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 8229 results

Global microplastic contamination in freshwater lakes: Spatial patterns, environmental drivers, and methodological challenges
(Elsevier, 2026) Jachimowicz, Piotr; Babkiewicz, Ewa; Gavlová, Anna; Lang, Jaroslav; Madzielewska, Weronika Irena; Maszczyk, Piotr; Mierzyńska, Karolina; Zieliński, Piotr
Microplastic (MP) pollution in freshwater lakes is an emerging global concern, yet comprehensive assessments remain limited. This review systematically analyzes 84 studies comprising 1268 individual sampling points across over 300 lakes worldwide, selecting only data based on FTIR and Raman spectroscopy to ensure identification reliability. MP concentrations in surface waters ranged from below 0.001 to over 200 MP/L, with the highest levels observed in shallow, lowland, and eutrophic systems. Fibers and fragments dominated MP shapes in both water and sediments, and polyethylene, polypropylene, and polyethylene terephthalate were the most commonly detected polymers, mirroring global plastic production trends. Environmental parameters such as trophic state, shoreline urbanization index and lake morphology were identified as key drivers of MP abundance and characteristics. A clear horizontal gradient was observed, with MP concentrations decreasing from shorelines toward lake centers. However, methodological inconsistencies remain a major obstacle to accurate assessments, including the dominance of surface-only sampling (96.5 % of lakes), limited spatial replication (over 70 % single-point sampling), and the frequent omission of MPs <300 mu m. These shortcomings highlight the urgent need for standardized, multi-depth, and year-round sampling strategies, as well as harmonized size fractionation and validation protocols, to ensure robust and comparable future assessments of MP pollution in freshwater ecosystems.
Shining the dynamics of the Economic Complexity Index on the European Union's climate change strategy: Evidence from the novel approach of MMQR
(Elsevier, 2026) Kömürcüoglu, Ömer Faruk; Kömürcüoglu, Elif Duygu; Koçak, Sinem; Çi̇l, Dilek; Karis, Çiğdem; Güven, Aykut Fatih; Bajaj, Mohit; Blažek, Vojtěch
For the European countries, the issue of combating climate change has become a matter of existence. Therefore, it is of extreme importance to present economic-based evidence for these countries' climate action. One emerging yet underexplored area is the environmental implications of the Economic Complexity Index (ECI), which reflects the knowledge intensity embedded in a country's production structure. Despite its relevance, studies examining the relationship between ECI and environmental degradation (ED) in the European context remain scarce. This paper aims to fill this gap by investigating the impact of ECI on ED between 1995 and 2021, focusing on the European Union countries recognized for their environmental sustainability efforts. For this purpose, the relationship between ECI and two of the pioneer indicators of ED-ecological footprint (EFP) and carbon emissions (CO2)-is assessed through two separate models. To address the dynamic and heterogeneous structure of the relationship, the novel Method of Moments Quantile Regression (MMQR) approach is employed. Empirical evidence suggests that ECI contributes to ED, with a stronger impact observed on CO2 emissions than on EFP. Another key finding is that higher levels of ED limit the negative environmental effects of ECI. However, the robustness of the findings is confirmed using the Driscoll-Kraay (D-K) standard error estimator and also, the symmetric causality test of Dumitrescu-Hurlin (D-H). As global leaders in environmental initiatives, EU countries must guarantee the availability and variety of green financing sources to expedite the transition to sustainable production methods in sectors impacting the ECI index via the European Investment Bank and the EU Innovation Fund. Policymakers can provide favorable tax incentives to industries that implement eco-friendly production methods to lower their expenses, thereby rewarding these industries and fostering acceptance of this strategy among sectors beyond this framework. Achieving higher ECI scores through the integration of renewable energy and green technologies is therefore essential for EU countries striving for a greener and more resilient future.
Multi-step-ahead forecasting of bike-sharing demand using multilayer perceptron model with additional timestamp features
(2026) Alfian, Ganjar; Saputra, Yuris Mulya; Ramadhani, Wildan Dzaky; Atmaji, Fransiskus Tatas Dwi; Farooq, Umar; Beneš, Filip; Fitriyani, Norma Latif; Syafrudin, Muhammad
Bike sharing is increasingly gaining popularity as an affordable and environmentally friendly mode of transportation in urban areas. However, the nature of bike sharing, where users can pick up and return bikes at different stations, often results in an uneven distribution of bikes across stations. Consequently, accurately predicting the future number of rented bikes at each station becomes crucial for bike-sharing operators to optimize the bike inventory at each location. This study introduces a multi-step-ahead forecasting model that employs machine learning methods to predict the hourly demand for rented bikes. We utilize information on rented bikes from the preceding day to forecast the forthcoming counts of rented bikes for the next 1, 3, 6, 12, and 24 h. Additional features extracted from timestamps are incorporated to enhance the accuracy of the model. We compare the proposed model, based on multilayer perceptron (MLP), with various machine learning prediction algorithms, including Support Vector Regression (SVR), K-Nearest Neighbor (KNN), Decision Tree (DT), Adaptive Boosting (AdaBoost), Random Forest (RF), and Linear Regression (LR). Applying the proposed MLP model to the Seoul bike-sharing dataset demonstrates a positive outcome, indicating a reduction in prediction error compared to other forecasting models. The proposed model achieves the highest R-2 (coefficient of determination) values when compared to other models, with values of 0.973, 0.882, 0.82, 0.807, and 0.79 for prediction horizons of 1, 3, 6, 12, and 24 h, respectively. By obtaining future values for predicted rented bikes, the trained model is anticipated to assist in optimizing the number of available bikes for bike-sharing companies.
Effect of NaBH4 loading and reduction temperature on defect-driven CO2 photoreduction over TiO2
(Elsevier, 2026) Ricka, Rudolf; Wanag, Agnieszka; Kusiak-Nejman, Ewelina; Reli, Martin; Filip Edelmannová, Miroslava; Łapiński, Marcin; Słowik, Grzegorz; Morawski, Antoni W.; Kočí, Kamila
This study investigates the role of defect engineering in enhancing TiO2-based photocatalysts for CO2 photoreduction through a systematically controlled synthesis. In contrast to previous reports focused on Ti3+ doping of commercial TiO2, here we combine sol-gel synthesis with post-synthetic chemical reduction using sodium borohydride (NaBH4) to obtain TiO2 materials with tunable concentrations of surface defects, specifically oxygen vacancies and Ti3+ sites. By varying both the reduction temperature and NaBH4 dosage, we introduce a new level of control over defect formation. The materials were characterized by X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), nitrogen physisorption, and photoelectrochemical measurements. Photocatalytic performance was assessed via CO2 photoreduction under UV-vis irradiation. The sample reduced at 350 degrees C with 1.5 g NaBH4 showed the highest activity and selectivity toward CH4 and CO, clearly surpassing the performance of commercial TiO2 (P25) and a sol-gel reference without chemical reduction (W-TiO2_350 degrees C). The improved performance is attributed to a synergistic balance of Ti3+ sites, oxygen vacancies, and surface hydroxyls, which enhance charge separation and CO2 activation. This work introduces new synthesis-structure-activity relationships and demonstrates the potential of defect-tuned TiO2 materials for efficient and selective CO2 valorization.
Zinc and copper metallic instability: Investigating altered metal functionality in both human and animal studies
(Springer Nature, 2026) Bhardwaj, Nidhi; Bhardwaj, Vandna; Choudhary, Ambika; Choudhary, Monika; Bhardwaj, Indu; Dulta, Kanika; Nagraik, Rupak; Ravi, Karthikeyan; Sharma, Avinash; Aman, Junaid
Homeostasis is the regulatory mechanism for the expression of all genes, the function of all metabolic pathways, the utilization of any essential trace element (TEs), while its disruptions lead to many pathological states. The pathologies include cardiovascular disease, anaemia, diabetes, neurological disorders, and cell death. For this, copper and zinc are two of the major TEs involved in controlling the physiological and pathological processes in both humans and animals. Zinc deficiency, for instance, is linked with decreased body weight, decreased ability to metabolize glucose, and impaired immune function. By contrast, deficiency of copper can lead to several neurological disorders, oxidative stress, mitochondrial dysfunction, and changes in lipid metabolism. On the other hand, there excessive exposure can have adverse effects on health, including the development of epilepsy, neuronal excitability, genotoxic effects, and cellular toxicity. Moreover, dual biological functions of zinc further complicate the understanding of their roles in both health and disease. Such as, zinc has a neuromodulatory function and helps to control excitably in neurons, but sometimes zinc in the synapse, inhibit the functioning of inhibitory neurotransmitter and cause damage to the neurons. Likewise, in metabolic diseases, particularly diabetes mellitus, there is often dysregulation of the levels of zinc and copper, resulting in steel-like interactions; elevated levels of copper and reduced levels of zinc contribute towards the pathogenesis of both the disease and the progression of dementia. Despite this antagonistic relationship, both trace metals act synergistically as necessary derivatives of superoxide dismutase; therefore, both play a vital role in maintaining cellular antioxidant defense systems. Therefore, this review covers published articles from 1992-2025 with regard to zinc and copper in their dietary and nanoparticle forms in animal and human models to demonstrate their differing roles and how they complement one another, or conflict with one another.Graphical AbstractA graphical summary of the percentage of publications (A), as well as the mechanism of neurotransmission by zinc ions (B), and the regulation of Zn2+ and Cu+ ions in both humans and animals, through either positive (regulation) or negative (regulation) pathways (C).
Comparative study on the wear resistance of C&B-type polymer materials for temporary crowns manufactured using 3D DLP printing technology
(MDPI, 2025) Firlej, Marcel; Pieniak, Daniel; Snarski-Adamski, Andrzej; Biedziak, Barbara; Niewczas, Agata; Petrů, Jana; Matijošius, Jonas; Krzysiak, Zbigniew; Zaborowicz, Katarzyna
DLP (Digital Light Processing) 3D printing enables precise fabrication of temporary crowns. Tribological properties of these materials affect clinical durability, wear resistance, and masticatory function. This study compared three C&B-type photopolymers for DLP-printed temporary crowns: Gr-17.1 temporary It, Gr-17 temporary (Pro3dure), and VarseoSmile Temp (BEGO). Samples were printed, post-processed, and polished. Surface topography (Sa, Sz) was measured via white light interferometry, and scratch resistance was evaluated with a Rockwell indenter. Sliding wear tests under wet conditions (37 degrees C, 90% RH) were conducted using an SRV 4 tester at 25 N for 20,000 cycles. VarseoSmile Temp showed the highest scratch and sliding wear resistance, with the lowest mean volumetric wear (0.025 mm(3)) and residual scratch depth, reflecting its higher inorganic filler content (30-50 wt%). Gr-17.1 had the most stable coefficient of friction (similar to 0.3), while Gr-17 experienced the greatest wear (0.235 mm(3)). No direct correlation between friction and wear was observed. These findings indicate that wear resistance depends on microstructure and filler content, supporting tribological testing as a tool to evaluate the durability of 3D-printed temporary crowns.
Photoexcited species localize on solvent-accessible fluorophore-rich domains inside carbon dots
(Elsevier, 2026) Langer, Michal; Zdražil, Lukáš; Rogach, Andrey L.; Osella, Silvio; Otyepka, Michal
Understanding the optical properties of luminescent carbon dots (CDs) at the electronic level is essential for engineering their light-responsive behavior. The localization of photoexcited species and the pathways of their de-excitation govern CD performance in sensing, bioimaging, and emerging photocatalytic applications. Yet, the underlying mechanisms remain unresolved. Here, we combine multiscale simulations with experiments on CDs synthesized from citric acid (CA) and ethylenediamine (EDA), precursors capable of forming the molecular fluorophore 5-oxo-1,2,3,5-tetrahydroimidazo[1,2-alpha]pyridine-7-carboxylic acid (IPCA). All-atom molecular dynamics simulations in water reveal that CA-EDA oligomeric condensation products containing IPCA units spontaneously assemble into dynamic similar to 2 nm nanoparticles with amorphous internal structures and stacked domains reminiscent of those observed in transmission electron microscopy images of CDs. Time-dependent density functional theory (TD-DFT) calculations show that photoexcited carriers are generated in these domains and remain spatially distributed, not confined to the CD core. Quenching experiments with Hg2+ confirm their accessibility to the environment. We therefore propose a structural model of fluorophore-rich domains embedded in an amorphous carbonaceous matrix, explaining the quasi-spherical morphology and characteristic blue photoluminescence. This model provides a mechanistic basis for fluorescence sensing and photocatalysis and establishes a framework for rational design of CDs with tailored photophysical and catalytic properties.
Cross-coupling reactions with nickel, visible light, and tert-butylamine as a bifunctional additive
(American Chemical Society, 2024) Düker, Jonas; Philipp, Maximilian; Lentner, Thomas; Cadge, Jamie A.; Lavarda, João E.A.; Gschwind, Ruth M.; Sigman, Matthew S.; Ghosh, Indrajit; König, Burkhard
Transition metal catalysis is crucial for the synthesis of complex molecules, with ligands and bases playing a pivotal role in optimizing cross-coupling reactions. Despite advancements in ligand design and base selection, achieving effective synergy between these components remains challenging. We present here a general approach to nickel-catalyzed photoredox reactions employing tert-butylamine as a cost-effective bifunctional additive, acting as the base and ligand. This method proves effective for C-O and C-N bond-forming reactions with a diverse array of nucleophiles, including phenols, aliphatic alcohols, anilines, sulfonamides, sulfoximines, and imines. Notably, the protocol demonstrates significant applicability in biomolecule derivatization and facilitates sequential one-pot functionalizations. Spectroscopic investigations revealed the robustness of the dynamic catalytic system, while elucidation of structure-reactivity relationships demonstrated how computed molecular properties of both the nucleophile and electrophile correlated to reaction performance, providing a foundation for effective reaction outcome prediction.
Federated-reinforcement learning-assisted IoT consumers system for kidney disease images
(IEEE, 2024) Mohammed, Mazin Abed; Lakhan, Abdullah; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Dutta, Ashit Kumar; Memon, Sajida; Marhoon, Haydar Abdulameer; Martinek, Radek
The number of people with kidney disease rises every day for many reasons. Many existing machine-learning-enabled mechanisms for processing kidney disease suffer from long delays and consume much more resources during processing. In this paper, the study shows how federated and reinforcement learning schemes can be used to develop the best delay scheme. The scheme must optimize both the internal and external states of reinforcement learning and the federated learning fog cloud network. This work presents the Adaptive Federated Reinforcement Learning-Enabled System (AFRLS) for Internet of Things (IoT) consumers' kidney disease image processing. The main relationship between IoT consumers and kidney image is that the data is collected from different IoT consumer sources, such as ultrasound and X-rays in healthcare clinics. In healthcare applications, kidney urinary tasks reduce the time it takes to preprocess federated learning datasets for training and testing and run them on different fog and cloud nodes. AFRLS decides the scheduling on other nodes and improves constraints based on the decision tree. Based on the simulation results, AFRLS is a new strategy that reduces the time tasks need to be delayed compared to other machine learning methods used in fog cloud networks. The AFRLS improved the delay among nodes by 55%, the delay among internal states by 40%, and the training and testing delay by 51%.
A deep transfer learning based convolution neural network framework for air temperature classification using human clothing images
(Springer Nature, 2024) Ahmed, Maqsood; Zhang, Xiang; Shen, Yonglin; Ali, Nafees; Flah, Aymen; Kanan, Mohammad; Alsharef, Mohammad; Ghoneim, Sherif S. M.
Weather recognition is crucial due to its significant impact on various aspects of daily life, such as weather prediction, environmental monitoring, tourism, and energy production. Several studies have already conducted research on image-based weather recognition. However, previous studies have addressed few types of weather phenomena recognition from images with insufficient accuracy. In this paper, we propose a transfer learning CNN framework for classifying air temperature levels from human clothing images. The framework incorporates various deep transfer learning approaches, including DeepLabV3 Plus for semantic segmentation and others for classification such as BigTransfer (BiT), Vision Transformer (ViT), ResNet101, VGG16, VGG19, and DenseNet121. Meanwhile, we have collected a dataset called the Human Clothing Image Dataset (HCID), consisting of 10,000 images with two categories (High and Low air temperature). All the models were evaluated using various classification metrics, such as the confusion matrix, loss, precision, F1-score, recall, accuracy, and AUC-ROC. Additionally, we applied Gradient-weighted Class Activation Mapping (Grad-CAM) to emphasize significant features and regions identified by models during the classification process. The results show that DenseNet121 outperformed other models with an accuracy of 98.13%. Promising experimental results highlight the potential benefits of the proposed framework for detecting air temperature levels, aiding in weather prediction and environmental monitoring.
SSO optimized FOFPID regulator design for performance enhancement of doubly fed induction generator based wind turbine system
(Springer Nature, 2024) Dembri, Rafik; Rahmani, Lazhar; Babes, Badreddin; Zaini, Hatim G.; Ghoneim, Sherif S. M.; Bojer, Amanuel Kumsa; Flah, Aymen; Sharaf, Ahmed B. Abou
A wind turbine system (WTS) is a highly coupled and nonlinear system where the output power depends upon highly uncertain wind speed. Therefore, the quality of produced power becomes a challenging problem for researchers. Direct Vector Control (DVC) is a powerful and widely utilized power control strategy to deal with winds that vary rapidly and randomly. As a result, this article employed the newly developed Social Spider Optimization (SSO) technique to optimize the design parameters of Fractional-Order Fuzzy Proportional-Integral with Derivative (FOFPID) regulator to maintain the output power of the studied DFIG-based WTS at the rated value under dynamic wind conditions. The suggested FOFPID controller integrates the capabilities of the Fuzzy intelligent regulator and the Fractional-Order controller, enhancing DFIG current control while allowing independent control of active and reactive power. The approach is incorporated within the DVC strategy of the DFIG's rotor-side converter (RSC), replacing the conventional Proportional-Integral (PI) regulator in the internal current loops. Extensive performance evaluations are conducted under various operating conditions, including active power reference changes, parameter uncertainties, and rapid wind speed variations. Comparative analyses with SSO-optimized PID and Fuzzy regulators show that the FOFPID regulator performs better in terms of maximum overshoot, extreme undershoot, settling time, Total Harmonic Distortion (THD), and Weighted Total Harmonic Distortion (WTHD). The suggested FOFPID regulator also displays stronger robustness against parameters mismatch and weather change than other regulator architectures.
An effective numerical method for studying the fractal-fractional smoking model
(World Scientific Publishing Co Pte Ltd, 2025) Adel, M.; Khader, M. M.; Alraddadi, I.; Sweilam, N. H.; Riaz, M. B.; Ahmad, Hijaz
Worldwide, smoking is a common social practice, especially in places like schools and on important occasions. The World Health Organization (WHO) states that smoking is the third leading cause of death worldwide and the most significant avoidable cause of disease. Thus in this study, we present the solution behavior of the fractal-fractional (FF) smoking model. Using an effective numerical integration technique, the discretized system of FF differential equations that results is numerically integrated. By using RK4 to evaluate the numerical solution, we can meet the requirements for both efficiency and accuracy in the given approach. In the hope of providing some recommendations through which to reduce the risks of this bad behavior, the effect of some parameters and external factors affecting the solution behavior of this proposed mathematical model was studied, including the recruitment rate (due to immigration or birth) and the smoking cessation rate. The results show that the implemented technique is a straightforward and efficient tool for modeling the solution for these models.
Methodological approaches to survey complex ice cave environments - the case of Dobšiná (Slovakia)
(Frontiers Media S.A., 2024) Pukanská, Katarína; Bartoš, Karol; Gašinec, Juraj; Pašteka, Roman; Zahorec, Pavol; Papčo, Juraj; Bella, Pavel; Andrássy, Erik; Dušeková, Laura; Bobíková, Diana; Kseňak, Ľubomír
Introduction Dobšiná; Ice Cave (Slovakia) has attracted the attention of many researchers since its discovery more than 150 years ago. Although the cave is located outside the high-mountain area, it hosts one of the largest volumes of underground perennial ice. The topographic mapping of this unique UNESCO Natural Heritage site has led to several historical surveys. In the last decades of rapid climate change, this natural formation has been subject to rapid changes that are dynamically affecting the shape of the ice body. Increased precipitation, the rise in year-round surface temperatures, and the gravity cause significant shape changes in the ice filling.Methods This paper describes modern technological tools to comprehensively survey and evaluate interannual changes in both the floor and wall of the underground ice body. Technologies such as digital photogrammetry, in combination with precise digital tacheometry and terrestrial laser scanning, make it possible to detect ice accumulation and loss, including the effect of sublimation due to airflow, as well as sliding movements of the ice body to the lower part of the cave. To get a comprehensive model of the ice volume, geophysical methods (microgravimetry and ground penetrating radar) have been added to determine the thickness of the floor ice in the upper parts of the cave in the last 2 years.Results Between 2018 and 2023, the ice volume in certain sections of the cave decreased by up to 667 m(3), with notable reductions in ice thickness ranging from 0.3 to 0.9 m in areas like the Small Hall and Collapsed Dome. The study also detected dynamic changes, such as the widening of the ice tunnel by 20 cm in some sections, and a vertical ice wall in Ruffinyi's Corridor showed localized volume losses up to 9 m3 (between 2018 and 2023). Additional geophysical methods - microgravimetry and ground penetrating radar - revealed an average ice thickness ranging from 10 to 25 m.Discussion The paper not only highlights the current technological possibilities but also points out the limitations of these technologies and then sets out solutions with a proposal of technological procedures for obtaining accurate geodetic and geophysical data.
Enhancing thermoelectric properties of ScN films through twin domains
(Elsevier, 2025) More-Chevalier, J.; Wdowik, U.D.; Martan, J.; Baba, T.; Cichoň, S.; Levinský, P.; Legut, D.; de Prado, E.; Hruška, P.; Pokorný, J.; Bulíř, J.; Beltrami, C.; Mori, T.; Novotný, M.; Gregora, I.; Fekete, L.; Volfová, L.; Lančok, J.
Tailoring thermoelectric properties of ScN-based materials is of vital importance for their application, particularly at high operating temperatures. Here, we report on the thermoelectric properties of the ScN layers deposited on MgO (001) substrates by the DC reactive magnetron sputtering. The microstructure of the produced thin films is examined by X-ray diffraction and atomic force microscopy, while their chemical composition and contamination by defects are determined by X-ray photoelectron spectroscopy. The effect of temperature on the phonon properties of ScN layers, having implications for their thermoelectric properties, is explored by Raman spectroscopy. The results of our experiments are confronted with those following from the first-principles studies. We find that the ScN/MgO(001) layers with twin-domain structure reveal enhanced thermoelectric properties at elevated temperature as compared to those measured for almost defect- and domain-free layers, namely, enlarged Seebeck coefficient by about 30% and over two and a half times increased figure of merit at 800 K. Therefore, structural twin domains in thin ScN film appear to be a simple and rather stable solution for the improvement of its thermoelectric properties at elevated temperatures.
Sensorless finite set predictive current control with MRAS estimation for optimized performance of standalone DFIG in wind energy systems
(Elsevier, 2024) Mebkhouta, Toufik; Golea, Amar; Boumaraf, Rabia; Benchouia, Toufik Mohamed; Karboua, Djaloul; Bajaj, Mohit; Chebaani, Mohamed; Blažek, Vojtěch
This paper introduces a sensorless control strategy combining Finite-Set Predictive Current Control (FSPCC) and Model Reference Adaptive System (MRAS) estimation to enhance the performance of standalone Doubly-Fed Induction Generators (DFIG) in wind energy systems. Addressing the challenges of cost and reliability, the proposed approach eliminates mechanical speed sensors by employing MRAS for real-time rotor speed and position estimation. FSPCC predicts rotor current one step ahead (K + 1), enabling precise control, optimal switching state selection, and improved current regulation with reduced ripple. The significance of this study lies in its potential to advance standalone wind energy systems by providing a robust, efficient, and reduced cost and effective solution for sensorless operation. The proposed strategy was experimentally validated using a 3 kW DFIG coupled with a turbine emulator, connected to a three-phase resistive load, and managed via a DS1104 control board. The system was tested under diverse operational conditions, including sudden load variations and dynamic speed changes, simulating real-time wind energy scenarios. The results demonstrate exceptional robustness and adaptability, with accurate speed estimation, effective voltage regulation, stable current waveforms, and enhanced power quality. The system also exhibited improved reactive power handling, ensuring smooth transitions under fluctuating loads and mitigating power oscillations. By addressing critical challenges in standalone DFIG applications, this work highlights the importance of integrating FSPCC and MRAS as a promising control solution. The results confirm its potential to improve system stability, efficiency, and reliability, offering significant advancements in renewable energy technologies and optimizing the performance of wind energy conversion systems. Also, this combination isn't applied before in the field in can be applied in many other fields like electric vehicles, robotics, aerospace systems and marines.
Distributional properties of the entropy transformed Weibull distribution and applications to various scientific fields
(Springer Nature, 2024) Sindhu, Tabassum Naz; Shafiq, Anum; Lone, Showkat Ahmad; Al-Mdallal, Qasem M.; Abushal, Tahani A.
A novel two-parameter continuous model titled the entropy-transformed Weibull (ET-W) distribution has been developed via the entropy transformation. A new framework has been investigated and found to meet the criteria of the probability function. By significantly improving the functional shape and having the ability to model the most likely form of the hazard rate function, this novel modification has increased the adaptability of typical model. Some of its core characteristics, such as its statistical and computational features, are simply and clearly presented. To examine the ultimate performance of maximum likelihood estimators during the process of estimating model parameters, a comprehensive simulation analysis has been conducted. The effectiveness of the suggested distribution is illustrated through the modeling of real datasets.
Phase transition driven Zn-Ion battery with laser-processed V2C/V2O5 electrodes for wearable temperature monitoring
(Wiley, 2025) Deshmukh, Sujit; Vaghasiya, Jayraj V.; Michalička, Jan; Langer, Rostislav; Otyepka, Michal; Pumera, Martin
Flexible power supply devices present significant potential for wearable bioelectronics within the Internet of Things. Aqueous zinc-ion batteries have emerged as a viable and safe alternative for power supply in flexible electronics. Nevertheless, typical battery behaviors are generally detrimental with unfavorable phase transition of electrodes, which invariably lead to rapid performance degradation. Here, extraordinary capacity enhancement of 150% is presented, sustained over 60 000 cycles, attained using vanadium carbide MXene (V2C)/vanadium pentoxide (V2O5) heterostructure as cathode. The unique cathode material is created through the rational engineering of MAX (V2AlC), employing a single-step laser writing process. The ultrastable Zn ion battery stands in stark contrast to all previously reported counterparts, which typically exhibit capacity degradation within a few hundred/thousand cycles. The primary mechanisms driving this enhancement include the delamination of V2C MXene and an unexpected favorable phase transition during cycling. Additionally, a wearable power supply is constructed using a series configuration and is integrated with a commercial temperature sensor for wireless, real-time body temperature monitoring. This study highlights the critical role of electrode design for advanced wearable bioelectronics.
Impact of antiphospholipid syndrome on placenta and uterine NK cell function: insights from a mouse model
(Springer Nature, 2024) Martirosyan, Anush; Kriegová, Eva; Savara, Jakub; Abroyan, Liana; Ghonyan, Susanna; Slobodová, Zuzana; Nesnadná, Romana; Manukyan, Gayane
Antiphospholipid syndrome (APS) is associated with recurrent pregnancy morbidity, yet the underlying mechanisms remain elusive. We performed multifaceted characterization of the biological and transcriptomic signatures of mouse placenta and uterine natural killer (uNK) cells in APS. Histological analysis of APS placentas unveiled placental abnormalities, including disturbed angiogenesis, occasional necrotic areas, fibrin deposition, and nucleated red blood cell enrichment. Analyses of APS placentas showed a reduced cell proliferation, lower protein content and thinning of endothelial cells. Disturbances in APS trophoblast cells were linked to a cell cycle shift in cytotrophoblast cells, and a reduced number of spiral artery-associated trophoblast giant cells (SpA-TGC). Transcriptomic profiling of placental tissue highlighted disruptions in cell cycle regulation with notable downregulation of genes involved in developmental or signaling processes. Cellular senescence, metabolic and p53-related pathways were also enriched, suggesting potential mechanisms underlying placental dysfunction in APS. Thrombotic events, though occasionally detected, appeared to have no significant impact on the overall pathological changes. The increased number of dysfunctional uNK cells was not associated with enhanced cytotoxic capabilities. Transcriptomic data corroborated these findings, showing prominent suppression of NK cell secretory capacity and cytokine signaling pathways. Our study highlights the multifactorial nature of APS-associated placental pathologies, which involve disrupted angiogenesis, cell cycle regulation, and NK cell functionality.
Complexity of synovial fluid-derived monocyte-macrophage-lineage cells in knee osteoarthritis
(Elsevier, 2024) Mikulková, Zuzana; Gallo, Jiří; Manukyan, Gayane; Trajerová, Markéta; Savara, Jakub; Shrestha, Bishu; Dýšková, Tereza; Nesnadná, Romana; Slobodová, Zuzana; Štefančík, Michal; Kriegová, Eva
Synovial fluid (SF)-derived monocyte-macrophage (MON-Mf)-lineage cells in knee osteoarthritis (KOA) remain poorly understood. We analyzed SF samples from 420 patients with KOA with effusion. The MONMf cells accounted for 47.4% (median; range 7.1%-94.4%) of CD45+ cells and consisted of four subpopulations that correlated with the distribution and activation of other immune cells. The most abundant subpopulation was that of inactive CD11b+CD14-CD16- myeloid dendritic cells (mDCs; cDC2), which exhibited low cytokine production, low T lymphocyte stimulation, and high migratory ability. Other major subpopulations included CD11b+CD14+CD16- monocyte-like cells and CD11b+CD14+CD16+ macrophages, which share a similar transcriptomic profile. A subpopulation of CD11b-CD14-CD16- mDCs (cDC1) was less common. A higher proportion of CD11b+CD14-CD16- mDCs was linked to early-stage KOA and mild joint pain. Dendritic cells were rarely present in KOA synovium. This study revealed the considerable complexity of SF-derived MON-Mf subpopulations and highlighted the role of inactive mDCs in KOA.
Parameter extraction model of wind turbine based on a novel pigeon-inspired optimization algorithm
(Taiwan Academic Network Executive Committee, 2024) Pan, Jeng-Shyang; Liu, Fei-Fei; Tian, Ai-Qing; Kong, Lingping; Chu, Shu-Chuan
This paper has been designed to address the problems of slow convergence and low convergence accuracy of the pigeon-inspired optimization (PIO) algorithm. The evolutionary mechanism of the PIO algorithm contains two stages, exploration and exploitation, which also exist to solve various numerical optimization problems not well. In order to solve the above problems, this paper proposes a novel pigeon-inspired optimization (NPIO) algorithm, which fuses the two stages of the operator into one stage, where exploitation and exploration are carried out simultaneously, and can assist the algorithm to find the optimal solution better. Numerical optimization problems can be solved with a smaller number of iterations. To verify the performance of the NPIO, standard test functions and practical application scenarios are selected for validation. Firstly, this paper uses 23 test functions to test and cross-sectionally compare with five optimization algorithms. The experimental results show that the NPIO is more competitive than the other five algorithms. Secondly, this paper is based on a high-precision mathematical model commonly used for wind turbines. It uses measurable quantities of wind turbines under actual operating conditions for the theoretical analysis of parameter identifiability. The results show that NPIO has a strong performance in wind turbine parameter identification.

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