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Moisture accumulates with the growing aging progress of oil-paper insulation and further shortens the remaining life of the transformer. The frequency-domain spectroscopy (FDS) technique can be used to realize the moisture estimation. However, the moisture estimation results would be unreliable once the aging effect on FDS was ignored. Given this issue, an alternative model including the aging effect is thus proposed using FDS and intelligent algorithm. In this work, the feature parameters of FDS data are used to build the databases for characterizing the aging degree and moisture. Then, the moisture estimation models are developed using the weighted K-nearest neighbor (K-NN) algorithm. The accuracy and applicability of the proposed models are finally discussed in laboratory and field conditions. In that respect, the findings reveal that the reported model is available for moisture estimation of transformer oil-paper insulation under various aging degrees and test temperatures. [ABSTRACT FROM AUTHOR]

With growing demand of paper products, the electric consumption in pulp and paper industry is increasing globally where a share of 30 % is attributed to the pulp pumping system. The efficient designing and running of pumping system are critical for energy efficiency. The incomplete information on pulp flows and the oversized designs are the major causes of low efficiency. In the first part of this research, the fiber level models and rheological properties covering a series of unit applications are examined. A loss model of pulp pump is set up by considering different factors influencing pumping performance. The efficiency improvement opportunities in pulp pumping are systematically investigated. The drag-reducing feature at low consistency pulp pumping is highlighted which will be further applied in energy optimized sizing and dimensioning of a pulp pump in the second part of this research. [ABSTRACT FROM AUTHOR]

Building and other construction assets constitute a significant part of a country's physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries' specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980-2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country's construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel statistical framework based on the first two quantile conditional moments that facilitates effective goodness-of-fit testing for one-sided Lévy distributions. The scale-ratio framework introduced in this paper extends our previous results in which we have shown how to extract unique distribution features using conditional variance ratio for the generic class of α-stable distributions. We show that the conditional moment-based goodness-of-fit statistics are a good alternative to other methods introduced in the literature tailored to the one-sided Lévy distributions. The usefulness of our approach is verified using an empirical test power study. For completeness, we also derive the asymptotic distributions of the test statistics and show how to apply our framework to real data. [ABSTRACT FROM AUTHOR]

Nowadays, reliability of power system is of great importance. Therefore, the condition of transformers as the expensive and critical components of a power system should be monitored and assessed. The dominant factor that causes limitation in transformers lifetime is the paper insulation. This paper aims to investigate the conspicuous influence of moisture content, especially in low oxygen and temperature on lifetime estimation under constant conditions, based on the results of previous experiments. Degree of polymerization (DP) is employed to quantify the effects of detrimental parameters with respect to their time-varying nature. This paper proposes a novel lifetime estimation-approach considering a yearly moisture growth for transformers. The results indicate different trend of DP profiles, which lead to an accurate lifetime estimation in comparison with DP obtained through ideal conditions. Furthermore, oscillations in the hot-spot temperature of transformers are demonstrated to have a negative effect on the lifetime. [ABSTRACT FROM AUTHOR]

Summary: The paper identifies a Wiener system, which is excited by a cyclostationary time series. To estimate the first subsystem's linear dynamic impulse response: this proposed algorithm first kernel‐windows the Wiener system's input measurements, then cross‐correlates with the output time series. To identify the second subsystem's static nonlinearity: this proposed algorithm first estimates the unobservable inter‐block internal signal (consistently in the statistical sense), and then kernel‐windows these estimates with the Wiener system output. This estimator provides the unusual capability to identify non‐invertible nonlinearities. This strategy removes any restrictive requirement for a Gaussian random excitation or a sinusoidal deterministic excitation. This paper further proves the estimator's asymptotic consistency and determines the kernel bandwidth for algorithmic convergence. The proposed algorithm's efficacy is verified in the context of two common applications: a servo mechanical system and a telecommunication channel. [ABSTRACT FROM AUTHOR]

A recent paper introduced the interpolating family (IF) of distributions, and they also derived various mathematical properties of the family. Some of the most important properties discussed were the integer order moments of the IF distributions. The moments were expressed as an integral (which were not evaluated) or as finite sums of the beta function. In this paper, more general expressions for moments of any integer order or any real order are derived. Apart from being more general, our expressions converge for a wider range of parameter values. The expressions for entropies are also derived, the maximum likelihood estimation is considered and the finite sample performance of maximum likelihood estimates is investigated. [ABSTRACT FROM AUTHOR]

There is a great desire to integrate multiple energy systems by forming low-carbon energy hubs. Among various energy platforms, the integration of power and gas networks via Power-to-Gas (PtG) technologies is introduced as a promising solution to enhance the flexibility of power grids and facilitate seamless adoption for high penetration levels of renewable energy resources. In addition, the production of green hydrogen from waste/inexpensive clean electricity has recently been identified as a potential means for large-scale energy storage, deployment of hydrogen mobility, and decarbonization of several processes that utilize hydrogen. As integrated hydrogen-based energy hubs (H 2 Hubs) attract more attention for widespread deployment, the security of such new platforms against various cyberattacks is also becoming a critical topic for investigation. This paper proposes a new data estimation technique, developed and integrated with H 2 Hubs scheduling models, for real-time optimal adaptive collection and organization of the system operating parameters for cybersecurity assessment. A new formulation is presented for the adaptive formation of a newly proposed concept, referred to as the system feasibility area , via real-time acquisition of the system operating information. The created feasibility areas are utilized for real-time post-occurrence detection of the bad scheduling data that is invisible to the existing cybersecurity layers. Numerical studies are conducted to demonstrate the efficacy and feasibility of the proposed model. The results demonstrate that the historical data can be adaptively collected in real time and successfully utilized to create feasibility areas to categorize the system operating points. • An optimal scheduling model is proposed for hydrogen-based energy hubs (H 2 Hubs). • A new data estimation method is developed and integrated with the scheduling model. • A new formulation is given for the adaptive creation of the feasibility area. • Simulation case studies are presented and discussed. [ABSTRACT FROM AUTHOR]

In this paper we introduce first-order spatial random coefficient non-negative integer-valued autoregressive (SRCINAR(1,1)) model to fit the spatial count data on a two-dimensional regular lattice. We use a thinning operator in order to define the processes. The mean, variance and autocorrelation function of the model are derived. Three methods (Yule-Walker, Conditional least squares and Conditional maximum likelihood) for the estimation of the parameters of the model are discussed and are compared with respect to the root mean squared error (RMSE), in a simulation study. Finally, SRCINAR(1,1) model is applied to a real data set of striga counts at the ICRISAT Sahelian Center near Niamey (Niger). [ABSTRACT FROM AUTHOR]

The goal for the next generation of terrestrial reference frames (TRF) is to achieve a 1-mm- and 0.1-mm/yr-accurate frame realization through the combination of reference station solutions by multi-technique geodetic observatories. A potentially significant source of error in TRF realizations is the inter-system ties between the instruments at multi-technique stations, usually independently determined through ground-based local surveying. The quality of local tie surveys is varied and inconsistent, largely due to differences in measurement techniques, surveying instruments, site conditions/geometries, and processing methods. The Global Geodetic Observing System (GGOS) has tried to address these problems by issuing guidelines for the construction and layout of future multi-technique observatories, promoting uniformity and quality while minimizing existing problems with local surveying that are exacerbated over longer baseline distances. However, not every observatory is going to be able to completely satisfy these guidelines, and in this work, a successful endeavor to satisfy the accuracy goals while exceeding the GGOS baseline guideline is detailed for the McDonald Geodetic Observatory (MGO) in the Davis Mountains of Texas, USA. MGO consists of a VLBI Geodetic Observing System (VGOS), infrastructure in place for a Space Geodesy Satellite Laser Ranging (SGSLR) telescope, and several Global Navigation Satellite Systems (GNSS) stations spanning a 900 m baseline and a 120 m elevation change. The results of the local ties between the GNSS stations across the near-kilometer baseline, as measured from their antenna reference points, show sub-mm precision and 1 mm accuracy validated through repeatability across several surveys conducted in 2021as well as 1 mm consistency with the monthly averaged daily solutions of the GNSS-based positioning. In this paper, we report these results as well as the framework of the surveys with sufficient detail and rigor in order to give confidence to the quality claims and to present the novel design and techniques employed in the procedure, processing, and error-budget analysis, which were determined through iterative research methods across repeated survey campaigns. [ABSTRACT FROM AUTHOR]

In this paper, we consider the on-line estimation of current subsequences for Partially Observable P-time Petri Nets and their starting markings on a sliding horizon composed of steps defined by two successive occurrences of observable transition firings. We propose a general strategy composed of two phases: Phase 1 exploits a simplification of the P-time Petri net under the form of a Timed Petri net; considering a candidate count vector and the relevant starting marking proposed at Phase 1, Phase 2 makes a schedulability analysis by building a system of relations which can be represented by an acyclic conflict-free computation graph. The complete approach avoids the generation of sets which is generally time and space consuming, and provides an optimal solution for each subproblem by using efficient standard tools. [ABSTRACT FROM AUTHOR]

There is a lack of point process models on linear networks. For an arbitrary linear network, we consider new models for a Cox process with an isotropic pair correlation function obtained in various ways by transforming an isotropic Gaussian process which is used for driving the random intensity function of the Cox process. In particular, we introduce three model classes given by log Gaussian, interrupted, and permanental Cox processes on linear networks, and consider for the first time statistical procedures and applications for parametric families of such models. Moreover, we construct new simulation algorithms for Gaussian processes on linear networks and discuss whether the geodesic metric or the resistance metric should be used for the kind of Cox processes studied in this paper. [ABSTRACT FROM AUTHOR]

Summary Multicollinearity results in inflation in the variance of the ordinary least squares estimators due to the correlation between two or more independent variables (including the constant term). A widely applied solution is to estimate with penalised estimators such as the ridge estimator, which trade off some bias in the estimators to gain a reduction in the variance of these estimators. Although the variance diminishes with these procedures, all seem to indicate that the inference and goodness of fit are controversial. Alternatively, the raise regression allows mitigation of the problems associated with multicollinearity without the loss of inference or the coefficient of determination. This paper completely formalises the raise estimator. For the first time, the norm of the estimator, the behaviour of the individual and joint significance, the behaviour of the mean squared error and the coefficient of variation are analysed. We also present the generalisation of the estimation and the relation between the raise and the residualisation estimators. To have a better understanding of raise regression, previous contributions are also summarised: its mean squared error, the variance inflation factor, the condition number, adequate selection of the variable to be raised, the successive raising, and the relation between the raise and the ridge estimator. The usefulness of the raise regression as an alternative to mitigate multicollinearity is illustrated with two empirical applications. [ABSTRACT FROM AUTHOR]

Image processing is an important area to bring out the best in an image for human interpretation. This process has been widely used in various fields such as machine vision, remote sensing image analysis, medical diagnosis, image restoration, image pattern recognition, and video processing. During the acquisition and transmission of digital images, several random signals, known as noise, can affect image quality. To reduce or to remove efficiently the noise in the acquired or transmitted images, various image denoising techniques can be applied. The performance of denoising methods increases progressively when the noise parameters are taken into account as input parameters. Traditional denoising approaches adopt some assumptions to model noise, such noise is known as purely additive or multiplicative, pixel-independent, and channel-invariant. Usually, these assumptions limit the denoising effect due to inaccurate estimation of noise parameters in these algorithm models. However, the real noise model is signal-dependent and even device-dependent. In this paper, a new denoising method called signal-dependant noise-reducing anisotropic diffusion is developed, which is a version of the speckle reducing anisotropic diffusion (SRAD) filter. It differs from the standard SRAD filter approach by the insertion of a suitable noise parameters estimation framework. The new filter is designed to handle a variety of images corrupted by several common types of signal-dependent noises that are produced by charge-coupled device sensors. As well as it offers great potential for denoising with preserving textures and fine details. Extensive experiments demonstrate a significant increase in the image denoising performance in terms of SNR and RMSE. Qualitative (visual) results underline the efficacy of the proposed algorithm for filtering mixed signal-dependent noise. [ABSTRACT FROM AUTHOR]

This paper investigates how ancillary geographic data – in particular, information on land or assessor parcels – might be used to improve estimates for small area populations and population characteristics. It seeks to determine whether parcel land use codes can be used to reliably replicate population and housing distributions within small (subcounty) areas and whether other parcel attributes – in addition to land use – exhibit any explanatory power in replicating population and housing characteristics within these same places. The basis for this paper was Professor Barbara Buttenfield’s service on the Census Scientific Advisory Committee, in which her working group explored the utility of administrative source data as an alternative or complement to federal survey data. This analysis highlights some of the benefits and complications of the incorporation of parcel data into geodemographic estimation, and the findings demonstrate that such a use is problematic but encouraging. [ABSTRACT FROM AUTHOR]

COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed. [ABSTRACT FROM AUTHOR]

Wave energy generation methods have significant energy costs. The implementation of sophisticated control techniques in wave energy generators can lower the cost of power generation by optimizing the energy recovered from wave energy converters (WECs). To determine control inputs, most control systems rely on knowledge of the wave excitation force, including information on past, present, and future excitation forces. For the excitation of WEC devices, wave excitation force can only be inferred and predicted because it is an unmeasurable quantity. One of the more widely used observers in wave excitation estimates at the moment is the Kalman filter, but its use is primarily restricted to linear Kalman filtering. The mooring system is an integral component of floating wave energy producers. The mooring force of the device is actually nonlinear; however, the majority of current studies on excitation estimates for wave energy producers based on Kalman filter methods employ an ideal motion model based on the linearization of the mooring force. This paper, in an attempt to make things more realistic, creates a WEC system with highly nonlinear mooring forces, suggests a way to build a wave excitation force estimator for a nonlinear WEC system using the extended Kalman filtering method, and assesses the impact of various factors, such as measurement noise, random phase, and the number of equal-energy methods dividing the frequency, on the accuracy of the wave excitation force estimate. [ABSTRACT FROM AUTHOR]

An application of moving-horizon strategy for nonlinear systems with possible outliers in measurements is addressed. With the increased success of movinghorizon strategy in the state estimation for linear systems with outliers acting on the measurement, investigating the nonlinear approach is highly required. In this paper we applied the nonlinear version which has been presented in the literature in term of discrete-time linear time-invariant systems, where the applied strategy considers minimizing a least-squares functions in which each measure possibly contaminated by outlier is left out in turn and the lowest cost is propagated. The moving horizon filter effectiveness as compared with the extended Kalman filter is shown by means of simulation example and estimation error comparison. The moving horizon filter shows the feature of resisting outliers with robust estimation. [ABSTRACT FROM AUTHOR]

AbstractThis paper wishes to propose a new model for count data and it is shortly called a Zero-Inflated Poisson - Probit (ZIP-P) model. The way of setting up for this new model is also based on the traditional model, the Zero-Inflated Poisson (ZIP) model. Note that this new model as well as the ZIP model has a wide range of practical applications and it can be easily applied and analyzed to the real data sets. More specifically, both of these models can be applied very well to count data sets where the frequency of zeros occupies a very high percentage. To achieve the goal of this study, we first recall the construction and general formula of the ZIP model. Then we present the most detailed way for building a new model includes the general formula and related equations necessary for estimating the parameters of the ZIP-P model. In addition, asymptotic results, numerical simulations and analysis of a real data set are also performed in this work. Moreover, we also clarify that this actual data set fits the ZIP-P model better than the other models considered. Finally, several lines of discussions and inferences are made in the study. [ABSTRACT FROM AUTHOR]

In this paper, adaptive controllers are designed to track a given trajectory for linear and nonlinear systems. No condition on the tracked trajectory, other than continuity and boundedness, is needed to simultaneously ensure exponential convergence to the tracking reference, exponential convergence to the identification of the plant, and robustness to nonparametric uncertainties. To achieve this, the formulation of the excitation condition associated with the identification part of the adaptive scheme is proposed without employing closed-loop signals, allowing the use of a transient enrichment of the reference. The effect of this transient modification is attenuated by using relaxed requirements for the identification, obtained through a generalization of several estimation algorithms found in recent literature that use memory mechanisms. Consequently, no spectral content of the tracked trajectory--a classic requirement in adaptive theory--is needed to guarantee the mentioned features when the proposed scheme is used. A numerical example is given to illustrate the design aspects involved and the distinctive features of the proposed strategy. [ABSTRACT FROM AUTHOR]

This paper proposes a partially periodic oscillation model, which is motivated by time series modelling for COVID-19 daily confirmed cases, in particular, to represent more accurately the dynamic features of the 7-day periodicity. In order to express the phenomenon of the partial 7-day cycle in the COVID-19 data, some partial periodic part is added to a heterogeneous autoregression model. Estimation algorithm based on the least squares errors and regression analysis is provided and parameter estimation consistency is given along with its proof. A Monte-Carlo simulation study is carried out to investigate the finite-sample performance. The proposed model is applied to the COVID-19 daily confirmed cases of the most affected eight countries which posses the partially periodic oscillation. Model criteria such as RMSE, MAE, HMAE, AIC and BIC are compared with other existing models. Efficiency of the model, relative to the benchmark, is evaluated to reveal its better accuracy performance. Out-of-sample forecasting analysis is conducted as well. The novelty is that this work is a challenging trial to identify the partially periodic oscillation of COVID-19 data, without smoothing, as well as the proposed model outperforms the existing time series models in the empirical analysis of the worldwide COVID-19. • A partially periodic oscillation model combined with heterogeneous autoregression is proposed. • The model is applied to the COVID-19 daily confirmed cases in the top eight countries with partially periodic oscillation. • A simple and robust estimation method is developed. • Model criteria measures such as RMSE, MAE, HMAE, AIC and BIC are evaluated and compared with existing models. • Efficiency of the proposed model, relative to benchmark models, is reported as 126%-163% improvement in some countries. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new bivariate random coefficient autoregressive (BOD-RCAR(1)) process driven by both explanatory variables and past observations. Firstly, some statistical properties of this model are derived. Secondly, three methods are used for estimating the unknown parameters: conditional least squares (CLS), conditional maximum likelihood (CML) and maximum empirical likelihood (MEL). The asymptotic properties of the estimators are given. Besides, two kinds of test based on empirical likelihood (EL) are established. A simulation experiment is presented to demonstrate the performance of the proposed method. Finally, an application to a real data example is investigated to assess the performance of the model. [ABSTRACT FROM AUTHOR]

INAR models have the great advantage of being able to capture the conditional distribution of a count time series based on their past observations, thus allowing it to be tailored to meet the unique characteristics of count data. This paper reviews the two-parameter Poisson extended exponential (PEE) distribution and its corresponding INAR(1) process. Then the INAR of order p (INAR(p)) model that incorporates PEE innovations is proposed, its statistical properties are presented, and its parameters are estimated using conditional least squares and conditional maximum likelihood estimation methods. Two practical data sets are analyzed and compared with competing INAR models in an effort to gauge the performance of the proposed model. It is found that the proposed model performs better than the competitors. [ABSTRACT FROM AUTHOR]

The paper presents an improvement of the n-shifting technique to identify the frequency response of an industrial process using a fully asymmetric and delaying relay. The n-shifting approach allows the calculation of n + 1 points of G(s) by an asymmetric relay experiment. This set of n points is composed of G(0), G(jωosc), ... , G(jnωosc), being ωosc the oscillation frequency, and where G(jωosc) is in most cases located in the third quadrant of the Nyquist map. By delaying the relay output and repeating a similar experiment it can be generated n additional points of G(s) where the first point is G(jω'osc) with 0 < ω'osc < ωosc. In this way, it is possible to depict the full output spectrum of G(s) from zero to very high frequencies by a short relay experiment. An example of identification and tuning of a PID controller with data from the n-shifting are presented to show the validity of the approach. [ABSTRACT FROM AUTHOR]

Most researchers focus on designing accurate crowd counting models with heavy parameters and computations but ignore the resource burden during the model deployment. A real-world scenario demands an efficient counting model with low-latency and high-performance. Knowledge distillation provides an elegant way to transfer knowledge from a complicated teacher model to a compact student model while maintaining accuracy. However, the student model receives the wrong guidance with the supervision of the teacher model due to the inaccurate information understood by the teacher in some cases. In this paper, we propose a dual-knowledge distillation (DKD) framework, which aims to reduce the side effects of the teacher model and transfer hierarchical knowledge to obtain a more efficient counting model. First, the student model is initialized with global information transferred by the teacher model via adaptive perspectives. Then, the self-knowledge distillation forces the student model to learn the knowledge by itself, based on intermediate feature maps and target map. Specifically, the optimal transport distance is utilized to measure the difference of feature maps between the teacher and the student to perform the distribution alignment of the counting area. Extensive experiments are conducted on four challenging datasets, demonstrating the superiority of DKD. When there are only approximately 6% of the parameters and computations from the original models, the student model achieves a faster and more accurate counting performance as the teacher model even surpasses it. [ABSTRACT FROM AUTHOR]

Reactor experiments were conducted to study aerobic sulfide oxidation in pulp and paper wastewaters under typical operating conditions. Data produced from these experiments were used to develop a set of sequential rate equations that describe the significant sulfide and oxygen processes in the reactors. Model parameters were estimated for three different pulp and paper wastewaters using a multiresponse procedure. The sulfide oxidation rate coefficients for the wastewaters ranged from 0.108 to 0.155 min-1 at 35°C and approximately neutral pH. The reaction order with respect to oxygen could not be precisely estimated but appeared to be near the lower boundary for the fitting procedure (essentially zero order). Modeling of the oxygen depletion data provided evidence that the first sulfide oxidation reaction product consumed 0.5 mg of oxygen per mg of sulfide oxidized, suggesting polysulfides and elemental sulfur as the initial reaction products. The oxygen depletion modeling also suggested oxidation of the initial reaction product to a more stable intermediate which is postulated to be thiosulfate. [ABSTRACT FROM AUTHOR]

Abstract This study proposes a new model in the family zero-inflated (ZI) binary models, it is called as a ZI Probit Bernoulli (ZIPBer) model. This model can be performed to model or simulate binary data that has an excessive amount of zero counts. At the framework of this research, we first introduce the classical formula, estimating equation (EE) and related functions for the ZIPBer model. Next, the results of asymptotic inferences for the ZIPBer model is studied based on the widespread regularity conditions. For the numerical illustrations, many simulation studies and a practical data set are executed in this paper. Specifically, we perform the fishing data set (FDS) to check the working effectiveness and robustness of the maximum likelihood estimation (MLE) approach in estimating parameters for the ZIPBer model. We also illustrate that this actual data set is more suitable for the proposed model: ZIPBer than other binary models such as logit, probit and ZIBer. The achieved results in this study are very unanimous with reality. It also illustrates that this is a dependable verification for us to know the solution as well as how to proceed to how to catch or take the most fish. This is the contribution of this work in terms of application. Finally, some crucial discussions and conclusions are given in this paper. [ABSTRACT FROM AUTHOR]

Background: The aim of the paper is to assess the cognitive and functional status of a segment of the population for a senescent age group. These assessments can help develop intervention strategies to improve the cognitive and functional status of these individuals as well as promote a healthy and active lifestyle. The assessment of cognitive and functional status in a segment of the population during senescence can identify certain problems and deficiencies in cognitive and physical functioning and can help develop physical activities to optimize the cognitive and functional state of senescent people. Objectives: To identify the cognitive and functional profile of the studied age group; Pointing out risk factors for cognitive and functional deterioration; Suggesting physical activities to improve cognitive and functional status. Methods: The recorded data helps us develop personalized aquatic programs to improve health and physical performance, tailored to the individual needs of participants, to be used over 6 months in 2024. Results: The findings suggested by the collected data, recognises a significant link between subjects' body mass index and their physical test scores. Conclusions: Analysis of the data allows us to identify risk factors, including chronic conditions, adopted lifestyle, genetic influences or other environmental conditioning, that could contribute to cognitive and functional decline in the older population. [ABSTRACT FROM AUTHOR]

This paper derives a new lifetime distribution called the unit inverse Weibull distribution(UIWD) from inverse weibull distribution. Various statistical properties such as the survival function, hazard rate function, revised hazard rate function, cumulative hazard rate function, moments, and quartiles have been discussed. Additionally, we have explored other properties like skewness, kurtosis, order statistics, and the quantile function. Various methods of estimation, including maximum likelihood, moments, percentiles, and the Cramer-Von Mises, have been discussed. Simulation studies were conducted to assess the accuracy and precision of the parameters. Comparative analyses were performed to highlight the effectiveness and utility of the proposed model in comparison to other existing models, using two real-life applications. Finally, real life data analysis reveals that derived distribution can provide a better fit than several well-known distributions. [ABSTRACT FROM AUTHOR]

Deep learning is used in various applications due to its advantages over traditional Machine Learning (ML) approaches in tasks encompassing complex pattern learning, automatic feature extraction, scalability, adaptability, and performance in general. This paper proposes an end-to-end (E2E) delay estimation method for 5G networks through deep learning (DL) techniques based on Gaussian Mixture Models (GMM). In the first step, the components of a GMM are estimated through the Expectation-Maximization (EM) algorithm and are subsequently used as labeled data in a supervised deep learning stage. A multi-layer neural network model is trained using the labeled data and assuming different numbers of E2E delay observations for each training sample. The accuracy and computation time of the proposed deep learning estimator based on the Gaussian Mixture Model (DLEGMM) are evaluated for different 5G network scenarios. The simulation results show that the DLEGMM outperforms the GMM method based on the EM algorithm, in terms of the accuracy of the E2E delay estimates, although requiring a higher computation time. The estimation method is characterized for different 5G scenarios, and when compared to GMM, DLEGMM reduces the mean squared error (MSE) obtained with GMM between 1.7 to 2.6 times. [ABSTRACT FROM AUTHOR]

AbstractPeriodic autoregressive (PAR) time series with finite variance is considered as one of the most common models of second-order cyclostationary processes. However, in the real applications, the signals with periodic characteristics may be disturbed by additional noise related to measurement device disturbances or to other external sources. Thus, the known estimation techniques dedicated for PAR models may be inefficient for such cases. When the variance of the additive noise is relatively small, it can be ignored and the classical estimation techniques can be applied. However, for extreme cases, the additive noise can have a significant influence on the estimation results. In this paper, we propose four estimation techniques for the noise-corrupted PAR models with finite-variance distributions. The methodology is based on Yule-Walker equations utilizing the autocovariance function. It can be used for any type of the finite-variance additive noise. The presented simulation study clearly indicates the efficiency of the proposed techniques, also for extreme case, when the additive noise is a sum of the Gaussian additive noise and additive outliers. The proposed estimation techniques are also applied for testing if the data correspond to noise-corrupted PAR model. This issue is strongly related to the identification of informative component in the data in case when the model is disturbed by additive non-informative noise. The power of the test is studied for simulated data. Finally, the testing procedure is applied for two real time series describing particulate matter concentration in the air. [ABSTRACT FROM AUTHOR]

In this paper, interval observers are designed for linear dynamic systems described by continuous-time models with exogenous disturbances, measurement noises, and parametric uncertainties. Jordan canonical form-based relations are presented for an interval observer that estimates the set of admissible values of a given linear function of the system state vector. The theoretical results are illustrated by a practical example. [ABSTRACT FROM AUTHOR]

Several recent works have tackled the estimation issue for the unidimensional four‐parameter logistic model (4PLM). Despite these efforts, the issue remains a challenge for the multidimensional 4PLM (M4PLM). Fu et al. (2021) proposed a Gibbs sampler for the M4PLM, but it is time‐consuming. In this paper, a mixture‐modelling‐based Bayesian MH‐RM (MM‐MH‐RM) algorithm is proposed for the M4PLM to obtain the maximum a posteriori (MAP) estimates. In a comparison of the MM‐MH‐RM algorithm to the original MH‐RM algorithm, two simulation studies and an empirical example demonstrated that the MM‐MH‐RM algorithm possessed the benefits of the mixture‐modelling approach and could produce more robust estimates with guaranteed convergence rates and fast computation. The MATLAB codes for the MM‐MH‐RM algorithm are available in the online appendix. [ABSTRACT FROM AUTHOR]

Abstract Process capability analysis is a widely used process improvement metric. There are several capability indices used in industries to assess the performance of production process. C p m is one of the most important capability index which is often used in many industries for assessing the performance of the production process by taking into consideration the target value. Although capability indices are estimated under the assumption that the data are independent but in many industries, process outputs are often autocorrelated. At the same time, no measuring device gives accurate results. In this paper, we discuss the statistical properties of the estimator of C p m for autocorrelated data and in the presence of random measurement errors. Here, we show that under the combined effect of autocorrelation and measurement errors, the estimator of C p m behaves differently depending upon the variability of the measurement errors. [ABSTRACT FROM AUTHOR]

Hydrogen energy has become an important way to solve energy crises owing to its non-pollution, high level of efficiency, and wide application. Proton exchange membrane fuel cells (PEMFCs) have received wide attention as an energy conversion device for hydrogen energy. The hydrogen concentration in the PEMFC anode directly determines the output voltage of the stack. The performance of the PEMFC gradually decreases due to the accumulation of nitrogen. However, the continuous circulation of anode gas and the nitrogen accumulation at the anode due to transmembrane diffusion lead to difficulties in estimating the anode gas concentration. The relationship between anode nitrogen concentration and voltage variation characteristics was studied by increasing the anode hydrogen concentration through the method of increasing nitrogen concentration and conducting experiments on a 16-cell stack. In this paper, an estimation method for nitrogen concentration in the anode is proposed to evaluate the nitrogen concentration in the anode on the basis of voltage variation characteristics, and the method was recalibrated and validated using experimental data. Due to the inhomogeneity of the gas distribution within the PEMFC stack, the mean cell voltage can provide a more accurate estimation of the anode nitrogen concentration compared to a single cell voltage. It is shown that the proposed approach can offer a new method to estimate anode nitrogen concentration. Compared with the conventional method, the new method is simpler as it does not require additional equipment or complex algorithms. In this paper, the anode nitrogen concentration was estimated by applying this method with a maximum error of only 0.35%. [ABSTRACT FROM AUTHOR]

As the second-largest contributor to historical global warming, methane emissions must be controlled to slow down temperature increase and achieve climate benefits. Due to a lack of knowledge about the specific sources and processes, a quantitative approach will lead to inaccurate estimation. In this paper, a typical local distribution company with more than 100 years of operation history was chosen. Detailed procedures of pipeline constructions and accidents were investigated, and critical steps leading to methane emissions were clarified. Then emission quantification methodologies for all processes were proposed, including a new pipeline connection, new regulator connection, emergency repair and third-party damages. As a basis for emission estimation, the distribution of parameters, such as diameter, length and pressure, was counted. Then the emission rates of all projects were calculated, and emission factors were established. The average emission rates of the new pipeline connection, new regulator connection, emergency repair, third-party damage (medium pressure) and third-party damage (low pressure) were 234 kg, 147 kg, 217 kg, 17,282 kg and 62 kg, respectively. In addition, the total methane emission in China from these sources was estimated to be about 5 × 104 t, which is large enough to attract attention. The work in this paper aims to establish a reasonable framework to evaluate venting methane emissions from the distribution process of natural gas. [ABSTRACT FROM AUTHOR]

This paper describes a coherent multi-dwell processing (CMDP) method for high velocity estimation and super-resolution in search and track, while search (TWS) radar modes use an un-conventional signal processing algorithm that exploits multi-dwell transmissions. The existence of the multi-dwell waveform is necessary for visibility needs by un-folding the target's velocity and range ambiguity and is proposed to be utilized for high velocity estimation and super-resolution. In this paper, the proposed scheme is shown to result in improved velocity estimation and doppler resolution performance for un-ambiguous targets in comparison to classical radar processing. The processing concept uses the same transmitted waveform (WF) and time duration without the need to increase the time on target (TOT) through sophisticated coherent concatenation of the received dwells with velocity compensation between the dwells. The phase compensation in receive mode is implemented for each target according to its characteristics, which means that target velocities are estimated in each dwell separately. The notable result of the CMDP is the linear doppler resolution improvement obtained with the given search resources and without knowing the target characteristics in advance or the dwell delay time. Other possible benefits of this process are the ability to achieve larger detection ranges and high-angle measurement precisions in search mode due to the higher signal-to-noise ratio (SNR) of the extended dwell and the ability to track more targets due to efficient time and resource management. An outstanding opportunity to exploit the CMDP is by combining missions in phased array (PA) radars, meeting the multi-objective needs of both high spatial scan rates for illuminating the target and high doppler estimation and resolution performance. [ABSTRACT FROM AUTHOR]

The Pareto Distribution has been found to be useful in describing socio-economic, physical, and biological phenomena. This paper explores different methods of estimating the shape parameter of the Pareto distribution and two of its functions (reliability and hazard functions), assuming the scale parameter is known. In particular, this paper shows that the Bayesian approach, using both the quadratic and entropy loss functions, can provide a general method for estimating the shape parameter and these two functions. These Bayesian estimators generalize the maximum likelihood estimator (MLE) and the uniformly most powerful unbiased estimator (UMVUE). Simulation results show that the general Bayesian method can perform better than the MLE and UMVUE, especially when the sample size is small. [ABSTRACT FROM AUTHOR]

The Laplace distribution, also known as the double exponential distribution, is a continuous probability distribution that is often used for modelling the data having heavy tails. In this paper, we proposed the Neutrosophic Laplace distribution which is the extension of the classical Laplace Distribution. We derived various statistical properties of the Neutrosophic Laplace Distribution such as mean, variance, skewness, rth moment, quartiles, and moment-generating function. The expressions for the estimation of the parameters are also derived using the maximum likelihood function of the distribution. A simulation study has been done to evaluate the performance of estimates. An application of the Neutrosophic Laplace Distribution is discussed to study the daily return of the NIFTY50 from Indian Stock Market. The analysis shows that the neutrosophic Laplace Model is acceptable, effective, and adequate for dealing with uncertainty in an unpredictable context. [ABSTRACT FROM AUTHOR]

This paper proposes a new statistical method for one-way classification which is based on the (reversed) hazard function of the response variable. The model parameters are estimated according the maximum likelihood approach. Several testing procedures, e.g., generalized likelihood ratio test, are investigated to assess homogeneity of populations. A non-parametric method for data analysis is also proposed. Two data sets are studied using the obtained results. [ABSTRACT FROM AUTHOR]

In this paper, two-parameter Poisson binomial-exponential 2 (PBE2) distribution is firstly reviewed, then a new integer-valued autoregressive (INAR) model with PBE2 innovations is proposed. The definition and statistical properties of the proposed model are given, including the mean, variance, covariance, strict stationarity and ergodicity. Two-step conditional least squares and conditional maximum likelihood estimation methods are considered to estimate the parameters. To assess the proposed model, a crime data set is analyzed and a comparison with other competing INAR models is given, which shows that the proposed model yields a better performance. [ABSTRACT FROM AUTHOR]

In this paper, a statistical model for COVID-19 infection dynamics is described, using only the observed daily statistics of infected individuals. For this purpose, two special classes of branching processes without or with an immigration component are considered. These models are intended to estimate the main parameter of the infection and to give a prediction of the mean value of the non-observed population of the infected individuals. This is a serious advantage in comparison with other more complicated models where the officially reported data are not sufficient for estimation of the model parameters. The model is applied for different regions in the world and the corresponding parameters of the infection dynamics are estimated. [ABSTRACT FROM AUTHOR]

In this paper some important statistical properties of three-parameter Sujatha distribution including descriptive measures based on moments, reliability properties, mean deviations, stochastic ordering and Bonferroni and Lorenz curves have been discussed. The estimation of parameters using maximum likelihood estimation has been discussed. Finally, the goodness of fit of the distribution has been presented for two real lifetime datasets and compared with several one and two-parameter well-known lifetime distributions. [ABSTRACT FROM AUTHOR]

Optimal building design in terms of comfort and energy performance means designing and constructing a building that requires the minimum energy demand under the given conditions while also providing a good level of human comfort. This paper focuses on replacing the complex energy and comfort simulation procedure with fast regression model-based processes that encounter the building shape as input. Numerous building shape descriptors were applied as inputs to several regression models. After evaluating the results, it can be stated that, with careful selection of building geometry describing design input variables, complex energy and comfort simulations can be approximated. Six different models with five different building shape descriptors were tested. The worst results were around R2 = 0.75, and the generic results were around R2 = 0.92. The most accurate prediction models, with the highest level of accuracy (R2 > 0.97), were linear regressions using 3rd power and dense neural networks using 1st power of inputs; furthermore, averages of mean absolute percentage errors are 1% in the case of dense neural networks. For the best performance, the building configuration was described by a discrete functional point cloud. The proposed method can effectively aid future building energy and comfort optimization processes. [ABSTRACT FROM AUTHOR]

This paper develops a multi-sector computable general equilibrium (CGE) model with specific features for Greece and the non-EU Rest of the World (RoW). The novelty of this work stems from the lack of energy-focused CGE models for Greece in the current literature. The study's objective is to determine how the Greek economy would react if a 30% import tariff and a quota of 67% on energy imports and 35% on remaining imports were implemented. Furthermore, if quotas and tariffs are in force, the Greek economy will initiate countermeasures by increasing investment in renewable energies through substitution and a 35% subsidy. To quantify this, the 2015 Input-Output (I-O) table for Greece and the non-EU RoW was used. To offer a better understanding, the 36 production sectors have been divided into Agriculture, Energy, Manufacturing, and Services. The quota scenario resulted in a reduction in all sectors in domestic production in terms of output and domestic uses, with some sectors experiencing around a 30% reduction. Renewable energy investments, on the other hand, have proven to be effective for domestic production, increasing output and domestic uses by (6.561%) and (7.886%), respectively. In terms of import tariffs, prices have increased, resulting in a significant decrease in household consumption that exceeds 30% in several sectors. Finally, policy recommendations for addressing these trade barriers and Greece's renewable energy opportunities are proposed. [ABSTRACT FROM AUTHOR]

High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained momentum due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size n, it is challenging to develop statistical inference methods for high-dimensional measurement error regression models due to the existence of bias, nonconvexity of the objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several novel statistical inference methods. This paper mainly reviews the current development on estimation, hypothesis testing and variable screening methods for high-dimensional measurement error regression models and shows the theoretical results of these methods with some directions worthy of exploring in future research. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for estimating the cyclic stress–strain curves of steels based on their monotonic properties and plastic strain amplitudes, utilizing artificial neural networks (ANNs). ANNs were trained on a substantial number of experimental data for steels, collected from relevant literature, and divided into subgroups according to alloying elements content (unalloyed, low-alloy, and high-alloy steels). Only monotonic properties that were proven to be relevant for the estimation of points on the stress–strain curve were used. The performance of the developed ANNs was assessed using an independent set of data, and the results were compared to experimental values, values obtained by existing empirical estimation methods, and by previously developed ANNs. The results showed that the new approach which combines relevant monotonic properties and plastic strain amplitudes as inputs to ANNs for cyclic stress–strain curve estimation is better than the previously used approach where ANNs estimate the parameters of the Ramberg–Osgood material model separately. This shows that a more favorable approach to the estimation of cyclic stress–strain behavior would be to directly estimate corresponding material curves using monotonic properties. Additionally, this may also reduce inaccuracies resulting from simplified representations of the actual material behavior inherent in the material model. [ABSTRACT FROM AUTHOR]

In this paper, we define a new generator to propose continuous as well as discrete families (or classes) of distributions. This generator is used for the DAL model (acronym of the last names of the authors, Dimitrakopoulou, Adamidis, and Loukas). This newly proposed family may be called the new odd DAL (NODAL) G-class or alternate odd DAL G-class of distributions. We developed both a continuous as well as discrete version of this new odd DAL G-class. Some mathematical and statistical properties of these new G-classes are listed. The estimation of the parameters is discussed. Some structural properties of two special models of these classes are described. The introduced generators can be effectively applied to discuss and analyze the different forms of failure rates including decreasing, increasing, bathtub, and J-shaped, among others. Moreover, the two generators can be used to discuss asymmetric and symmetric data under different forms of kurtosis. A Monte Carlo simulation study is reported to assess the performance of the maximum likelihood estimators of these new models. Some real-life data sets (air conditioning, flood discharges, kidney cysts) are analyzed to show that these newly proposed models perform better as compared to well-established competitive models. [ABSTRACT FROM AUTHOR]

In this paper, we study macroscopic growth dynamics of social network link formation. Rather than focusing on one particular dataset, we find invariant behavior in regional social networks that are geographically concentrated. Empirical findings suggest that the startup phase of a regional network can be modeled by a self-exciting point process. After the startup phase ends, the growth of the links can be modeled by a non-homogeneous Poisson process with a constant rate across the day but varying rates from day to day, plus a nightly inactive period when local users are expected to be asleep. Conclusions are drawn based on analyzing four different datasets, three of which are regional and a non-regional one is included for contrast. [ABSTRACT FROM AUTHOR]

In this paper, we propose a tractable Kumaraswamy Bell exponential (KwBE) distribution as a submodel of the Kumaraswamy Bell-G family of distributions. Several well-established properties are obtained for the KwBE distribution, such as the linear functional representation, $r$ r th moment, incomplete moment, moment generating function using Wright generalized hyper-geometric function, conditional moment and Réyni entropy. Based on the KwBE model, a group acceptance sampling plan (GASP) for the truncated life test is presented using median life as a quality index. Moreover, the essential design parameters are derived by defining the consumer risk and the test termination duration. The comparative study of GASP with ordinary sampling plan (OSP) is performed. A simulation study is performed to highlight the behavior of the estimates. On the inferential side, the associated parameters are estimated using a well-established maximum likelihood estimation method. The detailed model's comparison analysis, graphical as well as numerical evidence to real-data applications, supports the theoretical work. [ABSTRACT FROM AUTHOR]