Hybrid Computational Model Research Articles

Suggestion of a hybrid computational model to optimize EEG motor imagery signals classification

Brain-computer interfaces (BCIs) have proven to be innovative and promising technologies for human-machine interaction. However, distinguishing with relative precision between EEG motor signals generated by real or imagined movements of the left and right hands has still been a major challenge. By analyzing brain electrical signals and the processes of reading, processing, and interpreting data, the hybrid model suggested in this dissertation contributed to greater effectiveness of the algorithms, with accuracy above that normally found in most models present in the most recent literature. The experimental and observational research carried out in the creation of the methodology applied in this dissertation ensures the replication of automation methods, techniques, and algorithms, machine learning, and more specifically, in data preparation, feature extraction, signal classification, and obtaining results with greater accuracy for the implementation of brain-computer interfaces, BCIs. This dissertation opens new possibilities for future research and deepening of techniques, tools, and new experiments. With a premise to create personalized BCIs and trained specifically for individual pattern recognition, as they have shown greater potential results. Each human being is unique and has singular characteristics that distinguish them from all other beings.

Experimentally Aided Operational Virtual Prototyping to Predict Best Clamping Conditions for Face Milling of Large-Size Structures

Vibrations occurring during milling operations are one of the main issues disturbing the pursuit of better efficiency of milling operations and product quality. Even in the case of a stable cutting process, vibration reduction is still an important goal. One of the possible solutions to obtain it is selection of the favorable conditions for clamping the workpiece to the machine table. In this paper, a method for predicting and selecting the clamping condition of a large-size workpiece for the reduction in vibrations during milling is presented. A modal test of the workpiece is performed first for a selected set of tightening screw settings. Next, one milling pass is performed to obtain reference data which are then used to tune the hybrid computational model. In the subsequent step, milling simulations are performed for a set of tightening variants, and the best one is selected, providing the lowest vibrations, assessed as the root mean square (RMS) of vibration displacements. In this paper, the description of the clamping selection procedure, key elements of the simulation model, and simulation and experimental results obtained for the milling of the test workpiece performed for a set of different clamping conditions are provided. The proposed method accurately predicts not only the best but also the worst clamping conditions.

Compact hybrid type electronic neuron and computational model of its dynamics

Compact hybrid type electronic neuron and computational model of its dynamics

Thermodynamically consistent hybrid computational models for fluid-particle interactions

We introduce a novel computational framework designed to explore the dynamic interactions between fluid and solid particles or structures immersed in a viscous fluid medium adhering to the generalized Onsager principle. This innovative framework harnesses the power of the phase-field-embedding method, in which each solid component, whether rigid or elastic, is characterized by a volume-preserving phase field. The unified velocity within the fluid-solid ensemble governs the movement of both solid particles and the surrounding fluid, specifically for passive particles. Active particles, however, are not only influenced by this unified velocity but are also driven by their self-propelling velocities. To capture exclusive volume interactions among particles and between particles and boundaries, we employ repulsive potential forces at a coarser scale. These forces effectively model repulsion and collision effects. Rigid particles maintain structural integrity by enforcing a zero velocity gradient tensor within their spatial domains, necessitating the introduction of a constraining stress tensor. In contrast, elastic particles are governed by a quasi-linear constitutive equation describing the elastic stress within their domains, allowing for accurate modeling of their deformations. The motion of solid particles is tracked by monitoring the dynamics of their centers of mass. This approach facilitates the development of a hybrid, thermodynamically consistent hydrodynamic model applicable to both rigid and elastic particles. To numerically solve this thermodynamically consistent model for elastic particles, we present a structure-preserving numerical algorithm. Notably, in the limit of an infinite elastic modulus, this algorithm converges to the one employed for modeling rigid particles. Finally, we substantiate the effectiveness, accuracy, and stability of our proposed scheme through a series of numerical experiments. These experiments not only validate the computational framework but also showcase its capabilities, reinforcing the reliability of our approach.

Augmenting a Simulation Campaign for Hybrid Computer Model and Field Data Experiments

The Kennedy and O’Hagan (KOH) calibration framework uses coupled Gaussian processes (GPs) to meta-model an expensive simulator (first GP), tune its “knobs” (calibration inputs) to best match observations from a real physical/field experiment and correct for any modeling bias (second GP) when predicting under new field conditions (design inputs). There are well-established methods for placement of design inputs for data-efficient planning of a simulation campaign in isolation, that is, without field data: space-filling, or via criterion like minimum integrated mean-squared prediction error (IMSPE). Analogues within the coupled GP KOH framework are mostly absent from the literature. Here we derive a closed form IMSPE criterion for sequentially acquiring new simulator data for KOH. We illustrate how acquisitions space-fill in design space, but concentrate in calibration space. Closed form IMSPE precipitates a closed-form gradient for efficient numerical optimization. We demonstrate that our KOH-IMSPE strategy leads to a more efficient simulation campaign on benchmark problems, and conclude with a showcase on an application to equilibrium concentrations of rare earth elements for a liquid–liquid extraction reaction.

Analysis of nanonization and purification of organic compounds via green supercritical processing: Model development using advanced hybrid techniques

Complex relationship between process parameters and critical quality attributes in nanonization of drugs based on supercritical processing demands for developing advanced hybrid computational models. In this research, the complicated relationships between temperature, pressure, solvent density, and solubility of Nystatin in supercritical carbon dioxide for green processing of nanoparticles was explored via advanced hybrid models. For the first time, three base regression models: K-Nearest Neighbors (KNN), Bayesian Ridge Regression (BRR), and Polynomial Regression (PR) in combination with Political Optimizer (PO) for correlation of solubility of organic compound as well as solvent density was developed. Additionally, an ensemble approach, Bagging, is employed to enhance predictive performance of the models. Bagging ensemble technique combined with BRR predicts Nystatin solubility with an R2 of 0.89459. Bagging combined with KNN produces a score of 0.9162, showcasing its strong predictive capabilities. Moreover, bagging coupled with PR exhibits exceptional predictive performance, attaining a score of 0.99819. When predicting solvent density, bagging in conjunction with BRR yields an R2 value of 0.81069, while bagging paired with KNN achieves a higher score of 0.90542, demonstrating its proficiency in modeling solvent density. Bagging with PR surpasses the other models, attaining a significant R2 of 0.99035, highlighting its superior predictive accuracy.

Hybrid computational model depicts the contribution of non-significant lobes of human brain during the perception of emotional stimuli

Emotions are synchronizing responses of human brain while executing cognitive tasks. Earlier studies had revealed strong correlation between specific lobes of the brain to different types of emotional valence. In the current study, a comprehensive three-dimensional mapping of human brain for executing emotion specific tasks had been formulated. A hybrid computational machine learning model customized from Custom Weight Allocation Model (CWAM) and defined as Custom Rank Allocation Model (CRAM). This regression-based hybrid computational model computes the allocated tasks to different lobes of the brain during their respective executive stage. Event Related Potentials (ERP) were obtained with significant effect at P1, P2, P3, N170, N2, and N4. These ERPs were configured at Pz, Cz, F3, and T8 regions of the brain with maximal responses; while regions like Cz, C4 and F4 were also found to make effective contributions to elevate the responses of the brain, and thus these regions were configured as augmented source regions of the brain. In another circumstance of frequent –deviant - equal (FDE) presentation of the emotional stimuli, it was observed that the brain channels C3, C4, P3, P4, O1, O2, and Oz were contributing their emotional quotient to the overall response of the brain regions; whereas, the interaction effect was found presentable at O2, Oz, P3, P4, T8 and C3 regions of brain. The proposed computational model had identified the potential neural pathways during the execution of emotional task.

Development of advanced hybrid computational model for description of molecular separation in liquid phase via polymeric membranes

Separation of molecules using membranes has been computationally studied in this work for understanding the separation process and also assessment of integrating machine learning (ML) techniques to computational fluid dynamics (CFD) techniques. We initially performed CFD computations to simulate mass transfer in a membrane-based molecular separation system, and then exported the mass transfer data for training ML models. Indeed, a hybrid model was developed and employed for membrane process in separating a solute from a solution. We have modeled a data set containing more than 14 thousand data rows, which has two inputs r (m) and z (m) and single response, i.e., C (mol/m3) that is solute’ content in the solution. Decision tree-based models are used in this modeling. Simple Decision Tree (DT), Boosted Decision Tree (BDT) that is DT alongside AdaBoost, and Random Forest (RF) are the selected models for this research. The ML techniques were further optimized via a novel HHO algorithm. Final assessments show the R2 scores of 0.9991, 0.9874, and 0.9997 respectively for BDT, DT, and RF models. Based on this result and other analysis, RF is selected as the best method in this study for simulation of membrane process. This model has an RMSE error rate of 2.22 × 103.

Robust additive manufacturable Ni superalloys designed by the integrated optimization of local elemental segregation and cracking susceptibility criteria

To achieve an effective design of additively manufacturable Ni superalloys with decent service performance, a hybrid computational design model has been developed, where the strategy to tailor local elemental segregations was integrated within a scheme of minimizing the cracking susceptibility. More specifically, the phase boundary of primary NbC / γ matrix was introduced into the design routine to tune the spatial distribution of critical solutes at an atomic scale, thereby inhibiting the formation of borides and segregation-induced cracking. Based on the output of the design, new grades of Ni superalloy have been developed with excellent additive manufacturability, as confirmed by the robustness of printing parameters in fabricating low-defect-density samples. The capability of the phase boundaries to evenly distribute boron atoms was validated experimentally, and the cracking induced by uncontrolled boron segregation at grain boundaries was effectively prevented. The newly designed alloys showed good tensile properties and decent oxidation resistance at different service temperatures, which are comparable to those of conventionally produced superalloys. The finding that phase boundaries can be employed to prevent undesirable clustering of boron atoms can be extended to manipulate the distributions of other critical elements, which provides a new path for designing novel Ni superalloys with balanced printability and mechanical properties.

Multiformity of extracellular microelectrode recordings from Aδ neurons in the dorsal root ganglia: a computational modeling study.

Microelectrodes serve as a fundamental tool in electrophysiology research throughout the nervous system, providing a means of exploring neural function with a high resolution of neural firing information. We constructed a hybrid computational model using the finite element method and multicompartment cable models to explore factors that contribute to extracellular voltage waveforms that are produced by sensory pseudounipolar neurons, specifically smaller A-type neurons, and that are recorded by microelectrodes in dorsal root ganglia. The finite element method model included a dorsal root ganglion, surrounding tissues, and a planar microelectrode array. We built a multicompartment neuron model with multiple trajectories of the glomerular initial segment found in many A-type sensory neurons. Our model replicated both the somatic intracellular voltage profile of Aδ low-threshold mechanoreceptor neurons and the unique extracellular voltage waveform shapes that are observed in experimental settings. Results from this model indicated that tortuous glomerular initial segment geometries can introduce distinct multiphasic properties into a neuron's recorded waveform. Our model also demonstrated how recording location relative to specific microanatomical components of these neurons, and recording distance from these components, can contribute to additional changes in the multiphasic characteristics and peak-to-peak voltage amplitude of the waveform. This knowledge may provide context for research employing microelectrode recordings of pseudounipolar neurons in sensory ganglia, including functional mapping and closed-loop neuromodulation. Furthermore, our simulations gave insight into the neurophysiology of pseudounipolar neurons by demonstrating how the glomerular initial segment aids in increasing the resistance of the stem axon and mitigating rebounding somatic action potentials.NEW & NOTEWORTHY We built a computational model of sensory neurons in the dorsal root ganglia to investigate factors that influence the extracellular waveforms recorded by microelectrodes. Our model demonstrates how the unique structure of these neurons can lead to diverse and often multiphasic waveform profiles depending on the location of the recording contact relative to microanatomical neural components. Our model also provides insight into the neurophysiological function of axon glomeruli that are often present in these neurons.

Application of ANN-MOGA for nutrient sequestration for wastewater remediation and production of polyunsaturated fatty acid (PUFA) by Chlorella sorokiniana MSP1

Chlorella bears excellent potential in removing nutrients from industrial wastewater and lipid production enriched with polyunsaturated fatty acids. However, due to the changing nutrient dynamics of wastewater, growth and metabolic activity of Chlorella are affected. In order to sustain microalgal growth in wastewater with concomitant production of PUFA rich lipids, RSM (Response Surface Methodology) followed by heuristic hybrid computation model ANN-MOGA (Artificial Neural Network- Multi-Objective Genetic Algorithm) were implemented. Preliminary experiments conducted taking one factor at a time and design matrix of RSM with process variables viz. Sodium chloride (1 mM–40 mM), Magnesium sulphate (100 mg–800 mg) and incubation time (4th day to 20th day) were validated by ANN-MOGA. The study reported improved biomass and lipid yield by 54.25% and 12.76%, along with total nitrogen and phosphorus removal by 21.92% and 18.72% respectively using ANN-MOGA. It was evident from FAME results that there was a significantly improved concentration of linoleic acid (19.1%) and γ-linolenic acid (21.1%). Improved PUFA content makes it a potential feedstock with application in cosmeceutical, pharmaceutical and nutraceutical industry. The study further proves that C. sorokiniana MSP1 mediated industrial wastewater treatment with PUFA production is an effective way in providing environmental benefits along with value addition. Moreover, ANN-MOGA is a relevant tool that could control microalgal growth in wastewater.

The spatial dynamics of immune response upon virus infection through hybrid dynamical computational model.

The immune responses play important roles in the course of disease initiation and progression upon virus infection such as SARS-CoV-2. As the tissues consist of spatial structures, the spatial dynamics of immune responses upon viral infection are essential to the outcome of infection. A hybrid computational model based on cellular automata coupled with partial differential equations is developed to simulate the spatial patterns and dynamics of the immune responses of tissue upon virus infection with several different immune movement modes. Various patterns of the distribution of virus particles under different immune strengths and movement modes of immune cells are obtained through the computational models. The results also reveal that the directed immune cell wandering model has a better immunization effect. Several other characteristics, such as the peak level of virus density and onset time and the onset of the diseases, are also checked with different immune and physiological conditions, for example, different immune clearance strengths, and different cell-to-cell transmission rates. Furthermore, by the Lasso analysis, it is identified that the three main parameters had the most impact on the rate of onset time of disease. It is also shown that the cell-to-cell transmission rate has a significant effect and is more important for controlling the diseases than those for the cell-free virus given that the faster cell-to-cell transmission than cell-free transmission the rate of virus release is low. Our model simulates the process of viral and immune response interactions in the alveola repithelial tissues of infected individuals, providing insights into the viral propagation of viruses in two dimensions as well as the influence of immune response patterns and key factors on the course of infection.

Memory precision and age differentially predict the use of decision-making strategies across the lifespan

Memory function declines in normal aging, in a relatively continuous fashion following middle-age. The effect of aging on decision-making is less well-understood, with seemingly conflicting results on both the nature and direction of these age effects. One route for clarifying these mixed findings is to understand how age-related differences in memory affect decisions. Recent work has proposed memory sampling as a specific computational role for memory in decision-making, alongside well-studied mechanisms of reinforcement learning (RL). Here, we tested the hypothesis that age-related declines in episodic memory alter memory sampling. Participants (total N = 361; ages 18–77) performed one of two variants of a standard reward-guided decision experiment with additional trial-unique mnemonic content and a separately-administered task for assessing memory precision. When we fit participants’ choices with a hybrid computational model implementing both memory-based and RL-driven valuation side-by-side, we found that memory precision tracked the contribution of memory sampling to choice. At the same time, age corresponded to decreasing influence of RL and increasing perseveration. A second experiment confirmed these results and further revealed that memory precision tracked the specificity of memories selected for sampling. Together, these findings suggest that differences in decision-making across the lifespan may be related to memory function, and that interventions which aim to improve the former may benefit from targeting the latter.

Artificial dragonfly algorithm in the Hopfield neural network for optimal Exact Boolean k satisfiability representation.

This study proposes a novel hybrid computational approach that integrates the artificial dragonfly algorithm (ADA) with the Hopfield neural network (HNN) to achieve an optimal representation of the Exact Boolean kSatisfiability (EBkSAT) logical rule. The primary objective is to investigate the effectiveness and robustness of the ADA algorithm in expediting the training phase of the HNN to attain an optimized EBkSAT logic representation. To assess the performance of the proposed hybrid computational model, a specific Exact Boolean kSatisfiability problem is constructed, and simulated data sets are generated. The evaluation metrics employed include the global minimum ratio (GmR), root mean square error (RMSE), mean absolute percentage error (MAPE), and network computational time (CT) for EBkSAT representation. Comparative analyses are conducted between the results obtained from the proposed model and existing models in the literature. The findings demonstrate that the proposed hybrid model, ADA-HNN-EBkSAT, surpasses existing models in terms of accuracy and computational time. This suggests that the ADA algorithm exhibits effective compatibility with the HNN for achieving an optimal representation of the EBkSAT logical rule. These outcomes carry significant implications for addressing intricate optimization problems across diverse domains, including computer science, engineering, and business.

Probabilistic Slope Stability Analysis of Mount St. Helens Using Scoops3D and a Hybrid Intelligence Paradigm

In the past, numerous stratovolcanoes worldwide witnessed catastrophic flank collapses. One of the greatest risks associated with stratovolcanoes is a massive rock failure. On 18 May 1980, we witnessed a rock slope failure due to a volcano eruption, and a 2185.60 m high rock slope of Mount St. Helens was collapsed. Thus, from the serviceability perspective, this work presents an effective computational technique to perform probabilistic analyses of Mount St. Helens situated in Washington, USA. Using the first-order second-moment method, probability theory and statistics were employed to map the uncertainties in rock parameters. Initially, Scoops3D was used to perform slope stability analysis followed by probabilistic evaluation using a hybrid computational model of artificial neural network (ANN) and firefly algorithm (FF), i.e., ANN-FF. The performance of the ANN-FF model was examined and compared with that of conventional ANN and other hybrid ANNs built using seven additional meta-heuristic algorithms. In the validation stage, the proposed ANN-FF model was the best-fitted hybrid model with R2 = 0.9996 and RMSE = 0.0042. Under seismic and non-seismic situations, the reliability index and the probability of failure were estimated. The suggested method allows for an effective assessment of the failure probability of Mount St. Helens under various earthquake circumstances. The developed MATLAB model is also attached as a supplementary material for future studies.

Anomalously strong effects of plume contraction and material redeposition in nanosecond pulsed laser vaporization

Long-term dynamics of vapor plumes induced by irradiation of a copper target in argon background gas by nanosecond laser pulses is studied numerically based on a one-dimensional hybrid computational model. The model includes a thermal model of the irradiated target and a kinetic model of the gaseous plume flow. The latter is implemented in the form of the direct simulation Monte Carlo method. The simulations, performed at moderate laser fluences in the range from 1.5 to 4 Jcm−2 and background gas pressures between 0.01 and 1 bar, show that the long-term plume dynamics can be divided into three major stages: initial inertial expansion, plume contraction, and subsequent diffusive expansion. The simulations unexpectedly predict extremely strong effects of plume contraction, when the plume size can exhibit a two-fold reduction, and delayed material redeposition back to the irradiated surface, when ∼90% of vaporized material returns to the irradiated surface. Both plume contraction and delayed redeposition originate from the strong backward flow induced by the internal (secondary) shock wave that propagates from the mixing layer towards the irradiated surface. The major part of vaporized material condenses at the surface with a long time delay with respect to the laser pulse after the plume stopping and before the onset of diffusive expansion. As a result, only a marginal part of vapor, which can be as small as ∼5%, is retained in the plume by the beginning of the diffusive expansion. The obtained simulation results suggest that the plume contraction and delayed material redeposition are common phenomena for nanosecond laser vaporization and may affect the efficiency and quality of laser surface modification in the ablation regime as well.

Hybrid Machine Learning Approach to Predict the Site Selectivity of Iridium-Catalyzed Arene Borylation.

The borylation of aryl and heteroaryl C-H bonds is valuable for the site-selective functionalization of C-H bonds in complex molecules. Iridium catalysts ligated by bipyridine ligands catalyze the borylation of the C-H bond that is most acidic and least sterically hindered in an arene, but predicting the site of borylation in molecules containing multiple arenes is difficult. To address this challenge, we report a hybrid computational model that predicts the Site of Borylation (SoBo) in complex molecules. The SoBo model combines density functional theory, semiempirical quantum mechanics, cheminformatics, linear regression, and machine learning to predict site selectivity and to extrapolate these predictions to new chemical space. Experimental validation of SoBo showed that the model predicts the major site of borylation of pharmaceutical intermediates with higher accuracy than prior machine-learning models or human experts, demonstrating that SoBo will be useful to guide experiments for the borylation of specific C(sp2)-H bonds during pharmaceutical development.

HNSPPI: a hybrid computational model combing network and sequence information for predicting protein-protein interaction.

Most life activities in organisms are regulated through protein complexes, which are mainly controlled via Protein-Protein Interactions (PPIs). Discovering new interactions between proteins and revealing their biological functions are of great significance for understanding the molecular mechanisms of biological processes and identifying the potential targets in drug discovery. Current experimental methods only capture stable protein interactions, which lead to limited coverage. In addition, expensive cost and time consuming are also the obvious shortcomings. In recent years, various computational methods have been successfully developed for predicting PPIs based only on protein homology, primary sequences of protein or gene ontology information. Computational efficiency and data complexity are still the main bottlenecks for the algorithm generalization. In this study, we proposed a novel computational framework, HNSPPI, to predict PPIs. As a hybrid supervised learning model, HNSPPI comprehensively characterizes the intrinsic relationship between two proteins by integrating amino acid sequence information and connection properties of PPI network. The experimental results show that HNSPPI works very well on six benchmark datasets. Moreover, the comparison analysis proved that our model significantly outperforms other five existing algorithms. Finally, we used the HNSPPI model to explore the SARS-CoV-2-Human interaction system and found several potential regulations. In summary, HNSPPI is a promising model for predicting new protein interactions from known PPI data.

Gell: A GPU-powered 3D hybrid simulator for large-scale multicellular system.

As a powerful but computationally intensive method, hybrid computational models study the dynamics of multicellular systems by evolving discrete cells in reacting and diffusing extracellular microenvironments. As the scale and complexity of studied biological systems continuously increase, the exploding computational cost starts to limit large-scale cell-based simulations. To facilitate the large-scale hybrid computational simulation and make it feasible on easily accessible computational devices, we develop Gell (GPU Cell), a fast and memory-efficient open-source GPU-based hybrid computational modeling platform for large-scale system modeling. We fully parallelize the simulations on GPU for high computational efficiency and propose a novel voxel sorting method to further accelerate the modeling of massive cell-cell mechanical interaction with negligible additional memory footprint. As a result, Gell efficiently handles simulations involving tens of millions of cells on a personal computer. We compare the performance of Gell with a state-of-the-art paralleled CPU-based simulator on a hanging droplet spheroid growth task and further demonstrate Gell with a ductal carcinoma in situ (DCIS) simulation. Gell affords ~150X acceleration over the paralleled CPU method with one-tenth of the memory requirement.

Drug-target interactions prediction based on network topology feature representation embedded deep forest

Identifying drug-target interactions (DTIs) is instructive in drug design and disease treatment. Existing studies typically used the properties of nodes (drug chemical structure and protein sequence) to construct drug and target features while ignoring the influence of network topology information on the prediction of DTIs. In this study, a hybrid computation model is proposed to predict DTIs based on the network topological feature representation embedded the deep forest model (NTFRDF). The main idea is to capture the topological differences by learning the low-dimensional feature representation of drugs and targets from the heterogeneous network. In addition, the multi-similarity fusion strategy is proposed to mine hidden useful information in the known DTIs from multi-view to enrich network features of the heterogeneous network. Based on the deep forest framework, the performance of the proposed method is examined on four benchmark datasets. Our experimental results verify that the proposed method is competitive compared with some existing DTIs prediction models.