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The relationship between high-level vision and reading ability
Get PDFThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonReading is a skill that is required for formal education, human communication, and cognitive development. Approximately 5-12% of the population display difficulties with reading because they are affected by developmental dyslexia (DD), a reading disorder that has a neurobiological origin. The most popular and well-developed theory of DD is the phonological deficit theory which postulates that problems with reading are as a result of failing to learn the relationship between spelling and sounds. However, the phonological account does not explain why many dyslexic readers have measurable visual deficits. Specifically, there is increasing evidence to suggest that impaired function in the magnocellular pathway, which is sensitive to low contrast, low spatial and high temporal frequency stimuli, is associated with reading difficulties. The purpose of the current set of experiments is to test if performance in high-level visual tasks, e.g., object recognition, is associated with reading ability, and to include conditions designed to bias the magnocellular pathway, as previous work has focused on lower-level tasks, e.g., contrast detection, and/or has not specifically included a magnocellular condition. In conditions where the magnocellular pathway was stimulated, slow readers typically performed significantly worse over a range of high-level visual tasks: object recognition, visual search, scene gist classification, emotional face perception and optical illusions. In contrast, differences were not observed between groups in chromatic conditions which suggests that the parvocellular pathway, associated with high spatial acuity and colour vision, is intact. It is suggested that the deficits seen here are as a result of dysfunctional magnocellular input to various high-level regions interfering with top-down facilitation, attentional mechanisms, and global processing. Intervention programmes for DD often focus on improving phonological processing. The data presented across this thesis highlights the potential of visual training programmes to specifically target and strengthen the magnocellular pathway to improve visual-based tasks. Specifically, the use of games and illusions provide an engaging, practical method for potentially identifying those at risk in the pre-reading phase. Targeting those at risk during this critical period can prevent a spiral of negative effects that can occur in individuals with dyslexia. Overall, the findings provide new insight into the link between reading ability and high-level visual processing
Explanation–Question–Response dialogue: An argumentative tool for explainable AI
Get PDFAdvancements and deployments of AI-based systems, especially Deep Learning-driven generative language models, have accomplished impressive results over the past few years. Nevertheless, these remarkable achievements are intertwined with a related fear that such technologies might lead to a general relinquishing of our lives’s control to AIs. This concern, which also motivates the increasing interest in the eXplainable Artificial Intelligence (XAI) research field, is mostly caused by the opacity of the output of deep learning systems and the way that it is generated, which is largely obscure to laypeople. A dialectical interaction with such systems may enhance the users’ understanding and build a more robust trust towards AI. Commonly employed as specific formalisms for modelling intra-agent communications, dialogue games prove to be useful tools to rely upon when dealing with user’s explanation needs. The literature already offers some dialectical protocols that expressly handle explanations and their delivery. This paper fully formalises the novel Explanation–Question–Response (EQR) dialogue and its properties, whose main purpose is to provide satisfactory information (i.e., justified according to argumentative semantics) whilst ensuring a simplified protocol, in comparison with other existing approaches, for humans and artificial agents.This research was partially funded by the UK Engineering & Physical Sciences Research Council (EPSRC) under grant #EP/P010105/1
Participant Observers: Anthropology, Colonial Development, and the Reinvention of Society in Britain
No full textOn pages 139 - 141, Isak Niehaus reviews the following work: Freddy Foks (2023), Participant Observers: Anthropology, Colonial Development, and the Reinvention of Society in Britain (Oakland: University of California Press), 263 pp, £30.00 (pb), ISBN: 9780520390331
Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks
Get PDFData Availability Statement: The data are private and the University Ethics Committee does not allow public access to the data.Leukemia is a malignant disease that impacts explicitly the blood cells, leading to life-threatening infections and premature mortality. State-of-the-art machine-enabled technologies and sophisticated deep learning algorithms can assist clinicians in early-stage disease diagnosis. This study introduces an advanced end-to-end approach for the automated diagnosis of acute leukemia classes acute lymphocytic leukemia (ALL) and acute myeloid leukemia (AML). This study gathered a complete database of 44 patients, comprising 670 ALL and AML images. The proposed deep model’s architecture consisted of a fusion of graph theory and convolutional neural network (CNN), with six graph Conv layers and a Softmax layer. The proposed deep model achieved a classification accuracy of 99% and a kappa coefficient of 0.85 for ALL and AML classes. The suggested model was assessed in noisy conditions and demonstrated strong resilience. Specifically, the model’s accuracy remained above 90%, even at a signal-to-noise ratio (SNR) of 0 dB. The proposed approach was evaluated against contemporary methodologies and research, demonstrating encouraging outcomes. According to this, the suggested deep model can serve as a tool for clinicians to identify specific forms of acute leukemia.This research received no external funding
Moral education in multicultural societies: From the perspective of family and neighbour relationships
No full textThis thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPromoting social cohesion and harmonious coexistence is imperative within the context of human diversity. Navigating the complexities of communal living requires a moral pursuit of understanding and fostering interpersonal connections. Due to our society's growing diversity, these connections are more vital than ever. Fostering closer bonds among people is essential to overcome social disagreements, particularly in multicultural societies where diverse values and norms intersect. This research explores moral education in the context of diverse religious and cultural settings, focusing on how family and neighbour relationships contribute to individual moral development. By examining these fundamental social units, the research aims to unravel the intricacies of moral education aiming to enhance understanding and inform educational policy.
From a critical realist perspective, this study uses a mixed-methods approach, gathering data through a survey of 196 participants and conducting in-depth interviews with 12 individuals. This methodology allowed for a comprehensive understanding of the topic, combining quantitative insights with qualitative depth. The critical realism approach provided a robust analytical lens to interpret the findings, considering both the observable and underlying factors that may influence moral education.
Key findings from the study reveal a two-dimensional understanding of the concept of family and emphasise the crucial role of family relationships in shaping an individual's moral and character development. Furthermore, the study highlights the profound influence of recognition in interpersonal relationships, both within the family and with neighbours. This aspect of recognition plays an important role in how individuals interact and develop within their social spheres. Finally, the research offers valuable insights into the role of family and neighbour relationships in moral education, contributing to the discourse on educational policy in multicultural societies and underlining the need for inclusive and empathetic approaches in educational settings.Ministry of National Education of the Republic of Turkiye and the Turkish Governmen
Experimental investigation of humidified air condensation in different rows of serpantine heat exchanger – Cooling water flow rate effect
Get PDFData availability: Data will be made available on request.Condensing economizers are used in the industry as they enable the acquisition of additional heat during vapor condensation. In condensing heat exchangers, water is the usual cooling agent due to its high specific heat capacity and thus efficient heat removal. Therefore, experiments of hot humid air condensation in a serpentine economizer being cooled by water were performed to reveal the effect the cooling water flow rate has on humidified air condensation in separate rows of the serpentine type economizer (with vertical tubes). The results have shown that the cooling ratio (mass flow ratio between the coolant and the humid air) had a minor effect on the distribution of the humidified air temperature along test section for inlet Reynolds number of 3000–10000. The effects on the condensation flux distribution in different rows were much stronger. The most optimal cooling ratio was determined to be 3. The results have shown that the biggest condensation efficiency is up to 35 %. It was revealed that in some cases the convection was prevailing in the first rows of the economizer and this resulted in a decreased efficiency/performance of the exchanger.
The obtained results from the practical point of view will provide an extended basis for the optimisation of the design of economizers for waste heat recovery in the cases of different cooling water flow rates. It could also be applied to validate computational models developed for condensation heat transfer and condensate flux numerical modelling along economizer.This work was partly funded by the European Union H2020 programme project iWAYS under grant agreement number 958274.
The association between liking, learning and creativity in music
Get PDFData availability: The data underpinning this publication is available at https://osf.io/568jy/.Supplementary Information is available online at: https://www.nature.com/articles/s41598-024-70027-z#Sec21 .Aesthetic preference is intricately linked to learning and creativity. Previous studies have largely examined the perception of novelty in terms of pleasantness and the generation of novelty via creativity separately. The current study examines the connection between perception and generation of novelty in music; specifically, we investigated how pleasantness judgements and brain responses to musical notes of varying probability (estimated by a computational model of auditory expectation) are linked to learning and creativity. To facilitate learning de novo, 40 non-musicians were trained on an unfamiliar artificial music grammar. After learning, participants evaluated the pleasantness of the final notes of melodies, which varied in probability, while their EEG was recorded. They also composed their own musical pieces using the learned grammar which were subsequently assessed by experts. As expected, there was an inverted U-shaped relationship between liking and probability: participants were more likely to rate the notes with intermediate probabilities as pleasant. Further, intermediate probability notes elicited larger N100 and P200 at posterior and frontal sites, respectively, associated with prediction error processing. Crucially, individuals who produced less creative compositions preferred higher probability notes, whereas individuals who composed more creative pieces preferred notes with intermediate probability. Finally, evoked brain responses to note probability were relatively independent of learning and creativity, suggesting that these higher-level processes are not mediated by brain responses related to performance monitoring. Overall, our findings shed light on the relationship between perception and generation of novelty, offering new insights into aesthetic preference and its neural correlates
Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization
Get PDFData Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.This research is supported by Oracle for Research Grant (3146375)
Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine
No full textData availability: Data will be made available on request.Corrigendum to “Short-term high-speed rail passenger flow prediction by integrating ensemble empirical mode decomposition with multivariate grey support vector machine” [Eng. Appl. Art. Intellig. 136PB (2024) 109005]. : The authors regret the incorrect acknowledgments in the published article. The revised acknowledgments section is as follows:
Acknowledgements: This research is supported by the Fundamental Research Funds for the Central Universities, the Funds of the National Natural Science Foundation of China ( U2034208 ) and the Key Project of China State Railway Group Co., Ltd. ( N2023X034 ).
The authors would like to apologise for any inconvenience caused.Short-term prediction of high-speed rail (HSR) passenger flow provides a daily ridership estimation for the near future, which is critical to HSR planning and operational decision making. This paper proposes a new methodology that integrates ensemble empirical mode decomposition with multivariate support vector machines (EEMD-MSVM). There are four steps in this hybrid forecasting approach: (i) explore the correlation of multivariate HSR passenger flows at various stations based on archived data; (ii) decompose empirical modes of historical passenger flows for each HSR station, using EEMD to generate a number of intrinsic mode functions (IMFs) and a trend term; (iii) predict the IMF for each correlated station pair using MSVM; and (iv) reconstruct the refined IMF components to predict daily multivariate HSR passenger flows. The proposed EEMD-MSVM approach is demonstrated with multiple OD pairs along the Wuhan-Guangzhou HSR in China. Results from various origin-destination pairs, show that the EEMD-MSVM approach outperforms the existing ensemble empirical mode decomposition with grey support vector machine approach (EEMD-GSVM). With the multivariate approach, the mean absolute percentage error in demand prediction is reduced by 13.9%, 1.2%, 1.0%, 2.0%, and 2.7% and the mean absolute deviation is reduced by 78.8, 38.0, 4.4, 4.6, and 3.9, between these OD pairs respectively. Such increase in short-term demand prediction accuracy can significantly improve HSR service planning, operations, and revenue management in the real world.This research is supported by the Fundamental Research Funds for the Central Universities, the Funds of the National Natural Science Foundation of China ( U2034208 ) and the Key Project of China State Railway Group Co., Ltd. ( N2023X034 )
A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features
Get PDFData availability: Data will be made available on request.With the high penetration of renewable generation systems in the power grid, the accurate simulation of the uncertainty in renewable energy generation is vital to the safe operation of the power system This paper proposes a novel controllable method for renewable scenario generation based on the improved VAEGAN model. The standard VAEGAN model is first improved using spectral normalization technique and the generator of GAN is trained using VAE. Then, the external and internal interpretable features in the latent space are learned as the controllable vector utilizing the principle of mutual information maximization. Finally, the renewable energy scenarios with overall features are generated using the external universal meteorological features, and renewable energy scenarios with specific features are generated by tuning along the internal interpretable feature of the controllable vector in the latent space. The proposed approach is used to produce real-time series data for renewable energy including wind and solar power. Experiments demonstrate that our method has a better performance in terms of controllable generation and enables the generation of preference patterns covering various statistical features.This research was supported by the National Natural Science Foundation of China ( 62206062 ), the Science & Technology Program of Guangdong Power Grid Power Grid Co. Ltd. ( 031800KK52220014 )
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