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 5413 results

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.
High cumulative glucocorticoid dose is associated with increased levels of inflammation-related mediators in active rheumatoid arthritis
(Frontiers Media S.A., 2024) Petráčková, Anna; Horák, Pavel; Savara, Jakub; Skácelová, Martina; Kriegová, Eva
Glucocorticoids (GCs) are widely used as a treatment for rheumatoid arthritis (RA), leading to high cumulative doses in long-term treated patients. The impact of a high cumulative GC dose on the systemic inflammatory response in RA remains poorly understood. Methods We investigated long-treated patients with RA (n = 72, median disease duration 14 years) through blood counts and the serum levels of 92 inflammation-related proteins, and disease activity was assessed using the Simple Disease Activity Index (SDAI). Patients were grouped based on the cumulative GC dose, with a cut-off value of 20 g (low/high, n = 49/23). Results and discussion Patients with a high cumulative GC dose within the active RA group had elevated serum levels in 23 inflammation-related proteins compared with patients with a low dose (cytokines/soluble receptors: CCL3, CCL20, CCL25, IL-8, CXCL9, IL-17A, IL-17C, IL-18, sIL-18R1, IL-10, sIL-10RB, OSM and sOPG; growth factors: sTGF alpha and sHGF; other inflammatory mediators: caspase 8, STAMBP, sCDCP1, sirtuin 2, 4E-BP1, sCD40, uPA and axin-1; pcorr < 0.05). In non-active RA, the high and low GC groups did not differ in analysed serum protein levels. Moreover, patients with active RA with a high GC dose had an increased white blood cell count, increased neutrophil-lymphocyte and platelet-lymphocyte ratios and a decreased lymphocyte-monocyte ratio compared with the low dose group (p < 0.05). This is the first study to report elevated serum levels in inflammation-related proteins and deregulated blood counts in patients with active RA with a high cumulative GC dose. The elevated systemic inflammation highlights the importance of improving care for patients receiving high cumulative GC doses.
Enhanced wombat optimization algorithm for multi-objective optimal power flow in renewable energy and electric vehicle integrated systems
(Elsevier, 2025) Nagarajan, Karthik; Rajagopalan, Arul; Bajaj, Mohit; Raju, Valliappan; Blažek, Vojtěch
In this study, the authors propose the Enhanced Wombat Optimization Algorithm (EWOA) as a solution for the optimal power flow (OPF) issue that occurs in transmission networks. With the incorporation of different types of uncertainties like wind energy, solar photovoltaic (PV) systems, and plug-in electric vehicles (PEVs), the conventional OPF was made to undergo transformation as a stochastic OPF. In order to enhance the method's diversity, a Levy flight mechanism was integrated into the algorithm. For this study, the OPF problem was developed as a Multi-Objective Optimization (MOO) problem with the following objectives such as active power loss, emissions and generation cost. Then, the authors deployed the Monte Carlo simulations to determine the generation costs incurred upon wind energy, solar PV, and PEV sources. This was done so to reduce the overall costs and also overcome the system issues like feasibility and affordability. Further, the authors also used Weibull, lognormal and normal probability distribution functions (PDFs) for characterizing the uncertainties faced in solar PV, wind energy and PEV sources. In various scenarios, the proposed method was validated for its efficacy on IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems. This was done so to demonstrate its capability and address the complexities involved in OPF problem under different conditions. The key advancement of the proposed EWOA is that it integrates the Levy flight mechanism and chaotic sine map, which in turn dramatically boost its optimization capabilities. These mechanisms further contribute to optimal outcomes in terms of less active power loss and low operation costs and emissions. To be specific, the proposed EWOA attained the finest outcomes in terms of generation cost ($731.41/h) and 0.1989 ton/h for emissions in the altered IEEE 30-bus system, $35,642.53/h for cost and 0.8683 ton/h for emissions in the altered IEEE 57-bus system, and $127,753.82/h for cost and 33.2763 MW for real power loss in the altered IEEE 118-bus system. In line with the outcomes, the EWOA presented in this study exhibits strong convergence characteristics and effectively explores the Pareto front. In summary, the EWOA method surpasses the standard WOA outcomes by providing superior exploration capabilities, rapid convergence, robust constraint management, and low sensitivity to variations in the parameters. These advantages make EWOA an effective solution for tackling optimal power flow and other such complex multi-objective optimization challenges.
Techno-economic optimization and sensitivity analysis of off-grid hybrid renewable energy systems: A case study for sustainable energy solutions in rural India
(Elsevier, 2025) Kumar, Pujari Harish; Alluraiah, N. Chinna; Gopi, Pasala; Bajaj, Mohit; Kumar, P. Sunil; Kalyan, CH. Naga Sai; Blažek, Vojtěch
In the twenty-first century, global energy consumption is rapidly increasing, particularly in emerging nations, hastening the depletion of fossil fuel reserves and emphasizing the vital need for sustainable and renewable energy sources. This study aims to analyze hybrid renewable energy systems (HRESs) that use solid waste to generate power, focusing on difficulties linked to intermittent renewable sources using a techno-economic framework. Employing the HOMER Pro software, prefeasibility analysis is performed to meet the energy needs of an Indian community. System architecture optimization depends on factors like minimizing net present cost (NPC), achieving the lowest cost of energy (COE), and maximizing renewable source utilization. This study evaluates the technical, economic, and environmental feasibility of a hybrid renewable energy system (HRES) comprising a 400-kW solar photovoltaic (PV) array, a 100-kW wind turbine (WT), a 100-kW electrolyzer, 918 number of 12V batteries, a 200-kW converter, a 200-kW reformer, and a 15-kg hydrogen tank (H-tank). This optimal configuration has the lowest NPC of $26.8 million and COE of $4.32 per kilowatt-hour, and a Renewable Fraction (RF) of 100 %. It can provide a dependable power supply and satisfy 94 % of the daily onsite load demand, which is 1080 kilowatt-hours per day. The required electricity is sourced to load demand entirely from renewable energy at the given location. Additionally, the study highlights the benefits of HRES in solid waste management, considering technological advancements and regulatory frameworks. Furthermore, sensitivity analysis is conducted to measure economic factors that influence HRES, accounting for fluctuations in load demand, project lifespan, diesel fuel costs and interest rates. Installing an HRES custom-made to the local environmental conditions would provide a long-lasting, reliable, and cost-effective energy source. The results show that the optimal HRES system performs well and is a viable option for sustainable electrification in rural communities.
Implication of cenozoic tectono-sedimentary evolution for the geoenergy potential in the NW Transcarpathian Basin
(Ústav vied o Zemi Slovenskej akadémie vied, 2024) Subová, Viktória; Rybár, Samuel; Hudáčková, Natália; Jamrich, Michal; Jourdan, Fred; Mayers, Celia; Sliva, Ľubomír
The extensive Pannonian Basin System comprises several hydrocarbon-bearing sub-basins, including the moderately explored Transcarpathian Basin located in its NW part. Tectono-sedimentary and volcanic events have influenced the sub-basin's infill and geoenergy potential. Through a comprehensive analysis of petrophysical, organic geochemical, sedimentological, and biostratigraphic data, we aim to uncover the characteristics of petroleum and geothermal plays in the challenging-to-sample Prešov depocenter (NW corner of the Transcarpathian Basin) and its surrounding areas. The results highlight two significant tectono-sedimentary events: first, the opening and subsequent disintegration of the compressional foreland Central Carpathian Paleogene Basin, and its Lower Miocene continuation, which facilitated the deposition of source rocks. Second, the initial phase of rifting in the transtensional Prešov sub-basin, part of a broader back-arc system, created accommodation space for Karpatian to Badenian (Burdigalian to Serravallian) facies. This process led to the formation of fault system that deformed whole sedimentary infill, including the preCenozoic basement carbonates, which resulted in the creation of structural traps and pathways for horizontal and vertical migration. This research reaffirms the geoenergy potential of Paleogene sedimentary records in Central Europe as viable source rocks for hydrocarbons. Contrary to established knowledge, organic lean kerogen type III appears to not only produce methane gas but also wet gas. A promising hydrocarbon trap has been identified in the Triassic to basal Paleogene carbonate breccia reservoirs, though it includes a risk of CO2 and N2 contamination. Notably, this risk diminishes in the uppermost sections of the carbonate traps, where the highest concentrations of methane and wet gas are found, likely due to the gravitational separation of gases by molecular weight. Additionally, these carbonate breccias show moderate geothermal potential.
Cortisol detection using a long period fiber grating immunosensor coated with graphene oxide
(Elsevier, 2025) Soares, Simone; Giannetti, Ambra; Esposito, Flavio; Sansone, Lucia; Srivastava, Anubhav; Campopiano, Stefania; Giordano, Michele; Facão , Margarida; Santos, Nuno F.; Iadicicco, Agostino; Marques, Carlos; Chiavaioli, Francesco
Recirculating Aquaculture Systems (RAS) have revolutionized the protein production sector in aquaculture, leading to significant growth and expansion of the industry. Despite the success of RAS in aquaculture, there are challenges related to stress in fish raised in these systems, which can impact their food intake, growth, and overall well-being. One of the major limitations in the aquaculture industry is the lack of smart sensors for realtime detection of stress hormones like cortisol, hindering our ability to understand and effectively manage the welfare of fish in these systems. In this work, a graphene oxide (GO) coated long period grating (LPG) was fabricated into a double-clad optical fiber (DCF) with W-shaped refractive index profile. The working point of the device was tuned to the mode transition region to enhance its sensitivity against outer medium changes. It was further integrated into a microfluidic system and the fiber surface was functionalized with specific anti-cortisol antibodies for the detection of cortisol. Finally, the performance of this immunosensor was evaluated for a cortisol concentration range of 0.01 ng/mL to 100 ng/mL, a wide working range of concentrations of relevant interest, achieving a limit of detection (LOD) of 0.06 ng/mL. Moreover, a selectivity test using testosterone and glucose as interfering substances was carried out.

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