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ChesterRep is the University of Chester's institutional repository and an online platform designed to collate, store, and aid discoverability of research carried out at the university to the wider research community

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  • Dietary restriction and ageing: Recent evolutionary perspectives

    Mc Auley, Mark; University of Chester (Elsevier, 2022-09-24)
    Dietary restriction (DR) represents one of the most robust interventions for extending lifespan. It is not known how DR increases lifespan. The prevailing evolutionary hypothesis suggests the DR response redirects metabolic resources towards somatic maintenance at the expense of investment in reproduction. Consequently, DR acts as a proximate mechanism which promotes a pro-longevity phenotype. This idea is known as resource reallocation. However, growing findings suggest this paradigm could be incomplete. It has been argued that during DR it is not always possible to identify a trade-off between reproduction and lifespan. It is also suggested the relationship between reproduction and somatic maintenance can be uncoupled by the removal or inclusion of specific nutrients. These findings have created an imperative to re-explore the nexus between DR and evolutionary theory. In this review I will address this evolutionary conundrum. My overarching objectives are fourfold: (1) to outline some of the evidence for and against resource reallocation; (2) to examine recent findings which have necessitated a theoretical re-evaluation of the link between life history theory and DR; (3) to present alternatives to the resource reallocation model; (4) to present emerging variables which potentially influence how DR effects evolutionary trade-offs.
  • The physiological, perceptual and neuromuscular responses of team sport athletes to a running and cycling high intensity interval training session

    Twist, Craig; Bott, Richard; Highton, Jamie; University of Chester
    Purpose: The acute physiological, perceptual and neuromuscular responses to volume-matched running and cycling high intensity interval training (HIIT) were studied in team sport athletes. Methods: In a randomized cross-over design, 11 male team sport players completed 3 x 6 min (with 5 min between sets) repeated efforts of 15 s exercising at 120% speed (s"V" ̇O2max) or power (p"V" ̇O2max) at VO2max followed by 15 s passive recovery on a treadmill or cycle ergometer, respectively. Results: Absolute mean "V" ̇O2 (ES [95%CI] = 1.46 [0.47-2.34], p < 0.001) and heart rate (ES [95%CI] = 1.53 [0.53-2.41], p = 0.001) were higher in running than cycling HIIT. Total time at >90% VO2max during the HIIT was higher for running compared to cycling (ES [95%CI] = 1.21 [0.26-2.07], p = 0.015). Overall differential RPE (dRPE) (ES [95%CI] = 0.55 [-0.32-1.38], p = 0.094) and legs dRPE (ES [95%CI] = -0.65 [-1.48-0.23], p = 0.111) were similar whereas breathing dRPE (ES [95%CI] = 1.01 [0.08-1.85], p = 0.012) was higher for running. Maximal isometric knee extension force was unchanged after running (ES [95%CI] = -0.04 [-0.80-0.8], p = 0.726) compared to a moderate reduction after cycling (ES [95%CI] = -1.17 [-2.02- -0.22], p = 0.001). Conclusion: Cycling HIIT in team sport athletes is unlikely to meet the requirements for improving run-specific metabolic adaptation but might offer a greater lower limb neuromuscular load.
  • When My Work is Found Wanting: Power, intersectionality, postcolonialism, and the reflexive feminist researcher

    Llewellyn, Dawn; University of Chester (Routledge, 2021-12-31)
    Feminist research emerges out of a struggle with power. Ingrained in feminist studies of religion is the identification and dismantling of religious hierarchies and structures that disempower. Feminist scholarship has contended with the essentialist categories of ‘woman’ and ‘women’s experience’ without questioning that its rendering of ‘religion’ and ‘gender’ was premised on and benefited from its own modes of dominance and suppression, conditioned by Western colonialism. Taking up feminist research is a reflexive position that can assist in upsetting the established hierarchies of power and the binary oppositions of researcher and researched, knower and known, political and personal. However, feminist thinking in religion and gender, like the author own, has not always been reflexively attentive to its almost exclusive focus on the relationships between religion and gender and its own power as the product of Western, colonial, secular discourses.
  • Deep Learning based Human Detection in Privacy-Preserved Surveillance Videos

    Yousuf, Muhammad Jehanzaib; Kanwal, Nadia; Ansari, Mohammad Samar; Asghar, Mamoona; Lee, Brian; Technological University of the Shannon; Keele University; University of Chester; University of Galway
    Visual surveillance systems have been improving rapidly over the recent past, becoming more capable and pervasive with incorporation of artificial intelligence. At the same time such surveillance systems are exposing the public to new privacy and security threats. There have been an increasing number of reports of blatant abuse of surveillance technologies. To counteract this, data privacy regulations (e.g. GDPR in Europe) have provided guidelines for data collection and data processing. However, there is still a need for a private and secure method of model training for advanced machine learning and deep learning algorithms. To this end, in this paper we propose a privacy-preserved method for visual surveillance. We first develop a dataset of privacy preserved videos. The data in these videos is masked using Gaussian Mixture Model (GMM) and selective encryption. We then train high-performance object detection models on the generated dataset. The proposed method utilizes state-of-art object detection deep learning models (viz. YOLOv4 and YOLOv5) to perform human/object detection in masked videos. The results are encouraging, and are pointers to the viability of the use of modern day deep learning models for object detection in privacy-preserved videos.
  • A Novel Double-Threshold Neural Classifier for Non-Linearly Separable Applications

    Kashif, Mohd; Rahman, Syed Atiqur; Ansari, Mohammad Samar; Aligarh Muslim University; University of Chester
    Classification of data finds applications in various engineering and scientific problems. When real-time operation is desired, hardware solutions tend to be more amenable as compared to algorithmic/heuristic solutions. This paper presents a novel current-mode dual-threshold neuron designed and implemented at 32nm CMOS technology node. Subsequently, a current-mode double-threshold classifier is presented which is capable of classifying input patterns of non-linearly separable problems. Thereafter, application of the current-mode dual-threshold neuron in the realization of the XOR function using only a single neural unit is discussed. The proposed neuron as well as both the applications discussed are capable of operating from sub-1V power supplies. Computer simulations using HSPICE yield promising results with the values of delay and power consumption estimated to be lower than existing circuits.

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