A mathematical model is presented for investigating the temperature field caused by the rotary friction welding of dissimilar metals. For this purpose, an axisymmetric, nonlinear, boundary value problem of heat conduction is formulated with allowance for the frictional heating of two cylindrical specimens of finite length made of Al 6061 aluminium alloy and 304 stainless steel. The thermo-physical properties of materials change with increasing temperature. It was assumed that the coefficient of friction does not depend on the temperature. The mechanism of heat generation due to friction on the contact surface with the temperature field of samples is considered. The boundary problem of heat conduction was reduced to the set of nonlinear ordinary differential equations at time t relative to the values of temperature T at the finite elements nodes. The numerical solution of the problem was obtained with the inverse 2nd order differentiation method implemented in COMSOL FEM system (finite element method), with time step ∆t=0.1 (s). The influence of various values of friction coefficient is presented.

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In today's competitive industry landscape, it is crucial to assess manufacturing processes to enhance efficiency. However, identifying the critical factors that impact productivity can be a daunting task due to their intricate nature. To tackle this challenge, we propose a novel approach that combines fuzzy logic with TOPSIS to comprehensively evaluate manufacturing company efficiency. The method presented by the author treats this as a complex MCDM problem and accommodates diverse factors with distinct weights, which are crucial for a thorough efficiency analysis. This approach was applied to evaluate potential manufacturing entities in Cyprus through a three-step process. Firstly, relevant criteria were curated using literature and expert insights, endowing them with linguistic terms that were then translated into fuzzy values. Next, fuzzy TOPSIS evaluated efficiency, and sensitivity analysis gauged the criteria weight impact on decisions. This article introduces a new methodology for holistic manufacturing company evaluation. The synergy of fuzzy-set theory and TOPSIS proves effective amidst the ambiguity inherent in performance measurement. By uniting these methodologies, this study advances manufacturing performance evaluation, aiding informed decision-making. The research contributes a pioneering method to enhance manufacturing efficiency assessment while accommodating uncertainty through fuzzy logic integration.

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The study involved a numerical analysis of the water dropping process by fixed-wing aircraft. This method, also known as air attack, is used for aerial firefighting, primarily in green areas such as forests and meadows. The conducted calculations allowed for the analysis of the process over time. The calculations were performed based on a SolidWorks model of the M18B Dromader aircraft. After defining the computational domain and setting the boundary conditions, the simulations were carried out using the ANSYS Fluent software. The resulting water dropping area was used to analyze the intensity of water distribution. The volumetric distribution and airflow velocity distribution were analyzed for specified time steps. The boundary layer where air no longer mixes with water during the final phase of water dropping was also determined. The obtained results provide an important contribution to further analyses aimed at optimizing the water dropping process by fixed-wing aircraft.

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The study involved a numerical analysis of the water dropping process by fixed-wing aircraft. This method, also known as air attack, is used for aerial firefighting, primarily in green areas such as forests and meadows. The conducted calculations allowed for the analysis of the process over time. The calculations were performed based on a SolidWorks model of the M18B Dromader aircraft. After defining the computational domain and setting the boundary conditions, the simulations were carried out using the ANSYS Fluent software. The resulting water dropping area was used to analyze the intensity of water distribution. The volumetric distribution and airflow velocity distribution were analyzed for specified time steps. The boundary layer where air no longer mixes with water during the final phase of water dropping was also determined. The obtained results provide an important contribution to further analyses aimed at optimizing the water dropping process by fixed-wing aircraft.

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The paper describes one of the methods of data acquisition in data mining models used to support decision-making. The study presents the possibilities of data collection using the phases of the CRISP-DM model for an organization and presents the possibility of adapting the model for analysis and management in the decision-making process. The first three phases of implementing the CRISP-DM model are described using data from an enterprise with small batch production as an example. The paper presents the CRISP-DM based model for data mining in the process of predicting assembly cycle time. The developed solution has been evaluated using real industrial data and will be a part of methodology that allows to estimate the assembly time of a finished product at the quotation stage, i.e., without the detailed technology of the product being known. 

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Soil is a solid particle that covers the surface of the earth. Soil can be classified based on its color because the color indicates the nature and condition of the soil. CNN works well for image classification, but it requires large amounts of data. Augmentation is a technique to increase the amount of training data with various transformation techniques to the existing data. Rotation and Gamma Correction can be used simply as an augmentation technique and can reproduce an image with as many image variations as desired from the original image. CNN architecture has a convolution layer and Dense block has dense layers. The addition of Dense blocks to CNN aims to overcome underfitting and overfitting problems. This study proposes a combination of Augmentation and classification. In augmentation, a combination of rotation and Gamma correction techniques is used to reproduce image data. The CNN-Dense block is applied for classification. The soil image classification is grouped based on 5 labels black soil, cinder soil, laterite soil, peat soil, and yellow soil. The performances of the proposed method provide excellent results, where accuracy, precision, recall, and F1-Score performances are above 90%. It can be concluded that the combination of rotation and Gamma Correction as augmentation techniques and CNN-Dense blocks is powerful for use in soil image classification.

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This article has been retracted

Nowadays Mobile Ad Hoc Network (MANET) is an emerging area of research to provide various communication services to end users. Mobile Ad Hoc Networks (MANETs) are self-organizing wireless networks where nodes communicate with each other without a fixed infrastructure. Due to their unique characteristics, such as mobility, autonomy, and ad hoc connectivity, MANETs have become increasingly popular in various applications, including military, emergency response, and disaster management. However, the lack of infrastructure and dynamic topology of MANETs pose significant challenges to designing a secure and efficient routing protocol. This paper proposes an adaptive, secure, and efficient routing protocol that can enhance the performance of MANET. The proposed protocol incorporates various security mechanisms, including authentication, encryption, key management, and intrusion detection, to ensure secure routing. Additionally, the protocol considers energy consumption, network load, packet delivery fraction, route acquisition latency, packets dropped and Quality of Service (QoS) requirements of the applications to optimize network performance. Overall, the secure routing protocol for MANET should provide a reliable and secure commu­nication environment that can adapt to the dynamic nature of the network. The protocol should ensure that messages are delivered securely and efficiently to the intended destination, while minimizing the risk of attacks and preserving the network resources Simulation results demonstrate that the proposed protocol outperforms existing routing protocols in terms of network performance and security. The proposed protocol can facilitate the deployment of various applications in MANET while maintaining security and efficiency.

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Stock price prediction is an exciting issue and is very much needed by investors and business people to develop their assets. The main difficulties in predicting stock prices are dynamic movements, high volatility, and noises caused by company performance and external influences. The traditional method investors use is the technical analysis based on statistics, valuation of previous stock portfolios, and news from the mass media and social media. Deep learning can predict stock price movements more accurately than traditional methods. As a solution to the issue of stock prediction, the authors offer the Exponential Moving Average Gated Recurrent Unit (EMAGRU) model and demonstrate its utility. The EMAGRU architecture contains two stacked GRUs arranged in parallel. The inputs and outputs are the EMA10 and EMA20, formed from the closing prices over ten years. The authors also combine the AntiReLU and ReLU activation functions into the model so that EMAGRU has 6 model variants. The proposed model produces low losses and high accuracy. RMSE, MEPA, MAE, and R^2 are 0.0060, 0.0064, 0.0050, and 0.9976 for EMA10, and 0.0050, 0.0058, 0.0045, and 0.9982 for EMA20, respectively.

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