To analyze the cooling process of raw cotton, a mathematical model is proposed in the form of a boundary value problem for a system of thermal conductivity equations. The developed model makes it possible to describe the distribution and propagation of heat within a fibrous mass represented in the form of a ball. Such an approach provides an opportunity to investigate the thermal state of raw cotton during the cooling process and to determine the main regularities of heat transfer inside the fibrous material. The proposed mathematical formulation takes into account the thermal conductivity processes occurring within the fibrous mass and allows the temperature field to be determined as a function of spatial coordinates and time. The boundary and initial conditions are formulated in accordance with the physical characteristics of the cooling process. This makes it possible to obtain a mathematical description that adequately reflects the changes in temperature inside the considered fibrous medium during cooling. Using the method of separated variables, an analytical solution to the proposed initial-boundary value problem is obtained. The application of this method makes it possible to represent the solution in a convenient analytical form and to analyze the dependence of the temperature distribution on the main parameters of the process. The obtained analytical results provide a theoretical basis for studying the dynamics of heat propagation and evaluating the cooling efficiency of the fibrous mass. The results of the study can be used to improve and optimize technological processes associated with the cooling of raw cotton. In particular, the proposed mathematical model can contribute to the selection of appropriate cooling conditions and technological parameters aimed at reducing excessive temperature increases and minimizing possible losses in fiber quality. The obtained results may also be useful for developing more effective approaches to controlling thermal processes during the processing and storage of fibrous materials. The work is of interest to specialists in the fields of thermal physics, heat transfer, mathematical modeling, fibrous materials processing technology, cotton processing, and related scientific and engineering areas concerned with the analysis and control of thermal processes in fibrous materials.
Background: The disinfection of books and other printed materials is an important hygienic procedure in libraries, archives, educational institutions, and other facilities where large quantities of printed materials are stored and regularly used. Conventional disinfection procedures often require manual opening and turning of individual pages, which can be time-consuming and may result in uneven exposure of pages to the disinfectant. Therefore, the development of an automated mechanism capable of sequentially separating and turning individual book pages is an important technical task. Purpose: The purpose of this research is to develop and substantiate a mechanism for the automatic separation and page turning of individual book pages during the disinfection process. The proposed mechanism is intended to reduce manual intervention, ensure sequential page turning, improve the uniformity of disinfectant exposure, and minimize the risk of mechanical damage to books. Methods: The structural and functional principles of the proposed mechanism were investigated, with particular attention to the processes of separating adjacent pages, transferring a single page, and synchronizing page turning with the disinfection cycle. The operating sequence of the mechanical elements was analyzed to prevent simultaneous feeding of multiple pages, incomplete page turning, excessive deformation, and tearing. Controlled mechanical elements and sensors were incorporated into the system to detect the position and movement of pages and to coordinate the operation of the drive mechanism. The interaction between the page-separation unit, page-transfer mechanism, sensors, and drive system was considered to ensure stable and sequential operation. Results: The developed mechanism provides controlled separation and sequential turning of individual pages, allowing the pages to be exposed more uniformly during the disinfection process. The use of sensors for detecting page position and movement improves the coordination between the page-turning mechanism and the disinfection cycle. The proposed technical solution also reduces the probability of simultaneous feeding of several pages and mechanical damage associated with uncontrolled page movement. Conclusion: The proposed automatic page-separation and turning mechanism provides a technical basis for increasing the productivity and operational reliability of book disinfection equipment while preserving the physical condition of printed materials. The developed approach can be applied in libraries, archives, educational institutions, and other facilities requiring regular automated hygienic treatment of books and similar printed materials.
The development of functional knitted materials for cosmetological and post-surgical applications requires a comprehensive assessment of their technological, structural, and hygienic characteristics. This study investigates six variants of knitted fabrics intended for the manufacture of cosmetological compression mask products. The experimental materials were evaluated according to surface density, thickness, air permeability, moisture absorption, and volumetric density. These indicators are particularly important for mask products intended to maintain contact with the facial skin, provide controlled compression, preserve dimensional stability, and ensure an acceptable level of physiological comfort. The experimental results demonstrated considerable differences among the six variants. Surface density ranged from 379.5 to 661.8 g/m 2 , thickness from 1.20 to 2.20 mm, air permeability from 17.43 to 65.4 cm 3 /(cm 2 ·s), moisture absorption from 5.9 to 11.8%, and volumetric density from 205.2 to 413.6 mg/cm 3 . The results demonstrate that structural parameters of knitted fabrics significantly affect the functional performance of cosmetological mask materials. Variants with lower thickness and higher air permeability may provide improved physiological comfort, whereas materials with higher surface and volumetric density can provide a more compact structure and potentially greater mechanical stability. The obtained data can be used for selecting rational knitted structures for cosmetological compression masks and for further development of functional medical and cosmetological textile products.
In potato cultivation, mechanical inter-row tillage plays an important role in loosening the soil, eliminating weeds, crushing soil clods, and creating favorable soil conditions for the development of the plant root system. The effectiveness of inter-row tillage largely depends on the geometric and kinematic parameters of the working tools and their interaction with the soil. The aim of this study is to theoretically substantiate the main geometric and kinematic parameters of a rotary loosening blade designed for inter-row tillage in potato cultivation. The study analyzes the principal geometric parameters of the rotary blade, including the bending angle, lateral angle, bending line angle, tangential edge height, bending radius, rotational radius, working width, and cutting-edge characteristics. For the theoretical analysis, the rotational radius of the rotary loosener was assumed to be 0.17 m, with four blades mounted on one side of the disc. The blade wing length was taken as 0.07 m, while the sharpening angle of the cutting edge was set at 30°. Based on the obtained theoretical relationships, the main kinematic parameters of the rotary loosener were determined for different operating conditions. At a rotor rotational speed of 120–140 rpm, the corresponding angular velocity was calculated as 12.57–14.66 rad/s, while the peripheral velocity of the blade tip ranged from 2.14 to 2.49 m/s. At an aggregate travel speed of 3–5 km/h, the kinematic operating mode coefficient varied from 1.54 to 2.99. The obtained results provide a theoretical basis for selecting appropriate geometric and kinematic parameters of rotary loosening blades and can be used in the design and improvement of mechanical inter-row tillage equipment for potato cultivation.
Penetration rate prediction for hard rock tunnel boring machines (TBMs) remains challenging because rock mass conditions and machine operating regimes vary continuously along the tunnel alignment. Conventional static prediction models may not adequately represent such non-stationary construction conditions, particularly when geotechnical measurements are incomplete or obtained with delay during excavation. This study aims to develop a construction-phase prediction framework for ring-scale TBM penetration rate in a granite tunnel drive by integrating geotechnical data completion, sequence deep learning, and rolling-window model evaluation. A dataset of 1,000 consecutive rings was compiled, including boring-only penetration rate, thrust, torque, cutterhead rotational speed, rock mass type, and uniaxial compressive strength (UCS). Missing UCS measurements were completed using inverse distance weighting within a block model representation, resulting in estimated UCS values of approximately 33–177 MPa for rock masses dominated by massive and fractured granite. Three sequence deep learning models, namely long short-term memory (LSTM), gated recurrent unit (GRU), and temporal convolutional network (TCN), were evaluated using root mean square error (RMSE), mean absolute error (MAE), and a symmetric ±10% tolerance band adapted from accuracy-band concepts used in AACE-based project controls. The proposed rolling protocol used 100-ring validation and 100-ring test blocks to assess predictive performance under changing ground conditions. The results show that the optimized GRU model provided the most robust overall performance, achieving a mean test RMSE of approximately 0.229 m/h and a mean within-band compliance of approximately 54% across rolling folds. These findings indicate that rolling-window sequence learning can provide a practical and adaptable framework for construction-phase TBM performance prediction under evolving geological and operational conditions.
