Theoretical Modeling Approach Research Articles

3D Computational Modeling of Defective Early Endosome Distribution in Human iPSC-Based Cardiomyopathy Models

Intracellular cargo delivery via distinct transport routes relies on vesicle carriers. A key trafficking route distributes cargo taken up by clathrin-mediated endocytosis (CME) via early endosomes. The highly dynamic nature of the endosome network presents a challenge for its quantitative analysis, and theoretical modelling approaches can assist in elucidating the organization of the endosome trafficking system. Here, we introduce a new computational modelling approach for assessment of endosome distributions. We employed a model of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) with inherited mutations causing dilated cardiomyopathy (DCM). In this model, vesicle distribution is defective due to impaired CME-dependent signaling, resulting in plasma membrane-localized early endosomes. We recapitulated this in iPSC-CMs carrying two different mutations, TPM1-L185F and TnT-R141W (MUT), using 3D confocal imaging as well as super-resolution STED microscopy. We computed scaled distance distributions of EEA1-positive vesicles based on a spherical approximation of the cell. Employing this approach, 3D spherical modelling identified a bi-modal segregation of early endosome populations in MUT iPSC-CMs, compared to WT controls. Moreover, spherical modelling confirmed reversion of the bi-modal vesicle localization in RhoA II-treated MUT iPSC-CMs. This reflects restored, homogeneous distribution of early endosomes within MUT iPSC-CMs following rescue of CME-dependent signaling via RhoA II-dependent RhoA activation. Overall, our approach enables assessment of early endosome distribution in cell-based disease models. This new method may provide further insight into the dynamics of endosome networks in different physiological scenarios.

A Distributed Deployment Method for Wireless Sensor Networks Based on Multi Agent Systems

This paper proposes a distributed deployment method for wireless sensor networks based on multi-agent systems, which addresses the issues of poor self adaptive deployment capability and high deployment costs caused by numerous and diverse types of nodes in the deployment process of wireless sensor networks, leading to blind spots in coverage and redundant nodes. Due to the characteristics of intelligent perception, intelligent communication, and clustering of nodes in wireless sensor network, sensor nodes can be seen as sensing agents, and sensor networks can be seen as distributed multi-agent systems composed of numerous sensing agents. Through the sensing agents' perception of the surrounding environment and communication interaction, a unified deployment model and distributed deployment algorithm with multiple nodes (cluster head nodes) as leaders are established, achieving effective integration of autonomous and leadership mechanisms, distributed management, and centralized management. Taking the problem of sensor network coverage as an example, simulation experiments were conducted, including 12 sensor agents and 1 20 * 20 monitoring areas, to verify how the sensor agents perceive the environment and optimize the migration and deployment process under the coordination of cluster head nodes. The experimental results confirmed the effectiveness of the above models and algorithms. The research results provide a theoretical model and practical approach for the distributed deployment and scheduling of sensor nodes in wireless sensor networks, and can be extended to intelligent Internet of Things systems, such as urban intelligent agent systems, logistics intelligent agent systems, etc., to improve the intelligence of wireless sensor networks deployment and scheduling.

Experimental and theoretical investigation into flexural performance of thin-walled steel-laminated bamboo lumber truss beam

This study presents a novel truss beam called thin-walled steel-laminated bamboo lumber truss beam (SBTB). The SBTB is composed of cold-formed thin-walled steel chords and laminated bamboo lumber web members. A total of 24 SBTBs were divided into eight groups, each group containing 3 specimens, to investigate various parameters, including bamboo web member thickness and laminated direction, number of self-drilling screws, shear span ratio, and truss beam height and width. The flexural performance of SBTBs was evaluated through a four-point bending test, analyzing the failure mode, deflection, bearing capacity, and bending stiffness. The impact of different parameters on the flexural performance was thoroughly examined and discussed. In addition, a theoretical model and simplified prediction approach for the bending stiffness of SBTBs were developed. The results identified five failure modes during the flexural tests and demonstrated the linear elastic and nonlinear stages of the SBTB working process. Increasing the width and height of the beam, the number of self-drilling screws, and adopting plain pressed laminated bamboo lumber web members significantly improved the bending resistance of the composite truss beam. Notably, the theoretical model accurately predicted the stiffness of SBTBs, with a maximum discrepancy of less than 12.3 % between experimental and theoretical results.

Theoretical modeling and machine learning-based data processing workflows in comprehensive two-dimensional gas chromatography—A review

In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional gas chromatography (GC). Nonetheless, to fully benefit from the capabilities of GC × GC, a holistic approach to method development and data processing is essential for a successful and informative analysis. Method development enables the fine-tuning of the chromatographic separation, resulting in high-quality data. While generating such data is pivotal, it does not necessarily guarantee that meaningful information will be extracted from it. To this end, the first part of this manuscript reviews the importance of theoretical modeling in achieving good optimization of the separation conditions, ultimately improving the quality of the chromatographic separation. Multiple theoretical modeling approaches are discussed, with a special focus on thermodynamic-based modeling. The second part of this review highlights the importance of establishing robust data processing workflows, with a special emphasis on the use of advanced data processing tools such as, Machine Learning (ML) algorithms. Three widely used ML algorithms are discussed: Random Forest (RF), Support Vector Machine (SVM), and Partial Least Square–Discriminate Analysis (PLS-DA), highlighting their role in discovery-based analysis.

Disk and Envelope Streamers of the GGD 27-MM1 Massive Protostar

We present new Atacama Large Millimeter/submillimeter Array 0.98 mm observations of the continuum emission and several molecular lines toward the high-mass protostellar system GGD 27-MM1, driving the HH 80-81 radio jet. The detailed analysis of the continuum and the CH3CN molecular emission allows us to separate the contributions from the dust content of the disk (extending up to 190 au), the molecular content of the disk (extending from 140–360 au), and the content of the envelope, revealing the presence of several possible accretion streamers (also seen in other molecular tracers, such as CH3OH). We analyze the physical properties of the system, producing temperature and column density maps, and radial profiles for the disk and the envelope. We qualitatively reproduce the trajectories and line-of-sight velocities of the possible streamers using a theoretical model approach. An ad hoc model of a flared disk comprising a hot dust disk embedded in cold gas fits the H2S emission, which revealed the molecular disk as a crescent shape with a prominent central absorption. Another fit to the central absorption spectrum suggests that the absorption is probably caused by different external cold layers from the envelope or the accretion streamers. Finally, the analysis of the rotation pattern of the different molecular transitions in the molecular disk suggests that there is an inner zone devoid of molecular content.

Environmental Assessment of Controlled Landfill Site and Biogas Electric Potential Prediction of Moroccan Municipality Using Theoretical and Numerical Modeling Approaches

Environmental Assessment of Controlled Landfill Site and Biogas Electric Potential Prediction of Moroccan Municipality Using Theoretical and Numerical Modeling Approaches

Linking Redox Characteristics to Dissolved Organic Matter Derived from Different Biowaste Composts: A Theoretical Modeling Approach Based on FT-ICR MS Analysis.

Compost dissolved organic matter (DOM) is a complex mixture of redox-active organic molecules that impact various biogeochemical processes in soil environments. However, the impact of chemical complexity (heterogeneity and chemodiversity) on the electron accepting capacity (EAC) and electron donating capacity (EDC) of DOM molecules remains unclear, which hinders our ability to predict their environmental behavior and redox properties. In this study, the applicability of Vienna Soil Organic Matter Modeler 2 (VSOMM2) to the composting system based on the FT-ICR MS data has been validated. A molecular modeling approach using VSOMM2 and Schrödinger software was developed to quantitatively assess the redox sites and molecular interactions of compost DOM. Compost DOM molecules are categorized into three distinct groups based on their heterogeneous origins. In addition, we have developed 18 molecular models of compost DOM based on the links of molecules to EAC/EDC. Finally, Ar-OH, quinone, Ar-SH, and Ar-NH2 were identified as the redox sites; noncovalent contacts, H bonds, salt bridges, and aromatic-H bonds might be significant electronic transmission channels of compost DOM. Our findings contribute to the development of precise regulatory methods for functional molecules within compost DOM, providing the fine standards for composts matching specific ecosystem service requirements.

Theoretical modeling approach for adsorption of fibronectin on the nanotopographical implants.

The success of orthopedic implants depends on the sufficient integration between tissue and implant, which is influenced by the cellular responses to their microenvironment. The conformation of adsorbed extracellular matrix is crucial for cellular behavior instruction via manipulating the physiochemical features of materials. To investigate the electrostatic adsorption mechanism of fibronectin on nanotopographies, a theoretical model was established to determine surface charge density and Coulomb's force of nanotopography - fibronectin interactions using a Laplace equation satisfying the boundary conditions. Surface charge density distribution of nanotopographies with multiple random fibronectin was simulated based on random number and Monte Carlo hypothesis. The surface charge density on the nanotopographies was compared to the experimental measurements, to verify the effectiveness of the theoretical model. The model was implemented to calculate the Coulomb force generated by nanotopographies to compare the fibronectin adsorption. This model has revealed the multiple random quantitative fibronectin electrostatic adsorption to the nanotopographies, which is beneficial for orthopedic implant surface design.Significance: The conformation and distribution of adsorbed extracellular matrix on biomedical implants are crucial for directing cellular behaviors. However, the Ti nanotopography-ECM interaction mechanism remains largely unknown. This is mostly because of the interactions that are driven by electrostatic force, and any experimental probe could interfere with the electric field between the charged protein and Ti surface. A theoretical model is hereby proposed to simulate the adsorption between nanotopographies and fibronectin. Random number and Monte Carlo hypothesis were applied for multiple random fibronectin simulation, and the Coulomb's force between nanoconvex and nanoconcave structures was comparatively analyzed.

An extended universal morphological model for concrete mode‐I fracture: Parametric and case studies

AbstractThe purpose of this study is to develop a systematic meso‐level theoretical modeling approach for analyzing the mode‐I fracture failure behaviors of concrete. Firstly, the eXtended Universal Morphological Model (XUMM) is briefly reviewed. Secondly, a normalized analysis method for the evaluation of effects of properties and scales of meso structures on the performance of concrete is proposed based on the XUMM. Afterwards, parametric studies regarding the aggregate strength effect, the aggregate volume effect, the maximum aggregate size (MAS) effect, the tortuosity of mesoscopic crack, the roughness of fracture surface and the characteristic length are conducted. At last, a case study on the MAS effect based on experimental tests is provided to verify the applicability of the XUMM.

Enhancing interfacial shear debonding resistance by mechanical mismatch

Layered systems of similar/dissimilar soft materials are widely used in advanced device applications, including bioelectronics, soft robotics, and energy generators. Device designers strive to minimize the mechanical mismatch between the soft material layers, claiming that the mismatch could be associated with low interfacial debonding resistance and then cause device malfunction. Although strong interfacial bonding has been repeatedly achieved based on the principle of minimizing-mechanical-mismatch, the specific question of whether mechanical matching is the optimal solution for high debonding resistance has never been solidly answered, leaving ambiguity in the further optimization of the layered soft material systems. In this paper, the influence of the stiffness ratio between the soft material layers on the interfacial shear debonding resistance is systematically analyzed through theoretical modeling and finite element simulation approaches. Monotonic improvement of the shear debonding resistance with increasing the loading layer stiffness, even when exceeding that of the substrate layer, is analytically derived by establishing a shear debonding theory of a hyperelastic bilayer. This mechanical-mismatch induced improvement is due to the effective consumption of the external work by the strain energy storage of the loading layer and the large increase in the shear lag length. The theoretical predictions of the debonding resistance agree well with the finite element simulation results, which implies the local non-uniform deformation in the loading and substrate layers near the interfacial crack front causes insignificant influence on the shear debonding. Increasing the modulus or thickness of the loading layer or adding a stiff backing can equivalently improve the shear debonding resistance, which can be quantitatively predicted by the established shear debonding theory. Reasonable applications of the proposed debonding resistance enhancement strategy are also discussed. The findings provide good guidance for the safe service and further optimization of the layered soft material systems to improve the device reliability.

An extended universal morphological model for concrete mode‐I fracture: Formulations and preliminary verifications

AbstractThe purpose of this study is to develop a systematic meso‐level theoretical modeling approach for analyzing the mode‐I fracture failure behaviors of concrete. Firstly, the crack model is developed in a way to be equivalent to the continuum model at the macro‐level. Secondly, an extended universal morphological model (XUMM) for concrete mode‐I fracture analysis is proposed at the meso‐level. Afterwards, both the mesoscopic fracture characteristics and the macroscopic mechanical parameters of concrete are theoretically deduced based on the proposed XUMM. Comparison with existing meso‐level test results shows that the predictions from the XUMM have good consistency, but offsets are obvious as well. At last, the XUMM is calibrated using the limit state method and preliminarily verified via mesoscale numerical simulations.

Noncovalent Host-Guest Complexes of Artemisinin with α-, β-, and γ- Cyclodextrin Examined by Structural Mass Spectrometry Strategies.

Cyclodextrins (CDs) are a family of macrocyclic oligosaccharides with amphiphilic properties, which can improve the stability, solubility, and bioavailability of therapeutic compounds. There has been growing interest in the advancement of efficient and reliable analytical methods that assist with elucidating CD host-guest drug complexation. In this study, we investigate the noncovalent ion complexes formed between naturally occurring dextrins (αCD, βCD, γCD, and maltohexaose) with the poorly water-soluble antimalarial drug, artemisinin, using a combination of ion mobility-mass spectrometry (IM-MS), tandem MS/MS, and theoretical modeling approaches. This study aims to determine if the drug can complex within the core dextrin cavity forming an inclusion complex or nonspecifically bind to the periphery of the dextrins. We explore the use of group I alkali earth metal additives to promote the formation of various noncovalent gas-phase ion complexes with different drug/dextrin stoichiometries (1:1, 1:2, 1:3, 1:4, and 2:1). Broad IM-MS collision cross section (CCS) mapping (n > 300) and power-law regression analysis were used to confirm the stoichiometric assignments. The 1:1 drug:αCD and drug:βCD complexes exhibited strong preferences for Li+ and Na+ charge carriers, whereas drug:γCD complexes preferred forming adducts with the larger alkali metals, K+, Rb+, and Cs+. Although the ion-measured CCS increased with cation size for the unbound artemisinin and CDs, the 1:1 drug:dextrin complexes exhibit near-identical CCS values regardless of the cation, suggesting these are inclusion complexes. Tandem MS/MS survival yield curves of the [artemisinin:βCD + X]+ ion (X = H, Li, Na, K) showed a decreased stability of the ion complex with increasing cation size. Empirical CCS measurements of the [artemisinin:βCD + Li]+ ion correlated with predicted CCS values from the low-energy theoretical structures of the drug incorporated within the βCD cavity, providing further evidence that gas-phase inclusion complexes are formed in these experiments. Taken together, this work demonstrates the utility of combining analytical information from IM-MS, MS/MS, and computational approaches in interpreting the presence of gas-phase inclusion phenomena.

Local cation ordering in compositionally complex Ruddlesden–Popper n = 1 oxides

The Ruddlesden–Popper (RP) layered perovskite structure is of great interest due to its inherent tunability, and the emergence and growth of the compositionally complex oxide (CCO) concept endows the RP family with further possibilities. Here, a comprehensive assessment of thermodynamic stabilization, local order/disorder, and lattice distortion was performed in the first two reported examples of lanthanum-deficient Lan+1BnO3n+1 (n = 1, B = Mg, Co, Ni, Cu, Zn) obtained via various processing conditions. Chemical short-range order (CSRO) at the B-site and the controllable excess interstitial oxygen (δ) in RP-CCOs are uncovered by neutron pair distribution function analysis. Reverse Monte Carlo analysis of the data, Metropolis Monte Carlo simulations, and extended x-ray absorption fine structure analysis implies a modest degree of magnetic element segregation on the local scale. Further, ab initio molecular dynamics simulations results obtained from special quasirandom structure disagree with experimentally observed CSRO but confirm Jahn–Teller distortion of CuO6 octahedra. These findings highlight potential opportunities to control local order/disorder and excess interstitial oxygen in layered RP-CCOs and demonstrate a high degree of freedom for tailoring application-specific properties. They also suggest a need for expansion of theoretical and data modeling approaches in order to meet the innate challenges of CCO and related high-entropy phases.

Going Beyond Observable Actions

Abstract: Pauses represented in process data captured from digital learning and assessment tasks are defined as the time elapsed between two subsequent events. Pauses are used as a marker of unobservable cognitive processes, such as encoding, problem-solving, and planning, that underlie test takers’ subsequent observable actions in educational assessment. To make valid inferences about the underlying cognitive processes represented by pauses, we argue that applying a task-specific cognitive modeling approach is required. We discuss and demonstrate how to apply a task-specific, theoretical cognitive modeling approach for interpreting pauses. We believe that this approach will have value for educational researchers seeking to make valid, task-general inferences about test-taker cognition from pauses represented in process data.

Exploring Bipartite Network Approach in Hand, Foot and Mouth Disease Hotspot Identification

Mathematical modeling of hand, foot, and mouth disease (HFMD) mainly focuses on compartmental modeling approaches. It classifies human population into compartments and assumes homogeneity that regards every human has equal chance of contacting other individuals in the population. However, the transmission of HFMD is complicated and dynamic with the interactions of the intertwined biomedical and social factors. Describing the disease transmission dynamic that involves high-dimensional space is mathematically challenging. The graph theoretic bipartite network modeling (BNM) approach has the potential to handle this challenge by abstracting the real-world disease transmission system and incorporating the individual features of the bipartite nodes. This study aims to seize the advantages portrayed by the BNM approach in capturing the heterogeneous features of the entities within a disease transmission system. It intends to explore adopting the BNM approach in modeling the transmission of HFMD at Kuching, Malaysia and identify the hotspot by employing the BNM approach comprising a four-stage methodology adapted from the BNM methodology framework. The bipartite HFMD contact (BHC) network is formulated with the basic building block consisting of the location and human nodes. The individual parameters of the location and human node are incorporated. The resulting BHC network formulated comprises 10 human nodes, 20 location nodes, and 23 edges. Then, six top-ranked location nodes were identified and agreed with the chosen benchmark system. The potential HFMD hotspots are thus identified by determining the location nodes ranking. The result from this study has enabled timely and effective measures and policies to be customized accordingly by the public health authorities and related policymakers.

Association rules and prediction of transportation mode choice: Application to national travel survey data

Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules that have high confidence but low support, the information gain (IG) concept is introduced in the model building process. The modeling process in this study first involves extracting frequent items from the data using the FP-Growth algorithm and deriving CARs from these frequent items. Next, the IG statistic is used to construct a novel model (named CARIG), which consists of a set of decision rules that formally represent behavioral scripts, for predicting individuals’ transportation mode choice. The performance of CARIG is compared with the performance of conventional class-based association rules (CBA), decision trees (DT), a convolutional neural network (CNN) and a logistic regression (LR) model. In addition, a 10-fold cross validation test using a grid search parameter optimization method is conducted to validate the proposed approach. The results show that the proposed method is promising in predicting transportation mode choices observed in the national travel survey data.

Intrafasciomembranal Fluid Pressure: A Novel Approach to the Etiology of Myalgias, Part II.

The fascia forms a hierarchy of spaces (small and large compartments) that contain and enclose muscle fibers, fiber bundles, skeletal muscles, and compartments of several skeletal muscles. Solid fascia serves as a membrane that enables an increased volume and fluid pressure within such a fasciomembrane, an intrafasciomembrial fluid pressure (IFMFP). Increased IFMFP provides a theoretical model and a common explanation for the etiology of the myalgias: trigger point (TrP), chronic exertional compartment syndrome (CECS), overtraining syndrome (OTS), and delayed onset muscle soreness (DOMS). Many myalgias and their symptoms are poorly understood, and this review aims to provide an extension to this theoretical model and novel approach. This review suggests that the swelling from elevated IFMFP also likely leads to a longitudinal shortening of the same affected tissue. This model of swelling and shortening provides additional explanations for the changes in the lines of force through the body that can lead to changes in the body's posture and, thus, to compensatory movements. This new approximation also provides a biomechanical explanation for the thickening of the fascia and referred pain, and also suggests that IFMFP is a factor in weather-related pain.

Edge intelligence-driven digital twin of CNC system: Architecture and deployment

• Propose a theoretical modeling approach based on the hierarchical structure of CNC systems. • Proposes an edge intelligence-driven digital twin architecture for CNC system. • Proposes a generic digital twin modeling method. • Proposes a novel task partitioning method to improve system throughput. • Proposes an adaptive model selection algorithm . In recent years, digital twin (DT) technology has gradually become the primary way to achieve the intelligence of CNC systems. However, with the development of next-generation information technologies such as artificial intelligence (AI) and its wide application in CNC systems, the limitation of computing power and network resources has become one of the urgent problems that must be solved by the DT of CNC systems. To address these problems, a theoretical modeling method for CNC systems based on its hierarchical structure is proposed first, and the edge intelligence (EI) technology is introduced to support the deployment of DT models. Meanwhile, a model partitioning method and a model selection algorithm are proposed to support real-time model response in the model deployment process. In addition, an application case of EI-driven DT of CNC system is given to diagnose and predict the tool wear during machining processes.

Advanced Theory and Simulations in 2022 – Growing in Volume and Breadth

<i>Advanced Theory and Simulations</i> in 2022 – Growing in Volume and Breadth

Groundwater flow through fractured rocks and seepage control in geotechnical engineering: Theories and practices

Groundwater flow through fractured rocks has been recognized as an important issue in many geotechnical engineering practices. Several key aspects of fundamental mechanisms, numerical modeling and engineering applications of flow in fractured rocks are discussed. First, the microscopic mechanisms of fluid flow in fractured rocks, especially under the complex conditions of non-Darcian flow, multiphase flow, rock dissolution, and particle transport, have been revealed through a combined effort of visualized experiments and theoretical analysis. Then, laboratory and field methods of characterizing hydraulic properties (e.g. intrinsic permeability, inertial permeability, and unsaturated flow parameters) of fractured rocks in different flow regimes have been proposed. Subsequently, high-performance numerical simulation approaches for large-scale modeling of groundwater flow in fractured rocks and aquifers have been developed. Numerical procedures for optimization design of seepage control systems in various settings have also been proposed. Mechanisms of coupled hydro-mechanical processes and control of flow-induced deformation have been discussed. Finally, three case studies are presented to illustrate the applications of the improved theoretical understanding, characterization methods, modeling approaches, and seepage and deformation control strategies to geotechnical engineering projects.