Všechny publikace

Abstract This paper introduces an enhanced framework for performing division operations on interactive fuzzy numbers characterized by complete correlation. Unlike traditional methods reliant on the i…

Abstract This paper introduces an enhanced framework for performing division operations on interactive fuzzy numbers characterized by complete correlation. Unlike traditional methods reliant on the independence assumption, we build on the sup-J extension framework to support correlated input fuzzy values. The proposed method establishes precise conditions under which the result aligns with, diverges from, or subsumes conventional divisions such as Zadeh’s and the generalized Hukuhara division. Additionally, we investigate invertibility conditions for the proposed division with respect to multiplication. These refinements offer valuable theoretical insights and have implications for models involving uncertainty, including difference equations.  

In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we ass…

In this study, we present an analysis, driven by the Critical Discourse Studies, Frame Analysis, and Narrative Analysis, of how truancy is represented in the Czech media. Based on our findings, we assert that Czech media employ three frames (gender bias; moralisation and individualisation; and repression/retribution) for representing truancy, effectively depicting it as a criminal problem created by irresponsible mothers (and children they neglected) who must be punished to address the issue. Employing the cultural and feminist criminology framework, as a combination that is scarcely used in studies, we argue that media (re)produce criminalisation of mothers by amplifying deeply rooted and routinised gendered cultural stereotypes. In this sense, we show that cultural criminology is useful not only for analysing adrenaline/“exciting” transgression, subcultures, or edgework, but, especially when combined with feminist criminology, also for analysing possibilities of criminalisation rooted in mainstream culture. Furthermore, our analysis shows that Czech media representation of truancy is damaging both for society and for addressing the issue, reducing it to a mere neoliberal individual choice of irresponsible mothers, even though truancy is a much more complex phenomenon with communal and structural levels and can be seen rather as a product of advanced marginality.

Abstract   Intermediate quantifiers are expressions of natural language, for example “most, almost all, many, a few” using which we quantify a number of some objects in a given univer…

Abstract   Intermediate quantifiers are expressions of natural language, for example “most, almost all, many, a few” using which we quantify a number of some objects in a given universe. We have shown in [23] that all valid syllogisms with intermediate  quantifiers are a consequence of only two algebraic inequalities and one equality. The result was obtained in the formalism  of Lukasiewicz fuzzy type theory whose truth values form a linearly ordered complete MV-algebra. In this paper we will  prove that the same holds if we replace MV-algebra by a much more general IEQ-algebra (involutive EQ-algebra).   

Abstract This study examines the stressor-response process in physical activity among 226 participants across four countries. We analyzed their step count collected via activity monitors before and a…

Abstract This study examines the stressor-response process in physical activity among 226 participants across four countries. We analyzed their step count collected via activity monitors before and after a significant stressor: the COVID-19 lockdown. Results showed that a ‘local dynamic complexity’ metric significantly predicts the rate of recovery to pre-COVID levels of physical activity. These findings provide new opportunities for just-in-time interventions to support physical activity recovery after disruptive stressors. Data availability The data used in the analysis are available at https://osf.io/gsmhk/. Code availability The R scripts used for the analysis are available at https://osf.io/gsmhk/.

Abstract Background: Dyslipidemia and distorted fatty acid (FA) metabolism are frequent biochemical abnormalities associated with anorexia nervosa (AN). Gut microbiota is supposed to play an important…

Abstract Background: Dyslipidemia and distorted fatty acid (FA) metabolism are frequent biochemical abnormalities associated with anorexia nervosa (AN). Gut microbiota is supposed to play an important role in the etiopathogenesis of AN. Apart from the digestive function of bile acids (BAs), these compounds have multiple metabolic functions due to the activation of specific receptors. Objective/aims: The aims of the study were to investigate biochemical measures, including plasma lipids (lipoproteins, respectively), fatty acid (FA) patterns, and the profile of plasma Bas, in AN patients and healthy controls (CON). Methods: Plasma phospholipid FA and BAs profiles were analyzed in 39 women with a restrictive type of AN (AN-R; median age 17 years) and in 35 CON women (median age 20 years). Results: Compared to CON, AN had an increased concentration of HDL-C, increased content of palmitic acid, and decreased proportion of linoleic acid. Moreover, AN had a drop in the level of the sum of PUFAn-6 and increased delta 9 desaturase activity for stearic acid. In AN, we found decreased levels of plasma tauroursodeoxycholic acid (TUDCA). In AN, concentrations of 22:5n-6, 16:0, 20:3n-6 and fat mass index were predic-tors of HDL-C levels (R2 = 0.43). Conclusions: Patients with AN-R had an increased concentration of HDL-C, decreased levels of total PUFA n-6, and increased activity of D9D for stearic acid. Furthermore, AN exerted decreased levels of TUDCA. Therefore, a decreased level of TUDCA could potentially serve as a marker of AN.

Abstract Aristotle's Square also known as Square of Opposition, is a mathematical diagram dating back to Greek philosophy and exhibiting the connection between four logical propositions in a…

Abstract Aristotle's Square also known as Square of Opposition, is a mathematical diagram dating back to Greek philosophy and exhibiting the connection between four logical propositions in a simple graphical form. Fuzzy Relational Concept Analysis (FRCA) is a technique for extracting special clusters called fuzzy concepts from a Fuzzy Relational Context Family (FRCF), which is a dataset organized as multiple fuzzy object-attribute and object-object relations. The primary FRCA tools to obtain information from data are special fuzzy quantifiers viewed as interpretations in a model of formulas of the formal theory of the intermediate generalized quantifiers. This work focuses on the issue of generating a collection of fuzzy concepts from a certain FRCF, by choosing one of four particular FRCA quantifiers: the positive universal quantifier 𝒮1, the negative universal quantifier 𝒮−1, the positive existential quantifier 𝒮∃, and the negative existential quantifier 𝒮−∃. Certainly, the selection of the quantifier is crucial in the FRCA procedure since it affects the final concept classification: diverse fuzzy concepts arise from varying quantifiers. As the initial objective, this article introduces the logical relations involving 𝒮1,𝒮−1, 𝒮∃, and 𝒮−∃, in order to arrange them in a graded version of the Aristotelian square. The second goal of this study is to examine the connections among fuzzy concepts produced by distinct quantifiers in {𝒮1,𝒮−1,𝒮∃,𝒮−∃}. Therefore, our findings provide a twofold contribution to the advancement of Aristotle's square. Indeed, they reveal a novel interpretation of the square of opposition within the framework of Fuzzy Relational Concept Analysis, emphasizing its potential as a valuable tool for the analysis of data.

This paper examines the approximation capabilities of lattice integral transforms and their compositions in reconstructing lattice-valued functions. By introducing an integral kernel 𝑄 on the function…

This paper examines the approximation capabilities of lattice integral transforms and their compositions in reconstructing lattice-valued functions. By introducing an integral kernel 𝑄 on the function domain, we define the concept of a 𝑄-inverse integral kernel, which generalizes the traditional inverse kernel defined as a transposed integral kernel. Leveraging these 𝑄-inverses, we establish upper and lower bounds for a transformed version of the original function induced by the integral kernel 𝑄. The quality of approximation is analyzed using a lattice-based modulus of continuity, specifically designed for functions valued in complete residuated lattices. Additionally, under specific conditions, we demonstrate that the approximation quality for extensional functions with respect to the kernel 𝑄 can be estimated through the integral of the square of 𝑄, and in certain cases, these extensional functions can be perfectly reconstructed. The theoretical findings, illustrated through examples, provide a strong foundation for further theoretical advancement and practical applications.

A new concept, termed virtual neural networks, is introduced, where the count of trainable parameters is kept constant, and scalability is attained purely through computational resources. This concept…

A new concept, termed virtual neural networks, is introduced, where the count of trainable parameters is kept constant, and scalability is attained purely through computational resources. This concept is an abstract framework that can be realized using any standard convolutional neural network. It merges siamese neural networks with a deep ensemble technique by generating numerous virtual models that share weights derived from a small set of physical models. The ensemble comprises up to hundreds of trained models simultaneously. All virtual networks take the same input, and their interconnected structure induces an internal distortion that boosts the entire ensemble robustness. The accuracy of the ensemble improves as the number of virtual networks increases, without changing the capacity. Virtual neural networks outperform larger capacity models, typical deep ensembles, and contemporary approaches like SWA and Masksembles. Additionally, the highest performing individual model from the ensemble surpasses other models trained individually, even those with a greater number of parameters.