ISSN: 3106-0536

Issue 1, 2026

Articles
Information

Bridging Epidemiological Modelling and Quality Engineering: Optimal Mitigation of Viral Contagion
Appears in:
dtip 26(1):

Authors:
Daniel Botelho, M. Teresa Monteiro, Senhorinha Teixeira

Abstract:
Industry 5.0 is based on the digital technologies of Industry 4.0. However, it shifts the main focus from pure efficiency to a sustainable, resilient, human-centred approach. Since quality control and waste reduction are essential to sustainability, this exploratory study proposed an innovative app…Industry 5.0 is based on the digital technologies of Industry 4.0. However, it shifts the main focus from pure efficiency to a sustainable, resilient, human-centred approach. Since quality control and waste reduction are essential to sustainability, this exploratory study proposed an innovative approach: the adaptation of epidemiological models to quality control. Specifically, this paper investigates how the classical Susceptible-Infected-Recovered (SIR) model, usually applied to biological disease and information transmission, can be also adapted to determine the optimal moment to apply decisions of quality control and, or, predictive maintenance interventions. The methodology formulated an Optimal Control (OC) problem which is first validated using an empirical case of disinformation spread and solved numerically through an indirect method (using the CasADi software). Results show that OC strategies were able to minimise the system's global cost as well as reduce the number of infected individuals by 8.46\%, when comparing to a non controlled scenario. It is concluded that, by transferring this mathematical framework to physical manufacturing production lines, conceptualizing the propagation of defects in processes or components as an infectious phenomenon, engineers and managers can be equipped with a quantitative, data-driven tool. This allows the optimisation of timely interception of defects at their source, ensuring a sustainable reduction of industrial waste as well as the value of operators’ decisions within production centres in the context of Industry 5.0.
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Study of the influence of increased productivity on the efficiency of separation and dewatering of polydispersed coal enrichment sludges in a modular installation with filtration and sedimentation centrifuges
Appears in:
dtip 26(1):

Authors:
A. Shkop, N. Ponomarova, O. Shestopalov, A. Bosiuk, V. Babenko

Abstract:
Coal enrichment processes are accompanied by the formation of a significant amount of polydisperse sludges, the effective separation and dewatering of which is an important technological and environmental problem. The aim of this work was to study the influence of increasing the module productivity…Coal enrichment processes are accompanied by the formation of a significant amount of polydisperse sludges, the effective separation and dewatering of which is an important technological and environmental problem. The aim of this work was to study the influence of increasing the module productivity (increasing the solid phase flow rate by three times – up to 5.523 t/h on a dry basis) on the efficiency of separation and dewatering of polydisperse anthracite sludges of class 0–3 mm in the FGU-600 + OGS-469 modular installation. It was established that at increased load, the filtration centrifuge ensures the extraction of granular fraction with a moisture content of 8.8 % and ash content of 16 %, while the sedimentation centrifuge provides dewatering of fine fractions to a moisture content of 21.35–24 % at a solid phase concentration in the effluent of 2.5 g/L. Comparative analysis with previous results showed the preservation of high efficiency of the technological scheme, a slight improvement in the moisture content of the granular product and an acceptable increase in the loss of fine fractions. The obtained data confirm the possibility of scaling the proposed technology to industrial conditions.
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A Two-Phase Hybrid Metaheuristic Framework for Engineering Optimization
Appears in:
dtip 26(1):

Authors:
A. Baskar , Justine Yasappan, M. Anthony Xavior, Anna Burduk, Suthep Butdee

Abstract:
Population-based optimization metaheuristic algorithms generate a pool of candidate solutions in the “Initialization” phase and these approximate solutions are iteratively refined further in the subsequent “Improvement” phase(s) towards the optimal/near-optimal solution. Any population-based algori…Population-based optimization metaheuristic algorithms generate a pool of candidate solutions in the “Initialization” phase and these approximate solutions are iteratively refined further in the subsequent “Improvement” phase(s) towards the optimal/near-optimal solution. Any population-based algorithm may have a single or multiple “Improvement” phase(s). This paper analyses the impact of having two improvement phases. Different updating expressions are considered in each phase of the algorithm. In one case, the “C-Sine” algorithm, two new untested expressions are used, and performance is analysed. In the other case, the better performing Grey Wolf Optimizer (GWO) is applied in the first phase, and a new updating trigonometric expression is used in the second phase (termed as GWO:SineL algorithm) and analysis is carried out. The second phase applies the trigonometric "Sine" function over the random numbers generated using the Levy Flight Strategy. Mathematical functions, the CEC2019 dataset and a few real-world engineering problems are used for the analyses. Finally, the application of the “C-Sine” algorithm for solving multi-objective problems and the “GWO:SineL” algorithm for supply chain problems are studied. Codes are generated in MATLAB and run on an i5 PC with 4 GB RAM.
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Organizational Continuity and Human-Centered AI: Rethinking Governance in Aging Digital Economies
Appears in:
dtip 26(1):

Authors:
Teresa Dieguez, Jackson Barreto

Abstract:
INTRODUCTION: Digital transformation is often associated with technological sophistication, data availability, automation, and Artificial Intelligence ( AI)-mediated coordination. However, organizations may preserve more information while losing the human and institutional capacity to interpret, go…INTRODUCTION: Digital transformation is often associated with technological sophistication, data availability, automation, and Artificial Intelligence ( AI)-mediated coordination. However, organizations may preserve more information while losing the human and institutional capacity to interpret, govern, and use that information over time. This risk is especially relevant for small and medium-sized enterprises (SMEs), where tacit knowledge, contextual judgment, and governance memory are often concentrated in experienced professionals.
OBJECTIVES: This paper reframes digital transformation as an organizational continuity governance problem. It aims to explain how AI-mediated coordination, demographic aging, distributed work, and concentrated tacit knowledge may generate continuity asymmetry, understood as the gap between increasing technological coordination capacity and insufficient hu man-centered mechanisms fo r preserving interpretive continuity.
METHODS: The study develops an integrative conceptual analysis based on selective theoretical synthesis. It connects digital transformation, organizational memory, knowledge transfer and knowledge loss, sociomateriality, human-centered AI, AI governance, aging workforce research, and productive longevity to build a sociomaterial continuity perspective.
RESULTS: The paper distinguishes informational continuity from interpretive continuity and conceptualizes experienced professionals as continuity actors who sustain tacit knowledge, relational memory, contextual judgment, and governance understanding across time. It proposes the Sociomaterial Continuity Governance Framework, organized around four mutually reinforcing governance functions: knowledge risk and memory mapping, continuity actors and intergenerational transfer, human oversight of AI-mediated coordination, and modular continuity routines and audits for SMEs. The paper also formulates theoretical propositions to guide future empirical research.
CONCLUSION: Sustainable digital transformation depends not only on expanding technological capability, but also on preserving the interpretive conditions through which organizational knowledge remains meaningful, transferable, and governable. The article contributes by positioning organizational continuity as a sociomaterial governance achievement and by offering a conceptual framework for reducing continuity asymmetry in AI-mediated and aging SME environments.
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Application of Artificial Neural Networks for Quality Classification in Electromobility Manufacturing Processes
Appears in:
dtip 26(1):

Authors:
Paula Czarnik, Katarzyna Antosz, Jose Mendes Machado

Abstract:
INTRODUCTION: Modern manufacturing requires proactive, data-driven quality control methods that support process stability, reduce non-conformities and improve product reliability. OBJECTIVES: The objective of this paper is to evaluate the applicability of selected artificial neural network models …INTRODUCTION: Modern manufacturing requires proactive, data-driven quality control methods that support process stability, reduce non-conformities and improve product reliability.
OBJECTIVES: The objective of this paper is to evaluate the applicability of selected artificial neural network models for quality classification in an electromobility-related manufacturing process.
METHODS: The study used empirical production data, SIPOC process analysis and five neural network models evaluated with accuracy, error rate, validation cost, operational indicators, confusion matrices and ROC/AUC analysis.
RESULTS: The analysed models achieved validation accuracy above 95%, with the Narrow Neural Network obtaining the best overall result of 97.0% accuracy, 3.0% error rate and the lowest validation cost.
CONCLUSION: The results confirm that artificial neural networks can effectively support quality classification and proactive quality management in electromobility manufacturing processes.
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Regression analysis of the effect of Ge and Si on the torsional strength and fracture angle of aluminium alloys using one-way ANOVA
Appears in:
dtip 26(1):

Authors:
Umidjon Mardonov, Sarvar Tursunbaev, Nigora Rizaeva, Nuritdin Tadjiev, Nargiza Mahkmudova

Abstract:
INTRODUCTION: Aluminium alloys represent a highly sought-after category of structural materials, ranking just below iron-carbon alloys in their application within the manufacturing sector. In this study, the effect of germanium as an oxide (GeO2) form and Si, introduced as modifying additives into…INTRODUCTION: Aluminium alloys represent a highly sought-after category of structural materials, ranking just below iron-carbon alloys in their application within the manufacturing sector. In this study, the effect of germanium as an oxide (GeO2) form and Si, introduced as modifying additives into aluminium casting alloys on their torsional strength was investigated.
OBJECTIVES: Aluminium alloys ZL201 and AA5056 were selected as a study objective which are widely used in the production of shaped castings.
METHODS: During the study, germanium as an oxide (GeO2) form and silicon were introduced into the composition of selected aluminium alloy grades in various combinations, after which their torsional strength was tested using WP 500 testing equipment. Specimens melted in an induction furnace and cast at 720 °C into sand-clay moulds. The moulds were prepared as standardized specimens. During the study, the torsional failure angle and torsional strength of the aluminium specimens were determined. These parameters were calculated using a computer program linked to the WP 500 testing equipment. The effect of adding germanium and silicon on torsional strength and fracture angle of ZL201 and AA5056 aluminium alloys was investigated. One-way ANOVA was used to verify the reliability of the experimental tests.
RESULTS: It was found that the torsional strength of the alloy increased under the influence of germanium and silicon; however, when the germanium content exceeded 2%, the torsional strength of the studied aluminium alloys decreased. Based on the obtained results, graphs of the fracture angle and torsional strength as a function of the germanium and silicon content in the alloy were constructed.
CONCLUSION: Obtained results demonstrated that the addition of GeO2 with Si led to significant grain refinement, dispersion of eutectic and intermetallic phases, and improvement in structural homogeneity. The findings highlight the existence of a well-defined optimal Ge% concentration around 2%, at which both frictional and mechanical properties are maximized.

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Integrating Industry 4.0 Technologies into Project Management: A Framework Proposal
Appears in:
dtip 26(1):

Authors:
Lucas Gomes, Ana Cristina Ferreira

Abstract:
INTRODUCTION: The integration of Industry 4.0 technologies and Project Management represents a paradigm shift from traditional, experience-based practices to data-driven and intelligent systems. By leveraging advanced tools such as Artificial Intelligence, Big Data analytics, Internet of Things and…INTRODUCTION: The integration of Industry 4.0 technologies and Project Management represents a paradigm shift from traditional, experience-based practices to data-driven and intelligent systems. By leveraging advanced tools such as Artificial Intelligence, Big Data analytics, Internet of Things and Digital Twins, Project Management processes become increasingly predictive, adaptive and efficient.
OBJECTIVES: The objective of this study is to understand the main impacts of Industry 4.0 technologies and tools on the improvement of Project Management. There is limited research on the impacts and improvements brought by Industry 4.0 in Project Management, making it an interesting gap to study. It is also intended to propose a conceptual framework to guide the integration of Industry 4.0 tools into Project Management practices.
METHODS: A Systematic Literature Review was conducted using a mixed methodological approach, combining an inductive investigation approach, an exploratory research nature and grounded theory as the research strategy.
RESULTS: The results indicate that the application of Industry 4.0 technologies and tools improves Project Management. Regarding the technologies and tools of Industry 4.0 applied to enhance Project Management outcomes, Artificial Intelligence, Big Data and Building Information Modelling emerged as the key ones. Among the main impacts and improvements resulting from the integration of these technologies into Project Management, the enhancement of the decision-making process and tasks automation stands out. A correlation matrix was developed between Industry 4.0 technologies and the impacts/improvements generated in Project Management. Additionally, a framework for integrating Industry 4.0 and Project Management was proposed, based on the principles of continuous improvement.
CONCLUSION: This framework is relevant for researchers, organizations, project managers and their respective teams who aim to enhance Project Management in the Industrial Revolution paradigm.
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Machine Learning for Equipment Failure Prediction in Smart Manufacturing Plants: A Review
Appears in:
dtip 26(1):

Authors:
Kamil Musiał, Anna Barduk, Karolina Iwańczyk

Abstract:
INTRODUCTION:Smart manufacturing plants generate continuous streams of vibration, acoustic, thermal, electrical, process-control and quality data. These data make it possible to move from reactive or calendar-based maintenance toward predictive maintenance, but they also introduce problems of missi…INTRODUCTION:Smart manufacturing plants generate continuous streams of vibration, acoustic, thermal, electrical, process-control and quality data. These data make it possible to move from reactive or calendar-based maintenance toward predictive maintenance, but they also introduce problems of missing labels, non-stationary operating regimes, rare failures and unequal costs of false alarms and missed failures.
OBJECTIVES:This review analyses how machine learning is used to forecast equipment failures and remaining useful life in intelligent production environments, with emphasis on the suitability of methods for real industrial deployment rather than benchmark accuracy alone.
METHODS:A structured narrative review was conducted over established prognostics and health management literature, Industry 4.0 predictive-maintenance surveys, and representative empirical studies using public and industrial datasets such as C-MAPSS, PRONOSTIA, IMS bearings, Bosch Production Line Performance, semiconductor ion-implantation data and IoT-enabled production-line data.
RESULTS:The reviewed literature shows that feature-based classifiers, gradient boosting, support vector machines and random forests remain competitive when labels are scarce and process knowledge is available. Deep learning improves representation learning for multivariate sequences, especially for vibration and run-to-failure data, but requires careful validation against temporal leakage and domain shift. Hybrid approaches that combine signal processing, physics, digital twins and cost-aware decision rules are increasingly important for plant-level use.
CONCLUSION:Machine learning can substantially improve failure prediction in smart factories, yet its value depends on data governance, uncertainty handling, maintainability and integration with maintenance planning. The most credible systems treat prediction as a socio-technical decision process, not as a stand-alone model.
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Modern Approaches to Monitoring the Technical Condition of Heavy-Duty Gears
Appears in:
dtip 26(1):

Authors:
A. V. Gaydamaka, V. V. Klitnoi, V. V. Klitnoy, O. I. Naumov

Abstract:
INTRODUCTION: Gear transmissions of heavy-duty drive reducers operate under variable loads that cause fatigue damage (pitting and tooth fracture), therefore effective condition monitoring is essential to prevent failures.. OBJECTIVES: To conduct a review of the directions and methods for monitorin…INTRODUCTION: Gear transmissions of heavy-duty drive reducers operate under variable loads that cause fatigue damage (pitting and tooth fracture), therefore effective condition monitoring is essential to prevent failures..
OBJECTIVES: To conduct a review of the directions and methods for monitoring the technical condition of gearbox gears, to select an appropriate monitoring approach for the gears of high-power machine drives, and to propose the most effective method taking into account operating conditions and gear tooth control zones.
METHODS: The research employs an instrumental monitoring approach based on measuring the surface hardness of gear teeth using portable hardness testers. The methodology involves comparative analysis of metal hardness in two critical zones: the tooth end faces and the working surfaces.
RESULTS: Based on the conducted review, a method for monitoring the technical condition of gearbox gears in high-power drives has been developed. This method can be applied both in workshop and field operating conditions, enabling inspection of the working and non-working tooth surfaces with or without gearbox disassembly.
CONCLUSION: The developed monitoring method for gearbox gears of high-power drives will serve as a foundation for the development of a method for predicting the remaining service life of gearbox gears in high-power machine drives
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Digital Product Passport for Machine Tools as an Information Core of Sustainable Manufacturing Engineering
Appears in:
dtip 26(1):

Authors:
Sergey Dobrotvorskiy, Yevheniia Basova, Paweł Zawadzki, Mikołaj Kościński, Ruslan Mygushchenko, Oleksandr Permiakov, Brita Pyttel, Houssem Badreddine

Abstract:
INTRODUCTION: The transition toward Industry 5.0, sustainable manufacturing, and the European Digital Product Passport (DPP) concept creates new requirements for machine-building enterprises and engineering education. Modern machine tools are no longer considered isolated manufacturing units but co…INTRODUCTION: The transition toward Industry 5.0, sustainable manufacturing, and the European Digital Product Passport (DPP) concept creates new requirements for machine-building enterprises and engineering education. Modern machine tools are no longer considered isolated manufacturing units but complex cyber-physical systems generating continuous technological, operational, and diagnostic information throughout their lifecycle. In this context, the integration of Digital Product Passports into manufacturing systems becomes a key factor in ensuring traceability, sustainability, adaptability, and efficient lifecycle management.
OBJECTIVES: The objective of this paper is to develop a conceptual and practical framework for a Digital Product Passport of machine tools as an integrated information core for sustainable manufacturing engineering, lifecycle-oriented traceability, and technological decision-making within Industry 5.0 environments.
METHODS: The study is based on system analysis, lifecycle-oriented engineering approaches, digital manufacturing concepts, and information-flow modeling. A structured mathematical representation of technological information exchange between machine tool modules, technological parameters, monitoring systems, and decision-making units is proposed. The framework integrates CAD/CAM/CAE environments, QR-based identification, sensor data acquisition, cloud-oriented information support, and Alicona-based surface metrology according to ISO 25178.
RESULTS: A hierarchical Digital Product Passport architecture for machine tools was developed and experimentally validated using QR-linked engineering information structures and high-resolution surface metrology. The proposed model enables continuous synchronization of geometric, technological, operational, and diagnostic information throughout the machine lifecycle. Alicona InfiniteFocus G5 measurements confirmed the formation of stable microrelief structures suitable for reliable QR-code readability and engineering traceability. The developed framework additionally integrates Digital Twin structures, lifecycle-oriented monitoring, and educational engineering datasets within the Erasmus+ MechDiTS project, supporting Industry 5.0-oriented engineering education.
CONCLUSION: The proposed Digital Product Passport framework transforms machine tools into information-centric cyber-physical systems capable of supporting sustainable manufacturing, lifecycle traceability, predictive maintenance, and adaptive technological management. The integration of industrial engineering datasets, Digital Twins, and lifecycle-oriented information environments into engineering education creates new opportunities for preparing specialists capable of operating within data-driven Industry 5.0 manufacturing ecosystems.
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Scope

Digital transformation has given rise to a series of new innovative production processes, integration of supply chains for decision making, performance evaluation, optimization and adaptation, as well as greater complexity in the development of innovative products, simulation of complex behaviors and development of sophisticated equipment, on using advanced techniques and tools, as machine learning, artificial intelligence, Big Data And Analytics, Autonomous Robots, Industrial Internet Of Things (IIoT), Design of Cyber-Physical Production Systems (CPPS), Simulation/ Digital Twin, e-Maintenance, Augmented Reality, Additive Manufacturing and Systems Interoperability. All the papers developed on the mentioned above topics are welcome to this journal.

Indexing: CrossRef, Dimensions, Semantic Scholar, The Lens

eISSN: 3106-0536

Editor-in-Chief:
José Mendes Machado, University of Minho, Portugal
Co Editor-in-Chief:
Justyna Trojanowska, Poznan University of Technology, Poland
Associate Editors:
Bogdan Grămescu, National University of Science and Technology Politenhica Bucharest, Romania
Erika Ottaviano…

Editor-in-Chief:
José Mendes Machado, University of Minho, Portugal
Co Editor-in-Chief:
Justyna Trojanowska, Poznan University of Technology, Poland
Associate Editors:
Bogdan Grămescu, National University of Science and Technology Politenhica Bucharest, Romania
Erika Ottaviano, University of Cassino and Southern Lazio, Italy
Katarzyna Antosz, Rzeszow University of Technology, Poland
Lucia Knapčíková, Technical University of Košice, Slovakia
Pedro Torres, Polytechnic University of Castelo Branco, Portugal
Petr Valášek, Czech University of Life Sciences, Czech Republic
Suthep Butdee, Rajamangala University of Technology Krungthep, Thailand
Vijaya Kumar Manupati, Indian Institute of Mumbai, India
Yevheniia Basova, National Technical University "Kharkiv Polytechnic Institute" Ukraine
Editorial Board Members:
Abbas Al-Refaie, University of Jordan, Jordan
Agnieszka Kujawińska, Poznan University of Technology, Poland
Alejandro Pereira, University of Vigo, Spain
Alexandru Sover, Ansbach University of Applied Sciences, Germany
Aminul Islam, Technical University of Denmark, Denmark
Ana Simões, INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Portugal
Andre D. L. Batako, Liverpool John Moores University, UK
Anna Burduk, Wrocław University of Science and Technology, Poland
Anna Paraskevopoulou, Anglia Ruskin University, UK
Anthony Xavior Michael, Vellore Institute of Technology, India
Anton Rassõlkin, Tallinn University of Technology (TalTech), Estonia
Arkadiusz Gola, Lublin University of Technology, Poland
Arun Nagarajah, University of Duisburg-Essen, Germany
Arunita Jaekel, University of Windsor, Canada
Camelia Avram, Technical University of Cluj Napoca, Romania
Celina P. Leão, University of Minho, Portugal
Chi Hieu Le, University of Greenwich, United Kingdom
Dagmar Cagáňová, Newton University, Czech Republic
Daniela Cioboata, National Institute of Research and Development in Mechatronics and Measurement Technique, Romania
Darius Mazurkiewicz, Lublin University of Technology, Poland
Dragan Peraković, University of Zagreb, Croatia
Eduardo André Perondi , Universidade Federal do Rio Grande do Sul, Brazil
Eliani Ezani, Universiti Putra Malaysia, Malaysia
Enes Sukić, University Union-Nikola Tesla, Serbia
Filomena Soares, University of Minho, Portugal
Filip Górski, Poznan University of Technology, Poland
Francisco Silva, Polytechnic of Porto, Portugal
František Gazdoš, Tomas Bata University in Zlín, Czech Republic
Fumio Mizuno, Tohoku Institute of Technology, Japan
Gerhard Wilhelm Weber, Poznan University of Technology, Poland
Géza Husi, University of Debrecen, Hungary
Gisela Campos, Anhembi Morumbi University, Brazil
Filipe Pereira, Polytechnic Institute of Porto, School of Engineering of Porto, Portugal
Hajji Bekkay, National School of Applied Sciences, Mohamed Premier University, Malaysia
Hans Georg Gemuenden, Technische Universität Berlin, Germany
Ion Stiharu, Concordia University, Canada
Itsuro Kajiwara, Hokkaido University, Japan
Ivan Pavlenko, Sumy State University, Ukraine
Ján Piteľ, Technical University of Košice, Slovakia
Jiehan Zhou, Shandong University of Science and Technology, China
Jiri Vojtesek, Tomas Bata University in Zlin, Czech republic
José Carlos Sá, Polytechnic of Porto, Portugal
Jozef Husár, Technical University of Kosice, Slovakia
Judit T. Kiss, University of Debrecen, Hungary
Larry Xiancun Hu, University of Canberra, Australia
Leonilde Varela, University of Minho, Portugal
Liliana-Laura Badita-Voicu, National Institute of Research and Development in Mechatronics and Measurement Technique, Romania
Lorena Siguenza-Guzman, University of Cuenca, Ecuador | KU Leuven, Belgium
Magdalena Diering, Poznan University of Technology, Poland
Malebogo Ngoepe, University of Cape Town, South Africa
Mallikarjuna Reddy D, Vellore Institute of Technology, India
Marina Jarvis, Tallinn University of Technology (TalTech), Estonia
Maryna Ivanova, National Technical University, Ukraine
Michal Balog, AMBIS University, Czech republic
Milan Edl, University of West Bohemia,, Czech Republic
Mohammad Arani, Trine University, USA
Monia Najar, University of Tunis El Manar, Tunisia
Morteza Saberi, University of Technology Sydney, Australia
Naiming Xie, Nanjing University of Aeronautics and Astronautics, China
Oleg Zabolotnyi, Lutsk National Technical University, Ukraine
Olha Prokopenko, Estonian Entrepreneurship University of Applied Sciences, Estonia
Olena Hrybiuk, International Scientific and Technical University, Ukraine
Phatchani Srikhumsuk, Rajamangala University of Technology Krung, Thailand
Pierluigi Rea, University of Cagliari, Italy
Rajasekhara Reddy Mutra, VIT University,, India
Regita Bendikiene, Kaunas University of Technology, Lithuania
Sahin Yildirim, Erciyes University, Turkey
Salwan Khafaji, University of Babylon, Iraq
Stefan Vöth, Technische Hochschule Georg Agricola, Germany
Tomohiro Fukui, Kyoto Institute of Technology, Japan
Teresa Morgado, Polytechnic Institute of Lisbon, Portugal
Teresa Dieguez, Polytechnic Institute of Cávado and Ave, Portugal
Timo Kikas, Estonian University of Life Sciences, Estonia
Umidjon Mardonov, Tashkent State “Technical University named after Islam Karimov", Uzbekistan
Vitalii Ivanov, Sumy State University, Ukraine
Vìtor Carvalho, Polytecnic Institute of Cávado and Ave – IPCA, Portugal
Wenbin Dai, Shanghai Jiao Tong University, China
Weronika Kruszelnicka, Bydgoszcz University of Science and Technology, Poland
XiuRong Fang, Xi'an University of Science and Technology, China
Yanling Cai, Zhengzhou University, China

Publisher: EAI
ISSN: 3106-0536
Volume: 2

Published: 2026-05-21

EUDL

Bridging Epidemiological Modelling and Quality Engineering: Optimal Mitigation of Viral Contagion

Study of the influence of increased productivity on the efficiency of separation and dewatering of polydispersed coal enrichment sludges in a modular installation with filtration and sedimentation centrifuges

A Two-Phase Hybrid Metaheuristic Framework for Engineering Optimization

Organizational Continuity and Human-Centered AI: Rethinking Governance in Aging Digital Economies

Application of Artificial Neural Networks for Quality Classification in Electromobility Manufacturing Processes

Regression analysis of the effect of Ge and Si on the torsional strength and fracture angle of aluminium alloys using one-way ANOVA

Integrating Industry 4.0 Technologies into Project Management: A Framework Proposal

Machine Learning for Equipment Failure Prediction in Smart Manufacturing Plants: A Review

Modern Approaches to Monitoring the Technical Condition of Heavy-Duty Gears

Digital Product Passport for Machine Tools as an Information Core of Sustainable Manufacturing Engineering

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