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Nonlinear to Linear Modeling Toward an End-to-End Capability (poster).
Abstract not provided.
Nonlinear to Linear Modeling Toward an End-to-End Capability (slide).
Abstract not provided.
Nonlinear to Linear Modeling Toward an End-to-End Capability (poster).
Abstract not provided.
Nonlinear to Linear Modeling Toward an End-to-End Capability (slide).
Abstract not provided.
Linear sigma model for multiflavor gauge theories
We consider a linear sigma model describing $2N_f^2$ bosons ($\sigma$, a 0 , $\eta$' and π) as an approximate effective theory for a $SU(3)$ local gauge theory with $N_f$ Dirac fermions in the fundamental representation. The model has a renormalizable U(N f ) L Ⓧ invariant part, which has an approximate $O(2N_f^2)$ symmetry, and two additional terms, one describing the effects of a $SU(N_f)_V$ invariant mass term and the other the effects of the axial anomaly. We calculate the spectrum for arbitrary $N_f$. Using preliminary and published lattice results from the LatKMI collaboration, we found combinations of the masses that vary slowly with the explicit chiral symmetry breaking and $N_f$. This suggests that the anomaly term plays a leading role in the mass spectrum and that simple formulas such as $M_\sigma^2\simeq (2/N_f-C_\sigma)M_{\eta'}^2$ should apply in the chiral limit. Lattice measurements of $M_{\eta'}^2$ and of approximate constants such as $C_\sigma$ could help locating the boundary of the conformal window. Here, we show that our calculation can be adapted for arbitrary representations of the gauge group and in particular to the minimal model with two sextets, where similar patterns are likely to apply.
Morphological traits for allometric scaling of the European Sea Bass Dicentrarchus labrax (Linnaeus, 1758) from Southern Portugal population
Abstract The present study aimed to determine the allometric scaling among a selection of morphological traits in European sea bass ( Dicentrarchus labrax ) to estimate fish body weight. A set of morphological traits (fish body weight, length, height, and width) were directly measured in 146 fish of a recirculating aquaculture system, with body weights ranging from 17.11 to 652.21 g. In addition, a collection of digital imagery of each anesthetized fish from the side and top views were used to estimate other traits (indirect measures). Multiple regression analysis and regression coefficients were calculated using all possible combinations of biometric data (predictors) to estimate fish body weight, applying different numerical fitting models (linear, log‐linear, quadratic, exponential). The results showed that the best combination of traits for estimating fish body weight were fish body width, length and height, collected from direct measure ( R 2 = 0.995), for a log‐linear model fitting, which revealed more accurate determinations than the most commonly used length–weight relationship. Nevertheless, other combinations of morphological traits and fitting models were also found to be suitable in successfully predict fish body weight, with variability ranging between 92.5% and 98.5%. For indirect measures, the best predictor was a combination of traits from top view (width, eye distance and area without fins) fitted with a log‐linear function. These results comprise a relevant baseline in supporting the high potential of noninvasive methods to accurately follow the growth of European sea bass juveniles, recurring to imagery analysis of anesthetized fish. It has major potential applications in feeding consumption trials and fish growth models, as it allows for continuously following up fish growth under different experimental conditions without therein distress derived from manipulation.
Analytical model for quasi-linear flow response to resonant magnetic perturbation in resistive-inertial and viscous-resistive regimes
In this work, a quasi-linear model for plasma flow response to the resonant magnetic perturbation (RMP) in a tokamak has been rigorously developed in the resistive-inertial (RI) and viscousresistive (VR) regimes purely from the two-field reduced MHD model. Models for plasma response to RMP are commonly composed of equations for the resonant magnetic field response (i.e. the magnetic island) and the torque balance of plasma flow. However, in previous plasma response models, the magnetic island and the torque balance equations are often derived separately from reduced MHD and full MHD equations, respectively. By contrast, in this work we derive both the magnetic island response and the torque balance equations in a quasi-linear model for plasma flow response entirely from a set of two-field reduced MHD equations. Such a quasi-linear model can recover previous plasma flow response models within certain limits and approximations. Furthermore, the physical origins of quasi-linear forces and moments in the flow response equation are also accurately calculated and clarified self-consistently.
Quantifying improvements in water-stable aggregation caused by corn stover retention
Not provided.
Hyperplane decision trees as piecewise linear surrogate models for chemical process design
Recent trends in chemical engineering research point towards an increasing reliance on data-driven modeling approaches. Neural networks, for instance, have proven to be accurate when data is plentiful and high-dimensional, but in many cases, they require computationally-intensive training procedures. Here, in this work, we describe hyperplane decision trees (HT) as a highly expressive and low-compute machine learning model architecture. These models are locally linear and have linear decision boundaries, resulting in a piecewise linear model of the data. This property allows them to be converted into mixed-integer linear constraints which can be globally optimized. Our open-source PyTorch implementation of this method is a fast, flexible, and accessible way to build accurate piecewise linear models of data.
Linear relationships for modeling CO 2 absorption in aqueous alkanolamine solutions in a thermodynamically consistent way
A thermodynamically consistent model for the carbon dioxide (CO 2 ) absorption in aqueous alkanolamine system is of great importance in the research and development of a CO 2 capture process. To facilitate the development of thermodynamic models, linear Gibbs free energy, enthalpy, and heat capacity relationships using well-known amines as reference are used to correlate the standard reference state properties of ionic species with those of molecular species in the electrolyte system, which has been approved to provide a reliable and consistent way to estimate required parameters when there is minimal or no appropriate experimental data available. Here, the proposed relationships have been applied to the development of an electrolyte nonrandom two liquid (NRTL) activity coefficient model for CO 2 absorption in aqueous 1-amino-2-propanol (A2P) solution, as an example to demonstration the methodology. With limited vapor–liquid equilibrium data and other thermodynamic properties, the parameters in the electrolyte NRTL model are identified with good accuracy.
Topology of the $O(3)$ non-linear sigma model under the gradient flow
The $O(3)$ non-linear sigma model (NLSM) is a prototypical field theory for QCD and ferromagnetism, featuring topological qualities. Though the topological susceptibility should vanish in physical theories, lattice simulations of the NLSM find that the topological susceptibility diverges in the continuum limit. We introduce a $\theta$-term and analyze the topological charge as a function of $\theta$ under the gradient flow. Our results show that divergence persists in the presence of the flow, even at non-zero $\theta$.
A new approach to modeling temperature-related mortality: Non-linear autoregressive models with exogenous input
Highlights: • NARX models are established in other environmental research, but are unused in temperature-related mortality research. • NARX models do not assume any particular shape to the temperature-mortality association. • NARX models are cross-validated and are easily modified to take on real-time, multi-step-ahead predictions and hindcasts. • Year-round, daily mortality in 41 different US cities was modeled using an ensemble of NARX models in each location. • Models show skill everywhere. Results vary spatially and seasonally, improving upon a persistence model by 17% points. • When compared with distributed lag non-linear models, the output time series are largely similar Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975–2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6–24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34–71%). Mortality responded acutely to hot summer days, peaking at 0–2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2–4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences.
A Method to Capture Macroslip at Bolted Interfaces [PowerPoint]
Relative motion at bolted connections can occur for large shock loads as the internal shear force in the bolted connection overcomes the frictional resistive force. This macroslip in a structure dissipates energy and reduces the response of the components above the bolted connection. There is a need to be able to capture macroslip behavior in a structural dynamics model. A linear model and many nonlinear models are not able to predict marcoslip effectively. The proposed method to capture macroslip is to use the multi-body dynamics code ADAMS to model joints with 3-D contact at the bolted interfaces. This model includes both static and dynamic friction. The joints are preloaded and the pinning effect when a bolt shank impacts a through hole inside diameter is captured. Substructure representations of the components are included to account for component flexibility and dynamics. This method was applied to a simplified model of an aerospace structure and validation experiments were performed to test the adequacy of the method.
A Bayesian Approach to Regional Decadal Predictability: Sparse Parameter Estimation in High-Dimensional Linear Inverse Models of High-Latitude Sea Surface Temperature Variability
Stochastic reduced models are an important tool in climate systems whose many spatial and temporal scales cannot be fully discretized or underlying physics may not be fully accounted for. One form of reduced model, the linear inverse model (LIM), has been widely used for regional climate predictability studies—typically focusing more on tropical or midlatitude studies. However, most LIM fitting techniques rely on point estimation techniques deriving from fluctuation–dissipation theory. In this methodological study we explore the use of Bayesian inference techniques for LIM parameter estimation of sea surface temperature (SST), to quantify the skillful decadal predictability of Bayesian LIM models at high latitudes. We show that Bayesian methods, when compared to traditional point estimation methods for LIM-type models, provide better calibrated probabilistic skill, while simultaneously providing better point estimates due to the regularization effect of the prior distribution in high-dimensional problems. We compare the effect of several priors, as well as maximum likelihood estimates, on 1) estimating parameter values on a perfect model experiment and 2) producing calibrated 1-yr SST anomaly forecast distributions using a preindustrial control run of the Community Earth System Model (CESM). Finally, we employ a host of probabilistic skill metrics to determine the extent to which an LIM can forecast SST anomalies at high latitudes. We find that the choice of prior distribution has an appreciable impact on estimation outcomes, and priors that emphasize physically relevant properties enhance the model’s ability to capture variability of SST anomalies.
Ordinary and exotic mesons in the extended Linear Sigma Model
The extended Linear Sigma Model (eLSM) is a hadronic model based on the global symmetries of QCD and the corresponding explicit, anomalous, and spontaneous breaking patterns. In its basic three-flavor form, its mesonic part contains the dilaton/glueball as well as the nonets of pseudoscalar, scalar, vector, and axial–vector mesons, thus chiral symmetry is linearly realized. In the chiral limit and neglecting the chiral anomaly, only one term – within the dilaton potential – breaks dilatation invariance, and all terms are chirally symmetric. Spontaneous symmetry breaking is implemented by a generalization of the Mexican-hat potential, with explicit symmetry breaking responsible for its tilting. The overall mesonic phenomenology up to ~2 GeV is in agreement with the PDG compilation of masses and partial and total decay widths. The eLSM was enlarged in a straightforward way to include other conventional quark–antiquark nonets (pseudovector and orbitally excited vector mesons, tensor and axial-tensor mesons, radially excited (pseudo)scalar mesons, etc.), as well as two nonets of hybrid mesons, the lightest one with exotic quantum numbers J $\mathscr{PC}$ = 1 -+ not allowed for $\bar{q}q$ objects, such as the resonance π 1 (1600) and the recently discovered η 1 (1855) . In doing so, different types of chiral multiplets are introduced: heterochiral and homochiral multiplets, which differ in the way they transform under chiral transformations. Moreover, besides the scalar glueball that is present from the beginning as dilaton, other glueballs, the tensor, the pseudoscalar and the vector glueballs were coupled to the eLSM: the scalar resonance ƒ 0 (1710) turns out to be mostly gluonic, the tensor glueball couples strongly to vector mesons, and the pseudoscalar glueball couples sizably to $ππη'$ and can be assigned to X (2370) or X (2600). In all cases above, masses and decays can be analyzed allowing for a better understanding of both conventional and non-conventional mesons: whenever data are available, a comparison is performed and, when this is not the case, predictions of decay widths and decay ratios are outlined. The eLSM contains chiral partners on an equal footing and is therefore well suited for studies of chiral symmetry restoration at nonzero temperature and densities: this is done by coupling it to the Polyakov loop. Finally, the QCD phase diagram and the location of the critical endpoint were investigated within this framework.
Integrating proteomic or transcriptomic data into metabolic models using linear bound flux balance analysis
Abstract Motivation Transcriptomics and proteomics data have been integrated into constraint-based models to influence flux predictions. However, it has been reported recently for Escherichia coli and Saccharomyces cerevisiae, that model predictions from parsimonious flux balance analysis (pFBA), which does not use expression data, are as good or better than predictions from various algorithms that integrate transcriptomics or proteomics data into constraint-based models. Results In this paper, we describe a novel constraint-based method called Linear Bound Flux Balance Analysis (LBFBA), which uses expression data (either transcriptomic or proteomic) to predict metabolic fluxes. The method uses expression data to place soft constraints on individual fluxes, which can be violated. Parameters in the soft constraints are first estimated from a training expression and flux dataset before being used to predict fluxes from expression data in other conditions. We applied LBFBA to E.coli and S.cerevisiae datasets and found that LBFBA predictions were more accurate than pFBA predictions, with average normalized errors roughly half of those from pFBA. For the first time, we demonstrate a computational method that integrates expression data into constraint-based models and improves quantitative flux predictions over pFBA. Availability and implementation Code is available in the Supplementary data available at Bioinformatics online. Supplementary information Supplementary data are available at Bioinformatics online.
A Relaxed PV Bus Model in Linear Power Flow
The emerging multimodal active energy resource units make it necessary to introduce constant voltage amplitude buses (PV bus) in the operation of modern distribution systems. To enable the analysis of PV bus using linear power flow models, this letter proposes a relaxed PV bus model for solving power flow related problems using an indirect modeling approach. The developed PV bus model can be applied to linear power flow models in both rectangular and polar coordinates. Besides, the approximation error of the relaxed PV bus model can also be controlled and quantified. Lastly, the proposed relaxed PV bus model is validated using different test systems. According to the numerical studies, the maximum error on the PV bus approximation in all case studies is only 0.61%, and the maximum error of the supporting power at the PV buses is 6.5%.