This paper continues with the analysis of the impact of some frequently occurring and very intense galvanic faults on IPTV QoS parameters on VDSL lines in FTTB networks. In addition to the previously presented results for certain types of very intense faults, this paper will show the results of research into the impact of another significantly pronounced fault that can occur in drop wires, but also in other segments of access networks.

Landslides pose a serious hazard worldwide, and monitoring their slow displacements is crucial for early warning and risk mitigation [1]. Global Navigation Satellite System (GNSS) sensors provide continuous 3D positioning in all weather, but conventional geodetic-grade GNSS are expensive and fragile in harsh terrain. Recent years have seen the rise of low-cost GNSS units that offer centimeter-level accuracy at a fraction of the cost [2]. This paper reviews technical innovations that enable such performance, focusing on real field deployments. Key advances include high-precision positioning techniques (RTK and Precise Point Positioning, PPP) and hybrid PPP-RTK corrections that speed up convergence, data-driven approaches like “Virtual RINEX” (VRINEX) to emulate reference observations [3] [4], and integration of inexpensive MEMS inertial sensors to suppress GNSS noise [5]. Open-source processing (e.g. RTKLIB-based workflows) and community tools now make low-cost GNSS monitoring more accessible. Field tests confirm that properly deployed dual-frequency low-cost GNSS stations can track subcentimeter displacements. We summarize 14 representative studies, compare their setups and results (Tables 1&2), and conclude that multi-constellation dual-frequency receivers, short baselines or VRINEX references, and hybrid processing are recommended for cost-effective landslide monitoring. Future work should emphasize long-term autonomous networks and real-time PPP-RTK services to further democratize GNSS hazard monitoring.

This paper explores the integration of control and monitoring systems within a graphical environment where factory (production line) simulations can be conducted. Gamification of simulators enables realistic testing of proposed solutions, allowing errors to be identified and resolved before equipment is installed in real factories. In this way, the 1-10-100 rule can be applied, where quality issues and costs grow rapidly depending on the stage at which they are detected. The most effective approach is to use simulators to verify design of factories before purchasing and installing components. For this reason, the new generation of gamified simulators can significantly reduce both costs and development time for new factories. When including components from different manufacturers and various types of equipment such as PLCs, robots, and CNC machines, gamified simulators can support rapid prototyping of industrial environments. The Simulator Factory I/O, in combination with TIA Portal, is recognized as an adequate environment for verifying the proposed design of an entire factory or a specific part of a production line

This paper presents a methodological approach for integrating heterogeneous tourism data into a unified spatiotemporal analytical framework. By combining business intelligence, geospatial processing, and machine learning techniques, the proposed system enables a predictive and spatially aware analysis of tourist behavior. The approach is evaluated through a case study from Sarajevo Canton and demonstrates how fragmented data sources, temporal, spatial, and behavioral, can be semantically aligned to support strategic decision-making in tourism. Although the accuracy of the predictive model is constrained by data limitations, the integrated architecture reveals patterns in tourist flows and spatial clustering that are not captured by traditional methods. The main contribution lies in establishing a generalizable analytical approach to tourism intelligence, bridging data silos, and improving the analytical capabilities of destination management systems.

In Bosnia and Herzegovina, there is a significant lack of research combining Artificial Intelligence (AI), Business Intelligence (BI), and geospatial analysis in tourism planning. This paper aims to fill that gap by demonstrating the integration of these technologies applied to spatial data in order to analyze tourist movement patterns. Spatial data processing was performed using QGIS (Quantum Geographic Information System), an open-source Geographic Information System (GIS), while Python was used to apply clustering algorithms on the data points. The resulting insights are visualized clearly through Power BI dashboards for better interpretability. This approach allows for understanding the relationship between tourists' routes and the proximity of hotels and museums. By combining AI, BI, and spatial data, more effective tourism planning can be achieved, helping to maintain well-organized and sustainable destinations.

Non-aqueous rechargeable metal-air batteries are very attractive for energy storage due to their high theoretical specific energies compared to state-of-the art Li-ion batteries. While Li-O 2 batteries are often seen as the primary alternative, Na-O 2 cells offer advantages over their lithium counterparts due to more reversible chemistry. Since the (electro)chemistry of this system is still in its infancy, one aspect we address is the stability of the aprotic electrolytes due to the high oxidation and reduction potentials in the operating environment. There is intrinsic disparity in understanding the interfaces at atomic/molecular level in organic-based solvents. This is partly due to the previously used poorly defined, polycrystalline, and/or high-surface area electrode materials in organic electrolytes containing trace levels of impurities. By employing electrochemical and in situ surface characterization methods on well-defined metal single crystal surfaces, we establish the stability range and reveal the decomposition products. Additionally, we demonstrate the impact of impurities on interfacial properties in organic environments, adding another piece to the overall understanding of selected aprotic electrolyte stability. We believe that this fundamental insight provides a pathway for the rational design of stable organic electrolytes, which are essential for the development of high-capacity sodium-air batteries. ___________________ The submitted manuscript has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. The U.S. Government retains for itself, and others acting on its behalf, a paid-up nonexclusive, irrevocable worldwide license in said article to reproduce, prepare derivative works, distribute copies to the public, and perform publicly and display publicly, by or on behalf of the Government. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan. http://energy.gov/downloads/doe-public-access-plan

Shared administration – EU migration agencies – The European Border and Coast Guard Agency Frontex – European Integrated Border Management and responsibility gaps – Unclear division of obligations in Frontex’s mandate – EU composite legal order and limits of EU judicial remedies – National courts and the right to effective judicial protection under Article 19(1) TEU and Article 47 Charter – Domestic judges as European judges – Advantages and potential of domestic remedies in multi-actor situations – Importance of preliminary references in shared administration– Domestic adjudication of human rights violations in European Integrated Border Management – Adjudication on member state responsibility – Adjudication on individual responsibility – Strengthening domestic judicial systems.

The indoor environment of buildings is of fundamental importance for the health of people and other living organisms residing in them. From this perspective, key factors include indoor temperature, relative humidity and the concentration of CO2 or other pollutants. These healthy indoor conditions are typically maintained through functional heating and ventilation systems. However, in the case of indoor humidity, increasing moisture levels when they are low can be relatively challenging. There are more energy-efficient solutions that can be combined with ventilation systems. These include, for example, placing plants and green walls in the interior, which have a significant impact not only on microclimatic and acoustic conditions of the interior, but also on the overall psychological well-being of occupants. Green elements contribute to the effective regulation of CO2 and certain other harmful substances within the indoor environment. Another possible solution involves the use of sorption-active materials in the form of cladding panels—elements capable of functioning as indoor regulators, i.e., absorbing moisture and releasing it back into the indoor environment when necessary. This study investigates the moisture behavior of natural composites based on montmorillonite clay and straw fibers, as well as their possible integration with green elements to create healthy indoor conditions for their inhabitants. The developed clay composite can be classified as water and steam absorption class WSIII according to DIN 18948—the moisture buffering capacity value was 152.73 g/m2 after 12 h. Based on the research results, it can be stated that these composites could serve as interior cladding elements in synergy with green elements (Chlorophytum comosum, Epipremnum aureum), ideally regulating the indoor microclimatic conditions, especially as an effective solution for short-term humidity changes. The maximum difference in relative humidity between the reference testing chamber (without green elements and clay plates) and the chamber containing plant Chlorophytum comosum and three clay composite plates was 23.04%.

This article explores the robustness and explainability of a convolutional neural network-based fault detection method for medium-voltage circuit breakers. The robustness is analysed by evaluating the method's performance under the presence of stationary and non-stationary disturbances in the vibration signature. Additionally, the impact of sensor ageing on performance indices is investigated to assess long-term reliability. Since the condition assessment method is focused on binary classification, the detection outcome interpretation aspect is addressed by providing recommendations for operator or autonomous system actions. Both aspects are demonstrated using datasets collected from real-world medium-voltage circuit breakers.

The aim of this research was to determine the connection between body composition and explosive power of the lower extremities in top karate players, representatives of Bosnia and Herzegovina, and to identify the differences between men and women in these parameters. Correlation analysis was performed between body composition variables and lower extremity explosive strength, with additional use of independent samples t-test to examine gender differences. The results showed a significant positive correlation between body mass, muscle mass, total amount of water and jump height, while the percentage of fat tissue had a negative correlation with jump height. Also, significant gender differences were identified in variables such as fat tissue percentage (.004), muscle mass (.000), total water volume (.000) and jump height (.000), with men achieving better results. These differences indicate that body composition and explosive power are key factors in karate and highlight the need to adapt training and nutritional strategies to gender specificities in order to optimize results.

Brachial artery endothelial function, measured by the flow-mediated dilatation (FMD) technique, serves as a surrogate for coronary endothelial function and is recognized as an independent predictor of cardiovascular disease risk. Despite the known benefits of physical exercise interventions (PEI) in improving endothelial function, limited evidence exists to guide practitioners on the most effective form of PEI for enhancing endothelial function. The aim of this article is to investigate the effects of different PEI modalities on brachial artery FMD, and to establish the most effective PEI through a systematic review and network meta-analysis (NMA). PubMed, WoS, CINAHL, EMBASE, CENTRAL and EBSCOhost search was conducted from inception to February 20th, 2025. Randomized controlled studies investigating the effects of PEI on brachial artery FMD in adults were included. Both pairwise and Bayesian NMA were conducted using random-effects model to compare different PEI modalities within primary (aerobic training, resistance training and combined training) and secondary (continuous aerobic training vs. interval aerobic training vs. dynamic resistance training vs. combined training) categorizations. The PEI effectiveness was ranked using the surface under the cumulative ranking curve (SUCRA). In total, 84 studies with 3596 participants (43% females, 51.9 ± 15.1 years of age) were included in the analysis. Summarized evidence of 119 effect sizes through pairwise comparisons showed improvement in FMD (mean difference [MD], 2.24%; 95% confidence interval [CI] 1.90–2.58, p < 0.001) following different PEI, without difference between magnitude of the effect between healthy and asymptomatic individuals (Q, 1.27, p = 0.260). As shown in the NMA, the rank order within a primary classification showed aerobic training as the most effective (SUCRA: 89.8%, MD, 2.37%, 95% credible interval [CrI] 1.95–2.80) followed by resistance training (SUCRA: 66.0%, MD, 2.07%, 95% CrI, 1.34–2.79), and combined (aerobic and resistance) training (SUCRA: 44.1%, MD, 1.67%, 95% CrI, 0.73–2.6). Secondary NMA identified interval aerobic training as the most effective (SUCRA: 99.1%, MD, 3.07%, 95% CrI, 1.37–3.76), which showed to be more effective than continuous aerobic training (MD, 1.08%), dynamic resistance training (MD, 1.04%), and combined training (MD, 1.36%). Moreover, a negative association was found between FMD improvement and both intervention duration and overall training load, while positive associations were observed with weekly training frequency, single session duration, and weekly training duration. Various PEI modalities have demonstrated effectiveness in improving brachial artery FMD, with interval aerobic exercises of higher intensities emerging as the most effective based on current evidence, followed by dynamic resistance training, continuous aerobic training and combined training. These findings have significant implications for informing future exercise guidelines aimed at both prevention and treatment of endothelial dysfunction. The study protocol was prospectively registered in PROSPERO online registry: ID: CRD42023453202 In this meta-analysis of 84 randomized controlled trials, physical exercise interventions (PEIs) in general significantly increased brachial artery endothelial function measured by flow-mediated dilatation (FMD) technique in the adult population. This large-scale systematic review and network meta-analysis showed that aerobic training, resistance training and combined training are all effective in enhancing brachial artery FMD. Although various PEIs have demonstrated effectiveness in enhancing brachial artery FMD, interval aerobic exercises of higher intensities are emerging as the most effective based on current evidence. The findings from network meta-regression analyses suggest that greater improvements following PEI can be achieved by increasing the duration of acute stimuli (an additional half-hour of training = 0.80% increase in FMD) and weekly training exposure (an additional two and a half hours of training on weekly basis = 0.50% increase in FMD). In this meta-analysis of 84 randomized controlled trials, physical exercise interventions (PEIs) in general significantly increased brachial artery endothelial function measured by flow-mediated dilatation (FMD) technique in the adult population. This large-scale systematic review and network meta-analysis showed that aerobic training, resistance training and combined training are all effective in enhancing brachial artery FMD. Although various PEIs have demonstrated effectiveness in enhancing brachial artery FMD, interval aerobic exercises of higher intensities are emerging as the most effective based on current evidence. The findings from network meta-regression analyses suggest that greater improvements following PEI can be achieved by increasing the duration of acute stimuli (an additional half-hour of training = 0.80% increase in FMD) and weekly training exposure (an additional two and a half hours of training on weekly basis = 0.50% increase in FMD).

Stress is an integral component of modern sport and has a significant impact on athletes’ physical, emotional, and cognitive functioning. The aim of this review paper is to analyze existing literature on the effects of stress on sports performance, the underlying mechanisms through which stress operates, and potential strategies for mitigating its negative effects. Research findings indicate that stress may have both positive and negative consequences for athletic performance, depending on individual athlete characteristics, situational perception, and coping strategies. A proper understanding and effective management of stress can contribute to performance enhancement, burnout prevention, and the preservation of athletes’ mental health.

We consider the adaptive control problem for discrete-time, nonlinear stochastic systems with linearly parameterised uncertainty. Assuming access to a parameterised family of controllers that can stabilise the system in a bounded set within an informative region of the state space when the parameter is well-chosen, we propose a certainty equivalence learning-based adaptive control strategy, and subsequently derive stability bounds on the closed-loop system that hold for some probabilities. We then show that if the entire state space is informative, and the family of controllers is globally stabilising with appropriately chosen parameters, high probability stability guarantees can be derived.

Iron deficiency anemia (IDA) continues to pose one of the most significant challenges in maternal health, affecting nearly 40% of pregnant women worldwide according to the World Health Organization (2023).  Despite advances in obstetric screening, conventional diagnostic methods such as complete blood count (CBC) tests often fail to detect early or latent stages of anemia due to physiological changes associated with pregnancy.  This study introduces a robust machine learning framework integrating Extreme Gradient Boosting (XGBoost), and Conditional Tabular Generative Adversarial Networks (CTGAN) for the early detection of IDA in pregnant women. Our approach addresses the class imbalance inherent in clinical datasets and incorporates trimester-specific hematological adaptations.  Using 3,944 anonymized clinical records from ASA Hospital Sarajevo (January–July 2025), we evaluated model performance across hematological features commonly used in obstetric care. The optimized model achieved a precision of 100%, recall of 65.2%, specificity of 100%, and an AUC-ROC of 0.8686. Comparative analysis against conventional CBC screening, which reached only 40.5% sensitivity, demonstrated significant improvement in detection reliability. These findings demonstrate the potential of AI-enhanced diagnostics to support early detection of IDA in pregnant women, reduce missed diagnoses, and strengthen clinical decision-making. Further multi-center validation and integration of additional biomarkers are recommended to confirm generalizability.