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A method for Monte Carlo sensitivity analyses of α-eigenvalue (prompt neutron time decay constant) in a subcritical system is developed using the first-order differential operator sampling (DOS) method. The first-order derivative of α-eigenvalue with respect to nuclear data is calculated using the DOS method that includes the capability of calculating perturbed source effect. This paper is an extension of the author’s previous work for development of the sensitivity analysis method for keff-eigenvalue. Unlike the conventional Monte Carlo method for α-eigenvalue calculation that uses the power iteration of fission sources, this paper introduces a recently developed “time source method”. The “time source method” has a weakness for a void-containing subcritical system, which is overcome by assigning a virtual total cross section in the void region. The perturbed source effect, which is caused by the change of nuclear data in a subcritical system, can be calculated by two methods, the source perturbation iteration method and the superhistory method. The source perturbation iteration method is superior in terms of computation efficiency, but a huge computer memory is required. The superhistory method dramatically reduces the memory requirement, although it worsens the variance of the sensitivity coefficients. The method developed in this paper is applied to some numerical tests that use multi-group constants, and it is verified by comparing to the results obtained by a deterministic perturbation theory.

For a positive integer t, a t-spanner of a graph G is a spanning subgraph in which the distance between every pair of vertices is at most t times of their distance in G. In this paper, we consider the problem of finding a t-spanner with minimum number of edges in a given graph, which we call Minimum t -Spanner Problem . For t ≥ 2 , Minimum t -Spanner Problem is known to be NP-hard in general graphs. When the input graph is planar, it is shown by Brandes and Handke in 1997 that this problem is NP-hard for t ≥ 5 . Since then, the case of t ∈ { 2 , 3 , 4 } has been open for more than two decades. The main contribution of this paper is to settle this open problem by showing the NP-hardness of Minimum t -Spanner Problem in planar graphs for t ∈ { 2 , 3 , 4 } . As a byproduct, we show the NP-hardness of the problem on degree-bounded graphs, which improves previously known degree-bounds. We also present a fixed-parameter algorithm for this problem in which the number of removed edges is regarded as a parameter.

A fractional compartmental mathematical model for the spread of the COVID-19 disease is proposed. Special focus has been done on the transmissibility of super-spreaders individuals. Numerical simulations are shown for data of Galicia, Spain, and Portugal. For each region, the order of the Caputo derivative takes a different value, that is not close to one, showing the relevance of considering fractional models., This is a preprint of a paper whose final and definite form is published by 'Chaos Solitons Fractals' (ISSN: 0960-0779). Paper Submitted 04/June/2020; Revised 22/July/2020 and 02/Aug/2020; Accepted 04/Jan/2021

In this paper, we are interested in the following singular stochastic differential equation (SDE) d X t = b ( t , X t ) d t + d B t , 0 ≤ t ≤ T , X 0 = x ∈ R d , where the drift coefficient b : [ 0 , T ] × R d ⟶ R d is Borel measurable, possibly unbounded and has spatial linear growth. The driving noise B t is a d− dimensional Brownian motion. The main objective of the paper is to establish the existence and uniqueness of a strong solution and a Sobolev differentiable stochastic flow for the above SDE. Malliavin differentiability of the solution is also obtained (cf. [21] , [23] ). Our results constitute significant extensions to those in [31] , [30] , [14] , [21] , [23] by allowing the drift b to be unbounded. We employ methods from white-noise analysis and the Malliavin calculus. As application, we prove existence of a unique strong Malliavin differentiable solution to the following stochastic delay differential equation d X ( t ) = b ( X ( t − r ) , X ( t , 0 , ( v , η ) ) d t + d B ( t ) , t ≥ 0 , ( X ( 0 ) , X 0 ) = ( v , η ) ∈ R d × L 2 ( [ − r , 0 ] , R d ) , with the drift coefficient b : R d × R d → R d is a Borel-measurable function bounded in the first argument and has linear growth in the second argument.

The aim of this paper is to study the fast computation of the lower and upper bounds on the value function for utility maximization under the Heston stochastic volatility model with general utility functions. It is well known there is a closed form solution to the HJB equation for power utility due to its homothetic property. It is not possible to get closed form solution for general utilities and there is little literature on the numerical scheme to solve the HJB equation for the Heston model. In this paper we propose an efficient dual control Monte-Carlo method for computing tight lower and upper bounds of the value function. We identify a particular form of the dual control which leads to the closed form upper bound for a class of utility functions, including power, non-HARA and Yaari utilities. Finally, we perform some numerical tests to see the efficiency, accuracy, and robustness of the method. The numerical results support strongly our proposed scheme.

© 2019 The Authors. Background: The contingent valuation (CV) method is used to estimate the willingness to pay (WTP) for services and products to inform cost benefit analyses (CBA). A long-standing criticism that stated WTP estimates may be poor indicators of actual WTP, calls into question their validity and the use of such estimates for welfare evaluation, especially in the health sector. Available evidence on the validity of CV studies so far is inconclusive. We systematically reviewed the literature to (1) synthesize the evidence on the criterion validity of WTP/willingness to accept (WTA), (2) undertake a meta-analysis, pooling evidence on the extent of variation between stated and actual WTP values and, (3) explore the reasons for the variation. Methods: Eight electronic databases were searched, along with citations and reference reviews. 50 papers detailing 159 comparisons were identified and reviewed using a standard proforma. Two reviewers each were involved in the paper selection, review and data extraction. Meta-analysis was conducted using random effects models for ratios of means and percentage differences separately. Meta-bias was investigated using funnel plots. Results: Hypothetical WTP was on average 3.2 times greater than actual WTP, with a range of 0.7–11.8 and 5.7 (0.0–13.6) for ratios of means and percentage differences respectively. However, key methodological differences between surveys of hypothetical and actual values were found. In the meta-analysis, high levels of heterogeneity existed. The overall effect size for mean summaries was 1.79 (1.56–2.04) and 2.37 (1.93–2.80) for percent summaries. Regression analyses identified mixed results on the influence of the different experimental protocols on the variation between stated and actual WTP values. Results indicating publication bias did not account for differences in study design. Conclusions: The evidence on the criterion validity for CV studies is more mixed than authors are representing because substantial differences in study design between hypothetical and actual WTP/WTA surveys are not accounted for. Brunel University London, Health Economics Research Group.

Malmquist indices are often used to measure productivity changes in dynamic settings and have been widely applied. The indices are typically estimated using data envelopment analysis (DEA) estimators. Malmquist indices are often decomposed into sub-indices that measure the sources of productivity change (e.g., changes in efficiency, technology or other factors). Recently, Kneip et al. (2018) provide new theoretical results enabling inference about productivity change for individual firms as well as average productivity changed measured in terms of geometric means. This paper extends those results to components of productivity change arising from various decompositions of Malmquist indices. New central limit theorems are developed to allow inference about arithmetic means of logarithms of the sub-indices as well as geometric means of (untransformed) sub-indices. The results are quite general and extend to other sub-indices not explicitly considered in this paper.

We study a combinatorial problem arising from the microarray synthesis. The objective of the Border Minimization Problem (BMP) is to place a set of sequences in the array and to find an embedding of these sequences into a common supersequence such that the sum of the “border length” is minimized. A variant of the problem, called P-BMP, is that the placement is given and the concern is simply to find the embedding. An exponential time algorithm has been proposed for the problem but it is unknown whether the problem is NP-hard or not. In this paper, we give a comprehensive study of different variations of BMP by presenting NP-hardness proofs and approximation algorithms. We show that BMP, P-BMP, and 1D-BMP are all NP-hard and 1D-BMP is polynomial time solvable. The interesting implications include (i) the BMP is NP-hard regardless of the dimension (1D or 2D) of the array; (ii) the array dimension differentiates the complexity of the P-BMP; and (iii) for 1D array, whether placement is given differentiates the complexity of the BMP. Another contribution of the paper is devising approximation algorithms, and in particular, we present a randomized approximation algorithm for BMP with approximation ratio O ( n 1 ∕ 4 log 2 n ) , where n is the total number of sequences.

This paper studies vector quantile regression (VQR), which models the dependence of a random vector with respect to a vector of explanatory variables with enough flexibility to capture the whole conditional distribution, and not only the conditional mean. The problem of vector quantile regression is formulated as an optimal transport problem subject to an additional mean-independence condition. This paper provides results on VQR beyond the specified case which had been the focus of previous work. We show that even beyond the specified case, the VQR problem still has a solution which provides a general representation of the conditional dependence between random vectors. Keywords: Duality; Optimal transport; Vector quantile regression

Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these learning methods in repeated samples is assessed using the variance of the posterior distribution of the parameters of interest given the data. This may be permissible when the sample size is large because, under the conditions of the Bernstein--von Mises theorem, the posterior variance agrees asymptotically with the frequentist variance. In finite samples, however, things are less clear. In this paper we explore this issue by first considering the frequentist properties (bias and variance) of the posterior mean in the important case of the normal location model, which consists of a single observation on a univariate Gaussian distribution with unknown mean and known variance. Based on these results, we derive new estimators of the frequentist bias and variance of the WALS estimator in finite samples. We then study the finite-sample performance of the proposed estimators by a Monte Carlo experiment with design derived from a real data application about the effect of abortion on crime rates.

Computing reachable sets for continuous-stirred tank reactors (CSTRs) under uncertainty is crucial for designing efficient model-based control strategies or developing robust process monitoring protocols. This paper, the first in the three-part series, develops a linear transformation to project the dynamics of a CSTR reaction system onto a transformed state space. The proposed transformation is invertible, and leads to a “sparse” system representation in the transformed state space – a property crucial for the methods developed to compute reachable sets of CSTR reaction systems. The second and third papers in this series discuss how the transformation developed here can be used to compute effectively outer interval approximations to the reachable sets of CSTR reaction systems. To this effect, two new bounding methods – direct and indirect-bounding methods – are proposed in the second and third paper, respectively, to compute tight interval enclosures for the reachable sets of CSTR reaction systems. Several numerical examples are also provided to demonstrate efficacy of the proposed direct and indirect-bounding methods.

In Part I of this series, Bonet et al. (2015) introduced a new computational framework for the analysis of large strain isothermal fast solid dynamics, where a mixed set of Total Lagrangian conservation laws was presented in terms of the linear momentum and an extended set of strain measures, namely the deformation gradient, its co-factor and its Jacobian. The main aim of this paper is to expand this formulation to the case of nearly incompressible and truly incompressible materials. The paper is further enhanced with three key novelties. First, the use of polyconvex nearly incompressible strain energy functionals enables the definition of generalised convex entropy functions and associated entropy fluxes. Two variants of the same formulation can then be obtained, namely, conservation-based and entropy-based, depending on the unknowns of the system. Crucially, the study of the eigenvalue structure of the system is carried out in order to demonstrate its hyperbolicity and, thus, obtain the correct time step bounds for explicit time integrators. Second, the development of a stabilised Petrov–Galerkin framework is presented for both systems of hyperbolic equations, that is, when expressed in terms of either conservation or entropy variables. Third, an adapted fractional step method, built upon the work presented in Gil et al. (2014), is presented to extend the range of applications towards the incompressibility limit. Finally, a series of numerical examples are presented in order to assess the applicability and robustness of the proposed formulation. The overall scheme shows excellent behaviour in compressible, nearly incompressible and truly incompressible scenarios, yielding equal order of convergence for velocities and stresses.

In a mutual control structure agents exercise control over each other. Typical examples occur in the area of corporate governance: firms and investment companies exercise mutual control, in particular by owning each others’ stocks. In this paper we formulate a general model for such situations. There is a fixed set of agents, and a mutual control structure assigns to each subset (coalition) the subset of agents controlled by that coalition. Such a mutual control structure captures direct control. We propose a procedure in order to incorporate indirect control as well: if S controls T, and S and T jointly control R, then S controls R indirectly. This way, invariant mutual control structures result. Alternatively, mutual control can be described by vectors of simple games, called simple game structures, each simple game describing who controls a certain player, and also those simple games can be updated in order to capture indirect control. We show that both approaches lead to equivalent invariant structures. In the second part of the paper, we axiomatically develop a class of power indices for invariant mutual control structures. We impose four axioms with a plausible interpretation in this framework, which together characterize a broad class of power indices based on dividends resulting both from exercising and from undergoing control. By adding an extra condition a unique power index is singled out. In this index, each player accumulates his Shapley-Shubik power index assignments from controlling other players, diminished by the sum of the Shapley-Shubik power index assignments to other players controlling him.

Proceedings of the Seventh Spanish Conference on Programming and Computer Languages (PROLE 2007), In this paper we present a new approach for the analysis of rule-based specification of system dynamics. We model system states as simple digraphs, which can be represented with boolean matrices. Rules modelling the different state changes of the system can also be represented with boolean matrices, and therefore the rewriting is expressed using boolean operations only. The conditions for sequential independence between pair of rules are well-known in the categorical approaches to graph transformation (e.g. single and double pushout). These conditions state when two rules can be applied in any order yielding the same result. In this paper, we study the concept of sequential independence in our framework, and extend it in order to consider derivations of arbitrary finite length. Instead of studying one-step rule advances, we study independence of rule permutations in sequences of arbitrary finite length. We also analyse the conditions under which a sequence is applicable to a given host graph. We introduce rule composition and give some preliminary results regarding parallel independence. Moreover, we improve our framework making explicit the elements which, if present, disable the application of a rule or a sequence., Work sponsored by Spanish Ministry of Science and Education, project MOSAIC (TSI2005-08225-C07-06).

Murota (1998) and Murota and Shioura (1999) introduced concepts of M-convex function and M♮-convex function as discrete convex functions, which are generalizations of valuated matroids due to Dress and Wenzel (1992). In the present paper we consider a new operation defined by a convolution of sections of an M♮-convex function that transforms the given M♮-convex function to an M-convex function, which we call a compression of an M♮-convex function. For the class of valuated generalized matroids, which are special M♮-convex functions, the compression induces a valuated permutohedron together with a decomposition of the valuated generalized matroid into flag-matroid strips, each corresponding to a maximal linearity domain of the induced valuated permutohedron. We examine the details of the structure of flag-matroid strips and the induced valuated permutohedron by means of discrete convex analysis of Murota.

In this paper we propose an autoregressive wild bootstrap method to construct confidence bands around a smooth deterministic trend. The bootstrap method is easy to implement and does not require any adjustments in the presence of missing data, which makes it particularly suitable for climatological applications. We establish the asymptotic validity of the bootstrap method for both pointwise and simultaneous confidence bands under general conditions, allowing for general patterns of missing data, serial dependence and heteroskedasticity. The finite sample properties of the method are studied in a simulation study. We use the method to study the evolution of trends in daily measurements of atmospheric ethane obtained from a weather station in the Swiss Alps, where the method can easily deal with the many missing observations due to adverse weather conditions. (C) 2019 Elsevier B.V. All rights reserved.

Let X --> A(X) denote the algebraic K-theory of spaces functor. In the first paper of this series, we showed A(X x S-1) decomposes into a product of a copy of A(X), a delooped copy of A(X) and two homeomorphic nil terms. The primary goal of this paper is to determine how the "canonical involution" acts on this splitting. A consequence of the main result is that the involution acts so as to transpose the nil terms. From a technical point of view, however, our purpose will be to give another description of the involution on A(X) which arises as a (suitably modified) P.-construction. The main result is proved using this alternative discription. (C) 2002 Elsevier Science B.V. All rights reserved.

We consider the following variant of the Mortality Problem: given $k\times k$ matrices $A_1, A_2, \dots,A_{t}$, does there exist nonnegative integers $m_1, m_2, \dots,m_t$ such that the product $A_1^{m_1} A_2^{m_2} \cdots A_{t}^{m_{t}}$ is equal to the zero matrix? It is known that this problem is decidable when $t \leq 2$ for matrices over algebraic numbers but becomes undecidable for sufficiently large $t$ and $k$ even for integral matrices. In this paper, we prove the first decidability results for $t>2$. We show as one of our central results that for $t=3$ this problem in any dimension is Turing equivalent to the well-known Skolem problem for linear recurrence sequences. Our proof relies on the Primary Decomposition Theorem for matrices that was not used to show decidability results in matrix semigroups before. As a corollary we obtain that the above problem is decidable for $t=3$ and $k \leq 3$ for matrices over algebraic numbers and for $t=3$ and $k=4$ for matrices over real algebraic numbers. Another consequence is that the set of triples $(m_1,m_2,m_3)$ for which the equation $A_1^{m_1} A_2^{m_2} A_3^{m_3}$ equals the zero matrix is equal to a finite union of direct products of semilinear sets. For $t=4$ we show that the solution set can be non-semilinear, and thus it seems unlikely that there is a direct connection to the Skolem problem. However we prove that the problem is still decidable for upper-triangular $2 \times 2$ rational matrices by employing powerful tools from transcendence theory such as Baker's theorem and S-unit equations., Full version of the MFCS submission

In this paper, we consider clustering based on the kernel principal component analysis (KPCA) for high-dimension, low-sample-size (HDLSS) data. We give theoretical reasons why the Gaussian kernel is effective for clustering high-dimensional data. In addition, we discuss a choice of the scale parameter yielding a high performance of the KPCA with the Gaussian kernel. Finally, we test the performance of the clustering by using microarray data sets.

Supercapacitor (SC) is one of the most trending energy storage solutions. The SCs equivalent circuit models have been extensively applied to energy management because of their accuracy and simplicity. However, there is a difficulty in solving the conventional differential equation-based model used to characterize the electrical behavior of SCs operating in constant power applications. Hence, numerical or metaheuristic techniques have been used in the literature to derive SC's internal voltage at any time. In this work, a thorough mathematical analysis that enables a precise calculation of the electrical variables implied in the charge/discharge processes of SCs operated at constant power as a function of time is presented. First, the transcendental discharge voltage expression of SCs operating at constant power is formulated, and then it was solved using the Special Trans Function theory (STFT). The precision of calculation of the method used for solving the transcendental expression is presented and discussed. Second, the transcendental voltage of charging expression of SCs operating at constant power as a function of time is also formulated, and then it was solved using Lambert W equation. Third, a comparison of different methods for solving mentioned equations is presented. Fourth, the electrical variables involved in the charge/discharge processes of SCs – voltage, current, power, energy, state of charge, and power loss are investigated. Furthermore, the results obtained for the variation of parameters and their operating conditions demonstrate the proposed equations’ applicability and accuracy. Finally, the results obtained validate that the closed-form expressions suggested in this paper are accurate and straightforward, which can contribute to proper modeling, investigation, sizing, regulation, and control of constant power SCs in modern energy systems.

Considering system theory, the socio-economic variables that constitute a society should be able to capture the system response such as the number of weekly COVID-19 cases. A numerical approach has been presented in this paper to answer two vital questions; which variables are more important and how many variables are needed to capture the dynamics of the spread. Using the theory of least squares regression, two types of problems have been set up and solved using multilinear regression (MLR) and nonlinear powered function known as NLR in this study. Numerical techniques were applied to pre- and post-process the data and the vast number of outputs. Total 43 socio-economic and meteorological variables from 31 counties in California in the United States resulted about 37.4 millions of combinations for the analysis. Results show that variables related to total population, household income, occupation, and transportation are more important than the others. The frequency of having higher correlation for a variable increases as more variables are combined with it. Similarly, correlation increases as the number of variables in a combination increases. Some 5- variable combinations can capture the dynamics of the spread with higher accuracy having correlation coefficient as high as 0.985.

This paper presents a coupled thermo-hydro-mechanical (THM) finite element (FE) formulation which is capable of accounting for the effects of temperature change on the behaviour of unsaturated soils. Both vapour flow and density variation are taken into account in the development of this formulation. The full derivation procedure is provided and the adopted assumptions are stated and explained. To improve the efficiency of the nonlinear solution process while maintaining the accuracy of the prediction, a novel approach for determining iterative corrections when modelling coupled transient problems with the Newton-Raphson algorithm is established and presented here. The performance of the proposed FE formulation and of the new strategy for iterative corrections in a nonlinear solver is subsequently demonstrated and verified by simulations of laboratory experiments on unsaturated compacted bentonite, showing good agreement between the numerical and experimental results.

The Ramsey number $R_X(p,q)$ for a class of graphs $X$ is the minimum $n$ such that every graph in $X$ with at least $n$ vertices has either a clique of size $p$ or an independent set of size $q$. We say that Ramsey numbers are linear in $X$ if there is a constant $k$ such that $R_{X}(p,q) \leq k(p+q)$ for all $p,q$. In the present paper we conjecture that if $X$ is a hereditary class defined by finitely many forbidden induced subgraphs, then Ramsey numbers are linear in $X$ if and only if $X$ excludes a forest, a disjoint union of cliques and their complements. We prove the "only if" part of this conjecture and verify the "if" part for a variety of classes. We also apply the notion of linearity to bipartite Ramsey numbers and reveal a number of similarities and differences between the bipartite and non-bipartite case., 24 pages

We introduce in this paper the notion of dominance in the divisional efficiencies space in Network Data Envelopment Analysis. We argue that, irrespectively of the method used, a successful efficiency evaluation protocol should satisfy the dominance property at the divisional efficiencies level. In particular, there should not exist any other feasible solution in the assessment model, suboptimal in terms of the optimality criterion, that provides stage efficiencies scores at least as high as the assessed ones and higher for at least one stage. Then, we investigate the dominance property in different methods for two-stage series processes of various complexity. We prove that the additive efficiency decomposition method and the relational model provide non-dominated divisional efficiencies when they are applied to elementary two-stage processes, where nothing but the external inputs to the first stage enters the system and nothing but the external outputs of the second stage leaves the system. For more complex two-stage structures, however, we provide examples showing that these models do not comply with the dominance requirement at the divisional efficiencies level and lead to controversial results. Finally, we revisit some characteristic NDEA methods for which dominance is an inherent property.

In the two disjoint shortest paths problem ( 2-DSPP), the input is a graph (or a digraph) and its vertex pairs ( s 1 , t 1 ) and ( s 2 , t 2 ) , and the objective is to find two vertex-disjoint paths P 1 and P 2 such that P i is a shortest path from s i to t i for i = 1 , 2 , if they exist. In this paper, we give a first polynomial-time algorithm for the undirected version of the 2-DSPP with an arbitrary non-negative edge length function.

Lean Meat Yield (LMY, %) of carcass is an important industry trait, which currently is not routinely measured in Australian beef abattoirs. Objective on-line technology to determine LMY is key for wider adoption. This paper presents a proof-of-concept approach for estimating the LMY of beef carcasses from the 3D information provided by RGB-D cameras. Moreover, a specifically designed on-line data acquisition system for abattoir applications is presented, consisting of three cameras moving on a scanning rig to generate 3D carcass side reconstructions. The hindquarter is then segmented consistently across all the 3D models to extract curvature information and LMY estimated via non-linear regression based on Gaussian Process models. Sides from 119 carcasses at two different commercial abattoirs were used to evaluate this approach. Results from this preliminary study (RMSE = 3.91%, R2 = 0.69) using curvature, P8 fat and HSCW indicate that 3D imaging of beef carcasses is a viable and relatively accurate technology to estimate LMY.

In this paper, we sought and presented an 8-Dimensional deterministic mathematical COVID-19 dynamic model that accounted for the global stability analysis of the role of dual-bilinear treatment protocols of COVID-19 infection. The model, which is characterized by human-to-human transmission mode was investigated using dual non-pharmaceutical (face-masking and social distancing) and dual pharmaceutical (hydroxylchloroquine and azithromycin) as control functions following the interplay of susceptible population and varying infectious population. First, we investigated the model state-space and then established and computed the system reproduction number for both off-treatment ℜ 0 ( 1 ) = 10.94 and for onset-treatment ℜ 0 ( 2 ) = 3.224 . We considered the model for off-treatment and thereafter by incorporating the theory of LaSalle's invariant principle into the classical method of Lyapunov functions, we presented an approach for global stability analysis of COVID-19 dynamics. Numerical verification of system theoretical predictions was computed using in-built Runge-Kutta of order of precision 4 in a Mathcad surface. The set approach produces highly significant results in the main text. For example, while rapid population extinction was observed by the susceptible under off-treatment scenario in the first t f ≤ 18 days, the application of non-pharmaceuticals at early stage of infection proved very effective strategy in curtailing the spread of the virus. Moreso, the implementation of dual pharmacotherapies in conjunction with non-pharmaceuticals yields tremendous rejuvenation of susceptible population ( 0.5 ≤ S p ( t ) ≤ 3.143 c e l l s / m l 3 ) with maximal reduction in the rates of isolation, super spreaders and hospitalization of the infectives. Thus, experimental results of investigation affirm the suitability of proposed model for the control and treatment of the deadly disease provided individuals adheres to treatment protocols.

In this paper a fractional order mathematical model is constructed to study the dynamics of corona virus in Oman. The model consists of a system of eight non-linear fractional order differential equations in Caputo sense. Existence and uniqueness as well as the stability analysis of the solution of the model are given. The stability analysis is in the frame of Ulam-Hyers and generalized Ulam-Hyers criteria. Numerical simulations are given to support the theoretical results. Many informations on the dynamics of COVID -19 in Oman were obtained using this model. Also many informations on the qualitative behaviour of the model were obtained.

This paper concerns the minimax center of a collection of linear subspaces. When the subspaces are $k$-dimensional subspaces of $\mathbb{R}^n$, this can be cast as finding the center of a minimum enclosing ball on a Grassmann manifold, Gr$(k,n)$. For subspaces of different dimension, the setting becomes a disjoint union of Grassmannians rather than a single manifold, and the problem is no longer well-defined. However, natural geometric maps exist between these manifolds with a well-defined notion of distance for the images of the subspaces under the mappings. Solving the initial problem in this context leads to a candidate minimax center on each of the constituent manifolds, but does not inherently provide intuition about which candidate is the best representation of the data. Additionally, the solutions of different rank are generally not nested so a deflationary approach will not suffice, and the problem must be solved independently on each manifold. We propose and solve an optimization problem parametrized by the rank of the minimax center. The solution is computed using a subgradient algorithm on the dual. By scaling the objective and penalizing the information lost by the rank-$k$ minimax center, we jointly recover an optimal dimension, $k^*$, and a central subspace, $U^* \in$ Gr$(k^*,n)$ at the center of the minimum enclosing ball, that best represents the data., 26 pages

In this paper, we present a geometric approach for computing the controlled invariant set of a continuous-time control system. While the problem is well studied for in the ellipsoidal case, this family is quite conservative for constrained or switched linear systems. We reformulate the invariance of a set as an inequality for its support function that is valid for any convex set. This produces novel algebraic conditions for the invariance of sets with polynomial or piecewise quadratic support function. We compare it with the common algebraic approach for polynomial sublevel sets and show that it is significantly more conservative than our method., Comment: 7 pages, 2 figures, submitted to the 7th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS 2021)

The coronavirus disease-2019 (COVID-19) has been spreading rapidly in South Africa (SA) since its first case on 5 March 2020. In total, 674,339 confirmed cases and 16,734 mortality cases were reported by 30 September 2020, and this pandemic has made severe impacts on economy and life. In this paper, analysis and long-term prediction of the epidemic dynamics of SA are made, which could assist the government and public in assessing the past Infection Prevention and Control Measures and designing the future ones to contain the epidemic more effectively. A Susceptible-Infectious-Recovered model is adopted to analyse epidemic dynamics. The model parameters are estimated over different phases with the SA data. They indicate variations in the transmissibility of COVID-19 under different phases and thus reveal weakness of the past Infection Prevention and Control Measures in SA. The model also shows that transient behaviours of the daily growth rate and the cumulative removal rate exhibit periodic oscillations. Such dynamics indicates that the underlying signals are not stationary and conventional linear and nonlinear models would fail for long-term prediction. Therefore, a large class of mappings with rich functions and operations is chosen as the model class and the evolutionary algorithm is utilized to obtain the optimal model for long term prediction. The resulting models on the daily growth rate, the cumulative removal rate and the cumulative mortality rate predict that the peak and inflection point will occur on November 4, 2020 and October 15, 2020, respectively; the virus shall cease spreading on April 28, 2021; and the ultimate numbers of the COVID-19 cases and mortality cases will be 785,529 and 17,072, respectively. The approach is also benchmarked against other methods and shows better accuracy of long-term prediction.

This paper presents a novel Smooth Particle Hydrodynamics computational framework for the simulation of large strain fast solid dynamics in thermo-elasticity. The formulation is based on the Total Lagrangian description of a system of first order conservation laws written in terms of the linear momentum, the triplet of deformation measures (also known as minors of the deformation gradient tensor) and the total energy of the system, extending thus the previous work carried out by some of the authors in the context of isothermal elasticity and elasto-plasticity (Lee et al., 2016; Lee et al., 2017; Lee et al., 2019). To ensure the stability (i.e. hyperbolicity) of the formulation from the continuum point of view, the internal energy density is expressed as a polyconvex combination of the triplet of deformation measures and the entropy density. Moreover, and to guarantee stability from the spatial discretisation point of view, consistently derived Riemann-based numerical dissipation is carefully introduced where local numerical entropy production is demonstrated via a novel technique in terms of the time rate of the so-called ballistic free energy of the system. For completeness, an alternative and equally competitive formulation (in the case of smooth solutions), expressed in terms of the entropy density, is also implemented and compared. A series of numerical examples is presented in order to assess the applicability and robustness of the proposed formulations, where the Smooth Particle Hydrodynamics scheme is benchmarked against an alternative in-house Finite Volume Vertex Centred implementation.

In the paper, we are concerned with degenerate stochastic differential equations with jumps. We first establish two theorems about supports for the solution laws of the degenerate stochastic differential equations, under different (sufficient) conditions. We then apply one of our results to a class of degenerate stochastic evolution equations (that is, stochastic differential equations in infinite dimensions) with jumps to obtain a characterisation of path-independence for the densities of their Girsanov transformations.

A linear extension of a polytope Q is any polytope which can be mapped onto Q via an affine transformation. The extension complexity of a polytope is the minimum number of facets of any linear extension of this polytope. In general, computing the extension complexity of a given polytope is difficult. The extension complexity is also equal to the nonnegative rank of any slack matrix of the polytope. In this paper, we introduce a new geometric lower bound on the extension complexity of a polytope, i.e., which relies only on the knowledge of some of its geometric characteristics. It is based on the monotone behaviour of the f -vector of polytopes under affine maps. We present numerical results showing that this bound can improve upon existing geometric lower bounds, and provide a generalization of this lower bound for the nonnegative rank of any matrix.

Highlights • Potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 until 29 October 2020. • Proposing an algorithm to calculate the optimal parameters of SSA. • The results of V-SSA and R-SSA are compared to those from ARIMA, ARFIMA, Exponential Smoothing, TBATS, and NNAR. • SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models. • Providing 40 days ahead point forecasts for top ten countries affected by COVID-19., Coronavirus disease 2019 (COVID-19) is a pandemic that has affected all countries in the world. The aim of this study is to examine the potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19, which are the three main variables of interest. This paper contributes to the literature on forecasting COVID-19 pandemic in several ways. Firstly, an algorithm is proposed to calculate the optimal parameters of SSA including window length and the number of leading components. Secondly, the results of two forecasting approaches in the SSA, namely vector and recurrent forecasting, are compared to those from other commonly used time series forecasting techniques. These include Autoregressive Integrated Moving Average (ARIMA), Fractional ARIMA (ARFIMA), Exponential Smoothing, TBATS, and Neural Network Autoregression (NNAR). Thirdly, the best forecasting model is chosen based on the accuracy measure Root Mean Squared Error (RMSE), and it is applied to forecast 40 days ahead. These forecasts can help us to predict the future behaviour of this disease and make better decisions. The dataset of Center for Systems Science and Engineering (CSSE) at Johns Hopkins University is adopted to forecast the number of daily confirmed cases, deaths, and recoveries for top ten affected countries until October 29, 2020. The findings of this investigation show that no single model can provide the best model for any of the countries and forecasting horizons considered here. However, the SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models.

Highlights • A dynamical mathematical model for COVID-19 transmission involving the interactive effect of various factors with delay effect. • The rigorous analysis on the asymptotic behavior of the existing infection people. • Numerical predications and comparisons of our proposed model with officially issued data in China and Italy., The 2019 novel coronavirus (COVID-19) emerged at the end of 2019 has a great impact on China and all over the world. The transmission mechanism of COVID-19 is still unclear. Except for the initial status and the imported cases, the isolation measures and the medical treatments of the infected patients have essential influences on the spread of COVID-19. In this paper, we establish a mathematical model for COVID-19 transmission involving the interactive effect of various factors for the infected people, including imported cases, isolating rate, diagnostic rate, recovery rate and also the mortality rate. Under the assumption that the random incubation period, the cure period and the diagnosis period are subject to the Weibull distribution, the quantity of daily existing infected people is finally governed by a linear integral-differential equation with convolution kernel. Based on the asymptotic behavior and the quantitative analysis on the model, we rigorously prove that, for limited external input patients, both the quantity of infected patients and its variation ratio will finally tend to zero, if the infected patients are sufficiently isolated or the infection rate is small enough. Finally, numerical performances for the proposed model as well as the comparisons between our simulations and the clinical data of the city Wuhan and Italy are demonstrated, showing the validity of our model with suitably specified model parameters.

The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused million of infected cases globally and still pose an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 200,000. Recently, many mathematical models have been considered to better understand the coronavirus infection. Most of these models are based on classical integer-order derivative which can not capture the fading memory and crossover behavior found in many biological phenomena. Therefore, we study the coronavirus disease in this paper by exploring the dynamics of COVID-19 infection using the non-integer Caputo derivative. In the absence of vaccine or therapy, the role of non-pharmaceutical interventions (NPIs) is examined on the dynamics of theCOVID-19 outbreak in Pakistan. First, we construct the model in integer sense and then apply the fractional operator to have a generalized model. The generalized model is then used to present the detailed theoretical results. We investigate the stability of the model for the case of fractional model using a nonlinear fractional Lyapunov function of Goh-Voltera type. Furthermore, we estimate the values of parameters with the help of least square curve fitting tool for the COVID-19 data recorded in Pakistan since March 1 till June 30, 2020 and show that our considered model give an accurate prediction to the real COVID-19 statistical cases. Finally, numerical simulations are presented using estimated parameters for various values of the fractional order of the Caputo derivative. From the simulation results it is found that the fractional order provides more insights about the disease dynamics.

Most of the nations with deplorable health conditions lack rapid COVID-19diagnostic test due to limited testing kits and laboratories. The un-diagnosticmild cases (who show no critical sign and symptoms) play the role as a route that spread the infection unknowingly to healthy individuals. In this paper, we present a fractional order SIR model incorporating individual with mild cases as a compartment to become SMIR model. The existence of the solutions of the model is investigated by solving the fractional Gronwall's inequality using the Laplace transform approach. The equilibrium solutions (DFE & Endemic) are found to be locally asymptotically stable, and subsequently the basic reproduction number is obtained. Also the global stability analysis is carried out by constructing Lyapunov function. Lastly, numerical simulations that support analytic solution follow. It was also shown that when the rate of infection of the mild cases increases, there is equivalent increase in the overall population of infected individuals. Hence to curtail the spread of the disease there is need to take care of the Mild cases as well.

The dataset contains tree height data collected in 200 mangrove and non-mangrove trees sampled in various sites in Malaysia. Different height measurement methods were performed, including visual measurements (stick, thumb rule) and precision field instruments (clinometer, laser rangefinder and altimeter), which were compared against benchmark values obtained using an unmanned aerial vehicle (UAV) and a Leica distometer. The core data have been analysed and interpreted in the paper by Saliu et al. ‘’An accuracy analysis of mangrove tree height mensuration using forestry techniques, hypsometers and UAVs ’’ [1], in which the accuracy of each method for tree height measurement was discussed.

This paper investigates the scaling of the surface roughness of coronavirus, including the SARS-nCoV based on fractal and spectral analyses of their published electron microscopy images. The box-counting fractal dimensions obtained are subjected to ANOVA tests for statistical significance. Results show that the SARS-nCoV particles could not statistically be resolved by their shape on the basis of the fractal dimension values, but they could be distinguished from the earlier SARS-CoV particles. MANOVA test results require interaction of factors used for classifying virions into different types. The topological entropies, a measure of randomness in a system, measured for the images of varying size show correlation with the fractal dimensions. Spectral analyses of our data show a departure from power-law self-similarity, suggesting an apparent scaling of surface roughness over a band of maximum an order of magnitude. The spectral crossover that corresponds to characteristic length scale may represent average viral size. Our results may be useful in inferring the nature of surface-contact between the viral and human cell, causing infection and also in providing clues for new drugs, although it is too early to say. In addition, limitations of this study, including possible ways to avoid the bias in scaling exponents due to the use of different techniques are discussed., Highlights • Morphological diversity of nCoV particles is not statistically significant. • Classification requires the interaction of envelope-shape and spike distribution. • Slightly oval and elongated nCoV and CoV particles are statistically distinguished. • Viral surface roughness does not support power-law scaling.

The accuracy and accessibility of methods to calculate body segment inertial parameters are a key concern for many researchers. It has recently been demonstrated that the magnitude and orientation of principal moments of inertia are crucial for accurate dynamic models. This is important to consider given that the orientation of principal axes is fixed for the majority of geometric and regression body models. This paper quantifies the effect of subject specific geometry on the magnitude and orientation of second moments of volume in the trunk segment. The torsos of 40 male participants were scanned using a 3D imaging system and the magnitude and orientation of principal moments of volume were calculated from the resulting geometry. Principal axes are not aligned with the segment co-ordinate system in the torso segment, with mean Euler angles of 11.7, 1.9 and 10.3 in the ZXY convention. Researchers using anatomical modelling techniques should try and account for subject specific geometry and the mis-alignment of principal axes. This will help to reduce errors in simulation by mitigating the effect of errors in magnitude of principal moments.

Sensitivity analysis plays an important role in reliability evaluation, structural optimization and structural design, etc. The local sensitivity, i.e., the partial derivative of the quantity of interest in terms of parameters or basic variables, is inadequate when the basic variables are random in nature. Therefore, global sensitivity such as the Sobol’ indices based on the decomposition of variance and the moment-independent importance measure, among others, have been extensively studied. However, these indices are usually computationally expensive, and the information provided by them has some limitations for decision making. Specifically, all these indices are positive, and therefore they cannot reveal whether the effects of a basic variable on the quantity of interest are positive or adverse. In the present paper, a novel global sensitivity index is proposed when randomness is involved in structural parameters. Specifically, a functional perspective is firstly advocated, where the probability density function (PDF) of the output quantity of interest is regarded as the output of an operator on the PDF of the source basic random variables. The Frechet derivative is then naturally taken as a measure for the global sensitivity. In some sense such functional perspective provides a unified perspective on the concepts of global sensitivity and local sensitivity. In the case the change of the PDF of a basic random variable is due to the change of parameters of the PDF of the basic random variable, the computation of the Frechet-derivative-based global sensitivity index can be implemented with high efficiency by incorporating the probability density evolution method (PDEM) and change of probability measure (COM). The numerical algorithms are elaborated. Several examples are illustrated, demonstrating the effectiveness of the proposed method.

International audience; Since the founding theory established by G. Floquet more than a hundred years ago, computing the stability of periodic solutions has given rise to various numerical methods, mostly depending on the way the periodic solutions are themselves determined , either in the time domain or in the frequency domain. In this paper, we address the stability analysis of branches of periodic solutions that are computed by combining a pure Harmonic Balance Method (HBM) with an Asymptotic Numerical Method (ANM). HBM is a frequency domain method for determining periodic solutions under the form of Fourier series and ANM is continuation technique that relies on high order Taylor series expansion of the solutions branches with respect to a path parameter. It is well established now that this HBM-ANM combination is efficient and reliable, provided that the system of ODE is first of all recasted with quadratic nonlinearities, allowing an easy manipulation of both the Taylor and the Fourier series. In this context, Hill's method, a frequency domain version of Floquet theory, is revisited so as to become a by-product of the HBM applied to a quadratic system, allowing the stability analysis to be implemented in an elegant way and with good computing performances. The different types of stability changes of periodic solutions are all explored and illustrated through several academic examples, including systems that are autonomous or not, conservative or not, free or forced.

The goal of this paper is to advance the development of wave-guiding subwavelength crystals by developing designs whose properties are stable with respect to imperfections in their construction. In particular, we make use of a locally resonant subwavelength structure, composed of a chain of high-contrast resonators, to trap waves at deep subwavelength scales. We first study an infinite chain of subwavelength resonator dimers and define topological quantities that capture the structure's wave transmission properties. Using this for guidance, we design a finite crystal that is shown to have wave localization properties, at subwavelength scales, that are robust with respect to random imperfections., Journal de Mathématiques Pures et Appliquées, 144

This paper presents a numerical investigation on the gust load alleviation effects based on circulation control (CC) via blowing over airfoil trailing-edge Coanda surface. The NACA0012 airfoil was chosen for the study from subsonic to transonic speeds. The field velocity method is introduced to the unsteady Reynolds averaged Navier-Stokes solutions to simulate responses of the airfoil to arbitrarily shaped gust penetrations. For validation, gust responses to a step change in angle of attack, sharp-edged gusts and one-minus-cosine gusts from the numerical solutions are compared against available theories and other numerical references. Experimental data are used to validate the numerical results of CC. Thereafter circulation control via steady blowing with different momentum coefficients are tested for the gust load alleviation effects in terms of lift coefficients. The results show that circulation control can effectively suppress the lift disturbances caused by the gust for subsonic flows. Most importantly, CC has a fast frequency response characteristic as more than 50% of the total change in lift coefficient can be obtained within the non-dimensional time s = U ∞ t / b = 1 . Based on the rapid response characteristic, circulation control via unsteady blowing with dynamically adaptive momentum coefficients proportional to the vertical gust velocities is proposed and tested. The results demonstrate that a near constant lift coefficient can be achieved under gust condition for subsonic incoming flow indicating its potential for real-time adaptive load control. It has been demonstrated that CC is also able to reduce gust load at transonic speed, but it is less effective as compared with that at subsonic speed.

This paper aims to investigate the numerical approximation of semilinear non-autonomous stochastic partial differential equations (SPDEs) driven by multiplicative or additive noise. Such equations are more realistic than autonomous SPDEs while modeling real world phenomena. Numerical approximations for autonomous SPDEs are thoroughly investigated in the literature, while the non-autonomous case is not yet well understood. The non-autonomous SPDE is discretized in space by the finite element method and in time by the linear implicit Euler method. We break the complexity in the analysis of the time depending, not necessarily self-adjoint linear operators with the corresponding semi group and provide the strong convergence result of the fully discrete scheme toward the exact solution in the root-mean-square $L^2$ norm. The results indicate how the converge order depends on the regularity of the initial solution and the noise. In particular, for multiplicative trace class noise we achieve convergence order $\mathcal{O}(h^{2-\epsilon}+\Delta t^{1/2})$ and for additive noise with trace class, we achieve convergence order $\mathcal{O}(h^{2-\epsilon}+\Delta t^{1-\epsilon})$, for an arbitrarily small $\epsilon>0$. Numerical experiments to sustain our theoretical results are provided., Comment: arXiv admin note: text overlap with arXiv:1809.06234, arXiv:1803.00423, arXiv:1809.04438

This paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equation(SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the nonlinear part is stronger than the linear part, usually called stochastic dominated transport equations. Most standard numerical schemes lose their good stability properties on such equations, including the current linear implicit Euler method. We discretise the SPDE in space by the finite element method and propose a new scheme in time appropriate for such equations, called stochastic Rosenbrock-Type scheme, which is based on the local linearisation of the semi-discrete problem obtained after space discretisation. We provide a strong convergence of the new fully discrete scheme toward the exact solution for multiplicative and additive noise. Our convergence rates are in agreement with results in the literature. Numerical experiments to sustain our theoretical results are provided., Comment: arXiv admin note: text overlap with arXiv:1710.01386

In this paper, we propose a four compartmental model to understand the dynamics of infectious disease COVID-19. We show the boundedness and non-negativity of solutions of the model. We analytically calculate the basic reproduction number of the model and perform the stability analysis at the equilibrium points to understand the epidemic and endemic cases based on the basic reproduction number. Our analytical results show that disease free equilibrium point is asymptotically stable (unstable) and endemic equilibrium point is unstable (asymptotically stable) if the basic reproduction number is less than (greater than) unity. The dispersal rate of the infected population and the social awareness control parameter are the main focus of this study. In our model, these parameters play a vital role to control the spread of COVID-19. Our results reveal that regional lockdown and social awareness (e.g., wearing a face mask, washing hands, social distancing) can reduce the pandemic of the current outbreak of novel coronavirus in a most densely populated country like Bangladesh.

© 2019 Elsevier Ltd This paper investigates stability of Positive Markov Jump Linear Systems (PMJLSs) in the absence of a numerical realization. It considers the situation when only signs (and not magnitudes) of the entries of the subsystem matrices and the Markov transition matrices are known. The result is an analysis of a qualitative notion of stability known as sign-stability. Although the notions of sign-stability of PMJLSs are natural extensions of standard stochastic stability concepts such as exponential almost sure, mean and mean-square stability, the sign-stability notions are proven equivalent, which is not the case for their corresponding standard concepts. Moreover, for irreducible Markov chains, the particular structure of the Markov chain is shown to have no bearing on sign-stability.

There has been much research into freeness properties of finitely generated matrix semigroups under various constraints, such as the dimensions of the generator matrices and the semiring over which the matrices are defined. Most freeness problems have been shown to be undecidable starting from dimension three, even for upper-triangular matrices over the natural numbers. There are many open problems still remaining in dimension two. A recent paper has also investigated freeness properties of bounded languages of matrices, which are matrices from a set M∗1 M∗2· · · M∗k ⊆ Fn×n for some semiring F and a fixed value k ∈ N>0, where matrices M1, . . . , Mk are given [1]. We consider a notion of freeness and ambiguity for scalar reachability problems in matrix semigroups and bounded languages of matrices. Scalar reachability concerns the set {ρTMτ |M ∈ S}, where ρ, τ ∈ F n are vectors and S ⊆ F n×n is a finitely generated matrix semigroup. Ambiguity and freeness problems are defined in terms of the uniqueness of factorizations for each scalar. Such problems have also been studied in connection to formal power series. We show various undecidability results and their connections to weighted and probabilistic finite automata.