A novel nature-inspired optimization based neural network simulator to predict coal grindability index

Purpose Hardgrove grindability index (HGI) is an important physical parameter used to demonstrate the relative hardness of coal particles. Modeling of HGI based on coal conventional properties is a quite complicated procedure. The paper aims to develop a new accurate model for prediction of HGI that is called optimized evolutionary neural network (OPENN). Design/methodology/approach The procedure for generation of the proposed OPENN predictive model was performed in two stages. In the first stage, as the high dimensionality involved in the input space, a correlation-based feature selection (CFS) algorithm was used to select the most important influencing variables for HGI prediction. In the second stage, a combination of differential evolution (DE) and biography-based optimization (BBO) algorithms as a global search method were applied to evolve weights of a multi-layer perception neural network. Findings The proposed OPENN was examined and compared with other typical models using a wide range of Kentucky coal samples. The testing results showed that the accuracy of the proposed OPENN model is significantly better than the other typical models and can be considered as a promising alternative for HGI prediction. Originality/value As HGI test is relatively expensive procedure, there is an economical interest on HGI modeling based on coal conventional properties (proximate, ultimate and petrography); the proposed OPENN model to estimate HGI would be a valuable and practical tool for coal industry.

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Performance analysis of ant-lion optimization based routing algorithm for wireless sensor networks

International Journal of Intelligent Unmanned Systems ◽

10.1108/ijius-09-2019-0055 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Sasi B. Swapna ◽

R. Santhosh

Keyword(s):

Energy Consumption ◽

Performance Analysis ◽

Sensor Nodes ◽

Wireless Sensor ◽

Local Optimum ◽

Network Simulator ◽

Content Type ◽

Ant Lion Optimization ◽

Wide Range ◽

Ant Lion

PurposeThe miniscule wireless sensor nodes, engaged in the wide range of applications for its capability of monitoring the physical changes around, requires an improved routing strategy with the befitting sensor node arrangement that plays a vital part in ensuring a completeness of the network coverage.Design/methodology/approachThis paves way for the reduced energy consumption, the enhanced network connections and network longevity. The conventional methods and the evolutionary algorithms developed for arranging of the node ended with the less effectiveness and early convergence with the local optimum respectively.FindingsThe paper puts forward the befitting arrangement of the sensor nodes, cluster-head selection and the delayless routing using the ant lion (A-L) optimizer to achieve the substantial coverage, connection, the network-longevity and minimized energy consumption.Originality/valueThe further performance analysis of the proposed system is carried out with the simulation using the network simulator-2 and compared with the genetic algorithm and the particle swarm optimization algorithm to substantiate the competence of the proposed routing method using the ant lion optimization.

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CFD-based blade shape optimization of MGT-70(3)axial flow compressor

International Journal of Numerical Methods for Heat &amp Fluid Flow ◽

10.1108/hff-10-2018-0603 ◽

2019 ◽

Vol 30 (6) ◽

pp. 3307-3321 ◽

Cited By ~ 5

Author(s):

Mohammad Reza Pakatchian ◽

Hossein Saeidi ◽

Alireza Ziamolki

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Shape Optimization ◽

Fluid Dynamic ◽

Axial Compressor ◽

Axial Flow ◽

Design Parameters ◽

Content Type ◽

Wide Range ◽

Artificial Neural

Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

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Predicting delays in prefabricated projects: SD-BP neural network to define effects of risk disruption

Engineering Construction & Architectural Management ◽

10.1108/ecam-12-2020-1050 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ying Zhao ◽

Wei Chen ◽

Mehrdad Arashpour ◽

Zhuzhang Yang ◽

Chengxin Shao ◽

...

Keyword(s):

Neural Network ◽

Prediction Model ◽

Bp Neural Network ◽

Input Parameter ◽

Model System ◽

Extreme Condition ◽

Content Type ◽

Schedule Delay ◽

Wide Range ◽

Delay Prediction

PurposePrefabricated construction is often hindered by scheduling delays. This paper aims to propose a schedule delay prediction model system, which can provide the key information for controlling the delay effects of risk-related factors on scheduling in prefabricated construction.Design/methodology/approachThis paper combines SD (System Dynamics) and BP (Back Propagation) neural network to predict risk related delays. The SD-based prediction model focuses on dynamically presenting the interrelated impacts of risk events and activities along with workflow. While BP neural network model is proposed to evaluate the delay effect for a single risk event disrupting a single job, which is the necessary input parameter of SD-based model.FindingsThe established model system is validated through a structural test, an extreme condition test, a sensitivity test, and an error test, and shows an excellent performance on aspect of reliability and accuracy. Furthermore, 5 scenarios of case application during 3 different projects located in separate cities prove the prediction model system can be applied in a wide range.Originality/valueThis paper contributes to academic research on combination of SD and BP neural network at the operational level prediction, and a practical prediction tool supporting managers to take decision-making in a timely manner against delays.

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Artificial neural network model for evaluation the effect of surface properties amendment on slurry erosion behavior of AISI 5117 steel

Industrial Lubrication and Tribology ◽

10.1108/ilt-12-2015-0190 ◽

2016 ◽

Vol 68 (6) ◽

pp. 676-682 ◽

Cited By ~ 5

Author(s):

Bahaa Saleh ◽

Ayman A. Aly

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Operating Conditions ◽

Computational Technique ◽

Slurry Erosion ◽

Content Type ◽

Erosion Behavior ◽

Wide Range ◽

Artificial Neural ◽

Effect Of Surface

Purpose The aim of this paper is to evaluate the effect of surface treatment on slurry erosion behavior of AISI 5,117 steel using artificial neural network (ANN) technique. Design/methodology/approach The slurry erosion wear behavior of electroless nickel-phosphorus (Ni-P) coated, carburized and untreated AISI 5,117 alloy steel was investigated experimentally and theoretically using ANN technique based on error back propagation learning algorithm. Findings From the obtained results, it can be concluded that the proposed AAN model can be successfully used for evaluating slurry erosion behavior of the Ni-P coated, carburized and untreated AISI 5,117 steel for wide range of operating conditions and Ni-P coating and carburizing improve the slurry erosion resistance of AISI 5,117 steel; however, the coating is more efficient. Originality/value Slurry erosion is a serious problem for the performance, reliability and service life of engineering components used in many industrial applications. To improve the performance of these components, engineering surface technologies have been attracting a great deal of attention. The extent of slurry erosion is dependent on a wide range of variables. To account all variables that effect on erosion behavior, prediction of erosion behavior by soft computational technique is one of the most important requirements. ANN has the ability to tackle the problem of complex relationships among variables that cannot be accomplished by traditional analytical methods.

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Anti-CRISPR RNAs: designing universal riboregulators with deep learning of Csy4-mediated RNA processing

10.1101/2020.11.15.384107 ◽

2020 ◽

Author(s):

Haotian Guo ◽

Xiaohu Song ◽

Ariel B. Lindner

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Rna Processing ◽

Binding Sites ◽

Transcriptional Networks ◽

Promising Alternative ◽

Wide Range ◽

Flanking Sequences ◽

Structural Commonality

AbstractRNA-based regulation offers a promising alternative of protein-based transcriptional networks. However, designing synthetic riboregulators with desirable functionalities using arbitrary sequences remains challenging, due in part to insufficient exploration of RNA sequence-to-function landscapes. Here we report that CRISPR-Csy4 mediates a nearly all-or-none processing of precursor CRISPR RNAs (pre-crRNAs), by profiling Csy4 binding sites flanked by > 1 million random sequences. This represents an ideal sequence-to-function space for universal riboregulator designs. Lacking discernible sequence-structural commonality among processable pre-crRNAs, we trained a neural network for accurate classification (f1-score ≈ 0.93). Inspired by exhaustive probing of palindromic flanking sequences, we designed anti-CRISPR RNAs (acrRNAs) that suppress processing of pre-crRNAs via stem stacking. We validated machine-learning-guided designs with >30 functional pairs of acrRNAs and pre-crRNAs to achieve switch-like properties. This opens a wide range of plug-and-play applications tailored through pre-crRNA designs, and represents a programmable alternative to protein-based anti-CRISPRs.

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Functional Analysis and Antivirulence Properties of a New Depolymerase from a Myovirus That Infects Acinetobacter baumannii Capsule K45

Journal of Virology ◽

10.1128/jvi.01163-18 ◽

2018 ◽

Vol 93 (4) ◽

Cited By ~ 14

Author(s):

Hugo Oliveira ◽

Ana Rita Costa ◽

Alice Ferreira ◽

Nico Konstantinides ◽

Sílvio B. Santos ◽

...

Keyword(s):

Acinetobacter Baumannii ◽

Bacterial Infections ◽

Antimicrobial Agents ◽

Therapeutic Potential ◽

Host Immune System ◽

Promising Alternative ◽

Content Type ◽

A Genome ◽

Wide Range

ABSTRACT Acinetobacter baumannii is an important pathogen causative of health care-associated infections and is able to rapidly develop resistance to all known antibiotics, including colistin. As an alternative therapeutic agent, we have isolated a novel myovirus (vB_AbaM_B9) which specifically infects and makes lysis from without in strains of the K45 and K30 capsule types, respectively. Phage B9 has a genome of 93,641 bp and encodes 167 predicted proteins, of which 29 were identified by mass spectrometry. This phage holds a capsule depolymerase (B9gp69) able to digest extracted exopolysaccharides of both K30 and K45 strains and remains active in a wide range of pH values (5 to 9), ionic strengths (0 to 500 mM), and temperatures (20 to 80°C). B9gp69 was demonstrated to be nontoxic in a cell line model of the human lung and to make the K45 strain fully susceptible to serum killing in vitro. Contrary to the case with phage, no resistance development was observed by bacteria targeted with the B9gp69. Therefore, capsular depolymerases may represent attractive antimicrobial agents against A. baumannii infections. IMPORTANCE Currently, phage therapy has revived interest for controlling hard-to-treat bacterial infections. Acinetobacter baumannii is an emerging Gram-negative pathogen able to cause a variety of nosocomial infections. Additionally, this species is becoming more resistant to several classes of antibiotics. Here we describe the isolation of a novel lytic myophage B9 and its recombinant depolymerase. While the phage can be a promising alternative antibacterial agent, its success in the market will ultimately depend on new regulatory frameworks and general public acceptance. We therefore characterized the phage-encoded depolymerase, which is a natural enzyme that can be more easily managed and used. To our knowledge, the therapeutic potential of phage depolymerase against A. baumannii is still unknown. We show for the first time that the K45 capsule type is an important virulence factor of A. baumannii and that capsule removal via the recombinant depolymerase activity helps the host immune system to combat the bacterial infection.

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Systematic comparisons of customer base prediction accuracy: Pareto/NBD versus neural network

Asia Pacific Journal of Marketing and Logistics ◽

10.1108/apjml-09-2019-0520 ◽

2020 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Shao-Ming Xie ◽

Chun-Yao Huang

Keyword(s):

Neural Network ◽

Prediction Accuracy ◽

Relative Performance ◽

Customer Relationship ◽

Content Type ◽

Customer Data ◽

Customer Base ◽

Wide Range ◽

Real World Datasets ◽

Analytical Approaches

PurposePredicting the inactivity and the repeat transaction frequency of a firm's customer base is critical for customer relationship management. The literature offers two main approaches to such predictions: stochastic modeling efforts represented by Pareto/NBD and machine learning represented by neural network analysis. As these two approaches have been developed and applied in parallel, this study systematically compares the two approaches in their prediction accuracy and defines the relatively appropriate implementation scenarios of each model.Design/methodology/approachBy designing a rolling exploration scheme with moving calibration/holdout combinations of customer data, this research explores the two approaches' relative performance by first utilizing three real world datasets and then a wide range of simulated datasets.FindingsThe empirical result indicates that neither approach is dominant and identifies patterns of relative applicability between the two. Such patterns are consistent across the empirical and the simulated datasets.Originality/valueThis study contributes to the literature by bridging two previously parallel analytical approaches applicable to customer base predictions. No prior research has rendered a comprehensive comparison on the two approaches' relative performance in customer base predictions as this study has done. The patterns identified in the two approaches' relative prediction performance provide practitioners with a clear-cut menu upon selecting approaches for customer base predictions. The findings further urge marketing scientists to reevaluate prior modeling efforts during the past half century by assessing what can be replaced by black boxes such as NNA and what cannot.

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