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In this article, developed the compact implicit difference method based Grunwald Letnikov formula (GLF) to compute the solution of the time-fractional diffusion-wave equation (TFDWE) describing wave propagation phenomenan having order α ( 1 α 2 ) . The fractional derivative is in Caputo sense. The theoretical analysis such as stability, consistency, convergence and solvability of the said scheme are discussed and proved that the scheme is conditionally stable and convergent with the order ( τ 2 + ( Δ x ) 4 ) . The numerical results compared with the recent existed method. The results of the numerical examples show that the GLF and the proposed method is very accurate and efficient for time fractional diffusion-wave equation.

Financial and economic time series can feature locally explosive behaviour when bubbles are formed. We develop a time-varying parameter model that is capable of describing this behaviour in time series data. Our proposed dynamic model can be used to predict the emergence, existence and burst of bubbles. We adopt a flexible observation driven model specification that allows for different bubble shapes and behaviour. We establish stationarity, ergodicity, and bounded moments of the data generated by our model. Furthermore, we obtain the consistency and asymptotic normality of the maximum likelihood estimator. Given the parameter estimates in the model, the implied filter is capable of extracting the unobserved bubble process from the observed data. We study finite-sample properties of our estimator through a Monte Carlo simulation study. Finally, we show that our model compares well with existing noncausal models in a financial application concerning the Bitcoin/US dollar exchange rate.

Objective To study the value of multitime point salivary pepsin testing (MTPSPT) for the diagnosis of laryngopharyngeal reflux (LPR). Study Design Prospective noncontrolled. Methods For patients who met the enrollment criteria, the reflux symptom index (RSI) and reflux finding score (RFS) were calculated and salivary pepsin testing was performed. The pepsin test was performed every hour from 7:00 a.m. to 6:00 p.m. by collecting fresh saliva samples. A single positive test result was needed for the diagnosis of LPR. The consistency in the diagnosis of LPR between the two methods was compared with the weighted Cohen's kappa statistic. Results A total of 204 patients were included. The kappa value between the two methods was 0.566 (p = .00). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MTPSPT were 76.43%, 85.94%, 92.24%, and 62.5%, respectively. We also compared a single pepsin measure at 7 a.m. with the screening results based on the RSI and RFS, and found a much lower kappa agreement value (0.223, p = .00). The sensitivity, specificity, PPV, NPV, and false‐negative rate of pepsin testing at 7 a.m. (fasting) were 37.86%, 92.18%, 91.38%, 40.41%, and 58.57%, respectively. Conclusion The use of the result of a single salivary pepsin test in the morning yields a relatively higher rate of missed diagnosis of LPR, and multitime point testing through a day increased the accuracy and sensitivity of detection of LPR twofold compared to a single morning fasting sample. Level of Evidence 3

https://doi.org/10.1111/sjos.12552 Generalizations of the Ornstein-Uhlenbeck process defined through Langevin equations, such as fractional Ornstein-Uhlenbeck processes, have recently received a lot of attention. However, most of the literature focuses on the one-dimensional case with Gaussian noise. In particular, estimation of the unknown parameter is widely studied under Gaussian stationary increment noise. In this article, we consider estimation of the unknown model parameter in the multidimensional version of the Langevin equation, where the parameter is a matrix and the noise is a general, not necessarily Gaussian, vector-valued process with stationary increments. Based on algebraic Riccati equations, we construct an estimator for the parameter matrix. Moreover, we prove the consistency of the estimator and derive its limiting distribution under natural assumptions. In addition, to motivate our work, we prove that the Langevin equation characterizes essentially all multidimensional stationary processes.

The purpose of the present paper is to investigate on a class of spherical functional autoregressive processes in order to introduce and study LASSO (Least Absolute Shrinkage and Selection Operator) type estimators for the corresponding autoregressive kernels, defined in the harmonic domain by means of their spectral decompositions. Some crucial properties for these estimators are proved, in particular, consistency and oracle inequalities., Comment: 32 pages, 3 figures

Background We present findings from the multicenter, double-blind Phase 3 study, CENTURION. This study was designed to assess the efficacy of and consistency of response to lasmiditan in the acute treatment of migraine across four attacks. Methods Patients were randomized 1:1:1 to one of three treatment groups – lasmiditan 200 mg; lasmiditan 100 mg; or a control group that received placebo for three attacks and lasmiditan 50 mg for either the third or fourth attack. The primary endpoints were pain freedom at 2 h (first attack) and pain freedom at 2 h in ≥2/3 attacks. Secondary endpoints included pain relief, sustained pain freedom and disability freedom. Statistical testing used a logistic regression model and graphical methodology to control for multiplicity. Results Overall, 1471 patients treated ≥1 migraine attack with the study drug. Both primary endpoints were met for lasmiditan 100 mg and 200 mg ( p Conclusions These results confirm the early and sustained efficacy of lasmiditan 100 mg and 200 mg and demonstrate consistency of response across multiple attacks. Trial Registration Number: NCT03670810

In this paper, we consider the regression model with fixed design: Y i = g ( x i ) + ε i {Y}_{i}=g\left({x}_{i})+{\varepsilon }_{i} , 1 ≤ i ≤ n 1\le i\le n , where { x i } \left\{{x}_{i}\right\} are the nonrandom design points, and { ε i } \left\{{\varepsilon }_{i}\right\} is a sequence of martingale, and g g is an unknown function. Nonparametric estimator g n ( x ) {g}_{n}\left(x) of g ( x ) g\left(x) will be introduced and its strong convergence properties are established.

We establish strong consistency and asymptotic normality of the maximum likelihood estimator for stochastic time-varying parameter models driven by the score of the predictive conditional likelihood function. For this purpose, we formulate primitive conditions for global identification, invertibility, strong consistency, and asymptotic normality both under correct specification and misspecification of the model. A detailed illustration is provided for a conditional volatility model with disturbances from the Student’s t distribution.

The estimation of individual values (marks) in a finite population of units (e.g., trees) scattered onto a survey region is considered under 3P sampling. For each unit, the mark is estimated by means of an inverse distance weighting interpolator. Conditions ensuring the design-based consistency of maps are considered under 3P sampling. A computationally simple mean squared error estimator is adopted. Because 3P sampling involves the prediction of marks for each unit in the population, prediction errors rather than marks can be interpolated. Then, marks are estimated by the predictions plus the interpolated errors. If predictions are good, prediction errors are more smoothed than raw marks so that the procedure is likely to better meet consistency requirements. The purpose of this paper is to provide theoretical and empirical evidence on the effectiveness of the interpolation based on prediction errors to prove that the proposed strategy is a tool of general validity for mapping forest stands.

This paper axiomatically studies bankruptcy problems with nontransferable utility by focusing on generalizations of consistency and the contested garment principle. On the one hand, we discuss several consistency notions and introduce the class of parametric bankruptcy rules which contains the proportional rule, the constrained relative equal awards rule, and the constrained relative equal losses rule. On the other hand, we introduce the class of adjusted bankruptcy rules and characterize the relative adjustment principle by truncation invariance, minimal rights first, and a weak form of relative symmetry.

Research has attempted to explain perceived cues to deception based upon self‐report of what participants believe are ‘good’ cues to deception, or self‐report of what cues participants say they base their veracity judgements on. However, it is not clear to what extent participants can accurately self‐report what influences their decision‐making. Using a within‐subjects design, 285 participants completed a questionnaire regarding their beliefs about deception before rating a selection of truthful and deceptive statements on a variety of cues. Expert coders also rated the statements for the same cues. Laypeople and expert coders do not conceptualise between‐subject consistency in the same way. A lens model showed that whilst perceptions of cues, such as consistency and amount of detail, influence veracity judgements, these perceptions (and overall veracity judgements) are mostly inaccurate. Fundamentally, there seems to be inconsistencies between how deception research examines consistency and how it is understood and used by laypeople.

In this article, a numerical model for a Brusselator advection–reaction–diffusion (BARD) system by using an elegant numerical scheme is developed. The consistency and stability of the proposed scheme is demonstrated. Positivity preserving property of the proposed scheme is also verified. The designed scheme is compared with the two well-known existing classical schemes to validate the certain physical properties of the continuous system. A test problem is also furnished for simulations to support our claim. Prior to computations, the existence and uniqueness of solutions for more generic problems is investigated. In the underlying system, the nonlinearities depend not only on the desired solution but also on the advection term that reflects the pivotal importance of the study.

We consider sparse count data models with the sparsity rate ? = N/n = O(1) where N = N (n) is the number of observations and n ? ? is the number of cells. In this case the plug-in estimator of the structural distribution of expected frequencies is inconsistent. If ? = O(n ?? ) for some ? > 0, the nonparametric maximum likelihood estimator, in general, is also inconsistent. Assuming that some auxiliary information on the expected frequencies is available, we construct a consistent estimator of the structural distribution.

Pairwise comparisons constitute a popular and important tool for multiple-criteria decision making. Consistency is one particular aspect of pairwise comparisons that has been studied thoroughly in recent decades since human judgements are seldom fully consistent. The aim of this paper is to propose a new simple and intuitive iterative Step-by-Step (SBS) algorithm for inconsistency reduction of a (multiplicative) pairwise comparison matrix based on step-by-step changes of the most inconsistent matrix element, where the inconsistency is measured by a modified Koczkodaj’s inconsistency index. A comparison of the proposed SBS algorithm with other algorithms for inconsistency reduction is provided as well.

Statement Validity Assessment (SVA) proposes that baseline statements on different events can serve as a within-subject measure of a witness' individual verbal capabilities when evaluating scores from Criteria-based Content Analysis (CBCA). This assumes that CBCA scores will generally be consistent across two accounts by the same witness. We present a first pilot study on this assumption. In two sessions, we asked 29 participants to produce one experience-based and one fabricated baseline account as well as one experience-based and one fabricated target account (each on different events), resulting in a total of 116 accounts. We hypothesized at least moderate correlations between target and baseline indicating a consistency across both experience-based and fabricated CBCA scores, and that fabricated CBCA scores would be more consistent because truth-telling has to consider random event characteristics, whereas lies must be constructed completely by the individual witness. Results showed that differences in correlations between experience-based CBCA scores and between fabricated CBCA scores took the predicted direction (cexperience-based = .44 versus cfabricated =.61) but this difference was not statistically significant. As predicted, a subgroup of event-related CBCA criteria were significantly less consistent than CBCA total scores, but only in experience-based accounts. The discussion considers methodological issues regarding the usage of total CBCA scores and whether to measure consistency with correlation coefficients. It is concluded that more studies are needed with larger samples.

International audience; Decision Makers generally reason on several criteria, aiming to obtain a total consistency or partial order of several alternatives. MultiCriteria analysis is based on the assumption that such ordering exists. Decision Makers are supported by several kinds of approaches or tools. One approach consists in comparing the criteria two by two, i.e. pairwise comparison, to find the relative importance of each criterion. This relative importance, called weight of criteria, is used to find the final order of alternatives. One methodology, developed by Saaty, called Analytical Hierarchical Process (AHP), is based on this principle of pairwise comparison. Having the weights of criteria, the decision makers have then to compare the alternatives two by two for each criterion. Pairwise comparisons are simple to use; however, as the number of items to compare increases, so do the effort of conducting all comparisons and the probability of introducing inconsistencies. In this article we present an innovative approach to conduct pairwise comparisons based on a UI widget that resembles an interactive data plot. It uses the transitivity property of a consistent comparison matrix to infer comparisons. Our hypothesis is that this new approach is more efficient (as it reduces the number of actions the user must conduct to compare all items), more effective (as it limits the sources of inconsistencies), and yields better user satisfaction. We conducted a controlled experiment involving 50 participants. We observed that the proposed widget reduces the effort of making pairwise comparisons, improves the consistency of the comparisons, and leads to a better user experience.

In this paper, we construct a method with eight steps that belongs to the family of Obrechkoff methods. Due to the explicit nature of the new method, not only does it not require another method as predictor, but it can also be considered as a suitable predictive technique to be used with implicit methods. Periodicity and error terms are studied when applied to solve the radial Schrödinger equation, considering different energy levels. We show its advantages in terms of accuracy, consistency, and convergence in comparison with other methods of the same order appearing in the literature.

Rhythmic stimulation can be applied to modulate neuronal oscillations. Such ‘entrainment’ is optimized when stimulation frequency is individually calibrated based on magneto/encephalography markers. It remains unknown how consistent such individual markers are across days/sessions, within a session, or across cognitive states, hemispheres and estimation methods, especially in a realistic, practical, lab setting. We here estimated individual alpha frequency (IAF) repeatedly from short electroencephalography (EEG) measurements at rest or during an attention task (cognitive state), using single parieto-occipital electrodes in 24 participants on 4 days (between-sessions), with multiple measurements over an hour on 1 day (within-session). First, we introduce an algorithm to automatically reject power spectra without a sufficiently clear peak to ensure unbiased IAF estimations. Then we estimated IAF via the traditional ‘maximum’ method and a ‘Gaussian fit’ method. IAF was reliable within- and between-sessions for both cognitive states and hemispheres, though task-IAF estimates tended to be more variable. Overall, the ‘Gaussian fit’ method was more reliable than the ‘maximum’ method. Furthermore, we evaluated how far from an approximated ‘true’ task-related IAF the selected ‘stimulation frequency’ was, when calibrating this frequency based on a short rest-EEG, a short task-EEG, or simply selecting 10 Hz for all participants. For the ‘maximum’ method, rest-EEG calibration was best, followed by task-EEG, and then 10 Hz. For the ‘Gaussian fit’ method, rest-EEG and task-EEG-based calibration were similarly accurate, and better than 10 Hz. These results lead to concrete recommendations about valid, and automated, estimation of individual oscillation markers in experimental and clinical settings.

International audience; We consider a simple estimator of the density mode using the frequency polygon estimate. We investigate strong consistency of the estimator for strong mixing sequence of real variables under mild assumptions. We study the almost sure rate of convergence and we show that the estimator can achieve optimal almost sure rates of convergence for appropriate choices of the bin widths. The asymptotic normality of the simple estimator is given and a simulation study is performed. Our asymptotic results are obtained without any differentiability condition assumed on the density around the mode.

There are many factors underlying the instability of the consistency of the concrete mixture. The consistency of the concrete mixture was measured using a slump test. Slump tests are commonly used in measuring the quality of fresh concrete. The instability of the slump value becomes an unsolved problem. To facilitate predicting slump values, modeling is needed to reduce variations in concrete job mixs. Regression has been known as the basic method of predictive modeling. Collected data is divided according to the ratio of sand to: 44%. The sand ratio data

In recent years, there has been a transformation in the choice and lifestyle of Russian citizens. An increasing part of the population makes a more reasonable choice, taking into account all aspects, including the most important for meat products — these are organoleptic characteristics, in particular the consistency of the product. Consumers prefer products with the delicate, soft texture, expect good «biteness» and «cheweness». Therefore, preference is given to tender, juicy meat with a low content of connective tissue. The most commonly used method for testing meat consistency in laboratories around the world is a method that uses strength testing machines with a Warner-Bratzler blade (WB blade). In this work, the Shimadzu AGS-1kN universal testing machine (Japan) was chosen for research. Samples obtained from l. dorsi of pork and beef were selected as meat raw materials. To determine the optimal and reproducible method of sample preparation, some of them were subjected to heat treatment before analysis. In the study of samples without heat treatment, deviations from the average were more than 11%. After meat was cooked, a decrease in the relative standard deviation of the maximum shear stress from the average was achieved: from 11% in raw meat to 5% in a pork sample and 5.3% in a beef sample. The heat treatment of pre-cut samples led to a change in their geometric shape, which created additional difficulties for obtaining correct results, and also negatively affected the increase in the relative deviation to 15.5% for beef.

The aim of this paper is to develop some tools in order to obtain the weak consistency of (in other words, an analogue of the Lax–Wendroff theorem for) finite volume schemes for balance laws in the multi-dimensional case and under minimal regularity assumptions for the mesh. As in the seminal Lax–Wendroff paper, our approach relies on a discrete integration by parts of the weak formulation of the scheme. Doing so, a discrete gradient of the test function appears; the central argument for the scheme consistency is to remark that this discrete gradient is convergent in $$L^\infty $$ weak $$\star $$ .

Malnutrition appears in weaning age and is usually due to weaning food which is of low nutritional value. This problem led us to investigate the study of the physicochemical and functional properties of cassava flours and corn flours, and the fluidification of the gruels made from these flours by germinated yellow corn and sweet white potato flours. To do this, the approximate chemical composition, physical and functional properties, and ability of amylase‐rich flours to digest the starch in order to reduce consistency were evaluated. From these analyses, it emerges that the chemical composition, and physical and functional properties are influenced by the nature and the treatment undergone by the flours. It appears that the amylase‐rich flours that we used at a concentration of 1%–3% during the preparation of the gruels significantly reduced their consistencies. Given their strong liquefying power, this reduction was more marked with germinated corn flour where 1% permits to obtain desired consistency with 21.50 g of DM of bitter cassava flour, thereby multiplying the energy density and nutritional value of this flour by 5.18. It also appears that the action of flours rich in amylases was depending on the concentration, the nature of the flour, its composition, and the treatment undergone. In view of all these results, we can therefore consider the formulation of a weaning food with the consistency, and energy and nutritional value necessary for the proper growth of children.

Latent state-trait (LST) models are commonly applied to determine the extent to which observed variables reflect trait-like versus state-like constructs. Mixture distribution LST (M-LST) models relax the assumption of population homogeneity made in traditional LST models, allowing researchers to identify subpopulations (latent classes) with differing trait- and state-like attributes. Applications of M-LST models are scarce, presumably because of the analysis complexity. We present a step-by-step tutorial for evaluating M-LST models based on an application to mother, father, and teacher reports of children’s inattention (n = 811). In the application, we found three latent classes for mother and father reports and four classes for teacher reports. All reporter solutions contained classes with very low, low, and moderate levels of inattention. The teacher solution also contained a class with high inattention. Comparable mother and father (but not teacher) classes exhibited similar levels of trait and state variance.

The consistency test is a vital component of pairwise comparison matrices if meaningful results are to be guaranteed, and it has been studied extensively, since the analytic hierarchy process was developed by Saaty. However, how to deal with the consistency of a probability-hesitant analytic hierarchy process with different probability values is a specific problem which needs to be studied further. This paper provides a direct consistency test and an improvement method for the probability-hesitant analytic hierarchy process. A paradox in Saaty’s consistency test is first proposed, and then a direct consistency improvement method is suggested for dealing with the consistency of a probability-hesitant analytic hierarchy process with different probability values, from the perspective of three tuples, rather than carrying out matrix operations. In our method, only simple mathematical calculations are needed and there is no need for matrix operations. By using a partial correction, the proposed method can retain most of the information provided by the original comparison matrix, and simultaneously indicate the modification direction and provide the optimal values. At the same time, using the proposed method, the inconsistent elements in the probability-hesitant pairwise comparison matrix can be found rapidly and with a high degree of accuracy. Moreover, our proposed iterative algorithm is a method that is both very quick and leads to good results being obtained.

Two categories of hesitancy often exist in an intuitionistic multiplicative preference relation (IMPR). One is inconsistency among three or more preferences in an IMPR, and the other is hesitancy of a non-diagonal intuitionistic multiplicative judgment itself. Capturing such hesitancy plays an important role for deriving priority weights of IMPRs and measuring inconsistency degrees of IMPRs. This article first analyzes a recent consistency definition of IMPRs and reveals its deficiency. A normalized intuitionistic multiplicative weight vector is introduced and utilized as a benchmark of different intuitionistic multiplicative weight vectors with equivalency. A new notion of consistent IMPRs is then proposed by building the link between an IMPR and its normalized intuitionistic multiplicative weights. In order to find the closest IMPR with consistency to an original IMPR, the paper establishes a minimization model, in which the hesitancy difference between the two IMPRs is regarded as a constraint and the optimal goal function value is used to determine an inconsistency index of the original IMPR. The minimization model is further transformed into a least square model and its analytical solution is discovered by the Lagrangian multiplier method. Based on the analytical solution, an intuitionistic multiplicative judgment based index is devised to measure inconsistency of IMPRs, and an approach with checking acceptable consistency is developed for multi-criteria decision making with IMPRs. The presented models are illustrated and validated by three numerical examples including a hierarchical multi-criteria decision making problem.

ObjectivesThe aim of this study was to establish and validate a radiomics nomogram for predicting meningiomas consistency, which could facilitate individualized operation schemes-making.MethodsA total of 172 patients was enrolled in the study (train cohort: 120 cases, test cohort: 52 cases). Tumor consistency was classified as soft or firm according to Zada’s consistency grading system. Radiomics features were extracted from multiparametric MRI. Variance selection and LASSO regression were used for feature selection. Then, radiomics models were constructed by five classifiers, and the area under curve (AUC) was used to evaluate the performance of each classifiers. A radiomics nomogram was developed using the best classifier. The performance of this nomogram was assessed by AUC, calibration and discrimination.ResultsA total of 3840 radiomics features were extracted from each patient, of which 3719 radiomics features were stable features. 28 features were selected to construct the radiomics nomogram. Logistic regression classifier had the highest prediction efficacy. Radiomics nomogram was constructed using logistic regression in the train cohort. The nomogram showed a good sensitivity and specificity with AUCs of 0.861 and 0.960 in train and test cohorts, respectively. Moreover, the calibration graph of the nomogram showed a favorable calibration in both train and test cohorts.ConclusionsThe presented radiomics nomogram, as a non-invasive prediction tool, could predict meningiomas consistency preoperatively with favorable accuracy, and facilitated the determination of individualized operation schemes.

Much inter-intra-tester kinematic and kinetic repeatability research exists, with a paucity investigating inter-laboratory equivalence. The objective of this research was to evaluate the inter-laboratory equivalence between time varying unplanned kinematics and moments of unplanned sidestepping (UnSS). Eight elite female athletes completed an established UnSS procedure motion capture laboratories in the UK and Australia. Three dimensional time varying unplanned sidestepping joint kinematics and moments were compared. Discrete variables were change of direction angles and velocity. Waveform data were compared using mean differences, 1D 95%CI and RMSE. Discrete variables were compared using 0D 95% CI. The mean differences and 95%CI for UnSS kinematics broadly supported equivalence between laboratories (RMSE≤5.1°). Excluding hip flexion/extension moments (RMSE = 1.04 Nm/kg), equivalence was also supported for time varying joint moments between laboratories (RMSE≤0.40 Nm/kg). Dependent variables typically used to characterise UnSS were also equivalent. When consistent experimental and modelling procedures are employed, consistent time varying UnSS lower limb joint kinematic and moment estimates between laboratories can be obtained. We therefore interpret these results as a support of equivalence, yet highlight the challenges of establishing between-laboratory experiments or data sharing, as well as establishing appropriate ranges of acceptable uncertainty. These findings are important for data sharing and multi-centre trials. ispartof: SPORTS BIOMECHANICS ispartof: location:England status: published

This article presents a partially linear additive spatial error model (PLASEM) specification and its corresponding generalized method of moments (GMM). It also derives consistency and asymptotic normality of estimators for the case with a single nonparametric term and an arbitrary number of nonparametric additive terms under some regular conditions. In addition, the finite sample performance for our estimates is assessed by Monte Carlo simulations. Lastly, the proposed method is illustrated by analyzing Boston housing data.

This paper axiomatically studies the equal split-off set (cf. Branzei et al. (Banach Center Publ 71:39–46, 2006)) as a solution for cooperative games with transferable utility which extends the well-known Dutta and Ray (Econometrica 57:615–635, 1989) solution for convex games. By deriving several characterizations, we explore consistency of the equal split-off set on the domains of exact partition games and arbitrary games.

International audience; This paper introduces a new class of models for non-negative integer-valued time series with a periodic and seasonal autoregressive structure. Some properties of the model are discussed and the conditional quasi-maximum likelihood method is used to estimate the parameters. The consistency and asymptotic normality of the estimators are established. Their performances are investigated for finite sample sizes and the empirical results indicate that the method gives accurate estimates. The proposed model is applied to analyse the daily number of antibiotic dispensing medication for the treatment of respiratory diseases, registered in a health center of Vitória, Brazil.

Research on the consistency of suspended particulate matter (SPM) concentration retrieved from multisource satellite sensors can serve as long-time monitoring of water quality. To explore the influence of the atmospheric correction (AC) algorithm and the retrieval model on the consistency of the SPM concentration values, Landsat 8 Operational Land Imager (OLI) and Sentinel 2 MultiSpectral Imager (MSI) images acquired on the same day are used to compare the remote sensing reflectance (Rrs) SPM retrieval values in two high-turbidity lakes. An SPM retrieval model for Shengjin Lake is established based on field measurements and applied to OLI and MSI images: two SPM concentration products are highly consistent (R2 = 0.93, Root Mean Squared Error (RMSE) = 20.67 mg/L, Mean Absolute Percentage Error (MAPE) = 6.59%), and the desired results are also obtained in Chaohu Lake. Among the four AC algorithms (Management Unit of the North Seas Mathematical Models (MUMM), Atmospheric Correction for OLI’lite’(ACOLITE), Second Simulation of Satellite Signal in the Solar Spectrum (6S), Landsat 8 Surface Reflectance Code &amp, Sen2cor (LaSRC &amp, Sen2cor), the two Rrs products, as well as the final SPM concentration products retrieved from OLI and MSI images, have the best consistency when using the MUMM algorithm in SeaWIFS Data Analyst System (SeaDAS) software. The consistency of SPM concentration values retrieved from OLI and MSI images using the same model or same form of models is significantly better than that retrieved by applying the optimal models with different forms.

Purpose To evaluate the use of preoperative virtual Magnetic Resonance Elastography (vMRE) for patients undergoing transsphenoidal resection of pituitary adenomas (PA). Methods Ten patients (60.2 ± 19.6 years; 8 males) were prospectively examined with the vMRE-method prior to transsphenoidal surgery. vMRE-images, reflecting tissue stiffness were reconstructed. From these images, histograms as well as the mean stiffness values over the tumor body were extracted. Finally, vMRE-data was compared with the PA consistency at surgery blinded to vMRE. Results In all patients, successful vMRE-examination was performed enabling evaluation of even small PAs. For tumors with homogenous tissue, the mean stiffness value increased with surgical consistency grading. For heterogenous tumors, however, the mean stiffness value did not consistently reflect the grading at surgery. On the other hand, the vMRE-images and histograms were found to be able to characterize the tumor heterogeneity and display focal regions of high stiffness that were found to affect the surgery outcome in these PAs. The vMRE-images and histograms showed great promise in characterizing the consistency at surgery for these PAs. Conclusion Evaluation of PA consistency in preparation for surgery seems to be feasible using the vMRE-method. Our findings also address the need for high resolution diagnostic methods that can non-invasively display focal regions of increased stiffness, as such regions may increase the difficulty of transsphenoidal PA-resection.

A pair of errors regarding Proposition 3.4 and Theorem 5.2(ii) of Fissler and Ziegel (2016) have been noted. Corrections to them along with comments on imprecisions in two other results, Propositions 4.2 and 4.4 in the same article, are provided in the online supplement Fissler and Ziegel (2020).

ObjectiveAre people with a characteristically large physiological sway rendered particularly unstable when standing on a moving surface? Is postural sway in standing individuals idiosyncratic? In this study, we examine postural sway in individuals standing normally, and when subtle continuous sinusoidal disturbances are applied to their support platform. We calculate consistency between conditions to verify if sway can be considered characteristic of each individual. We also correlate two different aspects of participants’ responses to disturbance; their sway velocity and their regulation of body orientation.MethodsNineteen healthy adults (age 29.2 ± 3.2 years) stood freely on footplates coaxially aligned with their ankles and attached to a motorized platform. They had their eyes closed, and hips and knees locked with a light wooden board attached to their body. Participants either stood quietly on a fixed platform or on a slowly tilting platform (0.1 Hz sinusoid; 0.2 and 0.4 deg). Postural sway size was separated into two entities: (1) the spontaneous sway velocity component (natural random relatively rapid postural adjustments, RMS body angular velocity) and (2) the evoked tilt gain component (much slower 0.1 Hz synchronous tilt induced by the movement of the platform, measured as peak-to-peak (p-p) gain, ratio of body angle to applied footplate rotation).ResultsThere was no correlation between the velocity of an individual’s sway and their evoked tilt gain (r = 0.34, p = 0.15 and r = 0.30, p = 0.22). However, when considered separately, each of the two measurements showed fair to good absolute agreement within conditions. Spontaneous sway velocity consistently increased as participants were subjected to increasing disturbance. Participants who swayed more (or less) did so across all conditions [ICC(3,k) = 0.95]. Evoked tilt gain also showed consistency between conditions [ICC(3,k) = 0.79], but decreased from least to most disturbed conditions.ConclusionThe two measurements remain consistent between conditions. Consistency between conditions of two very distinct unrelated measurements reflects the idiosyncratic nature of postural sway. However, sway velocity and tilt gain are not related, which supports the idea that the short-term regulation of stability and the longer-term regulation of orientation are controlled by different processes.

AM, AP, LS, CC, and RW were supported by the Max Planck Society; AM was supported by the Wenner Gren Foundation (Grant Number 9095) and the British Academy Newton International Fellowship; AP was supported by the Leakey Foundation; LS was supported by the Minerva Foundation; JG was supported by an NSF Graduate Research Fellowship (DGE-1142336), the Canadian Institutes of Health Research's Strategic Training Initiative in Health Research's Systems Biology Training Program, an NSERC Vanier Canada Graduate Scholarship (CGS), and a long-term Research Grant from the German Academic Exchange Service (DAAD-91525837-57048249). CC was supported by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 679787). RW was supported by DFG Researcher Unit (FOR 2136) Sociality and Health in Primates (WI 2637/3-1). Research at the Taï Chimpanzee Project has been funded by the Max Planck Society since 1997. Predictability of social interactions can be an important measure for the social complexity of an animal group. Predictability is partially dependent on how consistent interaction patterns are over time: does the behavior on 1 day explain the behavior on another? We developed a consistency measure that serves two functions: detecting which interaction types in a dataset are so inconsistent that including them in further analyses risks introducing unexplained error; and comparatively quantifying differences in consistency within and between animal groups. We applied the consistency measure to simulated data and field data for one group of sooty mangabeys (Cercocebus atys atys) and to groups of Western chimpanzees (Pan troglodytes verus) in the Taï National Park, Côte d'Ivoire, to test its properties and compare consistency across groups. The consistency measures successfully identified interaction types whose low internal consistency would likely create analytical problems. Species-level differences in consistency were less pronounced than differences within groups: in all groups, aggression and dominance interactions were the most consistent, followed by grooming; spatial proximity at different levels was much less consistent than directed interactions. Our consistency measure can facilitate decision making of researchers wondering whether to include interaction types in their analyses or social networks and allows us to compare interaction types within and between species regarding their predictability. Publisher PDF

In this paper, we introduce a novel method to generate interpretable regression function estimators. The idea is based on called data-dependent coverings. The aim is to extract from the data a covering of the feature space instead of a partition. The estimator predicts the empirical conditional expectation over the cells of the partitions generated from the coverings. Thus, such estimator has the same form as those issued from data-dependent partitioning algorithms. We give sufficient conditions to ensure the consistency, avoiding the sufficient condition of shrinkage of the cells that appears in the former literature. Doing so, we reduce the number of covering elements. We show that such coverings are interpretable and each element of the covering is tagged as significant or insignificant. The proof of the consistency is based on a control of the error of the empirical estimation of conditional expectations which is interesting on its own.

In this research, we have shown that the goal of reducing the ecological system to a mathematical model cannot be achieved. Each mathematical model that is built has one of these two drawbacks: either some admissible outputs are not produced or some inadmissible outputs are produced. In this article, the authors aim to clarify to what point natural systems can be represented by formal languages. To this end, the concepts of consistent system and complete system are defined, from which it is demonstrated that the model system can be consistent and not complete. Initially, the authors define an axiomatic in which the inputs and outputs of natural systems and complex systems in general are classified, but from the point of view of the influence of objects from the system to the environment and vice versa. For each mathematical model that is generated, either of the two statements occurs: either some acceptable outputs are not produced, or some inadmissible outputs are produced. Finally, it is shown that if the set of inputs of the system is acceptable then the system is not complete or inconsistent.

International audience; The class of observation-driven models (ODMs) includes many models of non-linear time series which, in a fashion similar to, yet different from, hidden Markov models (HMMs), involve hidden variables. Interestingly, in contrast to most HMMs, ODMs enjoy likelihoods that can be computed exactly with computational complexity of the same order as the number of observations, making maximum likelihood estimation the privileged approach for statistical inference for these models. A celebrated example of general order ODMs is the GARCH$(p,q)$ model, for which ergodicity and inference has been studied extensively. However little is known on more general models, in particular integer-valued ones, such as the log-linear Poisson GARCH or the NBIN-GARCH of order $(p,q)$ about which most of the existing results seem restricted to the case $p=q=1$. Here we fill this gap and derive ergodicity conditions for general ODMs. The consistency and the asymptotic normality of the maximum likelihood estimator (MLE) can then be derived using the method already developed for first order ODMs.

Not just since the corona crisis have financial instruments in the form of guarantees, loans, and equity witnessed a growing popularity as a form of state aid vis-à-vis ‘regular’ grants. This thesis investigated whether the European Commission designs financial instruments and applies their legal framework in accordance with the principles of proportionality and consistency under European Union (EU) law. It found that the Commission does not adhere to the requirements of financial instruments under the case law of the EU Courts in a consistent manner. Specifically, it does not conduct the required balancing exercise of positive and negative effects of instruments, but applies maximum amounts and screening methods as well as a checklist in its compatibility assessment under the proportionality principle. Thus, the thesis recommends the introduction of so-called manifest negative effects, which are applied in other state aid areas and are apt to solve these inconsistencies: They would indicate to stakeholders which potential negative effects could disproportionally distort competition and markets and therefore be incompatible with EU law.

The initial boundary-value problem associated to a semilinear wave equation with time dependent boundary values was approximated by using the method of lines. Time integration is achieved by means of an explicit time method obtained from an arbitrarily high-order splitting scheme. We propose a technique to incorporate the boundary values that is more accurate than the one obtained in the standard way, which is clearly seen in the numerical experiments. We prove the consistency and convergence, with the same order of the splitting method, of the full discretization carried out with this technique. Although we performed mathematical analysis under the hypothesis that the source term was Lipschitz-continuous, numerical experiments show that this technique works in more general cases., This research was funded by the Ministerio de Ciencia y Educación, grant number PGC2018- 101443-B-I00, and the first author by Consejería de Educación, Junta de Castilla y León y Leónand Feder funds, grant number VA193P20.

We present the algebraic structures behind the approaches used to work with pairwise comparison matrices and, in general, the representation of preferences. We obtain a general definition of consistency and a universal decomposition in the space of PCMs, which allow us to define a consistency index. Also Arrow’s theorem, which is presented in a general form, is relevant. All the presented results can be seen in the main formulations of PCMs, i.e., multiplicative, additive and fuzzy approach, by the fact that each of them is a particular interpretation of the more general algebraic structure needed to deal with these theories.

Performance of Machine Learning models heavily depends on the quality of the training dataset. Among others, the quality of training data relies on the consistency of the labels assigned to similar items. Indeed, the labels should be coherently assigned (or collected) by avoiding inconsistencies for increasing the performance of the machine learning model. This study focuses on evaluating training data consistency for machine learning algorithms dealing with ranking problems, i.e., the Learning to Rank methods (LTR). This work defines a training data consistency measure based on the consensus value introduced in Group Decision Making. It investigates the statistical relationship between the proposed consistency measure and the performance of a deep neural network implementing an LTR method. This measure could drive data filtering at the training stage and guide model update decisions. Experimentation reveals a strong correlation between the proposed consistency measure and the performance of the model.

▪ Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1275). In all kinds of site investigation reports prepared to acquire the current situation of the project site, it is a common fact to perform the consistency tests which are specialized as Atterberg limit tests. Consistency can be defined as an important term, especially for fine-grained soils, to appoint the current state of the water content of soil formation in the field. Based on the ease and cost-effectiveness of the Atterberg tests, it has become a traditional solution to determine the fundamental design properties such as the rigidity and strength of the soil formation with the use of empirical approaches that are developed according to them. In this context, “compaction” can be an interesting term to investigate the appropriateness of determination of special characteristics of the phenomenon such as the optimum water content and the maximum dry unit weight with the development of a new perspective based on a simplest experimental process formed with only the evaluation of water content. Because it is a complicated and time-consuming process to apply the compaction test beginning of the sample preparation step to the ultimate evaluation step. Hence, in this paper, an integrated study is performed for highly plastic clays to acquire the consistency and the compaction properties together with a direct relationship. A huge database was prepared according to the data’s given in the well-accepted literature sources by the transmission of liquid limit and plastic limit test results conducted for only the high plastic clays. Besides, simple equations are tried to be obtained to calculate the plasticity index and approximations are proposed to find the maximum dry unit weight and the optimum water content of the soil, respectively. As a result, the applicability of both the regression analysis and the artificial neural network studies to the attainment process of both consistency characteristics and compaction problem were compared with each other to procure a reliable determination process. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

The paper presents the Triads Geometric Consistency Index ( T − G C I ), a measure for evaluating the inconsistency of the pairwise comparison matrices employed in the Analytic Hierarchy Process (AHP). Based on the Saaty’s definition of consistency for AHP, the new measure works directly with triads of the initial judgements, without having to previously calculate the priority vector, and therefore is valid for any prioritisation procedure used in AHP. The T − G C I is an intuitive indicator defined as the average of the log quadratic deviations from the unit of the intensities of all the cycles of length three. Its value coincides with that of the Geometric Consistency Index ( G C I ) and this allows the utilisation of the inconsistency thresholds as well as the properties of the G C I when using the T − G C I . In addition, the decision tools developed for the G C I can be used when working with triads ( T − G C I ), especially the procedure for improving the inconsistency and the consistency stability intervals of the judgements used in group decision making. The paper further includes a study of the computational complexity of both measures ( T − G C I and G C I ) which allows selecting the most appropriate expression, depending on the size of the matrix. Finally, it is proved that the generalisation of the proposed measure to cycles of any length coincides with the T − G C I . It is not therefore necessary to consider cycles of length greater than three, as they are more complex to obtain and the calculation of their associated measure is more difficult.

This paper is concerned with the least squares estimation of drift parameters for the Cox&ndash, Ingersoll&ndash, Ross (CIR) model driven by small symmetrical -stable noises from discrete observations. The contrast function is introduced to obtain the explicit formula of the estimators and the error of estimation is given. The consistency and the rate of convergence of the estimators are proved. The asymptotic distribution of the estimators is studied as well. Finally, some numerical calculus examples and simulations are given.

We consider a new nonparametric rule of classification, inspired from the classical moving window rule, that allows for the classification of spatially dependent functional data containing some completely missing curves. We investigate the consistency of this classifier under mild conditions. The practical use of the classifier will be illustrated through simulation studies.

The proposed method supports the determination of severity (S), occurrence (O), and detection (D) indices of Failure Modes and Effects Analysis (FMEA). Previously evaluated and previously not studied risks are compared in pairwise comparison. The analysis of the resulted pairwise comparison matrix provides information about the consistency of the risk evaluations and allows the estimation of the indices of the previously not evaluated risks. The advantages of the method include:•The pairwise comparison facilities the identification of risks that are otherwise difficult to evaluate•The inconsistency of existing FMEA studies can be highlighted and systematically reduced•The method can be generalized about a wide range of grading problems, Graphical abstract Image, graphical abstract

We introduce a family of various finite volume discretization schemes for the Fokker–Planck operator, which are characterized by different Stolarsky weight functions on the edges. This family particularly includes the well-established Scharfetter–Gummel discretization as well as the recently developed square-root approximation (SQRA) scheme. We motivate this family of discretizations both from the numerical and the modeling point of view and provide a uniform consistency and error analysis. Our main results state that the convergence order primarily depends on the quality of the mesh and in second place on the choice of the Stolarsky weights. We show that the Scharfetter–Gummel scheme has the analytically best convergence properties but also that there exists a whole branch of Stolarsky means with the same convergence quality. We show by numerical experiments that for small convection the choice of the optimal representative of the discretization family is highly non-trivial, while for large gradients the Scharfetter–Gummel scheme stands out compared to the others.