Vytautas Magnus University (VMU) (Lithuanian: Vytauto Didžiojo Universitetas (VDU)) is a public university in Kaunas, Lithuania. The university was founded in 1922 during the interwar period as an alternate national university.Initially it was known as the University of Lithuania, but in 1930 the university was renamed to Vytautas Magnus University, commemorating the 500th anniversary of the death of the Lithuanian ruler Vytautas the Great, who is known for the nation's greatest historical expansion in the 15th century.It is one of the leading universities of Lithuania, and has about 8,800 students, including Master's students and Ph.D. candidates. There are a little over 1000 employees, including approximately 90 professors.
Game species are valuable resources in many regions and contribute to a range of ecosystem services, yet they are often studied and managed individually despite responding to similar environmental conditions. This study examined spatial variation in 10 game species across a coastal-inland gradient in Norway by (1) examining regional variation in relative abundance, and (2) analysing spatial patterns of game species assemblages in relation to land-cover composition and elevation. We used harvest density as a proxy for relative abundance, and analysed data using negative binomial regression, self-organising maps, and principal component analysis. Significant regional differences were observed for several species, including moose (Alces alces), red deer (Cervus elaphus), roe deer (Capreolus capreolus), black grouse (Lyrurus tetrix), mountain hare (Lepus timidus), and capercaillie (Tetrao urogallus). The relative harvest density of red fox (Vulpes vulpes), pine marten (Martes martes), rock ptarmigan (Lagopus muta), and willow grouse (Lagopus lagopus) did not differ significantly among regions. Spatial clustering identified five game species assemblages (red deer, willow grouse, moose-forest grouse, roe deer-moose, willow grouse–ptarmigan) defined by distinct combinations of land-cover types, elevation and harvest densities across municipalities. Assemblages were distributed across gradients from coastal semi-natural landscapes to inland forest–agricultural mosaics and from lowland to higher-elevation environments. Our findings show that game species assemblages vary systematically with landscape composition and elevation across the coastal–inland gradient. This highlights the value of an assemblage-based perspective for understanding spatial patterns and integrating game species management with land-use planning.
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.
High-quality economic development is a prerequisite for sustainable growth. The key role is played by private companies in this domain. However, private companies often face difficulties in achieving high-quality development due to leverage control and/or credit constraints. Focusing on the bank credits, this study uses micro data of Chinese private companies from 2017 to 2022. By exploring the issues of leverage choice and allocation traps, we provide empirical evidence on how to promote the high-quality development of these firms. The benchmark regression indicates that increasing leverage can promote the high-quality development of private enterprises. In addition, the credit constraints are binding with respect to high-quality development. Second, the mechanism analysis reveals that the bank credit allocation bias exerts a negative effect, thereby weakening the role of leverage in promoting the high-quality development of private enterprises. Third, results suggest that private enterprises can enhance profitability or expand operation scale to mitigate the adverse effects of the credit allocation bias. The government policies can also be effective as suggested by the difference-in-difference model. Based on these results, policy implications are proposed for government, banks and private enterprises.
Stroke patients experience cognitive impairments affecting daily activities, necessitating effective rehabilitation methods. This study aimed to (1) assess the feasibility and effectiveness of short-term visual memory and selective attention training in an immersive virtual reality environment and (2) examine near and far transfer effects post-training. Twenty-seven stroke survivors were allocated to one of the two groups (Immersive virtual reality – iVR, and Control) and the pre-assessment was completed. Both groups participated in the conventional rehabilitation program. Additionally, the iVR group underwent 10 sessions of short-term visual memory and selective attention training in the iVR environment. After the intervention (or after 2 weeks for the control group) the post-assessment was completed. Cognitive functions were tested by the Addenbrooke’s Cognitive Examination-III and Trail Making Test Part A and B, Medical College of Georgia Complex Figures. The final data analysis included 20 participants (mean age – 62,15 ± 7,8), seven of them did not complete the post-assessment. The results indicated that iVR group (N = 13) showed a significant improvement in short-term visual memory retrieval time, whereas no statistically significant near- or far-transfer effects were observed. Anxiety and depression levels decreased similarly in both groups. In contrast, the control group (N = 7) improved only non-dominant hand psychomotor functions. These findings demonstrate that short-term visual memory and selective attention training tasks, when integrated into an immersive virtual reality environment, may contribute to conventional rehabilitation. However, the present results provide no clear evidence of near- or far-transfer effects following iVR training.
In the European Union, decarbonization has progressed unevenly across sectors and member states. This study examines sectoral CO2 trajectories in the EU-27 during 2000-2022 using a harmonized annual panel built primarily from the European Commission's Energy Statistical Country Datasheets and complemented with EDGAR/JRC sectoral emissions data. The empirical strategy combines descriptive analysis with OLS, fixed-effects, log-linear, and exploratory difference-in-differences specifications to assess conditional associations among per capita CO2 emissions, the renewable energy share, GDP per capita, and the carbon price. EU-wide CO2 emissions declined by 26.4% over the study period, with the largest contraction in the energy sector, while transport emissions remained comparatively stable. Across specifications, renewable energy share is consistently associated with lower emissions, although its magnitude weakens after controlling for time-invariant country heterogeneity. Carbon price is negatively associated with emissions in the baseline and log-linear models. In contrast, the exploratory DiD interaction is not statistically informative in the main treatment specification and yields negligible effect sizes in regional split models. The sign reversal in GDP between the pooled and within-country models indicates that cross-country differences and within-country dynamics should not be treated as equivalent. Overall, the findings support a heterogeneous and multi-speed decarbonization pattern and suggest that carbon pricing is better understood as part of a broader policy mix rather than as a stand-alone causal driver.
