Attitudes towards open peer review, open data and use of preprints influence scientists’ engagement with those practices. Yet there is a lack of validated questionnaires that measure these attitudes. The goal of our study was to construct and validate such a questionnaire and use it to assess attitudes of Croatian scientists. We first developed a 21-item questionnaire called Attitudes towards Open data sharing, preprinting, and peer-review (ATOPP), which had a reliable four-factor structure, and measured attitudes towards open data, preprint servers, open peer-review and open peer-review in small scientific communities. We then used the ATOPP to explore attitudes of Croatian scientists (n = 541) towards these topics, and to assess the association of their attitudes with their open science practices and demographic information. Overall, Croatian scientists’ attitudes towards these topics were generally neutral, with a median (Md) score of 3.3 out of max 5 on the scale score. We also found no gender (P = 0.995) or field differences (P = 0.523) in their attitudes. However, attitudes of scientist who previously engaged in open peer-review or preprinting were higher than of scientists that did not (Md 3.5 vs. 3.3, P<0.001, and Md 3.6 vs 3.3, P<0.001, respectively). Further research is needed to determine optimal ways of increasing scientists’ attitudes and their open science practices.

One of the main goals of the General Data Protection Regulation (GDPR) is to protect the personal data of individuals. Each organization (company, association, school, institution, university, etc.) has an obligation to protect all of the individual data that it obtains. Those data can belong to employees, members, students, clients, etc. The research in this paper is related to the higher education students in Croatia. This study is being conducted in three parts. The first part was conducted in April of 2017 (N=159) and the second in April/May of 2018 (N=141), in a period before the GDPR became valid (May 25th, 2018). In this paper, we are analysing the results of the second part of the study. Additionally, we are discussing risks that might appear if students do not know the GDPR. Risk matrix results are used to represent a basis which higher education administrations can utilize to make corrective decisions. The main conclusion of the research is that there are still issues with understanding the basic concepts of personal data and the GDPR, which may cause some problems during studying process. The main recommendation for HEIs or students organizations (such as student councils) is to organize lectures and workshops related to the GDPR.

One of the main characteristics of Intelligent and Adaptive Hypermedia E-Learning Systems (IAHe-LS) are adaptivity criteria. Selecting adaptivity criteria is one of the main steps in developing a prototype of a System for Dynamic Generating of Learning Objects (SDGLO) that will support the individual personalised learning process. The selection of those criteria has a high impact on the quality of usage of those systems. This paper presents research into prioritisation of adaptivity criteria from the perspective of their usage and the selection of adaptivity criteria for creating the SDGLO prototype. The methods that were used in the research are: descriptive statistics, Cronbach Alpha, one-way ANOVA, and Analytic Hierarchy Process (AHP) with final qualitative analysis. In conclusion, for the development of a prototype of SDGLO, the adaptivity criteria that are selected are learning style, cognitive style and learning goals.

Nectin-2 and Nectin-4 are cell adhesion molecules associated with the progression of various cancers. The main goal of this pilot study was to evaluate the expression patterns of Nectin-2 and Nectin-4 in laryngeal squamous cell carcinoma (LSCC). A retrospective study was conducted on tissue microarray (TMA) samples derived from 31 patients who underwent total laryngectomy. The findings revealed heterogenous expression of both Nectin-2 and Nectin-4 in tumor cells and surrounding stroma, with Nectin-4 expression being significantly higher than Nectin-2 expression. Specifically, 74% of cases showed weak cytoplasmic staining for Nectin-2, while 41.93% exhibited strong cytoplasmic staining for Nectin-4. Both Nectin-2 and Nectin-4 expressions were more pronounced at the invasive tumor margins. Although no significant differences in Nectin-4 expression were observed across tumor grades (W = 83.500; z = −0.463; p = 0.658), differences in expression patterns were noted. Well-differentiated tumors (Grade 1), 80.65% of cases, showed predominantly membranous Nectin-4 staining, including in squamous epithelial cells of the mucosal surface. Conversely, in less-differentiated tumors (Grade 2 and 3), a shift toward cytoplasmic staining was evident. Specifically, 74.19% of Grade 2 tumors and 100% of Grade 3 tumors showed a predominant cytoplasmic localization of Nectin-4. This transition from membranous to cytoplasmic localization was also evident in the progression from normal superficial epithelium to malignant tissue. These observations suggest that alterations in the expression and subcellular localization of Nectin-4 may be associated with carcinogenesis and could serve as potential markers for the assessment of precancerous lesions and the aggressiveness of laryngeal tumors.

Descriptive decision-making theories center on the analysis of individuals' decision-making styles (DMSs). Different approaches have been used to study DMSs, but this study focuses on two approaches: those of (1) Scott & Bruce and (2) Rowe. Scott & B...

E-learning systems that support personalized learning require sophisticated decision-making methods to adapt content to students optimally. This paper deals with applying multi-criteria decision-making methods in assigning learning objects in an e-learning system to students based on relevant customization criteria. The novelty of this study lies in the application of ANP and DEMATEL to improve content adaptation for students. Structuring the decision-making problem according to the DEMATEL and using ANP for prioritization has made the entire selection of learning objects better with respect to cognitive and learning styles and Bloom’s taxonomy levels. The method consists of various forms. In the first, DEMATEL has identified dependencies between criteria and clusters, mentioning their influence values on a 0–4 scale. A linear transformation model quantified the compatibility level of a student profile to a learning material. The transformed DEMATEL results were incorporated in all the interdependencies among criteria. The unweighted supermatrix was normalized by cluster weights assigned by experts before the iterative computation led to the converging weighted supermatrix. The outcome was that the individual students made these final priority rankings for learning materials. A pilot experiment was carried out to validate the system, and the results revealed that in the experimental group, the personalized learning environment showed the maximum statistical improvement over the control group. The research was conducted in Croatia, and the participants were students (N = 77) from two public universities and one polytechnic. Ultimately, the newly developed combined ANP-DEMATEL approach was effective in an instantaneous result-optimized dynamic learning path generation, ensuring knowledge acquisition. This research further contributes to developing intelligent educational systems by demonstrating how ANP and DEMATEL can be used synergistically to improve e-learning personalization. Future work could include optimizing weight assignment strategies or using new learning contexts to further adaptivity.