Search Results (261)

Blockchain is a decentralized digital ledger that records transactions across a distributed network of computers, enabling secure and transparent operations without requiring trust in a central authority. While initially developed for Bitcoin, blockchain technology now underpins many cryptocurrencies and other applications. It serves as an open trust layer without central reliance and is widely used in cryptocurrencies such as Bitcoin and Ethereum. However, this public and permanent open storage has raised concerns about its potential misuse for illegal trades or the distribution of unwanted content. In EuroS&P 2017, Ateniese et al. introduced the concept of the redactable blockchain, which utilizes the trapdoor collision function provided by chameleon hash to rewrite block contents without causing hashing inconsistencies. Recent research has continued to propose solutions for redactable blockchains, leveraging cryptographic algorithms such as chameleon hash and attribute-based encryption (ABE). Current solutions often employ sophisticated cryptographic schemes, such as ABE, but lack sufficient focus on developing secure and scalable solution for practical use. In this work, we propose the time-verifiable policy-based chameleon hash (TPCH) as a candidate solution for practical redaction to rewrite blockchain contents. Our solution for redactable blockchains enables the verification of whether a redaction was executed at a specific time, thereby offering time-based traceability for dominant algorithms in TPCH. Additionally, it restricts misbehavior or abuse of redaction powers by introducing a new trapdoor finding algorithm, Update, in addition to the adapt algorithm Adapt. We formally introduce TPCH with both black-box and white-box constructions. Our experimental and theoretical analysis demonstrates the feasibility and practicality of the proposed solution. Full article

Granule description is a fundamental problem in granular computing. However, how to describe indefinable granules is still an open, interesting, and important problem. The main objective of this paper is to give a preliminary solution to this problem. Before proceeding, the framework of approximate description is introduced. That is, any indefinable granule is characterized by an ordered pair of formulas, which form an interval set, where the first formula is the $\beta$-prior approximate optimal description and the second formula is the $\alpha$-prior approximate optimal description. More concretely, given an indefinable granule, by exploring the description of its lower approximate granule, its $\beta$-prior approximate optimal description is obtained. Likewise, by consulting the description of its upper approximate granule, its $\alpha$-prior approximate optimal description can also be derived. Following this idea, the descriptions of indefinable granules are investigated. Firstly, ∧-approximate descriptions of indefinable granules are investigated based on the classical concept lattice, and $(\wedge ,\vee )$-approximate descriptions of indefinable granules are given via object pictorial diagrams. And then, it is revealed from some examples that the classical concept lattice is no longer effective and negative attributes must be taken into consideration. Therefore, a three-way concept lattice is adopted instead of the classical concept lattice to study $(\wedge ,\neg )$-approximate descriptions and $(\wedge ,\vee ,\neg )$-approximate descriptions of indefinable granules. Finally, some discussions are presented to show the differences and similarities between our study and existing ones. Full article

Background/Objectives: Many commercial light-emitting diode (LED) devices are available for consumer home usage. The performance characteristics in respect to the dosimetry of many of the devices, currently on direct sale to the public, have not been subject to formal appraisal. In order to ‘bridge the gap’ between the evidence-based photobiomodulation therapy (PBMT) community and other interested parties, an evaluation is made of a selection of torch type hand-held LED PBMT products currently available for home use. Methods: Five randomly chosen intra-oral and hand-held LED PBMT devices were selected. The optical delivery parameters of the devices were measured, including the beam divergence angle, surface area exposure as well as the output power at the level of the LEDs. The surface and sub-surface temperature changes in porcine tissue samples were assessed under standardised conditions. The manufacturer’s patient instructions were correlated to the measured optical parameters. Calculations were made of irradiance and surface radiant exposure. Consumer satisfaction ratings and feedback data were collated, and a relevant statistical analysis conducted. Results: The results were heterogeneous with a wide range of applied wavelengths, output power and irradiance. Power output stability was variable, and, together with a wide beam divergence angle of 74°, the manufacturer’s directions for dosimetry were found to be inconsistent with an accurate dose delivery. Conclusions: The manufacturer’s proposed dosimetry fails to consider the relevance of the beam divergence angle and optical attenuation in view of the scatter and absorption. Appropriate instructions on how best to gain and optimise an acceptable clinical outcome were inconsistent with an evidence-based approach. Subject to validation by well-planned clinical trials, the concept of home PBMT may open interesting new therapeutic approaches. Full article

The formalism of Functional Language Logic (FLL) is presented, which is an extension of a logical formalism already introduced to represent sentences in natural languages. In the FLL framework, a sentence is represented by aggregating primitive predicates corresponding to words of a fixed language (English in the given examples). The FLL formalism constitutes a bridge between mathematical logic (high-order predicate logic) and the classical logical analysis of discourse, rooted in the Western linguistic tradition. Namely, FLL representations reformulate on a rigorous logical basis many fundamental classical concepts (complementation, modification, determination, specification, …), becoming, at the same time, a natural way of introducing mathematical logic through natural language representations, where the logic of linguistic phenomena is analyzed independently from the single syntactical and semantical choices of particular languages. In FLL, twenty logical operators express the mechanisms of logical aggregation underlying meaning constructions. The relevance of FLL in chatbot interaction is considered, and a problem concerning the relationship between embedding vectors in LLM (Large Language Model) transformers and FLL representations is posed. Full article

By exploring the positive elements of traditional business culture and combining them with modern enterprise management concepts, this paper aims to realize the sustainable development of enterprises and cultural heritage and innovation. In this context, this study empirically examines the impact and mechanisms of merchant guild culture (MGC) on corporate innovation, using A-share listed companies from 2010 to 2022 as a sample. The findings indicate that MGC positively influences contemporary corporate innovation through both external and internal channels. External channels include alleviating financing constraints and enhancing ESG performance, and internal influence channels such as improving integrity and emphasizing human capital. Additionally, social networks strengthen the relationship between MGC and corporate innovation. Furthermore, using the legal environment as a moderating variable has led to the discovery of a certain substitution relationship between formal and informal institutions. A heterogeneity analysis further shows that the effect of MGC on innovation is more pronounced in enterprises with low-risk preference and a foreign cultural impact. Full article

In this paper, new logical equivalences are presented within the simplification logic with mixed attributes paradigm, which allow the obtention of bases of shorter, easier-to-read attribute implications. In addition to the theoretical results which show that the proposed equivalences indeed hold in simplification logic with mixed attributes, experimental results which showcase the effectiveness of this method are also provided. Furthermore, the simplification method presented is iterative and gives sufficiently good results in only one or two iterations, therefore presenting itself as a reasonable procedure in time-sensitive experiments. Full article

In recent years, differential privacy has gained substantial traction in the medical domain, where the need to balance privacy preservation with data utility is paramount. As medical data increasingly relies on cloud platforms and distributed sharing among multiple stakeholders, such as healthcare providers, researchers, and policymakers, the importance of privacy-preserving techniques has become more pronounced. Trends in the field focus on designing efficient algorithms tailored to high-dimensional medical datasets, incorporating privacy guarantees into federated learning for distributed medical devices, and addressing challenges posed by adversarial attacks. Our work lays a foundation for these emerging applications by emphasizing the role of randomized response within the broader differential privacy framework, paving the way for advancements in secure medical data sharing and analysis. In this paper, we analyze the classical concept of a randomized response and investigate how it relates to the fundamental concept of differential privacy. Our approach is both mathematical and algorithmic in nature, and our purpose is twofold. On the one hand, we provide a formal and precise definition of differential privacy within a natural and convenient probabilistic—statistical framework. On the other hand, we position a randomized response as a special yet significant instance of differential privacy, demonstrating its utility in preserving individual privacy in sensitive data scenarios. To substantiate our findings, we include key theoretical proofs and provide indicative simulations, accompanied by open-access code to facilitate reproducibility and further exploration. Full article

Informal green spaces (IGSs) are vital yet under-researched urban areas that enhance biodiversity, provide ecosystem services, and improve the well-being of urban residents. However, the lack of a consistent definition and comprehensive understanding of their multifunctional roles has hindered their effective integration into urban planning. The current literature review aimed to clarify the concept of IGSs, analyze research trends, and identify further research areas. Using a combined bibliometric and systematic analysis approach, 150 articles from the Web of Science database, published from 1996 to 2024, were analyzed. The systematic analysis identified 54 relevant documents on the effects of green areas, revealing a diverse and growing body of research on IGSs, including their types, distribution, and socioeconomic contexts. The findings indicated an increasing trend in collaborative studies, using “informal green space” as the official term. This review proposed a clear and comprehensive definition of IGS, emphasizing its visibility, lack of formal recognition, minimal management, spontaneous vegetation, and temporary nature and underscoring its substantial environmental and social benefits. Furthermore, this review highlighted the need for standardized definitions and interdisciplinary studies to fully harness the potential of IGSs, thereby emphasizing their essential contribution to urban biodiversity and the regulation of urban microclimates. Full article

Background: On 16 January 2021 (EB148/18 Session), the World Health Organization (WHO) and Member States emphasized the importance of expanding the WHO Emergency Medical Teams (EMT) Initiative, investing in a global health workforce and multidisciplinary teams capable of being rapidly deployed, equipped, and fully trained to respond to all-hazard emergencies effectively. This resulted in the need to define a comprehensive framework. To achieve this, the EMT Initiative proposes the application of the four components of Surge Capacity, known as the 4“S” (Staff, Systems, Supplies, and Structure/Space), to build global capacities and capabilities, ensuring rapid mobilization and efficient coordination of national and international medical teams for readiness and response, complying with crisis standards of care defined in an ethical and evidence-based manner. Methods: A mixed-qualitative research approach was used, incorporating expert consensus through focus group discussions (FGDs), between 2021 and July 2022. This facilitated a detailed process analysis for the application of the surge capacity components to build global capacities and capabilities. This research highlighted the similarities between surge capacity and capacity building from an initial desk review and unified these concepts within the EMT Initiative. A standardized formal pathway was developed to enhance local, regional, and global capacities for emergency readiness and response. Results: The results showed that the framework successfully integrated the essential components of surge capacity and capacity building, making it adaptable to various settings. Conclusions: This framework provides a unified and replicable approach for readiness and response for all-hazards emergencies. Full article

A research framework is formed by the semantics of the “green” macrosystem, supported by a methodological approach, data analysis, and forecasting, with a focus on the dynamics of transition to a qualitatively new state. The purpose of the work is to develop conceptual provisions and methodological tools for assessing the implementation of the concept of a “green” macrosystem. Applying methods of system analysis, content analysis, formalization, statistical analysis (technologies of knowledge discovery in databases and time series analysis), and discriminant analysis contributed to achieving the goal. As a result of the research, the categorical apparatus of a “green” economy was clarified by outlining narrow and broad approaches to defining the “green” macrosystem; the author’s method of assessing the implementation of the concept of the “green” macrosystem was modified by expanding the list of factors of greening the economic system, transforming the condition of the “green” corridor to calculate the developed dynamic coefficient of transition to the concept of the “green” macrosystem (DCGM), and adapting the method to the macrosystem level; the regularities of a transition to the concept of a “green” macrosystem were revealed. The novelty of the study lies in the proposal of an integral DCGM indicator, which avoids the problems of normalization, weighting, and loss of relevant data, incorporates the determinants of the “green” economy (natural resources and pollution), and relies on available data. The formulated provisions develop the theoretical basis on which to transform the macrosystem to the “green” concept and can be taken into account in the implementation’s framework of strategic planning documents for the greening of production and economic systems. Full article

The design of complex aerospace systems requires a broad multidisciplinary knowledge base and an iterative approach to accommodate changes effectively. Engineering knowledge is commonly represented through engineering analyses and descriptive models with underlying semantics. While guidelines from systems engineering methodologies exist to guide the development of system models, creating a system model from scratch with every new application/system requires research into more adaptable and reusable modeling frameworks. In this context, this research demonstrates how a physics-based multidisciplinary analysis and optimization tool, SUAVE, can be leveraged to develop a system model. By leveraging the existing physics-based knowledge captured within SUAVE, the process benefits from the expertise embedded in the tool. To facilitate the systematic creation of the system model, an ontological metamodel is created in SysML. This metamodel is designed to capture the inner workings of the SUAVE tool, representing its concepts, relationships, and behaviors. By using this ontological metamodel as a modeling template, the process of creating the system model becomes more structured and organized. Overall, this research aims to streamline the process of building system models from scratch by leveraging existing knowledge and utilizing an ontological metamodel as a modeling template. This approach enhances formal knowledge representation and its consistency, and promotes reusability in multidisciplinary design problems. Full article

The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and a decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples t-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance. Full article

Our standard model-theoretic definition of logical consequence is originally based on Alfred Tarski’s (1936) semantic definition, which, in turn, is based on Rudolf Carnap’s (1934) similar definition. In recent literature, Tarski’s definition is described as a conceptual analysis of the intuitive ‘everyday’ concept of consequence or as an explication of it, but the use of these terms is loose and largely unaccounted for. I argue that the definition is not an analysis but an explication, in the Carnapian sense: the replacement of the inexact everyday concept with an exact one. Some everyday intuitions were thus brought into a precise form, others were ignored and forgotten. How exactly did the concept of logical consequence change in this process? I suggest that we could find some of the forgotten intuitions in Bernard Bolzano’s (1837) definition of ‘deducibility’, which is traditionally viewed as the main precursor of Tarski’s definition from a time before formalized languages. It turns out that Bolzano’s definition is subject to just the kind of natural features—paradoxicality of everyday language, Platonism about propositions, and dependence on the external world—that Tarski sought to tame by constructing an artificial concept for the special needs of mathematical logic. Full article

Statistical hypothesis testing, as formalized by 20th century statisticians and taught in college statistics courses, has been a cornerstone of 100 years of scientific progress. Nevertheless, the methodology is increasingly questioned in many scientific disciplines. We demonstrate in this paper how many of the worrisome aspects of statistical hypothesis testing can be ameliorated with concepts and methods from evidential analysis. The model family we treat is the familiar normal linear model with fixed effects, embracing multiple regression and analysis of variance, a warhorse of everyday science in labs and field stations. Questions about study design, the applicability of the null hypothesis, the effect size, error probabilities, evidence strength, and model misspecification become more naturally housed in an evidential setting. We provide a completely worked example featuring a two-way analysis of variance. Full article

The concept of the centaur, symbolizing the fusion of human and machine intelligence, has intrigued visionaries for decades. Recent advancements in artificial intelligence have made this concept not only realizable but also actionable. This synergistic partnership between natural and artificial intelligence promises superior outcomes by leveraging the strengths of both entities. Tracing its origins back to early pioneers of human–computer interaction in the 1960s, such as J.C.R. Licklider and Douglas Engelbart, the idea initially manifested in centaur chess but faced challenges as technological advances began to overshadow human contributions. However, the resurgence of generative AI in the late 2010s, exemplified by conversational agents and text-to-image chatbots, has rekindled interest in the profound potential of human–AI collaboration. This article formalizes the centaurian model, detailing properties associated with various centaurian designs, evaluating their feasibility, and proposing a design methodology that integrates human creativity with artificial intelligence. Additionally, it compares this model with other integrative theories, such as the Theory of Extended Mind and Intellectology, providing a comprehensive analysis of its place in the landscape of human–machine interaction. Full article