Humboldt University of Berlin (German: Humboldt-Universität zu Berlin, abbreviated HU Berlin) is a university in the central borough of Mitte in Berlin, Germany. It was established by Frederick William III on the initiative of Wilhelm von Humboldt, Johann Gottlieb Fichte and Friedrich Ernst Daniel Schleiermacher as the University of Berlin (Universität zu Berlin) in 1809, and opened in 1810, making it the oldest of Berlin's four universities. From 1810 until its closure in 1945, it was named Friedrich Wilhelm University (German: Friedrich-Wilhelms-Universität). During the Cold War the university found itself in East Berlin and was de facto split in two when the Free University of Berlin opened in West Berlin. The university received its current name in honour of Alexander and Wilhelm von Humboldt in 1949.The university is divided into nine faculties, including its medical school shared with the Free University of Berlin, has a student enrollment of around 32,000 students, and offers degree programmes in some 189 disciplines from undergraduate to postdoctorate level. Its main campus is located on the Unter den Linden boulevard in central Berlin. The university is known worldwide for pioneering the Humboldtian model of higher education, which has strongly influenced other European and Western universities.It was regarded as the world's preeminent university for the natural sciences during the 19th and early 20th century, as the university is linked to major breakthroughs in physics and other sciences by its professors, such as Albert Einstein. Past and present faculty and notable alumni include 53 Nobel Prize laureates (the most of any German university by a substantial margin), as well as eminent philosophers, sociologists, artists, lawyers, politicians, mathematicians, scientists, and Heads of State; among them are Albert Einstein, Karl Marx, Friedrich Engels, Otto von Bismarck, W. E. B. Du Bois, Angela Davis, Arthur Schopenhauer, Georg Wilhelm Friedrich Hegel, Walter Benjamin, Max Weber, Georg Simmel, Karl Liebknecht, Ernst Cassirer, Heinrich Heine, Max Planck and the Brothers Grimm. As one of Germany's most prestigious institutions of higher education, Humboldt University of Berlin has been conferred the title of "University of Excellence" under the German Universities Excellence Initiative.
Background Enhancing student learning with the use of cognitively and linguistically challenging questions is considered key for fostering student learning in classroom discourse. Theoretical models suggest that teachers’ use of questions is related to their achievement expectations for students. However, evidence for the interplay of teacher expectations, teacher questions, and student learning is scarce. Aims This study (1) describes the use of teacher questions in whole-classroom discourse, (2) examines relations between teacher expectations and teacher questions, (3) and investigates the effects of teacher questions on student achievement in reading, vocabulary, and mathematics. Sample The sample includes 329 first grade students and 17 teachers (nschools = 13) from 15 German language and 14 mathematics classrooms in Germany. Methods First, we coded the language-promoting quality of teacher questions based on video recordings of whole-classroom discourse. Second, we employed multilevel modeling to examine links between teachers’ (inaccurate) expectations, teacher questions, and later student achievement. Results Teacher questions were mostly of low language-promoting quality. Teachers asked significantly more questions in mathematics, including more open-ended questions. For mathematics, (inaccurately) low expectations were linked with higher frequencies of questions that promote elaboration and description. A positive effect on vocabulary emerged from questions that motivate students to hypothesize and conclude. Conclusions Overall, language-promoting questions occur comparatively rarely in content lessons. Evidence for class-level expectancy effects on teacher behavior was limited. Our findings suggest that the effectiveness of teacher questioning is domain-specific.
Eutrophication is a major environmental problem in reservoirs, particularly in mountainous regions where shallow inflows and agricultural runoff increase nutrient loading. Floating Treatment Wetlands (FTWs), as a general nature-based treatment technology, have been investigated for nutrient removal. This study evaluated Floating Treatment Islands (FTIs) for improving water quality in the Zhaveh Dam reservoir, Iran, targeting total phosphorus (TP), total nitrogen (TN), and organic matter. Experiments were conducted under contrasting hydraulic conditions in barrel, pool, canal, and reservoir-edge systems. Results showed reductions in TP, TN, and organic matter, with observed removal efficiencies varying across hydraulic conditions and generally tending to be higher under longer retention times and lower flow conditions. Native species such as Sparganium erectum demonstrated high adaptability and effective nutrient uptake under the tested conditions. The findings indicate that FTIs may provide a sustainable approach for eutrophication control and reservoir water quality improvement. This study presents a comparative multi-condition evaluation of FTIs under contrasting hydraulic conditions in a reservoir transition zone.
Over recent decades, European countries have supported the bioeconomy transition by implementing strategies and policy instruments. The bioeconomy is framed as a shift to a more sustainable future by increasing the use of biomass across sectors, and thereby reducing dependence on fossil resources and mitigating climate change, while stimulating biotechnological innovation and economic growth. However, an emerging academic discussion highlights the negative effects of the European bioeconomy transition, also those occurring in other locations. Our study analyzes this growing body of literature through a critical interpretive synthesis (CIS). Based on 108 publications, we assess the discussed environmental, social, and economic impacts, their interconnections, and the described mechanisms, including the link to bioeconomy policies. Our study shows that environmental impacts receive stronger attention, while economic and social aspects, such as equality or land ownership, have only recently been studied. Scholars predominantly frame rising biomass demand as the main driver of the cross-border impacts. However, the literature often remains vague in discussing causality. The characterization of the impacts ranges from unintended consequences to inherent features of the bioeconomy transition, depending on the underlying interpretations of the concept, the emphasized factors, and assumptions about the transition's underlying intentions in Europe.
This mapping review synthesizes 110 empirical studies using eye-tracking to examine map use and its implications for geography education. It identifies four core themes: traditional and digital map design, user interaction and behavior, and demographics. Methodologically, it highlights advances in mixed methods, Electroencephalography integration, machine learning, and open-source tools. Findings show that spatial abilities, strategies, and demographic factors influence map-reading. The review concludes each section with research questions to guide future work in educational settings.
The transferability of single or joint species distribution models ((j)SDMs) depends on their ability to predict beyond the observed environmental range and to remain consistent despite shifts in biotic interactions. Transfer accuracy may be improved by recent advances in the application of deep learning that provide greater flexibility and potentially superior predictive accuracy than traditional approaches. We implemented jSDMs with deep and machine learning algorithms and measured the transfer accuracy from continental to regional areas in communities with different species composition. We ran jSDMs with deep neural networks (DNN), elastic net (EN), and stacked SDMs (sSDM) with random forests (RF). We used 134 689 occurrence records representing 1776 species of six taxonomic groups (beetles, birds, bryophytes, fungi, lichens and plants) from 2387 forest plots in Europe. We employed an agnostic modelling approach that covered most of the environmental conditions by including more than 100 satellite-derived variables and 98 climatic variables. The predictive power of the models within the training continental area was evaluated using AUC, whereas the transfer accuracy in the regional area was evaluated with the Boyce index calculated with independent presence records. We found that the DNN-jSDMs outperformed other models at continental scale, but model transfer from continental to regional extent was less accurate. We found that the accuracy of regional predictions was higher for taxonomic groups with better representation in the continental data, such as birds, bryophytes and plants. Depending on the algorithm and the taxonomic group, we achieved acceptable (Boyce > 0) to accurate (Boyce > 0.5) transferability for 32-78% of the species. Our findings underscored the need of considering trade-offs among hyperparameter tuning, spatial scales and model complexity. Our findings also suggest that the varying biotic interaction structures and, particularly, the different species compositions of the transfer areas, may affect model transferability more than previously considered.
