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 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.

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.

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.

This paper proposes power generation forecasting for photovoltaic power plants by using Adaptive Neuro-Fuzzy Inference Systems library in MATLAB and considering meteorological factors. Renewable energy sources (RES) introduce compensation instability problems in the grid hence forecasting methods are considered. Especially important for grid operators is a day ahead forecasting as it can reduce negative imbalance price. Means of ensuring the balance reliability of the power system in terms of RES integration are presented. The installation of charging stations for electric vehicles or use of hydrogen technologies and modern storage systems can provide grid balance. In addition, decreasing the deviation of the current (real) value from the predicted value of power generation is a way to compensate for power unbalance.

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.

This paper develops a Generalized Linear Model using the Negative Binomial Regression with log link function to analyze the effects of mobility trends and seasons on COVID-19 cases. The data of four European countries was used, namely Austria, Greece, Italy, and Czech Republic. The dataset includes daily observations of registered COVID-19 cases, and the data of six types of mobility trends: retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential mobility for the period Feb 15 - Nov 15, 2020. The results suggest that the number of COVID-19 cases differs between seasons and different mobility trends.

Artificial intelligence has been used in the medical field for many years finding its way in assisting medical professionals to solve complex tasks in almost every application domain. Patient care and health care costs are in the forefront of any medical system. With the emergence of electronic games (EG), originally entertaining in nature, potential was seen for their application in the medical field in terms of exploring the capabilities of educating patients in the areas of need. Design of serious games emerged with a focus on helping with the patient’s therapeutic modality. Some of the medical field serious games are part of the daily work of a medical professional. With the development of mobile devices, this type of gaming has also become available to the wider population. The aim of this research was to demonstrate the possibilities and benefits of using EG in the daily clinical work of medical professionals. This work has a descriptive character where a section of the literature is given outlining the increased research interest in using serious games as therapy enhancements. We limited ourselves to research involving patients who need medical treatment or intervention.

Including biodiversity assessments in forest management planning is becoming increasingly important due to the importance of biodiversity for forest ecosystem resilience provision and sustainable functioning. Here we investigated the potential to include biodiversity indicators into forest management planning in Europe. In particular, we aimed to (i) identify biodiversity indicators and data collection methods for biodiversity assessments at the stand and landscape levels, and (ii) evaluate the practicality of those indicators for forest management planning. We performed a literature review in which we screened 188 research studies published between 1990 and 2020. We selected 94 studies that fulfilled the inclusion criteria and examined in more detail. We considered three aspects of biodiversity: structure, composition, and function, and four forest management categories: unmanaged, managed, plantation, and silvopastoral. We used three criteria to evaluate the practicality of forest biodiversity indicators: cost-effectiveness, ease of application, and time-effectiveness. We identified differences in the practicality of biodiversity indicators for their incorporation into management plans. Stand-level indicators are more practical than landscape-level indicators. Moreover, structural biodiversity indicators (e.g., large trees, canopy openness, and old forest stands) are more useful in management plans than compositional indicators, as these are easily observable by non-professionals and can be obtained by forest inventories. Compositional indicators such are vascular plants, fungi, bryophyte, lichens, and invertebrate species are hard to identify by non-professionals and thus are impractical. Functional indicators (e.g., nutrient cycling) are not sufficiently addressed in the literature. Using recently updated existing databases (e.g., national forest inventories and bird atlases) is very time and cost-efficient. Remote sensing and other technology (e.g., smartphone applications) are promising for efficient data collection in the future. However, more research is needed to make these tools more accurate and applicable to a variety of ecological conditions and scales. Until then, forest stand structural variables derived from inventories can help improve management plans to prepare European forests towards an uncertain future.