Upper limb rehabilitation exoskeletons face fundamental challenges achieving coordinated multi-joint assistance within portable configurations. Current cable-driven systems demonstrate effective single-joint support but lack coordinated shoulder-elbow capabilities due to anchor-point sensing constraints that limit workspace during simultaneous movements. To address these limitations, this paper presents a novel coordinated 3-Degree of Freedom (DOF) shoulder-elbow exosuit (3.3 kg) employing motor-proximal sensing architecture. Strategic load cell repositioning from anchor points to actuation unit locations eliminates spatial constraints, while geometric compensation algorithms maintain measurement accuracy, enabling coordinated assistance (shoulder flexion/extension, abduction/adduction, elbow flexion/extension) with preserved 0–$90^\circ$ kinematic workspace. Systematic development integrating product design specifications, multi-criteria decision-making, and biomechanical component dimensioning provided traceable design synthesis. Preliminary proof-of-concept tests with healthy participants (n = 5) provide an initial assessment of the system performance, demonstrating adequate sensing accuracy within the primary Activities of Daily Living (ADL) workspace, measurable muscle activation reduction, and preserved natural kinematics with minimal range-of-motion (ROM) constraint. An extensive experimental validation is out of the scope of this work. Results establish technical feasibility for coordinated cable-driven assistance, identifying requirements for future clinical translation.

The “differentiability gap” presents a primary bottleneck in Earth system deep learning: Since models cannot be trained directly on non-differentiable scientific metrics and must rely on smooth proxies (e.g., MSE), they often fail to capture high-frequency details, yielding “blurry” outputs. We develop a framework that bridges this gap using two different methods to deal with non-differentiable functions: The first is to analytically approximate the original non-differentiable function into a differentiable equivalent one; the second is to learn differentiable surrogates for scientific functionals. We formulate the analytical approximation by relaxing discrete topological operations using temperature-controlled sigmoids and continuous logical operators. Conversely, our neural emulator uses Lipschitz-convolutional neural networks to stabilize gradient learning via: (1) spectral normalization to bound the Lipschitz constant and (2) hard architectural constraints enforcing geometric principles. We demonstrate this framework’s utility by developing the Minkowski image loss, a differentiable equivalent for the integral-geometric measures of surface precipitation fields (area, perimeter, and connected components). Validated on the EUMETNET OPERA dataset, our constrained neural surrogate achieves high emulation accuracy, completely eliminating the geometric violations observed in unconstrained baselines, which generate physically impossible precipitation fields in up to 7.8% of cases. However, applying these differentiable surrogates to a deterministic super-resolution task reveals a fundamental trade-off: While strict Lipschitz regularization ensures optimization stability, it inherently over-smooths gradient signals, restricting the recovery of highly localized convective textures. This work highlights the necessity of coupling such topological constraints with stochastic generative architectures to achieve full morphological realism.

In the homology cobordism group $\Theta_\mathbb{Z}^3$, it is not known if there are non-trivial linear dependences between Seifert fibered spheres. Based on involutive Heegaard Floer theory, Hendricks, Manolescu and Zemke introduced the local equivalence group $\mathfrak{I}$ along with the homomorphism $h\;:\;\Theta_\mathbb{Z}^3 \rightarrow \mathfrak{I}$. Using the work of Dai and Stoffregen, one can find non-trivial linear dependences between the images of Seifert fibered spheres under h. Therefore, it is interesting to ask if such dependences in $\mathfrak{I}$ originate from $\Theta_\mathbb{Z}^3$. In this paper, by employing the $r_s$-invariants from the filtered instanton Floer homology developed by Nozaki, Sato and Taniguchi, we provide certain conditions to guarantee that such relations are not realised even in the rational homology cobordism group. We also discuss the local equivalence class of the $\operatorname{Pin(2)}$-equivariant Seiberg–Witten Floer stable homotopy type.

Central banking and monetary policy face particular challenges in heterogenous and multilevel currency areas. And yet, most literature on central banking, like most modern central banks themselves, assumes the validity of a unitary monetary authority implementing uniform monetary policy, even in compound polities. Here, I explore the dilemmas of central banking and monetary policy in politically decentralized currency areas by analyzing the origins of the Federal Reserve Act of 1913 and its provisions for a multilevel governance structure and decentralized monetary policy. The origins of the Federal Reserve remain puzzling because its decentralized structure is often dismissed as a failed experiment or a political compromise that nobody truly wanted. However, I find that its design was the product of a deliberate political effort to reimagine central banking in a federal society, and I trace the idea of a decentralized “central” bank to a distinct interpretation of the American political-constitutional tradition.

Belz and colleagues present GREAT, a seven-item clinical instrument for predicting electroconvulsive therapy response in unipolar depression, with promising discriminatory validity (AUC 0.841). We identify three methodological gaps – absent calibration, limited sample representativeness and unquantified incremental value – that must be addressed before GREAT can guide individual clinical decisions.

Insects are increasingly used in environmental education (EE) due to their high diversity, ecological relevance, and presence in urban environments. This study evaluates the use of insects as a pedagogical tool by implementing formal and non-formal EE activities with secondary students living in areas with a high incidence of vector-borne diseases, on the coast of Oaxaca, Mexico. A total of 118 students participated in theoretical, fieldwork, and laboratory activities, as well as a public outreach event (“Entomo-Expo”). Pre- and post-workshop questionnaires were used to assess changes in knowledge, perceptions, and attitudes. At the end of the workshop, students identified 11 insect orders. They communicated their knowledge using scientific terminology, and negative reactions toward insects decreased, particularly in reports on killing behaviour, while responses associated with observation and the release of insects increased. Participants also recognised key ecological roles of insects, especially pollination. These results are associated with improvements in students’ conceptual understanding and more positive attitudes towards insects following participation in the programme. However, given the pre–post-design without a control group, these findings should be interpreted as changes over time rather than causal effects. To address the persistence of negative behaviours in older students, EE should employ both formal and non-formal teaching strategies to support a sense of responsibility and encourage a cognitive shift toward sustainable development.

Radiocarbon dating of disarticulated human remains from Brading Roman villa (Isle of Wight, UK) assigns them to a single event or multiple events within a short duration in the Late Roman period, c. 220/50–360/400. The evidence of the coins suggests the possibility of a brief abandonment of the villa following a potentially murderous event in the 340s or 350s, perhaps to be linked to reprisals taken by Constantius against supporters of Magnentius. This event may in turn coincide with a significant reorganisation of space in the west range of the villa.

The growing involvement of private health insurers within universal health systems has intensified debate over their effects on access, equity, and long-term system sustainability. This paper examines the role of private insurers in the United Kingdom (UK) and South Africa through a case study of the Discovery Group, operating across both settings. We explore how private sector engagement shapes health financing, workforce dynamics, service delivery, digital infrastructure, and governance. Our analysis reveals that the impact of private health insurance on universal health systems is fundamentally context-dependent, mediated by institutional frameworks, regulatory environments, and the stage of universal coverage development. We find that private insurers can contribute meaningfully to digital health innovation and behavioural health interventions. However, expansion also introduces significant risks concerning workforce distribution, financing sustainability, and equity of access. These dynamics manifest differently across contexts. In the UK’s mature universal system, private insurance plays a supplementary role offering expedited access to care for members. In South Africa’s transitional dual system, private insurers more fundamentally shape whether quality care is accessible at all. As health systems evolve, the central challenge lies in developing governance frameworks that enable beneficial private sector contributions while safeguarding equitable access and national health system priorities.

Reinforcement Learning (RL) enables autonomous agents to learn policies from experience, but realistic problems often involve enormous state spaces, making learning and generalisation challenging. Abstraction and approximation are therefore essential. Relational Reinforcement Learning (RRL) offers a way to reason about objects and their relations, and the CARCASS framework by Martijn van Otterlo demonstrates how logical representations can model Markov Decision Processes (MDPs) in first-order domains. Originally implemented in Prologue, CARCASS leverages domain knowledge to create powerful abstractions. We explore Answer-Set Programming (ASP), which is a rich and, contrary to Prologue, fully declarative modelling language, to realise CARCASS abstractions. We evaluate our ASP-based implementation in case studies of two domains, viz. Blocks World and Minigrid. Our results indicate that CARCASS with ASP provides a promising approach to constructing abstractions for RL, especially when domain knowledge is available (our implementation is available at https://github.com/rbankosegger/RLASP-core. Further material (data, encodings, extended documentation) can be found here: https://www.bankosegger.at/iclp26/).

We compute the $K$-theory of the ${C}^{*}$-category generated by order zero, equivariant, properly supported, classical pseudodifferential operators acting on sections of homogeneous bundles over the symmetric space of a real reductive Lie group $G$. Our result uses the Connes-Kasparov isomorphism for $G$, and in fact it is equivalent to the Connes-Kasparov isomorphism. We relate our computation to David Vogan’s well-known parametrization of the tempered irreducible representations of $G$ with real infinitesimal character. When the reductive group $G$ has real rank one, we formulate and prove a Fourier isomorphism theorem for equivariant order zero pseudodifferential operators on the symmetric space, and use it to prove a $K$-theoretic version of Vogan’s theorem.

Learning diagnosis is essential for effective education, with formative assessments shown to significantly enhance academic performance. Diagnostic classification models (DCMs) have been developed to assess students’ learning status and provide remedial instruction. However, the impact of mastery or non-mastery of specific attributes on long-term learning development remains uncertain. If certain non-mastered attributes hinder the growth of mathematical ability, early intervention becomes essential. In this study, we developed a random-effects DCM for multilevel growth curves (RDC–MGC) model to identify the specific effects of attribute mastery on individual-level mathematics ability growth. The simulation studies showed that the Bayesian estimation procedure provided appropriate parameter recovery and coverage probabilities, whereas ignoring the multilevel structure resulted in biased parameter estimates. The model was applied to arithmetic test data from second- to sixth-grade elementary school students. Diagnosis was conducted in the second grade, and the effects of mastery on mathematics ability growth from the third to sixth grades were assessed. The results showed that attribute mastery in second grade was associated with both the intercept and slope of individual ability growth, suggesting the potential importance of early-stage diagnostic information for understanding later mathematical development. Potential extensions of the proposed RDC–MGC model are also discussed.

The Parker Solar Probe mission has observed near-continuous power in parallel ion cyclotron waves (PICWs) in the young, fast solar wind. These waves are unlikely to be directly produced by the turbulent cascade and are likely born of a local instability; yet, they are observed to both cool – and heat – the plasma. We propose that these observations can be self-consistently explained as the natural consequence of PICWs propagating in the inhomogeneous solar wind after they have been driven unstable. In this work, we argue that strong proton heating by a turbulent cascade of oblique ICWs will result in PICWs being driven unstable in a process known as quasi-linear focusing. Because the power in the turbulent cascade is concentrated at scales above the turbulent transition region, PICWs will be driven unstable within a range of wavenumbers parallel to the background magnetic field, $k_\parallel$, that is bounded from above by $k_{\parallel {\mathrm{P}}}^*$, corresponding to the start of the transition region. As unstable PICWs propagate away from the Sun to regions of lower proton density, their $k_\parallel$, multiplied by the proton inertial length $d_{\mathrm{p}}$, increases. Eventually, $k_\parallel d_{\mathrm{p}}$ of the PICWs becomes larger than $k_{\parallel {\mathrm{P}}}^*d_{\mathrm{p}}$ and the waves damp, heating the solar wind. We call this effect ‘cyclotron breaking’, in analogy with ocean waves breaking on the shore. We then discuss the testable predictions of the theory, including a distinct heating signature in which PICWs cool fast protons and heat slow protons at any given heliocentric distance $r$. Finally, we conjecture that cyclotron breaking can lead to net heating by PICWs if the power emitted as PICWs decreases sufficiently rapidly with $r$ that local emission of PICWs is overwhelmed by the local damping of PICWs generated closer to the Sun.