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In this work, we explore the parameter constraints of two dark energy models, namely the Modified Chaplygin–Jacobi gas (MCJG) and Modified Chaplygin–Abel gas (MCAG), within the context of f (T) gravity model in a non-flat Friedmann–Lemaître–Robertson–Walker (FLRW) universe. Our investigation involves comparing the equation of state for the MCJG and MCAG dark energy models with the equation of state derived from the f (T) gravity model. To derive constraints for the dark energy and f (T) gravity models, we use recent astronomical datasets, including H (z) data, type Ia supernovae observations, Gamma Ray Bursts data, quasar data, and Baryon Acoustic Oscillation (BAO) measurements. We present the reduced Hubble parameter in terms of observable parameters such as Ω r 0 (density parameter of radiation), Ω m 0 (density parameter of dark matter), Ω k 0 (density parameter associated with spatial curvature), Ω C J 0 (density parameter of Modified Chaplygin–Jacobi gas), Ω C A 0 (density parameter of Modified Chaplygin–Abel gas), and H 0 (present value of the Hubble parameter). We explore the cosmological evolution through various cosmic diagnostic parameters, including the deceleration parameter, Om (z) diagnostic, and statefinder diagnostic pair { r , s }. These diagnostic parameters offer valuable insights into the expansion dynamics and the nature of dark energy in the universe. We have also assessed the viability of the models using the information criteria. Our aim is to shed light on the nature of dark energy and its connection to the f (T) gravity model, and ultimately gain a deeper understanding of the underlying mechanisms driving the accelerated expansion of our universe. [ABSTRACT FROM AUTHOR]

This study investigates charge transfer during collisions between ice crystals and graupel of varying sizes within a simulated super-cooled cloud environment. Experiments were conducted inside a walk-in cold room reaching temperatures as low as −30°C. Graupel sizes of 2, 4, and 6 mm were collided with ice crystals averaging 70 µm and super-cooled cloud droplets averaging 25 µm at impact velocities of 1.2, 1.8, and 2.2 m s-1, respectively. Cloud temperature ranged from −6.5° to −18.5°C, and cloud water content varied between 0.1 and 1.72 g m-3. Results indicate that larger graupel particles acquire a greater negative charge upon collision with smaller ice crystals. Unlike some previous studies, a reversal of charge sign at lower temperatures was not observed for 2- and 6-mm graupel targets. The reversal of charge from +ve to −ve at lower temperatures was observed only for experiments with 4-mm graupel target. Parameterization equations for charge transfer to different graupel sizes across temperature and cloud water content ranges have been developed. [ABSTRACT FROM AUTHOR]

The modeling of the bioluminescent system of fireflies is key to understand the binding mode of the oxyluciferin/luciferase complex and its photophysical properties with the aim of developing high-efficiency devices and techniques. In this work, we present the OLUFF force field, which is able to describe the interactions to sample the conformational space of the four possible oxyluciferin emitters in both ground and excited state. This force field has been parameterized to reproduce quantum mechanical (QM) energies and geometrical parameters. Moreover, it has been validated by comparing probability distribution functions, minimized structures, infrared spectra and normal mode analysis obtained from OLUFF-based molecular dynamic (MD) simulations with their QM counterparts. Additionally, ground state simulations have also been performed using the general amber force field (GAFF) and compared with the OLUFF. It has been demonstrated that the OLUFF not only reproduces well the QM properties, but also improves the results from the GAFF. [ABSTRACT FROM AUTHOR]

The structural integrity and surface quality of parts produced using traditional fused deposition modeling depend on factors such as layer height, filament and build orientation, print speed, nozzle temperature, and, crucially for this study, both planar and non-planar slicing. Recent research on non-planar slicing techniques has shown significant improvements in surface smoothness and mechanical properties. Key approaches include non-planar slicing for 3-axis printers, adaptive slicing to optimize material placement in critical areas, and post-processing. However, current studies lack a comprehensive method for parameterizing filament direction across both planar and non-planar layers. This work presents an approach to generate optimal trajectories for planar and non-planar layers using contours derived from level set functions. The methodology demonstrates the advantages of non-planar printing, particularly with a filament orientation of 30° for inclined surfaces, ensuring better surface quality, uniformity, and structural integrity. This emphasizes the importance of trajectory planning and filament orientation in achieving high-quality prints on inclined geometries. This research highlights the necessity of a methodology that tailors filament paths based on the load-bearing requirements of each part, demonstrating its potential to enhance surface quality and structural performance, and further the advancement of the 3D printing industry. [ABSTRACT FROM AUTHOR]

A parametrization, given by the Euler angles, of Hermitian matrix generators of even and odd non-degenerate Clifford algebras is constructed by means of the Kronecker product of a parametrized version of Pauli matrices and by the identification of all possible anticommutation sets. The internal parametrization of the matrix generators allows a straightforward interpretation in terms of rotations, and in the absence of a similarity transformation can be reduced to the canonical representations by an appropriate choice of parameters. The parametric matrix generators of second and fourth-order are linearly decomposed in terms of Pauli, Dirac, and fourth-order Gell–Mann matrices establishing a direct correspondence between the different representations and matrix algebra bases. In addition, and with the expectation for further applications in group theory, a linear decomposition of GL(4) matrices on the basis of the parametric fourth-order matrix generators and in terms of four-vector parameters is explored. By establishing unitary conditions, a parametrization of two subgroups of SU(4) is achieved. [ABSTRACT FROM AUTHOR]

In the search for alternative cementitious materials, the alkali activation of aluminosilicates has been found to be a mechanically effective binder. Among precursors, metakaolin is most frequently used, with a primary source, kaolin, distributed globally in varying compositions. This variability may indicate potential compositional limitations for the large-scale production of such binders. Thus, four types of commercial calcined clays, activated under identical conditions, were evaluated, and their physicochemical characteristics were correlated with the mechanical properties of the resulting binder. Different characterization methods were used for the raw material and for each alkali-activated system. Anhydrous metakaolin was assessed through particle size distribution, specific surface area, zeta potential, vitreous phases, Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), amorphism, and pozzolanic activity. The pastes were evaluated in the fresh state through apparent activation energy progression and isothermal conduction calorimetry, and in the hardened state through compressive strength and dilatometry. Compressive strength values ranged from 7 to 42 MPa. From these results, a mathematical model was developed to estimate mechanical performance based on key variables, specifically amorphism, the pozzolanic index, and the silica-to-alumina ratio. This model allows for performance predictions without the need to prepare additional pastes. Interestingly, it was found that while some systems displayed low initial reactivity, their relative reactivity over time increased more significantly than those with higher early-stage reactivity, suggesting their potential for reconsideration in long-term applications. [ABSTRACT FROM AUTHOR]

There are two regular polygons Pn-(R1) and Pn-(R2) which correspond to the given polygonal distances {d1, d2, ..., dn. It is proved that the radii R1, R2 of the circumcircles of the regular polygons and the n distances are connected by the system: ... where m = 1, ..., n - 1. The solutions for the equilateral triangles and the squares are investigated. The two-parametric families of the solutions are given in both cases, when three distances are rational numbers. [ABSTRACT FROM AUTHOR]

Rotor blade tip has significant influence on turbine stage aerodynamics and heat transfer. Most previous efforts have been based on low-speed cascade settings. However, more recent research on transonic blade tips exhibits distinctive flow features with qualitatively different performance sensitivities. These prompt two key issues of interest on the related flow conditioning. First, the contrast between a low-speed flow and a transonic regime highlights the less studied high-subsonic flow regime, closely relevant to many realistic turbine designs. Second, the relative casing movement and upstream inflow conditions, known to have non-negligible effects, indicate the need to examine a rotor blade tip in a realistic stator-rotor stage environment, which is also lacking. To elaborate the Mach number effect in the flow regimes of practical interest, we aim to examine a high subsonic stage in a direct and consistent comparison with a transonic one. To this end, a high subsonic stage (exit Mach number of 0.7) and a transonic (exit Mach number of 1.1) are designed at the same Reynolds number with a three-dimensional parameterization and meshing system. The tip squealer height is used as a representative parameter to investigate the sensitivity of the stage aerothermal performance. The multi-objective optimization using the Kriging surrogated model is employed to identify the Pareto fronts for the stage efficiency and the heat transfer. The comparison of the optimized results between these two stages shows distinctively different trends in the performance variation with the squealer height. The efficiency of the subsonic stage increases with the squealer height reaching a plateau. In contrast, the efficiency in the transonic stage first increases and then drops to the level comparable to that of a flat tip. Significantly, the present results indicate, for the first time, that the squealer tip in a transonic stage may not be as effective as in a subsonic stage. On the other hand, for heat transfer, sensitivity variations are more complex. The overall heat load and the local nonuniformity lead to qualitatively different sensitivities with the squealer height, as well as completely incomparable Pareto fronts. These observed heat transfer sensitivities raise the question on how to effectively conduct a combined aerodynamic and heat transfer performance design optimization. The authors subsequently resort further aerothermal physics analyses described in a companion paper as Part II of the two-part article. In Part II, the physical interpretation of the contrasting aero-efficiency sensitivities for the two stages, as well as a physical understanding leveraged selection of the objective function for such combined blade tip aerothermal optimization will be presented. [ABSTRACT FROM AUTHOR]

The primary goal of this paper is to develop a formal foundation to design an adaptive output feedback predictor for a class of unknown systems where parameters and order are unknown or high-dimensional. We present a reduced-order adaptive output-predictor scheme based on modal reduction and Lyapunov's method. Moreover, the credibility of the proposed reduced-order adaptive output-predictor scheme is validated by mathematical proof and numerical studies. [ABSTRACT FROM AUTHOR]

In this paper we describe and compare the methods for the calculation of all the key points of the photovoltaic single-diode model. These include the short-circuit point, the open-circuit point, the maximum power point, the mean slope point, the maximum curvature point, and the jerk point. The main contribution of this paper is a new algorithm to obtain the maximum power point which is based on reducing its computation to solve a single-variable equation. Its unique solution leads to an explicit expression of the point by using a recent parametrization of the single-diode model current–voltage curve. In the numerical resolution of the previous equation, we will use as starting point the mean slope point which has been proved to be close to the maximum power point. Previously, we will provide for the first time in the literature an exact and explicit expression of the mean slope point. The new algorithm proposed reaches the accuracy of the best known numerical methods, but it is much faster, almost reaching the execution times of explicit formulas. [ABSTRACT FROM AUTHOR]

Abstract Currently, many utility‐scale wind turbine generator original equipment manufacturers are dependent on imported rare earth permanent magnets, which are susceptible to market risks from cost instability. To lower the production costs of these generators and stay competitive in the market, several small wind manufacturers are pursuing continuous improvements to both generator design and manufacturing. However, traditional design and manufacturing methods have yielded marginal improvements in wind power performance. This work presents novel methods to redesign a baseline 15‐kW wind turbine generator with reduced rare‐earth permanent magnets by leveraging cutting‐edge three‐dimensional (3D) printed polymer‐bonded permanent magnets and steel. Symmetric, asymmetric, and multimaterial‐magnet parametrization methods are introduced for shape optimization. We extend the symmetric and asymmetric methods to the back iron in the stator to further investigate the impact and opportunities for performance improvements with lesser active materials. We employ a design‐of‐experiments approach with parametric computer‐aided design for shape generation and evaluate different designs by magneto‐thermal modeling and finite‐element analysis. We use adaptive sampling technique to identify better performing designs with lesser magnet mass, higher efficiency, and lower cogging torque when compared with the baseline generator. Asymmetric pole designs resulted in a magnet mass in the range of 4.77–5.37 kg, which was 27%–35% lighter than the baseline generator, suggesting that a new design freedom exists that can be enabled by advanced manufacturing, such as 3D printing. Shaping the back iron in the stator resulted in material savings in electrical steel of up to 14.62 kg, which was 20% lighter than the baseline stator. We conducted a structural analysis to evaluate an optimized asymmetric rotor design from the point of view of mechanical integrity and air‐gap stiffness. The magnetically optimal shape profile was shown as having a positive impact on the radial stiffness, and an optimal solution was discovered to reduce the structural mass by nearly 30 kg, which was 29% lighter than the baseline.

In this paper, we describe an algorithm for fitting an analytic and bandlimited closed or open curve to interpolate an arbitrary collection of points in R 2 . The main idea is to smooth the parametrization of the curve by iteratively filtering the Fourier or Chebyshev coefficients of both the derivative of the arc-length function and the tangential angle of the curve and applying smooth perturbations, after each filtering step, until the curve is represented by a reasonably small number of coefficients. The algorithm produces a curve passing through the set of points to an accuracy of machine precision, after a limited number of iterations. It costs O(N log N) operations at each iteration, provided that the number of discretization nodes is N. The resulting curves are smooth, affine invariant, and visually appealing and do not exhibit any ringing artifacts. The bandwidths of the constructed curves are much smaller than those of curves constructed by previous methods. We demonstrate the performance of our algorithm with several numerical experiments. [ABSTRACT FROM AUTHOR]

In this paper, we employ parametrization techniques within the framework of f (T) gravity to investigate the deceleration parameter (DP), a key quantity characterizing the universe's expansion dynamics. By analyzing the DP, we gain valuable insights into the nature of cosmic constituents and their impact on the universe's evolution. We utilize a combination of observational data, including 31 Cosmic Chronometers (CC) measurements, 1048 Type Ia Supernovae (SNIa), 17 Baryon Acoustic Oscillation (BAO) measurements, Cosmic Microwave Background (CMB), 162 Gamma Ray Bursts (GRB), and 24 observations of compact radio quasars (Q). We employ the Markov chain Monte Carlo (MCMC) sampling technique to estimate the best-fit range of model parameters. Cosmological and cosmographic parameters are investigated, and their implications in the context of cosmology have been discussed. We also analyze the statefinder and Om diagnostic to gain deeper insights into the universe's behavior. Furthermore, we conduct statistical analyses to compare our model with the standard Λ CDM model. Our investigation also includes the study of physical parameters, providing comprehensive insights into the cosmological behavior within the f (T) gravity framework. The results and comparisons presented in this work contribute to a deeper understanding of the universe's dynamics and provide valuable implications for the cosmological model under consideration. [ABSTRACT FROM AUTHOR]

Currently, many utility‐scale wind turbine generator original equipment manufacturers are dependent on imported rare earth permanent magnets, which are susceptible to market risks from cost instability. To lower the production costs of these generators and stay competitive in the market, several small wind manufacturers are pursuing continuous improvements to both generator design and manufacturing. However, traditional design and manufacturing methods have yielded marginal improvements in wind power performance. This work presents novel methods to redesign a baseline 15‐kW wind turbine generator with reduced rare‐earth permanent magnets by leveraging cutting‐edge three‐dimensional (3D) printed polymer‐bonded permanent magnets and steel. Symmetric, asymmetric, and multimaterial‐magnet parametrization methods are introduced for shape optimization. We extend the symmetric and asymmetric methods to the back iron in the stator to further investigate the impact and opportunities for performance improvements with lesser active materials. We employ a design‐of‐experiments approach with parametric computer‐aided design for shape generation and evaluate different designs by magneto‐thermal modeling and finite‐element analysis. We use adaptive sampling technique to identify better performing designs with lesser magnet mass, higher efficiency, and lower cogging torque when compared with the baseline generator. Asymmetric pole designs resulted in a magnet mass in the range of 4.77–5.37 kg, which was 27%–35% lighter than the baseline generator, suggesting that a new design freedom exists that can be enabled by advanced manufacturing, such as 3D printing. Shaping the back iron in the stator resulted in material savings in electrical steel of up to 14.62 kg, which was 20% lighter than the baseline stator. We conducted a structural analysis to evaluate an optimized asymmetric rotor design from the point of view of mechanical integrity and air‐gap stiffness. The magnetically optimal shape profile was shown as having a positive impact on the radial stiffness, and an optimal solution was discovered to reduce the structural mass by nearly 30 kg, which was 29% lighter than the baseline. [ABSTRACT FROM AUTHOR]

The impact on global simulations of a new package of physical parametrizations in the Met Office Unified Model is documented. The main component of the package is an entirely new convection scheme, CoMorph. This has a mass‐flux structure that allows initiation of buoyant ascent from any level and the ability for plumes of differing originating levels to coexist in a grid box. It has a different form of closure, where the mass flux of initiation is dependent on local instability, and an implicit numerical solution for detrainment that yields smooth timestep behaviour. The scheme is coupled more consistently to the cloud, microphysics, and boundary‐layer parametrizations and, as a result, significant changes to these have also been made. The package, called CoMorph‐A, has been tested in a variety of single‐column and idealized regimes. Here we test it in global configurations and evaluate it against observations using a range of standard metrics. Overall it is found to perform well against the control. Biases in the climatologies of the radiative fluxes are significantly reduced across the Tropics and subtropics, tropical and extratropical cyclone statistics are improved, and the Madden–Julian oscillation and other propagating tropical waves are strengthened. It also improves overall scores in numerical weather prediction trials, without revisions to the data assimilation. There is still work to do to improve the diurnal cycle of precipitation over land, where the peak remains too close to the middle of the day. [ABSTRACT FROM AUTHOR]

ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory, which was designed for rapid assimilative runs. ANCHOR uses recently developed PyIRI model for the background and for the formation of the background covariance matrix. It only takes a few minutes for ANCHOR to complete the data assimilation (DA) for one day, including data pre‐processing and model set up. ANCHOR extracts ionospheric parameters from radio occultation (RO) and ionosonde data using PyIRI formalism and assimilates them as point measurements into maps of the background parameters using a Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment with synthetic data simulated using the SAMI3 model. ANCHOR reduces the root mean square errors in the analysis by more than a half for all of the ionospheric parameters in comparison to the background. Finally, this paper discusses advantages and limitations of the parametrized ionospheric DA, highlighting the avenues for its future improvement. Plain Language Summary: ANCHOR is a novel data assimilation model developed at the U.S. Naval Research Laboratory. It combines estimates of the electron density in the Earth's ionosphere from climatological model with observations, producing a better model representation that matches the data. It extracts ionospheric parameters from radio occultation and ionosonde data and assimilates them as point measurements into the maps of the background parameters using Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment. Key Points: Novel approach for rapid ionospheric data assimilation (DA) using anchor pointsGlobal ionospheric DA for plasma density parametersExtraction of the anchor points from the ionospheric data [ABSTRACT FROM AUTHOR]

The gravity field of the Earth is time-dependent due to several types of mass variations which take place on different time scales. Usually, the time-variability of the gravitational potential of the Earth is expressed by the monthly determination of a static geopotential model based on data from gravity field missions. In this paper, the variability of the potential is parameterized by a functional approach which contains a polynomial trend and periodic contributions. The respective parameters are estimated based on the monthly solutions derived from the GRACE and GRACE-FO gravity field mission up to a maximum degree of expansion n max = 96 . As a preliminary data analysis, a Fourier analysis is performed on selected potential coefficients from the available monthly solutions of the GFZ. The indicated frequency components are then used to formulate a time-dependent analytical approach to describe each Stokes coefficient's temporal behaviour. Different approaches are presented that include both polynomial and periodic components. The respective parameters for modelling the temporal variability of the coefficients are estimated in a Gauss-Markov model and tested for significance by statistical methods. Extensive comparative numerical studies are carried out between the newly generated model variants and the existing monthly GRACE, GRACE-FO and the existing time dependent EIGEN-6S4 solutions. The numerical comparisons make it clear that estimated models based on all available monthly solutions describe the essential periods very well, but such monthly events that deviate strongly from the mean behaviour of the signal show less precision in the space domain. Models that are estimated based on fourteen consecutive monthly solutions, covering one selected year, represent the amplitudes much more precise. The statements made apply to four initial data used, which are filtered to varying degrees. In particular, DDK2, DDK5 and DDK8, as well as unfiltered coefficients were used. For all the model approaches used, it can be seen that the potential coefficients contain up to about n ≈ 40 in case of DDK5 periodically signals with annual, semi-annual or quarterly, as well as Luna nodal periods and do not vary significantly beyond that degree. Only an offset can be estimated significantly for all Stokes coefficients. [ABSTRACT FROM AUTHOR]

To meet the ever-growing worldwide electric vehicle demand, the development of advanced generations of lithium-ion batteries is required. To this end, modelling is one of the pillars for the innovation process. However, modelling batteries containing a large number of different mechanisms occurring at different scales remains a field of research that does not provide consensus for each particular model or approach. Parametrization as part of the modelling process appears to be one of the issues when it comes to building a high-fidelity model of a target cell. In this paper, a particular parameter identification is therefore discussed. Indeed, even if Butler–Volmer is a well-known equation in the electrochemistry field, identification of its reaction rate constant or exchange current density parameters is lacking in the literature. Thus, we discuss the process described in the literature and propose a new protocol that expects to overcome certain difficulties whereas the hypothesis of calculation and measurement maintains high sensitivity. [ABSTRACT FROM AUTHOR]

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Global atmospheric models rely on parameterizations to capture the effects of gravity waves (GWs) on middle atmosphere circulation. As they propagate upwards from the troposphere, the momentum fluxes associated with these waves represent a crucial yet insufficiently constrained component. The present study employs three tree‐based ensemble machine learning (ML) techniques to probe the relationship between large‐scale flow and small‐scale GWs within the tropical lower stratosphere. The measurements collected by eight superpressure balloons from the Strateole 2 campaign, comprising a cumulative observation period of 680 days, provide valuable estimates of the gravity wave momentum fluxes (GWMFs). Multiple explanatory variables, including total precipitation, wind, and temperature, were interpolated from the ERA5 reanalysis at each balloon's location. The ML methods are trained on data from seven balloons and subsequently utilized to estimate reference GWMFs of the remaining balloon. We observed that parts of the GW signal are successfully reconstructed, with correlations typically around 0.54 and exceeding 0.70 for certain balloons. The models show significantly different performances from one balloon to another, whereas they show rather comparable performances for any given balloon. In other words, limitations from training data are a stronger constraint than the choice of the ML method. The most informative inputs generally include precipitation and winds near the balloons' level. However, different models highlight different informative variables, making physical interpretation uncertain. This study also discusses potential limitations, including the intermittent nature of GWMFs and data scarcity, providing insights into the challenges and opportunities for advancing our understanding of these atmospheric phenomena. Plain Language Summary: Part of the atmosphere's large‐scale circulation results from motions that are not resolved, or partly resolved, by weather or climate models. These include internal gravity waves, with horizontal scales from a few to hundreds of kilometers. The main sources occur in the troposphere, such as flow over mountains and cloud development. Their three‐dimensional propagation induces major aggregated impacts in the stratosphere and mesosphere, forcing key aspects of the circulation. This forcing is accounted for in climate models by "parameterizations," that mimics the effect of the unresolved waves based on the large‐scale, resolved flow. These parameterizations necessarily retain crude approximations and introduce significant uncertainty in the models. For gravity waves (GWs), sources are a major uncertainty. This study makes use of the high‐altitude balloon campaign Strateole 2 (October 2019–February 2020). Eight balloons circled Earth at heights around 18–20 km, providing unique observations of the GWs. These are used as targets for machine learning (ML) methods that take as inputs the information from outputs of a numerical weather prediction model describing the large‐scale flow. The successes and difficulties of ML provide insights which can guide improvements of parameterizations, such as the most informative large‐scale variables for estimating the unresolved waves. Key Points: Eight superpressure balloons from the Strateole 2 mission provide observations for accurate gravity wave momentum flux (GWMF) estimationThree machine learning (ML) methods are employed to probe the relationship between the GWMFs and ERA5's large‐scale flowsThe most informative large‐scale inputs are provided, along with a discussion of the successes and challenges of ML methods [ABSTRACT FROM AUTHOR]

In this work, we have proposed a simple parametrization for the pressure component p (z) of the dark energy model and have studied the cosmological implications of this model in the framework of f (Q) modified gravity theory, aka, the symmetric teleparallel gravity theory, where Q is known as the nonmetricity scalar. By considering a particular parametric form of p (z) , we obtained the Hubble solution for the f (Q) modified gravity model. In order to see whether this model is consistent with or challenges the Λ CDM limits, we tried to put constraints on the model parameters using the recent observational datasets like Hubble data, Cosmic Chronometer data, Type Ia Supernovae (SNIa) data, baryonic acoustic oscillations (BAO) data. We have employed the χ 2 minimization technique and have carried out the Markov Chain Monte Carlo (MCMC) analysis using emcee package. We have found that the deceleration parameter shows a smooth transition from positive to negative value in recent past which is essential for the structure formation of the Universe. It has been found that the parametric form of the dark energy pressure parameter is consistent with current cosmological scenario. [ABSTRACT FROM AUTHOR]

Using 3D models in the design phase of bridges helps the engineer to verify the bridge's geometry and to analyse its aesthetic qualities. When these models are parameterized, the whole design process becomes more dynamic and standardized, which is very attractive to the infrastructure area of bridge engineering. This work presents a technique for automated generation of box girder bridge models, in which the geometric parameters are defined based on current design standards and criteria. Fast generation of a bridge's model allows the engineer to make quicker decisions about the adopted structural solution, which expedites bridge design. Two case studies are chosen to present more details and validate the proposed technique: an existing overpass located in an urban area, and a fictitious bridge connecting two coastal cities. [ABSTRACT FROM AUTHOR]

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Introduction: Flatfoot is a condition commonly seen in children; however, there is general disagreement over its incidence, characterization and correction. Painful flatfoot accompanied with musculoskeletal and soft tissue problems requires surgery to avoid arthritis in adulthood, the most common surgical approach being two osteotomies to the calcaneus and medial cuneiform bones of the foot. Objectives: This study focuses on the parametrization of these two bones to understand their bone morphology differences in a population sample among 23 normal subjects. Population differences could help in understanding whether bone shape may be an important factor in aiding surgical planning and outcomes. Methods: A total of 45 sets of CT scans of these subjects were used to generate surface meshes of the two bones and converted to be iso-topological meshes, simplifying the application of Generalized Procrustes Analysis and Principal Component Analysis, allowing the main sources of variation between the subjects to be quantified. Results: For the calcaneus, 16 Principal Components (PCs) and, for the medial cuneiform, 12 PCs were sufficient to describe 90% of the dataset variability. The quantitative and qualitative analyses confirm that for the calcaneus PC1 describes the Achilles attachment location and PC2 largely describes the anterior part of the bone. For the medial cuneiform, PC1 describes the medial part of the bone, while PC2 mainly describes the superior part. Conclusion: Most importantly, the PCs did not seem to describe the osteotomy sites for both bones, suggesting low population variability at the bone cutting points. Further studies are needed to evaluate how shape variability impacts surgical outcomes. Future implications could include better surgical planning and may pave the way for complex robotic surgeries to become a reality. [ABSTRACT FROM AUTHOR]

Abstract ANCHOR is a novel assimilative model developed at the U.S. Naval Research Laboratory, which was designed for rapid assimilative runs. ANCHOR uses recently developed PyIRI model for the background and for the formation of the background covariance matrix. It only takes a few minutes for ANCHOR to complete the data assimilation (DA) for one day, including data pre‐processing and model set up. ANCHOR extracts ionospheric parameters from radio occultation (RO) and ionosonde data using PyIRI formalism and assimilates them as point measurements into maps of the background parameters using a Kalman Filter approach. This paper introduces the ANCHOR algorithm, discusses its coordinate system and background, explains the background covariance formation, discusses the extraction of the ionospheric parameters from the data and the assimilation process, and, finally, shows the results of the observing system simulation experiment with synthetic data simulated using the SAMI3 model. ANCHOR reduces the root mean square errors in the analysis by more than a half for all of the ionospheric parameters in comparison to the background. Finally, this paper discusses advantages and limitations of the parametrized ionospheric DA, highlighting the avenues for its future improvement.

The concept of dark energy (DE) emerged as a result of confirming the accelerated expansion of the universe. Since then, numerous models have been developed to explore the origin and nature of DE. In this study, we investigate several recent cosmological models (Models 1–9) based on the parametrization of the DE equation of state. Our analysis focuses on a homogeneous, isotropic flat universe comprising DE, dark matter (DM), and radiation. We assume the separate conservation of the dark components (DE and DM) and radiation. By employing various parametrizations of ω D (z) , we derive the corresponding Hubble function E (z). To understand the cosmic expansion history of the universe in a model-independent manner, we employ cosmography as an approach. We express important cosmographic parameters such as deceleration, jerk, snap, and lerk parameters in terms of the Hubble rate E (z) and its derivative up to the fourth order. Additionally, we examine the statefinder parameter and O m diagnostics to distinguish between different types of DE models. Finally, we compare the physical interpretations of these diagnostic parameters with the standard Λ CDM model to assess the viability of each model. [ABSTRACT FROM AUTHOR]

Coherent structures are characterized in high‐resolution simulations of three atmospheric boundary layers: dry convection, marine cumulus, and stratocumulus. Based on radioactive‐decaying tracers emitted at different altitudes (surface, top of well‐mixed layer, and cloud top), an object‐oriented methodology allows individual characterization of coherent tridimensional plumes within the flow. Each boundary layer shows updraft structures surrounded by subsiding shells that have similar thermodynamical characteristics. Well‐mixed downdrafts are located relatively close to updrafts and entrain dry, warm air from the free troposphere. While updrafts primarily carry the majority of heat and moisture within well‐mixed layers, accounting for 62‐70% of the total resolved flux, it is noteworthy that well‐mixed downdrafts also contibute a significant portion, ranging from 14% to 35%. Identified in all boundary layers, these subsiding structures are triggered by air mass convergence linked to updrafts' divergence and are thus part of an overturning circulation in well‐mixed layers. Close to the surface, downdrafts' divergence constrain updrafts' locations and thus shape a mesoscale cellular organization with cell sizes scaling with the boundary‐layer height (aspect ratio of around 2). Active cumulus formation does not strongly perturb the spatial organization of the sub‐cloud well‐mixed layer. The stratocumulus‐topped boundary layer also shares similarities with the overturning circulation despite having condensation and cloud‐radiation diabatic effects within the mixed layer. However, the visible mesoscale organization of stratocumulus shows larger cells than the boundary‐layer depth (aspect ratio >$$ > $$10) that suggest deviations from the clear‐sky conceptual view. The boundary‐layer decoupling influences mass fluxes of coherent structures and thus potentially plays a role in shaping the spatial organization. Since well‐mixed downdrafts contribute to a significant part of resolved flux of heat and moisture, our results suggest that downdraft properties in well‐mixed layers should be represented at the subgrid scale in climate models through non‐local mass‐flux parametrizations. [ABSTRACT FROM AUTHOR]

A crystal net can be derived from a 'generalized' voltage graph representing a graph analog of a fundamental domain of that crystal net along with a sufficient collection of its symmetries. The voltage assignments include not only isometries to the (oriented) edges, but also 'weight' groups assigned to vertices for generating the vertex figures around those vertices. By varying the voltage assignments, one obtains geometrically distinct – and occasionally topologically distinct – Euclidean graphs. The focus here is on deriving simple graphs, i.e. graphs with no loops or lunes, especially uninodal edge transitive graphs. [ABSTRACT FROM AUTHOR]

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In this paper, we investigate the dynamical behavior of the universe with a flat FLRW model in f (R , G) gravity, where R and G both signify the Ricci scalar and Gauss–Bonnet invariant. Furthermore, in order to determine the model's behavior, it must have the late-time universe's behavior, which involves both an accelerated expansion as well as ending in a big rip. We present a model that begins with a point-type singularity, i.e. a point with zero volume and infinite energy density, by using parametrization of the scale factor a (t). The model's actions are accelerating and expanding at the moment, and Λ CDM in late times. Our extensive analysis encompasses the energy conditions, dynamics of certain model solutions and additional cosmological tests through a dominant model. Finally, the proposed framework acts exactly like a quintessence dark energy model in the current time and is reliable with standard cosmology Λ CDM in late times. [ABSTRACT FROM AUTHOR]

Many parametrization and mapping-related problems in geometry processing can be viewed as metric optimization problems, i.e., computing a metric minimizing a functional and satisfying a set of constraints, such as flatness. Penner coordinates are global coordinates on the space of metrics on meshes with a fixed vertex set and topology, but varying connectivity, making it homeomorphic to the Euclidean space of dimension equal to the number of edges in the mesh, without any additional constraints imposed. These coordinates play an important role in the theory of discrete conformal maps, enabling recent development of highly robust algorithms with convergence and solution existence guarantees for computing such maps. We demonstrate how Penner coordinates can be used to solve a general class of optimization problems involving metrics, including optimization and interpolation, while retaining the key solution existence guarantees available for discrete conformal maps. [ABSTRACT FROM AUTHOR]

To meet the ever-growing worldwide electric vehicle demand, the development of advanced generations of lithium-ion batteries is required. To this end, modelling is one of the pillars for the innovation process. However, modelling batteries containing a large number of different mechanisms occurring at different scales remains a field of research that does not provide consensus for each particular model or approach. Parametrization as part of the modelling process appears to be one of the issues when it comes to building a high-fidelity model of a target cell. In this paper, a particular parameter identification is therefore discussed. Indeed, even if Butler–Volmer is a well-known equation in the electrochemistry field, identification of its reaction rate constant or exchange current density parameters is lacking in the literature. Thus, we discuss the process described in the literature and propose a new protocol that expects to overcome certain difficulties whereas the hypothesis of calculation and measurement maintains high sensitivity.

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Summary Tracking a moving target using only angle measurements is a challenging and complex problem. This is because the measurements received from a sensor on a moving observer provide very little information about the target's movements. Additionally, there are uncertainties regarding the target's initial range and speed, which can further complicate the estimation process. The angle measurements themselves carry a lot of uncertainties as they can be affected by significant outliers, which do not fit the standard Gaussian distribution that estimation algorithms typically assume. Furthermore, the observer's position can also be uncertain due to various environmental factors. To address these challenges and to propose a robust, unified and accurate solution, two new estimation frameworks are proposed. They involve making use of a range and speed parametrization approach to compensate for the initial uncertainty in the target's range and speed. They also involve using a maximum correntropy (MC) criterion and a numerically stable centered error entropy (CEE) criterion to deal with measurement outliers and the observer motion uncertainties. The performance of the proposed frameworks is evaluated by comparing them to the traditional unscented Kalman filter (UKF) and its parametrized versions using different performance metrics. The simulations conducted showed that the developed algorithms perform better in terms of estimation accuracy than conventional methods. Specifically, the second framework based on the CEE criterion outperformed MC‐based estimators. [ABSTRACT FROM AUTHOR]

The constraint of the deceleration parameter associated with dark energy stands as one of the most captivating subjects in the present cosmological framework. This study centers on the parametric reconstruction of the deceleration parameter in a flat Friedmann–Robertson–Walker (FLRW) Universe that encompasses radiation, dark energy, and pressure-less dark matter. In this context, we thoroughly investigate a highly motivated parametrization of q(z), which offers an evolutionary scenario from deceleration to the acceleration phase of the Universe. The crucial task of estimating the parametrization of the Hubble parameter is accomplished through its incorporation into the Friedmann equation. The free parameters are subsequently constrained utilizing a comprehensive set of observational data, including H(z), type Ia supernovae (SNIa), Baryon Acoustic Oscillation (BAO), Gamma Ray Burst (GRB), and Quasar (Q) measurements. Implementing the Markov Chain Monte Carlo (MCMC) technique and the H(z) + BAO + SNIa + GRB + Q dataset, we derive the best-fit values for the model parameters. Consequently, we provide a graphical analysis of the cosmographic parameters such as deceleration, jerk, and snap parameters by applying these optimized model parameter values. Finally, we compare our results with those of the standard Λ CDM paradigm to evaluate the viability of our proposed models. [ABSTRACT FROM AUTHOR]

Silicon is a promising anode material and can already be found in commercially available lithium-ion cells. Reliable modeling and simulations of new active materials for lithium-ion batteries are becoming more and more important, especially regarding cost-efficient cell design. Because literature lacks an electrochemical model for silicon-dominant electrodes, this work aims to close the gap. To this end, a Newman p2D model for a lithium-ion cell with a silicon-dominant anode and a nickel-cobalt-aluminum-oxide cathode is parametrized. The micrometer silicon particles are partially lithiated to 1200 mAh g Si − 1 . The parametrization is based on values from the electrode manufacturing process, measured values using lab cells, and literature data. Charge and discharge tests at six different C-rates up to 2C serve as validation data, showing a root-mean-squared error of about 21 mV and a deviation in discharge capacity of about 1.3%, both during a 1 C constant current discharge. Overall, a validated parametrization for a silicon-dominant anode is presented, which, to the best of our knowledge, is not yet available in literature. For future work, more in-depth studies should investigate the material parameters for silicon to expand the data available in the literature and facilitate further simulation work. [ABSTRACT FROM AUTHOR]