Materials science underpins national economies and infrastructures worldwide, contributing significantly to the delivery of key services, as well as supporting multiple industrial sectors, including space. The space sector, represents a cornerstone of technological progress, driving both direct and indirect innovation across many terrestrial fields. Taken together, materials science and the space sector represent a transformative frontier that remains only partially exploited, offering opportunities for scientific, economic, and societal advancement. In this context, this roadmap presents a special focus on where such interplay can yield fruitful outcomes over the next two decades, exploring specific intersections between these fields. The rationale behind this is routed in the current demand and forward view for specialized materials to address both terrestrial challenges and extraterrestrial ambitions, with the global space economy projected to reach $1.8 trillion by 2035, requiring a coordinated interdisciplinary effort. Microgravity provides a unique platform to achieve this, enabling critical insights, more precise control over material formation and the creation of advanced materials with enhanced properties for diverse applications. These breakthroughs are already informing applications across a range of industries on Earth, from semiconductors to pharmaceuticals, while laying the groundwork for advanced manufacturing in space. Emerging sectors such as the ‘In-Orbit Economy’ and in-situ resource utilization (ISRU) further underscore this critical opportunity, emphasizing the need for resilient materials that can withstand the harsh conditions of space and utilize extraterrestrial resources sustainably. This roadmap brings together a diverse array of contributions covering relevant subjects across these areas—from space exploration and ISRU to the production of new inorganic, organic, and even ‘living’ materials on microgravity platforms—while paying special attention to concrete examples, i.e. cases with a technological readiness level of 4 or higher, which warrant continued attention and effort.

Apstrakt: Ovaj rad sistematizuje i analizira zabilježene drvene mikrohabitate (tree-related microhabitats - TreMs) na jednom reprezentativnom starom primjerku hrasta lužnjaka u naselju Kreka, grad Tuzla. Rad prikazuje inventar prisutnih mikrostaništa, detaljno opisuje primijenjenu metodologiju praćenja TreMs prema preporukama Bütler et al. (2020), te prezentira primarne rezultate terenskog pregleda. Rad predstavlja pilot istraživanje mikrohabitata hrastova tuzlanske regije. Ključne riječi: microhabitat, hrast lužnjak, Tuzla.

ABSTRACT Introduction/Objective Acute intracranial stenting during endovascular thrombectomy (EVT) for ischemic stroke requires intraprocedural antiplatelet therapy (APT) to maintain patency. However, the hemorrhagic risk of combining APT with intravenous thrombolysis (IVT) remains uncertain. We evaluated the safety of IVT combined with conservative versus aggressive intraprocedural APT in patients requiring stenting during EVT. Methods This multicenter RESISTANT registry subanalysis (2016–2023) included 823 adults. APT was categorized as conservative (aspirin +/− oral P2Y12) or aggressive (including GPIIb/IIIa inhibitors or cangrelor). The primary outcome was a composite of symptomatic intracranial hemorrhage (sICH) and parenchymal hematoma (PH1/PH2). Multivariable logistic regression assessed associations and interactions between IVT and APT. Results A total of 823 patients were included: 44 (5.3%) received IVT + conservative APT, 130 (15.8%) No IVT + conservative APT, 145 (17.6%) IVT + aggressive APT, and 504 (61.2%) No IVT + aggressive APT. Frequencies of sICH‐PH1‐PH2 were 9.3% with IVT + conservative APT, 10.7% with IVT + aggressive APT, 3.2% with No IVT + conservative APT, and 9.9% with No IVT + aggressive APT. In multivariable analysis without interaction terms, neither IVT (aOR 1.18, 95% CI 0.58–2.27; p = 0.64) nor aggressive APT (aOR 2.10, 95% CI 0.92–5.69; p = 0.10) was independently associated with increased risk of sICH‐PH1‐PH2. However, in the interaction model, IVT within the conservative‐APT stratum (aOR 5.84, 95% CI 1.07–43.92; p = 0.05) and aggressive APT within the no‐IVT stratum (aOR 4.81, 95% CI 1.41–30.22; p = 0.03) were each associated with higher odds of sICH‐PH1‐PH2, while the IVT‐by‐APT interaction term was < 1 (aOR 0.15, 95% CI 0.02–0.94; p = 0.05), indicating attenuation of the joint effect on the multiplicative odds scale. Conclusion Among patients requiring intracranial stenting during EVT, we found no evidence that IVT and aggressive intraprocedural APT act synergistically to increase hemorrhagic risk. Rather, the negative IVT‐by‐APT interaction suggested attenuation of the joint effect on the multiplicative odds scale, although patients receiving both therapies remained at increased hemorrhagic risk relative to the reference group.

Apstrakt: Sumarni popis sandžaka Bosna iz 1468/69. godine preveo je prof. dr. Ahmed Aličić, jedan od najeminentnijih bosanskohercegovačkih filologa i poznavalaca historije Bosne pod osmanskom vlašću. Profesor Aličić je prijevodom ovog deftera zadužio bosanskohercegovačku historijsku nauku iz više razloga, ne samo zbog odličnog prijevoda, nego i zbog toga što je kao vrstan stručnjak historije Bosne pod osmanskom vlašću riješio mnoge dileme i nedoumice koje su bile prisutne u bosanskohercegovačkoj historiografiji, te je izvršio ispravnu ubikaciju mnogih mjesta koja se pominju u defteru. Nazivi većine naselja su se sačuvali pod istim ili malo izmijenjenim nazivom, dok su nazivi pojedinih mjesta iščezli tokom vremena. Ključne riječi: popis; Bosna; izvor, naselja, vjera, privreda, stanovništvo

The translation of blood-based proteomics into healthcare is no longer constrained primarily by technological limitations, but by unresolved challenges in standardization, validation, and implementation. In this Commentary, we identify four structural bottlenecks at the current translational inflection point that limit the opportunities for the adoption of multi-protein blood biomarkers in routine clinical care: lack of harmonized reference frameworks, uncertainty around fit-for-purpose biological resolution, complexity in validating multi-analyte and algorithm-based tests, and misalignment between the design of discovery workflows and clinical requirements. We argue that progress will depend on shifting from exploratory profiling toward decision-oriented proteome analytics, with early alignment across academic, regulatory, clinical, and technical domains. Establishing coherent validation pathways for clinically actionable use cases will be essential to enable reliable integration of proteomics into healthcare. This Comment identifies key bottlenecks limiting the clinical adoption of multi-protein blood biomarkers and argues that progress requires a shift from exploratory profiling to decision-oriented proteome analytics with early cross-sector alignment.

Stroke prediction plays an important role in healthcare, as it allows for the potential implementation of early measures and intervention. The Kaggle stroke dataset was used and compared between deep learning (DL) and traditional machine learning (ML) models. Preprocessing steps include imputation to the missing BMI values, one hot and label encoding, and z-score normalization, all applied before a stratified 5-fold cross validation with confidence intervals for the split. SMOTE was then applied exclusively to the training set to address class imbalance (~95% non-stroke). Eight classifiers were compared against each other which included five ML models: Random Forest (RF), Logistic Regression (LR), K-nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT) and three DL architectures: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN) and Artificial Neural Network (ANN). Due to the severe class imbalance in the dataset, F1-score was adopted as the primary evaluation metric, as it balances precision, recall and better reflects the model ability to correctly identify the minority stroke class. The most successful models were LR (30% F1-Score, 88% Accuracy) and MLP (28% F1-Score, 84% Accuracy). This suggests that for this dataset, classical ML models may offer competitive performance compared to deep learning on structured tabular data.

Conversational commerce agents that personalize assistance based on a user’s transactional state (cart contents, checkout progress, order completion) must model that state correctly, or downstream adaptive behavior will be misaligned with the user’s actual journey. We call mismatches between an agent’s claims and the observable event history journey hallucinations, and study a lightweight verification framework that reconstructs a minimal transactional user model from execution logs and checks agent claims against deterministic invariants. On 90 real sessions across four foundation models, trace-aware prompting reaches 99.5–100% user-state accuracy at 84–99% coverage, while unconstrained prompting produces unsupported state assertions at rates up to 8.5%. In a between-subjects user study (N = 42), verified responses were judged more trustworthy (p =.008, r =.43), better at reflecting journey understanding (p =.039, r =.32), and more often factually correct (p <.001, r =.56). The framework provides a practical reliability layer for transactional user-state modeling, helping personalization and dialog policies operate on verified, not hallucinated, user states.

Group Recommender Systems (GRSs) have yet to achieve widespread practical adoption, despite being an active area of study. This research aims to deepen our understanding of group decision-making by developing a conversational GRS embedded directly into an existing chat application in the form of a bot. We investigate how group dynamics and user roles influence the outcome of group decisions and how a bot can support group discussions through an agentic framework that continuously adapts its behavior based on interaction signals received from the group. To this end, we conduct a series of user studies to empirically evaluate the system across different decision-making domains, group sizes, and user demographics, with the ultimate goal of developing a conversational GRS framework that facilitates the decision-making process and guides groups toward more balanced and mutually satisfactory decisions.

Group Recommender Systems (GRSys) are designed to recommend items that address the needs of groups of people. Compared to individual users, groups are dynamic entities where interpersonal relationships, group dynamics, emotional contagion, etc., substantially affect the group’s needs. Nevertheless, these characteristics are often poorly defined or overlooked in system modeling. The fifth GMAP workshop brought together a community of scholars focused on group modeling, adaptation, and personalization. The event was dedicated to exploring the challenges and opportunities of supporting collective decision-making, fostering interdisciplinary dialogue, and forging new collaborations. The three presented papers covered a diverse range of topics, revisiting assumptions about similarity, task definitions, and fairness perception in particular scenarios within the realm of group modeling, adaptation, and personalization.

Adaptive and personalized systems increasingly mediate everyday digital experiences, and recent advances in LLMs, NLP, and Generative AI have amplified their reach, from intelligent user interfaces and conversational agents to AR/immersive interactions and autonomous assistants. These methods now support applications in health and well-being, behavior change and persuasion, e-learning and educational games, and group modeling for collaboration and team formation, all of which require increasingly rich and dynamic user models. At the same time, modern pipelines based on data mining, knowledge graphs/linked data, semantic representations, and affective computing raise urgent questions about transparency, privacy, fairness, accountability, and user understanding, reinforced by regulatory expectations such as the GDPR right to explanation. Yet research often optimizes personalization performance without comparable attention to interpretability and human comprehension. This workshop provides a forum for theoretical, methodological, and empirical work that bridges effectiveness and explainability, with emphasis on robust human-centered evaluation and reproducible practices, including benchmarks, datasets, and shared challenges that advance trustworthy personalization in an era of increasingly autonomous AI.

Abstract Introduction Transthoracic echocardiography (TTE) identifies a hypercontractile phenotype (HP) in chronic coronary syndromes (CCS), characterized by elevated resting left ventricular (LV) elastance (force = systolic blood pressure/end-systolic volume). To evaluate the prognostic significance and functional correlates of HP. Methods In a prospective multicentre study, 10 677 patients with CCS underwent resting TTE to assess LV ejection fraction (EF), stroke volume, and force by quantitative volumetric echocardiography. All patients were followed for the endpoint of all-cause mortality. In a subset of 5834 patients, stress echocardiography (exercise or dobutamine) was performed for LV contractile reserve and heart rate reserve. Results Patients were stratified into Force quintiles (Q1–Q5). Patients with hypercontractile phenotype exhibited lower stroke volume at rest (Q5 = 34.8 ± 12.3 vs Q1–Q4 = 57.4 ± 19.1 mL; P < .01) and higher EF at rest (Q5 = 64.8 ± 6.9% vs Q1–Q4 = 58.1 ± 8.7%, P < .01). During a median follow-up of 24 months (interquartile range = 12–40 months), 509 deaths occurred. The exposure-adjusted death rate was lowest in Q3 (3.53–4.51 mmHg/mL; 1.03 per 100 person/years) and higher in Q1 (≤2.62 mmHg/mL, 2.88), Q2 (2.63–3.52 mmHg/mL, 1.86), Q4 (4.52–6.11 mmHg/mL, 1.56), and Q5 (HP, >6.11 mmHg/mL; 1.88; P < .0001 vs Q1 and Q3). Multivariable analysis identified HP (Q5; HR 1.531 vs Q3, 95% CI 1.116–2.099; P = .006) and EF (HR 0.963, 95% CI 0.953–0.972; P < .0001) as independent predictors of death. During exercise or dobutamine stress, HP showed reduced LV contractile reserve (ΔEF: Q5 = 4.3 ± 9.4% vs Q1–Q4 = 7.0 ± 9.3%; P < .001) and blunted heart rate reserve (Q5 = 1.77 ± 0.33 vs Q1–Q4 = 1.85 ± 0.39; P < .01). All patients with force-based LV contractile reserve >4.1 (present in 185, 3.2% of the population) survived. Conclusion Patients with CCS with HP assessed by resting TTE demonstrate higher mortality and multilayered functional impairment, including reduced LV contractile and chronotropic reserves. Hypercontractile phenotype improved the prediction of mortality by EF. A ‘stronger’ heart is, in fact, functionally and prognostically weaker.

[This corrects the article DOI: 10.1016/j.euros.2025.12.004.].

We establish an equivalence between the existence of Costas polynomials and the existence of a special kind of orthomorphism such that their compositions are also orthomorphisms. Computations are easier over these orthomorphisms. We provide a lower bound for the number of Costas polynomials and derive some of their properties. We show that Costas polynomials, by virtue of being multiplicative analogs of planar polynomials, can also be used to construct complete families of mutually orthogonal Latin squares.

Introduction and Objective: Sex differences in macrovascular complications of type 2 diabetes (T2D) are known, but evidence on microvascular differences remain limited. Methods: We used an Austrian medical claims database of 8.9 million individuals (1997-2014). Comorbidities co-occurring with T2D were analyzed using sex- and age-specific contingency tables across 2-year intervals (2003-2014), with odds ratios estimated via the Cochran-Mantel-Haenszel method. Only comorbidities with sufficient case numbers were retained. Sex differences were quantified by the differences of logarithmic ORs between male and females in units of pooled standard errors assessed using a Bonferroni-corrected test. Results: In 40 to 49 year olds, females had more retinopathy (OR 9.3 vs. 5.5), chronic kidney failure (CKI: OR 9.8 vs. 6.5), depression (OR 3.1 vs. 1.9) and cardiovascular complications including chronic ischemic heart disease (CIHD: OR 10.8 vs. 5.6), heart failure (HF: OR 12.1 vs. 7.7) and acute myocardial infarction (MI: OR 9.4 vs. 3.9, all p-values <0.001) compared to males with T2D. In 50 to 59 year olds, females had higher rates of CKI (OR 9.2 vs. 6.2), depression (OR 2.8 vs. 1.9) and cardiovascular complications (CIHD: OR 6.8 vs. 4.4, HF: OR 8.7 vs. 5.3, MI: OR 5.7 vs. 2.7) compared to males. Retinopathy showed no sex differences, but cerebral infarction was more frequent in females (OR 4.4 vs. 3.2). In age group 60 to 69 year olds, females had higher rates in cardiovascular complications (CIHD: OR 4.8 vs. 3.7, HF: OR 6.4 vs. 4.3, MI: OR 3.8 vs. 2.5), retinopathy (OR: 2.5 vs. 3.0), CKI (OR 7.3 vs. 4.9) and depression (OR: 2.5 vs. 2.0). Lastly, in 70 to 79 year olds the least differences with a higher rate of cardiovascular complications (CIHD: OR 3.5 vs. 3.2, MI: OR 3.1 vs. 2.3) and CKI (OR 5.0 vs. 4.0, all p-values<0.001) in females compared to males was reported. Conclusion: More comorbidities were associated with T2D in females than in males, including cardiovascular disease, retinopathy, nephropathy, and neuropathy. These results support more personalized and effective management strategies. T. Gisinger: None. K. Fenz: None. P. Klimek: None. E. Dervic: None. A. Kautzky-Willer: None.