Gaps and requirements for applying automatic architectural design to building renovation

The renovation of existing buildings provides an opportunity to change the layout to meet the needs of facilities and accomplish sustainability in the built environment at high utilisation rates and low cost. However, building renovation design is complex, and completing architectural design schemes manually needs more efficiency and overall robustness. With the use of computational optimisation, automatic architectural design (AAD) can efficiently assist in building renovation through decision-making based on performance evaluation. This paper comprehensively analyses AAD's current research status and provides a state-of-the-art overview of applying AAD technology to building renovation. Besides, gaps and requirements of using AAD for building renovation are explored from quantitative and qualitative aspects, providing ideas for future research. The research shows that there is still much work to be done to apply AAD to building renovation, including quickly obtaining input data, expanding optimisation topics, selecting design methods, and improving workflow and efficiency

Real time tracking using nature-inspired algorithms

This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario

Evaluation of in silico and in vitro screening methods for characterising endocrine disrupting chemical hazards

Anthropogenic activities have drastically altered chemical exposure, with traces of synthetic chemicals detected ubiquitously in the environment. Many of these chemicals are thought to perturb endocrine function, leading to declines in reproductive health and fertility, and increases in the incidence of cancer, metabolic disorders and diabetes. There are over 90 million unique chemicals registered under the Chemical Abstracts Service (CAS), of which only 308,000 were subject to inventory and/or regulation, in September 2013. However, as a specific aim of the EU REACH regulations, the UK is obliged to reduce the chemical safety initiatives reliance on in vivo apical endpoints, promoting the development and validation of alternative mechanistic methods. The human health cost of endocrine disrupting chemical (EDC) exposure in the EU, has been estimated at €31 billion per annum. In light of the EU incentives, this study aims to evaluate current in silico and in vitro tools for EDC screening and hazard characterisation; testing the hypothesis that in silico virtual screening accurately predicts in vitro mechanistic assays. Nuclear receptor binding interactions are the current focus of in silico and in vitro tools to predict EDC mechanisms. To the author’s knowledge, no single study has quantitatively assessed the relationship between in silico nuclear receptor binding and in vitro mechanistic assays, in a comprehensive manner. Tripos ® SYBYL software was used to develop 3D-molecular models of nuclear receptor binding domains. The ligand binding pockets of estrogen (ERα and ERβ), androgen (AR), progesterone (PR) and peroxisome proliferator activated (PPARγ) receptors were successfully modelled from X-ray crystal structures. A database of putative-EDC ligands (n= 378), were computationally ‘docked’ to the pseudo-molecular targets, as a virtual screen for nuclear receptor activity. Relative to in vitro assays, the in silico screen demonstrated a sensitivity of 94.5%. The SYBYL Surflex-Dock method surpassed the OECD Toolbox ER-Profiler, DfW and binary classification models, in correctly identifying endocrine active substances (EAS). Aiming to evaluate the current in vitro tools for endocrine MoA, standardised ERα transactivation (HeLa9903), stably transfected AR transactivation (HeLa4-11) assays in addition to novel transiently transfected reporter gene assays, predicted the mechanism and potency of test substances prioritised from the in silico results (n = 10 potential-EDCs and 10 hormone controls). In conclusion, in silico SYBYL molecular modelling and Surflex-Dock virtual screening sensitively predicted the binding of ERα/β, AR, PR and PPARγ potential EDCs, and was identified as a potentially useful regulatory tool, to support EAS hazard identification

Enhancing the bees algorithm using the traplining metaphor

This work aims to improve the performance of the Bees Algorithm (BA), particularly in terms of simplicity, accuracy, and convergence. Three improvements were made in this study as a result of bees’ traplining behaviour. The first improvement was the parameter reduction of the Bees Algorithm. This strategy recruits and assigns worker bees to exploit and explore all patches. Both searching processes are assigned using the Triangular Distribution Random Number Generator. The most promising patches have more workers and are subject to more exploitation than the less productive patches. This technique reduced the original parameters into two parameters. The results show that the Bi-BA is just as efficient as the basic BA, although it has fewer parameters. Following that, another improvement was proposed to increase the diversification performance of the Combinatorial Bees Algorithm (CBA). The technique employs a novel constructive heuristic that considers the distance and the turning angle of the bees’ flight. When foraging for honey, bees generally avoid making a sharp turn. By including this turning angle as the second consideration, it can control CBA’s initial solution diversity. Third, the CBA is strengthened to enable an intensification strategy that avoids falling into a local optima trap. The approach is based on the behaviour of bees when confronted with threats. They will keep away from re-visiting those flowers during the next bout for reasons like predators, rivals, or honey run out. The approach will remove temporarily threatened flowers from the whole tour, eliminating the sharp turn, and reintroduces them again to the habitual tour’s nearest edge. The technique could effectively achieve an equilibrium between exploration and exploitation mechanisms. The results show that the strategy is very competitive compared to other population-based nature-inspired algorithms. Finally, the enhanced Bees Algorithms are demonstrated on two real-world engineering problems, namely, Printed Circuit Board insertion sequencing and vehicles routing problem

The blessings of explainable AI in operations & maintenance of wind turbines

Wind turbines play an integral role in generating clean energy, but regularly suffer from operational inconsistencies and failures leading to unexpected downtimes and significant Operations & Maintenance (O&M) costs. Condition-Based Monitoring (CBM) has been utilised in the past to monitor operational inconsistencies in turbines by applying signal processing techniques to vibration data. The last decade has witnessed growing interest in leveraging Supervisory Control & Acquisition (SCADA) data from turbine sensors towards CBM. Machine Learning (ML) techniques have been utilised to predict incipient faults in turbines and forecast vital operational parameters with high accuracy by leveraging SCADA data and alarm logs. More recently, Deep Learning (DL) methods have outperformed conventional ML techniques, particularly for anomaly prediction. Despite demonstrating immense promise in transitioning to Artificial Intelligence (AI), such models are generally black-boxes that cannot provide rationales behind their predictions, hampering the ability of turbine operators to rely on automated decision making. We aim to help combat this challenge by providing a novel perspective on Explainable AI (XAI) for trustworthy decision support.This thesis revolves around three key strands of XAI – DL, Natural Language Generation (NLG) and Knowledge Graphs (KGs), which are investigated by utilising data from an operational turbine. We leverage DL and NLG to predict incipient faults and alarm events in the turbine in natural language as well as generate human-intelligible O&M strategies to assist engineers in fixing/averting the faults. We also propose specialised DL models which can predict causal relationships in SCADA features as well as quantify the importance of vital parameters leading to failures. The thesis finally culminates with an interactive Question- Answering (QA) system for automated reasoning that leverages multimodal domain-specific information from a KG, facilitating engineers to retrieve O&M strategies with natural language questions. By helping make turbines more reliable, we envisage wider adoption of wind energy sources towards tackling climate change

Strategies towards reducing carbon emission in university campuses: A comprehensive review of both global and local scales

Universities and other Higher Education Institutions (HEIs) have a key role to play in promoting decarbonisation and sustainable development. The implementation of low-carbon and energy-efficient strategies in colleges and University Campuses (UCs) is of utmost importance, as the number of these buildings continues to grow rapidly worldwide. This paper uses an organized search strategy for reviewing the most impactful previous studies regarding decarbonisations strategies in UCs in different climate contexts. This research presents a comprehensive overview of influential parameters, which are practical to be considered in designing new or retrofitting existing UCs which has not been done before and also highlights relevant policies and guidelines required to implement these parameters. These factors are spatial planning and landscape, renewable and clean energy, energy systems, thermal envelope, green transportation, management and control, human-related performance and smartness. This review also explores the recent trends in the decarbonisation of UCs such as the application of smart technologies and implementation of real-time data-based control and management technologies. Finally, this review presents the research gaps, future trends and technologies which will facilitate the decarbonisation of UCs. This review would help researchers and designers to facilitate the transition towards net-zero carbon future in university campuses

Gene editing for resistance to influenza A virus in swine

Influenza A Virus (IAV) presents a major threat to human health and animal welfare. As pigs are susceptible to infection from avian and mammalian origin IAVs, they can be an intermediate host for onwards transmission and act as a mixing vessel in which novel IAVs are generated. ANP32 family proteins have been identified in humans and chickens as host proteins critical to the efficiency of viral genome replication and host factors involved in IAVs adaptation. Host factors recruited by IAV present potential gene-editing targets for controlling IAV transmission and the editing of ANP32 genes in swine represents a potential method of IAV control. Using CRISPR/Cas technology, ANP32A and ANP32B were disrupted in a porcine tracheal cell line (NPTr) to determine whether they are recruited in the same manner as in humans and chickens by IAV polymerase to support viral genome replication. Our results show that human, avian and swine adapted IAVs can recruit ANP32 family proteins in NPTr, and that ANP32A and ANP32B are functionally redundant for IAV and must both be functionally knocked out to reduce the capacity for IAV to propagate. To consider industrial applicability, we have modelled the introgression of IAV resistance alleles into a commercial pig breeding herd by one-step zygote gene-editing. Our model results show that more efficient gene-editing methods will reach fixation quicker, even with greater rates of zygote death, and that the level of germline transmission for the gene-edited alleles will have the largest effect on the flow of alleles to commercial breeders. Together, these results have identified genes for further consideration regarding IAV resistance in swine and that gene-editing will need optimisation in porcine zygotes for implementation in the near-term