This paper enhances vault security by integrating IoT, blockchain, and machine learning to monitor banknote weight. Blockchain ensures secure, tamper-proof storage of weight data, helping detect inconsistencies and potential theft. Machine learning models, including Linear Regression, Lasso Regression, KNN, SVM, and Random Forest, predict banknote count based on weight, with Linear and Lasso Regression achieving the highest accuracy. Challenges like data limitations and computational constraints are addressed, with recommendations for improvements. By combining these technologies, the system strengthens vault security, prevents theft, and ensures data integrity, offering a reliable solution for safeguarding physical currency.

This paper analyzes the behavior of photovoltaic (PV) power plants in low-demand power systems, with a focus on the power system of Bosnia and Herzegovina. To achieve a realistic representation of operational conditions, meteorological data specific to the region were incorporated into the analysis using a custom Python application for data collection and visualization. Simulations were performed using the EMTP software package to evaluate system performance under normal and faulty conditions. The behavior of both distant and close PV power plants was analyzed across various scenarios, with special attention given to the effects of different types of short circuits, the most common failures in power systems. The findings provide insights into the dynamic response of PV power plants in low-demand scenarios, contributing to improved stability and fault management strategies.

In order to research the advantages of usage of photovoltaic plants in smart grids, an analysis focused on the impact of photovoltaic systems on the stability and reliability of electrical grids is conducted in this paper. The paper addresses the technical aspects of integrating photovoltaic systems, including their variable production and how it affects the changes in electricity supply and demand in a real distributed power grid. Innovative technologies, such as energy storage devices and advanced communication systems, are also considered, which enable better control and management of the grid. The integration of a photovoltaic plant into a 20 kV network with consumers in the household and industrial categories, as well as an electric vehicle charging station, is analyzed with varying loads. The results obtained highlight the contribution of PV plants to the grid stability, reliability, voltage conditions, and total active and reactive power losses.

"The main purpose of this paper is to assess the seismic resistance of a masonry building from the Austro-Hungarian period in Sarajevo. The building is situated in Sarajevo's old Baščaršija, which is well-known for its marketplace of tiny adobe and wooden buildings from the Ottoman era. It is characterized by specific Austro-Hungarian architecture from the rebuilt Latin district next to the Miljacka River. European construction rules were created following a fire in 1879. The structure is notable for its size and design, which combines Ottoman surroundings with Austro-Hungarian influences. The original structure had two floors A business area occupied the ground floor, while residential apartments occupied the top floor of the original building, which was recorded by the Governmental Building Department in 1903. It was a typical residential rental building at the time. Later, a second level was constructed while keeping the same layout and structural elements. Typical Austro-Hungarian solid bricks from that era were used to construct the load-bearing walls, with lime mortar for the joints. Sand infill serves as fire-resistant insulation between the wooden beams and boards that form the floor structure. The original pitched roof was made of wood. Numerical modeling and nonlinear static (pushover) analysis were conducted using the 3Muri software package. The 3Muri software package, specialized in analyzing masonry structures, employs the innovative Frame by Macro Element (FME) method, enabling detailed seismic behavior analysis of walls. This paper presents detailed pushover analysis results, covering the distribution of lateral forces (uniform and static) for horizontal acceleration in the X and Y directions, considering the significant damage state for a 475-year return period. The main parameter monitored during the analysis was the vulnerability indexes. Results are presented for all walls, and wall damage was analyzed relative to the direction of seismic action, identifying walls most affected by bending or shear forces."

This paper considers the approach for overhead transmission lines’ (OHTL) electric and magnetic field reduction by finding the best phase conductors (PCs) and shield wires (SWs) positions. This approach combines an algorithm based on stochastic modeling for OHTL configuration generation, with the artificial neural networks (ANN) based method for the electric field strength and magnetic flux density determination. This approach enables the generation of an arbitrary number of different OHTL configurations, taking into account specific user-defined limitations. This further, enables to find the OHTL designs that are the best solution for the considered case study regarding electric and magnetic field levels. In this paper, a case study is presented where the considered approach is employed to find the OHTL designs that give the best results regarding the limitation of electric and magnetic field values.

Extensive construction of buildings with structural system made of reinforced concrete walls had been started in the early 60s of the last century, as a continuation of the rebuild of Europe after the World War II. This was especially true in the Western Balkan region. In some way these buildings replaced multistorey masonry buildings, enabled significantly higher number of floors and a larger number of apartments. A specific construction technology with the so-called tunnel formwork was applied, which enabled rapid construction progress in terms of the height of the building. Seismic resistant structure of the buildings consisted mainly of reinforced concrete slabs and walls, whereby the reinforcement detailing was performed according to the old technical codes and the ancient state of the art of the building’s construction. Regarding the structural system, the way of the construction and structural detailing of these buildings, they can be classified as a recent historical heritage. A high-rise building in Sarajevo, with 20 residential floors, about 55 years old, with a load-bearing system made of reinforced concrete walls and slabs, almost without any beams, was analyzed. According to the modern state of the praxis, the building does not meet the requirements of contemporary seismic codes, and this especially applies to the reinforcement design and detailing. Taking into account seismic vulnerability classification of the European Macroseismic Scale the building could suffer substantial damages when exposed to the stronger earthquake motions. We tried to capture the specific design of the existing reinforced concrete walls applying more sophisticated structural models, including confined and unconfined concrete. The mechanical properties of the built-in building materials in existing slabs and walls were obtained experimentally. The results of the nonlinear analysis show a relatively satisfactory global response of the structure, but with possible damages due to the rather poor reinforcement quantity in the walls. Just to mention that some of the main structural walls possess only few longitudinal reinforcement bars in the corners. An improvement of the structural system, in order to achieve a ductile response with the dissipation of the energy introduced by the earthquake, as proposed by the latest seismic codes and recommendations, has been discussed as well.

"In everyday engineering calculations, walls in masonry structures are typically analyzed as isolated from the rest of the structure. The corresponding gravitational load is determined, and the horizontal load is applied to the wall, assuming that floors are rigid within their plane and transfer horizontal loads according to the stiffness of the walls at the building's base. The wall's bearing capacity is verified on a model isolated from the structure, considering the effects of bending moments, normal forces, and shear forces. Spatial models that include other structural elements along with the walls are rarely created. This study focuses on slender walls, where height exceeds length, which are common in our architectural tradition. Reinforced concrete ring beams are regularly constructed at the top of such walls, transitioning into lintels or beams supporting the ceiling. The study aims to investigate whether these elements, along with the ceiling as a whole, influence wall behavior during earthquakes. Experiments and post-earthquake damage reports suggest that walls behave differently depending on the level of normal force stress. Wall behavior varies based on its position in the structure, load intensity, connections, and material and geometric characteristics. Less-loaded walls, typically on upper floors, tend to fail through full-wall rotation, with or without edge crushing. Sliding occurs with lower normal forces and high shear stresses, while diagonal fractures emerge at certain stress levels. This study develops a numerical model to explore the interaction between short walls and ceilings, especially in rocking and toe crushing, aiming to answer whether walls should be considered isolated or part of spatial frame systems."

The integration of renewable energy sources (RES) and battery energy storage systems (BESS) into electrical power distribution systems (EPDS) is growing rapidly, but presents challenges like increased energy losses, voltage deterioration, and rising costs. This paper proposes a multi-objective optimization framework for optimal BESS allocation in EPDS to reduce costs and improve voltage profiles. Using a genetic algorithm, Non-dominated Sorting Genetic Algorithm III (NSGA-III), it balances objectives while considering system and battery constraints. Python’s Pandapower and DEAP (Distributed Evolutionary Algorithms in Python) libraries are used for power flow analysis and optimization. The model is validated on a medium-voltage radial network with high renewable energy sources (RES) penetration, showing significant showing significant gains in network performance and highlight the potential for battery energy storage systems (BESS) to become standard components in modern power systems.

Air pollution, largely caused by activities in the construction sites, poses serious health and environmental risks to workers and people living nearby. This study focuses on predicting the concentrations of six major pollutants, i.e. PM2.5, PM10, NO2, CO, SO2, and O3. We train a Long Short-Term Memory network (LSTM) on each pollutant to forecast its levels twelve hours in advance. A window generator is used to map data into sequences, enabling the model to capture temporal patterns effectively. Extensive data pre-processing ensures accuracy, including handling missing values and transforming categorical variables. Specifically, the analysis of the pollutants is composed by the following steps: i) preparing the data, ii) building and training the model, iii) evaluating the model performance in terms of Root Mean Square Error (RMSE). We prove that LSTM performs outstandingly over other models, i.e. Random Forest and Artificial Neural Network. The obtained RMSE values ensure credibility and reliability of LSTM in air quality predictions. This predictive framework offers a practical approach for construction sites to manage air pollution and mitigate health and environmental impacts proactively.

Software developers often need guides navigating them in the process of choosing the most suitable frameworks and programming languages for their needs. In this study, the impact of the programming languages on the performance of four popular backend frameworks: Spring Boot, ASP.NET Core, Express.js, and Django is examined using tools such as Apache JMeter and Docker under uniform conditions. With metrics like latency, throughput, docker build time, and deployment time the experiments revealed that ASP.NET Core exhibited the lowest latency (1ms for HTTP POST and GET), while Django achieved the shortest deployment time (0.31 seconds). Spring Boot and Express.js occupied the middle ground, balancing flexibility and performance. Besides valuable insights into the efficiency of each framework in real-world applications, this paper also includes a review of similar studies while complementing them by providing additional perspectives through concrete measurements and analyses under realistic conditions. This study contributes to a better understanding of architectural decisions and their relationship to performance while making the way for further research, such as analyzing more complex applications and energy efficiency.

In recent years, we have seen a shift in the way we search, collect and verify knowledge on the Internet. Instead of the traditional way of typing questions into a web browser and selecting the appropriate answer, users are increasingly turning to chatbots to answer their questions. The answers provided by a generative chatbot are not always adequate and therefore it is important to use those chatbots that have a predefined set of knowledge that is used to get answers. In this paper, we present the results of the application of educational chatbots in different subjects studied in different study curriculums and at different universities.

This work looks into the utilization of blockchain technology within the telecommunications sector, emphasizing enhancements in security, privacy, and efficiency of data management. The “TelecomSecurity” smart contract, demonstrates blockchain’s features of decentralization and immutability, enabling robust user data protection, transparent identity management, and process automation. The paper focuses on protection mechanisms and resource optimization, showcasing detailed metrics of performance and gas consumption compared to traditional environments like Python and Flask. Additionally, it includes an analysis of the study “Blockchain technology empowers telecom network operation” by Dinh C. Nguyen, Pubudu N. Pathirana, and Ming Ding, published in IEEE Communications Magazine 2020, to discuss the blockchain’s potential to enhance operations within telecom networks, especially when integrating 5G technologies. The research establishes parallels between theoretical insights and practical findings, underscoring the blockchain’s relevance and use cases in real-world telecom scenarios. It also discusses potential applications in 5G networks and IoT devices, positioning blockchain as a transformative technology for the digital age, enhancing security, lowering costs, and improving operational efficiency. More specifically, this study explores how blockchain-based decentralized user management and smart contract automation can enhance telecom service agreements, reducing reliance on centralized authorities while improving transparency and operational efficiency.

This study investigates the influence of weather conditions and traffic flow dynamics on parcel delivery times. The main goal is to identify the factors contributing to delivery delays, which will help pinpoint key aspects that can improve logistics processes and enhance delivery accuracy. The Kruskal-Wallis test and Dunn’s post hoc analysis examined variations across data groups categorized by weather conditions (e.g., rain, snow, fog, storms) and traffic flow states (e.g., dense, congested, free-flowing, unobstructed). Additionally, a comparative approach was employed to assess the effects of different weather and traffic conditions. The results show that adverse weather conditions, such as rain and snow, combined with high traffic congestion, significantly increase delivery times compared to clear weather and normal or free-flowing traffic conditions. These findings offer valuable insights for future research and the optimization of logistics operations.

Abstract Background Phenotypic plasticity and inflammation, 2 well-established hallmarks of cancer, play key roles in local invasion and distant metastasis by enabling the rapid adaptation of tumor cells to dynamic micro-environmental changes. Results Here, we show that in oral squamous carcinoma cell carcinoma (OSCC), the competition between the Nucleosome Remodeling and Deacetylase (NuRD) and SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin remodeling complexes plays a pivotal role in regulating both epithelial-mesenchymal plasticity (EMP) and inflammation. By perturbing these complexes, we demonstrated their opposing downstream effects on the inflammatory pathways and EMP regulation. In particular, downregulation of the BRG1-specific SWI/SNF complex deregulates key inflammatory genes, such as TNF-α and IL6, in opposite ways when compared with the loss of CDK2AP1, a key member of the NuRD complex. We showed that CDK2AP1 genetic ablation triggers a pro-inflammatory secretome encompassing several chemokines and cytokines, thus promoting the recruitment of monocytes into the tumor microenvironment (TME). Furthermore, CDK2AP1 deletion stimulates their differentiation into M2-like macrophages, as validated on tumor microarrays from OSCC patient-derived tumor samples. Further analysis of the inverse correlation between CDK2AP1 expression and TME immune infiltration revealed specific downstream effects on the abundance and localization of CD68+ macrophages. Conclusions Our study sheds light on the role of chromatin remodeling complexes in OSCC locoregional invasion and highlights the potential of CDK2AP1 and other members of NuRD and SWI/SNF chromatin remodeling complexes as prognostic markers and therapeutic targets.

According to estimates by the United Nations' International Organization for Migration, in 2020 the global count of international migrants reached 281 million, nearly doubling the estimate from 1990. While a significant portion of emigration can be attributed to wars and conflicts, less developed countries have witnessed a surge in outward migration over the past few decades, extending beyond forced emigration. Among these migrants there is a considerable number of young, skilled, and educated individuals, whose departure has unfortunate effects on their countries of origin, impacting economic progress and demographics. The level of country development significantly influences migration, as migrants often move from less developed to more developed countries in search of better living conditions and more opportunities.This paper aims to identify the primary determinants of global migration movement between years 1990 and 2022, with a focus on evaluating the impact of country development level disparities on these flows. According to our static and dynamic estimation results the level of development is a significant driver of emigration while higher GDP per capita is associated with lower net emigration. These results imply that policies aimed at reducing migration pressures should focus on fostering economic development and increasing GDP per capita in low-income countries.