Yes / Critical technological systems exhibit complex dynamic characteristics such as time-dependent behavior, functional dependencies among events, sequencing and priority of causes that may alter the effects of failure. Dynamic fault trees (DFTs) have been used in the past to model the failure logic of such systems, but the quantitative analysis of DFTs has assumed the existence of precise failure data and statistical independence among events, which are unrealistic assumptions. In this paper, we propose an improved approach to reliability analysis of dynamic systems, allowing for uncertain failure data and statistical and stochastic dependencies among events. In the proposed framework, DFTs are used for dynamic failure modeling. Quantitative evaluation of DFTs is performed by converting them into generalized stochastic Petri nets. When failure data are unavailable, expert judgment and fuzzy set theory are used to obtain reasonable estimates. The approach is demonstrated on a simplified model of a cardiac assist system. / DEIS H2020 Project under Grant 732242.

Yes / Fault tree analysis (FTA) is a powerful technique that is widely used for evaluating system safety and reliability. It can be used to assess the effects of combinations of failures on system behaviour but is unable to capture sequence dependent dynamic behaviour. A number of extensions to fault trees have been proposed to overcome this limitation. Pandora, one such extension, introduces temporal gates and temporal laws to allow dynamic analysis of temporal fault trees (TFTs). It can be easily integrated in model-based design and analysis techniques. The quantitative evaluation of failure probability in Pandora TFTs is performed using exact probabilistic data about component failures. However, exact data can often be difficult to obtain. In this paper, we propose a method that combines expert elicitation and fuzzy set theory with Pandora TFTs to enable dynamic analysis of complex systems with limited or absent exact quantitative data. This gives Pandora the ability to perform quantitative analysis under uncertainty, which increases further its potential utility in the emerging field of model-based design and dependability analysis. The method has been demonstrated by applying it to a fault tolerant fuel distribution system of a ship, and the results are compared with the results obtained by other existing techniques.

Yes / Fault tree analysis is a widely used method of risk assessment in process industries. However, the classical fault tree approach has its own limitations such as the inability to deal with uncertain failure data and to consider statistical dependence among the failure events. In this paper, we propose a comprehensive framework for the risk assessment in process industries under the conditions of uncertainty and statistical dependency of events. The proposed approach makes the use of expert knowledge and fuzzy set theory for handling the uncertainty in the failure data and employs the Bayesian network modeling for capturing dependency among the events and for a robust probabilistic reasoning in the conditions of uncertainty. The effectiveness of the approach was demonstrated by performing risk assessment in an ethylene transportation line unit in an ethylene oxide (EO) production plant.

Yes / In different engineering processes, the reliability of systems is increasingly evaluated to ensure that the safety-critical process systems will operate within their expected operational boundary for a certain mission time without failure. Different methodologies used for reliability analysis of process systems include Failure Mode and Effect Analysis (FMEA), Fault Tree Analysis (FTA), and Bayesian Networks (BN). Although these approaches have their own procedures for evaluating system reliability, they rely on exact failure data of systems’ components for reliability evaluation. Nevertheless, obtaining exact failure data for complex systems can be difficult due to the complex behaviour of their components, and the unavailability of precise and adequate information about such components. To tackle the data uncertainty issue, this chapter proposes a framework by combining intuitionistic fuzzy set theory and expert elicitation that enables the reliability assessment of process systems using FTA. Moreover, to model the statistical dependencies between events, we use the BN for robust probabilistic inference about system reliability under different uncertainties. The efficiency of the framework is demonstrated through application to a real-world system and comparison of the results of analysis produced by the existing approaches. / The full text will be available at the end of the publisher's embargo, 9th April 2025

No / In recent decades, sustainable investing has caught on with investors, and it has now become the norm. In the age of start-ups, with scant information on the sustainability aspects of an asset, it becomes harder to pursue sustainable investing. To this end, this paper proposes a sustainable financial portfolio selection approach in an intuitionistic fuzzy framework. We present a comprehensive three-stage methodology in which the assets under consideration are ethically screened in Stage-I. Stage-II is concerned with cal- culating the sustainability scores, based on various social, environmental, and economic (SEE) criteria and an evaluation of the return and risk of the ethical assets. Intuitionistic fuzzy set theory is used to gauge the linguistic assessment of the assets on several SEE criteria from multiple decision-makers. A novel intuitionistic fuzzy multi-criteria group decision-making technique is applied to calculate the sustainability score of each asset. Finally, in Stage-III, an intuitionistic fuzzy multi-objective financial portfolio selection model is developed with maximization of the satisfaction degrees of the sustainabil- ity score, return, and risk of the portfolio, subject to several constraints. The ε-constraint method is used to solve this model, which yields various efficient, sustainable financial portfolios. Subsequently, investors can choose the portfolio best suited to their preferences from this pool of efficient, sustainable financial portfolios. A detailed empirical illustration and a comparison with existing works are given to substantiate and validate the proposed approach. / Institution of Eminence, University of Delhi, Delhi-110007 under Faculty Research Program / The full-text of this article will be released for public view at the end of the publisher embargo on 16 Jul 2024.

This essay explores whether the gender constructions in Joe Abercrombie’s Best Served Cold and Juliet Marillier’s Daughter of the Forest question or contribute to existing gender categories. The analysis is performed using Raewynn Connell’s gender structure model, Brian Attebery’s theory of fantasy as a "fuzzy set" and Maria Nikolajeva’s schedule for stereotypical gender traits. Thus, both of the texts were analyzed to determine if their contents, structures and reader responses create opportunities or act limiting, how the main characters are portrayed and how the books various power-, production-, emotional- and symbolic relations look like. The result of the analysis is that both of the books portray patriarchal worlds, sexual division of labor, misogyny and gender-binding statements. The characters in Daughter of the Forest are quite stereotypical, with some traits that exceed their gender, whilst the characters in Best Served Cold are all portrayed with traditionally manly traits (even the female main character). Therefor one can say that Best Served cold’s female protagonist is the only element in the books that fully questions prevailing gender categories.

Acquisitions are a major part of the growth and evolution of companies within varying markets. They are conducted under various circumstances with differing aims and rationales, and for different purposes. This thesis focuses on acquisitions carried out by serial investors acting in a private equity market. These acquisitions are characterised by a highly competitive nature, where access to a large pipeline of potential targets is believed to be a key success factor. As such, deal origination and sourcing of potential targets become important factors to facilitate a high-paced deal flow. Earlier research suggests that this is well researched with regards to venture capital, but to a large extent lacking within private equity. This thesis analyses and discusses the possibility of systematising early evaluation of potential acquisition targets within private equity. The aim is to suggest a framework that may be utilised, consisting of the criteria deemed important for early evaluation. Through utilising multi criteria decision making, the framework allows for a relative importance between criteria to contribute to assessment, while fuzzy set theory allows for a degree of uncertainty to be incorporated. This thesis is conducted through data collection from a number of professionals within private equity, selected from a non-probability sample. Semi-structured interviews and a 3 pairwise comparison is utilised to allow for analysis and discussion with regards to earlier research and theory, while allowing for comparison between respondents. The results of the thesis indicate that utilising a framework for early evaluation of potential targets may be beneficial. The key dimensions to consider are financial, commercial and market capabilities. However, the criteria constituting the dimensions may be firm or industry specific. / Förvärv är en viktig del av tillväxten och utvecklingen av företag i en rad olika marknader. De genomförs under varierande förutsättningar med olika mål och avsikter, och med olika syften. Denna uppsats fokuserar på förvärv utförda av serieinvesterare på en riskkapitalsmarknad. Dessa förvärv karaktäriseras av en konkurrensutsatt marknad, där tillgång till en bred pipeline av potentiella förvärvsmöjligheter förväntas vara en viktig framgångsfaktor. Således är hantering av leadsgenerering och sourcing av möjliga förvärvsobjekt viktiga faktorer för att främja ett aktivt förvärvsflöde. Tidigare forskning tyder på att detta är ett välutforskat område inom venture capital, men saknas i stor utsträckning inom riskkapital. Denna uppsats analyserar och diskuterar möjligheten att systematisera tidig utvärdering av potentiella förvärvsobjekt inom riskkapital. Avsikten är att föreslå ett ramverk som kan användas, bestående av de kriteria som anses viktiga vid tidig utvärdering. Genom att använda multi criteria decision making möjliggör ramverket en relativ betydelse mellan kriterier att påverka bedömningen, medan fuzzy set-teori möjliggör för en grad av osäkerhet att inkorporeras. Denna uppsats utförs genom datainsamling från ett antal yrkesverksamma inom riskkapital, utvalda från en icke-sannolikhetsprovtagning. Semi-strukturerade intervjuer och en parvis 5 jämförelse används för analys och diskussion i relation till tidigare forskning och teori, och tillåter även jämförelse mellan svarande. Resultaten av denna uppsats indikerar att användande av ett ramverk för tidig utvärdering av potentiella förvärvsobjekt kan vara positiv. De viktigaste dimensionerna att ta i beaktning är finansiella, kommersiella och marknadsmässiga förmågor. Kriterierna som utgör dessa dimensioner kan dock vara bolags- eller industrispecifika.

Many concrete problems, ranging from Portfolio selection to Water resource management, may be cast into a multiobjective programming framework. The simplistic way of superseding blindly conflictual goals by one objective function let no chance to the model but to churn out meaningless outcomes. Hence interest of discussing ways for tackling Multiobjective Programming Problems. More than this, in many real-life situations, uncertainty and imprecision are in the state of affairs. In this dissertation we discuss ways for solving Multiobjective Programming Problems with fixed and fuzzy coefficients. No preference, a priori, a posteriori, interactive and metaheuristic methods are discussed for the deterministic case. As far as the fuzzy case is concerned, two approaches based respectively on possibility measures and on Embedding Theorem for fuzzy numbers are described. A case study is also carried out for the sake of illustration. We end up with some concluding remarks along with lines for further development, in this field. / Operations Research / M. Sc. (Operations Research)

Fuzzy relational databases deal with imprecise data or fuzzy information in a relational database. The purpose of this fuzzy database implementation is to retrieve images by using fuzzy queries whose common-language descriptions are defined by the consensus of a particular user community. The fuzzy set, which is presentation of fuzzy attribute values of the images, is determined through membership function. This paper compares two methods of constructing membership functions, the Direct Rating and New Random Proportional, to determine which method gives maximum users satisfaction with minimum feedback from the community. The statistical analysis of results suggests the use of Direct Rating method. Moreover, the analysis shows that the performance of the New Random Proportional method can be improved with the inclusion of a "Not" modifier. This paper also identifies and analyzes issues that are raised by different versions of the database system.

The GUHA method is one of the oldest methods of exploratory data analysis, which is regarded as part of the data mining or knowledge discovery in databases (KDD) scienti_c area. Unlike many other methods of data mining, the GUHA method has firm theoretical foundations in logic and statistics. In scope of the method, finding interesting knowledge corresponds to finding special formulas in satisfactory rich logical calculus, which is called observational calculus. The main topic of the thesis is application of the "fuzzy paradigm" to the GUHA method By the term "fuzzy paradigm" we mean approaches that use many-valued membership degrees or truth values, namely fuzzy set theory and fuzzy logic. The thesis does not aim to cover all the aspects of this application, it emphasises mainly on: - Association rules as the most prevalent type of formulas mined by the GUHA method - Usage of fuzzy data - Logical aspects of fuzzy association rules mining - Comparison of the GUHA theory to the mainstream fuzzy association rules - Implementation of the theory using the bit string approach The thesis throughoutly elaborates the theory of fuzzy association rules, both using the theoretical apparatus of fuzzy set theory and fuzzy logic. Fuzzy set theory is used mainly to compare the GUHA method to existing mainstream approaches to formalize fuzzy association rules, which were studied in detail. Fuzzy logic is used to define novel class of logical calculi called logical calculi of fuzzy association rules (LCFAR) for logical representation of fuzzy association rules. The problem of existence of deduction rules in LCFAR is dealt in depth. Suitable part of the proposed theory is implemented in the Ferda system using the bit string approach. In the approach, characteristics of examined objects are represented as strings of bits, which in the crisp case enables efficient computation. In order to maintain this feature also in the fuzzy case, a profound low level testing of data structures and algoritms for fuzzy bit strings have been carried out as a part of the thesis.