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How does artistic practice lead to the production of knowledge?How does, in turn, artistic knowledge relate to its material base?How does contingent materiality guide the artist towards finding form and developing a statement?.

The historiography of science and religion has had a considerable impact in science and religion discussions, showing that there is no enduring essence to science or religion. Such suggestion is, however, fraught with philosophical issues that paradoxically prevent a foundational integration of the valuable insights from the historiographical work in science and religion discourses. This article proposes a hermeneutical approach to bridge the gap between historiography and philosophy; history and philosophy are interpretive paths that unnecessarily clash due to the philosophical import of anti-essentialist historiography. Science and religion discourses can be opened up by focusing on how the temporality of things (history) and the being of things (philosophy) are hermeneutically integrated to create these discourses.

This paper addresses three aspects of Bio Art: iconography, artificial life, and wetware. The development of models for innovation require hybrid practices which generate knowledge through epistemic experimental practices. The intersection of art and the biological sciences contain both scientific data as well as the visualization of its cultural imagination. In the Bio Art Lab at the School of Visual Arts, artists use the tools of science to make art.

The aim of this article is to discuss epistemology, focusing on the epistemic role of artwork in research projects. Porcelaneous is the initial phase of a three-year artistic research project with an artistic component comprising the making of porcelain boards. The  porcelain boards and the making process in this initial phase are used as examples from practice. At the core of the epistemological discussion is Hans-Jörg Rheinberger’s theory on experimental systems and epistemic things. Rheinberger advocates for an objective and practice-oriented approach rather than a theoretical approach to experimental research. In his setting, epistemic things are material. This article has concluded that Rheinberger’s theory is about attitudes rather than research methods and that this attitude to epistemological questions is relevant for artistic research.

The development and management of planted spaces in Northwestern Europe in the 17th and 18th centuries depended on the possibilities for circulation in the republic of letters of the Dutch golden age. Circulation was accompanied by questions of managing space, information and \"epistemic things\" (Rheinberger) for botanists. Against the conceptual backdrop of \"circulation\" (Raj), \"circulatory regimes\" (Saunier) and \"ensembles of things\" (Hahn), this paper analyses, first, flowerbeds as a script for managing information that shaped botanical gardens across Europe in Leiden, Uppsala, Coimbra, and as far as Batavia according to Linnaean principles. Second, it investigates hothouses as spaces for managing things, and with it the role of knowledge in things handled by professional and amateur gardeners, not least the stove for pineapple cultivation. The paper concludes with reflections on the community of the material and the social around epistemic things, and the differing influences of description and narration in garden spaces.

According to many philosophers there are only pro tanto oughts to believe relative to a standard of assessment: there are epistemic oughts to believe, moral oughts to believe, prudential oughts to believe etc. But there are no oughts to believe simpliciter. Many of the same philosophers who hold this view, also hold that ought to believe is to be understood deontologically – such that if S violates such an ought without excuse, S is blameworthy for doing so. I here argue that on a deontological understanding of ought to believe there must be ought to believe simpliciter and that it is the violation of this ought that determines whether we are to blame for our beliefs.

Data-driven forecasts of air quality have recently achieved more accurate short-term predictions. However, despite their success, most of the current data-driven solutions lack proper quantifications of model uncertainty that communicate how much to trust the forecasts. Recently, several practical tools to estimate uncertainty have been developed in probabilistic deep learning. However, there have not been empirical applications and extensive comparisons of these tools in the domain of air quality forecasts. Therefore, this work applies state-of-the-art techniques of uncertainty quantification in a real-world setting of air quality forecasts. Through extensive experiments, we describe training probabilistic models and evaluate their predictive uncertainties based on empirical performance, reliability of confidence estimate, and practical applicability. We also propose improving these models using “free” adversarial training and exploiting temporal and spatial correlation inherent in air quality data. Our experiments demonstrate that the proposed models perform better than previous works in quantifying uncertainty in data-driven air quality forecasts. Overall, Bayesian neural networks provide a more reliable uncertainty estimate but can be challenging to implement and scale. Other scalable methods, such as deep ensemble, Monte Carlo (MC) dropout, and stochastic weight averaging-Gaussian (SWAG), can perform well if applied correctly but with different tradeoffs and slight variations in performance metrics. Finally, our results show the practical impact of uncertainty estimation and demonstrate that, indeed, probabilistic models are more suitable for making informed decisions.

Byzantine fault tolerance (BFT) protocols serve as the cornerstone of data consistency in permissioned blockchains; however, their scalability is inherently constrained by stochastic leader-centric bottlenecks and rigid, non-adaptive timeout mechanisms. Existing rule-based heuristics often fail to capture high-entropy and time-varying network latency, leading to frequent view changes and severe performance degradation under network volatility. To mitigate this epistemic uncertainty, this paper proposes TimesNet-BFT, a novel entropy-aware optimization framework. By leveraging TimesNet’s transformation of one-dimensional time series into two-dimensional tensors for multi-periodicity analysis, the framework accurately characterizes stochastic nodal latency patterns to facilitate entropy-minimized dynamic leader election and adaptive timeout strategies. Extensive evaluations conducted on simulated and real-world trace-driven Internet of Vehicles (IoV) scenarios validate the proposed approach, achieving a prediction MAPE below 5% alongside robust zero-shot generalization. Notably, under high-entropy network conditions, the framework demonstrates up to a 191.9% increase in throughput and mitigates latency variance by 73.3%, effectively neutralizing the structural bottlenecks inherent to traditional information-agnostic protocols. Crucially, by mathematically decoupling consensus safety from AI prediction errors, the system introduces an aggressive liveness paradigm that maintains minimal control plane overhead while significantly enhancing the entropic stability of the consensus process.

Epistemic obligations are constraints on belief stemming from epistemic considerations alone. Booth (Synthese 187:509–517, 2012) is one of the many philosophers who deny that there are epistemic obligations. Any obligation pertaining to belief is an all things considered obligation, according to him—a strictly generic, rather than specifically epistemic, requirement. Though Booth's argument is valid, I will try to show that it is unsound. There are two central premises: (1) S is justified in believing that P iff S is blameless in believing that P; (2) S is blameless in believing that P iff S has not violated an all things considered duty in believing that P. Both premises are false. My argument against (1) depends on my own theory of epistemic obligations. My argument against (2) does not. This paper is part of a larger project—defending epistemic requirements in general against a series of objections and advancing a particular theory that solves various problems.

The rapid development of the Internet of Things (IoT) has enabled the implementation of interconnected intelligent systems in extremely dynamic contexts with limited resources. However, traditional paradigms, such as those using ECC-based heuristics and centralised decision-making frameworks, cannot be modernised to ensure resilience, scalability and security while taking quantum threats into account. In this case, we propose a modular architecture that integrates quantum-inspired cryptography (QI), epistemic uncertainty reasoning, the multiscale blockchain MuReQua, and the quantum-inspired decentralised storage engine (DeSSE) with fragmented entropy storage. Each component addresses specific cybersecurity weaknesses of IoT devices: quantum-resistant communication on epistemic agents that facilitate cognitive decision-making under uncertainty, lightweight adaptive consensus provided by MuReQua, and fragmented entropy storage provided by DeSSE. Tested through simulations and use case analyses in industrial, healthcare and automotive networks, the architecture shows exceptional latency, decision accuracy and fault tolerance compared to conventional solutions. Furthermore, its modular nature allows for incremental integration and domain-specific customisation. By adding reasoning, trust and quantum security, it is possible to design intelligent decentralised architectures for resilient IoT ecosystems, thereby strengthening system defences alongside architectures. In turn, this work offers a specific architectural response and a broader perspective on secure decentralised computing, even for the imminent advent of quantum computers.