Search results for: multi-objective evolutionary

Authors: Emilia Hunt, Sally C. Reynolds, Fiona Coward, Fabio Parracho Silva, Philip Hopley

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Ecological niche modeling is widely used in conservation studies, but application to the extinct hominin species is a relatively new approach. Being able to understand what ecological niches were occupied by respective hominin species provides a new perspective into influences on evolutionary processes. Niche separation or overlap can tell us more about specific requirements of the species within the given timeframe. Many of the ancestral species lived through enormous climate changes: glacial and interglacial periods, changes in rainfall, leading to desertification or flooding of regions and displayed impressive levels of adaptation necessary for their survival. This paper reviews niche modeling methodologies and their application to hominin studies. Traditional conservation methods might not be directly applicable to extinct species and are not comparable to hominins. Hominin niche also includes aspects of technologies, use of fire and extended communication, which are not traditionally used in building conservation models. Future perspectives on how to improve niche modeling for extinct hominin species will be discussed.

Keywords: hominin niche, climate change, evolution, adaptation, ecological niche modelling

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Authors: Mingzhu Lyu, Runlei Ren, Xinyu Dai, Jiaxuan Pi, Kanghua Li

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The study of video game policy in China has been divided into two branches: "pedagogy" and "game industry". The binary perspective of policy reveals the "contradictory" side of policy performance. Based on this suspicion, this paper constructs a three-dimensional sequence of time, content and institutions of game policy, and establishes the "contradictory" aspects of policy performance between 1949 and 2019. A central-level database of game policies, clarifying that our game policies follow a shift from reactive response to proactive guidance, stigmatization and de-stigmatization, the evolutionary logic. The study found that the central government has always maintained a strict requirement and prudent guidance for game policy, and the deep contradictions in game policy stem from the essential conflict between the natural amusement of games and the seriousness of the educational system, and the Chinese government's use of the understanding of the public and private domains and the Managing of the conflict.

Keywords: game industry, gaming policy, public domain, private domain

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Authors: Su Mon Saw, Joe Rothnagel

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Short open reading frames (sORFs) located in 5’UTR of mRNAs are known as uORFs. Characterization of uORF-encoded peptides (uPEPs) i.e., a subset of short open reading frame encoded peptides (sPEPs) and their translation regulation lead to understanding of causes of genetic disease, proteome complexity and development of treatments. Existence of uORFs within cellular proteome could be detected by LC-MS/MS. The ability of uORF to be translated into uPEP and achievement of uPEP identification will allow uPEP’s characterization, structures, functions, subcellular localization, evolutionary maintenance (conservation in human and other species) and abundance in cells. It is hypothesized that a subset of sORFs are translatable and that their encoded sPEPs are functional and are endogenously expressed contributing to the eukaryotic cellular proteome complexity. This project aimed to investigate whether sORFs encode functional peptides. Liquid chromatography-mass spectrometry (LC-MS) and bioinformatics were thus employed. Due to probable low abundance of sPEPs and small in sizes, the need for efficient peptide enrichment strategies for enriching small proteins and depleting the sub-proteome of large and abundant proteins is crucial for identifying sPEPs. Low molecular weight proteins were extracted using SDS-PAGE from Human Embryonic Kidney (HEK293) cells and Strong Cation Exchange Chromatography (SCX) from secreted HEK293 cells. Extracted proteins were digested by trypsin to peptides, which were detected by LC-MS/MS. The MS/MS data obtained was searched against Swiss-Prot using MASCOT version 2.4 to filter out known proteins, and all unmatched spectra were re-searched against human RefSeq database. ProteinPilot v5.0.1 was used to identify sPEPs by searching against human RefSeq, Vanderperre and Human Alternative Open Reading Frame (HaltORF) databases. Potential sPEPs were analyzed by bioinformatics. Since SDS PAGE electrophoresis could not separate proteins <20kDa, this could not identify sPEPs. All MASCOT-identified peptide fragments were parts of main open reading frame (mORF) by ORF Finder search and blastp search. No sPEP was detected and existence of sPEPs could not be identified in this study. 13 translated sORFs in HEK293 cells by mass spectrometry in previous studies were characterized by bioinformatics. Identified sPEPs from previous studies were <100 amino acids and <15 kDa. Bioinformatics results showed that sORFs are translated to sPEPs and contribute to proteome complexity. uPEP translated from uORF of SLC35A4 was strongly conserved in human and mouse while uPEP translated from uORF of MKKS was strongly conserved in human and Rhesus monkey. Cross-species conserved uORFs in association with protein translation strongly suggest evolutionary maintenance of coding sequence and indicate probable functional expression of peptides encoded within these uORFs. Translation of sORFs was confirmed by mass spectrometry and sPEPs were characterized with bioinformatics.

Keywords: bioinformatics, HEK293 cells, liquid chromatography-mass spectrometry, ProteinPilot, Strong Cation Exchange Chromatography, SDS-PAGE, sPEPs

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Authors: Samia Sadouki Chibani, Abdelkamel Tari

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Web service composition combines available services to provide new functionality. Given the number of available services with similar functionalities and different non functional aspects (QoS), the problem of finding a QoS-optimal web service composition is considered as an optimization problem belonging to NP-hard class. Thus, an optimal solution cannot be found by exact algorithms within a reasonable time. In this paper, a meta-heuristic bio-inspired is presented to address the QoS aware web service composition; it is based on Elephant Herding Optimization (EHO) algorithm, which is inspired by the herding behavior of elephant group. EHO is characterized by a process of dividing and combining the population to sub populations (clan); this process allows the exchange of information between local searches to move toward a global optimum. However, with Applying others evolutionary algorithms the problem of early stagnancy in a local optimum cannot be avoided. Compared with PSO, the results of experimental evaluation show that our proposition significantly outperforms the existing algorithm with better performance of the fitness value and a fast convergence.

Keywords: bio-inspired algorithms, elephant herding optimization, QoS optimization, web service composition

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Sadik Yigit

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The energy consumed in the buildings constitutes a large portion of the total energy consumption in the world. In this study, it was aimed to measure the impact of climate change on the energy consumption of residential building stock by analyzing a typical mid-rise residential building in four different climate regions of Turkey. An integrated system was developed using the "Distribution Evolutionary Algorithms in Python" tool and Energy Plus. By using the developed integrated system, the energy performance of the typical residential building was analyzed under the effect of different climate change scenarios. The results indicated that predicted overheating will be experienced in the future, which will significantly increase the cooling energy loads of the buildings. In addition, design solutions to improve the future energy performance of the buildings were proposed, considering budget constraints. The results of the study will guide researchers studying in this area of research and designers in the sector in finding climate change resilient design solutions.

Keywords: energy_efficient, residential buildings, climate change, energyplus

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Authors: Jean C. A. Sousa, Julio Cesar Maciel Santos, Andressa J. Rubio, Edneia A. S. Paccola, Natália U. Yamaguchi

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A bibliometric analysis in the Web of Science database was used to identify overall scientific results of graphene inks to date (2008 to 2018). The objective of this study was to evaluate the evolutionary tendency of graphene inks research and to identify its aspects, aiming to provide data that can guide future work. The contributions of different researches, languages, thematic categories, periodicals, place of publication, institutes, funding agencies, articles cited and applications were analyzed. The results revealed a growing number of annual publications, of 258 papers found, 107 were included because they met the inclusion criteria. Three main applications were identified: synthesis and characterization, electronics and surfaces. The most relevant research on graphene inks has been summarized in this article, and graphene inks for electronic devices presented the most incident theme according to the research trends during the studied period. It is estimated that this theme will remain in evidence and will contribute to the direction of future research in this area.

Keywords: bibliometric, coating, nanomaterials, scientometrics

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Authors: Abdelazeem Algammal, Reham Abouelmaatti, Xiaokun Li, Jisheng Ma, Eman Abdelnaby, Wael Elfeil

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Toll-like receptors (TLRs) are the best understood of the innate immune receptors that detect infections in vertebrates. However, the fish TLRs also exhibit very distinct features and a large diversity, which is likely derived from their diverse evolutionary history and the distinct environments that they occupy. Little is known about the fish immune system structure. Our work was aimed to identify and clone the Nile tilapiaTLR-3 as a model of freshwater fish species; we cloned the full-length cDNA sequence of Nile tilapia (Oreochromis niloticus) TLR-3 and according to our knowledge, it is the first report illustrating tilapia TLR-3. The complete cDNA sequence of Nile tilapia TLR-3 was 2736 pair base and it encodes a polypeptide of 912 amino acids. Analysis of the deduced amino acid sequence indicated that Nile tilapia TLR-3 has typical structural features and main components of proteins belonging to the TLR family. Our results illustrate a complete and functional Nile tilapia TLR-3 and it is considered an ortholog of the other vertebrate’s receptor.

Keywords: Nile tilapia, TLR-3, cloning, gene expression

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Authors: E. Sener, O. Isik, E. Eroglu, U. Sahin

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The main idea is to solve the system of Maxwell’s equations in accordance with the causality principle to get the energy quantities via Airy functions in a hollow rectangular waveguide. We used the evolutionary approach to electromagnetics that is an analytical time-domain method. The boundary-value problem for the system of Maxwell’s equations is reformulated in transverse and longitudinal coordinates. A self-adjoint operator is obtained and the complete set of Eigen vectors of the operator initiates an orthonormal basis of the solution space. Hence, the sought electromagnetic field can be presented in terms of this basis. Within the presentation, the scalar coefficients are governed by Klein-Gordon equation. Ultimately, in this study, time-domain waveguide problem is solved analytically in accordance with the causality principle. Moreover, the graphical results are visualized for the case when the energy and surplus of the energy for the time-domain waveguide modes are represented via airy functions.

Keywords: airy functions, Klein-Gordon Equation, Maxwell’s equations, Surplus of energy, wave boundary operators

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Authors: Abiodun Ladanu Ajala, Wuyi Oke

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Mathematical simulation and optimization models packaged within interactive computer programs provide a common way for planners and managers to predict the behaviour of any proposed water resources system design or management policy before it is implemented. Modeling presents a principal technique of predicting the behaviour of the proposed infrastructural designs or management policies. Models can be developed and used to help identify specific alternative plans that best meet those objectives. This study discusses various types of models, their development, architecture, data requirements, and applications in the field of engineering. It also outlines the advantages and limitations of each the optimization and simulation models presented. The techniques explored in this review include; dynamic programming, linear programming, fuzzy optimization, evolutionary algorithms and finally artificial intelligence techniques. Previous studies carried out using some of the techniques mentioned above were reviewed, and most of the results from different researches showed that indeed optimization and simulation provides viable alternatives and predictions which form a basis for decision making in building engineering structures and also in engineering planning and management.

Keywords: linear programming, mutation, optimization, simulation

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Authors: Gauthier Lefevre, Holger Kohlmann, Sebastien Saitzek, Rachel Desfeux, Adlane Sayede

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Hydrogen is a promising energy carrier, compatible with the sustainable energy concept. In this context, solid-state hydrogen-storage is the key challenge in developing hydrogen economy. The capability of absorption of large quantities of hydrogen makes intermetallic systems of particular interest. In this study, efforts have been devoted to the theoretical investigation of binary systems with constraints consideration. On the one hand, besides considering hydrogen-storage, a reinvestigation of crystal structures of the palladium-arsenic system shows, with experimental validations, that binary systems could still currently present new or unknown relevant structures. On the other hand, various binary Mg-based systems were theoretically scrutinized in order to find new interesting alloys for hydrogen storage. Taking the effect of pressure into account reveals a wide range of alternative structures, changing radically the stable compounds of studied binary systems. Similar constraints, induced by Pulsed Laser Deposition, have been applied to binary systems, and results are presented.

Keywords: binary systems, evolutionary algorithm, first principles study, pulsed laser deposition

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Authors: Mandeep Singh Azad.Dibyendu Chakraborty, Vikas Vohra

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Introduction: Poonch is one of the remotest districts of the Jammu and Kashmir (UT) and situated on international borders. This native poultry population in these areas is quite hardy and thrives well in adverse climatic conditions. Till date, no local breed from this area (Jammu Province) has been characterized thus present study was undertaken with the main objectives of molecular characterization of ChB6 gene in local native chicken of Poonch region located at international borders between India and Pakistan. The chicken B-cell marker (ChB6) gene has been proposed as a candidate gene in regulating B-cell development. Material and Method: RNA was isolated by Blood RNA Purification Kit (HiPura) and Trizol method from whole blood samples. Positive PCR products with size 1110 bp were selected for further purification, sequencing and analysis. The amplified PCR product was sequenced by Sangers dideoxy chain termination method. The obtained sequence of ChB6 gene of Poonchi chicken were compared by MEGAX software. BioEdit software was used to construct phylogenic tree, and Neighbor Joining method was used to infer evolutionary history. In order to compute evolutionary distance Maximum Composite Likelihood method was used. Results: The positively amplified samples of ChB6 genes were then subjected to Sanger sequencing with “Primer Walking. The sequences were then analyzed using MEGA X and BioEdit software. The sequence results were compared with other reported sequence from different breed of chicken and with other species obtained from the NCBI (National Center for Biotechnology Information). ClustalW method using MEGA X software was used for multiple sequence alignment. The sequence results of ChB6 gene of Poonchi chicken was compared with Centrocercus urophasianus, G. gallus mRNA for B6.1 protein, G. gallus mRNA for B6.2, G. gallus mRNA for B6.3, Gallus gallus B6.1, Halichoeres bivittatus, Miniopterus fuliginosus Ferringtonia patagonica, Tympanuchus phasianellus. The genetic distances were 0.2720, 0.0000, 0.0245, 0.0212, 0.0147, 1.6461, 2.2394, 2.0070 and 0.2363 for ChB6 gene of Poonchi chicken sequence with other sequences in the present study respectively. Sequencing results showed variations between different species. It was observed that AT content were higher then GC content for ChB6 gene. The lower AT content suggests less thermostable. It was observed that there was no sequence difference within the Poonchi population for ChB6 gene. The high homology within chicken population indicates the conservation of ChB6 gene. The maximum difference was observed with Miniopterus fuliginosus (Eastern bent-wing bat) followed by Ferringtonia patagonica and Halichoeres bivittatus. Conclusion: Genetic variation is the essential component for genetic improvement. The results of immune related gene Chb6 shows between population genetic variability. Therefore, further association studies of this gene with some prevalent diseases in large population would be helpful to identify disease resistant/ susceptible genotypes in the indigenous chicken population.

Keywords: ChB6, sequencing, ClustalW, genetic distance, poonchi chicken, SNP

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Authors: Seyedmirsajad Mokhtarimousavi, Danial Talebi, Hamidreza Asgari

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Gate Assignment Problem (GAP) is one of the most substantial issues in airport operation. In principle, GAP intends to maintain the maximum capacity of the airport through the best possible allocation of the resources (gates) in order to reach the optimum outcome. The problem involves a wide range of dependent and independent resources and their limitations, which add to the complexity of GAP from both theoretical and practical perspective. In this study, GAP was mathematically formulated as a three-objective problem. The preliminary goal of multi-objective formulation was to address a higher number of objectives that can be simultaneously optimized and therefore increase the practical efficiency of the final solution. The problem is solved by applying the second version of Non-dominated Sorting Genetic Algorithm (NSGA-II). Results showed that the proposed mathematical model could address most of major criteria in the decision-making process in airport management in terms of minimizing both airport/airline cost and passenger walking distance time. Moreover, the proposed approach could properly find acceptable possible answers.

Keywords: airport management, gate assignment problem, mathematical modeling, genetic algorithm, NSGA-II

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Authors: Yeong-Gyeong Choi, Kyoung-Seok Kim

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This study, literature review research, intends to deal with the problem of conceptual ambiguity among research on emotional labor, and to look into the evolutionary trends and changing aspects of defining the concept of emotional labor. In addition, in existing studies, deep acting and surface acting are highly related to a positive outcome variable and a negative outcome variable, respectively. It was confirmed that for employees performing emotional labor, deep acting and surface acting are highly related to OCB and CWB, respectively. While positive emotion that employees come to experience during job performance process can easily trigger a positive non-task behavior such as OCB, negative emotion that employees experience through excessive workload or unfair treatment can easily induce a negative behavior like CWB. The two management behaviors of emotional labor, surface acting and deep acting, can have either a positive or negative effect on non-task behavior of employees, depending on which one they would choose. Thus, the purpose of this review paper is to clarify the relationship between emotional labor and non-task behavior more specifically.

Keywords: emotion labor, non-task behavior, OCB, CWB

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Authors: Sanja Peter

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Transformation of urban environments can be compared to evolutionary processes. Systematic digital mapping of historical data can enable capturing some of these processes and their outcomes. For example, it may help reveal the structure of a city’s historical DNA. Gathering historical data for automatic processing may be giving a basis for cultural algorithms. Gothenburg City museum is trying to make city’s heritage information accessible through GIS-platforms and is now partnering with academic institutions to find appropriate methods to make accessible the knowledge on the city’s historical fabric. Hopefully, this will be carried out through a project called Digital Twin Cities. One part of this large project, concerning matters of Cultural Heritage, will be in collaboration with Chalmers University of Technology. The aim is to create a layered map showing historical developments of the city and extracting quantitative data about its built heritage, above and below the earth. It will allow interpreting the information from historic maps through, for example, names of the streets/places, geography, structural changes in urban fabric and information gathered by archaeologists’ excavations. Through the study of these geographical, historical and local metamorphoses, urban environment will reveal its metaphorical DNA or its MEM (Dawkins).

Keywords: Gothenburg, mapping, cultural heritage, city history

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Authors: Avantika Vats, Kushal Thakur

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This paper displays the point at issue, improvement, and utilization of a Monarch Butterfly Optimization (MBO) rather than a Genetic Algorithm (GA) in cognitive radio for the channel portion. This approach offers a satisfactory approach to get the accessible range of both the users, i.e., primary users (PUs) and secondary users (SUs). The proposed enhancement procedure depends on a nature-inspired metaheuristic algorithm. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. Southern Canada and the northern USA (land 1), and Mexico (Land 2). The positions of the monarch butterflies are modernizing in two ways. At first, the offsprings are generated (position updating) by the migration operator and can be adjusted by the migration ratio. It is trailed by tuning the positions for different butterflies by the methods for the butterfly adjusting operator. To keep the population unaltered and minimize fitness evaluations, the aggregate of the recently produced butterflies in these two ways stays equivalent to the first population. The outcomes obviously display the capacity of the MBO technique towards finding the upgraded work values on issues regarding the genetic algorithm.

Keywords: cognitive radio, channel allocation, monarch butterfly optimization, evolutionary, computation

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Authors: Anita Kushwaha

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We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

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Authors: Taisir Eldos, Aws Kanan, Waleed Nazih, Ahmad Khatatbih

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Chemical Reaction Optimization (CRO) is an optimization metaheuristic inspired by the nature of chemical reactions as a natural process of transforming the substances from unstable to stable states. Starting with some unstable molecules with excessive energy, a sequence of interactions takes the set to a state of minimum energy. Researchers reported successful application of the algorithm in solving some engineering problems, like the quadratic assignment problem, with superior performance when compared with other optimization algorithms. We adapted this optimization algorithm to the Printed Circuit Board Drilling Problem (PCBDP) towards reducing the drilling time and hence improving the PCB manufacturing throughput. Although the PCBDP can be viewed as instance of the popular Traveling Salesman Problem (TSP), it has some characteristics that would require special attention to the transactions that explore the solution landscape. Experimental test results using the standard CROToolBox are not promising for practically sized problems, while it could find optimal solutions for artificial problems and small benchmarks as a proof of concept.

Keywords: evolutionary algorithms, chemical reaction optimization, traveling salesman, board drilling

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Authors: Okay Gunes

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Unsatisfactory experiences due to an information shortage regarding the future pay-offs of actual choices, yield satisficing decision-making. This research will examine, for the first time in the literature, the motivation behind suboptimal decisions due to uncertainty by subjecting Adam Smith’s and Jeremy Bentham’s assumptions about the nature of the actions that lead to satisficing behavior, in order to clarify the theoretical background of a “consumption-based satisfactory time” concept. The contribution of this paper with respect to the existing literature is threefold: Firstly, it is showed in this paper that Adam Smith’s uncertainty is related to the problem of the constancy of ideas and not related directly to beliefs. Secondly, possessions, as in Jeremy Bentham’s oeuvre, are assumed to be just as pleasing, as protecting and improving the actual or expected quality of life, so long as they reduce any displeasure due to the undesired outcomes of uncertainty. Finally, each consumption decision incurs its own satisfactory time period, owed to not feeling hungry, being healthy, not having transportation…etc. This reveals that the level of satisfaction is indeed a behavioral phenomenon where its value would depend on the simultaneous satisfaction derived from all activities.

Keywords: decision-making, idea and belief, satisficing, uncertainty

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Authors: Akshay Paranjape, Nils Plettenberg, Robert Schmitt

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Producing faulty products can be costly for manufacturing companies and wastes resources. To reduce scrap rates in manufacturing, process parameters can be optimized using machine learning. Thus far, research mainly focused on optimizing specific processes using traditional algorithms. To develop a framework that enables real-time optimization based on a predictive model for an arbitrary production process, this study explores the application of reinforcement learning (RL) in this field. Based on a thorough review of literature about RL and process parameter optimization, a model based on maximum a posteriori policy optimization that can handle both numerical and categorical parameters is proposed. A case study compares the model to state–of–the–art traditional algorithms and shows that RL can find optima of similar quality while requiring significantly less time. These results are confirmed in a large-scale validation study on data sets from both production and other fields. Finally, multiple ways to improve the model are discussed.

Keywords: reinforcement learning, production process optimization, evolutionary algorithms, policy optimization, actor critic approach

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Authors: L. Salomon, P. Sklenar

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The Andes hold the highest plant species diversity in the world. How this occurred is one of the most intriguing questions in studies addressing the origin and patterning of plant diversity worldwide. Recently, the explosive adaptive radiations found in high Andean groups have been pointed as triggers to this spectacular diversity. The Andes is the species-richest area for the biggest genus from the Asteraceae family: Senecio. There, the genus presents an incredible diversity of species, striking growth form variation, and large niche span. Even when some studies tried to disentangle the evolutionary story for some Andean species in Senecio, they obtained partially resolved and low supported phylogenies, as expected for recently radiated groups. The high-throughput sequencing (HTS) approaches have proved to be a powerful tool answering phylogenetic questions in those groups whose evolutionary stories are recent and traditional techniques like Sanger sequencing are not informative enough. Although these tools have been used to understand the evolution of an increasing number of Andean groups, nowadays, their scope has not been applied for Senecio. This project aims to contribute to a better knowledge of the mechanisms shaping the hyper diversity of Senecio in the Andean region, using HTS focusing on Senecio ser. Culcitium (Asteraceae), recently recircumscribed. Firstly, reconstructing a highly resolved and supported phylogeny, and after assessing the role of allopatric differentiation, hybridization, and genome duplication in the diversification of the group. Using the Hyb-Seq approach, combining target enrichment using Asteraceae COS loci baits and genome skimming, more than 100 new accessions were generated. HybPhyloMaker and HybPiper pipelines were used for the phylogenetic analyses, and another pipeline in development (Paralogue Wizard) was used to deal with paralogues. RAxML was used to generate gene trees and Astral for species tree reconstruction. Phyparts were used to explore as first step of gene tree discordance along the clades. Fully resolved with moderated supported trees were obtained, showing Senecio ser. Culcitium as monophyletic. Within the group, some species formed well-supported clades with morphologically related species, while some species would not have exclusive ancestry, in concordance with previous studies using amplified fragment length polymorphism (AFLP) showing geographical differentiation. Discordance between gene trees was detected. Paralogues were detected for many loci, indicating possible genome duplications; ploidy level estimation using flow cytometry will be carried out during the next months in order to identify the role of this process in the diversification of the group. Likewise, TreeSetViz package for Mesquite, hierarchical likelihood ratio congruence test using Concaterpillar, and Procrustean Approach to Cophylogeny (PACo), will be used to evaluate the congruence among different inheritance patterns. In order to evaluate the influence of hybridization and Incomplete Lineage Sorting (ILS) in each resultant clade from the phylogeny, Joly et al.'s 2009 method in a coalescent scenario and Paterson’s D-statistic will be performed. Even when the main discordance sources between gene trees were not explored in detail yet, the data show that at least to some degree, processes such as genome duplication, hybridization, and/or ILS could be involved in the evolution of the group.

Keywords: adaptive radiations, Andes, genome duplication, hybridization, Senecio

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Authors: Feng-Sheng Wang, Chao-Ting Cheng

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Developing a drug from conception to launch is costly and time-consuming. Computer-aided methods can reduce research costs and accelerate the development process during the early drug discovery and development stages. This study developed a fuzzy multi-objective hierarchical optimization framework for identifying potential anticancer targets in a metabolic model. First, RNA-seq expression data of colorectal cancer samples and their healthy counterparts were used to reconstruct tissue-specific genome-scale metabolic models. The aim of the optimization framework was to identify anticancer targets that lead to cancer cell death and evaluate metabolic flux perturbations in normal cells that have been caused by cancer treatment. Four objectives were established in the optimization framework to evaluate the mortality of cancer cells for treatment and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. The applied nested hybrid differential evolution was applied to solve the trilevel MDM problem using two nutrient media to identify anticancer targets in the genome-scale metabolic model of colorectal cancer, respectively. Using Dulbecco’s Modified Eagle Medium (DMEM), the computational results reveal that the identified anticancer targets were mostly involved in cholesterol biosynthesis, pyrimidine and purine metabolisms, glycerophospholipid biosynthetic pathway and sphingolipid pathway. However, using Ham’s medium, the genes involved in cholesterol biosynthesis were unidentifiable. A comparison of the uptake reactions for the DMEM and Ham’s medium revealed that no cholesterol uptake reaction was included in DMEM. Two additional media, i.e., a cholesterol uptake reaction was included in DMEM and excluded in HAM, were respectively used to investigate the relationship of tumor cell growth with nutrient components and anticancer target genes. The genes involved in the cholesterol biosynthesis were also revealed to be determinable if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in cholesterol biosynthesis became unidentifiable if such a reaction was induced.

Keywords: Cancer metabolism, genome-scale metabolic model, constraint-based model, multilevel optimization, fuzzy optimization, hybrid differential evolution

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Authors: Theodorus Holtzhausen

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With a growing number of professionals in healthcare moving freely between countries and also in general a more mobile global workforce, awareness of cultural differences have become more urgent for health care workers to apply proper care. There is a slowly emerging trend in health care due to globalisation that may create a more uniform cultural base for administering healthcare, but it is still very vulnerable to being hijacked and misdirected by major commercial interests. Veterinary clinics and medical clinics promoting alternative remedies lacking evidence based support and simultaneously practicing medicine as a science have become more common. Such ‘holistic’ clinics see these remedies more as a belief system causing no harm with minimal impact but with added financial benefit to the facility. With the inarguable acceptance and realisation of the interconnection between evolutionary aspects of cognition, knowledge and culture as a global but vulnerable cognition-gaining process affecting us all, we can see the enormous responsibility we carry. Such a responsibility for creating global well-being calling for an universally applicable ethic. Such an ethic with the potential of having significant impact on our cognition gaining process.

Keywords: veterinary health care, ethics, wellbeing, veterinary clinics

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Authors: Mehdi Rostamzadeh

Abstract:

Genetic Algorithms (GAs) are an adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic. In this study, we apply GAs for fundamental and technical models of exchange rate determination in exchange rate market. In this framework, we estimated absolute and relative purchasing power parity, Mundell-Fleming, sticky and flexible prices (monetary models), equilibrium exchange rate and portfolio balance model as fundamental models and Auto Regressive (AR), Moving Average (MA), Auto-Regressive with Moving Average (ARMA) and Mean Reversion (MR) as technical models for Iranian Rial against European Union’s Euro using monthly data from January 1992 to December 2014. Then, we put these models into the genetic algorithm system for measuring their optimal weight for each model. These optimal weights have been measured according to four criteria i.e. R-Squared (R2), mean square error (MSE), mean absolute percentage error (MAPE) and root mean square error (RMSE).Based on obtained Results, it seems that for explaining of Iranian Rial against EU Euro exchange rate behavior, fundamental models are better than technical models.

Keywords: exchange rate, genetic algorithm, fundamental models, technical models

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Authors: Jun Zhou, Xingping Wang, Paul Milbourne

Abstract:

Evidences in the world shows that the industry and population have being greatly concentrated in metropolitan regions which is getting to be the most important area for the economic power and people living standard in the future. In the meanwhile, the relevant innovation theories of Agglomeration, New Industrial Geography and Modern Evolutionary innovation prove that the reason why the agglomeration in world-class city and metropolitan areas and also verify innovation is the key point for the development of metropolis. The primary purpose of this paper is to analyze the geographical spatial characteristics of innovative subjects which contain firm, university, research institution, government and intermediary organ in metropolis throughout the amount data analysis in Yangtze River Metropolis in China. The results show three main conclusions. The first is different subjects in different regions have different spatial characteristics. The second one is different structure and pattern between the subjects also can produce different innovative effect. The last but not the least is agglomeration of innovative subjects’ is not only influenced by the innovative network or local policies but also affected by the localized industry characteristics and culture which are getting to be the most important crucial factors.

Keywords: metropolitan development, innovative subject, spatial, Yangtze River Metropolis, China

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Authors: Christian H. Sanabria-Montaña, Rodrigo Huerta-Quintanilla

Abstract:

A lattice network is a special type of network in which all nodes have the same number of links, and its boundary conditions are periodic. The most basic lattice network is the ring, a one-dimensional network with periodic border conditions. In contrast, the Cartesian product of d rings forms a d-dimensional lattice network. An analytical expression currently exists for the clustering coefficient in this type of network, but the theoretical value is valid only up to certain connectivity value; in other words, the analytical expression is incomplete. Here we obtain analytically the clustering coefficient expression in d-dimensional lattice networks for any link density. Our analytical results show that the clustering coefficient for a lattice network with density of links that tend to 1, leads to the value of the clustering coefficient of a fully connected network. We developed a model on criminology in which the generalized clustering coefficient expression is applied. The model states that delinquents learn the know-how of crime business by sharing knowledge, directly or indirectly, with their friends of the gang. This generalization shed light on the network properties, which is important to develop new models in different fields where network structure plays an important role in the system dynamic, such as criminology, evolutionary game theory, econophysics, among others.

Keywords: clustering coefficient, criminology, generalized, regular network d-dimensional

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Authors: Mahmoud Enayati, Sirous Mohammadi

Abstract:

In this paper, a novel procedure to find the firing angles of the multilevel inverters of supply voltage and, consequently, to decline the total harmonic distortion (THD), has been presented. In order to eliminate more harmonics in the multilevel inverters, its number of levels can be lessened or pulse width modulation waveform, in which more than one switching occur in each level, be used. Both cases complicate the non-algebraic equations and their solution cannot be performed by the conventional methods for the numerical solution of nonlinear equations such as Newton-Raphson method. In this paper, Cuckoo algorithm is used to compute the optimal firing angle of the pulse width modulation voltage waveform in the multilevel inverter. These angles should be calculated in such a way that the voltage amplitude of the fundamental frequency be generated while the total harmonic distortion of the output voltage be small. The simulation and theoretical results for the 9-levels inverter offer the high applicability of the proposed algorithm to identify the suitable firing angles for declining the low order harmonics and generate a waveform whose total harmonic distortion is very small and it is almost a sinusoidal waveform.

Keywords: evolutionary algorithms, multilevel inverters, total harmonic content, Cuckoo Algorithm

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Authors: Atitallah Sofien, Bouyahia Olfa, Hadj Salah Ibrahim, Ben Saleh Foued, Missaoui Nada, Ben Rabeh Rania, Yahyaoui Salem, Mazigh Sonia, Boukthir Samir

Abstract:

Introduction: Carbon monoxide (CO) poisoning is a common pathology responsible for high morbidity and mortality worldwide. Aim: The purpose of this study was to determine the epidemiological profile of CO poisoning as well as its clinical, paraclinical, therapeutic, and evolutionary aspects. Methods: Our study included observations of CO poisoning in children hospitalized in the pediatric department C of the Children's Hospital in Tunis over a period of 3 years. Results: We have collected 199 cases of CO poisoning in children. The average age was 5.43 years, with a sex ratio of 0.98. The source of CO was inside the home in 73.2% of cases, and it was the gas bath heater in 68.8% of cases. The intoxication was collective in 93.5% of the cases, and it occurred during the month of January in 35.8% of the cases. The clinical manifestations were headaches in 69.5% of cases. The rate of carboxyhemoglobin was pathological in 73.9% of cases. All patients received normobaric oxygen therapy, and only 3.6% of patients had a hyperbaric oxygen therapy session. We did not deplore any case of death in our study. Conclusion: CO poisoning remains a public health problem in Tunisia with high morbidity. The risk of secondary complications, particularly neuropsychiatric, requires clinical and possibly neuroradiological monitoring of these victims.

Keywords: poisoning, carbon monoxide, children, hyperbaric oxygenation

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Authors: Anna Ouskova Leonteva, Michel Risser, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

Abstract:

This paper proposes an efficient optimization tool for an active magnetic regenerator (AMR) model, operating in two modes: magnetic refrigeration system (MRS) and thermo-magnetic generator (TMG). The aim of this optimizer is to improve the design of the AMR by applying a multi-physics multi-scales numerical model as a core of evaluation functions to achieve industrial requirements for refrigeration and energy conservation systems. Based on the multi-objective non-dominated sorting genetic algorithm 3 (NSGA3), it maximizes four different objectives: efficiency and power density for MRS and TMG. The main contribution of this work is in the simultaneously application of a CPU-parallel NSGA3 version to the AMR model in both modes for studying impact of control and design parameters on the performance. The parametric study of the optimization results are presented. The main conclusion is that the common (for TMG and MRS modes) optimal parameters can be found by the proposed tool.

Keywords: ecological refrigeration systems, active magnetic regenerator, thermo-magnetic generator, multi-objective evolutionary optimization, industrial optimization problem, real-world application

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