Tensors.jl — Tensor Computations in Julia

Tensors.jl is a Julia package that provides efficient computations with symmetric and non-symmetric tensors. The focus is on the kind of tensors commonly used in e.g. continuum mechanics and fluid dynamics. Exploiting Julia’s ability to overload Unicode infix operators and using Unicode in identifiers, implemented tensor expressions commonly look very similar to their mathematical writing. This possibly reduces the number of bugs in implementations. Operations on tensors are often compiled into the minimum assembly instructions required, and, when beneficial, SIMD-instructions are used. Computations involving symmetric tensors take symmetry into account to reduce computational cost. Automatic differentiation is supported, which means that most functions written in pure Julia can be efficiently differentiated without having to implement the derivative by hand. The package is useful in applications where efficient tensor operations are required, e.g. in the Finite Element Method.   Funding statement: Support for this research was provided by the Swedish Research Council (VR), grant no. 621-2013-3901 and grant no. 2015-05422

Forskningsdatahantering och infrastruktur vid Lunds universitet - en översikt av rapporter, undersökningar och utredningar 2015-2022 : behov, nuläge och framtid

Arbetet med stöd till forskningsdatahantering har utvecklats snabbt de senaste tio åren och kunskap har samlats både i publikationer och universitetsinterna rapporter. Forskarna efterfrågar infrastruktur, stöd och en universitetspolicy att luta sig emot. En analys av aggregerade behov och befintligt stöd visar att universitetet bör utveckla en samlad infrastruktur för forskningsdatalagring med möjlighet till samarbetsdelning, säker lagring, publicering, arkivering, sökning och bearbetning av forskningsdata. Bibliotek och andra stödfunktioner bör erbjuda stöd gällande rekommendationer och policydokument, samarbete och delning av gemensamma forskningsdata, upprättande av datahanteringsplaner, publicering, arkivering, upphovsrätt, säkerhetsaspekter, etikfrågor och återanvändning av forskningsdata. Som stöd till forskarna och stödverksamheten bör universitetet ta fram en forskningsdatapolicy

PCM4EU and PRIME-ROSE:Collaboration for implementation of precision cancer medicine in Europe

Background: In the two European Union (EU)-funded projects, PCM4EU (Personalized Cancer Medicine for all EU citizens) and PRIME-ROSE (Precision Cancer Medicine Repurposing System Using Pragmatic Clinical Trials), we aim to facilitate implementation of precision cancer medicine (PCM) in Europe by leveraging the experience from ongoing national initiatives that have already been particularly successful. Patients and methods: PCM4EU and PRIME-ROSE gather 17 and 24 partners, respectively, from 19 European countries. The projects are based on a network of Drug Rediscovery Protocol (DRUP)-like clinical trials that are currently ongoing or soon to start in 11 different countries, and with more trials expected to be established soon. The main aims of both the projects are to improve implementation pathways from molecular diagnostics to treatment, and reimbursement of diagnostics and tumour-tailored therapies to provide examples of best practices for PCM in Europe. Results: PCM4EU and PRIME-ROSE were launched in January and July 2023, respectively. Educational materials, including a podcast series, are already available from the PCM4EU website (http://www.pcm4eu. eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024. Conclusion: European collaboration can facilitate the implementation of PCM and thereby provide affordable and equitable access to precision diagnostics and matched therapies for more patients.</p

PCM4EU and PRIME-ROSE:Collaboration for implementation of precision cancer medicine in Europe

Background: In the two European Union (EU)-funded projects, PCM4EU (Personalized Cancer Medicine for all EU citizens) and PRIME-ROSE (Precision Cancer Medicine Repurposing System Using Pragmatic Clinical Trials), we aim to facilitate implementation of precision cancer medicine (PCM) in Europe by leveraging the experience from ongoing national initiatives that have already been particularly successful. Patients and methods: PCM4EU and PRIME-ROSE gather 17 and 24 partners, respectively, from 19 European countries. The projects are based on a network of Drug Rediscovery Protocol (DRUP)-like clinical trials that are currently ongoing or soon to start in 11 different countries, and with more trials expected to be established soon. The main aims of both the projects are to improve implementation pathways from molecular diagnostics to treatment, and reimbursement of diagnostics and tumour-tailored therapies to provide examples of best practices for PCM in Europe. Results: PCM4EU and PRIME-ROSE were launched in January and July 2023, respectively. Educational materials, including a podcast series, are already available from the PCM4EU website (http://www.pcm4eu. eu). The first reports, including an overview of requirements for the reimbursement systems in participating countries and a guide on patient involvement, are expected to be published in 2024. Conclusion: European collaboration can facilitate the implementation of PCM and thereby provide affordable and equitable access to precision diagnostics and matched therapies for more patients.</p

On computational homogenization of gradient crystal inelasticity

Crystal inelasticity is the main modeling technique employed to study the mechanical behavior of polycrystalline metallic materials. This class of models has the capability to represent micromechanical phenomena such as plastic slip, grain boundary interactions, and dislocation pile-up. An extended class of models, known as gradient crystal inelasticity, can also be used to predict size dependence of the grains, which is a property that has been observed experimentally. An increased understanding, in terms of the challenges in modeling polycrystalline materials, could aid in reducing costs and resources needed to determine the properties of these type of materials experimentally.The length scales that are characteristic for typical engineering applications and the length scale of the underlying microstructure often differ by many orders of magnitude. As a consequence, it is not computationally feasible to fully resolve the model at a fine enough scale to capture the microstructural characteristics. Instead, computational homogenization is a suitable framework for modeling structures exhibiting these scale separations. Homogenization allows for bridging the microstructural to the effective properties that pertain to the (structural) scale of engineering interest.In this work, different modeling aspects of gradient crystal inelasticity and their modeling capabilities, in a computational homogenization setting, are investigated. In particular, two variational formats are compared, specifically in terms of convergence rate with respect to mesh refinements, and the effect of applying certain boundary conditions. Furthermore, it is shown that certain effective properties (properties for sufficiently large microscopic models, called Representative Volume Elements) can be bounded from above and below based on simulations performed on finite size models (Statistical Volum Elements), that are amenable to simulation. The bounding property can be used towards estimating how large microscopic models that are needed to produce accurate results in the computational homogenization analysis. Several numerical examples, applied to both two and three-dimensional models, are given, demonstrating the validity of the theoretically made predictions