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Recoverable robust single machine scheduling with polyhedral uncertainty
Get PDFThis paper considers a recoverable robust single-machine scheduling problem under polyhedral uncertainty with the objective of minimising the total flow time. In this setting, a decision-maker must determine a first-stage schedule subject to the uncertain job processing times. Then following the realisation of these processing times, they have the option to swap the positions of up to Δ disjoint pairs of jobs to obtain a second-stage schedule. We first formulate this scheduling problem using a general recoverable robust framework, before we examine the incremental subproblem in further detail. We prove a general result for max-weight matching problems, showing that for edge weights of a specific form, the matching polytope can be fully characterised by polynomially many constraints. We use this result to derive a matching-based compact formulation for the full problem. Further analysis of the incremental problem leads to an additional assignment-based compact formulation. Computational results on budgeted uncertainty sets compare the relative strengths of the three compact models we propose
Investigating the impact of mathematics game-based learning among higher education students
Get PDFMany Caribbean students who enter higher education (HE) do not have a firm mathematics foundation. This impacts their academic outcome. A mathematics game-based learning (GBL) intervention was conducted to investigate its impact on students' learning outcomes and learning experiences and determine the potential of GBL as a pedagogical consideration in this context. This study examines the impact of mathematics GBL on students’ academic performance, their perceived satisfaction with the elements of the self-determination theory (SDT): autonomy, competence, and relatedness, their learning experience of flow, and their perceptions of the benefits and challenges of mathematics GBL in their classroom. The intervention was conducted in an HE institution in the Caribbean among three groups of undergraduate chemistry students. This is convergent mixed methods research conducted through a pragmatic lens and employed quantitative data (pretests and posttests, students' final grades, Likert responses) and qualitative data (questionnaire and focus group) to facilitate a rounded overview to answer the research questions. Results suggest that the intervention had a statistically significant impact on students' pretest to posttest scores and did not impact their final course grades negatively; students' basic psychological needs of autonomy, competence, and relatedness were satisfied; and some students may have experienced flow. Overall, the students overwhelmingly enjoyed the learning experience; it was positive for their well-being; they were motivated and engaged in the learning environment, and their mathematics knowledge and understanding were enhanced, resulting from the intervention. The findings add to the theoretical discourse of flow and SDT; for example, it is possible to optimise and enjoy a learning environment even if all flow elements are not in alignment; some Caribbean students are motivated by competition and leaderboard, and autonomy, competence, and relatedness promote flow in the GBL environment. These insights can inform practitioners, policymakers, and other education stakeholder
Lexical selection in Mandarin-English bilingual speakers
Get PDFThe ongoing debate in bilingualism research revolves the inhibition towards the nontarget language and the required speed to switch to another language. This inhibition has been investigated through various switching-paradigm, whereby suggesting the importance of language proficiency and use. However, given the growing body of bilingual speakers from different background, it is crucial to tap into the magnitude of inhibition and its dynamic nature within different language contexts and exposure. In the current thesis, I examined how language context affects the need of inhibition and how flexibly can bilingual speakers adapt themselves to optimise their language switching efficiency. In Study 1, Mandarin-English bilinguals completed language-switching tasks in different language contexts: (1) “L1-predominant” (most trials named in L1), (2) “L2- predominant” (most trials named in L2) and (3) “Mixed” (trials named half in L1 and half in L2). Based on our findings, I confirmed that switch cost asymmetry does not necessarily emerge during switching, and that the pattern of switch cost can also be modulated by the type of production process. In the production process with pictures named in either language (i.e., top-down processing), the degree of inhibition is dynamic and dependent on the predominant language. In Study 2, I then tested whether this switch cost pattern (asymmetry in the L1- predominant not in L2-predominant) would also emerge in other production-based tasks (here, reading aloud) or whether it was constrained to picture naming. Mandarin ChineseEnglish bilinguals were asked to read aloud Chinese characters and English words. The procedure was otherwise identical, including the context manipulation, but pictures were replaced with words. In contrast to study 1, I did not observe the asymmetry in both contexts. Study 3 tested whether the switch cost patterns (asymmetry in the L1-predominant, not in L2-predominant) would also emerge when participants could prepare for the target language (here, 250ms). Mandarin-English bilinguals saw a cue 250ms before the onset of the target pictures. The context manipulation remained the same. Here, I found different that magnitude of asymmetry was absent in both contexts. Study 4 was set out to investigate the brain activity before speech onset. The results showed that switching to L2 requires greater cognitive demands than switching to L1. To this end, I provide direct evidence with dynamics of inhibition, and importantly the anticipatory ability of the bilingual brain. This suggests that future work should continue to explore the language production processes in bilingual speakers. In summary, the results presented in this thesis demonstrated the flexibility of bilingual speakers when they switch languages. Furthermore, the effect of language context plays a role in bilingual language switching
Terahertz magnetic dynamics in rare-earth transition metal oxides
Get PDFOver the past decade, the field of terahertz driven magnetic phenomena has witnessed a remarkable surge of interest, particularly in the study of antiferromagnetic materials, which has emerged as a captivating sub-field within the area of ultrafast pump-probe spectroscopy studies. This thesis is dedicated to theoretical and experimental studies of a specific class of magnetic oxides, namely rare-earth orthoferrites (REOs) and the magnetic crystal known as Terbium Gallium Garnet (TGG). These materials are characterised by a plethora of fascinating physical phenomena, including the occurrence of spin reorientation phase transitions (SRTP), which can be effectively manipulated and controlled through the use of THz driving/excitation. The first two chapters of this thesis explain the motivations behind this research and provide an overview of the theoretical and experimental methods necessary for understanding the later chapters. Chapter 3 develops the theoretical formalism used to describe THz-driven magnetic switching phenomena in rare-earth orthoferrites with non-Kramers ions. It provides insights into the dynamics induced by THz radiation on iron spins and analyses the mechanisms that facilitate the iron spin-switching process during the spin-reorientation phase transition in Thulium orthoferrite (TmFeO3). Based on the available experimental data this chapter analyses static and dynamic properties of TmFeO3 in the course of SRPT, explains the effects responsible for the spin switching behaviour and presents theoretical results with the realistic values of threshold fields necessary for achieving effective and minimally dissipative iron spin switching showing a good match with experimental findings. Chapter 4 delves into our experimental results on the signatures of the magnetic analogue of the Jahn-Teller effect during the spin-reorientation phase transition in Terbium orthoferrite, (TbFeO3), supported by a developed theoretical analysis. It also explores the features of the strong coupling regime between Fe and Tb ions, comparing TbFeO3 with other strongly coupled systems as reported in previous research. Chapter 5 describes the experimental comparison of THz- and optically-induced spin dynamics of Tb ions in Terbium Gallium Garnet (Tb3Ga5O12). This crystal is well-known for its magneto-optical properties but lacks the specific magnetic order found in orthoferrites, making it an ideal candidate for investigating the features of low-temperature magnetism of pure Tb3+ ions unaffected by interactions with other magnetic ions. The final chapter summarises our theoretical and experimental results obtained throughout this research. It provides an outlook of how these results correlate with each other, and discusses future experimental and theoretical steps that emerged in the course of this work
IMAFD: An Interpretable Multi-stage Approach to Flood Detection from time series Multispectral Data
No full textIn this paper, we address two critical challenges in the domain of flood detection: the absence of a comprehensive flood detection framework and the lack of interpretable decision-making processes in explainable AI (XAI). To overcome these challenges, an Interpretable Multi-stage Approach to Flood Detection, IMAFD, has been proposed. The proposed IMAFD provides a comprehensive, efficient and interpretable solution suitable for large-scale remote sensing tasks and offers insight into decision-making. The proposed IMAFD approach combines the analysis of the dynamic time series image sequences to identify images with possible flooding with the static, within-image semantic segmentation. It combines anomaly detection (at both image and pixel level) with semantic segmentation. The flood detection problem is addressed through four stages: (1) at a sequence level: identifying the suspected images (2) at a multi-image level: detecting change within suspected images (3) at an image level: semantic segmentation of images into Land, Water or Cloud class (4) decision making. Our contributions are twofold. First, we provide a multi-stage holistic approach to flood detection, which efficiently reduces the number of images to be processed for semantic change detection in the later stage and reduces the processing time, which is critical for rapid disaster management such as flood. Secondly, the proposed semantic change detection method (stage 3) offers human users an interpretable decision-making process, while most explainable AI (XAI) methods provide post hoc explanations. The evaluation of the proposed IMAFD framework was performed on two datasets, WorldFloods and RavAEn. For both datasets, the proposed framework demonstrates a competitive performance compared to other methods while also providing interpretability. Specifically, the proposed IDSS+ outperformed U-Net on the Worldfloods dataset by 1.57% for IoU water and 0.19% for mIoU. On the Raven dataset, the proposed IMAFD first stage outperformed DINO by 0.16 for average precision, 0.25 for average recall and 0.21 for average F1. For the flood detection task, the proposed IMAFD achieved 100% accuracy, precision recall and F1 score by setting threshold at 4. Furthermore, our framework significantly reduces the time required to process the image sequences on the Raven dataset by 212.66 s in total compared to the state-of-the-art framework
Bouncing back : recovery from the impacts of COVID-19 on human wellbeing in Kenyan coastal fishing communities
No full textThe COVID-19 pandemic altered almost every aspect of people’s lives and undermined human wellbeing. Now that restrictions have lifted, we need to identify the lingering effects of the pandemic to strategically direct the ongoing recovery process. We conducted a mixed-methods longitudinal analysis of material, relational and subjective wellbeing in five coastal fishing communities in Kenya before, during and after the implementation of COVID-19 containment policies. We drew on qualitative analysis of interviews and quantitative analysis of surveys conducted with 32 fishers at three time points to explore how the pandemic affected wellbeing during the first year of the pandemic. We then used surveys conducted with the majority of fishers in each community in 2016, 2019 and 2022 to determine the scale of the impact of the pandemic proportionate to the impacts of ongoing changes in the communities. We identified a range of wellbeing impacts during the pandemic but also found that communities appear to be recovering. Although there were meaningful differences between our indicators of wellbeing immediately prior to (2019) and after (2022) the pandemic, our analysis leveraging data from 2016 suggests that these differences align with a longer-term trend likely associated with ongoing social-ecological changes. In all but one indicator (work enjoyment), we were unable to identify any significant long-term impacts of the pandemic on any of our wellbeing indicators. Our research provides compelling evidence of the capacity of coastal fishing communities to “bounce back” from the impacts of COVID-19, which likely has relevance for future shocks
Spatial distribution of Culex mosquitoes across England and Wales, July 2023
No full textWith medically important arboviruses such as West Nile virus (WNV) circulating in Europe and Usutu virus (USUV) currently present in the UK, it is imperative to identify areas in the UK at risk of establishment and spread of these viruses. Here, we describe a comprehensive nationwide field surveillance study conducted during July 2023 to map the distribution of the WNV and USUV competent vectors: Culex pipiens biotype pipiens, Culex pipiens biotype molestus and Culex torrentium, across England and Wales
Do we need simulation optimisation for queueing allocation problems?
No full textAs computational resources get cheaper and more convenient, largely through cloud technologies, the general trend is to solve larger simulation optimisation problems with more complex/realistic simulation models, sometimes in an exhaustive manner. This is despite the need for the computing sector and thus the simulation sector to reduce its carbon emissions. This paper advocates for using simpler models to aid the optimisation, particularly in queueing network problems, where many analytical models have been developed and system behaviours can make simulation optimisation more difficult. We focus on Jackson Networks for multi-fidelity optimisation of a server allocation problem, and set up a series of experiments to examine how well these models can aid simulation optimisation
Learning Algorithms for Verification of Markov Decision Processes
No full textWe present a general framework for applying learning algorithms and heuristical guidance to the verification of Markov decision processes (MDPs). The primary goal of our techniques is to improve performance by avoiding an exhaustive exploration of the state space, instead focussing on particularly relevant areas of the system, guided by heuristics. Our work builds on the previous results of Br{á}zdil et al., significantly extending it as well as refining several details and fixing errors. The presented framework focuses on probabilistic reachability, which is a core problem in verification, and is instantiated in two distinct scenarios. The first assumes that full knowledge of the MDP is available, in particular precise transition probabilities. It performs a heuristic-driven partial exploration of the model, yielding precise lower and upper bounds on the required probability. The second tackles the case where we may only sample the MDP without knowing the exact transition dynamics. Here, we obtain probabilistic guarantees, again in terms of both the lower and upper bounds, which provides efficient stopping criteria for the approximation. In particular, the latter is an extension of statistical model-checking (SMC) for unbounded properties in MDPs. In contrast to other related approaches, we do not restrict our attention to time-bounded (finite-horizon) or discounted properties, nor assume any particular structural properties of the MDP
On “Local Theory” neutrality with respect to “meta-theories”and data from a diversity of “native speakers”, including heritage speaker bilinguals : Commentary on Hulstijn (2024)
Get PDFThis commentary critically engages with Hulstijn’s revised Basic Language Cognition (BLC) Theory, which aims to enhance explanatory power and falsifiability regarding individual differences (IDs) in language proficiency across native and non-native speakers. While commending BLC Theory’s emphasis on separating oral and written language cognition, we raise two key concerns. First, we question the theory’s exclusive alignment with usage-based approaches, arguing that its core constructs are, in principle, compatible with multiple meta-theoretical frameworks, including generative ones. As such, BLC Theory should remain neutral to maximize its cross-paradigmatic utility. Second, we address the theory’s treatment of heritage speaker bilinguals (HSs), particularly the implication that they may not typically acquire BLC. We contend that this position overlooks robust empirical evidence demonstrating that HSs develop systematic, rule-governed grammars influenced by their individual input and usage conditions. Moreover, we highlight how IDs among HSs can provide a valuable testing ground for BLC Theory, particularly regarding the role of input and literacy. We conclude that embracing theory neutrality and integrating diverse speaker data—especially from heritage bilinguals—can enhance BLC Theory’s generalizability, empirical relevance, and theoretical utility across language acquisition research