Positive Real Values Research Articles

Evaluation of Abbott ID-NOW Rapid Assay with Real-Time Reverse Transcription PCR Assay for Detection of SARS-CoV-2

Rapid nucleic acid assays have been approved by FDA for managing the COVID-19 pandemic, however its analytical efficiency has not been thoroughly validated. This study evaluates the detection and identification of COVID-19 virus using Abbott ID-Now to rapidly identify cases and intervention practices in comparison to nucleic acid detection. Nasopharyngeal Swabs collected from 611 participants were tested for Abbott ID-NOW and LabGun COVID-19 ExoFast RT-PCR Kit as per manufacturer’s protocol. The results from the ID NOW™ COVID-19 assay were evaluated by comparing results with the standard RT-PCR, which served as a standard reference. The infection burden of SARS-CoV-2 in the population of UAE was 11.62%. Compared to detection using real time-based platforms, the sensitivity, specificity, positive and negative predictive values of the ID-Now were 84.51%, 99.81%, 98.36% and 98.00% respectively for COVID-19. A stratified analysis was also carried out using cycle threshold (Ct) values categorizing as Ct>33 as with low viral loads while those with Ct<33 as high. This demonstrated statistically significant (P<0.0001) decrease in sensitivity in samples (97.87% in low Ct value samples versus 58.33% in high Ct value samples). Even though the sensitivity for Abbott ID NOW™ in this study was lower, the specificity, positive predictive values and negative predictive values were significant in low viral load samples. It is easy to use and interpret, giving early information to support clinical decision-making ID-NOW could be possibly used as a point-of-care test after evaluation in epidemic and endemic settings.

Optimal permeance ratio, flux direction and layer distribution in composite asymmetric membranes composed of sequences of layers obeying real-power flux laws

In this study, the optimal permeance ratio and layers distribution in multilayer membranes and thin films in which each layer obeys the following real-power law: Flux=πi(P1,ini−P2,ini) are determined for any positive real values of ni. Such an exponent has a physical meaning in several cases, such as: diffusion-controlled permeation of hydrogen in metal membranes i) under infinite-dilution conditions (n = 0.5), and ii) under higher-concentration ones (0.5 < n < 1.5), iii) desorption-limited permeation (n = 1) in the same type of membranes, Knudsen-type diffusional mechanism (n = 1) and viscous flow (n = 2) in microporous membranes. Furthermore, n is equal to 4 in radiation-driven thermal flux. The work considers two generic layers of different thicknesses and exponents, for which it is proven the existence of a general optimal permeance ratio π1/π2 maximising the flux ratio, which is found to be equal to the inverse of the ratio between the respective maximum theoretical driving forces that can be established in each layer. Such an optimal permeance ratio is also proven to be a global optimum. Afterwards, we provide a theoretical generalisation of this optimality to whatever types of driving forces. Then, we provide a demonstration that the highest species permeating flux is always established when feeding it from the layer side with the highest exponent independently of the permeance ratio. As an important consequence, to maximise flux, a multilayer membrane/thin film should be fabricated by depositing the layers in cascade according to the ascending or descending exponents, and should be used in a configuration in which the flux flows from the layer with the highest pressure exponent to the layer with the lowest one. It is also shown that there exists an optimal value of the lower exponent n1 that maximizes the flux ratio keeping constant the other parameters. Finally, we showed that a virial-type linear combination of polynomial driving forces can be approximated by the empirical flux law investigated. Overall, our results are valid not only for whatever type of composite membranes and thin films (metallic, ceramic, polymeric and hybrid ones), but also for any sequential system involving a flux of whatever physical entity obeying the aforementioned law, such as heat transfer in the presence of radiation, non-linear electrical resistances and possible others.

PECULIARITIES OF THE VALUE OF BUILT HERITAGE IN CHISINAU

Valuation of privately owned real estate included in the Register of State Protected Monuments is a serious challenge for practicing values in the Republic of Moldova. The present research highlights methodological and conceptual peculiarities concerning the value and valuation of functional buildings, which cannot be modernized in accordance with the requirements and preferences of modern society. The objectives of the research include: identification of structural changes in the value of built heritage over time; identification of problems and recommendations on how to value a specific category of real estate. The authors find a negative economic value of the analyzed category of real estate, but a positive real value (especially market value). The peculiarities of the value of the land and the value of the building recognized as a national monument are highlighted. The influence of the value of the land on the value of the real estate and the structure of the value of the monument of local importance are illustrated.

Ordering dynamics of nonlinear voter models.

We study the ordering dynamics of nonlinear voter models with multiple states, also providing a discussion of the two-state model. The rate with which an individual adopts an opinion scales as the qth power of the number of the individual's neighbors in that state. For q>1 the dynamics favor the opinion held by the most agents. The ordering to consensus is driven by deterministic drift, and noise plays only a minor role. For q<1 the dynamics favors minority opinions, and for multistate models the ordering proceeds through a noise-driven succession of metastable states. Unlike linear multistate systems, the nonlinear model cannot be reduced to an effective two-state model. We find that the average density of active interfaces in the model with multiple opinion states does not show a single exponential decay in time for q<1, again at variance with the linear model. This highlights the special character of the conventional (linear) voter model, in which deterministic drift is absent. As part of our analysis, we develop a pair approximation for the multistate model on graphs, valid for any positive real value of q, improving on previous approximations for nonlinear two-state voter models.

Secrecy of WSN Data Over Nakagami m Fading Channels with Selection Combining Diversity

We consider the security of wireless sensor network (WSN) data over Nakagami – m fading channels at the physical layer. A WSN in which the fusion center performs selection diversity has been considered for better quality reception. The links between the WSN node and fusion center are assumed to follow Nakagami-m fading distribution. Closed-form expressions for secrecy outage probability (SOP) are derived, and it is established that SOP analysis also leads to the analysis of the existence of secrecy as a special case of SOP. The analytical expressions have been validated through results from simulations. The analysis is valid for all positive real values of the fading parameter, m. The limits on the signal-to-noise ratio can be obtained to secure the transmitted data against eavesdropping with the required SOP and secrecy rate using the analysis presented in this paper.

Euler–Lagrange-Type Equations for Functionals Involving Fractional Operators and Antiderivatives

The goal of this paper is to present the necessary and sufficient conditions that every extremizer of a given class of functionals, defined on the set C1[a,b], must satisfy. The Lagrange function depends on a generalized fractional derivative, on a generalized fractional integral, and on an antiderivative involving the previous fractional operators. We begin by obtaining the fractional Euler–Lagrange equation, which is a necessary condition to optimize a given functional. By imposing convexity conditions over the Lagrange function, we prove that it is also a sufficient condition for optimization. After this, we consider variational problems with additional constraints on the set of admissible functions, such as the isoperimetric and the holonomic problems. We end by considering a generalization of the fundamental problem, where the fractional order is not restricted to real values between 0 and 1, but may take any positive real value. We also present some examples to illustrate our results.

The Double Dyson Index β Effect in Non-Hermitian Tridiagonal Matrices.

The Dyson index, β, plays an essential role in random matrix theory, as it labels the so-called "three-fold way" that refers to the symmetries satisfied by ensembles under unitary transformations. As is known, its 1, 2, and 4 values denote the orthogonal, unitary, and symplectic classes, whose matrix elements are real, complex, and quaternion numbers, respectively. It functions, therefore, as a measure of the number of independent non-diagonal variables. On the other hand, in the case of β ensembles, which represent the tridiagonal form of the theory, it can assume any real positive value, thus losing that function. Our purpose, however, is to show that, when the Hermitian condition of the real matrices generated with a given value of β is removed, and, as a consequence, the number of non-diagonal independent variables doubles, non-Hermitian matrices exist that asymptotically behave as if they had been generated with a value 2β. Therefore, it is as if the β index were, in this way, again operative. It is shown that this effect happens for the three tridiagonal ensembles, namely, the β-Hermite, the β-Laguerre, and the β-Jacobi ensembles.

Confidence Interval Estimation for the Ratio of the Percentiles of Two Delta-Lognormal Distributions with Application to Rainfall Data

The log-normal distribution (skewed distribution or asymmetry distribution) is used to describe random variables comprising positive real values. It is well known that the logarithm values of these are normally distributed (symmetry distribution). Positively right-skewed data applicable to the log-normal distribution are frequently observed in the fields of environmental studies, biology, and medicine. The number of zero observations follows a binomial distribution. However, problems can arise in the analysis of data containing zero observations along with log-normally distributed data, for which the delta-lognormal distribution is often referred to for using the analysis of the data. In statistics, the percentile provides the relative standing of a numerical data point when compared to all of the others in a distribution with reference to the observations at or below it. In this study, estimates for the confidence interval for the ratio of the percentiles of two delta-lognormal distributions are constructed using fiducial generalized confidence interval approaches based on the fiducial quantity and the optimal generalized fiducial quantity, the Bayesian approach, and the parametric bootstrap method. As assessed by Monte Carlo simulations using the RStudio programming in terms of the coverage probability and the average length, the Bayesian approach performed quite well by providing adequate coverage probabilities along with the shortest average lengths in all of the scenarios tested. Daily rainfall data contain both zero and positive values. The daily rainfall data can usually be fitted to the delta-lognormal distribution. Their application to rainfall data is also provided to illustrate their efficacies with real data. The efficacy of the approach is used to compare two rainfall dispersion populations.

Analytical solutions for nonlinear systems using Nucci's reduction approach and generalized projective Riccati equations

&lt;abstract&gt;&lt;p&gt;In this study, the Nucci's reduction approach and the method of generalized projective Riccati equations (GPREs) were utilized to derive novel analytical solutions for the (1+1)-dimensional classical Boussinesq equations, the generalized reaction Duffing model, and the nonlinear Pochhammer-Chree equation. The nonlinear systems mentioned earlier have been solved using analytical methods, which impose certain limitations on the interaction parameters and the coefficients of the guess solutions. However, in the case of the double sub-equation guess solution, analytic solutions were allowed. The soliton solutions that were obtained through this method display real positive values for the wave phase transformation, which is a novel result in the application of the generalized projective Riccati method. In previous applications of this method, the real positive properties of the solutions were not thoroughly investigated.&lt;/p&gt;&lt;/abstract&gt;

A Novel Discrete-Time Chaos-Function-Based Random-Number Generator: Design and Variability Analysis

This paper presents a novel discrete-time (DT) chaotic map-based random-number generator (RNG), namely the Siponi map, which is a modification of the Logistic map. The Logistic map is usually applied to cryptosystems, mainly for the purposes of generating random numbers. In addition to being easy to implement, it has a better security level than other nonlinear functions. However, it can only process positive real-number inputs. Our proposed map is a deterministic function that can process positive and negative real values. We explored the map comprehensively and investigated its characteristics and parameters. We calculated the optimum parameter values using empirical and theoretical mathematical models to yield the maximum randomness of a sequence of bits. The limit variation of the maximum parameter value was determined based on a practical information measure. Empirical verification was performed for the Siponi map to generate bit sequences unrelated to the previous bit with high entropy values, and we found the extractor function threshold value to be 0.5, while the parameter control was −2 or 2. Using our proposed map, a simple RNG without post-processing passed DieHard statistical tests and all the tests on the NIST SP 800-22. Finally, we have implemented a Siponi map-based RNG on the FPGA board and demonstrated that the sources used are LUT = 4086, DSP = 62, and register = 2206.

Derivation of a statistical model for classical systems obeying the fractional exclusion principle.

The violation of the Pauli principle has been surmised in several models of the fractional exclusion statistics and successfully applied to several quantum systems. In this paper, a classical alternative of the exclusion statistics is studied using the maximum entropy methods. The difference between the Bose-Einstein statistics and the Maxwell-Boltzmann statistics is understood in terms of a separable quantity, namely the degree of indistinguishability. Starting from the usual Maxwell-Boltzmann microstate counting formula, a special restriction related to the degree of indistinguishability is incorporated using Lagrange multipliers to derive the probability distribution function at equilibrium under NVE conditions. It is found that the resulting probability distribution function generates real positive values within the permissible range of parameters. For a dilute system, the probability distribution function is intermediate between the Fermi-Dirac and Bose-Einstein statistics and follows the exclusion principle. Properties of various variables of this novel statistical model are studied and possible application to classical thermodynamics is discussed.

Spatial-temporal factors affecting monthly rainfall in some Central Asian countries assuming a Weibull regression model

Climate change has been observed worldwide in the last years. Among the different effects of climate change, rain precipitation is one of the effects that most challenge the population of all countries in the world. The main goal of this study is to introduce a data analysis of monthly rainfall data related to five countries in Central Asia (Kazakhstan, Kyrgyzstan, Tadjikistan, Turkmenistan and Uzbekistan) for a long period of time to discover the behavior of rain precipitation in these countries in the last decades and possible link with climate change. Since climate data are positive real values, Weibull regression models are used in the data analysis in presence of some spatial factors as latitude and longitude of the climate stations in each country, temporal factors (linear year effects), altitude of the climate station and categorical factors (countries).The obtained results show that some factors have different effects in the monthly rainfall of the assumed countries during the follow-up assumed period, possibly linked to the climate change observed in the last decades worldwide.

Normative Harmonization of Multinational EEG Norms

Various information-theoretic divergences have been proposed for the cost function in tasks such as matrix factorization and clustering. One class of divergence is called the Beta divergence. By varying a real-valued parameter β, the Beta divergence connects several well-known divergences, such as the Euclidean distance, Kullback-Leibler divergence, and Itakura-Saito divergence. Unfortunately, the Beta divergence is properly defined only for positive real values, hindering its use for measuring distances between complex-valued data points. We define a new divergence, the Complex Beta divergence, that operates on complex values, and show that it coincides with the standard Beta divergence when the data is restricted to be in phase. Moreover, we show that different values of β place different penalties on errors in magnitude and phase.

SqSelect: Automatic assessment of Failed Error Propagation in state-based systems

Current software systems are inherently complex and this fact strongly complicates, and makes more expensive, to validate them. Therefore, it is a must to provide methodologies, supported by tools, that can direct validation activities so that they focus on specific aspects of the system (e.g. its critical parts, common errors produced by developers, components that are expensive to fix after deployment, etc). Among the different validation techniques, testing is the most widely used. In this paper we focus on one of the main problems when testing systems with many components: the likelihood of Failed Error Propagation (FEP). FEP appears when we have faulty components such that their wrong behaviour is not revealed when isolatedly testing them but that might produce an error when they are combined with other components. Given a component, it is not possible to automatically assess the likelihood of FEP. However, previous work has shown that there is a strong correlation between the likelihood of FEP and an Information Theory notion called Squeeziness . Recent work has shown that it is possible to compute different values of Squeeziness (essentially, Squeeziness depends on a positive real value) and some of them are more suitable to estimate FEP. In this paper we present our tool SqSelect. Our tool receives either a specific system or its more important characteristics (number of states, maximum and minimum number of outgoing transitions from a state, size of the input and output alphabets) and returns interesting data that can help the tester to estimate the presence of FEP. In particular, our tool provides the most promising value(s) of the parameter associated with Squeeziness so that the likelihood of FEP can be more accurately estimated. In order to compute these values, our tool relies on an artificial neural network that has been extensively trained (compressing the information from around 250,000 systems and around 1,500,000 executions).

Extending the Beta divergence to complex values

• We state and prove a generalization of Young’s inequality to the complex plane. • We derive the Beta divergence for complex-valued scalars. • We show that several well-known divergences are special cases of the Complex Beta divergence. Various information-theoretic divergences have been proposed for the cost function in tasks such as matrix factorization and clustering. One class of divergence is called the Beta divergence. By varying a real-valued parameter β , the Beta divergence connects several well-known divergences, such as the Euclidean distance, Kullback-Leibler divergence, and Itakura-Saito divergence. Unfortunately, the Beta divergence is properly defined only for positive real values, hindering its use for measuring distances between complex-valued data points. We define a new divergence, the Complex Beta divergence, that operates on complex values, and show that it coincides with the standard Beta divergence when the data is restricted to be in phase. Moreover, we show that different values of β place different penalties on errors in magnitude and phase.

Application of the Poisson-Tweedie distribution in analyzing crash frequency data

This paper describes a study that applies the Poisson-Tweedie distribution in developing crash frequency models. The Poisson-Tweedie distribution offers a unified framework to model overdispersed, underdispersed, zero-inflated, spatial, and longitudinal count data, as well as multiple response variables of similar or mixed types. The form of its variance function is simple, and can be specified as the mean added to the product of dispersion and mean raised to the power P. The flexibility of the Poisson-Tweedie distribution lies in the domain of P, which includes positive real number values. Special cases of the Poisson-Tweedie distribution models include the linear form of the negative binomial (NB1) model with P equal to 1.0, the geometric Poisson (GeoP) model with P equal to 1.5, the quadratic form of the negative binomial (NB2) model with P equal to 2.0, and the Poisson Inverse Gaussian (PIG) model with P equal to 3.0. A series of models were developed in this study using the Poisson-Tweedie distribution without any restrictions on the value of the power parameter as well as with specific values of the power parameter representing NB1, GeoP, NB2, and PIG models. The effects of fixed and varying dispersion parameters (i.e., dispersion as a function of covariates) on the variance and expected crash frequency estimates were also examined. Three years (2012–2014) of crash data from urban three-leg stop-controlled intersections and urban four-leg signalized intersections in the state of Florida were used to develop the models. The Poisson-Tweedie models or the GeoP models were found to perform better when the dispersion parameter was constant or fixed. With the varying dispersion parameter, the NB2 and PIG models were found to perform better, with both performing equally well. Also, the fixed dispersion parameter values were found to be smaller in the models with a higher value of the power parameter. The variation across the models in their estimates of weight factor, expected crash frequency, and potential for safety improvement of hazardous sites based on the empirical Bayes method was also discussed.

Performance analysis for mixed [formula omitted] shadowed and exponentiated Weibull distributed dual-hop system with multiuser diversity in C-RAN

This paper investigates the performance of mixed radio-frequency/free-space optical (RF/FSO) system with multiuser diversity over mixed fading channels suitable for the Cloud Radio Access Network (C-RAN). Specifically, the considered system consists of K users, a decode-and-forward (DF) relay and a destination. The relay receives the data from the user with best channel conditions and forwards it to the destination. Furthermore, supposing that the RF link experiences κ−μ shadowed fading which combines small-scale multipath fading with line-of-sight shadowing, whereas the FSO link is subject to Exponentiated Weibull (EW) distributed turbulence. We first derived a novel probability density function (PDF) expression for the κ−μ shadowed fading so that the κ, m and can be equal to the arbitrary positive real value. Then, analytical expressions for the end-to-end outage probability (OP) and average bit error rate (ABER) of the overall network are derived assuming an intensity modulation with direct detection (IM/DD) scheme. The closed-form asymptotic expressions at high signal-to-noise (SNR) regimes are also presented. Capitalizing on the asymptotic analysis, we can obtain the diversity gain and discuss the bottlenecks in the mixed RF/FSO systems. Additionally, the impact of several parameters, including the number of users, atmospheric turbulence conditions and pointing errors on the overall system are studied. The simulation results are provided to prove the feasibility of deployment of the system in real-life scenarios.

Performance analysis for a mixed RF and multihop FSO communication system in 5G C-RAN

In this paper, we investigate the performance of a mixed radio frequency (RF) and multihop free space optical (FSO) system in a fifth-generation cloud radio access network (C-RAN) architecture. To cope with the practical channel characteristics, the RF link is assumed to experience the $ \kappa - \mu $ shadowed fading that has recently been proposed to model the small-scale multipath fading as well as line-of-sight shadowing. For FSO links, we adopt the exponentiated Weibull distribution, which can represent irradiance fluctuations at the finite receiving aperture over a wide range of turbulence conditions. Furthermore, we consider the pointing error between the FSO end devices. We first derive a novel probability density function (PDF) expression for the $ \kappa - \mu $ shadowed fading so that the $ \kappa $, $ m $, and $ \mu $ can be equal to the arbitrary positive real value. Then, we study the outage probability and the average symbol error rate (ASER) of the considered system. Our analysis is based on a decode-and-forward (DF) relaying scheme. We choose M-ary quadrature amplitude modulation (M-QAM) for the RF link and binary pulse position modulation (BPPM) for the FSO links. Finally, to provide more insights, we present the asymptotic approximate expressions in high signal-to-noise ratio regimes. Our research shows that the performance of the mixed RF and multihop FSO system can be improved by the large receiving aperture and the increasing number of hops.

Identifying the parametric occurrence of multiple steady states for some biological networks

We consider a problem from biological network analysis of determining regions in a parameter space over which there are multiple steady states for positive real values of variables and parameters. We describe multiple approaches to address the problem using tools from Symbolic Computation. We describe how progress was made to achieve semi-algebraic descriptions of the multistationarity regions of parameter space, and compare symbolic and numerical methods.The biological networks studied are models of the mitogen-activated protein kinases (MAPK) network which has already consumed considerable effort using special insights into its structure of corresponding models. Our main example is a model with 11 equations in 11 variables and 19 parameters, 3 of which are of interest for symbolic treatment. The model also imposes positivity conditions on all variables and parameters.We apply combinations of symbolic computation methods designed for mixed equality / inequality systems, specifically virtual substitution, lazy real triangularization and cylindrical algebraic decomposition, as well as a simplification technique adapted from Gaussian elimination and graph theory. We are able to determine semi-algebraic conditions for multistationarity of our main example over a 2-dimensional parameter space. We also study a second MAPK model and a symbolic grid sampling technique which can locate such regions in 3-dimensional parameter space.

Thermodynamics, stability and Hawking–Page transition of black holes from non-extensive statistical mechanics in quantum geometry

In this letter, the Tsallis theory of non-extensive statistical mechanics described by the parameter q>0 is applied in loop quantum gravity to calculate the black hole entropy, following ref. [11]. The black hole entropy is derived in terms of the Bekenstein-Hawking law for a given horizon area of mass M and arbitrary real positive values of the Immirzi parameter (γ). In this framework, it is shown that the black hole has a minimum temperature at Mmin which relies on the q-parameter, and the specific heat of system is positive with M>Mmin; this means that the large black hole is thermodynamically stable against radiation, in contrast to the standard result where all solutions appear to be unstable. This result is very similar to the ones announced from a black hole in Anti-de Sitter (AdS) space, where it is also proved that a Hawking-Page black hole phase transition results at a critical temperature which relies on the q parameter of the Tsallis formula.