Biomass-based hydrogen production represents a promising pathway to reduce the environmental impacts associated with fossil-based hydrogen. In thermochemical pathways, biomass is typically converted into intermediate streams such as bio-oil prior to hydrogen production. However, in bio-oil reforming processes, a key unresolved question is whether hydrogen production should prioritize complete conversion of the pyrolysis liquid or selective upgrading of its aqueous fraction, recovering the organic fraction as a fuel co-product.In this work, the implications of both alternatives at industrial scale are systematically evaluated through an integrated techno-economic and environmental assessment, comparing two process configurations for hydrogen production from pine wood waste via fast pyrolysis coupled with autothermal reforming, and benchmarking them against conventional steam methane reforming.The results show that both biomass-based scenarios can reduce climate change impacts by approximately 80% compared to steam methane reforming. However, this benefit is accompanied by higher impacts in categories such as acidification and resource use (minerals and metals) due to electricity consumption and forest management. From an economic perspective, the configuration based on complete conversion of bio-oil leads to a levelized cost of hydrogen of 5.2 €2025/kg H2, which decreases to 2.8 €2025/kg H2 when the organic fraction is co-produced as fuel for industrial heat boilers.Overall, the results demonstrate that the choice of bio-oil valorization strategy for hydrogen production constitutes a critical design variable, introducing a trade-off between hydrogen yield, economic performance and environmental impacts. The co-production strategy enhances economic robustness, while environmental differences between the two scenarios remain minor, and depend on the allocation approach and the impact category considered.
Herein, the aldol condensation of furfural (FAL) and levulinic acid (LA) toward the selective formation of β- and δ-furfurylidene-levulinic acid (C10 adducts) is investigated over a series of Zr-modified Beta zeolites under solvent-free conditions. Preliminary studies reveal that the parent commercial H-Beta zeolite exhibits limited performance, reaching low yield to C10 products due to strong Brønsted acidity promoting secondary reactions such as FAL polymerization. Progressive dealumination and Zr incorporation led to a marked increase in FAL conversion and C10 yield. The Zr/Al molar ratio, which governs the balance between Brønsted and Lewis acid sites, influences the catalytic activity and selectivity. Fully dealuminated Zr-Beta-4 catalyst was identified as the best-performing material in terms of FAL conversion, carbon balance, and yield/selectivity toward C10 adducts. Reaction time and temperature were simultaneously evaluated through a combined severity factor (CSF), achieving maximum selectivity and yield at intermediate CSF values (2.5 – 3). Subsequent optimization of reaction conditions, including LA/FAL molar ratio and FAL/catalyst mass ratio, demonstrated that operating at 120 °C provides the optimal compromise between activity and selectivity. Under optimized conditions (LA/FAL molar ratio = 6.8, FAL/Cat mass ratio = 2.5, 120 °C, 4 h), maximum response values of XFAL = 75.2% and YC10 = 48.8% were achieved. Catalyst reusability tests confirmed stable performance over three consecutive cycles without intermediate calcination, evidencing the robustness of Zr-Beta-4 catalyst. Overall, this study demonstrates that tuning zeolite acidity via controlled Al removal and Zr incorporation enables the efficient, stable, and solvent-free cross-aldol condensation of biomass-derived furfural and levulinic acid.
Oligometastatic disease represents an intermediate stage of cancer, often treated with surgery or ablative radiotherapy (ART). This scoping review aimed to systematically summarize current evidence on the use of radiomics, including machine learning and deep learning approaches, to predict response to ART. We also aimed to assess the methodological quality and reporting transparency of published studies, identifying gaps and opportunities for future research. A systematic search in PubMed, Web of Science, Scopus, Embase, Cochrane, and Google Scholar identified studies that used radiomics for predicting ART response. Two reviewers independently selected and assessed the methodological quality using the Radiomics Quality Score (RQS) and the METhodological RadiomICs Score (METRICS). In addition, reporting transparency was evaluated using the CheckList for EvaluAtion of Radiomics research (CLEAR). This scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews guidelines. The systematic search identified 9463 records, of which 29 studies (3946 patients) were included. Most studies used MRI-derived features, with 24 focusing on brain metastases. Radiomics-based models demonstrated variable predictive performance (area under the curve, AUC: 0.69–0.95), with deep learning models achieving the highest accuracies (AUC: 0.85–1.00). Methodological quality of the studies was moderate (mean RQS: 13; METRICS: 64.2–78
Digital technology is central to economic and social development, but it also raises social issues related to inequality and exclusion. Studies show that, without specific policies, technology can reproduce structural gaps in the labor market. In the case of women, data reveal persistent gaps in access to digital training and participation in highly skilled technology jobs. In this context, reducing the gender gap emerges as a necessary condition for digitalization to contribute to the SDG of the 2030 Agenda. This research aims to analyze the implementation of SDGs 4 (Quality Education) and 8 (Decent Work and Economic Growth) in the context of digital transformation, with an emphasis on their impact on gender equality (SDG 5) with regard to access to quality education and decent employment in Europe. Analyses Eurostat data between 2017 and 2023 using Jamovi software, a hierarchical multiple linear regression was applied. The results show that digitization emerges as a key factor in explaining the persistence of the gender gap in the 27 countries of the European Union, conditioned by the economic and social characteristics of each country. In conclusion, the advancement of digitization and technology in the European Union is conditioned by structural factors such as education, job stability, and gender equality in leadership, rather than by the mere availability of ICT specialists. These findings highlight the need for differentiated public policies that not only boost competitiveness but also address the social problems arising from the digital and gender divides, thus ensuring a more inclusive technological transition.
The aim of this cross-cultural study is to examine how preservice early childhood educators from Greece, Türkiye, Turkish Republic of North Cyprus, Estonia, Spain, and the United States perceive the teacher's role in children's play, and to identify similarities and differences in their perspectives. A total of 255 university students majoring in early childhood education completed a specially designed questionnaire. The findings emphasize that teacher involvement in children's play is seen as important across all countries, particularly in fostering social interactions among children. However, perceptions of specific roles vary and seem to be strongly influenced by the structure of teacher training programs. Notably, Greek preservice educators, who had the least practical experience but the most theoretical knowledge, rated certain teacher roles in play as less important compared to their peers from other countries. The study provides recommendations to enhance preservice educators' understanding of play, which may positively influence their future teaching practices.
