In this paper, an analytical study of the forced vibrations of hexapod table is studied. Considering external force as a time sinusoid force, forced vibrations of the platform are investigated. Resonance frequencies and vibrations of the moving platform are also calculated in different directions. The results of the analytical approach are verified using FEM simulation. Modelling the harmonic milling forces, a careful examination of the forced vibration are carried out in different cutting conditions and different configurations. Resonance frequencies and the range of vibrations are then calculated. Finally, knowing the resonance frequencies and the vibrations of hexapod table, different configurations of the table, which results in dynamic instability, are investigated.

Today, users generate various data increasingly using the Internet when choosing a product or service. This leads to the generation of data about the purchases and services of various consumers. In addition, consumers often leave feedback about the purchase. At the same time, consumers discuss their attitudes about goods and services on social networks, messengers, thematic sites, etc. This leads to the emergence of large volumes of data that contain useful information about various manufacturers of goods and services. Such information can be useful to both ordinary users and large companies. However, it is practically impossible to use this information due to the fact that it is located in different places, that is, it has a raw, unstructured character. At the same time, depending on the target group of users, not the entire data set is needed, but a specific target sample. To solve this problem, it is necessary to have a tool for structuring information arrays and their further analysis depending on the set goal. This can be done with the help of various frameworks that use methods of Machine learning and work with data. This work is devoted to elucidating the problem of creating means for evaluating consumer preferences based on the analysis of large volumes of data for its further use by the target audience.  The goal of the development of big data analysis systems is obtaining new, previously unknown information. The methodology of application of algorithms of work with large data sets and methods of Machine learning is used, namely the pandas library for operations on a data set and logistic regression for information classification As a result, a system was built that allows the analysis of lexical information, translate it into numerical format and create on this basis the necessary statistical samples. The originality of the work lies in the use of specialized libraries of data processing and Machine learning to create data analysis systems. The practical value of the work lies in the possibility of creating data analysis systems built using specialized Machine learning libraries.

Alzheimer's disease (AD) presents a significant challenge in healthcare, necessitating accurate and timely diagnosis for effective management. Resting-state functional magnetic resonance imaging (Rs-fMRI) has emerged as a valuable tool for understanding neural correlates and the early detection of AD. This article reviews recent advancements in utilizing Rs-fMRI in combination with Machine learning (ML) techniques for early AD diagnosis. First, we discuss the underlying principles of Rs-fMRI, highlighting its ability to detect alterations in brain functional connectivity (FC) patterns associated with AD. We then explore the potential of ML algorithms, particularly support vector Machines (SVMs), in analyzing Rs-fMRI data and discriminating between AD patients and healthy controls. We indicate the challenges and opportunities in integrating Rs-fMRI and ML, such as in data preprocessing, feature selection, and model interpretation. We also address the importance of large-scale, multi-site studies to validate the robustness and generalizability of the proposed approaches. Overall, the integration of Rs-fMRI and ML holds great promise as a non-invasive, objective, and sensitive diagnostic tool for AD, potentially enabling early detection and personalized treatment strategies. However, further studies are warranted to optimize methodologies, enhance interpretability, and facilitate clinical translation.

Today, the machining and milling of composites is of great importance,Because composites are used in various sectors and have found great efficiency in various industries. Considering this, in this article, in order to save the cost of milling composites, the effectiveness of various parameters that affect the cutting force and tool wear, including spindle speed (rpm), feed rate (mm/rev), cutting depth (mm) and percentage of sic, has been discussed,To minimize tool wear and cutting force by creating optimal conditions. Performing a sensitivity analysis according to the regression equations of cutting force and tool wear has shown that spindle speed with 63% and the percentage of composite silicon carbide with 22% have the greatest effect on tool wear and depth of cut with 47% and advance with 23% have the greatest effect on cutting force.

The course of change and transformation of the generations of shipyards shows that the occurrence of an accident or the invention of new technology and advanced Machine tools has caused the shipyard to change to the next generation every period. This generation change includes 5 levels so far. The difference between generations of shipyards includes three general parameters of production philosophy, production technology and factory layout. The realization and development of the first generation in the late 1940s, the second generation in the late 1960s, the third generation in the late 1980s, the realization and development of the fourth generation in the early 2000s, and the development of the fifth generation started in 2020. The most important factor in the change of generations of shipbuilding is to achieve the open index (better, cheaoer and sooner). In the research, an overview of important parameters in determining the generation of shipbuilding with advanced Machine tools, such as material flow, pre-outfit workshop, block making, factory expansion, mechanization, buffer area, process line, outfitting, and vessel dimensions, have been discussed.

Air spindles are one of the main elements of precise Machine tools.Vibration of these spindles is one of the vital issues necessary for investigation.Among parameters which influence air spindle vibrations are rotational speed, compression air method, input nozzle diameter, air gap pressure. In this study using ANSYS, the effects of air gap thickness on air spindle vibrations have been investigated. In this simulation the air gap is modeled by numbers of linear springs. Then the effect of air gap thickness on air spindle vibrations has been investigated. Rotor externally rotates around stator. Simulation results indicate that for static and transient analysis the values of radial displacements of rotor reduce by decreasing spring length (i.e. rotor and stator gap), and its minimum value are equal to 3.634 mm and 15.6 nm, respectively. Because of constant spring stiffness, in modal and harmonic analysis, results for different spring length have no variation and are equal to 1.053 mm and 23.7 nm, respectively.

One of the most critical problems of mechanized tunnelling is the abrasion of cutting tools. Soil abrasivity significantly reduces drilling efficiency and increases the operating costs of urban tunnels. There are extensive studies on abrasivity of rocks. However, limited studies have been performed on the influence of soil particle size distribution on tunnelling Machine cutting tools. Despite the wide range of methods and devices for measuring soil abrasivity, so far, no standard and comprehensive method for measuring soil abrasivity have been presented. In this study, considering the effect of some effective parameters on the abrasion of cutting tools, a new laboratory Machine to determine soil abrasivity was constructed. Then, using 8 different types of soil granulation, the effect of soil particle size distribution and density on cutting tool abrasion was studied. Also, using the Talbot curve, the abrasion values ​​of cutting tools in different particle sizes were compared. The results showed that the highest values ​​of cutting tools abrasion occur in soils with particle sizes according to the Talbot equation. As the soil granulation curve moves away from the Talbot curve, abrasivity decreases. Also, the maximum abrasion of cutting tools occurs in the amount of fine aggregate of 10% with an average abrasion percentage of 27.3%. By reducing the fine aggregate to values lower than 10%, the soil structure is disturbed and as a result, the average abrasion percentage of cutting tools decreases from 27.3% in soil with 10% fine aggregate to 2.37% in soil without fine aggregate. Also, by increasing soil density from 1.6 to 1.8, the average abrasion percentage of cutting tools increases from 8.1% to 31.4%.