Context. Gamma-ray burst GRB 241030A ( z  = 1.411) exhibited a particularly bright afterglow (similar to the ‘BOAT’, GRB 221009A), detected across gamma-ray, X-ray, UV, and optical bands. The extensive, multi-wavelength observations of this remarkable event provide a valuable opportunity to advance our understanding of GRB afterglow physics. Aims. We aim to constrain the physical properties of the jet, its microphysics, and the characteristics of the circumburst environment in the context of forward-shock emission. Methods. We compiled multi-wavelength observations spanning from a minute to a week after the prompt emission, processing the data through a unified photometry pipeline. Leveraging this comprehensive dataset, we analysed the observations both analytically and using Bayesian inference with two independent models. Our models assume that the afterglow emission arises from the strong forward shock of a laterally structured jet, with possible contributions from synchrotron self-Compton (SSC) scatterings. Results. We find that our models do reproduce the afterglow observations accurately, from the X-rays to the optical, favouring a jet propagating into a constant-density interstellar medium, with a viewing angle within the jet core. However, both analyses – with and without the inclusion of SSC scatterings – require parameter values that are extreme compared to expectations from standard theory. In particular, our results imply extremely energetic jets despite regular prompt energy, leading to a very inefficient prompt emission. Furthermore, the jets are particularly inefficient at accelerating particles, with low ϵ e and ϵ B , leading to significant SSC emission. Finally, our analyses indicate that the jets have large opening angles and propagate in high-density media. Conclusions. If the afterglow is indeed powered by radiation emitted behind a strong forward shock, our results place GRB 241030A within a sub-class of GRBs characterised by extreme kinetic energies, large jet opening angles, and very low prompt emission efficiencies, below 10 −3 , with strong SSC radiation. These predictions are difficult to reconcile with typical expectations from other GRBs. We therefore suggest that the afterglow of GRB 241030A is not solely powered by forward shock emission, and we discuss other options such as a long-lasting reverse-shock contribution.

Perfluorination of the terminal methyl group in ethanol gives rise to different thermodynamics of mixing with water. To understand its origin, we probe structure, thermodynamics and collective vibration modes in aqueous solutions of ethanol (EtOH) or 2,2,2-trifluoroethanol (TFE) by using Terahertz (THz) spectroscopy and molecular dynamics simulations. The THz spectra show mainly two features: a mostly entropy-related peak (below 200 cm-1) related to weaker water-water hydrogen bonds, and an enthalpy-related large band above 200 cm-1 related to water-solute hydrogen bonds. The entropic feature is red-shifted for TFE relative to EtOH, consistent with TFE's subpopulation of weaker solvation shell water-water hydrogen bonds found in the simulations. By contrast, the thermodynamics of mixing is dominated by three effects: the higher probability of forming water-water hydrogen bonds in the solvation shell of either solute than in the bulk; the fact that TFE induces a smaller perturbation per water molecule than EtOH, despite perturbing a slightly larger number of water molecules than EtOH, and TFE's weaker solute-water hydrogen bond. The three effects determine the more negative (favourable) enthalpy of mixing and more negative (unfavourable) entropy of mixing of EtOH relative to TFE at low concentrations. The results confirm that hydrophobic solvation of perfluorinated groups is fundamentally different from that of their alkylated equivalents and have implications for the development of models to predict solubility of perfluorinated molecules.

Malware detection using deep learning faces challenges in model selection for practical deployment. We systematically compare five transfer learning architectures (VGG16, ResNet50, DenseNet121, MobileNetV2, EfficientNetB0) on the MaleBin RGB malware dataset ($\text{1 2, 0 0 0 +}$ images through March 2025). Experiments on NVIDIA A100 GPU evaluated accuracy, efficiency, and deployment suitability. DenseNet121 achieved highest accuracy ($91.20 \%, 8 \mathrm{M}$ parameters), MobileNetV2 provided optimal edge deployment (90.39 %, 3.5 M parameters), while ResNet50 and EfficientNetB0 unexpectedly underperformed $(77.34 \%, 71.16 \%)$. Directions for practitioners are to deploy DenseNet121 for cloud environments, prioritizing accuracy, and MobileNetV2 for resource-constrained edge devices.

Deep learning has become increasingly significant in clinical medicine, including breast cancer detection, offering significant potential to improve patient outcomes. However, recurrent architectures like LSTM (Long Short-Term Memory) and BiLSTM (Bidirectional Long Short-Term Memory) remain underutilized for breast cancer prediction using structured tabular data, primarily due to the absence of explicit temporal dependencies, which are unsuitable for sequence-based modeling. This work presents a novel approach that redefines how LSTM architecture can be applied to the publicly available non-sequential Wisconsin Diagnostic Breast Cancer (WDBC), consisting of 569 samples and 30 features. The flat tabular input is reshaped into a fixed-length 3D tensor using a sliding window approach to adapt the data for sequence modeling. This transformation enables the model to leverage LSTM's sequential processing capabilities in a fundamentally new way, capturing implicit feature interactions across structured attributes without temporal context. Furthermore, Bayesian hyperparameter optimization techniques are applied to enhance the model's performance. The proposed model is evaluated against standard LSTM and state-of-the-art tabular Transformer architectures (FT-Transformer and SAINT). Results show that BiLSTM achieves the best overall performance (AUC 0.9985, accuracy 0.9824, RMSE 0.0964), while the LSTM baseline also surpasses both Transformerbased tabular models (AUC 0.9958, accuracy 0.9719). Performance gains are consistent across seven evaluation metrics, with statistical significance confirmed via paired t-tests $({p}<0.05)$. These findings demonstrate that, when appropriately adapted, recurrent architectures can outperform even advanced self-attention models in structured clinical prediction tasks.

Immunotherapy with immune checkpoint inhibitors (ICI) has become a transformative pillar in cancer treatment, offering significant improvements in survival and reducing treatment-related side effects compared to traditional therapies. In gynecologic cancers, ICIs have transformed the treatment of endometrial (EC) and cervical cancers, whereas they have not demonstrated clinical benefit in ovarian cancer. This review examines the current state of ICI advancements in EC. Given the unique immunological characteristics of EC, a comprehensive understanding of advancements is crucial for optimizing decision-making and patient outcomes. While ICIs have demonstrated robust and durable efficacy in dMMR/MSI-H EC, the magnitude of benefit in pMMR disease remains modest. Additionally, we examine promising future directions, including personalized immunotherapy approaches and novel combination therapies (e.g. antibody-drug conjugates, PARP inhibitors, antiangiogenic drugs).

Orthogonal signal generators are crucial for synchronization in single-phase systems, where accurate estimation of phase, frequency and amplitude is the focal point. Conventional generators are sensitive to a DC-offset in the input signal, which can degrade performance. This paper presents a modified Kalman-based generator with an additional feedback loop for DC elimination. A state-space model of proposed generator is developed, and parameters are calculated using a continuous Kalman estimator. The performance is validated in MATLAB/Simulink environment under several tests to determine performance of the presented orthogonal signal generator. Simulation results show that the generator is accurately tracking the input signal while generating its quadrature components demonstrating robust performance suitable for synchronization loop applications.

Chainsaw felling and processing work is conducted in various natural conditions and requires significant physical effort from the workers, movement in severe weather and environmental conditions, and has a high risk of injury. The aim of this study was to determine the physiological workload of chainsaw operators through continuous heart rate measurement during the entire working day. The research was carried out during the summer of 2024, encompassing different parts of the Federation of Bosnia and Herzegovina. Heart rate was measured using a Polar H10 Heart Rate Monitor Chest Strap with continuous data logging and storage of heart rate readings. A time study was performed based on recordings conducted simultaneously with the recording of heart rate, with the aim of determining the duration of individual work operations and identifying the work operation with the highest negative impact on the worker. The average working heart rate during productive work time for subject 1 was 104 bpm, 83 bpm for subject 2, 109 bpm for subject 3, 94 bpm for subject 4 and 129 bpm for subject 5. The results of the Kruskal-Wallis test showed a statistically significant difference in average heart rate in relation to the time study element. The heart rate reserve (%HRR) for the whole study time was estimated at 41.05 % for subject 1; 22.69% for subject 2; 44.50% for subject 3; 24.04% for subject 4, and 45.78% for subject 5. The results of the study showed that the %HRR of chainsaw operators during felling and processing exceeded the value of 40% for 3 out of 5 subjects, which corresponds to hard work and may have negative consequences for operators´ health.

With the growth of data volume and increased query complexity, the need for the application of various optimisation techniques that enable faster execution and more efficient use of resources is increasingly becoming evident. Research shows that indexing, query execution optimisation, and the use of caching significantly reduce processing time and increase system responsiveness. Given that databases are constantly growing in size due to the need to store and analyse data, efficient database architecture and organisation are imperative to the business environment. This paper deals with the topic of analysing databases with large data sets and how to retrieve them most efficiently, using web applications, which are today the most common UI for databases.

C-reactive protein structurally belongs to the pentraxin family, calcium-binding proteins with immune defense properties. In the serum of healthy adults and adolescents, there is less than 5 mg of C-reactive protein. Its concentration is increased in inflammatory diseases where values up to 500 mg/l can be found. The main role of C-reactive protein is complement activation and prevention of inflammation. It binds to bacteria or damaged cells and thus helps the activation of the classic complement pathway, opsonization and phagocytosis. Binding depends on calcium. Antibiotics are products of the metabolism of bacteria, fungi and molds, rarely higher plants, which in small concentrations prevent the growth and development of microorganisms or kill them. They belong to the group of antimicrobial drugs, which are used to treat and prevent bacterial infections. Cephalosporins are beta-lactam antibiotics with the same mechanism of action as penicillin, which means that they block the synthesis of the bacterial cell

Atrial fibrillation (AF) is the most common persistent cardiac arrhythmia in clinical practice and a significant, often underdiagnosed risk factor for stroke. The electrocardiogram (ECG) is the primary method for its detection, typically manifesting as irregular $\mathbf{R R}$ intervals and the absence of P-waves. Numerical ECG parameters enable quantitative analysis of these changes and provide a foundation for the development of automated detection systems. This study examines the association between atrial fibrillation and numerical ECG parameters using the ECG-ViEW II database. From 12-lead ECG recordings, key temporal and morphological parameters were extracted, and descriptive statistics were calculated to form the final dataset. Descriptive statistical analysis, inferential tests, and graphical visualizations were applied to compare AF and non-AF groups. The results indicate that parameters describing RR-interval variability show a strong association with atrial fibrillation, confirming their potential for application in automated systems for early AF detection.

The recent rise of large language models (LLMs) and other generative artificial intelligence (AI) tools like agents represent only the visible tip of the iceberg in artificial intelligence. Beneath the surface lies a vast foundation of advanced mathematics, statistics, signal processing techniques, countless smaller/domain-specific models, optimization algorithms, distributed computing infrastructure, specialized engineering tools, and domain knowledge that make these headline-grabbing AI systems possible. This paper uses the iceberg metaphor to illuminate these hidden layers and surveys the technical background, including the mathematical and statistical underpinnings of AI, but also limitations of LLMs in many engineering applications such as power system fault detection, simulation-based optimization, and time-series forecasting. By exploring these “submerged” components of the AI iceberg, this paper provides a comprehensive perspective on the true breadth of modern AI, bridging the gap between public perceptions of AI and the complex reality beneath with multiple supporting case studies. The findings contribute to the existing body of knowledge by underscoring that meaningful progress in AI requires not only visible breakthroughs in models and interfaces, but also continuous advances in the less glamorous but critical supporting layers and applications of the AI stack.

Implementing a cognitive Sense-Think-Act-Learn (STAL) architecture for automated cultural heritage visualization, this paper illustrates a multi-agent system. The system consists of three specialized agents: an Exhibition Curator Agent that applies Large Language Models (LLMs) to classify and generate exhibitions, a Conversational Guide Agent that provides interactive visitor engagement, and an Image Acquisition Agent that performs automated visual content enrichment. Evaluated on a dataset including 6,398 historical events across multiple civilizations and epochs, the system effectively automates the curation of the data by adopting a hybrid approach via deterministic classification algorithms as well as LLM-based analysis. The architecture allows adaptive learning based on administrative feedback, improving classification accuracy over time. This work provides a concrete framework to leverage generative $A I$ in cultural heritage digitization while also addressing issues related to scale, multilingual content, and domain-specific curation needs.

This paper explores the application of FPGA (Field Programmable Gate Array) technology based on the Basys 3 board in video game development. Given that the hardware of most classic games is no longer functional, the programmable nature of FPGA allows for precise replication, allowing modern devices to run these games and provide an authentic gaming experience. The paper presents the design and emulation of a video game on the Basys 3 FPGA development system. It also examines the use of open-source tools for the development of FPGA applications, comparing them with commercial alternatives. The Basys 3 system was prepared for game emulation by implementing a RISC-V processor, a VGA controller, and memory in Verilog. The game was initially developed in $\mathbf{C}$ for Windows and later ported to the FPGA environment, with particular attention paid to memory management to ensure proper image display via the VGA controller. The results demonstrate that FPGA systems are viable platforms for video game emulation and complex application development, with open-source tools proving efficient and effective.

This paper presents a novel UART-based debugging interface for resource-constrained RISC-V soft-core implementations on FPGAs. Unlike traditional JTAG-based approaches that require dedicated hardware and tools, our design leverages the ubiquitous UART peripheral to provide comprehensive debug capabilities through a dual-mode architecture. The interface operates in standard ASCII mode for command-line interaction and switches to a binary protocol for advanced operations including bulk instruction memory programming (IMPR), singleinstruction hot-patching (IMWR), and direct memory/CSR bus read/write operations (BUSR/BUSW). A key innovation is the bus mastering mechanism that enables real-time memory inspection and modification without permanent CPU halting, facilitating live debugging and in-field firmware updates. The FSM-based protocol incorporates checksum verification and timeout recovery for robust operation. Implemented on an 80 MHz RISC-V based SoC for WireGuard VPN acceleration, the interface consumes less than 2% additional FPGA resources while providing functionality comparable to more complex debug modules. Experimental results demonstrate successful hot-patching of running programs, sub-second firmware updates, and effective production diagnostics without requiring specialized JTAG hardware or tools.

The global transition to renewable energy faces challenges, particularly in integrating variable sources such as wind and solar. Battery Energy Storage Systems (BESS) provide a key solution for grid stabilization and peak load management. Peak shaving stores energy during low-demand periods and releases it during high-demand periods, reducing costs and stabilizing the grid. This research aims to model and analyze optimal BESS operation for peak shaving in industrial environments, highlighting both technical performance and contributions to sustainable energy systems. MATLAB/Simulink simulations evaluate effects on grid dependency, energy efficiency, and economic benefits, showing how BESS with photovoltaic generation can enhance efficiency, reduce grid reliance, and support environmentally friendly energy management.