The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system's accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients.
Citation: Swarnava Biswas, Debajit Sen, Dinesh Bhatia, Pranjal Phukan, Moumita Mukherjee. Chest X-Ray image and pathological data based artificial intelligence enabled dual diagnostic method for multi-stage classification of COVID-19 patients[J]. AIMS Biophysics, 2021, 8(4): 346-371. doi: 10.3934/biophy.2021028
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[9] | Yan Xia, Songhua Wang . Global convergence in a modified RMIL-type conjugate gradient algorithm for nonlinear systems of equations and signal recovery. Electronic Research Archive, 2024, 32(11): 6153-6174. doi: 10.3934/era.2024286 |
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The use of Artificial Intelligence (AI) in combination with Internet of Things (IoT) drastically reduces the need to test the COVID samples manually, saving not only time but money and ultimately lives. In this paper, the authors have proposed a novel methodology to identify the COVID-19 patients with an annotated stage to enable the medical staff to manually activate a geo-fence around the subject thus ensuring early detection and isolation. The use of radiography images with pathology data used for COVID-19 identification forms the first-ever contribution by any research group globally. The novelty lies in the correct stage classification of COVID-19 subjects as well. The present analysis would bring this AI Model on the edge to make the facility an IoT-enabled unit. The developed system has been compared and extensively verified thoroughly with those of clinical observations. The significance of radiography imaging for detecting and identification of COVID-19 subjects with severity score tag for stage classification is mathematically established. In a Nutshell, this entire algorithmic workflow can be used not only for predictive analytics but also for prescriptive analytics to complete the entire pipeline from the diagnostic viewpoint of a doctor. As a matter of fact, the authors have used a supervised based learning approach aided by a multiple hypothesis based decision fusion based technique to increase the overall system's accuracy and prediction. The end to end value chain has been put under an IoT based ecosystem to leverage the combined power of AI and IoT to not only detect but also to isolate the coronavirus affected individuals. To emphasize further, the developed AI model predicts the respective categories of a coronavirus affected patients and the IoT system helps the point of care facilities to isolate and prescribe the need of hospitalization for the COVID patients.
As we all know, Keller and Segel [1] first proposed the classical chemotaxis model (hereafter called K-S model), which has been widely applied in biology and medicine. The model can be given by the following:
{v1t=Δv1−χ∇⋅(v1∇v2)+f(v1), x∈Ω, t>0,τv2t=Δv2−v2+v1, x∈Ω, t>0, | (1.1) |
where v1 is the cell density, v2 is the concentration of the chemical signal, and f(v1) is the logistic source function. For the case of τ=1 and f(v1)=0, it has been proven that the classical solutions to system (1.1) always remain globally bounded when n=1 [2]. A critical mass phenomenon of system (1.1) has been shown in a two-dimensional space. Namely, if the initial data v10 satisfies ‖v10‖L1(Ω)<4πχ, then the solution (v1,v2) is globally bounded [3]. Alternatively, if the initial data v10 satisfies ‖v10‖L1(Ω)>4πχ, then the solution (v1,v2) is unbounded in finite or infinite time, provided Ω is simply connected [4,5]. In particular, for a framework of radially symmetric solutions in a planar disk, the solutions blow up in finite time if ‖v10‖L1(Ω)>8πχ [6]. When f(v1)=0, Liu and Tao [7] changed τv2t=Δv2−v2+v1 to v2t=Δv2−v2+g(v1) with 0≤g(v1)≤Kvα1 for K,α>0, and obtained the global well-posedness of model (1.1) provided that 0<α<2n. Later on, the equation τv2t=Δv2−v2+v1 was changed to 0=Δv2−ϖ(t)+g(v1) with ϖ(t)=1|Ω|∫Ωg(v1(⋅,t)) for g(v1)=vα1. Winkler [8] deduced that for any v10, the model (1.1) is globally and classical solvable if α<2n; conversely, if α>2n, then the solutions are unbounded in a finite-time for any ∫Ωv10=m>0. For τ=0, when f(v1)≤v1(c−dv1) with c,d>0, Tello and Winkler [9] deduced the global well-posedness of model (1.1) provided that d>n−2nχ. Afterwards, when f(v1)=cv1−dvϵ1 with ϵ>1,c≥0,d>0, Winkler [10] defined a concept of very weak solutions and observed that these solutions are globally bounded under some conditions. For more results on (1.1), the readers can refer to [11,12,13,14].
Considering the volume filling effect [15], the self-diffusion functions and chemotactic sensitivity functions may have nonlinear forms of the cell density. The general model can be written as follows:
{v1t=∇⋅(ψ(v1)∇v1−ϕ(v1)∇v2)+f(v1), x∈Ω, t>0,τv2t=Δv2−v2+v1, x∈Ω, t>0. | (1.2) |
Here, ψ(v1) and ϕ(v1) are nonlinear functions. When τ=1 and f(v1)=0, for any ∫Ωv10=M>0, Winkler [16] derived that the solution (v1,v2) is unbounded in either finite or infinite time if ϕ(v1)ψ(v1)≥cvα1 with α>2n,n≥2 and some constant c>0 for all v1>1. Later on, Tao and Winkler [17] deduced the global well-posedness of model (1.5) provided that ϕ(v1)ψ(v1)≤cvα1 with α<2n,n≥1 and some constant c>0 for all v1>1. Furthermore, in a high-dimensional space where n≥5, Lin et al. [18] changed the equation τv2t=Δv2−v2+v1 to 0=Δv2−ϖ(t)+v1 with ϖ(t)=1|Ω|∫Ωv1(x,t)dx, and showed that the solution (v1,v2) is unbounded in a finite time.
Next, we introduce the chemotaxis model that involves an indirect signal mechanism. The model can be given by the following:
{v1t=∇⋅(ψ(v1)∇v1−ϕ(v1)∇v2)+f(v1), x∈Ω, t>0,τv2t=Δv2−v2+w, x∈Ω, t>0,τwt=Δw−w+v1, x∈Ω, t>0. | (1.3) |
For τ=1, when ψ(v1)=1,ϕ(v1)=v1 and f(v1)=λ(v1−vα1), the conclusion in [19] implied that the system is globally classical solvable if α>n4+12 with n≥2. Furthermore, the authors in [20,21,22] extended the boundedness result to a quasilinear system. Ren [23] derived the global well-posedness of system (1.3) and provided the qualitative analysis of such solutions. For τ=0, when ψ(s)≥c(s+1)θ and |ϕ(s)|≤ds(s+1)κ−1 with s≥0,c,d>0 and θ,κ∈R, Li and Li [24] obtained that the model (1.3) is globally classical solvable. Meanwhile, they also provided the qualitative analysis of such solutions. More results of the system with an indirect signal mechanism can be found in [25,26,27,28].
Considering that the cell or bacteria populations have a tendency to move towards a degraded nutrient, the authors obtain another well-known chemotaxis-consumption system:
{v1t=Δv1−χ∇⋅(v1∇v2), x∈Ω, t>0,v2t=Δv2−v1v2, x∈Ω, t>0, | (1.4) |
where v1 denotes the cell density, and v2 denotes the concentration of oxygen. If 0<χ≤16(n+1)‖v20‖L∞(Ω) with n≥2, then the results of [29] showed that the system (1.4) is globally classical solvable. Thereafter, Zhang and Li [30] deduced the global well-posedness of model (1.4) provided that n≤2 or 0<χ≤16(n+1)‖v20‖L∞(Ω),n≥3. In addition, for a sufficiently large v10 and v20, Tao and Winkler [31] showed that the defined weak solutions globally exist when n=3. Meanwhile, they also analyzed the qualitative properties of these weak solutions.
Based on the model (1.4), some researchers have considered the model that involves an indirect signal consumption:
{v1t=Δv1−χ∇⋅(v1∇v2), x∈Ω, t>0,v2t=Δv2−v1v2, x∈Ω, t>0,wt=−δw+v1, x∈Ω, t>0, | (1.5) |
where w represents the indirect signaling substance produced by cells for degrading oxygen. Fuest [32] obtained the global well-posedness of model (1.5) provided that n≤2 or ‖v20‖L∞(Ω)≤13n, and studied the convergence rate of the solution. Subsequently, the authors in [33] extended the boundedness conclusion of model (1.5) using conditions n≥3 and 0<‖v20‖L∞(Ω)≤π√n. For more results on model (1.5), the readers can refer to [34,35,36,37,38,39].
Inspired by the work mentioned above, we find that there are few papers on the quasilinear chemotaxis model that involve the nonlinear indirect consumption mechanism. In view of the complexity of the biological environment, this signal mechanism may be more realistic. In this manuscript, we are interested in the following system:
{v1t=∇⋅(ψ(v1)∇v1−χϕ(v1)∇v2)+λ1v1−λ2vβ1, x∈Ω, t>0,v2t=Δv2−wθv2, x∈Ω, t>0,0=Δw−w+vα1, x∈Ω, t>0,∂v1∂ν=∂v2∂ν=∂w∂ν=0, x∈∂Ω, t>0,v1(x,0)=v10(x),v2(x,0)=v20(x), x∈Ω, | (1.6) |
where Ω⊂Rn(n≥1) is a bounded and smooth domain, ν denotes the outward unit normal vector on ∂Ω, and χ,λ1,λ2,θ>0,0<α≤1θ,β≥2. Here, v1 is the cell density, v2 is the concentration of oxygen, and w is the indirect chemical signal produced by v1 to degrade v2. The diffusion functions ψ,ϕ∈C2[0,∞) are assumed to satisfy
ψ(s)≥a0(s+1)r1 and 0≤ϕ(s)≤b0s(s+1)r2, | (1.7) |
for all s≥0 with a0,b0>0 and r1,r2∈R. In addition, the initial data v10 and v20 fulfill the following:
v10,v20∈W1,∞(Ω) with v10,v20≥0,≢0 in Ω. | (1.8) |
Theorem 1.1. Assume that χ,λ1,λ2,θ>0,0<α≤1θ, and β≥2, and that Ω⊂Rn(n≥1) is a smooth bounded domain. Let ψ,ϕ∈C2[0,∞) satisfy (1.7). Suppose that the initial data v10 and v20 fulfill (1.8). It has been proven that if r1>2r2+1, then the problem (1.6) has a nonnegative classical solution
(v1,v2,w)∈(C0(ˉΩ×[0,∞))∩C2,1(ˉΩ×(0,∞)))2×C2,0(ˉΩ×(0,∞)), |
which is globally bounded in the sense that
‖v1(⋅,t)‖L∞(Ω)+‖v2(⋅,t)‖W1,∞(Ω)+‖w(⋅,t)‖W1,∞(Ω)≤C, |
for all t>0, with C>0.
Remark 1.2. Our main ideas are as follows. First, we obtain the L∞ bound for v2 by the maximum principle of the parabolic equation. Next, we establish an estimate for the functional y(t):=1p∫Ω(v1+1)p+12p∫Ω|∇v2|2p for any p>1 and t>0. Finally, we can derive the global solvability of model (1.6).
Remark 1.3. Theorem 1.1 shows that self-diffusion and logical source are advantageous for the boundedness of the solutions. In this manuscript, due to the indirect signal substance w that consumes oxygen, the aggregation of cells or bacterial is almost impossible when self-diffusion is stronger than cross-diffusion, namely r1>2r2+1. We can control the logical source to ensure the global boundedness of the solution for model (1.6). Thus, we can study the effects of the logistic source, the diffusion functions, and the nonlinear consumption mechanism on the boundedness of the solutions.
In this section, we first state a lemma on the local existence of classical solutions. The proof can be proven by the fixed point theory. The readers can refer to [40,41] for more details.
Lemma 2.1. Let the assumptions in Theorem 1.1 hold. Then, there exists Tmax∈(0,∞] such that the problem (1.6) has a nonnegative classical solution (v1,v2,w) that satisfies the following:
(v1,v2,w)∈(C0(ˉΩ×[0,Tmax))∩C2,1(ˉΩ×(0,Tmax)))2×C2,0(ˉΩ×(0,Tmax)). |
Furthermore, if Tmax<∞, then
lim supt↗Tmax(‖v1(⋅,t)‖L∞(Ω)+‖v2(⋅,t)‖W1,∞(Ω))=∞. |
Lemma 2.2. (cf. [42]) Let Ω⊂Rn(n≥1) be a smooth bounded domain. For any s≥1 and ϵ>0, one can obtain
∫∂Ω|∇z|2s−2∂|∇z|2∂ν≤ϵ∫Ω|∇z|2s−2|D2z|2+Cϵ∫Ω|∇z|2s, |
for all z∈C2(ˉΩ) fulfilling ∂z∂ν|∂Ω=0, with Cϵ=C(ϵ,s,Ω)>0.
Lemma 2.3. (cf. [43]) Let Ω⊂Rn(n≥1) be a bounded and smooth domain. For s≥1, we have
∫Ω|∇z|2s+2≤2(4s2+n)‖z‖2L∞(Ω)∫Ω|∇z|2s−2|D2z|2, |
for all z∈C2(ˉΩ) fulfilling ∂z∂ν|∂Ω=0.
Lemma 2.4. Let Ω⊂Rn(n≥1) be a bounded and smooth domain. For any z∈C2(Ω), one has the following:
(Δz)2≤n|D2z|2, |
where D2z represents the Hessian matrix of z and |D2z|2=∑ni,j=1z2xixj.
Proof. The proof can be found in [41, Lemma 3.1].
Lemma 2.5. (cf. [44,45]) Let a1,a2>0. The non-negative functions f∈C([0,T))∩C1((0,T)) and y∈L1loc([0,T)) fulfill
f′(t)+a1f(t)≤y(t), t∈(0,T), |
and
∫t+τty(s)ds≤a2, t∈(0,T−τ), |
where τ=min{1,T2} and T∈(0,∞]. Then, one deduces the following:
f(t)≤f(0)+2a2+a2a1, t∈(0,T). |
In this section, we provide some useful Lemmas to prove Theorem 1.1.
Lemma 3.1. Let β>1, then, there exist M,M1,M2>0 such that
‖v2(⋅,t)‖L∞(Ω)≤M for all t∈(0,Tmax), | (3.1) |
and
∫Ωv1≤M1 for all t∈(0,Tmax). | (3.2) |
Proof. By the parabolic comparison principle for v2t=Δv2−wθ1v2, we can derive (3.1). Invoking the integration for the first equation of (1.6), one has the following:
ddt∫Ωv1=λ1∫Ωv1−λ2∫Ωvβ1 for all t∈(0,Tmax). | (3.3) |
Invoking the Hölder inequality, we obtain the following:
ddt∫Ωv1≤λ1∫Ωv1−λ2|Ω|β−1(∫Ωv1)β. | (3.4) |
We can apply the comparison principle to deduce the following:
∫Ωv1≤max{∫Ωv10,(λ1λ2)1β−1|Ω|}=M1. | (3.5) |
Thereupon, we complete the proof.
Lemma 3.2. For any γ>1, we have the following:
∫Ωwγ≤C0∫Ωvαγ1 for all t∈(0,Tmax), | (3.6) |
where C0=2γ1+γ>0.
Proof. For γ>1, multiplying equation 0=Δw−w+vα1 by wγ−1, one obtain the following:
0=−(γ−1)∫Ωwγ−2|∇w|2−∫Ωwγ+∫Ωvα1wγ−1≤∫Ωvα1wγ−1−∫Ωwγ for all t∈(0,Tmax). | (3.7) |
By Young's inequality, it is easy to deduce the following:
∫Ωvα1wγ−1≤γ−12γ∫Ωwγ+2γ−1⋅1γ∫Ωvαγ1. | (3.8) |
Thus, we arrive at (3.6) by combining (3.7) with (3.8).
Lemma 3.3. Let the assumptions in Lemma 2.1 hold. For any p>max{1,1θ−1}, there exists C>0 such that
12pddt∫Ω|∇v2|2p+12p∫Ω|∇v2|2p+14∫Ω|∇v2|2p−2|D2v2|2≤C∫Ωvθα(p+1)1+C, | (3.9) |
for all t∈(0,Tmax).
Proof. Using the equation v2t=Δv2−wθ1v2, we obtain the following:
∇v2⋅∇v2t=∇v2⋅∇Δv2−∇v2⋅∇(wθv2)=12Δ|∇v2|2−|D2v2|2−∇v2⋅∇(wθv2), | (3.10) |
where we used the equality ∇v2⋅∇Δv2=12Δ|∇v2|2−|D2v2|2. Testing (3.10) by |∇v2|2p−2 and integrating by parts, we derive the following:
12pddt∫Ω|∇v2|2p+∫Ω|∇v2|2p−2|D2v2|2+12p∫Ω|∇v2|2p=12∫Ω|∇v2|2p−2Δ|∇v2|2+∫Ω|∇v2|2p−∫Ω|∇v2|2p−2∇v2⋅∇(wθv2)=I1+12p∫Ω|∇v2|2p+I2. | (3.11) |
Using Lemma 2.3 and (3.1), one has the following:
∫Ω|∇v2|2p+2≤C1∫Ω|∇v2|2p−2|D2v2|2 for all t∈(0,Tmax), | (3.12) |
where C1=2(4p2+n)M2. In virtue of Lemma 2.2, Young's inequality, and (3.12), an integration by parts produces the following:
I1+12p∫Ω|∇v2|2p=12∫Ω|∇v2|2p−2Δ|∇v2|2+12p∫Ω|∇v2|2p=12∫∂Ω|∇v2|2p−2∂|∇v2|2∂ν−12∫Ω∇|∇v2|2p−2⋅∇|∇v2|2+12p∫Ω|∇v2|2p≤14∫Ω|∇v2|2p−2|D2v2|2+C2∫Ω|∇v2|2p−p−12∫Ω|∇v2|2p−4|∇|∇v2|2|2≤14∫Ω|∇v2|2p−2|D2v2|2+14C1∫Ω|∇v2|2p+2+C3≤12∫Ω|∇v2|2p−2|D2v2|2+C3 for all t∈(0,Tmax), | (3.13) |
with C2,C3>0. Due to |Δv2|≤√n|D2v2|, we can conclude from (3.1) and the integration by parts that
I2=−∫Ω|∇v2|2p−2∇v2⋅∇(wθv2)=∫Ωwθv2∇⋅(∇v2|∇v2|2p−2)≤∫Ωwθv2(Δv2|∇v2|2p−2+(2p−2)|∇v2|2p−2|D2v2|)≤∫Ω(√n+2(p−2))Mwθ|∇v2|2p−2|D2v2|=C4∫Ωwθ|∇v2|2p−2|D2v2| for all t∈(0,Tmax), | (3.14) |
with C4=(√n+2(p−2))M>0. Due to p>max{1,1θ−1}, we have θ(p+1)>1. With applications of Young's inequality, (3.12), and Lemma 3.2, we obtain the following from (3.14):
C4∫Ωwθ|∇v2|2p−2|D2v2|≤18∫Ω|∇v2|2p−2|D2v2|2+C5∫Ωw2θ|∇v2|2p−2≤18∫Ω|∇v2|2p−2|D2v2|2+18C1∫Ω|∇v2|2p+2+C6∫Ωwθ(p+1)≤14∫Ω|∇v2|2p−2|D2v2|2+C7∫Ωwθ(p+1)≤14∫Ω|∇v2|2p−2|D2v2|2+C8∫Ωvθα(p+1)1, | (3.15) |
with C5,C6,C7,C8>0. Substituting (3.13) and (3.15) into (3.11), we derive the following:
12pddt∫Ω|∇v2|2p+12p∫Ω|∇v2|2p+14∫Ω|∇v2|2p−2|D2v2|2≤C8∫Ωvθα(p+1)1+C3, | (3.16) |
for all t∈(0,Tmax). Thereupon, we complete the proof.
Lemma 3.4. Let the assumptions in Lemma 2.1 hold. If r1>2r2+1, then for any p>1, we obtain the following:
1pddt∫Ω(v1+1)p+1p∫Ω(v1+1)p≤14∫Ω|∇v2|2p−2|D2v2|2+(C+λ1+1p)∫Ω(v1+1)p−λ2∫Ωvp+β−11+C, | (3.17) |
for all t∈(0,Tmax), with C>0.
Proof. Testing the first equation of problem (1.6) by (v1+1)p−1, one can obtain the following:
1pddt∫Ω(v1+1)p+1p∫Ω(v1+1)p=−(p−1)∫Ω(v1+1)p−2ψ(v1)|∇v1|2+1p∫Ω(v1+1)p+χ(p−1)∫Ω(v1+1)p−2ϕ(v1)∇v1⋅∇v2+λ1∫Ωv1(v1+1)p−1−λ2∫Ωvβ1(v1+1)p−1, | (3.18) |
for all t∈(0,Tmax). In view of (1.7), the first term on the right-hand side of (3.18) can be estimated as follows:
−(p−1)∫Ω(v1+1)p−2ψ(v1)|∇v1|2≤−(p−1)a0∫Ω(v1+1)p+r1−2|∇v1|2. | (3.19) |
For the second term on the right-hand side of (3.18), we can see that
χ(p−1)∫Ω(v1+1)p−2ϕ(v1)∇v1⋅∇v2≤χ(p−1)b0∫Ωv1(v1+1)p+r2−2∇v1⋅∇v2. | (3.20) |
We can obtain the following from Young's inequality:
χ(p−1)b0∫Ωv1(v1+1)p+r2−2∇v1⋅∇v2≤χ(p−1)b0∫Ω(v1+1)p+r2−1∇v1⋅∇v2≤(p−1)a0∫Ω(v1+1)p+r1−2|∇v1|2+C1∫Ω(v1+1)p+2r2−r1|∇v2|2, | (3.21) |
with C1>0. Utilizing Young's inequality and (3.12), one has the following:
C1∫Ω(v1+1)p+2r2−r1|∇v2|2≤18(4p2+n)M2∫Ω|∇v2|2(p+1)+C2∫Ω(v1+1)(p+1)(p+2r2−r1)p≤14∫Ω|∇v2|2p−2|D2v2|2+C2∫Ω(v1+1)(p+1)(p+2r2−r1)p, | (3.22) |
where C2>0. Due to r1>2r2+1, for any p>1>r1−2r22r2−r1+1, we can obtain (p+1)(p+2r2−r1)p<p. Applying Young's inequality, we obtain the following:
C2∫Ω(v1+1)(p+1)(p+2r2−r1)p≤C3∫Ω(v1+1)p+C3, | (3.23) |
where C3>0. Hence, substituting (3.19)–(3.23) into (3.18), one obtains the following:
1pddt∫Ω(v1+1)p+1p∫Ω(v1+1)p≤14∫Ω|∇v2|2p−2|D2v2|2+(C3+λ1+1p)∫Ω(v1+1)p−λ2∫Ωvp+β−11+C4, | (3.24) |
for all t∈(0,Tmax), where C4>0.
Lemma 3.5. Let the assumptions in Lemma 2.1 hold. If r1>2r2+1, then for any p>max{1,1θ−1}, we obtain the following:
∫Ω(v1+1)p+∫Ω|∇v2|2p≤C, | (3.25) |
where C>0.
Proof. We can combine Lemma 3.3 with Lemma 3.4 to infer the following:
ddt(1p∫Ω(v1+1)p+12p∫Ω|∇v2|2p)+1p∫Ω(v1+1)p+12p∫Ω|∇v2|2p≤C1∫Ωvθα(p+1)1+(C1+λ1+1p)∫Ω(v1+1)p−λ2∫Ωvp+β−11+C1, | (3.26) |
where C1>0. Due to 0<α≤1θ and β≥2, we can obtain θα(p+1)≤p+1≤p+β−1. Using Young's inequality, we can obtain the following:
C1∫Ωvθα(p+1)1≤λ22∫Ωvp+β−11+C2, | (3.27) |
where C2>0. By the inequality (w+s)κ≤2κ(wκ+sκ) with w,s>0 and κ>1, we deduce the following:
(C1+λ1+1p)∫Ω(v1+1)p≤λ22∫Ωvp+β−11+C3, | (3.28) |
where C3>0, where we have applied Young's inequality. Thus, we obtain the following:
ddt(1p∫Ω(v1+1)p+12p∫Ω|∇v2|2p)+1p∫Ω(v1+1)p+12p∫Ω|∇v2|2p≤C4, | (3.29) |
where C4>0. Therefore, we can obtain (3.25) by Lemma 2.5. Thereupon, we complete the proof.
The proof of Theorem 1.1. Recalling Lemma 3.5, for any p>max{1,1θ−1}, and applying the Lp−estimates of elliptic equation, there exists C1>0 such that
supt∈(0,Tmax)‖w(⋅,t)‖W2,pα(Ω)≤C1 for all t∈(0,Tmax). | (3.30) |
The Sobolev imbedding theorem enables us to obtain the following:
supt∈(0,Tmax)‖w(⋅,t)‖W1,∞(Ω)≤C2 for all t∈(0,Tmax), | (3.31) |
with C2>0. Besides, using the well-known heat semigroup theory to the second equation in system (1.6), we can find C3>0 such that
‖v2(⋅,t)‖W1,∞(Ω)≤C3 for all t∈(0,Tmax). | (3.32) |
Therefore, using the Moser-iteration[17], we can find C4>0 such that
‖v1(⋅,t)‖L∞(Ω)≤C4 for all t∈(0,Tmax). | (3.33) |
Based on (3.31)–(3.33), we can find C5>0 that fulfills the following:
‖v1(⋅,t)‖L∞(Ω)+‖v2(⋅,t)‖W1,∞(Ω)+‖w(⋅,t)‖W1,∞(Ω)≤C5, | (3.34) |
for all t∈(0,Tmax). According to Lemma 2.1, we obtain Tmax=∞. Thereupon, we complete the proof of Theorem 1.1.
In this manuscript, based on the model established in [35], we further considered that self-diffusion and cross-diffusion are nonlinear functions, as well as the mechanism of nonlinear generation and consumption of the indirect signal substance w. We mainly studied the effects of diffusion functions, the logical source, and the nonlinear consumption mechanism on the boundedness of solutions, which enriches the existing results of chemotaxis consumption systems. Compared with previous results [29,32], the novelty of this manuscript is that our boundedness conditions are more generalized and do not depend on spatial dimension or the sizes of ‖v20‖L∞(Ω) established in [32], which may be more in line with the real biological environment. In addition, we will further explore interesting problems related to system (1.6) in our future work, such as the qualitative analysis of system (1.6), the global classical solvability for full parabolic of system (1.6), and so on.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
This work was partially supported by the National Natural Science Foundation of China (No. 12271466, 11871415).
The authors declare there is no conflict of interest.
[1] |
Sun T, Wang Y (2020) Modeling COVID-19 epidemic in Heilongjiang province, China. Chaos, Soliton Fract 138: 109949. doi: 10.1016/j.chaos.2020.109949
![]() |
[2] |
Melin P, Castillo O (2021) Spatial and temporal spread of the COVID-19 pandemic using self organizing neural networks and a fuzzy fractal approach. Sustainability 13: 8295. doi: 10.3390/su13158295
![]() |
[3] |
Castillo O, Melin P (2021) A novel method for a covid-19 classification of countries based on an intelligent fuzzy fractal approach. Healthcare 9: 196. doi: 10.3390/healthcare9020196
![]() |
[4] |
Castillo O, Melin P (2020) Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic. Chaos, Soliton Fract 140: 110242. doi: 10.1016/j.chaos.2020.110242
![]() |
[5] |
Boccaletti S, Ditto W, Mindlin G, et al. (2020) Modeling and forecasting of epidemic spreading: The case of Covid-19 and beyond. Chaos, Soliton Fract 135: 109794. doi: 10.1016/j.chaos.2020.109794
![]() |
[6] | Wang W, Xu Y, Gao R, et al. (2020) Detection of SARS-CoV-2 in different types of clinical specimens. Jama 323: 1843-1844. |
[7] | West CP, Montori VM, Sampathkumar P (2020) COVID-19 testing: the threat of false-negative results. Elsevier 95: 1127-1129. |
[8] |
Fang Y, Zhang H, Xie J, et al. (2020) Sensitivity of chest CT for COVID-19: comparison to RT-PCR. Radiology 296: E115-E117. doi: 10.1148/radiol.2020200432
![]() |
[9] |
Ng M-Y, Lee EY, Yang J, et al. (2020) Imaging profile of the COVID-19 infection: radiologic findings and literature review. Radiolo: Cardiothorac Imag 2: e200034. doi: 10.1148/ryct.2020200034
![]() |
[10] |
Huang C, Wang Y, Li X, et al. (2020) Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The lancet 395: 497-506. doi: 10.1016/S0140-6736(20)30183-5
![]() |
[11] |
Guan WJ, Ni ZY, Hu Y, et al. (2020) Clinical characteristics of coronavirus disease 2019 in China. New Engl J Med 382: 1708-1720. doi: 10.1056/NEJMoa2002032
![]() |
[12] |
Ai T, Yang Z, Hou H, et al. (2020) Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296: E32-E40. doi: 10.1148/radiol.2020200642
![]() |
[13] |
Khatami F, Saatchi M, Zadeh SST, et al. (2020) A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis. Sci Rep 10: 22402. doi: 10.1038/s41598-020-80061-2
![]() |
[14] |
Nair A, Rodrigues JCL, Hare S, et al. (2020) A British society of thoracic imaging statement: considerations in designing local imaging diagnostic algorithms for the COVID-19 pandemic. Clin Radiol 75: 329-334. doi: 10.1016/j.crad.2020.03.008
![]() |
[15] |
Jacobi A, Chung M, Bernheim A, et al. (2020) Portable chest X-ray in coronavirus disease-19 (COVID-19): A pictorial review. Clin Imag 64: 35-42. doi: 10.1016/j.clinimag.2020.04.001
![]() |
[16] | LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature . |
[17] | Gozes O, Frid-Adar M, Greenspan H, et al. Rapid ai development cycle for the coronavirus (covid-19) pandemic: Initial results for automated detection & patient monitoring using deep learning ct image analysis (2020) .arXiv preprint arXiv:2003.05037. |
[18] | Lessmann N, Sánchez CI, Beenen L, et al. (2020) Automated assessment of CO-RADS and chest CT severity scores in patients with suspected COVID-19 using artificial intelligence. Radiology . |
[19] | Li L, Qin L, Xu Z, et al. (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology . |
[20] |
Shi F, Xia L, Shan F, et al. (2021) Large-scale screening to distinguish between COVID-19 and community-acquired pneumonia using infection size-aware classification. Phys Med Biol 66: 065031. doi: 10.1088/1361-6560/abe838
![]() |
[21] | Magree H, Russell F, Sa'Aga R, et al. (2005) Chest X-ray-confirmed pneumonia in children in Fiji. B World Health Organ 83: 427-433. |
[22] |
Wong HYF, Lam HYS, Fong AHT, et al. (2020) Frequency and distribution of chest radiographic findings in patients positive for COVID-19. Radiology 296: E72-E78. doi: 10.1148/radiol.2020201160
![]() |
[23] |
Borghesi A, Maroldi R (2020) COVID-19 outbreak in Italy: experimental chest X-ray scoring system for quantifying and monitoring disease progression. La radiologia medica 125: 509-513. doi: 10.1007/s11547-020-01200-3
![]() |
[24] | Wang X, Peng Y, Lu L, et al. (2017) Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE conference on computer vision and pattern recognition 2097-2106. |
[25] | Rajpurkar P, Irvin J, Zhu K, et al. Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning (2017) .arXiv preprint arXiv:1711.05225. |
[26] |
Wang H, Jia H, Lu L, et al. (2019) Thorax-Net: An attention regularized deep neural network for classification of thoracic diseases on chest radiography. IEEE J Biomed Health 24: 475-485. doi: 10.1109/JBHI.2019.2928369
![]() |
[27] |
Rajaraman S, Candemir S, Kim I, et al. (2018) Visualization and interpretation of convolutional neural network predictions in detecting pneumonia in pediatric chest radiographs. Appl Sci 8: 1715. doi: 10.3390/app8101715
![]() |
[28] |
Kermany DS, Goldbaum M, Cai W, et al. (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172: 1122-1131. doi: 10.1016/j.cell.2018.02.010
![]() |
[29] |
Horry MJ, Chakraborty S, Paul M, et al. (2020) COVID-19 detection through transfer learning using multimodal imaging data. IEEE Access 8: 149808-149824. doi: 10.1109/ACCESS.2020.3016780
![]() |
[30] | Ahmed I, Ahmad A, Jeon G (2020) An iot based deep learning framework for early assessment of covid-19. IEEE Internet Things J . |
[31] |
Chowdhury MEH, Rahman T, Khandakar A, et al. (2020) Can AI help in screening viral and COVID-19 pneumonia? IEEE Access 8: 132665-132676. doi: 10.1109/ACCESS.2020.3010287
![]() |
[32] |
Han Z, Wei B, Hong Y, et al. (2020) Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE T Med Imaging 39: 2584-2594. doi: 10.1109/TMI.2020.2996256
![]() |
[33] |
Qian X, Fu H, Shi W, et al. (2020) M 3 Lung-Sys: A deep learning system for multi-class lung pneumonia screening from CT imaging. IEEE J Biomed Health 24: 3539-3550. doi: 10.1109/JBHI.2020.3030853
![]() |
[34] |
Sakib S, Tazrin T, Fouda MM, et al. (2020) DL-CRC: deep learning-based chest radiograph classification for COVID-19 detection: a novel approach. IEEE Access 8: 171575-171589. doi: 10.1109/ACCESS.2020.3025010
![]() |
[35] |
Waheed A, Goyal M, Gupta D, et al. (2020) Covidgan: data augmentation using auxiliary classifier gan for improved covid-19 detection. Ieee Access 8: 91916-91923. doi: 10.1109/ACCESS.2020.2994762
![]() |
[36] |
Varela-Santos S, Melin P (2021) A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks. Inform Sci 545: 403-414. doi: 10.1016/j.ins.2020.09.041
![]() |
[37] |
Brihn A, Chang J, OYong K, et al. (2021) Diagnostic performance of an antigen test with RT-PCR for the detection of SARS-CoV-2 in a hospital setting—Los Angeles county, California, June–August 2020. Morbid Mortal W Rep 70: 702. doi: 10.15585/mmwr.mm7019a3
![]() |
[38] |
Chawla NV, Bowyer KW, Hall LO, et al. (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16: 321-357. doi: 10.1613/jair.953
![]() |
[39] |
Hassan M, Ali S, Alquhayz H, et al. (2020) Developing intelligent medical image modality classification system using deep transfer learning and LDA. Sci Rep 10: 1-14. doi: 10.1038/s41598-019-56847-4
![]() |
[40] | He K, Zhang X, Ren S, et al. (2016) Identity mappings in deep residual networks Springer, 630-645. |
[41] |
Prakash C, Rajkumar S, Mouli PC (2012) Medical image fusion based on redundancy DWT and Mamdani type min-sum mean-of-max techniques with quantitative analysis. 2012 International conference on recent advances in computing and software systems IEEE, 54-59. doi: 10.1109/RACSS.2012.6212697
![]() |
[42] | Gayathri BM, Sumathi CP (2015) Mamdani fuzzy inference system for breast cancer risk detection. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) IEEE, 1-6. |
[43] | Terrada O, Raihani A, Bouattane O, et al. (2018) Fuzzy cardiovascular diagnosis system using clinical data. 2018 4th International Conference on Optimization and Applications (ICOA) IEEE, 1-4. |
[44] |
Yang W, Cao Q, Qin LE, et al. (2020) Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-center study in Wenzhou city, Zhejiang, China. J Infection 80: 388-393. doi: 10.1016/j.jinf.2020.02.016
![]() |
[45] |
Yoon SH, Lee KH, Kim JY, et al. (2020) Chest radiographic and CT findings of the 2019 novel coronavirus disease (COVID-19): analysis of nine patients treated in Korea. Korean J Radiol 21: 494-500. doi: 10.3348/kjr.2020.0132
![]() |
[46] |
Rodrigues JCL, Hare SS, Edey A, et al. (2020) An update on COVID-19 for the radiologist-A British society of thoracic imaging statement. Clin Radiol 75: 323-325. doi: 10.1016/j.crad.2020.03.003
![]() |
[47] |
Ludvigsson JF (2020) Systematic review of COVID-19 in children shows milder cases and a better prognosis than adults. Acta Paediatr 109: 1088-1095. doi: 10.1111/apa.15270
![]() |
[48] |
Holshue ML, DeBolt C, Lindquist S, et al. (2020) First case of 2019 novel coronavirus in the United States. N Engl J Med 382: 929-936. doi: 10.1056/NEJMoa2001191
![]() |
[49] |
Yang R, Li X, Liu H, et al. (2020) Chest CT severity score: an imaging tool for assessing severe COVID-19. Radiol: Cardiothorac Imag 2: e200047. doi: 10.1148/ryct.2020200047
![]() |
[50] |
Zhang W, Thurow K, Stoll R (2014) A knowledge-based telemonitoring platform for application in remote healthcare. Int J Comput Commun 9: 644-654. doi: 10.15837/ijccc.2014.5.661
![]() |
[51] |
Dong J, Zhuang D, Huang Y, et al. (2009) Advances in multi-sensor data fusion: Algorithms and applications. Sensors 9: 7771-7784. doi: 10.3390/s91007771
![]() |
[52] |
Gevaert CM, García-Haro FJ (2015) A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion. Remote Sens Environ 156: 34-44. doi: 10.1016/j.rse.2014.09.012
![]() |
[53] |
Fourati H (2014) Heterogeneous data fusion algorithm for pedestrian navigation via foot-mounted inertial measurement unit and complementary filter. IEEE T Instrum Meas 64: 221-229. doi: 10.1109/TIM.2014.2335912
![]() |
[54] |
Ambühl L, Menendez M (2016) Data fusion algorithm for macroscopic fundamental diagram estimation. Transport Res Part C: Emer Technol 71: 184-197. doi: 10.1016/j.trc.2016.07.013
![]() |