Publikační činnost VŠB-TUO ve Web of Science / Publications of VŠB-TUO in Web of Science

Permanent URI for this collectionhttp://hdl.handle.net/10084/56138

Kolekce obsahuje bibliografické záznamy článků akademických pracovníků VŠB-TUO v časopisech indexovaných ve Web of Science od roku 1990 po současnost. Odkaz na Web of Science je funkční ze sítě VŠB-TUO, vzdálený přístup viz web ÚK VŠB-TUO.

Do kolekce jsou zařazeny:

a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská – Technická univerzita Ostrava (VŠB-TUO),

b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

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Now showing 1 - 20 out of 8019 results

A double resistive-capacitive approach for the analysis of a hybrid battery-ultracapacitor integration study
(MDPI, 2025) Chmielewski, Adrian; Piórkowski, Piotr; Bogdziński, Krzysztof; Krawczyk, Paweł; Lorencki, Jakub; Kopczyński, Artur; Możaryn, Jakub; Costa-Castelló, Ramon; Ožana, Štěpán
The development of energy storage systems is significant for solving problems related to climate change. A hybrid energy storage system (HESS), combining batteries with ultracapacitors, may be a feasible way to improve the efficiency of electric vehicles and renewable energy applications. However, most existing research requires comprehensive modelling of HESS components under different operating conditions, hindering optimisation and real-world application. This study proposes a novel approach to analysing the set of differential equations of a substitute model of HESS and validates a model-based approach to investigate the performance of an HESS composed of a Valve-Regulated Lead Acid (VRLA) Absorbent Glass Mat (AGM) battery and a Maxwell ultracapacitor in a parallel configuration. Consequently, the set of differential equations describing the HESS dynamics is provided. The dynamics of this system are modelled with a double resistive-capacitive (2-RC) scheme using data from Hybrid Pulse Power Characterisation (HPPC) and pseudo-random cycles. Parameters are identified using the Levenberg-Marquardt algorithm. The model's accuracy is analysed, estimated and verified using Mean Square Errors (MSEs) and Normalised Root Mean Square Errors (NRMSEs) in the range of a State of Charge (SoC) from 0.1 to 0.9. Limitations of the proposed models are also discussed. Finally, the main advantages of HESSs are highlighted in terms of energy and open-circuit voltage (OCV) characteristics.
Design and analysis of automatic whole row tomato seedling transplanter technology with integrated controlling system
(Wiley, 2026) Ali, Addisu Negash; Nigus, Messele Gashaye; Paramasivam, Velmurugan; Petrů, Jana; Čep, Robert
In the small-scale farming, transplantation of tomato is performed manually using hand drilling without considering the standard agronomy practices. To develop innovative products with reduced size and automatic operations, the analysis and design of the feeding, picking, and planting components of the automatic transplanting machine need to be the focus area. The integrated approaches of conceptual design, concepts evaluation and selection, synthesis and numerical modeling of mechanisms, path manipulator design, components and assembly SolidWorks modeling, Matlab and ADAMS software validation simulations, and PLC based control system design are used to develop the target technology. During design and analysis, a 128 cell standard plug tray, 42 mm grid depth, 110 mm average seedling height, 35 cm plant spacing, 40 cm row spacing, and 192 seedlings/min planting capacity were selected as design criteria. The results from kinematics analysis and optimal design of gripper indicated that the clamping and insertion angles should be in the ranges of 16 degrees-22 degrees and 10.6 degrees-14.8 degrees respectively to prevent damage. Furthermore, the optimum clamping angle (beta) and insertion angle (alpha) were found to be 20 degrees and 13.4 degrees respectively for successful clamping and picking of seedlings. A combination of linear and fourth order polynomial models have been developed to provide accurate trajectory plans for the path manipulator. The ADAMS software simulation results are directly fitted with the theoretical results, and the modeling of the seedling pickup mechanisms provides a basis for future bench tests. For a standard 128 cells plug tray, and target frequency of 192 seedlings/min, the pickup device with eight grippers is designed to effectively pick the whole row of tomato seedlings within 2.5 s. Finally, to synchronize the transplanting operations and ensure a continuous supply of signals, photoelectric positioning sensors, magnetic switches, pneumatic components, and PLC control unit are selected and positioned at the optimum locations.
IndiVNet A region adaptive semantic image segmentation for autonomous driving in unstructured environments
(Springer Nature, 2025) Chakraborty, Pritam; Bandyopadhyay, Anjan; Bhattacharyya, Siddhartha, Siddhartha; Platoš, Jan
Autonomous navigation in developing regions is challenged by heterogeneous traffic, dynamic occlusions, and weak road structure. Existing segmentation models, largely trained on structured Western datasets, struggle to generalize under these conditions. To address this gap, we propose IndiVNet, a semantic segmentation architecture tailored for unstructured Indian driving environments. IndiVNet introduces a progressive dilation encoder (616) that captures fine-grained details and broad contextual cues without inducing oversparsity. Evaluated on the India Driving Dataset (IDD), it achieves 69.98% mIoU, outperforming CNN and Transformer baselines, and reaches 73.2% mIoU on CAMVID, demonstrating strong cross-domain generalization. By combining contextual adaptability with real-time efficiency, IndiVNet offers a scalable, region-aware solution for robust autonomous navigation in complex environments.
Thermoelectric power factors of defective scandium nitride nanostructures from first principles
(Elsevier, 2026) Cigarini, Luigi; Wdowik, Urszula D.; Legut, Dominik
The thermoelectric properties of scandium nitride are strongly influenced by structural and electronic factors arising from defects and impurities. Nevertheless, the mechanisms by which these microscopic features affect transport are not yet fully understood. Experiments show a large variability in the electronic transport properties, with a strong dependence on the experimental conditions, and attempts to improve thermoelectric efficiency often lead to conflicting effects. In this work, we employ the Landauer approach to analyze the effects of different kinds of structural defects and impurities on electronic transport in scandium nitride. This approach allows us to relate the transport mechanisms to the structural and electronic modifications introduced in the lattice, with atomistic resolution. In light of these new insights, we propose a rationale relating part of the experimental variability to its microscopic origin.
Development of optimally reinforced glass/bamboo fiber and rice husk/bagasse filler Agrostone composite panels for interior panel applications
(Wiley, 2026) Ali, Addisu Negash; Yityaw, Abebaw Abiyu; Paramasivam, Velmurugan; Rao, D. K. Nageswara
Agrostone panels, popularly used for interior walls in construction, are usually made from sustainable agricultural waste such as bagasse as filler and glass fiber as reinforcement in a binder of pumice, MgO, and MgCl2 solutions. The present work has gone ahead of one step by adding rice husk and bamboo fiber, which are abundantly available in Ethiopia, in addition to bagasse and glass fiber. It is aimed to investigate the effect of rice husk and bamboo fiber additives. Initial optimized parameters are obtained for two variables in three levels from the central composite design (CCD), and the samples are prepared for the design of experiments. Regression analysis and ANOVA are conducted using the experimental data. The data from ANOVA is fed to response surface methodology (RSM) to obtain the final optimized proportions of reinforcement fibers and fillers for the highest mechanical properties without changing the proportions of matrix materials. Using the proportions from RSM, the specimens are prepared for the final evaluation of mechanical properties. Weight percentages of bamboo fibers (0, 1.1, and 2.2) and rice husk (0, 3.515, and 7.03) are given by RSM. The optimization process in CCD consists of three levels for two variables in terms of weight percentage. The results show that 3.091 and 1.358 wt.% of rice husk and bamboo fiber, respectively, gave the highest tensile, compressive, flexural, and impact strength values of 64.92 MPa, 75.534 MPa, 65.168 MPa, and 63.485 J, respectively. The corresponding comparable values from the experiment are 65.02, 74.73, 73.42, and 63.95 MPa, respectively.
Rule-based profit taxation in dynamic Cournot oligopoly: Transmission, stability and welfare
(Elsevier, 2026) Nálepová, Veronika; Lampart, Marek
This study develops a dynamic Cournot model to examine whether profit taxation can stabilise oligopolistic markets hit by demand shocks. The tax rate is updated each period by a simple welfare rule, allowing fiscal policy to respond automatically to changing market conditions. The analysis connects the effectiveness with which taxes influence firms' decision-making (the transmission strength) to market stability. Simulations and chaos analysis show that when the tax signal is strong, firms adjust smoothly, volatility falls and competition is preserved. In contrast, when transmission is weak, feedback effects magnify shocks, increasing exit risk and market concentration. Moderate shocks are absorbed through temporary tax changes, while stronger demand shocks in the model mainly threaten the high-cost firm. Overall, transparent and predictable profit taxation serves as a practical stabiliser in concentrated industries, limiting volatility without ad hoc measures and providing a scalable framework for future fiscal design.
Optimizing monthly solar PV tilt angles and energy yield across global climate zones: a hybrid machine learning and PVLib approach
(Elsevier, 2026) Lin, Ohn Zin; Štěpanec, Libor; Koutroulis, Eftichios; Juchelková, Dagmar; Aye, Hnin Yee
Accurate determination of photovoltaic (PV) tilt angles is essential for maximizing energy yield, yet traditional methods often rely on fixed or latitude-based empirical rules that overlook dynamic climatic factors. While machine learning (ML) has been applied for solar irradiance prediction and system optimization, few studies have directly targeted monthly tilt angle prediction. This paper presents a hybrid framework that integrates PVLib-based simulations with supervised ML models to predict monthly optimal tilt angles for fixed PV installations across diverse climate zones. The framework uses simulated energy output as ground truth and trains models using localized features such as solar radiation, ambient temperature, humidity, and geographic data. Applied across 17 cities spanning tropical, dry, temperate, and continental climates, the Random Forest surrogate reproduced PVLib-optimal monthly tilts with MAE 2.04 ± 0.04° and R2 0.975 ± 0.001 under 5-fold cross-validation (67,287 out-of-fold samples). Relative to Klein's fixed-tilt rule, ML-guided monthly tilts increased annual yield by a median 7.8 % (interquartile range 7.0–8.5 %), while the maximum gain was 12.2 % in Reykjavík. Statistical validation using paired t-tests and Wilcoxon signed-rank tests confirms the robustness of the approach. Results highlight ML's scalability and adaptability, offering a measurement-free alternative for climate-responsive PV design.
Sustainable production of mullite grogs from industrial by-products
(MDPI, 2026) Škvarka, Josef; Janáková, Iva; Pticen, František; Kučerová, Radmila
This study focuses on preparing mullite grogs derived from selected waste materials and kaolin treated with advanced technologies to achieve high thermal resistance and low thermal expansion. The investigated waste materials include dust removal RON, slurry DE, feldspar dust removal from Halamky, and waste generated during the feldspar grinding at the Halamky I deposit. These materials (Red kaolin from Vidnava, Slurry DE, Dust-off RON, Feldspar dust-off Halamky) were processed into grogs and subsequently applied for the production of high-mullite ceramics. The influence of cristobalite admixture was also assessed. The chemical composition was determined by X-ray fluorescence (XRF), while the phase composition was analysed by X-ray diffraction (XRD). Amorphous mullite grogs with mullite contents greater than 40% were successfully prepared. Despite the relatively high iron content, the resulting products exhibited the desired white colour after firing and demonstrated properties that make them promising candidates for advanced refractory applications. The study highlights the potential to valorise industrial waste materials for high-value ceramic applications.
Mean–trend risk portfolio selection with non-dominated sorting asset preselection
(Palgrave Macmillan, 2026) Neděla, David; Ortobelli, Sergio; Tichý, Tomáš
In the vast landscape of financial markets, identifying potential investment assets such as stocks can be overwhelming and time-consuming. For portfolio managers, focusing on a specific selection of stocks through an effective filtering process can streamline this task. This paper introduces an efficient stock preselection method using multidimensional non-dominated sorting of selected return statistics. Unlike previous research, our approach leverages statistics derived from approximated return series through nonparametric regression and principal component analysis (PCA). We further explore the impact of this preselection on mean-variance and the newly proposed mean-trend risk large-scale portfolio selection strategies. By examining the efficient frontier of portfolios from various return and risk perspectives, our empirical analysis on US stock market data provides both ex-post and ex-ante results for 40 portfolio strategies. The findings suggest that for most risk-averse investors, mean-trend risk strategies with preselection significantly outperform both the same strategies without preselection and traditional mean-variance strategies.
Perseverance of management is needed - Efficient long-term strategy of Reynoutria management
(2024) Švec, Pavel; Perglová, Irena; Fröhlich, Václav; Laštovička, Josef; Seidl, Jakub; Růžičková, Kateřina; Horáková, Ivana; Lukavský, Jan; Ferko, Martin; Štych, Přemysl; Pergl, Jan
One of the most problematic invasive species in Europe are knotweeds from genus Reynoutria ( Fal- lopia) ) which have significant negative impact on the native communities as well on human activities. Therefore, they are a target of many control programmes. Due to their high regeneration potential, their management is problematic, and only chemical treatment is reported to be sufficiently effective. The aim of this paper was to describe and analyse the patterns of Reynoutria invasion under longterm chemical treatment with glyphosate-based herbicide in The Mor & aacute;vka river floodplain, Czech Republic. The data covers 17 years of management which started with the European project "Preservation of alluvial forest habitats in the Mor & aacute;vka river basin". We focus on (i) assessment of Reynoutria distribution during long-term management, (ii) analysis of the change of distribution according to the habitat, and (iii) discussion of the optimal management strategy based on the long-term data. Distribution data was obtained using GNSS field mapping. Before the start of the study in 2007, Reynoutria stands covered 29% of the study area (96.9 ha). As a result of systematic whole area chemical management, the extent decreased to 19.6% (65.3 ha) in 2009, and even reached 14.5% (48.2 ha) in 2013, three years after its end. Due to implementation of local chemical management in the following years, the area of Reynoutria was maintained at similar level, with minimum value 41.8 ha in 2018 and a slight increase in recent mapping in 2023. Beside the extent, the structure and coverage of invaded sites was analysed. There was a clear trend of fragmentation of larger polycormons with high coverage into many smaller and less dense ones as a result of chemical spraying. The average size of Reynoutria stand decreased from 0.61 ha in 2007 to half in 2013 (0.32 ha) to 0.15 ha in 2023. Testing of the effects of time, habitat, and biotope did not reveal significant differences of changes of extent and abundance over different environments (forest, open, bare ground), which indicates that there are no differences in reaction to management in the studied habitat and vegetation types. Our study provides a robust and unique overview of the invasion, reinvasion, and suppression dynamics for an important invasive species. If herbicide management is used, chemical treatment must be quite long-term as even three years of intensive glyphosate foliar spray application was not sufficient for the complete eradication of Reynoutria. . Therefore, we propose the following procedure for effective chemical management of Reynoutria: : 1) In largely infested sites, the first step is to reduce the distribution of Reynoutria stands to isolated polycormons. This phase can last 3-5 years. 2) After reaching the state of sparse distribution of Reynoutria, , we recommend herbicide application only in periods of every 3-5 years depending on the local context and rate of regrowth. 3) At sites exposed to soil disturbances, where the soil is contaminated by fragments of Reynoutria rhizomes, there is a need to apply herbicide immediately to target newly resprouting individuals.
Behavior of CuO as solid lubricant inside ZTA matrices
(AIP Publishing, 2024) Singh, Bipin Kumar; Kumar, Amit; Čep, Robert; Kumar, Ajay; Kumar, Ashwini; Dogra, Namrata; Logesh, K.
This investigation delves into the behavior of copper oxide (CuO) as a solid lubricant inside zirconia toughened alumina (ZTA) ceramic composites. The investigation starts with the preparation of ZTA through co-precipitation followed by powder metallurgy to develop CuO (1.5 wt. %)/ZTA composites. In all cases, hot isotactic pressing is applied for densification. The fully densified samples are thoroughly mirror-polished to investigate the mechanical and tribological properties. A 1.8% reduction in micro-hardness and 6% improvement in fracture toughness are observed with incorporation of CuO into the ZTA matrices. The analysis reveals that the presence of ionic copper at the grain boundary leads to the formation of copper-rich phases, causing a decrease in hardness. However, the softer CuO particles contribute to crack bridging and crack deflection, enhancing fracture toughness. Subsequent investigation into the tribological properties highlights the positive influence of the softer CuO phases acting as a secondary component within the ZTA matrix. A significant enhancement of 39.34% in the Coefficient of Friction (COF) is achieved by incorporating CuO into the ZTA matrix. This improvement can be attributed to the formation of a patchy layer through smearing and squeezing actions on wear debris during sliding. The uniform patchy layer results in smoother and more polished surfaces, leading to an improvement in both the COF and specific wear rate. Further wear analysis reveals various phenomena contributing to surface wear, including pullout of grain particles, micro-fracture, high abrasions, and laminar removal of grains. Overall, the introduction of CuO proves to be beneficial, showcasing improved mechanical and tribological properties in the developed composites, with application in dies, inserts, sparkplugs, etc.
Titanium-immobilized layered HUS-7 silicate as a catalyst for photocatalytic CO2 reduction
(Wiley, 2024) Ricka, Rudolf; Amen, Tareq W. M.; Tsunoji, Nao; Reli, Martin; Filip Edelmannová, Miroslava; Kormunda, Martin; Ritz, Michal; Kočí, Kamila
Utilizing photocatalytic CO2 reduction presents a promising avenue for combating climate change and curbing greenhouse gas emissions. However, maximizing its potential hinges on the development of materials that not only enhance efficiency but also ensure process stability. Here, we introduce Hiroshima University Silicate-7 (HUS-7) with immobilized Ti species as a standout contender. Our study demonstrates the remarkable photocatalytic activity of HUS-7 in CO2 reduction, yielding substantially higher carbonaceous product yields compared to conventional titanium-based catalysts TS-1 and P25. Through thorough characterization, we elucidate that their boosted photocatalytic performance is attributed to the incorporation of isolated Ti species within the silica-based precursor, serving as potent photoinduced active sites. Moreover, our findings underscore the crucial role of the Ligand-to-Metal Charge Transfer (LMCT) process in facilitating the photoactivation of CO2 molecules, shedding new light on key mechanisms underlying photocatalytic CO2 reduction.
DMT-OMPA: Innovative applications of an efficient adversarial Marine Predators Algorithm based on dynamic matrix transformation in engineering design optimization
(Elsevier, 2024) Zhang, Zhen; Chu, Shu-Chuan; Nguyen, Trong-The; Wang, Xiaopeng; Pan, Jeng-Shyang
This paper introduces an innovative variant of the Marine Predators Algorithm (MPA), termed the Dynamic Matrix Transformation-based Oppositional Marine Predators Algorithm (DMTOMPA), aimed at enhancing the efficiency of engineering optimization strategies. Traditional MPAs have several shortcomings, including insufficient solution diversity and coverage in the initialization phase, a tendency to become trapped in local optima, and inadequate search capabilities in the later stages of iteration, all of which negatively impact the algorithm's efficiency and effectiveness. To address these issues, the DMT-OMPA incorporates oppositional learning mechanisms and dynamic matrix transformation strategies, significantly enhancing global search capabilities and accelerating convergence speed, particularly in handling complex multidimensional optimization problems.Experimental results on the CEC2013 and CEC2017 test suites demonstrate that DMT-OMPA outperforms other recent MPA variants, various classical algorithm variants, and newly proposed algorithms, verifying its advantages in precision and reliability. Furthermore, the application of this algorithm to various real-world engineering problems substantiates its broad applicability and high efficiency. The study's findings not only deepen our understanding of swarm intelligence optimization algorithms but also provide a new efficient tool for solving complex engineering problems. The results indicate a promising potential for wider application in diverse fields, suggesting that the DMT-OMPA algorithm could become an effective tool for tackling complex optimization problems in the future.
Classification enhanced machine learning model for energetic stability of binary compounds
(Elsevier, 2024) Liu, Y. K.; Liu, Z. R.; Xu, T. F.; Legut, Dominik; Yin, X.; Zhang, R. F.
As contemporary computational technologies and machine learning methodologies rapidly evolve, machine learning (ML) models for predicting formation enthalpies of materials exhibited convincible numerical precision and remarkable predictive efficiency, thus establishing a solid foundation for materials thermodynamic design. Despite achieving numerically high global probability accuracy, current ML models for formation enthalpy nonetheless exhibit suboptimal local accuracy within specific physical domain, which can be attributed to the misalignment between the physical constraints of chemical bonds and the critical descriptors capturing classspecific traits. Herein, we propose a novel approach to improve the local precision of the ML model for predicting formation enthalpy by utilizing Miedema theory-based classification, which segments data into distinct categories according to the electronegativity difference, electron density discontinuity and atomic size difference. Utilizing ML algorithms to build surrogate models guided by the classification strategy significantly improves the local predictive accuracy of formation enthalpy for specific binary compounds, significantly raising the R2 value from 0.4-0.9 to 0.8-0.9 compared to an unclassified method. Furthermore, feature importance analysis demonstrates that the pivotal factors for each category vary in some manner, highlighting the insufficiency of a sole ML model in classifying large-dimensional data, which can be addressed by adopting a physicsinformed classification strategy. Our results suggest that employing physical-informed classification scheme into ML equips the models with broad applicability and local accuracy, which also shed light for other material properties predication.
Thermodynamic and multi-step kinetic analysis of slow pyrolysis of natural rubber-silanised cellulose composites with 30-55 phr filler content
(Elsevier, 2026) Dobrovská, Jana; Skalková, Petra; Iudina, Elizaveta; Holešová, Sylva; Kawuloková, Monika; Janík, Róbert
Pyrolysis is a promising thermochemical process for waste reduction and energy recovery. Natural rubber (NR) composites filled with 30, 45, and 55 phr silanised cellulose (CELS) were prepared and characterised by SEM and FTIR techniques. Thermogravimetric curves for heating rates of 2, 4, 6, 8, 10, and 20 °C·min−1 were measured in an inert gas. Kinetic parameters were determined by isoconversional kinetic analysis using the Friedman model-free method and a model-based method. By applying the generalised master plot method, it was found that the pyrolysis process follows an autocatalytic mechanism involving two kinetically independent, parallel pathways, each pathway consisting of two sequential steps. The results show that silanisation of cellulose has a positive effect on composite thermal stability, but only up to a specific content of CELS. At high loadings, the resulting silica-rich ash can act as a solid acid catalyst, accelerating secondary cracking reactions during pyrolysis. Innovative approaches for determining the formal thermodynamic parameters have been presented. The first method is based on the Eyring equation and the knowledge of Eα = f(α) and Aα = f(α) from the model-free method, providing the thermodynamic parameters as a function of the entire conversion range, α. The second method is based on the results of model-based kinetic analysis. The method makes it possible to determine these parameters for individual steps of a multi-step model and, thus, to compare the energy demand, spontaneity, and change in disorder of the system in the transition state for these steps.
Lexical predicates do substitute in fine-grained attitudes
(Springer Nature, 2025) Jespersen, Bjørn
Let {'is a woodchuck', 'is a groundhog'} be a pair of synonymous lexical predicates. Are they intersubstitutable within a fine-grained attitude ascription without affecting either the truth-value of the ascription or the content of the attitude? I will show that synonymy is sufficient to preserve substitutability within any non-quotational context. Only this requires that substitution is executed within a semantics that observes semantic and epistemic transparency also in contexts such as hyperintensional belief reports. I will develop my argument within Transparent Intensional Logic. I use my pro-substitution claim to argue against one wrong reason for fine-graining, which introduces logical distinctions without semantic differences.
Novel numerical approach toward hybrid nanofluid flow subject to Lorentz force and homogenous/heterogeneous chemical reaction across coaxial cylinders
(AIP Publishing, 2024) Janjua, Khuram Hina; Bilal, Muhammad; Riaz, Muhammad Bilal; Saqib, Abdul Baseer; Ismail, Emad A. A.; Awwad, Fuad A.
The combination of AA7075 and Ti6Al4V aluminum alloys provides an effective balance of endurance, corrosion resistance, and lightness. Some potential applications include aviation components, marine structures with anti-corrosion characteristics, surgical instruments, and athletic apparel. Therefore, the hybrid nanofluid (Hnf) consists of aluminum alloys (AA7075-Ti6Al4V), water (50%), and ethylene glycol (EG-50%) in the current analysis. The Hnf flow subject to heat radiation and Lorentz force is studied through coaxial cylinders. In addition, the flow has been observed under the impacts of homogeneous-heterogeneous (HH) chemical reaction and exponential heat source/sink. The modeled equations (continuity, momentum, HH, and heat equations) are renovated into the non-dimensional form through the similarity approach, which are further numerically computed by employing the ND-solve technique coupling with the shooting method. It can be noticed from the graphical results that the flow rate of Hnf drops with the rising effect of porosity and magnetic field parameters. The addition of AA7075-Ti6Al4V nanoparticles (NPs) also reduces the fluid temperature and velocity profile. Furthermore, the concentration distribution diminishes with the flourishing effect of HH parameters.
Graphene acid: A potent carbocatalyst for the friedel-crafts arylation of aldehydes with indoles
(Wiley, 2025) Galathri, Eirini M.; Hrubý, Vítězslav; Mountanea, Olga G.; Mantzourani, Christiana; Chronopoulos, Demetrios D.; Otyepka, Michal; Kokotos, Christoforos G.
Carbocatalysis represents a highly attractive and effective field within the realm of metal-free nanocatalysis, significantly advancing sustainability in synthetic chemistry. Graphene acid (GA) emerges as a well-defined graphene derivative, characterized by a high density of homogeneously distributed carboxylic groups over graphene lattice. This unique and uniform structure positions GA as an elegant alternative to other 2D carbocatalysts, namely graphene oxide. GA was successfully employed as the catalyst in a Friedel-Crafts-type reaction between indoles and aldehydes, facilitating the synthesis of bis(indolyl)methanes, organic compounds exhibiting interesting biological properties and significant pharmaceutical potential. The metal-free nature of GA, combined with the performance of the reaction "on water" under mild conditions, highlight the green credentials of the developed protocol. Comprehensive substrate screening, including a plethora of aliphatic or aromatic aldehydes and various 1- or 2-substituted indoles, resulted in moderate to high yields of variously functionalized bis(indolyl)methanes. Mechanistic and recovering studies showed that GA acts catalytically as a Br & oslash;nsted acid, maintaining its catalytic activity at a high rate for six subsequent cycles.
Using synthetic data for pretraining partial discharge detection in overhead transmission lines
(Springer Nature, 2025) Klein, Lukáš; Fulneček, Jan; Kabot, Ondřej; Dvorský, Jiří; Prokop, Lukáš
Accurate detection of partial discharges (PDs) in medium-voltage overhead transmission lines is critical for preemptive maintenance and avoiding costly outages, yet it is challenged by scarce labeled data and pervasive electromagnetic interference. This paper investigates a hybrid simulation-and-data-driven framework in which synthetically generated PD signals are used to pretrain deep neural networks and are subsequently fine-tuned on a limited set of real overhead-line measurements. The synthetic pipeline systematically varies PD repetition rates, amplitude distributions, vegetation-contact scenarios, and noise conditions, producing diverse time-series and spectrogram-like representations that approximate real operating environments. We conduct a comprehensive ablation study across multiple architectures—Convolutional Neural Networks (CNNs), a Vision Transformer (ViT), and a Long Short-Term Memory (LSTM) network—and analyze their sensitivity to granular sweeps of synthetic-data parameters. CNN-based models decisively outperform ViT and LSTM counterparts on the spectrogram-based classification task, while ViT and LSTM fail to learn meaningful representation. For the successful CNNs, pretraining on carefully parameterized synthetic datasets—particularly those reflecting higher PD activity, such as our Datasets 3 and 4—consistently improves downstream performance on real data, boosting the Matthews Correlation Coefficient (MCC) on imbalanced, cost-sensitive test sets by roughly 10–20% compared with training from scratch. At the same time, we show that poorly aligned synthetic data can degrade generalization, underscoring the need for accurate noise calibration and domain-aligned simulation. Overall, the results confirm that (i) architectural choice is pivotal for PD detection in overhead lines and (ii) well-designed synthetic data is a powerful, practical lever for achieving reliable and cost-effective PD monitoring when real labeled data are limited.
Analytical solutions and dynamical behaviors of the extended Bogoyavlensky-Konopelchenko equation in deep water dynamics
(IOP Publishing, 2025) Jhangeer, Adil; Beenish, Abdallah M.; Talafha, Abdallah M.; Ansari, Ali R.
In this study, we delve into the mathematical intricacies of the novel Bogoyavlensky-Konopelchenko equation, which finds practical applications in understanding the dynamics of internal waves in deep water. This equation holds significance across scientific fields such as plasma physics, nonlinear optics, and fluid dynamics. The equation extends the (2+1)-dimensional Bogoyavlensky-Konopelchenko equation by adding the second-order derivative terms B mu x mu x and B mu y mu y due to second-order dissipative elements. The generalized exponential rational function method, crucial in mechanical engineering, analyzes analytical solutions featuring symmetric waveform representations. The planar dynamical system, derived via Galilean transformation with mathematical models and parameter values, enhances problem comprehension. Sensitivity analysis and phase portraits of equilibrium points highlight symmetrical properties. The global analysis identifies periodic, quasi-periodic, and chaotic behaviors, corroborated by Poincar & eacute; maps, attractor, power spectrum, return map, and a symmetric basin of the largest Lyapunov exponent.

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