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Review

mTOR signaling in proteostasis and its relevance to autism spectrum disorders

  • Received: 24 October 2016 Accepted: 12 January 2017 Published: 19 January 2017
  • Proteins are extremely labile cellular components, especially at physiological temperatures. The appropriate regulation of protein levels, or proteostasis, is essential for all cells. In the case of highly polarized cells like neurons, proteostasis is also crucial at synapses, where quick confined changes in protein composition occur to support synaptic activity and plasticity. The accurate regulation of those cellular processes controlling protein synthesis and degradation is necessary for proteostasis, and its deregulation has deleterious consequences in brain function. Alterations in those cellular mechanisms supporting synaptic protein homeostasis have been pinpointed in autism spectrum disorders such as tuberous sclerosis, neurofibromatosis 1, PTEN-related disorders, fragile X syndrome, MECP2 disorders and Angelman syndrome. Proteostasis alterations in these disorders share the alterations in mechanistic/mammalian target of rapamycin (mTOR) signaling pathway, an intracellular pathway with key synaptic roles. The aim of the present review is to describe the recent literature on the major cellular mechanisms involved in proteostasis regulation in the synaptic context, and its association with mTOR signaling deregulations in various autism spectrum disorders. Altogether, the cellular and molecular mechanisms in synaptic proteostasis could be the foundation for novel shared therapeutic strategies that would take advantage of targeting common disorder mechanisms.

    Citation: Judit Faus-Garriga, Isabel Novoa, Andrés Ozaita. mTOR signaling in proteostasis and its relevance to autism spectrum disorders[J]. AIMS Biophysics, 2017, 4(1): 63-89. doi: 10.3934/biophy.2017.1.63

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  • Proteins are extremely labile cellular components, especially at physiological temperatures. The appropriate regulation of protein levels, or proteostasis, is essential for all cells. In the case of highly polarized cells like neurons, proteostasis is also crucial at synapses, where quick confined changes in protein composition occur to support synaptic activity and plasticity. The accurate regulation of those cellular processes controlling protein synthesis and degradation is necessary for proteostasis, and its deregulation has deleterious consequences in brain function. Alterations in those cellular mechanisms supporting synaptic protein homeostasis have been pinpointed in autism spectrum disorders such as tuberous sclerosis, neurofibromatosis 1, PTEN-related disorders, fragile X syndrome, MECP2 disorders and Angelman syndrome. Proteostasis alterations in these disorders share the alterations in mechanistic/mammalian target of rapamycin (mTOR) signaling pathway, an intracellular pathway with key synaptic roles. The aim of the present review is to describe the recent literature on the major cellular mechanisms involved in proteostasis regulation in the synaptic context, and its association with mTOR signaling deregulations in various autism spectrum disorders. Altogether, the cellular and molecular mechanisms in synaptic proteostasis could be the foundation for novel shared therapeutic strategies that would take advantage of targeting common disorder mechanisms.


    To evaluate a lot of genuine life troubles, MADM is the most dominant and feasible procedure from the decision-making strategy to survive with intricate data in authentic life scenarios. Under the beneficial processes of MADM conception, a lot of people have employed it in the circumstance of diverse territories. But, to accomplish with more than two sorts of opinions, Zadeh [1] firstly invented such sort of idea which includes more than two sorts of opinions in the shape of truth grade (TG) is called fuzzy set (FS). A large number of scholars have modified the conception of FS were to present the intuitionistic FS (IFS) [2], soft set (SS) [3], N-SS [4], fuzzy N-SS [5,6], Hesitant N-SS [7], bipolar valued FS (BFS) [8] and bipolar SS (BSS) [9]. All these theories have their importance but IFS proves to be a valuable procedure to convey the awkward fuzzy knowledge because it has TG TCfif(b) and falsity grade (FG) FCfif(b), with 0TCfif(b)+FCfif(b)1. Further, Yager [10] diagnosed another well-known conception of Pythagorean FS (PFS), with a valuable technique 0T2Cfpf(b)+F2Cfpf(b)1. Some implementations have been invented by distinct peoples likewise, correlation coefficient [11], different sort of methods [12], Pythagorean m-polar FSs [13], TOPSIS methods [14], and Chebyshev measure [15] by using the PFSs.

    The conception of FS was extended by Ramot et al. [16]. They initiated the technique of complex FS (CFS), signifying TG TCfcp(b)=TCfRT(b)ei2π(TCfIT(b)), with TCfRT(b),TCfIT(b)[0,1]. But in a lot of scenarios, they are unable to identify real-life dilemmas. In that spot, the complex IFS (CIFS), invented by Alkouri and Salleh [17]. CIFS includes the TG TCfcp(b)=TCfRT(b)ei2π(TCfIT(b)) and FG FCfcp(b)=FCfRT(b)ei2π(FCfIT(b)), with 0TCfRT(b)+FCfRT(b)1,0TCfIT(b)+FCfIT(b)1. Several modifications are illustrated here: for instance, CIF classes [18], CIF graph [19], CIF soft sets [20], CIF aggregation operators (AOs) [21], CIF quaternion number [22], CIF group [23], CIF algebraic structure [24]. Further, Ullah et al. [25] diagnosed the complex PFS (CPFS), which includes a new well-known strategy in the shape 0T2CfRT(b)+F2CfRT(b)1,0T2CfIT(b)+F2CfIT(b)1. A lot of applications have been employed by Akram and Naz [26], Akram and Sattar [27], Akram et al. [28], Ma et al. [29], Akram and Khan [30], and Garg [31].

    IFSs, PFSs, CIFSs, and CPFSs have achieved a lot of well-wishes from the side of scholars under the very well-known techniques of all prevailing conceptions. The distinct sort of implementations are stated in the shape: geometric AOs for IFSs [32], Hamacher AOs for IFSs [33], frank power AOs for IFSs [34], Heronian AOs for IFSs [35], Harmonic AOs for IFSs [36], Bonferroni AOs for IFSs [37], Prioritized AOs for IFSs [38], Power AOs for IFSs [39], Maclaurin symmetric mean for IFSs [40], AOs for PFSs [41], Einstein AOs for PFSs [42], Hamacher AOs for PFSs [43], Choquet frank AOs for PFSs [44], Heronian AOs for IFSs [45], Bonferroni AOs for PFSs [46], Prioritized AOs for PFSs [47], Power AOs for IFSs [48], Maclaurin symmetric mean for PFSs [49]. Under the above circumstances, a decision-making strategy always includes a lot of major hurdles as:

    1) How to demonstrate the data on the appropriate procedures to clarify the data.

    2) How to calculate the distinct attribute objects and arrange the generally favorite quantity.

    3) How to study the beneficial ideal from a lot of collections in the shape of alternatives.

    Hence, the major contribution of the theme is to invent the beneficial decision-making strategy under CPFSs by using the AOs for CLs. The prevailing conceptions are the specific parts of the invented CPFSs under implementing the distinct sort techniques. In CPFS, the intellectual faced two sorts of theory in the shape of real and unreal terms, which can help to decision-maker for taking a beneficial decision. But the prevailing IFSs, PFSs have a grip on one aspect at a time due to their weak mathematical structure. The unreal terms have been completely missed in these two ideas and due to these issues, the decision-maker loses a lot of data during the decision-making procedure. The importance of the unreal term in CPFS is that if an expert wants to lunch the car enterprise based on the well-known considerations in the shape of its name of the car and production dates. Here, the name of the car shows the real part, and the production of the car date shows the unreal part. For managing with such sort of scenario, the technique of IFS and PFS has been unsuccessful. For this, we consider the well-known conception of CPFS to try to demonstrate the beneficial results. The key factors of the invented works are implemented in the shape:

    1) To analyze some new operational laws based on CLs for CPF setting.

    2) To demonstrate the concept of CCPFWA, CCPFOWA, CCPFWG, and CCPFOWG operators are invented.

    3) Several significant features of the invented works are also diagnosed.

    4) To investigate the beneficial optimal from a large number of alternatives, a MADM analysis is analyzed based on CPF data. A lot of examples are demonstrated based on invented works to evaluate the supremacy and ability of the initiated works.

    5) To identify the sensitive analysis and merits of the invented works with the help of comparative analysis and they're graphical shown.

    The sensitive analysis of the prevailing and proposed works is diagnosed in the shape of Table 1.

    Table 1.  Show the invented works are more powerful is compared to prevailing works.
    Model IFSs PFSs CIFSs CPFSs
    Geometric AOs ×
    Hamacher AOs × ×
    frank power AOs × ×
    Heronian AOs × ×
    Harmonic AOs × ×
    Bonferroni AOs ×
    Prioritized AOs ×
    Power AOs ×
    Maclaurin symmetric mean × ×
    Einstein AOs × ×
    Proposed work × ×

     | Show Table
    DownLoad: CSV

    In Table 1, we easily get that the symbol " " stated the operators were developed someone and the symbol ×, stated the operators cannot be developed by anyone up to date. We know that the conception of CPFS is very well-known and interesting, but up to date, no one has employed it in the region of any sorts of operators to investigate the feasibility and consistency of the invented works. For more convenience, we illustrated Figure 1, which stated the invented works.

    Figure 1.  Stated the practical structure of the invented works.

    Lastly, the main characteristics of the CPFSs under a lot of points are illustrated in Table 2.

    Table 2.  Stated the sensitivity analysis.
    Model Uncertainty Falsity Hesitation Periodicity 2-D information Square in Power
    FSs × × × × ×
    IFSs × × ×
    PFSs × × ×
    CFSs × × ×
    CIFSs ×
    CPFSs

     | Show Table
    DownLoad: CSV

    Likewise, a narrative MADM process in the viewpoint of these recommended operators is constructed in which rankings are characterized in conditions of CPFNs. The feasibility down with the prevalence of methodology is illustrated over a genuine-life mathematical statistic and verified by differing their results with many prevalent approaches.

    This analysis is constructed in the shape: Section 2 includes some prevailing concepts of CPFSs and their related properties. Section 3 includes analyzing some new operational laws based on CLs for the CPF setting. Moreover, to demonstrate the closeness between finite numbers of alternatives, the conception of CCPFWA, CCPFOWA, CCPFWG, and CCPFOWG operators are invented. Several significant features of the invented works are also diagnosed. Section 4 investigates the beneficial optimal from a large number of alternatives, a MADM analysis is analyzed based on CPF data. A lot of examples are demonstrated based on invented works to evaluate the supremacy and ability of the initiated works. For massive convenience, the sensitivity analysis and merits of the identified works are also explored with the help of comparative analysis and they're graphically shown. The conclusion is referred to in Section 5.

    Several prevailing studies under the CIFSs, CPFSs, and their related properties. The mathematical symbol Uuni, diagnosed the universal set with TG and FG in the shape: TCfcp(b)=TCfRT(b)ei2π(TCfIT(b)) and FCfcp(b)=FCfRT(b)ei2π(FCfIT(b)). For the convenience of the reader, here we reviewed the conception of CIFSs, initiated by Alkouri and Salleh [17].

    Definition 1. [17] A mathematical structure of CIFSs Cfci, diagnosed by:

    Cfci={(TCfci(b),FCfci(b))/bUuni} (1)

    With a well-known characteristic 0TCfRT(b)+FCfRT(b)1 and 0TCfIT(b)+FCfIT(b)1. The mathematical shape of CIFN is diagnosed by: Cfcpj=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2,,nt.

    Definition 2. [17] Assume Cfcij=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2, stated two CIFNs, then

    Cfci1Cffci2=(max(TCfRT1(b),TCfRT2(b))ei2π(max(TCfIT1(b),TCfIT2(b))),min(FCfRT1(b),FCfRT2(b))ei2π(min(FCfIT1(b),FCfIT2(b)))) (2)
    Cfci1Cfci2=(min(TCfRT1(b),TCfRT2(b))ei2π(min(TCfIT1(b),TCfIT2(b))),max(FCfRT1(b),FCfRT2(b))ei2π(max(FCfIT1(b),FCfIT2(b)))) (3)
    Cfci1Cfci2=((TCfRT1(b)+TCfRT2(b)TCfRT1(b)TCfRT2(b))ei2π(TCfIT1(b)+TCfIT2(b)TCfIT1(b)TCfIT2(b)),FCfRT1(b)FCfRT2(b)ei2π(FCfIT1(b)FCfIT2(b))) (4)
    Cfci1Cfci2=(TCfRT1(b)TCfRT2(b)ei2π(TCfIT1(b)TCfIT2(b)),(FCfRT1(b)+FCfRT2(b)FCfRT1(b)FCfRT2(b))ei2π(FCfIT1(b)+FCfIT2(b)FCfIT1(b)FCfIT2(b))) (5)
    ΘscCfci1=((1(1TCfRT1(b))Θsc)ei2π(1(1TCfIT1(b))Θsc),FΘscCfRT1(b)ei2π(FΘscCfIT1(b))) (6)
    CfΘscci1=(TΘscCfRT1(b)ei2π(TΘscCfIT1(b)),(1(1FCfRT1(b))Θsc)ei2π(1(1FCfIT1(b))Θsc)) (7)

    Definition 3. [17] Assume Cfcij=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2, stated two CIFNs. The mathematical structure of score value (SV) is diagnosed by:

    Ssv(Cfcij)=TCfRT(b)FCfRT(b)+TCfIT(b)FCfIT(b)2[1,1] (8)

    Similarly, the mathematical structure of accuracy value (AV) is diagnosed by:

    Hav(Cfcij)=TCfRT(b)+FCfRT(b)+TCfIT(b)+FCfIT(b)2 (9)

    Definition 4. [17] Assume Cfcij=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2, stated two CIFNs. Then some techniques are diagnosed here, if Ssv(Cfcp1)>Ssv(Cfcp2)Cfcp1>Cfcp2;

    and if Ssv(Cfcp1)=Ssv(Cfcp2) then Hav(Cfcp1)>Hav(Cfcp2)Cfcp1>Cfcp2;

    and Hav(Cfcp1)=Hav(Cfcp2)Cfcp1=Cfcp2.

    For Hav(Cfcpj)[0,1].

    Further, several limitations lie in the technique of CIFSs, for this, here we reviewed the conception of CPFSs, initiated by Ullah et al. [25].

    Definition 5. [25] A mathematical structure of CPFSs Cfcp, diagnosed by:

    Cfcp={(TCfcp(b),FCfcp(b))/bUuni} (10)

    With a well-known characteristic 0T2CfRT(b)+F2CfRT(b)1 and 0T2CfIT(b)+F2CfIT(b)1. The mathematical shape of CPFN is diagnosed by: Cfcpj=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2,,nt. If TCfITj=FCfRTj=FCfITj=0 in Eq (10), then we get FSs, if TCfITj=FCfITj=0 in Eq (10), then we get PFSs, if TCfITj=FCfITj=0 , with putting "1" instead of "2", in Eq (10), then we get IFSs. Moreover, if FCfRTj=FCfITj=0 in Eq (10), then we get CFSs, by putting "1" instead of "2", in Eq (10), then we get CIFSs. Figure 2, which states the real structure of the unit disc in the complex plane.

    Figure 2.  Stated the geometrical shape of the unit disc.

    Definition 6. [25] Assume Cfcpj=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2, stated two CPFNs, then

    Cfcp1Cfcp2=(max(TCfRT1(b),TCfRT2(b))ei2π(max(TCfIT1(b),TCfIT2(b))),min(FCfRT1(b),FCfRT2(b))ei2π(min(FCfIT1(b),FCfIT2(b)))) (11)
    Cfcp1Cfcp2=(min(TCfRT1(b),TCfRT2(b))ei2π(min(TCfIT1(b),TCfIT2(b))),max(FCfRT1(b),FCfRT2(b))ei2π(max(FCfIT1(b),FCfIT2(b)))) (12)
    Cfcp1Cfcp2=((T2CfRT1(b)+T2CfRT2(b)T2CfRT1(b)T2CfRT2(b))12ei2π(T2CfIT1(b)+T2CfIT2(b)T2CfIT1(b)T2CfIT2(b))12,FCfRT1(b)FCfRT2(b)ei2π(FCfIT1(b)FCfIT2(b))) (13)
    Cfcp1Cfcp2=(TCfRT1(b)TCfRT2(b)ei2π(TCfIT1(b)TCfIT2(b)),(F2CfRT1(b)+F2CfRT2(b)F2CfRT1(b)F2CfRT2(b))12ei2π(F2CfIT1(b)+F2CfIT2(b)F2CfIT1(b)F2CfIT2(b))12) (14)
    ΘscCfcp1=((1(1T2CfRT1(b))Θsc)12ei2π(1(1T2CfIT1(b))Θsc)12,FΘscCfRT1(b)ei2π(FΘscCfIT1(b))) (15)
    CfΘsccp1=(TΘscCfRT1(b)ei2π(TΘscCfIT1(b)),(1(1F2CfRT1(b))Θsc)12ei2π(1(1F2CfIT1(b))Θsc)12) (16)

    Definition 7. [25] Assume Cfcpj=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2, stated two CPFNs. The mathematical structure of SV is diagnosed by:

    Ssv(Cfcpj)=T2CfRT(b)F2CfRT(b)+T2CfIT(b)F2CfIT(b)2[1,1] (17)

    Similarly, the mathematical structure of AV is diagnosed by:

    Hav(Cfcpj)=T2CfRT(b)+F2CfRT(b)+T2CfIT(b)+F2CfIT(b)2 (18)

    Definition 8. [25] Assume Cfcpj=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2, stated two CPFNs. Then some techniques are diagnosed here, if Ssv(Cfcp1)>Ssv(Cfcp2)Cfcp1>Cfcp2;

    and if Ssv(Cfcp1)=Ssv(Cfcp2) then Hav(Cfcp1)>Hav(Cfcp2)Cfcp1>Cfcp2;

    and Hav(Cfcp1)=Hav(Cfcp2)Cfcp1=Cfcp2.

    For Hav(Cfcpj)[0,1].

    This study includes demonstrating the closeness between a finite number of alternatives, the conception of CCPFWA, CCPFOWA, CCPFWG, and CCPFOWG operators are invented. Several significant features of the invented works are also diagnosed. In this all work, we used the CPFNs by Cfcpj=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))),j=1,2,,nt, and Δ:j be the CL of Cfcpj with 0Δ:j1. The weight vector is diagnosed by: ωwc={ωwc1,ωwc2,,ωwcnt} with ntj=1ωwcj=1,ωwcj[0,1].

    Definition 9. The CCPFWA operator is diagnosed by:

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=ntj=1ωwcj(Δ:jCfcpj)=ωwc1(Δ:1Cfcp1)ωwc2(Δ:2Cfcp2),ωwcnt(Δ:ntCfcpnt) (19)

    Several specific cases are gotten after the implementation of distinct techniques, for instance, if Δ:j=0 in Eq (19), then we get CPF weighted averaging operator. We got the theory of the CIF weighted averaging operator by changing the value of "2" in Eq (19) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (19), then we get PF weighted averaging operator. We got the theory of IF weighted averaging operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (19) into "1".

    Theorem 1. Under Eq (19), we diagnosed the Eq (20), such that

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=
    ((1ntj=1(1T2CfRTj)Δ:jωwcj)12ei2π(1ntj=1(1T2CfITj)Δ:jωwcj)12,ntj=1FΔ:jωwcjCfRTjei2π(ntj=1FΔ:jωwcjCfITj)) (20)

    Proof: Under the consideration of mathematical induction, in Eq (20), we fix nt=2, we gotten

    ωwc1(Δ:1Cfcp1)=((1(1T2CfRT1)Δ:1ωwc1)12ei2π(1(1T2CfIT1)Δ:1ωwc1)12,FΔ:1ωwc1CfRT1ei2π(FΔ:1ωwc1CfIT1))
    ωwc2(Δ:2Cfcp2)=((1(1T2CfRT2)Δ:2ωwc2)12ei2π(1(1T2CfIT2)Δ:2ωwc2)12,FΔ:2ωwc2CfRT2ei2π(FΔ:2ωwc2CfIT2))
    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2))=ωwc1(Δ:1Cfcp1)ωwc2(Δ:2Cfcp2)=((12j=1(1T2CfRTj)Δ:jωwcj)12ei2π(12j=1(1T2CfITj)Δ:jωwcj)12,2j=1FΔ:jωwcjCfRTjei2π(2j=1FΔ:jωwcjCfITj))

    In Eq (20), we fix nt=k, because for nt=2, hold.

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpk,Δ:k))=((1kj=1(1T2CfRTj)Δ:jωwcj)12ei2π(1kj=1(1T2CfITj)Δ:jωwcj)12,kj=1FΔ:jωwcjCfRTjei2π(kj=1FΔ:jωwcjCfITj))

    For nt=k+1, we have gotten

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpk+1,Δ:k+1))=CCQROFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpk,Δ:k))ωwck+1(Δ:ntCfcpk+1)=((1kj=1(1T2CfRTj)Δ:jωwcj)12ei2π(1kj=1(1T2CfITj)Δ:jωwcj)12,kj=1FΔ:jωwcjCfRTjei2π(kj=1FΔ:jωwcjCfITj))
    ((1(1T2CfRTk+1)Δ:k+1ωwck+1)12ei2π(1(1T2CfITk+1)Δ:k+1ωwck+1)12,FΔ:k+1ωwck+1CfRTk+1ei2π(FΔ:k+1ωwck+1CfITk+1))
    =((1k+1j=1(1T2CfRTj)Δ:jωwcj)12ei2π(1k+1j=1(1T2CfITj)Δ:jωwcj)12,k+1j=1FΔ:jωwcjCfRTjei2π(k+1j=1FΔ:jωwcjCfITj))

    Equation (20) holds for all possible values of nt.

    If Δ:j=0 in Eq (20), then we get CPF weighted averaging operator. We got the theory of the CIF weighted averaging operator by changing the value of "2" in Eq (20) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (20), then we get PF weighted averaging operator. We got the theory of IF weighted averaging operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (20) into "1".

    The conception of Idempotency, boundedness and monotonicity are illustrated below.

    Property 1. For fixing the value of Cfcpj=Cfcp=(TCfRT(b)ei2π(TCfIT(b)),FCfRT(b)ei2π(FCfIT(b))), then

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=Δ:Cfcp (21)

    Proof: Assume that Cfcpj=Cfcp=(TCfRT(b)ei2π(TCfIT(b)),FCfRT(b)ei2π(FCfIT(b))) i.e., TCfRT=TCfRTj,TCfIT=TCfITj,FCfRT=FCfRTj,FCIT=FCfITj, and Δ:=Δ:j, then

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))
    =((1ntj=1(1T2CfRT)Δ:jωwcj)12ei2π(1ntj=1(1T2CfIT)Δ:jωwcj)12,ntj=1FΔ:jωwcjCfRTei2π(ntj=1FΔ:jωwcjCfIT))
    =((1(1T2CfRT)Δ:ntj=1ωwcj)12ei2π(1(1T2CfIT)Δ:ntj=1ωwcj)12,FΔ:ntj=1ωwcjCfRTei2π(FΔ:ntj=1ωwcjCfIT))
    =((1(1T2CfRT)Δ:)12ei2π(1(1T2CfIT)Δ:)12,FΔ:CfRTei2π(FΔ:CfIT))=Δ:Cfcp

    Property 2. For fixing the value of

    Cfcpj=(minjTCfRTj(b)ei2π(minjTCfITj(b)),maxjFCfRTj(b)ei2π(maxjFCfITj(b))) and

    Cf+cpj=(maxjTCfRTj(b)ei2π(maxjTCfITj(b)),minjFCfRTj(b)ei2π(minjFCfITj(b))), then

    CfcpjCCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))Cf+cpj (22)

    Proof: Assume that

    Cfcpj=(minjTCfRTj(b)ei2π(minjTCfITj(b)),maxjFCfRTj(b)ei2π(maxjFCfITj(b))) and

    Cf+cpj=(maxjTCfRTj(b)ei2π(maxjTCfITj(b)),minjFCfRTj(b)ei2π(minjFCfITj(b))), then

    minjT2CfRTjT2CfRTjmaxjT2CfRTj1minjT2CfRTj1T2CfRTj1maxjT2CfRTj
    ntj=1(1minjT2CfRTj)minjΔ:jωwcjntj=1(1T2CfRTj)Δ:jωwcjntj=1(1maxjT2CfRTj)maxjΔ:jωwcj
    (1ntj=1(1minjT2CfRTj)minjΔ:jωwcj)12(1ntj=1(1T2CfRTj)Δ:jωwcj)12(1ntj=1(1maxjT2CfRTj)maxjΔ:jωwcj)12

    In the same way, we have

    (1ntj=1(1minjT2CfITj)minjΔ:jωwcj)12(1ntj=1(1T2CfITj)Δ:jωwcj)12
    (1ntj=1(1maxjT2CfITj)maxjΔ:jωwcj)12

    In the same way, we have

    ntj=1maxjFmaxjΔ:jωwcjCfRTjntj=1FΔ:jωwcjCfRTjntj=1minjFminjΔ:jωwcjCfRTj
    ntj=1maxjFmaxjΔ:jωwcjCfITjntj=1FΔ:jωwcjCfITjntj=1minjFminjΔ:jωwcjCfITj

    Under Eq (22), we obtained

    CfcpjCCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))Cf+cpj

    Property 3. Assume that CfcpjCfcpj, i.e., TCfRTjTCfRTj,TCfITjTCfITj,FCfRTjFCfRTj, and FCfITjFCfITj then

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt)) (23)

    Proof: Assume that CfcpjCfcpj, i.e., TCfRTjTCfRTj,TCfITjTCfITj,FCfRTjFCfRTj, and FCfITjFCfITj then

    1T2CfRTj1T2CfRTjntj=1(1T2CfRTj)Δ:jωwcjntj=1(1T2CfRTj)Δ:jωwcj
    (1ntj=1(1T2CfRTj)Δ:jωwcj)12(1ntj=1(1T2CfRTj)Δ:jωwcj)12

    In the same way, we demonstrated

    (1ntj=1(1T2CfITj)Δ:jωwcj)12(1ntj=1(1T2CfITj)Δ:jωwcj)12
    ntj=1FΔ:jωwcjCfRTjnti=1FΔ:jωwcjCfRTj,ntj=1FΔ:jωwcjCfITjnti=1FΔ:jωwcjCfITj

    Hence, we demonstrated CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=Cfcp,CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=Cfcp and Eq (17), some axioms are illustrated Here:

    1) If SSV(Cfcp)>SSV(Cfcp)Cfcp>Cfcp i.e., CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))<CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt));

    2) If SSV(Cfcp)=SSV(Cfcp)Cfcp=Cfcp i.e., CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt)), then by considering Eq (18), we demonstrated

    i. If HAV(Cfcp)>HAV(Cfcp)Cfcp>Cfcp i.e., CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))<CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt));

    ii. If HAV(Cfcp)=HAV(Cfcp)Cfcp=Cfcp i.e., CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt)).

    In last we determined

    CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))CCPFWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))

    Definition 10. The CCPFOWA operator is exhibited by:

    CCPFOWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))
    =ntj=1ωwcj(Δ:jCfcpj)=ωwc1(Δ:σ(1) CfCpσ(1))ωwc2(Δ:σ(2)CfCpσ(2)),
    ωwcnt(Δ:σ(nt)CfCpσ(nt)) (24)

    where σ(j) of (j=1,2,,nt), invented the permutations with σ(j1)σ(j). Several specific cases are gotten after the implementation of distinct techniques, for instance, if Δ:j=0 in Eq (24), then we get CPF ordered weighted averaging operator. We got the theory of CIF ordered weighted averaging operator by changing the value of "2" in Eq (24) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (24), then we get PF ordered weighted averaging operator. We got the theory of IF ordered weighted averaging operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (24) into "1".

    Theorem 2: Under Eq (24), we get

    CCPFOWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=((1ntj=1(1T2CfRTσ(j))Δ:σ(j)ωwcj)12ei2π(1ntj=1(1T2CfITσ(j))Δ:σ(j)ωwcj)12,ntj=1FΔ:σ(j)ωwcjCfRTσ(j)ei2π(ntj=1FΔ:σ(j)ωwcjCfITσ(j))) (25)

    If Δ:j=0 in Eq (25), then we get CPF ordered weighted averaging operator. We got the theory of CIF ordered weighted averaging operator by changing the value of "2" in Eq (25) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (25), then we get PF ordered weighted averaging operator. We got the theory of IF ordered weighted averaging operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (25) into "1". Idempotency, boundedness, and monotonicity, stated the properties for Eq (26).

    Property 4. If TCfRT=TCfRTj,TCfIT=TCfITj,FCfRT=FCfRTj,FCfIT=FCfITj, and Δ:=Δ:j, then

    CCPFOWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=Δ:Cfcp (26)

    Property 5. If Cfcpj=(minjTCfRTj(b)ei2π(minjTCfITj(b)),maxjFCfRTj(b)ei2π(maxjFCfITj(b))) and Cf+cpj=(maxjTCfRTj(b)ei2π(maxjTCfITj(b)),minjFCfRTj(b)ei2π(minjFCfITj(b))), then

    CfcpjCCPFOWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))Cf+cpj (27)

    Property 6. If CfcpjCfcpj, i.e., TCfRTjTCfRTj,TCfITjTCfITj,FCfRTjFCfRTj, and FCfITjFCfITj then

    CCPFOWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))
    CCPFOWA((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt)) (28)

    Definition 11. The CCPFWG operator is proved by:

    CCPFWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=ntj=1ωwcj(Δ:jCfcpj)=ωwc1(Δ:1CfCp1)ωwc2(Δ:2CfCp2),,ωwcnt(Δ:ntCfCpnt) (29)

    Several specific cases are gotten after the implementation of distinct techniques, for instance, if Δ:j=0 in Eq (29), then we get CPF weighted geometric operator. We got the theory of CIF weighted geometric operator by changing the value of "2" in Eq (29) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (29), then we get PF weighted geometric operator. We got the theory of IF weighted geometric operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (29) into "1".

    Theorem 3. Under Eq (29), we acquired

    CCPFWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))
    =(ntj=1TΔ:jωwcjCfRTjei2π(ntj=1TΔ:jωwcjCfITj),(1ntj=1(1F2CfRTj)Δ:jωwcj)12ei2π(1ntj=1(1F2CfITj)Δ:jωwcj)12) (30)

    If Δ:j=0 in Eq (30), then we get CPF weighted geometric operator. We got the theory of CIF weighted geometric operator by changing the value of "2" in Eq (30) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (30), then we get PF weighted geometric operator. We got the theory of IF weighted geometric operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (30) into "1". Idempotency, boundedness, and monotonicity stated some properties for Eq (30).

    Property 7. If TCfRT=TCfRTj,TCfIT=TCfITj,FCfRT=FCfRTj,FCfIT=FCfITj, and Δ:=Δ:j, then

    CCPFWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=Δ:Cfcp (31)

    Property 8. If Cfcpj=(minjTCfRTj(b)ei2π(minjTCfITj(b)),maxjFCfRTj(b)ei2π(maxjFCfITj(b))) and Cf+cpj=(maxjTCfRTj(b)ei2π(maxjTCfITj(b)),minjFCfRTj(b)ei2π(minjFCfITj(b))), then

    CfcpjCCPFWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))Cf+cpj (32)

    Property 9. If TCfRTjTCfRTj,TCfITjTCfITj,FCfRTjFCfRTj, and FCfITjFCfITj then

    CCPFWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))
    CCPFWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt)) (33)

    Definition 12. The CCPFOWG operator is confirmed by:

    CCPFOWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=ntj=1ωwcj(Δ:jCfcpj)=ωwc1(Δ:σ(1)CfCpσ(1))ωwc2(Δ:σ(2)CfCpσ(2)),,ωwcnt(Δ:σ(nt)CfCpσ(nt)) (34)

    Several specific cases are gotten after the implementation of distinct techniques, for instance, if Δ:j=0 in Eq (34), then we get CPF ordered weighted geometric operator. We got the theory of CIF ordered weighted geometric operator by changing the value of "2" in Eq (34) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (34), then we get PF ordered weighted geometric operator. We got the theory of IF ordered weighted geometric operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (34) into "1".

    Theorem 4. Under Eq (34), we achieved

    CCPFOWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))
    =(ntj=1TΔ:σ(j)ωwcjCfRTσ(j)ei2π(ntj=1TΔ:σ(j)ωwcjCfITσ(j)),(1ntj=1(1F2CfRTσ(j))Δ:σ(j)ωwcj)12ei2π(1ntj=1(1F2CfITσ(j))Δ:σ(j)ωwcj)12) (35)

    If Δ:j=0 in Eq (35), then we get CPF ordered weighted geometric operator. We got the theory of CIF ordered weighted geometric operator by changing the value of "2" in Eq (35) into "1". If TCfITj(b)=FCfITj(b)=0 in Eq (35), then we get PF ordered weighted geometric operator. We got the theory of IF ordered weighted geometric operator by changing the value of "2" with TCfITj(b)=FCfITj(b)=0, in Eq (35) into "1". Idempotency, boundedness, and monotonicity stated some properties for Eq (35).

    Property 10. If TCfRT=TCfRTj,TCfIT=TCfITj,FCfRT=FCfRTj,FCfIT=FCfITj, and Δ:=Δ:j, then

    CCPFOWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))=Δ:Cfcp (36)

    Property 11.

    If Cfcpj=(minjTCfRTj(b)ei2π(minjTCfITj(b)),maxjFCfRTj(b)ei2π(maxjFCfITj(b))) and Cf+cpj=(maxjTCfRTj(b)ei2π(maxjTCfITj(b)),minjFCfRTj(b)ei2π(minjFCfITj(b))), then

    CfcpjCCPFOWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))Cf+cpj (37)

    Property 12. If TCfRTjTCfRTj,TCfITjTCfITj,FCfRTjFCfRTj, and FCfITjFCfITj then

    CCPFOWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt))
    CCPFOWG((Cfcp1,Δ:1),(Cfcp2,Δ:2),,(Cfcpnt,Δ:nt)) (38)

    Ambiguity and intricacy are involved in every region of life like economics, engineering sciences, computer sciences, and medical sciences. A lot of people have investigated the beneficial ways how to evaluate their solutions. But, in the scenario of fuzzy circumstances, a lot of ambiguity has occurred if someone employed MADM techniques in the scenario of IFSs, PFSs, and CIFSs. The major theme of this theory is to demonstrate the beneficial ways for the selection of the most important and convenient optimal. For this, m alternatives and nt attributes are stated in the shape: {Cfal1,Cfal2,,Cfalm} and {Cfat1,Cfat2,,Cfatnt} with weight vectors ωwc={ωwc1,ωwc2,,ωwcnt} working under the technique ntj=1ωwcj=1,ωwcj[0,1]. For this, someone implemented the matrix D=[Cfalij]m×nt, includes the CPFNs with 0T2CfRT(b)+F2CfRT(b)1,0T2CfIT(b)+F2CfIT(b)1. A Mathematical structure Cfcpj=(TCfRTj(b)ei2π(TCfITj(b)),FCfRTj(b)ei2π(FCfITj(b))), stated the CPFNs.

    Several beneficial stages are diagnosed for demonstrating the qualitative optimal from the family of alternatives.

    Stage 1: In this, we consider the decision matrix which includes some rows and columns in the shape of CPFNs.

    Stage 2: Under the consideration of Eqs (20) and (30), we try to demonstrate the CPFN from the group of CPFNS given in Stage 1.

    Stage 3: Under the consideration of Eq (17), we try to demonstrate the single value from the CPFNS given in Stage 2.

    Stage 4: Explore some order in the shape of ranking values based on score values.

    Stage 5: Elaborate the beneficial optimal.

    COVID-19 is a novel and typical form of coronavirus that is not been before investigated in persons. The COVID-19 is one of the most intellectual and dangerous parts of the disease which was first diagnosed in December 2019. Up to date a lot of people have been affected by it, and many have passed away. When COVID-19 has discovered a lot of scholars have worked to make the best vaccination for it. Nowadays, a lot of vaccines have been found by different countries, but the Chines vaccine has gotten a lot of attention and several people have used it. Several important symptoms are diagnosed here, for instance, coughing, headache, loss of taste, sore throat, and muscle pain. For this, we suggested several alternatives, and their attributes are diagnosed in the shape of symptoms of the COVID-19, have specified below:

    Cfal1: Fever or chills.

    Cfal2: A dry hack and windedness.

    Cfal3: Feeling extremely drained.

    Cfal4: Muscle or body throbs.

    With four criteria in the shape of dangerous symptoms such as:

    Cfat1: Inconvenience relaxing.

    Cfat2: Steady agony or tension in your chest.

    Cfat3: Pale blue lips or face.

    Cfat4: Abrupt disarray.

    Several experts are given their opinions in the shape of (0.4,0.3,0.2,0.1), stated the weight vectors for four alternative and their four attributes. Several beneficial stages are diagnosed for demonstrating the qualitative optimal from the family of alternatives.

    Stage 1: In this, we consider the decision matrix which includes some rows and columns in the shape of CPFNs, stated in Table 3.

    Table 3.  Expressions of the arrangement of the CPFNs.
    Cfat1 Cfat2 Cfat3 Cfat4
    Cfal1 ((0.7ei2π(0.5),0.5ei2π(0.6)),0.8) ((0.71ei2π(0.51),0.51ei2π(0.61)),0.81) ((0.72ei2π(0.52),0.52ei2π(0.62)),0.82) ((0.73ei2π(0.53),0.53ei2π(0.63)),0.83)
    Cfal2 ((0.7ei2π(0.8),0.3ei2π(0.3)),0.7) ((0.71ei2π(0.81),0.31ei2π(0.31)),0.71) ((0.72ei2π(0.82),0.32ei2π(0.32)),0.72) ((0.73ei2π(0.83),0.33ei2π(0.33)),0.73)
    Cfal3 ((0.6ei2π(0.5),0.5ei2π(0.6)),0.8) ((0.61ei2π(0.51),0.51ei2π(0.61)),0.81) ((0.61ei2π(0.51),0.51ei2π(0.61)),0.81) ((0.62ei2π(0.52),0.52ei2π(0.62)),0.82)
    Cfal4 ((0.5ei2π(0.4),0.2ei2π(0.4)),0.8) ((0.51ei2π(0.41),0.21ei2π(0.41)),0.81) ((0.52ei2π(0.42),0.22ei2π(0.42)),0.82) ((0.53ei2π(0.43),0.23ei2π(0.43)),0.83)

     | Show Table
    DownLoad: CSV

    Stage 2: Under the consideration of Eqs (20) and (30), we try to demonstrate the CPFN from the group of CPFNS given in Stage 1, stated in Table 4.

    Table 4.  Aggregated values of the information are in Table 3.
    Method CCPFWA CCPFWG
    Cfal1 (0.6587ei2π(0.4656),0.5796ei2π(0.6701)) (0.7578ei2π(0.5796),0.4656ei2π(0.5607))
    Cfal2 (0.6266ei2π(0.7294),0.4354ei2π(0.4354)) (0.7842ei2π(0.8611),0.2634ei2π(0.2634))
    Cfal3 (0.5568ei2π(0.4618),0.578ei2π(0.6684)) (0.6684ei2π(0.578),0.4618ei2π(0.5568))
    Cfal4 (0.4656ei2π(0.3724),0.2824ei2π(0.4857)) (0.5796ei2π(0.4857),0.1898ei2π(0.3724))

     | Show Table
    DownLoad: CSV

    Stage 3: Under the consideration of Eq (17), we try to demonstrate the single value from the CPFNS given in Stage 2, conferred in Table 5.

    Table 5.  Expressions of the score values.
    Method CCPFWA CCPFWG
    Cfal1 0.0672 0.1895
    Cfal2 0.2728 0.6089
    Cfal3 0.1288 0.1288
    Cfal4 0.0199 0.1986

     | Show Table
    DownLoad: CSV

    Stage 4: Explore some order in the shape of ranking values based on score values, conferred in Table 6.

    Table 6.  Expressions of ranking values.
    Method Ranking values
    CCPFWA Cfal2Cfal4Cfal1Cfal3
    CCPFWG Cfal2Cfal4Cfal1Cfal3

     | Show Table
    DownLoad: CSV

    Stage 5: Elaborate the beneficial optimal, which is Cfal2. Moreover, Figure 3 states the practicality of the data in Table 5.

    Figure 3.  The practicality of the data is in Table 5.

    To evaluate the practicality of the invented works, we suggested several data from [17]. A lot of details are available in the prevailing works [17], for evaluating the feasibility and dominancy of the invented works, we suggested the data in Table 2 from [17], which includes the CIFNs. Then the final accumulated values are available in Table 7, under the weight vector (0.4,0.3,0.2,0.1), with the value of Δ:i,i=1,2,3,4,5, stated in the shape {0.8,0.81,0.82,0.83}. Under the consideration of Eqs (20) and (30), we try to demonstrate the CIFN from the group of CIFNS given in Table 2, stated in Table 7.

    Table 7.  Aggregated values of the information in Table 2 in [17].
    Method CCPFWA CCPFWG
    Cfal1 (0.65ei2π(0.6962),0.1735ei2π(0.2574)) (0.7262ei2π(0.4933),0.1145ei2π(0.2327))
    Cfal2 (0.5639ei2π(0.7074),0.3197ei2π(0.2636)) (0.6242ei2π(0.7483),0.2306ei2π(0.2185))
    Cfal3 (0.5462ei2π(0.6148),0.3024ei2π(0.2048)) (0.6499ei2π(0.702),0.2257ei2π(0.1426))
    Cfal4 (0.4994ei2π(0.5861),0.3709ei2π(0.2269)) (0.4829ei2π(0.5856),0.3421ei2π(0.2034))
    Cfal5 (0.4804ei2π(0.2332),0.4086ei2π(0.5341)) (0.5405ei2π(0.2888),0.3993ei2π(0.4592))

     | Show Table
    DownLoad: CSV

    Under the consideration of Eq (17), we try to demonstrate the single value from the CIFNS, conferred in Table 8.

    Table 8.  Expressions of the score values.
    Method CCPFWA CCPFWG
    Cfal1 0.4054 0.3517
    Cfal2 0.3233 0.4243
    Cfal3 0.2715 0.422
    Cfal4 0.202 0.2089
    Cfal5 0.0835 0.0026

     | Show Table
    DownLoad: CSV

    Moreover, Figure 4 states the practicality of the data in Table 8.

    Figure 4.  The practicality of the data in Table 8.

    Explore some order in the shape of ranking values based on score values, conferred in Table 9.

    Table 9.  Expressions of ranking values.
    Method Ranking values
    CCPFWA Cfal1Cfal2Cfal3Cfal4Cfal5
    CCPFWG Cfal1Cfal2Cfal3Cfal4Cfal5

     | Show Table
    DownLoad: CSV

    Elaborate the beneficial optimal, which is Cfal1.

    To evaluate the practicality of the invented works, we suggested several data from [27]. A lot of details are available in the prevailing works [27], for evaluating the feasibility and dominancy of the invented works, we suggested the data in Table 5 from [27], which includes the PFNs. Then the final accumulated values are available in Table 10, under the weight vector (0.4,0.3,0.2,0.1). Under the consideration of Eqs (20) and (30), we try to demonstrate the PFN from the group of PFNS given in Table 5, stated in Table 10.

    Table 10.  Expressions of the score values.
    Method CCPFWA CCPFWG
    Cfal1 0.2267 0.2741
    Cfal2 0.2028 0.2496
    Cfal3 0.3844 0.4194
    Cfal4 0.1766 0.2029
    Cfal5 0.2605 0.2814

     | Show Table
    DownLoad: CSV

    Explore some order in the shape of ranking values based on score values, conferred in Table 11. Moreover, Figure 5 stated the practicality of the data in Table 10.

    Table 11.  Expressions of ranking values.
    Method Ranking values
    CCPFWA Cfal3Cfal5Cfal1Cfal2Cfal4
    CCPFWG Cfal3Cfal5Cfal1Cfal2Cfal4

     | Show Table
    DownLoad: CSV
    Figure 5.  The practicality of the data in Table 10.

    Elaborate the beneficial optimal, which is Cfal3.

    Achievement without complication is very difficult due to ambiguity and rationality which is involved in genuine life dilemmas. MADM technique is one of the beneficial ways to determine our goal. The key technique of our works is to demonstrate the supremacy and effectiveness of the invented works. For this, several suggested works are discussed here: CLs for IFSs [50], CLs for PFSs [31], AOs for CIFSs [21], AOs for CPFSs [28], geometric AOs for IFSs [32], Hamacher AOs for IFSs [33], AOs for PFSs [41], Heronian AOs for IFSs [45], Bonferroni AOs for PFSs [46], and with several invented works are diagnosed in Table 12, under the consideration of data in Example 1. For more convenience, we illustrated Figure 6, which stated the invented works in Table 12.

    Table 12.  Stated the sensitivity analysis.
    Method Score values Ranking values
    Rahman et al. [50] Cannot be calculated Cannot be calculated
    Garg [31] Cannot be calculated Cannot be calculated
    Garg and Rani [21] Cannot be calculated Cannot be calculated
    Akram et al. [28] Ssv(Cfcp1)=0.0450,Ssv(Cfcp2)=0.0506,
    Ssv(Cfcp3)=0.0044,Ssv(Cfcp4)=0.0144
    Cfal2Cfal4Cfal1Cfal3
    Wang and Liu [32] Cannot be calculated Cannot be calculated
    Huang [33] Cannot be calculated Cannot be calculated
    Peng and Yuan [41] Cannot be calculated Cannot be calculated
    Li and Wei [45] Cannot be calculated Cannot be calculated
    Liang et al. [46] Cannot be calculated Cannot be calculated
    CCPFWA Ssv(Cfcp1)=0.0672,Ssv(Cfcp2)=0.2728,
    Ssv(Cfcp3)=0.1288,Ssv(Cfcp4)=0.0199
    Cfal2Cfal4Cfal1Cfal3
    CCPFWG Ssv(Cfcp1)=0.1895,Ssv(Cfcp2)=0.6089,
    Ssv(Cfcp3)=0.1288,Ssv(Cfcp4)=0.1986
    Cfal2Cfal4Cfal1Cfal3

     | Show Table
    DownLoad: CSV
    Figure 6.  The practicality of the data is in Table 12.

    For more convenience, we illustrated Figure 7, which stated the invented works in Table 13.

    Figure 7.  The practicality of the data is in Table 13.
    Table 13.  Stated the sensitivity analysis.
    Method Score Values Ranking Values
    Rahman et al. [50] Cannot be calculated Cannot be calculated
    Garg [31] Cannot be calculated Cannot be calculated
    Garg and Rani [21] Ssv(Cfcp1)=0.3043,Ssv(Cfcp2)=0.2122,
    Ssv(Cfcp3)=0.1604,Ssv(Cfcp4)=0.101
    Ssv(Cfcp5)=0.0724
    Cfal1Cfal2Cfal3Cfal4Cfal5
    Akram et al. [28] Ssv(Cfcp1)=0.5165,Ssv(Cfcp2)=0.4344,
    Ssv(Cfcp3)=0.3826,Ssv(Cfcp4)=0.313 1
    Ssv(Cfcp5)=0.1946
    Cfal1Cfal2Cfal3Cfal4Cfal5
    Wang and Liu [32] Cannot be calculated Cannot be calculated
    Huang [33] Cannot be calculated Cannot be calculated
    Peng and Yuan [41] Cannot be calculated Cannot be calculated
    Li and Wei [45] Cannot be calculated Cannot be calculated
    Liang et al. [46] Cannot be calculated Cannot be calculated
    CCPFWA Ssv(Cfcp1)=0.4054,Ssv(Cfcp2)=0.3233,
    Ssv(Cfcp3)=0.2715,Ssv(Cfcp4)=0.202
    Ssv(Cfcp5)=0.0835
    Cfal1Cfal2Cfal3Cfal4Cfal5
    CCPFWG Ssv(Cfcp1)=0.3517,Ssv(Cfcp2)=0.4243,
    Ssv(Cfcp3)=0.422,Ssv(Cfcp4)=0.2089
    Ssv(Cfcp5)=0.0026
    Cfal1Cfal2Cfal3Cfal4Cfal5

     | Show Table
    DownLoad: CSV

    Under the data in Table 2 from [17], several analyses are diagnosed in Table 13.

    Under the data in Table 5 from [27], several analyses are diagnosed in Table 14.

    Table 14.  Stated the sensitivity analysis.
    Method Score values Ranking values
    Rahman et al. [50] cannot be calculated cannot be calculated
    Garg [31] Ssv(Cfcp1)=0.1156,Ssv(Cfcp2)=0.1017,
    Ssv(Cfcp3)=0.2733,Ssv(Cfcp4)=0.0655
    Ssv(Cfcp5)=0.1504
    Cfal3Cfal5Cfal1Cfal2Cfal4
    Garg and Rani [21] Ssv(Cfcp1)=0.3367,Ssv(Cfcp2)=0.3128,
    Ssv(Cfcp3)=0.4944,Ssv(Cfcp4)=0.2866
    Ssv(Cfcp5)=0.3705
    Cfal3Cfal5Cfal1Cfal2Cfal4
    Akram et al. [28] Ssv(Cfcp1)=0.4467,Ssv(Cfcp2)=0.4228,
    Ssv(Cfcp3)=0.5944,Ssv(Cfcp4)=0.3966
    Ssv(Cfcp5)=0.4805
    Cfal3Cfal5Cfal1Cfal2Cfal4
    Wang and Liu [32] cannot be calculated cannot be calculated
    Huang [33] cannot be calculated cannot be calculated
    Peng and Yuan [41] Ssv(Cfcp1)=0.6452,Ssv(Cfcp2)=0.5817,
    Ssv(Cfcp3)=0.7562,Ssv(Cfcp4)=0.4127
    Ssv(Cfcp5)=0.6677
    Cfal3Cfal5Cfal1Cfal2Cfal4
    Li and Wei [45] Ssv(Cfcp1)=0.5561,Ssv(Cfcp2)=0.4926,
    Ssv(Cfcp3)=0.6671,Ssv(Cfcp4)=0.3236
    Ssv(Cfcp5)=0.5786
    Cfal3Cfal5Cfal1Cfal2Cfal4
    Liang et al. [46] Ssv(Cfcp1)=0.7361,Ssv(Cfcp2)=0.6726,
    Ssv(Cfcp3)=0.8471,Ssv(Cfcp4)=0.5036
    Ssv(Cfcp5)=0.7586
    Cfal3Cfal5Cfal1Cfal2Cfal4
    CCPFWA Ssv(Cfcp1)=0.2267,Ssv(Cfcp2)=0.2028,
    Ssv(Cfcp3)=0.3844,Ssv(Cfcp4)=0.1766
    Ssv(Cfcp5)=0.2605
    Cfal3Cfal5Cfal1Cfal2Cfal4
    CCPFWG Ssv(Cfcp1)=0.2741,Ssv(Cfcp2)=0.2496,
    Ssv(Cfcp3)=0.4194,Ssv(Cfcp4)=0.2029
    Ssv(Cfcp5)=0.2814
    Cfal3Cfal5Cfal1Cfal2Cfal4

     | Show Table
    DownLoad: CSV

    For more convenience, we illustrated Figure 8, which stated the invented works in Table 14.

    Figure 8.  The practicality of the data is in Table 14.

    After a long discussion, we have gotten the result that the invented works are massive feasible, and accurate to demonstrate the value of objects appropriately. Therefore, the invented works under the CPFSs are extensively reliable, and more consistent is compared to existing operators [21,28,31,32,33,41,45,46,50] and in future we will extend to compare with some new works which are discussed in [42,43,44,47,48,49].

    Ambiguity and uncertainty have been involved in several genuine life dilemmas, MADM technique is the most influential part of the decision-making technique to handle inconsistent data which occurred in many scenarios. The major construction of this works is exemplified in the succeeding ways:

    1) We analyzed some new operational laws based on CLs for the CPF setting.

    2) We demonstrated the closeness between a finite number of alternatives, the conception of CCPFWA, CCPFOWA, CCPFWG, and CCPFOWG operators are invented.

    3) Several significant features of the invented works are also diagnosed.

    4) We investigated the beneficial optimal from a large number of alternatives, a MADM analysis is analyzed based on CPF data.

    5) A lot of examples are demonstrated based on invented works to evaluate the supremacy and ability of the initiated works.

    6) For massive convenience, the sensitivity analysis and merits of the identified works are also explored with the help of comparative analysis and they're graphical shown.

    In the upcoming times, we will outspread the idea of complex q-rung orthopair FSs [51,52], complex spherical FSs [53,54], and Spherical fuzzy sets [55,56], etc. to advance the quality of the research works.

    The authors declare that they have no conflicts of interest about the publication of the research article.

    This research was supported by the Researchers Supporting Project number (RSP-2021/244), King Saud University, Riyadh, Saudi Arabia.

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