Simple Fitting Research Articles

Calculated Stark widths of the triply charged argon ion Ar iv

ABSTRACT In this work we studied the Stark widths of Ar iv ion which are important for modelling atmosphere of some particular stars. Using semiclassical perturbation and modified semi-empirical approach, we present in this work Stark widths of triply ionized argon Ar iv spectral lines which will be implemented in the STARK-B data base. Energy levels and oscillator strengths needed as input for Stark width calculations were obtained from the atomic structure data base TOPbase. If we need to interpolate these Stark widths at different temperatures for the studied transitions of Ar iv, we use a simple and accurate fitting formula based on a least-squares method. The calculated values were compared to the existing experimental and calculated Stark width values.

The effect of the simple fitness exercise model to improve the vocational students’ physical fitness

Physical fitness is needed by every human being to support his activities. Vocational Students are no exception when entering the industrial world. Because people who have good physical fitness will not feel tired quickly and still have other energy reserves to continue other activities. Vocational students who are basically required to have 70% skills must a healthty and fit body. As for the research objective, namely to determine the effect of the simple fitness training model to improve the physical fitness of Vocational students. The population of this research is vocational students with a sample of 24 students. The sampling technique uses a purposive sample with the criteria of final semester Indonesian Traditional Medicine Study Program students who are willing to take part in the study. This study uses mix of methods (qualitative and quantitative) with qualitative using the literature review and quantitative using the two group pretest and posttest design approach. Data were collected using the focus discussion grup and Cooper test instrument to determine cardiopulmonary fitness and the push-up sit-up test to determine the endurance of the arm and abdominal muscles. The results showed that the simple fitness training model had an effect on the physical fitness of vocational students with an average increase of 16%, the abdominal and arm muscle endurance groups by 15%. Based on the result of the sit up test and push up test. With the existence of a simple fitness training model, it can be carried out and is useful for students, especially vocational students.

Whole knee joint mapping using a phase modulated UTE adiabatic T1ρ (PM-UTE-AdiabT1ρ ) sequence.

To develop a 3D phase modulated UTE adiabatic T1ρ (PM-UTE-AdiabT1ρ ) sequence for whole knee joint mapping on a clinical 3 T scanner. This new sequence includes six major features: (1) a magnetization reset module, (2) a train of adiabatic full passage pulses for spin locking, (3) a phase modulation scheme (i.e., RF cycling pair), (4) a fat saturation module, (5) a variable flip angle scheme, and (6) a 3D UTE Cones sequence for data acquisition. A simple exponential fitting was used for T1ρ quantification. Phantom studies were performed to investigate PM-UTE-AdiabT1ρ 's sensitivity to compositional changes and reproducibility as well as its correlation with continuous wave-T1ρ measurement. The PM-UTE-AdiabT1ρ technique was then applied to five ex vivo and five in vivo normal knees to measure T1ρ values of femoral cartilage, meniscus, posterior cruciate ligament, anterior cruciate ligament, patellar tendon, and muscle. The phantom study demonstrated PM-UTE-AdiabT1ρ 's high sensitivity to compositional changes, its high reproducibility, and its strong linear correlation with continuous wave-T1ρ measurement. The ex vivo and in vivo knee studies demonstrated average T1ρ values of 105.6 ± 8.4 and 77.9 ± 3.9 ms for the femoral cartilage, 39.2 ± 5.1 and 30.1 ± 2.2 ms for the meniscus, 51.6 ± 5.3 and 29.2 ± 2.4 ms for the posterior cruciate ligament, 79.0 ± 9.3 and 52.0 ± 3.1 ms for the anterior cruciate ligament, 19.8 ± 4.5 and 17.0 ± 1.8 ms for the patellar tendon, and 91.1 ± 8.8 and 57.6 ± 2.8 ms for the muscle, respectively. The 3D PM-UTE-AdiabT1ρ sequence allows volumetric T1ρ assessment for both short and long T2 tissues in the knee joint on a clinical 3 T scanner.

Vibrio parahaemolyticus: Predicting effects of storage temperature on growth in Crassostrea gigas harvested in New Zealand

The frequency of gastroenteritis outbreaks caused by Vibrio parahaemolyticus has been increasing in many parts of the world including New Zealand. Some outbreaks in New Zealand have occurred at times when V. parahaemolyticus concentrations in shellfish in growing waters are low. V. parahaemolyticus is mesophilic, so is not expected to grow under refrigeration. Therefore, one possible explanation for the outbreaks is that the organism was able to grow to hazardous concentrations (e. g. >10,000/g) because of inadequate post-harvest refrigeration. To model the response of growth rates to temperature, we collected Pacific oysters (Crassostrea gigas) containing natural populations of V. parahaemolyticus over three consecutive summers, stored them for various times at 5, 10, 15, 20, 25 and 30 °C and determined V. parahaemolyticus concentrations. A simple log-linear fit was best to determine the growth rates at each temperature for each season. Applying a square root model (√growth rate (MPN/h) = b x (Temperature (°C) - T0) to all the growth rates gave a good fit (R2 = 0.84, RMSE = 0.0178) with b = 0.0096 and T0 = 8.44 °C. Fitted growth rates were considerably lower than previously reported, but the hypothetical minimal growth temperature was also lower. Thus, concentrations of V. parahaemolyticus are likely to increase more slowly in New Zealand oysters than was previously thought, but the organism may also be able to grow at lower temperatures. Both models suggest that the recommended minimum storage temperature of 7 °C in current New Zealand and US regulatory schemes is adequate. This information will be useful for evaluating food safety risks from storing Pacific oysters at different temperatures for different times. It will also assist in formulating time-temperature recommendations for safe handling of Pacific oysters.

The reactive Graetz problem for a first order homogeneous decomposition reaction, and the limit of moderately large reaction constants

The long term goal of the present study is to facilitate the inversion of experimental data on cluster evaporation in laminar flow inside tubes. This requires the determination of reactant and product fluxes as functions of three dimensionless governing parameters: the axial position x along the tube, the reaction rate coefficient K, and the ratio γ of product and reactant diffusivities. The reactant flux is first calculated by separation of variables, including the accurate determination of the first 35 eigenvalues for 13 discrete values of K in the range 0<K<500. This approach is far less efficient for the product flux because the eigenfunctions are not standard functions. A numerical solution is also implemented for the reactant and its product, though it can hardly cover the full range of the three relevant parameters. The most common experimental situation involves relatively large values of K, for which a three term asymptotic solution is developed in negative powers of K1/2, with errors of order K−2. These approximate results represent well the numerically calculated reactant fluxes for K > 10 in terms of two functions, H0(s) and H1(s), reported in tables. The product fluxes require two functions, I0(s,γ), I1(s,γ). These we compute for six values of γ, and report as simple analytical fits covering continuously the full physically relevant range 1<γ<1.5. The desired ability to analyze experimental data to yield kinetic information is therefore achieved without the need for additional numerical solutions to partial differential equations.

Field-Driven Physical Fitness Assessment for Firefighters: Overcoming Challenges and Improving Standards

Being a firefighter is a profession that requires a higher level of physical fitness compared to the general population. This fitness is crucial for effectively carrying out firefighting duties in specialized environments. The level of physical fitness is assessed during the initial employment examination and periodically evaluated throughout the firefighter's career. However, after conducting expert group interviews and surveys with current firefighters, several issues were identified with the current system, which mainly consists of basic physical fitness assessments. These issues include a lack of relevance to actual firefighting tasks, a high risk of injury for participants, inadequate reliability of measurement instruments and assessors, and a failure to incorporate appropriate exercises for improving physical fitness. To address these concerns, an analysis was conducted on the implementation of the U.S. Candidate Physical Ability Test (CPAT) and the performance evaluation of 629 domestic firefighters before and after an 8-week training program. The analysis revealed a significant reduction of approximately 18% in completion time and 17% in oxygen consumption pressure after the training period. These findings highlight the need to move beyond simple physical fitness assessments and develop a new fitness testing system that is specifically tailored to the demands of modern firefighting tasks. This study provides essential data that can serve as a foundation for future research on the development of an improved firefighting fitness testing system. The goal is to establish a reliable and trustworthy system that aligns with public expectations.

Unmanned Aerial System-Based Wheat Biomass Estimation Using Multispectral, Structural and Meteorological Data

Rapid estimation of above-ground biomass (AGB) with high accuracy is essential for monitoring crop growth status and predicting crop yield. Recently, remote sensing techniques using unmanned aerial systems (UASs) have exhibited great potential in obtaining structural information about crops and identifying spatial heterogeneity. However, methods of data fusion of different factors still need to be explored in order to enhance the accuracy of their estimates. Therefore, the objective of this study was to investigate the combined metrics of different variables (spectral, structural and meteorological factors) for AGB estimation of wheat using UAS multispectral data. UAS images were captured on two selected growing dates at a typical reclaimed cropland in the North China Plain. The spectral response was determined using the highly correlated vegetation index (VI). A structural metric, the canopy height model (CHM), was produced using UAS-based multispectral images. The measure of growing degree days (GDD) was selected as a meteorological proxy. Subsequently, a structurally–meteorologically weighted canopy spectral response metric (SM-CSRM) was derived by the pixel-level fusion of CHM, GDD and VI. Both correlation coefficient analysis and simple function fitting were implemented to explore the highest correlation between the measured AGB and each proposed metric. The optimal regression model was built for AGB prediction using leave-one-out cross-validation. The results showed that the proposed SM-CSRM generally improved the correlation between wheat AGB and various VIs and can be used for estimating the wheat AGB. Specifically, the combination of MERIS terrestrial chlorophyll index (MTCI), vegetation-masked CHM (mCHM) and normalized GDD (nGDD) achieved an optimal accuracy (R2 = 0.8069, RMSE = 0.1667 kg/m2, nRMSE = 19.62%) through the polynomial regression method. This improved the nRMSE by 3.44% compared to the predictor using MTCI × mCHM. Moreover, the pixel-level fusion method slightly enhanced the nRMSE by ~0.3% for predicted accuracy compared to the feature-level fusion method. In conclusion, this paper demonstrated that an SM-CSRM using pixel-level fusion with canopy spectral, structural and meteorological factors can obtain a good level of accuracy for wheat biomass prediction. This finding could benefit the assessment of reclaimed cropland or the monitoring of crop growth and field management in precision agriculture.

An Excel fitting routine for correcting protein absorption spectra for scatter

Protein concentrations are often determined in a non-destructive manner by measuring the absorbance at 280 nm. However, light scattering in protein samples can complicate such assessment. We here describe a simple Excel Solver-based fitting routine to correct full protein UV absorption spectra for both Rayleigh and Mie scattering. Using samples displaying various degrees of natural and artificially induced scattering, we show that our multi-wavelength fitting method is not only capable of aiding in the determination of protein concentrations but can also be employed in the spectral analysis of protein structural changes that are accompanied by alterations in scatter intensity.

Shock Cooling and Possible Precursor Emission in the Early Light Curve of the Type II SN 2023ixf

We present the densely sampled early light curve of the Type II supernova (SN) 2023ixf, first observed within hours of explosion in the nearby Pinwheel Galaxy (Messier 101; 6.7 Mpc). Comparing these data to recently updated models of shock-cooling emission, we find that the progenitor likely had a radius of 410 ± 10 R ⊙. Our estimate is model dependent but consistent with a red supergiant. These models provide a good fit to the data starting about 1 day after the explosion, despite the fact that the classification spectrum shows signatures of circumstellar material around SN 2023ixf during that time. Photometry during the first day after the explosion, provided almost entirely by amateur astronomers, does not agree with the shock-cooling models or a simple power-law rise fit to data after 1 day. We consider the possible causes of this discrepancy, including precursor activity from the progenitor star, circumstellar interaction, and emission from the shock before or after it breaks out of the stellar surface. The very low luminosity (−11 mag > M > −14 mag) and short duration of the initial excess lead us to prefer a scenario related to prolonged emission from the SN shock traveling through the progenitor system.

Progressive-fidelity computation of the genetic algorithm for energy-efficient virtual machine placement in cloud data centers

Energy efficiency is a critical issue in the management and operation of data centers, which form the backbone of cloud computing. Virtual machine placement has a significant impact on the energy efficiency of virtualized data centers. Among various methods to solve the virtual-machine placement problem, the genetic algorithm has been well accepted for its quality of solution. However, it is computationally demanding largely due to the complex form of fitness function, limiting its applications in data centers. To enhance the computational efficiency of the genetic algorithm while maintaining its quality of solution, a progressive-fidelity approach is developed in this paper for genetic-algorithm computation. It starts with a low-fidelity genetic algorithm with a simple fitness function. Then, for solution refinement, it switches to a medium-fidelity genetic algorithm with a more complicated fitness function. Finally, it progresses to the fine tuning of solution through a high-fidelity genetic algorithm with the energy consumption of data centers as fitness function. Heuristics are presented for the adaptive switching of genetic-algorithm computation from low fidelity to medium fidelity and finally to high fidelity. Experiments show that compared with the standard genetic algorithm of high fidelity, our progressive-fidelity approach of genetic algorithm computation is 50% faster for large-scale data centers while maintaining similar quality of solution in terms of the energy consumption of data centers.

Non-Settling Super-Strong Magnetorheological Fluids.

A magnetorheological (MR) fluid is generally called a suspension in which magnetic particles are dispersed in a non-magnetic medium. When an external magnetic field is applied, a pseudo-phase transition occurs within a short time to generate yield stress, and when the magnetic field is released, it returns to the suspended state. Due to these unique characteristics, it is classified as a smart material to be widely applied in various industries. High performance MR fluids require high yield stress and stability for long-term use. However, it is very difficult to improve performance and stability simultaneously due to the limited amount of magnetic particles in the suspension and particle sedimentation caused by the density mismatch between the suspending particles and the liquid phase. In this study, an MR slurry is developed that is completely different from the MR suspension, starting from the opposite concept. An innovative non-settling (i.e., permanently stable) magnetorheological slurry is successfully created that exhibits unprecedented ultra-high yield stress. This result is expected to be a turning point for applying MR fluids to more diverse industries. In addition, a simple fitting equation expressing the yield stress as a function of the particle volume fraction is proposed.

The Integral Momentum Balance Applied to Metering Orifices to Obtain the Pressure Drop and the Flow Rate With High Accuracy

Abstract The established method of calculating the pressure loss and the flow rate of flows through metering orifices requires empirical correction coefficients. In this paper, a completely momentum-based approach is presented to predict the flow rate and the pressure loss. It is shown that the proposed approach is as precise as the established method but without any empirical coefficient. The required physical coefficients solely base on physics. They pinpoint the sources of the inaccuracy that effects the employment of an empirical correction coefficient to correct the established method—and confirm the empirical correction coefficient on a physical foundation. Ultimately, the physical coefficients evade the effort to experimentally calibrate the theory with the experiments. The integral momentum balance is applied to a control volume comprising the orifice reaching up to the respective pressure metering cross section. Depending on the location of the control volume's open boundaries, the pressure loss or the flow rate can be obtained. To substitute the integral with averaged expressions, the introduced physical coefficients are deduced directly from numerical simulations and parametrized by simple analytical fit functions to complete the momentum-based approach. The numerical model and the proposed approach are both verified by the comparison with the results obtained by the standard ISO 5167:2003. The agreement with the classical calculation method as per ISO 5167:2003 is very high and within the specified limits, which shows that no empirical coefficient is necessary for high-accuracy flow metering using the momentum balance.

Drag model of finite-sized particle in turbulent wall-bound flow over sediment bed

Drag force acting on a particle is vital for the accurate simulation of turbulent multiphase flows, but the robust drag model is still an open issue. Fully resolved direct numerical simulation (DNS) with an immersed boundary method is performed to investigate the drag force on saltating particles in wall turbulence over a sediment bed. Results show that, for saltating particles, the drag force along the particle trajectories cannot be estimated accurately by traditional drag models originally developed for an isolated particle that depends on the particle-wall separation distance or local volume fraction in addition to the particle Reynolds number. The errors between the models and DNS are especially clear during the descending phase of the particles. Through simple theoretical analysis and DNS data fitting, we present a corrected factor using the classical, particle Reynolds number dependent drag force model as the benchmark model. The new drag model, which takes the particle vertical velocity into account, can reasonably predict the mean drag force obtained by DNS along a particle trajectory.

The IACOB project

Context. Blue supergiants (BSGs) are key objects for studying the intermediate phases of massive star evolution because they are very useful to constrain evolutionary models. However, the lack of a holistic study of a statistically significant and unbiased sample of these objects has lead to several long-standing questions about their physical properties and evolutionary nature to remain unsolved. Aims. This paper and other upcoming papers of the IACOB series are focused on studying from a pure empirical point of view a sample of about 500 Galactic O9–B9 stars with luminosity classes I and II (plus 250 late O- and early B-type stars with luminosity classes III, IV, and V) that cover distances up to ≈4 kpc from the Sun. Methods. We compiled an initial set of ≈11 000 high-resolution spectra from ≈1600 Galactic late O- and B-type stars. We used a novel spectroscopic strategy based on a simple fitting of the Hβ line to select stars in a specific region of the spectroscopic Hertzsprung–Russel diagram. We evaluated the completeness of our sample using the Alma Luminous Star catalog (ALS III) and Gaia-DR3 data. Results. We show the benefits of the proposed strategy for identifying BSGs that are descended in the context of single star evolution from stellar objects that are born as O-type stars. The resulting sample reaches a high level of completeness with respect to the ALS III catalog, gathering ≈80% of all-sky targets brighter than Bmag < 9 located within 2 kpc. However, we identify the need for new observations in specific regions of the southern hemisphere. Conclusions. We have explored a very fast and robust method for selecting BSGs. This provides a valuable tool for large spectroscopic surveys such as WEAVE-SCIP or 4MIDABLE-LR, and it highlights the risk of using spectral classifications from the literature. Upcoming studies will make use of this large and homogeneous spectroscopic sample to study the specific properties of these stars in detail. We initially provide first results for their rotational properties (in terms of projected rotational velocities, v sin i).

Using machine learning to compress the matter transfer function T(k)

The linear matter power spectrum $P(k,z)$ connects theory with large scale structure observations in cosmology. Its scale dependence is entirely encoded in the matter transfer function $T(k)$, which can be computed numerically by Boltzmann solvers, and can also be computed semianalytically by using fitting functions such as the well-known Bardeen-Bond-Kaiser-Szalay (BBKS) and Eisenstein-Hu (EH) formulas. However, both the BBKS and EH formulas have some significant drawbacks. On the one hand, although BBKS is a simple expression, it is only accurate up to 10%, which is well above the 1% precision goal of forthcoming surveys. On the other hand, while EH is as accurate as required by upcoming experiments, it is a rather long and complicated expression. Here, we use the genetic algorithms (GAs), a particular machine learning technique, to derive simple and accurate fitting formulas for the transfer function $T(k)$. When the effects of massive neutrinos are also considered, our expression slightly improves over the EH formula, while being notably shorter in comparison.

Accounting for endogenous effects in decision-making with a non-linear diffusion decision model

The Drift-Diffusion Model (DDM) is widely accepted for two-alternative forced-choice decision paradigms thanks to its simple formalism and close fit to behavioral and neurophysiological data. However, this formalism presents strong limitations in capturing inter-trial dynamics at the single-trial level and endogenous influences. We propose a novel model, the non-linear Drift-Diffusion Model (nl-DDM), that addresses these issues by allowing the existence of several trajectories to the decision boundary. We show that the non-linear model performs better than the drift-diffusion model for an equivalent complexity. To give better intuition on the meaning of nl-DDM parameters, we compare the DDM and the nl-DDM through correlation analysis. This paper provides evidence of the functioning of our model as an extension of the DDM. Moreover, we show that the nl-DDM captures time effects better than the DDM. Our model paves the way toward more accurately analyzing across-trial variability for perceptual decisions and accounts for peri-stimulus influences.

A Novel Strategy for Comprehensive Estimation of Lattice Energy, Bulk Modulus, Chemical Hardness and Electronic Polarizability of ANB8-N Binary Inorganic Crystals

How to search for a convenient method without a complicated calculation process to predict the physicochemical properties of inorganic crystals through a simple micro-parameter is a greatly important issue in the field of materials science. Herein, this paper presents a new and facile technique for the comprehensive estimation of lattice energy (U), bulk modulus (B), chemical hardness (ƞ), and electronic polarizability (α), just by using a simple mathematic fitting formula with a few structure parameters, such as the systems of rock salt crystals (group I–VII, II–VI) and tetrahedral coordinated crystals (group II–VI, III–V). For the typical binary ANB8-N crystal systems, our present conclusions suggest that a good quantitative correlation between U, B, ƞ, α and chemical bond length (d) is observed, the normal mathematical expression is P = a·db (P represents these physicochemical parameters), constants a and b depend on the type of crystals, and the relevant squares of the correlation coefficient (R2) are larger than 0.9. The results indicate that lattice energy, bulk modulus, and chemical hardness decrease with increases in chemical bond length, but electronic polarizability increases with an increase in chemical bond length. Meanwhile, the new data on the lattice energy, bulk modulus, chemical hardness, and electronic polarizability values of binary ANB8-N crystal systems considered in the present study are calculated via the obtained curve fitting equations without any complex calculation process. We find that there is a very good linear trend in our calculated results along with the values reported in the literature. The present study will be important in solid-state chemistry, which may give researchers useful guidance in searching for relevant data for predicting the properties of new materials or synthetic routes based on a simple mathematic empirical model.

An Enhanced Fusion Algorithm With Empirical Thermoelectric Models for Sensorless Temperature Estimation of Li-ion Battery Cells

An enhanced dual extended Kalman filter method is presented in this article for estimating and tracking the state-of-temperature of lithium-ion battery cells. A simple but effective dynamic and measurement empirical fit models are proposed and utilized to estimate the state-of-temperature concurrently with the state-of-charge. The proposed dual estimator improves the estimation accuracy of the temperature state by accounting for the variations in the state-of-charge. To test the performance of the proposed estimation method, two independent lithium-ion battery cell datasets were used to derive the empirical models and run the estimation algorithm. The obtained results show a promising performance of the estimation method in terms of the high estimation accuracy even in the case when the measurement contains high-magnitude noise or when the estimation algorithm is inaccurately initialized. The proposed models and the estimation algorithm are derived and experimentally tested in this article.

Fitting Power Spectrum of Scalar Perturbations for Primordial Black Hole Production during Inflation

A simple phenomenological fit for the power spectrum of scalar (curvature) perturbations during inflation is proposed to analytically describe slow roll of inflaton and formation of primordial black holes (PBH) in the early universe, in the framework of single-field models. The fit is given by a sum of the power spectrum of slow-roll inflation, needed for a viable description of the cosmic microwave background (CMB) radiation in agreement with Planck/BICEP/Keck measurements, and the log-normal (Gaussian) fit for the power spectrum enhancement (peak) needed for efficient PBH production, in the leading (model-independent) approximation. The T-type α-attractor models are used to get the simple CMB power spectrum depending upon the e-folds as the running variable. The location and height of the peak are chosen to yield the PBH masses in the asteroid-size window allowed for the whole (current) dark matter. We find the restrictions on the peak width.

Post-explant profiling of subcellular-scale carbon fiber intracortical electrodes and surrounding neurons enables modeling of recorded electrophysiology

Objective. Characterizing the relationship between neuron spiking and the signals that electrodes record is vital to defining the neural circuits driving brain function and informing clinical brain-machine interface design. However, high electrode biocompatibility and precisely localizing neurons around the electrodes are critical to defining this relationship. Approach. Here, we demonstrate consistent localization of the recording site tips of subcellular-scale (6.8 µm diameter) carbon fiber electrodes and the positions of surrounding neurons. We implanted male rats with carbon fiber electrode arrays for 6 or 12+ weeks targeting layer V motor cortex. After explanting the arrays, we immunostained the implant site and localized putative recording site tips with subcellular-cellular resolution. We then 3D segmented neuron somata within a 50 µm radius from implanted tips to measure neuron positions and health and compare to healthy cortex with symmetric stereotaxic coordinates. Main results. Immunostaining of astrocyte, microglia, and neuron markers confirmed that overall tissue health was indicative of high biocompatibility near the tips. While neurons near implanted carbon fibers were stretched, their number and distribution were similar to hypothetical fibers placed in healthy contralateral brain. Such similar neuron distributions suggest that these minimally invasive electrodes demonstrate the potential to sample naturalistic neural populations. This motivated the prediction of spikes produced by nearby neurons using a simple point source model fit using recorded electrophysiology and the mean positions of the nearest neurons observed in histology. Comparing spike amplitudes suggests that the radius at which single units can be distinguished from others is near the fourth closest neuron (30.7 ± 4.6 µm, ± S) in layer V motor cortex. Significance. Collectively, these data and simulations provide the first direct evidence that neuron placement in the immediate vicinity of the recording site influences how many spike clusters can be reliably identified by spike sorting.