Simple Interaction Research Articles

Singlet, triplet, and mixed all-to-all pairing states emerging from incoherent fermions

The electron-electron and electron-phonon coupling in complex materials can be more complicated than simple density-density interactions, involving intertwined dynamics of spin, charge, and spatial symmetries. This motivates studying universal models with complex interactions and whether BCS-type singlet pairing is still the “natural” fate of the system. To this end, we construct a Yukawa-SYK model with nonlocal couplings in both spin and charge channels. Furthermore, we provide for time-reversal-symmetry breaking dynamics by averaging over the Gaussian unitary ensemble rather than the orthogonal ensemble. We find that the ground state of the system can be an orbitally nonlocal superconducting state arising from incoherent fermions with no BCS-like analog. The superconductivity has an equal tendency to triplet and singlet pairing states separated by a non-Fermi liquid phase. We further study the fate of the system within the superconducting phase and find that the expected ground state, away from the critical point, is a mixed singlet/triplet state. Finally, we find that, while at Tc the triplet and singlet transitions are dual to one another, below Tc the duality is broken, with the triplet state more susceptible to orbital fluctuations just by its symmetry. Our results indicate that such fluctuation-induced mixed states may be an inherent feature of strongly correlated materials. Published by the American Physical Society 2024

Pattern-based Remote Switch for Domestic and Technological Operations

Abstract: This research explores the development of a remote-control system for domestic appliances using user-drawn patterns on a grid, aiming to provide an intuitive and accessible control method. The system is designed around a 3x3 grid of infrared (IR) LEDs and receivers that detect patterns drawn by the user with a finger or stylus. Each grid coordinate is mapped to a specific action, such as turning lights on or off or adjusting the speed of a fan. The detected patterns are transmitted to a NodeMCU ESP8266 microcontroller, which decodes the patterns and activates the corresponding appliance through Wi-Fi commands and relays. The innovative aspects of this system include its simple and intuitive interaction method, allowing users to define custom patterns for various actions, which enhances personalized control. The pattern-based interaction is particularly beneficial for individuals with limited dexterity or mobility, offering an accessible alternative to traditional remote controls. This project demonstrates the potential of combining hardware components, such as IR LEDs and receivers, with embedded C programming to create a user-friendly and efficient home automation solution. The proposed system not only simplifies the user experience but also provides a flexible and customizable interface for controlling domestic appliances.

Rapid separation of bile acid isomers via ion mobility mass spectrometry by complexing with spiramycin.

Bile acid (BA) is one of the main active components of bile and has multiple isomers, the structure or content of its isomers often changes due to diseases and other health problems; thus, the accurate detection of BA isomers is very important. In this study, two groups of BA isomers of glycine-conjugated BAs and taurine-conjugated BAs were simultaneously separated and quantitatively analyzed by ion mobility mass spectrometry (IM-MS). Especially, baseline mobility separation between the isomers was achieved by the formation of binary complexes via simple interaction with spiramycin (SPM), for which a separation resolution (Rp-p) of 1.96was reached. Moreover, BA isomers were quantitatively analyzed, and the limit of detection (LOD) of absolute quantification for TCDCA/TUDCA and GUDCA/GCDCA/GHDCA was 0.514 and 0.611ng∙mL-1, respectively; the LODs for molar ratio ranges of relative quantification for TCDCA/TUDCA, GUDCA/GHDCA, and GCDCA/GHDCA were 1:18-30:1, 1:18-21:1, and 1:19-21:1, respectively. Additionally, BA isomers analyzed in pig bile powder and bear bile powder were measured, which were in good consistency with those labeled, revealing the differences in BA composition and content between the two powders. Finally, BA detection and recovery analyses were performed on serum samples, with a recovery rate of ≥73.69%, RSD of ≤6.8%, and SR (standard deviation of recoveries, the degree of difference between measured values and average recovery) of ≤1.27. Due to the simple, rapid, and lack of need for complex sample preparation and chromatographic separation, the proposed method can be an effective method for BA detection in practical samples.

Genetic Variability in the Physicochemical Characteristics of Cultivated Coffea canephora Genotypes.

The objective of this study was to characterize the genetic divergence and selection gains of the physicochemical grains traits of 68 genotypes of C. canephora most cultivated in the Western Amazon. For this purpose, the following characteristics were evaluated over two harvests: aqueous extract, ash, acidity, pH, protein, ether extract, soluble solids, phenolic compounds, soluble sugars, reducing sugars, and non-reducing sugars. The genotype × measurement interaction effect was significant for all characteristics, with a predominant simple interaction, resulting in smaller changes in the ranking of genotypes. Out of a total of 45 genotypic correlation estimates, 8 were significant, of which 5 were related to acidity. The dispersion of the first two components associated with reference points shows that the genotypes BRS3193, AS1, AS2, AS3, N16, CA1, and AS7 were closest to the ideal type of higher performance. Selection for the main characteristic of soluble sugars resulted in estimates of genetic progress lower than those observed using selection indices. The genetic materials present high genetic diversity, allowing the selection of reference plants with high levels of sugars (BRS3193, AS3, GJ25, and LB30), proteins (BRS2357), lipids (GJ30), and phenolic compounds in their green beans (BRS3193) and high water solubility (AS2).

One-Click-Based Perception for Interactive Image Segmentation.

Existing deep learning-based interactive image segmentation methods have significantly reduced the user's interaction burden with simple click interactions. However, they still require excessive numbers of clicks to continuously correct the segmentation for satisfactory results. This article explores how to harvest accurate segmentation of interested targets while minimizing the user interaction cost. To achieve the above goal, we propose a one-click-based interactive segmentation approach in this work. For this particularly challenging problem in the interactive segmentation task, we build a top-down framework dividing the original problem into a one-click-based coarse localization followed by a fine segmentation. A two-stage interactive object localization network is first designed, which aims to completely enclose the target of interest based on the supervision of object integrity (OI). Click centrality (CC) is also utilized to overcome the overlapping problem between objects. This coarse localization helps to reduce the search space and increase the focus of the click at a higher resolution. A principled multilayer segmentation network is then designed by a progressive layer-by-layer structure, which aims to accurately perceive the target with extremely limited prior guidance. A diffusion module is also designed to enhance the information flow between layers. Besides, the proposed model can be naturally extended to multiobject segmentation task. Our method achieves the state-of-the-art performance under one-click interaction on several benchmarks.

An Analysis Phatic Expression on Hotel Transylvania Animated Movie

In contemporary filmmaking, the portrayal of emotions is pivotal, with phatic communication playing a crucial role. Phatic communication, involving verbal interactions aimed at maintaining social relationships rather than conveying information, enhances narrative and character development. This study examines phatic expressions in the animated film "Hotel Transylvania," directed by Genndy Tartakovsky, to uncover the deeper meanings and social significance of these seemingly simple interactions. The analysis reveals that greetings, casual conversations, and social courtesies in the film not only establish and maintain social bonds but also contributing to the film’s charm and authenticity. By exploring phatic communication in "Hotel Transylvania," this research offers insights into sociolinguistics and communication studies, emphasizing the importance of these expressions in storytelling and the functions of phatic expression in social context so that the readers can apply it to enhance their conversations.

A privacy-preserving framework with multi-modal data for cross-domain recommendation

Cross-domain recommendation (CDR) aims to enhance the recommendation accuracy in a target domain with sparse data by leveraging rich information in a source domain, thereby addressing the data-sparsity problem. Some existing CDR methods highlight the advantages of extracting domain-common and domain-specific features to learn comprehensive user and item representations. However, these methods cannot effectively disentangle these components, as they often rely on simple user-item historical interaction information (such as ratings, clicks, and browsing), neglecting the rich multi-modal features. In addition, they do not protect user-sensitive data from potential leakage during knowledge transfer between domains. To address these challenges, we propose a Privacy-Preserving Framework with Multi-Modal Data for Cross-Domain Recommendation, called P2M2-CDR. Specifically, we first design a multi-modal disentangled encoder that utilizes multi-modal information to disentangle more informative domain-common and domain-specific embeddings. Furthermore, we introduce a privacy-preserving decoder to mitigate user privacy leakage during knowledge transfer. Local differential privacy (LDP) is used to obfuscate disentangled embeddings before the inter-domain exchange, thereby enhancing privacy protection. To ensure both consistency and differentiation among these obfuscated disentangled embeddings, we incorporate contrastive learning-based domain-inter and domain-intra losses. Extensive experiments conducted on six CDR tasks from two real-world datasets demonstrate that P2M2-CDR outperforms other state-of-the-art single- and cross-domain baselines. The code is available at https://github.com/Lili1013/P2M2-CDR.

Knowledge-driven hierarchical intents modeling for recommendation

Previous studies on user–item interaction graphs have typically concentrated on simple interactions, often overlooking the significant role of user intent in shaping these interactions. While some recent research has explored intent relationships to enhance modeling, these approaches mainly focus on user preferences derived from interactions, ignoring the knowledge information with good interpretation in knowledge graphs. In addition, some recent work usually utilize undenoised graph structure information to learn the node representations, which introduces plenty of noise and impedes the well learning of users’ preference. In this paper, we utilize the rich interpretable knowledge information in the knowledge graph to design a novel knowledge-driven hierarchical intent modeling framework called KHIM. The focus is on designing a hierarchical user intent modeling process and an intent-based multi-view contrastive learning mechanism. The former extracts both the popular and personalized preferences of users from attribute tuples within the knowledge graph at the global-level and local-level, respectively. For global level, we capture the user’s true intents from positive view and augment the positive intent representation with negative view. While the latter generates high-quality user and item representations through multi-level cross-view contrastive learning. Two data augmentation strategies are designed during the contrastive process to mitigate the effects of noise in the learning process. Additionally, we also designed a neighbor filtering strategy based on semantic view to obtain more neighbor semantic information of user and item nodes, so as to further improve the recommendation performance. Experimental results on three benchmark datasets demonstrate that KHIM significantly outperforms various state-of-the-art approaches, highlighting its effectiveness in leveraging knowledge graph information for better recommendations.

STHKT: Spatiotemporal Knowledge Tracing with Topological Hawkes Process

Knowledge Tracing (KT) is a method that seeks to forecast students’ future performance based on their historical interactions with intelligent tutoring systems. Various KT techniques have been developed from distinct perspectives, such as probability, logic, and deep learning, to scrutinize interaction sequences. However, most current methods predominantly concentrate on temporal or structural information, considering simple question sequences or interaction sequences, but fail to describe the topological dependencies between sequences, which can lead to changes in causal structures,as referred to as dynamic structural information in the paper. The dynamic information includes the clustering effect among concepts and questions, as well as the group effect among students and questions: Questions with the same knowledge concepts tend to present increasingly similar response patterns in the sequence, and concurrently, students who answer similar questions often exhibit closer knowledge states. We introduce the STHKT method, which employs a tripartite heterogeneous graph to capture relationships among concepts, questions, and students. By incorporating temporal information through a topological graph, we employ two Graph Convolution Networks (GCNs) to model the two effects separately. Following this, we apply the Hawkes Process to introduce a spatiotemporal attention mechanism that combines both effects. Through comparative experiments, ablation studies and case studies, we validate the efficacy of key components in the model. Experiment results indicate that the STHKT model can effectively model the two major effects, integrate dynamic structural information, and to some extent improve the accuracy of predicting students’ response probabilities. It is the first time that these two effects have been considered in the process of knowledge tracing, and it is adaptable to large-scale online education platforms.

Ferroelastic twin walls for neuromorphic device applications

The possibility to use ferroelastic materials as components of neuromorphic devices is discussed. They can be used as local memristors with the advantage that ionic transport is constraint to twin boundaries where ionic diffusion is much faster than in the bulk and does not leak into adjacent domains. It is shown that nano-scale ferroelastic memristors can contain a multitude of domain walls. These domain walls interact by strain fields where the interactions near surfaces are fundamentally different from bulk materials. We show that surface relaxations (∼image forces) are curtailed to short range dipolar interactions which decay as 1/d2 where d is the distance between domain walls. In bigger samples such interactions are long ranging with 1/d. The cross-over regime is typically in the range of some 200–1500 nm using a simple spring interaction model.

An interpretable machine learning methodology to generate interaction effect hypotheses from complex datasets

AbstractMachine learning (ML) models are increasingly being used in decision‐making, but they can be difficult to understand because most ML models are black boxes, meaning that their inner workings are not transparent. This can make interpreting the results of ML models and understanding the underlying data‐generation process (DGP) challenging. In this article, we propose a novel methodology called Simple Interaction Finding Technique (SIFT) that can help make ML models more interpretable. SIFT is a data‐ and model‐agnostic approach that can be used to identify interaction effects between variables in a dataset. This can help improve our understanding of the DGP and make ML models more transparent and explainable to a wider audience. We test the proposed methodology against various factors (such as ML model complexity, dataset noise, spurious variables, and variable distributions) to assess its effectiveness and weaknesses. We show that the methodology is robust against many potential problems in the underlying dataset as well as ML algorithms.

Experience-dependent modulation of collective behavior in larval zebrafish.

Complex group behavior can emerge from simple inter-individual interactions. Commonly, these interactions are considered static and hardwired and little is known about how experience and learning affect collective group behavior. Young larvae use well described visuomotor transformations to guide interindividual interactions and collective group structure. Here, we use naturalistic and virtual-reality (VR) experiments to impose persistent changes in population density and measure their effects on future visually evoked turning behavior and the resulting changes in group structure. We find that neighbor distances decrease after exposure to higher population densities, and increase after the experience of lower densities. These adaptations develop slowly and gradually, over tens of minutes and remain stable over many hours. Mechanistically, we find that larvae estimate their current group density by tracking the frequency of neighbor-evoked looming events on the retina and couple the strength of their future interactions to that estimate. A time-varying state-space model that modulates agents' social interactions based on their previous visual-social experiences, accurately describes our behavioral observations and predicts novel aspects of behavior. These findings provide concrete evidence that inter-individual interactions are not static, but rather continuously evolve based on past experience and current environmental demands. The underlying neurobiological mechanisms of experience dependent modulation can now be explored in this small and transparent model organism.

Facilely incorporating aliphatic amines into a hydroxyl-containing robust metal-organic framework for enhancing CO2 adsorption

Two amine-incorporated composites, TETA@MIP-206-OH and TREN@MIP-206-OH, were facilely fabricated by incorporation of triethylenetetramine (TETA) and tris(2-aminoethyl)amine (TREN), respectively, into a hydroxyl-containing robust metal-organic framework, MIP-206-OH for enhancing CO2 adsorption. Characterizations revealed that the two amines were both successfully loaded into the pores by the interactions between the phenolic hydroxyl sites in the porous skeleton and the introduced amines groups without disrupting the inherent structure of MIP-206-OH. CO2 sorption experiments revealed that TETA@MIP-206-OH(1:1) possesses a CO2 adsorption capacity of 20.76 ± 2.3 cm3/g at 298 K and 1 atm, which is similar with that of MIP-206-OH itself (20.32 cm3/g) at the same condition. But, at relative lower pressure (P/P0 < 0.1), TETA@MIP-206-OH(1:1) exhibits much higher CO2 adsorption capacity that of MIP-206-OH itself, which finally is attributed to chemisorption feature verified by desorption hysteresis and Fourier transform infrared spectra measurements. TREN@MIP-206-OH(10:1) exhibits a higher CO2 adsorption capacity than both of them not only at lower pressure (P/P0 < 0.1) but also at about 1 atm with a value of 35.07 ± 0.59 cm3/g at 298 K. Moreover, the recycle sorption experiments revealed TETA@MIP-206-OH(1:1) composite can be reused for CO2 adsorption after being reactivated by heating, while the loaded aliphatic amines were still well immobilized in the framework even after running ten cycles of the CO2 sorption experiments. This work successfully demonstrated that incorporating aliphatic amines into hydroxyl-containing porous materials by simple acid-base interactions, effective recyclable adsorbents for CO2 capture can be facilely achieved.

Dual pH and Ca2+-Responsive PEG-Modified Pillar[5]arene-Based Supramolecular Nanodrug Delivery System: Excellent Cargo Encapsulation and Minimal Drug Toxicity.

Chemotherapy is one of the most employed strategies in clinical treatment of cancer. However, reducing medication adverse effects and improving the biological activity remains a significant issue for chemotherapy. We developed a pH and Ca2+-responsive pillar[5]arene-based supramolecular nanodrug delivery system (NDDS) WP5⊃EV@DOX to address the aforementioned challenges. The formation of this NDDS began with the spontaneous formation of supramolecular nanodrug carrier WP5⊃EV in water from PEG-modified pillar[5]arene and the bipyridilium salt derivative EV through simple host-guest interaction. Then the antitumor drug doxorubicin DOX was efficiently loaded with a high encapsulation rate of 84.6 %. Cytotoxicity results indicated that the constructed nanoplatform not only reduced DOX toxicity and side effects on normal cell (293T), but also significantly enhanced the antitumor activity on cancer cell (HepG2). Moreover, in vivo experiments showed that WP5⊃EV@DOX had a longer half-life and higher bioavailability in the blood of mice compared to the nake drug DOX, with increases to 212 % and 179 %, respectively. Therefore, WP5⊃EV@DOX has great potential in tumor therapy and provides a new idea for host-guest drug delivery system.

Extrapolating into no man’s land enables accurate estimation of surface properties with multiparameter equations of state

Thermodynamic properties of homogeneous fluids in the metastable and unstable regions are needed to describe confined fluids, interfaces, nucleating embryos and estimate critical mass flow rates. The most accurate equations of state (EoS) called multiparameter EoS, have a second, non-physical Maxwell loop that renders predictions unreliable in these regions. We elaborate how information from the stable region can be used to reconstruct the metastable and unstable regions. For a simple interaction potential, comparison to results from molecular simulations reveals that isochoric expansion of the pressure from stable states reproduces simulation results in the metastable regions. By constructing a dome that extends above the critical point, we obtain an extrapolated pressure from multiparameter EoS that is free of second Maxwell loops. A reconstructed EoS is developed next, by integrating the extrapolated pressure from a stable state to obtain the Helmholtz energy. The consistency of the reconstructed EoS is gauged by computing phase equilibrium densities, pressures, and enthalpies of evaporation, which are in reasonable agreement with experimental values. Combined with density gradient theory, the reconstructed EoS yields surface tensions of water, carbon dioxide, ammonia, hydrogen and propane that deviate, on average, 4.4%, 1.6%, 6.0%, 0.7% and 5.4% from experimental values respectively. The results reveal a potential to develop more accurate extrapolation protocols, which can be leveraged to obtain prediction of metastable properties, surface properties or used as constraints in fitting multiparameter EoS.

Adsorption isotherm and kinetics of diffusion of water accumulated between polypropylene thin film and Si substrate: Neutron reflectivity investigation

Isotactic polypropylene (PP) thin films deposited on Si substrates were subjected to neutron reflectivity (NR) measurements under various humidity conditions to evaluate the isothermal adsorption of water accumulated between the PP layer and Si substrate, with focus on the temperature-dependence of the adsorption isotherm. The adsorption of accumulated water followed a single type II isotherm according to the Brunauer-Emmett-Teller (BET) classification, regardless of temperature. This can be attributed to the weak interactions between the PP layer and Si substrate, and between the PP layer and water molecules. Hydrophobic PP does not significantly interact with water molecules and the hydrophilic Si surface. Therefore, the interfacial water molecules are simply adsorbed on the native oxide layer on Si via simple interaction between the water molecules and the silicon oxide surface without being affected by the PP layer. Consequently, the adsorption isotherm of the accumulated water follows the single type II adsorption isotherm regardless of temperature, similar to the adsorption isotherm of water simply adsorbed on exposed silica surfaces. These weak interactions of the PP layer with the Si surface and water molecules also lead to fast diffusion kinetics for the accumulated water along the interface.

Rational Tuning of the Concentration-independent Enrichment of Prion-like Domains in Stress Granules

Stress granules (SGs) are large ribonucleoprotein assemblies that form in response to acute stress in eukaryotes. SG formation is thought to be initiated by liquid–liquid phase separation (LLPS) of key proteins and RNA. These molecules serve as a scaffold for recruitment of client molecules. LLPS of scaffold proteins in vitro is highly concentration-dependent, yet biomolecular condensates in vivo contain hundreds of unique proteins, most of which are thought to be clients rather than scaffolds. Many proteins that localize to SGs contain low-complexity, prion-like domains (PrLDs) that have been implicated in LLPS and SG recruitment. The degree of enrichment of proteins in biomolecular condensates such as SGs can vary widely, but the underlying basis for these differences is not fully understood. Here, we develop a toolkit of model PrLDs to examine the factors that govern efficiency of PrLD recruitment to stress granules. Recruitment was highly sensitive to amino acid composition: enrichment in SGs could be tuned through subtle changes in hydrophobicity. By contrast, SG recruitment was largely insensitive to PrLD concentration at both a population level and single-cell level. These observations point to a model wherein PrLDs are enriched in SGs through either simple solvation effects or interactions that are effectively non-saturable even at high expression levels.

Interacting with the farthest neighbor promotes cohesion and polarization in collective motion

The collective behavior generated from self-organization is one of the most prominent characteristics of animal populations. Typically, it is assumed that the interactions between individuals are average and synchronous. However, recent empirical studies have shown that interactions between individuals are selective and update their positions and directions asynchronously. Especially the feature factor of distance is widely used in theoretical modeling, but there is no detailed analysis and discussion on it in pairwise interaction. In this work, the effect of pairwise interactions on group motion is investigated under three distance features: nearest, medium, and farthest, based on a self-propelled particle model and combined with an asynchronous update scheme. The research results indicate that simple pairwise interaction can achieve group-level cohesion, especially by selecting the farthest neighbor instead of only interacting with the nearest neighbor, and the proportion of topological neighbors required to achieve cohesion is independent of the group size. In addition, we found that there is a visual angle range that is conducive to achieving cohesion and consistent movement in the group. This study provides insight into selective interaction, especially the great significance of the distance factor in pairwise interaction.

Prototype as query for few shot semantic segmentation

Few-shot Semantic Segmentation (FSS) was proposed to segment unseen classes in a query image, referring to only a few annotated examples named support images. One of the characteristics of FSS is spatial inconsistency between query and support targets, e.g., texture or appearance. This greatly challenges the generalization ability of methods for FSS, which requires to effectively exploit the dependency of the query image and the support examples. Most existing methods abstracted support features into prototype vectors and implemented the interaction with query features using cosine similarity or feature concatenation. However, this simple interaction may not capture spatial details in query features. To address this limitation, some methods utilized pixel-level support information by computing pixel-level correlations between paired query and support features implemented with the attention mechanism of Transformer. Nevertheless, these approaches suffer from heavy computation due to dot-product attention between all pixels of support and query features. In this paper, we propose a novel framework, termed ProtoFormer, built upon the Transformer architecture, to fully capture spatial details in query features. ProtoFormer treats the abstracted prototype of the target class in support features as the Query and the query features as Key and Value embeddings, which are input to the Transformer decoder. This approach enables better capture of spatial details and focuses on the semantic features of the target class in the query image. The output of the Transformer-based module can be interpreted as semantic-aware dynamic kernels that filter the segmentation mask from the enriched query features. Extensive experiments conducted on PASCAL-5i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5^{i}$$\\end{document} and COCO-20i\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$20^{i}$$\\end{document} datasets demonstrate that ProtoFormer significantly outperforms the state-of-the-art methods in FSS.

Digital Twin-Enhanced Control for Fuel Cell and Lithium-Ion Battery Hybrid Vehicles

With the development of lithium-ion batteries and fuel cells, the application of hybrid power systems is becoming more and more widespread. To better optimize the energy management problem of fuel cell hybrid systems, the accuracy of system modeling and simulation is very important. The hybrid system is formed by connecting the battery to the fuel cell through an active topology. Digital twin technology is applicable to the mapping of physical entities to each other with high interactivity and fast optimization iterations. In this paper, a relevant model based on mathematical logic is established by collecting actual operational data; subsequently, the accuracy of the model is verified by combining relevant operating conditions and simulating the model. Subsequently, a three-dimensional visualization model of a hybrid power system-based sightseeing vehicle and its operating environment was established using digital twin technology to improve the model simulation of the fuel cell hybrid power system. At low speeds, the simulation results of the hybrid power system-based sightseeing vehicle have a small error compared with the actual running state, and the accuracy of the data related to each internal subcomponent is high. In the simple interaction between the model display vehicle and the environment, the communication state can meet the basic requirements of the digital twin model because the amount of data to be transferred is small. This study makes a preliminary attempt at digital parallelism by combining mathematical logic with visualization models and can be used as a basis for the subsequent development of more mature digital twin models.