Search Results (9,456)

This study presents an optimal bearing arrangement for the propulsion shafting system of ships equipped with multiple strut bearings, ensuring both structural stability and cost-effectiveness under shallow-draft conditions where the propeller must remain fully submerged. To this end, the shafting flexibility, alignment characteristics, and critical whirling speed were analyzed for various bearing arrangements. The analysis results show that removing the stern tube bearing and supporting the shaft using only the Y-type and I-type strut bearings, with the bearing span adjusted so that the L/d ratio remains within 15 to 18, minimizes the reaction influence number, shaft bending moments, and variations in bearing loads. At this configuration, the first natural frequency corresponding to the propeller blade order is also more than 30 percent higher than the service speed, thereby avoiding resonance caused by transverse vibration. Accordingly, this study confirms that adjusting the layout of strut bearings can simultaneously enhance both the structural reliability and dynamic stability of the propulsion shafting system. Full article

As the primary interface for integrating renewable energy sources such as wind and solar power into the grid, inverters are prone to inducing sub-/super-synchronous or medium-to-high-frequency oscillations during grid-connected operation under weak grid conditions. Optimizing the control structure of a single wind turbine inverter struggles to address multi-mode resonance issues comprehensively. Therefore, a cooperative control strategy for parallel-coupled inverters is proposed. First, a frequency-domain impedance reconstruction method for parallel wind turbines is proposed based on the phase-neutralizing characteristics and damping variation patterns of parallel-coupled impedances. Second, the damping characteristics of inverters are enhanced through the design of an additional damping controller, while the phase-frequency characteristics of wind turbines are improved using active damping based on notch filters. Finally, simulation models based on 2.5 MW permanent magnet synchronous generator (PMSG) units validate the effectiveness of the control strategy. Research results demonstrate that this cooperative control strategy effectively suppresses sub-/super-synchronous and medium-to-high-frequency oscillations: In the 0~300 Hz key oscillation band, the amplitude suppression rate of oscillating current reaches ≥60%, the total harmonic distortion (THD) of the 5th harmonic at the grid connection point decreases from 4.465% to 3.518%. Full article

This research focused on the synthesis of triangular silver nanoplates (TSNPs) with sharp corners using a photomediated seed growth method. The TSNPs produced had an average edge length of 27.2 ± 9.2 nm and a (110) crystalline plane structure. In terms of optical properties, the TSNPs displayed three key absorbance peaks at approximately 400 nm, 500 nm, and 660 nm, which correspond to out-of-plane dipolar resonance, in-plane quadrupolar resonance, and in-plane dipolar resonance, respectively. The prepared TSNP colloidal solutions served as surface-enhanced Raman spectroscopy (SERS)-active materials for detecting paraquat residue in aqueous samples. We optimized the mixing time of the liquid SERS with the sample, maintaining a 1:1 volume ratio. The findings showed a remarkable enhancement of the Raman signal with 10 min mixing time using laser excitation at a wavelength of 785 nm. This study achieved the development of novel SERS-active substrates capable of detecting pesticides with excellent accuracy, sensitivity, and reproducibility for both qualitative and quantitative analysis in tap water, river water, drinking water, and cannabis water. Additionally, it paved the way for using the SERS technique as a promising approach in the areas of food safety and environmental monitoring, especially in the organic farming field. Full article

While magnetic resonance imaging (MRI) is effective for detecting early-stage osteonecrosis of the femoral head (ONFH), it is often expensive and less accessible; conversely, radiography is more widely accessible but has limited sensitivity for early-stage diagnosis. We developed a deep learning approach using radiographic images to effectively classify ONFH stages, providing a more accessible method for early diagnosis and disease stage differentiation. The dataset consisted of 909 hip radiographs, yielding 1818 femoral head images (grade 0:1495; grade 1:80; grade 2:114; grade 3:93; grade 4:36). A U-Net model was used to segment the femoral heads, achieving a Dice similarity coefficient (DSC) of 0.977 on the test set, allowing precise localization of the region of interest. A variational autoencoder (VAE) was then trained using 1270 grade-0 images for training and 112 for validation to construct a normative latent distribution representing healthy femoral heads. When ONFH data from all grades were projected into the latent space, significant differences in Mahalanobis distance distributions were observed across most ONFH stages. No significant difference was found between grades 0 and 1 (p = 0.06), consistent with known radiographic subtlety. However, grades 2–4 showed significant deviation from grade 0, and significant differences were also observed among mid- and late-stage grades. These findings demonstrate that the proposed method effectively separates healthy and diseased femoral heads and captures gradewise structural progression within the latent space. This radiograph-based normative modeling approach offers an accessible alternative to MRI for ONFH stage differentiation, particularly in resource-limited clinical environments. Although early-stage differentiation remains challenging, the results highlight the potential of radiograph-based deep learning systems to improve diagnostic efficiency and support future automated ONFH staging workflows. Full article

The development of advanced semiconductor-based catalysts for the rapid degradation of emerging pharmaceutical pollutants in water remains a critical challenge in environmental science. In this study, we present the synthesis, characterization, and catalytic performance of zinc oxide (ZnO) microtetrapods decorated with plasmonic Ag nanoparticles. These microtetrapods have been designed to enhance piezo-, photo-, and piezo-photocatalytic degradation of metronidazole (MNZ), a persistent antibiotic contaminant. ZnO microtetrapods were synthesized by high-temperature pyrolysis and using atmospheric-pressure microwave nitrogen plasma, followed by photochemical deposition of Ag nanoparticles at various precursor concentrations (0–1 mmol AgNO₃). The structural integrity of the samples was confirmed through X-ray diffraction (XRD) analysis, while the morphology was examined using scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDX). Additionally, spectroscopic analysis, including Raman, electron paramagnetic resonance (EPR), and photoluminescence (PL) spectroscopy, was conducted to verify the successful formation of heterostructures with adjustable surface loading of Ag. It has been shown that ZnO microtetrapods decorated with plasmonic Ag nanoparticles exhibit Raman-active properties. A systematic evaluation under photocatalytic, piezocatalytic, and combined piezo-photocatalytic conditions revealed a pronounced volcano-type dependence of catalytic activity on Ag content, with the 0.75 mmol composition exhibiting optimal performance. In the presence of both light irradiation and ultrasonication, the optimized Ag/ZnO composite exhibited 93% degradation of MNZ within a span of 5 min, accompanied by an apparent rate constant of 0.56 min⁻¹. This value stands as a significant improvement, surpassing the degradation rate of pristine ZnO by over 24-fold. The collective identification of defect modulation, plasmon-induced charge separation, and piezoelectric polarization as the predominant mechanisms driving enhanced reactive oxygen species (ROS) generation is a significant advancement in the field. These findings underscore the synergistic interplay between plasmonic and piezoelectric effects in oxide-based heterostructures and present a promising strategy for the efficient removal of recalcitrant water pollutants using multi-field activated catalysis. Full article

Organizations increasingly rely on Agile software development to navigate the complexities of digital transformation. Agile emphasizes flexibility, empowerment, and emergent design, yet large-scale initiatives often extend beyond single teams to include multiple subsidiaries, business units, and regulatory stakeholders. In such contexts, team-level mechanisms such as retrospectives, backlog refinement, and planning events may prove insufficient to achieve alignment across diverse perspectives, organizational boundaries, and compliance requirements. To address this limitation, this paper introduces a complementary framework and a supporting software tool that enable systematic cross-stakeholder alignment. Rather than replacing Agile practices, the framework enhances them by capturing heterogeneous stakeholder views, surfacing tacit knowledge, and systematically reconciling differences into a shared alignment artifact. The methodology combines individual Functional Resonance Analysis Method (FRAM)-based process modeling, iterative harmonization, and an evidence-supported selection mechanism driven by quantifiable performance indicators, all operationalized through a prototype tool. The approach was evaluated in a real industrial case study within the regulated gaming sector, involving practitioners from both a parent company and a subsidiary. The results show that the methodology effectively revealed misalignments among stakeholders’ respective views of the development process, supported structured negotiation to reconcile these differences, and produced a consolidated process model that improved transparency and alignment across organizational boundaries. The study demonstrates the practical viability of the methodology and its value as a complementary mechanism that strengthens Agile ways of working in complex, multi-stakeholder environments. Full article

Capacitor-powered resonant inverters are often employed in pulsed-power applications to feed an equivalent series resistance–inductance–capacitance (RLC) load with time-varying component values. Establishing the short-time dynamics of such an arrangement is nontrivial since the system does not reach a steady state within a single pulse period. As a result, linearization around a single operation point cannot be applied for the sake of simplified system modeling. Consequently, the design of feedback controllers for such systems (aiming for, e.g., resonant frequency tracking or energy transfer rate regulation) is highly cumbersome and challenging since a linear time-invariant regulator is unable to bring the system to desired performance within the whole expected operation range. To cope with the modeling task, a reduced-order envelope model of a capacitor-fed resonant inverter feeding a time-varying RLC load was recently proposed by the authors. In this paper, this model is further simplified and split into linear and nonlinear parts, allowing the employment of a combination of feedback linearizing (nonlinear) action with a linear time-invariant regulator to form a nonlinear control structure allowing the attainment of resonant frequency tracking within a wide operation range. The proposed controller design methodology is accurately validated by multiple time-domain simulations. Full article

Background/Objectives: The cavernous segment of the internal carotid artery (ICA) is a critical neurovascular structure with complex cranial nerve relationships. Understanding its morphometric variability is essential for safe microsurgical and endovascular procedures. This study aimed to characterize the morphometry of the cavernous ICA using Magnetic resonance imaging (MRI) and assess associations with demographic variables. Methods: A retrospective observational study was conducted on 135 MRI scans of adult patients, distributed among 79 women and 56 men with an average age of 50.8 years, without cerebrovascular pathology, performed between March 2023 and January 2025. The diameters of the left and right cavernous ICA and the intercarotid distance were measured using RadiAnt DICOM Viewer. Statistical analyses included descriptive statistics, t-tests, correlations, and multivariate regression models adjusted for age and sex. Principal component and cluster analyses were applied to identify morphometric patterns. Results: The mean left and right ICA diameters were both 5.09 ± 0.65 mm, with a mean intercarotid distance of 17.4 ± 4.22 mm. No age-related associations were found (p > 0.05). Male patients showed significantly larger right ICA diameters (p = 0.008). Bilateral symmetry was confirmed (p > 0.05). Two morphometric clusters were identified: Morphotype 1 (larger ICA caliber and narrower spacing) and Morphotype 2 (smaller caliber and wider spacing), showing a significant sex distribution difference (p = 0.012). Conclusions: The cavernous ICA demonstrates stable bilateral symmetry with minor sex-dependent differences. Morphometric characterization supports safer planning of transsphenoidal, endovascular, and skull-base surgeries by reducing the risk of iatrogenic neurovascular injury. Full article

Traditional antibiotic agents are commonly employed in the control of pathogenic microorganisms. However, there is a growing need for novel alternative antimicrobial agents owing to the increasing prevalence of resistance to these treatments. Freshwater fungi, recognized for producing diverse secondary metabolites with biological activities, offer promising sources for drug development. However, studies on Trichoderma flavipes remain limited. Therefore, this study was conducted to investigate the antimicrobial properties of bioactive compounds derived from Trichoderma flavipes FBCC-F1632, a fungal species isolated from freshwater environments in Korea. The fungal strain FBCC-F1632 was isolated from stream soil obtained from Mungyeon-si, Republic of Korea, and identified through DNA extraction and phylogenetic analysis. Antimicrobial activity against Staphylococcus aureus and Bacillus cereus was assessed, revealing significant inhibitory rates. Potential bioactive compounds were extracted, purified, and structurally characterized using chromatographic and spectroscopic techniques, including nuclear magnetic resonance and mass spectrometry. Five bioactive compounds were identified: F1632-1 (cordyol C), F1632-2 (diorcinol), F1632-3 (violaceol I), F1632-4 (tryptophol), and F1632-6 (violaceol II). These compounds exhibited notable antimicrobial activities, particularly against Staphylococcus aureus and Bacillus cereus, underscoring their potential as candidates for the development of novel antimicrobial therapeutics. Full article

Alzheimer’s disease (AD) is a neurodegenerative condition that gradually deteriorates memory and cognitive abilities, posing a significant global health challenge. While convolutional neural networks (CNNs) applied to structural magnetic resonance imaging (MRI) have achieved high diagnostic accuracy, their decision-making processes often lack transparency, which can limit clinical trust. This study presents a structured evaluation framework by applying multiple gradient-based and model-agnostic interpretability methods, such as Grad-CAM, Grad-CAM++, HiRes-CAM, Backpropagation, Guided Backpropagation, Kernel SHAP, LIME, and RISE, to pre-trained and custom CNN architectures for AD classification. We utilized the ADNI MRI dataset and assessed models based on accuracy, sensitivity, specificity, and visual alignment of highlighted brain regions with established biomarkers. By analyzing both predictive performance and explanation validity, this study aims to assist clinicians in making informed diagnoses, ultimately strengthening trust in AI-assisted tools. Full article

Celtis africana, a rare plant native to southwestern Saudi Arabia, was explored for the first time as a source for the green synthesis of silver nanoparticles (AgNPs). Catechol-bearing phenolic amides in the aqueous leaf extract acted as both reducing and capping agents, enabling eco-friendly AgNP fabrication. The synthesized AgNPs were characterized using SEM, TEM, XRD, UV-Vis, and FTIR, revealing predominantly spherical nanoparticles with an average size of 9.28 ± 0.11 nm, a face-centered cubic crystalline structure, and a pronounced surface plasmon resonance at 424 nm. HPLC analysis confirmed the presence of caffeoyltryamine in the extract, while UV-Vis and FTIR indicated its attachment to the AgNP surface. The AgNPs exhibited broad-spectrum antimicrobial activity against Gram-positive bacteria (S. aureus, MRSA and E. faecalis) and Gram-negative bacteria (E. coli, K. pneumoniae, S. typhimurium, and P. aeruginosa), as well as pathogenic fungi such as C. albicans, C. glabrata, C. parapsilosis, and C. krusei with performance comparable to or exceeding that of AgNPs from Artemisia vulgaris, Moringa oleifera, and Nigella sativa. The MIC and MBC values for S. aureus, MRSA, E. coli, and S. typhimurium were consistently 6.25 µg/mL and 25 µg/mL, respectively, reflecting strong inhibitory and bactericidal effects at low concentrations. MTT assays demonstrated selective cytotoxicity, showing higher viability in normal human skin fibroblasts (HSF) than in MCF-7 breast cancer cells. The AgNPs also displayed strong antioxidant activity (IC₅₀ = 5.41 µg/mL, DPPH assay) and efficient catalytic reduction of 4-nitrophenol (4-NP) and methylene blue (MB), with rate constants of 0.0165 s⁻¹ and 0.0047 s⁻¹, respectively, exceeding most reported values. These findings identify Celtis africana as a promising source for eco-friendly AgNPs with strong antimicrobial, antioxidant, and catalytic properties for broad biological and environmental applications. Full article

This study develops an analytical framework for investigating in-plane nonlinear subharmonic resonance in fixed–fixed circular arches under a vertical base-excitation, a phenomenon not adequately addressed in previous research. Based on Hamilton’s principle, the governing partial differential equation for in-plane nonlinear motion is first derived. The tangential displacement is then expressed as a modal superposition, and the system is reduced to a set of second-order ordinary differential equations via the Galerkin method. Using the method of multiple scales, the nonlinear 1/2-subharmonic resonance is solved, yielding closed-form, steady-state amplitude–phase relations and corresponding stability conditions. Validation against finite element simulations and Runge–Kutta analyses confirms the accuracy of the proposed approach. Dimensionless fundamental frequencies match finite element results exactly, with discrepancies in critical base-excitation below 2.5%. A close agreement is observed in both the amplitude–frequency and force–response curves with numerical predictions and Bolotin’s method, accurately capturing the characteristic hardening nonlinearity and three distinct dynamic regions spanning negligible vibration, stable resonance, and instability. Parametric studies further reveal key trends. Larger included angles intensify the vibration amplitude and promote saddle-node bifurcation, while narrowing stable operating regions. Higher slenderness ratios enhance structural flexibility and nonlinearity, shifting resonant peaks toward higher frequencies. Increased damping suppresses the response amplitude and raises the thresholds for vibration initiation and bifurcation. Full article

Several recent studies have utilized neuroimaging to delineate the localization and function of brain regions involved in language. However, many uncertainties persist regarding the organization of the linguistic system in the human brain. The aim of the present study was to characterize the structural changes produced in a sample of 9 patients with post-stroke aphasia (4 women; mean age = 60 years, SD = 14.86) and their relationship with performance in the entire Boston Diagnostic Aphasia Examination (BDAE). Magnetic Resonance Imaging was acquired from the brain of each patient and brain lesions were assessed. Disconnection’s severity of each white matter tract by embedding the lesion into the streamline tractography atlas of the Human Connectome Project was analyzed, and grey matter lesion load using a 7-Network Cortical parcellation template was estimated, with additional subcortical, cerebellar and brainstem parcels. Finally, all data obtained was correlated with performance in the BDAE. Somatomotor network correlated with repetition scale. The disconnection of the left acoustic radiation and inferior longitudinal fasciculus correlated with repetition sub-scale. Finally, the left U-fibers correlated with severity (a BDAE sub-scale that assesses the patient’s communicative skills), conversational speech and reading sub-scales. These findings emphasized that the disconnection of these fronto-parieto-temporal structures correlate with deficits in repetition, beyond the classical hypothesis attributing such deficits solely to the impairment of the arcuate fasciculus. Full article

Biosensing technology serves as a cornerstone in biomedical diagnostics, environmental monitoring, personalized medicine, and wearable devices, playing an indispensable role in precise detection and real–time monitoring. Compared with traditional sensing platforms, functional nanomaterials—by virtue of their ultra–large specific surface area, exceptional optoelectronic properties, and superior catalytic activity—significantly enhance the sensitivity, selectivity, and response speed of biosensors. This has enabled ultrasensitive, rapid, and even in situ detection of disease biomarkers, pollutants, and pathogens. This review summarizes recent advances in five key categories of functional nanomaterials—metallic, semiconductor, carbon–based, two–dimensional, and stimulus–responsive materials—for advanced biosensing applications. It elucidates the structure–property relationships governing sensing performance, such as the surface plasmon resonance of gold nanoparticles and the high carrier mobility of graphene, and analyzes the core mechanisms behind optical sensing, electrochemical sensing, and emerging multimodal sensing strategies. With a focus on medical diagnostics, wearable health monitoring, and environmental and food safety surveillance, the review highlights the application value of functional nanomaterials across diverse scenarios. Current research is progressively moving beyond single–performance optimization toward intelligent design, multifunctional integration, and real–world deployment, though challenges related to industrial application remain. Finally, the review outlines existing issues in the development of functional nanomaterial–based biosensors and offers perspectives on the integration of nanomaterials with cutting–edge technologies and the construction of novel sensing systems. This work aims to provide insights for the rational design of functional nanomaterials and the cross–disciplinary translation of biosensing technologies. Full article

A rapid, colorimetric sensor for histamine detection is presented using citrate-stabilized gold nanoparticles enhanced with Cu²⁺ coordination. The sensing mechanism involves dual recognition: protonated histamine first adsorbs electrostatically onto AuNP surfaces at pH 5.5, followed by Cu²⁺-mediated coordination between imidazole rings that induces interparticle coupling, resulting in a characteristic shift of the localized surface plasmon resonance from 520 to 620 nm. The optical response, measured as the absorbance ratio A₆₂₀/A₅₂₀, exhibits excellent linearity over the range of 1.25–10 μM with a detection limit of 0.95 μM and total assay time under 30 min. The dual-recognition mechanism provides high selectivity for histamine over structural analogs, including L-histidine, imidazole, and L-lysine. The metal ion-mediated colorimetric approach described here achieves sub-micromolar sensitivity in simple buffer solutions, which is comparable to the histamine level used in in vitro cell assays and food-related studies. Thus, the present system is best viewed as a mechanistic model that can inform the design of future biosensing and analytical methods, rather than as a fully optimized sensor for direct clinical measurements in complex biofluids. Full article