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Background: Syndemic theory highlights the potential for health problems to interact synergistically, compounding impact. Young adults not in education, employment or training (NEET) are more likely to experience disadvantage and poorer general health outcomes. However, there is little research on their sexual health, or the extent to which this clusters with mental and physical health outcomes.Methods: Analysis of data from 16 to 24 year olds (1729 men, 2140 women) interviewed 2010-12 for Britain's third National Survey of Sexual Attitudes and Lifestyles. Natsal-3 is a national probability sample survey using computer-assisted personal interviewing with computer-assisted self-interviewing. Participants were classified as workers, students or NEET. We used multivariable logistic regression to examine associations between being NEET (relative to worker or student) and risk behaviours and outcomes in physical, sexual and mental health domains. We then examined how risk behaviours and poor health outcomes cluster within and across domains.Results: 15% men and 20% women were NEET; 36% men and 32% women were workers; and 49% men and 48% women were students. Young people who were NEET were more likely to report smoking and drug use (men) than other young people. There were few differences in sexual health, although NEETs were more likely to report condomless sex, and NEET women, unplanned pregnancy (past year). Risk behaviours clustered more within and across domains for NEET men. Among NEET women, poor health outcomes clustered across mental, physical and sexual health domains.Conclusions: Harmful health behaviours (men) and poor health outcomes (women) clustered more in those who are NEET. This points to a possible syndemic effect of NEET status on general ill health, especially for women. Our paper is novel in highlighting that elevated risk pertains to sexual as well as mental and physical health. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multi-resolution clustering (MRC) method that for the first time classifies end customers directly from massive, volatile and uncertain smart metering data. It will firstly extract spectral features of load profiles by multi-resolution analysis (MRA), and then cluster and classify these features in the spectral-domain instead of time-domain. The key advantage is that the proposed method will allow a dynamic load profiling to be flexibly re-constructed from each spectral level. MRC addresses three key limitations in time-series based load profiling: i) large sample size: sample size is reduced by a novel two-stage clustering, which firstly clusters each customers' massive daily profiles into several Gaussian mixture models (GMMs) and then clusters all GMMs; ii) volatility: it avoids the interferences between different load features (e.g. magnitude, overall trend, spikes) by decomposing them onto different resolution levels, and then clustering separately; iii) uncertainties: as the GMM can give a probabilistic cluster membership instead of a deterministic one, an additive classification model based on the posterior probability is proposed to reflect the uncertainty between days. The proposed method is implemented on 6369 smart metered customers from Ireland, and compared with the load profiles used by the U.K. industry and traditional K-means clustering. The results show that the developed MRC outperformed the traditional methods in its ability in profiling load for big, volatile and uncertain smart metering data. [ABSTRACT FROM PUBLISHER]

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required. [ABSTRACT FROM AUTHOR]

Notwithstanding their remarkable recent growth, surprisingly little research has hitherto been conducted on the evolving geography of professional and business services in Britain. This paper analyses the results of a detailed survey of 300 small and medium-sized management and engineering consultancies, in investigating the forces underpinning both the striking clustering of such firms in central London and their growth in decentralized locations of East Anglia and South West England. Particular attention is paid to the notion of clustering, the role of demand-side influences, localized ‘collective learning’ processes, and increasing globalization in clustering, and to so-called ‘enterprising behaviour theory’ in explaining decentralization. [ABSTRACT FROM AUTHOR]

Over the past years, studies shed light on how social norms and perceptions potentially affect loan repayments, with overtones for strategic default. Motivated by this strand of the literature, we incorporate collective social traits in predictive frameworks on credit card delinquencies. We propose the use of a two-stage framework. This allows us to segment a market into homogeneous sub-populations at the regional level in terms of social traits, which may proxy for perceptions and potentially unravelled behaviours. On these formed sub-populations, delinquency prediction models are fitted at a second stage. We apply this framework to a big dataset of 3.3 million credit card holders spread in 12 UK NUTS1 regions during the period 2015–2019. We find that segmentation based on social traits yields efficiency gains in terms of both computational and predictive performance compared to prediction in the overall population. This finding holds and is sustained in the long run for different sub-samples, lag counts, class imbalance correction or alternative clustering solutions based on individual and socio-economic attributes. [ABSTRACT FROM AUTHOR]

The evolution towards Smart Grids (SGs) represents an important opportunity for modernization of the energy industry. It is characterized by a bidirectional flow of information and energy between consumers and suppliers. However, the rapid increase of energy demands in residential areas is becoming a challenging problem. In order to address this issue, Demand-Side Management (DSM) has proven to be an effective solution. In this paper, we propose LOSISH, a price-based Demand Response (DR) system for load scheduling in residential Smart Homes (SHs) that achieves a trade-off between electricity payments and consumer's discomfort. Our proposed system considers Renewable Energy Sources (RESs), Battery Energy Storage System (BESS) and Plug-in Electric Vehicle (PEV). We formulate our scheduling as a constrained optimization problem and we propose a new hybrid algorithm to solve it. The latter combines two well known heuristic algorithms: Particle Swarm Optimization (PSO) and Binary Particle Swarm Optimization (BPSO). Moreover, we propose a new clustering algorithm based on Machine Learning (ML) to extract consumer's preferences from a real dataset that contains the historical consumption patterns of his smart appliances. We test our approach on real data traces obtained from a SH and we set up an experiment to evaluate our algorithm on a Raspberry Pi and measure its energy consumption. To prove the effectiveness of our approach, we compare our results with another approach from the literature in terms of electricity bill, Peak-to-Average Ratio (PAR), energy consumption, and execution time. Numerical results show that LOSISH outperforms the other approach in terms of electricity bill (up to 52.92% cheaper), PAR (up to 44% decrease in peak demands), energy consumption (up to 69.44% less consumption), and execution time (up to 63.15% faster). • The load scheduling problem in residential smart homes is studied. • A constrained problem is formulated to make a trade-off between costs and comfort. • A hybrid heuristic algorithm and a Machine Learning clustering model are proposed. • Real data collected from a smart home in the UK was used to validate our approach. • Simulation and experimental results demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Background: The complexities associated with delivering randomised surgical trials, such as clustering effects, by centre or surgeon, and surgical learning, are well known. Despite this, approaches used to manage these complexities, and opinions on these, vary. Guidance documents have been developed to support clinical trial design and reporting. This work aimed to identify and examine existing guidance and consider its relevance to clustering effects and learning curves within surgical trials.Methods: A review of existing guidelines, developed to inform the design and analysis of randomised controlled trials, is undertaken. Guidelines were identified using an electronic search, within the Equator Network, and by a targeted search of those endorsed by leading UK funding bodies, regulators, and medical journals. Eligible documents were compared against pre-specified key criteria to identify gaps or inconsistencies in recommendations.Results: Twenty-eight documents were eligible (12 Equator Network; 16 targeted search). Twice the number of guidance documents targeted design (n/N=20/28, 71%) than analysis (n/N=10/28, 36%). Managing clustering by centre through design was well documented. Clustering by surgeon had less coverage and contained some inconsistencies. Managing the surgical learning curve, or changes in delivery over time, through design was contained within several documents (n/N=8/28, 29%), of which one provided guidance on reporting this and restricted to early phase studies only. Methods to analyse clustering effects and learning were provided in five and four documents respectively (N=28).Conclusions: To our knowledge, this is the first review as to the extent to which existing guidance for designing and analysing randomised surgical trials covers the management of clustering, by centre or surgeon, and the surgical learning curve. Twice the number of identified documents targeted design aspects than analysis. Most notably, no single document exists for use when designing these studies, which may lead to inconsistencies in practice. The development of a single document, with agreed principles to guide trial design and analysis across a range of realistic clinical scenarios, is needed. [ABSTRACT FROM AUTHOR]

Background: In cluster randomised controlled trials (cRCTs), groups of individuals (rather than individuals) are randomised to minimise the risk of contamination and/or efficiently use limited resources or solve logistic and administrative problems. A major concern in the primary analysis of cRCT is the use of appropriate statistical methods to account for correlation among outcomes from a particular group/cluster. This review aimed to investigate the statistical methods used in practice for analysing the primary outcomes in publicly funded cluster randomised controlled trials, adherence to the CONSORT (Consolidated Standards of Reporting Trials) reporting guidelines for cRCTs and the recruitment abilities of the cluster trials design.Methods: We manually searched the United Kingdom's National Institute for Health Research (NIHR) online Journals Library, from 1 January 1997 to 15 July 2021 chronologically for reports of cRCTs. Information on the statistical methods used in the primary analyses was extracted. One reviewer conducted the search and extraction while the two other independent reviewers supervised and validated 25% of the total trials reviewed.Results: A total of 1942 reports, published online in the NIHR Journals Library were screened for eligibility, 118 reports of cRCTs met the initial inclusion criteria, of these 79 reports containing the results of 86 trials with 100 primary outcomes analysed were finally included. Two primary outcomes were analysed at the cluster-level using a generalized linear model. At the individual-level, the generalized linear mixed model was the most used statistical method (80%, 80/100), followed by regression with robust standard errors (7%) then generalized estimating equations (6%). Ninety-five percent (95/100) of the primary outcomes in the trials were analysed with appropriate statistical methods that accounted for clustering while 5% were not. The mean observed intracluster correlation coefficient (ICC) was 0.06 (SD, 0.12; range, - 0.02 to 0.63), and the median value was 0.02 (IQR, 0.001-0.060), although 42% of the observed ICCs for the analysed primary outcomes were not reported.Conclusions: In practice, most of the publicly funded cluster trials adjusted for clustering using appropriate statistical method(s), with most of the primary analyses done at the individual level using generalized linear mixed models. However, the inadequate analysis and poor reporting of cluster trials published in the UK is still happening in recent times, despite the availability of the CONSORT reporting guidelines for cluster trials published over a decade ago. [ABSTRACT FROM AUTHOR]

Background: Patient outcomes can depend on the treating centre, or health professional, delivering the intervention. A health professional's skill in delivery improves with experience, meaning that outcomes may be associated with learning. Considering differences in intervention delivery at trial design will ensure that any appropriate adjustments can be made during analysis. This work aimed to establish practice for the allowance of clustering and learning effects in the design and analysis of randomised multicentre trials.Methods: A survey that drew upon quotes from existing guidelines, references to relevant publications and example trial scenarios was delivered. Registered UK Clinical Research Collaboration Registered Clinical Trials Units were invited to participate.Results: Forty-four Units participated (N = 50). Clustering was managed through design by stratification, more commonly by centre than by treatment provider. Managing learning by design through defining a minimum expertise level for treatment provider was common (89%). One-third reported experience in expertise-based designs. The majority of Units had adjusted for clustering during analysis, although approaches varied. Analysis of learning was rarely performed for the main analysis (n = 1), although it was explored by other means. The insight behind the approaches used within and reasons for, or against, alternative approaches were provided.Conclusions: Widespread awareness of challenges in designing and analysing multicentre trials is identified. Approaches used, and opinions on these, vary both across and within Units, indicating that approaches are dependent on the type of trial. Agreeing principles to guide trial design and analysis across a range of realistic clinical scenarios should be considered. [ABSTRACT FROM AUTHOR]