Tomas Bata University in Zlín
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Metaheuristic-based model optimization of a steam-filled chamber
Full text linkSteam-filled chambers are an important part of many technological processes, among others, in tire manufacturing and electricity production from thermal power plants. This work proposes a chamber model as a strongly nonlinear process with time-delays, where parameters depend on operating conditions and may vary in time. To identify system parameters, a parametric optimization task is formulated that minimizes the fit of the model to factory-measured data under varying valve opening conditions. Two significantly different approaches were used to solve this nonlinear optimization task. The first utilized local and semi-local optimization with prior knowledge derived from solving a simplified variant of the task. The second used global optimization methods without any prior knowledge. The obtained parametric models were compared based on the quality of fit and the sensitivity and stability analysis of the obtained solutions. The achieved models reflect real data with high accuracy, with mean squared errors as low as 0.0138 on output values ranging from 0.0 to 20.0, representing less than 0.1% of the output range. The solutions differ significantly in the values of the obtained parameters. The use of multiple methods has thus made it possible to obtain a diverse set of solutions, which is particularly valuable in applications for difficult engineering problems. Results of this work can be further used e.g. for subsequent step - optimal control system design for the given process and operating conditions.Polish National Science Centre, Polish Ministry of Education and Science Funds [2020/39/I/ST7/02285]; Czech Science Foundation (GACR) [GF21-45465L]; Internal Grant Agency of the Tomas Bata University in Zlin [IGA/CebiaTech/2023/004
On the recontextualization of meme quiddity: A case study of the TikTok meme #аясейчасвампокажу
No full textThis paper analyzes the discursive operation of recontextualization in shaping the multimodal meme quiddity of #аясейчасвампокажу on TikTok (TT) by examining the interplay of platform design, the pragmatic aim of the creator and user reactions. Meme quiddity is understood as a flexible (multimodal) configuration of semiotic attributes recurrent in internet meme (IM) variants, serving as a contextualization cue and formative factor of an IM family (Segev et al. 2015). The study employs top-down and bottom-up procedures combining multimodal (critical) and socio-pragmatic discourse analyses. We focus on four user strategies for recontextualizing meme quiddities: combining memes, replacing the element of quiddity, placing the quiddity in an absurd context, and embedding the quiddity into new discourse. It was noted that the platform’s algorithm and affordances impact (1) the contextualization of quiddity in the TT derivatives within a family; and (2) the modes of quiddity entextualization. Humor plays a pivotal role in shaping social group cohesion and “playful patriotism”
Review of modern nonlinear control methods
No full textNonlinear control methods are essential for effective control of complicated dynamic systems, particularly when conventional linear techniques are ineffective. The paper presents an extensive review of contemporary nonlinear control techniques, paying particular attention to their categorization, theoretical backgrounds, and real-world applications. The study starts with the introduction of nonlinear system properties and mathematical modeling, followed by a bibliometric analysis that illustrates the increasing popularity of the research topic. The article classifies nonlinear control methodologies into two broad categories: system linearization-based and nonlinear control law-based direct approaches. Adaptive control, Nonlinear Model Predictive Control, and Artificial Intelligence-based control methodologies are investigated in detail and systematically compared based on recent experimental findings. Particular emphasis is placed on novel developments, e.g., data-driven control methods and optimization-based techniques, which have shown encouraging results in practical applications. The results emphasize the growing role of machine learning and model-free methods in nonlinear control. The review is a valuable resource for researchers and practitioners interested in getting acquainted with state-of-the-art nonlinear control techniques and their changing background.This research was funded by the Internal Grant Agency of Tomas Bata University supported under project No. IGA/CebiaTech/2024/002
Tunable electrical conductivity of nickel-polypyrrole microparticle suspensions under electric and magnetic fields
No full textThis study presents a new class of low-cost composite materials based on a silicone oil suspension containing nickel-based hybrid microparticles in equal volume fractions. Two types of suspensions were prepared using magnetic particles with different compositions. The nickel content of the hybrid microparticles was 78.3 and 83.4 wt% nickel core, respectively, with the rest being a polypyrrole coating composed of nanoparticles and nanotubes. Utilizing a specialized experimental setup, we investigated the influence of nickel concentrations on the electrical conduction properties of the suspensions. The measurements were performed under electric and combined electric and magnetic fields. Our findings demonstrate that the electrical conductivity has a non-linear response with external fields and it can be effectively tuned through the nickel content and the external fields. A model was developed to understand the observed trends. These findings have significant implications for the design and optimization of advanced materials in applications requiring the precise control of electrical properties under varying field conditions.The authors acknowledge the project DKRVO (RP/CPS/2024-28/007) supported by the Ministry of Education, Youth and Sports of the Czech Republic. The authors M. S., A. M., and L. M. wish to thank the Czech Science Foundation [23-07244S] for the financial support.Grantov Agentura Ccaron;esk Republiky [RP/CPS/2024-28/007]; Ministry of Education, Youth and Sports of the Czech Republic [23-07244S
Machining process optimization using a model based on criterial functional dependence
Full text linkThis research deals with the optimization of the machining process using a model based on criterial functional dependence hypothesis. The basis of this hypothesis is the assertion that for each production process of a given product with many input parameters, at given known requirements and conditions, it is possible to determine the minimum/maximum local extremum, that is, to find the most suitable conditions under which the criterion is achieved. To verify the optimization model, three different cutting tools (cutting inserts) were compared within the criteria functions set for cutting force Fc, process power P, and surface roughness characteristics Rz, all with two independent variables—cutting speed vc and feed f. The technology of turning with longitudinal external machining of the cylindrical surface was selected as the operation for the experiment. Taking into account the importance of individual criteria for real practice and the minimum extreme values achieved (a surface roughness Rz = 2.2 μm and cutting power p = 14,700 W at vc = 145 m/min and f = 0.8 mm), the tool with a linear cutting edge (LCE) designed at the authors' workplace appeared as the most suitable tool for machining operation under the given conditions when compared with commercially produced cutting tools TCMT 16T308-PR 4035 and CNMG 120408-WM 4025.The authors would like to express their gratitude to the Ministry of Education, Science, Research and Sports of the Slovak Republic for research support provided through the grants APVV-19-0550 and KEGA 042TUKE-4/2025, as well as the CEEPUS agency within the network SK-2026-01-2526.Ministry of Education, Science, Research and Sport of the Slovak Republic; CEEPUS agency [SK-2026-01-2526]; [APVV-19-0550]; [KEGA 042TUKE-4/2025
Integrated expert model for risk assessment and ensuring the safety of tourist trips: Economic and technological aspects
Full text linkThis study aims to develop an integrated expert model for assessing and ensuring the safety of tourist trips, with a particular emphasis on the economic and technological aspects. The model adopts a multi-level approach, spanning from individual perceptions of safety during travel to national assessments of regional tourism security. The economic factors, such as the financial implications of safety measures and their impact on regional tourism development, play a central role in this approach. The methodological foundation includes the theory of fuzzy sets and fuzzy logic, expert evaluations, and knowledge-based intelligent analysis. For the first time, we received: an information model for assessing the safety level of a tourist trip; a fuzzy method for determining the aggregated term risk assessment of one’s safety of a tourist trip; an expert method of assessing the level of the sense of security of the region on the part of the participants of the tourist movement; a hybrid method of determining the degree of risk to the safety of a tourist trip. The model not only provides a quantitative assessment but also highlights the safety risks associated with tourist trips, thereby enabling regional analyses that support decision-making aimed at ensuring tourism safety through both economic and technological means solutions.This research was funded by the Ministry of Education, Research, Development and Youth of the Slovak Republic and the Slovak Academy of Sciences as part of the research project VEGA No. 1/0700/25. This paper was supported by the EU Next Generation EU through the Recovery and Resilience Plan for Slovakia under project No. 09I03-03-V01-00059.Ministry of Education, Research, Development and Youth of the Slovak Republic; Slovak Academy of Sciences as part of the research project VEGA [1/0700/25]; EU Next Generation EU through the Recovery and Resilience Plan for Slovakia [09I03-03-V01-00059
Cooperation between parents and teachers in the early identification of mental disorders and problem behaviour of pupils at the primary level of education
Full text linkThe increase in the incidence of mental disorders and problem behaviour in students and their negative impact on their social life in the classroom and academic performance require increased attention to diagnosing various attributes of children's mental health. The first level of primary school is an ideal environment for implementing screening procedures to detect early symptoms. The aim of our contribution was to identify how to combine the views of teachers and parents on the child's behaviour in diagnosing mental health problems and behaviour disorders. Therefore, we compared and analysed the views of teachers and parents on the symptoms of mental and behavioural problems of students using the SDQ questionnaire. The results of our research confirmed the usefulness of using multiple informants in school screening and, in the context of the findings, we were able to identify ways to combine the teacher and parent perspectives in diagnosing externalizing and internalizing manifestations of sleep problems in primary school students.Scientific Grant Agency of the Ministry of Education, Science, Research and Sport of the Slovak Republic; Slovak Academy of Sciences [KEGA 019 Zcaron;U-4/2023
Changing participation in adult education and training in the Czech Republic: Who participates and who is likely to participate in AET?
Full text linkParticipation in adult education and training (AET) is one of the primary strategies of the European Commission for developing human capital and retaining a competitive labour force. AET is also regarded as a means to tackle educational inequality as it can provide a second chance at gaining qualifications or job-related skills. This study uses data from three rounds of the Adult Education Survey (AES) involving a total of 25,405 respondents to investigate empirically the changes in the participation of adults (ages 25 to 64) in AET in the Czech Republic (CZE). Building on the study by Simonov & aacute; and Hamplov & aacute; (2016), this article aims to show how participation in AET, as measured by the AES, evolved in the CZE from 2011 to 2022, and which educational, age, and socioeconomic groups had the greatest likelihood of engaging in AET. The study tests two competing hypotheses regarding inequality in AET participation: (1) the persistence of key sources of inequality and (2) the partial democratisation thesis, i.e., the gradual reduction of some factors of inequality. To test these hypotheses, the two types of AET in which adults in the CZE are most involved were selected: non-formal education (NFE) and employer-supported job-related NFE. The results of the empirical analysis indicate that AET participation has stagnated over the past decade, with more than half of the population not regularly engaging in AET annually. Using multinomial logistic regression (MLR), the study demonstrates that, although significant inequalities based on educational attainment, economic activity, age, and socioeconomic status are still evident, these inequalities have been weakening since 2011. This trend has occurred through a two-phase democratisation of participation, which exhibited different characteristics in 2016 and 2022
AI in supply chain: Techniques, applications, real-world cases and benefits under SCOR framework
No full textThis article focuses on practical perspectives of Artificial Intelligence (AI) applications in Supply Chain Management by exploring commonly used AI techniques, use cases and benefits of applying AI in Supply Chain Management with real-world examples from multinational corporations like DHL, IBM, Walmart, Amazon, Google, among others. The findings are grouped according to the four stages of the SCOR (Supply Chain Operations Reference) framework, i.e plan, source, make, deliver, to facilitate visualization. We find that AI techniques including Neural Networks, Genetic Algorithms, Support Vector Machines, Reinforcement Learning, Fuzzy Logic, and Natural Language Processing are applied to enhance supply chain efficiencies, lower costs, increase profits, improve customer satisfaction, save operational time, reduce potential disruption, better suppliers/customers relationships, improve product quality, enhance safety, and shorten lead times... These stem from nine benefit groups, namely PLAN (demand forecasting, inventory optimization, supply risk mitigation), SOURCE (procurement, supplier selection), MAKE (product quality assurance, smart warehouse management, predictive maintenance), DELIVER (route optimization, dynamic pricing, and last mile delivery, and customer service). Limitations and future research directions are discussed
Enhancing healthcare outcomes through big data and statistical models synergy
No full textBig data has become a crucial tool across multiple sectors worldwide. Due to the accumulative data inundation, it is cumbrous for humans to examine the vast amount of data. Therefore, big data along with AI (Artificial Intelligence) techniques are used for solving concerns associated to data storage, accessibility of important data and other aspects of big data. However, data in general can be both structured and unstructured in nature, which often affects the quality and accuracy for data analysis. Therefore, in order to overcome these pitfalls, advancements of AI technologies and big data analytics significantly aids in commendably extracting both structured and unstructured data which ultimately increases quality of the model. Owing to these advantages, big data is used across fields and more especially in healthcare industry, as big data aids in analyzing huge amount of data and offer valuable insights in terms of disease patterns, personalized treatment and ensures in discovering new drugs. Moreover, big data also plays a huge role towards cancer detection. Therefore, this paper focuses on reviewing big data, significance of big data, synergy of AI and big data approaches in healthcare sector. Further, different case studies are discussed in the paper, in addition, applications of big data in healthcare sector is reviewed in the current study for examining the importance of big data in healthcare sector. Eventually, the challenges are identified through the analysis of existing researchers and future recommendations are provided for overcoming the gaps that are intended to create encouraging work in this area