Computer Simulation Research Articles

Microscopic pathways of transition from low-density to high-density amorphous phase of water.

In recent years, much attention has been devoted to understanding the pathways of phase transition between two equilibrium condensed phases (such as liquids and solids). However, the microscopic pathways of transition involving non-equilibrium, non-diffusive amorphous (glassy) phases still remain poorly understood. In this work, we have employed computer simulations, persistence homology (a tool rooted in topological data analysis), and machine learning to probe the microscopic pathway of pressure-induced non-equilibrium transition between the low- and high-density amorphous (LDA and HDA, respectively) ice phases of the TIP4P/2005 and ST2 water models. Using persistence homology and machine learning, we introduced a new order parameter that unambiguously identifies the LDA- and HDA-like local environments. The LDA phase transitions continuously and collectively into the corresponding HDA phase via a pre-ordered intermediate phase during the isothermal compression. The local order parameter susceptibilities show a maximum near the transition pressure (P*)-suggesting maximum structural heterogeneities near P*. The HDA-like clusters are structurally ramified and spatially delocalized inside the LDA phase near the transition pressure. We also found manifestations of the first-order low-density to high-density liquid transition in the sharpness of the order parameter change during the LDA to HDA transition. We further investigated the (geometrical) structures and topologies of the LDA and HDA ices formed via different protocols and also studied the dependence of the (microscopic) pathway of phase transition on the protocol followed to prepare the initial LDA phase. Finally, the method adopted here to study the phase transition pathways is not restricted to the system under consideration and provides a robust way of probing phase transition pathways involving any two condensed phases at both equilibrium and out-of-equilibrium conditions.

Cognitive Technology to Evaluate the Academic Learning of Computational Cognition in Psychology Students

<p style="text-align:justify">This study illustrated an alternative way to evaluate students’ academic learning. It involved the joint and intertwined application of the natural semantic network technique, computer simulations, and semantic priming experiments to assess the cognitive changes in knowledge structures due to academic learning in two groups of psychology students. The experimental group was enrolled in a course on computational cognition, while the control group was oblivious to this course. The results indicated that the cognitive assessment tools discriminate the cognitive changes produced as a result of general training undertaken in a psychology degree versus the influence of a specific course. After the course, the experimental group increased their technical vocabulary, changed their conceptual valuation of definers related to computational theories of mind, and reorganized the relations among definers according to the computational cognition approach. Also, this group presented a higher connectivity index between the concepts of the semantic network, their conceptual activation level and conceptual co-activation pattern changed, and their access level to the evaluated schema’s concepts improved. In contrast, the control group did not show significant changes in their cognitive patterns after the course. These findings suggest that cognitive tools may be helpful in the diagnosis of academic learning.</p>

Quantification of Contributions from Different Dust Sources to the Distribution and the Variation of Spring Dust over the Tibetan Plateau and Corresponding Mechanisms since 2000

Abstract Dust is the predominant type of aerosol over the Tibetan Plateau (TP) due to the existence of surrounding significant dust sources. However, the contributions of different dust sources to the distribution and variation of dust over the TP and corresponding mechanisms are still being explored. By separating emissions from different dust sources in a numerical model, this study detected that dust originating from East Asia, the Middle East, and North Africa are the main contributors to spring dust over the TP, accounting for 42%–68%, 13%–27%, and 9%–25% of the total dust concentration, respectively. East Asian dust primarily affects the dust over the central and northern parts of the TP, whereas Middle Eastern and North African dust mainly contributes to the dust over the western and southern parts of the TP. Additionally, the variation in dust over the TP is related to East Asian and North African dust, which contribute 58% and 35% of the total variation, respectively. The mechanism underlying this association is attributable to the SST over the northern North Atlantic and updrafts over the northern slope of the TP: the increased SST enhances westerlies from North Africa to East Asia and northwesterly winds over the northern slope of the TP and combines with the stronger westerlies to promote the transport of East Asian and North African dust to the TP. Consequently, it is necessary to focus on the impact of multiple dust sources on the dust over the TP.

A geometric variation method to extract rock strength parameters via uniaxial compression testing

Numerical modelling in rock engineering and mechanics often requires inputting multiple sets of strength parameters, such as the commonly encountered tensile strength, cohesion, and friction angle. Usually, obtaining these parameters requires performing different types of rock mechanics experiments on certain rock specimens, such as tension tests and triaxial tests, which are time-consuming and labor-intensive. Uniaxial compression testing is simpler and requires less demanding equipment. This paper proposes a solution that relies solely on uniaxial compression testing to obtain multiple sets of rock strength parameters. The basic idea is to treat the process of acquiring multiple parameters as a problem of solving a set of mathematical equations. The uniaxial tests with different geometric specimens provide known quantities and the corresponding equations that link these known quantities (e.g., peak loads) with unknown quantities (e.g., strength parameters). We established three-dimensional numerical tests to represent the relationship between input strength parameters and the peak load. By transforming it into an optimization problem, numerical modelling is embedded into the objective function of the optimization algorithm, thereby achieving the automatic extraction of strength parameters. This method is verified through uniaxial compression tests using red sandstone specimens with various geometries. The results show that by changing the geometry of the rock specimens, multiple sets of strength parameters can be obtained. These parameters are successfully used to predict (extrapolate) peak loads of other geometric specimens. This method holds promise for achieving the automated extraction of material parameters for the numerical modelling of rock.

A bio-ionic liquid based self-healable and adhesive ionic hydrogel for the on-demand transdermal delivery of a chemotherapeutic drug.

The non-invasive nature and potential for sustained release make transdermal drug administration an appealing treatment option for cancer therapy. However, the strong barrier of the stratum corneum (SC) poses a challenge for the penetration of hydrophilic chemotherapy drugs such as 5-fluorouracil (5-FU). Due to its biocompatibility and capacity to increase drug solubility and permeability, especially when paired with chemical enhancers, such as oleic acid (OA), which is used in this work, choline glycinate ([Cho][Gly]) has emerged as a potential substance for transdermal drug delivery. In this work, we examined the possibility of transdermal delivery of 5-FU for the treatment of breast cancer using an ionic hydrogel formulation consisting of [Cho][Gly] with OA. Small angle neutron scattering, rheological analysis, field emission scanning electron microscopy, and dynamic light scattering analysis were used to characterize the ionic hydrogel. The non-covalent interactions present between [Cho][Gly] and OA were investigated by computational simulations and FTIR spectroscopy methods. When subjected to in vitro drug permeation using goat skin in a Franz diffusion cell, the hydrogel demonstrated sustained release of 5-FU and effective permeability in the order: [Cho][Gly]-OA gel > [Cho][Gly] > PBS (control). The hydrogel also demonstrated 92% cell viability after 48 hours for the human keratinocyte cell line (HaCaT cells) as well as the normal human cell line L-132. The breast cancer cell line MCF-7 and the cervical cancer cell line HeLa were used to study in vitro cytotoxicity that was considerably affected by the 5-FU-loaded hydrogel. These results indicate the potential of the hydrogel as a transdermal drug delivery vehicle for the treatment of breast cancer.

Thermodynamic and transport properties of triangular-well fluids from molecular dynamics

Thermodynamic and transport properties of particles interacting via the triangular-well potential in the range of 1.4 ≤ λ ≤ 2.6 have been studied using perturbation theory and Molecular Dynamics simulations. We present thermodynamic properties such as vapour-liquid coexistence, vapour pressures, and density for different values of the attractive range potential. Good agreement is observed between the theoretical approach and computer simulations in a wide range of densities, temperatures, and pressures. Additionally, we show Molecular Dynamic simulation results of transport properties such as the self-diffusion coefficient as a function of both density and temperature. Finally, thermodynamic and transport properties of real fluids like oxygen, methane, hydrogen sulfide, and fluoromethane have been predicted using both approaches. Results of vapour-liquid coexistence, vapour pressures, and critical points were in excellent agreement with experimental data. The coupling of perturbation theory, Molecular Dynamics simulations, and the triangular-well pair potential offers a versatile and efficient method to explore various real fluid systems, which would include associating fluids, complex molecular liquids, and colloidal systems. This approach can advance our understanding of these systems using a simple approach and to contribute the development of more accurate and predictive models.

Ultrathin TiS2@N,S-Doped Carbon Hybrid Nanosheets as Highly Efficient Photoresponsive Antibacterial Agents.

Nanobactericides are employed as a promising class of nanomaterials for eradicating microbial infections, considering the rapid resistance risks of conventional antibiotics. Herein, we present a pioneering approach, reporting the synthesis of two-dimensional titanium disulfide nanosheets coated by nitrogen/sulfur-codoped carbon nanosheets (2D-TiS2@NSCLAA hybrid NSs) using a rapid l-ascorbic acid-assisted sulfurization of Ti3C2Tx-MXene to achieve efficient alternative bactericides. The as-developed materials were systematically characterized using a suite of different spectroscopy and microscopy techniques, in which the X-ray diffraction/Raman spectroscopy/X-ray photoelectron spectroscopy data confirm the existence of TiS2 and C, while the morphological investigation reveals single- to few-layered TiS2 NSs confined by N,S-doped C, suggesting the successful synthesis of the ultrathin hybrid NSs. From in vitro evaluation, the resultant product demonstrates impressive bactericidal potential against both Gram-positive Staphylococcus aureus and Gram-negative Escherichia coli bacteria, achieving a substantial decrease in the bacterial viability under a 1.2 J dose of visible-light irradiation at the lowest concentration of 5 μg·mL-1 compared to Ti3C2Tx (15 μg·mL-1), TiS2-C (10 μg·mL-1), and standard antibiotic ciprofloxacin (15 μg·mL-1), respectively. The enhanced degradation efficiency is attributed to the ultrathin TiS2 NSs encapsulated within heteroatom N,S-doped C, facilitating effective photogenerated charge-carrier separation that generates multiple reactive oxygen species (ROS) and induced physical stress as well as piercing action due to its ultrathin structure, resulting in multimechanistic cytotoxicity and damage to bacterial cells. Furthermore, the obtained results from molecular docking studies conducted via computational simulation (in silico) of the as-synthesized materials against selected proteins (β-lactamasE.coli/DNA-GyrasE.coli) are well-consistent with the in vitro antibacterial results, providing strong and consistent validation. Thus, this sophisticated study presents a simple and effective synthesis technique for the structural engineering of metal sulfide-based hybrids as functionalized synthetic bactericides.

The effect of sand-crumb rubber mixture treatment on the seismic response of a low-rise building located on liquefiable soil

One of the most effective measures to reduce earthquake risks in high-seismic areas is to reduce the input forces caused by earthquakes to the structure. The properties of rubber materials in geotechnical projects are energy absorption, dynamic deformation change as well as damping of latent energy in loads, high shear modulus and damping ratio, easy access and their economic nature. In this research, with finite difference modeling, the effect of using sand- crumb rubber layer on the seismic behavior of the model has been obtained. In the following, by changing the thickness and depth of the sand-crumb rubber layer, the seismic behavior such as floor displacement and internal forces have been investigated. This research numerically models liquefaction (UBCsand with FLAC) and predicts the effects of the sand-crumb rubber layer of different thicknesses (as % weight of the mixture) under the foundation and evaluates the structure's damage and settlement interacting with the soil by validating the numerical model with the laboratory model and calibrating it with different ratios. The results show that liquefaction, pore water pressure excess ratio and soil settlement will be significantly reduced with the presence of sand-crumb rubber layer, and also the base shear has shown an increase, but the structural damage is reduced.Graphical

Diffusiophoretic Transport of Charged Colloids in Ionic Surfactant Gradients Entirely below versus Entirely above the Critical Micelle Concentration.

When placed in an ionic surfactant gradient, charged colloids will undergo diffusiophoresis at a velocity, uDP = MDP∇ ln S, where MDP is the diffusiophoretic mobility and S is the surfactant concentration. The diffusiophoretic mobility depends in part on the charges and diffusivities of the surfactants and their counterions. Since micellization decreases surfactant diffusivity and alters charge distributions in a surfactant solution, MDP of charged colloids in ionic surfactant gradients may differ significantly when surfactant concentrations are above or below the critical micelle concentration (CMC). The role of micelles in driving diffusiophoresis is unclear, and a previously published model that accounts for micellization suggests the possibility of a change in the sign of MDP above the CMC [Warren, P. B.; . Soft Matter 2019, 15, 278-288]. In the current study, microfluidic channels were used to measure the transport of negatively charged polystyrene colloids in sodium dodecyl sulfate (SDS) surfactant gradients established at SDS concentrations that are either fully above or fully below the CMC. Interpretation of diffusiophoresis was aided by measurements of the colloid electrophoretic mobility as a function of SDS concentration. A numerical transport model incorporating the prior diffusiophoretic mobility model for ionic surfactant gradients was implemented to elucidate signatures of positive and negative diffusiophoretic mobilities and compare with experiments. The theoretically predicted sign of the diffusiophoretic mobility below the CMC was determined to be particularly sensitive to uncertainty in colloid and surfactant properties, while above the CMC, the mobility was consistently predicted to be positive in the SDS concentration range considered in the experiments conducted here. In contrast, experiments only showed signatures of a negative diffusiophoretic mobility for these negatively charged colloids with no change of sign. Colloid diffusiophoretic transport measured in micellar solutions was more extensive than that below the CMC with the same ∇ ln S.

Recognition of local fiber orientation state in prepreg platelet molded composites via deep learning

This paper presents a novel deep learning approach for reconstruction of local through-the-thickness fiber orientation distribution (FOD) in discontinuous long-fiber, prepreg-platelet molded composites (PPMC). The thermal-residual strains on composite surfaces are used as inputs to train a fully convolutional neural network, named U-Net, to predict spatially varying, local FOD. High fidelity synthetic data was generated via computational simulation of PPMC with stochastic material orientation state and was used for training the deep learning model. The proposed U-Net model allowed for rapid recognition of PPMC morphology by solving the inverse structural mechanics problem of determining the average fiber orientation through the composite thickness based on the provided surface strain measurements. Upon training and validation, the U-Net deep learning model was deployed to rapidly predict complex distributions of the local through-the-thickness FOD in PPMC.

Gap junctions in Turing-type periodic feather pattern formation.

Periodic patterning requires coordinated cell-cell interactions at the tissue level. Turing showed, using mathematical modeling, how spatial patterns could arise from the reactions of a diffusive activator-inhibitor pair in an initially homogenous 2D field. Most activators and inhibitors studied in biological systems are proteins, and the roles of cell-cell interaction, ions, bioelectricity, etc. are only now being identified. Gap junctions (GJs) mediate direct exchanges of ions or small molecules between cells, enabling rapid long-distance communications in a cell collective. They are therefore good candidates for propagating nonprotein-based patterning signals that may act according to the Turing principles. Here, we explore the possible roles of GJs in Turing-type patterning using feather pattern formation as a model. We found 7 of the 12 investigated GJ isoforms are highly dynamically expressed in the developing chicken skin. In ovo functional perturbations of the GJ isoform, connexin 30, by siRNA and the dominant-negative mutant applied before placode development led to disrupted primary feather bud formation. Interestingly, inhibition of gap junctional intercellular communication (GJIC) in the ex vivo skin explant culture allowed the sequential emergence of new feather buds at specific spatial locations relative to the existing primary buds. The results suggest that GJIC may facilitate the propagation of long-distance inhibitory signals. Thus, inhibition of GJs may stimulate Turing-type periodic feather pattern formation during chick skin development, and the removal of GJ activity would enable the emergence of new feather buds if the local environment were competent and the threshold to form buds was reached. We further propose Turing-based computational simulations that can predict the sequential appearance of these ectopic buds. Our models demonstrate how a Turing activator-inhibitor system can continue to generate patterns in the competent morphogenetic field when the level of intercellular communication at the tissue scale is modulated.

Data-driven modelling makes quantitative predictions regarding bacteria surface motility.

In this work, we quantitatively compare computer simulations and existing cell tracking data of P. aeruginosa surface motility in order to analyse the underlying motility mechanism. We present a three dimensional twitching motility model, that simulates the extension, retraction and surface association of individual Type IV Pili (TFP), and is informed by recent experimental observations of TFP. Sensitivity analysis is implemented to minimise the number of model parameters, and quantitative estimates for the remaining parameters are inferred from tracking data by approximate Bayesian computation. We argue that the motility mechanism is highly sensitive to experimental conditions. We predict a TFP retraction speed for the tracking data we study that is in a good agreement with experimental results obtained under very similar conditions. Furthermore, we examine whether estimates for biologically important parameters, whose direct experimental determination is challenging, can be inferred directly from tracking data. One example is the width of the distribution of TFP on the bacteria body. We predict that the TFP are broadly distributed over the bacteria pole in both walking and crawling motility types. Moreover, we identified specific configurations of TFP that lead to transitions between walking and crawling states.

A Numerical Model of the Pollutant Transport in Rivers with Multi-Layer Rigid Vegetation

A Numerical Model of the Pollutant Transport in Rivers with Multi-Layer Rigid Vegetation

Analysis of time-dependent bearing capacity of pile foundation in marine soft soil region considering non-Darcy flow

Due to the deviation of pore water seepage in marine soft soils from Darcy flow under low hydraulic gradients, existing methods face challenges in accurately calculating the time-dependent bearing capacity of piles in such areas. This paper adopts the secondary development of Python to ABAQUS and employs a continuous coupling analysis method to establish a numerical model capable of simultaneously solving the large deformation of pile driving and non-Darcy flow. The model addresses issues such as mesh distortion during pile driving and the numerical implementation of three-dimensional non-Darcy flow. The accuracy of the model was verified through comparison with two experimental cases. Subsequently, the study analyzed how non-Darcy flow affects soil consolidation around the pile and the time-dependent bearing capacity of pile. Results indicate that when the permeability coefficient of the soil around the pile is less than 1 × 10−7 m/s, non-Darcy flow significantly delays the consolidation of soil within three times the pile diameter around the pile, thereby reducing the time-dependent bearing capacity of pile, with a greater impact observed for lower permeability coefficients of the soil. Once the soil around the pile reaches 90% consolidation, subsequent construction can proceed to shorten the project duration while maintaining safety.

Thermal enhancement of PEM fuel cell cooling with novel configurations of Tesla valve and hybrid nanofluids: A numerical study

The operating temperature and thermal distribution play a significant role in the appropriate performance of the proton exchange membrane fuel cell (PEMFC). A suitable cell cooling method can guarantee uniform temperature distribution and preservation of membrane water content under harsh conditions in a PEMFC. The compact structure of PEM fuel cells and the size of the cooling system impose a great challenge to achieve a proper design of cooling plate. In this numerical study, four designs of the multi-stage Tesla valve with straight and reverse configuration are proposed to be employed as a cooling channel in which three different hybrid nanofluids such as TiO2–Cu, Al2O3–CuO, and Ag–MgO dispersed in water and Ethylene glycol flow inside the channels. A validated numerical model is developed using a commercial computational fluid dynamics (CFD) code to investigate the effect of inlet temperatures and mass flow rates of cooling fluid and volume fraction of nanoparticles on heat transfer coefficient, pressure drop, index of uniform temperature (IUT), and performance evaluation criteria (PEC). The results demonstrated that the reverse configuration of the Tesla valve (CASE E) experienced outstanding uniform thermal distribution while the heat transfer coefficient increased by 15% in this case compared to the straight-line cooling channel. The Ag–MgO hybrid nanoparticles showed better thermal performance than the two other nanoparticles with a 4% reduction in temperature difference in the cooling plate.

Attack on optical cryptosystems by skip connection networks

Optical encryption methods, due to their efficient operation speed and parallel processing capabilities, hold significant importance in securing multidimensional and large-volume data. Enhancing the security of optical cryptosystems from the perspective of cryptanalysis holds significant importance currently. Presently, attack methods against optical encryption are complex, and the effectiveness of these attacks is insufficient. Security analysis solutions face limitations in both breadth and depth. Therefore, this paper proposes an attack on optical cryptosystems based on a skip connection network, demonstrating the susceptibility of optical cryptosystems to attacks based on neural network algorithms. The network model is trained on plaintext-ciphertext pairs, fitting equivalent keys without various additional conditions. It approximates plaintext information in high-dimensional space, directly obtaining corresponding plaintext through ciphertext information, expanding the applicability and enhancing the effectiveness of the attack scheme. Finally, the feasibility and effectiveness of the attack scheme were verified through computer simulations. The experiments indicate that the method proposed in this paper has low computational complexity, wide applicability, produces high-quality decrypted images, and high decipherment accuracy. This provides a universal approach for analyzing the security of various optical cryptosystems from the perspective of chosen plaintext attacks.

Influence of High-Frequency Operation on the Efficiency of a PMSM Drive with SiC-MOSFET Inverter

This paper investigates the effects of high-frequency switching and a high fundamental frequency on the parameters and efficiency of a high-speed permanent magnet synchronous machine (PMSM) drive. We discuss the design and modeling of the PMSM, taking into account these high-frequency effects. The impact of high frequencies is analyzed across three different inverters (IGBT, Fast IGBT, and SiC-MOSFET) and the motor, and we employ theoretical analysis, computer simulations, and experimental tests for validation. Our goal is to enhance our understanding of how these high-frequency factors affect the performance of the motor drive.

Leveraging Transfer Learning and U-Nets Method for Improved Gap Filling in Himawari Sea Surface Temperature Data Adjacent to Taiwan

Satellite sea surface temperature (SST) images are valuable for various oceanic applications, including climate monitoring, ocean modeling, and marine ecology. However, cloud cover often obscures SST signals, creating gaps in the data that reduce resolution and hinder spatiotemporal analysis, particularly in the waters near Taiwan. Thus, gap-filling methods are crucial for reconstructing missing SST values to provide continuous and consistent data. This study introduces a gap-filling approach using the Double U-Net, a deep neural network model, pretrained on a diverse dataset of Level-4 SST images. These gap-free products are generated by blending satellite observations with numerical models and in situ measurements. The Double U-Net model excels in capturing SST dynamics and detailed spatial patterns, offering sharper representations of ocean current-induced SST patterns than the interpolated outputs of Data Interpolating Empirical Orthogonal Functions (DINEOFs). Comparative analysis with buoy observations shows the Double U-Net model’s enhanced accuracy, with better correlation results and lower error values across most study areas. By analyzing SST at five key locations near Taiwan, the research highlights the Double U-Net’s potential for high-resolution SST reconstruction, thus enhancing our understanding of ocean temperature dynamics. Based on this method, we can combine more high-resolution satellite data in the future to improve the data-filling model and apply it to marine geographic information science.

Analysis of the Meso‐Mechanical Deformation Behavior of Al Foams and Ni/Al Hybrid Foams Using Time‐Lapse Micro‐Computed Tomography Measurements and 3D Optical Flow

Metal foams are an interesting class of bio‐inspired materials for lightweight construction and energy absorption. Commonly, aluminum (Al) foams are used. However, the mechanical properties have been improved by coating pure Al foams with a nanocrystalline nickel (Ni) coating resulting in Ni/Al hybrid foams. Herein, the meso‐mechanical deformation mechanisms in Al foams and the changes in the mechanisms in Ni/Al hybrid foams are studied using time‐lapse micro‐computed tomography measurements in comparison between numerical modeling using voxel finite element models and evaluation of displacement fields using 3D optical flow. This gives never‐seen insights into the highly localized 3D deformation mechanisms within the entire volume of the foams and not only on the surfaces as given by conventional digital image correlation methods. Displacements calculated by the 3D optical flow algorithm demonstrate its possibility to reveal a significant concentration of plastic deformation, particularly evident when deformation occurs within a distinct, slightly inclined band in the central region. Common numerical simulations using standard plasticity models do not accurately capture this localized deformation, underscoring the need to integrate damage and softening models into the simulation.

Design considerations for a long-wavelength InAsSb detector diode

Abstract InAsSb can absorb light across the entire long wavelength range (8-12 μm) and shares many of the other relevant basic materials properties of HgCdTe, the current incumbent detector technology for this band. We discuss here the device architectures in relation to the crystal growth technical aspects using molecular beam epitaxy and propose a simplified design consisting of an InAsSb absorber with a graded wider bandgap top layer of lattice matched AlInAsSb that exhibits a spontaneously formed p-n-junction. The 77 K device performance is predicted with a numerical model that indicates that quantum efficiencies of at least 75% should be achievable.