The growing problem of violence against older persons in Africa

This paper examines the growing problem of violence against older persons, particularly older women, in developing countries in general and in African countries in particular. An attempt is made to set out the nature and scale of the problem, and to examine some consequences of violence for older persons, based on local experience of HelpAge International (HAI) partners. Finally, selected innovative interventions designed and implemented by HAI partners to address the problem are described

Numerical analysis of one-dimensional waves in generalized thermoelasticity

Classical thermoelasticity theory is based on Fourier\u27s Law of heat conduction, which, when combined with the other fundamental field equations, leads to coupled hyperbolic-parabolic governing equations. These equations imply that thermal effects are to be felt instantaneously, far away from the external thermomechanieal load. Therefore, this theory admits infinite speeds of propagation of thermoelastic disturbances. This paradox becomes especially evident in problems involving very short time intervals, or high rates. of heat flux. Since infinite wave speeds are physically unrealistic in some situations, and since experiments have shown the existence of wavetype thermoelastic interactions, like in the observation of thermal pulses in dielectric crystals, generalized thermoelasticity theories have been developed. This thesis concentrates on one generalized thermoelasticity theory, proposed by Green and Lindsay, in which a generalized thermoelastic coupling constant, e, and two relaxation times, t0 and t, account for finite speed thermoelastic waves . A numerical analysis of an exact analytical solution, involving an instantaneous plane source of heat in an infinite body, is performed. The analysis reveals two finite speed wave fronts for each of the four fields: displacement, stress, temperature, and heat flux. The results are complimentary to previous analysis, and improve upon them, because a large range of parameters is involved, and the exact solution to the problem has been used

How young people from culturally and linguistically diverse backgrounds experience mental health: some insights for mental health nurses

This article reports on a part of a study which looked at the mental health of culturally and linguistically diverse (CALD) young people. The research sought to learn from CALD young people, carers, and service providers experiences relevant to the mental health of this group of young people. The ultimate goal was to gain insights that would inform government policy, service providers, ethnic communities and most importantly the young people themselves. To this end, qualitative interviews were undertaken with 123 CALD young people, 41 carers and 14 mental health service providers in Queensland, Western Australia and South Australia. Only one aspect of the study will be dealt with here, namely the views of the young CALD participants, which included risk factors, coping strategies and recommendations about how they could be supported in their struggle to maintain mental health. One of the most important findings of the study relates to the resilience of these young people and an insight into the strategies that they used to cope. The efforts of these young people to assist us in our attempts to understand their situation deserve to be rewarded by improvements in the care that we provide. To this end this article sets out to inform mental health nurses of the results of the study so that they will be in a position to better understand the needs and strengths of their CALD clients and be in a better position to work effectively with them

A Left-Sided Prevalence of Lentigo Maligna: A UK Based Observational Study and Review of the Evidence

Skin cancer has been shown to present asymmetrically, prevalent on the left side of the body, more so in subtypes of cutaneous melanoma such as lentigo maligna. Biases have been linked to cumulative UV light exposure and automobile driving patterns. Though left-right ratios have previously correlated with the side men or women tend to position themselves or countries drive on, more recent trends indicate a consistent left-sided bias. To clarify reasons for changing trends, a review of the evidence base and LM’s laterality in a UK cohort (99 cases 2000–2011) was conducted for the first time. The strong correlation of left-sided excess, found in both genders (ratios 1.381–1.5, P<0.05  X2 0.841), is congruent with more recent findings. Though evidence indicates that driving position is no longer a risk factor for LM, due most likely to improved car window UV protection, it remains the most commonly attributed cause. Understanding phenomena such as UV lights “scatter effect” or that cumulative exposure may not be a significant risk factor helps rationalize older conclusions that would otherwise appear contradictory. The reasons for left-sided excess remain unclear but may be due to factors requiring further research such as the body’s anatomical/embryological asymmetry

Anomaly Detection in Batch Manufacturing Processes Using Localized Reconstruction Errors From 1-D Convolutional AutoEncoders

Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD is possible, black box modelling approaches constrain model interpretability. These challenges obstruct the widespread adoption of Deep Learning solutions. The objective of the study is to demonstrate an AD approach which employs 1-Dimensional Convolutional AutoEncoders (1d-CAE) and Localised Reconstruction Error (LRE) to improve AD performance and interpretability. Using LRE to identify sensors and data that result in the anomaly, the explainability of the Deep Learning solution is enhanced. The Tennessee Eastman Process (TEP) and LAM 9600 Metal Etcher datasets have been utilised to validate the proposed framework. The results show that the proposed LRE approach outperforms global reconstruction errors for similar model architectures achieving an AUC of 1.00. The proposed unsupervised learning approach with AE and LRE improves model explainability which is expected to be beneficial for deployment in semiconductor manufacturing where interpretable and trustworthy results are critical for process engineering teams

Anomaly Detection in Batch Manufacturing Processes using Localised Reconstruction Errors from 1-Dimensional Convolutional AutoEncoders

Multivariate batch time-series data sets within Semiconductor manufacturing processes present a difficult environment for effective Anomaly Detection (AD). The challenge is amplified by the limited availability of ground truth labelled data. In scenarios where AD is possible, black box modelling approaches constrain model interpretability. These challenges obstruct the widespread adoption of Deep Learning solutions. The objective of the study is to demonstrate an AD approach which employs 1-Dimensional Convolutional AutoEncoders (1d-CAE) and Localised Reconstruction Error (LRE) to improve AD performance and interpretability. Using LRE to identify sensors and data that result in the anomaly, the explainability of the Deep Learning solution is enhanced. The Tennessee Eastman Process (TEP) and LAM 9600 Metal Etcher datasets have been utilised to validate the proposed framework. The results show that the proposed LRE approach outperforms global reconstruction errors for similar model architectures achieving an AUC of 1.00. The proposed unsupervised learning approach with AE and LRE improves model explainability which is expected to be beneficial for deployment in semiconductor manufacturing where interpretable and trustworthy results are critical for process engineering teams

Convolutional AutoEncoders for Anomaly Detection in Semiconductor Manufacturing

Semiconductor manufacturing, characterised by its complex processes, demands efficient anomaly detection (AD) systems for quality assurance. This study extends from previous work utilising unsupervised Convolutional AutoEncoders for AD in Semiconductor batch manufacturing by applying the technique to a novel dataset supplied by a local Semiconductor Manufacturer. Our method uses an approach that employs 1-dimensional Convolutional Autoencoders (1d-CAE) to improve AD performance and interpretability through the numerical decomposition of reconstruction errors. Identifying anomalies this way allows engineering resources to explain anomalies more effectively than traditional methods. We validate our approach with experiments, demonstrating its performance in accurately detecting anomalies while providing insights into the nature of these irregularities. The experiments also demonstrate the impact of training setup on detection capability, outlining an efficient framework for determining an optimal hyperparameter set-up in an industrial dataset. The proposed unsupervised learning approach with AE reconstruction error improves model explainability, which is expected to be beneficial for deployment in semiconductor manufacturing, where interpretable and trustworthy results are critical for solution adoption by process engineering teams

AN ELECTROPHORETIC ANALYSIS OF THE GENETIC VARIATION IN THE WILD AND CULTIVATED SOYBEAN GERMPLASM

Over 400 soybean (Glycine max (L.) Merr.) cultivars and plant introductions and more than 100 wild soybean (Glycine soja Sieb & Zucc.) plant introductions from a wide geographic distribution were examined by polyacrylamide, slab electrophoresis for genetic variation in 15 enzymes. Allozymes for 25 loci were found. All but three of these loci were confirmed by experimental crosses. Approximately 20 non-variable loci were also hypothesized. Models presented explaining the genetic control for each of the observed enzyme zymograms, were based on inheritance studies, on patterns of distribution of isozymes in various tissues or developmental stages, zymograms in related species, information from the literature, and the subcellular distribution of isozymes. Both G. max and G. soja were highly homogeneous within cultivars or plant introductions. However, G. soja was found to have a nine times greater observed heterozygosity (1.0% vs. 0.11%), a higher expected heterozygosity (12.1% vs. 10.3%), a significantly higher level of polymorphism (47% vs. 33%, at the 99% polymorphism level), and a significantly higher number of alleles/locus (1.8 vs. 1.5). Nearly 50% of the shared alleles had significant differences in frequencies. While the overall correlation between allele frequencies was high (R = .84, for variable loci), this correlation was reduced (R = .23) when frequencies were compared within geographic areas of seed origin. Alleles were more widely dispersed in G. max, while several alleles also appeared to have distinct distribution patterns. Northeast China had slightly higher levels of diversity for G. max, while Korea had significantly higher levels for G. soja. These observations suggest that domestication has reduced genetic diversity, changed the distribution of alleles, and in many cases allele frequencies. While sampling effects can\u27t be totally eliminated from consideration, the change in selection pressures and migrational influences brought about by domestication were probably responsible for the differences observed among these isozyme loci. While wild soybeans clearly had higher genetic diversity, the soybean germplasm appeared to have average diversity relative to other self-pollinating plants. Data were also included for using these isozyme loci in cultivar fingerprinting, linkage mapping and considering the possible polyploidy of the Glycine genus

A 10-year ecosystem restoration community of practice tracks large-scale restoration trends

In 2004, a group of large-scale ecosystem restoration practitioners across the United States convened to start the process of sharing restoration science, management, and best practices under the auspices of a traditional conference umbrella. This forum allowed scientists and decision makers to interact in a new type of setting, with science being presented from a perspective that informed ecosystem restoration decisions, and decision makers articulating their decision needs in a manner that informed the types of science questions that needed to be addressed. From that beginning, a core ecosystem restoration practitioner group has formed a community of practice that continues to build and maintain momentum for this type of ecosystem restoration engagement. In the fall of 2013, this community of practice became permanently organized as the Large-scale Ecosystem Restoration Section within the Society for Ecological Restoration. Over the past decade, this community has evaluated and expanded upon ecosystem restoration themes ranging from defining and measuring success, adaptive management, adaptive governance, and linking science with management decision-making. Current and future themes include novel ecosystems, ecosystem goods and services, urban ecosystem restoration, and climate change and ecosystem resilience