The fluent API, also known as Internal DSL, is one of the concepts introduced primarily for the purpose of increasing readability and maintainability in the process of software development. It is most commonly used when there is a need to perform operations according to precisely defined rules that determine their possible orders. However, implementing a Fluent API by manual coding can divert focus from defining these rules toward technical implementation, increasing the risk of errors, unexpected behavior, and higher development costs. To address these challenges, a model-driven engineering (MDE) approach can be applied, enabling the visual design of the Fluent API model and its transformation into a code skeleton.This paper aims to present how the previously created graphical development tool, implemented as a Microsoft Visual Studio extension for modeling Fluent API, can be enhanced with model-to-text transformation in order to provide code generation of a fluent API structure. That objective is achieved by using the template-based code generation technique, implemented by enabling the execution of the appropriate T4 text templates. The proposed approach is validated by demonstrating a real-life fluent API example's code generation from its model, resulting in C# source files that contain classes, interfaces, and their corresponding methods.

In modern medical circumstances, effective assessment of patients' conditions is recognized as crucial for quick decision-making, especially in critical situations. In these circumstances, the application of automated triage systems and their role in improving health care are considered key elements, with special focus being placed on the integration of technologies that enable a more accurate and faster assessment of the patient's condition. Based on the above, the paper analyzed various traditional methods of patient triage, as well as the potential for e-triage. Special importance is attached to reducing subjectivity in decision-making and improving the efficiency of emergency services. Challenges and advantages of implementing automated triage systems in real conditions were also discussed, with the aim of achieving optimal results. Considering the factors, a one of possible framework was proposed for the future development of advanced triage systems, which contribute to the improvement of the quality of health care provision.

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.

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.

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.

The architecture of a Deep Neural Network (DNN) plays a major role in determining its performance, yet the traditional methods for optimizing these architectures often depend on iterative trial-and-error processes requiring substantial expertise and manual effort. Neural Architecture Search (NAS) has emerged as a rapidly advancing field focused on automating the optimization of hyperparameters and network architectures. This study presents a comparative analysis of three heuristic approaches for NAS: Evolutionary Genetic Algorithms, Reinforcement Learning, and Random Forest Optimization. The efficacy of these methods is evaluated on two widely recognized benchmark classification datasets—MNIST and CREDIT CARD FRAUD—as well as a synthetically generated dataset. A comprehensive evaluation of performance metrics provides insight into the strengths, limitations, and relative effectiveness of each NAS methodology in optimizing neural network architectures for diverse data distributions.

The neural network training process produces black-box models with low explainability. In addition, the process itself is numerical, with parameters (such as learning rate, momentum, and early stopping trigger) being chosen ad hoc. During the training with chosen parameters, after each calculated update of the weights, the observed total change of weights indicates which training stage the network is currently in. At the same time, neural networks are limited in the data they can model due to various reasons, such as architecture, activation functions, data itself, and the training approach. This limitation is expressed in the phenomenon of the efficient computational frontier, which, it seems, cannot be crossed, no matter the hyperparameters of the network. This paper tackles the efficient usage of information regarding the total change of weights and the efficient computational frontier to determine when the training should be stopped. The results demonstrate the efficiency of training of simpler models compared to more complex models and prove that the general weight structure of models is formed very quickly in the training, while the forming of finer details takes up much more time.

"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."

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.

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.

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."

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.

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.

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.