Search Results (9,531)

This study investigates the thermal and vibration-attenuation performance of a novel 7-inch FPV drone frame manufactured from cast AZ31 magnesium alloy (MG), compared to 6061-T6 aluminum (AL) and carbon fiber (CF) composite structures under an extreme payload of 2 kg. Using quantitative spectral analysis of Blackbox flight logs, the research demonstrates that the MG frame provides superior system-level vibration damping, particularly under high-stress conditions. Under a 2 kg payload, the MG frame exhibited a 49% reduction in vibration power compared to the AL frame. Spectral data identified primary resonance peaks for the MG frame at 147 Hz (0 kg) and 204 Hz (2 kg), whereas the AL frame showed significantly higher frequency peaks at 179.5 Hz (0 kg) and 239.4 Hz (2 kg). Comparative modal hammer tests further validated these findings, with the magnesium design exhibiting lower impulse energy (0.22 mW/Hz) and faster decay than aluminum (0.24 mW/Hz). Thermal imaging analysis showed better motor cooling for the metallic frames; average motor temperatures on the magnesium frame (51.8 °C) and AL frame (50.3 °C) were significantly lower than on the CF structure (77.5 °C). The findings establish that AZ31 magnesium alloy offers an excellent synergy of lightweight stiffness and damping capacity, making it a viable alternative for heavy-duty FPV platforms requiring high signal integrity. Full article

Increasing pressure for innovation-driven competitiveness requires hospitality firms to integrate technological capability, market intelligence, and customer-focused innovation into coherent strategic configurations. However, prior research has largely examined these capabilities separately, limiting understanding of how their integration influences restaurant marketing performance in emerging markets. This study develops and empirically tests an integrated capability framework linking techno-resonance innovation capability, competitor orientation, consumer-centric innovation strategy, and new service development to restaurant marketing performance using survey data from 300 restaurant managers in Java and Bali, Indonesia. The results of PLS-SEM analysis indicate that techno-resonance innovation capability significantly strengthens consumer-centric innovation strategy and new service development, which subsequently improves marketing performance outcomes. The findings extend dynamic capabilities theory by demonstrating that capability integration—rather than isolated strategic actions—supports innovation-driven competitiveness in emerging hospitality markets and provides practical guidance for restaurant managers seeking to enhance performance under resource-constrained conditions. Full article

Electronic coupling within InAs/InP quantum dots (QDs) influences carrier lifetime and thus QD laser performance. In this work, vertical electronic coupling between QDs is theoretically investigated based on a structure of five-layer QD stacks. This analysis illustrates that the resonant tunneling, a consequence of coherent coupling between QDs, should be considered for carrier redistribution. The carrier tunneling time of ground states is estimated by studying two structures of uniform and chirped five-layer QD stacks. The impact of resonant tunneling on optical properties of InAs/InP QD Fabery–Perot (FP) lasers, such as threshold current, light power-current temperature dependence, and relative intensity noise, is investigated through a comparison of uniform and chirped QD lasers. It is found that the carrier resonant tunneling leads to an increase in the threshold current, low characteristic temperature, and high relative intensity noise. By using the chirped QD stacks, the optical properties are improved thanks to less resonant tunneling. Full article

Background: Amnestic mild cognitive impairment (aMCI) is considered a transitional state between normal aging and dementia, often without visible abnormalities on standard brain magnetic resonance (MR) images. The aim of the study was to analyze both microstructural and functional brain abnormalities using advanced MR techniques. Methods: The study included 27 patients with aMCI and an age-matched control group (CG) of 25 healthy subjects. All MR studies were performed on a 3T MR scanner (Philips, Ingenia) with a 32-channel head and neck coil using volumetric 3D T1 sequences, followed by a resting-state functional MRI (rs-fMRI) sequence. Volumetric analysis was performed using the Destrieux atlas to assess potential structural differences between groups. Seed-to-voxel functional connectivity analyses were conducted using the bilateral hippocampi and both anterior and posterior divisions of the parahippocampal gyri as seed regions. Results: Compared to healthy controls, reduced cortical thickness was observed in aMCI subjects in the temporal regions, frontal and orbitofrontal areas, limbic areas, parietal and sensorimotor cortices, as well as occipito-temporal regions. Additionally, significantly increased functional connectivity was observed between bilateral medial temporal lobe (MTL) regions and the right thalamus. Conclusions: Cortical thinning in various brain regions along with the increased functional connectivity between the MTL regions and the right thalamus may reflect potential compensatory mechanisms in response to initial subtle degenerative changes, emphasizing the importance of using both functional and structural imaging to detect early changes in aMCI patients. Full article

Fabry–Perot resonator (FPR) sensors are widely implemented in optical and microwave waveguides because their interference fringe spectra enable highly sensitive, stable, and calibration-free measurements. In contrast, despite the extensive use of beams and plates as waveguides in vibration- and ultrasound-based structural health monitoring (SHM), an explicit FPR framework for these mechanical waveguides has not been established. This paper demonstrates that flexural beams can be rigorously treated as FPRs despite their inherently dispersive nature. Through analytical derivation, wave-propagation analysis, and fringe-based group-velocity extraction, we show that flexural-beam resonances arise from multi-reflection interference analogous to Fabry–Perot interference. A closed-form relationship between the frequency-dependent group velocity and the FPR free spectral range (FSR) is established, enabling inverse determination of mechanical or environmental perturbance from the FPR fringe spectrum. By extending FPR-based fringe analysis to dispersive mechanical waveguides, this work introduces a theoretical framework for implementing dispersive mechanical waveguide-based FPR sensors. Full article

Brain tumor detection from magnetic resonance imaging (MRI) is fundamental to computer-aided diagnosis, yet automated models must remain robust to heterogeneous imaging conditions. Despite strong recent progress, many deep learning and transformer-based approaches primarily optimize performance accuracy without explicitly improving feature selectivity and spatial localization, and they typically produce deterministic output without uncertainty estimates, which limits reliability. To overcome these limitations, we introduce UA-EffNet-DA, an uncertainty-aware EfficientNet framework that addresses these limitations through three complementary components. First, EfficientNet-B4 serves as an efficient backbone for discriminative feature extraction. Second, lightweight dual attention modules, comprising channel and spatial attention in parallel, are applied to the model to emphasize what and where discriminative features to focus within MRI slices. Third, Monte Carlo dropout is employed during inference to quantify predictive uncertainty and enable confidence-aware decision. Experiments on two public benchmarks demonstrate strong performance, yielding accuracies of 98.73% on the Figshare dataset and 99.23% on the Kaggle dataset. In addition, explainable AI analysis using Gradient-weighted Class Activation Mapping (Grad-CAM) further indicates that the proposed model concentrates on diagnostically relevant tumor regions rather than background structures, supporting transparent decision-making. Ablation studies confirm the complementary contribution of dual attention refinement and uncertainty-aware inference. Overall, the proposed UA-EffNet-DA framework offers an efficient and interpretable approach for brain tumor detection that supports more reliable clinical decision support through uncertainty-aware predictions. Full article

A micro-cavity based on phase-change material is a very important strategy for the realization of tunable absorption and conversion of terahertz waves. In this work, a tunable terahertz metamaterial absorber based on the phase-change material germanium–antimony–tellurium (GST) is demonstrated. The device features a metal–insulator–metal triple-layer structure, where the dynamic switching of absorption characteristics is achieved via thermally controlled GST phase transition. In the amorphous state, the absorber exhibits a single absorption peak at 7.7 THz. Upon crystallization, the absorption switches to dual peaks at 5.1 THz and 8.3 THz, achieving near-perfect absorption in both states. Full-wave electromagnetic simulations and theoretical analysis based on a multiple-reflection interference model indicate that this performance tuning originates from the GST-phase-transition-induced change in the equivalent optical cavity length. This corresponds to a switch between two resonant modes: coupled inner–outer ring resonance and independent outer ring resonance. These results provide a foundation for developing dynamically tunable terahertz devices with promising applications in terahertz communications, imaging, and sensing. Full article

The present study focuses on liquid-precursor-mediated chemical vapor deposition (under ambient pressure and moderate temperature range) of WSe₂ on sapphire using ammonium meta-tungstate and sodium cholate. The investigation provides additional results and information for the WSe₂ cluster formations on sapphire as an extension of our previous study, especially based on structural, chemical and morphological characterization of the observed largest and predominant polygonal WSe₂ domains whose lateral size can reach several hundreds of micrometers. In addition, highly symmetrical shapes were also observed. The Raman spectroscopy and atomic force microscopy identified the formation of both mono- and multilayered WSe₂. Moreover, the Raman spectrum analysis shows a complex peak structure with unusual splitting effects in the second-order modes marking strong activity of excitonic-resonance processes. Full article

Background/Objectives: The Achilles tendon enthesis (ATE) is a key load-transmitting structure that is frequently affected in musculoskeletal disorders, including insertional tendinopathy, overuse injuries and inflammatory enthesopathies. Reliable non-invasive assessment of the enthesis structure is therefore of increasing clinical importance. This study evaluated the ability of advanced magnetic resonance (MR) microscopy to depict the ultrastructural organization of the ATE using histology as a reference standard. Methods: Five human ATEs from anatomical body donations were included. Two specimens were used for protocol development of the histological preparation, whereas three specimens underwent the full multimodal pipeline comprising undecalcified methyl methacrylate (MMA) thin-section histology with Giemsa staining, T2*-weighted 3D-variable echo time (vTE) MR microscopy at 7 Tesla, and microradiography. Results: Histological analysis demonstrated excellent preservation of fibrocartilage zones and mineralized interfaces. Corresponding MR microscopy data allowed the identification of major structural components of the enthesis, particularly mineralized regions, although fine ultrastructural details remained beyond the MR microscopy resolution. Microradiography supported interpretation of the mineralized tissue architecture and MR microscopy signal characteristics. Conclusions: These findings indicate that high-field MR microscopy can capture clinically relevant structural features of the Achilles tendon enthesis, while histology remains essential for detailed ultrastructural validation. The combined imaging approach provides a translational framework that may support improved diagnosis, monitoring and treatment evaluation in musculoskeletal disorders involving the osteotendinous junction. Full article

Heart disease remains a leading cause of mortality worldwide, and timely and accurate diagnosis is crucial for improving patient outcomes. Medical imaging plays a pivotal role in this process, yet traditional diagnostic methods often suffer from limitations, including dependency on manual interpretation, susceptibility to observer variability, and inefficiency in handling large-scale data. Deep learning has emerged as an innovative technology in medical imaging, providing unparalleled advancements in feature extraction, segmentation, classification, and prediction tasks. Despite its proven potential, comprehensive reviews of deep learning methods specifically targeted at cardiac imaging remain scarce. This review paper seeks to bridge this gap by analyzing the state-of-the-art deep learning applications for heart disease diagnosis, covering the period from 2015 to 2025. Employing a well-structured methodology, this review categorizes and examines studies based on imaging modalities: Ultrasound (US), Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT), and Electrocardiography (ECG). For each modality, the analysis focuses on utilized datasets, processing techniques (e.g., extraction, segmentation and classification), and paradigms (e.g., transfer learning, federated learning, explainability, interpretability, and uncertainty quantification). Additionally, the types of heart disease addressed and prediction accuracy metrics are also scrutinized. These findings point toward future opportunities, including the study of data quality, optimization, transfer learning, uncertainty quantification and model explainability or interpretability. Furthermore, exploring advanced techniques such as recurrent expansion, transformers, and other architectures may unlock new pathways in cardiac imaging research. This review is a critical synthesis offering a roadmap for researchers and practitioners to advance the application of deep learning in heart disease diagnosis. Full article

Wide-bandgap power devices, particularly silicon carbide (SiC) MOSFETs, have seen widespread adoption in electric vehicle (EV) motor drive systems due to their superior switching characteristics, including high switching speeds and high switching frequencies. However, these advantages exacerbate motor terminal overvoltage, with peaks reaching twice the inverter output voltage, causing insulation breakdown in windings and bearing electro-corrosion, which shorten motor lifespan. Traditional overvoltage prediction methods, such as distributed parameter models or detailed ladder network approaches, require extensive system parameters and involve high computational loads, while simplified models lack generality. To address these issues, this paper proposes a simplified prediction method based on a lumped ladder network model combined with frequency scanning. The approach uses impedance analysis to identify anti-resonance frequencies, enabling direct estimation of overvoltage amplitudes without prior knowledge of cable or motor specifics. Experimental validation on a SiC-based drive system demonstrates prediction errors below 10% and a reduction in computational time compared to conventional methods. Full article

Background/Objectives: Endophytic fungi represent an alternative source for resveratrol (RES) production. The present study aims to utilize mycoendophytic-derived resveratrol as a reducing agent for the synthesis of silver nanoparticles (AgNPs), in addition to further assay the cytotoxic activity of a RES-conjugated nanocarrier system toward human epidermoid carcinoma A-431 cells. Methods: Alternaria alternata AUMC 16209 was isolated from the stem of grapevine Vitis vinifera L. cultivar prime. Strain identification was achieved through morphological and molecular characterization using ITS sequencing. A. alternata AUMC 16209 exhibited RES production capability upon cultivation on PDB medium for seven days with a total of 8.25 mg/L as determined by HPLC. The crude RES was purified using flash chromatography followed by structure elucidation through ¹H and ¹³C NMR analyses. The purified RES was used for green synthesis of nanoparticles, acting as a reducing agent for silver ions. Results: Stable RES-AgNPs were fabricated at particle sizes ranging from 25 to 47 nm. RES-AgNPs observed a plasmon resonance absorption band at 415 nm with a negative zeta potential value of −38.5 mV. The crystalline structure of RES-AgNPs was addressed through X-ray diffraction analysis. FT-IR spectroscopy confirms the involvement of the functional –OH group and the aromatic C=C bond in the reduction and stabilization process. RES-AgNPs was more efficient to inhibit the cellular proliferation of human epidermoid carcinoma A-431 cells compared to RES alone. Conclusions: This report introduces for the first time an endophytic A. alternata as a sustainable source for RES production and emphasizes its potential for green synthesis of stable AgNPs with promising cytotoxic activity. Full article

Background: Prostate cancer (PCa) is the leading male urinary malignancy globally. Our previous article demonstrated the anti-PCa activity of Euphorbia humifusa Willd water extract (EHW) and some of its compounds via downregulating AR expression, but the anti-PCa active compounds from Euphorbia humifusa Willd (EH) and their mechanisms of action are yet to be clarified. Thus, the current article studied the in vitro anti-PCa effects of 3,3′-di-O-methylellagic acid (3,3′-di-O-Me-EA) derived from EHW and the related mechanism involved. Methods: 3,3’-di-O-Me-EA was isolated from EHW applying bioassay-guided fractionation. The spectroscopic methods were used to determining the structure of 3,3′-di-O-Me-EA. The drug-likeness and ADMET properties (absorption, distribution, metabolism, excretion, and toxicity) of 3,3′-di-O-Me-EA were analyzed in silico. Molecular docking and real-time surface plasmon resonance (SPR) analysis were performed to measure the interaction of 3,3′-di-O-Me-EA and VDAC1 protein. The viability and apoptosis of 22RV-1 and DU145 PCa cells were determined using MTT and Annexin V-FITC staining assay, respectively. q-PCR and Western blot experiments were used to analyzing the gene and protein expressions of VDAC1. Results: 3,3′-di-O-Me-EA was isolated and purified from EHW with a purity of ≥90.06%, and its structure was identified by HRTOF mass, NMR, and an authentic standard. In silico ADMET analysis indicated its favorable drug-like and pharmacokinetic properties. Molecular docking and SPR results confirmed that 3,3′-di-O-Me-EA could bind with the VDAC1 protein. Moreover, 3,3′-di-O-Me-EA dose- and time-dependently inhibited 22RV-1 and DU145 PCa cell viability, and induced apoptosis in a dose-dependent manner (p < 0.05). RT-qPCR and Western blot results showed that 3,3′-di-O-Me-EA dose-dependently up-regulated VDAC1 gene and protein expression levels in 22RV-1 and DU145 cells (p < 0.05). Meanwhile, in VDAC1-depleted 22RV-1 and DU145 cells, 3,3′-di-O-Me-EA down-regulated VDAC1 gene and protein expression levels, increased cell viability, and inhibited apoptosis compared to 22RV-1 and DU145 cells (p < 0.05). Furthermore, 3,3′-di-O-Me-EA enhanced VDAC1 gene and protein expression levels, inhibited cell viability, and induced apoptosis in VDAC1-overexpressed 22RV-1 and DU145 cells compared with 22RV-1 and DU145 cells (p < 0.05). Overall, EH active compound 3,3′-di-O-Me-EA may inhibit viability and induce apoptosis of 22RV-1 and DU145 PCa cells via up-regulating VDAC1 gene and protein expression levels. Conclusion: The results indicated that the 22RV1 and DU145 PCa cell viability inhibitory effects of 3,3′-di-O-Me-EA isolated from EH may be mediated by induction of apoptosis through up-regulation of VDAC1 gene and protein expression levels. Full article

Introduction: Clinical staging based on digital rectal examination is imprecise, leading to pathological upstaging in patients with prostate cancer (PCa). Accurate preoperative assessment remains a challenge despite the use of multiparametric magnetic resonance imaging (mpMRI) and fusion-guided biopsy. This study aims to identify key predictors of upstaging in preoperative patients. Materials and Methods: A retrospective analysis of 924 patients who underwent radical prostatectomy between July 2012 and January 2025 was performed. Variables included prostate-specific antigen, prostate volume, biopsy type, MRI, body mass index and age. Upstaging was defined as ≥pT3 in patients staged clinically as cT1–2. Optimal cut-offs for continuous variables were defined statistically. Multivariable logistic regression was applied to identify independent predictors of upstaging and minor staging upgrading (MSU)—defined as any upward shift in the pathological T stage relative to the clinical T stage. Model performance was evaluated using the area under the Receiver Operating Characteristic (ROC) curve (AUC). Results: Upstaging occurred in 31.9% and MSU in 50.6% of patients. The mean age was 65 years. Cut-off values for PSA density (PSAD) were 0.29 for upstaging and 0.28 for MSU. In the full-cohort model (AUC = 0.628), PSAD (odds ratio (OR) = 2.55), age (OR = 1.04), and hypertension (HT) (OR = 1.47) were associated with upstaging. In PIRADS-based models, PIRADS 5 and PSAD predicted both upstaging (OR = 1.62 and 6.10, respectively; AUC = 0.664) and MSU (OR = 1.75 and 4.67, respectively; AUC = 0.659). MSU was also associated with HT and a lack of fusion biopsy (AUC = 0.622). Conclusions: PSAD and PIRADS 5 lesions are strong determinants of pathological upstaging and MSU in PCa. These factors should be considered in preoperative risk stratification to improve staging accuracy. Despite advances in imaging and biopsy techniques, upstaging remains a common phenomenon, underlining the need for further refinement of diagnostic protocols. Full article

Understanding dry-out-induced weakening of reservoir and caprock rocks driven by gas displacement is critical for ensuring the operational safety and efficiency of underground gas storage (UGS). Using core samples from the Xiangguosi UGS collected from different regions and stratigraphic intervals, we quantify the evolution of dynamic elastic parameters during simulated downhole dry-out with a joint ultrasonic and nuclear magnetic resonance (NMR) monitoring system. The results show that as water saturation (S_w) decreases, the dynamic bulk modulus (K_d) and P-wave velocity (V_p) decline by varying degrees across specimens, with reductions ranging from 3.0% to 50.48% and from 1.34% to 17.56%, respectively, whereas the dynamic shear modulus (G_d) and S-wave velocity (V_s) show only minor variations throughout the process. These findings demonstrate that the sensitivity of dynamic parameters to dry-out is strongly specimen-dependent. Further analysis indicates that the dry-out response is highly variable and depends on a combination of petrophysical properties. Among these, the heterogeneity of the initial pore structure acts as an important factor, with its influence shaped by mineralogy and bulk frame rigidity. Cores with multimodal pore size distributions and well-developed macropores (long T₂ components) respond more strongly to dry-out, whereas higher clay mineral contents tend to mitigate modulus degradation by retaining water under stronger capillary confinement. Based on these observations, we propose a conceptual model of pore support and skeleton constraint. The model suggests that dry-out weakening arises from a progressive loss of pore fluid volumetric support to the rock skeleton as free water is preferentially displaced from meso- and macropores. These findings provide key experimental evidence and mechanistic insights for using geophysical methods to monitor dry-out zone expansion and to assess long-term formation stability in UGS. Full article