OpenAIRE

Permanent URI for this collectionhttp://hdl.handle.net/10084/89004

Kolekce určená pro sklízení infrastrukturou OpenAIRE; obsahuje otevřeně přístupné publikace, případně další publikace, které jsou výsledkem projektů rámcových programů Evropské komise (7. RP, H2020, Horizon Europe).

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Now showing 1 - 20 out of 5455 results

Pulse wave velocity estimation in a controlled In vitro vascular model: Benchmarking machine learning approaches
(MDPI, 2026) Barvík, Daniel; Černý, Martin; Procházka, Michal; Noury, Norbert
This study evaluates the feasibility of estimating stiffness-related parameters and pulse wave velocity (PWV) in a controlled in vitro circulatory setup using artificial silicone vessels with systematically varied Shore A hardness and wall thickness. From synchronized pressure and capacitive waveforms, fiducial points and engineered features are extracted, together with pump settings (stroke volume and heart rate). A Sugeno-type adaptive neuro-fuzzy inference system (ANFIS) is used for hardness-level prediction and benchmarked against linear regression and contemporary machine-learning/deep-learning baselines using stratified cross-validation. PWV estimates derived via hardness-to-elasticity conversion models and the Moens-Korteweg formulation are evaluated against a reference PWV obtained within the same experimental configuration. Under these controlled conditions, the proposed pipeline shows strong agreement with reference labels and measurements. The results should be interpreted as an in vitro validation step; translation to biological tissues or in vivo data will require external validation, calibration of material-property mapping, and robustness testing under physiological variability and measurement noise.
Social networks and social media as mediators of entrepreneurial entry among Indian women
(Springer Nature, 2026) Balcar, Jiří; Sinha, Prity; Johnson Filipová, Lenka; Horáková Hirschlerová, Nicole
This article examines how social networks and social media influence women's entrepreneurial entry in India. Using an explanatory sequential mixed-methods approach, we combine quantitative analysis of GEM data with qualitative interviews. The quantitative results show that having an entrepreneur in one's social network increases the probability of start-up involvement by 2.5 percentage points, while media exposure contributes an additional 1.8 percentage points. Interviews with women entrepreneurs illustrate how social media provides motivation, role-modeling, and perceived attainability, whereas personal networks offer emotional support, early clients, mentorship, and informal financing. Situating these findings within broader debates on social capital and female entrepreneurship, the study highlights how interpersonal and digital ties function as low-cost, relational resources in contexts of limited institutional support. This dual perspective advances existing research by clarifying the distinct yet complementary ways in which offline networks and online media shape women's early entrepreneurial decisions and help reduce perceived barriers.
Monitoring and assessment of potentially hazardous particles in high-risk workplaces in the production of Ni-Cd batteries
(Springer Nature, 2026) Tokarčíková, Michaela; Bernatíková, Šárka; Rössner ml., Pavel, Pavel; Vrbová, Kristýna; Šíma, Michal; Gabor, Roman; Běčák, Petr; Seidlerová, Jana
The manufacture of Ni-Cd batteries involves the risk of contact with and inhalation of dust particles containing heavy metals, including potential carcinogens. Three workplaces were selected that appeared to pose the greatest risk to workers in terms of exposure to heavy metal (nickel and cadmium) dust generated during the production and handling of nickel-cadmium plates. Although the limits for dust and carcinogens and mutagens are very strict, they are gradually being tightened. Therefore, the main motivation was to find the source of the highest amounts of respirable particles, determine the size of the particles, the composition of the fraction and evaluated potential toxicity of particles captured on the filters. This will help to propose additional measures to minimise concentrations of potentially carcinogenic particles in the working environment. The chemical analysis, particle size distribution, metal content and cytotoxicity of the particles trapped on the filters were investigated. While the concentration of heavy metals was well below the permissible exposure limits, the extracts obtained from the sampled filters had a significant effect on cytotoxicity, particularly those containing lower concentrations of particles.
A catalytic approach to the valorization of polyesters and biogenic waste for the production of amines
(Elsevier, 2026) Poovan, Fairoosa; Jagadeesh, Rajenahally V.; Beller, Matthias
Despite the numerous applications of polymers in human life, their excessive use has caused serious environmental hazards. Among polymer recycling methods, chemical recycling offers a streamlined approach to polymer degradation by enabling depolymerization without compromising functionality. Specifically, hydrogenolysis of polymers, a depolymerization technique that utilizes hydrogen to cleave bonds, has emerged as a powerful strategy for depolymerization, facilitating the upcycling of plastics into high-value chemicals. Previous research in this area has predominantly focused on noble metal catalysts, while the potential of base metals remains unexplored. Herein, we propose a cobalt-based hydrogenative amination approach for the depolymerization of polyesters to versatile diamines as well as amino alcohols. This strategy can also be applied to the one-pot synthesis of fatty amines from used cooking oil. The current system opens new avenues for the upcycling of waste into valuable amines, providing a viable solution to plastic pollution and a way to utilize waste as a resource.
Optimizing multi-step kinetic schemes for biomass pyrolysis in bioenergy production using multi-objective genetic algorithm: Transitioning from thermally thin to thermally thick regime
(Elsevier, 2026) Vasudev, Vikul; Tahir, Mudassir Hussain; Ram, Shri; Duan, Yanjun; Adamec, Tomáš; Najser, Tomáš
This study aims to optimize multi-step kinetic models for biomass pyrolysis to enhance bioenergy production. Utilizing three representative biomass types, i.e., Cunninghamia lanceolata (forestry-derived), rice straw (agricultural-derived), and green algae (aquatic-derived), thermogravimetric analysis was conducted across temperatures from 150 to 600 degrees C at heating rates of 5, 20, 30, and 40 degrees C/min. Three kinetic schemes with three, four, and nine reactions were parameterized using a parallelized multi-objective genetic algorithm fitting the model simultaneously to all heating rates. The four-reaction scheme achieved the highest fit quality (R-2 > 0.97 for all feedstocks), while the nine-reaction model provided detailed mechanistic insights but showed reduced accuracy for green algae (R-2 as low as 0.81). Coupling these kinetic models with a 2D heat transfer model of a thermally thick biomass particle revealed distinct thermal gradients and reaction front behaviors for each scheme, including average front propagation speeds of 0.19 mm/min and core conversion times between 27 and 34 min. The most complex scheme exhibited the fastest core conversion rate (up to 23 % faster), broadest and most diffuse reaction front, and greater char production. Whereas, simpler schemes produced sharper, localized fronts with slower conversion. This integrated experimental-computational approach quantitatively characterizes pyrolysis kinetics and thermal dynamics, advancing model development for optimized industrial bioenergy applications.
Assessing climate risk on the European financial system: a multi-scenario Analysis
(Springer Nature, 2026) Giacchetta, Gianandrea; Giacometti, Rosella; Torri, Gabriele
This paper investigates the impact of climate risk on the stability of the European financial system, with a particular focus on both transition and physical risk dimensions. Given the long-term and uncertain nature of climate-related risks, traditional econometric methods often fall short in capturing their systemic implications. To address this challenge, we develop a scenario-based framework grounded in realistic projections consistent to the theoretical framework of the Network for Greening the Financial System ("NGFS"), encompassing three climate pathways: orderly transition, disorderly transition, and hot house world. We model the relationship between climate risk drivers, European companies, and the financial system using vine copulas, enabling a flexible representation of complex dependencies. The effects of these scenarios on financial institutions are evaluated through key risk metrics (expected return, value at risk, and expected shortfall) conditioned on each climate scenario. Our results offer insights into how climate transition risks propagate through the financial system, with practical implications for financial stability assessment and systemic risk management. The primary contribution lies in integrating both transition and physical climate risks into a coherent and tractable risk assessment framework, offering valuable tools for policymakers, regulators, and financial practitioners. Results show a significant dependence of European financial system to brown companies and, thus, sizable losses in the disorderly transition scenario.
SDR-Based Vehicle-to-Vehicle OFDM VLC Communication System
(IEEE, 2026) Dratnal, Martin; Danys, Lukáš; Jaroš, René; Martinek, Radek
This paper presents a novel software-defined radio (SDR) platform designed to measure the bit error rate (BER) in vehicle-to-vehicle visible light communication (V2V VLC) technology under real-world conditions. Unlike previous studies that have primarily relied on simulations or static tests, this research provides empirical evidence from both stationary and dynamic vehicular scenarios. The proposed system functions as an orthogonal frequency division multiplexing (OFDM) transceiver, employing multistate quadrature amplitude modulation (M-QAM) to modulate individual subcarriers. Extensive experiments were conducted to evaluate the system's performance, including the effects of varying headlight beam angles and vehicle distances on BER. Notably, stable communication was achieved using 64-QAM modulation at distances up to 10 meters, maintaining a BER below the critical threshold of 10(-5 .) These findings demonstrate the feasibility and robustness of integrating VLC with standard vehicle headlight systems, offering a viable solution for enhancing vehicular communication in dynamic traffic environments.
Trajectory tracking control for autonomous vehicles: A systematic PRISMA review of models and strategies
(Elsevier, 2026) Albhaisi, Mohammed S.; Prauzek, Michal; Minh, Tri Tran Huu; Ožana, Štěpán; Konečný, Jaromír
Autonomous vehicles (AVs) are poised to redefine future mobility by offering enhanced safety, energy efficiency, and intelligent adaptability. A fundamental component enabling this transformation is trajectory tracking control, which ensures precise path-following despite environmental uncertainties, dynamic road conditions, and sensor noise. This systematic review follows the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology to analyze state-of-the-art trajectory tracking control strategies, categorizing them into traditional, adaptive, and learning-based methods. The study provides a comprehensive assessment of trajectory tracking models, highlighting their strengths, limitations, and applicability in real-world scenarios. Additionally, the review discusses key challenges, such as scalability, real-time adaptability, and the integration of multi-sensor data. By bridging theoretical advancements with practical implementations, this review contributes to the development of more robust, adaptive, and efficient trajectory tracking systems for autonomous mobility.
Equivalence between the co-rotational finite element method and the absolute coordinate formulation in multibody dynamics
(Wiley, 2026) Zwölfer, Andreas; Aubel, Maximilian; Páleník, Radek
Geometric nonlinearity arises in problems involving large displacements and rotations, where traditional linear assumptions fail. In the finite element community, several formulations have been developed to address these complexities; the corotational finite element formulation (CRF) has proven to be an efficient alternative to generic total Lagrangian (TL) and updated Lagrangian (UL) approaches for small-strain problems. In the multibody dynamics community, the floating frame of reference formulation (FFRF) is commonly used, employing a gross-motion-following local reference frame per body (also referred to as co-rotational or floating frame), in contrast to CRF's element-based approach. A less known but fully equivalent multibody formulation to FFRF is the absolute coordinate formulation (ACF), which uses absolute coordinates in contrast to rigid body coordinates plus local deformation as in FFRF. This paper demonstrates the equivalence of ACF and CRF, with the only difference being the number of reference frames-body-based versus element-based. Moreover, since CRF, while efficient, faces challenges in real-world multibody simulations due to the computational burden of assigning a reference frame to each finite element, this paper also discusses how CR/ACF can be applied when partitioning bodies into substructures, each equipped with its own reference frame.
Energy and environmental trade-offs in anaerobic digestion: Batch tests and LCA investigations of giant hogweed, canola straw, and manure
(Elsevier, 2026) Nwanegbo, Emmanuel; Mollashahi, Samaneh; Ferchau, Erik; Eckart, Hartmut Krause Sven
This study investigated the energy and environment trade-offs of anaerobic digestion (AD) using invasive giant hogweed, agricultural residue canola straw, and cow manure by combining mesophilic batch tests with a gate-togate life cycle assessment (LCA). Seven scenarios, comprising both mono-digestion and co-digestions were evaluated. Mono-digestion of Giant Hogweed achieved the highest specific biogas yield (671 LN/kg VS) and methane content (74.7%), with no detectable hydrogen sulphide (H2S), while co-digestion introduced mixdependent trade-offs between gas yield and emissions. The LCA identified the combined emissions from manure and digestate storage as the primary hotspot for Global Warming Potential (GWP), contributing 53-67% of total emissions. Crop-based mono-digestion scenarios exhibited higher GWP per tonne but lower emissions per kWh due to superior energy recovery; the same pattern held for abiotic depletion (fossil and elements). Sensitivity analysis showed that a 50% cut in storage emissions reduced GWP by 27-34% across all scenarios. Scenarios with higher net electricity output achieved lower impact intensities per kWh. The study concludes that optimizing feedstock ratios and implementing advanced storage practices is critical for maximizing both energy recovery and environmental performance of AD systems.
Abuse of tax law in a comparative perspective: An analysis of French and Czech regulations
(Akademia Leona Koźmińskiego, 2025) Mariański, Michał; Bartes, Richard
The concept of abuse in tax law is well established in many EU Member States, as it is an important element of the financial security and resilience of the economic system. However, the manner in which this concept is regulated is not uniform. It is therefore crucial to determine whether we can observe only small differences in this field or whether exists is a significate conceptual divergence among EU countries. The purpose of this article, which applies dogmatic, historical-descriptive, and, above all, comparative methods, is to examine the legal framework governing the abuse of tax law in France and the Czech Republic. The analysis leads to several conclusions that may be potentially interesting from the perspective of other EU countries. Another issue addressed concerns the compatibility of the examined solutions with the technological changes observable across almost all areas of the economy and braches of law. The different approaches to regulating the abuse of tax law in France and the Czech Republic presented in this paper may also serve as an impulse for further comparative or national studies aimed at identifying the most effective and efficient model in this field.
Machine learning in next-generation AEM fuel cells: a systematic review
(Royal Society of Chemistry, 2026) Ponnada, Srikanth
Fuel cells have lately garnered interest as a potentially advantageous technology for clean and efficient energy conversion. One type that has caught people's attention is the anion exchange membrane fuel cell (AEMFC), which can run on a variety of fuels and operates at low and high temperatures. Exploring its basic working principles, important materials, obstacles, and recent breakthroughs, this perspective presents a comprehensive introduction to AEMFC technology. The anion exchange membrane (AEM) and the electrodes of the AEMFC work together to improve the cell performance and the efficiency of the system as a whole. Furthermore, this review emphasizes the ways in which AEMFC technology is being improved by ML and AI technologies. Through the identification of crucial parameters and the improvement of the membrane electrode assembly (MEA), these technologies have the potential to optimize the performance of AEMFCs while drastically cutting down on the time and effort needed for experimental testing. Finally, we take a look at the possibilities and threats for further study of fuel cell technology-based sustainable energy generation using AEMs in conjunction with new electrode materials. This article introduces a structured framework and categorizes the following key concepts: need for anion exchange membranes (AEM) > mechanisms of anion conductivities > ORR (oxygen reduction reaction) > interfacial phenomena at the electrode-AEM interface > water management > integration of artificial intelligence (AI)/machine learning > neural networks (NN) > schemes for learning > predictive modeling > optimization algorithms and optimizing algorithms > AI in fault detection > AI in maintenance of fuel cells and in materials discovery.
CLM App: Interlamellar distance of pearlite via CLM revisited and automated
(MDPI, 2026) Zouhar, Martin; Mikmeková, Šárka; Hovjacký, Jan; Váňová, Petra
Pearlitic (stainless) steel is used in automotive, aerospace, and other industries where high strength, hardness, and wear resistance are required. Its quality control can be performed using mechanical tests or by examining the lamellar microstructure, namely, determining interlamellar distance. One of the related approaches is the circular line method (CLM). This paper reviews the challenges to automate employment of the CLM using custom Python code in order to reduce human time costs during image-based quality assessment of pearlite. The goal is to perform intersection counting automatically once the human operator has configured the application and selected the locations of measuring circles. Performance assessment using manually processed data from some 465 images is performed. We divide the imaged pearlite microstructures into different "types" when the code performs well or, respectively, not so well. We conclude with possible extensions of the work presented here.
Chaotic transitions and multistable regimes in a nonlinear wave model through hybrid analytical-numerical techniques
(Springer Nature, 2026) Iqbal, Muhammad; Kazmi, Syeda Sarwat; Riaz, Muhammad Bilal; Rehman, Muhammad Aziz
The modified Zakharov-Kuznetsov (mZK) model is a fundamental framework for describing nonlinear phenomena in plasma oscillations, optical systems, discrete electrical lattices, and oceanic wave dynamics. In this work, we establish a unified framework that combines exact solution construction with dynamical systems analysis of the mZK equation. Using the modified Khater method and the Sardar subequation technique under a traveling wave transformation, we derive new and more general families of solitary traveling wave solutions, including trigonometric, hyperbolic, rational, exponential-rational, and complex soliton forms. We show how free parameters govern the existence, shape, and stability of these solutions, with two- and three-dimensional MATLAB simulations illustrating their structures. Furthermore, we reformulate the mZK equation as a planar dynamical system and classify its qualitative features through phase portraits, identifying centers, saddles, and cusps. With the inclusion of periodic external forcing, the reduced system displays bifurcation and chaos, following a period-doubling route with multistability and sensitivity to parameter variations. To the best of our knowledge, this is the first study that simultaneously delivers exact soliton solutions and a complete nonlinear dynamics characterization of the mZK equation. This integrated approach provides a novel template for investigating broader classes of nonlinear evolution equations.
Optimizing calibration processes in automotive component manufacturing
(MDPI, 2026) Karásková, Jana; Slíva, Aleš; Nainaragaram Ramasamy, Mahalingam; Olivková, Ivana; Besta, Petr; Dizo, Jan
High-precision calibration of inertial measurement units for automotive safety systems combines fixed automated chamber cycles with semi-manual loading, alignment, and transfer. Motion waste and ergonomic constraints can therefore dominate throughput and cycle time stability. This study redesigns a production calibration workstation using time-and-motion analysis, operator observation, and structured root-cause analysis based on the Ishikawa diagram and the five whys. Three interventions were implemented and validated with pre- and post-measurements: bundled handling that consolidates full-set transfers and reduces non-value-adding motions; a fixture and material handling redesign with a manual lifting aid to reduce physical load and enable reliable single-operator operation; and a modular workstation layout that supports the phased addition of chambers. Total cycle time decreased from 4475 s to 1230 s, a 72 percent reduction, and weekly output rose from 800 to 4500 units without additional staffing or significant automation investment. Overall equipment efficiency improved from 75.3 percent to 85.2 percent, while the quality rate remained at 98.8 percent.
Assessment of the influence of electro slag remelting on the purity and mechanical properties of structural steel
(MDPI, 2026) Walek, Josef; Opěla, Petr; Vrána, František; Kocich, Radim
Electro slag remelting steel is a technology of tertiary metallurgy that can be used in the production of special structural steels where high purity is required to influence the quality of the final products. This work deals with the evolution of steel purity comparing vacuum degassing (VD) and electro slag remelting (ESR) technologies in terms of the chemical composition of non-metallic inclusions and their morphology. The present work primarily studies the creep behavior of special structural steel at two different initial material states (VD and ESR steel) tested in the range from 450 to 650 degrees C. A rather unique plastometric experimental methodology of accelerated creep testing, which consists of a slow plastic deformation of a material under long-term stress at an elevated temperature, was used to study the behavior of the prepared specimens. The results show that, after remelting the steel, there was an increase in micropurity due to a reduction in the average size and, in particular, a reduction in the maximum size of non-metallic inclusions. The results of creep behavior show a particular difference at 600 degrees C, where ESR steel shows higher relaxation phase stress values as well as higher creep strength factor values compared with VD steel.
Interactions of microplastics with heavy metals in the aquatic environment: Mechanisms and mitigation
(Elsevier, 2026) Rasool, Atta; Halfar, Jan; Brožová, Kateřina; Čabanová, Kristina; Chromíková, Jitka; Malíková, Petra; Motyka, Oldřich; Pertile, Eva; Drabinová, Silvie; Heviánková, Silvie
Microplastics (MPs) and heavy metals (HMs) are significant pollutants in aquatic ecosystems, posing substantial risks to both environmental and human health. Despite growing research interest, the quantitative understanding of MPs-HMs interactions remains limited due to methodological inconsistencies and insufficient cross-study data synthesis. This review presents a data-driven and mechanistic evaluation of these interactions, focusing on sorption and desorption mechanisms and the impact of key physicochemical parameters, including pH, salinity, temperature, and dissolved organic matter. Experimental data are analyzed to reveal trends in adsorption capacity across different polymers, particle sizes, and metal species. The review also synthesizes toxicological effects on aquatic organisms and humans and assesses recent advances in modeling and remediation approaches. Hybrid techniques integrating conventional and emerging technologies show promise for the simultaneous removal of MPs and HMs, though challenges persist for large-scale implementation. By linking quantitative trends with mechanistic insights, this review identifies critical knowledge gaps, outlines directions for future experimental validation, and supports the development of standardized protocols for environmental monitoring, risk assessment, and remediation.
Enhancement of strength properties and durability in high-performance concrete using polymer and steel fibers
(Sciendo, 2026) Němec, Jiří; Jeřábek, Jan; Gandel, Radoslav; Sucharda, Oldřich
Fiber-reinforced and high-performance concretes are elemental pillars of modern material engineering. Fibres are commonly used in combination with high-performance concrete. Important factor is to determine correct amount and type of fibres. This paper's focus is on high-performance fibre-reinforced concrete using polymer and steel varieties of fibres. For the experiment was determined main volume of fibres which was subsequently doubled in modified mixture to observe influence of highly increased number of fibres. Presented research introduces in total 4 mixtures of two different material types of fibres and two different dosages which were subjected to series of mechanical and durability tests including compressive, flexural and tensile-splitting strength, resistance against frost and defrosting chemicals and in the end were manufactured series of steel reinforced concrete beams without sheer reinforcement.The test showed a lot of mixed results, but it was determined that higher amount of steel fibres is usually beneficial for almost all measured characteristics, like compressive strength (137.3 MPa for prism fragments), mass loss after 400 cycles of resistance to frost and chemical de-icing agents (104.4 g/m2) and load tests of steel reinforced beams (125.13 kN). On the other hand, usage of polymer fibres especially in higher volume caused in some cases significant drawbacks like in the case of mass loss after 400 cycles of resistance to frost and chemical de-icing agents (116.9 g/m2) and load tests of steel reinforced beams (84.32 kN). Higher dosage of both types of fibres also influenced tensile-splitting strength in negative way.
Anthropometry and diagnostic aware deep learning for exercise assessment
(Frontiers Media S.A., 2026) Reyes Leiva, Karla Miriam; Nikelová, Pavla; Černý, Martin
Background: Correct technique during strength exercises such as squats and Romanian deadlifts (RDLs) is fundamental for performance and injury prevention. Objective: We introduce ADA (Anthropometry and Diagnostic Aware), a multimodal deep-learning framework that integrates IMU kinematics with anthropometric and diagnostic features to classify movement quality and predict movement related risk. Methods: Seventeen-sensor IMU data were collected from 15 healthy subjects performing correct and incorrect squat and RDL trials. A CNN-LSTM branch processed kinematic sequences and a fully connected branch processed static anthropometric/diagnostic inputs; feature fusion used attention weighting. Results: Incorporating anthropometry and diagnostic context increased sequence-level accuracy from 86.5% (kinematics only) to 94.8% (ADA) and enabled binary risk prediction at 97.8%. Personalized (transfer learning) fine tuning further improved accuracies (mean gains 3%–5% depending on window length). Conclusion: ADA demonstrates that subject-specific static features improve movement quality classification and risk stratification, supporting wearable-based personalized feedback in training and rehabilitation.
Functional safety-oriented risk analysis of heavy vehicle platooning
(University of Žilina, 2026) Mikula, Luboš; Famfulík, Jan
In this paper is presented platooning as a promising approach to reduce the greenhouse gas emissions, fuel consumption, and operation costs in heavy traffic. Attention is given to a risk assessment of vehicle-to-vehicle (V2V) communication in the context of ISO 26262, Edition 2: Road vehicles Functional safety. [ISO 26262-2 2018] The analysis focuses on safety-related hazards associated with convoy driving of heavy vehicles, utilizing the principles of functional safety. It applies hazard analysis and risk assessment (HARA) to classify potential risks according to their severity, exposure, and controllability. Automotive safety integrity level (ASIL) is later determined for each risk. The results provide the ASIL levels of identified hazards, which can be used for developing and validating functional safety measures for cooperative truck driving.

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