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Recommender systems are becoming crucial in academia, where the number of available scientific resources is continuously increasing. One of the main challenges of such systems is a cold start problem, which often occurs when new users have no preference for any items or recommend new items that no community user has recommended yet. In the case of academic systems, where researchers are usually reluctant to express their explicit feedback or scientific interests, the cold start problem has a considerable and long-term impact on the recommendation algorithms. To alleviate this problem, this paper discusses a graph-based recommendation approach extending the Page Rank algorithm by using a co-authorship network. The proposed approach aims to enhance existing recommendation capabilities in the European Open Science Cloud (EOSC). The first results of the evaluation indicate that the proposed recommendation model is promising and reduces the cold start user-side problem in the academic domain.

Surface curvature affects adhesion forces, deformations of surfaces in contact, leakage of mechanical seals, friction, wear, paintability, and electrical conductivity. However, potential benefits of surface curvature have not yet been fully utilized. One problem is the lack of comparison data helping to make an informed decision on the selection of curvature characterization method. In this paper, five multiscale curvature characterization methods, namely Nowicki, Bigerelle-Nowicki, Gleason-Heron, Kalin, and Bartkowiak are compared. The comparison was conducted on large image databases of computer-generated fractal surfaces, sine waves and real engineering surfaces. Specifically, the methods were evaluated for their ability to differentiate between surfaces that: (i) exhibit increasing curvature complexity, (ii) have varying curvatures at a single scale, and (iii) represent minute multiscale curvature changes encountered in real engineering applications. The results obtained indicate that the Bigerelle-Nowicki method exhibits the best overall performance.

Superparamagnetic iron oxide nanoparticles (SPIONs) are widely used for biomedical applications for their outstanding properties such as facile functionalization and doping with different metals, high surface-to-volume ratio, superparamagnetism, and biocompatibility. This study was designed to synthesize and investigate multifunctional nanoparticle conjugate to act as both a magnetic agent, anticancer immunological drug, and radiopharmaceutic for anticancer therapy. The carrier, 166Ho doped iron oxide, was coated with an Au layer, creating core-shell nanoparticles ([166Ho] Fe3O4@Au. These nanoparticles were subsequently modified with monoclonal antibody trastuzumab (Tmab) to target HER2+ receptors. We describe the radiobioconjugate preparation involving doping of a radioactive agent and attachment of the organic linker and drug to the SPIONs’ surface. The size of the SPIONs coated with an Au shell measured by transmission electron microscopy was about 15 nm. The bioconjugation of trastuzumab onto SPIONs was confirmed by thermogravimetric analysis, and the amount of two molecules per one nanoparticle was estimated with the use of radioiodinated [131I]Tmab. The synthesized bioconjugates showed that they are efficient heat mediators and also exhibit a cytotoxic effect toward SKOV-3 ovarian cancer cells expressing HER2 receptors. Prepared radiobioconjugates reveal the high potential for in vivo application of the proposed multimodal hybrid system, combined with magnetic hyperthermia and immunotherapy against cancer tissues.