Hybrid Computational Model Research Articles

Modified election algorithm in hopfield neural network for optimal randomksatisfiability representation

Election algorithm (EA) is a novel metaheuristics optimization model motivated by phenomena of the socio-political mechanism of presidential election conducted in many countries. The capability and robustness EA in finding an optimal solution to optimization has been proven by various researchers. In this paper, modified version of EA has been utilized in accelerating the searching capacity of Hopfield neural network (HNN) learning phase for optimal random-kSAT logical representation (HNN-R2SATEA). The utility of the proposed approach has been contrasted with the current standard exhaustive search algorithm (HNN-R2SATES) and the newly developed algorithm HNN-R2SATICA. From the analysis obtained, it has been clearly shown that the proposed hybrid computational model HNN-R2SATEA outperformed other existing model in terms of global minima ratio (Zm), mean absolute error (MAE), Bayesian information criterion (BIC) and execution time (ET). The finding portrays that the MEA algorithm surpassed the other two algorithms for optimal random-kSAT logical representation.

Photovoltaic power forecasting using wavelet Neuro-Fuzzy for active solar trackers

The generation of electric energy by photovoltaic (PV) panels depends on many parameters, one of them is the sun’s angle of incidence. By using solar active trackers, it is possible to maximize generation capacity through real-time positioning. However, if the engines that update the position of the panels use more energy than the difference in efficiency, the solar tracker system becomes ineffective. In this way, a time series forecasting method can be assumed to determine the generation capacity in a pre-established horizon prediction to evaluate if a position update would provide efficient results. Among a wide range of algorithms that can be used in forecasting, this work considered a Neuro-Fuzzy Inference System due to its combined advantages such as smoothness property from Fuzzy systems and adaptability property from neural networks structures. Focusing on time series forecasting, this article presents a model and evaluates the solar prediction capacity using the Wavelet Neuro-Fuzzy algorithm, where Wavelets were included in the model for feature extraction. In this sense, this paper aims to evaluate whether it is possible to obtain reasonable accuracy using a hybrid model for electric power generation forecasting considering solar trackers. The main contributions of this work are related to the efficiency improvement of PV panels. By assuming a hybrid computational model, it is possible to make a forecast and determine if the use of solar tracking is interesting during certain periods. Finally, the proposed model showed promising results when compared to traditional Nonlinear autoregressive model structures.

GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam

Landslides affect properties and the lives of a large number of people in many hilly parts of Vietnam and in the world. Damages caused by landslides can be reduced by understanding distribution, nature, mechanisms and causes of landslides with the help of model studies for better planning and risk management of the area. Development of landslide susceptibility maps is one of the main steps in landslide management. In this study, the main objective is to develop GIS based hybrid computational intelligence models to generate landslide susceptibility maps of the Da Lat province, which is one of the landslide prone regions of Vietnam. Novel hybrid models of alternating decision trees (ADT) with various ensemble methods, namely bagging, dagging, MultiBoostAB, and RealAdaBoost, were developed namely B-ADT, D-ADT, MBAB-ADT, RAB-ADT, respectively. Data of 72 past landslide events was used in conjunction with 11 landslide conditioning factors (curvature, distance from geological boundaries, elevation, land use, Normalized Difference Vegetation Index (NDVI), relief amplitude, stream density, slope, lithology, weathering crust and soil) in the development and validation of the models. Area under the receiver operating characteristic (ROC) curve (AUC), and several statistical measures were applied to validate these models. Results indicated that performance of all the models was good (AUC value greater than 0.8) but B-ADT model performed the best (AUC= 0.856). Landslide susceptibility maps generated using the proposed models would be helpful to decision makers in the risk management for land use planning and infrastructure development.

Role of hydrodynamic interactions in chemotaxis of bacterial populations

How bacteria sense local chemical gradients and decide to move has been a fascinating area of recent study. Chemotaxis of bacterial populations has been traditionally modeled using either individual-based models describing the motion of a single bacterium as a velocity jump process, or macroscopic PDE models that describe the evolution of the bacterial density. In these models, the hydrodynamic interaction between the bacteria is usually ignored. However, hydrodynamic interaction has been shown to induce collective bacterial motion and self-organization resulting in larger mesoscale structures. In this paper, the role of hydrodynamic interactions in bacterial chemotaxis is investigated by extending a hybrid computational model that incorporates hydrodynamic interactions and adding components from a classical velocity jump model. It is shown that by including hydrodynamic interactions, a suspension with a low initial volume fraction can exhibit locally high concentrations in bacterial aggregates. Also, it is shown that hydrodynamic interactions enhance the merging of the small aggregates into larger ones and lead to qualitatively different aggregate behavior than possible with pure chemotaxis models. Namely, differences in the shape, number, and dynamics of these emergent clusters.

Construction of realistic hybrid computational fetal phantoms from radiological images in three gestational ages for radiation dosimetry applications

Radiation exposure and associated radiation risks are major concerns for fetal development for pregnant patients who undergo radiation therapy or diagnostic imaging procedures. In order to accurately estimate the radiation dose to the fetus and assess the uncertainty of fetal position and rotation, three hybrid computational fetus phantoms were constructed using magnetic resonance imaging (MRI) for each fetus model as a starting point to construct a complete anatomically accurate fetus, gravid uterus, and placenta. A total of 27 fetal organs were outlined from radiological images via the Velocity Treatment Planning System. The DICOM-Structure set was imported to Rhinoceros software for further reconstruction of 3D fetus phantom model sets. All fetal organ masses were compared with ICRP-89 reference data. Our fetal model series corresponds to 20, 31, and 35 weeks of pregnancy, thus covering the second and third trimester. Fetal positions and locations were carefully adapted to represent the real fetus locations inside the uterus for each trimester of pregnancy. The new series of hybrid computational fetus models together with pregnant female models can be used in evaluating fetal radiation doses in diagnostic imaging and radiotherapy procedures.

Combining standard with optimised split‐step finite‐difference time‐domain methods for the study of graphene configurations

In this study, the authors propose a hybrid computational model for the reliable simulation of electromagnetic-wave phenomena emerging in graphene structures, which incorporates two variations of an unconditionally-stable finite-difference time-domain algorithm. The new approach features: (a) a dispersive model that relies on the auxiliary differential equation technique and takes into consideration the graphene's surface conductivity, and (b) a modified discretisation algorithm implementing error-optimised spatial approximations, which is appropriate for graphene-free mesh nodes. The resulting update equations are devoid of time-step restrictions, thus formulating a suitable and useful computational framework for the investigation of contemporary graphene-based problems, which commonly call for densely-sampled grids and/or prolonged simulations. After theoretically assessing the fundamental features of the proposed algorithm, the propagation properties of surface waves on graphene are extracted numerically and compared with analytical estimations, so that computational validation is provided. In addition, a realistic surface-wave coupler configuration is simulated and its main characteristics are extracted accurately, exemplifying the method's robustness on more complex setups.

A Hybrid Computational Intelligence Approach to Groundwater Spring Potential Mapping

This study proposes a hybrid computational intelligence model that is a combination of alternating decision tree (ADTree) classifier and AdaBoost (AB) ensemble, namely “AB–ADTree”, for groundwater spring potential mapping (GSPM) at the Chilgazi watershed in the Kurdistan province, Iran. Although ADTree and its ensembles have been widely used for environmental and ecological modeling, they have rarely been applied to GSPM. To that end, a groundwater spring inventory map and thirteen conditioning factors tested by the chi-square attribute evaluation (CSAE) technique were used to generate training and testing datasets for constructing and validating the proposed model. The performance of the proposed model was evaluated using statistical-index-based measures, such as positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity accuracy, root mean square error (RMSE), and the area under the receiver operating characteristic (ROC) curve (AUROC). The proposed hybrid model was also compared with five state-of-the-art benchmark soft computing models, including single ADTree, support vector machine (SVM), stochastic gradient descent (SGD), logistic model tree (LMT), logistic regression (LR), and random forest (RF). Results indicate that the proposed hybrid model significantly improved the predictive capability of the ADTree-based classifier (AUROC = 0.789). In addition, it was found that the hybrid model, AB–ADTree, (AUROC = 0.815), had the highest goodness-of-fit and prediction accuracy, followed by the LMT (AUROC = 0.803), RF (AUC = 0.803), SGD, and SVM (AUROC = 0.790) models. Indeed, this model is a powerful and robust technique for mapping of groundwater spring potential in the study area. Therefore, the proposed model is a promising tool to help planners, decision makers, managers, and governments in the management and planning of groundwater resources.

Unifying theories of reactive design contracts

Design-by-contract is an important technique for model-based design in which a composite system is specified by a collection of contracts that specify the behavioural assumptions and guarantees of each component. In this paper, we describe a unifying theory for reactive design contracts that provides the basis for modelling and verification of reactive systems. We provide a language for expression and composition of contracts that is supported by a rich calculational theory. In contrast with other semantic models in the literature, our theory of contracts allows us to specify both the evolution of state variables and the permissible interactions with the environment. Moreover, our model of interaction is abstract, and supports, for instance, discrete time, continuous time, and hybrid computational models. Being based in Unifying Theories of Programming (UTP), our theory can be composed with further computational theories to support semantics for multi-paradigm languages. Practical reasoning support is provided via our proof framework, Isabelle/UTP, including a proof tactic that reduces a conjecture about a reactive program to three predicates, symbolically characterising its assumptions and guarantees about intermediate and final observations. This allows us to verify programs with a large or infinite state space. Our work advances the state-of-the-art in semantics for reactive languages, description of their contractual specifications, and compositional verification.

Hybrid computational intelligence models for groundwater potential mapping

Groundwater is the most important natural resource in many parts of the world that requires advanced new technologies for monitoring and control. This study presents a comparative analysis of three novel hybrid computational intelligence models that consist of a base Decision Stump classifier and three ensemble learning techniques, i.e., Rotation Forest, MultiBoost, and Bagging, for the groundwater potential mapping. Ten influencing factors (i.e., slope, aspect, plan curvature, topographic wetness index, rainfall, river density, lithology, land use, and soil) and 34 groundwater wells from the Vadodara district, Gujarat, India, were used to prepare a geospatial database. Using this database, three hybrid groundwater models, i.e., Rotation Forest based Decision Stump, MultiBoost based Decision Stump, and Bagging based Decision Stump, were developed. Based on a variety of performance metrics, it is revealed that the Rotation Forest based Decision Stump model had the best performance, followed by the MultiBoost based Decision Stump and Bagging based Decision Stump models. However, all the novel hybrid computational models presented here provided improved estimates of groundwater potential compared to those in previous studies and are sufficiently general to be used in many different landscapes around the world.

Analysis of contact-impact dynamics of soft finger tapping system by using hybrid computational model

• An efficient hybrid computational model to analyze frictional impact responses during tapping of soft finger is developed. • A lumped-parameter model taking the tangential compliance into account is presented to calculate contact forces. • Event driven scheme and computational strategy are given to solve non-smooth differential equation. • Numerical results are compared with three-dimensional LS-DYNA solutions. • A ‘reserve slip’ phenomenon caused by large tangential contact compliance is captured. The aim of this paper is to develop an efficient hybrid computational model to analyze the frictional impact dynamic responses of soft finger during the tapping event. The large deformation field and inertial field of soft structure are discretized by absolute nodal coordinate formulation. Lagrange multiplier method is adopted to account for the constraints between neighbor phalanges. Considering tangential contact compliance, a lumped-parameter model at the local contact zone is presented to calculate contact forces. The governing equations of soft finger tapping system expressed by generalized coordinates are derived. An event driven scheme is given to analyze and evaluate the stick–slip transitions. The governing equation is integrated by generalized- α method. The applications of the hybrid computational model are demonstrated using various soft finger tapping systems acted by different driven moments. The feasibility of the proposed model is validated by comparing with the LS-DYNA solution. The error of the solution calculated by the proposed model for the peak value of contact force is less than 8%. Furthermore, numerical results show that the large structural compliance and driven moments have significant effect on the frictional impact responses. The normal relative motion between the fingertip and the rough target surface will experience 1∼4 compression-restitution transitions when Young's Modulus is from 0.01 GPa to 1 GPa or the slenderness ratio of phalanx is from 10.7 to 32. When the posture of soft finger is convex at impact instance, the tangential relative motion will experience 3 slip–stick transitions during the contact process. In addition, it also can be found that the tangential contact compliance can reverse the direction of slip (i.e. ‘reverse slip’ phenomenon).

A Hybrid Computational and Analytical Model of Inline Drip Emitters

This paper details a hybrid computational and analytical model to predict the performance of inline pressure compensating drip irrigation emitters. Pressure compensating emitters deliver a constant flow rate over a range of applied pressures to accurately meter water to crops. Flow rate is controlled within the emitter via a fixed resistance tortuous path, and a variable flow resistance composed of a flexible membrane that deflects under changes in pressure, restricting the flow path. A pressure resistance parameter was derived using an experimentally validated computational fluid dynamics (CFD) model to describe the flow behavior in tortuous paths. The bending mechanics of the membrane were modeled analytically and refined by deriving a correction factor using finite element analysis (FEA). A matrix formulation that calculates the force applied by a line or a patch load of any shape on a rectangular membrane, along which there is a prescribed deflection, was derived and was found to be accurate to be 1%. The combined hybrid computational–analytical model reduces the computational time of modeling emitters from hours to less than 30 min, dramatically lowering the time required to iterate and select optimal designs. The model was validated experimentally using three commercially available drip emitters and was accurate to within 12% of the experimental results.

Development a Hybrid Computational Model Based on Artificial Neural Network to Predict Natural Gas Global Demand

Development a Hybrid Computational Model Based on Artificial Neural Network to Predict Natural Gas Global Demand

Hybrid Intelligence Model on the Second Generation Neural network

In latest beyond, it has been visualised in various neural computing applications that the hybridisation of computational intelligence techniques yields better results over traditional methods. This proposed research introduces a new composite version (hybrid) computational model in both real and complex domain that is a unique fusion of revised evolutionary fuzzy clustering along with correlated neural networks. The whole methodology is based on two type of working framework as fuzzy assessment framework and neural categoriser framework. In first framework, suggest styles are disbursed as according to the quantity of clusters on the basis of learning strategy like revised evolutionary fuzzy clustering for generalisation of various evaluated cluster patterns that is totally responsible for selection process of network structure. The second framework of the proposed strategy is to fully based on the correlated neural network evolving the processes of training and subsequent generalisation. The existing real domain analysis is based on first generation neural network (RVNN) and for the analysis of proposed model in second generation neural network (CVNN), Hilbert transformation is used that is available in MATLAB for converting the real datasets in complex form.

Kinetic simulations of laser-induced plume expansion into a background gas under conditions of spatial confinement

Expansion of vapor plume induced by short-pulse laser irradiation of a bottom of a cylindrical cavity or planar trench in a copper target into helium, argon, and xenon background gases at pressure varying from zero to 1 bar is studied numerically. The simulations are performed based on a hybrid computational model that includes an unsteady heat conduction equation, Hertz-Knudsen model of evaporation, and a kinetic model of two-component gas flow implemented in the form of the Direct Simulation Monte Carlo method. It is found that increasing background pressure results in the formation of a complex structure of shock waves, which drastically changes the flow compared to the case of expansion into a vacuum. The moving shocks strongly affect the deposition of the ablated material inside the cavity. In particular, they induce the formation of a near-surface layer with a reduced fraction of vapor, which protects the cavity wall from vapor redeposition. The overall effect of the increasing background gas pressure on the flow structure and redeposition of vapor inside the cavity can be described as a trade-off between the confinement effect, i.e. a reduction in the propagation speed of the primary shock and overall rate of the plume expansion, and the focusing effect of moving shock waves that induce the radial flow towards the cavity axis, increase vapor density around the axis of symmetry, and reduce vapor density at the cavity wall. The efficiency of vapor removal out of the cavity can increase or decrease with increasing molar mass of the background gas depending on whether the flow is dominated by the focusing or confinement effect. These findings suggest that tuning the background gas parameters and geometrical parameters of spatial confinement can be used to effectively control the laser-induced plume expansion process in applications of laser ablation.

Validating quantum-classical programming models with tensor network simulations.

The exploration of hybrid quantum-classical algorithms and programming models on noisy near-term quantum hardware has begun. As hybrid programs scale towards classical intractability, validation and benchmarking are critical to understanding the utility of the hybrid computational model. In this paper, we demonstrate a newly developed quantum circuit simulator based on tensor network theory that enables intermediate-scale verification and validation of hybrid quantum-classical computing frameworks and programming models. We present our tensor-network quantum virtual machine (TNQVM) simulator which stores a multi-qubit wavefunction in a compressed (factorized) form as a matrix product state, thus enabling single-node simulations of larger qubit registers, as compared to brute-force state-vector simulators. Our simulator is designed to be extensible in both the tensor network form and the classical hardware used to run the simulation (multicore, GPU, distributed). The extensibility of the TNQVM simulator with respect to the simulation hardware type is achieved via a pluggable interface for different numerical backends (e.g., ITensor and ExaTENSOR numerical libraries). We demonstrate the utility of our TNQVM quantum circuit simulator through the verification of randomized quantum circuits and the variational quantum eigensolver algorithm, both expressed within the eXtreme-scale ACCelerator (XACC) programming model.

The Role of Dimensionality in Understanding Granuloma Formation.

Within the first 2–3 months of a Mycobacterium tuberculosis (Mtb) infection, 2–4 mm spherical structures called granulomas develop in the lungs of the infected hosts. These are the hallmark of tuberculosis (TB) infection in humans and non-human primates. A cascade of immunological events occurs in the first 3 months of granuloma formation that likely shapes the outcome of the infection. Understanding the main mechanisms driving granuloma development and function is key to generating treatments and vaccines. In vitro, in vivo, and in silico studies have been performed in the past decades to address the complexity of granuloma dynamics. This study builds on our previous 2D spatio-temporal hybrid computational model of granuloma formation in TB (GranSim) and presents for the first time a more realistic 3D implementation. We use uncertainty and sensitivity analysis techniques to calibrate the new 3D resolution to non-human primate (NHP) experimental data on bacterial levels per granuloma during the first 100 days post infection. Due to the large computational cost associated with running a 3D agent-based model, our major goal is to assess to what extent 2D and 3D simulations differ in predictions for TB granulomas and what can be learned in the context of 3D that is missed in 2D. Our findings suggest that in terms of major mechanisms driving bacterial burden, 2D and 3D models return very similar results. For example, Mtb growth rates and molecular regulation mechanisms are very important both in 2D and 3D, as are cellular movement and modulation of cell recruitment. The main difference we found was that the 3D model is less affected by crowding when cellular recruitment and movement of cells are increased. Overall, we conclude that the use of a 2D resolution in GranSim is warranted when large scale pilot runs are to be performed and if the goal is to determine major mechanisms driving infection outcome (e.g., bacterial load). To comprehensively compare the roles of model dimensionality, further tests and experimental data will be needed to expand our conclusions to molecular scale dynamics and multi-scale resolutions.

Predicting the vascular adhesion of deformable drug carriers in narrow capillaries traversed by blood cells

In vascular targeted therapies, blood-borne carriers should realize sustained drug release from the luminal side towards the diseased tissue. In this context, such carriers are required to firmly adhere to the vessel walls for a sufficient period of time while resisting force perturbations induced by the blood flow and circulating cells. Here, a hybrid computational model, combining a Lattice Boltzmann (LBM) and Immersed Boundary Methods (IBM), is proposed for predicting the strength of adhesion of particles in narrow capillaries (7.5μm) traversed by blood cells. While flowing down the capillary, globular and biconcave deformable cells (7μm) encounter 2μm discoidal particles, adhering to the vessel walls. Particles present aspect ratios ranging from 0.25 to 1.0 and a mechanical stiffness varying from rigid (Ca = 0) to soft (Ca = 10−3). Cell–particle interactions are quantitatively predicted over time via three independent parameters: the cell membrane stretching δp; the cell-to-particle distance r, and the number of engaged ligand–receptor bonds NL. Under physiological flow conditions (Re = 10−2), rigid particles are detached and displaced away from the wall by blood cells. This is associated with a significant cell membrane stretching (up to 10%) and rapid breaking of molecular bonds (t umax/H <1). Differently, soft particles deform their shape as cells pass by, thus reducing force perturbations and extending the life of molecular bonds. Yet, only the thinnest deformable particles (2 ×0.5μm) firmly adhere to the walls under all tested configurations. These results suggest that low aspect ratio deformable particles can establish long-lived adhesive interactions with capillary walls, enabling de facto vascular targeted therapies.

One-dimensional kinetic simulations of plume expansion induced by multi-pulse laser irradiation in the burst mode at 266 nm wavelength

Expansion of a plume into an argon background gas induced by irradiation of a copper target by a burst of nanosecond laser pulses is studied based on a one-dimensional hybrid computational model, including a thermal model of the irradiated target and a kinetic model of neutral gas flows. The kinetic model is implemented in the form of the Direct Simulation Monte Carlo method. The simulations are performed for a laser with 266 nm wavelength, producing bursts of 1, 3, and 7 pulses of 10 ns duration with the peak-to-peak separation varying from zero to 60 ns, in the range of argon pressure from zero to 1 bar. It is found that multi-pulse irradiation induces plumes with complicated internal structures, including cascades of primary and secondary shock waves. The mechanism of interaction between shock waves depends on the background gas pressure. The distributions of temperature and density in the plumes are strongly affected by the inter-pulse separation and number of pulses, which determine the conditions of plasma ignition. Simulations performed based on a model, where ionization and absorption in the plume are accounted for, suggest that tuning the parameters of the burst allows one to reduce the effect of plasma shielding.

A hybrid computational model for collective cell durotaxis.

Collective cell migration is regulated by a complex set of mechanical interactions and cellular mechanisms. Collective migration emerges from mechanisms occurring at single cell level, involving processes like contraction, polymerization and depolymerization, of cell-cell interactions and of cell-substrate adhesion. Here, we present a computational framework which simulates the dynamics of this emergent behavior conditioned by substrates with stiffness gradients. The computational model reproduces the cell's ability to move toward the stiffer part of the substrate, process known as durotaxis. It combines the continuous formulation of truss elements and a particle-based approach to simulate the dynamics of cell-matrix adhesions and cell-cell interactions. Using this hybrid approach, researchers can quickly create a quantitative model to understand the regulatory role of different mechanical conditions on the dynamics of collective cell migration. Our model shows that durotaxis occurs due to the ability of cells to deform the substrate more in the part of lower stiffness than in the stiffer part. This effect explains why cell collective movement is more effective than single cell movement in stiffness gradient conditions. In addition, we numerically evaluate how gradient stiffness properties, cell monolayer size and force transmission between cells and extracellular matrix are crucial in regulating durotaxis.

Computational model of enactive visuospatial mental imagery using saccadic perceptual actions

From the onset of cognitive revolution, the concept of mental imagery has been given different, many times opposing, theoretical accounts. Mental imagery appears to be a ubiquitous, yet wholly individual, easy to explain experience on the one hand, being hard to deal with scientifically on the other hand. The focus of this research is on an enactive approach to visuospatial mental imagery, inspired by Sima’s perceptual instantiation theory. We designed a hybrid computational model, composed of a forward model, an inverse model, both implemented as neural networks, and a memory/controller module, that grounds simple mental concepts, such as a triangle and a square, in perceptual actions, and is able to reimagine these objects by performing the necessary perceptual actions in a simulated humanoid robot. We tested the model on three tasks – salience-based object recognition, imagination-based object recognition and object imagination – and achieved very good results showing, as a proof of concept, that perceptual actions are a viable candidate for grounding the visuospatial mental concepts as well as the credible substrate of visuospatial mental imagery.