Evolutionary Prediction Research Articles

An evolutionary robust solar radiation prediction model based on WT-CEEMDAN and IASO-optimized outlier robust extreme learning machine

As a kind of clean energy, solar energy occupies a pivotal position in energy applications. Accurate and reliable solar radiation prediction is critical to the application of solar energy. In particular, a novel solar radiation prediction based on wavelet transform (WT), complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), improved atom search optimization (IASO) and outlier-robust extreme learning machine (ORELM) is proposed for solar radiation prediction. First, WT is used to denoise the original solar radiation time series, and CEEMDAN method is used to decompose the denoised sequence into intrinsic mode function (IMF) components with different distributions according to the fluctuation scale. Then the IASO algorithm is used to optimize the weights and thresholds of the ORELM to improve the performance of the ORELM model. Levy flight is added to the ASO to enhance the local and global search capability while the chaos population initialization based on piecewise linear chaotic map (PWLCM) is taken to improve the randomness and ergodicity of the initial population within the feasible region. Finally, the comparison with other benchmark models verifies the robustness and accuracy of the proposed solar radiation prediction model.

A miRNAs catalogue from third-stage larvae and extracellular vesicles of Anisakis pegreffii provides new clues for host-parasite interplay

Anisakids are widespread marine parasites of medical, veterinary and economic relevance. They infect marine natural hosts but humans can accidentally acquire the fish-borne zoonosis anisakiasis by ingesting infected raw fishes or mollusks. Among the several species described, Anisakis pegreffii is one of the main etiological agent of the disease, in particular in the Mediterranean area. Despite the growing evidence of miRNAs involvement in host-parasite interplay, and the emerging role of exosomal microvesicles in shuttling them between different cell types (and sometime across species), no information on miRNAs from any Anisakis species is presently available. In this study we isolated extracellular vesicles (EVs) released by Anisakis pegreffii infective third-stage larvae (L3) and analyzed by RNA-seq small RNAs from both L3 and EVs. We showed by nanoparticle tracking analysis that L3 release in culture medium particles of size compatible with the one of extracellular vesicles. A catalogue of 156 miRNAs from A. pegreffii was compiled by sequence comparison to evolutionary close species and miRNA prediction software. Using differential expression analysis, we identified a small number of highly abundant miRNAs in larvae and extracellular vesicles fractions whose potential biological relevance may deserve future investigation. Finally, A. pegreffii miRNAs were compared to those described in other parasitic helminths and predicted targets among human genes were searched, suggesting their potential involvement during infection.

Exploring the stable structures and photovoltaic properties of an ideal pseudo-binary alloy: Indium gallium phosphide

Pseudo-binary III-V semiconductor alloys are promising for applications in optical, photovoltaic and photoelectrochemical fields, for their tunable band gaps and band alignments. In this work, we carefully investigate structural and photovoltaic properties of indium gallium phosphide (InxGa1-xP; 0 < x < 1) alloys. Using evolutionary algorithm crystal structure prediction, we perform structure searches for indentifying potential crystal structures of InxGa1-xP alloys in nine chemical compositions at zero temperature and ambient pressure. We find that InxGa1-xP alloys can adopt zinc blende (ZB) and wurtzite (WT) structures and the former generally have lower energy than the latter at all compositions. Based on these predicted InxGa1-xP structures, we evaluated their spectroscopic limited maximum efficiencies (SLMEs) and find that InxGa1-xP alloys with high In concentrations (x ≥ 0.5) have higher SLME values than those of InxGa1-xP alloys with low In concentrations (x < 0.5). We also find that SLMEs of disordered InxGa1-xP alloys are larger than those of ordered alloys due to the enhanced oscillator strengths.

Interpretable EEG seizure prediction using a multiobjective evolutionary algorithm

Seizure prediction might be the solution to tackle the apparent unpredictability of seizures in patients with drug-resistant epilepsy, which comprise about a third of all patients with epilepsy. Designing seizure prediction models involves defining the pre-ictal period, a transition stage between inter-ictal brain activity and the seizure discharge. This period is typically a fixed interval, with some recent studies reporting the evaluation of different patient-specific pre-ictal intervals. Recently, researchers have aimed to determine the pre-ictal period, a transition stage between regular brain activity and a seizure. Authors have been using deep learning models given the ability of such models to automatically perform pre-processing, feature extraction, classification, and handling temporal and spatial dependencies. As these approaches create black-box models, clinicians may not have sufficient trust to use them in high-stake decisions. By considering these problems, we developed an evolutionary seizure prediction model that identifies the best set of features while automatically searching for the pre-ictal period and accounting for patient comfort. This methodology provides patient-specific interpretable insights, which might contribute to a better understanding of seizure generation processes and explain the algorithm’s decisions. We tested our methodology on 238 seizures and 3687 h of continuous data, recorded on scalp recordings from 93 patients with several types of focal and generalised epilepsies. We compared the results with a seizure surrogate predictor and obtained a performance above chance for 32% patients. We also compared our results with a control method based on the standard machine learning pipeline (pre-processing, feature extraction, classifier training, and post-processing), where the control marginally outperformed our approach by validating 35% of the patients. In total, 54 patients performed above chance for at least one method: our methodology or the control one. Of these 54 patients, 21 (approx38%) were solely validated by our methodology, while 24 (approx44%) were only validated by the control method. These findings may evidence the need for different methodologies concerning different patients.

Ultrahigh-Pressure Magnesium Hydrosilicates as Reservoirs of Water in Early Earth.

The origin of water on the Earth is a long-standing mystery, requiring a comprehensive search for hydrous compounds, stable at conditions of the deep Earth and made of Earth-abundant elements. Previous studies usually focused on the current range of pressure-temperature conditions in the Earth's mantle and ignored a possible difference in the past, such as the stage of the core-mantle separation. Here, using abinitio evolutionary structure prediction, we find that only two magnesium hydrosilicate phases are stable at megabar pressures, α-Mg_{2}SiO_{5}H_{2} and β-Mg_{2}SiO_{5}H_{2}, stable at 262-338GPa and >338 GPa, respectively (all these pressures now lie within the Earth's iron core). Both are superionic conductors with quasi-one-dimensional proton diffusion at relevant conditions. In the first 30 million years of Earth's history, before the Earth's core was formed, these must have existed in the Earth, hosting much of Earth's water. As dense iron alloys segregated to form the Earth's core, Mg_{2}SiO_{5}H_{2} phases decomposed and released water. Thus, now-extinct Mg_{2}SiO_{5}H_{2} phases have likely contributed in a major way to the evolution of our planet.

Predicting the evolution of number of native contacts of a small protein by using deep learning approach

Native contacts (NCs) are one of the most vital parameters in order to define the resemblance of a protein conformation with its native state. Prediction of number of native contacts in a protein is useful in protein folding mechanism. In this work, we focused to predict the time series of the number of NCs of a small protein, insulin monomer by using three neural network based models, namely; Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). The input data used in the study was the time evolution of NC values of the folded and unfolded protein conformations computed from the equilibrated trajectories of atomistic molecular dynamics (MD) simulations performed with the aqueous solution of the protein at ambient as well as at an elevated temperature. The evolutionary prediction accuracy of the three models was tested by calculating two error parameters; Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Our study revealed that, although these three models are successful in forecasting the time evolutions of the NCs in terms of lower RMSE and MAE, the prediction through static memoryless artificial neural network, MLP was relatively less precise as compared to other two recurrent units, LSTM and GRU. The study infers that by using the available input data generated from the MD trajectories; these neural network based models could be used to predict the complex evolution pattern of distanced based structural parameters of a protein with a satisfactory level.

First-principles search of hot superconductivity in La-X-H ternary hydrides

Motivated by the recent claim of hot superconductivity with critical temperatures up to 550 K in La + x hydrides, we investigate the high-pressure phase diagram of compounds that may have formed in the experiment, using first-principles calculations for evolutionary crystal structure prediction and superconductivity. Starting from the hypothesis that the observed Tc may be realized by successive heating upon a pre-formed LaH10 phase, we examine plausible ternaries of lanthanum, hydrogen and other elements present in the diamond anvil cell: boron, nitrogen, carbon, platinum, gallium, gold. We find that only boron and, to a lesser extent, gallium form metastable superhydride-like structures that can host high-Tc superconductivity, but the predicted Tc’s are incompatible with the experimental reports. Our results indicate that, while the claims of hot superconductivity should be reconsidered, it is very likely that unknown H-rich ternary or multinary phases containing lanthanum, hydrogen, and possibly boron or gallium may have formed under the reported experimental conditions, and that these may exhibit superconducting properties comparable, or even superior, to those of currently known hydrides.

Evolutionary Machine Learning-Based Energy Consumption Prediction for the industry

In the digitalization of industry and the industry 4.0 environment, it is important to master the accurate forecasting of energy demand in order to guarantee the continuity of production service as well as to improve the reliability of the electrical system while promoting energy efficiency strategies in the industrial sector. This paper proposes machine learning models to predict the energy consumption demand in an industrial plant, which takes into account the at-tributes that directly the consumption. The proposed models in this work include Multiple Linear Regression (MLR), Decision Tree (DT), Recurrent Neural Networks (RNN) and Gated Recurrent United (GRU), which are compared according to their performances criteria which help to find the best forecasting models. Basing on simulation results, it is proven that the MLR approach is the best forecasting method.

Emerging leadership and the general factor of personality (GFP): A quasi-experimental test of an evolutionary prediction.

Emerging leadership and the general factor of personality (GFP): A quasi-experimental test of an evolutionary prediction.

Hybrid driven strategy for constrained evolutionary multi-objective optimization

In the constrained multi-objective optimization problems, the pursuit of feasibility could improve convergence but will lead to the loss of diversity. For optimization algorithm, balancing the weight between convergence and diversity dynamically is a challenge, especially in problems with low proportion of feasible regions. In this paper, a constrained multi-objective optimization algorithm is proposed based on a hybrid driven strategy to enhance both the feasibility and diversity performance of the approximate Pareto solutions. The proposed algorithm contains two archives, that one is driven by feasibility information and the other is driven by diversity information. A self-adaptive archive selection mechanism and a conditional tournament selection strategy are proposed to provide mating parent solutions according to the evolutionary stage. Moreover, in the update of the feasibility archive, an evolutionary direction prediction mechanism is proposed and adopted to improve the evolutionary efficiency. Compared to four other multi-objective algorithms on three benchmark suits of different types, the performance of the proposed algorithm is better than the peer algorithms, especially in large-infeasible-regions multi-objective optimization problems.

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism

The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.

On the ground state crystal structure of (Ag0.5Cu0.5)2ZnSnSe4

In our quest to improve the device performance of kesterite based thin film solar cells a recent combined neutron and x-ray diffraction study on the structural properties of powder samples of (Ag1-xCux)2ZnSnSe4 yielded surprising results. Although both end members, Ag2ZnSnSe4 and Cu2ZnSnSe4 crystallise in the kesterite crystal structure, in the intermediate region x∼0.5 the structurally similar stannite crystal structure has been identified as the prevalent ground state crystal structure. Here, in order to understand this behaviour, we perform first-principles calculations based on density functional theory, combined with evolutionary crystal structure prediction investigation as implemented in the USPEX code. Our results show that (Ag0.5Cu0.5)2ZnSnSe4 crystallises in the kesterite crystal structure as well, with the Ag and Cu atoms being exclusively placed at the (2a) and (2c) Wyckoff positions, with no apparent mixing. A detailed structural analysis combined with electronic and optical properties will be discussed alongside experimental results.

Evolutionary Prediction of Soil Loss from Observed Rainstorm Parameters in an Erosion Watershed Using Genetic Programming

Various environmental problems such as soil degradation and landform evolutions are initiated by a natural process known as soil erosion. Aggregated soil surfaces are dispersed through the impact of raindrop and its associated parameters, which were considered in this present work as function of soil loss. In an attempt to monitor environmental degradation due to the impact of raindrop and its associated factors, this work has employed the learning abilities of genetic programming (GP) to predict soil loss deploying rainfall amount, kinetic energy, rainfall intensity, gully head advance, soil detachment, factored soil detachment, runoff, and runoff rate database collected over a three-year period as predictors. Three evolutionary trials were executed, and three models were presented considering different permutations of the predictors. The performance evaluation of the three models showed that trial 3 with the highest parametric permutation, i.e., that included the influence of all the studied parameters showed the least error of 0.1 and the maximum coefficient of determination (R2) of 0.97 and as such is the most efficient, robust, and applicable GP model to predict the soil loss value.

Mn(IV)‐Substituted Metal(II) Hexafluorido Metallates(IV): Synthesis, Crystal Structures, and Luminescence Properties

AbstractWe synthesized 16 novel red‐emitting Mn(IV)‐substituted phosphors of the compositions MgGeF6 : Mn, MgPbF6 : Mn, CaMF6 : Mn (M=Ge, Sn, Pb, Zr, Hf), SrSnF6 : Mn, “SrTiF6 : Mn”, BaPbF6 : Mn, ZnMF6 : Mn (M=Sn, Pb, Zr, Hf), and CdMF6 : Mn (M=Pb, Hf) using a dry‐chemical fluorination process completely avoiding hydrofluoric acid. In comparison with the commonly used red‐emitter K2SiF6 : Mn (KSF) with its emission maximum at 630.8 nm, all above Ca and Sr compounds and CdHfF6 : Mn show a blue‐shift of their emission maxima, the compound SrSnF6 : Mn even by 4.2 nm down to 626.6 nm. The compounds were characterized by powder X‐ray diffraction, IR and emission spectroscopy. We additionally present the crystal structures of the unsubstituted compounds CaZrF6, CaHfF6, CaPbF6, CdZrF6, CdHfF6, and MgHfF6, which all crystallize isotypic in the NaSbF6 structure type. The compound “SrTiF6” was characterized by powder XRD and studied with evolutionary structure prediction methods, but its crystal structure could not yet be solved.

LaN structural and topological transitions driven by temperature and pressure

We study lanthanum mononitride LaN by first-principles calculations. The commonly reported rock-salt structure of Fm3¯m symmetry for rare-earth monopnictides is found to be dynamically unstable for LaN at zero temperature. Using density functional theory and evolutionary crystal prediction, we discover a new, dynamically stable structure with P1 symmetry at 0 K. This P1-LaN exhibits spontaneous electric polarization. Our ab initio molecular dynamics simulations of finite-temperature phonon spectra further suggest that LaN will undergo ferroelectric and structural transitions from P1 to Fm3¯m symmetry, when temperature is increased. Moreover, P1-LaN will transform to a tetragonal structure with P4/nmm symmetry at a critical pressure P=18 GPa at 0 K. Electronic structures computed with an advanced hybrid functional show that the high-temperature rock-salt LaN can change from a trivial insulator to a strong topological insulator at P~14 GPa. Together, our results indicate that when P=14-18 GPa, LaN can show simultaneous temperature-induced structural, ferroelectric, and topological transitions. Lanthanum monopnictides thereby provide a rich playground for exploring novel phases and phase transitions driven by temperature and pressure.

Evolutionary theory prediction: Response rate as a joint function of reinforcement rate and reinforcer magnitude.

Artificial organisms (AOs) animated by an evolutionary theory of behavior dynamics (ETBD) worked on concurrent interval schedules with a standard reinforcer magnitude on 1 alternative and a range of reinforcer magnitudes on the other. The reinforcer magnitudes on the second alternative were hedonically scaled using the generalized matching law. The AOs then worked on single interval schedules that arranged various combinations of the scaled reinforcer magnitudes and a range of nominal schedule values. This produced bivariate response rate data to which 5 candidate equations were fitted. One equation was found to provide the best description of the bivariate data in terms of percentage of variance accounted for, information criterion value, and residual profile. This equation consisted of 2 factors, 1 entailing the scaled magnitude, 1 entailing the obtained reinforcement rate, and both expressed in the form of exponentiated hyperbolas. The theory's prediction of the bivariate equation, along with additional predictions of the theory, were tested on data from an experiment in which rats pressed levers for various concentrations of sucrose pellets. The bivariate equation predicted by the theory was confirmed, as were all the additional predictions of the theory that could be tested on this data set.

Machine learning and evolutionary prediction of superhard B-C-N compounds

We build random forests models to predict elastic properties and mechanical hardness of a compound, using only its chemical formula as input. The model training uses over 10,000 target compounds and 60 features based on stoichiometric attributes, elemental properties, orbital occupations, and ionic bonding levels. Using the models, we construct triangular graphs for B-C-N compounds to map out their bulk and shear moduli, as well as hardness values. The graphs indicate that a 1:1 B-N ratio can lead to various superhard compositions. We also validate the machine learning results by evolutionary structure prediction and density functional theory. Our study shows that BC10N, B4C5N3, and B2C3N exhibit dynamically stable phases with hardness values >40 GPa, which are superhard materials that potentially could be synthesized by low-temperature plasma methods.

Optimal foraging algorithm with direction prediction

Optimal foraging algorithm (OFA) was presented as a stochastic search algorithm to solve global optimization problems in 2017. As an emerging algorithm, there are many excellent potentials to be explored. To enhance the performance of OFA, a novel optimal foraging algorithm with direction prediction is presented in this paper, named OFA/DP. During the iterations, the population information can be fully utilized by OFA/DP. Once a new optimal solution is found, the evolutionary direction prediction strategy is applied to generate more potential candidates. In addition, considering the situation that the population does not evolve for a long time, which means that the algorithm has achieved the global optima or trapped into local optima. In this case, the Gaussian oscillation strategy is adopted to attempt to find a better solution, and escaping from local optima. To validate the efficiency of the proposed algorithm, the numerical experiments on 20 benchmark functions, 30 CEC test functions, 6 large scale functions, 28 CEC2017 constrained problems, 3 engineering problems, 6 unconstrained multi-objective functions and 10 constrained multi-objective functions are executed. The simulation results and the statistical test demonstrate that OFA/DP has a superior performance in most of functions with faster convergence speed.

Mating increases Drosophila melanogaster females' choosiness by reducing olfactory sensitivity to a male pheromone.

Females that are highly selective when choosing a mate run the risk of remaining unmated or delaying commencing reproduction. Therefore, low female choosiness would be beneficial when males are rare but it would be maladaptive if males become more frequent. How can females resolve this issue? Polyandry would allow mating-status-dependent choosiness, with virgin females selecting their first mate with little selectivity and becoming choosier thereafter. This plasticity in choosiness would ensure timely acquisition of sperm and enable females to increase offspring quality during later mating. Here, we show that Drosophila melanogaster females display such mating-status-dependent choosiness by becoming more selective once mated and identify the underlying neurohormonal mechanism. Mating releases juvenile hormone, which desensitizes Or47b olfactory neurons to a pheromone produced by males, resulting in increased preference for pheromone-rich males. Besides providing a mechanism to a long-standing evolutionary prediction, these data suggest that intersexual selection in D. melanogaster, and possibly in all polyandrous, sperm-storing species, is mainly the domain of mated females since virgin females are less selective. Juvenile hormone influences behaviour by changing cue responsiveness across insects; the neurohormonal modulation of olfactory neurons uncovered in D. melanogaster provides an explicit mechanism for how this hormone modulates behavioural plasticity.

Formation of copper boride on Cu(111)

Boron forms compounds with nearly all metals, with notable exception of copper and other group IB and IIB elements. Here, we report an unexpected discovery of ordered copper boride grown epitaxially on Cu(111) under ultrahigh vacuum. Scanning tunneling microscopy experiments combined with ab initio evolutionary structure prediction reveal a remarkably complex structure of 2D-Cu8B14. Strong intra-layer p–d hybridization and a large amount of charge transfer between Cu and B atoms are the key factors for the emergence of copper boride. This makes the discovered material unique and opens up the possibility of synthesizing ordered low-dimensional structures in similar immiscible systems.