Reducing sugars, particularly aldoses with cyclic structures, efficiently react with 1-phenyl-3-methyl-5-pyrazolone (PMP), forming derivatives that are detectable by high-performance liquid chromatography coupled with diode array detection (HPLC-DAD) at 248 nm. However, a major challenge in this type of analysis is the co-elution of xylose and arabinose, making their individual identification and quantification difficult. This study aimed to optimize an HPLC-DAD method to separate and quantify seven monosaccharides (mannose, rhamnose, galacturonic acid, glucose, galactose, xylose, and arabinose) in complex matrices while minimizing total analysis time.

A C18 column was used, and various chromatographic conditions were evaluated, including pH and composition of the mobile phase, gradient profile, flow rate, and column temperature. Derivatization with PMP was carried out under alkaline conditions. Method validation included assessment of linearity, limits of detection (LOD) and quantification (LOQ), repeatability, interday precision, and recovery rates.

The optimized method reduced the total analysis time to 15 minutes and achieved baseline separation between xylose and arabinose. Final chromatographic conditions included a flow rate of 0.6 mL/min, a column temperature of 15°C, and mobile phase A consisting of 50 mM ammonium acetate adjusted to pH 7.5.

This method enables individual identification and quantification of monosaccharides in natural matrices, resolving the co-elution of xylose and arabinose and significantly reducing analysis time.

The present study focuses on the formulation and evaluation of a novel transferosomal nanogel containing Tridax procumbens extract, aimed at enhancing topical antifungal therapy. Transferosomes, known for their ultra-deformable lipid bilayer and high skin penetration potential, were prepared using the ethanol injection method with varying concentrations of phospholipids (soya lecithin) and edge activator (Tween 80). The optimized formulation (F7) exhibited the highest entrapment efficiency (80.23%) and nanoscale particle size (~183 nm), with a stable zeta potential of –27 mV, indicating good colloidal stability.

The optimized transferosomal formulation was incorporated into a gel base using Carbopol 934 to form the transferosomal nanogel. Among the various formulations, TF7G2 showed optimal physicochemical properties including suitable pH (6.72), excellent spreadability (75.23 g.cm/sec), viscosity (41,256 cP), and high drug content (81.56%). In vitro diffusion studies using a Franz diffusion cell demonstrated a sustained drug release of 82.54% over 8 hours, following Peppas release kinetics.

The results suggest that the transferosomal nanogel significantly improves the skin permeability and retention of Tridax procumbens extract, making it a promising candidate for the effective topical treatment of fungal infections such as Tinea corporis. The integration of herbal medicine with nanocarrier systems like transferosomes offers a synergistic approach to enhance therapeutic efficacy, patient compliance, and stability in dermatological formulations.

Olive tree cultivation (Olea europaea) occupies around 12 million hectares worldwide, assuming particular economic and social relevance in Mediterranean regions. In Portugal, olive groves cover approximately 350,000 hectares, with a production of 160,800 tons of olive oil in 2023. However, the olive tree crop is severely affected by phytopathogenic agents, notably anthracnose, caused by species of the genus Colletotrichum, which is responsible for losses of over 50% in susceptible cultivars. The recurrent use of copper-based fungicides raises environmental, toxicological and regulatory concerns, reinforcing the need for sustainable and safe alternatives.

Algae extracts, rich in antifungal and antimicrobial compounds, are promising alternatives for the sustainable control of fungal diseases. Extracts of Asparagopsis armata, Amphidinium carterae, Fucus vesiculosus, Coolia monotis and Sargassum muticum were tested at concentrations of 0.1, 0.5 and 1 mg/mL against Colletotrichum sp. using in vitro assays of mycelial growth, spore germination inhibition and phytotoxicity on olive leaves. For the in vivo assays, Fucus vesiculosus showed a protective effect, reducing the incidence of infection in olives compared to the control.

The extracts of A. carterae (1 mg/mL) and F. vesiculosus (0.5 mg/mL) showed significant antifungal activity, with 60% and 31% inhibition of mycelial growth, respectively, at 24 hours. These results confirm the potential of algae as effective sources of antifungal compounds against olive anthracnose, constituting sustainable and viable alternatives to copper-based fungicides.

Addressing sulfate contamination from aqueous sources via adsorption presents challenges due to its high solubility and hydration characteristics. Herein, a Cu-modified granular chitosan-based adsorbent (CHP-Cu) was prepared, and its sulfate adsorption characteristics from both laboratory water sources and groundwater samples wereinvestigated. Batch equilibrium and dynamic (column) adsorption studies were conducted, focusing on environmentally relevant pH (ca. pH 7) conditions. Adsorbent characterization was accomplished via spectral techniques (IR, Raman, X-Ray Photoelectron spectroscopy), surface charge measurement, and thermogravimetry. The sulfate adsorption in laboratory water followed the Sips isotherm model (407 mg/g); the maximum adsorption capacity under dynamic column conditions reached 146 mg/ and was fitted with the Thomas and the Yoon–Nelson model. To investigate the role of Cu-coordination for sulfate removal, a brief comparison of the removal (%) in four well water samples was performed with three different granular composites (non-imbibed, Ca-imbibed, and Cu-imbibed). While non-imbibed and Ca-imbibed adsorbents showed low to negligible uptake, the Cu-imbibed adsorbent resulted in a considerable sulfate removal capacity from environmental water sources. The dynamic uptake capacity of CHP-Cu in well water samples showed that the matrix influences the dynamic uptake capacity, where the uptake increased from 120 mg/g (ca. 900 mg/L sulfate) to 134 mg/g (ca. 2100 mg/L sulfate), then 144 mg/g (ca. 2800 mg/L sulfate), and then to 153 mg/g (ca. 6800 mg/L sulfate).

Applied sciences and engineering education increasingly rely on advanced digital tools that foster practical skills and analytical thinking. Artificial intelligence (AI) solutions are gaining importance as they support the development of data-driven decision-making and problem-solving competencies.
The aim of this study was to develop and evaluate an AI-based educational chatbot to enhance student learning in data analysis and statistical process control, ensuring content quality and safe use through exclusive reliance on instructor-provided materials.
The chatbot was implemented on the ChatGPT-4o (Custom GPT) platform and generates tasks, data files, step-by-step instructions in Statistica, quizzes, and control questions, enabling self-paced and interactive learning. It was integrated into the Standardization of Production Processes course and evaluated using a student survey and a Computer-Assisted Video Interview (CAVI).
Evaluation results demonstrated high acceptance: 86.4% of students found the chatbot easy to use, 95.5% rated its responses as clear and understandable, 100% found Statistica's instructions helpful, and 95.5% reported that the content was tailored to their individual preferences. Quizzes and tasks supported learning for 90.9% of students and improved preparation for assignments and tests, while 77.2% reported increased motivation for regular revision, and 77.3% noted improvement in prompt formulation skills.
The AI chatbot thus provides a controlled environment for personalized learning and practical training in statistical process control, linking theoretical concepts with hands-on applications. Its design can be adapted to other technical and engineering courses, offering a scalable model for integrating AI-driven tools into higher education curricula focused on applied competencies.

Resource recommendation in electronic health records (EHR) represents a critical challenge in distributed computational environments, where efficient utilization of resources is essential for supporting large-scale healthcare data processing. In this work, we present a novel and effective schema for EHR resource requesting and resource monitoring, designed with a blockchain-integrated management approach to enhance transparency, security, and coordination. The proposed mechanism initiates with medical users generating a dual authentication token at the point of resource request, facilitated by a local resource manager. This ensures secure validation at the preliminary stage. The generated token is subsequently processed by a global resource manager, which functions as a distributed coordination server responsible for integrating multiple local resource managers to achieve synchronized resource allocation. To maintain reliability and trust, the blockchain repository is employed to validate, track, and record resource allocation events. These blockchain-integrated resources are further aligned with a centralized resource manager that ensures effective validation of requests and systematic updates to the allocation stack. By bridging blockchain governance with distributed resource management, the integrated blockchain manager plays a pivotal role in harmonizing coordination between global and local resource managers. Experimental evaluation demonstrates that the proposed approach achieves a scheduling accuracy of 94.32% and an allocation accuracy of 98.64% under unsupervised instructions compiled within the experimental setup. The findings confirm that the model significantly improves computation efficiency, resource utilization, and blockchain-based management in distributed EHR systems.

The purpose of this study is to analyze the amount of water-soluble vitamins in the table beet dried in an ultrasonic convective drying unit. An experimental study of table beet was conducted, as a result of which the following optimal drying parameters were determined: initial moisture content was 80-85%, final moisture content 15-17%, thickness 7-8 mm, air speed 1 m/s, ultrasound exposure at 30 kHz, drying temperature 55 °C, drying time 240 minutes. Drying is recommended to be carried out taking into account these parameters. Dried samples of table beet were chemically analyzed at the Institute of Bioorganic Chemistry of the Academy of Sciences of the Republic of Uzbekistan named after Academician A.S. Sadykov for the content of water-soluble vitamins: B-2 - 4.58 mg/g; B-6 - 5.92 mg/g; B-9 - 21.58 mg/g; B-3 - 7.02 mg/g; C – 24.04 mg/g. Dried beet leaves contain vitamins: B-2 – 14.68 mg/g; B-6 – 5.67 mg/g; B-9 – 171.86 mg/g; B-3 – 20.49 mg/g; C – 110.30 mg/g. Dried beet stems contain vitamins: B-2 – 4.13 mg/g; B-6 – 4.99 mg/g; B-9 – 20.77 mg/g; B-3 – 2.10 mg/g; C – 8.70 mg/g. The amount of water-soluble vitamins was studied by high-performance liquid chromatography. The results of chemical analysis showed that water-soluble vitamins are highly preserved in dried table beets using an ultrasonic-convective drying unit.

This study investigates the thermophysical, mechanical, and durability performance of adobes reinforced with Juncus maritimus fibres, a natural material recently considered for sustainable construction. The composites were prepared by mixing clay with different fibre volume fractions (20%, 40%, and 60%). Prior to fabrication, the raw materials were thoroughly characterised through physical, chemical, thermal, geotechnical, and mineralogical analyses to ensure a comprehensive understanding of their intrinsic properties. The elaborated adobe blocks were then subjected to a series of tests to evaluate their thermal performance using the Hot Disk method, as well as their mechanical behaviour through compression and flexural strength tests.

To assess durability, capillary water absorption experiments were conducted to examine the influence of fibre content on water resistance. The incorporation of fibres led to a significant improvement in thermal insulation, reducing heat transfer and increasing the material’s heat storage capacity. However, a progressive decrease in mechanical strength was observed with higher fibre content. In contrast, water resistance was enhanced, particularly with higher fibre incorporation, indicating better performance under humid conditions.

These findings demonstrate the potential of Juncus maritimus fibres as an effective natural reinforcement for adobe, resulting in composite materials that are not only more thermally efficient but also more durable, offering promising applications in sustainable and eco-friendly building practices.

This research examines the process of drying garlic on a vibration-convection drying unit. The results of experiments conducted at the Tashkent State Technical University made it possible to determine the optimal drying method that preserves the amino acid components of garlic (Allium sativum). The unit is equipped with three trays measuring 60×40 cm, located at a distance of 20 cm from each other. A special vibration device is fixed to the bottom of each tray. The vibrator operates at a power of 5 W, and its vibration acceleration is regulated in the range from 20 to 60 m/s², which allows the use of various drying modes. Dried garlic samples were studied at the Institute of Bioorganic Chemistry of the Academy of Sciences of the Republic of Uzbekistan for amino acid content. The results of eight samples of laboratory studies of the amino acid composition in garlic are presented. Each dried sample was prepared separately for analysis. Among the eight samples from three garlic drying methods, sample №3-garlic (cut into plate shape, 2 mm thick, dried at 30 °C, 2 m/s air flow velocity and 60 m/s² vibration acceleration, the drying time was 540 minutes) was dried in a vibration-convection drying unit. The amino acid concentration was as follows (mg/g): aspartic acid (2.080), glutamic acid (2.543), serine (6.928), glycine (9.691), asparagine (9.710), glutamine (1.678), threonine (1.662), arginine (9.806), alanine (9.097), proline (18.452), tyrosine (6.544), valine (4.027), histidine (9.810), isoleucine (4.299), leucine (3.579), tryptophan (4.463), and phenylalanine (4.201).

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.