Search Results (293)

A synthesis method for compact dual-band bandpass filters based on split-ring resonators (SRRs) is presented. The method combines coupling-matrix synthesis with an energy-based SRR model with a control technique of the center frequencies and the bandwidth ratio (BWR) of the two passbands. The proposed methodology is experimentally validated for prototypes implemented on Rogers RO3010. Although the synthesis procedure is general in formulation, any change of substrate requires re-optimization of the SRR dimensions, couplings, and achievable bandwidth ratio. Two third-order microstrip prototypes were fabricated on Rogers RO3010 (${ϵ}_r=10.2$, $h=0.64$ mm) to validate the approach. The first prototype operates at 1.9 and 2.4 GHz with measured −3 dB bandwidths of 200 and 100 MHz, insertion losses of 1.0 and 1.95 dB, and BWR ≈ 0.5. The second prototype operates at 1.9 and 2.4 GHz with measured bandwidths of 100 and 200 MHz, insertion losses of 1.8 and 0.6 dB, and BWR ≈ 1.9. The corresponding footprints are 32 × 12.37 ${\mathrm{mm}}^2$ and 27.87 × 12.42 ${\mathrm{mm}}^2$, respectively. The measured responses agree well with electromagnetic simulations and confirm that asymmetric dual-band bandwidths can be achieved in a compact planar topology without additional reconfigurable elements. Full article

This study examines whether firm-level digital transformation capability (DTC) is associated with stronger operational resilience and whether governance structures condition this relationship. Operational resilience is treated here as a business-sustainability dimension based on continuity and stability of operating outcomes, not as a broad measure of environmental, social, and governance (ESG), environmental, or social sustainability performance. Using an international firm-year panel that combines standardized financial data with disclosure-based measures of implemented digital practices and governance architecture, the analysis provides observational evidence on the role of DTC in strengthening firm adaptability. In the controlled fixed-effects models, DTC is positively associated with the sales resilience ratio (SRR) (β = 0.071) and the cash-flow stability index (CFSI) (β = 0.058); an interquartile increase in DTC corresponds to approximately 0.024 in SRR and 0.019 in CFSI, or roughly 16% and 10% of their sample standard deviations. The association is stronger in firms with stronger internal oversight, auditable review mechanisms, and external ecosystem monitoring. Mechanism analyses point to supply flexibility and data visibility as plausible transmission paths, while additional tests address reproducibility, disclosure-intensity bias, construct validity, alternative governance specifications, placebo timing, restricted-shock logic, and measurement boundaries. Overall, the findings provide evidence consistent with a contingent and observational association between DTC and operational resilience when digital capabilities are embedded within accountable governance frameworks. Full article

High-resolution satellite imagery is pivotal for accurate analysis in remote sensing applications, including land-use monitoring, urban planning, and environmental assessment. However, obtaining such data is often costly and limited. Consequently, super-resolution techniques, such as deep learning models and fine-tuning strategies like LoRA, offer a promising alternative to the critical research challenge, especially given the diversity and large scale of satellite datasets. While deep learning-based super-resolution models have been very promising recently, their effectiveness, efficiency, and scalability across heterogeneous satellite scenes are not well studied. This work studies the performance of representative deep learning Super-Resolution frameworks, including the Enhanced Super-Resolution Generative Adversarial Network. (ESRGAN), Swin Transformer for Image Restoration (SwinIR), and latent diffusion models (LDM), under unified experimental conditions using the WorldStrat dataset. The main goal is to establish whether adaptation strategies for parameter efficiency can boost reconstruction quality while reducing computational and training costs. Toward this goal, we investigate hybrid sequential pipelines, ensemble averaging, and Low-Rank Adaptation (LoRA)–based fine-tuning. The experiments indicate that these pipelines, which use multi-model methods, achieve only marginal performance gains while incurring substantial increases in computational complexity. LoRA-Based Fine-Tuning, by contrast, has demonstrated superiority in enhancing reconstruction accuracy and quality across all model families, despite using only a small percentage of trainable parameters. LoRA-based models demonstrate superiority over multi-model methods in both efficiency and performance. The presented results confirm that LoRA is an effective and accessible technique for high-fidelity satellite-based super-resolution image synthesis. The manuscript identifies LoRA as one of the enabling technologies advancing the state of the art in Deep Learning-based Super Resolution for large-scale satellite-based image synthesis. Full article

In this paper, we propose a single-layer metasurface structure with ultra-wideband operation and high polarization conversion efficiency, capable of transforming linearly polarized waves into cross-polarized waves. This structure excites additional electromagnetic resonance modes by integrating two symmetrical square patches within an anisotropic split-ring resonator (SRR). These new modes couple with the inherent resonance modes of the SRR, forming closely spaced multi-resonance characteristics across a wide frequency band. This multi-resonance capability enables broadband polarization conversion. This metasurface achieves an ultra-wideband performance spanning 10.89 GHz to 30.12 GHz, covering part of the X-band, the entire Ku-band, and the K-band, while maintaining a high polarization conversion efficiency exceeding 90%. Its broadband characteristics are attributed to the resonator’s ability to generate multiple resonances within a single unit cell. Both experimental and simulation results demonstrate the metasurface’s excellent polarization conversion performance. Furthermore, the proposed metasurface maintains acceptable oblique-incidence performance over a large portion of the operating band, although localized degradation appears at some frequencies. This structure offers significant advantages over traditional multilayer or active designs, featuring simple fabrication without assembly or welding. It may be useful for broadband polarization conversion and may also provide potential for scattering-control applications. Full article

Accurate permittivity characterization at terahertz frequencies is important for material analysis and device design, yet it remains challenging for small-volume samples and compact test structures. In this work, a terahertz permittivity sensor based on a spoof surface plasmon polariton (SSPPs) transmission line coupled to a backside split-ring resonator (SRR) is proposed and numerically studied. The SSPPs line is patterned on the top side of the substrate, while the SRR is etched on the backside, with the sample loaded into the SRR gap. The SSPPs mode penetrates through the substrate and excites the SRR, producing a pronounced transmission notch. Changes in the sample permittivity modulate the effective capacitance of the resonator, resulting in a monotonic shift in the notch center frequency. For relative permittivities from 1 to 8, the notch center frequency decreases from 152.1 GHz to 117.8 GHz, corresponding to a total shift of 34.3 GHz and an average sensitivity of about 4.90 GHz/ε_r. The minimum S₂₁ remains within approximately −23.80 to −21.56 dB, while the Q-factor stays in the range of 94.33–108.23, indicating good spectral readability. Tolerance analysis further shows that the resonance frequency is sensitive to critical structural dimensions and layer alignment, and practical implementation is therefore more suitable for single-device calibrated frequency-shift sensing. These results demonstrate the feasibility of the proposed dual-layer SSPPs–SRR configuration for compact permittivity sensing in the terahertz regime. Full article

This study evaluated the morphological development of 23 Coffea canephora clones in Espírito Santo to identify materials with superior vigor and quality for commercial and breeding purposes. Seedlings from cuttings were arranged in a completely randomized design with ten replicates and assessed at the commercial dispatch stage. Shoot and root growth, biomass, leaf area (LA), Dickson Quality Index (DQI), structural ratios (shoot/root ratio, SRR; height/diameter ratio, HDR), and anatomical traits were measured. Data were analyzed using analysis of variance with Scott–Knott clustering, Pearson correlation, and Principal Component Analysis (PCA). Significant variability was observed among clones. Clones 88, VR3, 8, and LB33 showed the highest stem diameter (SD), total dry mass (TDM), LA, and DQI, with balanced shoot and root development. Leaf area correlated strongly with SD, number of leaves (NL), biomass, and DQI, confirming its role as a seedling quality indicator. PCA identified two groups: a high-performance group with greater vigor and biomass, and a lower-performance group including clones 7, MR04, and VR4. The convergence of methods confirms the robustness of the results. Overall, clones 88, VR3, 8, and LB33 demonstrate superior agronomic potential at the seedling stage, offering promising options for nurseries, growers, and clonal selection programs. Full article

Planar microwave (MW) sensors offer high-resolution, non-invasive technology for monitoring critical soil properties, serving as a support for modern precision agriculture. While laboratory studies confirm their exceptional sensitivity, the widespread adoption of these sensors is severely impeded by critical translational challenges that constitute a defining “lab-to-field gap”. These barriers include high sensor-to-sensor variability, debilitating thermal cross-sensitivity, soil heterogeneity necessitating unique site-specific calibration, and the enduring tension between high-performance and cost-effective scaling. This review systematically synthesizes the current state of planar permittivity MW technology, moving beyond technical mechanisms to critically assess these operational limitations. We detail advanced architectural strategies designed to bridge this gap, focusing particularly on the transition toward more robust solutions. The key strategies analyzed include the adoption of differential sensor designs using microstrip patch antennas to mitigate common-mode environmental errors, the integration of ultra-compact metamaterial structures such as split-ring resonators (SRRs) and complementary split-ring resonators (CSRRs) for enhanced field robustness and deep soil sensing, and the necessity of multi-parameter sensing capabilities (moisture, pH, and salinity). By establishing a comprehensive roadmap that prioritizes field stability, cost efficiency, and seamless IoT integration, this review demonstrates that planar MW sensors are poised to become reliable and scalable tools. Addressing these critical translational hurdles will ensure optimal resource management, significantly enhance crop productivity, and enable sustainable practices within smart farming ecosystems. Full article

This paper presents the design of a compact four-element MIMO antenna based on a metamaterial structure and a reactive load generated by an open-circuit stub. The radiator array, arranged in an axial symmetry configuration, provides high inter-element isolation despite a sub-millimeter separation. The design is optimized for 5G n77/n78 band applications and employs a metamaterial structure composed of embedded octagonal split-ring resonators (SRRs) integrated on a Duroid RT5880 0500 (${ϵ}_r=2.2,h=1.27 \mathrm{m}\mathrm{m}$) substrate. This configuration achieves high miniaturization, with individual radiators of $19\times 9.53 \mathrm{m}{\mathrm{m}}^2$. Furthermore, through a stub-loading technique, the array is enhanced in two significant aspects: (a) it exhibits an increased impedance bandwidth, rising from a 23% fractional bandwidth in the stub-less design to 39% in the final architecture; and (b) a shift of the lower cut-off frequency toward lower values is obtained, resulting in a reduction of the radiator’s electrical length, which translates into physical size diminution. The total array has a size of only $28.8\times 28.8 \mathrm{m}{\mathrm{m}}^2$ ($0.24{\lambda }_0\times 0.24{\lambda }_0$, considering the lower cut-off frequency). Despite the proximity between radiators and the absence of electromagnetic decoupling structures, the design ensures inter-element isolation exceeding 15 dB in the lower band and reaching values above 20 dB in the mid and upper bands. Diversity metric analysis confirms high performance, yielding an Envelope Correlation Coefficient (ECC) $\ll 0.005$, Diversity Gain (DG) close to the ideal value ($\ge 9.9$), Total Active Reflection Coefficient (TARC) below −10 dB (converging in random phase analysis), and a Channel Capacity Loss (CCL) of less than $0.4$ bits/s/Hz. Therefore, the proposed antenna stands as an ideal design for compact 5G communication devices. Full article

This study details the design and development of an ultra-sensitive microwave sensor for non-invasive blood glucose monitoring, achieved by analyzing variations in the response of a split-ring resonator (SRR) through advanced engineering methodologies. There were three design phases in the development process. In the first phase, a standard SRR design was used. It had a resonant frequency of 2.975 GHz in S₂₁ and a sensitivity of only 0.0032 dB/(mg/dL). In the second phase, an interdigital capacitor (IDC) was added to the SRR structure. This made it work better and made it more sensitive, with a sensitivity of 0.015 dB/(mg/dL) at 4.1 GHz. The third phase was to use a fourth-order Moore fractal geometry to improve the resonance properties of the design a lot. From the obtained S₁₁, the maximum sensitivity was 0.042 dB/(mg/dL), which was a huge improvement in sensing efficiency compared to earlier designs. Several resonant frequencies were recorded between 4.84 and 7.56 GHz. The addition of the fractal structure made the electromagnetic field stronger in the resonant space and made the waves interact more with small changes in the biological medium, all without changing the sensor’s size (80 mm × 40 mm). These results show that fractal architecture is a promising way to create non-invasive, accurate, and easily integrated sensors in biological systems that can continuously measure blood glucose levels. Full article

Hyperspectral unmixing (HU) is fundamental for conducting quantitative analyses in remote sensing, yet existing methods face a persistent tradeoff between model performance and physical interpretability. Although deep learning models achieve superior performance, even “gray-box” models that incorporate physical constraints still suffer from an intrinsically opaque decision-making process, which hinders their trustworthiness in critical applications. To address this challenge, this paper introduces a theory-guided unmixing framework aimed at enhancing mechanistic interpretability called the sparse and subspace-attentive transformer unmixing network (SSTU-Net). Unlike heuristic architectures, SSTU-Net is rigorously derived from the first principles of sparse rate reduction (SRR) theory. Its core modules—the multi-head subspace self-attention (MSSA) and the iterative shrinkage-thresholding algorithm (ISTA)—directly implement the essential mathematical steps of information compression and sparsification within the SRR theory, respectively. Extensive experiments on both synthetic and real hyperspectral datasets demonstrate that SSTU-Net achieves competitive performance compared to representative state-of-the-art methods—including advanced autoencoder-based networks (e.g., CyCU-Net and DAAN) and recent transformer-based unmixing architectures (e.g., DeepTrans and MAT-Net)—while strictly adhering to theoretically predicted evolutionary trajectories. More importantly, a series of specifically designed structural interpretability validation experiments mechanistically confirm the theoretically predicted behaviors, such as layer-wise information compression, feature sparsification, and subspace orthogonalization. These results reveal the internal working mechanisms of SSTU-Net, validating the feasibility and significant potential of our principled theory-guided framework for developing high-performance and trustworthy intelligent models in remote sensing. Full article

Winter wheat (Triticum aestivum L.) productivity in intensive rice–wheat systems of the Jianghan Plain is constrained by sub-optimal nitrogen (N) management and residue handling. Straw residue return (SRR) can increase soil organic carbon and improve soil structure but may also immobilize N and alter the temporal pattern of soil mineral N (SMN). Although straw return and N fertilization have been widely studied, the combined effects on SRR and N applications on wheat yield and soil N dynamics in this region remain insufficiently resolved. In this study, we evaluated three SRR levels (0, 50, and 100% of approximately 3.5 t rice straw ha⁻¹) combined with four N application treatments over three years of field trials in the Jianghan Plain of Yangtze River Basin. Treatments were arranged in a randomized complete block design. Our results show that wheat performance is closely associated with SMN (NO₃⁻-N, NH₄⁺-N, total N) at 0–20 soil layers from booting to maturity. Grain yield increased sharply with N application, with SRR further enhancing yield. The combination of a 100% SRR and 70/30 basal-to-overwinter N split with a total N rate of 180 kg ha⁻¹ (T11) achieved the highest three-year mean grain yield. This superior performance was driven by optimized yield components, including a maximum of 55 grains per spike and a 1000-grain weight of 42.4 g under T11. Soil total N, nitrate-N, ammonium-N, and SOC were all significantly influenced by both N application timing and SRR. Across the three-year experiment, we concluded that 50–100% SRR combined with 70–100% basal N application represents an optimal agronomic practice for rice–wheat rotations in the Jianghan Plain. Full article

Heterogeneous ion-exchange membranes (IEMs) are cost-effective but suffer from low electrochemical efficiency due to surface inhomogeneities. While surface coating with homogeneous ionomers is a known modification strategy, its direct impact on electro-hydrodynamic behavior and desalination performance has rarely been visually verified. In this study, we employed a microfluidic platform to visualize and quantify the performance enhancement of Nafion-coated heterogeneous cation exchange membranes (CEMs). Contrary to conventional theories linking electro-convection (EC) to surface hydrophobicity, our results show that the hydrophilic Nafion coating significantly amplifies EC vortices. Direct visualization revealed that the coating layer acts as an electrical nozzle, inducing intense electric field focusing that triggers macroscopic vortex growth. Furthermore, we visually confirmed that the coating layer physically seals catalytic sites, effectively suppressing parasitic water-splitting reactions. In continuous desalination experiments, this hydrodynamic synergy led to a 32% increase in current efficiency (CE: 1.23) and an 18% increase in salt removal ratio (SRR: 79.4%) compared to bare membranes in the over-limiting regime. These findings demonstrate that inducing controlled hydrodynamic instability via surface modification is a dominant factor for high-efficiency desalination. Full article

Addressing the pressing need to extend the communication range of RF RFID tag antennas, this paper introduces a novel UHF RFID tag antenna technology based on resonant superstrate regulation using a Split-Ring Resonator (SRR). First, a finite element model of the UHF RFID folded dipole antenna was constructed based on the tag chip’s port impedance. Subsequently, a Two-element SRR resonant superstrate was employed to enhance the dipole antenna’s gain through “resonance and near-field coupling” technology. A folded dipole antenna gain-enhancing SRR resonant superstrate unit was designed, and a multi-parameter joint optimization method was adopted to obtain the optimal SRR resonant superstrate configuration for regulating the dipole antenna. Near-field coupling technology was used to design SRR resonant superstrate elements that enhance the folded dipole antenna’s gain. A multi-parameter joint optimization method was employed to obtain the optimal structural parameter set for the SRR resonant superstrate-controlled dipole antenna. Finally, simulations and experimental measurements of the RFID antenna performance revealed that: within the 920–925 MHz band, the maximum measured forward reading distance enhancement reached 62.1%. The research findings significantly enhance the practical performance of UHF RFID tags in complex environments, enabling more stable and efficient long-range identification in applications such as logistics tracking, asset management, and smart warehousing. Full article

Large language models (LLMs) are pretrained on massive internet data and inevitably memorize sensitive or copyrighted content. This continually raises privacy, legal, and security concerns. Machine unlearning has been proposed as an approach to remove the influence of undesired data while maintaining model utility. However, in real-world scenarios, unlearning requests continuously emerge, and existing approaches often struggle to handle these sequential requests, leading to utility degradation. To address this challenge, we propose the harmonization of Supervised fine-tuning and Reinforcement learning with Reward-based Sampling (SRRS) framework, which dynamically harmonizes supervised fine-tuning (SFT) and reinforcement learning (RL) via reward signals: SFT ensures forgetting efficacy, while RL preserves utility under continual adaptation. By harmonizing these paradigms, SRRS achieves reliable forgetting and sustained utility across sequential unlearning tasks, demonstrating competitive performance compared to baseline methods on TOFU and R-TOFU datasets. Full article

This study tests TiO₂ and SiO₂ nanolubricants in PAG oil using a Mini Traction Machine and an Ultra Shear Viscometer. The loads were 20 N and 40 N. The entrainment speeds ranged from 2.5 to 500 mm/s. The slide-to-roll ratio (SRR) ranged from 25 to 150%. The nanoparticle concentrations were 0.01, 0.03, and 0.05%. The ball size was 19.05 mm, and the disc was 46 mm. All tests were run at 40 °C. Only the 0.05% concentration lowered traction compared with PAG at a fixed SRR. TiO₂ at 0.05% showed the largest drop, up to 4.89% at 20 N and 2.99% at 40 N. However, lower concentrations increased traction. All the nanolubricants reduced wear. TiO₂ at 0.03% gave the lowest wear, with a reduction of about 35 µm at 40 N. Nanolubricant samples stayed between 40.2 and 40.5 °C, while PAG reached about 41.0 °C. TiO₂ produced slightly lower temperatures than SiO₂. Ultra-shear tests from 40 to 100 °C showed shear thinning. In most conditions, TiO₂ at 0.05% kept the highest viscosity at 40 and 60 °C, up to 12% above PAG. SiO₂ showed smaller changes. TiO₂ delivered better friction, wear, temperature, and viscosity performance. Overall, both nanolubricants at 0.03% are suitable when wear reduction and thermal stability are prioritised over traction reduction, such as in refrigeration applications, while the 0.05% suits high-load or high-shear use. Full article