Search

The large deformation of soft rock tunnel is an important problem to be solved urgently with the development of engineering technology. In order to improve the application effect of prestressed anchor cable adaptive active support system, this paper puts forward an optimization method based on adaptive genetic algorithm, reviews the research status of prestressed anchor support in soft rock tunnel, and analyzes and evaluates the existing methods. This paper introduces the construction process of AGA model, establishes the problem description model and genetic optimization algorithm selection model, and constructs the adaptive active support model of prestressed anchor cable. The model adopts an optimization algorithm with a total of 100 iterations. The Simple Genetic Algorithm and Parallel Genetic Algorithm are selected to evaluate the anchoring force, the control effect of surrounding rock displacement, the erosion of supporting structure and the construction time by comparative test. The test results show that AGA has a good effect on the anchoring force, the minimum anchoring force is 457 kN, and the displacement of surrounding rock is controlled within 1.21-1.59mm, which has a good influence on the quality of supporting structure and the construction completion time. Finally, the application of AGA model in parameter optimization of prestressed anchor cable in soft rock tunnel is summarized, and the future research direction is put forward. [ABSTRACT FROM AUTHOR]

The safety and stability of metal hydride reactor (MHR) structure are significantly important for normal operations of hydrogen isotope storage and supply system. Moreover, considering that the experiments of studying the fatigue and creep properties of MHR structure are always time, money, difficulty and energy consuming. To further improve the service life of MHR as well as lower the failure risk, this paper proposes a system integration method based on ASME code validation, which combines genetic algorithm (GA) and back propagation neural network (BPNN) for rapid assessment of creep-fatigue life (CFL) and optimal design of structural parameters. First, numerical simulation is applied to perform thermal-mechanical coupled finite element analysis and verified by ASME code. Then, parametric sensitivity analysis and experimental design of parameter-based training set based on Taguchi method were performed, and response values are obtained from physical models combined with numerical simulations. Finally, a parametric optimization procedure combining BPNN and GA is developed based on the training data. In the workflow, parametric data flow is passed through the experimental design, numerical simulation, prediction and optimization processes. The results show that the proposed BPNN proxy model based on ASME code validation has good performance in predicting the life of MHR. Based on this, the life of the MHR is improved by 8% compared to the reference sample. • The correlation between creep-fatigue life (CFL) of metal hydride reactor (MHR) and design parameters is established. • Numerical models combining BPNN with ASME Code are available for assessment of CFL of MHR. • The sensitivity analysis is applied to measure the effect of design parameters on CFL of MHR. • Optimal design scheme of MHR is performed through an integrated design optimization method. [ABSTRACT FROM AUTHOR]

The transport sector as a whole plays an important role in the economy of Bangladesh as its contribution to gross domestic product (GDP) stood at 10.98 percent in the fiscal year (FY) 2018-19 with a growth rate of 6.88 percent. However, this sector also accounted for 9.92 percent of the country's total greenhouse gas (GHG) emissions in 2020. This GHG emission is projected to grow by 116 percent by 2030, making decarbonization of transport an urgent priority by uptaking the electric vehicle (EV). Hence, the Government of Bangladesh is presently working towards issuing an EV policy to remove the barriers to accommodate this paradigm shift in the transportation sector. In order to develop an effective policy for scaling up EV uptake in Bangladesh, systematic and detailed forecasting needs to be carried out considering various influencing parameters. In this study, an EV outlook for Bangladesh from 2022 to 2040 has been formulated by applying the concepts of the additive linear programming mechanism. The predictive modelling includes linear growth of ICE vehicles, EV price reduction, and inclusion of carbon tax on ICE cars as contributing factors in this paper. It has been estimated that, with the inclusion of a 40% carbon tax, the forecasted number of EVs would reach around 3,000,000 in the year 2040. The outlook has led to a comparative financial analysis between ICE vehicles and EVs through the establishment of financial modelling. The genetic algorithm optimizes total tax incidence (TTI) that limits the payback period, and hence, reduces the price gaps between EVs and ICE vehicles. Based on the findings of the study, a number of policies are recommended on fiscal and financial incentives to pave the way for increased uptake of EVs in Bangladesh. • Electric vehicle outlook and policy formulation for decarbonizing the Bangladesh transport sector. • Factors such as growth, the global price of EVs, and the carbon tax for fossil fuel vehicles are considered. • Calibrated outlook has led to comparative financial analysis between fossil fuel vehicles and EVs. • A genetic algorithm is applied to optimize the total tax incidence (TTI) related to the investment payback period. • Policy recommendations are formulated to pave the way for increased uptake of EVs in Bangladesh. [ABSTRACT FROM AUTHOR]

The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines. [Display omitted] • Biochemical reaction networks can be studied as traces of reaction events. • Causally related sub-sequences of reaction events can be identified. • Reaction sub-sequences can be represented as a query and the corresponding response patterns. • NFsim software was modified to allow recording the reaction traces. • The simulations and evaluations of biochemical reaction networks were nearly fully automated. [ABSTRACT FROM AUTHOR]

Interbull's multiple across-country evaluaftion provides national breeding organizations with breeding values for internationally used bulls, which integrate performance data obtained in different breeding populations, environments, and production systems. However, breeding value-based selection decisions on domestic individuals born to foreign sires can only benefit from Interbull breeding values if they are integrated such that their information can contribute to the breeding values of all related domestic animals. For that purpose, several methods have been proposed which either model Interbull breeding values as prior information in a Bayesian approach, as additional pseudo data points, or as correlated traits, where these methods also differ in their software and parameterization requirements. Further, the complexity of integration also depends on the traits and genetic evaluation models. Especially random regression models require attention because of the dimensionality discrepancy between the number of Interbull breeding values and the number of modeled genetic effects. This paper presents the results from integrating 16,063 Interbull breeding values into the domestic single-step random regression test-day model for milk, fat, and protein yield for Australian Red dairy cattle breeds. Interbull breeding values were modeled as pseudo data points with data point-specific residual variances derived within animal across traits, ignoring relationships between integrated animals. Results suggest that the integration was successful with regard to alignment of Interbull breeding values with their domestic equivalent as well as with regard to the individual and population-wide increase in reliabilities. Depending on the relationship structure between integration candidates, further work is required to account for those relationships in a computationally feasible manner. Other traits with separate parity effects nationally could use a similar approach, even if not modeled with a test-day model. [ABSTRACT FROM AUTHOR]

• A two-stage adaptive surrogate model improves the surrogate model accuracy. • Trust region GA improves the stability of the contamination source inversion results. • TSASM-TRGA framework is a robust tool to improve the accuracy and reliability of GCSI. The inversion of contamination source characteristics is of great importance to groundwater remediation and risk management. The surrogate model-based simulation-optimization method is the most commonly used groundwater contamination source inversion (GCSI) approach. However, the static surrogate model and the genetic algorithm (GA) suffer from inaccuracy and instability problems when the nonlinearity of the numerical model or the dimension of the variables to be identified increase. In this paper, a two-stage adaptive surrogate model-assisted trust region GA (TSASM-TRGA) framework was developed to improve the accuracy and stability of the complex high dimensional nonlinear GCSI. In this framework, the two-stage adaptive surrogate model was applied to balance the global and local accuracy of the surrogate model, and the trust region GA was adopted to accelerate the convergence and improve the stability of optimal solutions. By comparing the accuracy of the TSASM-TRGA, two-stage adaptive surrogate model-assisted GA (TSASM-GA), one-stage adaptive surrogate model-assisted GA (OSASM-GA), and static surrogate model-assisted GA (SSM-GA) frameworks, the results showed that the identified contamination source locations with TSASM-TRGA framework are more consistent with the actual locations, and the identified contamination source fluxes are closer to the actual ones. The mean relative error (MRE) between TSASM-TRGA framework and the actual values is 0.31%, which is much smaller than that for the other three frameworks. The inversion results with the TSASM-TRGA framework show a higher accuracy than those with the simulation-statistical method. The present study suggests that the proposed TSASM-TRGA framework provides a more effective approach to improve the accuracy and stability of GCSI. [ABSTRACT FROM AUTHOR]

This paper presents the results of a comparative study of waves generated in water channels and in shaking tank systems with the aim of standardizing the use of different laboratories' facilities and establishing scales for analyzing the evolution of waves under deep water conditions. For this purpose, experimental models are built. The experiments encompass a collection of free surface level measurements obtained from progressive waves in a hydraulic channel and from stationary waves generated by imposing controlled motion to a rectangular tank. The experimental results are also evaluated by contrasting them with analytical correlations. In addition, a model based on a genetic algorithm is proposed to extrapolate the evolution of waves under other physical conditions not measured in the laboratory. The present analysis confirms the viability of wave height comparison between waves generated in a hydraulic channel and those generated by sloshing in a rectangular tank when its natural frequency coincides with the frequency of the progressive wave. Finally, a methodology for establishing a plausible scale for wave comparisons is described and applied. [ABSTRACT FROM AUTHOR]

• This paper proposes an optimal site selection for a heliostat field to improve optical efficiency. • Analyses of the site slope effect and the selection of the optimal site type are implemented. • Heliostat layout optimization is performed for further improvement on the optimal site. • This study reveals that the optimal site type varies according to the latitude. • Utilization of the most efficient site improves the optical efficiency by up to 1.65% point. This paper proposes a methodology to select a site for a heliostat field to further improve its optical efficiency. Site selection implies to select a location with advantageous geographical features for optical efficiency improvement. Therefore, analysis on the site slope effect and guideline to select the most optically efficient site type are the main concerns of this study. The paper consists of two parts: (1) analysis of the site slope effect and determination of the most efficient site type at each latitude, and (2) optimization of heliostat layout on the optimal site for further improvement. Latitude is treated as a control variable since it changes all the solar characteristics, so the site effects at different latitudes are examined independently in this study. Surrogate modeling and genetic algorithm are utilized for efficient and accurate optimization of the heliostat field layout. This study reveals that the optimal site type varies according to the latitude, and utilization of the most efficient heliostat field site improves the optical efficiency by up to 1.65% point that can hardly be achieved by field layout optimization only. Furthermore, this study suggests the applicability of central receiver system (CRS) at high latitude with appropriate site selection and layout optimization. [ABSTRACT FROM AUTHOR]

• Genetic model for Anopheles on the haploid level modulating double resistance. • Allele and epistasis models based on (in)dependent assortment principle. • Monomorphic and polymorphic equilibrium for competitive exclusion and genetic diversity. • Sensitivity analysis for model response studies and strategic interventions. • Optimal search for the best interventions based on habitat manipulation and larvicide. Continuous usage of insecticide in malaria control has decreased its viability due to the emergence of resistant mosquitoes. Strategic interventions, including the well-known rotation–mixing technique, have been implemented to inhibit (multiple) resistance problems. In this paper, a mathematical model describing the dynamic evolution of Anopheles's gametes in the gene pool is developed, taking into account double resistance. This model involves three essential factors in producing different genotypes, namely random mating, natural selection, and recombination. Two special cases of the model, the allele and epistasis model, are presented and evaluated for qualitative studies. The allele model admits three equilibria: two monomorphic equilibria corresponding to the competitive exclusion principle and one polymorphic equilibrium describing coexistence of the susceptible and resistant alleles. Meanwhile limit cycles cease to exist around these equilibria, the stability of the latter depends on the threshold measures R u , R v and dominance conditions on the fitness levels. The corresponding analysis suggests that overdominance condition for heterozygous gametes combined with R u , R v > 1 evoke the stability of the polymorphic equilibrium, maintaining the genetic diversity. The epistasis model is divided into two scenarios, namely recessive and dominant epistasis, which mediate different allele expressions in the two loci and recombination. Four monomorphic equilibria with their stability conditions are obtained explicitly. An analysis of the existence and stability of the polymorphic equilibrium is carried out numerically using Monte Carlo method. It is shown that the fitness level of resistant gametes has an essential role in the evolution of double resistance. In addition, parameter sensitivity analysis leads us to conclude that the intrinsic birth rate as well as logistic factors greatly affect the model behavior. This indicates that management of larval sources (habitat manipulation, larvicides) is an important strategy in suppressing double resistance. [ABSTRACT FROM AUTHOR]

Ply-drop (PD) is the termination of specific plies for laminated composite structures to obtain continuous thickness changes. It brings flexibility to the design of tapered composite laminates. However, as a structural defect, ply drops could have an impact on performance. Considering the impact of ply drop during stacking sequence design can provide more accurate performance analysis, but this will bring challenges in modeling and optimization. To consider the PD impact and achieve convenience in optimization, this paper proposes a high-fidelity finite element automatic modelling method of tapered laminates and corresponding optimization framework. By parameterizing the PD information and defining the basic elements and nodes of start stacking surface of the structure, the entire finite element model is layer-wisely constructed and controllable. Subsequently, based on the genetic algorithm framework, a repair strategy and its genetic operations are proposed to ensure that the design variables satisfy the ply-drop design guidelines. And a detailed optimization process is provided. Finally, the strength and deflection performance optimization problem of a tapered laminate with PD from 28 layers to 16 layers under three-point bending test is introduced for illustration of the proposed automatic modeling and optimization method. Comparisons between simulation results and experimental data of the obtained optimization solution verify the effectiveness of the proposed modeling and optimization method. [ABSTRACT FROM AUTHOR]

Indirect evaporative cooling has garnered considerable attention and research owing to its notable advantages, especially high cooling efficiency and low energy consumption. However, the complexity and diversity of parameters affecting indirect evaporative cooler (IEC) performance necessitate the development of a fast and efficient performance prediction model, which remains a critical research objective. To address the particular challenge, this paper introduces a novel approach that utilizes the principles of neural network theory to construct a predictive model based on backpropagation artificial neural networks (BP-ANN). The developed model is employed to investigate the impact of six main operating parameters of the water spray system on the IEC performance. Wet-bulb efficiency and pressure drop of secondary air channel are utilized as the performance evaluation indexes. The model is rigorously validated by relevant experimental data, enabling a comprehensive analysis of the influence of each parameter on the operational performance of the whole system. The significance of the influential parameters is evaluated through grey relational analysis. Furthermore, a multi-objective optimization approach employing a genetic algorithm (GA) is introduced to effectively determine the optimal operating parameters of the IEC system. The results demonstrate that the whole system can efficiently operate with low energy consumption when the wet-bulb efficiency and pressure drop weights are balanced. The optimal operating parameters include a nozzle pressure of 0.15 MPa, spacing of 85 mm, aperture of 6 mm, count of 11, spray density of 0.85 kg/(s ∙ m2) and air velocity of 2 m/s, at which time the wet-bulb efficiency was 0.85, the pressure drop was 210 Pa, and the coefficient of performance (COP) was 12.4, which demonstrated an efficiency increase of 23.2% in comparison with the base system. The findings of this research significantly contribute to a comprehensive understanding of the influence of water-spray operating conditions on IEC performance, and provide valuable insights for the design and operation of high-efficient IEC systems in practical applications. • The datasets were generated by a laboratory-scale prototype of the IEC system. • The ANN model for IEC considering spray operation was trained and validated. • Effects of nozzle and operation characteristics parameters were investigated. • The significance of the influential parameters was evaluated. • The optimal parameters for IEC were determined based on genetic algorithms. [ABSTRACT FROM AUTHOR]

• A Kriging model updating method combined of Kriging surrogate model and genetic algorithm is proposed to estimate parameters for horizontal multilayer earth model. • The efficiency and accuracy of the proposed method are analyzed. • The optimization accuracy influenced by initial data samples is discussed. • Verification cases have been illustrated for the feasibility of final estimated earth models. A Kriging model updating method combined with Kriging surrogate model and genetic algorithm is proposed in this paper, helping to improve the computational efficiency of parameters estimation for horizontal multilayer earth model around electrical substation. Based on the theory of widely used Wenner configuration method for apparent resistivity measurement, the optimization model for horizontal multilayer earth model parameters is built up. To avoid time-consuming calculation, Kriging surrogate model is introduced to replace the complicated optimization model using small amount of data samples. Then genetic algorithm is adopted to update this Kriging model and estimate the earth model parameters until the criterion achieved. The accuracy and efficiency of the proposed method are analyzed. And several cases of multilayer earth model are studied furtherly and compared with traditional genetic algorithm to verify its feasibility. [ABSTRACT FROM AUTHOR]

Searching for a near-optimal highway alignment is essential to achieve safe, efficient, and cost-effective transportation infrastructure. The challenges with the traditional design approach lie in its reliance on geometric elements which may lead to alignments that are complex, financially burdensome, and environmentally disruptive despite exploring a wide search area. This paper presents a novel and intelligent approach for highway alignment optimization, focusing on the development of the alignment using station points within a reduced domain. The approach involves utilizing a pre-generated Least Cost Path from a Geographic Information System (GIS) model, serving two purposes: first, determining the reduced search domain, and second, using it as input for genetic algorithms (GAs) until a near-optimal alignment is achieved. This approach simplifies the design process, reduces costs, and yields alignments that better harmonize with various relevant factors. The integrated GIS-GA model of this study helped reduce the whole search space to a confined tubular square corridor (CTSC), where the best alternative exists. Additionally, the method introduced a new perspective on highway alignment design, challenging conventional practices. The model is validated using comprehensive and sensitive scenarios for key parameter values. The results reveal that the search time is significantly reduced and that better solutions are located in real-time. This methodology has the potential to revolutionize the way transportation engineers and planners approach highway alignment projects, resulting in more efficient, well-informed, cost-effective, and context-sensitive designs. Further studies are highly recommended to fully explore the implications and applications of the proposed methodology. • Highway alignment development concerns researchers, aiming for optimal solutions, reducing time/cost, and lowering construction expenses. • This project reports the reduction of the search space from the whole to a CTSC, where optimal highway alignment exists in 3D space. • A problem oriented GIS model was integrated with a Genetic Algorithm Model into a single one to address the development of an optimum highway alignment solution in real time. • The resulting alignment was configured via a novel approach using station points along its length in 3D, rather than traditional highway design elements. • The resulting optimum highway alignment was promising, validating space reduction, cutting processing time, and improving solution quality. [ABSTRACT FROM AUTHOR]

Accurate prediction of rate of penetration is a prerequisite for optimization of drilling parameters. However, characteristics such as multiple drilling conditions, inconsistency in data length and various drilling variables can lead to inaccurate prediction of rate of penetration in actual drilling process. Therefore, it is important to use appropriate methods to predict rate of penetration for the above characteristics. This paper proposes an online prediction method of rate of penetration based on drilling conditions identification. First, data preprocessing and correlation analysis are employed to handle various drilling variables to obtain optimal model inputs. Then, dynamic time warping method is used to solve inconsistency in data length. Further, for multiple drilling conditions, the fuzzy c-means and dynamic time warping are combined to identify drilling conditions. Next, different extreme learning machine models optimized by genetic algorithm are built to predict rate of penetration for different drilling conditions. Finally, the results involving actual data illustrate that the prediction error of the proposed hybrid method of fuzzy c-means, dynamic time warping, extreme learning machine and genetic algorithm meets the requirements. The proposed hybrid method outperforms the comparative methods in four performance metrics, which verify the rationality and necessity of considering drilling conditions when predicting rate of penetration. [ABSTRACT FROM AUTHOR]

The existent algebraic models of the genetic code contribute to the understanding of the physio-chemical characteristics of the amino acids. However, the process of translating a gene into a phenotype is highly complex. Moreover, the intricacy of gene expression gets further multiplied due to the biases in the codon usage. This paper explores an algebraic structure called module on the set of codons as well as on that of RNA sequences. We study the potential implications of these structures on gene expression and the G C content of an RNA sequence. The base order { C , U , G , A } appears to possess greater biological significance than many of the orders previously studied. We have developed a novel algorithm to generate RNA sequences with high G C content, aiming to enhance the thermostability of biomolecules. The insights gained from this investigation may have applications in biomolecular modeling and docking, protein engineering, drug development, and related fields. • A module structure is constructed on genetic code and studied its implications. • Developed a novel algorithm to increase the G C content of RNA sequences. • This study may be utilized in computational protein engineering. [ABSTRACT FROM AUTHOR]

• The effects of design parameters and their interactions on the transmissibility. • The regression relationship between the transmissibility and input design parameters. • The response surface method (RSM) model validated by analysis of variance. • The RSM modelling and Genetic Algorithm applied to optimize the design. • Identify the parameters of seat suspension system from field vibration measurement. Low-frequency vibration is a significant hazard to commercial vehicle drivers. Research on how to reduce seat vibration and improve human comfort in a seating system through seat design optimization is important. The novelty of this paper is to study the sensitivity analysis of seat design parameters and their interaction effects and optimize seat design for the minimum vibration transmission to human drivers. The uniqueness of this paper is the identification of the system parameters from the measured vibration acceleration data rather than from the material test. The purpose of this paper is to develop a prediction model of vibration transmissibility from input design parameters using the response surface method modeling. The statistical significance of the RSM model will be validated by analysis of variance. The design parameters will be optimized through the RSM modeling and Genetic Algorithm. The method can enhance the vibration isolation performance of the existing vehicle seat suspension system with reduced cost and without additional parts and energy consumption and make the design change and optimization of the seat suspension system simple and easy to be implemented. According to the research results, the vibration transmissibility ratio of the optimized seat system is reduced by about 10%. [ABSTRACT FROM AUTHOR]

This paper demonstrates a novel application of the deep learning (DL) model designed by a genetic algorithm (GA) for modelling the figure of merits (FOMs) of gallium nitride (GaN) based high electron mobility transistors (HEMTs). To the best of our knowledge, it is the first paper that presents an end-to-end methodology to automate the optimization process of a DL model specifically for semiconductor devices. Such design automation of a DL model substantially reduces the barrier to adopting the DL techniques and encourages researchers from the semiconductor field to adopt the potential of deep learning techniques. To optimize and train the DL model, a dataset of 2160 unique GaN HEMTs for various epitaxial structures has been generated by experimentally validated simulation methodology. Four figure of merits (FOMs) i.e., on current (I o n), maximum transconductance (G m), subthreshold slope (SS) and threshold voltage (V t h) have been extracted to generate the dataset. Any combination of the three FOMs can be used as inputs to train the DL model for predicting the fourth one. In this particular application, I o n , G m , and SS are used to predict the V t h of the GaN HEMT. The GA converges after 15 generations for the population size of 250. The optimized solution obtained from the GA for the DL model has a batch size of 8, adam optimizer, 4 number of hidden layers with 16, 8, 16 and 16 neurons, respectively, RELU activation function and zero dropout rate. The optimized DL model provides accurate predictions of the FOMs even for unseen inputs at a very low computational cost in real-time (within a second). The performance of the optimized DL model has been also compared with Lasso, Ridge and support vector regression models and their mean square error and R-square are found to be 0.05, 1, 0.29, 0.17 and 0.978, 0.59, 0.88, 0.931, respectively. [ABSTRACT FROM AUTHOR]

Optimizing the geometry structures and operating conditions is an effective way to improve the performance of the segmented thermoelectric generator (STEG). A one-dimensional numerical model combined with genetic algorithm (GA) is presented for performance analysis and design optimization of the STEG. The model's predictions being in good agreement with experimental data in the published literature confirms the accuracy of the model. According to the compatibility factors, the Ba 0.4 ln 0.4 CoSb 12 , Bi 2 Te 0.7 Se 0.3 , Zn 4 Sb 3 and Bi 2 Te 3 are selected as materials for segments of N1, N2, P1, and P2, respectively. The p-segmented TEG is recommended through performance comparison between STEGs with four different structures. After that, the load following region and rated operating point are given, through load following characteristic analysis. At last, the effect of contact resistance on the performance of the STEG is analyzed. The analysis results show that, by reducing the contact resistance to 50 μΩ cm2 per leg, the peak conversion efficiency of the p-segmented TEG proposed in this paper can reach 9.83% at a temperature difference of 350 K, which is 25.4% higher than that of traditional thermoelectric generator. • The optimization method based on genetic algorithm is developed for the STEG. • Thermoelectric materials are selected according to the compatibility factors. • An integrative optimization of the P-segmented TEG is performed by GA. • Conversion efficiency and Power output of the STEG are improved significantly. [ABSTRACT FROM AUTHOR]

To realize both mechatronic properties of light mass and high control precision for a pan–tilt based on the multirotor unmanned aerial vehicle (MUAV) for aerial remote sensing applications, a structural multi-objective optimization approach based on the approximate model and genetic algorithm is proposed. The optimized structural parameters for establishing the approximate model are determined through the sensitivity analysis method, by which the predominant variables affecting mass and modal are selected. The approximate models on mass and first-order modal frequency are built through response surface methodology (RSM) and radial basis function (RBF) to heighten the optimization efficiency and smooth the numerical optimization results. Particularly, the non-dominated sorting genetic algorithm-II (NSGA-II) and Pareto Optimality are combined to acquire the parameters' optimal solutions. The modal tests based on hammering method and the control experiments are both conducted to validate the performance of the proposed approach. The results indicate that the structural multi-objective optimization approach presented in this paper is effective to reduce the mass meanwhile enhance its first-order modal frequency, hence leading to good stabilizing and tracking performances. • A structural multi-objective optimization approach is proposed for the pan-tilt. • A two-step optimization scheme is proposed to achieve optimization objectives. • The approximate models are built to heighten the optimization efficiency. • The NSGA-II and Pareto Optimality are combined to obtain the optimal solution. • The experiments are conducted to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Kernel partial least squares regression (KPLS) is a technique widely used in the construction of predictive models. However, the adjustment of both the parameter of the kernel function and the number of components supposes for the researcher an unavoidable additional task. This paper presents a procedure that optimizes the generalization capacity of KPLS multivariate models using genetic algorithms (GA), selects the values of the kernel function parameter and the number of components for which the value of the cross-validation coefficient Q2 cum is maximum, adds preliminary tests to configure the GA and defines a convergence criterion in terms of dispersion in the estimates. GA has demonstrated a good performance in the task of optimizing KPLS with convergent solutions towards a global optimum. [ABSTRACT FROM AUTHOR]

Organic Rankine cycle (ORC) can improve engine power by recovering exhaust energy. This paper co-optimizes the engine-ORC combined system's power and NOx emission, with decision variables of the engine's excess air ratio, spark advance angle, as well as ORC's pump and expander speeds. Firstly, a simulation model of the combined system is established and validated. Then, the initial dataset is generated by the D-optimum Latin hypercube method and simulation model. The artificial neural network (ANN) prediction models of NOx emission and power are established based on these datasets. Finally, the co-optimization is conducted using the ANN prediction model and genetic algorithm. Focusing on maximizing the combined system's power results in an 18.30 % increase in power, and a significant reduction in brake-specific fuel consumption (BSFC) and brake-specific NOx (BSNOx) by 10.10 % and 71.30 %, respectively, compared to the unoptimized basis. Targeting the lowest BSNOx leads to a limited 1.20 % increase in power output; however, it results in a 19.50 % increase in BSFC. When optimizing for both system output and BSNOx, the output remains 13.5 % above the unoptimized basis. Meanwhile, up to 89.8 % of BSNOx can be eliminated with negligible deterioration in BSFC. This study could be used for engine performance enhancements. • A data-driven ANN prediction model of engine-ORC combined system is established. • ANN cooperates with GA to co-optimize Combined system's power and NOx emission. • The optimum engine and ORC parameters for the different goals are obtained. [ABSTRACT FROM AUTHOR]

The accumulation of large amounts of historical data has led to the wide application of ensemble learning over the past few decades, but the balance between the individual accuracy of base classifiers (BCs) and the diversity among these BCs is rarely considered in the construction of ensemble models. Since such a balance is crucial to the success of ensemble models, this paper proposes a Genetic Algorithm-based sequential instance selection framework to address this research gap. The novelties of the proposed framework include: transforming the balance between the individual accuracy of BCs and the diversity among BCs into a general combinatorial optimization model and designing a Genetic Algorithm-based evolutionary instance selection method to solve this model. The proposed framework not only overcomes the inherent limitations of the Genetic Algorithm in some high-dimensional tasks but also provides an explicit and automatic way to balance the accuracy and diversity by searching appropriate training data subsets for different component BCs. Based on obtained training data subsets, the component BCs of the ensemble model are generated sequentially, and their predictions are further combined with the weighted majority voting rule. Using 30 real datasets collected from various practical applications, such as medicine, business, and industry, the effectiveness of the proposed framework in constructing powerful ensemble models is examined and compared with six benchmark ensemble learning methods. In addition, the capability of the proposed framework to improve convergence performances is also examined by the comparison with the traditional Genetic Algorithm. [ABSTRACT FROM AUTHOR]

There is an increasing industrial demand of using impedance fitting techniques to enable an equivalent circuit to approximate the electrical behavior of an electrical system. However, due to the high dependence on the prior physical information of the electrical system, previous impedance fitting techniques have long suffered from unacceptable implementation complexity and unsatisfactory fitting precision. To realize easy, cheap, and precise impedance fitting, a Physics-free genetic impedance fitting technique (PFGIFT) is proposed in this paper. Firstly, PFGIFT utilizes a five-component RLC-parallel (FCRLCP) equivalent circuit, which gets rid of expensive physics-based equivalent circuit modeling, to approximate the impedance of a two-terminal electrical system. Then, a novel bounded gaussian mixture model based genetic algorithm (BGMMBGA) is proposed to fully leverage the fitting capability of the FCRLCP equivalent circuit by embedding parameter tuning expertise into the evolutionary process. Under the combined effects of FCRLCP equivalent circuit and BGMMBGA, PFGIFT achieves precise impedance fitting performance without the use of expensive knowledge about physical information. The effectiveness of PFGIFT is verified separately on a human-designed impedance fitting task and an industrial application of fitting the common-mode impedance of a motor drive system. [ABSTRACT FROM AUTHOR]

[Display omitted] • Multi-objective optimization framework for thin-walled tubular deployable composite boom utilizing MOEA/D. • Discontinuous pareto front reveals distinct ply schemes with improved mechanical properties. • Cross-sectional radius influences bending stiffness while minimally affecting folding moment. • Hyperparameter adjustment improves computational efficiency without sacrificing optimization results. • Comparison of genetic algorithms highlights NSGA-III, NSGA-II, and MOEA/D as superior choices. As a crucial structural component in space applications such as solar sails and solar arrays, the thin-walled tubular deployable composite booms (DCBs) demonstrate extensive utilization by employing stored elastic strain energy to achieve folding and deploying functions. This paper introduces a multi-objective optimization framework that integrates an analytical model with a genetic algorithm. By utilizing a multi-objective evolutionary algorithm based on de-composition (MOEA/D), the optimization objectives of minimizing folding moment and maximizing bending stiffness are pursued. Multiple constraints associated with failure avoidance, laminate stacking sequence design principles, and the folding moment range of actuator in the folding mechanism are considered in the optimization. The multi-objective optimization design of the tubular DCBs is performed to obtain the optimal combinations of cross-sectional radius, central angle, and ply scheme. Experimental validation confirms the efficacy of the optimization results. Additionally, an in-depth analysis on the influence of genetic algorithm types, hyperparameters, and different design variables on the optimization outcomes is thoroughly discussed. The findings of this study offer significantly insights for the practical engineering applications of tubular DCBs. [ABSTRACT FROM AUTHOR]

Surrogate evaluation is a useful, if not the unique, technique in population-based evolutionary algorithms where exact fitness calculation is too expensive. This situation arises, for example, in Genetic Programming (GP) applied to evolve scheduling priority rules, since the evaluation of a candidate rule amounts to solve a large number of problem instances acting as training set. In this paper, a simplified model is proposed that relies on finding and then exploiting a small set of small problem instances, termed filter, such that the evaluation of a rule on the filter may help to estimate the performance of the same rule in solving the training set. The problem of finding the best filter is formulated as a variant of the optimal subset problem, which is solved by means of a Genetic Algorithm (GA). The surrogate evaluation of a new candidate rule consist in solving the instances of the filter. This model is exploited in combination with a Memetic Genetic Program (MGP); the resulting algorithm is termed Surrogate Model MGP (SM-MGP). An experimental study was performed on the problem of scheduling a set of jobs on a machine with varying capacity over time, denoted (1 , C a p (t) | | ∑ T i). The results of this study provided interesting insights into the problems of filter and rules calculation, and showcase that the priority rules evolved by SM-MGP outperform those evolved by MGP. • Formalization of the Optimal Filtering Set Problem (OFSP). • Development of a Genetic Algorithm (GA) for solving the OFSP. • Combination of surrogate evaluation and local search in Genetic Programming. • Analysis and comparison of eleven variants of Genetic Programming on performance. [ABSTRACT FROM AUTHOR]

• A novel DG hosting capacity evaluation method is proposed for multi-feeders system interconnected by external tie lines and SOPs. • An improved-IGDT model is presented to assess DG hosting capacity considering unpredictable integrated locations and uncertainty in outputs simultaneously. • A hybrid solution procedure integrated with genetic algorithm and model reformulation is developed to determine DG hosting capacity. Unknown integrated locations caused by unpredictable consumers' willingness and uncertainty in outputs induced by variable natural conditions are acknowledged challenges in the evaluation process of distributed generation (DG) hosting capacity (DGHC). For the former factor, this paper first proposes a novel assessment model to obtain the maximum feasible capacity no matter consumers in which locations have the possibility to stall DG. The decision variables of max operator and min operator are DG capacities and integrated locations respectively. Notably, unlike the previous works for one radial feeder, the interplay of DGHC in multi-feeders interconnected by tie lines and soft open points is also taken into consideration. After that, to combine uncertainties in integrated locations and outputs simultaneously, the above DGHC assessment model is further transformed into an improved information gap decision theory (IGDT) model with worst-case constraints for DG integrated locations. The specific solution procedure is also developed to solve this nonlinear IGDT model effectively. Case studies demonstrate that the evaluation results of DGHC based on this method always have the applicability and feasibility for any nodes in distribution system. [ABSTRACT FROM AUTHOR]

Nowadays, the development of new energy science has gradually entered a good situation, and accordingly, electric vehicles have been recognized by people all over the world and rapidly popularized in various countries. However, the rapid growth of electric vehicles has caused a series of problems, such as insufficient number of charging stations, uneven distribution, and high cost, which are becoming increasingly serious. In order to solve the above problems, this paper takes Ireland as an example for research. Based on the collection of relevant information, this paper first tries to build a social total cost model, and calculates the total operating cost of charging stations under various distribution conditions. In this model, the total social cost is subdivided into comprehensive economic cost and environmental cost. Economic costs include construction costs and fees, while environmental costs include electricity consumption and carbon dioxide emissions. Secondly, this paper established a charging station location optimization model based on genetic algorithm, which simplified the Irish territory into a rectangle with a length of 350 km and a width of 200 km, subdivided it into small squares, and adjusted the correlation coefficient to meet the charging demand. This solution solves the minimization problem of operating cost under the constraints of depreciation period of charging station, power consumption per unit distance of charging station and vehicle charging probability. Finally, in order to determine the factors that may have a significant impact on the total cost of charging stations, we conducted sensitivity analysis and found that the total cost is very sensitive to the number of charging stations and the possibility of EV charging per day. • This paper explores the optimal placement of charging stations in a certain environment. • Build the total social cost model based on economic and environmental cost. • Adopted algorithms to solve objective optimization problems. • Optimal placement of electric vehicle charging stations in Ireland is described. • Total social cost changes more in response to changes of charging stations and charging probability. [ABSTRACT FROM AUTHOR]

• Predictive performance of genetic risk model combining age had AUC value 0.728. • Age is more important than common genetic variants, showed Random Forest algorithm. • LSP1 gene polymorphism rs3817198 is specific for ER + breast cancer types. Breast cancer (BC) development is caused by the interaction of environmental and genetic factors. At least 90 susceptible genetic variants with different population penetration and incidence have been associated with BC. This paper therefore analysed the individual discrimination power of 8 low penetrant common genetic variants and calculated the predictive accuracy of the genetic risk model. The study enrolled 171 women with developed breast cancer (57.06 ± 11.60 years) and 146 control subjects (50.24 ± 10.69 years). The genotyping was performed by high resolution melting method (HRM) and confirmed by Sanger sequencing, and the Random Forest algorithm provided the ROC curve with AUC values. Significant association with BC was confirmed in 2 SNPs: rs2981582 FGFR2 and rs889312 MAP3K1 , and the odds ratios of homozygotes with two risk alleles in both SNP's were higher than in heterozygotes with one mutant allele, as follows: FGFR2 TT: 1.953 (95%CI 1.014–3.834, p = 0.049), CT 1.771 (95%CI 1.088–2.899, p = 0.026) and MAP3K1 CC 2.894 (95%CI 1.028–9.566, p = 0.048), AC 1.760 (95%CI 1.108–2.813, p = 0.019). FGFR2 had the best discrimination ability, followed by MAP3K1 and CASP8. Discriminative accuracy of the genetic risk model distinguishing the breast cancer patients and controls explained by AUC was 0.728, with 70.6% sensitivity and 65.1% specificity. Our study results therefore confirmed polygenic breast cancer inheritance with important involvement of FGFR2 , MAP3K1, LSP1 and CASP8 gene variants. [ABSTRACT FROM AUTHOR]

Abstract Rainfall derivatives are in their infancy since starting trading on the Chicago Mercantile Exchange (CME) in 2011. Being a relatively new class of financial instruments there is no generally recognised pricing framework used within the literature. In this paper, we propose a novel Genetic Programming (GP) algorithm for pricing contracts. Our novel algorithm, which is called Stochastic Model GP (SMGP), is able to generate and evolve stochastic equations of rainfall, which allows us to probabilistically transform rainfall predictions from the risky world to the risk-neutral world. In order to achieve this, SMGP's representation allows its individuals to comprise of two weighted parts, namely a seasonal component and an autoregressive component. To create the stochastic nature of an equation for each SMGP individual, we estimate the weights by using a probabilistic approach. We evaluate the models produced by SMGP in terms of rainfall predictive accuracy and in terms of pricing performance on 42 cities from Europe and the USA. We compare SMGP to 8 methods: its predecessor DGP, 5 well-known machine learning methods (M5 Rules, M5 Model trees, k-Nearest Neighbors, Support Vector Regression, Radial Basis Function), and two statistical methods, namely AutoRegressive Integrated Moving Average (ARIMA) and Monte Carlo Rainfall Prediction (MCRP). Results show that the proposed algorithm is able to statistically outperform all other algorithms. [ABSTRACT FROM AUTHOR]

Highlights • A novel finned tube heat exchanger is proposed for waste heat utilization. • The mechanism of heat transfer enhancement is revealed. • Response surface model is established by the central composite design. • Pareto optimal solution set is obtained by multi-objective genetic algorithm. • The overall performance of optimized structure is improved by 48–55%. Abstract Low-low temperature electrostatic precipitator technology is one of the important ways for energy saving and emission reduction of coal-fired power plants. In order to further improve the dust removal efficiency and enhance waste heat recovery performance, a novel H-type finned elliptical tube heat exchanger with longitudinal vortex generators is proposed. To achieve the maximum heat transfer enhancement with the minimum friction factor augmentation, the response surface model and multi-objective genetic algorithm are adopted to optimize the design parameters. Firstly, combined with the finite volume method and the central composite design method, the second-order response surface model between the design parameters (the length, height, angle, and position of longitudinal vortex generators) and the objective functions (Nusselt number and friction factor) is established. Then, based on the response surface model, the Pareto optimal solution set is obtained by the multi-objective genetic algorithm. Finally, by comprehensively comparing Nusselt number, friction factor and performance evaluation criteria of Pareto optimal solutions, the optimal combination is determined. Compared with the H-type finned tube heat exchanger, the performance evaluation criteria of the optimized novel heat exchanger is improved by 48–55%, which contributes to improve the overall performance of low-low temperature electrostatic precipitator system. The findings of this paper may provide practical guidelines for researchers and designers to develop efficient heat exchangers. [ABSTRACT FROM AUTHOR]

Abstract Indirect approaches for eliciting preference model parameters for multiple criteria decision aiding are of growing interest because they imply relatively less cognitive effort from the decision-maker (DM). Direct approaches are particularly hard in the case of the new ELECTRE TRI-nB method, because the task involves eliciting a number of limiting profiles for each category boundary. However, in ELECTRE methods, the simultaneous inference of the whole set of parameters needs the construction and resolution of a non-linear non-convex programming problem, which is typically very hard to solve. Therefore, an evolutionary-based method to infer the parameters of the ELECTRE TRI-nB model is proposed in this paper. The quality of the solutions is tested by measuring the capacity to restore the assignment examples and the capacity to make consistent assignments of new actions. In extensive computer experiments, using the pseudo-conjunctive assignment procedure, some main conclusions arise: (i) the capacity of the method to restore the training examples reaches high values, mainly with three and five limiting profiles per category; and (ii) the capacity to make appropriate assignments of new actions (not belonging to the training information) can be greatly improved by increasing the number of limiting profiles. Highlights • A complex non-linear problem is solved with evolutionary optimization. • The whole set of parameters of a multi-criteria sorting method is inferred. • Solutions tested with simulated and real data. • High quality of solutions when the boundaries between classes is enriched. • Dependence on the size of the problem is explored. [ABSTRACT FROM AUTHOR]

Highlights • The nectary is active during the two days of the anthesis. • Nectar is released mainly by four-celled trichomes. • The sugar content is equivalent in both anthesis days. • Cycles of contraction-expansion of the trichome cells protoplast are present. • Nectar is released through the cell wall and the cuticle of the trichome apical cell. Abstract Cabomba Aubl. is a genus considered as a potential genetic model for studies of early angiosperm evolution, accordingly, it is important to expand the knowledge of it. This paper reports the study of the anatomy and the ultrastructure of the nectary of Cabomba caroliniana Gray using bright-field microscope, scanning and transmission electron microscope in order to understand its secretion mechanism. C. caroliniana has protoginous flowers and the anthesis lasts two days. Nectaries of C. caroliniana are located in two basal lobes or yellow auricles of each white petal. Most nectar is observed in the area above the pronounced auricles. The secretion is released mainly by the four-cellular trichomes or hydropotens present in both nectary epidermis. The cellular ultrastructure indicates that the nectary is active during the two days of the anthesis. This agrees with the fact that in both anthesis days the fertile structures of the flower (first the stigmata and then the anthers) are disposed above the nectaries. The nectar secretion mechanism is discussed in relation to the present knowledge. The results of this study are related to what has been described for other basal angiosperms. [ABSTRACT FROM AUTHOR]

In half-wavelength (HWL) transmission lines, severe temporary overvoltages are usually observed during the occurrence of line-to-line faults in specific critical locations, and it is unrealistic to ignore the impact of the corona phenomenon. To represent the corona effect, an accurate Suliciu corona model was adopted and implemented in PSCAD software, and a new technique based on the genetic algorithm (GA) was proposed to fit the model parameters. Furthermore, the paper evaluated the performance of the overvoltage mitigation method based on the installation of two spark gaps (SGs) at strategic points to detune the resonance condition. As shown, the SGs turn the HWL line overvoltage levels similar to those observed in conventional AC lines, and the corona modelling further reduces the severity of the overvoltages. • Critical overvoltages and resonant faults in half-wavelength transmission lines. • Influence of corona effect on temporary and switching overvoltages. • Insulation coordination design of half-wavelength transmission lines. • Fitting Suliciu corona model parameters with genetic algorithm. • Energy stress in surge arresters of half-wavelength transmission lines. [ABSTRACT FROM AUTHOR]

The mitosis process of an eukaryotic cell can be represented by the structure constants of an evolution algebra. Any isotopism of the latter corresponds to a mutation of genotypes of the former. This paper uses Computational Algebraic Geometry to determine the distribution of three-dimensional evolution algebras over any field into isotopism classes and hence, to describe the spectrum of genetic patterns of three distinct genotypes during a mitosis process. Their distribution into isomorphism classes is also determined in case of dealing with algebras having a one-dimensional annihilator. [ABSTRACT FROM AUTHOR]

The partially encased concrete (PEC) column-steel beam frame steel plate shear wall (SPSW) structure of semi-rigid joint has good cooperative working performance and seismic performance. In this paper, based on the EC3 component model, considering the contribution of the new concrete components in the PEC column to the initial rotational stiffness of the joint domain, the component model of the flush end-plate PEC column joint was established. A 1/3-scale two-story single-span flush end-plate PEC column-steel beam frame SPSW structure was tested under quasi-static cyclic loading. The OpenSees model of this structure was established, which was validated by test results. Parametric analysis of the additional damping ratio for semi-rigid joints was conducted using OpenSees. The suggested value of additional damping ratios for semi-rigid joints was given. Finally, based on Matlab and OpenSees, the model was input in the form of tool command language (TCL), an optimal arrangement model of flush end-plate PEC column joints with the genetic algorithm (GA) as the main procedure was designed. The effects of different arrangement schemes of semi-rigid joints on the seismic response of PEC column-steel beam frame SPSW structures were analyzed. The results show that the seismic performance of the flush end-plate PEC column-steel beam frame SPSW structures is reliable. The results of the finite element (FE) are in good agreement with the test results, which indicates that the established OpenSees model is effective. The contribution of the new concrete components to the initial rotational stiffness of the middle joints, top and bottom joints is 45.02% and 49.68%, respectively. The optimal arrangement scheme of semi-rigid joints can be efficiently computed by the proposed GA model. • A component model for flush end-plate PEC column joints was given considering the contribution of new concrete components. • The PEC column-steel beam frame SPSW structure with flush end-plate joints was tested under quasi-static cyclic loading. • The parametric analysis of the additional damping ratio for semi-rigid joints was conducted using OpenSees software. • A computational model based the genetic algorithm was designed for the optimal arrangement of the flush end-plate joints. [ABSTRACT FROM AUTHOR]

• Investigating the coupling coordination of social economy–water environmental quality. • Improved CCD model, scissors difference were developed. • Spatiotemporal distribution and dynamic mechanism in CCD were discussed in detail. • Subsystem development modes and suggestions were proposed to promote CCD. Investigating the coordinated development between social economy and water environmental quality is an important basis for promoting the protection of water ecological environment. In this paper, a comprehensive index system was built to estimate the coupling coordination between social economy and water environmental quality subsystems in Nansi Lake catchment, China. Improved coupling coordination degree (CCD) model, scissors difference and projection pursuit model based on genetic algorithm were developed to investigate the spatiotemporal distribution and dynamic evolution mechanism of coupling coordination in social economy–water environment system. Results showed that: (1) Comprehensive level of two subsystems showed an overall uptrend, water environmental quality in the catchment was improved gradually with socio-economic development. (2) The status of coupling coordination between two subsystems had transformed from moderately unbalanced development (CCD of 0.265 in 2001) to favorably balanced development (CCD of 0.667 in 2017). The relatively low CCD in Heze generated the biggest restriction on the coordinated development between two subsystems, compared to Zaozhuang and Jining. (3) The speed difference between two subsystems showed a downward trend, the scissor difference was controlled effectively. And the social economy–water environmental quality system gradually tended to the coordinated development. Overall, the improved CCD model and scissors difference analysis in this paper may provide a new approach for empirical research on environment–economy nexus. We hope this study can help decision makers formulate measures to promote the coordinated development between social economy and water environmental quality subsystems. [ABSTRACT FROM AUTHOR]

Mammalian cell lines are characterized by a complex and flexible metabolism. A single model that could describe the variations in metabolic behavior triggered by variations in the culture conditions would be a precious tool in bioprocess development. In this paper, we introduce an approach to generate a poly-pathway model and use it to simulate diverse metabolic states triggered in response to removal, reduction or doubling of amino acids in the culture medium of an antibody-producing CHO cell line. Macro-reactions were obtained from a metabolic network via elementary flux mode enumeration and the fluxes were modeled by kinetic equations with saturation and inhibition effects from external medium components. Importantly, one set of kinetic parameters was estimated using experimental data of the multiple metabolic states. A good fit between the model and the data was obtained for the majority of the metabolites and the experimentally observed flux variations. We find that the poly-pathway modeling approach is promising for the simulation of multiple metabolic states. [ABSTRACT FROM AUTHOR]

Mooring lines load prediction typically requires data from several sensors and high computational power. In this paper, a soft computing methodology is proposed to estimate the load on mooring chains based on fuzzy logic that uses only some of the natural frequencies of these systems. Two fuzzy inference systems are constructed and optimized by genetic algorithm. The performance of triangular and Gaussian membership functions in modelling the natural frequencies and their behavior in the optimization process are compared. The accuracy of this methodology is validated by experimental data obtained from a test bench. In addition, the robustness of the fuzzy inference system in dealing with uncertainties is studied using statistical parameters. Furthermore, a novel normalization method is proposed to generalize the fuzzy inference systems and eliminate the training process. The results indicate that the load on the chain can be estimated with high precision, and the proposed methodology is suitable for monitoring loads in mooring lines. • The proposed methodology requires low computational power. • Load prediction is performed using only the natural frequencies. • Normalization method eliminates the training of the fuzzy inference system. • One model can be used to estimate the load of several chains. [ABSTRACT FROM AUTHOR]

Supplementary irrigation area in north China is an important agricultural production area, but water shortages have limited the agricultural production considerably. Irrigation is required to ensure crop yield during growth seasons with variable rainfall. Most irrigation schedules are based on typical meteorological years. However, it is very difficult to predict the meteorological information of irrigation year in advance, which limits the planning of irrigation schedules. Therefore, taking the central part of Shanxi Province (supplementary irrigation area) as an example, this study focuses on the optimal irrigation decision-making method of supplementary irrigation area. The irrigation schedule of summer maize from 1970 to 2020 was firstly optimized by using DSSAT model and genetic algorithm. On this basis, a decision-making method based on the ratio of cumulative rainfall to cumulative ETc between plantation date to the current date to determine the irrigation time is developed. The results indicated that using P/ETc (the ratio of cumulative rainfall to cumulative crop water requirement) as the criterion leads to superior decision-making. Two supplementary irrigation events can meet the growth demand of summer maize. Irrigation events decisions were made on days 44 and 58 after summer maize sowing, the accuracy of this method for two irrigation events decisions is 82.35 % and 86.27 %, respectively, and the average accuracy of the two irrigation event decisions was 84.31 %. At the same time, a support vector machine algorithm is used to make decisions about irrigation, comparing the decision accuracy with the results of the decision method proposed in this paper. The accuracy of the support vector machine decisions for the first and second irrigation events was 82.35 % and 80.40 %, respectively, and the average accuracy of the two irrigation event decisions was 81.40 %. The decision model was also superior to that involving support vector machine algorithms for the irrigation decision of summer maize can be used for irrigation decision-making in similar supplementary irrigation areas with variable rainfall. • Genetic algorithm and the DSSAT can effectively optimize the irrigation schedule. • Rainfall/ETc can be used as a criterion to determine the irrigation schedule. • The irrigation decision method is more effective than the SVM algorithm. [ABSTRACT FROM AUTHOR]

Influence maximization is recognized as a crucial optimization problem, which aims to identify a limited set of influencers to maximize the coverage of influence dissemination in social networks. However, real-world social networks are usually dynamic and large-scale, which leads to difficulty in capturing real-time user and diffusion features to effectively and accurately select the key influencers. In this paper, we propose an adaptive agent-based evolutionary approach to address this challenging issue with agent-based modeling and genetic algorithm. This novel approach identifies the users' influence capability in a distributed manner and optimizes the influencer set selection in a dynamic environment. An adaptive solution optimizer is proposed as one of the key components, driving the evolutionary process and adapting the candidate solutions dynamically. The proposed approach is also applicable to large-scale networks due to its distributed framework. Evaluation of our approach is performed by using both synthetic networks and real-world datasets. Experimental results demonstrate that the proposed approach outperforms state-of-the-art seeding algorithms in terms of maximizing influence. • Distributed approach combines genetic algorithm and agent-based modelling for influence maximization. • Genetic algorithm improved with solution adaptive optimizer to capture dynamic influencer candidates. • Proposed approach handles large scale and dynamics in real social networks simultaneously. [ABSTRACT FROM AUTHOR]

Co-clustering ensemble establishes a consensus co-clustering over the data, and the ensemble process can be described as an optimization problem that can be solved by genetic algorithms. However, co-clustering ensemble methods based on genetic models are very few, in which fuzzy clustering and hard clustering is not combined. In this paper, a multi-objective genetic model for co-clustering ensemble (GMCCE) is proposed, and the corresponding objective function is designed. First, to process fuzzy samples and general samples more appropriately, bilateral fuzzy clustering and hard co-clustering are combined organically. Then, chromosomes are encoded as the membership of rows and columns, and after evolution process, the best chromosome is the consensus result. Finally, the proposed model is used to design a GMCCE algorithm. To evaluate the potential of GMCCE, extensive experiments are carried out, including comparison with base co-clustering algorithms and state-of-the-art algorithms. The results demonstrate that the GMCCE algorithm outperforms other algorithms. • Bilateral fuzzy clustering and co-clustering are combined naturally • Base co-clustering results are integrated using multi-objective genetic algorithm • Chromosomes are encoded as the membership of rows and columns. • The performance of co-clustering algorithms can be optimized. [ABSTRACT FROM AUTHOR]

The genesis of orogenic gold deposits is commonly linked to hydrothermal ore fluids derived from metamorphic devolatilization reactions. However, there is considerable debate as to the ultimate source of these fluids and the metals they transport. Tourmaline is a common gangue mineral in orogenic gold deposits. It is stable over a very wide P – T range, demonstrates limited volume diffusion of major and trace elements and is the main host of B in most rock types. We have used texturally resolved B-isotope analysis by secondary ion mass spectrometry (SIMS) to identify multiple fluid sources within a single orogenic gold ore district. The Loulo Mining District in Mali, West Africa hosts several large orogenic gold ore bodies with complex fluid chemistry, associated with widespread pre-ore Na- and multi-stage B-metasomatism. The Gara deposit, as well as several smaller satellites, formed through partial mixing between a dilute aqueous-carbonic fluid and a hypersaline brine. Hydrothermal tourmaline occurs as a pre-ore phase in the matrix of tourmalinite units, which host mineralization in several ore bodies. Clasts of these tourmalinites occur in mineralized breccias. Disseminated hydrothermal and vein hosted tourmaline occur in textural sites which suggest growth during and after ore formation. Tourmalines show a large range in δ 11 B values from −3.5 to 19.8‰, which record a change in fluid source between paragenetic stages of tourmaline growth. Pre-mineralization tourmaline crystals show heavy δ 11 B values (8–19.8‰) and high X-site occupancy (Na ± Ca; 0.69–1 apfu) suggesting a marine evaporite source for hydrothermal fluids. Syn-mineralization and replacement phases show lighter δ 11 B values (−3.5 to 15.1‰) and lower X-site occupancy (0.62–0.88 apfu), suggesting a subsequent influx of more dilute fluids derived from devolatilization of marine carbonates and clastic metasediments. The large, overlapping range in isotopic compositions and a skew toward the opposing population in the δ 11 B data for both tourmaline groups reflects continual tourmaline growth throughout mineralization, which records the process of fluid mixing. A peak in δ 11 B values at ∼8‰ largely controlled by tourmalines of syn- to post-ore timing represents a mixture of the two isotopically distinct fluids. This paper demonstrates that B-isotopes in tourmaline can be instrumental in interpreting complex and dynamic hydrothermal systems. The importance of B as an integral constituent of orogenic ore forming fluids and as a gangue phase in orogenic gold deposits makes B-isotope analysis a powerful tool for testing the level of source region variability in these fluids, and by extension, that of metal sources. [ABSTRACT FROM AUTHOR]

Given observational data from systems of interacting galaxies, we seek to determine the values of various dynamical parameters through the optimization of numerical models via genetic algorithms. However, fitting these models can be quite difficult. In this paper, we show how naive implementations of fitness functions can yield unintuitive results. We then propose a novel fitness function which was developed by utilizing data from the Galaxy Zoo: Mergers project (GZM). The human-scored models obtained from GZM were used to validate our fitness function and led to the adoption of a tidal distortion term which dramatically improved results. We also give a characterization of various geometric and dynamical symmetries inherent within the system and show how the knowledge of these symmetries can be used to reduce the volume of the parameter search space when performing optimization. Lastly, we implement a real-coded genetic algorithm with features designed to address these symmetries. Using simulated target systems with known parameters as a surrogate for observational data, we test our fitness function and genetic algorithm for robustness, accuracy, and convergence. We discuss the link between the degree of tidal distortion present in a target image and the constraints on the dynamical parameters using three different target systems with varying morphology. [ABSTRACT FROM AUTHOR]

The distinctive features of the Lower Devonian rocks of the Longmenshan area in southwestern China are brachiopod shell concentrations, especially in the Bailiuping, Ganxi and Xiejiawan Formations, where brachiopod shell concentrations occur widely throughout. Depending on the dominant skeletal elements, six types of shell concentrations can be distinguished: Protochonete , Acrospirifer , Howellella , Orientospirifer , polyspecific shell and polyspecific fragments concentrations. According to the shell features, taphonomic signature, host sediments and their relationships, four genetic models of the various shell concentrations are described in this paper. The genetic processes and distributions along an onshore–offshore area were clarified on the base of taphonomic analysis. Pavements of opportunistic species of Protochonetes are autochthonous assemblages living in quieter, deeper, more offshore waters near the maximum storm wave base. The pavements are the result of reduced sedimentation; the substrate was silty and water-saturated with variable turbidity soupy-mud. Transport by high-energy processes is interpreted as the final formation process of polyspecific fragments concentrations with most extensive scope from intertidal zone to the maximum storm wave base. The Acrospirifer , Howellella , and Orientospirifer concentrations have been stirred by storm wave action and quickly buried after short transport tempestite model. They are most easily preserved around the average storm wave-base. The polyspecific shell concentrations, which include large bivalves are autochthonous assemblages living in shallow and relatively quieter water near shore environments. Autochthonous assemblages of the opportunist Protochonetes bailiupingensis occurring in the Bailiuping Formation of the Longmenshan area not only record of storm events, but are also important features to identify and correlate the Bailiuping Formation in the field. [ABSTRACT FROM AUTHOR]

Rapid urbanization and exacerbated natural hazards, for example, floods, have been the big challenges for the sustainable development of many countries. Understanding the coupling coordination between urbanization and flood is important for promoting the sustainable development. However, there are few quantitative studies on the interaction between these two in China which is experiencing accelerated urbanization and climate change. In this paper, we developed an improved coupling coordination degree (CCD) model, applied Moran's I and projection pursuit model based on genetic algorithm to analyze the spatiotemporal distribution and spatial clustering effects of coupling coordination in urbanization–flood disasters system under the context of China's Five-Year Plan for National Economic and Social Development. Our results showed an upward trend of the comprehensive level of urbanization subsystem and a decreasing first and rising later trend of flood disaster subsystem in China from 2001 to 2018. There was a U-shaped relationship between the two subsystems during the research period. From 2001 to 2018, provinces with the moderate and slight uncoordinated development decreased by 9.68% and 16.13%, respectively, while provinces with the barely coordinated development increased by 16.21%. Spatially, there was a remarkable spatial dependency on CCD. The CCD between two subsystems in Northwestern China was higher than that in Southeastern China. Due to the effects of geographic position, rainfall, human activities, and economic development, the low-low (L-L) clusters concentrated in Southeastern China, and the high-high (H–H) clusters appeared in Northwestern China. Our results show that the improved CCD model, along with Moran's I analysis, can provide theoretical basis for policy makers to formulate reasonable measures for promoting the coupling coordination between urbanization and flood disasters. • Spatiotemporal distribution and dynamic evolution of nexus between urbanization and flood disasters were investigated. • Improved CCD model, Moran's I and projection pursuit model based on genetic algorithm were developed. • There was a U-shaped relationship in comprehensive level between urbanization and flood disasters. • CCD between two subsystems in Northwestern China was higher than that in Southeastern China. • There was a remarkable spatial clustering effects in CCD. [ABSTRACT FROM AUTHOR]

Developing genetic models of porphyry Cu-, Mo-, and Cu-Mo deposits requires knowledge on the source of metals for these deposits. Even though porphyry Cu deposits worldwide were relatively well-studied and the corresponding sources of metals were considered, porphyry Cu-Mo deposits of the Urals were not previously investigated. The paper presents new data on two porphyry Cu-Mo deposits of Paleozoic age: namely Late Devonian to Early Carboniferous Verkhneuralskoe in the South Urals and Late Carboniferous to Early Permian Talitsa in the Middle Urals. Pyrite trace element data indicate minor input of Ni, Tl, and Ag from host rocks of the Talitsa deposit. Talitsa and Vekhneuralskoe deposits are heterogeneous in Pb isotope composition and, on the contrary, homogeneous in S isotope composition which is typical for porphyry deposits worldwide. The both deposits show similar age-corrected Pb isotope compositions of sulfides (18.228 to 18.521 for 206Pb/204Pb, 15.598 to 15.705 for 206Pb/204Pb, and 38.087 to 38.474 for 208Pb/204Pb) and of magmatic and hydrothermal K-feldspars (from 18.291 to 18.449 for 206Pb/204Pb, from 15.598 to 15.608 for 207Pb/204Pb, and from 38.023 to 38.127 for 208Pb/204Pb); δ34S varies from −0.4 to +3.4 ‰. The isotope data suggest that granitoid melts were the dominant source of both sulfides and ore-bearing magmatic rocks. Mantle-crustal mixing model calculated for the Urals shows 95 % of mantle Pb in porphyry Cu-Mo deposits. [Display omitted] • Talitsa and Verkhneuralskoe porphyry Cu-Mo deposits in the Urals were studied. • Pyrite trace element data and Pb and S isotope study indicate magmatic source. • Calculated mantle-crustal mixing model shows 95 % of mantle Pb in the both deposits. [ABSTRACT FROM AUTHOR]

The reconstruction of Gene Regulatory Networks (GRNs) from time series gene expression data is highly relevant for the discovery of complex biological interactions and dynamics. Various computational strategies have been developed for this task, but most approaches have low computational efficiency and are not able to cope with high-dimensional, low sample-number, gene expression data. In this paper, we introduce a novel combined filter feature selection approach for efficient and accurate inference of GRNs. A Boolean framework for network modelling is used to demonstrate the efficacy of the proposed approach. Using discretized microarray expression data, the genes most relevant to each target gene are first filtered using ReliefF, an instance-based feature ranking method that is here applied for the first time to GRN inference. Then, further gene selection from the filtered-gene list is done using a mutual information-based min-redundancy max-relevance criterion by eliminating irrelevant genes. This combined method is executed on resampled datasets to finalize the optimal set of regulatory genes. Building upon our previous research, a Pearson correlation coefficient-based Boolean modelling approach is utilized for the efficient identification of the optimal regulatory rules associated with selected regulatory genes. The proposed approach was evaluated using gene expression datasets from small-scale and medium-scale real gene networks, and was observed to be more effective than Linear Discriminant Analysis, performed better than the individual feature selection methods, and obtained improved Structural Accuracy with a higher number of true positives than other state-of-the-art methods, while outperforming these methods with respect to Dynamic Accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Every day the world produces an enormous amount of textual data. This unstructured text is of little use unless it is labeled using a combination of categories, keywords, tags. Humans can never annotate such massive data, and with a growing divide between the daily produced data and those annotated, the only alternative is to mechanize it. Automatic annotation process helps in saving resources in terms of time and cost. The process of multi-label annotation involves associating a document with multiple relevant labels. This paper proposes an unsupervised model to annotate corpus using multi-labels automatically. The model is based on multi-label topic modeling and genetic algorithm (GA). Topic modeling is a technique to extract the hidden topics from text, and the GA is used to find the optimal number of topics. We hyper-tuned the parameters of the topic modeling using two different training methods: variational Bayes and Gibbs sampling. The class imbalance in a corpus can affect the result of topic modeling, where the majority class dominates the minority class. We overcome this problem using the partitioning method. Though the proposed model was developed for the Arabic dataset, it is language neutral. We tested our model on three large Arabic corpora and three large English social media datasets. For the Arabic language, our work being the first work that tackles multi-label annotation, we needed a reference to compare our model. For the Arabic corpus, we compared the result of automatic annotation against humans using crowdsourcing (whose labeling was checked for quality). The analysis of the annotation shows an agreement among models (machine vs. human) of 79.30%. Moreover, for the English dataset, the results are quite competitive. • World produces huge amount of unstructured text, which is useless without labeling. • Humans can never annotate such massive textual data. • In multi-label classification, we assign multiple labels to each instance. • We propose multi-label topic modeling and genetic algorithms to annotate texts. • Our automatic annotation agrees 79.3% with crowdsourced humans. [ABSTRACT FROM AUTHOR]

—In this paper, an improved genetic algorithm-based hybrid direct-optimal extraction method for small-signal model of GaN HEMT devices is proposed. Simulated annealing algorithm and fuzzy adaptive strategy are incorporated into genetic algorithm for model parameter optimization, to overcome the shortcoming of conventional genetic algorithm that tend to fall into local optimum solutions. The validity of proposed algorithm is verified by using a 20-element GaN HEMT small-signal equivalent circuit model under different operating conditions. The results show that the modeled S-parameters agree well with the measured S-parameters in the frequency range of 0.5–20.5 GHz. [ABSTRACT FROM AUTHOR]