Search Results (34)

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

In this study, an evaluation of food waste generation was conducted, using images taken before and after the daily meals of people aged between 20 and 30 years in Serbia, for the period between 1 January and 31 April in 2022. A convolutional neural network (CNN) was employed for the tasks of recognizing food images before the meal and estimating the percentage of food waste according to the photographs taken. Keeping in mind the vast variates and types of food available, the image recognition and validation of food items present a generally very challenging task. Nevertheless, deep learning has recently been shown to be a very potent image recognition procedure, while CNN presents a state-of-the-art method of deep learning. The CNN technique was implemented to the food detection and food waste estimation tasks throughout the parameter optimization procedure. The images of the most frequently encountered food items were collected from the internet to create an image dataset, covering 157 food categories, which was used to evaluate recognition performance. Each category included between 50 and 200 images, while the total number of images in the database reached 23,552. The CNN model presented good prediction capabilities, showing an accuracy of 0.988 and a loss of 0.102, after the network training cycle. The average food waste per meal, in the frame of the analysis in Serbia, was 21.3%, according to the images collected for food waste evaluation. Full article

The world today faces several pressing challenges: energy from non-renewable sources is becoming increasingly expensive, while at the same time the use of agricultural land for food production is decreasing at the expense of biofuel production. Energy crops offer a potential solution to maximizing the use of land. In order to provide new value to the by-product, it is necessary to investigate its possible nutritional and functional potential. Therefore, the main objective of this study was to determine the energetic, nutritional, and functional potential of the species Sida hermaphrodita L. and Silphium perfoliatum L. in different phenophases. The analyzed energy potential of the mentioned species is not negligible due to the high determined calorific value (17.36 MJ/kg for Virginia mallow and 15.46 MJ/kg for the cup plant), high coke content (15.49% for the cup plant and 10.45% for Virginia mallow), and desirably high carbon content, almost 45%, in both species. The phenophase of the plant had a significant influence on the content of the analyzed specialized metabolites (SM) in the leaves, with a high content of ascorbic acid at the full-flowering stage in Virginia mallow (229.79 mg/100 g fw) and in cup plants at the end of flowering (122.57 mg/100 g fw). In addition, both species have high content of polyphenols: as much as 1079.59 mg GAE/100 g were determined in the leaves of Virginia mallow at the pre-flowering stage and 1115.21 mg GAE/100 g fw in the cup plants at the full-flowering stage. An HPLC analysis showed high levels of ellagic acid and naringin in both species. In addition, both species have high total chlorophyll and carotenoid concentrations. Due to their high content of SM, both species are characterized by a high antioxidant capacity. It can be concluded that, in addition to their energetic importance, these two plants are also an important source of bioactive compounds; thus, their nutritional and functional potential for further use as value-added by-products should not be neglected. Full article

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R² = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature. Full article

Eating habits are changing over time and new innovative nutrient-rich foods will play a great role in the future. Awareness of the importance of a healthy diet is growing, so consumers are looking for new creative food products rich in phytochemicals, i.e., specialized metabolites (SM). The consumption of fruits, vegetables and aromatic species occupies an important place in the daily diet, but different edible flower species are still neglected and unexplored. Flowers are rich in SM, have strong antioxidant capacities and also possess significant functional and biological values with favorable impacts on human health. The main aim of this study was to evaluate the content of SM and the antioxidant capacities of the edible flower species: Calendula officinalis L. (common marigold), Tagetes erecta L. (African marigold), Tropaeolum majus L. (nasturtium), Cucurbita pepo L. convar. giromontiina (zucchini) and Centaurea cyanus L. (cornflower). The obtained results showed the highest content of ascorbic acid (129.70 mg/100 g fw) and anthocyanins (1012.09 mg/kg) recorded for cornflower, phenolic compounds (898.19 mg GAE/100 g fw) and carotenoids (0.58 mg/g) for African marigold and total chlorophylls (0.75 mg/g) for common marigold. In addition to the esthetic impression of the food, they represent an important source of SM and thus can have a significant impact if incorporated in the daily diet. Full article