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Student evaluation of teaching (SeT) is ubiquitous in higher education but has been criticized by many scholars because of low use for course improvement benefiting student learning. Academic developers (ADs) are responsible for pedagogical courses and to support leadership and academics in processes enhancing educational quality. We could therefore expect ADs to play a key role in SeT practice. T

In this paper, we combine the operator splitting methodology for abstract evolution equations with that of stochastic methods for large-scale optimization problems. The combination results in a randomized splitting scheme, which in a given time step does not necessarily use all the parts of the split operator. This is in contrast to deterministic splitting schemes which always use every part at le

Mg alloys are prone to pitting due to their non-uniform protective corrosionCorrosion layers, which can lead to an increase in stress intensity based on the notch effect, pit-to-crack transition, and thus premature failure. A small set of analytical methods to determine the extent of pitting and its effect on the resulting residual strengthResidual tensile strength is presented. Micrographs, 3D mi

Outcrops of the Ordovician System in South Africa are extensive; they cover significant portions of the Northern, Western and Eastern Cape provinces as part of the Cape Fold Belt as well as the KwaZulu-Natal Province as supracrustal cover overlying the Natal sector of the Paleoproterozoic Namaqua-Natal metamorphic province. Within the Cape Fold Belt, Ordovician rocks of the Table Mountain Group (P

Objective: Obstetric anal sphincter injuries (OASIS) are severe complications to vaginal births with potential long-term consequences. Maternal origin has been proposed to affect the overall risk, but the association and underlying explanation are uncertain. The objective was to assess the association between maternal country of birth and OASIS. Methods: A Swedish nationwide cohort study including

This work proposes a stochastic variational deep kernel learning method for the data-driven discovery of low-dimensional dynamical models from high-dimensional noisy data. The framework is composed of an encoder that compresses high-dimensional measurements into low-dimensional state variables, and a latent dynamical model for the state variables that predicts the system evolution over time. The t

Structural health monitoring techniques aim at providing an automated solution to the threat of unsurveilled aging of structures that can have tremendous consequences in terms of fatalities, environmental pollution, and economic loss. To assess the state of damage of a complex structure, this paper proposes to fully characterize its behavior under multiple environmental and operational scenarios a

An energy-based a posteriori error bound is proposed for the physics-informed neural network solutions of elasticity problems. An admissible displacement-stress solution pair is obtained from a mixed form of physics-informed neural networks, and the proposed error bound is formulated as the constitutive relation error defined by the solution pair. Such an error estimator provides an upper bound of

Somaliasvenskar som flyttar till Storbritannien upplever inte bara att det är lättare att få jobb utan mycket lättare att starta eget, skriver bland andra ­ekonomihistorikern Benny Carlson.

I Kanada, USA och Storbritannien står etniska organisationer för centrala delar av integrationsarbetet - men i Sverige är det myndig­heter som ska integrera, skriver Benny Carlson.

Despite tremendous progress seen in the computational fluid dynamics community for the past few decades, numerical tools are still too slow for the simulation of practical flow problems, consuming thousands or even millions of computational core-hours. To enable feasible multi-disciplinary analysis and design, the numerical techniques need to be accelerated by orders of magnitude. Reduced-order mo

A data-driven reduced basis (RB) method for parametrized time-dependent problems is proposed. This method requires the offline preparation of a database comprising the time history of the full-order solutions at parameter locations. Based on the full-order data, a reduced basis is constructed by the proper orthogonal decomposition (POD), and the maps between the time/parameter values and the proje

Purpose - For eigenvalue problems containing uncertain inputs characterized by fuzzy basic parameters, first-order perturbation methods have been developed to extract eigen solutions, but either the result accuracy or the computational efficiency of these methods is less satisfactory. The purpose of this paper is to present an efficient method for estimation of fuzzy eigenvalues with high accuracy

We propose a non-intrusive reduced basis (RB) method for parametrized nonlinear partial differential equations (PDEs) that leverages models of different accuracy. From a collection of low-fidelity (LF) snapshots, parameter locations are extracted for the evaluations of high-fidelity (HF) snapshots to recover a reduced basis. Multi-fidelity Gaussian process regression (GPR) is employed to approxima

BACKGROUND: Our aim was to examine the prevalence and characteristics of difficult-to-treat HIV in the current Swedish HIV cohort and to compare treatment outcomes between people with difficult and non-difficult-to-treat HIV.METHODS: In this cross-sectional analysis of the Swedish HIV cohort, we identified all people with HIV currently in active care in 2023 from the national register InfCareHIV.

When evaluating quantities of interest that depend on the solutions to differential equations, we inevitably face the trade-off between accuracy and efficiency. Especially for parametrized, time-dependent problems in engineering computations, it is often the case that acceptable computational budgets limit the availability of high-fidelity, accurate simulation data. Multi-fidelity surrogate modeli

Highly accurate numerical or physical experiments are often very time-consuming or expensive to obtain. When time or budget restrictions prohibit the generation of additional data, the amount of available samples may be too limited to provide satisfactory model results. Multi-fidelity methods deal with such problems by incorporating information from other sources, which are ideally well-correlated

This work proposes a Bayesian inference method for the reduced-order modeling of time-dependent systems. Informed by the structure of the governing equations, the task of learning a reduced-order model from data is posed as a Bayesian inverse problem with Gaussian prior and likelihood. The resulting posterior distribution characterizes the operators defining the reduced-order model, hence the pred

A non-intrusive reduced basis (RB) method is proposed for parametrized nonlinear structural analysis undergoing large deformations and with elasto-plastic constitutive relations. In this method, a reduced basis is constructed from a set of full-order snapshots by the proper orthogonal decomposition (POD), and the Gaussian process regression (GPR) is used to approximate the projection coefficients.

In solid mechanics, linear structures often exhibit (local) nonlinear behavior when close to failure. For instance, the elastic deformation of a structure becomes plastic after being deformed beyond recovery. To properly assess such problems in a real-life application, we need fast and multi-query evaluations of coupled linear and nonlinear structural systems, whose approximations are not straight