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Recent Submissions

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.
Payment holidays, credit risk, and borrower-based limits: Insights from the Czech mortgage market
(Elsevier, 2026) Hodula, Martin; Pfeifer, Lukáš; Pacoň, Martin
This paper examines the design and outcomes of mortgage payment holidays introduced during the COVID-19 pandemic. The Czech Republic provides a useful setting, combining a broad legislative moratorium with a subsequent, eligibility-based bank moratorium. Using confidential loan-level data, we document that legislative moratoria were used mainly as a precautionary liquidity tool, while bank moratoria were accessed predominantly by higher-risk borrowers. A central contribution of the paper is to provide loan-level evidence on mortgage performance after these programs ended. We find that arrears rose only moderately once repayments resumed, though the increase was noticeably larger for bank-moratoria borrowers, reflecting their weaker risk profiles. We further show that stricter borrower-based regulations (LTV, DTI, DSTI) in place before the pandemic were associated with lower moratoria uptake and reduced post-moratoria arrears. The results illustrate how the interaction between program design and pre-existing regulation shaped both the use of payment holidays and their credit-risk implications.
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.
Blockchain-enhanced IoT forensics: Advancing security, trust, and efficiency in digital investigations
(World Scientific Publishing, 2026) Chen, Xiaoyun; Han, Weixue
The integration of blockchain technology into Internet of Things (IoT) forensics transforms digital investigations and resolves challenges in the security and validation of evidence within increasingly interconnected systems. This paper explores how blockchain's immutable, decentralized, and transparent ledger enhances the integrity, authenticity, and traceability of forensic data collected from IoT devices. By embedding blockchain within IoT ecosystems, investigators gain access to tamper-resistant records and establish credibility and admissibility in legal proceedings. Smart contracts and decentralized trust models automate security protocols to reduce human error while enhancing efficiency. Real-world applications in smart homes, healthcare, industrial automation, and communication networks demonstrate the framework's potential to strengthen forensic processes. This paper interprets blockchain's transformative role in advancing IoT forensics, for robust, transparent, and future-ready investigative methodologies.
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.