Mesoscopic Perspective Research Articles

Evaluation and characterization of rubber powder distribution on the ductility of rubber asphalt from the mesoscopic view

To investigate the effects of rubber powder’s distribution on the ductility of RA, the force-ductility test (FDT) was conducted with rubber contents of 15, 20, 25, and 30 wt%, and the results demonstrated that the dense rubber powder in RA will reduce its ductility. The microscopic tests discovered that the rubber powder distribution significantly impacted the mechanical performance of RA. Subsequently, based on the Monte Carlo method and Python, models of RA were established, and FDT was simulated from the mesoscopic view. The findings demonstrated that the dense distribution of rubber powder led to poor ductility of RA. Consequentially, this study offered an innovative mesoscopic perspective of studying RA, particularly the multiple impacts of rubber powder distribution on RA.

Strain-softening model for granite and sandstone based on experimental and discrete element methods

Combing macroscopic experimental method and mesoscopic numerical method, this study analyses the strain-softening behaviours of granite and sandstone. From the macroscopic perspective, the stress–strain curves of granite and sandstone under different confining pressures are studied by laboratory triaxial compression test. Variations of post-peak reduction modulus and critical plastic shear strain versus confining stress are obtained. Evolution of strength parameters at peak, residual and strain-softening stage are proposed. Then a method to develop the strain-softening model of hard and soft rocks is presented. From the mesoscopic perspective, based on the laboratory test results, the parameters of discrete element method PFC for the samples of the granite and sandstone are calibrated. Comparing the basically consistent results of laboratory experiment and numerical simulation, the feasibility of discrete element method is verified. Evolutions of mesoscopic crack propagation and mesoscopic particle displacement field in the complete failure process are analysed. Typical stresses of granite sample and sandstone sample in the failure stage are investigated. Above combined macroscopic experimental method and mesoscopic numerical method systematically analyse the characteristics of hard rock and soft rock in the strain-softening stage. Failure process and mechanical property of hard rock and soft rock are revealed at the macroscopic and mesoscopic levels. The initiation and propagation process of micro-cracks in rock are thoroughly investigated. The research results provide a scientific foundation for the analyse of strain-softening behaviour of hard rock and soft rock. The result shows that both the mesoscopic numerical method and macroscopic experimental method indicate that the failure pattern of sandstone is influenced by both confining pressure and axial stress, while granite is mainly affected by axial stress.

Modeling and failure mechanism study of unidirectional GFRP corrugated sandwich sheet piles based on discrete element method

In this paper, a DEM model of a unidirectional glass fiber reinforced polymer composite sandwich sheet pile (UGFRP-CSSP) is developed to elucidate the damage mechanisms under longitudinal bending and axial compression at the mesoscale, in order to bridge the gap between existing knowledge and its potential applications as sheet pile walls. A series of four-point bending and axial compression tests are performed on UGFRP-CSSP to validate the DEM model, which is calibrated through a sensitivity analysis of the mesoscopic parameters. The failure behavior, crack progress, and initial crack formation of the UGFRP-CSSP are investigated by correlating the results from physical experiments and numerical simulations. The research reveals that the DEM model accurately depicts the failure behavior under bending and compression of unidirectional (UD) composite elements, offering a mesoscopic perspective on the failure origins. It is identified that the tensile shear strength of the resin is the weak point leading to initial failure, as it is lower than that of the glass fibers. Longitudinal bending failure typically initiates at the skin-core interface and around the central axis of core, with cracks propagating longitudinally due to compression shear failure between fibers. Axial compression failure also occurs predominantly at the skin-core interface, with similar longitudinal cracking attributed to tensile shear failure at the acute angle fiber connections in the long span side. Finally, a new viewpoint was proposed that the fiber modulus may have a decisive impact on the failure behavior of UD composite materials.

Coevolution of non-pharmaceutical interventions and infectious disease spreading in age-structured populations

In the face of emerging major infectious diseases, non-pharmaceutical interventions (NPIs) such as mask-wearing are the primary means of disease control in the early stages due to the lack of effective pharmaceutical interventions. Yet the current understanding of the dynamic feedback loop between NPIs and infectious disease spreading remains limited. In this study, we proposed an asymmetrically coupled dynamic model of the coevolution of mask-wearing behaviour and infectious disease spreading in an age-structured population to describe the bidirectional relationship between NPIs such as mask-wearing and infectious disease spreading. A theoretical analysis framework was developed by extending the microscopic Markov chain method. To reflect the spreading process more realistically, we conducted numerical simulations from a mesoscopic perspective through an age-contact data-driven approach. Critical transition, multi-wave epidemics, multi-transitions, and mixed-phase transitions resulting from the coevolution of mask and disease are observed. The results highlight the critical role of NPIs in controlling disease spreading. The mask-wearing can effectively reduce the daily peak infection density and prevent large-scale outbreaks within a short period. However, it will also prolong the duration of the epidemic and cause the disease to exist in the population for a long time. There is a trade-off between the two. For certain diseases with specific spreading capabilities, pharmaceutical interventions such as vaccination, which can directly reduce the disease’s infectiousness, may result in more people being infected if NPIs are not implemented as early as possible. In addition, results from Anhui indicate that people aged 42 years are at high risk of being infected because of their large population base, which suggests that priority should be given to the prevention and control of this group.

Principles of Economic Externality Dynamics Economic Dynamics and the Standard Model (V)

The present paper discusses the importance of economic externality and the economic phenomena involved. Section 1 draws on the framework of weak force theory in physics, we propose a new approach of dynamic analysis for economic externality. From a mesoscopic (intermediate scale) perspective, no distinction is made between the macro-demand and individual micro-buyers nor between the macro-supply and individual micro-sellers. Section 2 applies the isospin modeling method to construct two economic isospin models. One is between the achieving impulse and the fear-of-failure impulse, under the effect of economic externality. The other is between the market behavior and market residues, namely, market aftertaste. Section 3 discusses the microstructural properties of the market, including Dirac's equation and the market, Dirac γmatrix notation, and the chirality and spin structure of market components. Section 4 discusses the helical structural characteristics of supply chains, including CPT theorems, Penrose zigzag helical processes, and several modeling directions of economic externality and buyer-seller interactions (weak interactions). Section 5 discusses the economic externality and the electroweak model, including extensive externality, externality mediations with Weinberg rotations, and economic analysis using lepton weak currents, including the analysis of lepton neutral weak currents. Finally, Section 6 provides a general discussion on the standard model of economic dynamics.

3D numerical study of axial compression behavior of concrete-filled steel tubular columns from mesoscopic perspective

This study investigates the axial compression behavior of Concrete-Filled Steel Tubular (CFST) short columns from a mesoscopic perspective. Concrete is regarded as a three-phase composite material consisting of aggregates, mortar, and interfacial transition zones (ITZs). The Concrete Damaged Plasticity (CDP) model was employed to simulate the nonlinear mechanical response and fracture characteristics of concrete. Verified against experimental results, the numerical model excels in realistically simulating the uniaxial compression behavior of CFST short columns. Subsequently, factors influencing the macroscopic behavior of CFST short columns were investigated. Results indicate that the random aggregate model adopted for concrete enables a more realistic prediction of the behavior of CFST short columns. Additionally, circular section steel tubes demonstrate a stronger confinement effect compared to square ones. With increasing lateral confinement, the properties of CFST short columns, such as bearing capacity and ductility, are significantly enhanced.

Low-Carbon Ecological Tea: The Key to Transforming the Tea Industry towards Sustainability

The realization of the value of ecological products has led to an economic means for reducing carbon emissions in China. Tea is one of the most important cash crops and one of the most popular beverages in the world. Due to the complex the tea industrial chain, it is considered to be an industry with high carbon emissions. Ecological tea products with low-carbon attributes can be considered a linkage of ecology, economy, and society. Based on this, this paper presents research on low-carbon ecological tea (LCT). Herein, we construct the formational logic of low-carbon ecological products, explore the connotations of LCT, and form a conceptual pathway for realizing LCT to contribute to climate change mitigation and adaptation. This paper starts from the upstream, midstream, and downstream of the industrial chain; it establishes three value realization pathways that keep, as a priority, the promotion of ecological industrialization, focus on restoration to improve the ecology of the industrial chain, and innovate technology to expand the industrial chain. The pathways are a set of low-emission production solutions that use techniques to enhance carbon sequestration in soil, reduce the use of fertilizers and pesticides, and help shift to clean energy from low-emission sources in the stages of plantation, processing, and distribution. In the process of realizing LCT, the government plays an important role, and its support and guidance are needed. Based on stakeholder theory, this paper builds an implementation mechanism that focuses on the micro perspective (users, organizations), integrates the mesoscopic perspective (industry), and relies on the macro perspective (government).

Revealing the mechanisms of semantic satiation with deep learning models

The phenomenon of semantic satiation, which refers to the loss of meaning of a word or phrase after being repeated many times, is a well-known psychological phenomenon. However, the microscopic neural computational principles responsible for these mechanisms remain unknown. In this study, we use a deep learning model of continuous coupled neural networks to investigate the mechanism underlying semantic satiation and precisely describe this process with neuronal components. Our results suggest that, from a mesoscopic perspective, semantic satiation may be a bottom-up process. Unlike existing macroscopic psychological studies that suggest that semantic satiation is a top-down process, our simulations use a similar experimental paradigm as classical psychology experiments and observe similar results. Satiation of semantic objectives, similar to the learning process of our network model used for object recognition, relies on continuous learning and switching between objects. The underlying neural coupling strengthens or weakens satiation. Taken together, both neural and network mechanisms play a role in controlling semantic satiation.

Mesoscopic model for the fracture of polymethyl methacrylate bone cement

Polymethyl methacrylate (PMMA) bone cement is widely used for biomedical applications. The fracture toughness (KIC) and tensile strength (ft) are two critical indicators directly influencing its safety and durability. However, different test methods are currently being used to determine the material parameters (KIC &ft) of PMMA bone cement. In order to address this issue, an innovative design method only employing the modified wedge split specimens is presented in this study to determine the PMMA bone cement material parameters (KIC &ft) simultaneously. Due to the discrete peak load Pmax, the resulting material parameters (KIC &ft) are discrete. Considering the discreteness of the material parameters (KIC &ft), their true values for PMMA bone cement were calculated through the standard statistical method. Further, a two-point simple method to determine the material parameters of PMMA bone cement has been proposed based on the equivalent area Ae. Finally, the numerical model of PMMA bone cement was developed from the mesoscopic level. The discrete mechanism of the peak load Pmax of PMMA bone cement due to the variation in particle contact strength was interpreted from the mesoscopic perspective.

A crystal plasticity model for multiaxial cyclic deformation of U75V rail steel

The theoretical investigations of cyclic deformation behavior and deformation mechanism of the rail steels under multiaxial loadings are of great significance to the study of rolling contact fatigue. Based on the classical crystal plasticity framework, a new nonproportional parameter at the mesoscale is proposed, and the isotropic and kinematic hardening models are modified. A series of finite element simulations of U75V rail steel under uniaxial and multiaxial cyclic loading are carried out using the proposed model. The simulation results show that the proposed model can reasonably reflect the cyclic softening under strain-controlled uniaxial cyclic loading, cyclic hardening under strain-controlled multiaxial cyclic loadings, and ratcheting behavior under stress-controlled uniaxial and multiaxial cyclic loadings of U75V rail steel. Moreover, the influence of multiaxial loading paths on the plastic strain is explained from a mesoscopic perspective. The values of nonproportional parameters of different slip systems under uniaxial and multiaxial paths are compared, and the difference of nonproportional parameters at the macroscale and mesoscale are discussed. Finally, the simulation results of the proposed model and the existing model with modified kinematic hardening rules are compared. The results show that the proposed model has better predictions on the evolution of ratcheting strain and nonproportionally additional hardening effect caused by nonproportionally multiaxial loadings.

Macro-Meso Damage Analysis of Tunnel Lining Concrete under Thermal-Mechanical Coupling Based on CT Images.

The mechanical properties and failure modes of concrete are controlled by its mesoscopic material composition and structure; therefore, it is necessary to study the deterioration characteristics of tunnel lining concrete under fire from a mesoscopic perspective. However, previous studies mostly analyzed the damage and failure process from a macro-homogeneous perspective, which has certain limitations. In this paper, a thermal-mechanical coupling test device was modified to simulate the state of concrete under tunnel fire conditions. Combined with CT technology, the macroscopic properties and mesoscopic characteristics of concrete were observed. Features were obtained, such as the change in compressive strength under fire, as well as mesoscopic deterioration characteristics. The damage variable D was defined to quantify mesoscopic damage, and the link between mesoscopic deterioration characteristics and macroscopic performance was established, which can be used to predict compressive strength loss through mesoscopic characteristics.

Ultrasonic computed tomography-based internal-defect detection and location of underwater concrete piers

Concrete piers are the key load-bearing components of wading bridges. Most detection equipment and methods focus on the surface defects of underwater piers, whereas the detection methods of internal defects are very deficient. This study proposes an ultrasonic computed tomography method combining linear travel time interpolation and simultaneous iterative reconstruction techniques for detecting internal defects in underwater concrete piers. The two-dimensional median filtering and self-organizing feature map are utilized to post-process tomographic images and the confidence approach is employed to detect defects. The efficacy of the proposed method is validated through numerical simulation and underwater experiments, respectively. The Comsol Multiphysics is utilized to simulate the two-dimensional random aggregate model of the concrete and internal defects are added to validate the method’s effectiveness from a mesoscopic perspective. Then actual scale piers containing embedded defects are cast and detected. The results demonstrate that the method can identify and locate internal defects in numerical simulations and practical tests. The numerical simulation experiments results are superior to those of scale concrete specimens. In addition, underwater concrete piers with larger internal defects, square holes, and no steel reinforcements tend to have better detection results.

Quantitative investigation of crack propagation and fracture mechanism of fissured granite from the mesoscopic perspective

Natural rock masses comprise numerous fissures, which affect the stability of rock mass engineering. It is crucial for rock engineering to comprehend the crack propagation and fracture mechanisms of fissured rock. In this study, uniaxial compression tests were conducted on fissured granite specimens with various flaw inclinations (0°, 30°, 45°, 60°, and 90°). After specimen failure, the cracks originating from pre-existing fissures were classified (tensile, shear, and tensile-shear cracks). To link the macroscopic failure behavior and mesoscopic fracture mechanism in fissured granite specimens, a quantitative method combining deep learning and scanning electron microscope (QMDL-SEM) was employed for the identification of the mesoscopic fracture mechanism of macroscopic cracks. Identified results suggested that the failure of fissured specimens was attributed to secondary cracks characterized by tensile stress (failure area caused by tensile stress accounting for more than 87%) and far-field cracks dominated by shear stress (failure area caused by shear stress accounting for more than 60%). Additionally, for secondary cracks, as the transition went from tensile crack to tensile-shear crack and then to shear crack in a macroscopic scale, the proportion of failure area caused by shear stress on the failure surface gradually increased. However, tensile stress still dominated the failure area.

Study on the shear strength of asphalt mixture by discrete element modeling with coarse aggregate morphology

High shear strength is crucial for asphalt pavements to resist permanent deformation at high temperatures, which is largely determined by the skeletal structure of coarse aggregates. In this study, four size ranges of polyhedral coarse aggregate clumps were built to simulate the morphology and dimensional characteristics of coarse aggregate. Subsequently, virtual triaxial shear tests were simulated using discrete element models. Three gradation types were analyzed to study the effects of aggregate structure, granularity composition, and coarse aggregate modulus on the shear strength of asphalt mixtures. The results show that it is feasible to obtain accurate morphological features of coarse aggregates by controlling the minimum diameter of the filled spheres in a polyhedral coarse aggregate box when constructing a discrete element model. The virtual triaxial shear test model developed in this study can be effectively utilized to simulate the shear mechanical properties of asphalt mixtures under various confining pressures. The numerical simulation results indicate that the cohesion of asphalt mixture AC-13 is the best, while the internal friction angle and shear strength of SMA-13 are the highest. The ranking of factors that affect the cohesion and internal friction can be summarized as: aggregate structure ≫ coarse aggregate modulus > granularity composition, while the order of factors that affect the shear strength is: aggregate structure ≫ granularity composition > coarse aggregate modulus. This indicates that the aggregate structure is crucial for the high-temperature performance of asphalt mixtures. This study addressed the issue of high variability in laboratory test results for macroscopic properties of asphalt mixtures and analyzed the skeleton role of coarse aggregate in asphalt mixtures from a mesoscopic perspective. The findings parameters for explaining the strength mechanism. The discrete element models also facilitate the optimization of asphalt mix design with reduced labor and time consumption on laboratory testing.

Robustness measurement of multiplex networks based on multiplex motifs

Multiplex network is a classic multilayer network model that can be used to model complex systems in the real world. Previous measures of network robustness mainly focused on the low-order characteristics of network topology, and the existence of motifs in the network is ignored. As simple building blocks of multiplex networks, multiplex motifs are indispensable tools to understand the structure of local networks. Therefore, from a mesoscopic perspective, we propose an index called normalized motif entropy to measure the robustness of multiplex networks under malicious attacks, and the effectiveness of our method is verified in real multiplex networks.

Spatiotemporal Analysis of Urban Blue Space in Beijing and the Identification of Multifactor Driving Mechanisms Using Remote Sensing

With rapid urban development in Beijing, there is a critical need to explore urban natural resources and understand their underlying mechanisms. Urban blue space (UBS) has gained increasing attention due to its potential to drive microcirculation, mitigate heat islands, and enhance residents’ well-being. In this study, we used remote sensing data to extract UBS in Beijing and employed exploratory spatial data analysis (ESDA) methods to examine its spatial and temporal development over the past two decades. We adopted a mesoscopic perspective to uncover the full spectrum of landscape patterns and quantitatively simulate the mechanisms influencing the area of UBS and landscape patterns. Our findings are as follows: (1) The UBS area in Beijing exhibited fluctuating growth from 2000 to 2020. (2) Spatial clustering of UBS was stable with subtle changes. (3) The ecological conditions in Beijing improved over the last 21 years, indicated by increased habitat diversity and richness, while notable landscape fragmentation posed significant challenges. (4) Science and technology management-related factors, such as UEM, EDUI, and STI, emerged as the most influential mechanisms for the UBS area. The coefficients for these factors were 0.798, 0.759, and 0.758, respectively. Following closely were vegetation conditions (NDVI) with a coefficient of 0.697 and an annual average temperature (T) with a coefficient of 0.692. (5) Precipitation was identified as the most vital influencing factor for the UBS landscape, with a significant correlation coefficient of 0.732. It was followed by residential population (POP), with a coefficient of 0.692, and economic conditions represented by gross domestic product (GDP), with a coefficient of 0.691.

Exploring the Impacts of COVID-19 and Lockdown on Online Car-Hailing Travel in Shanghai

The COVID-19 pandemic and lockdown have caused serious impacts on people’s lives, especially on daily travel like online car-hailing. Understanding the impacts of the pandemic on online car-hailing travel is essential for sustainable urban planning and governance, especially during public health emergencies including COVID-19. However, few studies have delved into the in-depth patterns and interpretations of crowd behaviors and mobility variations resulting from the lockdown, especially from different perspectives. This study attempts to make contributions to this gap by building a three-step method from a macroscopic to mesoscopic perspective. A dataset of online car-hailing trajectories for 15 days in 2018 and 3 special days (before and after the lockdown) in 2022 was used. Detailed analyses of the overall spatiotemporal patterns, the flows between administrative districts, and the four-perspective investigation in the central urban area were conducted. The main findings include a dramatic plunge in ride counts for online car-hailing due to the lockdown and a significant change in human mobility associated with hospitals and traffic hubs. Our study provides insights into the understanding of impacts of COVID-19 and lockdown and hopefully helps with the resilience and sustainability of the city. The workflow might also be inspiring for further studies.

Ecological regulation network of quality in American Ginseng: Insights from macroscopic-mesoscopic-microscopic perspectives

The environment profoundly influences the growth, development and constituents of plants. Nonetheless, a lack of comprehensive studies at various scales hinders our understanding of how environmental factors impact the quality of American ginseng. This study employed an integrated approach, considering macroscopic-mesoscopic-microscopic perspective, to reveal the ecological variables impacting the quality of American ginseng. From the macroscopic perspective, the MaxEnt model revealed that American ginseng is suitable for growth in temperate regions. The temperature seasonality and warmest quarter emerged as crucial bioclimatic factors that influence the distribution of American ginseng. Taking the mesoscopic perspective, the ginsenosides were significantly positively correlated with available phosphorus (AP) and soil water content (SWC), and significantly negatively correlated with ammonium nitrogen (NN), available potassium (AK), and microbial biomass carbon (MBC). From the microscopic perspective, a total of 16 microorganisms, including 14 bacteria and 2 fungi, were identified to have a significant impact on the synthesis of ginsenosides through 16 S and internally transcribed spacer (ITS) rRNA amplicon sequencing combine with machine learning. Notably, this is the first time to construct an ecological network for regulating the quality of American ginseng and identify the key ecological factors, which can serve as valuable references for targeted breeding and domestication of American ginseng. These findings not only established a theoretical foundation for the future investigation of biological fertilizers, but simultaneously provided crucial insights for cultivating high-quality medicinal plants.

Mesoscopic study on instability characteristics of residual coal pillars–roof system based upon domino effect in pillar goaf

The stability of pillar goaf is affected by the composite structure composed of residual coal pillars and roof, it is necessary to study the instability characteristics of residual coal pillars–roof system. Double coal pillar–roof combined bodies were constructed based on single coal pillar–roof combined body to characterize coal pillars–roof system in this paper. Through particle flow code (PFC), the instability modes of single combined body and double combined bodies with different combinations under uniaxial compression were studied from a mesoscopic perspective. With that, the instability criterions of double combined bodies were analyzed theoretically. The results show that the damage of single combined body and double combined bodies both have domino—type characteristics. During the single combined body is compressed, coal is broken firstly and induces rock damage. Meanwhile, the rock damage aggravates the destruction of coal in turn. Finally, the overall body loses bearing capacity based upon domino effect. During the double combined bodies with same mechanical properties are compressed, the component bodies bear the external load evenly and deform harmoniously. During the double bodies with different mechanical properties are compressed, the low-strength component body is destroyed and reaches its bearing limit firstly. Synchronously, the whole system reaches the bearing peak. Thereafter, the external load originally borne by low-strength body gradually transfers to high-strength body. The high-strength body also reaches the bearing limit over time, and the second bearing peak appears synchronously for the whole system. The instability of a single coal pillar is the initial cause of the instability of the whole coal pillars–roof system. The instability of any single component body can be regarded as the overall instability criterion for double bodies with same properties, while the instability of the single component body with high strength should be regarded as the instability criterion for double bodies with different properties.

Role of oil polarity on myofibrillar protein emulsions stability: A multi-scale research

The oil polarity was hypothesized to significantly influence the microscopic conformation of interfacial myofibrillar protein (MP) and thus the mesoscopic interfacial rheology of MP film, which ultimately tuned the macroscopic stability of MP-stabilized emulsions. In this research, the inner mechanism was revealed hierarchically based on emulsions multi-scale properties, by a combination of in vitro interfacial concentration measurement, dynamic interfacial tension measurement and interfacial dilatational rheology (highlighting Lissajous plots). Then, the above conclusion was verified through in silico molecular dynamic simulation. At macroscopic level, the light scattering results showed that emulsion prepared with higher polarity oil exhibited higher physical stability. This could be explained from the mesoscopic perspective that: (1) the adsorption dynamics of MP was faster (though lower diffusion rate, but higher penetration and rearrangement rate) on the higher polarity interface, which decreased interfacial tension more efficiently; (2) adsorption ratio, surface load and desorption ratio indicated that ‘multilayer interfacial film’ was formed by loosely-bound MP on the lower polarity interface, which decreased the effective interfacial coverage to impair emulsions stability; (3) Lissajous plots showed that interfacial structure with higher stability against large deformation was formed on the higher polarity oil interface. Finally, the microscopic molecular mechanism for explaining the above findings was revealed by in silico molecular dynamic simulation: though MP was less unfolded on the higher polarity interface leading to weaker intermolecular hydrophobic interactions, this compact molecular structure with stronger intramolecular rigidity predominantly contributed to steadier interfacial film structure. This multi-scale study provides a deep insight into the inner relationship between oil polarity and emulsions stability.