坦佩雷理工大学

This paper studies the problem of computing the stochastic probability (shortest code length) of the encoded vectors containing cluster structure using Normalized Maximum Likelihood (NML) model. This is of great theoretical and practical importance in data clustering based on Minimum Description Length (MDL) principle, such as for estimating the best number of clusters and best cluster structure for the data. Straightforward computation of the shortest code length of the vector containing cluster structure based on the NML model requires polynomial time with respect to the size of the vector and number of clusters. We show that this is a tractable problem by introducing a recursion formula for the efficient computation of normalizing constant from the NML model. The time complexity of the new formula is linear opposed to previous polynomial time with respect to the size of the vector and number of clusters.

PurposeThis study aims to understand the shared use of academic workspaces from the staff's perspective. The paper first examines the factors and readiness for shared use of academic workspaces. Second, it illustrates nine possible work modes and users' preferences. Thereby, the paper discusses the access-based consumption of academic workspaces.Design/methodology/approachThe study focuses on a Finnish case university, where both qualitative and quantitative data were collected in 2022 during the campus development process. The qualitative findings use an inductive analysis of 18 semi-structured and 10 focus group interviews. The interviewees represent two faculties and all levels of academia. The quantitative results use descriptive statistical analyses of university-wide workplace preference survey data (n = 1,507 respondents).FindingsThe findings illustrate that the shared use of academic workspaces depends on: the sharing partner level, the time and length of use and the access options and spaces. The respondents were more ready to share workspaces with a closer community. The nine work modes present possible shared use options for workspaces. Half of the respondents chose the typical non-sharing mode and the other half chose less typical solutions. The results indicate a move towards preferences for more varied work modes.Originality/valueTo the best of the authors' knowledge, this study is one of the first to examine the spectrum of shared use of academic workspaces on campus, which typically are not viewed as shared and are also under-researched. It systematically maps the factors to benefit the user-oriented campus development processes.

Despite the growing use of biopolymers in automotive, packaging and structural applications, predictive modelling of their elastic–viscoplastic deformation remains limited. In this work, a micromechanically based constitutive model is proposed to describe the micro‑ to macroscopic behaviour of a semi‑crystalline PLA matrix reinforced with short hemp fibers. The formulation relies on a multiplicative split of the deformation gradient into elastic and viscoelastic–plastic parts, with elasticity governed by fiber and crystalline phases and time‑dependent deformation localized in the amorphous phase. High fiber content and strong fiber–matrix bonding enable the suppression of lattice crystalline anisotropy, leading to a compact model with a reduced number of internal variables. The model is calibrated and validated using uniaxial tensile tests on pellet‑extrusion 3D‑printed specimens with controlled porosity and plasticiser content, and reproduces nonlinear loading, unloading, creep and stress relaxation. In a second step, synthetic data generated by the constitutive model are used to train surrogate machine‑learning models, which are discussed as a perspective for accelerating long‑term simulations and parametric studies in forming applications.

The increasing penetration of inverter-based resources has led to a significant reduction in system inertia, resulting in faster and more pronounced frequency deviations in modern low-inertia power systems. In such environments, the dynamic behavior of electrical loads becomes increasingly important in shaping overall system frequency response. This paper presents an enhanced load model that incorporates load-side dynamics in addition to conventional static behavior, thereby augmenting the representation of load-frequency control (LFC) models. This improved formulation increases the accuracy of frequency response studies in power systems. A comparison between the proposed augmented model and the conventional LFC representation demonstrates that relying solely on static load modeling can lead to inaccurate results and potentially misleading conclusions. Therefore, accurate modeling of load-side dynamics is essential for reliable frequency stability assessment in modern power systems.

QT interval prolongation is associated with an increased risk of torsades de pointes (TdP), a potentially life-threatening ventricular arrhythmia, making assessment of cardiac repolarization a critical component of drug development. This work demonstrates how integrated in silico methodologies can be used to predict and quantitatively characterize QT liability across the drug discovery and development continuum. A proof-of concept translational workflow combining molecular structure analysis, in vitro pharmacology, animal data, and clinical exposure-response modeling was evaluated using the retrospective case examples verapamil and dofetilide. Molecular structure- and pharmacology-based assessments predicted hERG channel interaction and potential QT risk for both compounds. While initial in silico analyses suggested a high QT liability for verapamil and dofetilide, mechanistic electrophysiological modeling differentiated their effects. For verapamil, concurrent calcium channel inhibition offset hERG-mediated effects, resulting in predicted reductions in action potential duration and no clinically relevant QT prolongation. In contrast, dofetilide produced concentration-dependent increases in action potential duration, consistent with its known proarrhythmic potential. Translation of nonclinical findings to humans accurately predicted the absence of clinically meaningful QT effects for verapamil and identified QT prolongation exceeding the regulatory 10 ms threshold for dofetilide at therapeutic concentrations. Drug exposure and pharmacokinetic properties could be anticipated based solely on molecular structure. This conceptual workflow demonstrates how integrated quantitative in silico approaches can support earlier identification, characterization, and communication of QT risk, enabling informed decision-making and improved development efficiency from bench to bedside.