In this research work, a video-related automated system, namely Robotic Key Image Frame Identification System (RKIFIS), is proposed, and it aims to instinctively identify the finite number of representative image frames over the video through the process of splitting the video contents into the optimal number of distinct clusters with different sizes using the Optimal-N-Means ONM clustering technique. The proposed RKIFI system contains five stages; in the beginning stage, the RKIFI converts the input video into a sequence of image frames using the standard open CV tool. Subsequently, the proposed system improves the image frame quality through pre-processing every individual image frame from the result of the previous stage. Afterward, the RKIFI system extracts highly relevant features from each image frame in the image frame set of the input video using standard arithmetic operations. Consecutively, the proposed system is iteratively split into the image frame vector set into a finite number of clusters through the process of iteratively identifying the optimal number of representative image frames over the input image frame set of the input video using the Optimal-N-Means clustering technique, where N denotes the optimal number of representative image frames in the image feature vector set of the input video. In the final stage, the RKIFI system validates the dissimilarity level among the key image frames which are identified in the clustering stage. The experimental result shows how the RKIFI system is well suited to automatically identifying the essential key image frames in the video data.

In this study, we developed a new type of composite with improved thermophysical and mechanical properties. This composite is made of aerated concrete based on two types of additives, and we aimed to choose the best one. The main raw materials for manufacturing cellular concrete are sand, lime, cement, water, and aluminium powder. We replaced natural sand with olive pomace sand and shell sand, using replacement percentages of 10%, 20%, 30%, and 40%. We conducted a comparative study of the results to ultimately choose the one that yielded the best results. The thermomechanical properties, durability, and physical properties of the composites were studied. We finally concluded that both types of replacement reduce the mechanical properties of the material, but shell sand gives better results than olive pomace sand. We also found that composites based on olive pomace sand have a higher water absorption coefficient than those with shell sand, which weakens the material, probably due to the porous structure of olive waste and the lack of bonding between the matrix and grain. In terms of thermal properties, both sands exhibited increased thermal conductivity by 28.5% and 32.7% for a 10% replacement rate for olive pomace waste and shell waste, respectively, but this remains an acceptable value, as aerated concrete is already known for its thermal insulation properties.

This work presents the development of an educational support system based on Convolutional Neural Networks (CNNs) applied to facial emotion recognition. The model was trained using public datasets of emotional expressions, enabling the real-time identification of affective states such as happiness, sadness, anger, surprise, and neutrality. From these detections, two dynamic indicators were defined: concentration and nervousness. Both were computed through a weighted mapping of emotions, where each recognized emotion contributed with specific coefficients to quantify levels of focus and stress. This methodology was inspired by studies in affective computing and educational psychology, which emphasize the influence of emotional states on attention and test anxiety.

The CNN model achieved an accuracy of approximately 70% during training and validation, ensuring reliable emotion detection for subsequent analysis. To determine readiness, a rule-based mechanism was applied: students were considered prepared when concentration reached at least 60 out of 100 while nervousness remained below 50. By combining these two indicators, the system provided an objective and interpretable evaluation of the student’s emotional readiness to answer questions or undertake an assessment.

The system was designed to support teachers in better understanding students’ emotional states during evaluative activities. By integrating emotional and cognitive factors, educators gain a more holistic view of the learning process, promoting fairer and more inclusive evaluation practices.

Experimental results confirmed consistent estimations of concentration and nervousness, with reliable classification of test readiness. These findings highlight the potential of artificial intelligence as an innovative tool in contemporary education.

Water scarcity remains one of the most critical challenges affecting agricultural productivity in arid and semi-arid regions. Hydrogels, as three-dimensional polymeric networks, have attracted increasing attention as soil conditioners due to their ability to retain water and act as carriers for nutrients or bioactive compounds. However, most commercial hydrogels are derived from synthetic polymers such as polyacrylates, which are not readily biodegradable and may generate environmental concerns. This has stimulated research into bio-based and biodegradable alternatives that combine high water retention with environmental compatibility.

In this study, we designed and developed superabsorbent hydrogels based on gellan gum (GG), karaya gum (KG), and humic acid (HA) as sustainable systems for agricultural applications. The hydrogels were characterized by means of FTIR, TGA, SEM, mechanical assays, and swelling kinetics. Soil water retention assays demonstrated that GG/HA and GG/KG/HA formulations preserved higher moisture levels than a commercial polyacrylate hydrogel. Biodegradation studies confirmed their environmental compatibility, showing weight losses greater than 30% after 30 days in soil extract.

Biological trials with sorghum (Sorghum sp.) seedlings under controlled chamber conditions (27±1 °C, 12 h photoperiod) revealed no phytotoxicity. Moreover, the GG/KG/HA hydrogel promoted superior growth and chlorophyll accumulation compared to GG/HA, highlighting the synergistic role of KG and HA in stimulating plant development.

These results confirm that GG/KG/HA hydrogels are biodegradable, biocompatible, and effective soil conditioners, offering a sustainable pathway to improve water-use efficiency, soil quality, and crop productivity.

Plant-based foods are essential to the human diet and offer solutions to health, environmental, and economic challenges. Plant proteins, especially when fermented, become more nutritionally complete. Controlled microbial fermentation enhances food quality and safety, suppresses anti-nutritional factors like phytic acid, and enables the development of innovative, functional food products. The objective of this study was to investigate the microbial fermentation of carob seeds (Ceratonia siliqua L.), a xyrophytic plant widely distributed across Mediterranean countries, and their potential applications in the food industry. These seeds were primarily selected and dried and then fermented in lab-scale solid-state type fermentation by lactic acid bacteria, Saccharomyces cerevisiae (yeast) and Aspergillus oryzae (fungus) for four days. The fermented carob seeds were then analyzed for the following: a) in-vitro digestibility was tested by applying appropriate proteolytic enzymes and measuring pH decrease, b) concentration of phytic acid was calculated through a biochemical assay that included measurements of free and total phosphorus and c) protein electrophoresis was applied to analyze the degree of fragmentation of the fermented proteins from carob seeds. The results have shown a mild proteolytic activity exerted by the microbial fermentation of the carob seeds which was evident in the in-vitro digestibility study and the decrease of the concentration of phytic assay. However, protein electrophoretic patterns have not revealed new protein zones as evidence of protein hydrolysis due to seed fermentation. Further research is needed to establish the optimum conditions for solid-state microbial fermentations of carob seeds to obtain a more digestible source of protein.

This study evaluates the impact of four drying methods—open sun drying, solar drying, infrared drying, and microwave drying—on the quality attributes and elemental retention of apricots (Prunus armeniaca L.). Experimental trials were conducted in June 2024 at the Tashkent Chemical–Technological Institute using equal quantities of fresh apricots. Drying was continued until the moisture content dropped below 20% (wet basis), followed by spectroscopic analysis to determine macro- and microelement concentrations. Solar-dried apricots showed the highest retention of essential nutrients: potassium (2.37%), silicon (0.538%), magnesium (0.145%), calcium (0.176%), and sulfur (0.152%). In contrast, open sun drying led to significant nutrient degradation and poor visual quality. Microwave drying preserved some micronutrients but resulted in surface scorching due to uneven heating. Infrared drying yielded acceptable results but required substantial energy input. Among all methods, solar drying provided the optimal balance of high product quality and energy efficiency. The specific energy consumption was effectively zero (0.00 kWh/kg) owing to exclusive reliance on solar radiation. This method supports sustainable food processing by reducing energy demand and greenhouse gas emissions while preserving nutritional quality. The results highlight solar drying as a promising, eco-friendly technique for preserving the nutritional integrity of agricultural products. These findings offer valuable scientific guidance for selecting appropriate drying technologies in the food processing industry, especially in regions with high solar potential.

The infection or contamination of fungi in crops or foods trigger serious food security and safety concern worldwide. Long-term application of conventional antifungal agents, such as azole, strobilurin or fludioxonil fungicides, during crop/food production can result in the emergence of fungicide-resistant fungi, for which effective agents for treating resistant fungal pathogens or contaminants are often very limited. Since the development of entirely new antifungal agents is an expensive and time-consuming process, we investigated an innovative approach termed "compound repurposing", which repositioned already marketed non-antifungal compounds as new antifungal agents for the control of fungal infections/contaminations. We also employed a "chemo-sensitization" strategy wherein co-application of potentiators (chemo-sensitizers), for example, redox-modulatory molecules, could enhance the antifungal efficacy of the screened compounds. We found that compounds such as polyphenol, terpenoid or medicinal agents, which have been used as nutrient supplements, non-antifungal medicines, etc., exerted potent antifungal activity against crop/food fungal pathogens or contaminants including Aspergillus sp. and Fusarium sp. Repurposed compounds also inhibited the biosynthesis of aflatoxins by Aspergillus flavus or Aspergillus parasiticus, thus ensuring safe production of crops/foods. Collectively, antifungal "compound repurposing" could serve as a promising strategy that can identify new crop/food protection molecules; "chemo-sensitization" significantly improved the antifungal efficacy of the screened compounds.

Cobalt oxide (Co₃O₄) nanoparticles were synthesized using the sol–gel method and calcined at various temperatures ranging from 300 °C to 600 °C to investigate the influence of thermal treatment on their structural, thermal and optical properties. X-ray diffraction (XRD) analysis confirmed the successful formation of a pure cubic spinel Co₃O₄ phase with nanocrystalline features, belonging to the Fd3m space group. As the calcination temperature increased, the samples exhibited enhanced crystallinity and sharper and more intense diffraction peaks, indicating grain growth and improved structural ordering. FTIR analysis confirmed the presence of functional groups and chemical bonding in Co₃O₄. Thermogravimetric analysis (TGA) indicated the elimination of surface adsorbed species and residual organics during the initial stages, succeeded by the stabilization of a cubic spinel Co₃O₄ phase, which exhibits remarkable thermal stability without any additional phase transitions. UV–Vis diffuse reflectance spectroscopy (DRS) analysis showed that the Co₃O₄ displayed significant absorption in the visible region, consistent with their intrinsic narrow bandgap characteristics. Unlike earlier sol–gel-synthesized Co₃O₄ nanoparticles, the present work highlights improved phase purity and long-term stability, making the material more stable for advanced applications. Optimized temperature Co₃O₄ nanostructures have great potential for next-generation energy storage devices, photocatalytic dye degradation, oxygen evolution reaction (OER) electrocatalysis, gas sensing, and biomedicine. This research not only provides valuable insights into the temperature-dependent development of Co₃O, but also sets a comparative standard for durable and scalable synthesis methods that align with modern trends in sustainable nanomaterials.

Renewable energy sources are essential for energy production and energy transfer systems. In this paper, we explore a novel approach that combines natural and social sciences through a machine learning (ML) technique, which integrates environmental and geographical information systems (GISs) and aligns with the United Nations' 17 Sustainable Development Goals (SDGs). With the help of a dataset, we derived one hundred regional observations that covered solar irradiation, wind energy, temperature, relative humidity, and altitude. Then, we enhanced this dataset using GIS information (latitude and longitude) and available energy production information at historical timestamps. This dataset was used for training the neural network. With the help of TensorFlow's Sequential Application Programming Interface (API), we used dropout regularisation and dense layers for overfitting prevention. The resulting model was a deep learning architecture that was capable of preventing overfitting. The dataset was standardised and split (80-20) for training and testing. Overfitting prevention deep learning architecture was used with a batch size of 16, trained for 50 epochs using the Adam optimiser, mean squared error (MSE) loss, and an initial training loss of 98,273.70 was obtained. The model trained loss was reduced to 16,651.12 while stabilising validation loss, indicating strong generalisation. Validation shows that the model GIS visualisations for energy generation aid in providing spatial dependence of energy interdependence for grid improvement and energy generation interdependence in spatial systems for grid planning. The proposed approach is, therefore, an integration of GIS and deep learning with the aim of obtaining spatially informative energy potential estimates.

Abstract

Elevated density in energy-advanced anodes is required for LIBs; however, problems like volume growth or reduced capacity make silicon (Si) and silicon dioxide (SiO₂) unsuitable. Using graphene to overcome these real-world constraints, this study compares Si–graphene oxide (Si-GO) and SiO₂–graphene oxide (SiO₂-GO) composite anodes.

The purpose of this study is to elucidate the different performance characteristics of Si-GO and SiO₂-GO anodes. By comprehending these distinctions, silicon-based materials may be rationally designed to meet specific energy density and cycle life needs for new batteries. This will allow for a customized selection or ideal blend of Si and SiO₂ in graphene composites.

A modified Hummer's method is proposed to manufacture graphene oxide (GO). GO is combined with either silicon from magnesiothermic reduction (for Si-GO) or a silica precursor (for SiO₂-GO) to generate composites. The morphology and structure of the materials are examined using elemental analysis, TGA, SEM, XRD, Raman spectroscopy, and XPS. Impedance spectroscopy, rate capability testing, galvanostatic cycling, and cyclic voltammetry are used to assess the electrochemical performance of LiFePO₄-based cells.

Si-GO is anticipated to provide a greater initial specific capacity, and because of its conversion reaction mechanism, SiO₂-GO should exhibit improved long-term cycle stability. Both composites should benefit from graphene's ability to improve electrical conductivity and buffer volume expansion. The trade-off between large capacity and long cycle life will be described in the analysis.

Acknowledgements. This research was supported by the Ministry of Science and Higher Education of the Russian Federation (project No. FSER-2025-0005).