Search Results (3,898)

The operational state of high-voltage disconnectors plays a critical role in ensuring the safety, stability, and power supply reliability of electrical systems. To enable accurate identification of the operational status of high-voltage disconnectors, this paper proposes a fault diagnosis method based on a Multi-Scale Residual Depthwise Separable Convolutional Neural Network (MSRDSN). First, wavelet transform is applied to vibration signals to perform multi-scale analysis and enhance detail resolution. Then, a novel network architecture, referred to as RDSN, is constructed to extract discriminative high-level features from vibration signals by integrating residual learning blocks and depthwise separable convolution blocks. Furthermore, a combined loss function is introduced to optimize the RDSN, which simultaneously maximizes inter-class distance, minimizes intra-class distance, and reduces feature redundancy. Experimental results show that the proposed method achieves a top accuracy of 99.44% on a balanced dataset, outperforming the sub-optimal approach by 1.11%. This study offers a novel and effective solution for fault diagnosis in high-voltage disconnectors. Full article

Background/Objectives: Vitamin C plays a vital role in human health, functioning as a powerful antioxidant and enzymatic cofactor. Although vitamin C bioavailability from food versus supplements has been debated, few studies have examined how intake form affects absorption and physiological markers. Methods: This randomized, controlled, crossover trial aimed to compare the bioavailability of vitamin C consumed as a supplement, through raw fruits and vegetables, or through fruit and vegetable juice. Twelve healthy adults underwent three 1-day crossover trials, each separated by a 2-week washout. Participants consumed 101.7 mg of vitamin C via powder, raw fruits and vegetables (186.8 g), or juice (200 mL). Plasma and urinary vitamin C concentrations, urinary metabolites (¹H NMR), and antioxidant activity (ORAC and TRAP) were assessed over 24 h. Results: All interventions elevated plasma vitamin C levels, with juice yielding the highest AUC (25.3 ± 3.2 mg/dL·h). Urinary vitamin C increased in all groups. Metabolomics revealed increased urinary excretion of mannitol, glycine, taurine, dimethylglycine (DMG), and asparagine, and decreased choline and dimethylamine (DMA). Notably, urinary mannitol increased only in the morning. Choline significantly decreased after powder intake (p = 0.001), with similar trends observed in the other groups. DMG and glycine increased following raw and juiced vegetable intake. Antioxidant activity showed transient ORAC elevation post-powder but no sustained improvements. Conclusions: Vitamin C is bioavailable from all intake forms, with juice providing the most efficient absorption. Urinary metabolite changes suggest microbiota-related modulation, while antioxidant activity improvements were limited. Full article

This study selected Abutilon theophrasti Medicus as the research object and optimized the ultrasonic-assisted heat reflux extraction process using response surface methodology to achieve efficient extraction of phenolic acids from its leaves. The optimized conditions were as follows: methanol was used as the extraction solvent, with a liquid–solid ratio of 30:1 (mL/g), ultrasonic power of 200 W, ultrasonic time of 30 min, and reflux temperature of 70 °C. Under these conditions, the extraction yield of total phenolic acid reached 213.29 μg/g, which significantly higher than those obtained using traditional extraction methods. Subsequently, six phenolic acid compounds, gallic acid, protocatechuic acid, chlorogenic acid, vanillic acid, syringic acid, and p-hydroxybenzoic acid, were successfully separated and identified from the leaf extract. Meanwhile, the phenolic acid contents in the roots, stems, and leaves of A. theophrasti were analyzed by HPLC method. The results showed that the phenolic acid content in the leaves was significantly higher than in the roots and stems. Furthermore, the antioxidant and antibacterial activities of extracts obtained from different plant parts, and those of the six separated phenolic acids, were systematically evaluated. The results demonstrated that all the samples exhibited notable antioxidant and antibacterial activities. Among them, gallic acid, protocatechuic acid, syringic acid, and vanillic acid displayed strong antioxidant activity, while gallic acid and vanillic acid showed the highest antibacterial efficacy. Full article

This study introduces a novel approach for analyzing theoretical Raman spectra, designed to facilitate spectral interpretation, particularly for complex systems such as functional mesoporous silica-based thin films. The proposed methodology relies on spectral decomposition supported by theoretical calculations, representing a step toward the development of autonomous research laboratories. The method assigns vibrational shifts to individual atoms within a molecular model and uses this information to generate partial spectra corresponding to specific atomic groupings. Unlike separate calculations for isolated components, this approach preserves the mutual interactions within the entire molecular structure, providing a more accurate representation of the vibrational environment. Decomposing the theoretical spectrum into contributions from atomic groups significantly simplifies the assignment of Raman bands to specific structural units, thereby enhancing the interpretative power of theoretical spectra and their correlation with experimental data. The method was demonstrated using real Raman spectroscopic data obtained from mesoporous SBA-15 silica thin films containing copper phosphonate groups. This work also highlights the critical role of molecular modeling and DFT calculations in Raman spectral analysis and outlines future perspectives for the use of artificial intelligence to automate and optimize the spectral interpretation process. Full article

In low-altitude multi-object tracking (MOT), challenges such as frequent inter-object occlusion and complex non-linear motion disrupt the appearance of individual targets and the continuity of their trajectories, leading to frequent tracking failures. We posit that the relatively stable spatio-temporal relationships within object groups (e.g., pedestrians and vehicles) offer powerful contextual cues to resolve such ambiguities. We present NOWA-MOT (Neighbors Know Who We Are), a novel tracking-by-detection framework designed to systematically exploit this principle through a multi-stage association process. We make three primary contributions. First, we introduce a Low-Confidence Occlusion Recovery (LOR) module that dynamically adjusts detection scores by integrating IoU, a novel Recovery IoU (RIoU) metric, and location similarity to surrounding objects, enabling occluded targets to participate in high-priority matching. Second, for initial data association, we propose a Graph Cross-Attention (GCA) mechanism. In this module, separate graphs are constructed for detections and trajectories, and a cross-attention architecture is employed to propagate rich contextual information between them, yielding highly discriminative feature representations for robust matching. Third, to resolve the remaining ambiguities, we design a cascaded Matched Neighbor Guidance (MNG) module, which uniquely leverages the reliably matched pairs from the first stage as contextual anchors. Through MNG, star-shaped topological features are built for unmatched objects relative to their stable neighbors, enabling accurate association even when intrinsic features are weak. Our comprehensive experimental evaluation on the VisDrone2019 and UAVDT datasets confirms the superiority of our approach, achieving state-of-the-art HOTA scores of 51.34% and 62.69%, respectively, and drastically reducing identity switches compared to previous methods. Full article

The timely and accurate prediction of flue-cured tobacco yield is crucial for its stable yield and income growth. Based on yield and meteorological data from 2003 to 2023 (from the NASA POWER database) of Yunnan Province, this study constructed a coupled framework of polynomial regression and a Stacking ensemble model. Four trend yield separation methods were compared, with polynomial regression selected as being optimal for capturing long-term trends. A total of 135 meteorological features were built using flue-cured tobacco’s growth period data, and 17 core features were screened via Pearson’s correlation analysis and Recursive Feature Elimination (RFE). With Random Forest (RF), Multi-Layer Perceptron (MLP), and Support Vector Regression (SVR) as base models, a ridge regression meta-model was developed to predict meteorological yield. The final results were obtained by integrating trend and meteorological yields, and core influencing factors were analyzed via SHapley Additive exPlanations (SHAP). The results showed that the Stacking model had the best predictive performance, significantly outperforming single models; August was the optimal prediction lead time; and the day–night temperature difference in the August maturity stage and the solar radiation in the April transplantation stage were core yield-influencing factors. This framework provides a practical yield prediction tool for Yunnan’s flue-cured tobacco areas and offers important empirical support for exploring meteorology–yield interactions in subtropical plateau crops. Full article

Wide-bandgap (WBG) perovskite solar cells (PSCs) are critical for high-efficiency tandem photovoltaic devices, but their practical application is severely limited by phase separation and poor film quality. To address these challenges, this study proposes a dual-additive passivation strategy using potassium thiocyanate (KSCN) and potassium chloride (KCl) to synergistically optimize the crystallinity and defect state of WBG perovskite films. The selection of KSCN/KCl is based on their complementary functionalities: K⁺ ions occupy lattice vacancies to suppress ion migration, Cl⁻ ions promote oriented crystal growth, and SCN⁻ ions passivate surface defects via Lewis acid-base interactions. A series of KSCN/KCl concentrations (relative to Pb) were tested, and the effects of dual additives on film properties and device performance were systematically characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), photoluminescence (PL), space-charge-limited current (SCLC), current-voltage (J-V), and external quantum efficiency (EQE) measurements. Results show that the dual additives significantly enhance film crystallinity (average grain size increased by 27.0% vs. control), reduce surface roughness (from 86.50 nm to 24.06 nm), and passivate defects-suppressing non-radiative recombination and increasing electrical conductivity. For WBG PSCs, the champion device with KSCN (0.5 mol%) + KCl (1 mol%) exhibits a power conversion efficiency (PCE) of 16.85%, representing a 19.4% improvement over the control (14.11%), along with enhanced open-circuit voltage (Voc: +2.8%), short-circuit current density (Jsc: +6.7%), and fill factor (FF: +8.9%). Maximum power point (MPP) tracking confirms superior operational stability under illumination. This dual-inorganic-additive strategy provides a generalizable approach for the rational design of stable, high-efficiency WBG perovskite films. Full article

Microbial priming is an emerging strategy in sustainable agriculture that involves the use of beneficial microorganisms to enhance agricultural productivity and sustainability. This innovative approach leverages the natural interactions between plants and microorganisms to promote plant growth and improve soil health. This study explores the application of microbial priming on almond seeds, focusing on the biostimulant effect of soil-based microbial extracts from a mediterranean shrub Pistacia lentiscus L. as an ecological strategy to improve the germination and seedling of almond (Prunus dulcis (Mill.)). The extraction process of soil differentiates three extracts: the first separates AMF spores (Myco) from all other bacterial and fungal consortia (MW), and the third combines the two previous extracts (MW + Myco). The experiment evaluated germination rates, seedling growth parameters, and conducted physico-chemical soil analyses. Arbuscular Mycorrhizal Fungi (AMF) colonization was also measured. Microbial priming significantly improved germination rates and enhanced seedling growth compared to untreated controls. The three microbial extracts showed significant effects on germination rate after 20 days, exceeding 90%. After 27 days, all treatments reach their maximum (100%). Seedling indicators allow MW + Myco extract to be considered as the most powerful extract on almond seedling growth. The combination of microbial and endomycorrhizal fungal extracts could be considered as a facilitator of seedling growth of almond. The AMF colonization was notably higher in treated plants. Overall, microbial priming effectively enhances almond seed germination and seedling growth, demonstrating its potential as a sustainable biostimulation strategy in agriculture. This practice boosts crop productivity and promotes soil health by enriching microbial communities and improving nutrient cycling. These results open up perspectives towards a natural-based strategy able to facilitate the germination and early seedling of almonds in both nurseries and in the field—and to enhance the productivity and health of almond cultivation in special Mediterranean area. Full article

Mixed eccentricity faults in squirrel cage induction motors (SCIMs) are challenging to diagnose due to their subtle influence on the stator-current signal. Several research gaps remain in this field, including the limited investigation of fault severity levels and the scarcity of studies addressing fault detection under full-load conditions. Motivated by these gaps, this study proposes a diagnostic approach based on the variational mode decomposition (VMD) of the stator current. This paper proposes a diagnostic approach based on VMD of the stator current. The current signal is decomposed into intrinsic mode components, which are further separated into approximated and detailed signals. By focusing on the detailed signals and removing the fundamental frequency, the proposed algorithm highlights the spectral components associated with the mixed eccentricity. Experimental validation was carried out on a 1.5 kW SCIM connected directly to the power grid and tested under three loading levels (12.5%, 50%, and 100% of the rated load). In all nine experimental scenarios, the method successfully distinguished the healthy motor from faulty conditions with 20% and 30% mixed eccentricity severities. These results demonstrate that the proposed VMD-based method provides a reliable and quantitative tool for rotor fault diagnosis under varying load conditions. Full article

The development of wind power aligns with the strategy of low-carbon development and plays a crucial role in the global transition to a green economy. The segmented steel–concrete wind turbine tower offers advantages such as modular fragment prefabrication, prestressed structural enhancement, and integrated intelligent construction. To investigate the structural performance of such towers, this paper established a numerical model based on an existing project. The model was validated against previous experiments and used for parametric analysis. A numerical model of a segmented steel–concrete wind turbine tower was developed to evaluate its overall deformation, stress distribution, and vertical and horizontal joint separation under various conditions. The concrete segment of the tower was numerically simplified, and a comparative analysis of structural performance was conducted between the detailed and simplified models. Based on the simplified model, the effects of the friction coefficient, prestress loss, and contact area on the anti-slip performance of the transition section of the towers were investigated and analyzed. The results indicated that the validity of the modeling approach was confirmed through the existing experimental results. The top displacement of the model incorporating vertical and horizontal joints (Model 1) did not exceed the limit of 1/100 under the safety factor considerations, indicating that the structure could ensure safety. The simplified model (Model 2) showed consistent behavior with Model 1, thereby providing a reliable basis for parametric studies. A reduction in the steel-to-steel friction coefficient, steel strand prestress, and contact area between the steel transition section and the embedded anchor plate resulted in an increase in the horizontal relative displacement between the steel transition section and the embedded anchor plate to varying extents. Notably, a more pronounced increase in displacement was observed under higher loading conditions. Overall, the horizontal relative displacement between the steel transition section and embedded anchor plate under single-loading conditions was below one millimeter in most of the studied conditions, which was relatively small compared to the assembly tolerance of the structure. Full article

Modern military aircraft integrate a large number of high-power-density electronic devices, which leads to a rapid increase in thermal load and poses significant challenges for heat dissipation. A promising thermal management approach is to intake ram air through a fuselage-mounted S-duct inlet and utilize it as a heat sink for the downstream heat exchanger. However, the S-duct’s geometry can induce significant flow separation and total pressure distortion, thereby limiting the mass flow rate. To address these challenges, this study investigates three flow-control strategies—vortex generators (VGs), Coanda injectors, and their combination—using high-fidelity three-dimensional numerical simulations validated against experimental data. The results indicate that VGs effectively suppress local separation and improve flow uniformity, although additional losses limit pressure recovery. The Coanda injector enhances boundary-layer momentum, substantially increasing mass flow throughput and pressure recovery. The combined VGs and Coanda injector approach achieves a lower distortion coefficient and provides a favorable balance between pressure recovery and flow uniformity. These findings demonstrate the potential of hybrid passive–active flow control in improving inlet aerodynamic quality and supporting integrated thermal management systems for future aircraft. Full article

A high-gain microstrip antenna array is proposed. The dual-frequency and dual-polarization characteristics of the array allow a satellite communication system to transmit and receive signals with a single antenna. To avoid high losses in microstrip feed lines for large apertures, the array is divided into subarrays, each fed by a low-loss separate feed network. The dual-frequency dual-polarization function is realized by utilizing two orthogonal modes of a corner-fed rectangular patch in a single-layer substrate. Moreover, to minimize losses in the separate feed network, semi-ridged coaxial lines and five four-way radial power dividers are employed. The power divider, composed of a cylindrical cavity and five SMA connectors, features very low insertion loss. Finally, to validate the design concept, a prototype of the proposed 32 × 32-element array operating at 12.5 GHz and 14.25 GHz is fabricated and measured. The measured results are in good agreement with the simulated ones. The −10 dB return loss frequency bands for the two operating frequencies are 12.04 GHz–12.69 GHz and 13.82 GHz–14.66 GHz, respectively. The measured gains at the two operating bands are 34.5 dBi and 35.2 dBi, respectively. Full article

To meet the demand for energy-efficient and high-performance computing in resource-limited sensor edge applications, this paper presents a reconfigurable memristor-based computing-in-memory circuit for Content-Addressable Memory (CAM). The scheme exploits the analog multi-level resistance characteristics of memristors to enable parallel multi-bit processing, overcoming the constraints of traditional binary computing and significantly improving storage density and computational efficiency. Furthermore, by employing dynamic adjustment of the mapping between input signals and reference voltages, the circuit supports dynamic switching between exact and approximate CAM modes, substantially enhancing functional flexibility. Experimental results demonstrate that the 32 × 36 memristor array based on a TiN/TiOx/HfO₂/TiN structure exhibits eight stable and distinguishable resistance states with excellent retention characteristics. In large-scale array simulations, the minimum voltage separation between state-representing waveforms exceeds 6.5 mV, ensuring reliable discrimination by the readout circuit. This work provides an efficient and scalable hardware solution for intelligent edge computing in next-generation sensor networks, particularly suitable for real-time biometric recognition, distributed sensor data fusion, and lightweight artificial intelligence inference, effectively reducing system dependence on cloud communication and overall power consumption. Full article

Although total knee replacements have an insignificant impact on patients’ mobility and quality of life, real-time performance monitoring remains a challenge. Monitoring the load over time can improve surgery outcomes and early detection of mechanical imbalances. Triboelectric nanogenerators (TENGs) present a promising approach as a self-powered sensor for load monitoring in TKR. A TENG was fabricated with dielectric layers consisting of Kapton tape and 3D-printed thermoplastic polyurethane (TPU) matrix incorporating CNT and BTO fillers, separated by an air gap and sandwiched between two copper electrodes. The sensor performance was optimized by varying the concentrations of BTO and CNT to study their effect on the energy-harvesting behavior. The test results demonstrate that the BTO/TPU composite that has 15% BTO achieved the maximum power output of 11.15 μW, corresponding to a power density of 7 mW/m², under a cyclic compressive load of 2100 N at a load resistance of 1200 MΩ, which was the highest power output among all the tested samples. Under a gait load profile, the same TENG sensor generated a power density of 0.8 mW/m² at 900 MΩ. By contrast, all tested CNT/TPU-based TENG produced lower output, where the maximum generated apparent power output was around 8 μW corresponding to a power density of 4.8 mW/m², confirming that using BTO fillers had a more significant impact on TENG performance compared with CNT fillers. Based on our earlier work, this power is sufficient to operate the ADC circuit. Furthermore, we investigated the durability and sensitivity of the 15% BTO/TPU samples, where it was tested under a compressive force of 1000 N for 15,000 cycles, confirming the potential of long-term use inside the TKR. The sensitivity analysis showed values of 37.4 mV/N for axial forces below 800 N and 5.0 mV/N for forces above 800 N. Moreover, dielectric characterization revealed that increasing the BTO concentration improves the dielectric constant while at the same time reducing the dielectric loss, with an optimal 15% BTO concentration exhibiting the most favorable dielectric properties. SEM images for BTO/TPU showed that the 10% and 15% BTO/TPU composites showed better morphological characteristics with lower fabrication defects compared with higher filler concentrations. Our BTO/TPU-based TENG sensor showed robust performance, long-term durability, and efficient energy conversion, supporting its potential for next-generation smart total knee replacements. Full article

Pomegranate (Punica granatum L.), valued for its health benefits and distinctive flavor, derives its characteristic aroma from volatile organic compounds (VOCs) that vary significantly with geographical origin. In this study, VOCs in pomegranates from six Chinese geographical regions were characterized using an electronic nose (E-nose), an electronic tongue (E-tongue), headspace gas chromatography–ion mobility spectrometry (HS-GC-IMS), and headspace solid-phase microextraction–gas chromatography–mass spectrometry (HS-SPME-GC-MS). To elucidate geographical variations in odor, taste, and volatile profiles, a comprehensive multivariate statistical analysis integrating principal component analysis (PCA), hierarchical cluster analysis, orthogonal partial least squares-discriminant analysis (OPLS-DA), and variable importance in projection (VIP) was employed. The results demonstrated that the E-nose and E-tongue effectively distinguished pomegranate by geographical origin, with aroma contributing more significantly than taste to regional differentiation. A total of 46 and 58 VOCs were identified using HS-GC-IMS and HS-SPME-GC-MS, respectively, with different characteristic volatile compounds in pomegranate from various origins, and alkenes, esters, and alcohols were the primary contributors to regional variations. Notably, OPLS-DA revealed that HS-GC-IMS exhibited superior discriminatory power in separating pomegranates of different geographical origins, with HY and HL displaying closely related odor profiles while the other samples showed the most pronounced odor differences, but these findings contrasted with HS-SPME-GC-MS results. Additionally, the VIP method and the relative odor activity value (ROAV) further identified six and eight key aroma compounds based on HS-GC-IMS and HS-SPME-GC-MS data; in particular, hexanal, nonanal, β-pinene, 3-hydroxybutan-2-one, and β-ocimene were identified as key aroma compounds in pomegranate as potential regional markers. These findings highlight VOC profiles as potential geographical origin markers, supporting origin traceability and quality control in the pomegranate industry. Full article