21st century social work: reducing re-offending - key practice skills

This literature review was commissioned by the Scottish Executive’s Social Work Services Inspectorate in order to support the work of the 21st Century Social Work Review Group. Discussions in relation to the future arrangements for criminal justice social work raised issues about which disciplines might best encompass the requisite skills for reducing re-offending in the community. Rather than starting with what is known or understood about the skills of those professionals currently involved in such interventions, this study sought to start with the research evidence on effective work with offenders to reduce re-offending and then work its way back to the skills required to promote this outcome

Effective pre-school, primary and secondary education project (EPPSE 3-14) : students' reports of their experiences in year 9

The Effective Pre-school, Primary and Secondary Education Project (EPPSE) has inves tigated the academic and social behavioural (+ in the later stages the affective) development of approximately 3,000 children from the age of 3+ years since 1997. This report presents the results of analyses related to student’s experiences in Year 9 (age 14), with the purpose of creating measures of both school and classroom life as experienced by students. These measures have been used in the analysis of academic and social-behavioural outcomes as well as dispositions to investigate whether a student’s reported experience of school can significantly predict outcomes in other areas. The findings highlight the importance of the ‘student voice’ and provide an insight into the experiences of teenagers in the first decade of the 21st Century

Effective pre-school, primary and secondary education project (EPPSE 3-14): students’ reports of their experiences of school in Year 9

The Effective Pre-school, Primary and Secondary Education Project (EPPSE) has inves tigated the academic and social behavioural (+ in the later stages the affective) development of approximately 3,000 children from the age of 3+ years since 1997. This report presents the results of analyses related to student’s experiences in Year 9 (age 14), with the purpose of creating measures of both school and classroom life as experienced by students. These measures have been used in the analysis of academic and social-behavioural outcomes as well as dispositions to investigate whether a student’s reported experience of school can significantly predict outcomes in other areas. The findings highlight the importance of the ‘student voice’ and provide an insight into the experiences of teenagers in the first decade of the 21st Century

Methods for anticipating governance breakdown and violent conflict

In this paper, authors Sarah Bressan, Håvard Mokleiv Nygård, and Dominic Seefeldt present the evolution and state of the art of both quantitative forecasting and scenario-based foresight methods that can be applied to help prevent governance breakdown and violent conflict in Europe’s neighbourhood. In the quantitative section, they describe the different phases of conflict forecasting in political science and outline which methodological gaps EU-LISTCO’s quantitative sub-national prediction tool will address to forecast tipping points for violent conflict and governance breakdown. The qualitative section explains EU-LISTCO’s scenario-based foresight methodology for identifying potential tipping points. After comparing both approaches, the authors discuss opportunities for methodological advancements across the boundaries of quantitative forecasting and scenario-based foresight, as well as how they can inform the design of strategic policy options

Assessing Prevalence of Known Risk Factors in a Regional Central Kentucky Medical Center Heart Failure Population as an Approach to Assessment of Needs for Development of a Program to Provide Targeted Services to Reduce 30 Day Readmissions

Abstract Objectives: Determine demographic, physiologic, and laboratory characteristics at time of admission of the heart failure (HF) population in a regional acute care facility in Central Kentucky through review of patient electronic medical records. Determine which HF population characteristics are significantly associated with readmissions to the hospital. Provide identification of the statistically significant common characteristics of the HF population to this facility so that they may work towards development of an electronic risk for readmission predictive instrument. Design: Retrospective chart review. Setting: Regional acute care facility in Central Kentucky. Participants: All patients (n = 175) with a diagnosis or history of HF (to include diagnosis related group (DRG) codes 402.01, 402.11, 402.91, 404.01, 404.03, 404.11, 404.13, 404.91, 404.93, 428.1, 428.41, 428.23, 428.43, 428.31, 428.33, 428.1, 428.20, 428.22, 428.30, 428.32, 428.40, 428.40, 428.42, 428.0, and 428.9; The Joint Commission, 2013) admitted to the acute care setting of a regional hospital in the Central Kentucky area between the dates of January 1, 2013 and July 31, 2013. Eligible participants were identified via an electronic discharge report listing all patients discharged during the study time period with a HF code. Main Outcome Measure: A chart review was performed to define the HF population within the regional acute care facility. Abstracted information was collected on data instruments (Appendices A,B, and C) and analyzed to define the overall HF population (n = 175). The data was then analyzed to determine significance between patient characteristics (demographic, physiologic, and laboratory) and 30 day readmissions. The data was examined both on the individual patient level and independent of patient level looking at each admission independently. Results: An in depth description of the HF patient population in this facility was obtained. Several patient characteristics including a history of anemia, COPD, ischemic heart disease, diabetes, and the laboratory values creatinine and BNP outside of the reference range were found to have a significant association with 30 day readmissions. Discharge to a skilled nursing facility (SNF) was also found to be a significant predictor of 30 day readmissions. Some social variables such as marital status were not found to have a significant relationship to 30 day readmissions. Conclusion: This investigation is a stepping stone to creating an electronic tool designed to reflect the characteristics of HF population admitted to a single facility and predict risk of HF readmissions within 30 days at the time of admission. Implementation of a plan of care designed to meet the needs of this HF population as well as identify those patients at high risk for will allow for provision of a comprehensive and timely individualized plan of care to reduce the incidence of 30 day readmissions

A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms

The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming paradigm, namely message passing in Forney-style factor graphs (FFGs), in the context of automated design of efficient Bayesian signal processing algorithms. To this end, we developed "ForneyLab" (https://github.com/biaslab/ForneyLab.jl) as a Julia toolbox for message passing-based inference in FFGs. We show by example how ForneyLab enables automatic derivation of Bayesian signal processing algorithms, including algorithms for parameter estimation and model comparison. Crucially, due to the modular makeup of the FFG framework, both the model specification and inference methods are readily extensible in ForneyLab. In order to test this framework, we compared variational message passing as implemented by ForneyLab with automatic differentiation variational inference (ADVI) and Monte Carlo methods as implemented by state-of-the-art tools "Edward" and "Stan". In terms of performance, extensibility and stability issues, ForneyLab appears to enjoy an edge relative to its competitors for automated inference in state-space models.Comment: Accepted for publication in the International Journal of Approximate Reasonin

The current state of biomarker research for Friedreich's ataxia: a report from the 2018 FARA biomarker meeting

The 2018 FARA Biomarker Meeting highlighted the current state of development of biomarkers for Friedreich's ataxia. A mass spectroscopy assay to sensitively measure mature frataxin (reduction of which is the root cause of disease) is being developed. Biomarkers to monitor neurological disease progression include imaging, electrophysiological measures and measures of nerve function, which may be measured either in serum and/or through imaging-based technologies. Potential pharmacodynamic biomarkers include metabolic and protein biomarkers and markers of nerve damage. Cardiac imaging and serum biomarkers may reflect cardiac disease progression. Considerable progress has been made in the development of biomarkers for various contexts of use, but further work is needed in terms of larger longitudinal multisite studies, and identification of novel biomarkers for additional use cases

Analytical and simulator study of advanced transport

An analytic methodology, based on the optimal-control pilot model, was demonstrated for assessing longitidunal-axis handling qualities of transport aircraft in final approach. Calibration of the methodology is largely in terms of closed-loop performance requirements, rather than specific vehicle response characteristics, and is based on a combination of published criteria, pilot preferences, physical limitations, and engineering judgment. Six longitudinal-axis approach configurations were studied covering a range of handling qualities problems, including the presence of flexible aircraft modes. The analytical procedure was used to obtain predictions of Cooper-Harper ratings, a solar quadratic performance index, and rms excursions of important system variables

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science