Fish Health Unit Report of Activities Undertaken in 2022

This report summarises the activities undertaken by the Fish Health Unit of the Marine Institute in 2022. Regulation (EU) 2016/429 lays down the rules for the prevention and control of animal diseases which are transmissible to animal or humans and the Marine Institute is the Competent Authority responsible for implementation of this regulation in Ireland. The purpose of this report is to provide all stakeholders with an improved understanding of the operations of the Marine Institute in fish health, and the findings encountered by the Fish Health Unit in 2022

The importance of the space program

This IQP explores the importance of the space program in society. It focuses on the impact of how space technology enhances our lives on Earth. We present a history of the space program and describe five examples of space technology and specific uses: remote sensing, GPS, radar, SAR, and lidar. We conclude with a brief cost assessment and survey in support of the view that space funding is justifiably valuable

Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain

Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP). The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP

Machine Learning Derived Lifting Techniques and Pain Self-Efficacy in People with Chronic Low Back Pain

This paper proposes an innovative methodology for finding how many lifting techniques people with chronic low back pain (CLBP) can demonstrate with camera data collected from 115 participants. The system employs a feature extraction algorithm to calculate the knee, trunk and hip range of motion in the sagittal plane, Ward’s method, a combination of K-means and Ensemble clustering method for classification algorithm, and Bayesian neural network to validate the result of Ward’s method and the combination of K-means and Ensemble clustering method. The classification results and effect size show that Ward clustering is the optimal method where precision and recall percentages of all clusters are above 90, and the overall accuracy of the Bayesian Neural Network is 97.9%. The statistical analysis reported a significant difference in the range of motion of the knee, hip and trunk between each cluster, F (9, 1136) = 195.67, p < 0.0001. The results of this study suggest that there are four different lifting techniques in people with CLBP. Additionally, the results show that even though the clusters demonstrated similar pain levels, one of the clusters, which uses the least amount of trunk and the most knee movement, demonstrates the lowest pain self-efficacy

Effects of lumbar extensor muscle strengthening and neuromuscular control retraining on disability in patients with chronic low back pain:A protocol for a randomised controlled trial

INTRODUCTION: Chronic low back pain (CLBP) is the leading cause of disability worldwide. However, there is no consensus in the literature regarding optimal management. Exercise intervention is the most widely used treatment as it likely influences contributing factors such as physical and psychological. Literature evaluating the effects of exercise on CLBP is often generalised, non-specific and employs inconsistent outcome measures. Moreover, the mechanisms behind exercise-related improvements are poorly understood. Recently, research has emerged identifying associations between neuromuscular-biomechanical impairments and CLBP-related disability. This information can be used as the basis for more specific and, potentially more efficacious exercise interventions for CLBP patients. METHODS AND ANALYSIS: Ninety-four participants (including both males and females) with CLBP aged 18-65 who present for treatment to a Melbourne-based private physiotherapy practice will be recruited and randomised into one of two treatment groups. Following baseline assessment, participants will be randomly allocated to receive either: (i) strengthening exercises in combination with lumbar force accuracy training exercises or (ii) strengthening exercises alone. Participants will attend exercise sessions twice a week for 12 weeks, with assessments conducted at baseline, midway (ie, 6 weeks into the trial) and at trial completion. All exercise interventions will be supervised by a qualified physiotherapist trained in the intervention protocol. The primary outcome will be functional disability measured using the Oswestry Disability Index. Other psychosocial and mechanistic parameters will also be measured. ETHICS AND DISSEMINATION: This study was given approval by the University of Melbourne Behavioural and Social Sciences Human Ethics Sub-Committee on 8 August 2017, reference number 1 749 845. Results of the randomised controlled trial will be published in peer-reviewed journals. TRIAL REGISTRATION NUMBER: ACTRN12618000894291