Teesside University

About: Teesside University is a education organization based out in Middlesbrough, Middlesbrough, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2155 authors who have published 5540 publications receiving 118373 citations. The organization is also known as: University of Teesside.

The Correlation between Running Economy and Maximal Oxygen Uptake: Cross-Sectional and Longitudinal Relationships in Highly Trained Distance Runners

Abstract: A positive relationship between running economy and maximal oxygen uptake (VO2max) has been postulated in trained athletes, but previous evidence is equivocal and could have been confounded by statistical artefacts Whether this relationship is preserved in response to running training (changes in running economy and VO2max) has yet to be explored This study examined the relationships of (i) running economy and VO2max between runners, and (ii) the changes in running economy and VO2max that occur within runners in response to habitual training 168 trained distance runners (males, n = 98, VO2max 730 ± 63 mL∙kg-1∙min-1; females, n = 70, VO2max 652 ± 59 mL kg-1∙min-1) performed a discontinuous submaximal running test to determine running economy (kcal∙km-1) A continuous incremental treadmill running test to volitional exhaustion was used to determine VO2max 54 participants (males, n = 27; females, n = 27) also completed at least one follow up assessment Partial correlation analysis revealed small positive relationships between running economy and VO2max (males r = 026, females r = 025; P 85% of the variance in these parameters unexplained by this relationship, these findings reaffirm that running economy and VO2max are primarily determined independently

Lateral weight transference exercises following acute stroke: a preliminary study of clinical effectiveness

Abstract: Objectives: To evaluate a training programme aimed at improving lateral weight transference in patients following acute stroke to determine main treatment effects, if any, to inform the design of future studies.Design: A single-blind randomized controlled trial.Setting: The Stroke Unit at The James Cook University Hospital, Middlesbrough, UK.Subjects: Thirty-five patients with an acute stroke.Interventions: All subjects received their usual care, including physiotherapy. The treatment group (n / 17) received 12 additional therapy sessions (over four weeks) comprising exercises aimed at improving lateral weight transference in sitting delivered by trained physiotherapy assistants.Main outcome measures: Measures of dynamic reaching, sitting and standing, and static standing balance were undertaken by a blind independent observer.Results: Specific measures of weight displacement in standing and reaching, and timed standing up and sitting down did not detect any differences over time regardless of group. Neit...

Deep-IFS: Intrusion Detection Approach for Industrial Internet of Things Traffic in Fog Environment

Abstract: The extensive propagation of industrial Internet of Things (IIoT) technologies has encouraged intruders to initiate a variety of attacks that need to be identified to maintain the security of end-user data and the safety of services offered by service providers. Deep learning (DL), especially recurrent approaches, has been applied successfully to the analysis of IIoT forensics but their key challenge of recurrent DL models is that they struggle with long traffic sequences and cannot be parallelized. Multihead attention (MHA) tried to address this shortfall but failed to capture the local representation of IIoT traffic sequences. In this article, we propose a forensics-based DL model (called Deep-IFS) to identify intrusions in IIoT traffic. The model learns local representations using local gated recurrent unit (LocalGRU), and introduces an MHA layer to capture and learn global representation (i.e., long-range dependencies). A residual connection between layers is designed to prevent information loss. Another challenge facing the current IIoT forensics frameworks is their limited scalability, limiting performance in handling Big IIoT traffic data produced by IIoT devices. This challenge is addressed by deploying and training the proposed Deep-IFS in a fog computing environment. The intrusion identification becomes scalable by distributing the computation and the IIoT traffic data across worker fog nodes for training the model. The master fog node is responsible for sharing training parameters and aggregating worker node output. The aggregated classification output is subsequently passed to the cloud platform for mitigating attacks. Empirical results on the Bot-IIoT dataset demonstrate that the developed distributed Deep-IFS can effectively handle Big IIoT traffic data compared with the present centralized DL-based forensics techniques. Further, the results validate the robustness of the proposed Deep-IFS across various evaluation measures.

Shape Analysis via Second-Order Bi-Abduction

Abstract: We present a new modular shape analysis that can synthesize heap memory specification on a per method basis. We rely on a second-order biabduction mechanism that can give interpretations to unknown shape predicates. There are several novel features in our shape analysis. Firstly, it is grounded on second-order bi-abduction. Secondly, we distinguish unknown pre-predicates in pre-conditions, from unknown post-predicates in post-condition; since the former may be strengthened, while the latter may be weakened. Thirdly, we provide a new heap guard mechanism to support more precise preconditions for heap specification. Lastly, we formalise a set of derivation and normalization rules to give concise definitions for unknown predicates. Our approach has been proven sound and is implemented on top of an existing automated verification system.We show its versatility in synthesizing a wide range of intricate shape specifications.