Články z časopisů s impakt faktorem / Articles from Impact Factor Journals

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Now showing 1 - 20 out of 6825 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.
A novel analytical methodology for estimating high-frequency lumped model inductances and series capacitance of transformer winding: an indirect measurement procedure
(Elsevier, 2026) Chaouche, Moustafa Sahnoune; Didi, Faouzi; Amara, Abderrazak; Houassine, Hamza; Yousof, Mohd Fairouz Mohd; Tazay, Ahmad F.; Flah, Aymen; Metwaly, Mohamed K.; Ghaly, Ramy N. R., Ramy N. R.; Ghoneim, Sherif S. M.
In this article, a new analytical method is introduced to effectively estimate the self-inductance, mutual inductances, and series capacitance of transformer windings. The approach uses FR data collected at the winding terminals with the neutral open test. It applies an analytical formula that converts the sum of the inverse squares of both short-circuit and open-circuit natural frequencies, derived from the FR curve, into a polynomial function. These formulas are based on a lumped, mutually coupled equivalent model of the winding, with relationships expressed as a polynomial function connected by a factor relating the inductances, generalized to an N-1 degree for the N-th section of the model. By solving this polynomial, all winding inductance values can be accurately estimated, enabling the determination of the series capacitance. Notably, this method relies solely on measurements of the FR curve, ground capacitance, and equivalent inductance, providing an indirect yet highly efficient way to determine all parameters of the lumped mutually coupled equivalent model. This technique has been rigorously validated through experimental frequency response measurements on two air-core insulated windings, producing remarkably precise results that demonstrate its effectiveness in the field of frequency modeling.
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.
Experimental examination of thermohydraulic characteristics of a new vibrating rubber tube turbulator with multiple air bubble outlets inserted inside a double-pipe heat exchanger
(Elsevier, 2026) Afridi, Muhammad Idrees; Pourahmad, Saman; Maleki, Nemat Mashoofi; Tavousi, Ebrahim; Rahbari, Alireza; Adibi, Tohid; Sharifpur, Mohsen
This experimental study explores a new method for improving heat transfer in heat exchangers by utilizing bubble injection alongside electromagnetic vibration techniques. Instead of conventional bubble injection, a vibrating rubber tube with multiple air outlets is employed to introduce bubbles into the working fluid. This configuration ensures uniform bubble distribution along both axial and radial directions, while the rubber tube's continuous vibration disrupts the thermal boundary layer, promoting turbulence and further enhancing heat transfer. The effects of various parameters are investigated, including Reynolds numbers spanning from 1050 to 7370, bubble injection flow rates between 0.5 and 2 l/min, rubber tube diameters of 3-5 mm, and air outlet numbers ranging from 30 to 90. Results show that increasing the bubble flow rate and tube diameter enhances both heat transfer and the friction coefficient. In contrast, increasing the number of air outlets improves heat transfer while reducing the friction coefficient. A maximum TEF of 4.42 is achieved at a bubble flow rate of 1.5 l/ min, a tube diameter of 5 mm, and 90 air outlets. Under these conditions, the Nusselt number and friction coefficient are up to 10.43 and 13.1 times higher, respectively, compared to those of a plain tube.
Strain-induced enhancement of spin pumping in Pt/YIG bilayers
(IOP Publishing, 2026) Solis, Lara M.; Carreira, Santiago J.; Gómez, Javier; Butera, Alejandro; Abellán, Maria; García, Carlos; Bonetto, Fernando; Vavassori, Paolo; Briático, Javier; Steren, Laura B.; Aguirre, Myriam H.
Enhancing spin-to-charge (S -> C) conversion efficiency remains a key challenge in spintronic materials research. In this work we investigate the effect of substrate-induced strains onto the S -> C efficiency. On one hand, we analyze strains-induced magnetic anisotropies in yttrium iron garnet (Y3Fe5O12, YIG) by comparing the magnetic and structural properties of YIG films grown on Gd3Ga5O12 (GGG) and (CaGd)3(MgZrGa)5O12 (SGGG) substrates. Differences in lattice mismatch-YIG//GGG ( eta=-0.06%) and YIG//SGGG ( eta=-0.83%)-lead to out-of-plane tensile strains in the first case and unexpected compressive strain in the latter. On the other hand, we study the spin injection efficiency on Pt/YIG bilayers evaluated by the inverse spin Hall effect (ISHE). We find that the resulting perpendicular magnetic anisotropy in YIG//SGGG, while not dominant over shape anisotropy, correlates with enhanced ISHE signals as observed in spin pumping ferromagnetic resonance and spin Seebeck effect experiments. Strain engineering proves effective in enhancing S -> C conversion, providing insight into the design of efficient spintronic devices.
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.
Schrodinger optimizer: A quantum duality-driven metaheuristic for stochastic optimization and engineering challenges
(Elsevier, 2025) Hussein, Nazar K.; Qaraad, Mohammed; El Najjar, Abdelwahab M.; Farag, M. A.; Elhosseini, Mostafa A.; Mirjalili, Seyedali; Guinovart, David
This paper introduces the Schrodinger Optimizer (SRA), a new metaheuristic algorithm motivated by principles of quantum mechanics, specifically Schrodinger's equation and wave-particle duality. SRA possesses a twin update mechanism that balances probabilistic exploration and deterministic exploitation, facilitating effective navigation in high-dimensional, intricate search spaces. The algorithm was extensively tested on benchmark suites such as CEC 2019 (low-dimensional), CEC 2017 (50D and 100D), CEC 2022 (20D), and eight real-world engineering design optimization problems. Comparison tests with state-of-the-art physics-inspired and advanced metaheuristic algorithms revealed SRA's superior performance. In the 100D CEC 2017 benchmark, SRA ranked the best average rank (1.87) among the physics-based algorithms and performed better than its rivals on 20 of the 29 functions. It also performed best (2.92) among emerging metaheuristic variants. Statistical tests (Friedman and Wilcoxon signed rank) confirmed the significance of these results. In engineering applications, SRA consistently obtained better solutions with fewer computations. These findings accentuate SRA's potential in solving complex optimization problems efficiently. This study opens up new possibilities for powerful and versatile optimization methods through the integration of quantum-inspired concepts into the metaheuristic paradigm. The source code is available at https://github.com/MohammedQaraad/SRA/blob/main/SRA_framework.ipynb
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.
Validation of the Tena pregnancy phantom and fetal dose assessment in proton scanning beam therapy
(Elsevier, 2026) Mojżeszek, Natalia; Brkić, Hrvoje; Foltyńska, Gabriela; Van Hoey, Olivier; Jabłoński, Hubert; Kasabasic, Mladen; Kopeć, Renata; Krzempek, Katarzyna; Lipa, Monika; Matamoros, Andrea; Radolińska, Monika; Rydygier, Marzena; Skóra, Tomasz; Granja, Carlos; Stolarczyk, Liliana; Krzempek, Dawid; De Saint-Hubert, Marijke
Background: Intensity modulated proton therapy (IMPT) is the preferred option during pregnancy, as it reduces out-of-field doses compared to photon techniques. A physical pregnancy phantom was validated for in-field proton dosimetry and used to assess fetal dose across four IMPT plans. Methods: The 18-week pregnancy Tena phantom was composed of bone, soft tissue, and lung substitutes. Proton relative stopping power (RSP) for Tena tissues was measured and compared with treatment planning system (TPS) and Monte Carlo (MC) calculations. Experimental TPS dose verification was performed using gamma index (GI). Fetal dose was measured for IMPT of glioma, Hodgkin lymphoma without (HL) and with a range shifter (HL-RS), and submandibular gland (neck) cancer using a Timepix and bubble detectors. Results: Differences between TPS-assigned and MC-simulated relative to the measured RSP values were up to -7.4 %. GI(3 %/3 mm) values were above 93.38 %. The neutron dose equivalent in the fetus position ranged between 2.5 and 49.4 mu Sv/Gy(RBE) for glioma and HL-RS plans, respectively. The HL plan reduced neutron dose equivalent to 15.8 mu Sv/Gy(RBE), while for the neck 20 mu Sv/Gy(RBE) was measured. Neutrons were dominant with similar to 80 % contribution to the total dose equivalent. A summed fetal dose was calculated considering the prescribed dose per treatment and ranged between 0.17 mSv and 1.89 mSv for glioma and HL-RS, respectively. Conclusions: The Tena phantom is suitable for proton dosimetry and enables accurate TPS calculations. The use of a range shifter increased the fetal dose by more than threefold. Fetal doses for all IMPT plans remained below 2 mSv.
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.
Dynamic graph learning for bus passenger profiling in urban transportation networks
(IEEE, 2026) Hou, Mingliang; Tahir, Muhammad; Frnda, Jaroslav; Zheng, Xiaoa; Anwar, Muhammad Shahid; Tang, Yongwei; Hussain, Imtiaz
Bus passenger profiling is a critical task for optimizing urban transportation, but it is hindered by three key challenges: the heterogeneity of passenger behaviors, complex station-level interactions, and the prevalence of sparse, noisy transit data. Conventional end-to-end models that operate on aggregated traffic flow often fail to address these issues systematically. To overcome these limitations, this paper proposes GRASP, a novel two-stage paradigm for passenger profiling and flow prediction. In the first stage, GRASP acts as a disentangling module, constructing a passenger-centric graph to cluster individuals into distinct behavioral profiles based on their co-occurrence patterns. In the second stage, it performs profile-aware forecasting by learning group-specific, dynamic spatio-temporal dependencies using an adaptive station graph. This station-level model is further enhanced by a contrastive learning objective to ensure robustness against data imperfections. Extensive experiments on three real-world datasets demonstrate that GRASP not only achieves significantly superior flow prediction accuracy but also uncovers actionable passenger profiles. By structurally decoupling passenger behavior from station-level dynamics, GRASP offers a more interpretable and effective solution for data-driven public transportation management.
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.
Entropy-driven disordered surface formation with durable anti-water stability for ultra-stable layered oxide cathodes in sodium-ion batteries
(Elsevier, 2026) Wang, Jiaqi; Zhou, Junhua; Shi, Qitao; Zhang, Cheng; Wang, Zhipeng; Bachmatiuk, Alicja; Shen, Yanbin; Choi, Jinho; Yang, Ruizhi; Rümmeli, Mark H.
Conventional layered oxide cathodes for sodium-ion batteries (SIBs) suffer from severe capacity degradation due to crystalline surface reactivity, which triggers parasitic reactions with ambient H2O/O-2, leading to surface corrosion and bulk structural collapse. Herein, we introduce a high-entropy engineering strategy that designs a self-protective cathode, Na0.8Mg0.1Zn0.1Cu0.1Fe0.1Mn0.6O2 (HEO). This material spontaneously forms an entropy-stabilized amorphous surface coating with an ultralow formation energy of 0.16 eV. The coating acts as a kinetic barrier, raising the activation energy for detrimental H2O/O2 reactions by 160 % compared to a low-entropy counterpart (Na0.8Mg0.2Mn0.8O2). The synergy between entropy stabilization and surface amorphization delivers exceptional environmental robustness. After 90 days of water exposure, HEO retains 98 % of its initial capacity, and 99 % capacity retention over 100 cycles, surpassing state-of-the-art layered cathodes in cycling stability. This work establishes a universal framework for designing air/water-resilient cathodes, with immediate implications for scalable manufacturing and long-term storage stability of SIB systems.
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.

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