Methodology for identifying alternative solutions in a population based data generation approach applied to synthetic biology

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonDesign is an essential component of sustainable development. Computational modelling has become a useful technique that facilitates the design of complex systems. Variables that characterises a complex system are encoded into a computational model using mathematical concepts and through simulation each of these variables alone or in combination are modified to observe the changes in the outcome. This allows the researchers to make predictions on the behaviour of the real system that is being studied in response to the changes. The ultimate goal of any design process is to come up with the best design; as resources are limited, to minimize the cost and resource consumption, and to maximize the performance, profits and efficiency. To optimize means to find the best solution, the best compromise among several conflicting demands subject to predefined requirements. Therefore, computational optimization, modelling and simulation forms an integrated part of the modern design practice. This thesis defines a data analytics driven methodology which enables the identification of alternative solutions of computational design by analysing the generational history of the population based heuristic search used to generate the templates. While optimisation is focused on obtaining the optimal solution this methodology focuses on alternative solutions which are sub optimal by fitness or solutions with similar fitness but different structures. When the optimal design solution is less robust, alternative solutions can offer a sufficiently good accuracy and an achievable resource requirement. The main advantage of the methodology is that it exploits the exploration process of the solution space during a single run, by focusing also on suboptimal solutions, which usually get neglected in the search for an optimal one. The history of the heuristic search is analysed for the emergence of alternative solutions and evolving of a solution. By examining how an initial solution converts to an optimal solution core design patterns are identified, and these were used to improve the design process. Further, this method limits the number of runs of the heuristic search as more solution space is covered. The methodology is generic because it can be used to any instance where a population based heuristic search is applied to generate optimal designs. The applicability of the methodology is demonstrated using three case studies from mathematics (building of a mathematical function for a set target) and biology (obtaining alternative designs for genomic metabolic models [GEM] and DNA walker circuits). In each case a different heuristic search method was used: Gene expression programming (mathematical expressions), genetic algorithms (GEM models) and simulated annealing (DNA walker circuits). Descriptive analytics, visual analytics and clustering was mainly used to build the data analytics driven approach in identifying alternative solutions. This data analytics driven methodology is useful in optimising the computational design of complex systems

Underutilised Crops: Diversity and Distribution in Moneragala District, Sri Lanka

Human being mainly depends on a very limited number of major crops to meet their needs giving their less attention to minor crops. Those minor crop species are categorised as underutilised crop species (UUCs) in which potential to improve livelihoods, as well as food security and sovereignty is not being fully recognised and many of the plant species that are cultivated across the world are underutilised. Therefore, it was needed to identify the importance that village farmers have paid for these crops. This study was carried out to assess their diversity and identify the priority species in Moneragala district of Sri Lanka. Uva and Eastern provinces in Sri Lanka are popularised for backyard underutilised crop cultivation. Moneragala district which belongs to Uva province of Sri Lanka was purposively selected as the study area, since higher number of UUC farmers are recorded and could be observed in this region of the country. For the district to be sufficiently covered and for an exhaustive species inventory, 3 DS divisions were purposively selected with the help of officers from Department of Agriculture working in the area and through field observations. For the DS division to be covered enough, 3 GN divisions were selected in each DS division accordingly. Following that procedure, Thenegallanda, Kahambana and Nakkala GNs from Moneragala DS, Kahakurullanpelessa, Mahawewa and Bodagama GNs from Thanamalwila DS and Adawelayaya, Nugayaya and Buduruwagala GNs from Wellawaya were selected. 10 farmers from each GN division were interviewed using snowball sampling method by first meeting the president of the farmers’ association in each GN division. Accordingly, the sample size was 90. Through the interview some key information was recorded on each of the species identified. Local vernacular names of the crops each farmer consider as importance to them among the crops they are involved in were recorded. They were asked to cite the crops based on the extent of the crops, extent of the consumption, degree of consumption, perceived nutritional value, cultural importance, medicinal properties, market use, market value, and contribution to household income. Data were analysed using descriptive statistics. The study revealed that 38 underutilised crop species including cereals (18.42%), vegetables (15.79%), leafy vegetables (13.16%), legumes (7.89%), oil crops (5.26%), root and tuber crops (7.89%), fruit crops (18.42%) and herbal crops (13.17%). Among them 4 species were identified as of priority based on two main criteria among which included their growing severity within the whole district and degree of distribution. Accordingly, Moneragala district of Sri Lanka has a great diversity of underutilised crops species. Vigna unguiculata, Vigna radiata, Arachis hypogaea, Eleusine coracana and Citrus aurantifolia appeared as the most widely grown species within the whole district. Unfortunately, the distribution of most of the crops were not at a satisfactory level. Vigna unguiculata, Vigna radiata, Solanum melongena and A. hypogea were appeared as prioritised corps which are commonly grown and evenly distributed within the district. For the promotion of these underutilised crop species in Moneragala district, it will be important to put in place a national and special research and development programme under the joint umbrella of the ministries of agriculture and scientific research sponsored by the government involving all possible actors including researchers, developers and producers.Keywords: Underutilised crops, Diversity, Livelihoods, Food security, Consumptio

From Jargon to Clarity: Enhancing Science Communication with ChatGPT

ChatGPT, an advanced language model developed by OpenAI, represents a groundbreaking leap in the realm of science communication. This remarkable chatbot seamlessly generates clear and concise explanations, unraveling the intricacies of complex scientific concepts for many fraternities. It goes beyond mere elucidation, actively addressing public inquiries and dispelling common misconceptions and flaws, resulting in a more informed and scientifically literate society. The conversational prowess of ChatGPT empowers even the general public to initiate and sustain meaningful dialogues with individuals from diverse backgrounds. By leveraging ChatGPT's interactive capabilities, many stimulate thought-provoking conversations, fueling curiosity and fostering a deeper engagement with scientific topics. Moreover, ChatGPT's extensive training imbues it with a vast knowledge base, enabling it to provide highly informative responses to a wide range of questions in no time

A Comprehensive Study On Developing Neural Network Models For Predicting The Coagulant Dosage And Treated Water Qualities For A Water Treatment Plant

Determination of the optimum coagulant dosage for water treatment is traditionally carried out using the jar test, which is a time consuming procedure incapable of responding to sudden changes in water qualities. Therefore, data driven modeling techniques such as neural networks are used for developing predictive models for the coagulation process. In this work, three different neural network models, namely, the general regression neural network (GRNN), extreme learning machine single layer feed forward neural network (ELM-SLFN) and the extreme learning machine radial basis function neural network (ELM-RBF) were developed to predict the coagulant dosage, and their performances were compared with the commonly used multilayer perceptron neural network (MLP). It was shown that the ELM and the GRNN models consumed significantly lesser effort and time for training compared to the MLP. The ELM-RBF demonstrated the best tradeoff between prediction accuracy and computational requirement. Therefore, the ELM-RBF was used to develop models for predicting the coagulant dosage, treated water (TW) turbidity and residual aluminum with R values of 0.9752, 0.8239 and 0.9019 respectively. The input parameters required to develop each model was determined using a global exhaustive search algorithm as it was shown that the Pearson correlation coefficient and the principal component analysis were not suitable techniques for selecting input parameters for this study. Thus, inputs used for predicting the coagulant dosage were raw water (RW) turbidity, RW color and alum (t-1). The effectiveness of the coagulant dosage and the TW quality models were improved using an imputation model and a genetic algorithm. The imputation model was developed using K-means clustering with an imputation accuracy similar to a self-organizing map, to cope with failures in hardware sensors causing downtime in fully automated water treatment plants and ensure the continual use of the coagulant dosage model. The imputation model reconstructed missing values of RW turbidity and RW color with R values of 0.9075 and 0.8250 respectively. Subsequently, the reconstructed RW turbidity and RW color were used to predict the coagulant dosage with R values of 0.9742 and 0.9809 respectively, which are highly satisfactory. Meanwhile, the GA improved the R value of the TW turbidity model to 0.8294. The GA significantly improved the ability of the ELM-RBF to identify the required response of TW turbidity to the alum dosage

The behaviour of small clusters of bodies falling in a viscous fluid

Imperial Users onl

Results from Process Modeling of the Mixed-salt Technology for CO2 Capture from Post-combustion-related Applications

Mixed-salt technology, a solvent-based technology for removing CO2 from flue gas streams offers a significant advantage over conventional amine-based CO2 removal technologies (e.g., Fluor Econamine FG PlusSM technology). SRI International (SRI) is currently investigating the application of mixed-salt technology for pulverized coal combustion (PCC) power plant retrofit applications for removing >90% CO2 at a cost not to exceed $40/tonne of CO2 captured. The research was performed at a large bench-scale level with funding from the United States Department of Energy (DOE), National Energy Technology Laboratory (NETL). Very recently, a successful demonstration of mixed-salt technology at 0.25 tonne/day system was conducted in the USA, and the data obtained from the tests was used to develop a rate-based model to determine the mass and energy balance for a carbon dioxide recovery (CDR) removing 90% CO2 from a 550-MW supercritical power plant. In this paper, we present the process modeling data including the preliminary techno-economic evaluation (TEA) of mixed-salt technology. CO2 capture and CO2 pipeline purity specifications were met in all the process configurations investigated in this study. SRI's mixed-salt process can strip CO2 at high pressure as the stripper for rich-solvent regeneration is operated at higher pressure than the Fluor Econamine FG PlusSM process. Thus, the electrical power required for compressing CO2 to delivery pressures (> 130 atm) is greatly reduced in the mixed-salt process compared to other solvent-based technologies operating with lower-pressure regenerations. Ammonia-based technologies require absorber solvent cooling and treated gas washing to reduce ammonia emissions, and the raw water consumption of the process combines the water being used in the two water-wash sections. The Fluor Econamine FG PlusSM technology requires a large water recycle in the CDR unit for cooling purposes (1,173,350-1,286,900 lpm or 310,000-340,000 gpm), which greatly exceeds the PC plant cooling water requirement (643,450-757,000 lpm or 170,000-200,000 gpm). SRI's mixed-salt process requires a relatively smaller recycle for cooling purposes, and the overall cooling water recycled was 71% less in the mixed-salt process compared to the baseline case. As such, the auxiliary power required for mixed-salt process CDR unit was 60% less than the baseline case. The heat duty for the mixed-salt process was calculated to be 2.0 MJ/Kg of CO2 recovered (in the stripper reboiler). This accounts for a 44% decrease in the heat duty requirement in the mixed-salt process compared to the baseline case. Published by Elsevier Ltd