Choice Of Parameters Research Articles

Sensitivity-Aware Density Estimation in Multiple Dimensions.

We formulate an optimization problem to estimate probability densities in the context of multidimensional problems that are sampled with uneven probability. It considers detector sensitivity as an heterogeneous density and takes advantage of the computational speed and flexible boundary conditions offered by splines on a grid. We choose to regularize the Hessian of the spline via the nuclear norm to promote sparsity. As a result, the method is spatially adaptive and stable against the choice of the regularization parameter, which plays the role of the bandwidth. We test our computational pipeline on standard densities and provide software. We also present a new approach to PET rebinning as an application of our framework.

Influence of Gas Oven Temperature on the Microstructure and Extrudability of Al-Mg-Si Aluminium Alloys

The microstructure of Al-Mg-Si aluminium alloys is well characterised when it comes to the initial state of the cast billet or the artificially aged profile. Typically, a gas oven coupled with an induction oven is used to preheat the billets before extrusion. Preheating reduces the flow stress of the aluminium and dissolves the phases that have precipitated during cooling after homogenisation. In this paper, the influence of the gas oven temperature on the partial or complete dissolution of the precipitated phases will be shown. In addition, the negative effect of a wrong choice of parameters on the volume content of the MgSi phases will be shown.

Effect of Einasto Spike on the Gravitationally Decoupled Self-gravitating Dark Matter Halos

Abstract In this work, we consider the possibility of constructing
gravitationally bound, charged dark matter halos motivated by
Einasto density model. This model generalizes the concept of
charged, non-commutative mini-compact objects by including dark
matter as its primary component through the principles of the
minimal geometric deformation strategy. We point out that the
coupling of the non-commutativity inspired Einasto spike density
model with a non-isotropic fluid, within the context of gravitational
decoupling allows the formation of different minimally deformed
dark matter halos, corresponding to each value of the deformation
parameter. We assume the Tolman-Kuchowicz cosmological model as a
seed metric within the geometric deformation scheme to
initially generate an electrically charged isotropic solution.
Subsequently, we employ a density-like constraint to produce a
second anisotropic charged dark matter stellar model via the
Einasto density profile. A complete graphical analysis of the
structural variables and stability of both models indicate that,
for the considered choice of parameters, both cosmological models
are well-behaved, exhibiting expected physical behavior.

A Space Distributed Model and Its Application for Modeling the COVID-19 Pandemic in Ukraine

A space distributed model based on reaction–diffusion equations, which was previously developed, is generalized and applied to COVID-19 pandemic modeling in Ukraine. Theoretical analysis and a wide range of numerical simulations demonstrate that the model adequately describes the second wave of the COVID-19 pandemic in Ukraine. In particular, comparison of the numerical results obtained with the official data shows that the model produces very plausible total numbers of the COVID-19 cases and deaths. An extensive analysis of the impact of the parameters arising from the model is presented as well. It is shown that a well-founded choice of parameters plays a crucial role in the applicability of the model.

Techno-economic optimization and feasibility of PCM-based seasonal thermal energy storage systems for district heating and cooling

Phase change materials (PCM) are an attractive seasonal thermal energy storage solution for load shifting due to relatively high energy density. Nevertheless, the choice of the right design parameters, such as storage size and phase-change temperature, is nontrivial, as these are tightly linked to the expected seasonal operation of the storage. This paper presents a combined design and operation optimization framework for sizing PCM-based seasonal thermal storage, which can capture dynamic behaviour of the storage in sensible and latent phases, and its impact on the efficiency of connected heat pumps and chillers. The proposed method, formulated as a mixed integer quadratically-constrained programming problem, was applied to a case study of heating-dominated district. It was found that the optimal PCM melting temperature equals to the heating demand supply temperature, thus removing the need of a heat pump to discharge the storage. The optimal operation of storage requires a full charging and discharging cycle of both latent and liquid phase. Higher CO2 emission price does not lead to a significant reduction of CO2 emissions, but the storage size does, leading to a higher heating coverage ratio of the storage, and consequently CO2 emissions and cost savings, which can be further enhanced with PV integration. The economic analysis indicates that, unless other benefits such as storage volume reduction are accounted for, PCM cost should drop by a factor 4 to make this solution economically viable for seasonal storage.

Bringing the Peccei-Quinn mechanism down to Earth

It is conventionally assumed that the physics underlying the Peccei-Quinn (PQ) mechanism for addressing the strong CP problem is at very high energies, orders of magnitude above the weak scale. However, this may not be the case in general and the associated PQ boson ϕ, besides the signature state, i.e., the ultralight axion a, may emerge well below the weak scale. We consider this possibility and examine some of the conditions for its viability. The example model proposed here may also provide the requisite Standard Model Higgs mass parameter, without invoking new scalars above the giga-electron-volt (GeV) scale. The corresponding parameter space can maintain against quantum corrections. This scenario, depending on the choice of parameters, can potentially be constrained by flavor data. We point out that the current mild excess in B+→K+νν¯, reported by the Belle II experiment, could in principle be explained in this setup as B+→K+ϕ and B+→K+a, with both ϕ and a escaping the detector as missing energy. For a sufficiently heavy PQ boson, in the GeV regime (as the preferred explanation of the Belle II excess), one can separate these two contributions, due to the difference in K+ momenta. In this case, the axion may also affect lighter meson, e.g., kaon, decays while ϕ would not be a kinematically allowed final state. Published by the American Physical Society 2024

Hard jet substructure in a multistage approach

We present predictions and postdictions for a wide variety of hard jet-substructure observables using a multistage model within the framework. The details of the multistage model and the various parameter choices are described in []. A novel feature of this model is the presence of two stages of jet modification: a high-virtuality phase [modeled using the modular all twist transverse-scattering elastic-drag and radiation model ()], where modified coherence effects diminish medium-induced radiation, and a lower virtuality phase [modeled using the linear Boltzmann transport model ()], where parton splits are fully resolved by the medium as they endure multiple scattering induced energy loss. Energy-loss calculations are carried out on event-by-event viscous fluid dynamic backgrounds constrained by experimental data. The uniform and consistent descriptions of multiple experimental observables demonstrate the essential role of modified coherence effects and the multistage modeling of jet evolution. Using the best choice of parameters from [], and with no further tuning, we present calculations for the medium modified jet fragmentation function, the groomed jet momentum fraction zg and angular separation rg distributions, as well as the nuclear modification factor of groomed jets. These calculations provide accurate descriptions of published data from experiments at the Large Hadron Collider. Furthermore, we provide predictions from the multistage model for future measurements at the BNL Relativistic Heavy Ion Collider. Published by the American Physical Society 2024

Matter bounce cosmology within Finsler-Randers geometry: A comprehensive study of anisotropic influences

In this study, we explore the dynamics of matter bounce cosmology within the framework of Finsler-Randers geometry, focusing on the role of the Finslerian correction term η(t). By integrating Finsler geometry into cosmological models, we introduce anisotropic effects that significantly impact the evolution of the universe, particularly during the bounce phase. The research examines various cosmological parameters, including the deceleration (qη(t)), jerk (jη(t)), and snap (sη(t)) parameters, highlighting the influence of the Finsler correction on these key indicators. Our results demonstrate that the Finslerian framework leads to more complex and abrupt transitions in the universe's expansion dynamics compared to traditional Riemannian models. The study also reveals that the Finslerian correction intensifies the violations of energy conditions, such as the null energy condition (NEC), which are crucial for the occurrence of a successful bounce. Furthermore, the analysis of the squared sound speed vs2 indicates that the model's stability is highly sensitive to the choice of the Finslerian parameters, with certain configurations leading to instability during the bounce. Our findings underscore the unique contributions of Finsler geometry to cosmological models, offering deeper insights into the behavior of the universe under anisotropic influences and providing a potential avenue for addressing longstanding challenges in cosmology.

Dimension of Planar Non-conformal Attractors with Triangular Derivative Matrices

We study the dimension of the attractor and quasi-Bernoulli measures of parametrized families of iterated function systems of non-conformal and non-affine maps. We introduce a transversality condition under which, relying on a weak Ledrappier-Young formula, we show that the dimensions equal to the root of the subadditive pressure and the Lyapunov dimension, respectively, for almost every choice of parameters. We also exhibit concrete examples satisfying the transversality condition with respect to the translation parameters.

Sensitivity of Bayesian 21 cm power spectrum estimation to foreground model errors

Abstract Power spectrum estimators are an important tool in efforts to detect the 21 cm brightness temperature fluctuations from neutral hydrogen at early times. An initial detection will likely be statistical in nature, meaning that it will not be possible to make a coherent map of the brightness temperature fluctuations; instead, only their variance will be measured against a background of noise and residual systematic effects. Optimal Quadratic Estimator (OQE)-based methods often apply an inverse covariance weighting to the data. However, inaccurate covariance modelling can lead to reduced sensitivity and, in some cases, severe signal loss. We recently proposed a Bayesian method to jointly estimate the 21 cm fluctuations, their power spectrum, and foreground emission. Instead of requiring a fixed a priori estimate of the covariance, we estimate the covariance as part of the inference. Choices of parametrization, particularly of the foregrounds, are subject to model errors and could lead to biases and other ill effects if not properly controlled. In this paper, we investigate the effects of inaccurate foreground models on 21 cm power spectrum recovery. Using simulated visibilities, we find that, even in the most extreme scenarios tested, our approach is capable of recovering 21 cm delay power spectrum estimates consistent with a known input signal for delays ≳ 300 ns (∼88% of the available Fourier modes). This is true even when using foreground models derived from modified foreground catalogs containing spatial and spectral perturbations at the quoted level of uncertainty on our foreground catalogs.

Deconstructing the Mapper algorithm to extract richer topological and temporal features from functional neuroimaging data

ABSTRACT Capturing and tracking large-scale brain activity dynamics holds the potential to deepen our understanding of cognition. Previously, tools from topological data analysis, especially Mapper, have been successfully used to mine brain activity dynamics at the highest spatiotemporal resolutions. Even though it is a relatively established tool within the field of topological data analysis, Mapper results are highly impacted by parameter selection. Given that noninvasive human neuroimaging data (e.g., from fMRI) is typically fraught with artifacts and no gold standards exist regarding “true” state transitions, we argue for a thorough examination of Mapper parameter choices to better reveal their impact. Using synthetic data (with known transition structure) and real fMRI data, we explore a variety of parameter choices for each Mapper step, thereby providing guidance and heuristics for the field. We also release our parameter exploration toolbox as a software package to make it easier for scientists to investigate and apply Mapper to any dataset.

Design and performance evaluation of a reversible bladeless turbo machine

It is well known that bladeless or Tesla turbomachinery idea was proposed by Nikola Tesla over a century ago, and it has several distinct features, such as reversibility of operation, which includes expander and compressor operations obtained by reversing the rotational speed, provided that the statoric channels are purposely designed.The main objective of this paper is to design a reversible Tesla or bladeless machine with optimal design parameter choice that can operate both in compressor and expander modes. As a consequence of the high losses due to rotor and stator interactions, statorless (volute) configurations are investigated here, showing superior performance in both compressor and expander modes of operation. Numerical study has been conducted for both compressor and expander modes of this machine using air as working fluid. In compressor mode, numerical results have been validated with experimental data, while results for expander mode will be validated in the near future. The numerical results predict peak isentropic efficiencies of 57.5 % and 63.5 %, for the compressor and expander modes of operation excluding assembly losses, respectively, while rotor efficiency is quite high > 90 % at very low mass flow rates. Tests on Tesla machine prototype have shown an experimental isentropic efficiency of maximum 32.4 % when operating in compressor mode. The overall assembly efficiency is reduced, compared to numerical predictions, due to leakage and end wall losses. The CFD simulations could match the experimental results at high pressure ratio and low mass flow, with less than 2 % error on pressure ratio. Through the improvement of the sealing system and the reduction of losses (leakage and end wall friction), it is expected that overall prototype efficiency could improve by at least 10 percentage points.

Improved liver fat and quantification at 0.55 T using locally low-rank denoising.

To improve liver proton density fat fraction (PDFF) and quantification at 0.55 T by systematically validating the acquisition parameter choices and investigating the performance of locally low-rank denoising methods. A Monte Carlo simulation was conducted to design a protocol for PDFF and mapping at 0.55 T. Using this proposed protocol, we investigated the performance of robust locally low-rank (RLLR) and random matrix theory (RMT) denoising. In a reference phantom, we assessed quantification accuracy (concordance correlation coefficient [ ] vs. reference values) and precision (using SD) across scan repetitions. We performed in vivo liver scans (11 subjects) and used regions of interest to compare means and SDs of PDFF and measurements. Kruskal-Wallis and Wilcoxon signed-rank tests were performed (p < 0.05 considered significant). In the phantom, RLLR and RMT denoising improved accuracy in PDFF and with >0.992 and improved precision with >67% decrease in SD across 50 scan repetitions versus conventional reconstruction (i.e., no denoising). For in vivo liver scans, the mean PDFF and mean were not significantly different between the three methods (conventional reconstruction; RLLR and RMT denoising). Without denoising, the SDs of PDFF and were 8.80% and 14.17 s-1. RLLR denoising significantly reduced the values to 1.79% and 5.31 s-1 (p < 0.001); RMT denoising significantly reduced the values to 2.00% and 4.81 s-1 (p < 0.001). We validated an acquisition protocol for improved PDFF and quantification at 0.55 T. Both RLLR and RMT denoising improved the accuracy and precision of PDFF and measurements.

No evidence that visual impulses enhance the readout of retrieved long-term memory contents from EEG activity

Abstract The application of multivariate pattern analysis (MVPA) to electroencephalography (EEG) data allows neuroscientists to track neural representations at temporally fine-grained scales. This approach has been leveraged to study the locus and evolution of long-term memory contents in the brain, but a limiting factor is that decoding performance remains low. A key reason for this is that processes like encoding and retrieval are intrinsically dynamic across trials and participants, and this runs in tension with MVPA and other techniques that rely on consistently unfolding neural codes to generate predictions about memory contents. The presentation of visually perturbing stimuli may experimentally regularize brain dynamics, making neural codes more stable across measurements to enhance representational readouts. Such enhancements, which have repeatedly been demonstrated in working memory contexts, could offer a tool to improve decoding in long-term memory tasks. In this study, we evaluated whether visual perturbations—or pings—improve our ability to predict the category of retrieved images from EEG activity during cued recall. Overall, our findings suggest that while pings evoked a prominent neural response, they did not reliably produce improvements in MVPA-based classification across several analyses. We discuss possibilities that could explain these results, including the role of experimental and analysis parameter choices and mechanistic differences between working and long-term memory.

An analysis of the Crossbred Algorithm for the MQ Problem

The Crossbred algorithm is currently the state-of-the-art method for solving overdetermined multivariate polynomial systems over 𝔽 2 . Since its publication in 2017, several record breaking implementations have been proposed and demonstrate the power of this hybrid approach. Despite these practical results, the complexity of this algorithm and the choice of optimal parameters for it are difficult open questions. In this paper, we prove a bivariate generating series for potentially admissible parameters of the Crossbred algorithm.

SimpleMetaPipeline: Breaking the bioinformatics bottleneck in metabarcoding

Abstract The democratisation of next‐generation sequencing has vastly increased the availability of sequencing data from metabarcoding. However, to effectively prepare these metabarcoding data for subsequent analysis, researchers must consistently apply several different bioinformatic tools—including those which denoise reads, cluster sequences and assign taxonomic identities. This often creates a bioinformatics bottleneck in workflows for non‐specialists due to obstacles around: (a) integrating different tools, (b) the inability to easily modify and rerun bioinformatic pipelines involving non‐scripted (‘point‐and‐click’) elements and (c) the multiple outputs that may be required of a single dataset (e.g. amplicon sequence variants [ASVs] and operational taxonomic units [OTUs]), which often results in users running pipelines multiple times. Here, we introduce SimpleMetaPipeline, an open‐source bioinformatics pipeline implemented in R, which addresses these obstacles. SimpleMetaPipeline integrates the most robust and commonly used existing bioinformatic tools in a single reproducible pipeline, with a streamlined choice of parameters, to generate a sequence data table containing alternative clustering and assignment options. SimpleMetaPipeline accepts demultiplexed paired‐end and single reads from multiple sequencing runs. We describe the pipeline and demonstrate how alternative annotations enable the easy implementation of multi‐algorithm agreement tests to strengthen inferences. SimpleMetaPipeline represents a valuable addition to the existing library of pipelines, providing easy and reproducible bioinformatics, including a range of commonly desired clustering and assignment options, such as OTUs and ASVs.

Influence of the sound propagation models to predict far field wind turbine noise

During the development process of a wind farm, the noise impact on neighboring residents is of crucial importance to limit annoyance and comply with local regulations. In France, this assessment is carried out by modelling the propagation of sound from each wind turbine to residents. For wind farm owners, this phase will in fine define the operation of the wind farm and therefore its electricity production. However, there are major concerns and uncertainties during this modelling phase. A concrete example of the influence of the choice of propagation model on the production of a wind farm is presented, giving an idea of its economic impact. Comparisons of the impact of wind farm noise modelled by engineering models with measurements taken on wind farms in operation provide a better understanding of the choice of certain methods or parameters. Taken together, these observations suggest areas for further research and development, with the aim of perhaps defining a more restricted framework for the sound propagation modelling stage, in order to help operators predict wind farm noise more accurately.

Impact of traffic simulation parameters on the estimation of noise exposure in an urban environment

Since road traffic is the most impactful noise source in the European cities, the evaluation of citizens' exposure is crucial. Although the noise map accuracy is affected by uncertainties in traffic volume, to perform extensive traffic monitoring is not practical and expensive, even in small cities. Traffic simulation software uses routing algorithms to suggest the fastest path to distribute vehicles from an origin through a network to a desired destination which interacts within the urban environment. Therefore, it could represent an important tool for noise mapping in the critical task to fill the data gaps in traffic volumes, also for implementing action plans using a suitable traffic management approach. However, there is a lack of evidence on how the choice of traffic simulation parameters influences noise estimation. In this work, we design a simulation framework to evaluate the impact of routing algorithms on the estimation of population noise exposure in an urban area. Using an open-source pipeline based on public databases and open-source software SUMO and NoiseModelling, we evaluated the implication on the traffic distribution and resulting noise exposure for a set of traffic model key parameters.

Hierarchical porous metamaterial for sound absorption

Absorption of sound is of great importance in several engineering applications, from room acoustics to engine noise reduction. Porous media are practical and commonly used to address this issue. However, their performance is limited at large wavelengths, where more material is required. Inspired by recursive patterns at multiple scale lengths, typical of many biological systems, we propose here a new design based on hierarchical multilayered porous materials. Two hierarchical designs are investigated: periodic and gradient. In both cases it is found that the increase of the hierarchy level allows to simultaneously (i) increase the absorption, and (ii) reduce the quantity of porous material required. Both the cases of normal and oblique incidences are examined. The absorption coefficient is calculated via the transfer matrix method and the finite element method, supporting measurements in an impedance tube. The porous media are described through the JCA model. An optimization algorithm based on the metaheuristic Greedy Randomized Adaptive Search Procedure is developed to optimize the organization of the porous layers, and practical tips are given on the most suitable choice of parameters (static air-flow resistivity, angle of incidence) to obtain the highest absorption amplification at desired low frequencies.

THz optical response of Ba(Fe[formula omitted]Ni[formula omitted])[formula omitted]As[formula omitted] films analyzed within the three-band Eliashberg s[formula omitted]-wave model

The uncertainty of the nature of the normal state and superconducting condensate of unconventional superconductors continues to stimulate considerable speculation about the mechanism of superconductivity in these materials. Of particular interest are the type of symmetry of the order parameter and the basic electronic characteristics of the superconducting and normal states. We report the derivation of temperature dependences of the superconducting condensate plasma frequency, superfluid density, and London penetration depth by measuring terahertz spectra of conductivity and dielectric permittivity of the Ba(Fe1−xNix)2As2 thin films with different Ni concentrations. A comprehensive analysis of the experimental data was performed in the framework of the simple three-band Eliashberg model under the assumption that the superconducting coupling mechanism is mediated by antiferromagnetic spin fluctuations. The results of independent experiments support the choice of model parameters. Based on calculations of the temperature dependences of superconducting gaps, we may conclude that the obtained results are compatible with the scenario, in which Ba(Fe1−xNix)2As2 is a multiband superconductor with s±-wave pairing symmetry.