Research article Special Issues

Regularization effect of the mixed-type damping in a higher-dimensional logarithmic Keller-Segel system related to crime modeling


  • Received: 12 September 2022 Revised: 07 November 2022 Accepted: 06 November 2022 Published: 26 December 2022
  • We study a logarithmic Keller-Segel system proposed by Rodríguez for crime modeling as follows:

    {ut=Δuχ(ulnv)κuv+h1,vt=Δvv+u+h2,

    in a bounded and smooth spatial domain ΩRn with n3, with the parameters χ>0 and κ>0, and with the nonnegative functions h1 and h2. For the case that κ=0, h10 and h20, recent results showed that the corresponding initial-boundary value problem admits a global generalized solution provided that χ<χ0 with some χ0>0.

    In the present work, our first result shows that for the case of κ>0 such problem possesses global generalized solutions provided that χ<χ1 with some χ1>χ0, which seems to confirm that the mixed-type damping κuv has a regularization effect on solutions. Besides the existence result for generalized solutions, a statement on the large-time behavior of such solutions is derived as well.

    Citation: Bin Li, Zhi Wang, Li Xie. Regularization effect of the mixed-type damping in a higher-dimensional logarithmic Keller-Segel system related to crime modeling[J]. Mathematical Biosciences and Engineering, 2023, 20(3): 4532-4559. doi: 10.3934/mbe.2023210

    Related Papers:

    [1] A. El-Mesady, Y. S. Hamed, M. S. Mohamed, H. Shabana . Partially balanced network designs and graph codes generation. AIMS Mathematics, 2022, 7(2): 2393-2412. doi: 10.3934/math.2022135
    [2] Doris Dumičić Danilović, Andrea Švob . On Hadamard 2-(51,25,12) and 2-(59,29,14) designs. AIMS Mathematics, 2024, 9(8): 23047-23059. doi: 10.3934/math.20241120
    [3] Menderes Gashi . On the symmetric block design with parameters (280,63,14) admitting a Frobenius group of order 93. AIMS Mathematics, 2019, 4(4): 1258-1273. doi: 10.3934/math.2019.4.1258
    [4] Muhammad Sajjad, Tariq Shah, Huda Alsaud, Maha Alammari . Designing pair of nonlinear components of a block cipher over quaternion integers. AIMS Mathematics, 2023, 8(9): 21089-21105. doi: 10.3934/math.20231074
    [5] James Daniel, Kayode Ayinde, Adewale F. Lukman, Olayan Albalawi, Jeza Allohibi, Abdulmajeed Atiah Alharbi . Optimised block bootstrap: an efficient variant of circular block bootstrap method with application to South African economic time series data. AIMS Mathematics, 2024, 9(11): 30781-30815. doi: 10.3934/math.20241487
    [6] Hye Kyung Kim . Note on r-central Lah numbers and r-central Lah-Bell numbers. AIMS Mathematics, 2022, 7(2): 2929-2939. doi: 10.3934/math.2022161
    [7] Cui-Xia Li, Long-Quan Yong . Modified BAS iteration method for absolute value equation. AIMS Mathematics, 2022, 7(1): 606-616. doi: 10.3934/math.2022038
    [8] Ahmad Y. Al-Dweik, Ryad Ghanam, Gerard Thompson, M. T. Mustafa . Algorithms for simultaneous block triangularization and block diagonalization of sets of matrices. AIMS Mathematics, 2023, 8(8): 19757-19772. doi: 10.3934/math.20231007
    [9] Shakir Ali, Amal S. Alali, Atif Ahmad Khan, Indah Emilia Wijayanti, Kok Bin Wong . XOR count and block circulant MDS matrices over finite commutative rings. AIMS Mathematics, 2024, 9(11): 30529-30547. doi: 10.3934/math.20241474
    [10] Abd El-Raheem M. Abd El-Raheem, Mona Hosny . Saddlepoint p-values for a class of nonparametric tests for the current status and panel count data under generalized permuted block design. AIMS Mathematics, 2023, 8(8): 18866-18880. doi: 10.3934/math.2023960
  • We study a logarithmic Keller-Segel system proposed by Rodríguez for crime modeling as follows:

    {ut=Δuχ(ulnv)κuv+h1,vt=Δvv+u+h2,

    in a bounded and smooth spatial domain ΩRn with n3, with the parameters χ>0 and κ>0, and with the nonnegative functions h1 and h2. For the case that κ=0, h10 and h20, recent results showed that the corresponding initial-boundary value problem admits a global generalized solution provided that χ<χ0 with some χ0>0.

    In the present work, our first result shows that for the case of κ>0 such problem possesses global generalized solutions provided that χ<χ1 with some χ1>χ0, which seems to confirm that the mixed-type damping κuv has a regularization effect on solutions. Besides the existence result for generalized solutions, a statement on the large-time behavior of such solutions is derived as well.



    Regular two-level designs are widely used in factorial experiments. When the size of experimental units is large, inhomogeneity of experimental units has bad influences on estimating treatment effects (see, [1,2]). A useful way to reduce such bad influences is to block the experimental units into categories known as blocks. Thus, choosing optimal blocked regular two-level designs becomes an important issue.

    As pointed out by [1], there are two kinds of blocking problems. One is called the single block variable problem which involves only one block variable and the other is called the multi block variables problem which considers two or more block variables. In the last decades, most of the literature were concerned with the single block variable problem. Sitter et al. [3], H. Chen and C. S. Cheng [4], R. C. Zhang and D. K. Park [5], and S. W. Cheng and C. F. J. Wu [6] respectively proposed different minimum aberration (MA) criteria which are suitable for selecting blocked designs with single block variable. Under these MA criteria, the construction methods of blocked designs with single block variable were discussed in [7,8,9].

    Zhang et al. [10] proposed the general minimum lower-order confounding (GMC) criterion for choosing optimal regular two-level designs. The GMC criterion is preferable when there are some prior knowledge on the importance ordering of treatment effects. R. C. Zhang and R. Mukerjee [11] extended the GMC criterion to blocked designs with single block variable, referred as B-GMC criterion, and gave the construction methods of B-GMC blocked designs via complementary designs. From different considerations, [12] proposed another GMC criterion for blocked designs with single block variable, referred as B1-GMC. Zhao et al. [13] and Zhao et al. [14] studied the construction methods of B1-GMC designs. Zhang et al. [15] proposed multi-stage differential evolution algorithm for constrained D-optimal design. Gashi [16] considered symmetric block design.

    Compared to the experiments involving a single block variable, the experiments involving multi block variables are often encountered in practice. As has been mentioned in [1], in the agricultural context, when designs are laid out in rectangular schemes, both row and column inhomogeneity effects probably exist in the soil. Another example of multi block variables problem is from [2], which considers comparing two gasoline additives by testing them on two cars with two drivers over two days. In this experiment, three variables, cars, drivers and days, have to be considered to partition the experimental units.

    Despite the wide application background, the multi block variables problem is less studied due to its complexity. In particular, constructing optimal designs with multi block variables is considerably challenging. Under the MA criterion, [17] developed some rules for constructing optimal regular two-level blocked designs with multi block variables. Zhang et al. [18] extended the idea of the GMC criterion to the case of multi block variables problem and developed the blocked GMC criterion, called B2-GMC criterion. Inheriting the advantage of the GMC criterion, a B2-GMC design is particularly preferable when some prior information on importance ordering of treatment effects is present. Zhang et al. [18] tabulated some B2-GMC designs with small numbers of treatment factors and small run sizes by computer search. When n or N is large, computer search becomes computationally challenging, where N=2nm. Zhao et al. [19] studied the B2-GMC criterion and constructed a small number of B2-GMC designs. In this paper, the B2-GMC designs with the number of treatment factors n all over 5N/16+1nN1 are constructed. The construction results cover all that of [19]. The structures of the constructed B2-GMC designs are concise and easy to implement.

    The rest of the paper is organized as follows. Section 2 reviews the B2-GMC criterion and introduces some notation. The construction methods of B2-GMC designs are provided in Section 3. Section 4 gives concluding remarks. Some proofs are deferred to Appendix.

    Let q=nm and N=2q. Denote the regular two-level saturated design as

    Hq={1,2,12,3,13,23,123,,123q}

    in Yates order, where the columns 1, 2,, and q are q independent columns in the form of

    1=(1,1,1,1,,1,1)2q,2=(1,1,1,1,,1,1,1,1)2q,q=(1,,1,1,,1)2q,

    where the superscript of each column denotes transpose, in 1 the entry 1, followed by 1, is repeated 2q1 times, and in 2 the two successive entries 1's, followed by two successive 1's, are repeated 2q2 times, , and in q the 2q1 successive entries 1's are followed by 2q1 successive 1's. The remaining columns in Hq are generated by taking the component-wise products of any k of the q independent columns, where k=2,3,,q. For example, the column 12 is generated by taking component-wise products of the independent columns 1 and 2. Denote H1={1},Hr={Hr1,r,rHr1} for r=2,,q, where rHr1={rd:dHr1} and Hr consists of the first 2r1 columns of Hq. Let Fqr={q,qHr1} for r=2,,q, then Fqr consists of the first 2r1 columns of Fqq and Fqq consists of the last 2q1 columns of Hq.

    Suppose that the inhomogeneity of the units of an experiment comes from s different sources, i.e., s block variables, denoted as b1,b2,,bs. Suppose the block variable bj has 2lj levels, i.e., the 2nm units are grouped into 2lj blocks with respect to the block variable bj. Then there should be lj independent columns of Hq to implement such a blocking schedule. Such columns are called block columns in the following. Denote Fj as the collection of the lj independent block columns corresponding to the block variable bj. It is worth noting that the columns in Fj have the following two relations:

    (i) the lj columns in Fj are independent of each other for j=1,2,,s;

    (ii) a column from Fj is not necessarily independent of the columns from Fi, ij.

    Clearly, when s=1, the blocking problem with multi block variables reduces to that with a single block variable. For simplicity, we consider only the case of lj=1 for j=1,2,,s. Then there are s block columns and each of them blocks the 2nm experimental units into 2 groups. Here, we would like to emphasize that the s block columns are not necessarily independent of each other.

    Throughout the paper, we use D=(Dt,Db) to denote a blocked regular 2nm:2s design, where Dt is a regular 2nm design, and Db consists of s block columns. We will not differentiate block variables, block columns, and block factors in the following. B. Tang and C. F. J. Wu [20] introduced the concept of isomorphism which helps to narrow down the search for optimal blocked designs in this paper. An isomorphism ϕ is a one-to-one mapping from Hq to Hq such that ϕ(xy)=ϕ(x)ϕ(y) for every xyHq. Two 2nm:2s designs D1=(D1t,D1b) and D2=(D2t,D2b) are isomorphic if there exists an isomorphism ϕ that maps D1t onto D2t, and D1b onto D2b.

    Zhang et al. [18] put forward the effect hierarchy principle for blocked designs with multi block variables as follows:

    (i) Lower order treatment factorial effects are more likely to be important than higher order ones, and treatment factorial effects of the same order are equally likely to be important.

    (ii) Lower order block factorial effects are more likely to be important than higher order ones, and block factorial effects of the same order are equally likely to be important.

    (iii) All the interactions between treatment and block factors are negligible.

    With the effect hierarchy principle and weak assumption of effects involving three or more factors are usually not important and negligible, [18] proposed the B2-GMC criterion which considers only the confounding among the main effects and two-factor interactions. As a common assumption in the blocking issues, if a treatment effect is confounded with a potentially significant block effect, the treatment effect cannot be estimated. Thus, confounding of the main effects of treatment factors with any potentially significant block effect is not allowed. In the following, we always suppose the main effects and the two-factor interactions of the block factors are potentially significant.

    Denote #1C(p)2(D) as the number of main treatment effects which are aliased with p two-treatment-factor interactions (2tfi's) but not with any potentially significant block effects, where p=0,1,2,,P, P=n(n1)/2. Similarly, #2C(p)2(D) denotes the number of 2tfi's which are aliased with the other p 2tfi's but not with any potentially significant block effects. Denote

    #1C2(D)=(#1C(0)2(D),#1C(1)2(D),,#1C(P)2(D)),#2C2(D)=(#2C(0)2(D),#2C(1)2(D),,#2C(P)2(D)),#C(D)=(#1C2(D),#2C2(D)). (2.1)

    A blocked design D=(Dt,Db) is called a B2-GMC design if D sequentially maximizes (2.1). Let #iC(p)j(Dt) be the number of i-th order effects which are aliased with p j-th order effects of Dt. Let

    #1C2(Dt)=(#1C(0)2(Dt),#1C(1)2(Dt),,#1C(P)2(Dt)),#2C2(Dt)=(#2C(0)2(Dt),#2C(1)2(Dt),,#2C(P)2(Dt)),#C(Dt)=(#1C2(Dt),#2C2(Dt)). (2.2)

    A 2nm design Dt is called a GMC design if Dt sequentially maximizes (2.2).

    Let

    U(Db)={γHq:γDb   or   γ=ab   with   a,bDb},

    i.e., U(Db) contains the potentially significant block effects. As aforementioned, confounding between main treatment effects and potentially significant block effects is not allowed which leads to DtU(Db)=, the empty set, and consequently #1C(p)2(D)=#1C(p)2(Dt), p=0,1,2,,P.

    As a preparation of deriving B2-GMC designs, we introduce one more piece of notation. For DtHq and γHq, define

    B2(Dt,γ)=#{(d1,d2:d1,d2Dt,d1d2=γ},

    where # denotes the cardinality of a set, and d1d2 stands for the two-factor interaction of d1 and d2. Thus, B2(Dt,γ) equals the number of 2tfi's of Dt appearing in the alias set that contains γ.

    To construct B2-GMC designs, one should first consider the first part in (2.1), i.e., #1C2(D). Recall that #1C(p)2(D)=#1C(p)2(Dt) for p=0,1,,P, then choosing D to maximize #1C2(D) reduces to choosing Dt to maximize #1C2(Dt).

    A 2nm design Dt is said to have resolution R if no c-factor interaction is confounded with any other interaction involving less than Rc factors (see, [21]). Note that a 2nm design Dt with resolution at least IV has #1C(0)2(Dt)=n and #1C(p)2(Dt)=0 for p=1,P. This implies that a 2nm design Dt with resolution at least IV must maximize #1C2(Dt). When 5N16+1nN2, if Dt has resolution at least IV, then DtFqq (see, [22]). In the remaining part of this section, we suppose DtFqq. By Lemma 1 in [13], to choose Db from Hq, there are two possibilities: (i) DbFqq=, and (ii) DbFqq. As has been pointed out, when constructing B2-GMC designs, there should be DtU(Db)=. This leads to the constraint

    #{U(Db)Fqq}N2n. (3.1)

    Certainly, for Db in the case (i), U(Db)Hq1 and thus Db satisfies the constraint (3.1). For Db in the case (ii), there must be U(Db)Fqq resulting in the necessity to investigate the number of columns in U(Db)Fqq. The following lemma addresses this question.

    Lemma 1. Let Db be any s-projection of Hq with DbFqq. If 2ks2k+11 for some kq2, then #{U(Db)Fqq}2k.

    The proof of Lemma 1 is lengthy and thus deferred to Appendix.

    Lemmas 2 and 3 below are straightforward extensions of some results in [19] and [23], respectively. These two lemmas are helpful in deriving the construction methods of B2-GMC designs.

    Lemma 2. Let Db be any s-projection of Hq with 2ks2k+11 for some kq1.

    (i) If DbFqq=, then #{U(Db)Hq1}2k+11 and the equality holds when Db has k+1 independent columns.

    (ii) If DbFqq, then #{U(Db)Hq1}2k1 and the equality holds when Db has k+1 independent columns.

    (iii) If DbHk+1, then U(Db)=Hk+1.

    (iv) If DbHkFq(k+1), then U(Db)=HkFq(k+1).

    Lemma 3. Suppose Dt consists of the last n columns of Hq. For any two columns γ1 and γ2 in Hq1, if γ1 is ahead of γ2 in Yates order, then B2(Dt,γ1)B2(Dt,γ2).

    Combining Lemmas 1, 2 and 3, the following theorem provides the constructions of B2-GMC designs with 5N16+1nN2, where N16.

    Theorem 1. Suppose D=(Dt,Db) is a 2nm:2s design with 2rN2n2r+11 for some rq3 and 2ks2k+11 for some kq2. The design D=(Dt,Db) is a B2-GMC design if Dt consists of the last n columns of Fqq and

    (i) Db is any s-projection of HkFq(k+1) when 1kr,

    (ii) Db is any s-projection of Hk+1 when r+1kq2.

    Proof. Let D=(Dt,Db) be a 2nm:2s design with DtFqq and U(Db)HqDt. According to Lemma 1 of [22],

    B2(Dt,γ)={0, if   γFqq,B2(ˉDt,γ)+nN4, if   γHq1,

    where ˉDt=FqqDt. Note that Dt has resolution at least IV, then #1C2 is maximized by D. Therefore, we consider only #2C2 in (2.1) in the following.

    For (i). From Theorem 2 of [22], Dt is a GMC 2nm design and thus Dt maximizes (2.2) among all Dt. From Lemma 2 (iv), if Db is any s-projection of HkFq(k+1), then U(Db)=HkFq(k+1). Suppose γ0 is the last column of Hk in Yates order and B2(Dt,γ0)=p0, where p0nN4N16+12. From Lemma 3, for any γHk, we have B2(Dt,γ)p0. Since DtFqq, we have B2(Dt,γ)=0 for any γFqq. By the definition of #2C(p)2(D), when pp02, we have 1p+1p01 and

    #2C(p)2(D)=(p+1)#{γHq:γU(Db),B2(Dt,γ)=p+1}=(p+1)#{γHq:B2(Dt,γ)=p+1}=#2C(p)2(Dt). (3.2)

    From Lemma 3, for any γHqHk, we have B2(Dt,γ)p0. Therefore, when pp0, we have

    #2C(p)2(D)=(p+1)#{γHq:γU(Db),B2(Dt,γ)=p+1}=(p+1)#{γ(Hq1Hk)(FqqFq(k+1)):B2(Dt,γ)=p+1}=0. (3.3)

    From (3.2), we obtain that D sequentially maximizes

    (#2C(0)2(D),#2C(1)2(D),,#2C(p02)2(D))

    among all D since Dt is a GMC design.

    Suppose D is not a B2-GMC design, then there exists a D and some p1p01 such that

    (#2C(0)2(D),#2C(1)2(D),,#2C(p11)2(D))=(#2C(0)2(D),#2C(1)2(D),,#2C(p11)2(D)), (3.4)

    and

     #2C(p1)2(D)>#2C(p1)2(D). (3.5)

    Recall the definitions of #2C(p)2(D) and #2C(p)2(Dt), we have

    1p+1#2C(p)2(D)=#{γHq:γU(Db),B2(Dt,γ)=p+1}=#{γHq1:B2(Dt,γ)=p+1}#{γHk:B2(Dt,γ)=p+1}, (3.6)

    where the second equality is due to B2(Dt,γ)=0 for any γFqq. From (3.6), it is obtained that

    Pp=01p+1#2C(p)2(D)=Pp=0#{γHq1:B2(Dt,γ)=p+1}Pp=0#{γHk:B2(Dt,γ)=p+1}=#{Hq1Hk}=2q12k. (3.7)

    By (3.2), (3.3) and (3.7), it is obtained that

    Pp=01p+1#2C(p)2(D)=p02p=01p+1#2C(p)2(D)+1p0#2C(p01)2(D)=2q12k. (3.8)

    Similarly, for D we have

    Pp=01p+1#2C(p)2(D)=Pp=0#{γHq1:B2(Dt,γ)=p+1}Pp=0#{γU(Db)Hq1:B2(Dt,γ)=p+1}=2q11#{U(Db)Hq1}. (3.9)

    From (3.2)–(3.5), we obtain

    Pp=01p+1#2C(p)2(D)=p11p=01p+1#2C(p)2(D)+1p1+1#2C(p1)2(D)<p11p=01p+1#2C(p)2(D)+1p1+1#2C(p1)2(D)p11p=01p+1#2C(p)2(D)+Pp=p11p+1#2C(p)2(D)=Pp=01p+1#2C(p)2(D).

    Then it leads to #{U(Db)Hq1}<2k1 from Eqs (3.8) and (3.9). This contradicts Lemma 2 (ii) and completes the proof of (i).

    For (ii). From Lemma 1, if DbFqq, then #{U(Db)Fqq}2k2r+1 which implies U(Db)Dt. This is not allowed as has been pointed out. Therefore, if r+1kq2, there should be DbHq1. According to Lemma 2 (iii), if Db is an s-projection of Hk+1, then U(Db)=Hk+1. The remainder of the proof is similar to that of (i) and omitted. This completes the proof.

    In the following, an example is provided to illustrate the constructions of B2-GMC 2nm:2s designs with 5N16+1nN2.

    Example 1. Consider constructing B2-GMC 2127:22 and 2127:29 designs. For both B2-GMC designs to be constructed, we have q=nm=5, N=32 and r=2 as 22N2n231. The values of the parameters N and n satisfy 5N16+1nN2. Therefore, to construct these two B2-GMC designs, Dt should be the last 12 columns of F55.

    For the case 2127:22, we have s=2 which gives k=1. Therefore, we should choose 2 block columns according to Theorem 1 (i) as k<r. Without loss of generality, let Db1={1,5} be the 2-projection of H1F52. Then D=(Dt,Db1) is a B2-GMC 2127:22 design.

    For the case 2127:29, we have s=9 which gives k=3. Therefore, we should choose 9 block columns according to Theorem 1 (ii) as k>r. Without loss of generality, let Db2={1,2,12,3,13,23,123,4,14} be the 9-projection of H4. Then D=(Dt,Db2) is a B2-GMC 294:29 design.

    Similar to the discussion in the first paragraph of Section 3.1, when constructing B2-GMC designs with n>N2, we should also first maximize #1C2(Dt). Suppose the number of columns in HqDt satisfies 2rN1n2r+11 for some rq2. According to the first paragraph of Section 3.2 in [22], when n>N2, if Dt maximizes #1C2(Dt), then HqDtHr+1 up to isomorphism. This implies that DbHr+1.

    Suppose 2ks2k+11 for some kq2. According to Lemma 2 (i) and (ii) combined with Lemma 1, we have #U(Db)2k+11 no matter DbFqq= or DbFqq. Therefore, there should be k=r with N1n=2k+11 or k<r, otherwise U(Db)Dt. For the case of k=r with N1n=2k+11, it is trivial since Dt=HqHk+1 and thus DbHk+1. This obtains that the design D=(Dt,Db) with Dt=HqHk+1 and Db being any s-projection of Hk+1 is a B2-GMC design. The following theorem considers the constructions of B2-GMC designs for the case of k<r.

    Theorem 2. Suppose D=(Dt,Db) is a 2nm:2s design with 2rN1n2r+11 for some rq2 and 2ks2k+11 for some k<r. The design D=(Dt,Db) is a B2-GMC design if Dt consists of the last n columns of Hq and Db is any s-projection of Hk+1.

    Proof. Let D=(Dt,Db) be a 2nm:2s design with HqDtHr+1. Then we have U(Db)HqDt. From Lemma 3 of [22],

    B2(Dt,γ)={nN/2, if   γHqHr+1,B2(ˉDt,γ)+N22r, if   γHr+1,

    where ˉDt=HqDt.

    By Lemma 2 (iv), if Db is any s-projection of Hk+1, then U(Db)=Hk+1. Suppose γ0 is the last column of Hk+1 in Yates order and B2(Dt,γ0)=p0, where p0N22r>nN21. From Lemma 3, if γHk+1, then B2(Dt,γ)p0. By the definition of #2C(p)2(D), when pp02, we have 1p+1p01 and

    #2C(p)2(D)=(p+1)#{γHq:γU(Db),B2(Dt,γ)=p+1}=(p+1)#{γHq:B2(Dt,γ)=p+1}=#2C(p)2(Dt). (3.10)

    From Lemma 3, for any γHqHk+1, we have B2(Dt,γ)p0. Therefore, when pp0, we have

    #2C(p)2(D)=(p+1)#{γHq:γU(Db),B2(Dt,γ)=p+1}=0. (3.11)

    Since Dt consists of the last n columns of Hq, from Theorem 3 of [22], Dt is a GMC 2nm design. Then Dt sequentially maximizes (2.2). Recall that #1C(p)2(D)=#1C(p)2(Dt) for p=0,1,,P. Thus, (3.10) implies that D maximizes

    (#1C(0)2(D),#1C(1)2(D),#1C(P)2(D),#2C(0)2(D),#2C(1)2(D),#2C(p02)2(D))

    among all D.

    Suppose D is not a B2-GMC design, then there exists a D and some p1p01 such that

    (#1C(0)2(D),#1C(1)2(D),,#1C(P)2(D),#2C(0)2(D),#2C(1)2(D),#2C(p11)2(D))=(#1C(0)2(D),#1C(1)2(D),,#1C(P)2(D),#2C(0)2(D),#2C(1)2(D),,#2C(p11)2(D))

    and #2C(p1)2(D)>#2C(p1)2(D). With a similar argument to the proof of Theorem 1 (i), such a D results in #{U(Db)Hq1}<2k+11 which contradicts Lemma 2 (i). This completes the proof.

    In the following, an example is provided to illustrate the constructions of B2-GMC 2nm:2s designs with n>N2.

    Example 2. Consider constructing B2-GMC 295:22 and 2128:23 designs.

    For the case 295:22, we have q=nm=4, N=16 and r=2 as 22Nn1231. Since s=2, we obtain k<r as k=1. According to Theorem 3.2, let Dt1 be the last 9 columns of H4, and Db1={1,2} be a 2-projection of H2. Then D=(Dt1,Db1) is a B2-GMC 295:22 design.

    For the case 2128:23, we have q=nm=4, N=16 and r=1 as 21Nn1221. Since s=3, we obtain k=r as k=1. As discussed in the second paragraph in Section 3.2, let Dt2 be the last 12 columns of H4, and Db2={1,2,12}=H2. Then D=(Dt2,Db2) is a B2-GMC 2128:23 design.

    Regular two-level factorial designs are widely used in factorial experiments. Inhomogeneity of the units has bad influences on estimating the treatment effects when size of experimental units is large. To reduce such bad influences, a useful way is to block the experimental units into categories. As has been pointed out in [1], there are two types of blocking problems. One is the single block variable problem and the other is the multi block variables problem. In the last decades, the single block variable problem was maturely investigated in the literature.

    As has been exemplified in Section 1, multi block variables problem is more widely encountered in practice compared to the blocking problem with a single block variable. However, the studies on multi block variables problem are relatively primitive. The GMC criterion is welcome in the situations where the importance ordering of treatment effects is present. Zhang et al. [18] proposed the B2-GMC criterion for choosing optimal blocked regular two-level designs. Construction methods on B2-GMC designs can only be found in [19] in which the B2-GMC designs of some n from 5N16+1nN2 are constructed. In this paper, the B2-GMC designs of n all over 5N16+1nN1 are systemically constructed. The structures of the constructed B2-GMC designs are concise and easy to implement.

    This work was supported by the National Natural Science Foundation of China (Grant No. 11801331).

    The authors declare that there is no conflict of interest.

    We only need to prove #{U(Db)Fqq}2k for the case of s=2k. Recall that

    U(Db)=Db{γHq:γ=ab,a,bDb}.

    We have

    U(Db)Fqq=(DbFqq)({γHq:γ=ab,a,bDb}Fqq).

    Denote A=DbFqq and E=DbHq1. Then Db=AE, AE=,

    {γHq:γ=ab,a,bDb}Fqq=AE,

    and

    U(Db)Fqq=AAE,

    where AE={γHq:γ=ab,aA,bE}. Thus, it suffices to prove

    #{AAE}2k (A.1)

    for #A+#E=2k.

    Regarding to the columns in A and E, there are two cases:

    (B1) #A>#E, or

    (B2) #A#E.

    The global line of the remaining proof for Lemma 1 is as follows. In Lemma A.1, we first show that if (A.1) holds for the case (B1), then (A.1) holds for the case (B2). Afterwards, with Lemma A.2–A.5, we prove that (A.1) indeed holds for the case (B1).

    Lemma A.1. Suppose that (A.1) holds for the case (B1), then (A.1) holds for the case (B2).

    Proof. For the A and E in the case (B2), without loss of generality, we suppose qA. Then,

    #{AAE}=#{A({IN}E)}=#{(qA)({q}qE)}=#{({IN}˜A)˜E}=#{˜E˜E˜A},

    where ˜A=q(Aq)Hq1 and ˜E={q}qEFqq.

    Note that #˜A=#A12k11, #˜E=#E+12k1+1 and #˜A+#˜E=2k, then #˜E>#˜A and the case (B2) converts into the case (B1). Therefore, if (A.1) holds for the case (B1), then (A.1) holds for the case (B2). This completes the proof.

    In the following, we only need to prove that (A.1) holds for the case (B1).

    For A in the case (B1), we have #A2k1+1. Then, A has at least k+1 independent columns. We suppose A has h+1(k+1) independent columns. Let e denote the hth independent column in Hq in Yates order. Up to isomorphism, A can be expressed as

    A=A1{ea1,ea2,,eav}, (A.2)

    where A1 has h independent columns with A1Fqh and {a1,a2,,av}Fqh.

    For E in the case (B1), if #E=0, then #A=2k and (A.1) holds. In the following, we consider only 1#E2k11. Up to isomorphism, there are three cases for the columns in E:

    (C1) all the columns are from Hh1;

    (C2) some columns are from Hh1 and the others are from HhHh1;

    (C3) some columns are from Hh1, some are from HhHh1 and the others are from HqHh.

    We first consider the case (C2) with h>k. Note that AAE=A({IN}E). Denote B={IN}E. Then B can be represented as

    B=B1{eb1,eb2,,ebw}, (A.3)

    where INB1, B1{IN}Hh1 and {b1,,bw}Hh1.

    Recall that 2k1<#A2k1<2h1, we can always find a column rHh1 or r=IN such that at least one column in {ea1,ea2,,eav}, say ea1, satisfies (re)(ea1)=ra1FqhA1. Without loss of generality, we assume that there is some t1(1t1v) such that re{ea1,,eat1}FqhA1 and re{eat1+1,,eav}A1. Meanwhile, there is some t2(0t2w) such that re{eb1,,ebt2}Hh1B1 and re{ebt2+1,,ebw}B1.

    Let A2={eat1+1,,eav} and A3={ea1,,eat1}. Then,

    A=A1A2A3. (A.4)

    Let B2={ebt2+1,,ebw} and B3={eb1,,ebt2}. Then,

    B=B1B2B3. (A.5)

    Let

    A=A1A2A3 (A.6)

    and

    B=B1B2B3, (A.7)

    where A3=reA3 and B3=reB3.

    Lemma A.2. Suppose A,B,A and B are defined as in (A.4)–(A.7), respectively, then #{AB}#{AB}.

    Proof. Let

    Q1=(A1B2)(A2B1),Q2=(A1B1)(A3B3),Q3=(A1B3)(A3B1),Q4=(A2B3)(A3B2),Q3=(A3B2)(A2B3),Q4=(A1B3)(A3B1).

    Since reA2A1 and reB2B1, we have A2B2Q2. Thus

    AB=Q1Q2(A2B2)Q3Q4=Q1Q2Q3Q4.

    Since Q1Q3Fq(h+1)Fqh and Q2Q4Fqh are mutually exclusive, thus

    #{AB}=#{Q1Q3}+#{Q2Q4}=#Q1+#Q2+#Q3+#Q4#{Q1Q3}#{Q2Q4}.

    Note that A3B3=A3B3, then

    AB=Q1Q2(A2B2)Q3Q4=Q1Q2Q3Q4.

    Since Q1Q3Fq(h+1)Fqh and Q2Q4Fqh are mutually exclusive, thus

    #{AB}=#{Q1Q3}+#{Q2Q4}=#Q1+#Q2+#Q3+#Q4#{Q1Q3}#{Q2Q4}=#Q1+#Q2+#Q3+#Q4#{Q1Q3}#{Q2Q4},

    where the third equality is due to reQ3=Q4 and reQ4=Q3. Therefore, to prove Lemma A.2, it suffices to prove

    #{Q1Q3}+#{Q2Q4}#{Q1Q3}+#{Q2Q4}

    or equivalently

    #{Q2Q4}#{Q2Q4}#{Q1Q3}#{Q1Q3}. (A.8)

    Note that A2reA1 and B2reB1, then

    A2B3(reA1)B3=A1B3

    and

    A3B2A3(reB1)=(reA3)B1=A3B1

    which implies that Q4Q4. Similarly, we can obtain Q3Q3. Therefore, (A.8) is equivalent to

    #{(Q2Q4)(Q2Q4)}#{(Q1Q3)(Q1Q3)}. (A.9)

    Thus, we only need to prove (A.9).

    For the left hand side of (A.9), we have

    #{(Q2Q4)(Q2Q4)}=#{Q2(Q4Q4)}.

    For the right hand side of (A.9), we have

    #{(Q1Q3)(Q1Q3)}=#{Q1(Q3Q3)}=#{(reQ1)(reQ3reQ3)}=#{(reQ1)(Q4Q4)}.

    Since reQ1=(A1(reB2))((reA2)B1)A1B1, we have reQ1Q2. Then (A.9) holds. This completes the proof of Lemma A.2.

    Remark 1. Lemma A.2 indicates that for any A defined in (A.2) and B defined in (A.3), we can always find A, which has less columns out of Fqh than A, and B, which has no more columns out of Hh1 than B, such that #{AB}#{AB}. Repeatedly applying Lemma A.2, we can finally find AFqh and B, which has no more columns out of Hh1 than B, such that #{AB}#{AB}.

    For simplicity of notation, we still denote A as A and B as B. Then we can assume that AFqh. Note that there might be t2=0 in the procedure above. Then, following Remark 1, B has the following cases:

    (D1) B{IN}Hh1, or

    (D2) (B{IN})(HhHh1),

    For the case (D2), we write B as B=B1{eb1,,ebt3}, where INB1, B1{IN}Hh1 and {b1,,bt3}Hh1. Note that 1#(B{IN})2k11<2h11. We can always find a column r1Hh1 or r1=IN such that at least one column in {eb1,,ebt3}, say eb1, satisfies (r1e)(eb1)=r1b1Hh1B1. Without loss of generality, suppose there is some t4 with 1t4t3 such that r1e{eb1,,ebt4}Hh1B1 and r1e{ebt4+1,,ebt3}B1. Denote B2={ebt4+1,,ebt3} and B3={eb1,,ebt4}, then

    B=B1B2B3. (A.10)

    Denote

    B=B1B2B3, (A.11)

    where B3=r1eB3.

    Lemma A.3. Suppose AFqh, B and B are defined as in (A.10) and (A.11), respectively, then #{AB}#{AB}.

    Proof. Note that AB1Fqh and (AB2)(AB3)Fq(h+1)Fqh. Therefore,

    #{AB}=#{AB1}+#{(AB2)(AB3)}=#{AB1}+#{AB2}+#{AB3}#{(AB2)(AB3)}.

    Since (AB1)(AB3)Fqh and AB2Fq(h+1)Fqh, we have

    #{AB}=#{(AB1)(AB3)}+#{AB2}=#{AB1}+#{AB2}+#{AB3}#{(AB1)(AB3)}=#{AB1}+#{AB2}+#{AB3}#{(AB1)(AB3)},

    where the third equality is due to r1e(AB3)=AB3. On one hand,

    #{(AB1)(AB3)}=#{r1e((AB1)(AB3))}=#{(A(r1eB1))(AB3)}. (A.12)

    On the other hand, B2r1eB1, which leads to

    (AB2)(AB3)(A(r1eB1))(AB3). (A.13)

    From (A.12) and (A.13), we obtain that

    #{(AB1)(AB3)}#{(AB2)(AB3)}.

    This implies #{AB}#{AB} and completes the proof.

    Remark 2. By repeatedly applying Lemma A.3, we can finally find BHh1 such that #{AB}#{AB}. This result is also true for the cases (C1) and (C3) with h>k due to the following reasons. When {ea1,ea2,,eav}=, the case (C2) reduce to the case (C1). For the case (C3), with a similar argument to Lemma A.3, we can find a T with INT and (TIN)Hh such that #{AT}#{AB}. Then the case (C3) reduces to case (C2).

    The following remark considers the case of h=k.

    Remark 3. For h=k, up to isomorphism, AFq(k+1). In this situation, h should equal to k in the cases (C1), (C2) and (C3). Especially, in the cases (C1) and (C2), E is already a subset of Hk. By repeatedly applying Lemma A.3 to E in the case (C3), we can find a set, say P, such that PHk and #{A({IN}P)}#{A({IN}E)}.

    In summary, for any A and B defined in (A.2) and (A.3), by repeatedly applying Lemma A.2 and A.3, we can always find AFq(k+1) and B{IN}Hk with INB, such that #{AB}#{AB}. Next, we denote A as A and B as B and prove that #{AB}=2k for any AFq(k+1), B{IN}Hk with INB and #A+#B=2k. We first introduce a useful lemma from [24].

    Denote IS as the set consisting of the distinct columns generated by taking component-wise products of any two columns of S.

    Lemma A.4. Let S be an s-subset of Fqq, s=2k1+δ3 and 0<δ2k1, then #IS2k1, where the equality holds when the number of independent columns of S is k+1.

    Lemma A.5. Suppose AFq(k+1), INB, BINHk with #A+#B=2k+1 and kq2, then #{AB}=2k.

    Proof. Without loss of generality, suppose e is the (k+1)th independent column in Hq. Then #{AB}=#{A(e\bmqB)} and e\bmqBFq(k+2)Fq(k+1). Next, we show #{A(e\bmqB)}=2k. Since Ae\bmqB=, we have #{Ae\bmqB}=#A+#{e\bmqB}=2k+1 and Ae\bmqB has k+2 independent columns. By Lemma A.4, we have

    #IAe\bmqB=#{IAI\bmeqB(A(eqB))}=2k+11. (A.14)

    Note that IAIeqBHk then #{IAIeqB}2k1, and A(eqB)Hk+1Hk then #{A(eqB)}2k. From (A.14), there should be #{IAIeqB}=2k1 and #{A(eqB)}=2k. This completes the proof.

    Proof of Lemma 1. According to the proofs of Lemma A.2, A.3 and A.5, we can immediately obtain that #{U(Db)Fqq}2k. This completes the proof.



    [1] N. Rodríguez, On the global well-posedness theory for a class of PDE models for criminal activity, Phys. D Nonlinear Phenom., 260 (2013), 191–200. https://doi.org/10.1016/j.physd.2012.08.003 doi: 10.1016/j.physd.2012.08.003
    [2] M. Short, M. D'Orsogna, V. Pasour, G. Tita, P. Brantingham, A. Bertozzi, et al., A statistical model of criminal behavior, Math. Mod. Meth. Appl. Sci., 18 (2008), 1249–1267. https://doi.org/10.1142/S0218202508003029 doi: 10.1142/S0218202508003029
    [3] M. Short, A. Bertozzi, P. Brantingham, G. Tita, Dissipation and displacement of hotspots in reaction-diffusion model of crime, Proc. Natl. Acad. Sci. USA, 107 (2010), 3961–3965. https://doi.org/0.1073/pnas.0910921107 doi: 10.1073/pnas.0910921107
    [4] H. Berestycki, J. Wei, M. Winter, Existence of symmetric and asymmetric spikes for a crime hotspot model, SIAM J. Math. Anal., 46 (2014), 691–719. https://doi.org/10.1137/130922744 doi: 10.1137/130922744
    [5] R. Cantrell, C. Cosner, R. Manásevich, Global bifurcation of solutions for crime modeling equations, SIAM J. Math. Anal., 44 (2012), 1340–1358. https://doi.org/10.1137/110843356 doi: 10.1137/110843356
    [6] Y. Gu, Q. Wang, G. Yi, Stationary patterns and their selection mechanism of urban crime models with heterogeneous near-repeat victimization effect, Eur. J. Appl. Math., 28 (2017), 141–178. https://doi.org/10.1017/S0956792516000206 doi: 10.1017/S0956792516000206
    [7] T. Kolokolnikov, M. Ward, J. Wei, The stability of steady-state hot-spot patterns for a reaction-diffusion model of urban crime, Discrete Contin. Dyn. Syst. Ser. B, 19 (2014), 1373–1410. https://doi.org/10.3934/dcdsb.2014.19.1373 doi: 10.3934/dcdsb.2014.19.1373
    [8] D. Lloyd, H. O'Farrell, On localised hotspots of an urban crime model, Phys. D Nonlinear Phenom., 253 (2013), 23–39. https://doi.org/10.1016/j.physd.2013.02.005 doi: 10.1016/j.physd.2013.02.005
    [9] D. Lloyd, N. Santitissadeekorn, M. Short, Exploring data assimilation and forecasting issues for an urban crime model, Eur. J. Appl. Math., 27 (2016), 451–478. https://doi.org/10.1017/S0956792515000625 doi: 10.1017/S0956792515000625
    [10] L. Mei, J. Wei, The existence and stability of spike solutions for a chemotax is system modeling crime pattern formation, Math. Models Methods Appl. Sci., 30 (2020), 1727–1764. https://doi.org/10.1142/S0218202520500359 doi: 10.1142/S0218202520500359
    [11] M. Short, A. Bertozzi, P. Brantingham, Nonlinear patterns in urban crime: hotspots, bifurcations, and suppression, SIAM J. Appl. Dyn. Syst., 9 (2010), 462–483. https://doi.org/10.1137/090759069 doi: 10.1137/090759069
    [12] W. Tse, M. Ward, Hotspot formation and dynamics for a continuum model of urban crime, Eur. J. Appl. Math., 27 (2016), 583–624. https://doi.org/10.1017/S0956792515000376 doi: 10.1017/S0956792515000376
    [13] N. Rodríguez, A. Bertozzi, Local existence and uniqueness of solutions to a PDE model for criminal behavior, Math. Models Methods Appl. Sci., 20 (2010), 1425–1457. https://doi.org/10.1142/S0218202510004696 doi: 10.1142/S0218202510004696
    [14] N. Rodríguez, M. Winkler, On the global existence and qualitative behavior of one-dimensional solutions to a model for urban crime, Eur. J. Appl. Math., 33 (2022), 919–959. https://doi.org/10.1017/S0956792521000279 doi: 10.1017/S0956792521000279
    [15] Q. Wang, D. Wang, Y. Feng, Global well-posedness and uniform boundedness of urban crime models: One-dimensional case, J. Differ. Equations, 269 (2020), 6216–6235. https://doi.org/10.1016/j.jde.2020.04.035 doi: 10.1016/j.jde.2020.04.035
    [16] M. Freitag, Global solutions to a higher-dimensional system related to crime modeling, Math. Meth. Appl. Sci., 41 (2018), 6326–6335. https://doi.org/10.1002/mma.5141 doi: 10.1002/mma.5141
    [17] J. Shen, B. Li, Mathematical analysis of a continuous version of statistical models for criminal behavior, Math. Meth. Appl. Sci., 43 (2020), 409–426. https://doi.org/10.1002/mma.5898 doi: 10.1002/mma.5898
    [18] J. Ahn, K. Kang, J. Lee, Global well-posedness of logarithmic Keller-Segel type systems, J. Differ. Equations, 287 (2021), 185–211. https://doi.org/10.1016/j.jde.2021.03.053 doi: 10.1016/j.jde.2021.03.053
    [19] Y. Tao, M. Winkler, Global smooth solutions in a two-dimensional cross-diffusion system modeling propagation of urban crime, Commun. Math. Sci., 19 (2021), 829–849. https://doi.org/10.4310/CMS.2021.v19.n3.a12 doi: 10.4310/CMS.2021.v19.n3.a12
    [20] M. Winkler, Global solvability and stabilization in a two-dimensional cross-diffusion system modeling urban crime propagation, Ann. Inst. Henri Poincaré, Anal. Non Linéaire, 36 (2019), 1747–1790. https://doi.org/10.1016/j.anihpc.2019.02.004 doi: 10.1016/j.anihpc.2019.02.004
    [21] Y. Jiang, L. Yang, Global solvability and stabilization in a three-dimensional cross-diffusion system modeling urban crime propagation, Acta Appl. Math., 178 (2022). https://doi.org/10.1007/s10440-022-00484-z doi: 10.1007/s10440-022-00484-z
    [22] N. Rodríguez, M. Winkler, Relaxation by nonlinear diffusion enhancement in a two-dimensional cross-diffusion model for urban crime propagation, Math. Models Methods Appl. Sci., 30 (2020), 2105–2137. https://doi.org/10.1142/S0218202520500396 doi: 10.1142/S0218202520500396
    [23] F. Heihoff, Generalized solutions for a system of partial differential equations arising from urban crime modeling with a logistic source term, Z. Für Angew. Math. Phys., 71 (2020). https://doi.org/10.1007/s00033-020-01304-w doi: 10.1007/s00033-020-01304-w
    [24] B. Li, L. Xie, Generalized solution to a 2D parabolic-parabolic chemotaxis system for urban crime: Global existence and large time behavior, submitted for publication, 2022.
    [25] P. Jones, P. Brantingham, L. Chayes, Statistical models of criminal behavior: The effects of law enforcement actions, Math. Models Methods Appl. Sci., 20 (2010), 1397–1423. https://doi.org/10.1142/S0218202510004647 doi: 10.1142/S0218202510004647
    [26] A. Pitcher, Adding police to a mathematical model of burglary, Eur. J. Appl. Math., 21 (2010), 401–419. https://doi.org/10.1017/S0956792510000112 doi: 10.1017/S0956792510000112
    [27] J. Zipkin, M. Short, A. Bertozzi, Cops on the dots in a mathematical model of urban crime and police response, Discrete Contin. Dyn. Syst. Ser. B, 19 (2014), no. 5, 1479–1506. https://doi.org/10.3934/dcdsb.2014.19.1479 doi: 10.3934/dcdsb.2014.19.1479
    [28] W. Tse, M. Ward, Asynchronous instabilities of crime hotspots for a 1-D reaction-diffusion model of urban crime with focused police patrol, SIAM J. Appl. Dyn. Syst., 17 (2018), 2018–2075. https://doi.org/10.1137/17M1162585 doi: 10.1137/17M1162585
    [29] A. Buttenschoen, T. Kolokolnikov, M. Ward, J. Wei, Cops-on-the-dots: the linear stability of crime hotspots for a 1-D reaction-diffusion model of urban crime, Eur. J. Appl. Math., 31 (2020), 871–917. https://doi.org/10.1017/S0956792519000305 doi: 10.1017/S0956792519000305
    [30] B. Li, L. Xie, Global large-data generalized solutions to a two-dimensional chemotaxis system stemming from crime modelling, Discrete Contin. Dyn. Syst. Ser. B, 2022 (2022). https://doi.org/10.3934/dcdsb.2022167 doi: 10.3934/dcdsb.2022167
    [31] N. Rodríguez, Q. Wang, L. Zhang, Understanding the effects of on- and off-hotspot policing: Evidence of hotspot, oscillating, and chaotic activities, SIAM J. Appl. Dyn. Syst., 20 (2021), 1882–1916. https://doi.org/10.1137/20M1359572 doi: 10.1137/20M1359572
    [32] E. Keller, L. Segel, Initiation of slime mold aggregation viewed as an instability, J. Theor. Biol., 26 (1970), 399–415. https://doi.org/10.1016/0022-5193(70)90092-5 doi: 10.1016/0022-5193(70)90092-5
    [33] K. Fujie, Boundedness in a fully parabolic chemotaxis system with strongly singular sensitivity, J. Math. Anal. Appl., 424 (2015), 675–684. https://doi.org/10.1016/j.jmaa.2014.11.045 doi: 10.1016/j.jmaa.2014.11.045
    [34] M. Winkler, Global solutions in a fully parabolic chemotaxis system with singular sensitivity, Math. Methods Appl. Sci., 34 (2011), 176–190. https://doi.org/10.1002/mma.1346 doi: 10.1002/mma.1346
    [35] J. Lankeit, A new approach toward boundedness in a two-dimensional parabolic chemotaxis system with singular sensitivity, Math. Meth. Appl. Sci., 39 (2016), 394–404. https://doi.org/10.1002/mma.3489 doi: 10.1002/mma.3489
    [36] C. Stinner, M. Winkler, Global weak solutions in a chemotaxis system with large singular sensitivity, Nonliear Anal. Real Word Appl., 12 (2011), 3727–3740. https://doi.org/10.1016/j.nonrwa.2011.07.006 doi: 10.1016/j.nonrwa.2011.07.006
    [37] J. Lankeit, M. Winkler, A generalized solution concept for the Keller-Segel system with logarithmic sensitivity: global solvability for large nonradial data, NoDEA-Nonlinear Differ. Equ. Appl., 24 (2017). https://doi.org/10.1007/s00030-017-0472-8 doi: 10.1007/s00030-017-0472-8
    [38] A. Zhigun, Generalised supersolutions with mass control for the Keller-Segel system with logarithmic sensitivity, J. Math. Anal. Appl., 467 (2018), 1270–1286. https://doi.org/10.1016/j.jmaa.2018.08.001 doi: 10.1016/j.jmaa.2018.08.001
    [39] M. Winkler, Unlimited growth in logarithmic Keller-Segel systems, J. Differ. Equations, 309 (2022), 74–97. https://doi.org/10.1016/j.jde.2021.11.026 doi: 10.1016/j.jde.2021.11.026
    [40] M. Winkler, T. Yokota, Stabilization in the logarithmic Keller-Segel system, Nonlinear Anal., 170 (2018), 123–141. https://doi.org/10.1016/j.na.2018.01.002 doi: 10.1016/j.na.2018.01.002
    [41] J. Ahn, Global well-posedness and asymptotic stabilization for chemotaxis system with signal-dependent sensitivity, J. Differ. Equations, 266 (2019), 6866–6904. https://doi.org/10.1016/j.jde.2018.11.015 doi: 10.1016/j.jde.2018.11.015
    [42] Q. Hou, Z. Wang, K. Zhao, Boundary layer problem on a hyperbolic system arising from chemotaxis, J. Differ. Equations, 261 (2016), 5035–5070. https://doi.org/10.1016/j.jde.2016.07.018 doi: 10.1016/j.jde.2016.07.018
    [43] H. Jin, J. Li, Z. Wang, Asymptotic stability of traveling waves of a chemotaxis model with singular sensitivity, J. Differ. Equations, 255 (2013), 193–219. doilinkhttps://doi.org/10.1016/j.jde.2013.04.002 doi: 10.1016/j.jde.2013.04.002
    [44] H. Li, K. Zhao, Initial-boundary value problems for a system of hyperbolic balance laws arising from chemotaxis, J. Differ. Equations, 258 (2015), 302–308. https://doi.org/10.1016/j.jde.2014.09.014 doi: 10.1016/j.jde.2014.09.014
    [45] J. Li, T. Li, Z. Wang, Stability of traveling waves of the Keller-Segel system with logarithmic sensitivity, Math. Models Methods Appl. Sci., 24 (2014), 2819–2849. https://doi.org/10.1142/S0218202514500389 doi: 10.1142/S0218202514500389
    [46] Y. Tao, L. Wang, Z. Wang, Large-time behavior of a parabolic-parabolic chemotaxis model with logarithmic sensitivity in one dimension, Discrete Contin. Dyn. Syst. Ser. B, 18 (2013), 821–845. https://doi.org/10.3934/dcdsb.2013.18.821 doi: 10.3934/dcdsb.2013.18.821
    [47] Z. Wang, Z. Xiang, P. Yu, Asymptotic dynamics in a singular chemotaxis system modeling onset of tumor angiogenesis, J. Differ. Equations, 260 (2016), 2225–2258. https://doi.org/10.1016/j.jde.2015.09.063 doi: 10.1016/j.jde.2015.09.063
    [48] M. Winkler, Renormalized radial large-data solutions to the higher-dimensional Keller-Segel system with singular sensitivity and signal absorption, J. Differ. Equations, 264 (2018), 2310–2350. https://doi.org/10.1016/j.jde.2017.10.029 doi: 10.1016/j.jde.2017.10.029
    [49] M. Winkler, The two-dimensional Keller-Segel system with singular sensitivity and signal absorption: global large-data solutions and their relaxation properties, Math. Models Methods Appl. Sci., 26 (2016), 987–1024. https://doi.org/10.1142/S0218202516500238 doi: 10.1142/S0218202516500238
    [50] B. Li, L. Xie, Generalized solution and its eventual smoothness to a logarithmic Keller-Segel system for criminal activities, submitted for publication, 2022.
    [51] M. Winkler, Large-data global generalized solutions in a chemotaxis system with tensor-valued sensitivities, SIAM J. Math. Anal., 47 (2015), 3092–3115. https://doi.org/dx.doi.org/10.1137/140979708 doi: 10.1137/140979708
    [52] M. Aida, K. Osaka, T. Tsujikawa, M. Mimura, Chemotaxis and growth system with sigular sensitivity function, Nonliear Anal. Real Word Appl., 6 (2005), 323–336. https://doi.org/10.1016/j.nonrwa.2004.08.011 doi: 10.1016/j.nonrwa.2004.08.011
    [53] X. Cao, Global bounded solutions of the higher-dimensional Keller-Segel system under smallness conditions in optimal spaces, Discrete Contin. Dyn. Syst., 35 (2015), 1891–1904. https://doi.org/10.3934/dcds.2015.35.1891 doi: 10.3934/dcds.2015.35.1891
    [54] M. Winkler, Aggregation vs. global diffusive behavior in the higher-dimensional Keller-Segel model, J. Differ. Equations, 248 (2010), 2889–2905. https://doi.org/10.1016/j.jde.2010.02.008 doi: 10.1016/j.jde.2010.02.008
    [55] J. Simon, Compact sets in the space Lp(0,T;B), Ann. Mat. Pura Appl., 146 (1996), 65–96. https://doi.org/10.1007/BF01762360 doi: 10.1007/BF01762360
    [56] O. Ladyzhenskaya, N. Ural'tseva, Linear and Quasilinear Elliptic Equations, Translated from the Russian by Scripta Technica, Inc. Translation editor: Leon Ehrenpreis, Academic Press, New York-London, 1968.
  • This article has been cited by:

    1. Yuna Zhao, Gengxin Sun, General Minimum Lower-Order Confounding Designs with Multi-Block Variables, 2021, 2021, 1563-5147, 1, 10.1155/2021/5548102
  • Reader Comments
  • © 2023 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(1698) PDF downloads(97) Cited by(6)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog