Computational Model Research Articles

Single-Atom Catalysts: Are You Really Single?

Single-atom catalysts (SACs) have attracted the attention of the scientific community due to a number of attractive properties allowing their application in different catalytic processes, including electrochemical ones. Due to the nature of the active site, consisting of only a few atoms, SACs are also very attractive for theoretical modeling as the active site can be directly translated into the computational model. However, as a rule, the possibility of the active site change induced by pH and electrode potential is disregarded in theoretical models. This Perspective emphasizes the need to address the actual state of SA electrocatalysts under operating conditions before considering their catalytic activity and selectivity. The concept of surface Pourbaix plot, well-known in electrochemistry, can be directly transferred to SACs, providing valuable insights and guidance for developing novel catalytic materials. We discuss recent approaches to designing Pourbaix plots for SACs and outline the importance of properly treating the actual state of SACs and other emerging types of catalysts.

Exploring the Lifeline: Unpacking the Complexities of Placental Vascular Function in Normal and Preeclamptic Pregnancies.

The proper development and function of the placenta are essential for the success of pregnancy and the well-being of both the fetus and the mother. Placental vascular function facilitates efficient fetal development during pregnancy by ensuring adequate gas exchange with low vascular resistance. This review focuses on how placental vascular function can be compromised in the pregnancy pathology preeclampsia, and conversely, how placental vascular dysfunction might contribute to this condition. While the maternal endothelium is widely recognized as a key focus in preeclampsia research, this review emphasizes the importance of understanding how this condition affects the development and function of the fetal placental vasculature. The placental vascular bed, consisting of microvasculature and macrovasculature, is discussed in detail, as well as structural and functional changes associated with preeclampsia. The complexity of placental vascular reactivity and function, its mediators, its impact on placental exchange and blood distribution, and how these factors are most affected in early-onset preeclampsia are further explored. These factors include foremost lipoproteins and their cargo, oxygen levels and oxidative stress, biomechanics, and shear stress. Challenges in studying placental pathophysiology are discussed, highlighting the necessity of innovative research methodologies, including ex vivo experiments, in vivo imaging tools, and computational modeling. Finally, an outlook on the potential of drug interventions targeting the placental endothelium to improve placental vascular function in preeclampsia is provided. Overall, this review highlights the need for further research and the development of models and tools to better understand and address the challenges posed by preeclampsia and its effects on placental vascular function to improve short- and long-term outcomes for the offspring of preeclamptic pregnancies. © 2024 American Physiological Society. Compr Physiol 14:5763-5787, 2024.

Investigating Trajectories Linking Social Cognitive Capacity, Bias, and Social Isolation Using Computational Modeling.

Despite theoretical emphasis on loneliness affecting social information processing, empirical studies lack consensus. We previously adopted a clinical science framework to measure the association between social cognitive capacity and both objective and perceived social isolation in nonclinical participants. Our prior study found that while objective social isolation is linked to both social cognitive capacity and bias, loneliness is associated only with the latter. This study extended our previous model using a computational approach to capture implicit cognitive processes. We replicated and extended our earlier findings with a new sample of 271 participants, using neuropsychological tasks and a dot-probe paradigm analyzed via Drift Diffusion Model. We presented two complementary trajectories of how social cognitive bias may arise: the increased propensity to engage with salient social stimuli or a decreased information processing capacity dependent on the presence or absence of potential social threats. Furthermore, we found evidence that loneliness is associated with the time needed for perceptual processing of stimuli, both directly and indirectly, via social cognitive bias. Taken together, the complex and context-dependent nature of information processing biases observed in the current study suggests that complex and multifaceted interventions should be implemented to counter social information processing biases in lonely individuals.

Test-Retest Reliability of Two Computationally-Characterised Affective Bias Tasks

Affective biases are commonly seen in disorders such as depression and anxiety, where individuals may show attention towards and preferential processing of negative or threatening stimuli. Affective biases have been shown to change with effective intervention: randomized controlled trials into these biases and the mechanisms that underpin them may allow greater understanding of how interventions can be improved and their success be maximized. For such trials to be informative, we must have reliable ways of measuring affective bias over time, so we can detect how and whether they are altered by interventions: the test-retest reliability of our measures puts an upper bound on our ability to detect any changes. In this online study we therefore examined the test-retest reliability of two behavioural affective bias tasks (an ‘Ambiguous Midpoint’ and a ‘Go-Nogo’ task). 58 individuals recruited from the general population completed the tasks twice, with at least 14 days in between sessions. We analysed the reliability of both summary statistics and parameters from computational models using Pearson’s correlations and intra-class correlations. Standard summary statistic measures from these affective bias tasks had reliabilities ranging from 0.18 (poor) to 0.49 (moderate). Parameters from computational modelling of these tasks were in many cases less reliable than summary statistics. However, embedding the covariance between sessions within the generative modelling framework resulted in higher estimates of stability. We conclude that measures from these affective bias tasks are moderately reliable, but further work to improve the reliability of these tasks would improve still further our ability to draw inferences in randomized trials.

Biased expectations about future choice options predict sequential economic decisions

Considerable research has shown that people make biased decisions in “optimal stopping problems”, where options are encountered sequentially, and there is no opportunity to recall rejected options or to know upcoming options in advance (e.g. when flat hunting or choosing a spouse). Here, we used computational modelling to identify the mechanisms that best explain decision bias in the context of an especially realistic version of this problem: the full-information problem. We eliminated a number of factors as potential instigators of bias. Then, we examined sequence length and payoff scheme: two manipulations where an optimality model recommends adjusting the sampling rate. Here, participants were more reluctant to increase their sampling rates when it was optimal to do so, leading to increased undersampling bias. Our comparison of several computational models of bias demonstrates that many participants maintain these relatively low sampling rates because of suboptimally pessimistic expectations about the quality of future options (i.e. a mis-specified prior distribution). These results support a new theory about how humans solve full information problems. Understanding the causes of decision error could enhance how we conduct real world sequential searches for options, for example how online shopping or dating applications present options to users.

Covalent integration of polymers and porous organic frameworks

Covalent integration of polymers and porous organic frameworks (POFs), including metal-organic frameworks (MOFs), covalent organic frameworks (COFs) and hydrogen-bonded organic frameworks (HOFs), represent a promising strategy for overcoming the existing limitations of traditional porous materials. This integration allows for the combination of the advantages of polymers, i.e., flexibility, processability and chemical versatility etc., and the superiority of POFs, like the structural integrity, tunable porosity and the high surface area, creating a type of hybrid materials. These resulting polymer-POF hybrid materials exhibit enhanced mechanical strength, chemical stability and functional diversity, thus opening up new opportunities for applications across a large variety of fields, such as gas separation, catalysis, biomedical applications, environmental remediation and energy storage. In this review, an overview of synthetic routes and strategies on how to covalently integrate different polymers with various POFs is discussed, especially with a particular focus on methods like polymerization within, on and among POF structures. To investigate the unique properties and functions of these resultant hybrid materials, the characterization techniques, including nuclear magnetic resonance spectroscopy (NMR), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), transmission electron microscopy (TEM) and scanning electron microscopy (SEM), gas adsorption analysis (BET) and computational modeling and machine learning, are also presented. The ability of polymer-POFs to manipulate the pore environments at the molecular level affords these materials a wide range of applications, providing a versatile platform for future advancements in material science. Looking forward, to fully realize the potential of these hybrid materials, the authors highlight the scalability, green synthesis methods, and potential for stimuli-responsive polymer-POF materials as critical areas for future research.

Toward the Bayesian brain: a generative model of information transmission by vestibular sensory neurons

The relative accessibility and simplicity of vestibular sensing and vestibular-driven control of head and eye movements has made the vestibular system an attractive subject to experimenters and theoreticians interested in developing realistic quantitative models of how brains gather and interpret sense data and use it to guide behavior. Head stabilization and eye counter-rotation driven by vestibular sensory input in response to rotational perturbations represent natural, ecologically important behaviors that can be reproduced in the laboratory and analyzed using relatively simple mathematical models. Models drawn from dynamical systems and control theory have previously been used to analyze the behavior of vestibular sensory neurons. In the Bayesian framework, which is becoming widely used in cognitive science, vestibular sense data must be modeled as random samples drawn from probability distributions whose parameters are kinematic state variables of the head. We show that Exwald distributions are accurate models of spontaneous interspike interval distributions in spike trains recoded from chinchilla semicircular canal afferent neurons. Each interval in an Exwald distribution is the sum of an interval drawn from an Exponential distribution and a Wald or Inverse Gaussian distribution. We show that this abstract model can be realized using simple physical mechanisms and re-parameterized in terms of the relevant kinematic state variables of the head. This model predicts and explains statistical and dynamical properties of semicircular canal afferent neurons in a novel way. It provides an empirical foundation for realistic Bayesian models of neural computation in the brain that underlie the perception of head motion and the control of head and eye movements.

Characterization of the PaHAK Gene and Its Expression During the In Vitro Seed Germination of Two Botanical Avocado Varieties Under Saline Stress

Soil salinity is one of the main challenges that modern agriculture faces. Avocado, which is classified as a glycophyte, is very sensitive to salt stress. There are botanical varieties of avocado that differ in their salt tolerance. This study investigated how salt stress affects the in vitro germination of two avocado botanical varieties americana (West Indian breed) and drymifolia (Mexican native) with different salt tolerances. This study also assessed the potential role of the avocado PaHAK2 high-affinity K+ transporter HAK/KUP/KT in response to saline stress during germination. Salinity (60 mM NaCl) delayed the germination speed of the drymifolia variety relative to the americana variety. A computational 3D inference protein model of the PaHAK2 protein showed 10 highly conserved transmembrane domains. During the imbibition period, there was a differential increase in the expression of the PaHAK2 gene at 60 mM NaCl in both varieties, which suggests the presence of osmotic adjustment and regulation. The enhanced expression of PaHAK2 in the americana variety suggests an adaptive advantage to salinity. We conclude that PaHAK2 participates in the response of avocado to salt stress during seed germination.

Task-relevant stimulus design improves P300-based brain-computer interfaces.

In the pursuit of refining P300-based brain-computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands. In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses. With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Signficance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.

The metabolic hypothesis for restrictive eating behaviors: A computational and evolutionary approach.

Restrictive eating behaviors, widespread in humans and animals, are often conceptualized as maladaptive, but may serve adaptive purposes under specific circumstances. To investigate the adaptive potential of restrictive eating behaviors. Computational models explored the relationship between food availability, basal metabolic rate, and restrictive eating behaviors. The evolutionary conservation of genes associated with both basal metabolic rate and restrictive eating behaviors was evaluated. The propensity to engage in restrictive eating behaviors protected against negative energy balances at times of food volatility, implying ecological fitness potential. A high degree of conservation across species was observed in retrieved genes, implying selective evolutionary constraints. Restrictive eating behaviors may represent a maladaptive outcome of evolutionary constraints on protective metabolic mechanisms. The higher prevalence of restrictive eating in women could stem from a greater reliance on protective strategies, highlighting the need for further exploration of sex-specific genetic and environmental interactions.

Experimental Testing of the Theoretically Predicted Magnetic Properties for Kagomé Compounds in the Li-Fe-Ge System.

Investigating material properties is essential to assessing their application potential. While computational methods allow for a fast prediction of the material structure and properties, experimental validation is essential to determining the ultimate material potential. Herein, we report the synthesis and experimental magnetic properties of three previously reported Kagome compounds in the Li-Fe-Ge system. LiFe6Ge4, LiFe6Ge5, and LiFe6Ge6 were predicted to have ferromagnetic or antiferromagnetic ground states. The hydride route that replaces the ductile Li metal with salt-like LiH proved to be an excellent alternative for the facile synthesis of the Li-Fe-Ge powders with appreciable purity, permitting the investigation of their bulk magnetic properties. Magnetometry below room temperature and room-temperature 57Fe Mössbauer spectroscopy collectively indicate an antiferromagnetic ground state for the three compounds with ordering temperatures above 300 K, contrary to the prediction of ferromagnetic ground states. Moreover, Mössbauer spectroscopy reveals a magnetization of 1.1-1.3 μB/Fe atom for the Li-Fe-Ge compounds, while higher moments of 1.63-2.90 μB/Fe atom were theoretically predicted. Experimental (in)validation addresses the issue of inaccuracy in determining material properties in silico only and helps to improve the prediction power of the computational models. This work underlines that the contribution of experimentalists continues to be valuable for the accurate determination of structure-property relationships in solid-state materials.

Integrating Artificial Intelligence and Computational Algorithms to Optimize the 15-Minute City Model

The 15-minute city concept, designed to ensure that all essential services and amenities are accessible within a 15 min walk or bike ride from home, presents a transformative vision for urban living. This paper explores the concept of a 15-minute city and its implications, along with its main features and pillars. Furthermore, it elaborates on how the integration of artificial intelligence (AI) and computational tools can be utilized in optimizing the 15-minute city model. We reveal how AI-driven algorithms, machine learning techniques, and advanced data analytics can enhance urban planning, improve accessibility, and foster social integration. Our paper focuses on the practical applications of these technologies in creating pedestrian-friendly neighborhoods, optimizing public transport coordination, and enhancing the quality of life for urban residents. By executing some of these computational models, we demonstrate the potential of AI and computational tools to realize the vision of the 15-minute city, making urban spaces more inclusive, resilient, and adaptive to the evolving needs of their inhabitants.

Numerical Simulations of Thermodynamic Processes in the Chamber of a Liquid Piston Compressor for Hydrogen Applications

This paper presents the results of numerical simulations examining the thermodynamic processes during hydraulic hydrogen compression, using COMSOL Multiphysics® 6.0. These simulations focus on the application of hydrogen compression systems, particularly in hydrogen refueling stations. The computational models employ the CFD and heat transfer modules, along with deforming mesh technology, to simulate gas compression and heat transfer dynamics. The superposition method was applied to simplify the analysis of hydrogen and liquid piston interactions within a stainless-steel chamber, accounting for heat exchange between the hydrogen, the oil (working fluid), and the cylinder walls. The study investigates the effects of varying compression stroke durations and initial hydrogen pressures, providing detailed insights into temperature distributions and energy consumption under different conditions. The results reveal that the upper region of the chamber experiences significant heating, highlighting the need for efficient cooling systems. Additionally, the simulations show that longer compression strokes reduce the power requirement for the liquid pump, offering potential for optimizing system design and reducing equipment costs. This study offers crucial data for enhancing the efficiency of hydraulic hydrogen compression systems, paving the way for improved energy consumption and thermal management in high-pressure applications.

Synthesis and Study of Oxide Semiconductor Nanoheterostructures in SiO2/Si Track Template

In this study, chemical deposition was used to synthesize structures of Ga2O3 -NW/SiO2/Si (NW—nanowire) at 348 K and SnO2-NW/SiO2/Si at 323 K in track templates SiO2/Si (either n- or p-type). The resulting crystalline nanowires were δ-Ga2O3 and orthorhombic SnO2. Computer modeling of the delta phase of gallium oxide yielded a lattice parameter of a = 9.287 Å, which closely matched the experimental range of 9.83–10.03 Å. The bandgap is indirect with an Eg = 5.5 eV. The photoluminescence spectra of both nanostructures exhibited a complex band when excited by light with λ = 5.16 eV, dominated by luminescence from vacancy-type defects. The current–voltage characteristics of δ-Ga2O3 NW/SiO2/Si-p showed one-way conductivity. This structure could be advantageous in devices where a reverse current is undesirable. The p-n junction with a complex structure was formed. This junction consists of a polycrystalline nanowire base exhibiting n-type conductivity and a monocrystalline Si substrate with p-type conductivity. The I–V characteristics of SnO2-NW/SiO2/Si suggested near-metallic conductivity due to the presence of metallic tin.

Numerical study of the variable-order time-fractional KP-BBM equation in 2D using RBFs

Abstract Water waves are a complex phenomenon often examined due to their unpredictable nature and potential hazards in oceans and coastal areas. This study investigates the non-linear variable order time-fractional Kadomtsev-Petviashvili-Benjamin-Bona-Mahony (KP-BBM) equation across three different two-dimensional space domains. We employ a finite difference method for temporal variables and radial basis functions (RBFs) for spatial variables to solve the model. The computational model is validated by comparing it with exact solutions for classical integer-order models, ensuring that it aligns with the classical integer-order model as the time-fractional order approaches one. The study also aims to evaluate the impact of various parameters in the equation and the dispersion effects of different time-fractional variable orders compared to classical waves.

STUDY OF THE DYNAMICS OF A VIBRATION MACHINE CONSIDERING THE INFLUENCE OF THE PROCESSING MEDIUM

This paper presents the results of studying the dynamics of a vibration machine, takinginto account its interaction with the processing medium based on representing the medium as a continuoussystem. The application of the complex number method significantly simplifies the formation of equations ofmotion for the discrete-continuous system, which is a computational model of the vibration system "machinemedium."The study substantiates and develops a method for considering the mutual influence of the machineand the processing medium, based on representing the medium as a continuous system. The dynamicsof the vibration system "machine-medium" is studied, and oscillation parameters are determined withoutconsidering resistance forces. It is found that the overall motion of the vibration system is described by fourcomponents. The first three describe the natural oscillations of the system, of which the first two are determinedonly by initial conditions, and the third reflects the accompanying oscillations caused by the externalforce applied to the system. The last component defines the forced oscillations following the external force'schange law. This result shows that the oscillations of the vibration system are not strictly harmonic, which isconfirmed by the provided graphs. The dynamics of the "machine-medium" vibration system, consideringresistance forces, are studied, and analytical dependencies for determining oscillation amplitudes and natural and resonant frequencies are obtained. The results of the amplitude calculations of the vibration platform forvarious heights of compacted concrete mixtures reveal the influence of the resistance coefficient and theratio of oscillation frequency to the wave propagation speed in the mixture. Analysis of the obtained graphsshows that the resistance coefficient has a different effect on the amplitude of oscillations for different heightsof the compacting mixture. Certain mixture height zones of the vibration system "machine-medium" operatein a near-resonant mode. A significant influence on the amplitude is exerted by the wave propagation speed,which is included in the analytical formulas for determining the numerical values of the coefficients accountingfor reactive and active components of resistance.

A model for the interaction of a pathogen and an innovative antimicrobial nanocoating

AbstractIn the ongoing battle against infectious diseases, innovative nanotextured antipathogenic coatings provide promising avenues for preventing pathogens from spreading. Understanding their interactions at the micro/nanoscale is crucial for improving their design and efficacy. This work proposes a model for the interaction between pathogens and nanotextured antipathogenic coated surfaces. For this purpose, the forces and deformations experienced by pathogens upon contact with nanotextured surfaces are studied. To achieve this goal, a computational model based on the molecular dynamics software ESPResSo and, particularly, the Object-In-Fluid implementation, has been developed, extending the previous works of I. Jančigova et al. and G. Lazzini et al. More specifically, a Staphylococcus Aureus bacterium is modelled as an elastic cell represented by a triangular mesh and immersed in a computational Lattice-Boltzmann fluid. The size, shape and elastic properties of the pathogen cell are tuned, emulating those of Staphylococcus Aureus. A poly(methyl methacrylate) substrate laser-sintered with Ag nanoparticles is modelled with a triangular mesh, and the interactions between the cell and the substrate are introduced through a Lennard-Jones potential. The simulations performed reveal the influence of surface geometry and dispersion in the coated substrate, providing critical insights into designing more effective antibacterial surfaces that inhibit pathogen proliferation.

Computational Modeling of Biomass Fast Pyrolysis in Fluidized Beds with Eulerian Multifluid Approach

This study investigated the fast pyrolysis of biomass in fluidized-bed reactors using computational fluid dynamics (CFD) with an Eulerian multifluid approach. A detailed analysis was conducted on the influence of various modeling parameters, including hydrodynamic models, heat transfer correlations, and chemical kinetics, on the product yield. The simulation framework integrated 2D and 3D geometrical setups, with numerical experiments performed using OpenFOAM v11 and ANSYS Fluent v18.1 for cross-validation. While yield predictions exhibited limited sensitivity to drag and thermal models (with differences of less than 3% across configurations and computational codes), the results underline the paramount role of chemical kinetics in determining the distribution of bio-oil (TAR), biochar (CHAR), and syngas (GAS). Simplified kinetic schemes consistently underestimated TAR yields by up to 20% and overestimated CHAR and GAS yields compared to experimental data (which is shown for different biomass compositions and different operating conditions) and can be significantly improved by redefining the reaction scheme. Refined kinetic parameters improved TAR yield predictions to within 5% of experimental values while reducing discrepancies in GAS and CHAR outputs. These findings underscore the necessity of precise kinetic modeling to enhance the predictive accuracy of pyrolysis simulations.

Smart energy: application of the photovoltaic system using genetic algorithms for decision making in industry 4.0

The growing demand for sustainable solutions and the digitalization of industrial processes have driven the adoption of photovoltaic systems and advanced decision-making technologies. In the context of Industry 4.0, where automation and artificial intelligence are fundamental, these systems stand out as a clean energy alternative, promoting savings and reducing pollutant emissions. This study aims to develop a photovoltaic energy control model that uses genetic algorithms to optimize energy efficiency in industrial environments, reducing costs and dependence on non-renewable sources. The methodology included the computational modeling of a photovoltaic system and the application of genetic algorithms to optimize parameters such as panel angle and operating hours, adapting the system in real time to variable consumption and generation conditions. The results showed that the use of genetic algorithms increased the system's efficiency by up to 20% compared to traditional methods, as well as minimizing consumption from the electricity grid at peak times. This study reinforces the importance of artificial intelligence in optimizing renewable resources, contributing to energy efficiency and sustainability in Industry 4.0.

The Effect of Barrel Configurations on the Porosity During HVAF Thermal Spray Ni-Based Amorphous Coatings: Numerical Simulation and Experiment

The relationship between barrel configurations and porosity of a Ni-based amorphous coating (AM) that is fabricated using a high-velocity air fuel (HVAF) process was revealed by both numerical and experimental methods. A computational fluid dynamics model was applied to investigate the gas-flow field and the behavior of in-flight particles with various barrel configurations. It is found that barrel length obviously affects the particle velocity and temperature while it has a slight influence on the particle velocity and temperature. The longer the barrel length (diameter), the higher the flame (particle) velocity and temperature. By analyzing both particle velocity and temperature, the optimal barrel configuration (4E) to achieve low-porosity coatings was predicted. These calculations were experimentally verified by the production of a low-porosity (2.09%) Ni-based AM that was fabricated by HVAF using the predicted optimal barrel configuration.