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 5427 results

Peroneal electric transcutaneous neuromodulation versus solifenacin in the treatment of the overactive bladder wet
(2025) Krhut, Jan; Rejchrt, Michal; Slovák, Martin; Peter, Lukáš; Zvara, Peter
Introduction Peroneal electrical Transcutaneous NeuroModulation (peroneal eTNM (R)) is a non-invasive treatment for overactive bladder (OAB). In the previous randomized study in female patients with OAB, both dry and wet, peroneal eTNM (R) demonstrated significantly better safety and comparable efficacy to solifenacin. This subgroup analysis aimed to compare the safety and efficacy of peroneal eTNM (R) versus solifenacin in OAB wet population. Material and methods In the primary study, eligible subjects were randomized in a 2 : 1 ratio to receive either 12 weeks of daily peroneal eTNM (R) for 30 minutes or solifenacin 5 mg daily. This subgroup analysis included participants who presented with at least one incontinence episode at baseline and completed the study according to protocol. The primary endpoint was safety, secondary endpoint was proportion of continent subjects after treatment. Additional efficacy assessments included change in bladder diary variables, OAB V8 score, and quality of life (QoL). Results In the peroneal eTNM (R) group (n = 26), three treatment-related adverse events (TRAEs) were recorded, while nine TRAEs occured in the solifenacin group (n = 16). The proportion of patients who achieved continence after 4, 8 and 12 weeks of treatment was 50%, 62%, and 65% in the peroneal eTNM (R) and 56%, 50%, and 56% in the solifenacin group, respectively. Both treatments led to significant and similar improvements in all bladder diary variables, OAB V8 score, and QoL. Conclusions The results of this secondary analysis confirm that peroneal eTNM (R) has significantly better safety profile and comparable efficacy versus solifenacin in the subgroup of incontinent OAB patients.
Nanomaterial-based inkjet printing for electrochemical sensing
(Wiley, 2026) Panáček, David; Urban, Massimo; Silvestri, Alessandro; Dědek, Ivan; Nalepa, Martin-Alex; Merkoçi, Arben; Prato, Maurizio; Otyepka, Michal
Inkjet printing (IJP) has emerged as a transformative technology for printed and flexible electronics, redefining electrode engineering for (bio)chemical sensing. It enables maskless, picoliter-scale, additive deposition with high spatial precision, uniformity, and material efficiency. We provide a comprehensive overview of IJP as both a fabrication and post-fabrication functionalization platform for electrochemical working electrodes and fully printed devices. We integrate advances in ink formulation, jetting behavior, and substrate interactions with performance metrics such as layer thickness, roughness, electrochemical surface area, sensitivity, detection limit, and reproducibility. Comparative analyses with drop-casting and screen-printing highlight IJP's advantages in reproducibility, scalability, and material economy. Particular emphasis is placed on nanomaterial- and bioink-based systems, including carbon nanomaterials, MXenes, and hybrid inks, where controlled deposition governs electrode functionality. We also discuss emerging opportunities in hybrid architectures, reactive printing, and sustainable approaches using biodegradable substrates and water-based inks. Finally, we outline a roadmap toward automated, digitally controlled, and environmentally responsible manufacturing of customizable sensors for wearable, biomedical, food, and environmental applications. Collectively, these developments position inkjet printing as an enabling framework for the next generation of intelligent, reproducible, and sustainable sensing technologies.
Comprehensive experimental performance investigation of conducted electromagnetic interference in split-phase induction motors: Common-mode
(Sage Publications, 2026) Miloudi, Mohamed; Miloudi, Houcine; Ardjoun, Sid Ahmed El Mehdi; Elzein, I. M.; Mahmoud, Mohamed Metwally; Mbasso, Wulfran Fendzi; Hussein, Hany S.; Ewais, Ahmed M.
Motors in Adjustable Speed Drive (ASD) systems are the major sources of conducted Electromagnetic Interference (EMI), and they are mainly the Common-Mode (CM) currents and voltages. Compliance with Electromagnetic Compatibility (EMC) standards is of utmost importance when maintaining system reliability in the face of ever-stricter Electromagnetic Compatibility standards in the industrial sectors. This work presents the first systematic experimental evaluation of CM impedance in Split Phase Induction Motors (SPIMs) in a wide frequency range (100 Hz to 100 MHz). Unlike prior studies that were limited to either a differential-mode analysis or limited frequencies in the experiment, the study provides comprehensive CM impedance data of two different SPIM setups, explaining resonance and anti-resonance behaviors that have direct implications on EMC performance. It is experimentally proven that high impedance designed motors significantly reduce CM current transfer, thus reducing EMI emissions and enhance EMC compliance. Particularly, the impedance peak of SPIM (I) was 8k at 100 MHz that translated to a 45% decrease in CM current and -15 dB attenuation of conducted EMI compared to SPIM (II). The resonance and anti-resonance frequencies determined the influence of motor architecture on its susceptibility to EMI. As a result, the findings provide prescriptive design information to the optimization of SPIMs in applications, for example, industrial automation and electric vehicle platforms, where very high EMI mitigation levels are of crucial importance.
Exploring the hepatoprotective and cytotoxic activities of Thalictrum foliolosum and Cordia dichotoma for targeting acute liver injury
(Elsevier, 2026) Raghuvanshi, Disha; Raghuvanshi, Komal; Kumar, Sunil; Thakur, Mehak; Kumar, Deepak; Khan, Azhar; Kumar, Dinesh; Verma, Rachna; Farshori, Nida N.; Al-Sheddi, Ebtesam S.; Al-Oqail, Mai M.; Malik, Tabarak
Liver diseases remain a significant global health burden despite advancements in hepatology. Plant-based therapies offer promising hepatoprotective potential, highlighting the need to evaluate medicinal plants with therapeutic activity. Therefore, the present study aims to evaluate the methanolic extracts of the root and leaves of Thalictrum foliolosum and the leaves of Cordia dichotoma for antibacterial, anti-inflammatory, cytotoxic, and hepatoprotective effects. Antimicrobial analysis revealed that T. foliolosum leaves extract showed maximum inhibition against E. coli (19.0 f 1.0 mm) and the root extract against S. typhi (22.0 f 1.0 mm), while C. dichotoma leaves extract against Bacillus sp. (17.3 f 1.5 mm). Anti-inflammatory analysis showed that at 300 mu g/mL, C. dichotoma leaves exhibited 48.10 f 0.34 % inhibition, while T. foliolosum root and leaves extracts showed 46.35 f 0.90 % and 44.77 f 1.49 % inhibition, respectively. Furthermore, both extracts exhibited dosedependent cytotoxicity toward HepG2 cells, with T. foliolosum root and C. dichotoma leaf extracts showing CTC50 values of 110.7 and 250.7 mu g/mL, respectively. In-vivo studies showed that both the extracts significantly restored liver biomarkers in CCl4-induced hepatotoxicity in Wistar albino rats. T. foliolosum roots extract (200 mg/kg) reduced total bilirubin to 0.33 f 0.06 mg%, conjugated bilirubin to 0.05 f 0.02 mg%, serum glutamate oxaloacetate transaminase (SGOT) to 120.50 f 12.02 IU/L, serum glutamate pyruvate transaminase (SGPT) to 52.00 f 16.97 IU/L, and alkaline phosphate (ALP) to 205.50 f 27.58 IU/L, while restoring total protein (5.70 f 0.14 g%) and albumin (3.30 f 0.14 g%). Similarly, C. dichotoma leaves extract (200 mg/kg) lowered total bilirubin to 0.34 f 0.03 mg%, conjugated bilirubin to 0.06 f 0.03 mg%, SGOT to 122.00 f 2.83 IU/L, SGPT to 44.50 f 3.54 IU/L, and ALP to 185.00 f 29.70 IU/L, with improved total protein (5.60 f 0.57 g%) and albumin (3.30 f 0.14 g%). Molecular docking further supported the bioactivity of the extracts. Senecionine showed good affinity for the antibacterial target 4KR4 (-7.6 kcal/mol), while rutin exhibited the strongest binding to the antiinflammatory (5IKR, -8.5 kcal/mol) and hepatoprotective (3SU4, -7.7 kcal/mol) targets. Overall, these findings revealed that C. dichotoma leaf extract exhibits stronger hepatoprotective activity than T. foliolosum root extract, supporting its further investigation in future studies.
Frozen slab method mediated sulfur-affinitive single-atom catalysts for efficient reversible sodium storage
(Royal Society of Chemistry, 2026) Cui, Kai; Qi, Zijia; Legut, Dominik; Zhao, Wanxiang; Chen, Biao; Wu, Ningning; Zhang, Qiuyu; Wang, Tianshuai
Carbon-supported single-atom catalysts (C-SAMs) have recently emerged as a frontier strategy to address the issue of irreversible reactions in MoS2-based sodium-ion batteries. However, conventional C-SAMs designed solely considering the d-p orbital coupling theory often yield distorted adsorption energy predictions for Na2S, as it overlooks the roles of Na-N bond interactions and structural deformation. Herein, we introduce the frozen slab method to evaluate the influence of C-SAMs' affinities toward Na and S on Na2S adsorption. Based on their relative adsorption strengths, C-SAMs are classified into three categories: S-affinitive, amphiphilic, and Na-affinitive. Theoretical calculations reveal that S-affinitive C-SAMs strongly adsorb S atoms, thereby weakening the Na-S bond in Na2S and facilitating bond cleavage during charging. This reduces the decomposition energy barrier of Na2S and enhances the reversibility of the conversion reaction. Experimental results confirm that S-affinitive C-SAV can accelerate Na+ storage kinetics in MoS2, enabling highly efficient reversible conversion during charging. As a result, after 1000 cycles at a high current density of 5 A g-1, the MoS2/C-SAV electrode exhibits a specific capacity of 332.8 mAh g-1, with a capacity retention rate as high as 98.87% and an average capacity decay of only 0.001% per cycle.
Design of novel exponential PDN controller via quadratic interpolation optimiser for nonlinear and unstable ball and beam system
(Wiley, 2026) Izci, Davut; Ekinci, Serdar; Çelik, Emre; Uyar, Murat; Bajaj, Mohit; Blažek, Vojtěch; Rubanenko, Olena
This study presents a novel exponential proportional-derivative controller with filter (exp-PDN) for stabilising the nonlinear and underactuated ball and beam system. Unlike conventional PID-based approaches, the proposed controller removes the integral term, resulting in faster transient responses and improved robustness. It incorporates nonlinear exponential shaping of both the error and its derivative, along with a filtered derivative path for enhanced noise handling. A custom multi-objective cost function, comprising the squared error, settling time, and percent overshoot, is proposed to evaluate control performance. The quadratic interpolation optimiser (QIO), a recently developed metaheuristic based on analytical interpolation, is employed to optimise the controller parameters. To validate its effectiveness, the exp-PDN controller is compared against five state-of-the-art metaheuristic algorithms: QIO, spider wasp optimiser, komodo mlipir algorithm, golden eagle optimiser, and slime mould algorithm. The QIO-optimised exp-PDN achieves the best performance, with the lowest cost value (0.3211), minimal overshoot (5.52%), fast rise time (0.97 s), and smallest steady-state error (4.1643 x 10- 4). Further comparisons with QIO-optimised phase-lead and PID-with-filter controllers demonstrate the superiority of the proposed method in both transient and steady-state behaviour. In summary, this work advances the control of nonlinear unstable systems by delivering a structurally simple yet highly responsive control architecture. The combination of dual-channel exponential shaping and efficient metaheuristic optimisation results in state-of-the-art closed-loop performance, highlighting the practical value of the proposed exp-PDN framework for real-world control applications.
Lunar regolith simulant-based triboelectric nanogenerators: Toward sustainable energy harvesting from resources on the moon
(Elsevier, 2026) Yohannan, Alex; Vaghasiya, Jayraj V.; Sonigara, Keval K.; Pumera, Martin
The exploration of extraterrestrial materials for energy harvesting, generation and storage is important for futuristic material evolution and use. Thus, study and use of extraterrestrial materials simulants becomes straightforward way to identify potential of those materials. Such as Lunar Regolith Simulants are tested as reference material to explore suitability for construction, solar cell components and beyond. However, aiming futuristic space exploration, on-site energy generator development from Lunar regolith materials is unexplored and necessary to unveil it. In this work, we introduce a lightweight, flexible triboelectric nanogenerator (TENG) that uses lunar regolith simulant particles (LRPs) embedded in polydimethoxysilane (PDMS) to harvest mechanical energy as first proof-of-concept. Under cyclic contact-separation, the optimized device containing 30 wt % of <= 45 mu m LRPs yields an open-circuit voltage V-oc of similar to 10.5 V, a short-circuit current I-sc of similar to 2.2 mu A, and a peak power density reached its maximum at 3.0 mu W cm(-)(2) under a force of 2.5 N at 10 Hz. Systematic optimization of grain size and weight fraction of LRPs in PDMS film is analyzed and resulted in the voltage output of 1.6 times and current density by 2.1 times compared to the bare PDMS material. Furthermore, the device shows 95 % performance retention of its output after 36,000 operation cycles, underscoring its good stability and potential for sustainable energy harvesting in ambient environments. These results demonstrate that utilizing extraterrestrial fillers, such as LRPs, is a useful approach for enhancing TENG performance in future terrestrial settings, offering insight for future space materials employed in composite design for TENG devices.
Fractional analysis for multiple solutions of thermodynamic model of Casson fluid under hydrodynamic and non-hydrodynamic optimization
(Elsevier, 2026) Abro, Shahnila Yaseen; Souayeh, Basma; Flah, Aymen; Hamdi, Monia; Abro, Kashif Ali; Faizan, Muhammad
This study investigates the flow behavior of a non-Newtonian Casson fluid influenced by hydromagnetic and non-hydromagnetic effects over an oscillating plate, subject to combined gradients of temperature and mass concentration. The analysis is framed within the context of linear fractional differential equations incorporating the Caputo-Fabrizio fractional derivative with a non-singular kernel. A mathematical model is developed, employing a linear boundary condition to characterize the temperature distribution, mass concentration, and velocity profiles. The governing equations are first non-dimensionalized and then extended into their fractional forms. An analytical solution is obtained using integral transform techniques, specifically the Laplace transform with its inversion and the Fourier sine transform with inversion. The break down the data analysis process under rheological variation for temperature and concentration is explored through which generalization and comparison is investigated. The key findings are focused on the flow and heat transfer characteristics, examining the influence of key dimensionless parameters. Moreover, the comparison between fractional and classical approaches are found in excellent agreement.
Optimizing feature selection with random reversal and adaptive Gaussian based Dung beetle optimizer for intrusion detection system in IoT
(Springer Nature, 2025) Vurubindi, Padmavathi; Frnda, Jaroslav; Sujatha, Canavoy Narahari; Divakarachari, Parameshachari Bidare; Nijaguna, G. S.; Mahendar, A.
The Internet of Things (IoT) is an emerging, promising technology developed with the objective of establishing global connectivity among devices. IoT is highly susceptible to malicious attacks, owing to its resource-constrained architecture, insecure wireless communication, diverse device ecosystems, and the vast volume of sensor data transmitted over networks. An effective Intrusion Detection System (IDS) is essential to address these security concerns. However, challenges such as irrelevant features and poor class separability complicate its development. This research proposes a novel IDS by introducing an Improved Random Reversal Learning (IRRL) and Dimensional Adaptive Gaussian Variation (DAGV)-based Dung Beetle Optimizer (RGDBO) for optimal feature selection, enhancing exploration, and avoiding premature convergence. For classification, a Convolutional Neural Network (CNN) integrated with CosFace and ArcFace loss functions, termed CACNN, is employed to enhance intrusion classification through more efficient discrimination among classes. The combined RGDBO-CACNN framework is evaluated on three benchmark datasets: UNSW-NB15, NSL-KDD, and CICIDS-2017, using accuracy, recall, precision, and F1-score as performance metrics. A comparative analysis of existing methods, including GA-FR-CNN, GTO-BSA, and BMRF-RF, demonstrates the superiority of the proposed model, with RGDBO-CACNN achieving an accuracy of 99.999% on the UNSW-NB15 dataset.
Design and optimization of localized plasmon resonance sensing via square-slotted Ag-graphene-dielectric metasurfaces for dermatological cancer identification using machine learning
(Springer Nature, 2025) Alsharari, Meshari; Flah, Aymen; Aliqab, Khaled; Pergl, Ivo; Kumar, Abhinav; Armghan, Ammar
Skin cancer is a dangerous, life-threatening illness impacting countless individuals globally, requiring urgent awareness, prevention, and early detection. It is one of the most common forms of cancer, often caused by excessive sun exposure or tanning, and requires early detection for effective treatment. Early detection of skin cancer is achievable through advanced sensor designs that utilize graphene material. Graphene's exceptional properties make it extremely appropriate for creating sensitive, accurate, and non-invasive diagnostic tools to identify cancer at early stages. The integration of silver (Ag), graphene, and silicon dioxide (SiO2) materials forms a highly sensitive multilayer structure, significantly enhancing the surface plasmon resonance response, which enables precise detection of skin cancer biomarkers at extremely low concentrations. An exceptional sensitivity of 1050 nm/RIU is attained, enabling efficient skin cancer detection through advanced plasmonic biosensing technology. Optimizing the biosensor design by systematically varying key physical parameters-such as layer thicknesses, slot dimensions, and material configurations-significantly enhanced its sensitivity. The optimization is also achieved by using a Machine learning algorithm. The highest R2 value of 0.99 is achieved for this research. This strategic tuning of the structural and optical characteristics enabled more accurate detection capabilities, making the sensor highly effective for early skin cancer diagnosis through plasmonic resonance.
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.
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.

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