With interest in camel milk growing due to its nutrients and biologically active compounds, research into methods of processing and drying it is crucial. In recent decades, extensive studies have explored its chemical composition and health benefits with a focus on drying techniques and their effects on its properties. This review systematically summarizes the available literature on camel milk drying processes and their effects on its chemical composition with a view to shortening the drying time. To achieve this goal, we meticulously reviewed numerous studies published between 2014 and 2024 to identify optimal drying methods that maximize the preservation of camel milk’s nutrient components and bioactive compounds. Our analysis revealed significant findings: freeze drying preserves nutrients better than spray drying, but is less efficient. Spray drying, while faster, tends to compromise some nutritional values. Conclusively, optimizing drying parameters can improve production efficiency and nutrient retention.

Typical leg exoskeletons employ open-loop kinematic chains with motors placed directly on movable joints; while this design offers flexibility, it leads to increased costs and heightened control complexity due to the high number of degrees of freedom. The use of heavy servo-motors to handle torque in active joints results in complex and bulky designs, as highlighted in the existing literature. In this analytical study, we introduced a novel synthesis method with analytical solutions provided for synthesizing the lower-limb exoskeleton. Furthermore, we proposed a mathematical model of multicriteria optimization; as a result, we obtained several lower-limb exoskeleton mechanisms comprising only six links, well-suited to the human anatomical structure, exhibit superior trajectory accuracy, efficient force transmission, satisfactory step height, and having internal transfer segment of the foot.

This paper proposes a robot designed for automated routine or emergency disinfection in closed premises. The robot is related to the combined type robots. The robot consists of two functional parts: a universal mobile platform (lower part) and a disinfector (upper part), which, if necessary, can be freely moved by personnel on 4 wheels. In the initial position, the upper part of the disinfection robot is at the charging station. The mobile robot drives up to the disinfector, «hooks» it (puts it on itself) and moves along the planned route. The upper part of the disinfector will have its own independent intelligent system, separate from the mobile robot, which, when a person is recognized, stops liquid disinfection: in this case, the UV lamps turn through 180°, the cylindrical body closes and ventilation of the disinfected air from the enclosed space is turned on. In addition, liquid disinfection is only enabled when detecting beds, tables and chairs. With the spray nozzles located at a height of 400 mm, the disinfector can carry out a simultaneous combined treatment of rooms with equipment and furniture, including high-quality processing of the lower surfaces of tables, chairs and beds. To improve the functional characteristics of robotic disinfectors and to simplify their design, a multifunctional robotic disinfector has been proposed. It was found that the result is achieved by the fact that in a multifunctional disinfection robot containing a mobile cart with an autonomous positioning and navigation system, a disinfection platform with a disinfection liquid spraying system and UV lamps with reflectors installed on it, the disinfection platform will have its own autonomous control and power systems.

This study presents a combined numerical and experimental analysis of heat and mass transfer in mare’s milk during vacuum sublimation drying, which is a process essential for producing high-quality powdered products. The numerical model was developed using two-dimensional simulations and validated against experimental data obtained for varying sample thicknesses (3 mm, 5 mm, and 7 mm). Results demonstrated a strong agreement between the model and experimental temperature data, with a coefficient of determination ($R^2$) of 95% during the sublimation process. The findings revealed that thinner samples (3 mm) exhibited a 20% faster drying rate than thicker samples (7 mm), highlighting the critical role of sample thickness in sublimation dynamics. Additionally, the effects of heat flux distribution and mass loss due to sublimation were analyzed to understand the drying dynamics. This study highlights the importance of optimizing process parameters such as chamber pressure, shelf temperature, and sample thickness to enhance drying efficiency and reduce processing time. The findings provide valuable insights for scaling vacuum sublimation drying of mare’s milk for industrial applications.

The article presents the main characteristics and results of studies of the chemical composition of camel milk and shubat during the winter period, produced in the economy of the Almaty region. Milk consumption of almost all types, except cow's milk, has increased by 17% over the past 50 years in all countries. Studies have revealed the potential value of camel milk in the treatment of various human diseases such as asthma, tuberculosis, jaundice and diseases of the gastrointestinal tract. The conducted research is based on the requirements of relevant regulatory documents and includes the results of the study of this topic by both Kazakhstani and foreign scientists. We determined that in winter, the fat content of camel milk exceeds the average values by 0.57% and amounted to 5.14%. The protein and casein content turned out to be lower than the average data by 0.26% and 0.59%, respectively. The lactose content of milk sugar is below average. It was also found that during the fermentation process, the amount of vitamins B1, B2, B3 and B5 increased by 12-85% compared with their content in the original camel milk. These results are an important contribution to the study of the nutritional properties of camel milk and shubat, as well as their potential use in the food and medical industry.

Producing high-quality camel milk powder requires precise control of vacuum sublimation drying parameters due to the product's sensitivity to thermal stress and oxidation. This study addresses the optimization of the drying process by integrating advanced machine learning (ML) techniques to predict optimal drying conditions. In this research, four ML algorithms were developed and evaluated: Logistic Regression, Random Forest, Decision Tree, and Extreme Gradient Boosting (XGBoost). Each algorithm was trained on detailed sensor data, including chamber temperatures, vacuum pressure, and milk thickness, with drying success defined by maintaining temperature deviations within ±5°C and completing drying cycles within 24 hours. The XGBoost model exhibited the best performance, achieving an accuracy of 99.3%, precision and recall of 96.4%, and the highest F1-score. Temperature parameters, particularly in specific chamber locations, emerged as critical predictors of successful drying outcomes. By enabling accurate forecasting and real-time parameter adjustments, this ML-driven approach significantly enhances drying efficiency, product quality, and sustainability, offering substantial economic and logistical benefits, particularly for remote regions in Kazakhstan where camel milk production is prominent.