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Incorporating the 4 Pillars of Advanced Practice in Radiation Therapy: Guidance for Integration and Education in the US
No full textA national assessment of the drinking water infrastructure deficit in New Zealand by territorial authority and sociodemographic characteristics
Get PDFThe quality of drinking water reticulation networks is central to ensuring the provision of safe water. We conducted a national assessment of public drinking water reticulation condition in New Zealand (NZ) derived from information on pipe material and age and investigated regional and sociodemographic variations in the reticulation network. In total, 30.7% of the 57,174 km of drinking water pipes in NZ were in poor or very poor condition, while 18.5% were past their life expectancy. We identified wide variation in the proportion of pipes in poor or very poor condition amongst Territorial Authorities (TAs) and between areas of varying socioeconomic deprivation within TAs. Using nationally consistent data, our findings suggest that the current drinking water infrastructure deficit in NZ may be larger than previously estimated. Our results also highlight potential challenges to TA-based amalgamation of water services under the new legislation
Exploring the mental health needs, concerns and experiences of young asylum seekers in the UK: a qualitative literature review
Get PDFYoung asylum seekers often experience traumatic events before arriving in their host country and are therefore vulnerable to developing serious mental health issues, including post-traumatic stress disorder. They also experience numerous barriers to accessing mental health support. This literature review explored the qualitative evidence on the mental health needs, concerns and experiences of young asylum seekers in the UK. Four main themes emerged from the analysis of the ten included studies: feelings of loss and insecurity; challenges integrating into the host culture; perceptions of mental health; and personalised and culturally appropriate mental health support. The article provides mental health nurses with an insight into the mental health needs, concerns and experiences of young asylum seekers. It also describes some of the ways in which nurses can provide mental health support to this vulnerable group, including by adopting trauma-informed and culturally sensitive approaches to care
Income Inequality, Structural Change, and Inclusive Economic Growth
Get PDFSub-Saharan African countries have experienced significant structural change and economic
growth in recent decades; however, inequality levels remain high raising concerns that the
growth is not inclusive enough to reduce inequality levels. There are extensive debates on the
relationship between income inequality and economic growth because this can vary across and
along the growth path of countries. This study explores the effect of economic growth and
structural change on income inequality using a panel dataset of 40 sub-Saharan African
countries over the period 2001-2015. The study employs the iterated Generalized Method of
Moment (GMM) estimator for analysis. The findings suggest that although increased income
levels in the region fuel inequality, the transition of the economies towards the services sector
could reduce income inequality. However, the overall contribution of structural change to
reducing inequality levels has been minimal suggesting that the growth experiences of the
region, especially over the last two decades, may not have been inclusive, hence the need for
enhanced redistributive policies to deepen inclusivity of the growth process
Empirical Analysis of Variations of Matrix Factorization in Recommender Systems
Get PDFRecommender systems recommend products to users. Almost all businesses utilize recommender systems to sug- gest their products to customers based on the customer's previous actions. The primary inputs for recommendation algorithms are user preferences, product descriptions, and user ratings on prod- ucts. Content-based recommendations and collaborative filtering are examples of traditional recommendation systems. One of the mathematical models frequently used in collaborative filtering is matrix factorization (MF). This work focuses on discussing five variants of MF namely Matrix Factorization, Probabilistic MF, Non-negative MF, Singular Value Decomposition (SVD), and SVD++. We empirically evaluate these MF variants on six benchmark datasets from the domains of movies, tourism, jokes, and e-commerce. MF is the least performing and SVD is the best-performing method among other MF variants in terms of Root Mean Square Error (RMSE)
Beyond the Archive podcast series for ORIGIN
No full textThis research seeks to build on a expand a pilot online platform (Ways of Being) developed in 2020. The platform hosts a series of human centred stories all based on artefacts found in museums, galleries and archives, and creates a virtual online museum.The stories in the pilot were shown to have mental health benefits for young people age 16-24, particularly those from under-supported communities. The data from this pilot generated a conceptual framework of the story features that had most benefit.In this programme of development we seek to under further understand the features of the stories, the user experience of the platform and the contexts in which young people might be using the platform, in order to co-design content that enhances the mental health benefits further. This will refine the conceptual framework from the pilot.A team of young people with lived experienced of mental health challenges have been employed on the Lab4Living design team to create the story content guided by the conceptual frameworkThis podcast captures some of the work of this team of people; how they go about searching archives, finding stories, examples of those stories, how they consider ethical issues of reporting other peoples stories.The podcast series has been Produced, Directed, Edited and Hosted by Sophie Cochrane-Powell, with a variety of guests each week, including other members of the team.
Podcast episodes originally published on Spotify
Effects of manufacturing direction, heat-treatment and surface operations on fatigue life in additively manufactured metals: An analysis based on statistics and artificial intelligence.
Get PDFThis study aimed to establish whether useful fatigue design stress-life curves could be estimated for additively manufactured metals through statistical and machine learning analysis of a large quantity of experimental fatigue data. The study focused on additively manufactured aluminium, steel and titanium. Three manufacturing parameters were considered, namely the manufacturing direction, heat-treatment and surface operations, with the results presented for 0.1 and −1 loading ratios. By gathering experimental data for all parameters, the negative inverse slopes were found to be concentrated between 3 and 6, and the mean endurance limit as a ratio to ultimate tensile strength was 0.18 and 0.21 for 0.1 and −1 loading ratios, respectively, without any statistical analysis. Surface operations were observed to have a significant effect on the fatigue strength of additively manufactured aluminium, steel and titanium regardless of other manufacturing parameters. Multiple linear regression analysis and several machine learning methods (Decision Tree, Support Vector Machines, K-Nearest Neighbour, Multi-Layer Perceptron, Partial Least Squares and Gaussian Process Regression) were used to develop predictive models. The results of these analyses highlight that the conventional approach applied to fatigue of traditional metals does not suffice for additively manufactured metals. While artificial intelligence presents a promising solution, our investigation indicates it is necessary to account for parameters in addition to those considered here such as manufacturing processes, material properties, material microstructure and defects to make reliable fatigue property estimates for additively manufactured metals using machine learning
The Potential of Pulsed Electric Field in the Postharvest Process of Fruit and Vegetables: A Comprehensive Perspective.
Get PDFPulsed electric field (PEF) is a novel non-thermal treatment for quality retention of fruits and vegetables (F&V) during postharvest processing. PEF helps to microbial control and retain several attributes such as the physical and chemical of F&V. This review outlines requirements and advances in electrical systems applied in PEF for F&V. In addition, it reviews the effect of PEF application on antioxidant activity, color, texture, weight loss, and other chemical properties affecting the shelf life of F&V. Attention is also drawn to the applicability of PEF technology as a pretreatment to assist design in the case of the emergence of sustainable bio-refineries based on F&V. PEF pretreatment enhances the extraction of valuable bioactive compounds and maintains quality characteristics of F&V which include color, phytochemicals, antioxidant capacity, proteins, volatile compounds, and sensory attributes. Furthermore, the current study highlights that electroporation of the cell membrane by PEF treatment enhances mass transfer during the drying and moisture loss processes of F&V. In this context, the extraordinary rapidity of treatment applications leads to considerable reductions in processing time and total energy consumption concerning traditional methods. The adaptability and scalability of PEF secure its application in sizes varying from small-scale operations driven by supermarket demand up to food units. However, PEF has limitations in the postharvest process of F&V due to its potential for the high energy costs associated with the technology. In addition, PEF cannot guarantee the inactivation of all microorganisms, particularly the spores and certain resilient bacterial strains that cause microbial regrowth on storage. Overall, this technology can further increase the yield obtained from extraction and extend shelf life, which is essential for processing facilities and consumers’ benefit
Mobilising UK Data and AI for All with a National Grid of Civic Learning Systems
Get PDFAim:
We propose regional incentives and national infrastructure developments to build a national
grid of civic learning systems – the Grid – harnessing multi-source data for public service
innovation, science, AI engineering and inclusive economic growth.
Objectives:
The following objectives establish the critical national structural factors required to enable a
unified network of civic data and learning systems across the UK. These components will
serve as the foundational architecture – analogous to power lines and pylons connecting
civic data-action ‘power stations’ on a national grid. This infrastructure will facilitate secure
data sharing, standardised practices, and collaborative innovation while ensuring local
autonomy and flexibility to address regional needs within a diameter of trust (public trust and
practical joint accountability and mutual aid between organisations). By creating these
essential connecting elements, we can transform currently isolated data initiatives into an
interconnected ecosystem that drives public service improvement, scientific advancement,
and economic growth through data-driven innovation