Abstract: With new web applications rapid growth in size, the monolithic frontend approach has been increasingly challenged over the last years. The micro frontends concept has been proposed to match the architectural needs facing increasing complexity, e.g. for newly emerging cloud-native solutions. It provides function-level granularity and lets the developer adjust each process’s performance. In the majority of relevant cases for which the micro frontends paradigm is considered, the system should be migrated, or an existing monolith should be converted to new technologies, whereas, the scientific community mainly focuses on greenfield designs. In this paper, we validate the paradigm, with a case study, in the context of migration for enterprise information systems, in a multi-vendor environment. Based on our analysis, we propose a method for the migration of frontend monoliths, along with guidelines and recommendations for future work.

Keywords: micro frontends; multi-vendor; migration

Abstract: The application of torsional vibration dampers is reasonable in every internal combustion engine with high performance output, where the unbalanced gas and inertial forces cause harmful torsional oscillations on the crankshaft. These oscillations can lead to the fatigue and damage of engine components. A visco-damper is a type of torsional vibration damper. Temperature represents one of the highest effects, on the lifetime of its operational fluid, in this case, silicone oil. Design solutions are proposed and realized for increasing cooling capabilities and the durability of the silicone oil. The heat transfer processes inside and outside of the damper are determined by coupled fluid dynamics and heat transfer simulations. The plausibility of the results is confirmed by using a modified version of the Iwamoto equation. A temperature-based lifetime prediction method has been used for determining the lifespan of the synthetic damping medium. The proposed geometrical modifications, decrease the level of the temperature distributions and thereby, improve the durability of the silicone oil.

Keywords: torsional vibration damper; silicone oil; design modifications; thermal and life-time management; CFD

Abstract: This paper examines the issues of domain-specific hierarchical data structures, based on directed acyclic graphs, dedicated to the representation of the geometry of three-dimensional scenes. In this paper, the authors introduce two versions (out-of-core and semi-out-of-core) of an algorithm to transform hierarchical data structures - pointerless sparse voxel directed acyclic graphs, into sparse voxel directed acyclic graphs. Pointerless sparse voxel directed acyclic graphs are not suitable for immediate traversing, due to the absence of pointers to the child nodes; however, they are suitable for archiving and streaming, as they have a more compact binary-level representation. Sparse voxel directed acyclic graphs, on the other hand, allow quick traversing during visualization or other forms of processing, since their nodes include pointers to child nodes. The disadvantage of this, is that the binary-level representation, requires more operating memory or secondary storage space. Both hierarchical data structures - sparse voxel directed acyclic graphs and pointerless sparse voxel directed acyclic graphs - and both versions of the proposed conversion algorithm are described in the first part of the paper. Results of tests, performed on various models - previously surface polygonal models, stored in the Wavefront Technologies geometry definition file format (OBJ) - now voxelized to the respective resolutions, are summarized in the second part of the paper. The binary-level representation lengths of both data structures, along with the time consumption of both versions of the proposed conversion algorithms, are detailed in the last part of the paper.

Keywords: pointerless sparse voxel octrees; PSVO; sparse voxel octrees; SVO; pointerless sparse voxel directed acyclic graphs; PSVDAG; sparse voxel directed acyclic graphs; SVDAG; hierarchical data structures; volume dataset; three-dimensional image; lossless data compression

Abstract: Within the digital culture, the increasing internet consumption and the constant development of technology, especially smartphones have made cyber awareness turn to be increasingly urgent. This study focuses on comparing the level of cyber security awareness, knowledge and behavior among university students in general and between Hungary and Vietnam in particular. Research data was collected, using a set of questionnaires and the 313 responses from University Students, in different school years and fields of study, in Hungary and Vietnam. Quantitative analysis was conducted using SPSS. Results show that all respondents possess a lack of knowledge of cyber security, leading to a low level of cyber threat awareness, beyond the differences in respondent countries. However, there are minor differences in the behavior, between respondents in Hungary and Vietnam, which were measured through four dimensions of cyber security: malware items, password usage issues, social engineering and online scam issues. This research helps to raise awareness of differences in cyber security mindfulness, due to cultural characteristics, that can be considered, when developing global mega-systems, such as, social platforms.

Keywords: Cyber security; internet security; online threats; smartphone usage; student awareness; student behavior; Hungary, Vietnam

Abstract: Internet of Digital Reality (IoD) is a concept that extends the Internet of Things (IoT) with the management, transmission and harmonization of digital realities. IoD covers aspects of connectivity, accessibility and usability with respect to different cognitive entities present in the digital world, via a confluence of technologies including virtual reality, artificial intelligence and 2D digital environments, in a way that recognizes human factors and cognitive aspects as key issues. Devices, interfaces, and interacting entities can be enabled through IoD to share digital realities and to thereby build a new level of reality, using intelligent connections mostly based on immersive virtual scenarios and multi-modal interactions in both public and private networks. In this paper, we look into the infrastructural requirements of and challenges behind the Internet of Digital Reality, which must be solved in order to deliver a high-quality user experience while keeping the increasing complexity of these networks at bay.

Keywords: Digital Reality; Future Internet; Internet of Things; Internet of Everything; Internet of Digital Reality

Abstract: This paper highlights ethnocentric food consumption or the favoring of domestically produced food products. One of the theoretical pillars of the study is the trend toward ethnocentrism. Several studies proved that consumers are more positive about products from their own region. We would like to examine the Hungarians’ opinion concerning their own products within the Hungarian food market. The other pillars of the theoretical background of the study are general food consumer behavior models and theories within the frame, of which, we analyzed the most important changes of the mindset of food consumers and the main structural diversion of food preferences. In the primary research, quantitative strategy, has been used, with the help of a pre-tested standardized questionnaire and snowball sample taking methods, 1447 questionnaires were evaluated. These research tools typically include closed-ended questions: selective, combinative semantic differential scales and ranking in the form of question types. The main aim of the research was to analyze the Hungarian consumers’ preferences, for case of food consumption and characterize the most important target markets of the Hungarian food industry. We examined the attitude of the respondents toward Hungarian foods, from affective, cognitive and conative aspects. We also investigated the general food-consumer preferences of our respondents and the main elements of preference for Hungarian food consumption. As a result, we could characterize the most important features that Hungarian food-consumers associate with Hungarian food, we could also understand the advantages and disadvantages of various Hungarian foods. In addition, we could distinguish food consumer patterns, based on food consumption preferences. The results of this work can serve as an orientation for the players in the Hungarian food markets, to characterize their potential target markets and access the most important consumer groups, for the case of Hungarian food promotion.

Keywords: food consumption; preferences; ethnocentrism

Abstract: Demand forecasting for business practice is one of the biggest challenges of current business research. However, the discussion on the use of forecasting methods in business is still at the beginning. Forecasting methods are becoming more accurate. Accuracy is often the only criterion for forecasting. In the reality of business practice or management is also influenced by other factors such as runtime, computing demand, but also the knowledge of the manager. The goal of this article is to verify the possibilities demand forecasting using deep learning and statistical methods. Suitable methods are determined on based multi-criteria evaluation. Accuracy according to MSE and MAE, runtime and computing demand and knowledge requirements of the manager were chosen as the criteria. This study used univariate data from an e-commerce entity. It was realized 90-days and 365-days demand forecasting. Statistical methods Seasonal naïve, TBATS, Facebook Prophet and SARIMA was used. These models will be compared with a deep learning model based on recurrent neural network with Long short-term memory (LSTM) layer architecture. The Python code used in all experiments and data is available on GitHub (https://github.com/mrnavrc/demand_forecasting). The results show that all selected methods surpassed the benchmark in their accuracy. However, the differences in the other criteria were large. Models based on deep learning have proven to be the worst on runtime and computing demand. Therefore, they cannot be recommended for business practice. As a best practice model has proven Prophet model developed at Facebook.

Keywords: demand forecasting; deep learning model; TBATS; Prophet; SARIMA

Abstract: Three-dimensional (3D) printing is an astonishing technology that has enabled the manufacturing of complex structures, with comparatively shorter times and the least material consumption. Currently, Graphene is gaining remarkable attention, as a filler material, used for the reinforcement of metal and polymer composites. In this paper, the 3D printing system, based on the digital light processing (DLP) method, is employed for the fabrication of bio-based resin specimens, to estimate their dynamic mechanical properties. For this purpose, two graphene concentrations (0.5 and 1 wt%) were mixed in resin (matrix) by a vortex mixer/shaker. The resultant mixture, in addition to the neat resin, was utilized for producing the test pieces, at three different layer thicknesses (35, 50, 100 μm). A comparison of the mechanical properties, between the DLP-printed neat resin and graphene/resin composite materials, was accomplished, to illustrate the impact of filler (graphene nanoplatelets) and the printing process settings (layer thickness). These determinants were assessed according to the microstructure and tensile characteristics of the examined materials. The results of scanning electron microscopy (SEM) showed a fairly even dispersion of graphene in the resin matrix. Moreover, it was found that smaller layer thicknesses provide a higher tensile strength. Further, a decrease in Young's modulus, tensile strength and elongation can be observed, with higher graphene concentrations.

Keywords: Photopolymerization; 3D Printing settings; Graphene platelets; Polymer composites; Tensile strength; Young’s Modulus

Abstract: Herein, a disturbance observer is designed for linear non-minimum phase systems. A Smith Predictor is added to the system, using Recursive Least Squares (RLS), with a forgetting factor algorithm. The combination of both approaches, eliminates the restrictive feature of the classical disturbance observer, for non-minimum phase systems and removes the necessity for precise delay measurements, for the Smith Predictor structure. The results show that the proposed design procedure preserves system stability, in the presence of disturbances and time delays.

Keywords: Disturbance Observer; Recursive Least Squares; Smith Predictor

Abstract: Joint torque prediction plays an important role in quantitative limb rehabilitation training and the exoskeleton robot. The Surface electromyography signal (sEMG) with the advantages of non-invasive and easy collection can be applied to the prediction of human muscle force. By utilizing the sEMG, the recurrent cerebellar model neural network (RCMNN), which has better generalization and computational power than the traditional neural network has been used to predict the joint torque. In this work, a smooth function with adaptive coefficient is employed to polish the results of RCMNN, the proposed method shows great performance on torque prediction with the correlation coefficient between the torque and the estimation result up to 98.43%, such advanced model paves the way to the application on the quantitative rehabilitation training.

Keywords: Torque prediction; ankle joint; sEMG; RCMNN

Abstract: In this paper, we show that a high-level of correlation exists between a simple image feature - mean gradient magnitude and the peak signal-to-noise ratio (PSNR) of the first just noticeable difference point for JPEG image compression. On the basis of this observation, we proposed a method to estimate the JPEG quality factor which represents the effective limit between perceptually lossy and lossless coding as the PSNR of the first just noticeable difference point. The goal is optimal image/video coding, at the lowest compression bit-rate that ensures perceptually lossless output image quality. We also show that this feature can be used to predict higher PSNR just noticeable difference points.

Keywords: JPEG; just noticeable difference points; peak signal-to-noise ratio; perceptually lossless

Abstract: This paper addresses the case when acoustic energy is applied to plastically deformed metals when the “Ultrasonic recovery phenomenon” is observed, which manifests itself in a decrease of dislocation density, hardness and yield strength. Based on the analysis of the processes occurring in the sonicated material's microstructure, a model has been developed within the terms of the synthetic theory of irrecoverable deformation. We introduce into the equation governing recovery processes a term for expressing the oscillating stress amplitude. The model results show good agreement with the experimental data.

Keywords: ultrasound; plastic deformation; recovery

Abstract: Maintenance is one of the more important processes that affect productivity and creates added value, for the main process within every company. The aim of this paper is to highlight the process of vehicle fleet maintenance and the evaluate the economic and technical benefits, in terms of optimizing the maintenance process. We used economic, comparative analysis to evaluate the maintenance process. Pareto analysis on the principle (80/20%) presents critical service activities for each type of bus. Critical services of the maintenance process are shock absorbers, cooler, planning service, brake lines. Important features of the maintenance process are cost optimization, capacity planning, workload utilization, optimal material delivery and monitoring the fulfillment of the plan for every company, in various areas of industry. The solution to the problems of fleet maintenance is the introduction of a proactive maintenance approach, aimed at improving the individual activities and capabilities of the vehicles used in the process.

Keywords: maintenance; efficiency; plan; life cycle; vehicle fleet