Search Results (38)

Biosecurity plays a central role in preventing disease transmission in dairy production systems and animal welfare. However, quantitative data on biosecurity implementation in smallholder and medium-scale dairy farms remains inconsistent, especially in developing countries. This study provides a structured assessment of on-farm biosecurity practices in northern Kosovo using a standardized, risk-based scoring approach. A cross-sectional survey was conducted on 55 dairy farms using the unmodified Biocheck.UGent™ dairy questionnaire. External and internal biosecurity scores were calculated through predefined, weighted algorithms and analyzed using non-parametric descriptive statistics. Farm-level results were subsequently compared with international reference values derived from the Biocheck.UGent™ global database. The median biosecurity scores for Kosovo farms were 47.8% for external biosecurity and 29.0% for internal biosecurity, indicating uneven implementation with pronounced weaknesses in measures designed to limit within-herd transmission. The lowest-scoring domains were purchase and reproduction and feed and water within external biosecurity, and working organization and equipment, calf management, and calving management within internal biosecurity. In contrast, visitors and farmworkers, control of vermin and other animals among external measures, and adult cattle management among internal measures, showed relatively higher scores, although all remained below international reference levels. When compared with the global overall biosecurity reference median of 76.7% derived from the Biocheck.UGent™ database, the biosecurity performance of the surveyed dairy farms in Kosovo was substantially lower. This gap does not indicate a complete absence of biosecurity measures but rather an uneven application, with the most pronounced deficiency observed in routine practices that govern within-herd disease transmission. The use of a risk-based scoring system allowed these weaknesses to be identified in a structured manner and placed the Kosovo results within an international benchmarking framework. In this context, the approach functions as a practical diagnostic tool, enabling farmers and veterinarians to prioritize feasible, epidemiological-relevant improvements within small- and medium-scale dairy production settings. Full article

Food waste remains a global challenge, particularly among younger generations. This study examines the attitudes and behaviours of 330 Generation Z individuals (aged 18–24 years) from Italy, Estonia, Croatia, Romania, and Serbia using an extended Theory of Planned Behaviour (TPB). The TPB model was expanded to include moral social values, awareness of health risks, and good provider identity. A mixed-methods approach was applied, combining 7-day food waste diaries, visual plate-waste analysis, and self-administered questionnaires. Food recognition analysis showed that Estonian participants wasted less food per meal (3.43%) than those from Italy, Serbia, Croatia, and Romania (12.53%, 12.57%, 14.53%, and 17.18%). Nationality-specific patterns emerged: Romanians mainly discarded meat and potatoes, while participants from Estonia, Croatia, and Serbia wasted fruit and vegetables; Italians most frequently wasted fish and dairy. The extended TPB effectively predicted intentions to reduce food waste, identifying key behavioural determinants that can inform targeted interventions for young consumers. Full article

Hazelnut (Corylus avellana L.) shells, typically discarded as agro-industrial by-products, represent a potentially valuable source of bioactive polyphenolic compounds with significant antioxidant properties. This study aimed to evaluate and compare the polyphenol composition and antioxidant capacity of the kernels and shells of two hazelnut varieties, ‘Rimski’ and ‘Istarski duguljasti’. High-intensity ultrasound-assisted extraction (UAE) was applied to enhance the recovery of bioactive compounds under optimized conditions (80% ethanol, high amplitude, and 25 min treatment). The extracts were analyzed for total polyphenols, total flavonoids, total non-flavonoids, and individual phenolic compounds. Hazelnut shells exhibited significantly higher levels of total polyphenols, flavonoids, and antioxidant capacity compared to kernels. The dominant individual polyphenolic compounds identified in the shell were kaempferol, gallic acid, naringin, rutin trihydrate, quercetin-3-glucoside, chlorogenic acid, quercetin, ferulic acid, rosmarinic acid, and vanillic acid. Application of UAE notably improved extraction efficiency and overall yield compared to conventional extraction methods. The findings underscore hazelnut shells as a nutritionally and functionally valuable by-product and confirm UAE as a green, efficient extraction technique. These results provide a strong basis for developing high-value-added products for the cosmetic, pharmaceutical, and food industries, thereby supporting circular bioeconomy and sustainable chemistry principles. Full article

In this paper, an artificial neural network (ANN) model of the MLP 5-17-1 type was developed to predict the gross calorific value (HHV) of various waste types based on ultimate analysis. The dataset comprised heterogeneous samples, including biomass, municipal and industrial waste, sludges, and derived fuels, ensuring the model’s diversity and universality. The model achieved high accuracy (R² = 0.92; RMSE = 2.36; MAE = 1.68; MAPE = 10.99%), comparable to previous research results. The heterogeneity of the samples confirmed wide variations in composition and energy properties, which are crucial for developing a universal predictive model. The results confirm that ANN is a reliable tool for assessing the energy potential of waste and highlight the importance of expanding databases and optimizing parameters in future research. Full article

Throughout the development process of the education industry, the core competitiveness of education focuses on the output of high-quality content, and the emergence of a virtual human provides a more efficient carrier for the education industry, which can fundamentally help the education industry transform to improve efficiency. By combining virtual reality technology with artificial intelligence, this paper designs a virtual teacher based on the VOCA model for real-time interaction. The learned model, VOCA (voiceoperated character animation) takes any speech signal as input and realistically animates a wide range of adult faces. Compared with the traditional virtual teacher based on text or speech, the virtual teacher in this paper provides human-like interaction, which is a new teaching form for people involved in the field of artificial intelligence. According to the appearance, movement, and behavior characteristics of real teachers, the virtual teacher image is designed, and the interaction mode of virtual teachers is enriched from facial expression, body posture, voice, and speech. A virtual teacher with personalized, interactive, and intelligent characteristics is developed by combining voice, image and natural language-processing technology. It enables virtual teachers to interact with students in a more intuitive and personalized way, provide real-time feedback and personalized guidance, and provide better learning support and teaching experience for online education. Full article

Green leafy vegetables are very challenging in terms of storage and preservation, while packaging in controlled conditions with the selection of appropriate polymer material is crucial for maintaining their nutritional value and quality. Various packaging materials have different gas and water vapor permeability as well as physicochemical properties that can create a specific environment inside the package, therefore affecting the chemical composition, sensory characteristics, and overall quality of packed leafy vegetables. Stinging nettle is an edible plant with a high antioxidant content, making it a valuable leafy vegetable. Therefore, this study aimed to evaluate the influence of four packaging materials (biaxially oriented polypropylene (BOPP), low-density polyethylene (LDPE), polyamide/polyethylene (PA/PE), and polylactic acid (PLA)) on the antioxidant content of packed fresh nettle leaves during 14-day storage. Ascorbic acid content was the highest after 6 days of storage, equally well preserved in all tested films, with an average of 86.74 mg/100 g fm (fresh mass). After 14 days of storage, the total phenolic content was best preserved when packed in LDPE. The content of caffeoylmalic and chlorogenic acids was the highest in LDPE after 6 days. In addition, leaves packed in LDPE after 6 days of storage had the highest content of all photosynthetic pigments. According to FRAP analysis, the antioxidant capacity was best maintained in LDPE (at the 14th day, the measured capacity was 43.61 µmol TE/g). This study shows that the type of packaging material (BOPP, LDPE, PA/PE, and PLA) and storage duration (6 and 14 days) have a great impact on the level of antioxidant compounds in the nettle leaves, where LDPE and BOPP can be highlighted as the most favorable for the preservation of total and individual phenolic compounds, photosynthetic pigments, and antioxidant capacity. Full article

The aim of this study was to analyze the content of fatty acids and tocopherols in various components (pulp, seeds, peel) of avocado (Persea americana), which are often neglected as by-products. In addition, the effects of different drying processes on these components were investigated and the health benefits of the main fatty acids contained in avocados were highlighted. The samples were subjected to three drying processes: hot air (HAD), vacuum (VD), and hot-air microwave (HAMD). In all parts of fresh avocado, oleic acid was the most abundant (41.28–57.93%), followed by palmitic acid (19.90–29.45%) and linoleic acid (8.44–14.95%). Drying led to a significant reduction in the oleic acid content, with palmitic acid showing the greatest stability. HAD resulted in higher levels of oleic acid and linoleic acid in dried pulp and peel samples compared with VD and HAMD, while HAMD had the highest content of α-linolenic acid in all parts. In addition, HAMD had the shortest drying time. HAMD duration was 35 min, which was 76.7% shorter than HAD (150 min) and 82.5% shorter than VD (200 min). Considering fatty acid retention and drying efficiency, HAMD appears to have been the most effective method, especially for the avocado peel. Remarkably, the avocado peel consistently contained higher total tocopherol, with δ-tocopherol generally being the most abundant form. The high content of tocopherols, oleic acid, and linoleic acid in the avocado peel suggests promising health benefits. Full article

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R² of 0.92. SVM (R² = 0.81), RFR, and polynomial models (R² = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure. Full article

This research aims to use artificial neural networks (ANNs) to estimate the yield and energy characteristics of Miscanthus x giganteus (MxG), considering factors such as year of cultivation, location, and harvest time. In the study, which was conducted over three years in two different geographical areas, ANN regression models were used to estimate the lower heating value (LHV) and yield of MxG. The models showed high predictive accuracy, achieving R² values of 0.85 for LHV and 0.95 for yield, with corresponding RMSEs of 0.13 and 2.22. A significant correlation affecting yield was found between plant height and number of shoots. In addition, a sensitivity analysis of the ANN models showed the influence of both categorical and continuous input variables on the predictions. These results highlight the role of MxG as a sustainable biomass energy source and provide insights for optimizing biomass production, influencing energy policy, and contributing to advances in renewable energy and global energy sustainability efforts. Full article

As the impact of global warming intensifies drought effects, plants need to adapt to drought and other climate change-induced stresses through various defense mechanisms. One of them is the increased synthesis of bioactive compounds (BCs), which helps plants overcome adverse environmental conditions. This effect can be used in sustainable controlled cultivation as a tool for the nutritional improvement of crops, so this study focused on growing stinging nettle (Urtica dioica L.) for human consumption in a controlled environment. Since nettle can be consumed as a green leafy vegetable due to its nutritional value, the aim of this study was to determine the content of BCs (ascorbic acid, phenolic compounds, and pigments) and antioxidant capacity of nettle leaves grown under different drought stress conditions in an ebb and flow hydroponic system. During the experiment, plants were treated with a nutrient solution adjusted for nettle cultivation for 1 hour and then exposed to three different drought intervals: 24, 48, and 96 h. During the 48 h drought interval, the plants accumulated the highest amounts of total phenolic content and total non-flavonoid content (400.21 and 237.33 mg GAE/100 g, respectively), and during the 96 h drought interval, the nettle accumulated the highest amount of ascorbic acid (96.80 mg/100 g fw). The highest antioxidant capacity was recorded during the 24 and 48 h treatments (2435.07 and 2444.83 µmol/TE, respectively) according to the ABTS and during the 48 h treatment (3773.49 µmol/TE) according to the FRAP assay. The obtained results show that different drought stress durations caused by the absence of nutrient solutions can have a positive effect on the accumulation of nettle BCs. Full article

Miscanthus and Virginia Mallow are energy crops characterized by high yields, perenniality, and low agrotechnical requirements and have great potential for solid and liquid biofuel production. Later harvest dates result in lower yields but better-quality mass for combustion, while on the other hand, when biomass is used for biogas production, harvesting in the autumn gives better results due to lower lignin content and higher moisture content. The aim of this work was to determine not only the influence of the harvest date on the energetic properties but also how accurately artificial neural networks can predict the given parameters. The yield of dry matter in the first year of experimentation for this research was on average twice as high in spring compared to autumn for Miscanthus (40 t/ha to 20 t/ha) and for Virginia Mallow (11 t/ha to 8 t/ha). Miscanthus contained 52.62% carbon in the spring, which is also the highest percentage determined in this study, while Virginia Mallow contained 51.51% carbon. For both crops studied, delaying the harvest date had a positive effect on ash content, such that the ash content of Miscanthus in the spring was about 1.5%, while in the autumn it was 2.2%. Harvest date had a significant effect on the increase of lignin in both plants, while Miscanthus also showed an increase in cellulose from 47.42% in autumn to 53.5% in spring. Artificial neural networks used to predict higher and lower heating values showed good results with lower errors when values obtained from biomass elemental composition were used as input parameters than those obtained from proximity analysis. Full article

The aim of this study was to investigate the potential of using structural analysis parameters for estimating the higher heating value (HHV) of biomass by obtaining information on the composition of cellulose, lignin, and hemicellulose. To achieve this goal, several nonlinear mathematical models were developed, including polynomials, support vector machines (SVMs), random forest regression (RFR) and artificial neural networks (ANN) for predicting HHV. The performed statistical analysis “goodness of fit” showed that the ANN model has the best performance in terms of coefficient of determination (R² = 0.90) and the lowest level of model error for the parameters X² (0.25), RMSE (0.50), and MPE (2.22). Thus, the ANN model was identified as the most appropriate model for determining the HHV of different biomasses based on the specified input parameters. In conclusion, the results of this study demonstrate the potential of using structural analysis parameters as input for HHV modeling, which is a promising approach for the field of biomass energy production. The development of the model ANN and the comparative analysis of the different models provide important insights for future research in this field. Full article

In this study, the physical properties of two hazelnut species were investigated before and after drying at different temperatures and durations. The results showed that the physical properties of the hazelnut samples, including size, volume, density, weight, kernel mass, and shell mass, were significantly affected by temperature, duration, and their interactions. In addition, the moisture content of the samples decreased with increasing temperature and drying duration. The lowest value for the Istarski duguljasti variety was 5.36% (160 °C and 45 min), while the lowest value for Rimski okrugli was measured at 160 °C and 60 min (5.02%). Ash content was affected by both temperature and time, with the Istarski duguljasti variety having a minimum value of 0.84% at 120 °C and 60 min and Rimski okrugli a maximum value of 1.24% at 100 °C and 30 min. The variables of the ultimate analysis, such as nitrogen, carbon, sulfur, and hydrogen, increased with increasing temperature and time. The oxygen content and the higher heating value decreased with increasing temperature. Energy optimization in the drying process is crucial to reduce costs and save time. Effective energy optimization measures can lead to significant cost savings and improved operational efficiency in the drying process. Full article

One of the essential factors for the selection of the drying process is energy consumption. This study intended to optimize the drying treatment of capers using convection (CD), refractive window (RWD), and vacuum drying (VD) combined with ultrasonic pretreatment by a comparative approach among artificial neural networks (ANN) and response surface methodology (RSM) focusing on the specific energy consumption (SEC). For this purpose, the effects of drying temperature (50, 60, 70 °C), ultrasonication time (0, 20, 40 min), and drying method (RWD, CD, VD) on the SEC value (MJ/g) were tested using a face-centered central composite design (FCCD). RSM (R²: 0.938) determined the optimum drying-temperature–ultrasonication-time values that minimize SEC as; 50 °C-35.5 min, 70 °C-40 min and 70 °C-24 min for RWD, CD and VD, respectively. The conduct of the ANN model is evidenced by the correlation coefficient for training (0.976), testing (0.971) and validation (0.972), which shows the high suitability of the model for optimising specific energy consumption (SEC). Full article

Hazelnut fruit samples were collected over 2 years (2020 and 2021) and subjected to four different drying temperatures (100, 120, 140, and 160 °C) and four different drying times of 15, 30, 45, and 60 min using conduction drying. The analyses performed showed that conduction drying at different temperatures and different drying times had a significant effect on the change in the composition of the hazelnut shell fuel and its mass properties. Comparing the untreated samples over two years and the samples after drying, it can be seen that in 2020, the drying treatment causes a decrease in the percentage of C and H, while in 2021, drying at 160 °C and 45 min causes an increase in C and H values. After treatment, the S content decreased on average, while the value of O increased or remained the same. The greatest increase in heating values (HHV and LHV) was observed at temperatures of 140 °C and 120 °C and the duration of 45 min. When drying was applied, a significant difference in mass change was observed at 120 °C, 100 °C, and 140 °C and 30 and 60 min process durations. The change in heating value is significantly affected by the parameters year of sampling, temperature, and time, while the change in mass of the hazelnut shell is most affected by drying time. Full article