M | e1 | e2 | N | e1 | e2 |
x1 | ⟨.2,.3,.4⟩ | ⟨.3,.5,.5⟩ | x1 | ⟨.3,.6,.1⟩ | ⟨.4,.5,.4⟩ |
x2 | ⟨.3,.4,.3⟩ | ⟨.6,.2,.4⟩ | x2 | ⟨.6,.5,.2⟩ | ⟨.7,.3,.2⟩ |
x3 | ⟨.3,.5,.2⟩ | ⟨.4,.4,.3⟩ | x3 | ⟨.4,.5,.3⟩ | ⟨.6,.3,.3⟩ |
x4 | ⟨.2,.7,.6⟩ | ⟨.3,.4,.3⟩ | x4 | ⟨.9,1,.4⟩ | ⟨.4,.5,.1⟩ |
Ultrahigh molecular weight polyethene (UHMWPE) is employed as a bearing material in a range of applications due to its improved elasticity, compatibility, and impact resistance, processing conditions for a suitable surface texture are necessary. Surface texture processing on microchannels using lasers is always associated with the effect of heat damage on the polymer specimen surface. This study aims to explore the use of polydimethylsiloxane (PDMS) and polyacrylic acid (PAA) in the form of liquid gel coatings in order to reduce heat damage to surfaces during the laser processing of ultrahigh molecular weight polyethene (UHMWPE). First, PDMS and PAA were coated on the surface of the UHMWPE material specimen, and then texturing was performed using a laser diode and cleaned using the ultrasonic method. Second, the dimensions and texture profiles of all the samples from this study were measured using a confocal microscope and open source software. In addition, the effect of adding liquid gel on the surface at 150 µm thickness and laser power parameters was determined. The results show that the PDMS and PAA liquid gel layers help regulate the dimensional bulge of the fabricated microchannels at laser powers below 6 watts, compared to those produced without the coating.
Citation: Eko Sasmito Hadi, Ojo Kurdi, Ari Wibawa BS, Rifky Ismail, Mohammad Tauviqirrahman. Influence of laser processing conditions for the manufacture of microchannels on ultrahigh molecular weight polyethylene coated with PDMS and PAA[J]. AIMS Materials Science, 2022, 9(4): 554-571. doi: 10.3934/matersci.2022033
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Ultrahigh molecular weight polyethene (UHMWPE) is employed as a bearing material in a range of applications due to its improved elasticity, compatibility, and impact resistance, processing conditions for a suitable surface texture are necessary. Surface texture processing on microchannels using lasers is always associated with the effect of heat damage on the polymer specimen surface. This study aims to explore the use of polydimethylsiloxane (PDMS) and polyacrylic acid (PAA) in the form of liquid gel coatings in order to reduce heat damage to surfaces during the laser processing of ultrahigh molecular weight polyethene (UHMWPE). First, PDMS and PAA were coated on the surface of the UHMWPE material specimen, and then texturing was performed using a laser diode and cleaned using the ultrasonic method. Second, the dimensions and texture profiles of all the samples from this study were measured using a confocal microscope and open source software. In addition, the effect of adding liquid gel on the surface at 150 µm thickness and laser power parameters was determined. The results show that the PDMS and PAA liquid gel layers help regulate the dimensional bulge of the fabricated microchannels at laser powers below 6 watts, compared to those produced without the coating.
Data is a valuable source of knowledge that contains helpful information if exploited effectively [1]. One of the challenges facing data researchers is the ambiguity and uncertainty of the data they have access to, which makes it difficult for them to process information. But these challenges are, in a positive sense, opportunities for the development of new techniques and tools, such as they various approaches based on fuzzy set theory [2]. The advent of fuzzy theory has prompted extensive work on ideas such as fuzzy sets [3], vague sets [4], soft sets [5], and neutrosophic sets [6]. It was originally thought that the development of new theories would eclipse fuzzy theory, but that does not seem to be the case [7]. This research field is becoming more and more active, with a number of fundamental contributions to the rapid development of new theories [8,9]. One of the most prominent applications is the use of fuzzy set theory in emerging and vibrant fields like machine learning [10,11] or topological data analysis [12,13].
In recent years, the study of soft sets [5] and neutrosophic sets [14] has become an attractive research area. Neutrosophic sets recently emerged as a tool for dealing with imprecise, indeterminate, and inconsistent data [15]. In contrast, soft sets show potential for dealing with uncertainties that classical methods cannot control [16]. Combining these two types of sets results in a unique hybrid structure, a neutrosophic soft set (NS-set) [17], for working effectively in uncertain environments. Maji proposed this [17,18] in 2013 and it was modified by Deli and Broumi [19] in 2015. Furthermore, Karaaslan [20] redefined this concept and its operations to be more efficient and complete. Since then, this structure has proved to be quite effective when applied in real life in many fields, such as decision making [17], market prediction [21], and medical diagnosis [22,23].
The topology on NS-sets is one of the issues that needs more attention, alongside neutrosophic topology [24,25] and soft topology [26]. This issue has emerged recently to help complete the overall picture for NS and aid its practical applications based on topology [27,28]. In 2017, Bera and Mahapatra [29] gave general operations to construct a topology on NS-sets. They also presented concepts related to topological space such as interior, closure, neighborhood, boundary, regularity, base, subspace, separation axioms, along with specific illustrations and proofs. In 2018, these authors [30] continued to develop further studies on connectedness and compactness on NS-topological space. In 2019, Ozturk, Aras, and Bayramov [31] introduced a new approach to topology on NS-sets. This approach is quite different from the previous work [29], and was further developed by constructing separation axioms [32] in the same year, 2018. Recently, the continuum [33] or compactness [34] on the topological space generated on NS-sets has also been studied with the same properties as the normal space. Many variations [35] of the topological space on NS-sets have also attracted the attention of researchers, and most of the related works are inspired by topology on neutrosophic and soft sets with the idea of a hybrid structure [36,37].
In this work, we construct the topological space and related concepts on NS-sets through general operations in a way that is very different from the work of Bera and Mahapatra [29,30], but more general than the work of Ozturk, Aras, and Bayramov [31,32], with our operations based on the generality of min and max operations. This work begins by defining two new operations to create the relationships between NS-sets. These relations are then used as the kernel for forming topology and topological relations on NS-sets. One emphasis shown here is on elucidating the relationship between the topology on NS-sets and the component fuzzy soft topologies. All the ideas in this work are presented convincingly and clearly through definitions, theorems, and their consequences.
In summary, the significant contributions of this study are as follows:
(1) Defining two novel concepts, called min−norm and max−norm, to provide a theoretical foundation for determining operations on NS-sets, including intersection, union, difference, AND, and OR.
(2) Constructing the topology, open set, closed set, interior, closure, and regularity concepts on NS-sets based on just determined operations.
(3) Elucidating the relationship between the topology on NS-sets and the fuzzy soft topologies generated by truth, indeterminacy, falsity degrees by the theorems and counterexamples.
(4) The concepts are well-defined, and the theorems are proved convincingly and logically.
This work is organized as follows: Section 1 presents the motivation and introduces the significant contributions. Section 2 briefly introduces NS-sets and related concepts. The two new ideas, min−norm and max−norm, are provided in Section 3 as a theoretical foundation for determining operations on NS-sets, including intersection, union, difference, AND, and OR. In Section 4, the topology on NS-sets is defined with related concepts such as open set, closed set, interior, closure, and regularity. Furthermore, the relationship between the topology on NS-sets and the fuzzy soft topologies generated by truth, indeterminacy, and falsity functions by theorems and counterexamples in Section 5. The last section presents conclusions and future research trends in this area.
This section recalls the NS-set proposed in 2013 by Maji [17,18], then modified and improved in 2015 by Deli and Broumi [19]. This concept is based on combining soft [5] and neutrosophic [6] sets. Some background related to NS-sets is briefly presented below so that readers can better understand the following sections.
Without loss of generality, we consider X to be a universal set, E to be a parameter set, and N(X) to denote the collection of all neutrosophic sets on X.
Definition 1. ([18,19]). The pair (A,E) is a NS-set on X where A:E⟶N(X) is a set valued function determined by e⟼A(e)≔Ae with
Ae:X⟶]−0;1+[×]−0;1+[×]−0;1+[ |
x⟼Ae(x)≔⟨TAe(x);IAe(x);FAe(x)⟩ | (1) |
for all e∈E, and the real function triples TAe,IAe,FAe:X⟶]−0;1+[ indicate truth, indeterminacy, and falsity degrees, respectively, with no restriction on their sum.
In other words, the NS-set can be described as a set of ordered tuples as follows:
(A,E)={(e,A(e)):e∈E,A(e)∈N(X)} | (2) |
={(e,⟨x,TAe(x),IAe(x),FAe(x)⟩):e∈E,x∈X} | (3) |
:={(e,xTAe(x),IAe(x),FAe(x)):e∈E,x∈X}. | (4) |
If nothing changes, the symbol NS(X) indicates the collection of all NS-sets on X. Besides, if the NS-sets consider the same parameter set E, then it is not mentioned repeatedly in order to simplify the notations. Moreover, because the values of T, I, F belong to the unit interval [0;1], the integral part of the values is almost zero. Typically, it may occur that the integer part is omitted (for example, .1 instead of 0.1). Therefore, if it does not lead to confusion, this omitted format of a decimal is always used in all the tables used in this paper.
a. ∅E is a null NS-set if
∀e∈E,∀x∈X,{T∅E(x)=0I∅E(x)=0F∅E(x)=1. | (5) |
b. ∅˜E is a semi-null NS-set if
∃e∈E,∀x∈X,{T∅˜E(x)=0I∅˜E(x)=0F∅˜E(x)=1. | (6) |
c. XE is an absolute NS-set if
∀e∈E,∀x∈X,{TXE(x)=1IXE(x)=1FXE(x)=0. | (7) |
d. X˜E is a semi-absolute NS-set if
∃e∈E,∀x∈X,{TX˜E(x)=1IX˜E(x)=1FX˜E(x)=0. | (8) |
Definition 3. ([19,31]). Let A and B be two NS-sets on X.
a. A is a NS-subset of B,writtenasA⊆B, if
∀e∈E,∀x∈X,{TAe(x)≤TBe(x)IAe(x)≤IBe(x)FAe(x)≥FBe(x). | (9) |
b. A is a NS-superset of B,writtenasA⊇B, if B is a NS-subset of A.
c. ¯A is the complement of A if
∀e∈E,∀x∈X,{T¯Ae(x)=FAe(x)I¯Ae(x)=1−IAe(x)F¯Ae(x)=TAe(x). | (10) |
Example 1. Let two NS-sets M and N be represented in Table 1 as follows:
M | e1 | e2 | N | e1 | e2 |
x1 | ⟨.2,.3,.4⟩ | ⟨.3,.5,.5⟩ | x1 | ⟨.3,.6,.1⟩ | ⟨.4,.5,.4⟩ |
x2 | ⟨.3,.4,.3⟩ | ⟨.6,.2,.4⟩ | x2 | ⟨.6,.5,.2⟩ | ⟨.7,.3,.2⟩ |
x3 | ⟨.3,.5,.2⟩ | ⟨.4,.4,.3⟩ | x3 | ⟨.4,.5,.3⟩ | ⟨.6,.3,.3⟩ |
x4 | ⟨.2,.7,.6⟩ | ⟨.3,.4,.3⟩ | x4 | ⟨.9,1,.4⟩ | ⟨.4,.5,.1⟩ |
Based on Eq (9) of Definition 3, M⊆N.
Example 2. The NS-set P and its complement ¯P are represented according to Eq (10) in Table 2 as follows:
P | e1 | e2 | e3 | ¯P | e1 | e2 | e3 |
x1 | ⟨.9,.8,.2⟩ | ⟨.3,.7,.2⟩ | ⟨.4,.6,.3⟩ | x1 | ⟨.2,.2,.9⟩ | ⟨.2,.3,.3⟩ | ⟨.3,.4,.4⟩ |
x2 | ⟨.7,.6,.2⟩ | ⟨.3,.4,.6⟩ | ⟨.4,.3,.2⟩ | x2 | ⟨.2,.4,.7⟩ | ⟨.6,.6,.3⟩ | ⟨.2,.7,.4⟩ |
x3 | ⟨.3,.3,.5⟩ | ⟨.1,.2,.3⟩ | ⟨.9,.5,.8⟩ | x3 | ⟨.5,.7,.3⟩ | ⟨.3,.8,.1⟩ | ⟨.8,.5,.9⟩ |
Theorem 1. If A∈NS(X),
(1) ¯¯A=A,
(2) ¯∅E=XE,
(3) ¯∅˜E=X˜E,
(4) ¯XE=∅E,
(5) ¯X˜E=∅˜E.
Proof. These properties are directly inferred from the definitions of the null, semi-null, absolute, semi-absolute NS-sets and the complement operation.
In this section, we focus on defining two novel norms, called min−norm and max−norm, as the foundations for determining operations on NS-sets in general. Each operation is well-defined along with its well-proven properties.
Definition 4. A min−norm is the binary operation ∙:[0;1]×[0;1]→[0,1] that obeys the conditions as follows:
(a) ∙ has the commutative and associative properties,
(b) ∀x∈[0,1],x∙1=1∙x=x,
(c) ∀x∈[0,1],x∙0=0∙x=0,
(d) ∀x,y∈[0,1],x≥x∙y.
Definition 5. A max−norm is the binary operation ∘:[0;1]×[0;1]→[0,1] that obeys the following conditions:
(a) ∘ has the commutative and associative properties,
(b) ∀x∈[0,1],x∘1=1∘x=1,
(c) ∀x∈[0,1],y∘0=0∘x=x,
(d) ∀x,y∈[0,1],x≤x∘y.
Definition 6. The min−norm ∙ and max−norm ∘ satisfy De Morgan's law if they obey the following conditions:
∀x,y∈[0,1],(1−x)∘(1−y)=1−x∙y, | (11) |
∀x,y∈[0,1],(1−x)∙(1−y)=1−x∘y. | (12) |
Some commonly used min−norm and max−norm are shown in Table 3. On the other hand, all of these norms satisfy De Morgan's law in pairs.
min−norms | max−norms | |
1 | ∀x,y∈[0,1],x∙y=xy | ∀x,y∈[0,1],x∘y=x+y−xy |
2 | ∀x,y∈[0,1],x∙y=min{x,y} | ∀x,y∈[0,1],x∘y=max{x,y} |
3 | ∀x,y∈[0,1],x∙y=max{x+y−1,0} | ∀x,y∈[0,1],x∘y=min{x+y,1} |
Definition 7. The intersection of the two NS-sets A and B,writtenasA∩B, is determined by
∀e∈E,∀x∈X,{TA∩Be(x)=TAe(x)∙TBe(x)IA∩Be(x)=IAe(x)∙IBe(x)FA∩Be(x)=FAe(x)∘FBe(x). | (13) |
Example 3. Let two NS-sets A and B be represented in Table 4 as follows:
A | e1 | e2 | B | e1 | e2 |
x1 | ⟨.2,.4,.5⟩ | ⟨.5,.2,.8⟩ | x1 | ⟨.7,.2,.7⟩ | ⟨.6,0,.5⟩ |
x2 | ⟨.1,.4,.3⟩ | ⟨.8,.9,.4⟩ | x2 | ⟨.3,.9,.1⟩ | ⟨.7,.7,.9⟩ |
x3 | ⟨.1,.2,.7⟩ | ⟨.8,.9,.4⟩ | x3 | ⟨.6,.4,.7⟩ | ⟨.8,.1,0⟩ |
If using min−norms x∙y=max{x+y−1,0} and max−norms x∘y=min{x+y,1}, the intersection A∩B of the two above NS-sets is described according to Eq (13) in Table 5 as follows:
A∩B | e1 | e2 |
x1 | ⟨0,0,1⟩ | ⟨.1,0,1⟩ |
x2 | ⟨0,.3,.4⟩ | ⟨.5,.6,1⟩ |
x3 | ⟨0,0,1⟩ | ⟨.6,0,.4⟩ |
Theorem 2. If A,B,C∈NS(X),
(1) A∩A=A,
(2) A∩∅E=∅E,
(3) A∩∅˜E=∅˜E,
(4) A∩XE=A,
(5) A∩X˜E=A,
(6) A∩(B∩C)=(A∩B)∩C,
(7) A∩B=B∩A.
Proof. These properties are directly inferred from the definitions of norms and intersection operation.
Definition 8. Let (Ai)i∈I be a collection of NS-sets on X. The intersection of the collection of NS-sets (Ai)i∈I,writtenas⋂i∈IAi, is determined by
∀e∈E,∀x∈X,{T⋂i∈IAie(x)=∙i∈I{TAie(x)}I⋂i∈IAie(x)=∙i∈I{IAie(x)}F⋂i∈IAie(x)=∘i∈I{FAie(x)}. | (14) |
Definition 9. The union of the two NS-sets A and B,writtenasA∪B, is determined by
∀e∈E,∀x∈X,{TA∪Be(x)=TAe(x)∘TBe(x)IA∪Be(x)=IAe(x)∘IBe(x)FA∪Be(x)=FAe(x)∙FBe(x). | (15) |
Example 4. If using min−norms x∙y=max{x+y−1,0} and max−norms x∘y=min{x+y,1}, the union A∪B of the two above NS-sets A and B in Example 3 is described according to Eq (14) in Table 6 as follows:
A∪B | e1 | e2 |
x1 | ⟨.9,.6,.2⟩ | ⟨1,.2,.3⟩ |
x2 | ⟨.4,1,1⟩ | ⟨1,1,.3⟩ |
x3 | ⟨.7,.6,.4⟩ | ⟨1,1,0⟩ |
Theorem 3. If A,B,C∈NS(X),
(1) A∪A=A,
(2) A∪∅E=A,
(3) A∪∅˜E=A,
(4) A∪XE=XE,
(5) A∪X˜E=X˜E,
(6) A∪(B∪C)=(A∪B)∪C,
(7) A∪B=B∪A.
Proof. These properties are directly inferred from the definitions of norms and union operation.
Theorem 4. If the min−norm and max−norm satisfy De Morgan's law, for all A,B∈NS(X),
(1) ¯A∩B=¯A∪¯B,
(2) ¯A∪B=¯A∩¯B.
Proof.
(1) ∀e∈E,∀x∈X,
{T¯A∩Be(x)=FA∩Be=FAe(x)∘FBe(x)I¯A∩Be(x)=1−IA∩Be=1−IAe(x)∙IBe(x)F¯A∩Be(x)=TA∩Be=TAe(x)∙TBe(x), | (16) |
and
{T¯A∪¯Be(x)=T¯Ae(x)∘T¯Be(x)=FAe(x)∘FBe(x)I¯A∪¯Be(x)=I¯Ae(x)∘I¯Be(x)=(1−IAe(x))∘(1−IBe(x))F¯A∪¯Be(x)=F¯Ae(x)∙F¯Be(x)=TAe(x)∙TBe(x). | (17) |
Moreover,
(1−IAe(x))∘(1−IBe(x))=1−IAe(x)∙IBe(x), | (18) |
due to De Morgan's law of the min−norm and max−norm. Therefore,
∀e∈E,∀x∈X,{T¯A∩Be(x)=T¯A∪¯Be(x)I¯A∩Be(x)=I¯A∪¯Be(x)F¯A∩Be(x)=F¯A∪¯Be(x). | (19) |
(2) ∀e∈E,∀x∈X,
{T¯A∪Be(x)=FA∪Be=FAe(x)∙FBe(x)I¯A∪Be(x)=1−IA∪Be=1−IAe(x)∘IBe(x)F¯A∪Be(x)=TA∪Be=TAe(x)∘TBe(x), | (20) |
and
{T¯A∩¯Be(x)=T¯Ae(x)∙T¯Be(x)=FAe(x)∙FBe(x)I¯A∩¯Be(x)=I¯Ae(x)∙I¯Be(x)=(1−IAe(x))∙(1−IBe(x))F¯A∩¯Be(x)=F¯Ae(x)∘F¯Be(x)=TAe(x)∘TBe(x). | (21) |
Moreover,
(1−IAe(x))∙(1−IBe(x))=1−IAe(x)∘IBe(x), | (22) |
due to De Morgan's law of the min−norm and max−norm. Therefore,
∀e∈E,∀x∈X,{T¯A∪Be(x)=T¯A∩¯Be(x)I¯A∪Be(x)=I¯A∩¯Be(x)F¯A∪Be(x)=F¯A∩¯Be(x). | (23) |
The distributive properties between intersection and union operations are not satisfied in the case of these general operations. Counterexamples are shown in Example 5.
Example 5. Let the NS-set C be represented in Table 7 as follows:
C | e1 | e2 |
x1 | ⟨.2,.1,.9⟩ | ⟨.3,.2,.6⟩ |
x2 | ⟨.3,.7,.6⟩ | ⟨.8,.2,.5⟩ |
x3 | ⟨.2,.1,.4⟩ | ⟨.3,.2,.5⟩ |
If using min−norm x∙y=max{x+y−1,0} and max−norm x∘y=min{x+y,1} with the two above NS-sets A and B in Example 3, the two NS-sets A∩(B∪C) and (A∩B)∪(A∩C) can be described in Table 8 as follows:
A∩(B∪C) | e1 | e2 | (A∩B)∪(A∩C) | e1 | e2 |
x1 | ⟨.1,0,1⟩ | ⟨.4,0,.9⟩ | x1 | ⟨0,0,1⟩ | ⟨.1,0,1⟩ |
x2 | ⟨0,1,.3⟩ | ⟨.8,.8,.8⟩ | x2 | ⟨0,.4,.3⟩ | ⟨.6,.1,.9⟩ |
x3 | ⟨0,0,.8⟩ | ⟨.8,.2,.4⟩ | x3 | ⟨0,0,1⟩ | ⟨.1,.1,.9⟩ |
Therefore, A∩(B∪C)≠(A∩B)∪(A∩C). Similarly, see Table 9:
A∪(B∩C) | e1 | e2 | (A∪B)∩(A∪C) | e1 | e2 |
x1 | ⟨.2,.4,.5⟩ | ⟨.5,.2,.8⟩ | x1 | ⟨.3,.1,.6⟩ | ⟨.8,0,.7⟩ |
x2 | ⟨.1,1,0⟩ | ⟨1,.9,.4⟩ | x2 | ⟨0,1,1⟩ | ⟨1,1,.3⟩ |
x3 | ⟨.1,.2,.7⟩ | ⟨.9,.9,0⟩ | x3 | ⟨0,0,.5⟩ | ⟨1,1,0⟩ |
Therefore, A∪(B∩C)≠(A∪B)∩(A∪C).
Definition 10. Let (Ai)i∈I be a collection of NS-sets on X. The union of the collection of NS-sets (Ai)i∈I,writtenas⋃i∈IAi, is determined by
∀e∈E,∀x∈X,{T⋃i∈IAie(x)=∘i∈I{TAie(x)}I⋃i∈IAie(x)=∘i∈I{IAie(x)}F⋃i∈IAie(x)=∙i∈I{FAie(x)}. | (24) |
Definition 11. The difference of the two NS-sets A and B,writtenasA∖B, is determined by A∖B=A∩¯B, i.e.,
∀e∈E,∀x∈X,{TA∖Be(x)=TAe(x)∙FBe(x)IA∖Be(x)=IAe(x)∙(1−IBe(x))FA∖Be(x)=FAe(x)∘TBe(x). | (25) |
Example 6. If using min−norm x∙y=max{x+y−1,0} and max−norm x∘y=min{x+y,1}, the difference A∖B of the two above NS-sets A and B in Example 3 is described according to Eq (25), see Table 10:
A∖B | e1 | e2 |
x1 | ⟨0,.2,1⟩ | ⟨0,.2,1⟩ |
x2 | ⟨0,0,.6⟩ | ⟨.7,.2,1⟩ |
x3 | ⟨0,0,1⟩ | ⟨0,.8,1⟩ |
Theorem 5. If the min−norm and max−norm satisfy De Morgan's law, for all A,B,C∈NS(X),
(1) A∖B⊆A,
(2) ¯A∖B=¯A∪B,
(3) ¯A∖¯B=B∖A,
(4) A∖(B∪C)=(A∖B)∩(A∖C),
(5) (A∩B)∖C=(A∖C)∩(B∖C),
(6) (A∖B)∩(C∖D)=(C∖B)∩(A∖D)=(A∩C)∖(B∪D).
Proof.
(1) ∀e∈E,∀x∈X,{TA∖Be(x)=TAe(x)∙TBe(x)≤TAe(x)IA∖Be(x)=IAe(x)∙(1−IBe(x))≤IAe(x)FA∖Be(x)=FAe(x)∘FBe(x)≥FAe(x). This implies that A∖B⊆A.
(2) ¯A∖B=¯A∩¯B=¯A∪¯¯B=¯A∪B due to Theorem 1.
(3) ¯A∖¯B=¯A∩¯¯B=¯A∩B=B∩¯A=B∖A.
(4) A∖(B∪C)=A∩¯B∪C=A∩(¯B∩¯C)=(A∩¯B)∩(A∩¯C)=(A∖B)∩(A∖C) due to Theorems 3 and 4.
(5) (A∩B)∖C=(A∩B)∩¯C=(A∩¯C)∩(B∩¯C)=(A∖C)∩(B∖C) due to Theorem 3.
(6) (A∖B)∩(C∖D)=(A∩¯B)∩(C∩¯D)=(C∩¯B)∩(A∩¯D)=(C∖B)∩(A∖D) due to Theorem 3.
(7) (A∖B)∩(C∖D)=(A∩¯B)∩(C∩¯D)=(A∩C)∩(¯B∩¯D)=(A∩C)∩¯B∪D =(A∩C)∖(B∪D) due to Theorems 3 and 4.
Definition 12. The AND operation of the two NS-sets A and B with the same parameter set E, written as A∧B, is determined over the same parameter set E×E by
∀(e1,e2)∈E×E,∀x∈X,{TA∧B(e1,e2)(x)=TAe1(x)∙TBe2(x)IA∧B(e1,e2)(x)=IAe1(x)∙IBe2(x)FA∧B(e1,e2)(x)=FAe1(x)∘FBe2(x). | (26) |
Definition 13. The OR operation of the two NS-sets A and B with the same parameter set E, written as A∧B, is determined over the same parameter set E×E by
∀(e1,e2)∈E×E,∀x∈X,{TA∨B(e1,e2)(x)=TAe1(x)∘TBe2(x)IA∨B(e1,e2)(x)=IAe1(x)∘IBe2(x)FA∨B(e1,e2)(x)=FAe1(x)∙FBe2(x). | (27) |
Example 7. If using min−norm x∙y=max{x+y−1,0} and max−norm x∘y=min{x+y,1}, the AND A∧B and OR A∨B operations of the two above NS-sets A and B in Example 3 is described according to Eqs (26) and (27) in Table 11 as follows:
A∧B | (e1,e1) | (e1,e2) | (e2,e1) | (e2,e2) | A∨B | (e1,e1) | (e1,e2) | (e2,e1) | (e2,e2) |
x1 | ⟨0,0,1⟩ | ⟨0,0,1⟩ | ⟨.2,0,1⟩ | ⟨.1,0,1⟩ | x1 | ⟨.9,.6,.2⟩ | ⟨.8,.4,1⟩ | ⟨1,.4,.5⟩ | ⟨1,.2,.3⟩ |
x2 | ⟨0,.3,.4⟩ | ⟨0,.1,1⟩ | ⟨.1,.8,.5⟩ | ⟨.5,.6,1⟩ | x2 | ⟨.4,1,1⟩ | ⟨.8,1,.2⟩ | ⟨1,1,0⟩ | ⟨1,1,.3⟩ |
x3 | ⟨0,0,1⟩ | ⟨0,0,.7⟩ | ⟨.4,.3,.1⟩ | ⟨.6,0,.4⟩ | x3 | ⟨.7,.6,.4⟩ | ⟨.9,.3,0⟩ | ⟨1,1,.1⟩ | ⟨1,1,0⟩ |
Theorem 6. If the min−norm and max−norm satisfy De Morgan's law, for all A,B∈NS(X),
(1) ¯A∧B=¯A∨¯B,
(2) ¯A∨B=¯A∧¯B.
Proof.
(1) ∀(e1,e2)∈E×E,∀x∈X,
{T¯A∧B(e1,e2)(x)=FA∧B(e1,e2)=FAe1(x)∘FBe2(x)I¯A∧B(e1,e2)(x)=1−IA∧B(e1,e2)=1−IAe1(x)∙IBe2(x)F¯A∧B(e1,e2)(x)=TA∧B(e1,e2)=TAe1(x)∙TBe2(x), | (28) |
and
{T¯A∨¯B(e1,e2)(x)=T¯Ae1(x)∘T¯Be2(x)=FAe1(x)∘FBe2(x)I¯A∨¯B(e1,e2)(x)=I¯Ae1(x)∘I¯Be2(x)=(1−IAe1(x))∘(1−IBe2(x))F¯A∨¯B(e1,e2)(x)=F¯Ae1(x)∙F¯Be2(x)=TAe1(x)∙TBe2(x). | (29) |
Moreover,
(1−IAe1(x))∘(1−IBe2(x))=1−IAe(x)∙IBe(x), | (30) |
due to De Morgan's law of the min−norm and max−norm. Therefore,
∀(e1,e2)∈E×E,∀x∈X,{T¯A∧B(e1,e2)(x)=T¯A∨¯B(e1,e2)(x)I¯A∧B(e1,e2)(x)=I¯A∨¯B(e1,e2)(x)F¯A∧B(e1,e2)(x)=F¯A∨¯B(e1,e2)(x). | (31) |
(2) ∀(e1,e2)∈E×E,∀x∈X,
{T¯A∨B(e1,e2)(x)=FA∨B(e1,e2)=FAe1(x)∙FBe2(x)I¯A∨B(e1,e2)(x)=1−IA∨B(e1,e2)=1−IAe1(x)∘IBe2(x)F¯A∨B(e1,e2)(x)=TA∨B(e1,e2)=TAe1(x)∘TBe2(x), | (32) |
and
{T¯A∧¯B(e1,e2)(x)=T¯Ae1(x)∙T¯Be2(x)=FAe1(x)∙FBe2(x)I¯A∧¯B(e1,e2)(x)=I¯Ae1(x)∙I¯Be2(x)=(1−IAe1(x))∙(1−IBe2(x))F¯A∧¯B(e1,e2)(x)=F¯Ae1(x)∘F¯Be2(x)=TAe1(x)∘TBe2(x). | (33) |
Moreover,
(1−IAe1(x))∙(1−IBe2(x))=1−IAe1(x)∘IBe2(x), | (34) |
due to De Morgan's law of the min−norm and max−norm. Therefore,
∀(e1,e2)∈E×E,∀x∈X,{T¯A∨B(e1,e2)(x)=T¯A∧¯B(e1,e2)(x)I¯A∨B(e1,e2)(x)=I¯A∧¯B(e1,e2)(x)F¯A∨B(e1,e2)(x)=F¯A∧¯B(e1,e2)(x). | (35) |
This section uses the operations just constructed above as the core to build the topology and related concepts on NS-sets. It is important to note that the norms used must satisfy De Morgan's law.
Definition 14. A collection τ⊆NS(X) is NS-topology on X if it obeys the following properties:
(a) ∅E and XE belongs to τ,
(b) The intersection of any finite collection of τ's elements belongs to τ,
(c) The union of any collection of τ's elements belongs to τ.
Then, the pair (X,τ) is a NS-topological space and each element of τ is a NS-open set.
Example 8. Let three NS-sets K1, K2, K3 be represented in Table 12 as follows:
K1 | e1 | e2 | K2 | e1 | e2 | K3 | e1 | e2 |
x1 | ⟨.2,.2,1⟩ | ⟨.5,.5,.7⟩ | x1 | ⟨.3,.3,.9⟩ | ⟨.6,.6,.6⟩ | x1 | ⟨.4,.4,.8⟩ | ⟨.7,.7,.5⟩ |
x2 | ⟨.3,.3,.9⟩ | ⟨.6,.6,.6⟩ | x2 | ⟨.4,.4,.8⟩ | ⟨.7,.7,.5⟩ | x2 | ⟨.5,.5,.7⟩ | ⟨.8,.8,.4⟩ |
x3 | ⟨.4,.4,.8⟩ | ⟨0,.9,1⟩ | x3 | ⟨.5,.5,.7⟩ | ⟨.8,.8,.4⟩ | x3 | ⟨.6,.6,.6⟩ | ⟨.9,.9,.3⟩ |
If using the min−norm x∙y=min{x,y}, max−norm x∘y=max{x,y}, the collection τ={∅E,XE,K1,K2,K3} is a NS-topology.
Theorem 7.
(1) τ0={∅E,XE} is a NS-topology (anti-discrete).
(2) τ∞=NS(X) is a NS-topology (discrete).
(3) If τ1 and τ2 are two NS-topologies, τ1∩τ2 is a NS-topology.
Proof. This proof focuses on the proof of Property 3 because Properties 1 and 2 are directly inferred.
• ∅E,XE∈τ1;∅E,XE∈τ2⇒∅E,XE∈τ1∩τ2.
• If {Kj}n1 is a finite family of NS-sets in τ1∩τ2, Ki∈τ1 and Ki∈τ2 for all i. So ∩{Kj}n1∈τ1 and {Kj}n1∈τ2. Thus ∩{Kj}n1∈τ1∩τ2.
• If letting {Ki|i∈I} be a family of NS-sets in τ1∩τ2, Ki∈τ1 and Ki∈τ2 for all i∈I. So ∪i∈IKi∈τ1 and ∪i∈IKi∈τ2. Therefore, ∪i∈IKi∈τ1∩τ2.
It should be noted that if τ1 and τ2 are two NS-topologies, τ1∪τ2 cannot be a NS-topology. Counterexamples are shown in Example 9.
Example 9. Let three NS-sets H1, H2, H3 be represented in Table 13 as follows:
H1 | e1 | e2 | H2 | e1 | e2 | H3 | e1 | e2 |
x1 | ⟨1,0,1⟩ | ⟨0,1,0⟩ | x1 | ⟨0,1,0⟩ | ⟨1,0,1⟩ | x1 | ⟨1,0,1⟩ | ⟨1,0,1⟩ |
x2 | ⟨1,0,1⟩ | ⟨0,1,0⟩ | x2 | ⟨0,1,0⟩ | ⟨1,0,1⟩ | x2 | ⟨1,0,1⟩ | ⟨1,0,1⟩ |
If using the min−norm x∙y=xy, max−norm x∘y=x+y−xy and letting τ1={∅E,XE,H1,H2} and τ2={∅E,XE,H3} be two NS-topologies, the collection τ1∪τ2={∅E,XE,H1,H2,H3} is not a NS-topology due to H1∪H2∉τ1∪τ2, see Table 14.
H1∪H2 | e1 | e2 |
x1 | ⟨1,0,1⟩ | ⟨1,1,0⟩ |
x2 | ⟨1,0,1⟩ | ⟨1,1,0⟩ |
Definition 15. A NS-set A∈NS(X) is NS-closed set if it has the complement ¯A is a NS-open set. The symbol ¯τ is denoted as the collection of all NS-closed sets.
Theorem 8.
(1) ∅E and XE belongs to ¯τ.
(2) The union of any finite collection of ¯τ's elements belongs to ¯τ.
(3) The intersection of any collection of ¯τ's elements belongs to ¯τ.
Proof. These properties are directly inferred from the definitions of a NS-closed set and De Morgan's law for intersection and union.
Definition 16. The NS-interior of a NS-set A,writtenasint(A), is the union of all NS-open subsets of A. It is considered the biggest NS-open set which is contained by A.
Example 10. Let three NS-sets L1, L2, K be represented as follows:
L1 | e1 | e2 | L2 | e1 | e2 | K | e1 | e2 |
x1 | ⟨.7,.8,.3⟩ | ⟨.4,.5,.8⟩ | x1 | ⟨.3,.2,.7⟩ | ⟨.6,.5,.2⟩ | x1 | ⟨.8,.8,.3⟩ | ⟨.8,.8,.3⟩ |
x2 | ⟨.5,.2,.6⟩ | ⟨.3,.4,.2⟩ | x2 | ⟨.5,.8,.4⟩ | ⟨.7,.6,.8⟩ | x2 | ⟨.4,.6,.5⟩ | ⟨.4,.6,.5⟩ |
If using the min−norm x∙y=max{x+y−1,0}, max−norm=min{x+y,1}, the collection τ={∅E,XE,L1,L2} is the NS-topology. It is easy to see that ∅E,L1⊆K and ∅E∪L1=L1⊆K. Therefore, int(K)=A.
Theorem 9. A NS-set A is a NS-open set if and only if A=int(A).
Proof. If A∈τ then A is the biggest NS-open set that is contained by A. So A=int(A). Conversely, A=int(A)∈τ.
Theorem 10. If A,B∈NS(X),
(1) int(int(A))=int(A),
(2) int(∅E)=∅E and int(XE)=XE,
(3) A⊆B⇒int(A)⊆int(B),
(4) int(A∩B)=int(A)∩int(B),
(5) int(A)∪int(B)⊆int(A∪B).
Proof.
(1) Due to int(A)∈τ, int(int(A))=int(A).
(2) ∅E∈τ⇒int(∅E)=∅E and XE∈τ⇒int(XE)=XE.
(3) Due to A⊆B, int(A)⊆A⊆B and int(B)⊆B. Because int(B) is the biggest NS-open set contained in B, int(A)⊆int(B).
(4) Since int(A)∈τ and int(B)∈τ, then int(A)∪int(B)∈τ. It is known that int(A)⊆A and int(B)⊆B, so int(A)∪int(B)⊆A∪B. Moreover, int(A∪B) is the biggest NS-open set contained in A∪B. Therefore, int(A)∪int(B)⊆int(A∪B).
(5) Since int(A∩B)⊆A∩B, so int(A∩B)⊆A and int(A∩B)⊆B. Therefore, int(A∩B)⊆int(A) and int(A∩B)⊆int(B) or int(A∩B)⊆int(A)∩int(B).
Moreover,
{int(A)∩int(B)⊆int(A)⊆Aint(A)∩int(B)⊆int(B)⊆B⇒int(A)∩int(B)⊆A∩B |
and int(A∩B) is the biggest NS-open set contained in A∩B, so
int(A)∩int(B)⊆int(A∩B). |
Thus, int(A∩B)=int(A)∩int(B).
Definition 17. The NS-closure of a NS-set A,writtenascl(A), is the intersection of all NS-closed supersets of A. The cl(A) is the smallest NS-closed set which contains A.
Example 11. For the NS-topology τ given in Example 10, let NS-set H be represented in Table 16 as follows:
H | e1 | e2 | ¯L1 | e1 | e2 | e1 | e2 |
x1 | ⟨.2,.2,.8⟩ | ⟨.2,.2,.8⟩ | x1 | ⟨.3,.2,.7⟩ | ⟨.8,.5,.4⟩ | ⟨.7,.8,.3⟩ | ⟨.2,.5,.6⟩ |
x2 | ⟨.3,.4,.8⟩ | ⟨.3,.4,.8⟩ | x2 | ⟨.6,.8,.5⟩ | ⟨.2,.6,.3⟩ | ⟨.4,.2,.5⟩ | ⟨.8,.4,.7⟩ |
It is easy to see that ¯∅E=XE, ¯XE=∅E. So ∅E, XE, ¯L1, ¯L2 are all NS-closed sets. Since H⊆XE, cl(H)=L2.
Theorem 11. A NS-set A is a NS-closed set if and only if A=cl(A).
Proof. Let A be a NS-closed set. Because A⊆A and cl(A) is the smallest NS-closed set that contains A, cl(A)⊆A. Therefore, A=cl(A). Conversely, if A=cl(A) then A is a NS-closed set.
Theorem 12. If A,B∈NS(X),
(1) cl(cl(A))=cl(A),
(2) cl(∅E)=∅E and cl(XE)=XE,
(3) A⊆B⇒cl(A)⊆cl(B),
(4) cl(A∩B)⊆cl(A)∩cl(B),
(5) cl(A∪B)=cl(A)∪cl(B).
Proof.
(1) Directly inferring from Theorem 9.
(2) Directly inferring from Definition 14 and Theorem 9.
(3) Since A⊆B⊆cl(B) and cl(A) is the smallest NS-closed set containing A, cl(A)⊆cl(B).
(4) Since A∩B⊆A⊆cl(A) and A∩B⊆B⊆cl(B), A∩B⊆cl(A)∩cl(B). Therefore, cl(A∩B)⊆cl(A)∩cl(B).
(5) It is easy to see that A⊆A∪B⊆cl(A∪B), B⊆A∪B⊆cl(A∪B), cl(A) is the smallest NS-closed set that contains A, and cl(B) is the smallest NS-closed set that containing B. So cl(A)⊆cl(A∪B) and cl(B)⊆cl(A∪B). Therefore, cl(A)∪cl(B)⊆cl(A∪B).
Moreover, since A⊆cl(A) and B⊆cl(B), A∪B⊆cl(A)∪cl(B). Therefore, cl(A∪B)⊆cl(A)∪cl(B).
Thus, cl(A∪B)=cl(A)∪cl(B).
Theorem 13. If A,B∈NS(X),
(1) ¯int(A)=cl(¯A),
(2) ¯cl(A)=int(¯A).
Proof.
(1) Because
int(A)=∪i∈I{Hi∈τ:Hi⊆A}, |
¯int(A)=¯∪i∈I{Hi∈τ:Hi⊆A}=∩i∈I{¯Hi∈¯τ:¯Hi⊇¯A}=cl(¯A). | (36) |
(2) Because
cl(A)=∪i∈I{Hi∈¯τ:Hi⊇A}, |
¯cl(A)=¯[∩i∈I{Hi∈¯τ:Hi⊇A}]=∪i∈I{¯Hi∈τ:¯Hi⊆¯A}=int(¯A). | (37) |
Definition 18. The NS-boundary of a NS-set A,writtenas∂A, is the intersection of the NS-closure of A and the NS-closure of ¯A.
Example 12. For the NS-topology τ given in Example 10 and the NS-set H given in Example 11, the complement of H is represented in Table 17 as follows:
¯H | e1 | e2 |
x1 | ⟨.8,.8,.2⟩ | ⟨.8,.8,.2⟩ |
x2 | ⟨.8,.6,.3⟩ | ⟨.8,.6,.3⟩ |
It is easy to see that cl(H)=XE and cl(¯H)=XE. So ∂H=XE∩XE=XE.
Theorem 14. If A∈NS(X),
(1) ∂A=cl(A)∩¯int(A),
(2) int(A)∩∂A=∅E,
(3) ∂A=∅E if and only if A is a NS-open and NS-closed set.
Proof.
(1) ∂A=cl(A)∩cl(¯A) = cl(A)∩¯int(A) due to Theorem 13.
(2) It is easy to see that
int(A)∩∂(A)=int(A)∩cl(A)∩cl(¯A)=int(A)∩cl(A)∩¯int(A) |
=int(A)∩¯int(A)∩cl(A)=∅E |
due to Theorem 13.
(3) Since
∂(A)=cl(A)∩cl(¯A)=cl(A)∩¯int(A)=∅E, |
cl(A)∩int(A)≠∅E. So A⊆cl(A)⊆int(A)⊆A. Therefore, A=cl(A)=int(A) or A is a NS-open and NS-closed set.
Conversely, if A is a NS-open and NS-closed set, A=int(A) and A=cl(A). Therefore,
∂(A)=cl(A)∩cl(¯A)=cl(A)∩¯int(A)=cl(A)∩¯cl(A)=∅E. |
Definition 19.
a. The NS-open set M is regular if M=int(cl(M)).
b. The NS-closed set M is regular if M=cl(int(M)).
Theorem 15. If M,N∈NS(X),
(1) If M is a NS-closed set, int(M) is a regular NS-open set.
(2) If M is a NS-open set, cl(M) is a regular NS-closed set.
(3) If M and N are two regular NS-open sets, M⊆N⟺cl(M)⊆cl(N).
(4) If M and N are two regular NS-closed sets, M⊆N⟺int(M)⊆int(N).
(5) If M is a regular NS-closed set, ¯M is a regular NS-open set.
(6) If M is a regular NS-open set, ¯M is a regular NS-closed set.
Proof.
(1) If M is a NS-closed set,
int(M)⊆M⇒cl[int(M)]⊆cl(M)=M⇒int[cl(int(M))]⊆int(M). | (38) |
int(M)⊆cl(int(M))⇒int(int(M))=int(M)⊆int[cl(int(M))]. | (39) |
Therefore, int(M)=int[cl(int(M))] or int(M) is regular.
(2) If M is a NS-open set,
int(cl(M))⊆cl(M)⇒cl(int(cl(M)))⊆cl(cl(M))=cl(M). | (40) |
Because int(M)=M,
M⊆cl(M)⇒int(M)=M⊆int(cl(M))⇒cl(M)⊆cl(int(cl(M))). | (41) |
Therefore, cl(M)=cl(int(cl(M))) or cl(M) is regular.
(3) Clearly, M⊆N⇒cl(M)⊆cl(N) and int(cl(M))=M, int(cl(N))=N due to M,N are regular. Conversely,
cl(M)⊆cl(N)⇒int(cl(M))=M⊆int(cl(N))=N⇒M⊆N. |
(4) Clearly, M⊆N⇒int(M)⊆int(N) and M=cl(int(M)), N=cl(int(N)) due to M,N are regular. Conversely,
int(M)⊆int(N)⇒cl(int(M))=M⊆cl(int(N))=N⇒M⊆N. |
(5) If M is a regular NS-open set, M=int(cl(M)). So
cl(int(¯M))=cl(¯cl(M))=¯int(cl(M))=¯M. |
Therefore, ¯M is a regular NS-closed set.
(6) Similarly, if M is a regular NS-closed set, int(cl(¯M))=¯M. So ¯M is a regular NS-open set.
Theorem 16. Let τ={Ki:i∈I} be NS-topology on X where
Ki={(e,xTKie(x),IKie(x),FKie(x)):e∈E,x∈X}. | (42) |
Three collections
T=(Ti)i∈I={(e,⟨x,TKie(x)⟩):e∈E,x∈X}, | (43) |
I=(Ii)i∈I={(e,⟨x,IKie(x)⟩):e∈E,x∈X}, | (44) |
F=(Fi)i∈I={(e,⟨x,1−FKie(x)⟩):e∈E,x∈X}, | (45) |
are the fuzzy soft topologies on X.
Proof.
• ∅E∈τ⇒˜∅∈T,˜∅∈I,˜∅∈F.
• XE∈τ⇒˜X∈T;˜X∈I;˜X∈F.
• Let (Ti)i∈I be a family of fuzzy soft sets in T, (Ii)i∈I be a family of fuzzy soft sets in I, and (Fi)i∈I be a family of fuzzy soft sets in F. They make a family of NS-sets {Ki:i∈I} where
Ki={(e,xTKie(x),IKie(x),FKie(x)):e∈E,x∈X}∈τ. | (46) |
So ∪i∈IKi∈τ or
∪i∈IKi={(e,x∘i∈I{TKie(x)},∘i∈I{IKie(x)},∙i∈I{FKie(x)}):e∈E,x∈X}∈τ. | (47) |
Therefore,
{[⟨∘i∈I{TKie(x)}⟩:x∈X]e∈E}=˜∪i∈I{TKie(X):e∈E}∈T, | (48) |
{[⟨∘i∈I{TKie(x)}⟩:x∈X]e∈E}=˜∪i∈I{IKie(X):e∈E}∈I, | (49) |
{[⟨∙i∈I{FKie(x)}⟩:x∈X]e∈E}C |
={[⟨1−∙i∈I{FKie(x)}⟩:x∈X]e∈E} |
={[⟨∘i∈I{(1−FKi(e)(a))}⟩:a∈X]e∈E} |
=˜∪i∈I{(IKi(e)(X))Ce∈E}∈F. | (50) |
• Let {Tj∈T}n1, {Ij∈I}n1,{Fj∈F}n1 be finite families of fuzzy soft sets on X and satisfy
Kj={(e,xTKje(x),IKje(x),FKje(x)):e∈E,x∈X}∈τ. | (51) |
So, we have ∩n1Kj∈τ, i.e.,
∩n1Kj={(e,x{∙TKie(x)}n1,{∙IKie(x)}n1,{∘FKie(x)}n1):e∈E,x∈X}. | (52) |
Hence,
[{{∙TKie(x)}n1:x∈X}e∈E]=˜∩n1{[TKie(X)]e∈E}∈T, | (53) |
[{{∙IKie(x)}n1:x∈X}e∈E]=˜∩n1{[IKie(X)]e∈E}∈I, | (54) |
{[{∘FKie(x)}n1:x∈X]e∈E}C |
={[1−{∘FKie(x)}n1:x∈X]e∈E} |
={[∙{1−FKie(x)}n1:x∈X]e∈E} |
=˜∩n1{[IKie(X)]e∈E}∈F. | (55) |
In the general case, the opposite of Theorem 16 is not true. This is demonstrated through a counterexample, as shown in Example 13.
Example 13. Let four NS-sets H1, H2, and H3be represented in Table 18 as follows:
e1 | e2 | ||
H1 | x1 | ⟨.25,.25,.75⟩ | ⟨.25,.25,.75⟩ |
x2 | ⟨13,13,13⟩ | ⟨13,13,13⟩ | |
H2 | x1 | ⟨.5,.75,.5⟩ | ⟨.5,.75,.5⟩ |
x2 | ⟨13,23,23⟩ | ⟨13,23,23⟩ | |
H3 | x1 | ⟨.75,.5,.25⟩ | ⟨.75,.5,.25⟩ |
x2 | ⟨23,13,13⟩ | ⟨23,13,13⟩ | |
H1∪H2 | x1 | ⟨.75,1,.25⟩ | ⟨.75,1,.25⟩ |
x2 | ⟨23,1,0⟩ | ⟨23,1,0⟩ |
If using the min−norm x∙y=max{x+y−1,0}, max−norm=min{x+y,1}, three collections defined in Table 19 as follows are the fuzzy soft topologies on X.
e1 | e2 | ||
T | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
T1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
T2 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
T3 | ⟨.75,23⟩ | ⟨.75,23⟩ | |
I | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
I1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
I2 | ⟨.75,23⟩ | ⟨.75,23⟩ | |
I3 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
F | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
F1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
F2 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
F3 | ⟨.75,23⟩ | ⟨.75,23⟩ |
The T,I,F are fuzzy soft topologies on X, but τ={∅E,XE,H1,H2,H3} is not a NS- topology on X because H1∪H2∉τ.
Theorem 17. Let three collections
T=(Ti)i∈I={(e,⟨x,TKie(x)⟩):e∈E,x∈X}, | (56) |
I=(Ii)i∈I={(e,⟨x,IKie(x)⟩):e∈E,x∈X}, | (57) |
F=(Fi)i∈I={(e,⟨x,1−FKie(x)⟩):e∈E,x∈X}, | (58) |
be the fuzzy soft topologies on X. Let τ={Ki:i∈I} where
Ki={(e,xTKie(x),IKie(x),FKie(x)):e∈E,x∈X}. | (59) |
If for all l,m,n, we have
Tl∩Tm=Tn⇒{Il∩Im=InFl∩Fm=Fn, | (60) |
Tl∪Tm=Tn⇒{Il∩Im=InFl∩Fm=Fn. | (61) |
Then τ is the NS-topology on X.
Proof.
• Obviously, ∅E,XE∈τ.
• Let {Ki:i∈I}⊂τ be a family of NS-sets on X. We have {Ti},{Ii},{Fi} are families of fuzzy soft sets on X. So,
∃n0∈I,Tn0=⋃i∈ITi∈T,In0=⋃i∈IIi∈I,Fn0=⋃i∈IFi∈F. | (62) |
Thus, ⋃i∈IKi=Tn0∈τ.
• Let {Kj∈τ}n1 be a finite family of NS-sets on X. We have {Tj}n1, {Ij}n1,{Fj}n1as finite families of fuzzy soft sets on X. So,
∃m0∈I,Tm0=∩n1Tj∈T,Im0=∩n1Ij∈I,Fm0=∩n1Fj∈F, | (63) |
Thus, ∩n1Kj∈τ.
Theorem 18. Let τ={Ki:i∈I} be the NS-topology on X where
Ki={(e,xTKie(x),IKie(x),FKie(x)):e∈E,x∈X}. | (64) |
For each e∈E, three collections
Te=(Tei)i∈I={⟨x,TKie(x)⟩:x∈X}, | (65) |
Ie=(Iei)i∈I={⟨x,IKie(x)⟩:x∈X}, | (66) |
Fe=(Fei)i∈I={⟨x,1−FKie(x)⟩,x∈X}, | (67) |
are the fuzzy topologies on X.
Proof. It can be implied from Theorem 17.
In the general case, the opposite of Theorem 18 is not true. This is demonstrated through the counterexample shown in Example 14.
Example 14. We return to Example 12 with the same hypothesis. Then,
Te1={(0,0),(1,1),(.25,13),(.5,13),(.75,23)}, | (68) |
Ie1={(0,0),(1,1),(.25,13),(.75,23),(.5,13)}, | (69) |
Fe1={(0,0),(1,1),(.25,13),(.5,13),(.75,23)}, | (70) |
are fuzzy topologies on X. Similarly,
Te2={(0,0),(1,1),(.25,13),(.5,13),(.75,23)}, | (71) |
Ie2={(0,0),(1,1),(.25,13),(.5,13),(.75,23)}, | (72) |
Fe2={(0,0),(1,1),(.25,13),(.5,13),(.75,23)}, | (73) |
are also fuzzy topologies, but τ={∅E,XE,H1,H2,H3} is not a NS-topology on X because K1∪K2∉τ.
In this paper, two novel norms are proposed to serve as the core for determining operations on NS-sets. These operations are used to construct the topology and related concepts such as open set, closed set, interior, closure, and regularity. Another highlight of this work is demonstrating the relationship between the topologies on NS-sets and fuzzy soft sets. The topology on NS-sets can parameterize the topologies on fuzzy soft sets, but the reverse is not guaranteed. This work's advantage is the structure's logic is presented with well-defined concepts and convincingly proven theorems.
Determining these concepts in a novel way enables a variety of methods for studying NS-sets, and offers a unique opportunity for future research and development in this field. Such research could focus on separation axioms, continuity, compactness, and paracompactness on NS-sets. Moreover, the relationship between topology on hybrid structure, NS-sets, and component structures, neutrosophic sets and soft sets, is also of research interest. In addition, applications of neutrosophic soft topological spaces can be investigated to handle decision-making problems.
Furthermore, we are also turning our interests to building topology on a new type of set, neutrosophic fuzzy sets. We believe these results will be helpful for future studies on neutrosophic fuzzy topology to develop a general framework for practical applications. These issues present opportunities but also challenges for researchers interested in the field of fuzzy theory.
The authors declare that they have no competing interests in this paper.
[1] |
Drakopoulos SX, Psarras GC, Forte G, et al. (2018) Entanglement dynamics in ultra-high molecular weight polyethylene as revealed by dielectric spectroscopy. Polymer 150: 35–43. https://doi.org/10.1016/j.polymer.2018.07.021. doi: 10.1016/j.polymer.2018.07.021
![]() |
[2] |
Golchin A, Simmons GF, Glavatskih S, et al. (2013) Tribological behaviour of polymeric materials in water-lubricated contacts. P I Mech Eng J-J Eng 227: 811–825. https://doi.org/10.1177/1350650113476441. doi: 10.1177/1350650113476441
![]() |
[3] |
Chang T, Yuan C, Guo Z (2019) Tribological behavior of aged UHMWPE under water-lubricated condition. Tribol Int 133: 1–11. https://doi.org/10.1016/j.triboint.2018.12.038. doi: 10.1016/j.triboint.2018.12.038
![]() |
[4] |
Chen S, Li J, Wei L, et al. (2017) Tribological properties of polyimide-modified UHMWPE for bushing materials of seawater lubricated sliding bearings. Tribol Int 115: 470–476. https://doi.org/10.1016/j.triboint.2017.06.011. doi: 10.1016/j.triboint.2017.06.011
![]() |
[5] |
Cho MH, Bahadur S, Pogosian AK (2005) Friction and wear studies using Taguchi method on polyphenylene sulfide filled with a complex mixture of MoS2, Al2O3, and other compounds. Wear 258: 1825–1835. https://doi.org/10.1016/j.wear.2004.12.017. doi: 10.1016/j.wear.2004.12.017
![]() |
[6] |
Ramadan MA (2018) Friction and wear of sand-contaminated lubricated sliding. Friction 6: 457–463. https://doi.org/10.1007/s40544-017-0192-4. doi: 10.1007/s40544-017-0192-4
![]() |
[7] |
Golchin A, Villain A, Emami N (2017) Tribological behaviour of nanodiamond reinforced UHMWPE in water-lubricated contacts. Tribol Int 110: 195–200. https://doi.org/10.1016/j.triboint.2017.01.016. doi: 10.1016/j.triboint.2017.01.016
![]() |
[8] |
Bruck AL, Karuppiah KS, Sundararajan S, et al. (2010) Friction and wear behavior of ultrahigh molecular weight polyethylene as a function of crystallinity in the presence of the phospholipid dipalmitoyl phosphatidylcholine. J Biomed Mater Res B 93: 351–358. https://doi.org/10.1002/jbm.b.31587. doi: 10.1002/jbm.b.31587
![]() |
[9] |
Atwood SA, Van Citters DW, Patten EW, et al. (2011) Tradeoffs amongst fatigue, wear, and oxidation resistance of cross-linked ultra-high molecular weight polyethylene. J Mech Behav Biomed Mater 4: 1033–1045. https://doi.org/10.1016/j.jmbbm.2011.03.012. doi: 10.1016/j.jmbbm.2011.03.012
![]() |
[10] |
Dougherty PSM, Srivastava G, Onler R, et al. (2015) Lubrication enhancement for UHMWPE sliding contacts through surface texturing. Tribol Trans 58: 79–86. https://doi.org/10.1080/10402004.2014.933935. doi: 10.1080/10402004.2014.933935
![]() |
[11] |
Kustandi TS, Choo JH, Low HY, et al. (2009) Texturing of UHMWPE surface via NIL for low friction and wear properties. J Phys D Appl Phys 43: 015301. https://doi.org/10.1088/0022-3727/43/1/015301. doi: 10.1088/0022-3727/43/1/015301
![]() |
[12] |
Nakatsuji T, Mori A (2001) The tribological effect of mechanically produced micro-dents by a micro diamond pyramid on medium carbon steel surfaces in rolling-sliding contact. Meccanica 36: 663–674. https://doi.org/10.1023/A:1016348803781. doi: 10.1023/A:1016348803781
![]() |
[13] |
Wang X, Adachi K, Otsuka K, et al. (2006) Optimization of the surface texture for silicon carbide sliding in water. Appl Surf Sci 253: 1282–1286. https://doi.org/10.1016/j.apsusc.2006.01.076. doi: 10.1016/j.apsusc.2006.01.076
![]() |
[14] |
Etsion I (2004) Improving tribological performance of mechanical components by laser surface texturing. Tribol Lett 17: 733–737. https://doi.org/10.1007/s11249-004-8081-1. doi: 10.1007/s11249-004-8081-1
![]() |
[15] |
Etsion I (2005) State of the art in laser surface texturing. J Tribol 127: 248–253. https://doi.org/10.1115/1.1828070. doi: 10.1115/1.1828070
![]() |
[16] |
Zhang YL, Zhang XG, Matsoukas G (2015) Numerical study of surface texturing for improving tribological properties of ultra-high molecular weight polyethylene. Biosurface Biotribology 1: 270–277. https://doi.org/10.1016/j.bsbt.2015.11.003. doi: 10.1016/j.bsbt.2015.11.003
![]() |
[17] |
Riveiro A, Soto R, Del Val J, et al. (2014) Laser surface modification of ultra-high-molecular-weight polyethylene (UHMWPE) for biomedical applications. Appl Surf Sci 302: 236–242. https://doi.org/10.1016/j.apsusc.2014.02.130. doi: 10.1016/j.apsusc.2014.02.130
![]() |
[18] |
Hussain M, Sufyan M, Abbas N, et al. (2019) Influence of laser processing conditions for texturing on ultra-high-molecular-weight-polyethylene (UHMWPE) surface. Case Studies in Thermal Engineering 14: 100491. https://doi.org/10.1016/j.csite.2019.100491. doi: 10.1016/j.csite.2019.100491
![]() |
[19] |
Tangwarodomnukun V, Chen HY (2015) Laser ablation of PMMA in air, water, and ethanol environments. Mater Manuf Process 30: 685–691. https://doi.org/10.1080/10426914.2014.994774. doi: 10.1080/10426914.2014.994774
![]() |
[20] |
Gao K, Liu J, Fan Y, et al. (2019) Ultra-low-cost fabrication of polymer-based microfluidic devices with diode laser ablation. Biomed Microdevices 21: 83. https://doi.org/10.1007/s10544-019-0433-6. doi: 10.1007/s10544-019-0433-6
![]() |
[21] |
Katayama S, Kubo Y, Yamada N (2002) Characterization and mechanical properties of flexible dimethylsiloxane‐based inorganic/organic hybrid sheets. J Am Ceram Soc 85: 1157–1163. https://doi.org/10.1111/j.1151-2916.2002.tb00238.x. doi: 10.1111/j.1151-2916.2002.tb00238.x
![]() |
[22] |
Aoki Y (2012) Electrical treeing characteristics in polydimethylsiloxane-based organic-inorganic hybrid materials. Mol Cryst Liq Cryst 568: 186–191. https://doi.org/10.1080/15421406.2012.708841. doi: 10.1080/15421406.2012.708841
![]() |
[23] |
Aoki Y (2016) Heat-resistant, thermally conductive coating of alumina on metal via electrophoretic deposition with added polydimethylsiloxane-based organic–inorganic hybrid materials. Polym Bull 73: 2605–2614. https://doi.org/10.1007/s00289-016-1700-9. doi: 10.1007/s00289-016-1700-9
![]() |
[24] |
Mata A, Fleischman AJ, Roy S (2005) Characterization of polydimethylsiloxane (PDMS) properties for biomedical micro/nanosystems. Biomed Microdevices 7: 281–293. https://doi.org/10.1007/s10544-005-6070-2. doi: 10.1007/s10544-005-6070-2
![]() |
[25] |
Torrisi L, Cutroneo M, Torrisi A, et al. (2020) IR ns pulsed laser irradiation of Polydimethylsiloxane in vacuum. Vacuum 177: 109361. https://doi.org/10.1016/j.vacuum.2020.109361. doi: 10.1016/j.vacuum.2020.109361
![]() |
[26] | Kurtz SM (2016) A primer on UHMWPE, UHMWPE Biomaterials Handbook, Amsterdam, Netherlands: William Andrew Publishing, 1–6. |
[27] | Material Property Database (polydimethylsiloxane). Available from: https://www.mit.edu/~6.777/matprops/pdms.htm. |
[28] |
Price EJ, Covello J, Tuchler A, et al. (2020) Intumescent, epoxy-based flame-retardant coatings based on poly(acrylic acid) compositions. ACS Appl Mater Interfaces 12: 18997–19005. https://doi.org/10.1021/acsami.0c00567. doi: 10.1021/acsami.0c00567
![]() |
[29] |
Sonnier R, Otazaghine B, Iftene F, et al. (2016) Predicting the flammability of polymers from their chemical structure: an improved model based on group contributions. Polymer 86: 42–55. https://doi.org/10.1016/j.polymer.2016.01.046. doi: 10.1016/j.polymer.2016.01.046
![]() |
[30] |
Xie X, Li D, Tsai TH, et al. (2016) Thermal conductivity, heat capacity, and elastic constants of water-soluble polymers and polymer blends. Macromolecules 49: 972–978. https://doi.org/10.1021/acs.macromol.5b02477. doi: 10.1021/acs.macromol.5b02477
![]() |
[31] | ChemSrc. Polyacrylic Acid. CAS#: 9003-01-4. Available from: https://www.chemsrc.com/en/cas/9003-01-4_453957.html#wuHuaDiv. |
[32] |
Wang Y, Li P, Sun Z, et al. (2018) A model of screen reaction force for the 3D additive screen printing. The Journal of The Textile Institute 109: 1000–1007. https://doi.org/10.1080/00405000.2017.1397834. doi: 10.1080/00405000.2017.1397834
![]() |
[33] | ASTM International (2013) Standard test methods for measurement of wet film thickness of organic coatings. ASTM D1212-91. Available from: https://www.astm.org/Standards/D1212.htm. |
[34] |
Nečas D, Klapetek P (2012) Gwyddion: an open-source software for SPM data analysis. Cent Eur J Phys 10: 181–188. https://doi.org/10.2478/s11534-011-0096-2. doi: 10.2478/s11534-011-0096-2
![]() |
[35] | Mahmoudzadeh R, Salabati M, Hsu J, et al. (2021) Agreement of optical coherence tomography thickness measurements between Heidelberg Eye Explorer and ImageJ software. CJO (In press). https://doi.org/10.1016/j.jcjo.2021.05.018. |
[36] |
Duangwas S, Tangwarodomnukun V, Dumkum C (2014) Development of an overflow-assisted underwater laser ablation. Mater Manuf Process 29: 1226–1231. https://doi.org/10.1080/10426914.2014.930896. doi: 10.1080/10426914.2014.930896
![]() |
[37] |
Mannion PT, Magee J, Coyne E, et al. (2004) The effect of damage accumulation behaviour on ablation thresholds and damage morphology in ultrafast laser micro-machining of common metals in air. Appl Surf Sci 233: 275–287. https://doi.org/10.1016/j.apsusc.2004.03.229. doi: 10.1016/j.apsusc.2004.03.229
![]() |
[38] |
Furzikov N (1990) Approximate theory of highly absorbing polymer ablation by nanosecond laser pulses. Appl Phys Lett 56: 1638–1640. https://doi.org/10.1063/1.103150. doi: 10.1063/1.103150
![]() |
[39] |
Kamal A, Bashir M, Firdous S, et al. (2016) Optical properties of ultra-high molecular weight polyethylene (UHMWPE): a material of choice for total joint applications. Radiat Phys Chem 118: 102–106. https://doi.org/10.1016/j.radphyschem.2015.03.012. doi: 10.1016/j.radphyschem.2015.03.012
![]() |
[40] |
Nunes dos Santos W, Mummery P, Wallwork A (2005) Thermal diffusivity of polymers by the laser flash technique. Polym Test 24: 628–634. https://doi.org/10.1016/j.polymertesting.2005.03.007. doi: 10.1016/j.polymertesting.2005.03.007
![]() |
[41] | Brown MS, Arnold CB (2010) Fundamentals of laser-material interaction and application to multiscale surface modification, In: Sugioka K, Meunier M, Piqué A, Laser Precision Microfabrication, Berlin, Heidelberg: Springer Berlin Heidelberg, 91–120. |
[42] |
Ahmed N, Darwish S, Alahmari AM (2016) Laser ablation and laser-hybrid ablation processes: a review. Mater Manuf Process 31: 1121–1142. https://doi.org/10.1080/10426914.2015.1048359. doi: 10.1080/10426914.2015.1048359
![]() |
[43] |
Von der Linde D, Sokolowski-Tinten K (2000) The physical mechanisms of short-pulse laser ablation. Appl Surf Sci 154: 1–10. https://doi.org/10.1016/S0169-4332(99)00440-7. doi: 10.1016/S0169-4332(99)00440-7
![]() |
[44] |
Hoffman J (2015) The effect of recoil pressure in the ablation of polycrystalline graphite by a nanosecond laser pulse. J Phys D Appl Phys 48: 235201. https://doi.org/10.1088/0022-3727/48/23/235201. doi: 10.1088/0022-3727/48/23/235201
![]() |
[45] |
Tangwarodomnukun V, Likhitangsuwat P, Tevinpibanphan O, et al. (2015) Laser ablation of titanium alloy under a thin and flowing water layer. Int J Mach Tool Manu 89: 14–28. https://doi.org/10.1016/j.ijmachtools.2014.10.013. doi: 10.1016/j.ijmachtools.2014.10.013
![]() |
[46] |
Singh S, Argument M, Tsui Y, et al. (2005) Effect of ambient air pressure on debris redeposition during laser ablation of glass. J Appl Phys 98: 113520. https://doi.org/10.1063/1.2138800. doi: 10.1063/1.2138800
![]() |
[47] |
Miotello A, Kelly R (1999) Laser-induced phase explosion: new physical problems when a condensed phase approaches the thermodynamic critical temperature. Appl Phys A 69: S67–S73. https://doi.org/10.1007/s003399900296. doi: 10.1007/s003399900296
![]() |
[48] |
Bulgakova N, Bulgakov A (2001) Pulsed laser ablation of solids: transition from normal vaporization to phase explosion. Appl Phys A 73: 199–208. https://doi.org/10.1007/s003390000686. doi: 10.1007/s003390000686
![]() |
[49] |
Sarma U, Joshi SN (2020) Numerical modelling and simulation of microchannel fabrication on polycarbonate using Laser-Induced Plasma Assisted Ablation (LIPAA). Optik 223: 165379. https://doi.org/10.1016/j.ijleo.2020.165379. doi: 10.1016/j.ijleo.2020.165379
![]() |
[50] |
Aoki Y, Yoshioka K (2014) Preparation and characterization of highly heat-resistant organic–inorganic hybrid materials made from two-component polydimethylsiloxane. Mol Cryst Liq Cryst 597: 59–64. https://doi.org/10.1080/15421406.2014.932224. doi: 10.1080/15421406.2014.932224
![]() |
[51] |
Samant AN, Dahotre NB (2008) Computational predictions in single-dimensional laser machining of alumina. Int J Mach Tool Manu 48: 1345–1353. https://doi.org/10.1016/j.ijmachtools.2008.05.004. doi: 10.1016/j.ijmachtools.2008.05.004
![]() |
[52] |
Zhou J, Shen H, Pan Y, et al. (2016) Experimental study on laser microstructures using long pulse. Opt Laser Eng 78: 113–120. https://doi.org/10.1016/j.optlaseng.2015.10.009. doi: 10.1016/j.optlaseng.2015.10.009
![]() |
1. | Adem Yolcu, Aysun Benek, Taha Yasin Öztürk, A new approach to neutrosophic soft rough sets, 2023, 0219-1377, 10.1007/s10115-022-01824-z | |
2. | Quang-Thinh Bui, My-Phuong Ngo, Vaclav Snasel, Witold Pedrycz, Bay Vo, Information measures based on similarity under neutrosophic fuzzy environment and multi-criteria decision problems, 2023, 122, 09521976, 106026, 10.1016/j.engappai.2023.106026 | |
3. | Bing Chen, Xiao Long Xin, Xiao Fei Yang, Distance functions and filter topological residuated lattices1, 2024, 10641246, 1, 10.3233/JIFS-238147 | |
4. | Nechervan B. Ibrahim, Alias B. Khalaf, On maximal block topological space, 2023, 45, 10641246, 8541, 10.3233/JIFS-223749 | |
5. | Aysun Benek, Taha Yasin Öztürk, Adem Yolcu, 2024, chapter 4, 9798369320853, 67, 10.4018/979-8-3693-2085-3.ch004 |
M | e1 | e2 | N | e1 | e2 |
x1 | ⟨.2,.3,.4⟩ | ⟨.3,.5,.5⟩ | x1 | ⟨.3,.6,.1⟩ | ⟨.4,.5,.4⟩ |
x2 | ⟨.3,.4,.3⟩ | ⟨.6,.2,.4⟩ | x2 | ⟨.6,.5,.2⟩ | ⟨.7,.3,.2⟩ |
x3 | ⟨.3,.5,.2⟩ | ⟨.4,.4,.3⟩ | x3 | ⟨.4,.5,.3⟩ | ⟨.6,.3,.3⟩ |
x4 | ⟨.2,.7,.6⟩ | ⟨.3,.4,.3⟩ | x4 | ⟨.9,1,.4⟩ | ⟨.4,.5,.1⟩ |
P | e1 | e2 | e3 | ¯P | e1 | e2 | e3 |
x1 | ⟨.9,.8,.2⟩ | ⟨.3,.7,.2⟩ | ⟨.4,.6,.3⟩ | x1 | ⟨.2,.2,.9⟩ | ⟨.2,.3,.3⟩ | ⟨.3,.4,.4⟩ |
x2 | ⟨.7,.6,.2⟩ | ⟨.3,.4,.6⟩ | ⟨.4,.3,.2⟩ | x2 | ⟨.2,.4,.7⟩ | ⟨.6,.6,.3⟩ | ⟨.2,.7,.4⟩ |
x3 | ⟨.3,.3,.5⟩ | ⟨.1,.2,.3⟩ | ⟨.9,.5,.8⟩ | x3 | ⟨.5,.7,.3⟩ | ⟨.3,.8,.1⟩ | ⟨.8,.5,.9⟩ |
min−norms | max−norms | |
1 | ∀x,y∈[0,1],x∙y=xy | ∀x,y∈[0,1],x∘y=x+y−xy |
2 | ∀x,y∈[0,1],x∙y=min{x,y} | ∀x,y∈[0,1],x∘y=max{x,y} |
3 | ∀x,y∈[0,1],x∙y=max{x+y−1,0} | ∀x,y∈[0,1],x∘y=min{x+y,1} |
A | e1 | e2 | B | e1 | e2 |
x1 | ⟨.2,.4,.5⟩ | ⟨.5,.2,.8⟩ | x1 | ⟨.7,.2,.7⟩ | ⟨.6,0,.5⟩ |
x2 | ⟨.1,.4,.3⟩ | ⟨.8,.9,.4⟩ | x2 | ⟨.3,.9,.1⟩ | ⟨.7,.7,.9⟩ |
x3 | ⟨.1,.2,.7⟩ | ⟨.8,.9,.4⟩ | x3 | ⟨.6,.4,.7⟩ | ⟨.8,.1,0⟩ |
A∩B | e1 | e2 |
x1 | ⟨0,0,1⟩ | ⟨.1,0,1⟩ |
x2 | ⟨0,.3,.4⟩ | ⟨.5,.6,1⟩ |
x3 | ⟨0,0,1⟩ | ⟨.6,0,.4⟩ |
A∪B | e1 | e2 |
x1 | ⟨.9,.6,.2⟩ | ⟨1,.2,.3⟩ |
x2 | ⟨.4,1,1⟩ | ⟨1,1,.3⟩ |
x3 | ⟨.7,.6,.4⟩ | ⟨1,1,0⟩ |
C | e1 | e2 |
x1 | ⟨.2,.1,.9⟩ | ⟨.3,.2,.6⟩ |
x2 | ⟨.3,.7,.6⟩ | ⟨.8,.2,.5⟩ |
x3 | ⟨.2,.1,.4⟩ | ⟨.3,.2,.5⟩ |
A∩(B∪C) | e1 | e2 | (A∩B)∪(A∩C) | e1 | e2 |
x1 | ⟨.1,0,1⟩ | ⟨.4,0,.9⟩ | x1 | ⟨0,0,1⟩ | ⟨.1,0,1⟩ |
x2 | ⟨0,1,.3⟩ | ⟨.8,.8,.8⟩ | x2 | ⟨0,.4,.3⟩ | ⟨.6,.1,.9⟩ |
x3 | ⟨0,0,.8⟩ | ⟨.8,.2,.4⟩ | x3 | ⟨0,0,1⟩ | ⟨.1,.1,.9⟩ |
A∪(B∩C) | e1 | e2 | (A∪B)∩(A∪C) | e1 | e2 |
x1 | ⟨.2,.4,.5⟩ | ⟨.5,.2,.8⟩ | x1 | ⟨.3,.1,.6⟩ | ⟨.8,0,.7⟩ |
x2 | ⟨.1,1,0⟩ | ⟨1,.9,.4⟩ | x2 | ⟨0,1,1⟩ | ⟨1,1,.3⟩ |
x3 | ⟨.1,.2,.7⟩ | ⟨.9,.9,0⟩ | x3 | ⟨0,0,.5⟩ | ⟨1,1,0⟩ |
A∖B | e1 | e2 |
x1 | ⟨0,.2,1⟩ | ⟨0,.2,1⟩ |
x2 | ⟨0,0,.6⟩ | ⟨.7,.2,1⟩ |
x3 | ⟨0,0,1⟩ | ⟨0,.8,1⟩ |
A∧B | (e1,e1) | (e1,e2) | (e2,e1) | (e2,e2) | A∨B | (e1,e1) | (e1,e2) | (e2,e1) | (e2,e2) |
x1 | ⟨0,0,1⟩ | ⟨0,0,1⟩ | ⟨.2,0,1⟩ | ⟨.1,0,1⟩ | x1 | ⟨.9,.6,.2⟩ | ⟨.8,.4,1⟩ | ⟨1,.4,.5⟩ | ⟨1,.2,.3⟩ |
x2 | ⟨0,.3,.4⟩ | ⟨0,.1,1⟩ | ⟨.1,.8,.5⟩ | ⟨.5,.6,1⟩ | x2 | ⟨.4,1,1⟩ | ⟨.8,1,.2⟩ | ⟨1,1,0⟩ | ⟨1,1,.3⟩ |
x3 | ⟨0,0,1⟩ | ⟨0,0,.7⟩ | ⟨.4,.3,.1⟩ | ⟨.6,0,.4⟩ | x3 | ⟨.7,.6,.4⟩ | ⟨.9,.3,0⟩ | ⟨1,1,.1⟩ | ⟨1,1,0⟩ |
K1 | e1 | e2 | K2 | e1 | e2 | K3 | e1 | e2 |
x1 | ⟨.2,.2,1⟩ | ⟨.5,.5,.7⟩ | x1 | ⟨.3,.3,.9⟩ | ⟨.6,.6,.6⟩ | x1 | ⟨.4,.4,.8⟩ | ⟨.7,.7,.5⟩ |
x2 | ⟨.3,.3,.9⟩ | ⟨.6,.6,.6⟩ | x2 | ⟨.4,.4,.8⟩ | ⟨.7,.7,.5⟩ | x2 | ⟨.5,.5,.7⟩ | ⟨.8,.8,.4⟩ |
x3 | ⟨.4,.4,.8⟩ | ⟨0,.9,1⟩ | x3 | ⟨.5,.5,.7⟩ | ⟨.8,.8,.4⟩ | x3 | ⟨.6,.6,.6⟩ | ⟨.9,.9,.3⟩ |
H1 | e1 | e2 | H2 | e1 | e2 | H3 | e1 | e2 |
x1 | ⟨1,0,1⟩ | ⟨0,1,0⟩ | x1 | ⟨0,1,0⟩ | ⟨1,0,1⟩ | x1 | ⟨1,0,1⟩ | ⟨1,0,1⟩ |
x2 | ⟨1,0,1⟩ | ⟨0,1,0⟩ | x2 | ⟨0,1,0⟩ | ⟨1,0,1⟩ | x2 | ⟨1,0,1⟩ | ⟨1,0,1⟩ |
H1∪H2 | e1 | e2 |
x1 | ⟨1,0,1⟩ | ⟨1,1,0⟩ |
x2 | ⟨1,0,1⟩ | ⟨1,1,0⟩ |
L1 | e1 | e2 | L2 | e1 | e2 | K | e1 | e2 |
x1 | ⟨.7,.8,.3⟩ | ⟨.4,.5,.8⟩ | x1 | ⟨.3,.2,.7⟩ | ⟨.6,.5,.2⟩ | x1 | ⟨.8,.8,.3⟩ | ⟨.8,.8,.3⟩ |
x2 | ⟨.5,.2,.6⟩ | ⟨.3,.4,.2⟩ | x2 | ⟨.5,.8,.4⟩ | ⟨.7,.6,.8⟩ | x2 | ⟨.4,.6,.5⟩ | ⟨.4,.6,.5⟩ |
H | e1 | e2 | ¯L1 | e1 | e2 | e1 | e2 |
x1 | ⟨.2,.2,.8⟩ | ⟨.2,.2,.8⟩ | x1 | ⟨.3,.2,.7⟩ | ⟨.8,.5,.4⟩ | ⟨.7,.8,.3⟩ | ⟨.2,.5,.6⟩ |
x2 | ⟨.3,.4,.8⟩ | ⟨.3,.4,.8⟩ | x2 | ⟨.6,.8,.5⟩ | ⟨.2,.6,.3⟩ | ⟨.4,.2,.5⟩ | ⟨.8,.4,.7⟩ |
¯H | e1 | e2 |
x1 | ⟨.8,.8,.2⟩ | ⟨.8,.8,.2⟩ |
x2 | ⟨.8,.6,.3⟩ | ⟨.8,.6,.3⟩ |
e1 | e2 | ||
H1 | x1 | ⟨.25,.25,.75⟩ | ⟨.25,.25,.75⟩ |
x2 | ⟨13,13,13⟩ | ⟨13,13,13⟩ | |
H2 | x1 | ⟨.5,.75,.5⟩ | ⟨.5,.75,.5⟩ |
x2 | ⟨13,23,23⟩ | ⟨13,23,23⟩ | |
H3 | x1 | ⟨.75,.5,.25⟩ | ⟨.75,.5,.25⟩ |
x2 | ⟨23,13,13⟩ | ⟨23,13,13⟩ | |
H1∪H2 | x1 | ⟨.75,1,.25⟩ | ⟨.75,1,.25⟩ |
x2 | ⟨23,1,0⟩ | ⟨23,1,0⟩ |
e1 | e2 | ||
T | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
T1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
T2 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
T3 | ⟨.75,23⟩ | ⟨.75,23⟩ | |
I | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
I1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
I2 | ⟨.75,23⟩ | ⟨.75,23⟩ | |
I3 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
F | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
F1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
F2 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
F3 | ⟨.75,23⟩ | ⟨.75,23⟩ |
M | e1 | e2 | N | e1 | e2 |
x1 | ⟨.2,.3,.4⟩ | ⟨.3,.5,.5⟩ | x1 | ⟨.3,.6,.1⟩ | ⟨.4,.5,.4⟩ |
x2 | ⟨.3,.4,.3⟩ | ⟨.6,.2,.4⟩ | x2 | ⟨.6,.5,.2⟩ | ⟨.7,.3,.2⟩ |
x3 | ⟨.3,.5,.2⟩ | ⟨.4,.4,.3⟩ | x3 | ⟨.4,.5,.3⟩ | ⟨.6,.3,.3⟩ |
x4 | ⟨.2,.7,.6⟩ | ⟨.3,.4,.3⟩ | x4 | ⟨.9,1,.4⟩ | ⟨.4,.5,.1⟩ |
P | e1 | e2 | e3 | ¯P | e1 | e2 | e3 |
x1 | ⟨.9,.8,.2⟩ | ⟨.3,.7,.2⟩ | ⟨.4,.6,.3⟩ | x1 | ⟨.2,.2,.9⟩ | ⟨.2,.3,.3⟩ | ⟨.3,.4,.4⟩ |
x2 | ⟨.7,.6,.2⟩ | ⟨.3,.4,.6⟩ | ⟨.4,.3,.2⟩ | x2 | ⟨.2,.4,.7⟩ | ⟨.6,.6,.3⟩ | ⟨.2,.7,.4⟩ |
x3 | ⟨.3,.3,.5⟩ | ⟨.1,.2,.3⟩ | ⟨.9,.5,.8⟩ | x3 | ⟨.5,.7,.3⟩ | ⟨.3,.8,.1⟩ | ⟨.8,.5,.9⟩ |
min−norms | max−norms | |
1 | ∀x,y∈[0,1],x∙y=xy | ∀x,y∈[0,1],x∘y=x+y−xy |
2 | ∀x,y∈[0,1],x∙y=min{x,y} | ∀x,y∈[0,1],x∘y=max{x,y} |
3 | ∀x,y∈[0,1],x∙y=max{x+y−1,0} | ∀x,y∈[0,1],x∘y=min{x+y,1} |
A | e1 | e2 | B | e1 | e2 |
x1 | ⟨.2,.4,.5⟩ | ⟨.5,.2,.8⟩ | x1 | ⟨.7,.2,.7⟩ | ⟨.6,0,.5⟩ |
x2 | ⟨.1,.4,.3⟩ | ⟨.8,.9,.4⟩ | x2 | ⟨.3,.9,.1⟩ | ⟨.7,.7,.9⟩ |
x3 | ⟨.1,.2,.7⟩ | ⟨.8,.9,.4⟩ | x3 | ⟨.6,.4,.7⟩ | ⟨.8,.1,0⟩ |
A∩B | e1 | e2 |
x1 | ⟨0,0,1⟩ | ⟨.1,0,1⟩ |
x2 | ⟨0,.3,.4⟩ | ⟨.5,.6,1⟩ |
x3 | ⟨0,0,1⟩ | ⟨.6,0,.4⟩ |
A∪B | e1 | e2 |
x1 | ⟨.9,.6,.2⟩ | ⟨1,.2,.3⟩ |
x2 | ⟨.4,1,1⟩ | ⟨1,1,.3⟩ |
x3 | ⟨.7,.6,.4⟩ | ⟨1,1,0⟩ |
C | e1 | e2 |
x1 | ⟨.2,.1,.9⟩ | ⟨.3,.2,.6⟩ |
x2 | ⟨.3,.7,.6⟩ | ⟨.8,.2,.5⟩ |
x3 | ⟨.2,.1,.4⟩ | ⟨.3,.2,.5⟩ |
A∩(B∪C) | e1 | e2 | (A∩B)∪(A∩C) | e1 | e2 |
x1 | ⟨.1,0,1⟩ | ⟨.4,0,.9⟩ | x1 | ⟨0,0,1⟩ | ⟨.1,0,1⟩ |
x2 | ⟨0,1,.3⟩ | ⟨.8,.8,.8⟩ | x2 | ⟨0,.4,.3⟩ | ⟨.6,.1,.9⟩ |
x3 | ⟨0,0,.8⟩ | ⟨.8,.2,.4⟩ | x3 | ⟨0,0,1⟩ | ⟨.1,.1,.9⟩ |
A∪(B∩C) | e1 | e2 | (A∪B)∩(A∪C) | e1 | e2 |
x1 | ⟨.2,.4,.5⟩ | ⟨.5,.2,.8⟩ | x1 | ⟨.3,.1,.6⟩ | ⟨.8,0,.7⟩ |
x2 | ⟨.1,1,0⟩ | ⟨1,.9,.4⟩ | x2 | ⟨0,1,1⟩ | ⟨1,1,.3⟩ |
x3 | ⟨.1,.2,.7⟩ | ⟨.9,.9,0⟩ | x3 | ⟨0,0,.5⟩ | ⟨1,1,0⟩ |
A∖B | e1 | e2 |
x1 | ⟨0,.2,1⟩ | ⟨0,.2,1⟩ |
x2 | ⟨0,0,.6⟩ | ⟨.7,.2,1⟩ |
x3 | ⟨0,0,1⟩ | ⟨0,.8,1⟩ |
A∧B | (e1,e1) | (e1,e2) | (e2,e1) | (e2,e2) | A∨B | (e1,e1) | (e1,e2) | (e2,e1) | (e2,e2) |
x1 | ⟨0,0,1⟩ | ⟨0,0,1⟩ | ⟨.2,0,1⟩ | ⟨.1,0,1⟩ | x1 | ⟨.9,.6,.2⟩ | ⟨.8,.4,1⟩ | ⟨1,.4,.5⟩ | ⟨1,.2,.3⟩ |
x2 | ⟨0,.3,.4⟩ | ⟨0,.1,1⟩ | ⟨.1,.8,.5⟩ | ⟨.5,.6,1⟩ | x2 | ⟨.4,1,1⟩ | ⟨.8,1,.2⟩ | ⟨1,1,0⟩ | ⟨1,1,.3⟩ |
x3 | ⟨0,0,1⟩ | ⟨0,0,.7⟩ | ⟨.4,.3,.1⟩ | ⟨.6,0,.4⟩ | x3 | ⟨.7,.6,.4⟩ | ⟨.9,.3,0⟩ | ⟨1,1,.1⟩ | ⟨1,1,0⟩ |
K1 | e1 | e2 | K2 | e1 | e2 | K3 | e1 | e2 |
x1 | ⟨.2,.2,1⟩ | ⟨.5,.5,.7⟩ | x1 | ⟨.3,.3,.9⟩ | ⟨.6,.6,.6⟩ | x1 | ⟨.4,.4,.8⟩ | ⟨.7,.7,.5⟩ |
x2 | ⟨.3,.3,.9⟩ | ⟨.6,.6,.6⟩ | x2 | ⟨.4,.4,.8⟩ | ⟨.7,.7,.5⟩ | x2 | ⟨.5,.5,.7⟩ | ⟨.8,.8,.4⟩ |
x3 | ⟨.4,.4,.8⟩ | ⟨0,.9,1⟩ | x3 | ⟨.5,.5,.7⟩ | ⟨.8,.8,.4⟩ | x3 | ⟨.6,.6,.6⟩ | ⟨.9,.9,.3⟩ |
H1 | e1 | e2 | H2 | e1 | e2 | H3 | e1 | e2 |
x1 | ⟨1,0,1⟩ | ⟨0,1,0⟩ | x1 | ⟨0,1,0⟩ | ⟨1,0,1⟩ | x1 | ⟨1,0,1⟩ | ⟨1,0,1⟩ |
x2 | ⟨1,0,1⟩ | ⟨0,1,0⟩ | x2 | ⟨0,1,0⟩ | ⟨1,0,1⟩ | x2 | ⟨1,0,1⟩ | ⟨1,0,1⟩ |
H1∪H2 | e1 | e2 |
x1 | ⟨1,0,1⟩ | ⟨1,1,0⟩ |
x2 | ⟨1,0,1⟩ | ⟨1,1,0⟩ |
L1 | e1 | e2 | L2 | e1 | e2 | K | e1 | e2 |
x1 | ⟨.7,.8,.3⟩ | ⟨.4,.5,.8⟩ | x1 | ⟨.3,.2,.7⟩ | ⟨.6,.5,.2⟩ | x1 | ⟨.8,.8,.3⟩ | ⟨.8,.8,.3⟩ |
x2 | ⟨.5,.2,.6⟩ | ⟨.3,.4,.2⟩ | x2 | ⟨.5,.8,.4⟩ | ⟨.7,.6,.8⟩ | x2 | ⟨.4,.6,.5⟩ | ⟨.4,.6,.5⟩ |
H | e1 | e2 | ¯L1 | e1 | e2 | e1 | e2 |
x1 | ⟨.2,.2,.8⟩ | ⟨.2,.2,.8⟩ | x1 | ⟨.3,.2,.7⟩ | ⟨.8,.5,.4⟩ | ⟨.7,.8,.3⟩ | ⟨.2,.5,.6⟩ |
x2 | ⟨.3,.4,.8⟩ | ⟨.3,.4,.8⟩ | x2 | ⟨.6,.8,.5⟩ | ⟨.2,.6,.3⟩ | ⟨.4,.2,.5⟩ | ⟨.8,.4,.7⟩ |
¯H | e1 | e2 |
x1 | ⟨.8,.8,.2⟩ | ⟨.8,.8,.2⟩ |
x2 | ⟨.8,.6,.3⟩ | ⟨.8,.6,.3⟩ |
e1 | e2 | ||
H1 | x1 | ⟨.25,.25,.75⟩ | ⟨.25,.25,.75⟩ |
x2 | ⟨13,13,13⟩ | ⟨13,13,13⟩ | |
H2 | x1 | ⟨.5,.75,.5⟩ | ⟨.5,.75,.5⟩ |
x2 | ⟨13,23,23⟩ | ⟨13,23,23⟩ | |
H3 | x1 | ⟨.75,.5,.25⟩ | ⟨.75,.5,.25⟩ |
x2 | ⟨23,13,13⟩ | ⟨23,13,13⟩ | |
H1∪H2 | x1 | ⟨.75,1,.25⟩ | ⟨.75,1,.25⟩ |
x2 | ⟨23,1,0⟩ | ⟨23,1,0⟩ |
e1 | e2 | ||
T | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
T1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
T2 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
T3 | ⟨.75,23⟩ | ⟨.75,23⟩ | |
I | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
I1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
I2 | ⟨.75,23⟩ | ⟨.75,23⟩ | |
I3 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
F | ˜∅ | ⟨0,0⟩ | ⟨0,0⟩ |
˜X | ⟨1,1⟩ | ⟨1,1⟩ | |
F1 | ⟨.25,13⟩ | ⟨.25,13⟩ | |
F2 | ⟨.5,13⟩ | ⟨.5,13⟩ | |
F3 | ⟨.75,23⟩ | ⟨.75,23⟩ |