Implicit Model Research Articles

A fully implicit and thermodynamically consistent finite element framework for bone remodeling simulations

This work addresses the thermodynamically consistent formulation of bone remodeling as a fully implicit finite element material model. To this end, bone remodeling is described in the framework of thermodynamics for open systems resulting in a thermodynamically consistent constitutive law. In close analogy to elastoplastic material modeling, the constitutive equations are implicitly integrated in time and incorporated into a finite element weak form. A consistent linearization scheme is provided for the subsequent incremental non-linear boundary value problem, resulting in a computationally efficient description of bone remodeling. The presented model is suitable for implementation in any standard finite element framework with quadratic or higher-order element types. Two numerical examples in three dimensions are shown as proof of the efficiency of the proposed method.

Parallel fully implicit chemical potential-based modeling of unconventional shale gas reservoirs

The design of novel mathematical models and state-of-the-art simulators for unconventional shale gas reservoirs plays an increasingly important role in the current energy mix of the world’s growing energy demand. In this paper, we present a robust and scalable framework, which is based on the chemical potential-based modeling and the fully implicit method (FIM), to model and simulate this highly nonlinear flow transport problem on a large scale. In the proposed approach, a thermodynamically consistent mathematical model of gas flow in shale formations, which employs the gas density and the chemical potential gradient, is developed to satisfy the second law of thermodynamics and meanwhile guarantee the energy dissipation property. And then the family of fully implicit finite element algorithms is utilized to accurately capture the complicated flow physics behind the transport process in shale media on high resolution grids. In particular, our approach further enhances the numerical formulation by proposing the family of Newton–Krylov methods for efficiently computing, and the parallel implementation of the simulator is achieved by using the domain decomposition technique. Numerical experiments are presented to demonstrate the robustness and parallel scalability of the solution strategies for several interesting shale gas flow problems in two or three dimensions. With the proposed parallel method, large-scale reservoir simulation can be obtained on the Shaheen-II supercomputer with up to 40960 processors, which enables to achieve a good strong scalability by saving more than 90 percent of computing time, when the problem size is enlarged to hundreds of millions of degrees of freedom.

Identification of parametric dynamical systems using integer programming

Identification of nonlinear dynamical systems using data-driven frameworks facilitates the prediction and control of systems in a range of applications. Identification of a single system from the measurements of the system’s states leads to the discovery of explicit or implicit models that cannot generalize beyond the system for which the data are provided. By learning the effect of parameters in the system, we propose a generalizable model for the Identification of Parametric forms of dynamical systems using Integer Programming (IP2). We first build general libraries of basis functions that take into account both states and parameters. Subsequently, leveraging dimension analysis and the assumption of having integer coefficients in the equations, we show that our framework can identify the exact forms of parametric mechanical dynamical systems like an ideal pendulum or an inverted pendulum on a cart. Moreover, by applying object tracking techniques and taking advantage of a sequential filtering scheme, we can identify the state and energy equations of these dynamical systems from videos of the systems, i.e. pixel space noisy data, rather than state-space measurements. The results show that using integer programming makes the proposed framework significantly (more than 40 times in the case of inverted pendulum on a cart) more robust to noise compared to previous optimization models.

An Attempt of Seeking Favorable Binding Free Energy Prediction Schemes Considering the Entropic Effect on Fis-DNA Binding.

Protein-DNA binding mechanisms in a complex manner are essential for understanding many biological processes. Over the past decades, numerous experiments and calculations have analyzed the specificity of protein-DNA binding. However, the accuracy of binding free energy prediction for multi-base DNA systems still needs to be improved. Fis is a DNA-binding protein that regulates various transcription and recombination reactions. In the present work, we tested several methods of predict binding free energy based on this system to find a favorable prediction scheme and explore the binding mechanism of Fis protein and DNA. Two solvent models (explicit and implicit solvent models) were chosen for the dynamics process, and the predicted binding free energy was more accurate under the explicit solvent model. When different Poisson-Boltzmann/Generalized Born (PB/GB) models were tested for DNA force fields (BSC1 and OL15), it was found that the binding free energy predicted by the selected OL15 force field performed better and the correlation between predicted and experimental values was improved with the increasing interior dielectric constant (Dk). Finally, using Dk = 8, the GBOBC1 model combined with interaction entropy (IE), which was calculated for entropic contribution (GBOBC1_IE_8), was screened out for the binding free energy prediction and analysis of the Fis-DNA system, and the validity of the method was further verified by testing the Cren7-DNA system. By performing conformational analysis of the minor groove, it was found that mutation of the DNA central sequence A/T to C/G and deletion of the guanine 2-amino group would change the minor groove width and thus affect the formation of the major groove, altering the interaction and atomic contact between the protein and the major groove, thus changing the binding affinity of Fis and DNA. Hopefully, the series of tests in this work can shed some light on the related studies of protein and DNA systems.

Clustering and Mining Algorithm of Factor Structure Data of Physical Education Teachers' Teaching Behavior Based on Internet Information Retrieval Algorithm

Integrating visual word list and Rocchio algorithm, constructs an implicit relevance feedback retrieval model in semantic space, and judges information retrieval preference. User demand mining is introduced, Jensen-Shannon divergence is used to calculate the relative entropy distance between the probability distributions of document sets, and similarity matching is calculated to complete interactive information retrieval. Teaching ability is gradually formed by teachers in long-term teaching practice, and is stably and comprehensively reflected in teaching viewpoints, teaching methods, teaching skills, teaching style and so on. The weight analysis of each index in the evaluation index system was carried out by using the analytic hierarchy process, and the selection of the evaluation subject was completed through the third round of expert questionnaires, and the evaluation criteria were formulated in combination with the opinions of tutors and some expert teachers.

Coarse-grained intrinsically disordered protein simulations with explicit ions parameterized by salting-out constants.

Implicit solvent coarse-grained models have emerged as an effective method for simulating intrinsically disordered proteins (IDPs) due to the long length and time-scale need to effectively simulate their equilibrium behavior. These coarse-grained methods typically rely on Debye-Hückel theory to explain the effects of salt in the simulations. However, Debye-Hückel electrostatics are only accurate over a limited range of ionic strengths and are also unable to capture ion-type specific effects, typically called Hofmeister effects that arise at higher salt concentrations. To address this, we developed an explicit ion model of potassium chloride that can interface with the otherwise implicit solvent hydrophobicity scale model for intrinsically disordered protein. Parameters for the explicit ion model were determined by matching the salting-out constants of several small peptide-like molecules. Salting-out constants were determined in the simulation by creating a system with an ion concentration gradient via an external potential and performing umbrella sampling along the concentration gradient. The remaining amino acids for which there is no data for the salting-out constant are determined using a scaled Kapcha-Rosky hydrophobicity scale determined from the umbrella sampling simulations. The parameters are validated against a set FRET data from a diverse set of linker peptides at several salt concentrations. This explicit ion model is able to capture the changes in protein expansion as a function of salt concentration at concentration above 0.5 M where Debye-Hückel screened electrostatics predict no change in protein expansion.

Putative pore structure of amyloid beta and alpha synuclein in lipid bilayers.

Many degenerative diseases are characterized by aberrant protein aggregation. One possible mechanism of toxicity is the perturbation or permeabilization of cell membranes by certain types of oligomers. However, the structure of such membrane-inserted pore-forming complexes is unknown. Here we consider putative membrane-inserted β-barrels of Amyloid β and α-synuclein and evaluate them first in an implicit membrane model and then by multi-microsecond all atom simulation. The 11 amino acid peptide sequence of Aβ25-35 is relatively hydrophobic and readily embeds into synthetic bilayers producing ion channels. We consider octameric and decameric β barrels of different topology, strand orientation, and shear. Two decameric β barrels remain stably embedded in the membrane at the conclusion of these 5-μs all atom simulations: an antiparallel barrel with K28 in the pore lumen and a parallel barrel with the K28 in the membrane interface. Examination of the α-synuclein sequence reveals two regions that could form membrane-embedded β-hairpins: 35-56 and 64-92. We find that a 35-56 barrel remains inserted but dehydrates and collapses if all His50 are neutral. If half of the His50 are doubly protonated, however, the barrel takes an oval shape but remains hydrated and open. The 64-92 barrel remains hydrated for at least 10 µs. Our results for these pore structures may carry implications for familial Parkinson's mutations.

An overview of implicit and explicit phase field models for quasi-static failure processes, implementation and computational efficiency

Phase field models have become a promising tool in modelling failure processes for various engineering applications. However, their computational inefficiency has posed challenges for a wider application which is desired in engineering. While most papers present implicit phase field models, only a few studies devoted to explicit phase field models have been reported in recent years and most papers report their high computational efficiency compared to implicit phase field models. The purpose of this contribution is to present an overview of the implicit and explicit phase field models for quasi-static failure processes, detailing about their theories and implementation in the commercial software ABAQUS. The provided ABAQUS implementation programs can serve as a numerical tool for modelling engineering problems. Several numerical examples are then presented and a comparison of their computational efficiency is subsequently provided. Moreover, a mass/viscosity scaling scheme for the explicit phase field model and a non-iteration scheme for the implicit phase field model are introduced both improve their efficiency. The comparison results clearly reveal that the computational efficiency can be improved significant by using the explicit phase field model, and more attention should be paid to it.

The creation and diffusion of international business research

Abstract“W(h)ither U.S. Hegemony in International Business Research?” by Grigorios Livanis and Michael Geringer, is an excellent effort to encompass the growth of international business (IB) scholarly research worldwide. This commentary examines the implicit assumptions and models of their analysis, showing that IB models are useful in explaining the scholarly growth of IB.

Predicting flower induction of litchi (Litchi chinensis Sonn.) with machine learning techniques

The flower induction is a critical physiological change during which vegetative buds transit to floral buds. The duration of flower induction (DFI) for litchi plays determinative role for the success and the quality of flowering. It is hard to be reliably predicted because multiple factors including ages of trees, variety and dynamically environmental and climate variables have important impacts. Here we predicted the DFI for four litchi varieties using random forest (RF) implicit and stepwise regression (STR) explicit machine learning models. These models were trained and validated from the data consisting phenological phases from the mature of the last autumn shoots to the flower shedding, and the corresponding meteorological factors from 2009 to 2020. The DFI predictive models consider timescales from 1 h up to 10 days, and the determination coefficients from the 5-fold cross validation achieves 0.96 to 0.99. The high accuracy was maintained in the blind test, with the determination coefficients of 0.97–0.98 for the data in 2019 and 0.78–0.88 for 2020. The reliability is still sufficient to guide the cultivation of litchi although the dramatic climate change weaken the inherent multiple correlations for DFI. From these robust predictive models, we found ten important features that can be used to determine the DFI with consistent positive or negative contributions. They are the minimum cooling rate, the maturity time of the last autumn shoot, the maximum heating rate, the mean of minimum temperature, the mean of maximum temperature, the amount of heat accumulation at hot day with the minimum daily temperature above 22 °C, the maximum daily temperature below 26 °C, the minimum daily temperature below 6 °C, age, and the atmospheric relative humidity.

Conformations of Steroid Hormones: Infrared and Vibrational Circular Dichroism Spectroscopy.

Steroid hormone molecules may exhibit very different functionalities based on the associated functional groups and their 3D arrangements in space, i.e., absolute configurations and conformations. Infrared (IR) and vibrational circular dichroism (VCD) spectra of four different steroid hormones, namely dehydroepiandrosterone (DHEA), 17α-methyltestosterone (MTTT), (16α,17)-epoxyprogesterone (Epoxy-P4), and dehydroepiandrosterone acetate (AcO-DHEA), were measured in deuterated dimethyl sulfoxide and some also in carbon tetrachloride. Extensive conformational searches were carried out using the recent developed conformer-rotamer ensemble sampling tool (CREST) which also accounts for solvent effects using an implicit solvation model. All the CREST conformational candidates were then reoptimized at the B3LYP-D3BJ/def2-TZVPD with the PCM of solvent. The good agreements between the experimental IR and VCD spectra and the theoretical simulations provide a conclusive information about their conformational distribution and absolute configurations. The experimental and theoretical IR and VCD spectra of AcO-DHEA in the carbonyl and alkene stretching region showed some discrepancies, and the possible causes related to solvent effects, large amplitude motions and levels of theory used in the modelling were explored in detail. As part of the investigation, additional calculations at the B3LYP-D3BJ/6-31++G (2d,p) and B3LYP-D3BJ/cc-pVTZ levels, as well as some 'mixed' calculations with the double-hybrid functional B2PLYP-D3 were also carried out. The results indicate that the double-hybrid functional is important for predicting the correct IR band pattern in the carbonyl and alkene stretching region.

Manufacturing cost estimation based on similarity

ABSTRACT Accurate and rapid estimation of manufacturing costs can improve the competitiveness of manufacturers and optimize part design. Several factors affect the cost, including the part design model, machining processing progress, and materials. Extracting a unified dataset from the manufacturing information is complicated owing to different information expressions, such as geometrical-topological, digital, text, and symbol representations. This study proposed a manufacturing cost estimation method based on similarity. The main contribution of this study is the calculation of the similarity of manufacturing factors, particularly the similarity of 3D geometrical models: ① the geometrical-topological representations of a model was transformed into point cloud data and a point cloud similarity calculation method based on the implicit shape model was developed, from which the similarity of the 3D model of the part was obtained;② the manufacturing factors were transformed into digital representations and the similarity of these factors was calculated using Euclidean distance. The weights of these factors were determined using subjective weight coefficients and the similarity of the parts was obtained based on the weights. Finally, the manufacturing cost was estimated using the smoothing index method. The accuracy of the proposed method has a state-of-the-art effect compared to other methods.

Accurate and Cost-Effective NMR Chemical Shift Predictions for Nucleic Acids Using a Molecules-in-Molecules Fragmentation-Based Method.

We have developed, implemented, and assessed an efficient protocol for the prediction of NMR chemical shifts of large nucleic acids using our molecules-in-molecules (MIM) fragment-based quantum chemical approach. To assess the performance of our approach, MIM-NMR calculations are calibrated on a test set of three nucleic acids, where the structure is derived from solution-phase NMR studies. For DNA systems with multiple conformers, the one-layer MIM method with trimer fragments (MIM1trimer) is benchmarked to get the lowest energy structure, with an average error of only 0.80 kcal/mol with respect to unfragmented full molecule calculations. The MIMI-NMRdimer calibration with respect to unfragmented full molecule calculations shows a mean absolute deviation (MAD) of 0.06 and 0.11 ppm, respectively, for 1H and 13C nuclei, but the performance with respect to experimental NMR chemical shifts is comparable to the more expensive MIM1-NMR and MIM2-NMR methods with trimer subsystems. To compare with the experimental chemical shifts, a standard protocol is derived using DNA systems with Protein Data Bank (PDB) IDs 1SY8, 1K2K, and 1KR8. The effect of structural minimizations is employed using a hybrid mechanics/semiempirical approach and used for computations in solution with implicit and explicit-implicit solvation models in our MIM1-NMRdimer methodology. To demonstrate the applicability of our protocol, we tested it on seven nucleic acids, including structures with nonstandard residues, heteroatom substitutions (F and B atoms), and side chain mutations with a size ranging from ∼300 to 1100 atoms. The major improvement for predicted MIM1-NMRdimer calculations is obtained from structural minimizations and implicit solvation effects. A significant improvement with the explicit-implicit solvation model is observed only for two smaller nucleic acid systems (1KR8 and 7NBK), where the expensive first solvation shell is replaced by the microsolvation model, in which a single water molecule is added for each solvent-exposed amino and imino protons, along with the implicit solvation. Overall, our target accuracy of ∼0.2-0.3 ppm for 1H and ∼2-3 ppm for 13C has been achieved for large nucleic acids. The proposed MIM-NMR approach is accurate and cost-effective (linear scaling with system size), and it can aid in the structural assignments of a wide range of complex biomolecules.

A generic framework for representing and analyzing model concurrency

Recent results in language engineering simplify the development of tool-supported executable domain-specific modeling languages (xDSMLs), including editing (e.g., completion and error checking) and execution analysis tools (e.g., debugging, monitoring and live modeling). However, such frameworks are currently limited to sequential execution traces and cannot handle execution traces resulting from an execution semantics with a concurrency model supporting parallelism or interleaving. This prevents the development of concurrency analysis tools, like debuggers supporting the exploration of model executions resulting from different interleavings. In this paper, we present a generic framework to integrate execution semantics with either implicit or explicit concurrency models, to explore the possible execution traces of conforming models, and to define strategies for helping in the exploration of the possible executions. This framework is complemented with a protocol to interact with the resulting executions and hence to build advanced concurrency analysis tools. The approach has been implemented within the GEMOC Studio. We demonstrate how to integrate two representative concurrent meta-programming approaches (MoCCML/Java and Henshin), which use different paradigms and underlying foundations to define an xDSML’s concurrency model. We also demonstrate the ability to define an advanced concurrent omniscient debugger with the proposed protocol. The paper, thus, contributes key abstractions and an associated protocol for integrating concurrent meta-programming approaches in a language workbench, and dynamically exploring the possible executions of a model in the modeling workbench.

Free-view Face Relighting Using a Hybrid Parametric Neural Model on a SMALL-OLAT Dataset

The development of neural relighting techniques has by far outpaced the rate of their corresponding training data (e.g., OLAT) generation. For example, high-quality relighting from a single portrait image still requires supervision from comprehensive datasets covering broad diversities in gender, race, complexion, and facial geometry. We present a hybrid parametric neural relighting (PN-Relighting) framework for single portrait relighting, using a much smaller OLAT dataset or SMOLAT. At the core of PN-Relighting, we employ parametric 3D faces coupled with appearance inference and implicit material modelling to enrich SMOLAT for handling in-the-wild images. Specifically, we tailor an appearance inference module to generate detailed geometry and albedo on top of the parametric face and develop a neural rendering module to first construct an implicit material representation from SMOLAT and then conduct self-supervised training on in-the-wild image datasets. Comprehensive experiments show that PN-Relighting produces comparable high-quality relighting to TotalRelighting (Pandey et al., 2021), but with a smaller dataset. It further improves shape estimation and naturally supports free-viewpoint rendering and partial skin material editing. PN-Relighting also serves as a data augmenter to produce rich OLAT datasets beyond the original capture.

Polyelectrolyte complexation of two oppositely charged symmetric polymers: A minimal theory.

Interplay of Coulomb interaction energy, free ion entropy, and conformational elasticity is a fascinating aspect in polyelectrolytes (PEs). We develop a theory for complexation of two oppositely charged PEs, a process known to be the precursor to the formation of complex coacervates in PE solutions, to explore the underlying thermodynamics of complex formation, at low salts. The theory considers general degrees of solvent polarity and dielectricity within an implicit solvent model, incorporating a varying Coulomb strength. Explicit calculation of the free energy of complexation and its components indicates that the entropy of free counterions and salt ions and the Coulomb enthalpy of bound ion-pairs dictate the equilibrium of PE complexation. This helps decouple the self-consistent dependency of charge and size of the uncomplexed parts of the polyions, derive an analytical expression for charge, and evaluate the free energy components as functions of chain overlap. Complexation is observed to be driven by enthalpy gain at low Coulomb strengths, driven by entropy gain of released counterions but opposed by enthalpy loss due to reduction of ion-pairs at moderate Coulomb strengths, and progressively less favorable due to enthalpy loss at even higher Coulomb strengths. The total free energy of the system is found to decrease linearly with an overlap of chains. Thermodynamic predictions from our model are in good quantitative agreement with simulations in literature.

The Active Inference Model of Coherence Therapy.

Coherence Therapy is an empirically derived experiential psychotherapy based on Psychological Constructivism. Symptoms are viewed as necessary output from an implicit model of the world. The therapist curates experiences and directs attention toward discovering the model. Rendered explicit, the model is juxtaposed with contradictory knowledge driving memory re-consolidation with resolution of the symptom. The Bayesian Brain views perception and action as inferential processes. Prior beliefs are combined in a generative model to explain the hidden causes of sensations through a process of Active Inference. Prior beliefs that are poor fits to the real world are suboptimal. Suboptimal priors with optimal inference produce Bayes Optimal Pathology with behavioral symptoms. The Active Inference Model of Coherence Therapy posits that Coherence Therapy is a dyadic act of therapist guided Active Inference that renders the (probable) hidden causes of a client's behavior conscious. The therapist's sustained attention on the goal of inference helps to overcome memory control bias against retrieval of the affectively charged suboptimal prior. Serial experiences cue memory retrieval and re-instantiation of the physiological/affective state that necessitates production of the symptom in a particular context. As this process continues there is a break in modularity with assimilation into broader networks of experience. Typically, the symptom produced by optimal inference with the suboptimal prior is experienced as unnecessary/inappropriate when taken out of the particular context. The implicit construct has been re-represented and rendered consciously accessible, by a more complex but more accurate model in which the symptom is necessary in some contexts but not others. There is an experience of agency and control in symptom creation, accompanied by the spontaneous production of context appropriate behavior. The capacity for inference has been restored. The Active Inference Model of Coherence Therapy provides a framework for Coherence Therapy as a computational process which can serve as the basis for new therapeutic interventions and experimental designs integrating biological, cognitive, behavioral, and environmental factors.

Equilibrium Image Denoising with Implicit Differentiation.

Recent efforts on learning-based image denoising approaches use unrolled architectures with a fixed number of repeatedly stacked blocks. However, due to difficulties in training networks corresponding to deeper layers, simply stacking blocks may cause performance degradation, and the number of unrolled blocks needs to be manually tuned to find an appropriate value. To circumvent these problems, this paper describes an alternative approach with implicit models. To our best knowledge, our approach is the first attempt to model iterative image denoising through an implicit scheme. The model employs implicit differentiation to calculate gradients in the backward pass, thus avoiding the training difficulties of explicit models and elaborate selection of the iteration number. Our model is parameter-efficient and has only one implicit layer, which is a fixed-point equation that casts the desired noise feature as its solution. By simulating infinite iterations of the model, the final denoising result is given by the equilibrium that is achieved through accelerated black-box solvers. The implicit layer not only captures the non-local self-similarity prior for image denoising, but also facilitates training stability and thereby boosts the denoising performance. Extensive experiments show that our model leads to better performances than state-of-the-art explicit denoisers with enhanced qualitative and quantitative results.

Analysis of Online Marketing Strategies Post Pandemic COVID-19

This research examines the analysis of marketing strategies carried out online after the COVID-19 pandemic. The research used in this research is descriptive qualitative research. Various sources were processed in this study such as books, research results, journals, and research articles. This research explains that the challenges and opportunities for business marketing that rely on digital applications during a pandemic are felt to be very diverse and complex. Post-covid-19 shopping behavior has changed based on the relationship between technology & culture. The pattern of spending on basic needs and shopping has changed due to the ease of using the internet for shopping and the existence of online services. Digital-based marketing for a profitable and sustainable business is proven to face many challenges and opportunities. The problem is that not all business people and customers understand and can apply technology, while the opportunity is that digital technology applications can innovate business marketing efforts efficiently, effectively, and productively. In addition, the role of digital marketing on the financial performance of several organizations and the implicit digital transformation model has been identified as the most important aspects related to the current COVID-19 situation.

A Hierarchical Decoder Architecture for Multilevel Fine-Grained Disaster Detection

As a cutting-edge challenge in the field of disaster evaluation, the detection of disasters in remote sensing images is crucial. However, most existing approaches to disaster detection simply solve the problem as a naive multi-class change detection, lacking accurate damage-level classification. In this paper, we propose a new approach to disaster detection called multi-level disaster detection (MLDD) that focuses on fine-grained damage-level classification. Our proposed approach tackles MLDD through hierarchical-correlation modeling and presents a universal disaster detection architecture. Specifically, we summarize two existing applicative methods, one-step training and pre-training, which are compatible with our proposed architecture. In addition, we propose two novel hierarchical approaches, namely the multi-task (MT) based and graph-encoding (GE) based approaches. The MT approach resolves MLDD through layer-wise learning in a progressive manner, building explicit multi-stage and implicit joint models to probe into the coarse-to-fine correlation for damage-level evaluation. The GE approach enhances hierarchical relationships by encoding multifold messaging directions and probabilities using a graph neural network. Furthermore, all four hierarchical paradigms can be embedded in our hierarchical MLDD architecture, which outperforms state-of-the-art methods on the xBD dataset, particularly in fine-grained damage-level classification. Overall, our proposed approach represents a significant improvement over existing disaster detection methods and has the potential to advance the field of disaster evaluation.