Biomolecular condensates govern essential cellular processes yet elude description by traditional equilibrium models. This roadmap, distilled from structured discussions at a workshop and reflecting the consensus of its participants, clarifies key concepts for researchers, funding bodies, and journals. After unifying terminology that often separates disciplines, we outline the core physics of condensate formation, review their biological roles, and identify outstanding challenges in nonequilibrium theory, multiscale simulation, and quantitative in-cell measurements. We close with a forward-looking outlook to guide coordinated efforts toward predictive, experimentally anchored understanding and control of biomolecular condensates.
Purpose The purpose of this study is to investigate whether dilute ferrofluids subjected to static magnetic fields can enhance convective heat transfer in turbulent channel flow. Although nanoparticle suspensions are known to increase thermal conductivity, high particle concentrations significantly raise viscosity and pumping requirements. Using ferrofluids with low particle loadings in combination with magnetic forcing has been proposed as an alternative mechanism to promote mixing without incurring excessive flow resistance. Design/methodology/approach Large eddy simulations of turbulent channel flow at a friction Reynolds number Ret = 395 are conducted for homogeneous dilute ferrofluids exposed to static magnetic fields. The ferrofluid magnetization is modeled using both a saturation-based nonlinear constitutive law and a commonly applied linearized approximation. Magnetic field strengths range from 1 mT to 3 T, allowing assessment of magnetically induced flow modification and its impact on momentum and heat transport. Findings For the physically realistic saturation-based magnetization, static magnetic fields cause a slight suppression of near-wall turbulence, resulting in marginally reduced heat transfer performance. This effect is linked to the saturation of particle magnetization at elevated field intensities. In contrast, the linearized magnetization model produces pronounced secondary flow structures that artificially enhance heat transfer, demonstrating that nonphysical model assumptions can lead to misleading conclusions regarding ferrofluid-based thermal enhancement in turbulent regimes. Originality/value This study clarifies that static magnetic fields do not enhance heat transfer for dilute ferrofluids in turbulent flows and helps researchers and engineers assess the practical limits of magnetically assisted thermal management. Furthermore, this study emphasizes the necessity of using realistic magnetization models for accurate predictions.
Self-sovereign identity (SSI) is a novel approach to digital identity management, which is controversially discussed in technological communities and academia and lately also in the political space. Positions in the debate range from touting SSI as introducing a paradigm shift in internet identity and user privacy, while others dismiss the concept as libertarian hyperbole. SSI aims to give individuals an independent digital existence and control over their digital identities. Technically, this is achieved by providing individuals with digital identity wallet applications, which allow them to store and present digitally verifiable credentials. Despite its transformative potential, SSI is not comprehensively conceptualized in information system research. Therefore, in this Fundamentals article, we offer the following contributions: First, based on existing information systems research, we provide a consolidated definition and a conceptual framework of SSI structured along five analytic levels: (1) foundational principles, (2) credential exchange, (3) technical building blocks, (4) applications, and (5) governance. Second, we present an information systems research agenda on SSI, including concrete research questions and promising theoretical directions.
The integrated density of states (IDS) is a fundamental spectral quantity for quantum Hamiltonians modeling condensed matter systems, describing how densely energy levels are distributed. It can be interpreted as a volume-averaged spectral distribution. Hence, there are two equivalent definitions of the IDS related by the Pastur-Shubin formula: an operator-theoretic trace formula and a limit of normalized eigenvalue counting functions on finite volumes. We study a discrete random Schrödinger operator with bounded random potentials of finite-range correlations and prove a quantitative concentration inequality ensuring, with explicit high probability, that the empirical IDS (normalized eigenvalue counting function) uniformly approximates the abstract IDS trace formula within a prescribed error, thereby implying confidence regions for the IDS.
Chatbots are increasingly used in e-commerce for customer service, product recommendations, and sales advice. Although they offer efficiency and personalization, they also carry risks: responses may exhibit bias, contain toxic language, or apply inappropriate personalization that can erode customer trust, harm brand reputation, and conflict with emerging regulations. Existing datasets address bias or toxicity in short, user-generated texts such as tweets or comments, but they do not capture the multi-turn, transactional nature of e-commerce dialogues or the interplay between multiple ethical dimensions. To close this gap, we present FairBotBench, a German–English benchmark dataset for evaluating chatbot behavior in e-commerce. The dataset comprises 600 dialogues, generated from practice-driven scenarios and diverse user profiles, and enriched with ethically problematic variants and linguistic transformations. Each dialogue is annotated by humans across three ethical dimensions, namely bias, toxicity, and personalization. To demonstrate the usefulness and potential of the benchmark, we apply it to several SOTA LLMs in an LLM-as-a-Judge scenario, evaluating how well they can detect ethically problematic chatbot responses. FairBotBench provides the first multilingual, domain-specific, and ethically comprehensive benchmark for conversational AI in e-commerce, offering a robust foundation for research, evaluation, and regulatory compliance .
