Singidunum University (Serbian: Универзитет Сингидунум, romanized: Univerzitet Singidunum) is a higher education institution in Belgrade, Serbia which offers undergraduate, master and doctoral academics studies in three scientific fields – social sciences and humanities; technical sciences; and natural sciences and mathematics. The university consists of three faculties, and has around 7,300 enrolled students as of 2018–19 school year, which makes it the largest private university in Serbia.The first faculty was founded in 1999, and the University itself was established on 17 January 2005. The University is organized according to the principles of the Bologna Declaration (Bologna Process) education model and it applies the European Credit Transfer System (ECTS). The curricula and study programmes were designed in line with respectable European universities and colleges models, whilst relying on Serbian education system best practices..
Holocaust memorial cycling represents a form of mobile commemorative leisure grounded in cycling practice, where physical movement, ethical reflection, and emotional engagement intersect across landscapes marked by atrocity. As an organized cycling practice, it situates remembrance within embodied mobility rather than static heritage visitation. This study conceptualizes memorial cycling as embodied leisure in which remembrance emerges through cycling experiences shaped by moral purpose, technological mediation, and sociocultural selectivity. Drawing on a survey of 881 cyclists and employing factor analysis and structural equation modelling, seven dimensions were identified: participation motivation, emotional reflection, ethical concerns, recreational engagement, technology potential, technology acceptance, and future impact. Findings show that cycling-based leisure practices structure engagement with commemorative landscapes, with remembrance emerging as an experiential outcome of embodied mobility, while remaining shaped by ethical expectations associated with Holocaust memory.
Increasing turbulence in contemporary business environments has made the quantitative analysis of unstructured textual data a central methodological challenge for researchers and decision-makers. The increasing availability of large-scale textual data has heightened the need for quantitative frameworks that can transform unstructured language into analyzable numerical representations. Transformer-based language models address this need by encoding text into high-dimensional semantic embeddings. Yet, these representations are commonly treated as black-box inputs for downstream tasks, with limited examination of their intrinsic numerical and geometric properties. The research in this manuscript addresses this gap by proposing a quantitative framework for analyzing transformer-based semantic embeddings as high-dimensional metric spaces prior to task-specific modeling. We employ an innovative methodological approach, considering vector norms regarding examining the dispersion of vector norms to detect concentration of measure, cosine similarity in the context of evaluating the distribution of pairwise cosines between vectors, and principal component analysis. For the purpose of the research, 3034 visitor-generated reviews related to national park experiences were used. Textual inputs are deterministically mapped into a normalized 384-dimensional embedding space using a transformer-based encoder. The analysis examines numerical stability through vector norm dispersion, semantic organization via cosine similarity distributions, variance structure using principal component analysis, and internal organization through unsupervised clustering validity metrics. Clustering is successful when high separation between clusters and high cohesion within clusters are achieved, which is why a single measure combining separation and cohesion metrics was proposed in the research. The results show almost perfect norm stability, backing up the choice of angular similarity as the right semantic metric. Variance decomposition and clustering results share a continuous high-dimensional semantic structure with no dominant latent components or clearly separable clusters. These results suggest that semantic meaning is best thought of as a continuous metric space rather than discrete categories, highlighting the need for representational diagnostics before predictive modeling.
The global shift toward renewable energy is driven by the dual imperatives of rising energy demand and the need to reduce environmental harm caused by fossil fuels. However, renewables like wind and solar power pose unique challenges, particularly due to their intermittent generation and current limitations in energy storage technologies. Battery banks, commonly used to store surplus energy, degrade over time, making accurate forecasting of their remaining usable lifecycles critical for maintaining system reliability and efficiency. This study proposes a novel approach for forecasting battery health using an optimized long short-term memory (LSTM) network. To address the complexity of deep learning hyperparameter selection, a modified metaheuristic optimization algorithm is developed and integrated into a broader optimization framework aimed at improving model performance while minimizing overfitting. The method is benchmarked against several state-of-the-art optimizers, with results validated through comprehensive simulations and statistical analysis. This work contributes a scalable forecasting methodology, an effective optimization strategy, and interpretable results to support sustainable energy storage solutions.
The aim of this article is to explore memes created and circulated by students and citizens taking part in the protests in Serbia. Memes from two social media platforms, X and Instagram, were collected and analysed in terms of their visual form and content, with emphasis on recurring themes. Based on these, six types of pictorial and one type of video memes in relation to the Serbian student protest are discussed. The article explores the meanings the memes created and distributed during the protests carried, understanding them as instances of political aims being achieved by non-political means. We further discuss the ethical implications of memes, both pictorial and AI-generated. The general issues the article tackles are those of protest mobilisation, humour, memes, ethics and AI.
The limited availability and non-sustainability of fossil fuels have led to the increasing interest in renewable energy alternatives. Significant obstacles must be addressed to fully integrate renewable energy into the existing power distribution grids. While reliability is a key factor in ensuring sustainable energy generation, solar power plants heavily depend on weather conditions which pose a challenge to maintain consistent and uninterrupted output without incurring substantial energy storage costs. As a result, accurate prediction of photovoltaic power production is crucial for efficient grid control and energy market operations. Traditional forecasting methods often struggle with nonlinear dependencies, while deep learning approaches are highly sensitive to hyperparameter tuning. This study proposes the application of metaheuristic optimization techniques to improve different lightweight recurrent neural network models and also considers attention mechanisms to forecast photovoltaic power generation. Additionally, an adapted metaheuristic optimizer is introduced to effectively overcome the obstacles of hyperparameter tuning. Extensive simulations are conducted using real-world dataset. The best-produced model in the simulations, which combines the gated recurrent unit with an attention mechanism, obtained a promising mean squared error score of 0.007713, indicating a significant perspective for further use in this area, with potential for deployment in resource-constrained environments like embedded and TinyML platforms.
