
Assessing potential threats typically necessitates the use of a robust mathematical model, a comprehensive evaluation method and universal decision rules. A novel approach is utilized in this study to optimize existing threat assessment (TA) algorithms and three-way decision models (3WDMs) are leveraged that incorporate decision-theoretic rough sets (DTRSs) within dynamic intuitionistic fuzzy (IF) environments to create a mixed-attitude 3WDM based on the IF-VIKOR-GRA method in the context of aviation warfare. The primary objectives of this study include determining conditional probabilities for IF three-way decisions (3WDs) and establishing mixed-attitude decision thresholds. Both the target attribute and loss function are expressed in the form of intuitionistic fuzzy numbers (IFNs). To calculate these conditional probabilities, an IF technique is used to combine the multi-attribute decision-making (MADM) method VIKOR and the grey relational analysis (GRA) method, while also taking into account the risk-related preferences of decision-makers (DMs). Optimistic and pessimistic 3WDMs are developed from the perspectives of membership degree and non-membership degree, then subsequently integrated into the comprehensive mixed-attitude 3WDM. The feasibility and effectiveness of this methodology are demonstrated through a numerical example and by comparison to other existing approaches.
Citation: Qiwen Wang, Guibao Song, Xiuxia Yang. Mixed-attitude three-way decision model for aerial targets: Threat assessment based on IF-VIKOR-GRA method[J]. Mathematical Biosciences and Engineering, 2023, 20(12): 21514-21536. doi: 10.3934/mbe.2023952
[1] | Sugenendran Supramani, Rahayu Ahmad, Zul Ilham, Mohamad Suffian Mohamad Annuar, Anita Klaus, Wan Abd Al Qadr Imad Wan-Mohtar . Optimisation of biomass, exopolysaccharide and intracellular polysaccharide production from the mycelium of an identified Ganoderma lucidum strain QRS 5120 using response surface methodology. AIMS Microbiology, 2019, 5(1): 19-38. doi: 10.3934/microbiol.2019.1.19 |
[2] | Hafidh Shofwan Maajid, Nurliyani Nurliyani, Widodo Widodo . Exopolysaccharide production in fermented milk using Lactobacillus casei strains AP and AG. AIMS Microbiology, 2022, 8(2): 138-152. doi: 10.3934/microbiol.2022012 |
[3] | Maria Parapouli, Anastasios Vasileiadis, Amalia-Sofia Afendra, Efstathios Hatziloukas . Saccharomyces cerevisiae and its industrial applications. AIMS Microbiology, 2020, 6(1): 1-31. doi: 10.3934/microbiol.2020001 |
[4] | Tru Tran, Stephanie N. Dawrs, Grant J. Norton, Ravleen Virdi, Jennifer R. Honda . Brought to you courtesy of the red, white, and blue–pigments of nontuberculous mycobacteria. AIMS Microbiology, 2020, 6(4): 434-450. doi: 10.3934/microbiol.2020026 |
[5] | Nurul Alia Syufina Abu Bakar, Nur Aliyyah Khuzaini, Siti Baidurah . Co-fermentation involving Lysinibacillus sp. and Aspergillus flavus for simultaneous palm oil waste treatment and renewable biomass fuel production. AIMS Microbiology, 2022, 8(3): 357-371. doi: 10.3934/microbiol.2022025 |
[6] | Ogueri Nwaiwu, Chiugo Claret Aduba . An in silico analysis of acquired antimicrobial resistance genes in Aeromonas plasmids. AIMS Microbiology, 2020, 6(1): 75-91. doi: 10.3934/microbiol.2020005 |
[7] | Dimitra Papadopoulou, Vasiliki Chrysikopoulou, Aikaterini Rampaouni, Alexandros Tsoupras . Antioxidant and anti-inflammatory properties of water kefir microbiota and its bioactive metabolites for health promoting bio-functional products and applications. AIMS Microbiology, 2024, 10(4): 756-811. doi: 10.3934/microbiol.2024034 |
[8] | Thomas Bintsis . Lactic acid bacteria as starter cultures: An update in their metabolism and genetics. AIMS Microbiology, 2018, 4(4): 665-684. doi: 10.3934/microbiol.2018.4.665 |
[9] | Kholoud Baraka, Rania Abozahra, Maged Wasfy Helmy, Nada Salah El Dine El Meniawy, Sarah M Abdelhamid . Investigation of the protective and therapeutic effects of Lactobacillus casei and Saccharomyces cerevisiae in a breast cancer mouse model. AIMS Microbiology, 2022, 8(2): 193-207. doi: 10.3934/microbiol.2022016 |
[10] | Oluwafolajimi Adesanya, Tolulope Oduselu, Oluwawapelumi Akin-Ajani, Olubusuyi M. Adewumi, Olusegun G. Ademowo . An exegesis of bacteriophage therapy: An emerging player in the fight against anti-microbial resistance. AIMS Microbiology, 2020, 6(3): 204-230. doi: 10.3934/microbiol.2020014 |
Assessing potential threats typically necessitates the use of a robust mathematical model, a comprehensive evaluation method and universal decision rules. A novel approach is utilized in this study to optimize existing threat assessment (TA) algorithms and three-way decision models (3WDMs) are leveraged that incorporate decision-theoretic rough sets (DTRSs) within dynamic intuitionistic fuzzy (IF) environments to create a mixed-attitude 3WDM based on the IF-VIKOR-GRA method in the context of aviation warfare. The primary objectives of this study include determining conditional probabilities for IF three-way decisions (3WDs) and establishing mixed-attitude decision thresholds. Both the target attribute and loss function are expressed in the form of intuitionistic fuzzy numbers (IFNs). To calculate these conditional probabilities, an IF technique is used to combine the multi-attribute decision-making (MADM) method VIKOR and the grey relational analysis (GRA) method, while also taking into account the risk-related preferences of decision-makers (DMs). Optimistic and pessimistic 3WDMs are developed from the perspectives of membership degree and non-membership degree, then subsequently integrated into the comprehensive mixed-attitude 3WDM. The feasibility and effectiveness of this methodology are demonstrated through a numerical example and by comparison to other existing approaches.
Dedicated to our friend Giuseppe (Rosario) Mingione on his 50th birthday.
Let us consider the functional
F(v)=∫Ω[|Dv|p+|detDv|r]dx, |
where v:Ω⊂Rn→Rn, n≥2, Ω a bounded open set, p>1, r>0.
It is well known that, if u is a minimizer for F(v), the maximum principle holds, namely, each component uα of u=(u1,...,un) satisfies the following condition
uα(x)≤sup∂Ωuα,α∈{1,2,…,n}. |
Indeed, maximum principle holds true, in general, for minimizers of the class of functionals
F(v)=∫ΩΨ(|Dv|,|detDv|)dx, | (1.1) |
where the integrand Ψ(s,t) is such that s→Ψ(s,t) strictly increases, and t→Ψ(s,t) is increasing (see [39]).
What happens when we only have that s→Ψ(s,t) is increasing and not necessarily strictly increasing? Two examples are Ψ(s,t)=|t| that gives
F(v)=∫Ω|detDv|dx, | (1.2) |
and Ψ(s,t)=max{|s|p−1;0}+|t|r that gives
F(v)=∫Ω(max{|Dv|p−1;0}+|detDv|r)dx, | (1.3) |
with p>1 and r>0. Maximum principle fails. Namely, consider n=2, Ω⊂R2 is the ball B(0;π) centered in the origin and with radius π.
The map u:=(1,1+sin|x|) has gradient
Du=[00x1|x|cos|x|x2|x|cos|x|], |
detDu=0, and |Du|2=cos2|x|≤1. It minimizes both the functionals (1.2) and (1.3). Moreover, the
second component u2=1+sin|x| equals 1 on the boundary of Ω, and is strictly greater than 1 inside. Therefore, the second component of the minimizer u does not satisfy the maximum principle. This example was given to the last author by V. Sverak a few years ago. F. Leonetti gladly takes the opportunity to thank V. Sverak for his kindness.
Furthermore, regarding the previous example, it is worth pointing out that the level set {x∈Ω:u2(x)>1=u2∂Ω} has positive measure
L2({x∈Ω:u2(x)>1=u2∂Ω})=L2(Ω)>0, | (1.4) |
on the other hand, the measure of the image of the same level set, by means of u, is zero
L2(u({x∈Ω:u2(x)>1=u2∂Ω}))=0, | (1.5) |
see Figure 1.
We ask ourselves whether the previous example shows a common feature to all minimizers when t→Ψ(s,t) strictly increases.
In this paper, we give a positive answer to previous question obtaining a modified version of maximum principle in the case the integrand Ψ(s,t) of the functional (1.1) strictly increases only with respect to the second variable t.
We will suppose p>n in order to ensure semicontinuity property and consequent existence of minimizers (see [17]), and also to apply the area formula, that reveals to be a key tool in our proof.
In addition, we can still get a similar maximum principle by using a version of the area formula for u∈W1,1(Ω,Rn), see [34,35], provided a suitable negligible set S=Ω∖AD is removed (see definition 2.1).
Let us come back to the functional (1.3): coercivity holds true with exponent p and growth from above with exponent q=:nr that could be different from p. When we deal with functionals with different growth, regularity for minimizers is usually obtained when the two exponents of growth and coercivity are not too far apart, see [3,6,10,11,12,13,18,32,49,50]. In our case, we do not assume anything on the distance between the two exponents p and q. This is not in contradiction with the counterexamples in the double phase case [22,25], since our functional (1.3) is autonomous, neither is in contrast with counterexamples in the autonomous case [33,38,47,48], since they show blow up along a line that intersects the boundary of Ω while, in our case, minimizers are bounded on ∂Ω.
With regard to the regularity of minimizers u of (1.1), let us mention partial regularity results in [9,23,26,27,28,30,36,52]. Everywhere regularity results can be found in [7,19,29,31], for n=2. We also mention global L∞ bounds in [4,5,21,39,40,41,42,43,44], and local L∞ regularity in [8,14,15,16,20]. Furthermore, concerning nonlinear elasticity, we cite, in particular, the results in [1,37,45,46,51].
In the next section 2 we write some preliminaries. In section 3 we state our result and we give the proof.
In order to obtain our result, we need that the area formula holds. Therefore, let us recall the following
Definition 2.1. Let u:Rn→Rn be a map which is almost everywhere approximately differentiable and let A be a measurable subset of Rn. We define the Banach indicatrix of u by
N(u,A,y):=♯{x:x∈A∩AD(u),u(x)=y} |
where
AD(u)={x:uisapproximatelydifferentiableatx}, |
and the theorem
Theorem 2.2. (see Theorem 1 in section 1.5, chapter 3, at page 220 of [35]) Let Ω be an open subset of Rn and u be an almost everywhere approximately differentiable map, in particular let u∈W1,1(Ω;Rn). Then for any measurable subset A of Ω we have that N(u,A,⋅) is measurable and
∫A|detDu(x)|dx=∫RnN(u,A,y)dy | (2.1) |
holds.
Furthermore, a related condition we will refer to is the Lusin property (N) that is so defined
Definition 2.3. (Lusin property (N)) Let Ω⊂Rn be an open set and f:Ω→Rn a mapping. We say that f satisfies Lusin property (N) if the implication
Ln(E)=0⟹Ln(f(E))=0 |
holds for each subset E⊂Ω.
Let Ψ:[0,+∞)×[0,+∞)→R be a continuous non negative function such that
s→Ψ(s,t) is increasing for every t∈[0,+∞) | (H1) |
t→Ψ(s,t) is strictly increasing for every s∈[0,+∞), | (H2) |
and let us denote Ω⊂Rn a bounded open set. We will consider integral functional of the type
F(u):=∫ΩΨ(|Du|,|detDu|) dx. | (3.1) |
Definition 3.1. Let p≥1 and u∈W1,p(Ω;Rn) such that F(u)<∞. We will say that u is a minimizer of F in Ω, if and only if
F(u)≤F(v)∀v∈u+W1,p0(Ω;Rn). | (3.2) |
The main result is the following
Theorem 3.2. Let u∈W1,p(Ω;Rn), p>n, be the continuous representative of a minimizer of the functional (3.1), under assumptions (H1) and (H2). Fix α∈{1,…,n}, and let us denote
Lα:=supx∈∂Ωuα(x)<+∞,BLα:={x∈Ω:uα(x)>Lα}, |
BLα is the set of points in Ω where the maximum principle is violated, then
Ln(u(BLα))=0. | (3.3) |
Proof. Let us define
vβ(x):={uβ(x)ifβ≠αmin{uα(x);Lα}ifβ=α. |
It results that v is a good test function in (3.2), namely u−v∈W1,p0(Ω;Rn), then we deduce that
F(u)=∫ΩΨ(|Du|,|detDu|) dx≤∫ΩΨ(|Dv|,|detDv|) dx=F(v). | (3.4) |
Let us denote
GLα:={x∈Ω:uα(x)≤Lα}, thenBLα=Ω∖GLα={x∈Ω:uα(x)>Lα}, |
and let us split the integrals in (3.4) on the sets GLα and BLα. Observing that Du≡Dv on the set GLα we can get rid of the common part in (3.4) thus obtaining
∫BLαΨ(|Du|,|detDu|) dx≤∫BLαΨ(|Dv|,|detDv|) dx. |
Now we observe that on BLα, Dvα=0 and detDv=0, then
∫BLαΨ(|Du|,|detDu|) dx≤∫BLαΨ(|Dv|,0) dx |
Now, argue by contradiction, by assuming that
Ln(BLα∩{|detDu|>0})>0. | (3.5) |
At this stage, we recall that |Dv|≤|Du| on BLα, and we use the strict monotonicity of Ψ with respect to the second argument (H2), and hypothesis (H1), to deduce
∫BLαΨ(|Du|,|detDu|) dx≤∫BLαΨ(|Dv|,0) dx<∫BLαΨ(|Dv|,|detDu|) dx≤∫BLαΨ(|Du|,|detDu|) dx, | (3.6) |
thus reaching a contradiction. The previous argument shows that
Ln(BLα∩{|detDu|>0})=0. |
Using the area formula (2.1) we conclude
Ln(u(BLα∩AD(u)))=∫u(BLα∩AD(u))1dy≤∫u(BLα∩AD(u))N(u,BLα,y)dy≤∫RnN(u,BLα,y)dy=∫BLα|detDu| dx=0. | (3.7) |
To conclude the proof we recall that the condition p>n ensures that u:Ω→Rn satisfies the Lusin property (N), that is Ln(u(E))=0 whenever E⊂Ω and Ln(E)=0. In particular Ln(BLα∖AD(u))=0 and this implies that
Ln(u(BLα∖AD(u)))=0. | (3.8) |
Connecting (3.7) and (3.10) we get (3.3).
It is worth pointing out some comments concerning the hypotheses in Theorem 3.2.
As a matter of fact, assuming u∈W1,p(Ω;Rn) for p>n ensures some fundamental conditions.
The first point concerns the existence of minimizers of the functional (3.1). Assuming that p>n guarantees not only that detDu∈L1, but more that the map
u∈W1,p(Ω;Rn)→detDu∈Lpn |
is sequentially continuous with respect to the weak topology (see Theorem 8.20 in [17]). The aforementioned property, that is no longer true for p<n, see [2], is one of the main ingredients to prove the lower semicontinuity of the functional (3.1). The second main ingredient to deduce the existence of minimizers of the functional (3.1) is a kind of convexity assumption on the function Ψ. Precisely, we have that if the function
(X,detX)∈Rn×n×R→Ψ(|X|,|detX|)∈R |
is convex and
C|X|p≤Ψ(|X|,|detX|)∀X∈Rn×n, |
then the functional (3.1) is weakly lower semicontinuous and coercive in W1,p(Ω;Rn). The existence of minimizers of the functional (3.1) follows for any fixed boundary datum u∈W1,p(Ω;Rn) such that F(u)<+∞ (see Theorem 8.31 in [17]; see also [24]).
The second main point, where the assumption p>n is crucial, concerns the Lusin property (N) quoted in the Definition 2.3. It is known that the Lusin property (N) still holds true for u∈W1,n(Ω;Rn), if u is a homeomorphism. Moreover, there are also other results about the validity of the Lusin property (N) for suitable p<n, or with integrability rate close to n under particular assumptions, but, beyond that, the Lusin property (N) is no longer true, in general, for u∈W1,p(Ω;Rn) with p≤n. In this case we can carry on the proof of Theorem 3.2 as before, but we can not conclude in the same way because we do not have any information regarding the set Ln(u(BLα∖AD(u))). Nevertheless we can state the Theorem 3.2 in a weaker form. We need to stress the dependence of the level set BLα={x∈Ω:uα(x)>Lα}=BLα(u) on the considered representative u of the minimizer.
Theorem 3.3. Let u∈W1,p(Ω;Rn), p≥1, be a minimizer of the functional (3.1) under assumptions (H1) and (H2). Fix α∈{1,…,n}, then
Ln(u(BLα(u)∩AD(u)))=0. | (3.9) |
Remark 3.4. We note that (3.9) holds true for every representative u of a W1,p- minimizer (see section 1.5, chapter 3 of [35]). Moreover, in accordance with Corollary 1, chapter 3 of [35], if we consider a Lusin representative u, it satisfies Lusin property (N) in whole Ω so that
Ln(u(BLα(u)∖AD(u)))=0 | (3.10) |
holds, and for such a representative we come to the conclusion that
Ln(u(BLα(u)))=0. | (3.11) |
We acknowledge support by GNAMPA, INdAM, MUR, UNIVAQ, UNISA, UNISANNIO, Università di Napoli "Parthenope" through the Project CoRNDiS, DM MUR 737/2021, CUP I55F21003620001.
The authors declare no conflict of interest.
[1] |
S. Y. Wang, G. Wang, J. R. Zhang, Threat assessment method for air defense targets based on variable weight TOPSIS algorithm, J. Proj. Rockets Missiles Guid., 39 (2019), 171–176. https://doi.org/10.15892/j.cnki.djzdxb.2019.06.037 doi: 10.15892/j.cnki.djzdxb.2019.06.037
![]() |
[2] |
J. J. Yang, K. Li, Two dimensional evaluation of air attack target threat based on parameter and time dimension, J. Ordnance Equip. Eng., 42 (2021), 239–243. https://doi.org/10.11809/bqzbgcxb2021.05.043 doi: 10.11809/bqzbgcxb2021.05.043
![]() |
[3] |
K. Pan, X. H. Pan, X. Q. Guo, Target threat judgment in surface antiaircraft based on MUDP, Comput. Digit. Eng., 42 (2014), 802–805+821. https://doi.org/10.3969/j.issn1672-9722.2014.05.016 doi: 10.3969/j.issn1672-9722.2014.05.016
![]() |
[4] |
H. M. Chai, Y. Zhang, X. Y. Li, Y. N. Song, Aerial target threat assessment method based on deep learning, J. Syst. Simul., 34 (2022), 1459–1467. https://doi.org/10.16182/j.issn1004731x.joss.21-0080 doi: 10.16182/j.issn1004731x.joss.21-0080
![]() |
[5] |
S. P. Kong, H. R. Zhang, X. P. Liao, D. P. Hong, Aerial targets threat assessment based on AHP and entropy weight method, Tactical Missile Technol., 39 (2018), 79–84. https://doi.org/10.16358/j.issn.1009-1300.2018.01.14 doi: 10.16358/j.issn.1009-1300.2018.01.14
![]() |
[6] |
D. Kong, T. Chang, Q. Wang, H. Sun, W. Dai, A threat assessment method of group targets based on interval-valued intuitionistic fuzzy multi-attribute group decision-making, Appl. Soft Comput., 67 (2018), 350–369. https://doi.org/10.1016/j.asoc.2018.03.015 doi: 10.1016/j.asoc.2018.03.015
![]() |
[7] |
J. Feng, Q. Zhang, J. Hu, A. Liu, Dynamic assessment method of air target threat based on improved GIFSS, J. Syst. Eng. Electron., 30 (2019), 525–534. https://doi.org/10.21629/JSEE.2019.03.10 doi: 10.21629/JSEE.2019.03.10
![]() |
[8] |
Y. Q. Lu, C. L. Fan, Q. Fu, X. W. Zhu, W. Li, Missile defense target threat assessment based on improved similarity measure and information entropy of IFRS, Syst. Eng. Electron., 44 (2022), 1230–1238. https://doi.org/10.12305/j.issn.1001-506X.2022.04.20 doi: 10.12305/j.issn.1001-506X.2022.04.20
![]() |
[9] |
C. Jin, J. Sun, Y. J. Wang, P. S. Cai, X. Rong, Threat comprehensive assessment for air defense targets based on intuitionistic fuzzy TOPSIS and variable weight VIKOR, Syst. Eng. Electron., 44 (2022), 172–180. https://doi.org/10.12305/j.issn.1001-506X.2022.01.22 doi: 10.12305/j.issn.1001-506X.2022.01.22
![]() |
[10] |
M. S. Zhang, K. H. Xu, L. Z. Li, Multi-target threat assessment based on intuitionistic fuzzy set and VIKOR, J. Ordnance Equip. Eng., 40 (2019), 62–67. https://doi.org/10.11809/bqzbgcxb2019.06.014 doi: 10.11809/bqzbgcxb2019.06.014
![]() |
[11] |
D. J. Chen, J. Wang, H. W. Zhang, Dynamic threat assessment model based on intuitionistic fuzzy multiple attribute decision making, Comput. Sci., 46 (2019), 183–188. https://doi.org/10.11896/j.issn.1002-137X.2019.04.029 doi: 10.11896/j.issn.1002-137X.2019.04.029
![]() |
[12] |
J. T. Yao, N. Azam, Web-based medical decision support systems for three-way medical decision making with game-theoretic rough sets, IEEE Trans. Fuzzy Syst., 23 (2015), 3–15. https://doi.org/10.1109/TFUZZ.2014.2360548 doi: 10.1109/TFUZZ.2014.2360548
![]() |
[13] |
H. X. Li, L. B. Zhang, B. Huang, X. Z. Zhou, Sequential three-way decision and granulation for cost-sensitive face recognition, Knowl.-Based Syst., 91 (2016), 241–251. https://doi.org/10.1016/j.knosys.2015.07.040 doi: 10.1016/j.knosys.2015.07.040
![]() |
[14] |
Y. Li, Z. H. Zhang, W. B. Chen, F. Min, TDUP: An approach to incremental mining of frequent itemsets with three-way-decision pattern updating, Int. J. Mach. Learn. Cybern., 8 (2017), 441–453. https://doi.org/10.1007/s13042-015-0337-6 doi: 10.1007/s13042-015-0337-6
![]() |
[15] |
G. M. Lang, D. Q. Miao, M. J. Cai, Three-way decision approaches to conflict analysis using decision-theoretic rough set theory, Inf. Sci., 406–407 (2017), 185–207. https://doi.org/10.1016/j.ins.2017.04.030 doi: 10.1016/j.ins.2017.04.030
![]() |
[16] |
J. B. Liu, H. X. Li, B. Huang, Y. Liu, D. Liu, Convex combination-based consensus analysis for intuitionistic fuzzy three-way group decision, Inf. Sci., 574 (2021), 542–566. https://doi.org/10.1016/j.ins.2021.06.018 doi: 10.1016/j.ins.2021.06.018
![]() |
[17] |
X. Ye, D. Liu, A cost-sensitive temporal-spatial three-way recommendation with multi-granularity decision, Inf. Sci., 589 (2022), 670–689. https://doi.org/10.1016/j.ins.2021.12.105 doi: 10.1016/j.ins.2021.12.105
![]() |
[18] |
J. Liu, X. Guo, P. Ren, L. Zhang, Z. Hao, Consensus of three-way group decision with weight updating based on a novel linguistic intuitionistic fuzzy similarity, Inf. Sci., 648 (2023), 119537. https://doi.org/10.1016/j.ins.2023.119537 doi: 10.1016/j.ins.2023.119537
![]() |
[19] |
J. M. Zhan, J. J. Wang, W. P. Ding, Y. Y. Yao, Three-way behavioral decision making with hesitant fuzzy information systems: Survey and challenges, IEEECAA J. Autom. Sin., 10 (2023), 330–350. https://doi.org/10.1109/JAS.2022.106061 doi: 10.1109/JAS.2022.106061
![]() |
[20] |
Y. F. Yin, R. T. Zhang, Q. R. Su, Threat assessment of aerial targets based on improved GRA-TOPSIS method and three-way decisions, Math. Biosci. Eng., 20 (2023), 13250–13266. https://doi.org/10.3934/mbe.2023591 doi: 10.3934/mbe.2023591
![]() |
[21] |
J. M. Zhan, H. B. Jiang, Y. Y. Yao, Three-way multiattribute decision-making based on outranking relations, IEEE Trans. Fuzzy Syst., 29 (2021), 2844–2858. https://doi.org/10.1109/TFUZZ.2020.3007423 doi: 10.1109/TFUZZ.2020.3007423
![]() |
[22] |
W. J. Wang, J. M. Zhan, C. Zhang, Three-way decisions based multi-attribute decision making with probabilistic dominance relations, Inf. Sci., 559 (2021), 75–96. https://doi.org/10.1016/j.ins.2021.01.028 doi: 10.1016/j.ins.2021.01.028
![]() |
[23] |
J. H. He, H. R. Zhang, Z. Y. Zhang, J. P. Zhang, Probabilistic linguistic three-way multi-attibute decision making for hidden property evaluation of judgment debtor, J. Math., 2021 (2021), 1–16. https://doi.org/10.1155/2021/9941200 doi: 10.1155/2021/9941200
![]() |
[24] |
J. Ye, J. M. Zhan, B. Z. Sun, A three-way decision method based on fuzzy rough set models under incomplete environments, Inf. Sci., 577 (2021), 22–48. https://doi.org/10.1016/j.ins.2021.06.088 doi: 10.1016/j.ins.2021.06.088
![]() |
[25] |
J. M. Zhan, J. Ye, W. P. Ding, P. D. Liu, A novel three-way decision model based on utility theory in incomplete fuzzy decision systems, IEEE Trans. Fuzzy Syst., 30 (2022), 2210–2226. https://doi.org/10.1109/TFUZZ.2021.3078012 doi: 10.1109/TFUZZ.2021.3078012
![]() |
[26] |
J. J. Wang, X. L. Ma, J. H. Dai, J. M. Zhan, A novel three-way decision approach under hesitant fuzzy information, Inf. Sci., 578 (2021), 482–506. https://doi.org/10.1016/j.ins.2021.07.054 doi: 10.1016/j.ins.2021.07.054
![]() |
[27] |
W. Li, Y. Q. Lu, C. L. Fan, R. Z. Huo, Dynamic threat assessment based on combination weighting and improved VIKOR, AERO Weapon., 29 (2022), 66–75. https://doi.org/10.12132/ISSN.1673-5048.2021.0254 doi: 10.12132/ISSN.1673-5048.2021.0254
![]() |
[28] |
T. C. Li, J. Ye, K. Lv, Air combat threat assessment based on improved three-way decision-TOPSIS method, J. Gun Launch Control, 43 (2022), 1–8+20. https://doi.org/10.19323/j.issn.1673-6524.2022.06.001 doi: 10.19323/j.issn.1673-6524.2022.06.001
![]() |
[29] | S. Opricovic, Multicriteria optimization of civil engineering systems, Fac. Civ. Eng. Belgrade, 2 (1998), 5–21. |
[30] |
Y. J. Lai, T. Y. Liu, C. L. Hwang, TOPSIS for MODM, Eur. J. Oper. Res., 76 (1994), 486–500. https://doi.org/10.1016/0377-2217(94)90282-8 doi: 10.1016/0377-2217(94)90282-8
![]() |
[31] |
Y. Gao, D. Li, H. Zhong, A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment, Eng. Appl. Artif. Intell., 87 (2020), 103276. https://doi.org/10.1016/j.engappai.2019.103276 doi: 10.1016/j.engappai.2019.103276
![]() |
[32] |
Y. Gao, Y. C. Huang, G. B. Cheng, L. Duan, Multi-target threat assessment method based on VIKOR and three-way decisions under intuitionistic fuzzy information, ACTA Electron. Sin., 49 (2021), 542–549. https://doi.org/10.12263/DZXB.20190150 doi: 10.12263/DZXB.20190150
![]() |
[33] |
D. T. Wei, X. D. Liu, J. Deng, Group decision-making method and application based on intuitionistic fuzzy similarity and gray relation, J. Ordnance Equip. Eng., 42 (2021), 172–177. https://doi.org/10.11809/bqzbgcxb2021.07.030 doi: 10.11809/bqzbgcxb2021.07.030
![]() |
[34] |
Y. Y. Yao, S. K. M. Wong, A decision theoretic framework for approximating concepts, Int. J. Man-Mach. Stud., 37 (1992), 793–809. https://doi.org/10.1016/0020-7373(92)90069-W doi: 10.1016/0020-7373(92)90069-W
![]() |
[35] |
Y. Y. Yao, Probabilistic approaches to rough sets, Expert Syst., 20 (2003), 287–297. https://doi.org/10.1111/1468-0394.00253 doi: 10.1111/1468-0394.00253
![]() |
[36] |
Y. Y. Yao, Decision-theoretic rough set models, Rough Sets Knowl. Technol., 4481 (2007), 1–12. https://doi.org/10.1007/978-3-540-72458-2_1 doi: 10.1007/978-3-540-72458-2_1
![]() |
[37] |
M. F. Scheier, C. S. Carver, Optimism, coping, and health: Assessment and implications of generalized outcome expectancies., Health Psychol., 4 (1985), 219–247. https://doi.org/10.1037/0278-6133.4.3.219 doi: 10.1037/0278-6133.4.3.219
![]() |
[38] |
B. B. Fan, J. Lin, Y. Wang, Y. J. Chen, Multi-granulation intuitive fuzzy three-way decision and its application in targets recognition, Fire Control Command Control, 44 (2019), 49-56+62. https://doi.org/10.3969/j.issn.1002-0640.2019.07.010 doi: 10.3969/j.issn.1002-0640.2019.07.010
![]() |
[39] |
Q. H. Zhang, C. C. Yang, G. Y. Wang, A sequential three-way decision model with intuitionistic fuzzy numbers, IEEE Trans. Syst. Man Cybern. Syst., 51 (2021), 2640–2652. https://doi.org/10.1109/TSMC.2019.2908518 doi: 10.1109/TSMC.2019.2908518
![]() |
[40] | K. T. Atanassov, Intuitionistic Fuzzy Sets, Physica, Heidelberg, 1999. https://doi.org/10.1007/978-3-7908-1870-3_1 |
[41] |
S. Opricovic, G.-H. Tzeng, Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS, Eur. J. Oper. Res., 156 (2004), 445–455. https://doi.org/10.1016/S0377-2217(03)00020-1 doi: 10.1016/S0377-2217(03)00020-1
![]() |
[42] |
X. R. Tan, J. L. Deng, Grey relational analysis: a new method for multivariate statistical analysis, Stat. Res., 12 (1995), 46–48. https://doi.org/10.19343/j.cnki.11-1302/c.1995.03.011 doi: 10.19343/j.cnki.11-1302/c.1995.03.011
![]() |
[43] |
D. Q. Miao, Q. G. Duan, H. Y. Zhang, N. Jiao, Rough set based hybrid algorithm for text classification, Expert Syst. Appl., 36 (2009), 9168–9174. https://doi.org/10.1016/j.eswa.2008.12.026 doi: 10.1016/j.eswa.2008.12.026
![]() |
[44] |
Y. Y. Yao, Three-way decisions with probabilistic rough sets, Inf. Sci., 180 (2010), 341–353. https://doi.org/10.1016/j.ins.2009.09.021 doi: 10.1016/j.ins.2009.09.021
![]() |
[45] |
Z. S. Xu, Approaches to multi-stage multi-attribute group decision making, Int. J. Inf. Technol. Decis. Mak., 10 (2011), 121–146. https://doi.org/10.1142/S0219622011004257 doi: 10.1142/S0219622011004257
![]() |
[46] |
F. Shen, X. S. Ma, Z. Y. Li, Z. S. Xu, D. L. Cai, An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation, Inf. Sci., 428 (2018), 105–119. https://doi.org/10.1016/j.ins.2017.10.045 doi: 10.1016/j.ins.2017.10.045
![]() |
1. | Wan Abd Al Qadr Imad Wan-Mohtar, Zul Ilham, Adi Ainurzaman Jamaludin, Neil Rowan, Use of Zebrafish Embryo Assay to Evaluate Toxicity and Safety of Bioreactor-Grown Exopolysaccharides and Endopolysaccharides from European Ganoderma applanatum Mycelium for Future Aquaculture Applications, 2021, 22, 1422-0067, 1675, 10.3390/ijms22041675 | |
2. | Ismail Fitri Mohd Hafidz, Muhamad Syaffuan Ramli, Nur Raihan Abdullah, Wan Abd Al Qadr Imad Wan-Mohtar, Nur Hafizah Azizan, Faez Sharif, 2022, 2454, 0094-243X, 050027, 10.1063/5.0078615 | |
3. | Rahayu Ahmad, Srivani Sellathoroe, Ehwan Ngadi, Tengku Shafazila Tengku Saharuddin, Iffah Izzati Zakaria, Suguna Selvakumaran, Wan Abd Al Qadr Imad Wan-Mohtar, Isolation, identification, cultivation and determination of antimicrobial β-glucan from a wild-termite mushroom Termitomyces heimii RFES 230662, 2021, 37, 18788181, 102187, 10.1016/j.bcab.2021.102187 | |
4. | Sugenendran Supramani, Nur Ardiyana Rejab, Zul Ilham, Rahayu Ahmad, Pau-Loke Show, Mohamad Faizal Ibrahim, Wan Abd Al Qadr Imad Wan-Mohtar, Performance of Biomass and Exopolysaccharide Production from the Medicinal Mushroom Ganoderma lucidum in a New Fabricated Air-L-Shaped Bioreactor (ALSB), 2023, 11, 2227-9717, 670, 10.3390/pr11030670 | |
5. | Nur Raihan Abdullah, Mohd Hamzah Mohd Nasir, Nur Hafizah Azizan, Wan Abd Al Qadr Imad Wan-Mohtar, Faez Sharif, Bioreactor-grown exo- and endo-β-glucan from Malaysian Ganoderma lucidum: An in vitro and in vivo study for potential antidiabetic treatment, 2022, 10, 2296-4185, 10.3389/fbioe.2022.960320 | |
6. | Siti Rokhiyah Ahmad Usuldin, Wan Abd Al Qadr Imad Wan-Mohtar, Zul Ilham, Adi Ainurzaman Jamaludin, Nur Raihan Abdullah, Neil Rowan, In vivo toxicity of bioreactor-grown biomass and exopolysaccharides from Malaysian tiger milk mushroom mycelium for potential future health applications, 2021, 11, 2045-2322, 10.1038/s41598-021-02486-7 | |
7. | Yuhang Ma, Liang Sun, Rui Wang, Yian Gu, Hong Xu, Peng Lei, High-Efficiency Extraction of Pantoea alhagi Exopolysaccharides Driven by pH-Related Changes in the Envelope Structure, 2022, 27, 1420-3049, 7209, 10.3390/molecules27217209 | |
8. | Siti Rokhiyah Ahmad Usuldin, Zul Ilham, Adi Ainurzaman Jamaludin, Rahayu Ahmad, Wan Abd Al Qadr Imad Wan-Mohtar, Enhancing Biomass-Exopolysaccharides Production of Lignosus rhinocerus in a High-Scale Stirred-Tank Bioreactor and Its Potential Lipid as Bioenergy, 2023, 16, 1996-1073, 2330, 10.3390/en16052330 | |
9. | Sidra Bibi, Zhong-Liang Wang, Chitsan Lin, Sheng-Hua Min, Chih-Yu Cheng, Two-stage cultivation strategies for optimal production of Ganoderma pellets with potential application in the vegan food industry, 2023, 0022-1155, 10.1007/s13197-023-05719-x | |
10. | Sharareh Rezaeian, Hamid R. Pourianfar, Samaneh Attaran Dowom, Comparative Assessment of β-glucan Composition and Structural Characterization in Wild and Commercial Enoki Mushrooms, Flammulina velutipes, 2023, 08891575, 105286, 10.1016/j.jfca.2023.105286 | |
11. | Ignat V. Sonets, Nikita V. Dovidchenko, Sergey V. Ulianov, Maria S. Yarina, Stanislav I. Koshechkin, Sergey V. Razin, Larissa M. Krasnopolskaya, Alexander V. Tyakht, Unraveling the Polysaccharide Biosynthesis Potential of Ganoderma lucidum: A Chromosome-Level Assembly Using Hi-C Sequencing, 2023, 9, 2309-608X, 1020, 10.3390/jof9101020 | |
12. | Km Jyoti, Kuldeep Soni, Ram Chandra, Optimization of the production of Exopolysaccharide (EPS) from biofilm-forming bacterial consortium using different parameters, 2024, 4, 29501946, 100117, 10.1016/j.microb.2024.100117 | |
13. | Danial ‘Aizat Norhisham, Norsharina Md Saad, Siti Rokhiyah Ahmad Usuldin, Diwiya A G Vayabari, Zul Ilham, Mohamad Faizal Ibrahim, Pau-Loke Show, Wan Abd Al Qadr Imad Wan-Mohtar, Performance of Malaysian kenaf Hibiscus cannabinus callus biomass and exopolysaccharide production in a novel liquid culture , 2023, 14, 2165-5979, 10.1080/21655979.2023.2262203 | |
14. | Sameh Samir Ali, Eman Elgibally, Maha A. Khalil, Jianzhong Sun, Abd El-Raheem R. El-Shanshoury, Characterization and bioactivities of exopolysaccharide produced from Azotobacter salinestris EPS-AZ-6, 2023, 246, 01418130, 125594, 10.1016/j.ijbiomac.2023.125594 | |
15. | Afnan Ahmadi Zahuri, Wan Hanna Melini Wan Mohtar, Zarimah Mohd Hanafiah, Muhamad Fazly Abdul Patah, Pau-Loke Show, Yusufjon Gafforov, Wan Abd Al Qadr Imad Wan-Mohtar, Mycoremediation of Industrial Textile Wastewater Using Ganoderma lucidum Pellets and Activated Dolomite in Batch Bioreactor, 2024, 1073-6085, 10.1007/s12033-023-01035-z | |
16. | Henan Zhang, Jingsong Zhang, Yanfang Liu, Chuanhong Tang, Recent Advances in the Preparation, Structure, and Biological Activities of β-Glucan from Ganoderma Species: A Review, 2023, 12, 2304-8158, 2975, 10.3390/foods12152975 | |
17. | Teik Chee Lim, Zul Ilham, Wan Abd Al Qadr Imad Wan-Mohtar, Production of Ganodiesel from the biomass of Ganoderma lucidum in air-L-shaped bioreactor (ALSB), 2024, 10, 24058440, e35170, 10.1016/j.heliyon.2024.e35170 | |
18. | Si Zhang, Yu Wang, Bohan Wang, Shenyan Wang, A review on superiority of mycelial pellets as bio-carriers: Structure, surface properties, and bioavailability, 2024, 58, 22147144, 104745, 10.1016/j.jwpe.2023.104745 | |
19. | Jiahua Liu, Zengjun Yang, Khinkhin Phyu, Yu’ang Cao, Han Wang, Xiaoyu Xu, Junfeng Liang, Keqiang Zhang, Chein-Chi Chang, Suli Zhi, Mixed microalgal culture enhances biofilm adhesion during treatment of livestock wastewater: The role of extracellular polymeric substances, 2024, 498, 13858947, 155580, 10.1016/j.cej.2024.155580 | |
20. | Zarimah Mohd Hanafiah, Wan Hanna Melini Wan Mohtar, Noureddine El Messaoudi, Youssef Miyah, Ali Maged, Salah Knani, Besma Graba, Yusufjon Gafforov, Wan Abd Al Qadr Imad Wan-Mohtar, Wastewater treatment using Ganoderma species via ganoremediation bioreactor: A comprehensive review, 2025, 2589014X, 102105, 10.1016/j.biteb.2025.102105 |