Mälardalen University (Swedish: Mälardalens universitet), or MDU, is a Swedish university located in Västerås and Eskilstuna, Sweden. It has 16,000 students and around 1000 employees, of which 91 are professors, 504 teachers, and 215 doctoral students. Mälardalen University is the world's first environmentally certified school according to the international standard ISO 14001.In December 2020, the Löfven government proposed that the university should receive university status from 1 January 2022.
Abstract Research on how socioeconomic status (SES) and gender interact to affect student achievement has produced contradictory findings. Some studies suggest boys are more vulnerable to socioeconomic disadvantage; others find the opposite. This paper argues that these conflicting results stem from a methodological artifact: because boys report their parents’ education less accurately than girls do, the source of parental education data in large-scale assessments fundamentally shapes estimates of the SES–gender interaction. PISA 2006 and 2009 provide data on 151,269 fifteen-year-old students from 20 countries for whom both parent-reported and student-reported parental education were available. This within-subject design allowed a direct comparison of the parental-education–gender interaction in mathematics achievement, isolating the effect of the data source. The direction of the interaction systematically reverses depending on the data source. When using reliable parent-reported data, the parental-education gradient is steeper for boys in the majority of country-waves (18 of 31), consistent with the “vulnerable boys” hypothesis. In 12 of those 18 cases, the results reverse to indicate “vulnerable girls” when using the more commonly available student-reported proxy data. A sign test confirmed that the interaction estimate was more negative with student data in 28 of 31 country-waves (p < 0.001). The choice of data source is not a minor technical detail but a factor that can reverse conclusions about educational equity.
We prove the existence and uniqueness of viscosity solutions to quasi-variational inequalities (QVIs) with both upper and lower obstacles. In contrast to most previous works, we allow all involved coefficients to depend on the state variable and do not assume any type of monotonicity. It is well known that double obstacle QVIs are related to zero-sum games of impulse control, and our existence result is derived by considering a sequence of such games. Full generality is obtained by allowing one player in the game to randomize their control. A by-product of our result is that the corresponding zero-sum game has a value. Utilizing recent results for backward stochastic differential equations (BSDEs), we find that the unique viscosity solution to our QVI is related to optimal stopping of BSDEs with constrained jumps and, in particular, to the corresponding non-linear Snell envelope. This gives a new probabilistic representation for double obstacle QVIs. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
This brief presents a unified control framework that integrates a feedback linearization (FL) controller in the inner loop with an adaptive data-enabled policy optimization (DeePO) controller in the outer loop to balance an autonomous bicycle. While the FL controller stabilizes and partially linearizes the inherently unstable and nonlinear system, its performance is compromised by unmodeled dynamics and time-varying characteristics. To overcome these limitations, the DeePO controller is introduced to enhance adaptability and robustness. The initial control policy of DeePO is obtained from a finite set of offline, persistently exciting (PE) input and state data. To improve stability and compensate for system nonlinearities and disturbances, a robustness-promoting regularizer refines the initial policy, while the adaptive section of the DeePO framework is enhanced with a forgetting factor to improve adaptation to time-varying dynamics. The proposed FL-DeePO approach is evaluated through simulations and real-world experiments on an instrumented autonomous bicycle. Results demonstrate its superiority over the FL-only approach and a reinforcement learning (RL) controller, achieving more precise tracking of the reference lean angle and lean rate.
Thermal energy systems in buildings play a central role in global decarbonization efforts, accounting for a significant share of energy use and carbon emissions. This study addresses a key research question: how can advanced control strategies further enhance the performance of already energy-efficient, low-exergy thermal systems in low-energy buildings? To address this, a model predictive control (MPC) framework is designed to optimize the operation of an advanced thermal system based on modern concepts of low-temperature heating and high-temperature cooling, including ground-source heat pumps, borehole thermal storage, and modern air handling units. This approach employs a multi-layered MPC cost function, considering both immediate operational costs (electricity and heating) as well as system impact penalties, such as COQ emissions, thermal energy storage preservation, comfort violations, and peak load shaving, in response to fluctuating market cost signals, outdoor temperature, and thermal storage limitations. Applied to a validated, ultra-efficient commercial building, the MPC framework achieves a 13 % reduction in annual market-responsive operational costs, a 20 % improvement in long-term savings, and a four-year shorter payback period compared to existing well-established rule-based control. The results further confirm the robustness of predictive control under realistic forecast errors, as demonstrated by Monte Carlo simulations. From an environmental perspective, the COQ emission index stays below both Swedish electricity and district heating baselines, demonstrating the environmental benefits of predictive control through strategic sector coupling. Beyond the case study, the proposed method provides a scalable pathway for integrating predictive control into next-generation smart buildings. It highlights the potential of MPC as the final optimization layer in advanced thermal systems, aligning with global objectives for cost-promising and carbon-neutral building operations.
Classical Ewald methods for Coulomb and Stokes interactions rely on “kernel-splitting," using decompositions based on Gaussians to divide the resulting potential into a near field and a far field component. Here, we show that a more efficient splitting for the scalar biharmonic Green's function can be derived using zeroth-order prolate spheroidal wave functions (PSWFs), which in turn yields new efficient splittings for the Stokeslet, stresslet, and elastic kernels, since these Green's tensors can all be derived from the biharmonic kernel. This benefits all fast summation methods based on kernel splitting, including FFT-based Ewald summation methods, that are suitable for uniform point distributions, and DMK-based methods that allow for nonuniform point distributions. The DMK (dual-space multilevel kernel-splitting) algorithm we develop here is fast, adaptive, and linear-scaling, both in free space and in a periodic cube. We demonstrate its performance with numerical examples in two and three dimensions.
