We show that the uncertainty in distance and time measurements found by the heuristic combination of quantum mechanics and general relativity is reproduced in a purely classical and flat multi-fractal spacetime whose geometry changes with the probed scale (dimensional flow) and has non-zero imaginary dimension, corresponding to a discrete scale invariance at short distances. Thus, dimensional flow can manifest itself as an intrinsic measurement uncertainty and, conversely, measurement-uncertainty estimates are generally valid because they rely on this universal property of quantum geometries. These general results affect multi-fractional theories, a recent proposal related to quantum gravity, in two ways: they can fix two parameters previously left free (in particular, the value of the spacetime dimension at short scales) and point towards a reinterpretation of the ultraviolet structure of geometry as a stochastic foam or fuzziness. This is also confirmed by a correspondence we establish between Nottale scale relativity and the stochastic geometry of multi-fractional models.
The existing methods of determining an α-cut of a fuzzy set to construct its underlying shadowed set do not fully comply with the concept of shadowed sets, namely, a retention of the total amount of fuzziness and its localized redistribution throughout a universe of discourse. Moreover, no closed formula to calculate the corresponding α-cut is available. This paper proposes analytical formulas to calculate threshold values required in the construction of shadowed sets. We introduce a new algorithm to design a shadowed set from a given fuzzy set. The proposed algorithm, which adheres to the main premise of shadowed sets of capturing the essence of fuzzy sets, helps localize fuzziness present in a given fuzzy set. We represent the fuzziness of a fuzzy set as a gradual number. Through defuzzification of the gradual number of fuzziness, we determine the required threshold (i.e., some α-cut) used in the formation of the shadowed set. We show that the shadowed set obtained in this way comes with a measure of fuzziness that is equal to the one characterizing the original fuzzy set.
In this paper we review our recent findings on the different interaction mechanisms of the C-terminal domain of the nucleoprotein (N) of measles virus (MeV) N-TAIL, a model viral intrinsically disordered protein (IDP), with two of its known binding partners, i.e., the C-terminal X domain of the phosphoprotein of MeV XD (a globular viral protein) and the heat-shock protein 70 hsp70 (a globular cellular protein). The N-TAIL binds both XD and hsp70 via a molecular recognition element (MoRE) that is flanked by two fuzzy regions. The long (85 residues) N-terminal fuzzy region is a natural dampener of the interaction with both XD and hsp70. In the case of binding to XD, the N-terminal fuzzy appendage of N-TAIL reduces the rate of alpha-helical folding of the MoRE. The dampening effect of the fuzzy appendage on XD and hsp70 binding depends on the length and fuzziness of the N-terminal region. Despite this similarity, N-TAIL binding to XD and hsp70 appears to rely on completely different requirements. Almost any mutation within the MoRE decreases XD binding, whereas many of them increase the binding to hsp70. In addition, XD binding is very sensitive to the -helical state of the MoRE, whereas hsp70 is not. Thus, contrary to hsp70, XD binding appears to be strictly dependent on the wild-type primary and secondary structure of the MoRE.
Managers often deal with uncertainty of a different nature in their decision processes. They can encounter uncertainty in terms of randomness or fuzziness (i.e., mist, obscurity, inaccuracy or vagueness). In the first case (randomness), it can be described, for example, by probability distribution, in the second case (fuzziness) it cannot be characterized in such a way. The methodological part of the paper presents basic tools for dealing with the uncertainty of both of these types, which are techniques of probability theory and fuzzy approach technique. The original contribution of the theoretical part is the interpretation of these different techniques based on the existence of fundamental analogies between them. These techniques are then applied to the problem of the project valuation with its “internal” value. In the first case, the solution is the point value of the statistical E[PV], in the second case the triangular fuzzy number of the subjective E[PV]. The comparison of the results of both techniques shows that the fuzzy approach extends the standard outcome of a series useful information. This informative “superstructure” of the fuzzy approach compared to the standard solution is another original benefit of the paper.
This study proposes the use of fuzzy AHP method to evaluate the structure of airline business model attributes and its corresponding hierarchy of evaluation index. As the AHP method requires, several pairwise comparisons were made using linguistic variable to determine the weights of criteria and sub-criteria. According to the results, value added processes have been found as the most important criteria while core products and services under value proposition criterion have been identified as the most important sub-criteria. This study suggests fuzzy AHP as a useful method in determining the important attributes of business model. In addition, the results of the present study contribute to the business model literature by determining importance and weights of criteria and sub-criteria for the airline business model and may act as a strategic tool to modify or generate a winning business model for the airline industry.
Customer interaction in new service development has a positive impact on the performance of new services. In addition, prior studies recognize the importance of the fuzzy front-end stages of new service development. Yet, the researchers have not taken the next step to explore the relationship between these two key areas of service innovation. To address this critique of the literature, the process of customer interaction in the fuzzy front-end of new service development is investigated by conducting a rigorous qualitative field research involving 26 financial services firms. The findings suggest that the fuzzy front-end can be much less ‘fuzzy’ if customers are involved in the front-end stages of new service development.
Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal training samples to enhance generalization performance for two different kinds of classifiers, discriminative and generative (e.g. SVM and KNN). The optimal samples are selected by first estimating the boundary of each class and then calculating the fuzziness-based distance between each sample and the estimated class boundaries. Those samples that are at smaller distances from the boundaries and have higher fuzziness are chosen as target candidates for the training set. Through detailed experimentation on three publically available datasets, we showed that when trained with the proposed sample selection framework, both classifiers achieved higher classification accuracy and lower processing time with the small amount of training data as opposed to the case where the training samples were selected randomly. Our experiments demonstrate the effectiveness of our proposed method, which equates favorably with the state-of-the-art methods.
Purpose The purpose of this paper is to explore conceptualizations of mindset across disciplines with particular attention to scholars’ care in defining and operationalizing the construct of mindset. Theories of mindset have witnessed increased attention through a variety of disciplines for their applicability as processes with the potential to influence individual and/or organizational outcomes. Exploration of mindset conceptualizations and characterizations reveal substantial divergences. Design/methodology/approach This conceptual paper generally examines the utilization of mindset constructs via a multidisciplinary review of literature and specifically details three mindset theories (implemental and deliberative, global and growth and fixed mindsets) to illuminate such disparities. Findings This paper categorizes the significant variations of the mindset construct and research via three distinct streams. Each stream highlights knowledge as instrumental in the mindset construct; however, the ways in which varying aspects of knowledge, knowledge mechanisms or knowledge as a component of an individuals and/or organization’s identity correspond to the inherent presuppositions of varying articulations of mindset theory and praxis. Practical implications Effectively influencing an individual and/or organization’s mindset necessitates an accurate assessment of the mindset construct. Further, evaluating the applicability of mindset research and/or feedback from a consultant warrants attention to the assumptions undergirding the mindset construct. Originality/value Generally, mindset studies and theories have scantly attended to both the historical development of mindset research as well as divergences in the research record within and across disciplines. This paper attempts to address this deficiency. Further, this paper appears to be the first attempt to compare and identify varying conceptualizations and characterizations of mindset theory and, therefore, identifies previously unidentified assumptions.
This paper investigates the stress–strength reliability in the presence of fuzziness. The fuzzy membership function is defined as a function of the difference between stress and strength values, and the fuzzy reliability of single unit and multicomponent systems are calculated. The inclusion of fuzziness in the stress–strength interference enables the user to make more sensitive analysis. Illustrations are presented for various stress and strength distributions.