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Pet kompleksnih indeksa (PLI, RI, mERMq, TRI, CSI) određeno je za svaku od 40 lokacija tokom perioda istraživanja (2005, 2007, 2013, 2019) uzimajući u obzir osam teških metala. Primenom klasterske analize (CA) i analize glavnih komponenti (PCA) na vrednosti indeksa zagađenja, izvršeno je prostorno i vremensko poređenje kvaliteta sedimenta. Rezultati statističke analize pokazali su povišen stepen kontaminacije sedimenta u 2005. godini u Kotorskom i Tivatskom zalivu, dok je u 2007., 2013., i 2019. godini bio u Tivatskom zalivu. PCA, CA i indeksi zagađenja ukazali su na povećanje nivoa zagađenja sedimenta u Tivatskom zalivu u periodu od 2005. do 2019.

Background and Aims In the global distribution of persistent organic pollutants (POPs) forests play a key role due to the forest canopy efect. In this study, mountain beech forests were selected to identify the level and distribution of selected POPs. Methods Organochlorine pesticides (OCPs) and polybrominated diphenyl ethers (PBDEs) were ana lyzed by capillary gas chromatography with an elec tron capture detector (GC–ECD). Analysis of poly chlorinated biphenyls (PCBs) was performed by gas chromatography with a mass spectrometry detector (GC–MS). The polycyclic aromatic hydrocarbons (PAHs) were analyzed by liquid chromatography with a diode array detector (HPLC–DAD). Results The total ∑20OCP concentrations in the soil from mountain beech forests at a depth of 0 – 40 cm ranged from 24.46 µg kg−1 to 331.11 µg kg−1, and at a depth of 40 – 80 cm from 15.80 µg kg−1 to 247.12 µg kg−1. The mass concentration for ∑6PCB ranges from 9.21 to 95.28 µg kg−1 in the surface soil layer and from 8.07 to 66.56 µg kg−1 in the soil sub layer. The total concentration of ∑7PBDE congeners measured in soil samples from mountain beech forests across Europe was from 1.38 to 29.68 µg kg−1 in the surface soil layer and from 1.01 to 53.31 µg kg−1 in the soil sublayer. The total concentration of ∑16PAH varied from 271.52 to 1154.52 µg kg−1 in the surface soil layer and from 318.95 to 1052.54 µg kg−1 in the soil sublayer. Conclusion The results of this study can serve as an important basis for international, national, and regional measures of environmental protection of beech forests from POPs pollution

Purpose To anticipate the impact of illegal landfills, development of new models should become a part of environmental risk management strategies. One of such approaches includes applications of the artificial neural network (ANN). The main objective of this study was to elucidate the impact of illegal landfilling on the surrounding soil environment and human health, as well as to establish an artificial neural network (ANN) models for predicting the hazards of illegal landfilling as an effective tool in decision-making and environmental risk management. Methods The identification of heavy metals source in soil was performed by principal component analysis (PCA). To assess the sensitivity of the soil ecosystem to heavy metal concentrations, Soil Quality standards and quantitative indices were used. The possible health effects were valued using the average daily doses (ADDs), hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR). ANN modeling was used for the prediction of heavy metal concentrations in the soil based on landfill size, municipality size, the number of residents, plant species, soil, and landform types. Results The average values of the pollution indexes for Cd were in the moderately contaminated and very high contamina tion categories. The HQ values were lower than the safe level. Cr and Pb posed a significant CR for adults and children, and Ni for children. The ANN models have exhibited good generalization power and accurately predicted the output parameters with a high value of the coefficient of determination. Conclusion Concerning heavy metal concentrations, illegal landfills near agricultural soil have a significant impact on the soil ecosystem and people’s health. The developed ANN models can be applied generally to anticipate the heavy metal concentrations in soil, according to the before mentioned input parameters, with high accuracy.

The main objective of this study is to determine the possibility of predicting the impact of land use and soil type on concentrations of heavy metals (HMs) and phthalates (PAEs) in soil based on an artificial neural network model (ANN). Qualitative analysis of HMs was performed with inductively coupled plasma-optical emission spectrometry (ICP/OES) and Direct Mercury Analyzer. Determination of PAEs was performed with gas chromatography (GC) coupled with a single quadrupole mass spectrometry (MS). An ANN, based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) iterative algorithm, for the prediction of HM and PAE concentrations, based on land use and soil type parameters, showed good prediction capabilities (the coefficient of determination (r(2)) values during the training cycle for HM concentration variables were 0.895, 0.927, 0.885, 0.813, 0.883, 0.917, 0.931, and 0.883, respectively, and for PAEs, the concentration variables were 0.950, 0.974, 0.958, 0.974, and 0.943, respectively). The results of this study indicate that HM and PAE concentrations, based on land use and soil type, can be predicted using ANN.

Sprouted seeds and microgreens are emerging as innovative specialty raw salad crops, valued for their health-promoting properties. However, the risk of foodborne illnesses associated with microbial contamination in microgreens underscores the need for effective sanitization in their production. This study explores using hydrolates, by-products of essential oil distillation from six plants, as natural sanitizers in alfalfa microgreen production. The research investigates their impact on seed germination, antimicrobial activity, and sensory attributes. Results revealed that oregano, fennel, lavender, and lemon catmint hydrolates decrease alfalfa seed germination, while peppermint and hop hydrolates have no significant impact. Peppermint and fennel hydrolates demonstrate notable efficacy in antimicrobial testing. Sensory analysis indicates differences in odour and flavour, with peppermint, oregano, and lavender receiving high scores. According to the results, peppermint hydrolate can be considered a favourable option for alfalfa micro sprout production, contributing to sustainable and organic approaches in urban agriculture and underlining the importance of natural sanitizers for food safety.

The aim of this study was to find the chemical parameters for the differentiation of plum cultivars grown along the fjord areas of Western Norway and Eastern Norway, having specific agroclimatic conditions. Chemical analysis of the fruits confirmed the contents of 13 quantified elements, 22 sugar compounds, 11 organic acids, 19 phenolic compounds, and antioxidant activity in 68 plum cultivars. Dominated contents were noted for nitrogen (with the maximum mean value of 3.11%), potassium (8055.80 mg/kg), and phosphorous (7878.88 mg/kg). Averagely, the highest level of sugars was determined for glucose (244.46 g/kg), fructose (197.92 g/kg), sucrose (208.25 g/kg), and sorbitol (98.02 g/kg), organic acids for malic acid (24.06 g/kg), and for polyphenol compounds were 5-O-caffeoylquinic acid (66.31 mg/kg), and rutin (58.06 mg/kg). Applied principal component analysis has been useful for distinguishing the plum cultivars from three areas in Norway where copper, iron, potassium, magnesium, manganese, and sodium; sucrose, ribose, maltose, and raffinose; p-hydroxybenzoic acid, rutin, ferulic acid, kaempferol 7-O-glucoside, p-coumaric acid, and 5-Ocaffeoylquinic acid were the most influential. In regard to human health and future breeding work that will have the aim to produce functional food with high health-related compounds, the plum cultivar ‘Mallard’ should be underlined due to the high level of elements, ‘Valor’ due to high sugar content, ‘Helgøyplomme’ due to content of organic acids, and ‘Diamond’ due to the content of phenolic compounds.

The machine learning technique for prediction and optimization of building material performances became an essential feature in the contemporary civil engineering. The Artificial Neural Network (ANN) prognosis of mortar behavior was conducted in this study. The model appraised the design and characteristics of seventeen either building or high-temperature mortars. Seven different cement types were employed. Seventeen mineral additives of primary and secondary origin were embedded in the mortar mixtures. Cluster Analysis and Principal Component Analysis designated groups of similar mortars assigning them a specific purpose based on monitored characteristics. ANN foresaw the quality of designed mortars. The impact of implemented raw materials on the mortar quality was assessed and evaluated. ANN outputs highlighted the high suitability level of anticipation, i.e., 0.999 during the training period, which is regarded appropriate enough to correctly predict the observed outputs in a wide range of processing parameters. Due to the high predictive accuracy, ANN can replace or be used in combination with standard destructive tests thereby saving the construction industry time, resources, and capital. Good performances of altered cement mortars are positive sign for widening of economical mineral additives application in building materials and making progress towards achieved carbon neutrality by reducing its emission.

The purpose of the study was to identify and predict the optimized parameters for phosphoric acid production. This involved modeling the crystal reactor, UCEGO filter (as a detailed model of the filter is not available in Aspen Plus or other simulation software), and acid separator using Sci-Lab to develop Cape-Open models. The simulation was conducted using Aspen Plus and involved analyzing 10 different phosphates with varying qualities and fractions of P2O5 and other minerals. After a successful simulation, a sensitivity analysis was conducted by varying parameters such as capacity, filter speed, vacuum, particle size, water temperature for washing the filtration cake, flow of recycled acid and strong acid from the separator below the filter, flow of slurry to reactor 1, temperature in reactors, and flow of H2SO4, resulting in nearly one million combinations. To create an algorithm for predicting process parameters and the maximal extent of recovering H3PO4 from slurry, ANN models were developed with a determination coefficient of 99%. Multi-objective optimization was then performed using a genetic algorithm to find the most suitable parameters that would lead to a higher reaction degree (96–97%) and quantity of separated H3PO4 and lower losses of gypsum. The results indicated that it is possible to predict the influence of process parameters on the quality of produced acid and minimize losses during production. The developed model was confirmed to be viable when compared to results found in the literature.

Road traffic accidents are a significant public health issue, accounting for almost 1.3 million deaths worldwide annually, with millions more experiencing non-fatal injuries. A variety of subjective and objective factors contribute to the occurrence of traffic accidents, making it difficult to predict and prevent them on new road sections. Artificial neural networks (ANN) have demonstrated their effectiveness in predicting traffic accidents using limited data sets. This study presents two ANN models to predict traffic accidents on common roads in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors that can be easily determined, such as road length, terrain type, road width, average daily traffic volume, and speed limit. The models predict the number of traffic accidents, as well as the severity of their consequences, including fatalities, injuries and property damage. The developed optimal neural network models showed good generalization capabilities for the collected data foresee, and could be used to accurately predict the observed outputs, based on the input parameters. The highest values of r2 for developed models ANN1 and ANN2 were 0.986, 0.988, and 0.977, and 0.990, 0.969, and 0.990, accordingly, for training, testing and validation cycles. Identifying the most influential factors can assist in improving road safety and reducing the number of accidents. Overall, this research highlights the potential of ANN in predicting traffic accidents and supporting decision-making in transportation planning.

With the goal of enhancing the quality of the environment, urban green infrastructure (UGI) is an essential element in sustainable cities, and nature-based solutions (NBS) are being carried out as new infrastructure solutions that increase the resilience of cities. In this research, the method of theoretical analysis and the content analysis as the basic fact-gathering technique was applied to answer to following questions: What are the hindrances and bottlenecks in implementing NBS? Are the current decision-making mechanisms helping NBS get in route to shape cities? Is there any binding policy in practice that promotes NBS? In Belgrade is planned Type 3 of the degree of intervention/level and engineering type-Creation and new ecosystem management in the classifications of intensive urban green space management; urban planning strategies; urban water management; ecological restoration of degraded terrestrial ecosystems; and restoration and creation of semi-natural water bodies and hydrographic networks. In the future, it is essential to implement policies and incentives on national, regional, and local scales that help encourage the usage of NBS in the development of urban infrastructure.

The aim of this study was to find the chemical parameters for the differentiation of plum cultivars grown along the fjord areas of Western Norway and Eastern Norway, having specific agroclimatic conditions. Chemical analysis of the fruits confirmed the contents of 13 quantified elements, 22 sugar compounds, 11 organic acids, 19 phenolic compounds, and antioxidant activity in 68 plum cultivars. Dominated contents were noted for nitrogen (with the maximum mean value of 3.11%), potassium (8055.80 mg/kg), and phosphorous (7878.88 mg/kg). Averagely, the highest level of sugars was determined for glucose (244.46 g/kg), fructose (197.92 g/kg), sucrose (208.25 g/kg), and sorbitol (98.02 g/kg), organic acids for malic acid (24.06 g/kg), and for polyphenol compounds were 5-O-caffeoylquinic acid (66.31 mg/kg), and rutin (58.06 mg/kg). Applied principal component analysis has been useful for distinguishing the plum cultivars from three areas in Norway where copper, iron, potassium, magnesium, manganese, and sodium; sucrose, ribose, maltose, and raffinose; p-hydroxybenzoic acid, rutin, ferulic acid, kaempferol 7-O-glucoside, p-coumaric acid, and 5-O-caffeoylquinic acid were the most influential. In regard to human health and future breeding work that will have the aim to produce functional food with high health-related compounds, the plum cultivar ‘Mallard’ should be underlined due to the high level of elements, ‘Valor’ due to high sugar content, ‘Helgøyplomme’ due to content of organic acids, and ‘Diamond’ due to the content of phenolic compounds. © 2023 by the authors.

Stinging nettle (Urtica dioica L.) is one fantastic plant widely used in folk medicine, pharmacy, cosmetics, and food. This plant’s popularity may be explained by its chemical composition, containing a wide range of compounds significant for human health and diet. This study aimed to investigate extracts of exhausted stinging nettle leaves after supercritical fluid extraction obtained using ultrasound and microwave techniques. Extracts were analyzed to obtain insight into the chemical composition and biological activity. These extracts were shown to be more potent than those of previously untreated leaves. The principal component analysis was applied as a pattern recognition tool to visualize the antioxidant capacity and cytotoxic activity of extract obtained from exhausted stinging nettle leaves. An artificial neural network model is presented for the prediction of the antioxidant activity of samples according to polyphenolic profile data, showing a suitable anticipation property (the r2 value during the training cycle for output variables was 0.999).

The problem of microbial biofilms has come to the fore alongside food, pharmaceutical, and healthcare industrialization. The development of new antibiofilm products has become urgent, but it includes bioprospecting and is time and money-consuming. Contemporary efforts are directed at the pursuit of effective compounds of natural origin, also known as “green” agents. Mushrooms appear to be a possible new source of antibiofilm compounds, as has been demonstrated recently. The existing modeling methods are directed toward predicting bacterial biofilm formation, not in the presence of antibiofilm materials. Moreover, the modeling is almost exclusively targeted at biofilms in healthcare, while modeling related to the food industry remains under-researched. The present study applied an Artificial Neural Network (ANN) model to analyze the anti-adhesion and anti-biofilm-forming effects of 40 extracts from 20 mushroom species against two very important food-borne bacterial species for food and food-related industries—Listeria monocytogenes and Salmonella enteritidis. The models developed in this study exhibited high prediction quality, as indicated by high r2 values during the training cycle. The best fit between the modeled and measured values was observed for the inhibition of adhesion. This study provides a valuable contribution to the field, supporting industrial settings during the initial stage of biofilm formation, when these communities are the most vulnerable, and promoting innovative and improved safety management. © 2023 by the authors.

The main idea of the present study was to investigate the impact of the ongoing world crisis on the socio-economic issues in Serbia concerning building materials usage and purchase. This research fills in the gaps in the literature and contributes to the comprehension of how the crisis has affected salaries, market pricing, and materials consumption in the building sector. The data are gathered through a questionnaire and analyzed using a statistical methodology (frequencies, descriptive statistics, and Spearman’s correlations). Most of the former studies investigated the surveys conducted on specialists in the field, while this study analyzed the perspectives of random people. Socio-demographic issues are analyzed along with materials consumption before and after the crisis. A special emphasis is given to ecological awareness and novel materials usage. Additionally, it captures a broad shift in the economy and ecological consciousness in a developing country. The majority of respondents are open to using novel building materials and products, but their choice would largely be influenced by cost, the amount of effort involved, and their understanding of the advantages. Statistical approaches revealed that the crisis has a considerable impact on the markets for construction and building supplies, altering consumers’ decisions when purchasing. This contribution lays the groundwork for developing countries in the modern world to improve sustainability and adopt circular thinking. Professionals in Serbia need to have a more eco-aware mindset and enhance how they provide pertinent information to potential clients. This study is limited by the number of respondents. For future mathematical modeling and forecasting, more answerers are needed.

The main idea of the present study was to investigate the impact of the ongoing world crisis on the socio-economic issues in Serbia concerning building materials usage and purchase. This research fills in the gaps in the literature and contributes to the comprehension of how the crisis has affected salaries, market pricing, and materials consumption in the building sector. The data are gathered through a questionnaire and analyzed using a statistical methodology (frequencies, descriptive statistics, and Spearman’s correlations). Most of the former studies investigated the surveys conducted on specialists in the field, while this study analyzed the perspectives of random people. Socio-demographic issues are analyzed along with materials consumption before and after the crisis. A special emphasis is given to ecological awareness and novel materials usage. Additionally, it captures a broad shift in the economy and ecological consciousness in a developing country. The majority of respondents are open to using novel building materials and products, but their choice would largely be influenced by cost, the amount of effort involved, and their understanding of the advantages. Statistical approaches revealed that the crisis has a considerable impact on the markets for construction and building supplies, altering consumers’ decisions when purchasing. This contribution lays the groundwork for developing countries in the modern world to improve sustainability and adopt circular thinking. Professionals in Serbia need to have a more eco-aware mindset and enhance how they provide pertinent information to potential clients. This study is limited by the number of respondents. For future mathematical modeling and forecasting, more answerers are needed.

The main challenge in controlling the microbiological contamination of historical paper is finding an adequate method that includes the use of cost-effective, harmless, and non-toxic biocides whose effectiveness is maintained over time and without adverse effects on cultural heritage and human health. Therefore, this study demonstrated the possibility of using a non-invasive method of historical paper conservation based on plant essential oils (EOs) application. Evaluation of antimicrobial effects of different EOs (lemongrass, oregano, rosemary, peppermint, and eucalyptus) was conducted against Cladosporium cladosporoides, Aspergillus fumigatus, and Penicillium chrysogenum, which are commonly found on archive papers. Using a mixture of oregano, lemongrass and peppermint in ratio 1:1:1, the lower minimal inhibition concentration (0.78%) and better efficiency during a vapour test at the highest tested distance (5.5 cm) compared with individual EOs was proven. At the final step, this EOs mixture was used in the in situ conservation of historical paper samples obtained from the Archives of Vojvodina. According to the SEM imaging, the applied EOs mixture demonstrates complete efficiency in the inhibition of fungi colonization of archive papers, since fungal growth was not observed on samples, unlike the control samples.

This article describes still insufficiently known technology of pot marigold cultivation with the compost produced from the organic waste of the processing of medicinal plants. For the first time the application was analyzed of different amounts of compost (control – without compost, 2, 10 and 30 kg/m2 ) on the morphological, productive and qualitative parameters of two pot marigold varieties (Domaći oranž and Plamen Plus). During the five-year period, the best results in both tested pot marigold varieties were achieved with the 30 kg/m2 compost application. The yield of dry flower was higher for the Domaći oranž pot marigold variety fertilized with 30 kg/m2 compost (1957.4 kg/ha) compared with the Plamen Plus variety (451.1 kg/ha). A significantly higher fresh flower yield of the Domaći oranž variety greatly influenced the increase in the quantities of examined quality parameters (total carotenoids, total phenolic, total flavonoids, and DPPH reduction), whose content was higher in the Plamen Plus variety. The artificial neural network model, was built applying the Broyden-FletcherGoldfarb-Shanno algorithm, exerted the adequate forecasting abilities for the productivity and quality of pot marigold flowers and the influence of compost material, produced from medicinal plants waste (R2 was 0.837 for the training period). This research demonstrates that it is possible to use organic waste obtained in the processing of medicinal plants, supporting the effectiveness of a circular economy model in the cultivation of pot marigold.