Weight changes following lower limb arthroplasty : a prospective observational study

The aim of this study was to assess patterns of weight loss/gain following total hip or knee joint replacement. Four hundred and fifty primary lower limb arthroplasty patients, where the current surgery was the last limiting factor to improved mobility, were selected. Over a one year period 212 gained weight (mean 5.03kg), 92 remained static, and 146 lost weight. The median change was a weight gain of 0.50Kg (p=0.002). All patients had a significant improvement in Oxford outcome scores. Hip arthroplasty patients were statistically more likely to gain weight than knee arthroplasty patients. A successful arthroplasty, restoring a patient's mobility, does not necessarily lead to subsequent weight loss. The majority of patients put on weight with an overall net weight gain. No adverse effect on functional outcome was noted

Exploring the challenges in developing a multi-criteria assessment for smart local energy systems

Several countries worldwide, including the United Kingdom, are investing in and introducing policies to foster the development and deployment of Smart Local Energy Systems. Smart Local Energy Systems are complex and socio-technical, with a wide range of stakeholders and multiple social, technical, environmental and economic aims. It is, therefore, essential to develop a standardised assessment tool to monitor the implementation of these systems and their social, technological, environmental and economic benefits and impacts. This paper presents work related to developing such a multi-criteria assessment tool, focusing on exploring and identifying the challenges of applying multi-criteria assessment to the development and deployment of Smart Local Energy Systems. The research involved semi-structured interviews with relevant expert stakeholders concerning six core assessment themes, corresponding sub-themes, and associated criteria/metrics. The results provide insights into the challenges of applying multi-criteria assessment to Smart Local Energy Systems and highlight the complex nature of these systems. Furthermore, stakeholder burnout (due to too many stakeholder engagement activities), data collection issues, and the broad definition and/or limited scope of assessment criteria were identified as the principal challenges faced in developing such an assessment tool, potentially affecting the reliability of its outputs

Effect of daily restriction and age at initiation of a skip-a-day program for young broiler breeders.

Two experiments were conducted with Cobb feather sex broiler breeders comparing skip-a-day (SAD) feeding programs which began at either 2, 4, 6 or 8 wk of age. A fifth program, daily restriction started at 2 wk of age, was also compared. Chicks hatched in December and July, respectively, in Experiments 1 and 2 were exposed to natural daylight until 20 wk of age. All birds were fed ad libitum until the respective restriction programs began. All grower programs terminated at 20 wk of age. A breeder diet was given daily after 20 wk. Males and females were grown together. Sexual maturity was reached earlier in the 2-wk restriction groups (2-wi SAD in Experiment 1 and the 2-wk daily restriction in both experiments) than in the 8-wk SAD group. Egg production in Experiment 1 was also improved by the early restriction. Fertility and hatchability were not significantly affected by treatment. Based on the results of these experiments a SAD program beginning at 2 wk of age was as good as or better than one initiated at later ages. The 2-wk daily restriction program was equivalent to the 2-wk SAD program

Self-Updating Models with Error Remediation

Many environments currently employ machine learning models for data processing and analytics that were built using a limited number of training data points. Once deployed, the models are exposed to significant amounts of previously-unseen data, not all of which is representative of the original, limited training data. However, updating these deployed models can be difficult due to logistical, bandwidth, time, hardware, and/or data sensitivity constraints. We propose a framework, Self-Updating Models with Error Remediation (SUMER), in which a deployed model updates itself as new data becomes available. SUMER uses techniques from semi-supervised learning and noise remediation to iteratively retrain a deployed model using intelligently-chosen predictions from the model as the labels for new training iterations. A key component of SUMER is the notion of error remediation as self-labeled data can be susceptible to the propagation of errors. We investigate the use of SUMER across various data sets and iterations. We find that self-updating models (SUMs) generally perform better than models that do not attempt to self-update when presented with additional previously-unseen data. This performance gap is accentuated in cases where there is only limited amounts of initial training data. We also find that the performance of SUMER is generally better than the performance of SUMs, demonstrating a benefit in applying error remediation. Consequently, SUMER can autonomously enhance the operational capabilities of existing data processing systems by intelligently updating models in dynamic environments.Comment: 17 pages, 13 figures, published in the proceedings of the Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II conference in the SPIE Defense + Commercial Sensing, 2020 symposiu