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.

The expansion of telecommunication access networks is constrained by static planning methods unable to process diverse, dynamic data. To address this, we propose a novel Multi-Agent System (MAS) where autonomous, domain-specialized AI agents collaboratively evaluate criteria for network expansion. The framework uniquely integrates structured and geospatial data with insights from unstructured documents via a Retrieval-Augmented Generation (RAG) component and synthesizes the agents' collective findings using the Analytic Hierarchy Process (AHP) to transparently weigh decision factors. This work provides a scalable, explainable, and methodologically robust framework for dynamic network planning.

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.

Monitoring landslide activity demands positioning systems that can operate continuously in difficult terrain while maintaining high accuracy. Traditional geodetic GNSS receivers provide excellent precision but are often too costly and delicate for large-scale deployments. Recent developments in affordable GNSS hardware have opened new opportunities for building dense monitoring networks at a fraction of the expense. This paper reviews the hardware components most critical to such systems, including receiver types, antennas, power solutions, and communication links. Low-cost single-frequency devices, such as u-blox modules, demonstrate promising results under favorable conditions, though they require longer convergence times. Dual-frequency receivers, such as the ZED-F9P, deliver faster initialization and more reliable precision, albeit with higher cost. Antenna configuration further influences performance, with geodetic-grade options ensuring stability and calibrated patch antennas offering practical compromises. Field deployments typically integrate solar panels with battery storage and rely on cellular or radio communication for real-time data transfer. With overall system costs ranging from €500 to €1500 per station, properly configured low-cost units have proven capable of tracking ground displacements with sufficient accuracy for landslide monitoring. The evidence suggests that careful hardware integration, balancing receiver choice, antenna performance, autonomous power supply, and connectivity is key to designing effective and resilient GNSS monitoring networks.

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

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.

Throughput prediction in cellular networks has garnered considerable interest in recent years due to its demonstrated positive impact on quality of experience. Existing proposals operate by having each user device make its own predictions, in a standalone manner, on the basis of its local measurements. Our hypothesis is that pooling of device measurements in a collaborative way can yield more accurate predictions, by allowing a broader set of observations from within a cell to be combined. To this end, we identify shortcomings in existing datasets, and then present our collaborative approach, along with an extensive evaluation. When compared to operating standalone, the results show a reduction in prediction error of up to 66% for users that have been inactive, and up to 17% for active users.

This paper presents the control of DC-DC buck converter. The control strategy is based on the theory of discrete-time sliding mode control to ensure robustness against parameter variations and external disturbances. The output voltage regulation is achieved using a digital controller that combines discrete-time first-order sliding mode control with a discrete-time realization of the super-twisting algorithm, commonly used in second-order sliding mode control. The design of the proposed controller is performed using a discrete-time model of the DC-DC buck converter. Extensive simulations were conducted in Matlab/Simulink to validate the performance of the control algorithm under varying load conditions and input voltage fluctuations. A comparative study between conventional PI controller and the proposed discrete-sliding mode based controller in tracking of various voltage references under action of disturbances is given.

The aim of the paper is to investigate the influence of the RTV silicon coating thickness on the electric field intensity values on the surface of high-voltage overhead transmission line conductors. For this study, the mathematical model based on the Charge Simulation Method (CSM) was used. The analysis are performed for the case of 400 kV overhead transmission line. The electric field intensity is calculated over the circumference and in the immediate vicinity of the conductors. The obtained results show that increasing the thickness of the RTV coating has a strong influence on reducing electric field intensity values on the conductor surface and in its immediate vicinity. Furthermore, increasing the thickness of the RTV coating decreases the electric field intensity gradient in the vicinity of the conductor and consequently decreases the probability of the stationary AC corona occurrence.

This paper examines the integration of Big Data analytics with the Aura Blockchain platform to enhance supply chain management in the luxury goods industry. The Aura Blockchain Consortium, established in 2019-2021 by leading luxury brands including LVMH, Prada, and Cartier, provides a permissioned blockchain infrastructure for product traceability and authenticity verification. By 2025, the consortium has grown to 50+ member brands with over 50 million luxury products registered on its blockchain. This study explores how combining blockchain's immutable ledger capabilities with big data analytics techniques can yield valuable insights including end-to-end traceability, anti-counterfeiting measures, supply chain optimization, quality control, and enhanced customer engagement. The paper presents a technical architecture for integration, discusses real-world implementations such as digital product passports, and addresses challenges including data privacy, scalability, interoperability, and organizational adoption. The findings suggest that this technological convergence enables luxury brands to transition from reactive to proactive supply chain management, meeting both regulatory requirements like the EU's Digital Product Passport initiative and evolving consumer expectations for transparency and sustainability.

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

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%.

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.

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.