The digital era generates extremely complex transformative processes in educational institutions, particularly universities worldwide. We are witnessing a transformation of the entire concept of knowledge and the understanding of its purpose and role. The implementation of digital resources and platforms opens up a whole network of possibilities for greater access to knowledge/information and global connectivity among academic communities. Tools such as online libraries, digital databases, digital archives, and various platforms for virtual collaboration are now available to students and educators. However, with this progress come significant challenges and open questions, especially for the social sciences and humanities. These disciplines face a dilemma: will the digital transformation advance or marginalize them? Digital platforms offer advantages in terms of accessibility and innovation, but they also bring dangers. It is inevitable that digital platforms enable faster access to information and innovative teaching approaches, but they also carry numerous risks for the future of education as a whole. There is an increasing trend of pronounced marginalization of the social sciences and humanities, partly as a consequence of the strong focus on STEM disciplines, which are often at the center of attention due to their technological nature and the profit they generate. Moreover, the hegemony of capital-interest trends, which favor technical and market-oriented approaches to education, threatens the traditional mission of universities as spaces for the development and generation of healthy trends in contextual critical thinking. Profit-oriented concepts of education, supported by neoliberal ideology, focus on technological and market-valuable disciplines, while the social sciences and humanities risk being pushed aside. There is a clear trend of favoring various forms/models of hybrid teaching, which combine online and in-person lectures. The loss of physical interaction can negatively affect the development of critical thinking and dynamic discussion in the social sciences and humanities. At universities that have historically been bastions of critical thinking, neoliberal pressures, and direct attacks on critical/liberatory thought are supported by rapidly growing concepts of exclusively profit-oriented paradigms of rationality. Additionally, the digitalization of education and the digital transformation raise the question of the future of the concept of the "knowledge society," which is increasingly being profaned. In this context, the "knowledge society" becomes a concept losing its authenticity, as knowledge is increasingly used as a means for market prosperity, rather than as a tool for the development of broader societal progress. Controlled neoliberal societies, driven by the hegemony of capital-interest trends, increasingly influence the direction of university development, leading to attacks on critical thought.

Abstract Background and purpose Due to their unique application and action, inhalation products require specific quality tests, such as Uniformity of Delivered Dose and Aerodynamic Assessment of Fine Particles. While there's no current official requirement for dissolution tests, new draft guidelines are introducing them as a supportive or required measure; however, a universally accepted methodology for such testing remains elusive. The aim of the present study was to explore the discriminatory ability and in vivo predictability of the newly developed dissolution assembly. Experimental approach The applied experimental approach to biopharmaceutical characterization of inhalation products involved developing a biorelevant method for testing the dissolution rate of the selected active substances. Seven commercially available products, formulated as pressurized metered dose inhalers, containing either salmeterol xinafoate or beclomethasone dipropionate, have been studied. The research strategy combined in vitro testing within silico simulations. Key results The developed dissolution method did not detect significant differences in the case of products containing highly soluble salmeterol, but it did reveal differences for products containing poorly soluble beclomethasone dipropionate. Moreover, a correlation was identified between the dissolution test results and absorption constants for beclomethasone dipropionate. Conclusion The obtained results indicated that the investigated products would not be considered bioequivalent based on the aerodynamic particle size distribution. It was demonstrated that a discriminative dissolution method can be developed through a well-established paradigm of dissolution testing, while taking into account the specificities of the inhalation route of administration.

This paper examines the status and potential of gender studies programs at the University of Sarajevo. It presents the history of gender/women's/feminist studies in Bosnia and Herzegovina, with a primary focus on the postgraduate and doctoral gender studies programs. The postgraduate program in "Gender Studies" was offered from 2006 to 2012, while in 2013, a doctoral program in "Gender Studies" was launched, admitting only the first generation of students. Through an analysis of relevant documents, including curricula, announcements, reports, and other supporting materials, the key aspects of the program are discussed—from the content of the courses to institutional and economic barriers. The study identifies circumstances that have negatively impacted the further development and institutionalization of the program, as well as links to a decline in student interest, limited financial resources, program expenditures, and the commercialization of the program at the University of Sarajevo. Although the "Gender Studies" program formally exists and represents the only such educational program at the University of Sarajevo and in Bosnia and Herzegovina, no calls for new admissions have been made for over a decade. This analysis invites a discussion on the potential for reactivating the program, with the aim of advancing gender studies in the academic context of the country.

Background: Between 10% and 80% of surgical patients experience some form of fear and anxiety before surgery. This is often attributed to inadequate or incorrect preoperative information. Objectives: This study aimed to critically evaluate and compile research that describes the impact of preoperative information on the patient's well-being before surgery. Methods: A systematic search was conducted on PubMed, Medline, CHINAL, Embase, and the Cochrane Library database for qualitative and quantitative literature regarding factors influencing patients' well-being before surgery. An inductive thematic analysis generated categories and subcategories. Nineteen studies were included. Results: Two main categories emerged from the thematic analysis of the included articles. These were the direct impact of information on fear and anxiety and the indirect impact of information on fear and anxiety. Information from healthcare professionals, alternative sources of information, shortage of healthcare professionals, music, and inability to receive information were some of the factors that can influence the well-being of patients before surgery. There are different reasons for the patient's fear and anxiety preoperatively, as well as the importance of direct and indirect information and other methods. For some patients, however, too much information could cause more fear and anxiety. Conclusion: The importance of the patient's discomfort being highlighted by the healthcare professionals emerges clearly and shows negative experiences in those cases where the patient feels his fears and concerns are not being addressed. More qualitative and quantitative research in the same theme, education and using person-centred care, and the right amount of information based on the patient's wishes are needed to improve the patient's well-being.

axiomFP.py is an open-source software developed to diagnose ploidy level and call quality for samples genotyped on Affymetrix Axiom SNP arrays by making frequency plots of normalized SNP call positions among SNPs meeting specific clustering parameters. This research outlines the methods employed in the development of the software, and presents the results obtained through its application on a dataset of mixed ploidy apple (Malus spp.) cultivars and germplasm accessions. The tools required to prepare the input files and operate the software are also described. The frequency plots generated by the software require a visual inspection to assess ploidy and call quality. The results have been validated using the available ploidy data, as well as flow cytometry, and have shown complete accuracy. The software is available on GitHub at https://github.com/allmiraria/axiomFP.

This paper presents the design and evaluation of a Virtual Reality (VR) application developed to educate young adults on flood safety. The simulation, playable on the Oculus Quest 2, was created using Unity and features assets modeled in Blender. It adopts a scenario-based learning approach, set within a school setting, where users navigate a seven-stage flood emergency by locating survival equipment and making contextually relevant decisions. The effectiveness of the application was assessed using pre- and post-intervention Likert scale questionnaires. The results indicate improved knowledge retention, enhanced decision-making skills, and increased user engagement. Qualitative feedback highlighted the simulation’s realism and emotional resonance. This preliminary study highlights the potential of VR-enabled experiential learning in disaster preparedness, providing ethical considerations and recommendations for broader implementation.

The paper initially delineates the problem of aging in composite polymer insulators utilized in overhead transmission lines, followed by a comprehensive examination of the underlying basic mechanisms contributing to this phenomenon. Subsequently, the paper addresses aspects of accelerated artificial aging specific to this class of high-voltage insulation systems. A critical analysis of both standardized and selected non-standardized testing methodologies employed to simulate aging processes is presented. Furthermore, the study incorporates simulation modeling via COMSOL Multiphysics to identify and emphasize critical stress regions within the insulators, which are posited as key contributors to the overall aging behavior. Based on the findings, important conclusions relevant to practical application are drawn, offering valuable insights that can inform future strategies and decision-making processes.

In this paper, we analyze the secrecy outage performance of the classical Wyner’s model, where both the legitimate receiver and the eavesdropper experience gamma-shadowed two-wave with diffuse power (GS-TWDP) composite fading. We derive expressions for the lower bound of the secrecy outage probability (SOP) and the probability of strictly positive secrecy capacity (SPSC) in terms of Meijer’s G-function, which can be efficiently implemented in MATLAB® and MATHEMATICA®. The derived expressions are validated through Monte Carlo simulations and used to perform detailed analysis of the impact of shadowing and multipath severity on secrecy outage performance in channels with composite fading.

High-throughput plant phenotyping using RGB imaging offers a scalable and non-invasive solution for monitoring plant growth and extracting various traits. However, achieving accurate segmentation across experiments remains a challenging task due to image variability usually caused by shifts in pot positions. This study introduces a customized image stabilization method to align pots consistently across time-series images of Arabidopsis thaliana, enhancing spatial consistency. A large-scale RGB dataset was collected and prepared, with 4,000 manually annotated images used to train multiple encoder–decoder deep learning models. Various CNN-based encoders were paired with well-known decoders, including U-Net, $\mathbf{U}^{2}$-Net, PANet, and DeepLabv3. Stabilization significantly improved performance of models, with the $EffNetB1 +\mathbf{U}^{2}$-Net encoder-decoder combination achieving the highest precision score of 0.95 and Intersection over Union of 0.96. These results demonstrate the value of spatial consistency and offer a robust, scalable pipeline for automated plant segmentation in indoor phenotyping systems.

With the emergence of advanced techniques in the field of artificial intelligence, risk management in the financial sector has undergone significant transformation. This paper proposes a deep learning–based approach for risk modeling using Bidirectional Long Short-Term Memory (BiLSTM) networks, adapted for tabular data by excluding explicit temporal dependencies. The model is tailored to support accurate decision-making in financial risk assessment. One notable component of this study is the use of automated hyperparameter optimization (HPO) methods, which further enhance the model’s overall effectiveness. These tuning strategies yield models that are less complex, faster to train, and capable of adapting to dynamic data environments, making them suitable for integration into automated credit approval systems. The model was evaluated against baseline approaches, demonstrating improved predictive performance across key evaluation metrics, with statistical significance confirmed by McNemar’s test.

This paper presents a PMU-data-based methodology for estimating regional inertia constants in power systems during the initial transient period following a disturbance. The power system is partitioned into dynamically coherent regions based on frequency signals from all monitored buses. Empirical Mode Decomposition (EMD) is applied to each nodal frequency signal to extract Intrinsic Mode Functions (IMFs), and the dominant IMF is identified through an energy ratio criterion. Pairwise correlation analysis of these dominant IMFs is then used to group buses with similar dynamic behavior, forming coherent regions. Within each region, the active power imbalance is computed from Phasor Measurement Units (PMU)-measured tie-line power deviations, while the rate of change of frequency (RoCoF) is estimated from residual trends of EMD-processed frequency signals. These residuals are shown to accurately follow the center of inertia (CoI) frequency trajectory, allowing precise CoI RoCoF estimation. To improve robustness against noise and oscillatory distortions, an adaptive Least Mean Squares (LMS) filter is applied. The regional inertia constants are subsequently estimated using an adapted swing equation during the initial transient period. The method is validated on the IEEE 39-bus test system, yielding estimation errors below 3% relative to reference values, demonstrating its effectiveness for inertia monitoring in low-inertia systems.

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and the most common cause of dementia worldwide. Early and accurate forecasting of cognitive decline in AD patients is essential for personalized treatment planning and effective clinical trial design. However, modeling disease progression is complicated by the irregular timing of clinical visits and heterogeneous data sources. This study presents a Time-aware Long Short-Term Memory (T-LSTM) model that captures temporal dependencies in patient data by integrating time gaps between visits directly into the learning process. Data from multiple large-scale cohorts—including ADNI, NACC, and CPAD—are harmonized and preprocessed to construct a unified longitudinal dataset for training and evaluation. Our approach forecasts Mini-Mental State Examination (MMSE) scores for an unlimited time horizon, demonstrating strong predictive performance and highlighting the effectiveness of temporally sensitive neural network architectures for long-term cognitive trajectory modeling in AD.

Manipulating deformable objects such as textiles remains a significant challenge in robotics due to their complex dynamics, unpredictable configurations, and high-dimensional state space. In this paper, we present an integrated planning and execution framework for robotic cloth manipulation that combines symbolic task planning, physics-based simulation, and vision-guided real-world control. High-level plans are generated using Planning Domain Definition Language (PDDL) and translated into low-level primitive actions executed on a Franka Emika Panda robot. To enable perceptual grounding, we employ a vision-based pipeline using an adapted CeDiRNet model for cloth corner detection and grasp point estimation. The system is first validated in IsaacSim using a particle-based deformable cloth model and then transferred to a real-world setup with consistent performance. Our results demonstrate successful Sim2Real transfer of high-level plans, enabling reliable execution of folding tasks in both simulated and physical environments.

Air pollution, particularly the concentration of particulate matter ($\mathbf{P M}_{\mathbf{1 0}}$), poses significant risks to human health and the environment. In this study, we employed the FB Prophet forecasting model to predict PM10 levels in Sarajevo and applied SHAP to enhance model interpretability. Using historical meteorological data and PM10 concentration records, we evaluated the model’s performance across three prediction horizons: 30, 60, and 90 days ahead. SHAP analysis identified the key meteorological drivers influencing $\mathbf{P M}_{10}$ concentrations. Accurate long-horizon predictions can support timely planning and decision-making, while also enhancing our understanding of air pollution dynamics in Sarajevo and providing valuable guidance for environmental management and public health strategies.