Recovery Of Parameters Research Articles

Separating deterministic and stochastic gravitational wave signals in realistic pulsar timing array datasets

Recent observations by pulsar timing arrays (PTAs) suggest the presence of gravitational wave (GW) signals that potentially originate from supermassive black hole binaries (SMBHBs). These binaries can generate two kinds of signals: a stochastic gravitational wave background (GWB) or a deterministic continuous gravitational wave (CGW). The ability to correctly recognize and separate them is crucial for accurate signal recovery and astrophysical interpretation. This paper is aimed at investigating the interaction between stochastic GWB and deterministic CGW signals with the analysis pipelines currently available. We focus on understanding potential misinterpretations and biases in the parameter estimation when these signals are analysed separately or altogether. To this end, we performed several realistic simulations based on the European PTA 24.8yr dataset. We first injected either a GWB or a CGW into five datasets (of three GWB realisations and two CGW realisations) with identical noise. We analysed each signal type independently and then we analysed data sets containing both a stochastic GWB and a single resolvable CGW. We compared the parameter estimations using different search models, including Earth term (ET) only or combined Earth and pulsar term (ET+PT) CGW templates, along with correlated or uncorrelated power law GWB templates. We show that when searched for independently, the GWB and CGW signals can be misinterpreted (i.e. they can be confused with each other) and only a combined search is able to recover the true signal present. For datasets containing both a GWB and a CGW, failure to account for the latter biases the recovery of the GWB; however, when we perform a combined search, both GWB and CGW parameters can be recovered without any strong bias. Care must be taken with the method used to perform combined searches on these multi-component datasets, as the CGW PT can be misinterpreted as a common uncorrelated red noise. However, this can be avoided by conducting direct searches for a correlated GWB plus a CGW (ET+PT). Our study underscores the importance of combined searches to ensure unbiased recovery of GWB parameters in the presence of strong CGWs. This is crucial to accurately interpreting the signal recently found in PTA data and it is a first step towards a robust framework for disentangling stochastic and deterministic GW components in more sensitive datasets in the future.

A ranking forced choice diagnostic classification model for psychological assessment using forced choice questionnaires.

The diagnostic classification model (DCM) has been widely utilized in non-cognitive tests, offering diagnostic information on latent attributes. However, the model's reliance on single-stimulus (SS) items may lead to response biases (e.g., social desirability), jeopardizing the psychometric properties. As an alternative to SS scales, forced choice questionnaires (FCQ) can effectively control response biases. The combination of FCQs and the DCM not only circumvents response bias but also yields fine-grained diagnostic information on latent attributes. To the best of our knowledge, only one study (Huang, Educ. Psychol. Meas., 83, 2022, 146) has explored this topic and developed a DCM for forced choice (FC) items. However, the existing model has limitations in terms of its modelling assumption, the FC format and the number of attributes measured by statement. To address these limitations, this study proposes a ranking FC-DCM that (1) adopts a generalized assumption, (2) covers all FC formats and (3) eases the limitation on the number of attributes measured by each statement. The simulation study demonstrated that the proposed model exhibited satisfactory person and item parameter recovery under all conditions. This study provided an illustrative example by developing an FC version questionnaire to further explore the applications and advantages of the proposed model in real-world settings.

Research on the Mechanism of Spinal Stability Under Body Load

In modern society, the prevalence of musculoskeletal disorders and spinal problems has become increasingly concerning, particularly among students and working professionals who regularly carry heavy loads. The growing awareness of health issues related to load carrying has sparked significant research interest in this field. This study investigated the mechanisms of spinal stability under various loading conditions among college students. While backpacks are essential in daily life, their impact on spinal biomechanics and potential injury risks remains a concern. Twenty university students (10 males, 10 females) participated in this research examining the effects of different load magnitudes (0%, 5%, 10%, 20%, and 30% body weight) and carrying durations on spinal stability. Using three-dimensional motion capture, force platform measurements, and surface electromyography, we analyzed participants' postural control and muscle activity during both static stance and dynamic walking conditions at various gradients (0°, 5°, 10°, 20°). Results showed that loads exceeding 20% body weight caused significant alterations in spinal alignment, with forward lean angles increasing by 7-8 degrees at 30% body weight loading. During inclined walking, the combination of slope and load had multiplicative effects, with 30% body weight load at 20° slope resulting in approximately 10-12 degrees more spine forward flexion compared to level ground. Prolonged loading (60 minutes) led to a 30-35% increase in center of pressure sway range, indicating deteriorated postural control. EMG analysis revealed significant muscle fatigue, with erector spinae and multifidus muscles showing primary roles in maintaining spinal stability. Recovery of spinal stability parameters required approximately 30 minutes following heavy load carrying. These findings provide important guidance for establishing evidence-based recommendations for load carrying among college students and emphasize the need for appropriate rest periods and carrying techniques to maintain spinal health.

Exploiting the diversity of modeling methods to probe systematic biases in strong lensing analyses

Challenges inherent to high-resolution and high signal-to-noise data as well as model degeneracies can cause systematic biases in analyses of strong lens systems. In the past decade, the number of lens modeling methods has significantly increased, from purely analytical methods, to pixelated and non-parametric ones, or ones based on deep learning. We embraced this diversity by selecting different software packages and use them to blindly model independently simulated Hubble Space Telescope (HST) imaging data. To overcome the difficulties arising from using different codes and conventions, we used the COde-independent Organized LEns STandard (COOLEST) to store, compare, and release all models in a self-consistent and human-readable manner. From an ensemble of six modeling methods, we studied the recovery of the lens potential parameters and properties of the reconstructed source. In particular, we simulated and inferred parameters of an elliptical power-law mass distribution embedded in a shear field for the lens, while each modeling method reconstructs the source differently. We find that, overall, both lens and source properties are recovered reasonably well, but systematic biases arise in all methods. Interestingly, we do not observe that a single method is significantly more accurate than others, and the amount of bias largely depends on the specific lens or source property of interest. By combining posterior distributions from individual methods using equal weights, the maximal systematic biases on lens model parameters inferred from individual models are reduced by a factor of 5.4 on average. We investigated a selection of modeling effects that partly explain the observed biases, such as the cuspy nature of the background source and the accuracy of the point spread function. This work introduces, for the first time, a generic framework to compare and ease the combination of models obtained from different codes and methods, which will be key to retain accuracy in future strong lensing analyses.

Deconstructing delay discounting in human cocaine addiction using computational modelling and neuroimaging.

A preference for sooner-smaller over later-larger rewards, known as delay discounting, is a candidate transdiagnostic marker of waiting impulsivity and a research domain criterion. While abnormal discounting rates have been associated with many psychiatric diagnoses and abnormal brain structure, the underlying neuropsychological processes remain largely unknown. Here, we deconstruct delay discounting into choice and rate processes by testing different computational models and investigate their associations with white matter tracts. Patients with cocaine use disorder (CUD, n=107) and healthy participants (n=81) completed the Monetary Choice Questionnaire. We computed their discounting rate using the well-known Kirby method, plus logistic regression, single-subject and full hierarchical Bayesian models. In Bayesian models, we additionally included a choice sharpness parameter. Seventy CUD patients and 69 healthy participants also underwent diffusion tensor imaging tractography to quantify streamlines connecting the executive control and valuation brain networks. CUD patients showed significantly higher discounting rates, and lower choice sharpness, suggesting greater indifference in their choices. Importantly, the full Bayesian model had the greatest reliability for parameter recovery compared with Kirby and logistic regression methods. Using Bayesian estimates, we found that white matter streamlines connecting executive control network with the nucleus accumbens predicted discounting rate in healthy participants, but not in CUD patients. We demonstrate that measuring delay discounting and choice sharpness directly with a novel computational model explains impulsive choices in CUD patients better than standard hyperbolic discounting. Our findings highlight a distinct neuropsychological phenotype of impulsive discounting, which may be generalizable to other patient groups.

Parameter recovery of skew multidimensional multiple-group item response models (MGM-MIRT): a comparison of MCMC algorithms and assessment of effects of interest

Multidimensional item response theory (MIRT) models are powerful tools to analyse item response data. Most of them rely on the assumption that the latent traits are normally distributed. However, when such assumption does not hold, the item and the latent traits estimates can be inaccurate. This paper deals, in a very detailed way, through simulation studies, with the utility of the class of the MIRT models developed by Padilla et al. [Multidimensional multiple-group IRT models with skew normal latent trait distributions. J Multivariate Anal 2018;167:250–268], where the multivariate skew-normal distribution under the centred parameterization is employed for modelling the latent traits. The impact of important factors of interest on the parameter recovery is investigated. Such factors are the number of subjects, number of items, number of groups and asymmetry level. In a general way, the results show that our approach outperforms some traditional models when the latent traits follow asymmetric distributions. In addition, different Monte Carlo Markov Chain (MCMC) algorithms for parameter estimation are compared, by considering two schemes of data augmentation and a proposal to speed up their convergence, based on approaches found in the literature, for binary regression models. Guidelines are provided for the correct using of Bayesian inferential methods for the MIRT models.

A refreshing take on the inverted Dirichlet via a mode parameterization with some statistical illustrations

AbstractThe inverted Dirichlet (IDir) distribution is a popular choice for modeling multivariate data with positive support; however, its conventional parameterization can be challenging to interpret. In this paper, we propose a refreshing take on the IDir distribution through a convenient mode-based parameterization, resulting in the mode-reparameterized IDir (mIDir). This new parameterization aims to enhance the use of the IDir in various contexts. We provide relevant statistical illustrations in robust and nonparametric statistics, model-based clustering, and semiparametric density estimation, all benefiting from this novel perspective on the IDir for computation and implementation. First, we define finite mIDir mixtures for clustering and semiparametric density estimation. Secondly, we introduce a smoother based on mIDir kernels, which, by design, avoids allocating probability mass to unrealistic negative values, thereby addressing the boundary bias issue. Thirdly, we introduce a heavy-tailed generalization of the mIDir distribution, referred to as the contaminated mIDir (cmIDir), which effectively handles and detects mild outliers, making it suitable for robust statistics. Maximum likelihood estimates of the parameters for the parametric models are obtained using a developed EM algorithm as well as direct numerical optimization. A parameter recovery analysis demonstrates the successful application of the estimation method, while a sensitivity analysis examines the impact of mild outliers on both the mIDir and cmIDir models. The flexibility and advantages of the proposed mIDir-based models are showcased through several real data analyses and illustrations.

Characterisation of Electrochemical Systems from Periodic Chronoamperometry Signals Using Computational Tools

Electrochemical systems are complex, involving various chemical and physical processes (e.g. electron transfer reactions, diffusive and convective mass transfer, charge transfer processes). In most cases, electrochemical systems are summarised using simple signals (e.g. amperometric or potentiometric signals), which do not, however, adequately capture their complexity. These mostly unidimensional signals are usually insufficient to facilitate the full understanding of the electrochemical system and its underlying, simultaneously occurring processes.Established electroanalytical methods are commonly utilised to characterise specific (electro-) chemical and physical processes of electrochemical systems (e.g. potentiometry, amperometry/voltammetry, electrochemical impedance spectroscopy, etc.). Despite the usefulness of these traditional electroanalysis techniques, they are often restricted to the analysis of a very specific set of system characteristics and parameters. Moreover, the experimental nature of these techniques, usually requiring a specific protocol, sets limits to their employment for on-line measurements of industrial electrochemical systems (e.g. reactors, fuel cells etc.).To avoid these limitations, a new alternative approach to understanding and characterising electrochemical systems is proposed. Computational techniques have the potential to extract various system parameters from periodic chronoamperometry signals of electrochemical systems. Such techniques include methods that are commonly used in chemometric investigations in addition to other modern signal processing tools.Early findings from this ongoing study include the separation of Faradaic and non-Faradaic current contributions from a variety of simulated chronoamperometric signals. Moreover, qualitative sensitivity studies appear promising for the subsequent quantitative recovery of multiple parameters from stagnant and hydrodynamic electrochemical systems (e.g. capacitance, resistance).As a consequence, this novel computer-aided approach can pave the way for real-time analyses of electrochemical applications. Furthermore, this strategy will likely reduce the need for specific electroanalytic experiments to determine certain parameters. Instead, complex electrochemical systems and their underlying chemical and physical processes have the potential to be characterised from simple, continuous chronoamperometric signals by means of computational techniques.

A CIGALE module tailored (not only) for low-luminosity active galactic nuclei

The spectral energy distribution (SED) of low-luminosity active galactic nuclei (LLAGN) presents unique challenges as the emission from these objects is comparable to the radiation from their host galaxy and the accretion physics involved is particularly complex. This study introduces a novel CIGALE module specifically designed to address these challenges. The module combines the empirical $L_ X $--$L_ m $ relationship with physically motivated accretion models, such as advection-dominated accretion flows (ADAFs) and truncated accretion disks, providing a more accurate depiction of LLAGN central engine emission. A mock analysis of the module revealed good recovery of true parameters, with only a slight bias toward higher input values, further validating its reliability. We tested the module on a sample of 50 X-ray-detected local galaxies, including low-ionization nuclear emission-line regions (LINERs) and Seyferts, and demonstrated its capacity to accurately estimate bolometric luminosities, even in the presence of significant galaxy contamination. Notably, the previous X-ray module failed to provide AGN solutions for this sample, stressing the need for a novel approach. Comparisons with mid-luminosity AGN datasets confirm the module’s robustness and applicability up to $L_ X $ erg/s. We also expanded the X-ray-to-bolometric correction formula, making it applicable to AGN spanning ten orders of magnitude in luminosity, and revealing lower $k_ X $ values for LLAGN than typically assumed. Additionally, our analysis of the $ ox $ index, which represents the slope between UV and X-ray emissions, uncovered trends that differ from those observed in high-luminosity AGN. Unlike quasars, where $ ox $ correlates with $ Edd $, LLAGN exhibit nearly constant or weakly correlated $ ox $ values, suggesting a shift in accretion physics and photon production mechanisms in low-luminosity regimes. These results underscore the importance of a multiwavelength approach in AGN studies and reveal distinct behaviors in LLAGN compared to quasars. Our findings significantly advance our understanding of LLAGN and offer a comprehensive framework for future research to complete the AGN population census.

Test-retest reliability of behavioral and computational measures of advice taking under volatility.

The development of computational models for studying mental disorders is on the rise. However, their psychometric properties remain understudied, posing a risk of undermining their use in empirical research and clinical translation. Here we investigated test-retest reliability (with a 2-week interval) of a computational assay probing advice-taking under volatility with a Hierarchical Gaussian Filter (HGF) model. In a sample of 39 healthy participants, we found the computational measures to have largely poor reliability (intra-class correlation coefficient or ICC < 0.5), on par with the behavioral measures of task performance. Further analysis revealed that reliability was substantially impacted by intrinsic measurement noise (indicated by parameter recovery analysis) and to a smaller extent by practice effects. However, a large portion of within-subject variance remained unexplained and may be attributable to state-like fluctuations. Despite the poor test-retest reliability, we found the assay to have face validity at the group level. Overall, our work highlights that the different sources of variance affecting test-retest reliability need to be studied in greater detail. A better understanding of these sources would facilitate the design of more psychometrically sound assays, which would improve the quality of future research and increase the probability of clinical translation.

Multi-Layered Implant Approach for Hemilaryngectomy Reconstruction in a Porcine Model.

Partial laryngectomies result in voice, swallowing, and airway impairment for thousands of patients in the United States each year. Treatment options for dynamic restoration of laryngeal function are limited. Thus, there is a need for new reconstructive approaches. Here, we evaluated early (4 week) outcomes of multi-layered mucosal-myochondral (MMC) implants when used to restore laryngeal form and function after hemilaryngectomy in a porcine model. Six Yucatan minipigs underwent transmural hemilaryngectomies followed by reconstruction with customized MMC implants aiming to provide site-appropriate localization of regenerated laryngeal tissues, while supporting laryngeal function. All implants were fabricated from polymeric collagen, with a subset of muscle and cartilage implants containing motor endplate-expressing muscle progenitor cells or cartilage-like cells differentiated from adipose stem cells, respectively. Vocalization and laryngeal electromyography (L-EMG) measurements with nerve conduction studies were performed post-operatively and compared with baseline along with gross and histological analyses of the healing response. All animals (n = 6) survived and maintained airway patency, safe swallowing, and phonation, without the use of tracheostomy and/or gastrostomy tubes. Histological evaluation indicated no adverse tissue reaction or implant degradation, showing progressive regenerative remodeling with mucosa reformation and ingrowth of new muscle and cartilage. Preliminary L-EMG suggested weak but detectable motor unit action potentials. Although vocalization duration, frequency, and intensity decreased post-operatively, all animals retained vocal capacity and parameter recovery was evident over the study duration. Engineered collagen polymeric implants in the presence or absence of autologous cell populations may serve as a feasible reconstructive option to restore dynamic function after hemilaryngectomy. Long-term follow-up is needed to further assess functional outcomes. NA Laryngoscope, 2024.

Development and validation of HPLC-UV and LC-MS/MS methods for the quantitative determination of a novel aminothiazole in preclinical samples

Aminothiazoles are the important class of chemical groups which have proven their broad range of biological activities. A novel aminothiazole (21MAT) was quantified in analytical solutions using a high-performance liquid chromatography (HPLC) approach that was developed and partially validated for the analysis of in vitro experimental samples. An isocratic elution on reverse phase Phenomenex® Luna C18 (50 mm × 4.6 mm, 5 μm) column with 55% 0.1% v/v orthophosphoric acid in water and 45% of orthophosphoric acid in acetonitrile at a flow rate of 1 mL/min was used. The analyte was detected at 272 nm. Similar to this, a robust bioanalytical technique, LC-mass spectrometry (LC-MS/MS) was created and verified to measure 21MAT in rat plasma for use in in vitro screening study samples and early-stage pharmacokinetic research. The protein precipitation method was used to extract 21MAT from plasma. The mixture of 95: 5% v/v methanol: acetonitrile and 0.1% v/v formic acid, along with 15% of 5 mM ammonium formate solution, was used to separate the mixture on a reverse phase Waters Xterra RP® C18 (150 mm × 4.6 mm, 5 μm) column at a flow rate of 1 mL/min. Using electro spray ionisation mode in multiple reaction monitoring mode, the analyte and internal standard (a structural analogue) were both identified. According to current criteria, all validation parameters (specificity, selectivity, accuracy, precision, recovery, matrix factor, hemolysis effect, and stability) were evaluated in rat plasma. The area response of 21MAT was found to be linear over the concentration range of 1.25–1250 ng/mL in rat plasma. Both techniques are suitable for use in any format of preclinical research and were sufficiently reliable to measure 21MAT precisely in various matrices. In silico prediction helped in understanding absorption, distribution, metabolism, excretion, and toxicity (ADMET) behaviour of the molecule. Both developed LC-MS/MS and HPLC-UV methods were successfully used to quantify the analyte in in vitro screening study samples.

The Impact of Different Revascularization Strategies Implemented in Acute Myocardial Infarction on the Recovery of Left Ventricular Functional and Deformational Parameters

Background: Despite well-established therapeutic techniques, such as direct revascularization through percutaneous coronary intervention (PCI), acute myocardial infarction (AMI) remains a leading cause of mortality and morbidity. Objectives: To determine if two-dimensional speckle tracking echocardiography (STE) deformation parameters and the early recovery of left ventricular (LV) functions are affected by the timing of PCI in AMI. Methods: A total of 200 cases with newly-onset acute myocardial infarction (AMI) who had a baseline left ventricular ejection fraction (LVEF) higher than 40% and received effective therapy with percutaneous coronary intervention (PCI) were included in this investigation. cases were categorized as either ST-elevation myocardial infarction (STEMI) or non-ST-elevation myocardial infarction (NSTEMI). cases were grouped into four groups according to the time between presentation and PCI. Using standard echocardiography and two-dimensional (2D) ST-elevation STE, individuals were re-evaluated three months later to find out if remodeling had taken place or if the LV function had returned. Results: Of the 200 AMI patients, including 140 males (70%), improvement in global longitudinal strain (GLS) and harmed longitudinal strain (HLS) were better in STEMI and NSTEMI patients received urgent revascularization with PCI (groups I and III) versus patients with pharmacoinvasive strategy or routine invasive strategy (Groups II and IV) (P &lt; 0.05) while there was an insignificant difference between group I and III (P = 0.79). Of the 200 patients, 47 patients (23.5%) presented signs of LV remodeling at 3 months follow up. Age, smoking history, hypertension, dyslipidemia, Killip class, peak creatine phosphokinase - MB level, baseline left ventricular end diastolic volume (LVEDV), HLS, and harmed longitudinal strain rate (HLSR) were all factors that were found to be significantly associated with left ventricular remodeling (P&lt;0.05) in the univariate logistic regression analysis. The following factors were identified as independent predictors of left ventricular remodeling in multivariate logistic regression analysis: damaged left ventricular ejection fraction (EF) and end-systolic volume, peak troponin I, Killip class, culprit left anterior descending (LAD), 2 and 3-vessel coronary artery disease (CAD), and wall motion score index (WMSI). Conclusion: Earlier PCI in AMI helps earlier improvement in myocardial strain parameters. HLS and HLSR are excellent predictors for LV remodeling and may do better than global parameters.

A comment on the Revised Diffusion Model for Conflict tasks (RDMC).

In conflict tasks, such as the Simon, Eriksen flanker, or Stroop task, a relevant and an irrelevant feature indicate the same or different responses in congruent and incongruent trials, respectively. The congruency effect refers to faster and less error-prone responses in congruent relative to incongruent trials. Distributional analyses reveal that the congruency effect in the Simon task becomes smaller with increasing RTs, reflected by a negative-going delta function. In contrast, for other tasks, the delta function is typically positive-going, meaning that congruency effects become larger with increasing RTs. The Diffusion Model for Conflict tasks (DMC; Ulrich et al., Cognitive Psychology, 78, 148-174, 2015) accounts for this by explicitly modeling the information accumulated from the relevant and the irrelevant features and attributes negatively- versus positively-sloped delta functions to different peak times of a pulse-like activation resulting from the task-irrelevant feature. Because the underlying function implies negative drift rates, Lee and Sewell (Psychonomic Bulletin & Review, 31(5), 1-31, 2024) recently questioned this assumption and suggested their Revised Diffusion Model for Conflict tasks (RDMC). We address three issues regarding RDMC compared to DMC: (1) The pulse-like function is not as implausible as Lee and Sewell suggest. (2) RDMC itself comes with a questionable assumption that different parameters are required for congruent and incongruent trials. (3) Moreover, we present data from a new parameter recovery study, suggesting that RDMC lacks acceptable recovery of several parameters (in particular compared to DMC). In this light, we discuss RDMC as not (yet) a revised version of DMC.

Public Authentic-Replica Sampling Mechanism in Distributed Storage Environments

With the rapid development of wireless communication and big data analysis technologies, the storage of massive amounts of data relies on third-party trusted storage, such as cloud storage. However, once data are stored on third-party servers, data owners lose physical control over their data, making it challenging to ensure data integrity and security. To address this issue, researchers have proposed integrity auditing mechanisms that allow for the auditing of data integrity on cloud servers without retrieving all the data. To further enhance the availability of data stored on cloud servers, multiple replicas of the original data are stored on the server. However, in existing multi-replica auditing schemes, there is a problem of server fraud, where the server does not actually store the corresponding data replicas. To tackle this issue, this paper presents a formal definition of authentic replicas along with a security model for the authentic-replica sampling mechanism. Based on time-lock puzzles, identity-based encryption (IBE) mechanisms, and succinct proof techniques, we design an authentic replica auditing mechanism. This mechanism ensures the authenticity of replicas and can resist outsourcing attacks and generation attacks. Additionally, our schemes replace the combination of random numbers and replica correspondence tables with Linear Feedback Shift Registers (LFSRs), optimizing the original client-side generation and uploading of replica parameters from being linearly related to the number of replicas to a constant level. Furthermore, our schemes allow for the public recovery of replica parameters, enabling any third party to verify the replicas through these parameters. As a result, the schemes achieve public verifiability and meet the efficiency requirements for authentic-replica sampling in multi-cloud environments. This makes our scheme more suitable for distributed storage environments. The experiments show that our scheme makes the time for generating copy parameters negligible while also greatly optimizing the time required for replica generation. As the amount of replica data increases, the time spent does not grow linearly. Due to the multi-party aggregation design, the verification time is also optimal. Compared to the latest schemes, the verification time is reduced by approximately 30%.

Order-disorder behavior in the ferroelectric nematic phase investigated via Raman spectroscopy.

Polar-ordered fluids are of interest both fundamentally and from an application standpoint. The recently discovered ferroelectric nematic phase is an example of a polar-ordered fluid, and while there has been extensive research interest in these materials, some of the fundamental properties are yet to be fully understood. Here, we report the order parameters of one of the first known materials that exhibit a ferroelectric nematic phase, RM734, determined via Raman spectroscopy. Raman spectroscopy been used extensively to determined order parameters in liquid crystals systems but also to probe ferroelectric behavior in solid ferroelectric systems and is therefore a powerful technique to study the ferroelectric nematic phase. A reduction and subsequent recovery of order parameters (Δ〈P_{2}〉≈0.1, Δ〈P_{4}〉≈0.06) is observed near the onset of the N to N_{F} transition, a feature that is confirmed via complementary birefringence measurements. This dip in order parameters has been attributed to splay fluctuations, which occur at the onset of the N_{F} transition; here we suggest a different explanation. A broadening of the full-width half-maxima (FWHM), of the order of 1 cm^{-1}, of the selected Raman peak is observed near the N to N_{F} phase transition, which we relate to either a change in reorientational dynamics or the onset of polar order. The N_{F} transition is analyzed using standard solid ferroelectric frameworks. An energetic barrier associated with the para- to ferroelectric transition is found to be of the order of 2.5±0.6kJ/mol, which is comparable to solid ferroelectric materials.

Identifying effective evolutionary strategies-based protocol for uncovering reaction kinetic parameters under the effect of measurement noises

BackgroundThe transition from explanative modeling of fitted data to the predictive modeling of unseen data for systems biology endeavors necessitates the effective recovery of reaction parameters. Yet, the relative efficacy of optimization algorithms in doing so remains under-studied, as to the specific reaction kinetics and the effect of measurement noises. To this end, we simulate the reactions of an artificial pathway using 4 kinetic formulations: generalized mass action (GMA), Michaelis–Menten, linear-logarithmic, and convenience kinetics. We then compare the effectiveness of 5 evolutionary algorithms (CMAES, DE, SRES, ISRES, G3PCX) for objective function optimization in kinetic parameter hyperspace to determine the corresponding estimated parameters. ResultsWe quickly dropped the DE algorithm due to its poor performance. Baring measurement noise, we find the CMAES algorithm to only require a fraction of the computational cost incurred by other EAs for both GMA and linear-logarithmic kinetics yet performing as well by other criteria. However, with increasing noise, SRES and ISRES perform more reliably for GMA kinetics, but at considerably higher computational cost. Conversely, G3PCX is among the most efficacious for estimating Michaelis–Menten parameters regardless of noise, while achieving numerous folds saving in computational cost. Cost aside, we find SRES to be versatilely applicable across GMA, Michaelis–Menten, and linear-logarithmic kinetics, with good resilience to noise. Nonetheless, we could not identify the parameters of convenience kinetics using any algorithm.ConclusionsAltogether, we identify a protocol for predicting reaction parameters under marked measurement noise, as a step towards predictive modeling for systems biology endeavors.

Effects of Injected Current Streams on MHD Equilibrium Reconstruction of Local Helicity Injection Plasmas in a Spherical Tokamak

Open field line currents are intrinsic to DC helicity injection plasma startup and pose a challenge for inferring the plasma equilibrium with standard reconstruction analysis. Local helicity injection (LHI) is a type of DC helicity injection which uses small, modular current sources to drive force-free current along helical field lines to produce tokamak plasmas. MHD modeling and magnetic measurements during LHI indicate the injected current streams remain coherent as helical structures on the outboard edge of a core toroidal plasma that is tokamak-like in a toroidally averaged sense. To extract core plasma equilibrium properties, external magnetic diagnostics corrected for contributions from the injected current streams are fitted by a standard Grad-Shafranov equilibrium code. An iterative approach for estimating and subtracting the stream contributions from the diagnostic signals is described and applied to a model equilibrium database to reduce systematic errors introduced by the streams. Convergence is usually attained with 2 to 4 iterations, with derived equilibrium parameters matching the prescribed axisymmetric core values to within estimated experimental uncertainties. Accurate recovery of core parameters occurs when the ratio of the net toroidal windup current from the streams to the core plasma current is less than 0.2, which is typically satisfied in most experiments.

An Item Response Tree Model for Items with Multiple‐Choice and Constructed‐Response Parts

AbstractTraditional IRT and IRTree models are not appropriate for analyzing the item that simultaneously consists of multiple‐choice (MC) task and constructed‐response (CR) task in one item. To address this issue, this study proposed an item response tree model (called as IRTree‐MR) to accommodate items that contain different response types at different steps and multiple different cognitive processes behind each score to effectively investigate the cognitive process and achieve a more accurate evaluation of examinees. The proposed model employs appropriate processing function for each task and allows multiple paths to an observed outcome. The simulation studies were conducted to evaluate the performance of the proposed IRTree‐MR, and results show the proposed model outperforms the traditional IRT model in terms of parameters recovery and model‐fit. Moreover, an empirical study was carried out to verify the advantages of the proposed model.

Enhancing Effort-Moderated Item Response Theory Models by Evaluating a Two-Step Estimation Method and Multidimensional Variations on the Model.

Rapid-guessing behavior in data can compromise our ability to estimate item and person parameters accurately. Consequently, it is crucial to model data with rapid-guessing patterns in a way that can produce unbiased ability estimates. This study proposes and evaluates three alternative modeling approaches that follow the logic of the effort-moderated item response theory model (EM-IRT) to analyze response data with rapid-guessing responses. One is the two-step EM-IRT model, which utilizes the item parameters estimated by respondents without rapid-guessing behavior and was initially proposed by Rios and Soland without further investigation. The other two models are effort-moderated multidimensional models (EM-MIRT), which we introduce in this study and vary as both between-item and within-item structures. The advantage of the EM-MIRT model is to account for the underlying relationship between rapid-guessing propensity and ability. The three models were compared with the traditional EM-IRT model regarding the accuracy of parameter recovery in various simulated conditions. Results demonstrated that the two-step EM-IRT and between-item EM-MIRT model consistently outperformed the traditional EM-IRT model under various conditions, with the two-step EM-IRT estimation generally delivering the best performance, especially for ability and item difficulty parameters estimation. In addition, different rapid-guessing patterns (i.e., difficulty-based, changing state, and decreasing effort) did not affect the performance of the two-step EM-IRT model. Overall, the findings suggest that the EM-IRT model with the two-step parameter estimation method can be applied in practice for estimating ability in the presence of rapid-guessing responses due to its accuracy and efficiency. The between-item EM-MIRT model can be used as an alternative model when there is no significant mean difference in the ability estimates between examinees who exhibit rapid-guessing behavior and those who do not.