10876nam 2200553 450 99649034720331620230726154705.09789811914492(electronic bk.)9789811914485(MiAaPQ)EBC7083141(Au-PeEL)EBL7083141(CKB)24814912600041(PPN)26495940X(EXLCZ)992481491260004120230211d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierQ-Rung orthopair fuzzy sets theory and applications. /edited by Harish GargSingapore :Springer,[2022]©20221 online resource (558 pages)Print version: Garg, Harish Q-Rung Orthopair Fuzzy Sets Singapore : Springer,c2022 9789811914485 Includes bibliographical references.Intro -- Preface -- Contents -- About the Editor -- 1 q-Rung Orthopair Fuzzy Supra Topological Applications in Data Mining Process -- 1.1 Introduction -- 1.2 Preliminary -- 1.3 q-Rung Orthopair Fuzzy Supra Topological Spaces -- 1.4 Mappings of q-Rung Orthopair Fuzzy Spaces -- 1.5 Algorithm for Data Mining Problem Via q-Rung Orthopair Fuzzy Supra Topology -- 1.6 Numerical Example -- 1.7 Conclusion and Future Work -- References -- 2 q-Rung Orthopair Fuzzy Soft Topology with Multi-attribute Decision-Making -- 2.1 Introduction -- 2.2 Some Elementary Models -- 2.3 q-Rung Orthopair Fuzzy Soft Sets -- 2.4 q-Rung Orthopair Fuzzy Soft Topology -- 2.4.1 q-ROFS Separation Axioms -- 2.5 Multi-attribute Decision-Making -- 2.5.1 Numerical Application -- 2.5.2 Generalized Choice Value Method -- 2.6 Conclusion -- References -- 3 Decision-Making on Patients' Medical Status Based on a q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation -- 3.1 Introduction -- 3.2 Preliminaries -- 3.2.1 q-Rung Orthopair Fuzzy Sets -- 3.3 q-Rung Orthopair Fuzzy Max-Min-Max Composite Relation -- 3.3.1 Numerical Application -- 3.4 Application of qROFMMMCR in Disease Diagnosis -- 3.4.1 qROFMMMCR in Terms of Patients and Diseases with Respect to Symptoms -- 3.4.2 Experiment of Disease Diagnosis -- 3.5 Conclusion -- References -- 4 Soergel Distance Measures for q-Rung Orthopair Fuzzy Sets and Their Applications -- 4.1 Introduction -- 4.2 Background -- 4.2.1 q-Rung Orthopair Fuzzy Sets -- 4.2.2 Some Existing Information Measures for q-ROFSs -- 4.3 Soergel Distance Measures for q-ROFSs and Their Validation/Efficiency -- 4.3.1 Twelve Types of Soergel Distance Measures for q-ROFSs -- 4.3.2 Twelve Types of Weighted Soergel Distance Measures for q-ROFSs -- 4.3.3 The Validation/Efficiency of SoDMs and SoSMs for q-ROFSs -- 4.4 Applications of SoDMs -- 4.4.1 Proposed Decision-Making Method.4.4.2 Illustrative Examples -- 4.5 Comparison Analysis -- 4.6 Sensitivity Analysis and Advantages of SoDMs -- 4.6.1 Sensitivity Analysis of SoDMs for the Value of q -- 4.6.2 Advantages of Proposed Approaches -- 4.6.3 Limitations of Proposed Approaches -- 4.7 Conclusion -- References -- 5 TOPSIS Techniques on q-Rung Orthopair Fuzzy Sets and Its Extensions -- 5.1 Introduction -- 5.2 Preliminaries -- 5.2.1 TOPSIS -- 5.3 TOPSIS Techniques on q-ROFS -- 5.4 Combined Weighting TOPSIS MADM Using q-ROHFS -- 5.5 TOPSIS Techniques on q-ROFSfS -- 5.6 Applications -- 5.7 Conclusions -- References -- 6 Knowledge Measure-Based q-Rung Orthopair Fuzzy Inventory Model -- 6.1 Introduction -- 6.1.1 Literature Review -- 6.1.2 Research Gap and the Contribution -- 6.2 Preliminaries -- 6.3 Model Formulation -- 6.3.1 Case (I): Replacement of the Faulty Option by Warranty Claiming and Repair Option -- 6.3.2 Case (ii): Replacement of the Faulty Option by Warranty Claiming and the Emergency Purchase Option -- 6.3.3 Inventory Model with q-Rung Orthopair Fuzzy Variables -- 6.3.4 Vendor's Optimal Policy -- 6.4 Numerical Computation -- 6.4.1 Sensitive Analysis -- 6.4.2 Comparison Study -- 6.5 Conclusion -- Appendix -- References -- 7 Higher Type q-Rung Orthopair Fuzzy Sets: Interval Analysis -- 7.1 Introduction -- 7.2 Basic Concepts of q-RIVOFSs -- 7.3 Some Novel Measures for q-RIVOFSs -- 7.3.1 Cross-Entropy Measure for q-RIVOFSs -- 7.3.2 Hausdorff Distance for q-RIVOFSs -- 7.4 Multi-Attribute Decision-Making Method Under q-RIVOF Circumstances -- 7.4.1 TODIM Method with q-RIVOFSs -- 7.5 Illustrative Example -- 7.5.1 Case Description -- 7.5.2 Illustration of the Proposed Q-RIVOFS-TODIM Approach -- 7.5.3 Sensitivity Analysis -- 7.5.4 Comparative Analysis -- 7.6 Conclusion -- References.8 Evidence-Based Cloud Vendor Assessment with Generalized Orthopair Fuzzy Information and Partial Weight Data -- 8.1 Introduction -- 8.2 Literature Review -- 8.2.1 CV Selection Using Decision Models -- 8.2.2 GOFS-Based Decision Approaches -- 8.3 A New Scientific Framework for CV Selection -- 8.3.1 Preliminaries -- 8.3.2 Mathematical Model with GOFS -- 8.3.3 Evidence-Based Ranking Algorithm with GOFS -- 8.4 Real Case Example-Selection of CVs -- 8.5 Comparative Analysis -- 8.6 Conclusion -- References -- 9 Supplier Selection Process Based on CODAS Method Using q-Rung Orthopair Fuzzy Information -- 9.1 Introduction -- 9.2 q-Rung Orthopair Fuzzy Sets (q-ROFS) -- 9.2.1 Algebraic Operations q-ROFS -- 9.3 Combinative Distance-Based Assessment (CODAS) -- 9.3.1 Steps for the CODAS Method -- 9.4 CODAS and q-Rung Orthopair Fuzzy Sets for the Supplier Selection Process -- 9.5 Case Numeric -- 9.6 Discussions -- 9.7 Conclusions -- References -- 10 Group Decision-Making Framework with Generalized Orthopair Fuzzy 2-Tuple Linguistic Information -- 10.1 Introduction -- 10.2 Preliminaries -- 10.2.1 The 2-Tuple Linguistic Representation Model -- 10.2.2 The MSM Operator and its Weighted Form -- 10.3 The GOFTLMSM Aggregation Operator and its Weighted Form -- 10.3.1 The GOFTLMSM Operator -- 10.3.2 The GOFTLWMSM Operator -- 10.4 The GOFTLDMSM Aggregation Operator and its Weighted Form -- 10.4.1 The GOFTLDMSM Operator -- 10.4.2 The GOFTLWDMSM Operator -- 10.5 An MAGDM Model with GOFTL Information -- 10.6 Illustrative Example and Discussion -- 10.6.1 Evaluation Process of the Proposed Method -- 10.6.2 Sensitivity Analysis -- 10.6.3 Comparative Analysis -- 10.6.4 Advantages and Superiorities of the Proposed Work -- 10.7 Conclusions -- References -- 11 3PL Service Provider Selection with q-Rung Orthopair Fuzzy Based CODAS Method -- 11.1 Introduction -- 11.2 Literature Survey.11.3 q-ROF CODAS Method -- 11.3.1 q-Rung Orthopair Fuzzy Sets -- 11.3.2 q-ROF CODAS Methodology -- 11.4 Case Study -- 11.5 Conclusion -- References -- 12 An Integrated Proximity Indexed Value and q-Rung Orthopair Fuzzy Decision-Making Model for Prioritization of Green Campus Transportation -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 Case Study -- 12.3.1 Definition of Alternatives and Criteria -- 12.4 Preliminaries -- 12.5 Proposed Methodologies -- 12.5.1 Proximity Indexed Value (PIV) Method -- 12.5.2 Proposed q-ROF PIV Method -- 12.6 Experimental Results -- 12.7 Discussion -- 12.8 Conclusion -- References -- 13 Platform-Based Corporate Social Responsibility Evaluation with Three-Way Group Decisions Under Q-Rung Orthopair Fuzzy Environment -- 13.1 Introduction -- 13.2 Preliminaries -- 13.2.1 q-rung Orthopair Fuzzy Sets (q-ROFSs) -- 13.2.2 Three Way Decisions (TWDs) -- 13.3 CSR Evaluation Method Based on TWDs with q-ROFSs -- 13.3.1 Information Fusion Method -- 13.3.2 CSR Classification of Platform-Based Enterprises with TWDs -- 13.4 An Illustrative Example -- 13.4.1 Decision Analysis with Our Proposed Method -- 13.4.2 Comparative Experiment -- 13.4.3 Sensitivity Analysis -- 13.5 Conclusions -- References -- 14 MARCOS Technique by Using q-Rung Orthopair Fuzzy Sets for Evaluating the Performance of Insurance Companies in Terms of Healthcare Services -- 14.1 Introduction -- 14.2 Preliminaries -- 14.3 Q-ROF-MARCOS Method -- 14.4 Analysis of Healthcare Services Under Q-ROFS-MARCOS Technique -- 14.5 Sensitivity Analysis -- 14.6 Conclusion -- References -- 15 Interval Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Cite Selection of Electric Vehicle -- 15.1 Introduction -- 15.2 Preliminaries -- 15.3 Interval Complex q-Rung Orthopair Fuzzy Sets.15.4 Interval Complex q-Rung Orthopair Fuzzy Aggregate Operators for MADM Problems -- 15.5 The MADM Model Based on IVCq-ROFWA and IVCq-ROFGA Operators -- 15.6 An Illustrative Example for the Validation of the Proposed MADM Model -- 15.7 Conclusion and Future Work -- References -- 16 A Novel Fermatean Fuzzy Analytic Hierarchy Process Proposition and Its Usage for Supplier Selection Problem in Industry 4.0 Transition -- 16.1 Introduction -- 16.2 Supplier Selection in Industry 4.0 Transition -- 16.3 Preliminaries: Fermatean Fuzzy Sets -- 16.4 A Novel Fermatean Fuzzy AHP Extension -- 16.5 An Application in Turkey -- 16.6 Discussion and Concluding Remarks -- References -- 17 Pentagonal q-Rung Orthopair Numbers and Their Applications -- 17.1 Introduction -- 17.2 Preliminary -- 17.3 Pentagonal q-Rung Orthopair Numbers -- 17.4 Multi-criteria Decision-Making Method Based on Pq-RO-Numbers -- 17.5 Conclusion -- References -- 18 q-Rung Orthopair Fuzzy Soft Set-Based Multi-criteria Decision-Making -- 18.1 Introduction -- 18.2 q-ROFSSs -- 18.2.1 Weighted SM for q-ROFSSs -- 18.3 MCDM Using q-Rung Orthopair Fuzzy Soft Information -- 18.4 MCDM with TOPSIS Approach Based on q-ROFSSs -- 18.5 MCDM Using q-ROFS VIKOR Method -- 18.6 Practical implementation of proposed SM related to COVID-19 -- 18.7 Conclusion -- References -- 19 Development of Heronian Mean-Based Aggregation Operators Under Interval-Valued Dual Hesitant q-Rung Orthopair Fuzzy Environments for Multicriteria Decision-Making -- 19.1 Introduction -- 19.2 Preliminaries -- 19.2.1 DHq-ROFS -- 19.2.2 IVDHq-ROFS -- 19.2.3 Operations on IVDHq-ROFNs -- 19.2.4 HM Operator -- 19.2.5 GHM Operator -- 19.3 HM-Based IVDHq-ROF Aggregation Operators and Its Properties -- 19.3.1 IVDHq-ROFHM Operator -- 19.3.2 IVDHq-ROFWHM Operator -- 19.3.3 IVDHq-OFGHM Operator -- 19.3.4 IVDHq-ROFWGHM Operator.19.4 Approach to MCDM with HM-Based IVDHq-ROF Information.Fuzzy setsPresa de decisionsthubMatemàticathubConjunts borrososthubLlibres electrònicsthubFuzzy sets.Presa de decisionsMatemàticaConjunts borrosos511.3223Garg HarishMiAaPQMiAaPQMiAaPQ996490347203316Q-Rung orthopair fuzzy sets3008974UNISA02230nam 2200481 450 991055503430332120230824214659.01-5231-2790-21-119-60558-X1-119-60553-91-119-60566-0(CKB)4100000008780154(MiAaPQ)EBC5838460(EXLCZ)99410000000878015420190817d2019 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdhesives for wood and lignocellulosic materials /Ramamurtinanda Kumar and Antonio PizziHoboken, New Jersey :Scrivener Publishing :Wiley,2019.1 online resource (518 pages)THEi Wiley ebooks.1-119-60543-1 Includes bibliographical references and index."The book is a comprehensive treatment of the subject covering a wide range of subjects uniquely available in a single source for the first time. A material science approach has been adopted in dealing with wood adhesion and adhesives. The approach of the authors was to bring out hierarchical cellular and porous characteristics of wood with polymeric cell wall structure, along with the associated non-cell wall extractives, which greatly influence the interaction of wood substrate with polymeric adhesives in a very unique manner not existent in the case of other adherends. Environmental aspects, in particular formaldehyde emission from adhesive bonded wood products, has been included. A significant feature of the book is the inclusion of polymeric matrix materials for wood polymer composites"--Provided by publisher.WoodBondingAdhesivesEngineered woodWoodBonding.Adhesives.Engineered wood.668/.3Kumar Ramamurtinanda1219409Pizzi A(Antonio),1946-MiAaPQMiAaPQMiAaPQBOOK9910555034303321Adhesives for wood and lignocellulosic materials2819621UNINA