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The resolution sensitivity of the Kain–Fritsch (KF) convection scheme and the role of interactions between the physics and dynamics within the gray zone (<10 km) were investigated using the Separate Physics and Dynamics Experiment (SPADE) framework. Two groups of experiments were conducted using the Weather Research and Forecasting (WRF) model via traditional (Tradition) runs and SPADE runs with resolutions of 1, 2, 4, and 8 km during the wet period of the Tropical Warm Pool–International Cloud Experiment (TWP‐ICE). Results show that the KF scheme simulates the weakened convective processes well as the resolution increases in both groups, and the changes in the convective variables with resolution in SPADE are smaller than in the Tradition group. This indicates the important effects of interactions between model components on convection parameterizations as the resolution changes. Additionally, the microphysics variables remain nearly unchanged with resolution in SPADE and weaken slightly in Tradition as the resolution decreases, suggesting the relatively weaker influences of model interactions for the resolved‐cloud parameterization. Therefore, the scale‐aware behavior of KF scheme is further strengthened in Tradition runs, primarily through inhibiting the strength of stratiform processes through physics–dynamics interactions and physical components. Plain Language Summary The resolution sensitivity of Convective Parameterization Schemes (CPS) is a critical and challenging issue in the Earth's weather and climate system, particularly within the gray‐zone scale (1–10 km). This paper analyzes the resolution sensitivity of the KF convection scheme and isolates the effects of other model components (mainly the dynamics) on the CPS through designing two groups of experiments under the backgrounds with and without the physics‐dynamics feedbacks using the WRF model with resolutions of 1, 2, 4, and 8 km. The results demonstrate that the KF scheme shows scale‐aware performance in simulating convection under the strong tropical rainfall condition. Furthermore, the scale‐awareness of the KF scheme could be strengthen with the interactions from other model components. This paper provides a new perspective for evaluating the scale‐aware CPS and understanding the effects of physics‐dynamics interactions on the simulations. Key Points The resolution sensitivity of the Kain–Fritsch scheme was analyzed using the SPADE framework The KF scheme shows the scale adaptability across the gray zone during the wet period of TWP‐ICE The scale‐dependence of the KF scheme is strengthened by physics–dynamics interactions across the gray zone

CD44 ligand-receptor interactions are known to be involved in regulating cell migration and tumor cell metastasis. High expression levels of CD44 correlate with a poor prognosis of melanoma patients. In order to understand not only the mechanistic basis for dacarbazine (DTIC)-based melanoma treatment but also the reason for the poor prognosis of melanoma patients treated with DTIC, dynamic force spectroscopy was used to structurally map single native CD44-coupled receptors on the surface of melanoma cells. The effect of DTIC treatment was quantified by the dynamic binding strength and the ligand-binding free-energy landscape. The results demonstrated no obvious effect of DTIC on the unbinding force between CD44 ligand and its receptor, even when the CD44 nanodomains were reduced significantly. However, DTIC did perturb the kinetic and thermodynamic interactions of the CD44 ligand-receptor, with a resultant greater dissociation rate, lower affinity, lower binding free energy, and a narrower energy valley for the free-energy landscape. For cells treated with 25 and 75 μg/mL DTIC for 24 hours, the dissociation constant for CD44 increased 9- and 70-fold, respectively. The CD44 ligand binding free energy decreased from 9.94 for untreated cells to 8.65 and 7.39 kcal/mol for DTIC-treated cells, which indicated that the CD44 ligand-receptor complexes on DTIC-treated melanoma cells were less stable than on untreated cells. However, affinity remained in the micromolar range, rather than the millimolar range associated with nonaffinity ligands. Hence, the CD44 receptor could still be activated, resulting in intracellular signaling that could trigger a cellular response. These results demonstrate DTIC perturbs, but not completely inhibits, the binding of CD44 ligand to membrane receptors, suggesting a basis for the poor prognosis associated with DTIC treatment of melanoma. Overall, atomic force microscopy-based nanoscopic methods offer thermodynamic and kinetic insight into the effect of DTIC on the CD44 ligand-binding process.

In this article, the computational methodology of the catenary–train–track system vibration analysis is presented and used to estimate the influence of vehicle body vibrations on the pantograph–catenary dynamic interaction. This issue is rarely referred in the literature, although any perturbations appearing at the pantograph–catenary interface are of great importance for high-speed railways. Vehicle body vibrations considered in this article are induced by the passage of train through the track stiffness discontinuity, being a frequent cause of significant dynamic effects. First, the most important assumptions of the computational model are presented, including the general idea of decomposing catenary–train–track dynamic system into two main subsystems and the concept of one-way coupling between them. Then, the pantograph base vibrations calculated for two train speeds (60 m/s, 100 m/s) and two cases of track discontinuity (a sudden increase and a sudden decrease in the stiffness of track substrate) are analyzed. Two cases of the railway vehicle suspension are considered – a typical two-stage suspension and a primary suspension alone. To evaluate catenary–pantograph dynamic interaction, the dynamic uplift of the contact wire at steady arm and the pantograph contact force is computed. It is demonstrated that an efficiency of the two-stage suspension grows with the train speed; hence, such vehicle suspension effectively suppresses strong sudden shocks of vehicle body, appearing while the train passes through the track stiffness discontinuity at a high speed. In a hypothetical case when the one-stage vehicle suspension is used, the pantograph base vibrations may increase the number of contact loss events at the catenary–pantograph interface.

The overlapping displacement fields among closely spaced piles termed as pile-to-pile interactions, increase the overall settlement of pile groups. Resultantly, under static loading, these interactions invariably decrease the group stiffness of piles than the collective stiffnesses of corresponding single piles. Whereas under dynamic loading, the group stiffness may increase or decrease than the cumulative stiffnesses of single piles depending on the loading frequency. As soil exhibits nonlinear behaviour under strong motions, in addition to the consideration for soil nonlinearity to obtain the response of piles, nonlinearity generated at the interface between the soil and pile needs to be appropriately considered as it can significantly change the response of piles. To assess the influence of mentioned nonlinearities on the vertical pile-to-pile interaction factors, a scale model test on closely spaced piles is carried out under 1 g conditions. At very low loading amplitudes wherein soil exhibits close-to-elastic behaviour, the experimental interactions are drastically smaller than those obtained from closed-form solutions assuming soil as an elastic material, highlighting the influence of soil-pile interface nonlinearity. Under higher loading amplitudes, results indicate that the increased nonlinearities strengthen the amplitude dependency of interactions. To minutely assess the effects of soil-pile interface nonlinearity on the response, three-dimensional nonlinear finite element modelling (FEM) is carried out. Results obtained from FEM considering soil and soil-pile interface nonlinearities validate the experimental results well. Whereas, assuming soil as an elastic material leads to a noticeable reduction in interactions due to stiffnesses of neighbouring piles; interactions get further reduced when the number of adjacent piles increases.

Autonomous driving is currently an issue of heated debate in automotive engineering. Accurate prediction of the future trajectory of self-driving cars can significantly reduce the occurrence of traffic accidents. However, predicting the future trajectories of vehicles is a challenging task since it is influenced by the interaction behaviours of neighbouring vehicles. This paper proposes a framework that allows for parameter sharing and cross-layer independence, based on a dynamic graph convolutional spatiotemporal network, to study the interactions between vehicles and the temporal dynamics in historical trajectories. By extracting dynamic adjacency matrices from different vehicle interaction features, the model can describe dynamic spatiotemporal relationships and facilitate addressing changes in traffic scenarios. Finally, the proposed model is experimentally compared with existing mainstream trajectory prediction methods using the NGSIM dataset. The results demonstrate that our trajectory prediction model achieved excellent performance in terms of model parameters and prediction accuracy. Compared to the four mainstream models, our model improved accuracy by 35.73%. In addition, we also analyze the relationship between model complexity and efficiency.

This paper presents incorporation of the microphysical piggybacking into the Weather Research and Forecasting (WRF) model. Microphysical piggybacking is to run a single simulation applying two microphysical schemes, the first scheme driving the simulation and the second piggybacking this simulated flow. “Driving the simulation” means that the simulated microphysical processes, affect the cloud buoyancy and thus force the simulated flow. In contrast, the piggybacking variables are advected by the simulated flow and undergo microphysical transformation, but they do not affect the simulated flow (like in prescribed flow—kinematic—simulations). The two sets of variables (driver and piggybacker) include temperature, water vapor mixing ratio, and all microphysical variables. We provide details of implementing piggybacking into the WRF model, illustrate its applications, and demonstrate the benefits of this methodology in two idealized three‐dimensional cases: (a) a squall line case applying two microphysics schemes, the Thompson bulk microphysics scheme and the University of Pécs/NCAR bin (UPNB) scheme. The piggybacking simulations revealed that the microphysics‐dynamics interaction plays a more important role than the pure microphysical size sorting effect in the transition zone formation. (b) A case of daytime shallow‐to‐deep convective development over land. This case uses the UPNB scheme and contrasts convection developing in environments with either pristine or polluted cloud condensation nuclei (CCN). The piggybacking results indicated that the increase of cloud cover and decrease of supersaturation are mainly associated with the microphysical effect of increasing CCN while the change of precipitation on the ground is also influenced by microphysics‐dynamics interactions. Plain Language Summary Weather forecast, especially precipitation and cloud formation prediction is a challenging part of numerical modeling. They have to interpret processes that take place on a wide range of scales in space and time (dynamical and microphysical processes). A novel simulation methodology was implemented in a widely used weather prediction model (WRF). This method helps us to understand better the interactions between different processes. On one hand, this unique technique operates as a normal weather prediction model, which drives the simulation (the different processes interact with each other in this “driver” run). On the other hand, a second package of model variables just follows the flow produced by the “driver,” which is called piggybacking the simulation. This second set of variables just go through the same calculations as in a normal model run, except that they do not have any feedback on the simulated flow. We have tested various cases to investigate the interactions in different types of weather situations. We have found that it's a useful technique to understand the relationship between dynamic and microphysical processes. It also helps us better understand how weather works so we can make more accurate predictions. Key Points Piggybacking methodology implemented into the Weather Research and Forecasting model to examine microphysics‐dynamics interactions Idealized squall line case presented the importance of microphysics‐dynamics interactions in transition zone formation The advantage of the method is the gridpoint‐by‐gridpoint comparison, for example, to state the cloud condensation nuclei effect on microphysics

A stochastic horizontal subgrid‐scale mixing scheme is evaluated in ensemble simulations of a tropical oceanic deep convection case using a horizontal grid spacing (Δh) of 3 km. The stochastic scheme, which perturbs the horizontal mixing coefficient according to a prescribed spatiotemporal autocorrelation scale, is found to generally increase mesoscale organization and convective intensity relative to a non‐stochastic control simulation. Perturbations applied at relatively short autocorrelation scales induce differences relative to the control that are more systematic than those from perturbations applied at relatively long scales that yield more variable outcomes. A simulation with mixing enhanced by a constant factor of 4 significantly increases mesoscale organization and convective intensity, while turning off horizontal subgrid‐scale mixing decreases both. Total rainfall is modulated by a combination of mesoscale organization, areal coverage of convection, and convective intensity. The stochastic simulations tend to behave more similarly to the constant enhanced mixing simulation owing to greater impacts from enhanced mixing as compared to reduced mixing. The impacts of stochastic mixing are robust, ascertained by comparing the stochastic mixing ensembles with a non‐stochastic mixing ensemble that has grid‐scale noise added to the initial thermodynamic field. Compared to radar observations and a higher resolution Δh = 1 km simulation, stochastic mixing seemingly degrades the simulation performance. These results imply that stochastic mixing produces non‐negligible impacts on convective system properties and evolution but does not lead to an improved representation of convective cloud characteristics in the case studied here. Plain Language Summary Regional weather prediction and climate models commonly have horizontal grid lengths of 2–4 km that cannot resolve mixing of air in cumulonimbus clouds with surrounding cooler, drier environmental air, a key process that modulates cloud and storm properties. This study evaluates a method to represent such mixing in models that induces random variability in the magnitude of mixing for a tropical oceanic deep convection case. This approach is found to alter both the intensity and areal coverage of precipitation. As space and time scales of the variability are increased, changes to precipitation coverage and intensity become greater and less systematic relative to a control simulation. Altering mixing also changes the sizes of convective clouds and the degree to which they cluster around one another. Ultimately, the new mixing approach induces variable responses in simulated convective clouds but generally makes them more intense with wider cloud cores, which does not improve upon the control simulation relative to radar observations and a higher resolution simulation. Key Points A stochastic subgrid‐scale mixing scheme was evaluated in a tropical oceanic deep convection case using 3‐km horizontal grid spacing Stochastic mixing modulates the area and intensity of convection, producing more organization and less dilute updrafts relative to a control More systematic impacts on convection properties occur for relatively short spatial and time scale variability of stochastic mixing

We advance interactionist perspectives on how organizational structures emerge in new issue domains. Our study is grounded in field data collected over 18 months at a large biomedical company that sought to become more sustainable. Over that period, some sustainability-related issues became firmly embedded in formal structures and procedures, while others faltered. We identify the quality of situational interactions among organizational members as the engine behind the structuring of organizational sustainability efforts. Successful interactions generated traces of attention, motivation, knowledge, relationships, and resources that linked fleeting interactions to emergent organizational structures. Our findings point to the importance of internal advocates and distributed processes at middle and lower levels for developing organizational structures, and we show that advocates’ interests, commitments, and identities are altered in the course of repeated interactions, as are the political resources available to them. Paying attention to situation-level interactions thus results in a more dynamic view of the emergence of formal structures through political processes. We develop a process model that informs structuration perspectives on organizational change by showing how social interaction dynamics can account for divergent levels of structuring within the same domain. The model also advances political perspectives on organizational change by unpacking the situational underpinnings of advocacy efforts and collective mobilization around issues.

The main attention of this study is focused on the interaction dynamics of breather, and hybrid solitons and breather for an extended generalization of Vakhnenko equation. The general form of the N -order auxiliary function is first derived by the Hirota bilinear method. Then, the N -order solutions can be obtained. When N ≥ 2 , the loop-like breather may emerge by taking the dispersion coefficients as conjugate complex number. The breather is like a magical spring with good elasticity, changeable period and thickness. Furthermore, novel interaction features between breathers, between soliton/solitons and breather/breathers, are observed by visualizing method. The results show that there are abundant dynamics during the interactions, such as elastic collision, phase shift, amplitude amplification, collapse and bulge effect.

The method of inputting the seismic wave determines the accuracy of the simulation of soil-structure dynamic interaction. The wave method is a commonly used approach for seismic wave input, which converts the incident wave into equivalent loads on the cutoff boundaries. The wave method has high precision, but the implementation is complicated, especially for three-dimensional models. By deducing another form of equivalent input seismic loads in the finite element model, a new seismic wave input method is proposed. In the new method, by imposing the displacements of the free wave field on the nodes of the substructure composed of elements that contain artificial boundaries, the equivalent input seismic loads are obtained through dynamic analysis of the substructure. Subsequently, the equivalent input seismic loads are imposed on the artificial boundary nodes to complete the seismic wave input and perform seismic analysis of the soil-structure dynamic interaction model. Compared with the wave method, the new method is simplified by avoiding the complex processes of calculating the equivalent input seismic loads. The validity of the new method is verified by the dynamic analysis numerical examples of the homogeneous and layered half space under vertical and oblique incident seismic waves.