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Recent Submissions

Computation of dynamic deflection in thin elastic beam via symmetries
(Elsevier, 2024) Majeed, Zain; Jhangeer, Adil; Mahomed, F. M.; Zaman, F. D.
The deflection profiles governed by Euler Bernoulli's fourth-order equations under varied applied loads are investigated in this research. This study provides essential insights for engineers designing aircraft components, bridges, and similar structures, ensuring system safety and efficiency. The investigation emphasizes critical factors such as amplitude and frequency, load history, and material properties. Initially, conservation laws of the equations with applied loads are derived by expressing them in the Euler-Lagrange form, where the resultant conservation laws satisfy the divergence expression. The association between symmetries and conservation laws is demonstrated, followed by the application of double reduction theory, which reduces both the variables and the order of the equation. Graphical representations of the outcomes illustrate the impact of load variations on the beam's deflection profiles. These visual aids facilitate a deeper understanding of the influence of different loading conditions. A comparison between varying loads is presented, showcasing the impact of these variations on structural behavior. The findings are crucial for enhancing structural design and ensuring safety under varied loading conditions, showcasing the novelties in the analytical approach and the practical applications of the derived results.
Systemic risk detection using an entropy approach in portfolio selection strategy
(Springer Nature, 2024) Neděla, David; Tichý, Tomáš; Torri, Gabriele
This paper focuses on the investigation and detection of systemic risk. Such risk significantly affects the financial markets and the banking sector, and is fundamental for macro-prudential regulation. To address this issue, we propose an early warning system to anticipate periods of distress. In particular, we consider systemic risk from the investors' perspective, developing optimal portfolio strategies that incorporate such an early warning system based on different entropy measures to predict and hedge the occurrence of systemic risk. On top of this, we introduce a rule that, in periods of crisis, triggers a switch to a risk-free portfolio. In order to determine the optimal composition of a portfolio, we use a new double-optimization strategy, which consists of the maximization of selected performance ratios in the first step and the minimization of selected systemic risk indicators (CoVaR, Marginal expected shortfall) for a given expected return in the second step. An empirical analysis shows that the proposed strategy allows reducing the total risk of the portfolio and generally improves its profitability. We finally discuss how the introduction of these investment strategies may affect the overall stability of the financial system.
Analysis of the computational costs of an evolutionary fuzzy rule-based internet-of-things energy management approach
(Elsevier, 2025) Mikuš, Miroslav; Konečný, Jaromír; Krömer, Pavel; Bančík, Kamil; Konečný, Jiří; Choutka, Jan; Prauzek, Michal
This study presents an in-depth analysis of the computational costs associated with the application of an Evolutionary Fuzzy Rule-based (EFR) energy management system for Internet of Things (IoT) devices. In energy-harvesting IoT nodes, energy management is critical for sustaining long-term operation. The proposed EFR approach integrates fuzzy logic and genetic programming to autonomously control energy consumption based on available resources. The study evaluates the system's computational performance, particularly focusing on processing time, RAM and flash memory usage across various hardware configurations. Different compiler optimization levels and floating-point unit (FPU) settings were also explored, comparing standard and pre-compiled algorithms. The results reveal computational times ranging from 2.43 to 5.23 ms, RAM usage peaking at 6.23 kB, and flash memory consumption between 19 kB and 32 kB. A significant reduction in computational overhead is achieved with optimized compiler settings and hardware FPU, highlighting the feasibility of deploying EFR-based energy management systems in low-power, resource-constrained IoT environments. The findings demonstrate the trade-offs between computational efficiency and energy management, with particular benefits observed in scenarios requiring real-time control in remote and energy-limited environments.
Review of authentication, blockchain, driver ID systems, economic aspects, and communication technologies in DWC for EVs in smart cities applications
(MDPI, 2024) Rajamanickam, Narayanamoorthi; Vishnuram, Pradeep; Abraham, Dominic Savio; Gono, Miroslava; Kačor, Petr; Mlčák, Tomáš
The rapid advancement and adoption of electric vehicles (EVs) necessitate innovative solutions to address integration challenges in modern charging infrastructure. Dynamic wireless charging (DWC) is an innovative solution for powering electric vehicles (EVs) using multiple magnetic transmitters installed beneath the road and a receiver located on the underside of the EV. Dynamic charging offers a solution to the issue of range anxiety by allowing EVs to charge while in motion, thereby reducing the need for frequent stops. This manuscript reviews several pivotal areas critical to the future of EV DWC technology such as authentication techniques, blockchain applications, driver identification systems, economic aspects, and emerging communication technologies. Ensuring secure access to this charging infrastructure requires fast, lightweight authentication systems. Similarly, blockchain technology plays a critical role in enhancing the Internet of Vehicles (IoV) architecture by decentralizing and securing vehicular networks, thus improving privacy, security, and efficiency. Driver identification systems, crucial for EV safety and comfort, are analyzed. Additionally, the economic feasibility and impact of DWC are evaluated, providing essential insights into its potential effects on the EV ecosystem. The paper also emphasizes the need for quick and lightweight authentication systems to ensure secure access to DWC infrastructure and discusses how blockchain technology enhances the efficiency, security, and privacy of IoV networks. The importance of driver identification systems for comfort and safety is evaluated, and an economic study confirms the viability and potential benefits of DWC for the EV ecosystem.
AI-based data mining approach to control the environmental impact of conventional energy technologies
(Elsevier, 2024) Szramowiat-Sala, Katarzyna; Penkala, Roch; Horák, Jiří; Krpec, Kamil; Hopan, František; Ryšavý, Jiří; Borovec, Karel; Górecki, Jerzy
Environmental pollution remains one of the foremost existential threats to human well-being, despite the concerted efforts and implementation of various programmes aimed at fostering cleaner air. The contemporary global economic and energy landscape, characterised by multifaceted challenges, has undeniably hindered the efficacy of efforts to kerb air pollutant emissions. Solid fuels persist as primary sources of energy production in numerous countries, serving both the residential and industrial sectors. However, combustion of such fuels, particularly within domestic heating units (DHUs), engenders the release of a diverse array of organic compounds characterised by intricate structures and potent mutagenic and environmentally hazardous properties. However, the combustion process, if properly regulated, can be carried out in an environmentally sustainable manner. The intricate interplay of myriad factors that influence the composition and quality of chimney flue gases underscores the complexity inherent in controlling the combustion process. Artificial intelligence (AI) has emerged as a versatile tool with applications that span various domains, including environmental monitoring systems. In this study, we posit the utilisation of artificial neural networks (ANNs) as a sophisticated data mining technique to control the emission of flue gases contingent on the specific boiler and fuel utilised. Feed forward predictive models with back propagation were utilized for AI-based data mining aiming at the prediction of the concentration of flue gas components. The highest coefficients of model fit goodness were obtained for CO2, 2 , NOx x and SO2 2 with R2 2 equal to 0.99, 0.98 and 0.99, respectively. The study demonstrated the feasibility and effectiveness of using AI-based data mining to predict emissions from conventional energy technologies. By leveraging the predictive capabilities of ANNs, it is possible to significantly reduce the environmental impact of solid fuel combustion, contributing to cleaner air and improved public health.