Multiscale computational models

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

We present a flexible and efficient framework for multiscale modeling in computational chemistry (MiMiC). It is based on a multiple-program multiple-data (MPMD) 1 model with loosely coupled programs. Fast data exchange between programs is achieved through the use of MPI intercommunicators. This allows exploiting the existing parallelization strategies used by the coupled programs while maintaining a high degree of flexibility. MiMiC has been used in a new electrostatic embedding quantum mechanics/molecular mechanics (QM/MM) implementation coupling the highly efficient CPMD and GROMACS programs but it can also be extended to use other programs. The framework can also be utilized to extend the partitioning of the system into several domains that can be treated using different models, such as models based on wave function or density functional theory as well as coarse-graining and continuum models. The new QM/MM implementation treats long-range electrostatic QM-MM interactions through the multipoles of the QM subsystem which substantially reduces the computational cost without loss of accuracy compared to an exact treatment. This enables QM/MM molecular dynamics (MD) simulations of very large systems.

We propose a numerical solution to incorporate in the simulation of a system of conservation laws boundary conditions that come from a microscopic modeling in the small mean free path regime. The typical example we discuss is the derivation of the Euler system from the BGK equation. The boundary condition relies on the analysis of boundary layers formation that accounts from the fact that the incoming kinetic flux might be far from the thermodynamic equilibrium.

We present a MULTICOMP package developed for multiscale modeling of polymer-based nanomaterials. The package implements GUI-based tools for the automatic construction of various types of polymer systems with the transition from the atomistic level of modeling to the mesolevel and from the mesolevel to the macrolevel. Using automated scripts, researchers can construct different kinds of nanomaterials, create flexible simulation schemas and define how data are transferred between engaged software modules and tools. The package makes it possible to analyze structural, thermophysical, mechanical properties, the cohesive energy density, the viscosity of polymer melts, and the diffusion of small molecules. Due to the client–server organization, the package can perform calculations on local and remote computing facilities and transfer the most time-consuming calculations to supercomputers. Examples of preparation and characterization of a cross-linked polymer matrix, a clay-based nanocomposite, and analysing their properties illustrate MULTICOMP package operation.

The role of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) has been highlighted in mechanisms underlying inflammatory and neuropathic pain processes. The present study was designed to investigate whether NF-κB signaling is associated with pain-related neuropeptide expression in patients with chronic back pain related to degenerative disc disease (DDD). Intervertebral disc (IVD) tissues were collected from forty DDD patients undergoing disc replacement or fusion surgery, and from eighteen postmortem (PM) control subjects. RELA, NFKB1, CGRP, TAC1, TRPV1, and MMP-3 gene expression were analyzed by RT-qPCR, while NF-κB subunit RelA and NF-κB1–DNA binding in nuclear extracts and calcitonin gene related peptide (CGRP), substance P (SP), and transient receptor potential, subfamily V, member 1 (TRPV1) protein levels in cytosolic extracts of tissues were assessed by enzyme-linked immunosorbent assay (ELISA). An upregulated NF-κB1–DNA binding, and higher CGRP and TRPV1...

Over the last decade, the first principle study, has achieved a great importance in the field of quantum chemistry, solid state physics and material science. This study is about to investigate the different prosperities of many-body electronic systems and it is a quantum mechanical simulation approach. Eventually, this approach has a tool to predict some new materials and their properties computationally. This method can save the use of chemical methods, which can be harmful to the environment. In this review article, the research field of first principle techniques has been explored for some important semiconductor materials.

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 HS were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE in the antero-posterior direction at τ 4 and τ 5, and MSE in the ML direction at τ 4 outperformed the other entropy measures in terms of discriminative ability and characterized the gait of swPD with 79%, 82%, and 78% probabi...

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Vision Computing is a branch of artificial intelligence that focuses on providing computers with the functions typical of human vision. Due to recent advances made in technology, scientists have incorporate portable imagers into computer vision systems that can be connected via wireless networks. Advances in Vision Computing: An International Journal (AVC) is an open access, peer-reviewed journal that publishes articles which contribute new results in all areas of the advanced vision computing. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in vision computing and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of vision computing.

This paper considers a single-layer 2D triangular lattice system as an ideal higher-order rod and/or beam. The objective is to compare the solutions of these continuum models with the standard atomistic solutions. The accuracy of these higher-order rod and beam theories, in modeling several nano-systems, depends on the frequency of vibration. The existence of a second spectrum associated with these higherorder continuum theories and its contribution to the continuum solutions are studied on the basis of molecular dynamics (MD) results. It is shown that the second spectrum of a Timoshenko beam is less accurate with respect to the MD results but cannot be disregarded totally. Also, the existence of a second propagating mode associated with a Mindlin-Herrmann rod is substantiated by MD simulation results. The accuracy of these higher order rod and beam theories deteriorates beyond their cut-on frequencies. However, the use of higher-order rod and beam theories in several high-frequency dynamic and multiscale analyses is still justifiable.

The paper presents a multi-scale modeling of Boundary Value Problem (BVP) approach involving cohesive-frictional granular materials in the FEM × DEM multi-scale framework. On the DEM side, a 3D model is defined based on the interactions of spherical particles. This DEM model is built through a numerical homogenization process applied to a Volume Element (VE). It is then paired with a Finite Element code. Using this numerical tool that combines two scales within the same framework, we conducted simulations of biaxial and pressuremeter tests on a cohesive-frictional granular medium. In these cases, it is known that strain localization does occur at the macroscopic level, but since FEMs suffer from severe mesh dependency as soon as shear band starts to develop, the second gradient regularization technique has been used. As a consequence, the objectivity of the computation with respect to mesh dependency is restored.

The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected stud...

A comprehensive modeling of the evolution of a slope failure has to address two important issues. One is how the slope becomes unstable; the other how the unstable mass separates itself. This study tackles the first issue considering that the formation the unstable mass to be a mixed mode crack propagation problem. A mesh based partition of unity method, also known as the manifold method, is employed in this undertaking. Because of the method’s root in the discrete element method, it is also capable of modeling discretecontinuum interaction problem. The use of this method, therefore, also takes care of the second issue. Sample problems are solved as an illustration. The problem configuration consists of a simple slope that has a preexisting tensile crack along its crest. The slope failure is triggered by rainfall which raises water pressure in the crack. As the stress around the crack tip increases, an existing crack grows and a failure surface is eventually developed. The maximum s...

In this paper an adaptive algorithm for Smoothed Particle Hydrodynamics (SPH) for the Shallow Water Equations (SWEs) is presented. The area of a particle is inversely proportional to depth giving poor resolution in small depths without particle refinement. This is a particular limitation for flooding problems of interest here. Higher resolution is created by splitting the particles, while particle coalescing (or merging) improves efficiency by reducing the number of the particles when acceptable. The new particle coalescing procedure merges two particles together if their area becomes less than a predefined threshold value. Both particle splitting and coalescing procedures conserve mass and momentum and the smoothing length of new particles is calculated by minimizing the density error of the SPH summation. The new dynamic particle refinement procedure is assessed by testing the numerical scheme against analytical, experimental and benchmark test cases. The analytical cases show that with particle splitting and coalescing typical convergence rates remain faster than linear. For the practical test case, in comparison to using particle splitting alone, the particle coalescing procedure leads to a significant reduction of computational time, by a factor of 15. This makes the computational time of the same order as mesh-based methods with the advantage of not having to specify a mesh over a flood domain of unknown extent a priori.

Atomistic and coarse-grained simulations can be a great help in uncovering the mechanisms of physical processes at microscopic and mesoscopic levels at time scales ranging from femtoseconds to milliseconds. Any simulation study involves (1) setting up an appropriate simulation system representing the physical problem, (2) running the simulation and collecting information about the system, and (3) analyzing the collected data. The last step eventually leads to final conclusions about the system. Software for molecular simulation has been in development for many years and a number of high quality freely distributed general purpose simulation packages is available for researchers. Data analysis tools are usually less general as they often depend on a specific research project and the system under investigation. While many simulation packages come with a set of some general data analysis utilities, it is not unusual for such analysis tools to be developed on a per project basis inside research groups. Interestingly, there is a very limited set of available tools for setting up simulation systems, even though this is the very first and vital step of every simulation study. This lack of convenient general simulation system generators sometimes may even dictate the kind of simulations done based on the available initial systems rather than on the system being the best for a particular problem. In this work we describe a general software tool, bio.b-gen, for the creation of initial systems for biological molecular simulations. A number of case systems are demonstrated using an atomistic force field as well as the coarse grained MARTINI force field. The tool is designed to generate initial systems for the GROMACS general simulation package.

In linear Rankine panel method, the discrete linear dispersion relation is solved on a discrete free-surface to capture the free-surface waves generated due to wave-body interactions. Discretization introduces numerical damping and dispersion, which depend on the discretization order and the chosen methods for differentiation in time and space. The numerical properties of a linear Rankine panel method, based on a direct boundary integral formulation, for capturing two and three dimensional freesurface waves were studied. Different discretization orders and differentiation methods were considered, focusing on the linear distribution and finite difference schemes. The possible sources for numerical instabilities were addressed. A series of cases with and without forward speed was selected, and numerical investigations are presented. For the waves in three dimensions, the influence of the panels' aspect ratio and the waves' angle were considered. It has been shown that using the cancellation effects of different differentiation schemes the accuracy of the numerical method could be improved.

Although deep networks have recently emerged as the model of choice for many computer vision problems, in order to yield good results they often require time-consuming architecture search. To combat the complexity of design choices, prior work has adopted the principle of modularized design which consists in defining the network in terms of a composition of topologically identical or similar building blocks (a.k.a. modules). This reduces architecture search to the problem of determining the number of modules to compose and how to connect such modules. Again, for reasons of design complexity and training cost, previous approaches have relied on simple rules of connectivity, e.g., connecting each module to only the immediately preceding module or perhaps to all of the previous ones. Such simple connectivity rules are unlikely to yield the optimal architecture for the given problem. In this work we remove these predefined choices and propose an algorithm to learn the connections between modules in the network. Instead of being chosen a priori by the human designer, the connectivity is learned simultaneously with the weights of the network by optimizing the loss function of the end task using a modified version of gradient descent. We demonstrate our connectivity learning method on the problem of multiclass image classification using two popular architectures: ResNet and ResNeXt. Experiments on four different datasets show that connectivity learning using our approach yields consistently higher accuracy compared to relying on traditional predefined rules of connectivity. Furthermore, in certain settings it leads to significant savings in number of parameters.

Among total scattering (TS) techniques, the Debye Scattering Equation (DSE) based approach has become a valuable tool for characterizing nanomaterials with different structure, size and morphology, with all the wellknown advantages associated to the use of reciprocal space methods [1]. Since the computation operates starting from real space atomistic models, structural and microstructural information can be simultaneously derived within a unified approach. Moreover, the possibility of modeling both the small-and wide-angle regions of the X-ray TS pattern, with the DSE method, taking advantage of their complementarity in terms of length scales (from the atomic to the nanometer one), offers the opportunity of detecting sophisticate nanocrystals morphologies and disentangling defects-induced peak broadening in the TS pattern, from size-induced effects [2,3]. Here, experimental and modeling aspects related to the DSE approach, along with an overview of applications, from nanosized colloidal quantum dots and perovskites up to innovative engineered nanoapatites, will be presented.

Maximum strength sets the limit of a material's intrinsic resistance to permanent deformation. Its significance, however, lies not in the highest strength value that a solid can possibly achieve, but rather in how this quantity is degraded, from which one could decipher the underlying mechanisms of yielding in a real material. A wide range of maximum strength values have been measured experimentally for metallic glasses. However, the true maximum strength remains unknown to date. Here, using finite deformation theory, we give the first theoretical estimate of the ultimate strength of metallic glasses. Our theoretical results, along with those from experiment and simulation, lead us to several mechanisms of degradation of the theoretical strength that are closely connected to correlated atomic motion with varying characteristic length in real metallic glasses.

The present work reports on the development of a cokingresistant NiSn-based membrane electrode assembly (MEA) for internal CH 4 reforming in solid oxide fuel cells (SOFCs). Catalyst powder was prepared in a centrifugal casting oven by melting stoichiometric amounts of Ni and Sn under vacuum. The formation of Ni 3 Sn 2 intermetallic phase was confirmed by XRD analysis. Catalytic activity for CH 4 reforming and stability of the NiSn powder were first evaluated in a quartz glass reactor for 4 h at 600-1,000°C. The main reaction products H 2 and CO were detected by gas chromatography while no carbon formation was detected during the experiments. Then, 3YSZ electrolyte-supported MEAs were fabricated with a Ni 3 Sn 2 /YSZ anode and LSM/YSZ cathode and characterized under SOFC conditions. The MEA showed an excellent stability under CH 4 atmosphere (3% H 2 O) at 850°C over more than 650 h. No substantial decrease in cell potential was observed during this period.

The diffusive scaling of many finite-velocity kinetic models leads to a small-relaxation time behavior governed by reduced systems which are parabolic in nature. Here we demonstrate that standard numerical methods for hyperbolic conservation laws with stiff relaxation fail to capture the right asymptotic behavior. We show how to design numerical schemes for the study of the diffusive limit that possess the discrete analogue of the continuous asymptotic limit. Numerical results for a model of relaxing heat flow and for a model of nonlinear diffusion are presented.

This paper presents a new numerical method for multiscale modeling of composite materials. The new numerical model, called DECM, consists of a DEM (Discrete Element Method) approach of the Cell Method (CM) and combines the main features of both the DEM and the CM. In particular, it offers the same degree of detail as the CM, on the microscale, and manages the discrete elements individually such as the DEM-allowing finite displacements and rotations-on the macroscale. Moreover, the DECM is able to activate crack propagation until complete detachment and automatically recognizes new contacts. Unlike other DEM approaches for modeling failure mechanisms in continuous media, the DECM does not require prior knowledge of the failure position. Furthermore, the DECM solves the problems in the space domain directly. Therefore, it does not require any dynamic relaxation techniques to obtain the static solution. For the sake of example, the paper shows the results offered by the DECM for axial and shear loading of a composite two-dimensional domain with periodic round inclusions. The paper also offers some insights into how the inclusions modify the stress field in composite continua.

Cinnamons are a new computation model intended to form a theoretical foundation for Control Network Programming (CNP). CNP has established itself as a programming approach combining declarative and imperative features. It supports powerful tools for control of the computation process; in particular, these tools allow easy, intuitive, visual development of heuristic, nondeterministic, or randomized solutions. The paper providesrigorous definitions of the syntax and semantics of the new model of computation, at the same time trying to keep the intuition behind clear. The purposely simplified theoretical model is then compared to both WHILE-programs (thus demonstrating its Turing completeness), and the "real" CNP. Finally, future research possibilities are mentioned that would eventually extend the cinnamon programming and its theoretical foundation into the directions of nondeterminism, randomness and fuzziness.

This paper presents a multiscale model based on a FEM×DEM approach, a method that couples discrete elements at the microscale and finite elements at the macroscale. FEM×DEM has proven to be an effective way to treat real-scale engineering problems by embedding constitutive laws numerically obtained using discrete elements into a standard finite element framework. This proposed paper focuses on some numerical open issues of the method. Given the nonlinearity of the problem, Newton's method is required. The standard full Newton method is modified by adopting operators different from the consistent tangent matrix and by developing ad-hoc solution strategies. The efficiency of several existing operators is compared, and a new and original strategy is proposed, which is shown to be numerically more efficient than the existing propositions. Furthermore, a shared memory parallelization framework using OpenMP directives is introduced. The combination of these enhancements allows to overcome the FEM×DEM computational limitations, thus making the approach competitive with classical FEM in terms of stability and computational cost.

The paper presents a multi-scale modeling of Boundary Value Problem (BVP) approach involving cohesive-frictional granular materials in the FEM × DEM multi-scale framework. On the DEM side, a 3D model is defined based on the interactions of spherical particles. This DEM model is built through a numerical homogenization process applied to a Volume Element (VE). It is then paired with a Finite Element code. Using this numerical tool that combines two scales within the same framework, we conducted simulations of biaxial and pressuremeter tests on a cohesive-frictional granular medium. In these cases, it is known that strain localization does occur at the macroscopic level, but since FEMs suffer from severe mesh dependency as soon as shear band starts to develop, the second gradient regularization technique has been used. As a consequence, the objectivity of the computation with respect to mesh dependency is restored.

The CraikLeibovich (CL) equations are a surface-wave filtered version of the instantaneous NavierStokes equations in which the rectified effects of the surface waves are captured through a so-called "vortex force" term:the cross-product of the Stokes, or Lagrangian, mass drift associated with the filtered surface waves and the filtered vorticity vector.For locally generated wind waves, the Stokes drift is very strongly surface confined.In this scenario, the induced body force may be represented as a surface, or skin, effect. Using matched asymptotic analysis in this limit, we derive effective boundary conditions (BCs) for the flow beneath the Stokes drift layer (i.e. in the bulk of the mixed layer). We establish the regime of validity of the resulting formulation by performing linear stability analyses and numerical simulations of both the asymptotic model and the full CL equations for a variety of vertical Stokes drift profiles.The effective BC formulation offers both theoretical and computational advantages, and should be particularly useful for LES of Langmuir turbulence for which the need to resolve very small scale near-surface flow structures imposes severe computational constraints.

Conventional energy sources such as petroleum and coal are currently the primary sources for electricity production. However, these energy sources are depleting, and are also unfriendly to the environment. Renewable energy sources such as solar energy, wind energy, photovoltaic cell energy etc. are very effective in reducing the greenhouse gas emissions serving as cleaner alternatives. Solid oxide fuel cells (SOFC) serve as one such alternate clean source of energy. These unlike other fuel cells which can only operate with specific fuel sources, exhibit fuel flexibility by operating at high temperatures. These high temperature operations however come with several inherent challenges such as suitable choice of material, stability of the auxiliary systems and so on, for the optimum performance of the fuel cell. Hence critical understanding of corelation between high temperatures and the performance of the SOFC is required so that the controlling factors can be optimised further to suit more small-scale applications. To aid this aspect in our study, simulation of a three-dimensional modelled single cell planar solid oxide fuel cell was performed under steady-state and counter-current flow condition in the temperature range of 500 0 C to 1200 0 C using a computational dynamics software, COMSOL Multiphysics. The choice of materials for the fuel cell involved the use of, nickel oxide as anode, lanthanum strontium cobalt ferrite as cathode and yttria-stabilized zirconia the electrolyte. Hydrogen was supplied as anode side fuel while oxygen was fed as the cathode side fuel. The performance of the fuel cell was then determined from the polarization and power characteristics, which were then analyzed, and it was finally interpreted that with the increase in operating temperature, the performance of the solid oxide fuel cell increases.

Neural stimulation can be used as a tool to elicit natural sensations or behaviors by modulating neural activity. This can be potentially used to mitigate the damage of brain lesions or neural disorders. However, in order to obtain the optimal stimulation sequences, it is necessary to develop neural control methods, for example by constructing an inverse model of the target system. For real brains, this can be very challenging, and often unfeasible, as it requires repeatedly stimulating the neural system to obtain enough probing data, and depends on an unwarranted assumption of stationarity. By contrast, detailed brain simulations may provide an alternative testbed for understanding the interactions between ongoing neural activity and external stimulation. Unlike real brains, the artificial system can be probed extensively and precisely, and detailed output information is readily available. Here we employed a spiking network model of sensorimotor cortex trained to drive a realistic virtual musculoskeletal arm to reach a target. The network was then perturbed, in order to simulate a lesion, by either silencing neurons or removing synaptic connections. All lesions led to significant behvaioral impairments during the reaching task. The remaining cells were then systematically probed with a set of single and multiple-cell stimulations, and results were used to build an inverse model of the neural system. The inverse model was constructed using a kernel adaptive filtering method, and was used to predict the neural stimulation pattern required to recover the pre-lesion neural activity. Applying the derived neurostimulation to the lesioned network improved the reaching behavior performance. This work proposes a novel neurocontrol method, and provides theoretical groundwork on the use biomimetic brain models to develop and evaluate neurocontrollers that restore the function of damaged brain regions and the corresponding motor behaviors.

The paper presents a multi-scale modeling of Boundary Value Problem (BVP) approach involving cohesive-frictional granular materials in the FEM × DEM multi-scale framework. On the DEM side, a 3D model is defined based on the interactions of spherical particles. This DEM model is built through a numerical homogenization process applied to a Volume Element (VE). It is then paired with a Finite Element code. Using this numerical tool that combines two scales within the same framework, we conducted simulations of biaxial and pressuremeter tests on a cohesive-frictional granular medium. In these cases, it is known that strain localization does occur at the macroscopic level, but since FEMs suffer from severe mesh dependency as soon as shear band starts to develop, the second gradient regularization technique has been used. As a consequence, the objectivity of the computation with respect to mesh dependency is restored.

Regularization is commonly used in machine learning for alleviating overfitting. In convolutional neural networks, regularization methods, such as Dropout and Shake-Shake, have been proposed to improve the generalization performance. However, these methods are lack of self-adaption throughout training, i.e., the regularization strength is fixed to a predefined schedule, and manual adjustment has to be performed to adapt to various network architectures. In this paper, we propose a dynamic regularization method which can dynamically adjust the regularization strength in the training procedure. Specifically, we model the regularization strength as a backward difference of the training loss, which can be directly extracted in each training iteration. With dynamic regularization, the large model is regularized by the strong perturbation and vice versa. Experimental results show that the proposed method can improve the generalization capability of off-the-shelf network architectures and outperforms state-of-the-art regularization methods.

Although computer-aided engineering (CAE) software has been used for many years in the plastic industry, identifying the most appropriate mesh geometry and density remains a challenge. It can affect the accuracy of the simulation, the time and the costs. The evaluation of the most suitable mesh is not easy because the difficulties to obtain the real the values of the pressure and temperature inside the mold. The current work investigates this issue. A mold was manufactured and sensors were installed in its interior. CAE simulations using different mesh geometries and densities were evaluated against the experimental data. The results showed that the computational time was mostly influenced by the mesh geometry. The use of 2D mesh and lower density can lead to a faster and more precise simulation of pressure inside the mold and 3D mesh with lower density can provide a faster and precise simulation of the temperature.

The implementation of the highly active antiretroviral therapy (HAART) and the combination of anti-HIV drugs have resulted in longer survival and a better quality of life for the people infected with the virus. In this work, a method is proposed to map complex networks of AIDS prevalence in the US counties, incorporating information about the chemical structure, molecular target, organism, and results in preclinical protocols of assay for all drugs in the cocktail. Different machine learning methods were trained and validated to select the best model. The Shannon information invariants of molecular graphs for drugs, and social networks of income inequality were used as input. The nodes in molecular graphs represent atoms weighed by Pauling electronegativity values, and the links correspond to the chemical bonds. On the other hand, the nodes in the social network represent the US counties and have Gini coefficients as weights. We obtained the data about anti-HIV drugs from the ChEMBL database and the data about AIDS prevalence and Gini coefficient from the AIDSVu database of Emory University. Box-Jenkins operators were used to measure the shift with respect to average behavior of drugs from reference compounds assayed with/in a given protocol, target, or organism. To train/validate the model and predict the complex network, we needed to analyze 152,628 data points including values of AIDS prevalence in 2310 counties in the US vs. ChEMBL results for 21,582 unique drugs, 9 viral or human protein targets, 4856 protocols, and 10 possible experimental measures. The best model found was a linear discriminant analysis (LDA) with accuracy, specificity, and sensitivity above 0.80 in training and external validation series. Abbreviations a th , County; ANNs, Artificial neural networks; BI k , Balaban's information indices; b j , Experimental conditions; CDR ac (b j), Cocktail-AIDS disease ratio; c th , Cocktails; D a , AIDS prevalence rate for the county; d ij , Topological distance between atoms i and j; d th , Drug; G a , Gini measure in the county; I a 0 , Shannon information index of income inequality; IC k , Neighborhood symmetry indices; L ac (b j) obs , Observed variable of formation of links between counties and cocktails; MA, Moving average operators; MI k , Molecular information indices; ML, Machine learning; nAT, Number of molecule atoms; n g , Number of elements in the g th class; nSK, Number of non-H atoms; v d (b j), Value of biological activity; W, Wiener index; z c , Average of the z-scores of the biological activity of each drug; z d (b j), z-score of the biological activity; η i , Atom eccentricity; σ i ,, i th vertex distance degree

It is known that in pathological conditions, physiological systems develop changes in the multiscale properties of physiological signals. However, in real life, little is known about how changes in the function of one of the two coupled physiological systems induce changes in function of the other one, especially on their multiscale behavior. Hence, in this work we aimed to examine the complexity of cardio-respiratory coupled systems control using multiscale entropy (MSE) analysis of cardiac intervals MSE (RR), respiratory time series MSE (Resp), and synchrony of these rhythms by cross multiscale entropy (CMSE) analysis, in the heart failure (HF) patients and healthy subjects. We analyzed 20 min of synchronously recorded RR intervals and respiratory signal during relaxation in the supine position in 42 heart failure patients and 14 control healthy subjects. Heart failure group was divided into three subgroups, according to the RR interval time series characteristics (atrial fibrillation (HFAF), sinus rhythm (HFSin), and sinus rhythm with ventricular extrasystoles (HFVES)). Compared with healthy control subjects, alterations in respiratory signal properties were observed in patients from the HFSin and HFVES groups. Further, mean MSE curves of RR intervals and respiratory signal were not statistically different only in the HFSin group (p = 0.43). The level of synchrony between these time series was significantly higher in HFSin and HFVES patients than in control subjects and HFAF patients (p < 0.01). In conclusion, depending on the specific pathologies, primary alterations in the regularity of cardiac rhythm resulted in changes in the regularity of the respiratory rhythm, as well as in the level of their asynchrony.

Intervertebral disc (IVD) degeneration is a major risk factor of low back pain. It is defined by a progressive loss of the IVD structure and functionality, leading to severe impairments with restricted treatment options due to the highly demanding mechanical exposure of the IVD. Degenerative changes in the IVD usually increase with age but at an accelerated rate in some individuals. To understand the initiation and progression of this disease, it is crucial to identify key top-down and bottom-up regulations’ processes, across the cell, tissue, and organ levels, in health and disease. Owing to unremitting investigation of experimental research, the comprehension of detailed cell signaling pathways and their effect on matrix turnover significantly rose. Likewise, in silico research substantially contributed to a holistic understanding of spatiotemporal effects and complex, multifactorial interactions within the IVD. Together with important achievements in the research of biomaterials,...

Semantic annotation of images is an important research topic on both image understanding and database or web image search. Image annotation is a technique to choosing appropriate labels for images with extracting effective and hidden feature in pictures. In the feature extraction step of proposed method, we present a model, which combined effective features of visual topics (global features over an image) and regional contexts (relationship between the regions in Image and each other regions images) to automatic image annotation.In the annotation step of proposed method, we create a new ontology (base on WordNet ontology) for the semantic relationships between tags in the classification and improving semantic gap exist in the automatic image annotation.Experiments result on the 5k Corel dataset show the proposed method of image annotation in addition to reducing the complexity of the classification, increased accuracy compared to the another methods.

Defects in the annulus fibrosus (AF) of intervertebral discs allow nucleus pulposus tissue to herniate causing painful disability. Microdiscectomy procedures remove herniated tissue fragments, but unrepaired defects remain allowing reherniation or progressive degeneration. Cell therapies show promise to enhance repair, but methods are undeveloped and carriers are required to prevent cell leakage. To address this challenge, this study developed and evaluated genipin-crosslinked fibrin (FibGen) as an adhesive cell carrier optimized for AF repair that can deliver cells, match AF material properties, and have low risk of extrusion during loading. Part 1 determined that feasibility of bovine AF cells encapsulated in high concentration FibGen (F140G6: 140 mg/mL fibrinogen; 6 mg/mL genipin) for 7 weeks could maintain high viability, but had little proliferation or matrix deposition. Part 2 screened tissue mechanics and in situ failure testing of nine FibGen formulations (fibrin: 35-140 mg/...

We investigated the effects of the cytokine inhibitors IL-1 receptor antagonist (IL-1Ra) and soluble tumor necrosis factor receptor-1 (sTNFR1) on the extracellular matrix metabolism of human intervertebral discs (IVDs) and the roles of IL-1β and TNF in the homeostasis of IVD cells. The 1.2% alginate beads and the explants obtained from 35 human lumbar discs were treated with cytokine inhibitors. Extracellular matrix metabolism was evaluated by proteoglycan (PG) and collagen syntheses and IL-1β, TNF, and IL-6 expressions after three days of culture in the presence or absence of IL-1Ra, sTNFR1, and cycloheximide. Simultaneous treatment with IL-1Ra and sTNFR1 stimulated PG and collagen syntheses in the NP and AF cells and explants. The IL-1β concentration was significantly correlated to the relative increase in PG synthesis in AF explants after simultaneous cytokine inhibitor treatment. The relative increase in PG synthesis induced by simultaneous cytokine treatment was significantly h...

Wear is a complex phenomenon taking place as two bodies in relative motion are brought into contact with each other. There are many different types of wear, for example, sliding, fretting, surface fatigue, and combinations thereof. Wear occurs over a wide range of scales, and it largely depends on the mechanical properties of the material. For instance, at the micro-scale, sliding wear is the result of material detachment that occurs due to fracture. An accurate numerical simulation of sliding wear requires a robust and efficient solver, based on a realistic fracture mechanics model that can handle large deformations. In the present work, a fully coupled thermo-mechanical and meshfree approach, based on the momentum-consistent smoothed particle Galerkin (MC-SPG) method, is adapted and employed to predict wear of colliding asperities. The MC-SPG-based approach is used to study how plastic deformation, thermal response, and wear are influenced by the variation of the vertical overlap between colliding spherical asperities. The findings demonstrate a critical overlap value where the wear mechanism transitions from plastic deformation to brittle fracture. In addition, the results reveal a linear relationship between the average temperature and the increasing overlap size, up until the critical overlap value. Beyond this critical point, the average temperature reaches a steady-state value.

Wear is a complex phenomenon taking place as two bodies in relative motion are brought into contact with each other. There are many different types of wear, for example, sliding, fretting, surface fatigue, and combinations thereof. Wear occurs over a wide range of scales, and it largely depends on the mechanical properties of the material. For instance, at the micro-scale, sliding wear is the result of material detachment that occurs due to fracture. An accurate numerical simulation of sliding wear requires a robust and efficient solver, based on a realistic fracture mechanics model that can handle large deformations. In the present work, a fully coupled thermo-mechanical and meshfree approach, based on the momentum-consistent smoothed particle Galerkin (MC-SPG) method, is adapted and employed to predict wear of colliding asperities. The MC-SPG-based approach is used to study how plastic deformation, thermal response, and wear are influenced by the variation of the vertical overlap between colliding spherical asperities. The findings demonstrate a critical overlap value where the wear mechanism transitions from plastic deformation to brittle fracture. In addition, the results reveal a linear relationship between the average temperature and the increasing overlap size, up until the critical overlap value. Beyond this critical point, the average temperature reaches a steady-state value.

In this paper we present the foundation of a unified, object-oriented, three-dimensional (3D) biomodeling environment, which allows us to integrate multiple submodels at scales from subcellular to tissues and organs. Our current implementation combines a modified discrete model from statistical mechanics, the Cellular Potts Model (CPM), with a continuum reaction-diffusion (RD) model and a state automaton with well-defined conditions for cell differentiation transitions to model genetic regulation. This environment allows us to rapidly and compactly create computational models of a class of complex developmental phenomena. To illustrate model development, we simulate a simplified version of the formation of the skeletal pattern in a growing embryonic vertebrate limb.

In this paper, a unified nonlocal rational continuum enrichment technique is presented for improving the dispersive characteristics of some well known classical continuum equations on the basis of atomistic dispersion relations. This type of enrichment can be useful in a wide range of mechanical problems such as localization of strain and damage in many quasibrittle structures, size effects in microscale elastoplasticity, and multiscale modeling of materials. A novel technique of transforming a discrete differential expression into an exact equivalent rational continuum derivative form is developed considering the Taylor 's series transformation of the continuous field variables and traveling wave type of solutions for both the discrete and continuum field variables. An exact equivalent continuum rod representation of the 1D harmonic lattice with the non-nearest neighbor interactions is developed considering the lattice details. Using similar enrichment technique in the variational framework, other useful higher-order equations, namely nonlocal rational Mindlin-Herrmann rod and nonlocal rational Timoshenko beam equations, are developed to explore their nonlocal properties in general. Some analytical and numerical studies on the high frequency dynamic behavior of these novel nonlocal rational continuum models are presented with their comparison with the atomistic solutions for the respective physical systems. These enriched rational continuum equations have crucial use in studying high-frequency dynamics of many nano-electro-mechanical sensors and devices, dynamics of phononic metamaterials, and wave propagation in composite structures. These new models can help to circumvent the biggest problem regarding size and time restrictions in many atomistic simulations.