Search Results (1,510)

This article presents the design and characterization process of a lightweight Vivaldi antenna for high-voltage ultra-wideband systems. The proposed antenna consists of two radiating arms with different exponential curves on their inner and outer edges fed with an insulated-coplanar-plates transmission line. Weight reduction is achieved by implementing the antenna with sheets composed of a polyester layer between two aluminum layers, with a polylactic acid insulator inserted between the arms. The reflection coefficient of the implemented antenna demonstrates an impedance bandwidth ranging from 0.61 GHz to 3.44 GHz. High-voltage operation of up to 12.4 kV is also experimentally demonstrated. In addition to satisfying the high-voltage and ultra-wideband operational requirements, the proposed antenna is shown to achieve, among antennas with comparable characteristics, the most effective combination of low minimum operating frequency and low weight. The transfer function between the voltage applied to the antenna, $V_s$, and the radiated electric field, $E_r$, is measured. Using this transfer function, the radiated electric field is calculated for an input voltage pulse with a rise time of 110 ps to confirm the antenna’s capability of producing radiated pulses with low distortion. The calculated radiated electric field pulse closely matches the results obtained with full-wave simulation. To assess the similarity between the radiated and applied pulses, the pulse width stretch ratio is calculated, yielding a variation of 3.86% for the direction of maximum gain and 9.36% for 30° in the H-plane of the antenna. This feature is desirable for EMC, EMI and sensing applications. The antenna is also characterized in the frequency domain, achieving a maximum gain of 10.09 dBi at 3.63 GHz and a 30° 3 dB beamwidth for ultra-wideband pulses. Full article

Bi₂O₂Se is an emerging oxyselenide semiconductor, noted for its promising thermoelectric properties and excellent chemical stability, and it is often regarded as an n-type counterpart to p-type BiCuSeO. However, its intrinsic thermoelectric figure of merit (zT) is severely limited by low electron concentration and high thermal conductivity. In this work, we employed Cl element as donator dopant to substantially enhance the carrier concentration. The room-temperature carrier concentration of Bi₂O₂Se_0.98Cl_0.02 thereby reached 2.70 × 10²⁰ cm⁻³, representing an improvement of two orders of magnitude compared to that of pristine Bi₂O₂Se. Subsequently, multiple hot-pressing cycles were applied to the optimized composition Bi₂O₂Se_0.98Cl_0.02. The process induced significant grain refinement, and the resulting high density of grain boundaries effectively suppressed the lattice thermal conductivity, reducing it to 0.95 W·m⁻¹·K⁻¹ at 823 K. Eventually, a maximum zT of 0.24 was achieved at 823 K for the three-time hot-pressed Bi₂O₂Se_0.98Cl_0.02 sample, representing a 71% improvement compared with the pristine Bi₂O₂Se sample prepared by single hot-pressing (zT = 0.14 at 823 K). This work provides a new strategy for enhancing the thermoelectric performance of oxyselenide through the synergistic regulation of doping and microstructure. Full article

Polycrystalline La_2−xBi_xNiMnO₆ (x = 0.2, 0.5, 1.0) samples were synthesized via a high-temperature and high-pressure method, with their structural, magnetic, and electrical properties systematically characterized. X-ray diffraction (XRD) confirms a monoclinic double perovskite structure (space group P2₁/n) for all samples, while Bi³⁺ induces a lattice volume expansion trend inferred from XRD peak shifts due to its larger ionic radius than La³⁺. Magnetically, all exhibit ferromagnetism and soft magnetic features, with magnetization decreasing as Bi content increases. The x = 0.2 and 0.5 samples show two distinct Curie temperatures, both decreasing with Bi substitution, whereas the higher Curie temperature vanishes in the x = 1.0 sample, likely due to Bi-induced structural changes. Electrically, all display semiconducting behavior (resistivity: x = 0.5 > x = 0.2 > x = 1.0) and negative magnetoresistance (MR) at 200 K, peaking at 12% (x = 0.5) and 7.5% (x = 1.0). For the x = 1.0 sample, negative magnetoresistance strengthens with decreasing temperature (130–200 K), with magnetoresistance-field (MR-H) curves showing herringbone and arc shapes. Full article

Bismuth oxychloride (BiOCl) has garnered significant research interest owing to its non-toxicity, affordability, and distinct layered structure. Although BiOCl possesses promising photocatalytic potential, its large band gap and rapid photocarrier recombination restrict its practical use. In this work, a natural tourmaline mineral was effectively integrated with BiOCl to form a composite (TBO). Comprehensive characterization and photocatalytic assessments revealed that the intrinsic electric field of tourmaline notably strengthened both the adsorption capacity and the light-driven catalytic efficiency of BiOCl. Under visible-light irradiation, ofloxacin (OFX, 10 ppm) was eliminated by approximately 98% within 60 min. The apparent reaction rate constant (k) of TBO was 0.0407 min⁻¹, which was approximately 184.8 and 2.26 times those of tourmaline alone and pristine BiOCl, respectively. Furthermore, both the visible-light absorption and the separation efficiency of photogenerated electron–hole pairs were significantly enhanced. After evaluating its behavior under various simulated natural environmental conditions, TBO displayed strong potential for practical application. Reactive species trapping and analysis identified singlet oxygen (¹O₂) and superoxide radicals (·O₂⁻) as the primary active species in photocatalysis. Moreover, the degradation route of ofloxacin and the toxicity of its intermediates were systematically examined. These findings offer meaningful guidance for improving photocatalytic materials by utilizing naturally occurring minerals. Full article

Hybrid electric mining trucks operating in open-pit environments encounter highly variable gradients and payload conditions that standard energy management strategies fail to address adequately. Existing approaches are predominantly calibrated for full-load scenarios and neglect the accelerated battery degradation resulting from sustained high-power cycling, undermining long-term operational viability. This study presents a multi-objective energy management framework that couples real-time driving condition recognition with dynamic programming (DP) optimization for a 130-tonne hybrid mining truck. Field data collected from an open-pit mine in Heilongjiang Province were used to construct six physically representative driving conditions via principal component analysis and K-means clustering. A Bidirectional Gated Recurrent Unit (Bi-GRU) network (2 layers, 128 hidden units per direction) was trained on a route-based temporal split, attaining 95.8% classification accuracy across all six conditions. Condition-specific powertrain modes were subsequently defined, and a DP formulation with a weighted-sum cost function was solved to jointly minimize diesel consumption and battery capacity fade—quantified through a semi-empirical effective electric quantity metric. A marginal rate of substitution (MRS) analysis was conducted to identify the optimal trade-off between fuel economy and battery life preservation. In the DP cost function, the weight coefficient μ (ranging from 0 to 1) governs the relative emphasis placed on battery degradation minimization versus fuel consumption minimization: μ = 0 corresponds to pure fuel minimization, whereas μ = 1 corresponds to pure battery degradation minimization. The MRS analysis identified μ = 0.1 as the knee point of the Pareto trade-off: relative to pure fuel minimization (μ = 0), this setting reduces effective electric quantity by 6.1% while increasing fuel consumption by only 1.4% (MRS = 4.36). Against a rule-based baseline, the proposed strategy improves fuel economy by 12.3% and extends battery service life by 15.7%. Co-simulation results were validated against onboard fuel-flow measurements; absolute simulated and measured fuel consumption values are reported route-by-route, with deviations within 4.5%. A three-layer BP neural network (3 inputs, two hidden layers of 20 and 10 neurons, 1 output) trained on the DP solution reproduces near-optimal performance—with fuel consumption and effective electric quantity increases below 1.0% and 1.1%, respectively—while reducing computation time by over 96% (from approximately 52,860 s to 1836 s for the 1800 s driving cycle), demonstrating practical feasibility for real-time deployment. Full article

Objectives: Variations in composite resin composition and aging time remain one of the main reasons for replacing esthetic restorations. This in vitro study aimed to evaluate the microtensile bond strength of a low-shrinkage composite resin on a demineralized dentin surface following adhesive interface degradation. Methods: Seventy-eight extracted human molars were prepared, and artificial caries lesions were induced. For microtensile bond strength (μTBS) testing, 60 teeth were randomly assigned to six experimental subgroups (n = 10 per subgroup) based on restorative system and thermal cycling condition. An additional 18 teeth were randomly assigned to six experimental subgroups (n = 3 each) for SEM analysis. Three restorative systems were evaluated, Z250 (conventional resin), K (Kalore resin), and P90 (Filtek P90 resin), each subjected to two thermal cycling conditions: without thermal cycling (NTC) and 12,000 thermal cycles (TC). Results: In the NTC groups, Z250 exhibited a significantly higher bond strength (25.29 ± 10.91 MPa) compared to K (9.69 ± 11.63 MPa) and P90 (9.81 ± 8.49 MPa) (p < 0.05). Following TC, a numerical decrease in bond strength was observed across all groups. Z250 (13.00 ± 10.76 MPa) maintained a significantly higher bond strength compared to K (4.30 ± 6.40 MPa) and P90 (0 ± 0 MPa) (p = 0.001). Notably, the P90 group showed a near-complete loss of bond strength after TC (0 ± 0 MPa), which was a statistically significant reduction compared to its NTC condition (p = 0.002). SEM analysis revealed a predominance of mixed failures in most experimental groups, while the P90 TC group showed a clear predominance of adhesive failures. Conclusions: This study demonstrates that the conventional Bis-GMA resin (Z250) consistently exhibited superior bond strength to demineralized dentin compared to the low-shrinkage resins (Kalore and Filtek P90) under both non-aged and aged conditions. While all materials experienced a reduction in bond strength after thermal cycling, the Filtek P90 system showed a catastrophic loss of adhesion after aging, indicating its particular susceptibility to degradation. These results emphasize the critical roles of resin chemistry and adhesive system selection in long-term bond durability in compromised dentin. Full article

The rapid advancement of deepfake generation technologies presents significant challenges to the verification of digital video authenticity. These time-dependent artifacts are difficult to detect using conventional frame-based detection approaches. This paper introduces AT-HSTNet, an Action-Transformer-based Hierarchical Spatiotemporal Network designed for robust and computationally efficient deepfake video detection. The proposed framework adopts a multi-stage hierarchical architecture in which frame-level visual features are extracted using an EfficientNet-B0 backbone, short- and medium-range temporal patterns are modeled through Bidirectional Long Short-Term Memory (BiLSTM) networks, and long-range temporal dependencies are captured using an action-aware Transformer operating on temporally aggregated representations. Unlike conventional video transformers that apply self-attention directly to raw frame-level features, the proposed action-aware attention mechanism reduces redundant computation and improves stability in temporal reasoning. Extensive experiments on the balanced FFIW-10K dataset demonstrate that AT-HSTNet achieves an accuracy of 98.7%, with 98.0% precision, 96.0% recall, and a 96.9% F1-score, outperforming representative CNN–BiLSTM and CNN–Transformer baseline architectures. In addition, AT-HSTNet is highly efficient, requiring only 0.45 GFLOPs and achieving an inference speed of approximately 30 FPS on consumer-grade GPU hardware. As a result of this study, we found hierarchical temporal modeling more effective when combined with action-aware attention for any deepfake video detection. Full article

Wood ash from biomass power plants and coarse, low-grade sheep wool from farming are underutilized biowastes that are often landfilled. Their valorization could reduce waste and emissions, decrease reliance on virgin materials, and support the circular economy and European Green Deal targets. However, both materials may contain naturally occurring radionuclides, primarily ⁴⁰K, as well as trace uranium and thorium isotopes, with higher concentrations typically found in wood ash due to combustion processes. Assessing their activity concentrations and bioavailability is therefore essential to ensure regulatory compliance and protect public health. This study quantified radionuclide levels in wood ash and sheep wool samples collected in Croatia and evaluated their suitability for agricultural applications. Natural radionuclides (⁴⁰K, ²³²Th, ²³⁸U, ²¹⁴Pb, ²¹⁴Bi, ²²⁶Ra, ²¹⁰Pb, ²¹⁰Po) and ¹³⁷Cs were determined using high-resolution gamma-ray and alpha spectrometry. The influence of different factors on radionuclide content was discussed, and transfer factors within the soil–hay–wool pathway were calculated to assess bioavailability. Measured activity concentrations were consistently low, and transfer factors indicated minimal radionuclide mobility. The results support the safe agricultural reuse of these materials and provide baseline data for radiological safety assessments in sustainable waste management practices. Full article

The Yechangping super-large porphyry–skarn deposit is a key component of the East Qinling molybdenum metallogenic belt, central China. Magnetite is widely developed across all mineralization stages of this deposit, yet its systematic geochemical evolution and prospecting significance remain poorly constrained. This study presents in situ major- and trace-element analyses of magnetite via electron probe microanalysis (EPMA), laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and elemental mapping, to unravel the ore-forming hydrothermal evolution and establish reliable prospecting indicators. Four magnetite generations are identified based on petrography and paragenetic relationships: late skarn stage (Mt1), oxide stage (Mt2 and Mt3), and polymetallic sulfide stage (Mt4). Magnetite has total iron contents (TFeO, total Fe calculated as FeO) of 82.72–95.46 wt.% (values above the 93 wt.% stoichiometric limit of pure magnetite stem from minor oxidation), with dominant isovalent Fe³⁺ and Al³⁺ lattice substitution supported by a significant negative Fe–Al correlation. Systematic stage-dependent geochemical variations are observed: Mt1 has the highest Ti (mostly >1500 ppm), V and Cr, while Mt2–Mt4 show progressive Ti depletion (mostly <100 ppm), recording continuous cooling of the hydro-thermal system. V and Cr contents decrease markedly from Mt1 to Mt3, with secondary enrichment in Mt4; Mo concentrations peak in Mt2 (average 5.06 ppm), coupled with elevated chalcophile metalloid Te, As, Pb and Bi. Elemental mapping results show that K occurs as discrete hotspots, which may be mainly derived from feldspar microinclusions, rather than lattice substitution in magnetite. These geochemical fingerprints record a transition from high-temperature magmatic–hydrothermal fluids to late contact-metasomatic fluids, with evolving fluid–rock interaction and oxygen fugacity. Our results demonstrate that magnetite chemistry is a reliable tool for discriminating mineralization stages and vectoring prospecting targets in porphyry–skarn Mo–W systems. Full article

Two-dimensional materials have broad application prospects in the field of optoelectronic devices. As a next-generation power electronic device, AlN materials have obvious advantages in power processing, and their monolayer structure has excellent optoelectronic properties, which is of great significance for the study of 2D AlN monolayers. Properties such as electronic and optical properties of metal-adsorbed AlN (M-AlN) systems have been systematically investigated using density functional theory from first principles. The results of the energy bands of the M-AlN system indicate that the adsorption of Al, Li, Ag, Au, Bi, Cr, Mn, Na, Pb, Sn, Ti, and K metals makes the monolayer AlN magnetic, the incorporation of two metals, Al and Li, is the transition of the monolayer AlN from a semiconductor to a semi-metal, and the introduction of K metal makes the monolayer AlN transition from a semiconductor to a metal. The work function of the M-AlN system shows that the introduction of the metal reduces the work function of the monolayer AlN, especially for K-AlN, which is reduced by 56.12% compared to the monolayer AlN. In addition, the results of the optical absorption spectra of the M-AlN system revealed that the introduction of the metals made the monolayer AlN exhibit high absorption peaks in the visible and near-infrared regions; in particular, the intensity of the absorption peaks of the Ti-AlN system at 557.8 nm reached 7.4 × 10⁴ cm⁻¹ and the intensity of the absorption peaks of the K-AlN system at 1109.3 nm reached 1.01 × 10⁵ cm⁻¹. This indicates that the introduction of Ti and K metal atoms enhances the absorption properties of monolayer AlN in the visible and near-infrared regions. Finally, the time-domain finite difference using spherical metal nanoparticles is used to excite the localized surface plasmon resonance, and the results show a small area of strong electric field around the electric field hotspot of Cr and Li particles, and a good concentration of the electric field strength in the x and y directions. In summary, the system of metal atoms adsorbed on AlN will be favorable for the design of spintronics, field-emitting devices and solar photovoltaic devices. Full article

Thermoelectric (TE) materials offer a promising route for direct thermal-to-electrical energy conversion via the Seebeck effect. Among them, GeTe exhibits superior performance in the mid-temperature range (500–800 K), whereas (Bi,Sb)₂Te₃ is widely regarded as the benchmark material for near low-temperature applications (< 450 K). To improve TE efficiency over a wider temperature range, segmented GeTe/(Bi,Sb)₂Te₃-based single-leg TE devices were developed. Specifically, based on nanocomposite technology, B₄C and SiC nanoparticles were, respectively, introduced into GeTe and (Bi,Sb)₂Te₃, achieving optimization of electrical conductivity alongside reduction in thermal conductivity, thereby enhancing the thermoelectric figure of merit (ZT). Finite element simulations were used to optimize the geometric structure of the segmented device, determining the ideal ratio of GeTe to (Bi,Sb)₂Te₃. The simulations predicted a maximum conversion efficiency (η_max) of 16.9% when the ratio of GeTe to (Bi,Sb)₂Te₃ was 0.24, with a power density of 18.5 mW/mm². Experimentally, the fabricated segmented device attained a peak conversion efficiency of 7.14% and a power density of 12.5 mW/mm² under a hot-side temperature of 773 K. These findings confirm that strategic segmentation, combined with nanoscale phonon scattering engineering, substantially improves overall TE device performance across broad temperature range, underscoring its potential for high-efficiency thermoelectric energy conversion systems. Full article

This study develops a kinetic model that captures poly-ether-ether-ketone (PEEK) crystallization over a temperature $T$ window from glass transition ($T_g$) to melting ($T_m$) temperature, and across cooling rates from 5 to ~10³ °C/min. The framework is a parallel dual-Nakamura formulation whose isokinetic parameters {$k_i\left(T\right),n_i,w_i\left(T\right)$} are obtained from a bi-level non-linear regression of isothermal crystallization tests conducted using a flash-differential scanning calorimeter (FSC). The weight $w_i\left(T\right)$ partitions the faster primary and slower secondary crystallization and is represented by a physics-based analytical function that captures its dome-shaped temperature dependence. A maximum isothermally achievable enthalpy function is introduced so that the model predicts enthalpy $\Delta H\left(t\right)$ natively under arbitrary thermal profiles. To extend this isothermal backbone to non-isothermal conditions, two explicit cooling-rate-dependent scalars are introduced, $\omega \left(\dot{T}\right)$ and $\chi \left(\dot{T}\right)$, which shift $w_i\left(T\right)$ and limit attainable crystallinity at high cooling rates respectively. Finally, a rate-dependent induction time relation is added to adjust the onset of crystallization. Calibrating these rate functions against non-isothermal experiments, while keeping the isokinetic parameters fixed, yields a single isothermal–non-isothermal model that predicts $\Delta H\left(t\right)$ under arbitrary $T\left(t\right)$ profiles. Model performance is validated using an interrupted FSC experiment with a multi-segment cooling program that mimics a local transient thermal history of PEEK during additive manufacturing. The sample is cooled through successive constant-rate segments with intermittent quench–remelt cycles to probe the accumulated crystallinity along the path. Without additional fitting, the model predicts the measured enthalpy evolution with R² ≈ 0.95. The framework thus provides a practical route for predicting polymer crystallinity under processing-relevant thermal histories. Full article

Soil salinity severely threatens global ecosystems and agriculture, making accurate monitoring an ongoing priority. Currently, efficiently utilizing multi-source datasets to enhance monitoring accuracy while minimizing computational resources remains a critical challenge. This study evaluated several modeling strategies, including full-dataset modeling, variance inflation factor (VIF), Boruta, particle swarm optimization, ant colony optimization and recursive feature elimination (RFE), and validated results across diverse regions (Almaty, Kazakhstan; Shandong, China). We further validated the results using multiple algorithms, including linear regression, partial least squares regression, extreme gradient boosting, k-nearest neighbor and random forest (RF), with topsoil (0–20 cm) electrical conductivity inverted via the optimal method. Results indicate that input feature numbers substantially impact model performance: regional-scale feature selection is indispensable, with RFE outperforming full-dataset modeling (R² improves by up to 0.28, while RMSE decreases by 2.21 dS m⁻¹) and VIF performing the worst. Transferability is also demonstrated in Almaty and Shandong. Additionally, the RF algorithm shows superior performance in soil salinity mapping (overall accuracy = 0.73; kappa coefficient = 0.65). And, the RFE and SHAP results highlight CRSI, BI, and MSAVI2 as particularly important predictors for estimating soil salinity in our study area. Collectively, this study highlights the critical importance of feature optimization and interpretability in soil attribute mapping through the integration of multi-source remote sensing data. Full article

Severe foliage occlusion and dynamically changing lighting conditions in complex orchard environments pose significant challenges for visual perception systems in automated apple harvesting, including low detection accuracy, poor robustness, and insufficient real-time performance. To address these issues, this study proposes an improved lightweight detection network based on YOLOv11, named YOLO-WBL, along with a precise yield estimation algorithm based on 3D point clouds, termed CLV. The YOLO-WBL network is optimized in three aspects: (1) A C3K2_WT module integrating wavelet transform is introduced into the backbone network to enhance multi-scale feature extraction capability; (2) A weighted bidirectional feature pyramid network (BiFPN) is adopted in the neck network to improve the efficiency of multi-scale feature fusion; (3) A lightweight shared convolution separated batch normalization detection head (Detect-SCGN) is designed to significantly reduce the parameter count while maintaining accuracy. Based on this detection model, the CLV algorithm deeply integrates depth camera point cloud information through 3D coordinate mapping, irregular point cloud reconstruction, and convex hull volume calculation to achieve accurate estimation of individual fruit volume and total yield. Experimental results demonstrate that: (1) The YOLO-WBL model achieves a precision of 93.8%, recall of 79.3%, and mean average precision (mAP@0.5) of 87.2% on the apple test set; (2) The model size is only 3.72 MB, a reduction of 28.87% compared to the baseline model; (3) When deployed on an NVIDIA Jetson Xavier NX edge device, its inference speed reaches 8.7 FPS, meeting real-time requirements; (4) In scenarios with an occlusion rate below 40%, the mean absolute percentage error (MAPE) of yield estimation can be controlled within 8%. Experimental validation was conducted using apple images selected from the dataset under varying lighting intensities and fruit occlusion conditions. The results demonstrate that the CLV algorithm significantly outperforms traditional average-weight-based estimation methods. This study provides an efficient, accurate, and deployable visual solution for intelligent apple harvesting and yield estimation in complex orchard environments, offering practical reference value for advancing smart orchard production. Full article

To overcome the limitations imposed by the intermittent nature of sunlight in photocatalytic applications, this research constructs a round-the-clock purification system. We integrated an optimized S-scheme CoFe₂O₄/BiVO₄ (CFO/BV) heterojunction (synthesized via ultrasonic self-assembly at a 0.5:0.5 ratio) with a thermal energy storage (TES) unit consisting of SiO₂-encapsulated Na₂SO₄·10H₂O phase change materials (PCMs). Comprehensive characterization techniques, including XRD, HRTEM, UV-Vis DRS, EPR, and DSC, confirmed the successful formation of the interface, a broadened visible-light response (λ > 650 nm), efficient radical production, and a high latent heat storage capacity (>200 J/g). Under simulated solar irradiation, the composite exhibited superior performance, degrading 98% of the Rhodamine B within 6 h (k = 0.00994 min⁻¹), significantly surpassing single-component counterparts. More importantly, during the subsequent 12 h dark period, the heat released from the PCM maintained the reaction temperature above 35 °C, driving a 64% degradation efficiency via a thermocatalytic pathway. The system demonstrated robust stability (>90% efficiency after five cycles), excellent magnetic recoverability (98%), and high tolerance to saline textile wastewater (<10% activity loss). Furthermore, Life Cycle Assessment (LCA) indicated a 40% reduction in energy consumption compared to conventional UV/TiO₂ processes, highlighting a sustainable strategy for continuous wastewater remediation through synergistic photocatalysis and thermocatalysis. Full article