Microscopic Model Research Articles

Modeling, Monitoring, and Controlling Road Traffic Using Vehicles to Sense and Act

This review offers a comprehensive overview of current traffic modeling, estimation, and control methods, along with resulting field experiments. It highlights key developments and future directions in leveraging technological advancements to improve traffic management and safety. The focus is on macroscopic, microscopic, and micro-macro models, as well as state-of-the-art control techniques and estimation methods for deploying vehicles in traffic field experiments.

A stochastic and microscopic model to predict road traffic noise by random generation of single vehicles' speeds.

Road traffic noise is the major component of acoustic environmental pollution both in urban and rural areas. For this reason, much effort has been put into developing models to assess its impact. However, literature models are often suitable for standard conditions but can fail in non-standard ones, i.e., when the single vehicle speed cannot be neglected. Moreover, input data to literature models are not always available, e.g., if the road infrastructure is still in the design phase. The presented approach aims to try to overcome these shortcomings using a microscopic and stochastic-core model, in which the speed of each vehicle can be randomly generated using a specific speed distribution. The validation of the model, investigated through a statistical analysis of simulated continuous equivalent sound pressure levels, the error distribution, and the calculation of commonly used error metrics suggests that the proposed methodology provides good estimations of traffic noise. The errors of the model computed as the differences between measured and simulated sound levels, can be described as a distribution curve with a -0.6 dBA mean and a standard deviation of 2.3 dBA. The error metrics confirm the model's goodness, with a mean absolute error of 1.84 dBA and a coefficient of variation error of 0.03.

Calibrating Microscopic Traffic Simulation Model Using Connected Vehicle Data and Genetic Algorithm

This study introduces a data-driven approach to calibrate microscopic traffic simulation models like VISSIM using high-resolution trajectory data, aiming to improve simulation accuracy and fidelity. The study focuses on a highway segment of NJ-3 and NJ-495 in Hudson County, New Jersey, selected as a case study for its high traffic volume and strategic significance. Trajectory data from 338 connected vehicles, sourced from the Wejo dataset, a global provider of anonymized, high-resolution vehicle movement data, along with traffic volume data from Remote Traffic Microwave Sensors (RTMS), served as inputs. The trajectories produced by the simulation model were compared to the ground truth to measure discrepancies. By adjusting driving behavior parameters (e.g., car-following and lane-changing behaviors) and other factors (e.g., desire speed), a Genetic Algorithm was adopted to minimize these differences. Results showed significant improvements, including a 14.19% reduction in mean error, an 18.27% reduction in median error, and a 22.57% reduction in the 75th percentile error during calibration. In the validation phase, the calibrated parameters yielded a 32.68% reduction in mean error, demonstrating the framework’s robustness. This study presents a scalable calibration framework using connected vehicle data, providing tools for accurate simulation, real-time traffic management, and infrastructure planning.

Mechanical and failure behaviors of columnar jointed basalts (CJBs): 3D microscopic reconstruction and modeling

Mechanical and failure behaviors of columnar jointed basalts (CJBs): 3D microscopic reconstruction and modeling

Magnetic particle capture in high‐gradient magnetic separation: A theoretical and experimental study

AbstractHigh‐gradient magnetic separation (HGMS) has traditionally been used in mineral processing, with many effective models developed for typically employed rod‐wire shaped matrices. However, its potential in bioprocessing, especially for high‐value products, introduces new demands on plant and matrix design. This study presents a multi‐scale model for HGMS that simulates new complex geometries, which enhance particle recovery. We have developed microscopic models to accurately simulate the trajectories of magnetic particles within the fluid flow and magnetic fields of HGMS systems. A pivotal aspect of our work is the effective translation of microscopic relationships into macroscopic transport models. The model is validated experimentally using a rotor‐stator HGMS system tailored for bioprocessing, with magnetic particle concentration measurements showing strong alignment with the model's predictions. The model's flexibility enables its application across various matrix shapes, overcoming the limitations of traditional rod‐wire models, and providing a robust framework for improved HGMS in‐silico process understanding and optimization.

A thermodynamically consistent approach to the energy costs of quantum measurements

Considering a general microscopic model for a quantum measuring apparatus comprising a quantum probe coupled to a thermal bath, we analyze the energetic resources necessary for the realization of a quantum measurement, which includes the creation of system-apparatus correlations, the irreversible transition to a statistical mixture of definite outcomes, and the apparatus resetting. Crucially, we do not resort to another quantum measurement to capture the emergence of objective measurement results, but rather exploit the properties of the thermal bath which redundantly records the measurement result in its degrees of freedom, naturally implementing the paradigm of quantum Darwinism. In practice, this model allows us to perform a quantitative thermodynamic analysis of the measurement process. From the expression of the second law, we show how the minimal required work depends on the energy variation of the system being measured plus information-theoretic quantities characterizing the performance of the measurement – efficiency and completeness. Additionally, we show that it is possible to perform a thermodynamically reversible measurement, thus reaching the minimal work expenditure, and provide the corresponding protocol. Finally, for finite-time measurement protocols, we illustrate the increasing work cost induced by rising entropy production inherent in finite-time thermodynamic processes. This highlights an emerging trade-off between velocity of the measurement and work cost, on top of a trade-off between efficiency of the measurement and work cost. We apply those findings to bring new insights in the thermodynamic balance of the measurement-powered quantum engines.

A Microscopic Traffic Model to Investigate the Effect of Connected Autonomous Vehicles at Bottlenecks and the Impact of Cyberattacks

Bottlenecks reduce both traffic safety and efficiency, resulting in congestion and collisions. The introduction of connected autonomous vehicles (CAVs) has had a significant impact on road networks and can improve traffic efficiency at bottlenecks. This paper proposes a microscopic traffic model to investigate CAV behavior at bottlenecks and examine the effect of cyberattacks. The model is developed using data collected from a roadside sensor node. It is implemented in MATLAB using the Euler scheme to simulate a platoon of vehicles on a circular road of length 1 km. The performance is compared with the intelligent driver (ID) model. The results obtained indicate that the road capacity with the proposed model is 1.4 times higher than with the ID model. Further, the proposed model results in nearly constant speeds with small variations, which is realistic. Conversely, the ID model produces large speed variations that are unrealistic. In addition, the proposed model results in less acceleration and deceleration, which leads to lower vehicle emissions and pollution. The efficiency is better than with the ID model due to CAV communication and coordination, so queues dissipate faster. The traffic flow with the proposed model increases as the density decreases, which is consistent with traffic dynamics. It is also shown that the proposed model can characterize CAV behavior under cyberattacks that cause disruptions in the data. Thus, it can be employed for traffic control and forecasting when bottleneck conditions exist and there is malicious behavior.

Superconductor-Insulator Transition Induced by Precise Subtripled Vapor Chemical Gating.

Recent progress in superconductor-insulator transition has shed light on the intermediate metallic state with unique electronic inhomogeneity. The microscopic model, suggesting that carrier spatial distribution plays a decisive role in the intermediate state, has been instrumental in understanding the quantum transition. However, the narrow carrier density window in which the intermediate state exists necessitates precise control of the gate dielectric layer, presenting a challenge to in situ map the carrier spatial distribution. Herein, a subtripled vapor chemical gating strategy has been proposed to precisely control carrier density and map spatial distribution in the LixZrNCl system. The chemical gating strategy utilizes subtripled vapor to quasi-continuously reduce the Li doping level, driving the ground-state transition from superconductor to quantum metal to quantum Griffiths singularity (QGS) to insulator. In situ optical mapping demonstrates an inhomogeneous electronic state in the intermediate metallic state and an evolution to a stripe-like pattern at 4 K, offering new insights into the nature of the intermediate metallic state.

Metal-organic frameworks as anchors for giant unilamellar vesicle immobilization.

Giant unilamellar vesicles (GUVs) are ideal for studying cellular mechanisms due to their cell-mimicking morphology and size. The formation, stability, and immobilization of these vesicles are crucial for drug delivery and bioimaging studies. Separately, metal-organic frameworks (MOFs) are actively researched owing to their unique and varied properties, yet little is known about the interaction between MOFs and phospholipids. This study investigates the influence of the metal-phosphate interface on the formation, size distribution, and stability of GUVs with different lipid compositions. GUVs were electroformed in the presence of a series of MOFs. The results show Al, Zn, Cu, Fe, Zr, and Ca metal centers of MOFs can coordinate to phospholipids on the surface of GUVs, leading to the formation of functional GUV@MOF constructs, with stablilities over 12 hours. Macroscopically, society has seen biology (people, plants, microbes) interacting with inorganic materials regularly. We now explore how microscopic biological models behave in the presence of inorganic constructs. This research opens new avenues for advanced biomedical applications interacting tailored frameworks with liposomes.

Bose-Einstein condensation of a two-magnon bound state in a spin-1 triangular lattice.

In ordered magnets, the elementary excitations are spin waves (magnons), which obey Bose-Einstein statistics. Similarly to Cooper pairs in superconductors, magnons can be paired into bound states under attractive interactions. The Zeeman coupling to a magnetic field is able to tune the particle density through a quantum critical point, beyond which a 'hidden order' is predicted to exist. Here we report direct observation of the Bose-Einstein condensation of the two-magnon bound state in Na2BaNi(PO4)2. Comprehensive thermodynamic measurements confirmed the two-dimensional Bose-Einstein condensation quantum critical point at the saturation field. Inelastic neutron scattering experiments were performed to establish the microscopic model. An exact solution revealed stable two-magnon bound states that were further confirmed by electron spin resonance and nuclear magnetic resonance experiments, demonstrating that the quantum critical point is due to the pair condensation, and the phase below the saturation field is likely the long-sought-after spin nematic phase.

Study of Gas–Liquid Two-Phase Flow Characteristics at the Pore Scale Based on the VOF Model

To study the effects of liquid properties and interface parameters on gas–liquid two-phase flow in porous media. The volume flow model of gas–liquid two-phase flow in porous media was established, and the interface of the two-phase flow was reconstructed by tracing the phase fraction. The microscopic imbibition flow model was established, and the accuracy of the model was verified by comparing the simulation results with the classical capillary imbibition model. The flow characteristics in the fracturing process and backflow process were analyzed. The influence of flow parameters and interface parameters on gas flow was studied using the single-factor variable method. The results show that more than 90% of the flowing channels are invaded by fracturing fluid, and only about 50% of the fluid is displaced in the flowback process. Changes in flow velocity and wetting angle significantly affect Newtonian flow behavior, while variations in surface tension have a pronounced effect on non-Newtonian fluid flow. The relative position of gas breakthrough in porous media is an inherent property of porous media, which does not change with fluid properties and flow parameters.

A cross-scale transfer learning framework: prediction of SOD activity from leaf microstructure to macroscopic hyperspectral imaging.

Superoxide dismutase (SOD) plays an important role to respond in the defence against damage when tomato leaves are under different types of adversity stresses. This work employed microhyperspectral imaging (MHSI) and visible near-infrared (Vis-NIR) hyperspectral imaging (HSI) technologies to predict tomato leaf SOD activity. The macroscopic model of SOD activity in tomato leaves was constructed using the convolutional neural network in conjunction with the long and short-term temporal memory (CNN-LSTM) technique. Using heterogeneous two-dimensional correlation spectra (H2D-COS), the sensitive macroscopic and microscopic absorption peaks connected to tomato leaves' SOD activity were made clear. The combination of CNN-LSTM algorithm and H2D-COS analysis was used to research transfer learning between microscopic and macroscopic models based on sensitive wavelengths. The results demonstrated that the CNN-LSTM model, which was based on the FD preprocessed spectra, had the best performance for the microscopic model, with RC and RP reaching 0.9311 and 0.9075, and RMSEC and RMSEP reaching 0.0109 U/mg and 0.0127 U/mg respectively. There were 10 macroscopic and 10 microscopic significant sensitivity peaks found. The transfer learning was carried out using sensitive wavelengths, and the model performed well with an RP value of 0.7549 and an RMSEP of 0.0725 U/mg. The combined CNN algorithm and H2D-COS analysis demonstrated the viability of transfer learning across microscopic and macroscopic models for quantitative tomato leaf SOD prediction.

Predicting 2D Crystal Packing in Thin Films of Small Molecule Organic Materials

AbstractThe large variety of structural morphologies realized in organic semiconductors is a big challenge for the microscopic modeling of such systems. A global computational solution is still out of reach due to prevalent molecular flexibility. However, the specific case of crystalline thin films that exhibit surface alignment of molecular π‐systems for optoelectronic applications of high technological relevance, seems to be a simpler task. This study proposes an approach for the structure prediction of two‐dimensional (2D) molecular layers as precursors for the three‐dimensional (3D) structure of deposited crystalline thin films. Based on grid search sampling for the layer's degrees of freedom, it requires only a small number of trial structures to find complex packing motifs of layered molecular materials. It facilitates parallel screening among multiple molecular conformers, which is usually very difficult and expensive, using the latest 3D‐based prediction methods. The study researches theoretically and experimentally a set of known and newly crystallized compounds of evaporable flexible molecules with interesting optoelectronic properties, predicts their packing in 2D layers, and compares them with experimentally resolved crystal structures, obtaining very good agreement in the packing of these molecules within layers. The computational costs are estimated to be several orders of magnitude lower than with 3D methods.

A new three-dimensional microscopic modeling strategy in studying coupled electro-mechanical properties for piezoelectric composites

In this study, an innovative micromechanical modeling framework based on the three-dimensional finite-volume direct averaging micromechanics theory is presented to investigate mechanical properties of 0–3 type piezoelectric composites subjected to a coupled electro-mechanical environment. To this end, the electro-mechanical coupling constitutive relation is presented. Moreover, the parametric elements are further constructed to improve the calculation precision. In order to verify the proposed micromechanical model, the numerical results of local stress and electric field distribution are also obtained by finite element method. The convergence and calculation accuracy of the proposed model are demonstrated by excellent consistency of the numerical results. On this basis, The elastic modulus, piezoelectric coupling coefficient and dielectric permittivity coefficient are obtained by three-dimensional FVDAM model at different particle volume fractions. By applying different loading conditions, the local stress and electric field intensity distributions on the interface between particle and matrix are investigated. It can be concluded that the proposed micromechanical model offers a useful tool in studying the effective properties and local responses of the piezoelectric composites.

The Nordic-walking mechanism and its explanation of deconfined pseudocriticality from Wess-Zumino-Witten theory

The understanding of phenomena falling outside the Ginzburg-Landau paradigm of phase transitions represents a key challenge in condensed matter physics. A famous class of examples is constituted by the putative deconfined quantum critical points between two symmetry-broken phases in layered quantum magnets, such as pressurised SrCu2(BO3)2. Experiments find a weak first-order transition, which simulations of relevant microscopic models can reproduce. The origin of this behaviour has been a matter of considerable debate for several years. In this work, we demonstrate that the nature of the deconfined quantum critical point can be best understood in terms of a novel dynamical mechanism, termed Nordic walking. Nordic walking denotes a renormalisation group flow arising from a beta function that is flat over a range of couplings. This gives rise to a logarithmic flow that is faster than the well-known walking behaviour, associated with the annihilation and complexification of fixed points, but still significantly slower than the generic running of couplings. The Nordic-walking mechanism can thus explain weak first-order transitions, but may also play a role in high-energy physics, where it could solve hierarchy problems. We analyse the Wess-Zumino-Witten field theory pertinent to deconfined quantum critical points with a topological term in 2+1 dimensions. To this end, we construct an advanced functional renormalisation group approach based on higher-order regulators. We thereby calculate the beta function directly in 2+1 dimensions and provide evidence for Nordic walking.

Green behavior propagation analysis based on statistical theory and intelligent algorithm in data-driven environment.

The correlation between green behavior and energy efficiency is growing due to the heightened focus on energy efficiency among individuals. This paper introduces a three-layer network model to analyze the relationships among information diffusion, awareness and green behavior spreading. We have analyzed the probability tree of state transfer across 12 states by using Microscopic Markov Chain Approach (MMCA) and derived the state transfer equations for each state to compute the state transition threshold. In addition, we use the reaction-diffusion system to model the interaction between space and time changes for each state in the green behavior propagation layer. The equilibrium point of the system is defined, and the criteria for Turing bifurcation are identified. The optimal control approach achieves parameter identification, and this study validates the theory through several numerical simulations. Meanwhile, the effectiveness of parameter identification based on the convolutional neural network (CNN) and optimal control is compared. The data on China's electrical energy generation is predicted and compared by using three neural networks and an autoregressive integrated moving average (ARIMA) model. Further, considering clean energy generation as a green behavior, we fit the data on the percentage of clean energy generation by applying a Microscopic Markov Chain model and a reaction-diffusion system.

Dynamic fracture toughness and fractal characteristics of crack in pressurized acidified Qinshui coal under impact loading

To investigate the effect of acid fracturing fluid on the fracture toughness and fractal properties of crack propagation in Qinshui coal under impact load, the 50 mm diameter split Hopkinson pressure bar device was employed to carry out mode I dynamic fracture toughness tests on Qinshui anthracite samples treated with acid fracturing fluid and water-based fracturing fluid under different impact pressures. Coal samples were subjected to force saturation and acidity treatment using an innovative apparatus. The fracture propagation phase of the specimen was acquired by a high-speed camera sensor. Combined with Image J analysis software and PCAS image recognition system, the macroscopic crack propagation trajectory and probability entropy of micro pores in coal samples were quantitatively analyzed. These findings revealed that dynamic fracture toughness endowed a strong rate-response relationship. When the impact pressure is 0.3, 0.4, and 0.5 MPa, the average fracture toughness of the water-based fracturing fluid group coal specimens was respectively 0.64, 1.20, and 1.31 times better that of the acidic fracturing fluid group. The rate of crack propagation and the dynamic fracture toughness of coal were reduced after acidification of the specimens. The crack growth rate initially surged, then decreased rapidly until it reached a stable state under impact load, while the variation in crack growth length and opening breadth showed a time-dependent increase. Crack propagation resistance and dynamic fracture toughness of coal are reduced by the acidification of the specimens. The fractal dimension of cracks in specimen increased under the impact of pressure growth. The fractal dimensions of crack in coal samples under the action of acidic fracturing fluid at 0.30, 0.40, and 0.50 MPa are 1.066, 1.078, and 1.087 times that of water-based fracturing fluid, as well as 1.119, 1.136, and 1.157 times that of natural state coal samples. With the increase in impact pressure, the entropy magnitude of the pore probability on the fracture surface of the coal sample also increased. The fracture surface morphology of coal sample transformed from compact and neat to loose and porous with the action of acidification. The dual mechanism of weakening and enhancing the fracture behavior of anthracite coal by fracturing fluid under different loading rates was explored, and a microscopic fracture mechanics model incorporating loading rate was developed based on the dual nature of the fracturing fluid and linear elastic fracture mechanics theory. The study results offer empirical evidence to investigate the process of fracture initiation and propagation in acid fracturing in Qinshui coal, and provide theoretical direction for designing acid fracturing in coal seams and controlling complicated fracture network.

Microscopic kinetic model of gas hydrate and the effect of brine: a case study of natural gas hydrate from the seabed off the Tokachi coast

Microscopic kinetic model of gas hydrate and the effect of brine: a case study of natural gas hydrate from the seabed off the Tokachi coast

Synthesis, characterization and corrosion inhibition of 1-(4-anisyl)-3-(4-(pyridin-4-ylmethyl)phenyl)carbamide on mild steel in 1M HCl: A comprehensive study from experimental, theoretical and microscopic modelling perspectives

Synthesis, characterization and corrosion inhibition of 1-(4-anisyl)-3-(4-(pyridin-4-ylmethyl)phenyl)carbamide on mild steel in 1M HCl: A comprehensive study from experimental, theoretical and microscopic modelling perspectives

Dynamic behavior of ultrasonic frequency pulsed current-assisted gas metal arc welding

Investigating ultrasonic frequency pulsed current (UFPC)-assisted welding is crucial owing to its significant advantages over traditional welding methods, including enhanced weld quality, reduced defects, and increased efficiency. This study elucidated the generation mechanism of arc ultrasonic waves influenced by UFPC through theoretical derivation. A microscopic model of arc plasma under the influence of UFPC was developed, and the equations governing the thermophysical properties of the plasma were theoretically derived and calculated. A three-dimensional numerical simulation model was developed to analyze the dynamic behavior of arc plasma and molten metal in UFPC-assisted gas metal arc welding (UFPC-GMAW). The simulation results revealed that the incorporation of UFPC increased both the maximum arc temperature and plasma velocity, resulting in a conical shape of the arc. Additionally, the maximum arc temperature amplitude decreased from 13.6% in conventional GMAW (C-GMAW) to 9.6% in UFPC-GMAW, indicating improved arc stability. The increase in electromagnetic force led to a higher droplet transfer frequency. Moreover, the weld pool in UFPC-GMAW exhibited greater weld penetration than in C-GMAW. Notably, the variations in maximum arc temperature and velocity were synchronized with UFPC, resulting in localized thermal contraction and expansion within the arc. These changes affected the arc volume and shape, leading to the excitation of ultrasonic waves. The calculated droplet size and transfer frequency were consistent with the experimental results, confirming the reliability of the models. These simulation results provide valuable guidance for engineers in designing UFPC-GMAW technology to achieve high-quality welds.