Direct sequential based firefly algorithm for the α-pinene isomerization problem

Publicado em: "Computational science and its applications – ICCSA 2016: 16th International Conference, Beijing, China, July 4-7, 2016, Proceedings, Part I"The problem herein addressed is a parameter estimation problem of the α-pinene process. The state variables of this bioengineering process satisfy a set of differential equations and depend on a set of unknown parameters. A dynamic system based parameter estimation problem aiming to estimate the model parameter values in a way that the predicted state variables best fit the experimentally observed state values is used. A numerical direct method, known as direct sequential procedure, is implemented giving rise to a finite bound constrained nonlinear optimization problem, which is solved by the metaheuristic firefly algorithm (FA). A Matlab programming environment is developed with the mathematical model and the computational application of the method. The results produced by FA, when compared to those of the fmincon function and other metaheuristics, are competitive.COMPETE: POCI-01- 0145-FEDER-007043FCT - Fundação para a Ciência e Tecnologia, within the projects UID/CEC/00319/2013 and UID/MAT/00013/201

Review of Anaerobic Digestion Modeling and Optimization Using Nature-Inspired Techniques

Although it is a well-researched topic, the complexity, time for process stabilization, and economic factors related to anaerobic digestion call for simulation of the process offline with the help of computer models. Nature-inspired techniques are a recently developed branch of artificial intelligence wherein knowledge is transferred from natural systems to engineered systems. For soft computing applications, nature-inspired techniques have several advantages, including scope for parallel computing, dynamic behavior, and self-organization. This paper presents a comprehensive review of such techniques and their application in anaerobic digestion modeling. We compiled and synthetized the literature on the applications of nature-inspired techniques applied to anaerobic digestion. These techniques provide a balance between diversity and speed of arrival at the optimal solution, which has stimulated their use in anaerobic digestion modeling

Machine Learning Research Trends in Africa: A 30 Years Overview with Bibliometric Analysis Review

In this paper, a critical bibliometric analysis study is conducted, coupled with an extensive literature survey on recent developments and associated applications in machine learning research with a perspective on Africa. The presented bibliometric analysis study consists of 2761 machine learning-related documents, of which 98% were articles with at least 482 citations published in 903 journals during the past 30 years. Furthermore, the collated documents were retrieved from the Science Citation Index EXPANDED, comprising research publications from 54 African countries between 1993 and 2021. The bibliometric study shows the visualization of the current landscape and future trends in machine learning research and its application to facilitate future collaborative research and knowledge exchange among authors from different research institutions scattered across the African continent

Improved differential search algorithms for metabolic network optimization

The capabilities of Escherichia coli and Zymomonas mobilis to efficiently converting substrate into valuable metabolites have caught the attention of many industries. However, the production rates of these metabolites are still below the maximum threshold. Over the years, the organism strain design was improvised through the development of metabolic network that eases the process of exploiting and manipulating organism to maximize its growth rate and to maximize metabolites production. Due to the complexity of metabolic networks and multiple objectives, it is difficult to identify near-optimal knockout reactions that can maximize both objectives. This research has developed two improved modelling-optimization methods. The first method introduces a Differential Search Algorithm and Flux Balance Analysis (DSAFBA) to identify knockout reactions that maximize the production rate of desired metabolites. The latter method develops a non-dominated searching DSAFBA (ndsDSAFBA) to investigate the trade-off relationship between production rate and its growth rate by identifying knockout reactions that maximize both objectives. These methods were assessed against three metabolic networks – E.coli core model, iAF1260 and iEM439 for production of succinic acid, acetic acid and ethanol. The results revealed that the improved methods are superior to the other state-of-the-art methods in terms of production rate, growth rate and computation time. The study has demonstrated that the two improved modelling-optimization methods could be used to identify near-optimal knockout reactions that maximize production of desired metabolites as well as the organism’s growth rate within a shorter computation time

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

Prediction of multi-inputs bubble column reactor using a novel hybrid model of computational fluid dynamics and machine learning

The combination of machine learning and numerical methods has recently become popular in the prediction of macroscopic and microscopic hydrodynamics parameters of bubble column reactors. Such numerical combination can develop a smart multiphase bubble column reactor with the ability of low-cost computational time when considering the big data. However, the accuracy of such models should be improved by optimizing the data parameters. This paper uses an adaptivenetwork-based fuzzy inference system (ANFIS) to train four big data inputs with a novel integration of computational fluid dynamics (CFD) model of gas. The results show that the increasing number of input variables improves the intelligence of the ANFIS method up to R = 0.99, and the number of rules during the learning process has a significant effect on the accuracy of this type of modeling. Furthermore, the proper selection of model’s parameters results in higher accuracy in the prediction of the flow characteristics in the column structure

Improvements on the bees algorithm for continuous optimisation problems

This work focuses on the improvements of the Bees Algorithm in order to enhance the algorithm’s performance especially in terms of convergence rate. For the first enhancement, a pseudo-gradient Bees Algorithm (PG-BA) compares the fitness as well as the position of previous and current bees so that the best bees in each patch are appropriately guided towards a better search direction after each consecutive cycle. This method eliminates the need to differentiate the objective function which is unlike the typical gradient search method. The improved algorithm is subjected to several numerical benchmark test functions as well as the training of neural network. The results from the experiments are then compared to the standard variant of the Bees Algorithm and other swarm intelligence procedures. The data analysis generally confirmed that the PG-BA is effective at speeding up the convergence time to optimum. Next, an approach to avoid the formation of overlapping patches is proposed. The Patch Overlap Avoidance Bees Algorithm (POA-BA) is designed to avoid redundancy in search area especially if the site is deemed unprofitable. This method is quite similar to Tabu Search (TS) with the POA-BA forbids the exact exploitation of previously visited solutions along with their corresponding neighbourhood. Patches are not allowed to intersect not just in the next generation but also in the current cycle. This reduces the number of patches materialise in the same peak (maximisation) or valley (minimisation) which ensures a thorough search of the problem landscape as bees are distributed around the scaled down area. The same benchmark problems as PG-BA were applied against this modified strategy to a reasonable success. Finally, the Bees Algorithm is revised to have the capability of locating all of the global optimum as well as the substantial local peaks in a single run. These multi-solutions of comparable fitness offers some alternatives for the decision makers to choose from. The patches are formed only if the bees are the fittest from different peaks by using a hill-valley mechanism in this so called Extended Bees Algorithm (EBA). This permits the maintenance of diversified solutions throughout the search process in addition to minimising the chances of getting trap. This version is proven beneficial when tested with numerous multimodal optimisation problems

An investigation into the biosynthesis of proximicins

PhD ThesisThe proximicins are a family of three compounds – A-C – produced by two marine Actinomycete Verrucosispora strains – V. maris AB18-032 and V. sp. str. 37 - and are characterised by the presence of 2,4-disubstituted furan rings. Proximicins demonstrate cell-arresting and antimicrobial ability, making them interesting leads for clinical drug development. Proximicin research has been largely overshadowed by other Verrucosispora strain secondary metabolites (SM), and despite the publication of the V. maris AB18-032 draft, the enzymatic machinery responsible for their production has not been established. It has been noted in related research into a pyrrole-containing homolog – congocidine –due to the structural similarity exhibited, proximicins likely utilise a similar biosynthetic route. The initial aim of this research was to confirm the presumed pathway to proximicin biosynthesis. Following the sequencing, assembly and annotation of the second proximicin producer, Verrucosispora sp. str. MG37, and genome mining of V. maris AB18-032, no common clusters mimicked that of congocidine, casting doubt on the previously assumed analogous biosynthetic routes. A putative proximicin biosynthesis (ppb) cluster was identified, containing non-ribosomal peptide synthetase (NRPS) enzymes, exhibiting some homology with congocidine. NRPSsystems represent a network of interacting proteins, which act as a SM assembly line: crucially, adenylation (A)- domain enzymes act as the ‘gate-keeper’, determining which precursors are included into the elongating peptide. To elucidate the route to proximicins, activity characterisation of the four A-domains present in ppb cluster was attempted. The A-domain Ppb120 was shown to possess novel activity, demonstrating a high promiscuity towards heterocycle containing precursors, in addition to the absence of an apparent essential domain. This discovery refutes previous work outlining the core residues which dictate A-domain activity, while also presenting a facile route to novel heterocycle-containing compounds. Despite extensive work, A-domains ppb195 and ppb210, were ineffectively purified in the active form – informing future work into A-domains activity characterisation. Finally, the ppb220 A-domain which lies at the border of ppb, was inactive suggesting over-estimation of the cluster margins. To confirm ppb220 redundancy and confirm ppb boundaries, CRISPR/Cas gene editing studies were done. The gene responsible for the orange pigment of Verrucosispora strains was initially targeted and successfully deleted, and ppb studies commenced. The research here refutes the previously presumed route to proximicin biosynthesis; the ppb cluster instead comprises enzymes exhibiting unique activity and structure. The findings represent the foundations for allowing exploitation of chemistry exhibited within the proximicin family. The novelty exhibited can be utilised in the search for antimicrobial clinical leads, by allowing the production of compounds containing previously inaccessible heterocycle chemistry