AR Parameters Research Articles

The normal ranges of selected acoustic rhinometry parameters depending on age and sex-component of standarization in nasal provocation test

IntroductionAcoustic rhinometry is a non-invasive method of measuring intranasal spaces. As one of the objective techniques for nasal patency, it plays a critical role in evaluating the nasal allergen challenge.Aim: To establish the typical ranges of selected AR parameters for age and sex.Material and methodsThe study was conducted in a group of 583 randomly selected subjects (329 females and 254 males). Acoustic rhinometry was done before and 15 min after a two-time application of 0.1% solution of the decongestant xylomethazoline. The evaluated AR parameters (the cross-sectional area 1 (CSA-1) at the level of the isthmus nasi and the distance between the external nares and the isthmus nasi (dCSA-1)) were measured separately for both nasal cavities. Another evaluated parameter was the mean CSA measured 3 cm beyond CSA-1 on the AR curve (CSA-3).ResultsThe site of the minimum CSA on the acoustic rhinometry curve in the study group was the second notch on the acoustic rhinometry curve (CSA-1), corresponding to the nasal isthmus. With increasing age of the subjects, we observed a systematic increase in nasal spaces. The difference between sexes reached statistical significance at the ages of 17 years and older.ConclusionsThe evaluated males show larger nasal cavity volumes than females. The difference between the sexes reaches statistical significance at older ages.

The Effective Brain Areas in Recognition of Dyslexia

Background: The brain has four lobes consist of frontal, parietal, occipital, and temporal. Most researchers have reported that the left occipitotemporal region of the brain, which is the combined region of the occipital and temporal lobes, is less active in children with dyslexia like Sklar, Glaburda, Ashkenazi and Leisman. Methods: There are different methods and tools to investigate how the brain works, such as magnetic resonance imaging (MRI), positron emission tomography (PET), magneto-encephalography (MEG) and electroencephalography (EEG). Among these, EEG determines the electrical activity of the brain with the electrodes placed on the special areas on the scalp. In this research, we processed the EEG signals of dyslexic children and healthy ones to determine what the areas of the brain are most likely to cause the disease. Results: For this purpose, we extracted 43 features, including relative spectral power (RSP) features, mean, standard deviation, skewness, kurtosis, Hjorth, and AR parameters. Then an SVM classifier is used to separate two classes. Finally, we show the particular brain activation pattern by calculating the correlation coefficients and co-occurrence matrices, which suggests the activation of the working memory region as an active area. Conclusion: By identifying the brain areas involved in reading activity, it has expected that psychologists and physicians will be able to design the therapeutic exercises to activate this part of the brain.

Identifikasi dan Klasifikasi Benih Padi Menggunakan Analisis Citra Digital Berdasarkan Bentuk Fisik

Seed quality testing is one of aspect control and quality assurance for certified seeds. The grain shape is one of the important parameter components tested in the seed certification process especially in the field. The common method used were using human/ analysts visual observation. It has a high degree of subjectivity and low efficiency. Observation of complex samples in the field requires an alternative observation that is more subjective and accurate. An alternative technology for identifying seeds during certification and production is identification based on digital images. The purpose of this study were to identify and classify rice seeds based on physical form using digital image analysis. A total of 20 varieties with various shapes have been taken with a microscope that connected to the camera and computer. The resulting image file was analyzed using imageJ 1.51k software and analyzed statistic to discriminate the seeds tested according to their group. Results from this study indicated that digital image analysis is able to identify and classify seeds. The grouping of seeds into long, medium and round seed categories based on perimeter, circularity, AR, and round parameters with successive correlation levels is 95.4%; 82.5%; 45.3% and 38.9%. This method is more sensitive to identifying seed characteristics than eye visualization of seeds whose physical size is outside the range of numbers specified in the description. For example Cisadane, Gilirang and Ketonggo seeds.

Robust estimation of stationary continuous‐time arma models via indirect inference

In this article, we present a robust estimator for the parameters of a stationary, but not necessarily Gaussian, continuous‐time ARMA(p,q) (CARMA(p,q)) process that is sampled equidistantly. Therefore, we propose an indirect estimation procedure that first estimates the parameters of the auxiliary AR(r) representation (r ≥ 2p−1) of the sampled CARMA process using a generalized M‐ (GM‐)estimator. Since the map which maps the parameters of the auxiliary AR(r) representation to the parameters of the CARMA process is not given explicitly, a separate simulation part is necessary where the parameters of the AR(r) representation are estimated from simulated CARMA processes. Then, the parameters which take the minimum distance between the estimated AR parameters and the simulated AR parameters give an estimator for the CARMA parameters. First, we show that under some standard assumptions the GM‐estimator for the AR(r) parameters is consistent and asymptotically normally distributed. Then, we prove that the indirect estimator is also consistent and asymptotically normally distributed when the asymptotically normally distributed LS‐estimator is used in the simulation part. The indirect estimator satisfies several important robustness properties such as weak resistance, ‐robustness and it has a bounded influence functional. The practical applicability of our method is illustrated in a small simulation study with replacement outliers.

ON THE FIRST PASSAGE TIME FOR AUTOREGRESSIVE PROCESSES

The first passage time problem for an autoregressive process AR(p) is examined. When the innovations are gaussian, the determination of the first pas- sage time probability distribution is closely related to computing a multidimensional integral of a suitable gaussian random vector, known in the literature as orthant prob- ability. Recursive equations involving the first passage time probability distribution are given and a numerical scheme is proposed which takes advantage of the recursion. Compared with the existing procedures in the literature, the algorithm we propose is computationally less expensive and reaches a very good accuracy. The accuracy is tested on some closed form expressions we achieve for special choices of the AR(p) parameters.

Application of Kalman Filter Model in the Landslide Deformation Forecast

Nonlinear exponential trend model is linearized into the linear model, then linearized model parameters are regarded as the state vector containing the dynamic noise to erect Kalman filter model based on exponential trend model to predict the deformation of the rock landslide. Deformation observation values of the landslide are regarded as a time series to erect AR(1) model, then model parameters of AR(1) model are regarded as the state vector containing the dynamic noise to erect Kalman filter model based on AR(1) model to predict the deformation of the rock landslide. The deformation of the landslide is regarded as the function of the time, then Taylor series is used to determine the functional relationship between the deformation of the landslide and the time, and Taylor series is spread to erect Kalman filter model based on Taylor series to predict the deformation of the earthy landslide. The deformation of landslides relates to many factors, the rainfall and the temperature influence the deformation of landslides specially, thus Kalman filter model based on multiple factors is erect to predict the deformation of the earthy landslide on the basis of Taylor series. Numerical examples show that the fitting errors and the forecast errors of the four Kalman filter models are little.

Non identification of structural change in non stationary AR(1) models

This article studies the identification problem in AR(1) models with a change in the AR parameter at an unknown time k 0. Consider the model where denotes the indicator function and is a sequence of i.i.d. random variables which are in the domain of attraction of the normal law with zero means and possibly infinite variances. Two cases were investigated: case (I) and case (II) where c is a fixed constant. We derived the limiting distributions of the least squares estimator of the break fraction τ 0() for the cases above under some mild conditions. The results showed that the change point (or k 0) is non identifiable for the aforementioned two cases by the least squares method. Monte Carlo experiments were conducted to examine the identification of τ 0 under finite sample situations. Our theoretical results are supported by the Monte Carlo experiments.

Effect of data error correlations on trans-dimensional MT Bayesian inversions

Real magnetotelluric (MT) data errors are commonly correlated, but MT inversions routinely neglect such correlations without an investigation on the impact of this simplification. This paper applies a hierarchical trans-dimensional (trans-D) Bayesian inversion to examine the effect of correlated MT data errors on the inversion for subsurface geoelectrical structures, and the model parameterization (the number of conductivity interfaces) is treated as an unknown. In the inversion considering error correlations, the data errors are parameterized by the first-order autoregressive (AR(1)) process, which is included as an unknown in the inversion. The data information itself determines the AR(1) parameter. The trans-D inversion applies the reversible-jump Markov chain Monte Carlo algorithm to sample the trans-D posterior probability density (PPD) for the model parameters, model parameterization and AR(1) parameters, accounting for the uncertainties of the model dimension and data error correlation in the uncertainty estimates of the conductivity profile. In the inversion ignoring the correlation, we neglect the correlation effect by turning off the AR(1) parameter. Then the correlation effect on the MT inversion can be examined upon comparing the posterior marginal conductivity profiles from the two inversions. Further investigation is then carried out for a synthetic case and a real MT data example. The results indicate that for strong correlation cases, neglecting error correlations can significantly affect the inversion results.

Thermohydraulic sensitivity analysis and multi-objective optimization of Fe3O4/H2O nanofluid flow inside U-bend heat exchangers with longitudinal strip inserts

U-bend double pipe heat exchangers (UDPHEs) are one of the simple and low-costing heat exchangers (HEs) which have been used in various energy and thermal engineering applications. In present study, a comprehensive sensitivity analysis and multi-objective optimization of the thermo-hydraulic and performance characteristics is done for Fe3O4/water nanofluid flow inside UDPHEs with longitudinal strip inserts. The flow is turbulent and all of the important fluid conditions (including nanoparticles concentration ϕ, Reynolds number Re and Prandtl number Pr) and geometric parameters (including the aspect ratio of longitudinal strip inserts AR and the diameter ratio DR) are taken into account in investigations. The optimization is carried out using by genetic algorithm (GA) with considering entropy generation number (EGN) and performance evaluation criteria (PEC) as objective functions. The results of sensitivity analysis illustrate that, among all of the parameters, the parameters of DR and AR respectively has the highest and lowest impact on all of the objectives. Also, the outputs of the optimization procedure are 0.02298, 22478.25, 3.1205, 3.4593 and 0.5175 for optimum value of ϕ, Re, Pr, AR and DR, respectively (the optimum value of objective functions is EGNopt = 0.002593 and PECopt = 1.8489).

BIOCENOTIC HETEROGENEITY IN ANTHROPOZOONOSIS FOR EGGS - ECOTECHNOLOGICAL CHAINS OF DETRITY TYPE FOR THE LIMITING OF LINEARITY OF TECHNOLOGIES

Experiments have been conducted that looked into the transformed ecotope in eggs that have undergone anthropozoonosis, in which there was introduced, a group of age matched, biomass and health ascribed status of birds of species of Gallus gallus category laying hens, breed ISA-Brough (ISA-Brown) at 18 weeks of age, 5 lines aligned for age, and grown for 300 days.Involved are 3 models of detritus trophic chain, which are trophotechnological, and also are representative of reproducing technologies for the processing of manure on the basis of the degradation of organic substances used in practice: biotechnological chain for anaerobic degradation of fertilizer (AR); biotechnology for controlled aerobic degradation (composting) (K), and detrital technological chain for extensive aerobic degradation, replicating good farming practices (DFP), which include the creation of conditions for uncontrolled aeration for duration of 180 days.Aerobic technologies use open technology systems with free (DFP) or regulated (K) atmospheric air flow. Providing aerobic conditions is related to the release of some of the biogenic elements in the manure as gaseous products that pollute the atmosphere. The nitrogen is partially released in the form of ammonia (under the simulated/modeled conditions, the amount of ammonia reaches 20 ppm). For this reason, there is a redistribution compared to the outputted raw material: the content of nitrogen in the standing fertilizer (DFP) is 74.6%, in the compost - 94%, in AR the amount is 135%. The dynamics of the other investigated macronutrients listed in Table 1 are analogous to this. Redistribution of toxic elements is also established (Table 2). Regardless of the differences in their dynamics, in all three technologies of processing, the content of toxic elements is regulated as acceptable levels for application and use in soil, with the exception of zinc, which has a low toxicity. A modified mathematical model is proposed to optimize the technological parameters of AR. Organic fertilizer with a dry matter content of 48.16% is introduced at the entrance to the system with AR. In order to obtain optimal concentration (7-8%) for maximum ecological efficiency (criteria R – which is the maximum degree of degradation of organic matter in the fertilizer), a sevenfold dilution of the substrate is required. A sevenfold increase in product (biosludge) with a relatively low dry matter content of 4.08% is obtained.

BIOCENOTIC HETEROGENEITY IN ANTHROPOZOONOSIS FOR EGGS - TRIMODULATE ECOTECHNICAL AGROCENOSIS FOR EFFECTIVE USE OF MANURE

There are three models of detritus chains that reproduce the technology for processing manure applied in practice for utilization of fertilizer from hens that were raised with floor adherence to the technological standards: biotechnological chain for anaerobic degradation of fertilizer (AR); for controlled aerobic degradation (composting, K) and installation of a plant that carries out processes of extensive aerobic degradation, and there is also reproduction of good farming practices (GFP) which include the creation of conditions for uncontrolled aeration for the duration of 180 days. It is established that the AR combines to a maximal degree, environmental priorities with economic priorities as there is the production of a gas fuel and a biosludge, which exists in its highest value as a fertilizer. A modified mathematical model is proposed to optimize the technological parameters of AR. In order to obtain optimal concentration (7-8%) to achieve maximum environmental efficiency (criteria R - maximum degree of degradation of organic matter in fertilizer), a sevenfold dilution of the substrate in water is required. Seven times the amount of product (biosludge) with a relatively low dry matter content- 4.08 %, is obtained.The possibility of separating the biosludge has been studied. The liquid phase, which contains 65% of the dry matter, is found to include 60% of the organic matter and forms the predominant part of the biogenic elements: 87% of the nitrogen, in point, and 92% of the ammonium ions; 90% of the potassium, 74% of the magnesium and 79% of the calcium. More than a 1/3 of the dry matter (35%) and 40% of the organic matter remain in the solid phase, but its content of the plant-based macronutrients is much lower: for nitrogen- 13%, for phosphorus- 24%, for potassium- 10%, for magnesium- 26% and for calcium- 21%.About 20% of the energy generated as biogas is consumed for the separation and granulation of the solid fraction. To improve energy efficiency, proposed is the creation of a three-module ecotechnical system for efficient utilization of biosludge, by depositing in the soil of the agrocenosis for feed production by the method of Petrov et al (1981) every 10 months yearly and separating the biosludge during the other two months, through which soil tillage is impossible due to meteorological conditions. During this period, the liquid phase is stored in cement reservoirs and separation is managed appropriately to avoid sedimentation of the solid phase of the biosludge during storage.

Sparse time series modeling of the baseline vibration from a gearbox under time-varying speed condition

Abstract Time series model-based approach (TSMBA) is promising in processing vibration signals and assessing the health condition of gearboxes. Accurate time series modeling of the baseline vibration is critical to the TSMBA. Gearboxes often operate under time-varying speed condition, which makes the baseline vibration non-stationary. To accurately model such signals, non-stationary time series models are in demand. Conventional functional pooled autoregression (FP-AR) model is a good option. However, conventional FP-AR assumed 1) consecutive AR terms and 2) identical functional space that describes the dependency between AR parameters and rotating speed, which limited its modeling accuracy. To improve modeling accuracy, this paper proposes a sparse FP-AR model that uses sparse AR terms and non-identical functional spaces. To obtain such a sparse FP-AR model, a new model selection procedure is developed by adopting the least absolute shrinkage and selection operator. The sparse FP-AR model has been validated using simulation signals from a simulation model for a fixed-axis gearbox and experimental signals from two independent fixed-axis gearbox test-rigs. The modeling accuracy was measured by mean squared errors and randomness tests of the modeling residuals, goodness-of-fit between the one-step ahead prediction and real gear vibration, and time-frequency spectra. Results have shown that the proposed sparse FP-AR model has higher modeling accuracy than the conventional one. Meanwhile, TSMBA that uses the sparse FP-AR model was applied for detecting gear tooth crack faults under time-varying speed condition. Results have shown that the proposed method benefits the fixed-axis gearbox in early detection of faults and better assessment of fault progressions.

Kalman Filter-Based Channel Estimation for Mobile-to-Mobile and Relay Networks

This letter deals with channel estimation in mobile-to-mobile communications, possibly including amplify and forward mobile relays, assuming a two-dimensional scattering environment. A second-order autoregressive [AR(2)] model with the Kalman filter is used to estimate the channel gain of the global multihop channel. The parameters of the AR(2) model can be tuned by using a classic correlation matching criterion. Alternatively, the use of a minimization of the asymptotic variance criterion permits a strong performance improvement, although the corresponding analytical study is still missing in the literature—which is the topic of this letter. The closed-form expressions for the optimal tuning of the AR(2) parameters and mean square error performance have been first established as a function of the second and fourth moments of the Doppler spectrum of the global channel. Because the analytical formulas of these moments were not given in the literature, we derive them by exploiting cumulant property of convolution of density functions, after decomposition of the global channel as a cascade of Jakes Doppler power spectral density channels. Consequently, the provided closed-form formula are helpful for the design of Kalman filters for a given state of the source, destination, and mobile relays [Doppler spread and signal to noise ratio].

Effect of Left Ventricular Reverse Remodeling on Long-term Outcomes After Aortic Valve Replacement

There have been few studies with a large number of patients on the effect of left ventricular (LV) reverse remodeling and long-term outcomes after aortic valve replacement (AVR). This study aimed to investigate long-term outcomes and the prognostic impact of follow-up echocardiographic parameters after AVR. We evaluated 456 consecutive patients from a retrospective multicenter registry in Japan (J-PROVE-Retro) who underwent AVR for aortic valve diseases (predominantly aortic stenosis [AS]; 326 patients and aortic regurgitation [AR]; 130 patients). Preoperative and follow-up echocardiography at 1 year after AVR was evaluated. The primary outcome measure was a composite of cardiac death or hospitalization due to heart failure. The median follow-up period was 9.2 years in AS group and 9.7 years in AR group. The freedom rate from the primary outcome was 92% at 5 years and 79% at 10 years in AS, and 97% at 5 years, and 93% at 10 years in AR. LV end-diastolic and end-systolic diameters, and the LV mass index decreased and LV ejection fraction increased after AVR in both AS and AR, and LV mass index was normalized in more than half of the patients. In the Cox proportional hazard model, echocardiographic parameters at 1 year after AVR were more strongly related to long-term outcomes than preoperative echocardiographic parameters. In conclusion, echocardiographic parameters at 1 year after AVR are more important as predictors of long-term outcomes than preoperative parameters in both AS and AR. More attention should be paid on early postoperative remodeling for long-term follow-up of patients after AVR.

Identification in Binary Response Panel Data Models: Is Point-Identification More Common Than We Thought?

This paper investigates identification in binary response models with panel data. Conditioning on sufficient statistics can sometimes lead to a conditional maximum likelihood approach that can be used to identify and estimate the parameters of interest in such models. Unfortunately it is often difficult or impossible to find such sufficient statistics, and even if it is possible, the approach sometimes leads to conditional likelihoods that do not depend on some interesting parameters. Using a range of different data generating processes, this paper calculates the identified regions for parameters in panel data logit AR(2) and logit VAR(1) models for which it is not known whether the parameters are identified or not. We find that identification might be more common than was previously thought, and that the identified regions for non-identified objects may be small enough to be empirically useful.

Analysis of the Precision of Bispectral Estimation Methods

In order to evaluate the accuracy of bispectral estimation method, signals of cosine function were adopted. Because the cosine signals’ three order moment spectrum and three order cumulant spectrum are zero, Non zero part of the bispectrum estimated by no matter which method is the estimation error. Through the comparison of three kinds of estimation methods: the direct method, indirect method and AR parameter method, errors of various estimation methods were obtained, then changing the values of different parameters in all methods, and observing the bispectral error values changes with the parameters, so as to provide a basis for the various bispectrum estimation method and the selection.

Effect of cladding on thermal behavior of nuclear fuel element with non-uniform heat generation

In nuclear power reactors if the maximum temperature (2000ᵒC) of the fuel element like Uranium-dioxide increments and reaches 2865ᵒC then the fuel element melts down leading to heavy loss and damage. To maintain this maximum temperature well below the allowable limit the heat in the fuel element should be conducted and dissipated to the surrounding medium. In this article, the numerical prediction and comparison of thermal performance characteristics of a nuclear fuel element with and without cladding (bare fuel element) having non-uniform energy generation in axial direction is studied in detail. Accordingly, the two-dimensional heat conduction equation having a cosine variation of heat generation term in the axial direction, along with appropriate boundary conditions is solved using second order accurate finite difference schemes. The results are presented for a wide range of parameters like Aspect Ratio Ar, Biot number, Bi, Conductivity ratio, Cr, Thickness ratio, Th, and total heat generation parameter, Qt in the form of transverse temperature profiles and axial temperature profiles. Effect of Cr and Th due to presence of cladding is thoroughly studied. Different range of parameters for which the temperature of the fuel element is within the maximum temperature range is obtained. Critical values of these parameters for which the temperature increments above maximum temperature is also investigated. From the detailed discussion of the results it is concluded that the maximum temperature attained in the fuel element without cladding (bare fuel element) is very much higher than that attained in fuel element with cladding. Also it is observed that for fixed values of Ar, Cr and Th, there exists a critical value of Qt and Bi beyond and below which maximum temperature in the fuel element exceeds its allowable limit.

Estimating nonlinear additive models with nonstationarities and correlated errors

AbstractIn this paper, we study a nonparametric additive regression model suitable for a wide range of time series applications. Our model includes a periodic component, a deterministic time trend, various component functions of stochastic explanatory variables, and an AR(p) error process that accounts for serial correlation in the regression error. We propose an estimation procedure for the nonparametric component functions and the parameters of the error process based on smooth backfitting and quasimaximum likelihood methods. Our theory establishes convergence rates and the asymptotic normality of our estimators. Moreover, we are able to derive an oracle‐type result for the estimators of the AR parameters: Under fairly mild conditions, the limiting distribution of our parameter estimators is the same as when the nonparametric component functions are known. Finally, we illustrate our estimation procedure by applying it to a sample of climate and ozone data collected on the Antarctic Peninsula.

Asymptotic theory for M-estimates in unstable AR([formula omitted]) processes with infinite variance innovations

In this paper, we present the asymptotic distribution of M-estimates for parameters in unstable AR(p) processes. The innovations are assumed to be in the domain of attraction of a symmetric stable law with index 0<α≤2. In particular, in the case of repeated unit roots or conjugate complex unit roots, M-estimates have a higher asymptotic rate of convergence compared to the least square estimates and the asymptotic results can be written as Itô stochastic integrals.

Efficacy and Safety of Autologous Cell-based Therapy in Patients with No-option Critical Limb Ischaemia: A Meta-Analysis.

Revascularisation therapy is the current gold standard of care for critical limb ischemia (CLI), although a significant proportion of patients with CLI either are not fit for or do not respond well to this procedure. Recently, novel angiogenic therapies such as the use of autologous cellbased therapy (CBT) have been examined, but the results of individual trials were inconsistent. To pool all published studies that compared the safety and efficacy of autologous CBT derived from different sources and phenotypes with non cell-based therapy (NCT) in CLI patients. We searched Medline, Embase, Cochrane Library and ClinicalTrials.gov from 1974-2017. Sixteen randomised clinical trials (RCTs) involving 775 patients receiving the following interventions: mobilised peripheral blood stem cells(m-PBSC), bone marrow mononuclear cells(BM-MNC), bone marrow mesenchymal stem cells(BM-MSC), cultured BM-MNC(Ixmyelocel-T), cultured PB cells(VesCell) and CD34+ cells were included in the meta-analysis. High-quality evidence (QoE) showed similar all-cause mortality rates between CBT and NCT. AR reduction by approximately 60% were observed in patients receiving CBT compared to NCT (moderate QoE). CBT patients experienced improvement in ulcer healing, ABI, TcO2, pain free walking capacity and collateral vessel formation (moderate QoE). Low-to-moderate QoE showed that compared to NCT, intramuscular BM-MNC and m-PBSC may reduce amputation rate, rest pain, and improve ulcer healing and ankle-brachial pressure index, while intramuscular BM-MSC appeared to improve rest pain, ulcer healing and pain-free walking distance but not AR. Efficacy of other types of CBT could not be confirmed due to limited data. Cell harvesting and implantation appeared safe and well-tolerated with similar rates of adverse-events between groups. Implantation of autologous CBT may be an effective therapeutic strategy for no-option CLI patients. BM-MNC and m-PSBC appear more effective than NCT in improving AR and other limb perfusion parameters. BM-MSC may be beneficial in improving perfusion parameters but not AR, however, this observation needs to be confirmed in a larger population of patients. Generally, treatment using various sources and phenotypes of cell products appeared safe and well tolerated. Large-size RCTs with long follow-up are warranted to determine the superiority and durability of angiogenic potential of a particular CBT and the optimal treatment regimen for CLI.