finding was that the service helped older people to gain confidence, re-engage with the community and become socially active again. Three topics were identified: why older people valued the service, what impact it had made on their health and well-being and what they wanted from the service. In addition, nine subthemes emerged: life is worth living, gaining a sense of belonging, knowing they had a friend, a healthy mind is a healthy body, the alleviation of loneliness and anxiety, increased self-confidence, ordinary conversation, a trusted and reliable service, the future--giving something back. In conclusion, the findings present in-depth qualitative evidence of the impact of telephone befriending on older people's well-being. Befriending schemes provide low-cost means for socially isolated older people to become more confident and independent and develop a sense of self-respect potentially leading to increased participation and meaningful relationships

Self-assembled germanium islands grown on (001) silicon substrates by low-pressure chemical vapor deposition

The time evolution of self-assembled Ge islands, during low-pressure chemical vapor deposition (LPCVD) of Ge on Si at 650 Deg C using high growth rates, has been investigated by atomic force microscopy, transmission electron microscopy, and Rutherford backscattering spectrometry. We have found three different island structures The smallest islands are "lens-shaped" and characterized by a rather narrow size distribution, ~4nm high and ~20nm wide. Next to form are a distinct population of multifaceted "dome shaped" islands, up to 25nm high and 80-150 nm wide. Finally, the largest islands that form are square-based truncated pyramids with a very narrow size distribution ~50nm high and ~250nm wide. The pyramidal islands normally seen in the intermediate size range (~150nm) are not observed. The small lens-shaped islands appear to be defect free, while some of the multifaceted islands as well as all the large truncated pyramids contain misfit dislocations. The existence of multifaceted islands, in the size range where multifaceted "dome shaped" islands have previously been reported, is attributed to the high growth rate used. Furthermore, under the growth conditions used, the truncated-pyramid-shaped islands are characterized by a very narrow size distribution

Wide-field Magnetic Field and Temperature Imaging using Nanoscale Quantum Sensors

The simultaneous imaging of magnetic fields and temperature (MT) is important in a range of applications, including studies of carrier transport, solid-state material dynamics, and semiconductor device characterization. Techniques exist for separately measuring temperature (e.g., infrared (IR) microscopy, micro-Raman spectroscopy, and thermo-reflectance microscopy) and magnetic fields (e.g., scanning probe magnetic force microscopy and superconducting quantum interference devices). However, these techniques cannot measure magnetic fields and temperature simultaneously. Here, we use the exceptional temperature and magnetic field sensitivity of nitrogen vacancy (NV) spins in conformally-coated nanodiamonds to realize simultaneous wide-field MT imaging. Our "quantum conformally-attached thermo-magnetic" (Q-CAT) imaging enables (i) wide-field, high-frame-rate imaging (100 - 1000 Hz); (ii) high sensitivity; and (iii) compatibility with standard microscopes. We apply this technique to study the industrially important problem of characterizing multifinger gallium nitride high-electron-mobility transistors (GaN HEMTs). We spatially and temporally resolve the electric current distribution and resulting temperature rise, elucidating functional device behavior at the microscopic level. The general applicability of Q-CAT imaging serves as an important tool for understanding complex MT phenomena in material science, device physics, and related fields

Agent-based autonomous systems and abstraction engines: Theory meets practice

We report on experiences in the development of hybrid autonomous systems where high-level decisions are made by a rational agent. This rational agent interacts with other sub-systems via an abstraction engine. We describe three systems we have developed using the EASS BDI agent programming language and framework which supports this architecture. As a result of these experiences we recommend changes to the theoretical operational semantics that underpins the EASS framework and present a fourth implementation using the new semantics

The material role of digital media in connecting with, within, and beyond museums

The connective potentials of digital media have been positioned as a key part of a contemporary museum visitor experience. Using a sociology of translation, we construct a network of visitor experiences using data from a digital media engagement project at a large and multi-sited museum in the United Kingdom. These experiences relate to (dis)connections with the museum, museum objects, and other visitors. Through this analysis we disclose the often contradictory roles of the non-human, including and going beyond the digital, as contributors to the success and failure of attempts to change museum visitor experiences through engagement activities rooted in narratives of participation and connectivity

Timage -- A Robust Time Series Classification Pipeline

Time series are series of values ordered by time. This kind of data can be found in many real world settings. Classifying time series is a difficult task and an active area of research. This paper investigates the use of transfer learning in Deep Neural Networks and a 2D representation of time series known as Recurrence Plots. In order to utilize the research done in the area of image classification, where Deep Neural Networks have achieved very good results, we use a Residual Neural Networks architecture known as ResNet. As preprocessing of time series is a major part of every time series classification pipeline, the method proposed simplifies this step and requires only few parameters. For the first time we propose a method for multi time series classification: Training a single network to classify all datasets in the archive with one network. We are among the first to evaluate the method on the latest 2018 release of the UCR archive, a well established time series classification benchmarking dataset.Comment: ICANN19, 28th International Conference on Artificial Neural Network

Classification of time series by shapelet transformation

Time-series classification (TSC) problems present a specific challenge for classification algorithms: how to measure similarity between series. A \emph{shapelet} is a time-series subsequence that allows for TSC based on local, phase-independent similarity in shape. Shapelet-based classification uses the similarity between a shapelet and a series as a discriminatory feature. One benefit of the shapelet approach is that shapelets are comprehensible, and can offer insight into the problem domain. The original shapelet-based classifier embeds the shapelet-discovery algorithm in a decision tree, and uses information gain to assess the quality of candidates, finding a new shapelet at each node of the tree through an enumerative search. Subsequent research has focused mainly on techniques to speed up the search. We examine how best to use the shapelet primitive to construct classifiers. We propose a single-scan shapelet algorithm that finds the best $k$ shapelets, which are used to produce a transformed dataset, where each of the $k$ features represent the distance between a time series and a shapelet. The primary advantages over the embedded approach are that the transformed data can be used in conjunction with any classifier, and that there is no recursive search for shapelets. We demonstrate that the transformed data, in conjunction with more complex classifiers, gives greater accuracy than the embedded shapelet tree. We also evaluate three similarity measures that produce equivalent results to information gain in less time. Finally, we show that by conducting post-transform clustering of shapelets, we can enhance the interpretability of the transformed data. We conduct our experiments on 29 datasets: 17 from the UCR repository, and 12 we provide ourselve

On the segmentation and classification of hand radiographs

This research is part of a wider project to build predictive models of bone age using hand radiograph images. We examine ways of finding the outline of a hand from an X-ray as the first stage in segmenting the image into constituent bones. We assess a variety of algorithms including contouring, which has not previously been used in this context. We introduce a novel ensemble algorithm for combining outlines using two voting schemes, a likelihood ratio test and dynamic time warping (DTW). Our goal is to minimize the human intervention required, hence we investigate alternative ways of training a classifier to determine whether an outline is in fact correct or not. We evaluate outlining and classification on a set of 1370 images. We conclude that ensembling with DTW improves performance of all outlining algorithms, that the contouring algorithm used with the DTW ensemble performs the best of those assessed, and that the most effective classifier of hand outlines assessed is a random forest applied to outlines transformed into principal components

Anomalous Lattice Vibrations of Single and Few-Layer MoS2

Molybdenum disulfide (MoS2) of single and few-layer thickness was exfoliated on SiO2/Si substrate and characterized by Raman spectroscopy. The number of S-Mo-S layers of the samples was independently determined by contact-mode atomic-force microscopy. Two Raman modes, E12g and A1g, exhibited sensitive thickness dependence, with the frequency of the former decreasing and that of the latter increasing with thickness. The results provide a convenient and reliable means for determining layer thickness with atomic-level precision. The opposite direction of the frequency shifts, which cannot be explained solely by van der Waals interlayer coupling, is attributed to Coulombic interactions and possible stacking-induced changes of the intralayer bonding. This work exemplifies the evolution of structural parameters in layered materials in changing from the 3-dimensional to the 2-dimensional regime.Comment: 14 pages, 4 figure

Removal of steroid estrogens in carbonaceous and nitrifying activated sludge processes

This is the post-print version of the final paper published in Chemosphere. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2010 Elsevier B.V.A carbonaceous (heterotrophic) activated sludge process (ASP), nitrifying ASP and a nitrifying/denitrifying ASP have been studied to examine the role of process type in steroid estrogen removal. Biodegradation efficiencies for total steroid estrogens (ΣEST) of 80 and 91% were recorded for the nitrifying/denitrifying ASP and nitrifying ASP respectively. Total estrogen biodegradation (ΣEST) was only 51% at the carbonaceous ASP, however, the extent of biodegradation in the absence of nitrification clearly indicates the important role of heterotrophs in steroid estrogen removal. The low removal efficiency did not correlate with biomass activity for which the ASPcarbonaceous recorded 80 μg kg−1 biomass d−1 compared to 61 and 15 μg kg−1 biomass d−1 at the ASPnitrifying and ASPnitrifying/denitrifying respectively. This finding was explained by a moderate correlation (r2 = 0.55) between total estrogen loading (ΣEST mg m−3 d−1) and biomass activity (μg ΣEST degraded kg−1 d−1) and has established the impact of loading on steroid estrogen removal at full-scale. At higher solids retention time (SRT), steroid estrogen biodegradation of >80% was observed, as has previously been reported. It is postulated that hydraulic retention time (HRT) is as important as SRT as this governs both reaction time and loading. This observation is based on the high specific estrogen activity determined at the ASPcarbonaceous plant, the significance of estrogen loading and the positive linear correlation between SRT and HRT.Public Utilities Board of Singapore, Anglian Water Ltd., Severn Trent Water Ltd., Thames Water Utilities Ltd., United Utilities Plc., and Yorkshire Water Services Ltd