Search Results (2,340)

In this article, a compact wideband three-slot filtering antenna is proposed. The antenna consists of a U-shaped driven slot, a folded resonant slot, and a linear resonant slot. A microstrip feedline with a shorting via is employed to excite the antenna. Mixed electric and magnetic couplings enable the driven slot to couple to the two resonant slots. Three resonant frequencies lie within the passband, resulting in wideband operation. The lowest resonant frequency is determined by the folded resonant slot, while the highest resonant frequency is determined by the linear resonant slot. The center resonant frequency is influenced by the combined effects of the U-shaped driven slot, the folded resonant slot, and the linear resonant slot. A low-frequency radiation null at 1.68 GHz and a high-frequency radiation null at 3.19 GHz are generated. These two radiation nulls enable the proposed antenna to achieve excellent filtering performance. A prototype was fabricated and measured. The measured results are in good agreement with the simulated ones. The measurements show that the proposed three-slot filtering antenna exhibits a relative impedance bandwidth of 39.1%. The out-of-band suppression levels reach 12.5 dB and 14.8 dB in the lower and upper sidebands, respectively. The proposed three-slot filtering antenna is suitable for applications in wireless communication systems. Full article

The impact of environmental disturbances and sensor deployment variations on damage identification represents a critical bottleneck that constrains the practical effectiveness of structural health monitoring. Existing methods addressing these challenges often suffer from poor interpretability due to information loss during feature extraction or exhibit insufficient sensitivity in identifying early-stage minor damage. This paper proposes a damage identification method based on the Shapelet Transform and Random Forest classifier, which extracts highly interpretable local shape features from vibration response signals to achieve robust identification of structural state changes. The study utilizes measured random vibration response data from a timber truss bridge. The dataset comprises four reference states collected on different dates and five damage states simulated by additional masses ranging from +23.5 g to +193.7 g, with sensors deployed in both vertical and horizontal directions. The Shapelet Transform selects local subsequences with high information gain from the original time series as features, which are subsequently classified using the Random Forest algorithm. The experimental design systematically investigates the influence of different damage severities, sensor locations, and environmental variations on method performance. The results demonstrate that with a Shapelet extraction time of 10 min, the method achieves 100% identification accuracy across multiple operating conditions comprehensively considering environmental variations, sensor location differences, and varying damage severities. When the extraction time is reduced to 5 min, 3 min, and 1 min, the average accuracies are 93.98%, 89.51%, and 58.48%, respectively. The method effectively identifies the minimum simulated damage (+23.5 g), which represents only 0.07% of the total structural mass, while maintaining stable performance under varying sensor locations and environmental conditions. Compared to traditional methods based on global frequency-domain features or statistical characteristics, the proposed method extracts physically meaningful local Shapelet features, offering significant advantages in interpretability. In contrast to deep learning approaches, this method demonstrates greater robustness under limited sample conditions. This study confirms that the combined framework of the Shapelet Transform and Random Forest can effectively address multiple real-world challenges in structural health monitoring, delivering high accuracy, strong robustness, and excellent interpretability, thereby providing a novel approach for developing practical real-time damage identification systems. Full article

Line spectrum purification is a fundamental task in underwater detection and identification tasks. A dual-input architecture based on Dense U-net is introduced to extract clean line spectra from strong interference. The U-net model features a symmetric encoder–decoder structure that accepts two-dimensional data as both input and output. The DenseBlock, a core component of DenseNets, offers greater parameter efficiency compared to conventional convolutional layers. In this paper, standard convolutional layers inside the original U-net are replaced by DenseBlocks. This model possesses two input channels, thus allowing the time–frequency feature of the interference and that of the interference–target mixture to be fed simultaneously. With supervised learning, the model is capable of eliminating the strong interference components and background noise from the superimposed spectrum, thereby producing a purified target line spectrum. Compared to traditional interference suppression methods, this approach offers higher feature accuracy and greater signal-to-interference-and-noise ratio (SINR) gain. Moreover, the model is trainable using simulation datasets and then deployed to real-world measurements, demonstrating strong generalization capabilities—a valuable property given the limited availability of labeled samples in underwater detection tasks. Being data-driven, this method operates without requiring prior assumptions about the array configuration, and consequently exhibits greater resilience to array imperfections relative to conventional model-based interference suppression techniques. Simulation and experimental results demonstrate that the proposed method achieves an output SINR improvement of more than 8 dB under low SINR conditions and exhibits significantly better robustness to array position errors than conventional methods, verifying its excellent line spectrum purification capability. Full article

Background/Objectives: The objective assessment of natural tooth mobility remains challenging in clinical practice. This pilot study aimed to investigate the feasibility, repeatability, and agreement of a modified implant stability measurement system adapted for natural teeth using a custom-fabricated titanium bracket and a modified SmartPeg. Methods: Sixteen systemically healthy patients (10 males, six females) and 94 single-rooted permanent teeth with varying mobility grades were included. The tooth mobility was assessed using the Miller Mobility Index, Periotest M, and resonance frequency analysis (RFA) with the Osstell Beacon device. For the Osstell measurements, a custom titanium bracket bonded to the buccal tooth surface allowed for the placement of a modified SmartPeg. Each tooth was measured twice under standardized conditions, and mean values were recorded. The statistical analyses included Spearman correlation analysis, Cohen’s kappa for agreement with Miller categories, and intraclass correlation coefficients (ICCs) to assess the measurement repeatability. Results: The mean Periotest value was 12.70 ± 13.69, and the mean ISQ (implant stability quotient) value was 69.45 ± 19.37. The repeated measurements demonstrated excellent intra-examiner repeatability for both devices (ICC > 0.95). The Periotest values showed substantial agreement with the Miller mobility grades (κ = 0.763; p < 0.001), whereas the Osstell values demonstrated weak agreement with these ordinal categories (κ = 0.094; p = 0.048). A strong negative correlation was observed between the Periotest and Osstell measurements irrespective of the scales (r = −0.865; p < 0.001). Conclusions: In natural dentition, the resonance frequency analysis demonstrated reproducible measurements under controlled experimental conditions and showed measurable associations with conventional mobility assessments. However, the method remains investigational. The findings do not establish clinical validity for the routine assessment of natural tooth mobility. Further studies with larger sample sizes and statistical models accounting for patient-level clustering are required before clinical implementation can be considered. This study is registered at ClinicalTrials.gov (NCT07188168). Full article

In this paper, a novel multiband microwave resonator is proposed and investigated for non-invasive glucose sensing applications. The structure is based on a compact, planar spiral-like geometry fed by a Coplanar waveguide (CPW) transmission line, designed to support multiple resonant modes through nested concentric rings. A full electromagnetic model was developed to predict the resonance behavior analytically, achieving excellent agreement with Computer Simulated Technology (CST) simulations across four resonant frequencies (2.7, 6.44, 8.0, and 12.8 GHz). The sensor demonstrated high glucose sensitivity at multiple frequencies, with peak values reaching 0.05 dB/mg/dL and 0.038 dB/mg/dL at 10.1 GHz and 6.22 GHz, respectively. To enhance conformability and skin contact, the antenna was further transformed into a semi-cylindrical flexible form suitable for finger-wrapping. Despite the mechanical deformation, the structure preserved its resonance while offering enhanced near-field interaction with biological tissues. The folded sensor achieved a sensitivity of 0.032 dB/mg/dL at 5.25 GHz and a peak gain of 6.05 dB, validating its robustness for wearable deployment. The clear correlation between reflection magnitude and glucose level (with R > 0.99) confirms the sensor’s potential as a passive, multiband, and non-invasive glucose monitoring platform. The physics-informed residual deep learning framework significantly enhances prediction accuracy, achieving an RMSE of 0.28 mg/dL, MARD of 0.13%, and confining 100% of both training and holdout predictions within the <5% ISO-like risk region, thereby ensuring robust and clinically reliable non-invasive glucose estimation. Full article

Phenetole derivatives with dual ethoxy substituents exhibit rich conformational diversity and complex vibronic characteristics, making them important model compounds for understanding substituent effects on molecular structure and spectroscopy. In this work, we systematically investigated the stable rotamers, vibronic spectra, and cationic ground-state features of p-diethoxybenzene (PDEB) using resonance-enhanced multiphoton ionization (REMPI), UV-UV hole-burning (HB), and mass-analyzed threshold ionization (MATI) spectroscopies, combined with density functional theory (DFT) calculations. The ground-state potential energy surface (PES) of PDEB was calculated at the B3LYP/6-311++G(d,p) level, identifying eight rotamers with distinct statistical weights and relative energies. Hole-burning spectroscopy resolved two dominant rotamers (cis/up–up and trans/up–down) in the supersonic molecular beam, with their S₁←S₀ transition origins determined as 33,824 cm⁻¹ and 33,613 cm⁻¹, respectively. Franck-Condon simulations of the vibronic transitions showed excellent agreement with the experimental REMPI spectra, enabling precise assignment of substituent and benzene ring vibrational modes. MATI experiments yielded accurate adiabatic ionization energies (AIEs) of the cis and trans rotamers as 59,629 ± 5 cm⁻¹ and 59,432 ± 5 cm⁻¹, respectively, and identified active cationic vibrational modes in the D₀ state. Geometric parameters of PDEB in the S₀, S₁, and D₀ states were calculated at the B3PW91/aug-cc-pVTZ, TD-B3PW91/aug-cc-pVTZ, and UB3PW91/aug-cc-pVTZ levels, revealing structural evolution during electronic excitation and ionization. The effects of ethoxy substituent orientation on molecular energy, vibrational frequencies, and ionization energy are discussed, and differences in spectral characteristics between PDEB and its meta isomer (MDEB) are compared. This work provides a comprehensive spectral and structural database for p-diethoxybenzene and deepens the understanding of structure–property relationships in diethoxybenzene isomers. Full article

Within the framework of consistent couple stress theory (CCST) and employing Hamilton’s principle, we derive a Galerkin weak formulation to analyze the three-dimensional (3D) size-dependent free vibration characteristics of a simply supported, functionally graded (FG) magneto-electro-elastic (MEE) doubly curved (DC) microscale shell subjected to a uniform temperature change. Incorporating the differential reproducing kernel (DRK) interpolants into the weak formulation, we further develop an element-free Galerkin (EFG) method. The microscale shell of interest is composed of two-phase MEE materials, and its material properties are assumed to vary through its thickness according to a power-law distribution of the volume fractions of the constituents. The results show that the natural frequency solutions obtained using the EFG method are in excellent agreement with the reported 3D solutions for laminated composite and FG-MEE macroscale plates, with the material length-scale parameter and the inverse of the curvature radii set to zero. The effects of the material length-scale parameter, temperature change, inhomogeneity index, and mid-surface radius and length-to-thickness ratios on the FG-MEE microscale shell’s free vibration characteristics in a thermal environment are examined and appear to be significant. Full article

Transmission towers, as critical infrastructure in power systems, are frequently threatened by multiple hazards such as strong winds and flood scour. Traditional structural health monitoring methods face limitations in data feedback timeliness and mechanical interpretation, making real-time condition awareness and early warning under disaster scenarios challenging. To address these issues, this paper proposes a digital twin framework for transmission tower structures, integrating Building Information Modeling (BIM), Internet of Things (IoT) technology, and the Finite Element Method (FEM) for structural health monitoring and visual warning under wind loads and flood scour effects. The framework achieves cross-platform collaboration through the FEM Open Application Programming Interface (OAPI) and Python scripts. In the physical domain, fluctuating wind loads are simulated based on the Davenport spectrum, flood scour depth is modeled using the HEC-18 formulation, and foundation constraint degradation is represented through nonlinear spring stiffness reduction. In the FEM domain, dynamic time-history analyses are conducted to obtain structural responses. In the BIM domain, a three-level warning mechanism based on stress change rate (ΔR) is established to achieve intuitive rendering and dynamic feedback of structural damage. A 44.4 m high latticed angle steel tower is employed as the case study for validation. Results demonstrate that the simulated wind spectrum closely matches the theoretical target spectrum, confirming the validity of the load input. A critical scour evolution threshold of 40% is identified, beyond which the first two natural frequencies exhibit nonlinear decay with a maximum reduction of 80.9%. Non-uniform scour induces significant load transfer, with axial forces at leeside nodes increasing from 27 kN to 54 kN. During the 0–60 s wind loading process, BIM visualization accurately captures the full stress evolution from the tower base to the upper structure, showing excellent agreement with FEM results. The proposed framework establishes a closed-loop interaction mechanism of “physical sensing–digital simulation–visual warning”, effectively enhancing the timeliness and interpretability of structural health monitoring for transmission towers under multiple hazards, providing an innovative approach for intelligent disaster prevention in power infrastructure. Full article

Dust pollution induced by blasting during tunnel construction via the drill-and-blast method poses a severe threat to workers’ health and construction safety. To address this issue, a wet chord grid dust removal and purification device adaptable to deep-buried long tunnels was developed in this study. The device integrates dust control and removal functions, featuring mobility, high purification efficiency, and water recycling capability. Through experimental tests, the optimal operating parameters of the system were determined: the dust removal efficiency reached a peak of 94.3% (laboratory optimal value from the basic parameter optimization test) when the frequency of the extraction axial flow fan was set to 30 Hz and the cross-sectional wind speed of the chord grid reached 3.34 m/s. The circulating water tank achieved the optimal water treatment performance under the conditions of a relative buried depth of 0.42 for the water inlet, a volume ratio of 1:2 for the sedimentation area to the clear water area, and a relative baffle height of 0.65. Numerical simulations based on CFD software (2021) revealed that the on-site dust removal efficiency of the device reached 79.86% and 87.9% under the working conditions where the tunnel face was 10 m and 100 m away from the connecting passage, respectively, which are in good agreement with the field measurement results. In the practical application at the Shierpo Tunnel of the Guangxi Tianba Expressway, the device achieved an average total dust removal efficiency of 78.4%, with 81.2% removal efficiency for PM₁₀ and 76.5% for PM_2.5, demonstrating excellent engineering applicability and dust removal performance for respirable dust. This study provides effective technical support and a theoretical basis for improving the construction environment of drill-and-blast tunnels. Full article

The reliability of high-frequency (HF) frequency selection technology relies on the prediction accuracy of the Maximum Usable Frequency of the ionospheric F2 layer (MUF-F2). To improve its short-term prediction performance, a novel hybrid deep learning prediction model is proposed, which achieves accurate modeling of the complex spatiotemporal variation patterns of MUF-F2 by integrating a feature enhancement mechanism, a dual-branch feature extraction structure, and a bidirectional temporal dependency capture network. The hybrid prediction model integrates the Channel Attention mechanism (CA), Dual-Branch Convolutional Neural Network (DCNN), and Bidirectional Long Short-Term Memory network (BiLSTM). The model is trained and validated using MUF-F2 data from 5 communication links over China during geomagnetically quiet periods and 4 during geomagnetic storm periods, with the difference in the number of links attributed to experimental constraints and the disruptive effects of geomagnetic storms. Its performance is evaluated via multiple metrics, and a comparative analysis is conducted with commonly used prediction models such as the Long Short-Term Memory (LSTM) network. Experimental results show that during geomagnetically quiet periods, the proposed model achieves lower prediction errors (Root Mean Square Error (RMSE) < 1.1 MHz, Mean Absolute Percentage Error (MAPE) < 3.8%) and a higher goodness of fit (coefficient of determination (R²) > 0.94), with the average error reduction across all links ranging 8 from 6.2% to 46.9% compared with the baseline model. Under geomagnetic storm disturbance conditions, the model still maintains robust prediction performance, with R² > 0.89 for all communication links, as well as RMSE < 0.6 MHz, Mean Absolute Error (MAE) < 0.4 MHz, and MAPE < 3.3%. The study demonstrates that the proposed CA-DCNN-BiLSTM model exhibits excellent prediction accuracy and anti-interference capability under different geomagnetic activity conditions, which can effectively improve the short-term prediction accuracy of MUF-F2 and provide more reliable technical support for HF communication frequency decision-making. Full article

Birefringence, arising from the low-symmetry structure in orthorhombic LaFeO₃, limits the observation and utilization of magneto-optical effects. In this study, the pure-phase perovskite-typed La_1−xCe_xFe_1−xTi_xO₃/SiO₂ thin films were successfully fabricated via radio-frequency magnetron sputtering, where the co-doping of Ce³⁺ and Ti⁴⁺ ions effectively induced a structure transition from orthorhombic to a highly symmetric cubic phase, eliminating birefringence effect and thus reducing optical transmission loss. At the same time, the doped Ce³⁺ ions also effectively enhanced the magnetic and magneto-optical effects of the system due to their strong spin coupling effect and superexchange interaction with Fe³⁺ ions. The results show that the cubic-phase La_0.5Ce_0.5Fe_0.5Ti_0.5O₃/SiO₂ thin film exhibits excellent magnetic and magneto-optical performance. Their saturation magnetization reaches 180 emu/cm³ with an in-plane easy magnetic axis. And their magnetic circular dichroic ellipticity |ψ_F| reaches 3054 degrees/cm. Full article

This study systematically investigated the combined effects of drying method (mid- and short-wave infrared drying, MSID; hot air drying, HAD; radio frequency-hot air combined drying, RF-HAD), drying temperature (35, 45, 55, 60 °C), and initial wet-basis moisture content (20%, 25%, 30%) on drying characteristics, nutritional quality, texture, and oxidative stability of peanuts. RF-HAD achieved the shortest drying time, followed by MSID and HAD. Protein content remained stable across all treatments. Fat, oleic acid, and total amino acids were significantly affected by all three factors with significant two-way interactions; linoleic acid exhibited significant method × moisture and three-way interactions. Hardness, adhesiveness, springiness, gumminess, and chewiness showed significant three-way interactions, indicating interdependent effects. All samples met national standards for acid value and peroxide value. MSID yielded the lowest acid value and peroxide value immediately after drying, suggesting better initial oxidative quality. Acid value was primarily influenced by method and temperature, with significant two-way interactions, whereas peroxide value showed significant main effects and a highly significant three-way interaction. No single drying condition optimized all quality attributes. RF-HAD excels in drying efficiency and texture enhancement but requires temperature control to limit oxidation; MSID offers superior initial oxidative stability and amino acid retention. Initial moisture content acts as an active variable that modulates the effects of drying method and temperature. Full article

Wire rope isolators (WRIs) exhibit typical nonlinear and asymmetric hysteretic behavior, with their mechanical performance being significantly influenced by the coupled effects of multiple parameters. This study investigates the dynamic response of large-sized spiral WRIs under axial loading. Within the framework of an asymmetric hysteresis model, a novel algebraic closed-form formulation is adopted for parameter identification and numerical simulation. Furthermore, a characteristic parameter, A, is introduced to quantify the unique mechanical behavior induced by the structural configuration of WRIs. Five types of large-sized spiral WRIs are selected as test specimens. For each WRI, tests are conducted under 30 distinct working conditions, yielding a total of 150 cyclic loading tests across all scenarios. By systematically varying the displacement amplitude, loading frequency, and preloading pressure, the influences of these key parameters on the dynamic characteristics of WRIs are comprehensively analyzed. These characteristics encompass the axial hysteresis loop shape, energy dissipation capacity, equivalent viscous damping, and average secant stiffness. The results indicate that these three loading parameters exert substantial effects on the mechanical properties of large-sized WRIs. Additionally, the simulated hysteresis curves derived from the identified parameters exhibit excellent agreement with the experimental observations. Compared with conventional mechanical models, the proposed algebraic closed-form model demonstrates slightly higher fitting accuracy, thereby validating its effectiveness and applicability in characterizing the mechanical behavior of large-sized WRIs. This research provides a crucial reference for the engineering application of large-sized spiral WRIs and facilitates the broader adoption of the proposed modeling approach. Full article

The high penetration of renewables introduces stability challenges to modern power grids due to their intermittency and lack of inertia. Unlike conventional grid-following controls, this paper proposes a Virtual Synchronous Machine (VSM) control strategy for a three-level neutral-point clamped (NPC) battery energy storage system (BESS), enabling autonomous voltage and frequency support. The VSM control comprises voltage reference generation and voltage tracking. A step-by-step derivation of inverter output impedance is also provided, along with an analysis of how key parameters affect it. Simulation results in MATLAB R2021a/Simulink demonstrate excellent dynamic performance and grid-supporting functionalities, validating the effectiveness of the proposed design and the accuracy of the impedance analysis. Full article

The prediction of drying kinetics in hygroscopic biological materials remains challenging due to the strong coupling between internal moisture diffusion, evolving surface wettability, material deformation and thermolabile bioactive compounds degradation. In this context, periodic temperature variations are inherent to many industrial and solar drying systems, yet most experimental and modeling studies evaluate product quality under constant-temperature conditions. This work provides a demonstration that periodic drying can alter quality degradation pathways in ways that may not be captured by constant-temperature experiments. A coupled non-isothermal lattice Boltzmann method (LBM) model for heat and moisture transport was integrated with a Weibull kinetic formulation to describe the degradation of total carotenoids, total polyphenols, and antioxidant activity in carrot slices. Validation against experimental data across 50–70 °C demonstrates excellent agreement (R² > 0.96 for moisture ratio; quality retention within ±2% of the literature values). Seven drying scenarios were systematically evaluated: constant temperature (60 °C), fast and slow periodic oscillations, high-amplitude cycles, a mixed strategy combining constant initial drying with subsequent oscillations, and two intermittent ON/OFF profiles. Results reveal that while total polyphenol degradation within the present model is constrained to ~13.3% retention under the adopted kinetic parameters, carotenoid and antioxidant retention are highly sensitive to temperature history. The mixed strategy (60 °C for 2 h followed by 50–60 °C oscillations) achieves the highest quality retention (TC: 51.6%, AA: 34.4%) while requiring the lowest energy input (0.512 kJ), outperforming constant drying (TC: 48.8%, AA: 32.9%, 0.563 kJ). Conversely, high-amplitude intermittent drying (70/25 °C) accelerates carotenoid degradation (TC: 46.7%) despite shorter drying time (8.81 h), and low-amplitude intermittent cycling (65/55 °C) yields the poorest mean quality (31.4%) with the highest energy consumption (0.583 kJ). The framework reveals that oscillation frequency critically determines quality outcomes: slow cycles (8 h period) marginally improve retention, while fast cycles (2 h) offer no benefit over constant drying. These findings provide quantitative insights toward the design of drying strategies, demonstrating that optimal strategies must account for the coupling between temperature history and moisture-dependent vulnerability, with the mixed strategy emerging as the best-performing strategy among the tested scenarios. Full article