Effectiveness of a school-based high-intensity interval training programme in adolescents – The PRO-HIIT intervention

High-intensity interval training (HIIT) has garnered considerable interest in promoting health and mental health in children and adolescents and remains a prominent topic in school-based research recently. However, the effectiveness of school-based HIIT on many outcome variables remains underexplored, and the process evaluation for conducting school-based interventions was frequently overlooked. The aim of this thesis was to assess the effectiveness of a school-based HIIT intervention – the PRO-HIIT study – in promoting Chinese secondary school students’ physical fitness, cognitive and mental health, and academic performance. The initial four chapters consist of the preparation phase of the PRO-HIIT intervention, including an introduction (Chapter 1), a literature review on process and outcome evaluation of school-based HIIT interventions (Chapter 2), and validation of rating of perceived exertion (RPE, Chapter 3) and Polar Verity Sense (PVS, Chapter 4) for the purpose of developing feasible fidelity monitoring tools for the PRO-HIIT study. Findings from Chapter 2 revealed that process evaluation is frequently overlooked in the literature of school-based HIIT interventions, with only half of the process evaluation measures were reported and only five interventions (11%) reported process evaluation in either a section or a separate publication. Furthermore, both RPE (Chapter 3) and PVS (Chapter 4) were demonstrated to be valid in monitoring HIIT intensity, with RPE showing acceptable criterion validity (r = 0.53 - 0.74, p < 0.01) compared to electrocardiogram measured heart rate and PVS showing very strong within-participant correlation (r = 0.93, p < 0.01) against Polar H10, a chest-based electrocardiogram heart rate monitor. Chapter 5 detailed the protocol of the PRO-HIIT intervention, an enhancement of traditional warm-up periods by replacing it with a 6-8-minute of HIIT in physical education and physical activity lessons. Chapter 6 reported the effectiveness of the PRO-HIIT intervention. The results showed improved CRF immediately post-intervention (6.1 laps, 95% CI = 4.1, 8.1), with the favourable effect lasting for at least two months (4.2 laps, 95% CI = 2.2, 6.2). In addition, participants in the intervention group demonstrated improvements in handgrip strength, bone health, and inhibitory control compared to the control group at post-intervention only. Our findings suggest that the accumulated fitness gains were likely to be lost or stagnated over the summer holiday, regardless of intervention groups. However, the PRO-HIIT intervention showed some potential in mitigating this negative trend. Chapter 7 is a comprehensive process evaluation of the PRO-HIIT intervention, and demonstrated successful implementation in terms of reach, recruitment, retention, and fidelity, despite delivering fewer HIIT sessions due to the sessions in physical activity lessons being cancelled from the fifth intervention week onwards. Nevertheless, delivered HIIT sessions were well received, with positive responses from both teachers and students, and no injuries reported over the intervention period. Chapter 8 summarised the findings of the thesis and discussed its novel contribution to the literature of school-based high-intensity interval training interventions. In addition, a logical model was developed for guiding future school-based HIIIT interventions, with the PRO-HIIT intervention used as an example to support the utility of the proposed model. Furthermore, the limitations of the PRO-HIIT intervention, such as assessment of fidelity, student autonomy, and the unique context of the intervention, were discussed, along with suggestions for future directions

Data and Python code.rar

The datasets include "test data" and "training data", which were used to establish five machine learning models (LR, SVM, RF, CNN, and LGBM ) for mineral prospectivity mapping. </p

Roles of Different Dimensions of Vocabulary Knowledge in L2 Reading Comprehension: Readers, Texts, and Tasks

Abstract Informed by the Lexical Quality Hypothesis (Perfetti, 2007) and the Reading Systems Framework (Perfetti & Stafura, 2014), this thesis aims to further current understandings about roles of vocabulary knowledge in second language (L2) reading comprehension. It sets out to test 1) how different dimensions of vocabulary knowledge (i.e., size vs. depth), particularly different aspects of vocabulary depth knowledge, collectively and relatively predict reading comprehension for different types of texts and tasks; and 2) how the contribution of different dimensions and aspects of vocabulary knowledge to reading comprehension may be mediated by readers’ inference making skills. Two distinct but related studies were conducted that involved separate groups of participants of the same background, that is, Chinese-speaking university students in the UK. While the two studies followed the same design and answered the same set of research questions, they purposefully differed on the type of texts for reading comprehension. Study 1 (N = 123) focused on the comprehension of narrative texts, whereas Study 2 (N = 121) focused on that of informational texts. For each study, a battery of established and researcher-developed tests was administered to participants on a group basis or individually to test their vocabulary size, vocabulary depth (semantic network knowledge and polysemous word knowledge), inferencing skills (bridging and elaborative), working memory (digital span and operation span), and passage comprehension (long vs. short passages). In both studies, participants’ different aspects of vocabulary knowledge and reading comprehension were measured through a set of paper-based tasks, while their inference making skills and working memory were measured with computer-based decision tasks. Various correlation-based methods, including hierarchical regression analysis and structural equation modelling (SEM), were conducted to answer the research questions in each study. Hierarchical regression analyses revealed that in both studies, the three vocabulary knowledge measures collectively explained a substantial amount of variance in reading comprehension; and each was a significant unique predictor. The relative effects of different types of vocabulary knowledge on reading comprehension, however, varied between the two studies. In Study 1, when narrative reading comprehension was the outcome measure, vocabulary size had the greatest unique effect, followed by semantic network knowledge and polysemous knowledge. In contrast, in Study 2, where the focus was on comprehension of informational texts, semantic network knowledge was found to be the strongest predictor, and the two depth aspects demonstrated a stronger effect than did vocabulary size. Further, in both studies, differential patterns were revealed when the three vocabulary measures were regressed on different reading tasks (long vs. short passages). In both studies, subsequent SEM analysis showed that, controlling for working memory and inference making, a latent variable of vocabulary knowledge represented by the three measures directly and significantly predicted reading comprehension; its indirect effect on reading comprehension, through the mediation of inference making, was also significant. Thus, disregarding text type, the effects of vocabulary knowledge on reading comprehension were partially mediated by inference making skills. The mediating effect in Study 2 for informational texts appeared to be smaller than that in Study 1 for narrative texts. Further SEM analysis tested, in both studies, how different vocabulary knowledge, particularly the two depth aspects, may differentially contribute to reading comprehension through the mediation of bridging and elaborative inferences. Among the various mediated routes in the two studies, only the indirect effect of word associations (an aspect of vocabulary depth) on reading comprehension through the mediation of bridging inference was found to be significant in Study 1 for narrative comprehension. Taken together, the findings of the two studies in this thesis have filled several important gaps in the literature on L2 vocabulary knowledge and reading comprehension. In particular, they have expanded our understanding of how various types of vocabulary knowledge, inference making skills, and working memory interrelate and work in tandem in L2 reading development. By attending to both reader and non-reader factors, this research has generated new insights into the lexical basis of L2 reading. It highlights the complexity of lexical involvement in L2 text reading and comprehension in relation to dimensions/aspects of vocabulary knowledge, mediating functions of inference making, types of texts and comprehension tasks that may involve different reading purposes and activities. Pedagogically, the findings across the two studies suggest that, to enable successful reading comprehension, disregarding text type, readers should have a deep understanding of words or a high-quality vocabulary; and vocabulary depth deserves special instructional attention even among advanced L2 learners

Distributed Learning for Metaverse over Wireless Networks

Metaverse is envisioned to be a human-centric framework that creates an interface for users to immerse themselves in education, professional training, and entertainment by accessing a virtual world. The quality of immersive experiences (QoIE) naturally comes out as a metric to measure the multi-sensory multimedia (MSMM) communication provided by Metaverse networks, we first propose a human-centric MSMM communication framework and highlight the asymmetric uplink-downlink transmission mechanism by identifying their different responsibilities. This MSMM framework raises the need for advanced communication technologies and more computational resources to support the deployment of AI-enabled Metaverse services. Task-oriented communication (TOC), can enhance conventional data-oriented communication by shifting from data rate maximization to task completion communication, especially deep learning-based TOC (DL-TOC) can build up a joint communication and task completion architecture. The idea of investigating distributed computational resources of end users to perform local learning, and only share model parameters with the central server, known as distributed learning framework, becomes popular, which saves communication resources and provides privacy protection. Then, it is introduced as a beneficial scheme to enable training ML models for both Al-enabled services and the DL-TOC scheme in a distributed manner. Specifically, we propose three distributed learning variants to address the heterogeneity of Metaverse networks from different aspects. Next, a case study is proposed to demonstrate how the proposed distributed learning frameworks can assist attention-aware communication for Metaverse. Finally, we identify the challenges and some promising research directions.</p

The Compulsive Brain Disease Model of Addiction Lowers Problematic Alcohol Drinkers' Confidence to Reduce their Addictive Behaviour

Background Critical addiction theorists have proposed that public dissemination of the compulsive brain disease model of addiction (cBDMA), as opposed to choice-based theories, might be iatrogenic in reducing problematic substance users' confidence to limit their consumption, but only two preliminary experimental studies have tested this claim. Methods In this online between-subjects framing design, 1204 UK-based weekly alcohol drinkers (stratified into three severity levels by AUDIT: low-risk, hazardous and dependent) watched either a short video of Dr. Nora Volkow describing addiction as a compulsive brain disease or Prof. Marc Lewis describing addiction as a value-based choice (clipped from public lectures) or a neutral video describing UK geography. Participants then reported their agreement with and unpleasantness of the videos and their number of previous attempts, desire and confidence to reduce their addictive behaviour. Results Participants agreed more with the compulsion video but also rated it as more unpleasant. Low-risk drinkers reported greater desire to reduce addictive behaviour following the choice and compulsion than neutral video. Both hazardous and dependent drinkers reported lower confidence to reduce addictive behaviour following the compulsion than choice video. Effect sizes were small. Conclusions The study corroborated two previous studies in suggesting that public dissemination of the cBDMA, compared to choice-based theories of addiction, appears to be iatrogenic for hazardous and dependent drinkers in lowering their confidence to reduce addictive behaviour. The study strengthens demand for research testing whether dissemination of the cBDMA in the natural environment unintentionally promotes addictive behaviour, directly contradicting its purpose.</p

Controlling Multistability in a Vibro-Impact Capsule System

This work concerns the control of multistability in a vibro-impact capsule system driven by a harmonic excitation. The capsule is able to move forward and backward in a rectilinear direction, and the main objective of this work is to control such motion in the presence of multiple coexisting periodic solutions. A position feedback controller is employed in this study, and our numerical investigation demonstrates that the proposed control method gives rise to a dynamical scenario with two coexisting solutions, corresponding to forward and backward progression. Therefore, the motion direction of the system can be controlled by suitably perturbing its initial conditions, without altering the system parameters. To study the robustness of this control method, we apply numerical continuation methods in order to identify a region in the parameter space in which the proposed controller can be applied. For this purpose, we employ the MATLAB-based numerical platform COCO, which supports the continuation and bifurcation detection of periodic orbits of non-smooth dynamical systems

Seismic vibration control analysis of single layer spherical reticulated shell based on the natural modes

The developments and research status of seismic vibration control of long-span spatial structures using multiple tuned mass damper (MTMD) are introduced in this paper. The dynamic equation of reticulated shell with MTMD vibration control system is derived and solved. Based on controlling selected natural modes, the design procedure of distributed multiple tuned mass damper (D-MTMD) seismic control method of the single layer spherical reticulated shell is proposed. A numerical model of single layer reticulated shell is built. By the method of time-history analysis with “El Centro seismic waves”, the dynamic responses of the reticulated shell structure equipped with different seismic vibration control schemes are computed, analysed and compared under horizontal and vertical seismic excitations. The numerical results show that the proposed seismic vibration control method is efficient in reducing dynamic response of reticulated shell structures. Beyond that, some suggestions for seismic vibration control optimization design are summarized

Longevity risk and survivor derivative pricing

Purpose – Longevity risk, that is, the uncertainty of the demographic survival rate, is an important risk for insurance companies and pension funds, which have large, and long-term, exposures to survivorship. The purpose of this paper is to propose a new model to describe this demographic survival risk. Design/methodology/approach – The model proposed in this paper satisfies all the desired properties of a survival rate and has an explicit distribution for both single years and accumulative years. Findings – The results show that it is important to consider the expected shift and risk premium of life table uncertainty and the stochastic behaviour of survival rates when pricing the survivor derivatives. Originality/value – This model can be applied to the rapidly growing market for survivor derivatives

A viscoelastic nonlinear energy sink with an electromagnetic energy harvester: Narrow-band random response

Nonlinear energy sink is a passive energy absorption device that surpasses linear dampers, and has gained significant attention in various fields of vibration suppression. This is owing to its capacity to offer high vibration attenuation and robustness across a wide frequency spectrum. Energy harvester is a device employed to convert kinetic energy into usable electric energy. In this paper, we propose an electromagnetic energy harvester enhanced viscoelastic nonlinear energy sink (VNES) to achieve passive vibration suppression and energy harvesting simultaneously. A critical departure from prior studies is the investigation of the stochastic P-bifurcation of the electromechanically coupled VNES system under narrow-band random excitation. Initially, approximate analytical solutions are derived using a combination of multiple-scale method and a perturbation approach. The substantial agreement between theoretical analysis solutions and numerical solutions obtained from Monte Carlo simulation underscores the method's high degree of validity. Furthermore, the effects of system parameters on system responses are carefully examined. Additionally, we demonstrate that stochastic P-bifurcation can be induced by system parameters, which is further verified by the steady-state density functions of displacement. Lastly, we analyze the impacts of various parameters on mean square current and mean output power, crucial for selecting suitable parameters to enhance energy harvesting performance

Performance enhancement of a viscoelastic bistable energy harvester using time-delayed feedback control

This paper focuses on the stochastic analysis of a viscoelastic bistable energy harvesting system under colored noise and harmonic excitation, and adopts the time-delayed feedback control to improve its harvesting effciency. Firstly, to obtain the dimensionless governing equation of the system, the original bistable system is approximated as a system without viscoelastic term by using the stochastic averaging method of energy envelope, and then is further decoupled to derive an equivalent system. The credibility of the proposed method is validated by contrasting the consistency between the numerical and the analytical results of the equivalent system under different noise conditions. The influence of system parameters on average output power is analyzed, and the control effect of the time-delayed feedback control on system performance is compared. The output performance of the system is improved with the occurrence of stochastic resonance(SR). Therefore, the signal-to-noise ratio expression for measuring SR is derived, and the dependence of its SR behavior on different parameters is explored