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.

Aim of study: Practically and simply assessing biodiversity by using inventory variables in four types of forest plantation stands (mixed and pure) including species such are chestnut, blue gum and maritime pine. Area of study: Northwest Portugal in Vale do Sousa (14,840 ha), which is 97% covered with plantation forests. Material and methods: Simulated data, from 90-year stand-level forest management planning, were considered using three indicators: tree species (number of different species and species origin—native or exotic), mean diameter at breast height (DBH), and shrub biomass. Two shrub regeneration types (fully regeneration by seed and fully regeneration by resprouting), and three site quality conditions were also considered. Main results: Mean biodiversity scores varied between very low (10.13) in pure blue gum stands on lowest-quality sites with shrub regeneration by seed, and low (29.85) in mixed stands with a dominance of pine, on best-quality sites with shrub regeneration by resprouting. Site quality and shrub regeneration type significantly affected all biodiversity scores in mixed stands dominated by pine and pure chestnut stands, while less affected pure blue gum stands and mixed stands dominated by blue gum. Research highlights: The considered biodiversity indicators cover the major biodiversity aspects and allow biodiversity assessment over time. The findings are relevant for biodiversity conservation and fire protection management.

The possibilities of using artificial neural networks (ANNs) for quick decision-making in the events of prolonged surges are presented in this paper considering that neural networks can establish non-linear relationships between the parameters of an ultra-high voltage transmission line. Research has been carried out based on theoretical models as well as practical problems aiming at the analysis of resonant overvoltages during their occurrence, development and existence. Determining of overvoltage characteristics was carried out in the presence of a significant number of fuzzy specified factors affecting the accuracy. The multilayer model, suitable for identifying the factors having the greatest impact on the occurrence, frequency and multiplicity of overvoltages in electrical networks, is applied. The resonant overvoltages were generated by connecting the autotransformer to the electrical bulk network. The results of determining the characteristics of resonant overvoltages using ANNs are presented in this paper. To achieve this goal, the following four tasks were formulated: (i) overvoltage characteristics using neural network methods were determined, (ii) neural network model corresponding to power line initial data was built, (iii) forecasted results were obtained, and (iv) the accuracy of constructed model was evaluated.

The Church of the Holy Archangels Michael and Gabriel located in Sarajevo is a national monument belonging to Eastern Orthodox cultural heritage. It is a very well-preserved sacral object considering the date of first mention is 1539 and it has been used to date for the religious purposes. On the other hand, deterioration of aging historical/religious buildings is inevitable process composed of cumulative, progressive and nonlinear factors. Hence, in order to maintain their best condition for as long as possible compliance with guidelines and procedures for cultural heritage preservation is needed. Climate control within historical/religious buildings surfaced as an important research area as indoor climate is changing in recent times. Humans have always shaped their environment by desire to enjoy concurrently the comfort of modern living as well as preserve the monuments for future generations. For example, use of heating systems in historical/religious buildings are creating new challenges for their preservation. This paper is an attempt towards the implementation of Internet of Things (IoT) system with focus on preservation of the national monument using a simulation of climate control in the Church of the Holy Archangels Michael and Gabriel.

Europe, with all its common sights, has an enviable wealth of history and cultural heritage. With its many monuments, sites, traditions, history, art, and culture, it has always attracted curious views and tells centuries-old stories to many tourists and visitors. At the heart of Europe, Bosnia and Herzegovina (BiH), founded in the 11th century, with its picturesque past, has always been at the crossroads of faith and civilizations. The key audience of tourism in BiH are nature lovers, adventurers and young and digital nomads, who represent great potential for the development of this sector given their nature of work, to be able to work from any location, and during the COVID-19 period. Furthermore, the importance of the diaspora for the development of tourism in BiH goes beyond tourist visits and helps BiH on its path to digital transformation. Digital tourism refers to how we use digital tools to organize, manage and even enjoy the travel experience. It uses all of the tools of digital transformation to change how we travel and experience different sites. Through digitalization, we want to reach every individual who passes through this country and further attract lovers of European history and culture, offer them a different, more creative, and innovative approach to learning about the cultural and historical treasures it hides. The goal of digital tourism is to raise awareness of the importance of cultural heritage, provide new opportunities for visitors and bring new knowledge. Therefore, this paper provides an overview of the possibilities of digital representations of the medieval historical period of BiH through identified pillars of digital reconstruction, and ways to connect the movable cultural heritage residing in the museums with real sites in an attempt to contribute to its promotion through digital tourism. © 2022 Copyright for this paper by its authors.

Every year installed capacity of renewable energy sources in the World and Ukraine increases. This paper presents a method of determining of technical condition of the photovoltaic model (PVM) with the usage of neuro-fuzzy modeling. The relevance of the transition from traditional to renewable energy sources (RES) is investigated in the article. The most popular RESs for Ukraine and the world are highlighted. The tendency of change of electricity generation by photovoltaic stations is analyzed. Peculiarities in functioning of the electric network employing RES are considered.The optimality criterion components of the power system (PS) normal mode with high level of photovoltaic power plants integration is presented. Technical condition of the PVM was estimated by means of residual resource coefficient. PVM residual resource coefficient which considers the values of all diagnostic parameters was determined using ANFIS library in MATLAB.