Primitive Model Research Articles

How to Tell Easy from Hard: Complexities of Conjunctive Query Entailment in Extensions of ALC

It is commonly known that the conjunctive query entailment problem for certain extensions of (the well-known ontology language) ALC is computationally harder than their knowledge base satisfiability problem while for others the complexities coincide, both under the standard and the finite-model semantics. We expose a uniform principle behind this divide by identifying a wide class of (finitely) locally-forward description logics, for which we prove that (finite) query entailment problem can be solved by a reduction to exponentially many calls of the (finite) knowledge base satisfiability problem. Consequently, our algorithm yields tight ExpTime upper bounds for locally-forward logics with ExpTime-complete knowledge base satisfiability problem, including logics between ALC and µALCHbregQ (and more), as well as ALCSCC with global cardinality constraints, for which the complexity of querying remained open. Moreover, to make our technique applicable in future research, we provide easy-to-check sufficient conditions for a logic to be locally-forward based several versions of the on model-theoretic notion of unravellings. Together with existing results, this provides a nearly complete classification of the “benign” vs. “malign” primitive modelling features extending ALC, missing out only the Self operator. We then show a rather counter-intuitive result, namely that the conjunctive entailment problem for ALCSelf is exponentially harder than for ALC. This places the seemingly innocuous Self operator among the “malign” modelling features, like inverses, transitivity or nominals.

A primitive-based representation of dance: modulations by experience and perceptual validity.

The generation of complex movements such as dance might be possible due to the utilization of movement building blocks, i.e., movement primitives. However, it is largely unexplored how the temporal structure of a movement sequence and the recruitment of these primitives change with experience. Therefore, we obtained a representation of primitives with the temporal movement primitive model from the motion capture data of dancers with varying experiences, both for improvised and choreographed movements (elements from contemporary/modern/jazz) with different qualitative expressions. We analyzed differences between movement conditions regarding the number of temporal segments and the number of primitives, as well as their association with dance experience. Especially for the choreography with a neutral expression, the results indicate a negative association between experience and the number of segments and a positive association between experience and the number of primitives. The variation in the recruitment of these primitives suggests an increased consistency of modular control with experience, particularly for improvised dance. A prerequisite for the meaningful interpretation of these results regarding human movement production is that the model can generate perceptually valid dance movements. This was confirmed in a subsequent experiment, although the validity was slightly impaired for improvised movements. Overall, the results of the choreographed movement sequences suggest that experience is associated with an increase in motor repertoire that might facilitate fewer and longer temporal segments.NEW & NOTEWORTHY This study demonstrates that a temporal movement primitive model, trained with movements performed by dancers with different levels of experience, is able to generate natural-looking dance movements. The results suggest that motor experience in dance is associated not only with fewer temporal segments but also with an increase in the number of underlying movement building blocks. The recruitment of these primitives, which might be used to simplify movement production, additionally seems to become more consistent with experience.

Grand-canonical molecular dynamics simulations powered by a hybrid 4D nonequilibrium MD/MC method: Implementation in LAMMPS and applications to electrolyte solutions.

Molecular simulations in an open environment, involving ion exchange, are necessary to study various systems, from biosystems to confined electrolytes. However, grand-canonical simulations are often computationally demanding in condensed phases. A promising method [L. Belloni, J. Chem. Phys. 151, 021101 (2019)], one of the hybrid nonequilibrium molecular dynamics/Monte Carlo algorithms, was recently developed, which enables efficient computation of fluctuating number or charge density in dense fluids or ionic solutions. This method facilitates the exchange through an auxiliary dimension, orthogonal to all physical dimensions, by reducing initial steric and electrostatic clashes in three-dimensional systems. Here, we report the implementation of the method in LAMMPS with a Python interface, allowing facile access to grand-canonical molecular dynamics simulations with massively parallelized computation. We validate our implementation with two electrolytes, including a model Lennard-Jones electrolyte similar to a restricted primitive model and aqueous solutions. We find that electrostatic interactions play a crucial role in the overall efficiency due to their long-range nature, particularly for water or ion-pair exchange in aqueous solutions. With properly screened electrostatic interactions and bias-based methods, our approach enhances the efficiency of salt-pair exchange in Lennard-Jones electrolytes by approximately four orders of magnitude, compared to conventional grand-canonical Monte Carlo. Furthermore, the acceptance rate of NaCl-pair exchange in aqueous solutions at moderate concentrations reaches about 3% at the maximum efficiency.

Eddy‐Mean Flow Interaction With a Multiple Scale Quasi Geostrophic Model

AbstractParameterization of mesoscale eddies in coarse resolution ocean models is necessary to include the effect of eddies on the large‐scale oceanic circulation. We propose to use a multiple‐scale Quasi‐Geostrophic (MSQG) model to capture the eddy dynamics that develop in response to a prescribed large‐scale flow. The MSQG model consists in extending the traditional quasi geostrophic (QG) dynamics to include the effects of a variable Coriolis parameter and variable background stratification. Solutions to this MSQG equation are computed numerically and compared to a full primitive equation model. The large‐scale flow field permits baroclinically unstable QG waves to grow. These instabilities saturate due to non‐linearities and a filtering method is applied to remove large‐scale structures that develop due to the upscale cascade. The resulting eddy field represents a dynamically consistent response to the prescribed background flow, and can be used to rectify the large‐scale dynamics. Comparisons between Gent‐McWilliams eddy parameterization and the present solutions show large regions of agreement, while also indicating areas where the eddies feed back onto the large scale in a manner that the Gent‐McWilliams parameterization cannot capture. Also of interest is the time variability of the eddy feedback which can be used to build stochastic eddy parameterizations.

Subgrid Parameterizations of Ocean Mesoscale Eddies Based on Germano Decomposition

AbstractOcean models at intermediate resolution (1/4°), which partially resolve mesoscale eddies, can be seen as Large eddy simulations of the primitive equations, in which the effect of unresolved eddies must be parameterized. In this work, we propose new subgrid models that are consistent with the physics of two‐dimensional flows. We analyze subgrid fluxes in barotropic decaying turbulence using Germano (1986, https://doi.org/10.1063/1.865568) decomposition. We show that Leonard and Cross stresses are responsible for the enstrophy dissipation, while the Reynolds stress is responsible for additional kinetic energy (KE) backscatter. We utilize these findings to propose a new model, consisting of three parts, that is compared to a baseline dynamic Smagorinsky model. The three‐component model accurately simulates the spectral transfer of energy and enstrophy and improves the representation of KE spectrum, resolved KE and enstrophy decay in a posteriori experiments. The backscattering component of the new model (Reynolds stress) is implemented both in quasi‐geostrophic and primitive equation ocean models and improves statistical characteristics, such as the vertical profile of eddy KE, meridional overturning circulation and cascades of kinetic and potential energy.

Phase transitions of ionic fluids in nanoporous electrodes

In this work, we utilise grand canonical Metropolis Monte Carlo simulations, to establish pore-induced freezing of restricted primitive model fluids. A planar pore model is utilised, with walls that are initially neutral, and either non-conducting or perfectly conducting. The phase of the confined electrolyte (solid/fluid) displays an oscillatory dependence on surface separation, in narrow pores. Conditions are chosen so that the bulk is composed of a stable fluid electrolyte. The tendency for the electrolyte to freeze in narrow pores is somewhat stronger in systems with non-conducting walls. We also demonstrate that an applied potential will, above a threshold value, melt a frozen electrolyte. In these cases, the capacitance, as measured by the average surface charge density divided by the applied potential, will be almost vanishing if the applied potential is below this threshold value. We do not see any evidence for a “superionic fluid”, which has been hypothesised to generate a strong capacitance in narrow pores, due to an efficient screening of like-charge repulsions by image charges.Graphic abstract

Application of the symmetric Poisson-Boltzmann theory to equilibrium thermodynamic properties of primitive model electrolyte mixtures

A symmetric Poisson-Boltzmann theory is applied to a primitive model electrolyte solution containing 7 different ion species. Only 7 coupled equations need to be solved for the mean electrostatic potential around the ions once the uncharged hard sphere radial distribution function is specified. Comparison is made with the individual activity and osmotic coefficients of a simulation study of a sea water mixture. Good overall agreement is found with the simulation results.

Operation Optimization Adapting to Active Distribution Networks Under Three-Phase Unbalanced Conditions: Parametric Linear Relaxation Programming

Asymmetric phase distributed generation, which is widely used in distribution systems, aggravates the severity of the intrinsic three-phase unbalanced condition of distribution systems. To overcome these problems, operation optimization formulas of the three-phase unbalanced active distribution network (ADN) are established considering soft open point (SOP) and belong to a nonconvex quadratic programming (NQP) problem. However, in conventional methods, either approximations that are not valid for an unbalanced distribution system are used or convex relaxation methods are applied to primitive nonlinear optimization models. However, these methods do not guarantee a feasible optimization solution. Therefore, determining the solution to the NQP problem is challenging. In this article, we propose a parametric linear relaxation algorithm to obtain the optimal feasible solution by solving the operation optimization problem of the three-phase unbalanced ADN with the SOP. The parametric linear relaxation technique is constructed based on the properties of quadratic functions and the mean value theorem, and the NQP is converted to a series of parametric linear relaxation programming problems. The results revealed that the proposed algorithm can achieve the optimal and feasible solution and considerably reduce the computation time compared with an equivalent NLP optimization model for the same distribution system.

Molecular thermodynamic model for O-(2-hydroxyethyl) cellulose (HEC) intrinsic viscosity

We present a coarse-grained model for O-(2-hydroxyethyl) cellulose (HEC) in order to obtain its intrinsic viscosity by applying three theoretical models and Monte Carlo computer simulations (MC). Results are based on a previous experimental study for this substance at neutral conditions and at pH 4.5, at temperatures of 25∘C and 40∘C (Martínez-Richa, 2012 [3]). The MC primitive model consists of spherocylinder particles whose aspect ratio was determined for 13 samples of HEC of different molecular weights. We consider the Onsager, Simha and Kuhn-Kuhn models, in order to relate the intrinsic viscosity with the aspect ratio of the particles.

Arrested spinodal decomposition of the screened symmetric restricted primitive model.

Amorphous solids, such as glasses and gels, arise as the asymptotic limit of non-equilibrium and irreversible relaxation aging processes. These amorphous solids form when the system is suddenly and deeply quenched in the dynamic arrest region. We use the non-equilibrium self-consistent generalized Langevin equation (NE-SCGLE) theory to investigate the formation of such structures via arrested spinodal decomposition in the screened symmetric restricted primitive model. We propose a direct correlation function that allows us to derive an expression for the functional derivative of the chemical potential, which serves as the necessary input in the NE-SCGLE theory. By analyzing the asymptotic localization length and the asymptotic characteristic length, we identify different classes of dynamic arrest states as a function of the density and the final quench temperature. The system features simultaneously attractive and repulsive interactions, resulting in different arrested regions in the non-equilibrium phase diagram for a given screened parameter: (i) ionic glasses, (ii) electrostatic gels, and (iii) attractive glasses. Finally, by collapsing the asymptotic effective structure factors at a typical fractal dimension of df = 3, we confirm the formation of gels slightly above the glass-gel transition in the arrested phase diagram.

Anisotropic diffusion filters for flow-dependent variational data assimilation of sea surface temperature

It is well known that the background error covariance matrix in the data assimilation is anisotropic and varied with the flow field, that is, flow-dependent. However, it is generally assumed to be isotropic and static for ease of implementation. Therefore, to enhance the quality of the background error covariance, two easy-to-implement, flow-dependent three-dimensional variational analysis (3DVar) schemes, the advection–diffusion filter (ADF) and the Smagorinsky diffusion filter (SDF), are tentatively proposed. These serve as alternative schemes for the traditional methods that are based on the isotropic assumption. First, a correctness test of the gradient for the ADF is performed to verify the validity of the adjoint code. Then, the response analysis for the 1D advection–diffusion​ equation is investigated to validate the anisotropic characteristic of the ADF with various flow velocities. Second, the two flow-dependent 3DVar schemes are verified via single-observation assimilation experiments. Compared to the existing diffusion filter with a static diffusion coefficient, both new schemes show a flow-dependent capability in the assimilation experiment because they successfully spread the observational information anisotropically. Finally, a retroactive real-time forecast experiment framework based on a 3D baroclinic primitive equation ocean model is used to investigate the effects of the ADF/SDF on the analysis and forecast accuracy of sea surface temperature (SST). While the traditional variational assimilation method with an isotropic diffusion filter generally reduces initial field errors, both the ADF and SDF can further enhance the accuracy of the initial fields in strong current areas. In addition, better forecast skills can be obtained with the two flow-dependent schemes in the East China Sea.

Nested Monte Carlo simulation of ionic systems with the primitive model using Debye-Hückel (DH) potential as an importance function and optimizing the DH potential with Kullback-Leibler divergence minimization

Nested Monte Carlo simulation of ionic systems with the primitive model using Debye-Hückel (DH) potential as an importance function and optimizing the DH potential with Kullback-Leibler divergence minimization

Intelligent generation of combat simulation scenarios based on UML diagram recognition

The traditional generation of combat simulation scenarios often requires a manual understanding of conceptual scenarios and transformation into simulation scenarios. This method has the problems of long development time and high development threshold. Conceptual scenarios are usually visual Unified Modeling Language (UML) diagrams, so we can use artificial intelligence technology to extract key semantics from them, and automatically map the extracted semantics to simulation scenarios. This method is called the intelligent generation of combat simulation scenarios. To extract the key semantics from conceptual scenarios in UML form, we propose the UML diagram recognition method based on Keypoint Region-based Convolutional Neural Network (R-CNN). This method includes three parts: image character recognizer, primitive object detector, and image semantic extractor. First, we use optical character recognition (OCR) technology to achieve image character recognition. Second, we manually annotate the primitive target-detection dataset and propose a new primitive target-detection model—Keypoint R-CNN. This model considers the direction of connecting lines and realizes the detection of symbols and connecting lines. Third, we propose a targeted combined primitive detection and primitive relationship extraction method to extract the high-level semantics of UML diagrams. Then, we carried out experiments and evaluations on the self-made dataset. Compared with other methods, the F1 score of our method is improved by about 7%, and the Jaccard coefficient is improved by about 10%. Finally, we use a case study to implement the intelligent generation process of combat simulation scenarios using the UML recognition method we proposed. This case shows the operability and efficiency of our method. Our method greatly reduces the labor cost and development threshold of combat simulation scenario generation and improves the development efficiency of the combat simulation system.

Effect of solvent on electric double layer capacitance and over screening inside spherical cavity by density functional theory

In this article, the solvent primitive model (SPM) and the restricted primitive model (RPM) in the framework of modified fundamental measure theory are compared for the prediction of the electric double layer (EDL) at the contact surface of the concave wall of a charged spherical cavity. The structure of EDL, EDL capacitance, Stern and diffuse contributions, zeta potential, and over screening phenomenon are predicted and compared with the two models. Our results show that considering the solvent molecules as hard spheres according to SPM leads to an appearance of layering structure and oscillatory behavior of density profiles inside the cavity. Also, the amount of ion adsorption in SPM is greater than that of RPM at the same condition. As a result of more counter-ion adsorption in comparison to co-ion adsorption, the total net charge inside the cavity increases. That way, the predicted amount of EDL capacitance and its diffuse contribution by SPM are greater than the corresponding values by RPM. Note that the Stern contribution to the capacitance has the same value in both models at fixed and equal sizes of ions and solvent molecules. Finally, the mean electrostatic potential (MEP) in the cavity, which is strongly affected by more counter-ion adsorption in SPM, shows evidence of an over screening phenomenon in the stern region at a lower concentration in comparison to RPM. Also, the slope of MEP is higher for SPM (in the Stern region), which leads to lower absolute values for its zeta potential.

SUPPORTING CONNECTIONS BETWEEN FIRST MOTHERS AND THEIR UNBORNS: AN ANTIDOTE TO DISSOCIATION?

The old saw in child welfare circles— “A baby is the only living thing without a past, only a future”—was always self-serving. Now we know it was always patently false. The new science in infant mental health and prenatal psychology makes it possible to reconsider the matter of various risks in prenatal life. We are then released to wonder what life is like in there, for example, for a child scheduled to be surrendered for adoption. While we don’t yet proclaim to have special avenues into the mental life of prenates—or even systematic means to investigate it—evidence mounts that there IS such life, that it’s responsive to the mother’s mental life, and that such early and interactive mental life is of consequence for the baby’s later development. While there is still much to learn, we can surely say that it’s incomprehensible that the first nine months of the life of a child whose mom does not intend to keep him is anything but innocuous. What if we considered these questions non-rhetorically and non-judgmentally: “How could a woman ever be expected to forget a baby she had carried for nine months? What would it take to ‘move on’ from the experience…” (Glaser, 2021, p. 4). Might we even conclude that we’re disrespecting first mothers to assume that they pay no attention to what is happening, and have no psychological response to it? And if moms do have a response, is it detectable by the babies within? Finally, are early and primitive working models of self and other created in utero? Might these early working models be brought by the adoptee to his relationship with his adoptive mother? If so, to what effect? This paper focuses on one aspect of the mental life of a mother who intends to surrender her child for adoption: the possibility that she may, as a defense, dissociate—a kind of withdrawal from her own experience—and that such withdrawal may be experienced by the baby as maternal absence and rejection. We will try to show what this experience is like for the unborn, what defenses may be erected, and how such early experience may shape later relationships.

Friction heating and stress-strain state of ventilated disc brakes

This paper describes the study of ventilated car disc brakes stress-strain conditions and friction under the pressure using the ANSYS environment. Such influencing factors are taken into account in the course of research as angular speed value, the pressure of the pads on the disk, the nature of the load application, convection, thermal expansion, etc. Computer modelling of the stress field and the transient thermal field in the area of contact between the pads and the disk is provided by the method of sequential thermostructural communication of the intermediate states of the brake model directly in the ANSYS Coupled Field Transient environment. Besides, the ANSYS calculations were also performed based on the primitive assembly model of two steel blocks (the discrepancy was less than 3%) to determine the identity of the theoretical knowledge about the heating of bodies as a result of the work to overcome frictional forces. Finally, a high level of calculation results convergence by analytical formulas and computer modelling was established. Since this approach justified itself, its principles were taken as a basis in the calculations of ventilated disc brakes of cars, which significantly facilitates their application, knowing the area of the active part of the disc (the rest of the boundary conditions are typical and correspond to the normal operating modes of the vehicle).

Confined Electrolytes Show Bulk Dynamics Modulated by Hydrodynamic Couplings with the Walls

We use numerical simulations at the mesoscopic scale, namely, multiparticle collision dynamics (MPCD), to investigate the properties of electrolyte solutions in a charged slit pore. The solution is described within the primitive model of electrolytes, where ions are charged hard spheres embedded in a dielectric medium. Hydrodynamic couplings between ions and with the charged walls are precisely accounted for by the MPCD algorithm. We show that the dynamic properties of ions in this situation strongly differ from the behavior at infinite dilution (ideal case), contrary to what is usually assumed in the usual Poisson-Nernst-Planck description of this kind of systems. As a consequence of confinement, the diffusion coefficients of ions unexpectedly increase with the average ionic density in the systems. This is due to a decrease of the proportion of ions that are slowed down by the wall. Moreover, nonequilibrium simulations are used to estimate the electrical conductivity of these confined electrolytes. We show that the simulation results can be accounted for quantitatively by combining bulk descriptions of the electrical conductivity of electrolytes with a simple description of the hydrodynamics of ions in a slit pore.

Algebraic infection of charge correlations at the ionic critical point

AbstractWe consider a classical two‐component plasma analog of the restricted primitive model of electrolyte, where the hard‐core interaction is replaced by a soft differentiable potential. Within the Born–Green–Yvon hierarchy for the equilibrium distribution functions, we shed light on an infection mechanism where the charge correlations are polluted by the density correlations at the critical point of the liquid–gas transition. This should imply an algebraic decay of critical charge correlations, consistently with the violation of the Stillinger–Lovett sum rule observed in Monte Carlo simulations. In connection with this effect of density correlations on the charge correlations, we also provide a discussion on an extended version of the Debye–Hückel theory that calculates charge correlations in the restricted primitive model for an externally imposed spatial fluctuation of charges.PACS Numbers: 05.30.‐d, 05.70.Ce, 52.25.Kn.

Seasonal Variability of the Ocean Circulation in Queen Charlotte Strait, British Columbia

ABSTRACT Queen Charlotte Strait (QCST) is a large marine area separating northern Vancouver Island from the British Columbia mainland. Although it is an important waterway connecting Queen Charlotte Sound with Johnstone Strait and eventually the Strait of Georgia, complex dynamics in QCST, including tides, surface winds, river runoffs, and coastal currents, are yet understudied. In this numerical study, a high-resolution (up to approximately 10 m in the horizontal) model was developed for QCST and adjacent areas to investigate the circulation in the strait, using the unstructured grid, Finite-Volume, primitive equation Community Ocean Model (FVCOM). Two operational larger-scale models force the FVCOM model: the Coastal Ice Ocean Prediction System for the West coast (CIOPS-W) at the open ocean (one-way nesting), and the High-Resolution Deterministic Prediction System (HRDPS) at surface boundaries. Prevailing winds over QCST are southeasterly in winter and northwesterly in summer and the model demonstrated the capacity to reproduce the ocean circulation regimes in both seasons of 2019. Complex empirical orthogonal function (CEOF) and correlation analyses determined that surface winds play a dominant role on the variability of residual flow in QCST in the winter, while river runoffs strongly influence the variability during the summer. The analyses highlight that QCST is a complex estuarine system with significant seasonal variability rather than just a simple conduit between Queen Charlotte Sound and the Strait of Georgia.

Day-ahead dispatch of novel battery charging and swapping station based on distributionally robust optimization

Battery swapping station (BSS) is a promising way to support the proliferation of electric vehicles (EVs). This paper upgrades BSS to a novel battery charging and swapping station (NBCSS) with wind power, photovoltaic power, energy storage and gas turbine integrated, which is equivalent to a microgrid with flexibility further enhanced. An integrated model of batteries based on the state of charge (SOC) interval is put forward to release the complexity of separate modeling of each battery, where the charging and discharging priorities are embedded. Then, a distributionally robust optimization (DRO) model for the day-ahead dispatch of NBCSS is presented considering the uncertainties of wind power, photovoltaic power and load. This model minimizes the worst-case expected total cost over a family of distributions characterized by an ambiguity set. By employing the affine decision rules, the primitive two-stage DRO model can be eventually reformulated as a tractable mixed-integer linear program. Finally, case studies are conducted to demonstrate the effectiveness of the proposed method. The results show that the charging and discharging freedom of batteries enhances the operational flexibility of NBCSS and reduces the 22.9 % of the total cost. And the proposed method has the superiority of decision-making over the deterministic and adaptive robust optimization ones.