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FilippovPliss lemma for dynamical inclusions on a time scale
 M Rafaqat^{1}Email authorView ORCID ID profile,
 R Ahmed^{2},
 T Donchev^{3} and
 V Lupulescu^{4}
https://doi.org/10.1186/s1366201713606
© The Author(s) 2017
 Received: 20 May 2017
 Accepted: 12 September 2017
 Published: 29 September 2017
Abstract
In this paper we prove two variants of the wellknown FilippovPliss lemma in the case of dynamical inclusions on a time scale. The first variant is when the righthand side is Lipschitz continuous on the state variable. Afterward we introduce onesided Perron conditions for multifunctions on a time scale and prove the second variant of that lemma. Some discussions on relaxed systems are presented.
Keywords
 dynamical inclusions on a time scale
 onesided Perron condition
 FilippovPliss lemma
MSC
 34N05
 39N99
1 Introduction
Time scale theory is introduced in [1, 2] in order to unify the continuous and discrete systems. We refer the reader to [3, 4] for the theory of dynamical equations on a time scale and [5, 6], where some applications are given. Among others notice [7] where the theory of dynamical systems in measure chains is studied. In [8] the integration on a time scale is investigated and its connections with standard Lebesgue integral are considered in [9].
In the last years different optimization problems on time scales have been studied. We refer the reader to [10–13]. The theory of dynamical inclusions on a time scale is presented in [14–16]. In the later paper [17] the authors prove the analogy of Filippov’s selection theorem, which shows that the optimal control of Caratheodory controlled systems can be written equivalently as differential inclusions.
One of the most useful results in optimal control is the significant lemma of FilippovPliss (see, e.g., [18]). It was proved first time by Filippov in the case of Lipschitzian righthand side in [19] and afterward extended by Pliss in [20] under much weaker conditions. We refer the reader to [21, 22] for the main applications of this result. Notice also [23] where the history and review of this lemma is presented. This result has been extended to the case of onesided Lipschitz differential inclusions in [24].
We refer the reader to [25, 26] for the needed facts in setvalued analysis and differential inclusions.
In this paper we prove a variant of this lemma in the case of Lipschitz dynamical inclusion. Afterward onesided Perron condition is introduced in the case of a time scale, and a variant of FilippovPliss lemma is proved in the case of onesided Perron inclusions on a time scale. One of the most important theorems in the optimal control is that under some additional hypotheses the closure of the solution set of the original system is the solution set of the convexified one. We discuss its extension on dynamical inclusions.
The paper is organized as follows. In the next section the needed definitions, notations and preliminary results are given. Third section deals with system description. The main results of the presented paper are given in the fourth section, where we study the FilippovPliss lemma and relaxation theorem. Finally, we present conclusion with discussion of other possibilities for the definition of onesided Perron condition.
2 Preliminaries
Recall that (see, e.g., [3]) every closed subset \(\mathbb{T} \subset \mathbb{R}\) is called a time scale, and hence the time scale \(\mathbb{T}\) is a complete metric space with the usual metric on \(\mathbb{R}\). Furthermore, the intersection of \(\mathbb{T}\) with any closed bounded interval is a compact set.
Now we recall some properties of the Δderivative.
Proposition 2.1
 (a)
If f is Δdifferentiable at t, then f is continuous at t.
 (b)If f is continuous at \(t\in \mathbb{T}_{\mathrm {rs}}\), then$$ f^{\Delta }(t) = \frac{f(\sigma (t))  f(t)}{\mu (t)} . $$
 (c)The map f is Δdifferentiable at \(t \in \mathbb{T}^{k} \backslash \mathbb{T}_{\mathrm {rs}}\) if and only if$$ f^{\Delta }(t) = \lim_{s \rightarrow t} \frac{f(t)  f(s)}{t  s}. $$
 (d)Let \(f,g:\mathbb{T}\to \mathbb{R}^{n}\) be Δdifferentiable, then the scalar product \(\langle f,g\rangle \) is also Δdifferentiable and$$ \bigl\langle f(t),g(t) \bigr\rangle ^{\Delta }= \bigl\langle f(t),g^{\Delta }(t) \bigr\rangle + \bigl\langle f^{\Delta }(t),g \bigl(t+ \mu (t) \bigr) \bigr\rangle . $$
It follows from Proposition 2.1 that \(f(\sigma (t)) = f(t) + \mu (t)f^{\Delta }(t)\). Evidently a vectorvalued function \(f(\cdot )\) is Δdifferentiable at t iff every coordinate function \(f_{i}(\cdot )\) is Δdifferentiable at t and \(f^{\Delta }(t) = (f_{1}^{\Delta }(t), \ldots ,f_{n}^{\Delta }(t))\).
Definition 2.2

If \(A_{1}\cap A_{2}= \emptyset \) are Δmeasurable and \(A= A_{1}\cup A_{2}\), then the multifunction \(H: A\rightrightarrows \mathbb{R}^{n}\) is Δmeasurable if and only if H is Δmeasurable as a map from \(A_{i}\) into \(\mathbb{R}^{n}\) for \(i= 1,2\).
Furthermore, every Δmeasurable function \(f(\cdot )\) satisfies Lusin’s property, i.e., there exists a sequence of pairwise disjoint closed sets \(\mathbb{I}_{n}\subset \mathbb{I}\) such that the Δmeasure of \(\mathbb{I}\setminus \bigcup_{m=1}^{\infty } \mathbb{I}_{m}\) is 0 and f is continuous on \(\mathbb{I}_{k}\times \mathbb{R}^{n}\) for every \(k\geq 1\).
Definition 2.3
A realvalued function \(f:[a,b]\longrightarrow \mathbb{R}^{n}\) defined on \([a,b]\) is said to be absolutely continuous if, for every \(\epsilon >0\), there exists \(\delta >0\) such that \(\sum_{k=1} ^{n}\vert f(b_{k})f(a_{k})\vert <\epsilon \) for every n disjoint subintervals \((a_{k},b_{k})\) of \([a,b]\), \(a_{k}< b_{k}\), \(k=1,2,\ldots, n\), such that \(\sum_{k=1}^{n}\vert b_{k}a_{k}\vert <\delta \).
Proposition 2.4
Proposition 2.13 in [15]
If the function \(f: \mathbb{I}\to \mathbb{R}^{n}\) is absolutely continuous, then the Δmeasure of the set \(\{ t\in \mathbb{I} _{\mathrm {rd}}:f(t)=0\textit{ and }f^{\Delta }(t)\neq 0\}\) is zero.
As it is well known (see, e.g., [26]) every Δmeasurable multifunction \(F(\cdot )\) admits Δmeasurable selection \(f(t)\in F(t)\).
Definition 2.5

Upper semicontinuous (USC) at \((\tau ,y)\) if, for every \(\varepsilon >0\), there exists δ such that \(F((\tau \delta , \tau +\delta )\cap \mathbb{I},x+ \delta \mathbb{B}) \subset F( \tau ,y)+ \varepsilon \mathbb{B}\), where \(\mathbb{B}\) is the closed unit ball.

Lower semicontinuous (LSC) at \((\tau ,y)\) if, for every \(\mathbb{I}\ni t_{i}\to \tau \), \(x_{i}\to y\) and \(f\in F(\tau ,y)\), there exists \(f_{i}\in F(t_{i},x_{i})\) with \(f_{i}\to f\).

Continuous if it is simultaneously USL and LSC.
3 System description
The Δabsolute continuous function \(x(\cdot )\) is said to be a solution of (3.1) if it satisfies inclusion (3.1) for Δ a.a. \(t\in \mathbb{I}\).
Denote by \(\mathcal{B}\) the Borel σ algebra on \(\mathbb{R} ^{n}\).
Definition 3.1
 F1. :

\(\vert F(t,x)\vert \leq \lambda (1+ \vert x\vert )\) (sublinear growth), where λ is a positive constant.
Remark 3.2
 F2. :

The map \(F(\cdot ,\cdot )\) is \(\Delta \times \mathcal{B}\) measurable and, for every \(t\in \mathbb{I}\), the map \(F(t,\cdot )\) is USC.
We are going to prove the existence theorem, because we did not see the following variant of it. For other existence results, we refer the reader to [15, 16].
Theorem 3.3
Under F1, F2 system (3.1) has a solution. The solution set is \(C(I_{\mathbb{I}},\mathbb{R}^{n})\) compact.
Proof
Case (i): If \(\tau \in \mathbb{T}_{\mathrm {rd}}\), then \({\tau }'\tau \leq h _{n}\), i.e., \(\vert x(t)x(\tau )\vert \leq Nh_{n}\), where N is the constant from Remark 3.2. Therefore \(x_{n}^{\Delta }(t)\in F(t,x_{n}(t)+ Nh _{n})\).
Case (ii): If \(\tau \in \mathbb{T}_{\mathrm {rs}}\), then \({\tau }'=\sigma ( \tau )\). In this case \([\tau ,{\tau }')\cap \mathbb{T}=\tau \) and hence \(t=\tau \). However, \(x_{n}^{\Delta }(t)\in F(t,x_{n}^{\Delta }(t))\). The claim is proved because \(\lim_{h_{n}\to 0}Nh_{n}=0\).
From Theorem 3.5 of [17] we know that there exists a subsequence \(x_{n_{k}}(\cdot )\) which converges uniformly to a solution \(x(\cdot )\) of (3.1). The proof is therefore complete. □
Recall that the multimap \(F(\cdot ,\cdot )\) is said to be almost USC (LSC, continuous) if, for every \(\varepsilon > 0\), there exists a set \(N_{\varepsilon }\subset \mathbb{I}\) with Δ measure less than ε and such that \(F(\cdot ,\cdot )\) is USC (LSC, continuous) on \((\mathbb{I}\setminus I_{\varepsilon })\times \mathbb{R}^{n}\).
We can prove the following proposition.
Proposition 3.4
Let \(F(\cdot ,\cdot )\) have nonempty convex compact values, and let it be almost USC, then F is \(\Delta \times \mathcal{B}\) measurable and \(F(t,\cdot )\) is USC for Δ a.a. \(t\in \mathbb{I}\).
Proof
It is easy to see that \(F(\cdot ,\cdot )\) is almost USC iff there exists a sequence of pairwise disjoint closed sets \(\mathbb{I}_{n}\subset \mathbb{I}\) such that Δmeasure of \(\mathbb{I}\setminus \bigcup_{m=1}^{\infty }\mathbb{I}_{m}\) is 0 and F is USC on \(\mathbb{I}_{k}\times \mathbb{R}^{n}\) for every \(k\geq 1\). Therefore F is \(\Delta \times \mathfrak{B}\) measurable on \(\mathbb{I}_{k} \times \mathbb{R}^{n}\) for every \(k\geq 1\) and hence on \(\mathbb{I} \times \mathbb{R}^{n}\). Also \(F(t,\cdot )\) is USC for Δ a.e. t. □
4 FilippovPliss lemma on a time scale
In this section we prove the main results in the paper. We prove two versions of the FilippovPliss lemma which have many applications in optimal control (cf. [18]).
We need the following result, which is a particular case of Proposition 1.43 of [25].
Proposition 4.1
Let \(F, G: \mathbb{I}\times \mathbb{R}^{n}\rightrightarrows \mathbb{R}^{n}\) be \(\Delta \times \mathcal{B}\) measurable and at least one with compact values. Then the map \(H(t,x)= F(t,x)\cap G(t,x)\) is also \(\Delta \times \mathcal{B}\) measurable.
Now we will prove two variants of the FilippovPliss theorem for dynamical inclusion on a time scale. The first proof deals with Lipschitz righthand side.
Theorem 4.2
Proof
Namely, \(G(t, u)\) admits nonempty values because \(F(t,\cdot )\) is Lipschitz and it is with nonempty convex compact values. We are going to prove that \(G(t, u)\) is closed and convex. Indeed if \(v_{i} \in F(t, u)\) and \(v_{i}\rightarrow v\), we know that \(\vert v  y^{\Delta }(t)\vert = \lim_{i\rightarrow \infty } \vert v_{i}  y^{\Delta }(t)\vert \). Therefore, if \(v_{1}, v_{2} \in G(t, u)\), then we have that \(\vert y^{\Delta }(t)  \lambda v_{1}  (1  \lambda )v_{2}\vert \leq \lambda \vert y^{\Delta }(t)  v _{1}\vert + (1  \lambda )\vert y^{\Delta }(t)  v_{2}\vert \leq L\vert y(t)  u\vert + f(t)\), \(\forall \lambda \in (0, 1)\). Also \(G(t, u)\) is USC. For this, it is enough to see that \(G(t,\cdot )\) has a closed graph. Let \(v_{i} \in G(t, u_{i})\), \(u_{i} \rightarrow u\) and \(v_{i} \rightarrow v\). Since \(F(t,\cdot )\) is USC, one has that \(\lim_{i \rightarrow \infty }v_{i} = v \in F(t,u)\). Furthermore, \(\vert v_{i}  y^{\Delta }(t)\vert \leq L\vert y(t)  u_{i}\vert + f(t)\), and hence \(\vert v  y^{\Delta }(t)\vert \leq L\vert y(t)  u\vert + f(t)\).
Now, we have to show that \(G(\cdot ,\cdot )\) is \(\Delta \times \mathcal{B}\) measurable. Let \(X\in \mathbb{R}^{n}\). Since \(y(\cdot)\) is AC, then \(y^{\Delta }(\cdot)\) is Δmeasurable, and hence the multimap \(H(t,X) = \{ (t,z) \in [t_{0},T)_{\mathbb{T}}\times \mathbb{R}^{n}:\vert z  y^{\Delta }(t)\vert \leq L\vert y(t)  X\vert + f(t) \}\) is \(\Delta \times \mathcal{B}\) measurable. Due to Proposition 4.1, the map \(G(t, X) = H(t,X) \cap F(t,X)\) is also \(\Delta \times \mathcal{B}\) measurable. The claim is therefore proved. It follows from Theorem 3.3 that \(x^{\Delta } \in G(t, x(t))\), \(x(t_{0}) = x_{0}\) admits a solution \(x(\cdot )\).
From Theorem 1.67 of [3] we know that \(\vert x(t)  y(t)\vert = r(t)\), where \(r^{\Delta }(t) \leq L r(t) + f(t)\) for Δa.e. t and \(r(t_{0}) =\vert x_{0}  y_{0}\vert \).
The definition of \(G(\cdot ,\cdot )\) then implies the last statement of the theorem. □
Now we will prove a FilippovPliss type theorem under much weaker condition, which gives the estimation only of the difference between \(x(\cdot )\) and \(y(\cdot )\) but not between their derivatives.
Definition 4.3
The multivalued map \(F: \mathbb{I}\times \mathbb{R}^{n}\rightrightarrows \mathbb{R}^{n}\) is said to be OSP (onesided Perron) (on the state variable) if there exists a Perron function \(w(\cdot ,\cdot )\) such that:

\(v(\cdot ,\cdot )\) is \(\Delta \times \mathcal{B}\) measurable, \(v(t,\cdot )\) is continuous;

\(v(\cdot ,\cdot )\) is Δintegrally bounded on the bounded sets and \(v(t,0)= 0\);

the unique solution of \(r^{\Delta }(t)= v(t,r(t))\), \(r(t_{0})=0\) is \(r(t)= 0\).
\(v(\cdot ,\cdot )\) is called module if it satisfies only the first two conditions, but not necessarily the third one.
The definition of OSP condition on a time scale is different than in ordinary differential inclusions. Here it depends also on the point t.
Now we extend the previous theorem to the case of OSP multifunctions.
Theorem 4.4
Let \(F(\cdot ,\cdot )\) satisfy F1, F2, and let \(F(t,\cdot )\) be OSP w.r.t. a Perron function \(w(\cdot ,\cdot )\). If \(f(\cdot )\) is a Δintegrable function on \(\mathbb{I}\) and if \(y(\cdot )\) is an AC function with \(\operatorname{dist}(y^{\Delta }(t), F(t, y(t))) \leq f(t)\), then there exists a solution \(x(\cdot )\) of (3.1) such that \(\vert x(t)  y(t)\vert \leq r(t)\), where \(r^{\Delta }(t) = w(t,r(t))+ f(t)\) and \(r(t_{0}) = \vert x_{0}  y_{0}\vert \).
Proof
Clearly, the setvalued map \(t\to y^{\Delta }(t)+f(t)\mathbb{B}\) is Δmeasurable. Therefore \(H(t)=F(t,y(t))\cap (y^{\Delta }(t)+f(t) \mathbb{B})\) is also Δmeasurable and hence \(t\to F(t,y(t))\) is Δmeasurable. Thus there exists a Δmeasurable selection \(h(t)\in H(t)\). Evidently \(h(t)\in F(t,y(t))\).
We claim that \(G(t,\cdot )\) is upper semicontinuous for every \(t\in \mathbb{I}\). Indeed we have to prove that the graph of \(G(t,\cdot )\) is compact. However, the graph is bounded, and hence it remains to show that it is closed.
Let \(u_{i}\to u\), \(v_{i}\in G(t,u_{i})\) and \(v_{i}\to v\). We have to show that \(v\in G(t,u)\). Clearly \(v\in F(t,u)\) because \(F(t,\cdot )\) is USC. If t is right dense, then \(\langle y(t)u_{i},h(t)v_{i}\rangle \to \langle y(t)u,h(t)v\rangle \), \(\vert y(t)u_{i}\vert \to \vert y(t)u\vert \) and \(w(\vert y(t)u_{i}\vert )\to w(t,\vert y(t)u\vert )\). Thus \(\langle y(t)u,h(t)v\rangle \leq w(t,\vert y(t)u\vert )\vert y(t)u\vert \), i.e., \(v\in G(t,u)\). If t is right scattered, then \(\vert h(t)v\vert \leq w(t,\vert y(t)u\vert )\) because \(\lim_{i\to \infty }\vert h(t)v_{i}\vert =\vert h(t)v\vert \).
We have to prove that \(G(\cdot ,\cdot )\) is \(\Delta \times \mathcal{B}\) measurable.
Consider first the case \(G: \mathbb{T}_{\mathrm {rs}}\times \mathbb{R}^{n} \rightrightarrows \mathbb{R}^{n}\). Since \(w(t,\cdot )\) and \(y(\cdot )\) are continuous as well as \(h(\cdot )\) is Δmeasurable, one has that \(S(t,u)=\{v\in \mathbb{R}^{n}:\vert h(t)v\vert \leq w(t, \vert y(t)u\vert )\}\) is \(\Delta \times \mathcal{B}\) measurable. Then \(\overline{G}(t,u)=F(t,u) \cap \overline{S}(t,u)\) is \(\Delta \times \mathcal{B}\) measurable.
Therefore \(\overline{G}(t,u)=F(t,u)\cap \overline{S}(t,u)\) is also Δmeasurable. Consequently, \(G(\cdot ,\cdot )\) is \(\Delta \times \mathcal{B}\) measurable.
From Proposition 2.4 we know that the intersection of the sets \(\{t\in \mathbb{T}:\vert x(t)y(t)\vert =0\}\) and \(\{t\in \mathbb{T}: \vert x^{\Delta }(t)y^{\Delta }(t)\vert \neq 0\}\) has Δmeasure zero. Thus \(\vert x(t)y(t)\vert ^{\Delta }\leq w(t,\vert y(t)x(t)\vert ) +f(t)\) for Δ almost every \(t\in \mathbb{T}_{\mathrm {rd}}\).
If \(t\in \mathbb{T}_{\mathrm {rs}}\), then \(\vert y^{\Delta }(t)x^{\Delta }(t)\vert \leq \vert y^{\Delta }(t)h(t)\vert + \vert h(t)x^{\Delta }(t)\vert \leq w(t,\vert y(t)x(t)\vert )\). However, \(\vert x(t)y(t)\vert ^{\Delta }\leq \vert x^{\Delta }(t) y^{\Delta }(t)\vert \) and hence \(\vert x(t)y(t)\vert ^{\Delta }\leq w(t,\vert x(t)y(t)\vert )+f(t)\).
Remark 4.5
It is easy to see that Theorem 4.2 remains true also when \(w(\cdot ,\cdot )\) is only module.
Theorem 4.6
Example 4.7
Let \(G: \mathbb{T}\times \mathbb{R}^{n} \rightrightarrows \mathbb{R} ^{n}\) be bounded full Perron. Define \(F(t,x)= H(x)+ G(t,x)\). Then \(F(\cdot ,\cdot )\) satisfies all the assumptions of Theorem 4.2.
Then clearly system (3.1) with \(t_{0}=0\), \(T=1\) and \(x_{0}=0\) satisfies the conditions of Theorem 4.4.
Now we discuss the closure of the solution set for system (3.1) when \(F(\cdot ,\cdot )\) is almost continuous and not necessarily convexvalued. Notice that we only show some further studying directions.
Theorem 4.8
Proof
Let \(x_{m}(\cdot )\) be a sequence of solutions of (3.1) such that \(x_{m}(t)\to x(t)\) uniformly on \(\mathbb{I}\). As in [9] we can extend every \(x_{k}^{\Delta }(\cdot )\) on I as a Lebesgue integrable function \(g_{k}(\cdot )\). Due to Diestel criterion (see, e.g., [27]), the sequence \(\{ g_{k}(\cdot )\}_{k}\) is weakly \(L_{1}\) precompact and passing to subsequences if necessary \(g_{k}(t)\to g(t)\) \(L_{1}\) weakly. Due to Mazur’s lemma, there exists a convex combination \(\sum_{i=k}^{k_{i}} \alpha_{i} g_{i}(t)\) converging to \(g(t)\) \(L_{1}\) strongly and passing to subsequences for a.a. \(t\in I\). Clearly its restriction to \(\mathbb{I}\) \(g_{\mathbb{I}}(t) \in \overline {\operatorname {\mathit {co}}}F(t,x(t))\) for Δ a.e. t. Notice that every \(g_{(}\cdot )\) is constant on \([t,\sigma (t)]\), the latter is not a single point in the case \(t\in \mathbb{I}_{\mathrm {rs}}\). Since \(\mathbb{I} _{\mathrm {rs}}\) is countable, then it is easy to show that in \(g(t)\in F(t,x(t))\) for Δ a.a. \(t\in \mathbb{I}_{\mathrm {rs}}\). Consequently, \(g(t)\in H(t,x(t))\). □
It will be interesting to prove or disprove the following conjecture, which is an analogue of the very important in the optimal control relaxation theorem.
Conjecture 1
Let \(F(\cdot ,\cdot )\) be almost continuous with compact values. Suppose that F1 holds and \(F(t,\cdot )\) is OSP. Then the closure of the solution set of (3.1) is the solution set of (4.2).
We hope that the reader will be able to prove this conjecture.
5 Conclusion
In this paper we prove a FilippovPliss type theorem for time scale dynamical inclusion, when the righthand side satisfies the wellknown Lipschitz condition on the state variable. Afterward we extend the onesided Perron condition to time scale systems. The FilippovPliss lemma is then extended to OSP dynamical inclusions.
Notice that we have to distinguish the right dense and right scattered points in the FilppovPliss lemma and relaxation theorem. The conditions are different than in the case of continuous systems.
We prove also the existence of solutions to socalled almost LSC dynamical inclusions and pose a conjecture that a variant of the socalled relaxation theorem is true.
Now we discuss some open problems.
5.1 Almost LSC problem
It seems that the following result is true.
Proposition 5.1
Let \(F(\cdot ,\cdot )\) be almost LSC with closed nonempty values. Under F1 the dynamical inclusion (3.1) admits a solution.
Proof
(Idea). Consider the compacts \(\mathbb{I}_{k}\) as in the previous proof, where \(F(\cdot ,\cdot )\) is LSC on \(\mathbb{I}_{k}\times \mathbb{R}^{n}\). Let N be from Remark 3.2. Consider the cone \(K_{N}= \{ (t,x)\in \mathbb{I}\times \mathbb{R}^{k}: \vert x\vert \leq (N+1)t\}\).
It is well known that every LSC multifunction has a \(\Gamma^{N+1}\) continuous selection, i.e., selection \(f(t,x)\in F(t,x)\) such that \(f_{k}(t_{i},x_{i})\to f_{k}(t,x)\) if \(\mathbb{I}_{k}\ni t_{i}\to t\) and \(\vert x_{i}x\vert \leq (N+1)(t_{i}t)\) (see, e.g., Lemma 6.2 in [28]).
Dealing as in the proof of Lemma 6.1 of [28] (see also [29]), one can try to prove that \(y(\cdot )\) is also a solution (3.1). The obstacle is that we work with right dense points. □
Declarations
Acknowledgements
The authors would like to thank all the anonymous reviewers and the auditors for their helpful advice and hardwork. The work was supported by the University of Lahore, Lahore, Pakistan.
Authors’ contributions
RA, TD and VL contributed to Sections 1, 2, 3 and MR contributed to Section 4. All authors read and approved the final manuscript
Competing interests
The authors declare that they have no competing interests.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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