In this paper, a learning framework is introduced which incorporates principles of probabilistic inference, variational optimization, and geometry-preserving operations inspired by quantum transformations. The central innovation of this quantum-inspired variational convolution (QiVC) lies in its quantum-inspired rotated ensemble (QiRE) mechanism. QiRE performs differentiable low-dimensional subspace rotations of convolutional weights. By drawing a mathematical analogy from unitary evolution, this approach enables structured uncertainty modeling that respects the intrinsic geometry of the parameter space. To demonstrate its practical potential, the concept is instantiated in a QiVC-based convolutional network (QiVC-Net) and evaluated in the context of biosignal classification, focusing on phonocardiogram (PCG) recordings. The proposed QiVC-Net integrates an architecture in which the QiVC layer does not introduce additional parameters, instead performing an ensemble rotation of the convolutional weights through a structured mechanism ensuring robustness without added highly computational burden. Experiments on two benchmark datasets, PhysioNet CinC 2016 and PhysioNet CirCor DigiScope 2022, show that QiVC-Net achieves high performance, reaching accuracies of 97.84% and 97.89%, respectively. These findings highlight the versatility of the QiVC framework and its promise for advancing uncertainty-aware modeling in real-world biomedical signal analysis. The implementation of the QiVConv layer is available in GitHub for public use.
This paper assesses the impact of the Swiss CO2 levy on residential heating energy demand and the associated CO2 emissions. Using the synthetic control method, the results show that the levy led to an average annual reduction in CO2 emissions of 6.5% during the post-treatment period (2008–2021), corresponding to a decrease of 0.1 metric tons of CO2 per capita per year. Furthermore, the empirically estimated price elasticities for heating oil indicate that the short-run elasticity for the retail price is −0.044, while the elasticity for the CO2 levy is −0.997, demonstrating that consumers respond more strongly to policy-induced price changes than to market-driven price changes. In the long run, these elasticities increase to −0.054 (retail price) and −1.215 (levy), highlighting that over time, households adjust their demand more significantly in response to sustained price changes. A similar pattern is observed for natural gas, with short-run elasticities of −0.216 (retail) and −0.547 (levy), increasing to −0.552 and −1.396, respectively, in the long run. These findings are consistent with the Swiss CO2 levy contributing to lower fossil-fuel demand and building-sector emissions, although the SCM inference does not reach conventional levels of statistical significance. The results underline the importance of policy-induced price instruments and highlight the necessity of high levy rates to ensure a measurable impact on consumption behavior.
Many physical systems, such as vibrating elastic bodies, acoustic media and electromagnetic fields, are governed by second-order evolution equations ẍ+Dẋ+Sx=0 and interact with their environment through their boundary. The classical analysis via the operator matrix 𝒜=[[ 0 I; -S -D ]] requires S to be self-adjoint, which typically fixes a boundary condition a priori and thus excludes boundary control. We therefore use S only to define the energy space and govern the dynamics by a maximal operator S_max⊃ S whose domain carries no boundary condition. Based on a factorization S=A^*A, we construct a boundary triplet for the associated operator matrix and define boundary inputs and outputs of Robin type, parametrized by an operator P on the boundary space. We show that the resulting system is impedance passive and internally well-posed if P is a contraction, and that for D=0 this covers all boundary conditions under which the free dynamics generate a contraction semigroup. Moreover, we show that the position-momentum formulation (e.g., [[ 0 I; Δ 0 ]] for the wave equation) and the strain-momentum formulation (e.g., [[ 0 ∇; div 0 ]]) describe the same open system, including its boundary inputs and outputs. We illustrate our results by an n-dimensional wave equation and Maxwell's equations.
This study examines the influences of milling-induced surface topography and residual stresses on the fretting fatigue behaviour of high-strength steel 34CrNiMo6 + QT (AISI 4340) under flat-flat contact conditions. Various milling parameters (feed rate, cutting depth, and tool configuration) were systematically varied to characterize their influence on surface roughness and residual stress states, which ranged from tensile to compressive depending on tool stiffness and cutting conditions. Derived milling configurations were subsequently employed to produce specimens for plain fatigue and fretting fatigue experiments under actively controlled slip amplitude and defined contact pressure. The resulting S-N curves revealed a 78% reduction in fretting fatigue strength compared to plain fatigue attributable to tribological damage mechanisms. While lower surface roughness promotes the crack growth in the inclined section, compressive residual stresses appear to moderately improve the fretting fatigue limit by enhancing crack arrest, as indicated by marker load tests. Moreover, the breadth of the surface power spectrum, expressed by the Nayak parameter, exerts only a minor influence on the normalized number of load cycles. Instead, the fretting fatigue life is primarily affected by adhesive wear and mechanical interlocking influencing the crack initiation. Adhesion formation is largely governed by macroscopic surface waviness and mostly independent of residual stresses. These results indicate that the residual stresses mainly influence the fretting fatigue limit through crack arrest while the surface topography modifies the level and depth affected by contact stresses and primarily governs the specimen lifetime in the HCF regime.
Although cycling offers numerous advantages, existing research identifies significant barriers to its uptake. These include feeling unsafe in traffic. While cyclist-centered infrastructure may address objective safety deficits, its impact on perceived insecurity remains uncertain. To address this gap and as an approach to map the safety continuum, we conducted a national field study in Germany that combined on-site observations and interviews to determine the number of so far undocumented safety-critical events (SCEs), which are precursors to crashes and indicators of infrastructure quality. Specifically, we investigated SCEs and their characteristics (involved interaction partners, causes, evasive actions) in relation to infrastructure type and assessment method (observation, interview). Over 12 weeks, we observed 34,676 cyclists in three medium-sized German cities, and surveyed 978. The results clearly show that SCEs occur frequently. From the perspective of cyclists, 10 to 28 times more SCEs are reported than observed, depending on the infrastructure. These results have several implications: SCEs should be considered when designing infrastructure, as they can reduce subjective perceptions of safety and potentially lead to less sustainable mobility. Infrastructure should be designed so that all road users are not only objectively safe but also subjectively, i.e., fear-free, when travelling. The findings underscore the importance of incorporating both cyclists' perspectives and SCEs into infrastructure planning, as cyclists perceive risk differently and often avoid crashes through defensive riding, so SCEs are not recorded in official crash statistics.
