Curve Data Research Articles

Rheological Characteristics of Fe–C–Cr(Ni) Alloys

The principal objective of this project was to investigate the rheological properties of Fe-C-Cr and Fe-C-Ni-based low-alloy steels using an Anton Paar high-temperature rotational viscometer up to 1550 °C. The emphasis was placed on determining the liquidus temperatures and evaluating the flow and viscosity curves and the temperature dependence of dynamic viscosity. All were studied depending on the change in the content of chromium (0.010-4.863 wt%), nickel (0.001-4.495 wt%), and carbon (0.043-1.563 wt%). It was shown that the dynamic viscosity decreases with increasing nickel content and increases with increasing carbon and chromium content. The experimental data of the flow curves were fitted using the Herschel-Bulkley model with a good agreement between the measured and calculated values. Characterization of the internal structure was performed by SEM and EDX analyses, confirming non-significant changes in the microstructure of the original and remelted samples. The phase composition of the selected samples was also determined using JMatPro 12.0 simulation software (Sente Software Ltd., Guildford, UK).

Critical, supercritical and phase-transition properties of binary 1-propanol + n-heptane mixtures

PρTx relationship of four binary 1-propanol + n-heptane mixtures was measured using high-temperature and high-pressure constant-volume piezometer technique in the critical, supercritical, and in the immediate vicinity of the liquid–gas phase transition curve. The experimental temperature and pressure ranges were from (373 to 623) K and up to 60 MPa. The liquid–gas critical loci behavior in the (Tc-x), (Pc-x), (ρc-x), and (Pc-Tc) projections, were studied based on the measured PρTx data. The values of the Krichevskii parameter for the (1-propanol + n-heptane) mixture were calculated using the measured critical curves data. Pure- and mixture-like behavior of the weakly (Cvx) and strongly (KTx and CPx) divergent properties has been studied based on measured critical curve data. The measured mixture molar volumes and pure components data were used to calculate the excess molar volumes. The values of the second virial coefficient of the mixture were calculated based on the measured PVTx data.

Frequency Analysis of High Amplitude δ Scuti Star V593 Lyr by American Association of Variable Stars Observers International Database

The new results of light variability and frequency analysis of a high amplitude δ Scuti star (HADS) V593 Lyr were reported. δ Scuti-type variable stars are fascinating short-period stars with luminosity classes from III to V and spectral types from A0 to F6. Their location on the Hertzsprung-Russell (H-R) diagram is in the classical cepheid instability strip. The photometric data in the V filter and clear filter of V593 Lyr were collected from the American Association of Variable Stars Observers (AAVSO) international database. Discrete Fourier transformation and pre-whitening methods were used in the Period04 package to examine time-series light curve data. During 15 observational nights, we obtained 28 times of new light maxima magnitude variability and significant pulsation frequencies for the star. The study found that the fundamental frequency of V593 Lyr is 9.7894180 ± 0.0000002 d−1, corresponding to the main pulsation period of about 0.1021511 d and detected its second to fifth harmonics with a signal-to-noise ratio (S/N) of more than four. A study of the pulsation frequencies and period change of HADS stars can estimate their pulsation modes and evolution. Moreover, HADS stars are suitable targets for understanding asteroseismology, which is the subject that applies a star's pulsations to investigate its internal structure, and the high amplitude stars provide reference points for calculating cosmic distances in the Universe.

Studying the Exoplanets HAT-P-56 b and HATS-36 b with Kepler

Exoplanet has become one of the most popular research subjects in astronomy. This study analyzes light curve data from the K2 mission of the Kepler space telescope and measure the properties of two exoplanets, HAT-P-56 b and HATS-36 b. With the transit method, the radii are obtained, orbital periods and semi-major axes of these two exoplanets. For HAT-P-56 b, the period is , the radius is and the semi-major axis is . For HATS-36 b, the period is , the radius is and the semi-major axis is . Compared to previous studies, it is found that radii and semi-major axes measured in this paper are consistent with their results, whereas the calculated orbital period is a bit bigger. In addition, the large radii and short orbital periods suggest that both exoplanets are Hot Jupiters. These results shed light on the exploring of the exoplanets around the two host stars, HAT-P-56 and HATS-36.

A high efficiency CO2 concentration interval optimization method for lettuce growth

The decline in carbon fertilization effects has shifted scientific focus toward the efficient and suitable regulation of CO2 concentration ([CO2]) for plant growth. In this study, the rapid A/CO2 response curve (RAC) data of lettuce were analyzed statistically under nine photosynthetic photon flux densities (PPFDs) and four temperatures. An efficient CO2 supplementation interval acquisition method based on the frequency distribution characteristics of RACs was proposed. The characteristic subsections of jumping were obtained depending on the frequency distribution of RACs. The cumulative contribution rate (CCR) of the characteristic subsections were >97 %, which showed the efficiency of the method. Additionally, U-chord curvature theory was used to simultaneously obtain the optimal regulated [CO2] for the same RAC curves, and the results showed that the [CO2] obtained by U-chord length were all within the interval obtained by the method, which proved the rationality of the method. The [CO2] interval supplement improved the daily CO2 exchange rate by 20.27 % and 21.64 % at 150 and 200 μmol·m−2·s−1, and increased the lettuce fresh biomass by 26.78 % at 150 μmol·m−2·s−1. Based on the interval of [CO2] efficient utilization regulation at various temperatures and PPFDs, a genetic algorithm-support vector regression model was built with R2 of the model was >0.84 and the root mean square error was <35.2256 μmol·mol−1. In conclusion, the [CO2] interval obtained by this method has a positive effect on lettuce growth. This work provides a new method for obtaining high-efficiency supplementary concentration of CO2 during the growth of lettuce.

PK/PD modeling and simulation of the in vitro activity of the combinations of isavuconazole with echinocandins against Candida auris.

In vitro combination of echinocandins and isavuconazole against the emerging species Candida auris is mainly synergistic. However, this combination has not been evaluated in clinical settings. A pharmacokinetic/pharmacodynamic modeling and simulation approach based on in vitro data may be helpful to further study the therapeutic potential of these combinations. Therefore, the aims of this study were to characterize the time course of growth and killing of C. auris in response to the combination of the three approved echinocandins and isavuconazole using a semimechanistic model and to perform model-based simulations in order to predict the in vivo response to combination therapy. In vitro static time-kill curve data for isavuconazole and echinocandins combinations against six blood isolates of C. auris were best modeled considering the total killing of the fungal population as dependent on the additive effects of both drugs. Once assessed, the predictive performance of the model using simulations of different dosing and fungal susceptibility scenarios were conducted. Model-based simulations revealed that none of the combinations at standard or higher dosages would be effective against the studied isolates of C. auris and it was predicted that the combinations of isavuconazole with anidulafungin or caspofungin would be effective for minimum inhibitory concentrations up to 0.03 and 0.06 mg/L respectively, whereas the combination with micafungin would lead to treatment failure. The current approach highlights the importance of bridging the in vitro results to the clinic.

The Blinded-Dose Purchase Task: assessing hypothetical demand based on cocaine, methamphetamine, and alcohol administration

RationaleBehavioral economic drug purchase tasks quantify the reinforcing value of a drug (i.e., demand). Although widely used to assess demand, drug expectancies are rarely accounted for and may introduce variability across participants given diverse drug experiences.ObjectivesThree experiments validated and extended previous hypothetical purchase tasks by using blinded drug dose as a reinforcing stimulus, and determined hypothetical demand for experienced effects while controlling for drug expectancies.MethodsAcross three double-blind, placebo-controlled, within-subject experiments, cocaine (0, 125, 250 mg/70 kg; n=12), methamphetamine (0, 20, 40 mg; n=19), and alcohol (0, 1 g/kg alcohol; n=25) were administered and demand was assessed using the Blinded-Dose Purchase Task. Participants answered questions regarding simulated purchasing of the blinded drug dose across increasing prices. Demand metrics, subjective effects, and self-reported real-world monetary spending on drugs were evaluated.ResultsData were well modeled by the demand curve function, with significantly higher intensity (purchasing at low prices) for active drug doses compared to placebo for all experiments. Unit-price analyses revealed more persistent consumption across prices (lower α) in the higher compared to lower active dose condition for methamphetamine (a similar non-significant finding emerged for cocaine). Significant associations between demand metrics, peak subjective effects, and real-world spending on drugs also emerged across all experiments.ConclusionsOrderly demand curve data revealed differences across drug and placebo conditions, and relations to real-world measures of drug spending, and subjective effects. Unit-price analyses enabled parsimonious comparisons across doses. Results lend credence to the validity of the Blinded-Dose Purchase Task, which allows for control of drug expectancies.

Recurrence quantification analysis of isokinetic strength tests: A comparison of the anterior cruciate ligament reconstructed and the uninjured limb

BackgroundDespite widespread use of return to sport testing following anterior cruciate ligament reconstruction, studies suggest inadequacy in current testing criteria, such as limb symmetry index calculations, to determine athletes' readiness to return to play. Recurrence quantification analysis, an emerging non-linear data analysis tool, may reveal subtle neuromuscular differences between the injured and uninjured limb that are not captured by traditional testing. We hypothesized that isokinetic torque curve data of the injured limb would demonstrate lower determinism and entropy as compared to the uninjured limb. Methods102 patients (44 M, 58F, 10 ± 1 months post-anterior cruciate ligament reconstruction) underwent isokinetic quadriceps strength testing using a HumacNorm dynamometer. Patients completed maximum effort knee extension and flexion at 60°/sec. Data were post-processed with a MATLAB CRQA Graphical User Interface and determinism and entropy values were extracted. Paired-sample t-tests (α = 0.05) were used to compare data from the injured and uninjured limb. FindingsDeterminism and entropy values in the torque curves were lower in the injured limb than the uninjured limb (p < 0.001). Our findings indicate there is less predictability and complexity present in the torque signals of injured limbs. InterpretationRecurrence quantification analysis can be used to assess neuromuscular differences between limbs in patients who have undergone anterior cruciate ligament reconstruction. Our findings offer further evidence that there are changes to the neuromuscular system which persist following reconstruction. Further investigation is needed to establish thresholds of determinism and entropy values needed for safe return to sport and to evaluate the utility of recurrence quantification analysis as a return to sport criterion.

Realizing the value in "non-standard" parts of the qPCR standard curve by integrating fundamentals of quantitative microbiology.

The real-time polymerase chain reaction (PCR), commonly known as quantitative PCR (qPCR), is increasingly common in environmental microbiology applications. During the COVID-19 pandemic, qPCR combined with reverse transcription (RT-qPCR) has been used to detect and quantify SARS-CoV-2 in clinical diagnoses and wastewater monitoring of local trends. Estimation of concentrations using qPCR often features a log-linear standard curve model calibrating quantification cycle (Cq) values obtained from underlying fluorescence measurements to standard concentrations. This process works well at high concentrations within a linear dynamic range but has diminishing reliability at low concentrations because it cannot explain "non-standard" data such as Cq values reflecting increasing variability at low concentrations or non-detects that do not yield Cq values at all. Here, fundamental probabilistic modeling concepts from classical quantitative microbiology were integrated into standard curve modeling approaches by reflecting well-understood mechanisms for random error in microbial data. This work showed that data diverging from the log-linear regression model at low concentrations as well as non-detects can be seamlessly integrated into enhanced standard curve analysis. The newly developed model provides improved representation of standard curve data at low concentrations while converging asymptotically upon conventional log-linear regression at high concentrations and adding no fitting parameters. Such modeling facilitates exploration of the effects of various random error mechanisms in experiments generating standard curve data, enables quantification of uncertainty in standard curve parameters, and is an important step toward quantifying uncertainty in qPCR-based concentration estimates. Improving understanding of the random error in qPCR data and standard curve modeling is especially important when low concentrations are of particular interest and inappropriate analysis can unduly affect interpretation, conclusions regarding lab performance, reported concentration estimates, and associated decision-making.

MeltR software provides facile determination of nucleic acid thermodynamics

Thermodenaturation (melting) curves of macromolecules are used to determine folding thermodynamic parameters. Notably, this insight into RNA and DNA stability underlies nearest neighbor theory and diverse structure prediction tools. Analysis of UV-detected absorbance melting curves is complex and multivariate, requiring many data preprocessing, regression, and error analysis steps. The absorbance melting curve-fitting software MeltWin, introduced in 1996, provided a consistent and facile melting curve analysis platform used in a generation of folding parameters. Unfortunately, MeltWin software is not maintained and relies on idiosyncratic choices of baselines by the user. Herein, we provide MeltR, an open-source, curve-fitting package for analysis of macromolecular thermodynamic data. The MeltR package provides the facile conversion of melting curve data to parameters provided by MeltWin while offering additional features including global fitting of data, auto-baseline generation, and two-state melting analysis. MeltR should be a useful tool for analyzing the next generation of DNA, RNA, and nonnucleic acid macromolecular melting data.

Simple VHF and UHF Propagation Loss Model

Empirical propagation loss models play a crucial role in a wireless communication system with their cost-effectiveness and simplicity. In this letter, we present a mathematical characterization of the Federal Communication Commission (FCC) F(50,50) curve for the Very High Frequency (VHF) and the Ultra High Frequency (UHF) band. The proposed empirical model is based on a novel step-wise curve fitting the FCC signal strength data. The F(50,50) model is intended for the TV broadcasting system and has been employed effectively in different literary works. Following this, we proposed a simple mathematical expression applicable to a 100 km distance with antenna height ranging from 30 to 300 m. The results showed that the proposed mathematical formulation agreed well with the F(50,50) curve data. Furthermore, the model presented is straightforward and can readily be implemented in VHF and UHF signal loss calculation requiring minimal input parameters.

Robotic-assisted radical prostatectomy: learning curves and outcomes from an Australian perspective.

Robot-assisted radical prostatectomy (RARP) has been a treatment for men who suffer from intermediated to high-risk prostate cancer in Australia since 2003. The primary outcomes in relation to learning curves in robotic surgery have been extensively researched in overseas populations, but there is no study from a cohort of Australian surgeons performing RARP. This study aims to highlight the effect of RARP learning curves on primary surgical outcomes in a high-volume Australian centre. A retrospective audit of all RARP performed at Epworth Healthcare from 2016 to 2021 was performed. The primary outcome data collected included operating time (OT), estimated blood loss (EBL), and positive surgical margins (PSM). Exclusion criteria were applied. Positive outcomes were set at OT 240min, blood loss 310mL, and negative surgical margins. A total of 3969 cases were analysed for a cohort of 53 surgeons. Of these surgeons, 24 surgeons have performed >50 operations to be able to undergo learning curve analysis. The median OT was 229min, the median blood loss was 353mL, and most cases had negative surgical margins (>1mm, n=3681, 92.7%). The mean learning curve transition point was 65 cases. There was a significant difference in the EBL and rate of PSM for the higher volume cohort (p=0.002 and <0.0001, respectively). We perform a retrospective study of all RARP performed at a high-volume Australian centre. Higher volume surgeons demonstrate that primary outcomes improve with a higher caseload (EBL, PSM). Learning curve transition points for RARP are comparable to international high-volume surgeons. Learning curve data could form the benchmark for RARP training and skills development.

Galaxy Rotation Curves and Universal Scaling Relations: Comparison between Phenomenological and Fermionic Dark Matter Profiles

Galaxies show different halo scaling relations such as the radial acceleration relation, the mass discrepancy acceleration relation (MDAR), or the dark matter (DM) surface density relation. At difference with traditional studies using phenomenological ΛCDM halos, we analyze the above relations assuming that DM halos are formed through a maximum entropy principle (MEP) in which the fermionic (quantum) nature of the DM particles is dully accounted for. For the first time, a competitive DM model based on first physical principles, such as (quantum) statistical-mechanics and thermodynamics, is tested against a large data set of galactic observables. In particular, we compare the fermionic DM model with empirical DM profiles: the Navarro–Frenk–White (NFW) model, a generalized NFW model accounting for baryonic feedback, the Einasto model, and the Burkert model. For this task, we use a large sample of 120 galaxies taken from the Spitzer Photometry and Accurate Rotation Curves data set, from which we infer the DM content to compare with the models. We find that the radial acceleration relation and MDAR are well explained by all the models with comparable accuracy, while the fits to the individual rotation curves, in contrast, show that cored DM halos are statistically preferred with respect to the cuspy NFW profile. However, very different physical principles justify the flat inner-halo slope in the most-favored DM profiles: while generalized NFW or Einasto models rely on complex baryonic feedback processes, the MEP scenario involves a quasi-thermodynamic equilibrium of the DM particles.

Liquid-liquid equilibria of the ternary systems water + C1 – C3 alcohols + dimethyl adipate at 298.15 K and atmospheric pressure: Experimental data and modeling

The aim of this study was to examine the possibility of using a green organic solvent, dimethyl adipate (DMA), as a separation agent in the extraction of different alcohols from aqueous solutions. For that purpose, the liquid–liquid equilibria (LLE) of four ternary systems were analytically determined at 298.15 K and atmospheric pressure using stirred and thermo-regulated cells: {water + methanol + dimethyl adipate (DMA)}, {water + ethanol + DMA}, {water + 1-propanol + DMA}, {water + 2-propanol + DMA}. Phase diagrams for these four ternary systems were obtained by determining a binodal curve and tie-lines data using a visual cloud-point method, but also gas chromatography. Distribution coefficients and separation factors were calculated for the immiscibility region. Hand and Othmer-Tobias correlations were used to examine the reliability of the tie-line data. In addition, the experimental ternary LLE data were correlated with the UNIversal QUAsiChemical (UNIQUAC) activity coefficient models. The obtained results showed that DMA is a suitable candidate for the extraction of methanol, ethanol, 1-propanol, and 2-propanol from water. With some alcohols, this potential is more noticeable than with others. The potential of DMA as a separation agent is increasing in the following sequence: methanol < ethanol < 2-propanol < 1-propanol, and that also could be noticed from the selectivity values that also increase in this sequence. The UNIQUAC model proved as successful in correlating the LLE data.

A Longitudinal Dyadic Study of Six Leisure Activities in Swedish Couples During the Transition to Parenthood

Parents are not only caregivers to their children; they also have leisure routines that can impact their own well-being. However, little is known about how leisure activities change within the context of a couple during the transition to parenthood. This study uses latent growth curve models and data from the Swedish Pregnancy Panel to examine how often 918 first-time, heterosexual couples participated in six leisure activities from around pregnancy week 19 to one year postpartum. Compared to fathers, mothers less frequently exercised and listened to news, and more frequently read newspapers, spent time for themselves, and spent time with friends. Over time, mothers increased their frequency of praying to God and decreased spending time for themselves and with friends. Fathers decreased frequency of exercise. Within couples, there was a positive correlation between mothers’ and fathers’ frequency of engaging in leisure activities, although most changes over time were not associated. Our finding that two individuals within a couple may change their leisure activities independently of each other during the transition to parenthood can help healthcare professionals and researchers prepare expectant couples for upcoming changes (or lack thereof) and promote parent well-being. Our findings also highlight the possibility that in contexts with more state support for families, parenthood may not exacerbate gender gaps in leisure.

Anterior vertebral body tethering for adolescent idiopathic scoliosis associated with less early post-operative pain and shorter recovery compared with fusion

PurposeWhile posterior spinal instrumentation and fusion (PSIF) for severe adolescent idiopathic scoliosis (AIS) is the gold standard, anterior vertebral body tethering (AVBT) is becoming an alternative for select cases. Several studies have compared technical outcomes for these two procedures, but no studies have compared post-operative pain and recovery.MethodsIn this prospective cohort, we evaluated patients who underwent AVBT or PSIF for AIS for a period of 6 weeks after operation. Pre-operative curve data were obtained from the medical record. Post-operative pain and recovery were evaluated with pain scores, pain confidence scores, PROMIS scores for pain behavior, interference, and mobility, and functional milestones of opiate use, independence in activities of daily living (ADLs), and sleeping.ResultsThe cohort included 9 patients who underwent AVBT and 22 who underwent PSIF, with a mean age of 13.7 years, 90% girls, and 77.4% white. The AVBT patients were younger (p = 0.03) and had fewer instrumented levels (p = 0.03). Results were significant for decreased pain scores at 2 and 6 weeks after operation (p = 0.004, and 0.030), decreased PROMIS pain behavior at all time points (p = 0.024, 0.049, and 0.001), decreased pain interference at 2 and 6 weeks post-operative (p = 0.012 and 0.009), increased PROMIS mobility scores at all time points (p = 0.036, 0.038, and 0.018), and faster time to functional milestones of weaning opiates, independence in ADLs, and sleep (p = 0.024, 0.049, and 0.001).ConclusionIn this prospective cohort study, the early recovery period following AVBT for AIS is characterized by less pain, increased mobility, and faster recovery of functional milestones, compared with PSIF.Level of evidenceIV.

Potential distribution of a noxious weed (Solanum viarum Du-nal), current status, and future invasion risk based on MaxEnt modeling

ABSTRACT Species distribution modeling and niche dynamics of alien plants can aid in understanding the existing and future invasion potential in the wake of climate change. We describe the niche dynamics of S. viarum Dunal, and report on its present and predicted future distribution in Pakistan under scenarios of climate change. The results revealed that S. viarum had an extensive range of prospective distribution zones in Pakistan, with Central Punjab, Eastern Khyber Pakhtunkhwa, and Southern Balochistan being the most suitable. According to ROC curve data, the MaxEnt model has a great prediction precision and credible outcomes. The most important variables influencing S. viarum latent distribution may be precipitation and temperature. The current potential area of S. viarum is approximately 105,750 km2 (12%). Under climate change regimes, the highly suitable area for S. viarum increases by 2–9% under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The outcomes of this study could help to develop control of regional invasions and climate change approaches in Pakistan. More importantly, the findings of niche dynamics in native and invaded S. viarum areas contribute to a better understanding of the nature of niche modifications in invasive species and the potential for invasion under climate change.

Influence of Microstructural Characteristics on Wear and Corrosion Behaviour of Si3N4-Reinforced Al2219 Composites

The fabrication of the Al2219 + Si3N4 composites was done by means of stir casting process. The specimen was subjected to microstructural studies. The density of Si3N4 employed in the fabrication of the composites has been evaluated. Generally, the density of the composites increases as the weight percent of Si3N4 increases. TGA and DTA were performed at temperatures ranging from 30°C to 800°C, with some mass loss noted as well as the thermal stability of an AA2219 matrix improved. Under various parameters of applied load, operating duration, and speed, the wear behaviour of the produced aluminium matrix composite (AMC) was examined. Corrosion behaviour was examined using potentiodynamic polarization curve data, and it was discovered that the rate of corrosion decreases with increasing Si3N4 content, adding to the AA2219 matrix. The research work is intended to produce a totally new composite material with various compositions of reinforcement and to investigate their mechanical properties and a result of simulation.

Comparison of statistical models for characterizing continuous differences between two biomechanical measurement systems

Most biomechanical processes are continuous in nature. Measurement systems record this continuous behavior as curve data, which is often treated inappropriately in validation studies. The current paper compares different statistical models for analyzing the agreement of curves from two measurement systems. All models were evaluated in various error scenarios (simulated and real-world data). Excellent results were obtained using a functional method, with coverage probabilities close to the desired level in all data sets. Pointwise constructed bands had a lower coverage probability, but still contained most of the curve points and may thus be an option in scenarios where assumptions of functional models are violated (e.g., when curves are much noisier than those presented here, or in the presence of drift). Models that account for within-subject variation showed a higher coverage probability and less uncertainty about the variation of band limits. We hope this study, along with the provided research code, will inspire researchers to use methods for curve data more frequently and appropriately.

Semi-Autonomic AI LF-NMR Sensor for Industrial Prediction of Edible Oil Oxidation Status.

The evaluation of an oil's oxidation status during industrial production is highly important for monitoring the oil's purity and nutritional value during production, transportation, storage, and cooking. The oil and food industry is seeking a real-time, non-destructive, rapid, robust, and low-cost sensor for nutritional oil's material characterization. Towards this goal, a 1H LF-NMR relaxation sensor application based on the chemical and structural profiling of non-oxidized and oxidized oils was developed. This study dealt with a relatively large-scale oil oxidation database, which included crude data of a 1H LF-NMR relaxation curve, and its reconstruction into T1 and T2 spectral fingerprints, self-diffusion coefficient D, and conventional standard chemical test results. This study used a convolutional neural network (CNN) that was trained to classify T2 relaxation curves into three ordinal classes representing three different oil oxidation levels (non-oxidized, partial oxidation, and high level of oxidation). Supervised learning was used on the T2 signals paired with the ground-truth labels of oxidation values as per conventional chemical lab oxidation tests. The test data results (not used for training) show a high classification accuracy (95%). The proposed AI method integrates a large training set, an LF-NMR sensor, and a machine learning program that meets the requirements of the oil and food industry and can be further developed for other applications.