ANALYSIS OF THE RECOVERING PHASE AFTER THE CYCLING PRACTICE USING AUGMENTED VISUAL FEEDBACK

The objective of this study was to use a system of augmented visual feedback to improve the pedaling technique in the recovery phase. Nineteen people from 14 to 16 years old with no experience in cycling divided in experimental (n=10) and control (n=9) group took part in this study. Two first evaluations were done to determine the maximal oxygen uptake and work load. Right after the second evaluation seven section pedaling practice was done and after the last one, a post test was conducted. After a week a retention test was done. The results showed that both groups increased their performance, but the experimental group showed better results in the retention moment and could be considered as a more appropriate tool for the teaching of cycling

Cattle Weight Gain and Sward-Animal Nitrogen Relationships in Grazed Hemarthria altissima Pastures

Limpograss (Hemarthria altissima [Poir.] Stapf et C.E. Hubb.) in vitro digestion is greater than most perennial grasses adapted to Florida. Weight gain of cattle grazing limpograss has been lower than expected due in part to low herbage N concentration. Experiments were conducted on limpograss pastures to assess N fertilization, overseeding of the legume aeschynomene (Aeschynomene americana L), and feeding of N supplements to cattle as means of overcoming this limitation. Greater N fertilization, overseeding aeschynomene, and supplementation (corn-urea mixtures) increased weight gain 0.30, 0.23, and 0.24-0.35 kg d-1, respectively, over control treatments. Herbage digestible organic matter:crude protein ratio and cattle blood urea N concentration showed potential as predictors of response to N supplementation. Limpograss leaf blade crude protein (90-130 g kg-1) was two to three times greater than stem plus sheath. Stem plus sheath:leaf ratio was more than three times as great in the bottom as in the top half of the canopy, suggesting that lower stocking rates that allow greater diet selection may increase protein concentration of herbage consumed and increase gain

A genome-scale shRNA resource for transgenic RNAi in Drosophila

Existing transgenic RNAi resources in Drosophila melanogaster based on long double-stranded hairpin RNAs are powerful tools for functional studies, but they are ineffective in gene knockdown during oogenesis, an important model system for the study of many biological questions. We show that shRNAs, modeled on an endogenous microRNA, are extremely effective at silencing gene expression during oogenesis. We also describe our progress toward building a genome-wide shRNA resource. © 2011 Nature America, Inc. All rights reserved

SPARC 2022 book of abstracts

Welcome to the Book of Abstracts for the 2022 SPARC conference. Our conference is called “Moving Forwards” reflecting our re-emergence from the pandemic and our desire to reconnect our PGR community, in celebration of their research. PGRs have continued with their research endeavours despite many challenges, and their ongoing successes are underpinned by the support and guidance of dedicated supervisors and the Doctoral School Team. To recognise supervision excellence we will be awarding our annual Supervisor of the Year prizes, based on the wonderful nominations received from their PGR students.Once again, we have received a tremendous contribution from our postgraduate research community; with over 60 presenters, 12 Three-Minute Thesis finalists, and 20 poster presentations, the conference showcases our extraordinarily vibrant, inclusive, and resilient PGR community at Salford. This year there will be prizes to be won for ‘best in conference’ presentations, in addition to the winners from each parallel session. Audience members too could be in for a treat, with judges handing out spot prizes for the best questions asked, so don’t miss the opportunity to put your hand up. These abstracts provide a taster of the diverse and impactful research in progress and provide delegates with a reference point for networking and initiating critical debate. Take advantage of the hybrid format: in online sessions by posting a comment or by messaging an author to say “Hello”, or by initiating break time discussions about the amazing research you’ve seen if you are with us in person. Who knows what might result from your conversation? With such wide-ranging topics being showcased, we encourage you to take up this great opportunity to engage with researchers working in different subject areas from your own. As recent events have shown, researchers need to collaborate to meet global challenges. Interdisciplinary and international working is increasingly recognised and rewarded by all major research funders. We do hope, therefore, that you will take this opportunity to initiate interdisciplinary conversations with other researchers. A question or comment from a different perspective can shed new light on a project and could lead to exciting collaborations, and that is what SPARC is all about. SPARC is part of a programme of personal and professional development opportunities offered to all postgraduate researchers at Salford. More information about this programme is available on our website: Doctoral School | University of Salford. Registered Salford students can access full details on the Doctoral School hub: Doctoral School Hub - Home (sharepoint.com) You can follow us on Twitter @SalfordPGRs and please use the #SPARC2022 to share your conference experience.We particularly welcome taught students from our undergraduate and master’s programmes as audience members. We hope you enjoy the presentations on offer and that they inspire you to pursue your own research career. If you would like more information about studying for a PhD here at the University of Salford, your lecturers can advise, or you can contact the relevant PGR Support Officer; their details can be found at Doctoral School | University of Salford. We wish you a rich and rewarding conference experience

Enhancing Gearbox Fault Diagnosis through Advanced Feature Engineering and Data Segmentation Techniques

Efficient gearbox fault diagnosis is crucial for the cost-effective maintenance and reliable operation of rotating machinery. Despite extensive research, effective fault diagnosis remains challenging due to the multitude of features available for classification. Traditional feature selection methods often fail to achieve optimal performance in fault classification tasks. This study introduces diverse ranking methods for selecting the relevant features and utilizes data segmentation techniques such as sliding, windowing, and bootstrapping to strengthen predictive model performance and scalability. A comparative analysis of these methods was conducted to identify the potential causes and future solutions. An evaluation of the impact of enhanced feature engineering and data segmentation on predictive maintenance in gearboxes revealed promising outcomes, with decision trees, SVM, and KNN models outperforming others. Additionally, within a fully connected network, windowing emerged as a more robust and efficient segmentation method compared to bootstrapping. Further research is necessary to assess the performance of these techniques across diverse datasets and applications, offering comprehensive insights for future studies in fault diagnosis and predictive maintenance