Search Results (211)

Millimeter-wave signals experience substantial path loss when penetrating common building materials, hindering seamless indoor coverage from outdoor networks. To address this limitation, we present the 28-GHz “Meta-Window”, a mass-producible, visible transparent device designed to enhance millimeter-wave signal focusing. Fabricated via metal sputtering and etching on a standard soda-lime glass substrate, the meta-window incorporates subwavelength metallic structures arranged in a rotating pattern based on the Pancharatnam–Berry phase principle, enabling 0–360$°$ phase control within the 25–32 GHz frequency band. A 210 mm × 210 mm prototype operating at 28 GHz was constructed using a 69 × 69 array of metasurface unit cells, leveraging planar electromagnetic lens principles. Experimental results demonstrate that the meta-window achieves greater than 20 dB signal focusing gain between 26 and 30 GHz, consistent with full-wave electromagnetic simulations, while maintaining up to 74.93% visible transmittance. This dual transparency—for both visible light and millimeter-wave frequencies—was further validated by a communication prototype system exhibiting a greater than 20 dB signal-to-noise ratio improvement and successful demodulation of a 64-QAM single-carrier signal (1 GHz bandwidth, 28 GHz) with an error vector magnitude of 4.11%. Moreover, cascading the meta-window with a reconfigurable reflecting metasurface antenna array facilitates large-angle beam steering; stable demodulation (error vector magnitude within 6.32%) was achieved within a ±40$°$ range using the same signal parameters. Compared to conventional transmissive metasurfaces, this approach leverages established glass manufacturing techniques and offers potential for direct building integration, providing a promising solution for improving millimeter-wave indoor penetration and coverage. Full article

Accurate glaucoma diagnosis relies on precise segmentation of the optic disc (OD) and optic cup (OC) in retinal images. However, despite the development of numerous automatic segmentation models, the lack of annotations in the target domain and domain shift among datasets continue to limit their segmentation performance. To address these issues, we propose a Causal Self-Supervised Network (CSSN) that leverages self-supervised learning to enhance model performance. First, we construct a Structural Causal Model (SCM) and employ backdoor adjustment to convert the conventional conditional distribution into an interventional distribution, effectively severing the influence of style information on feature extraction and pseudo-label generation. Subsequently, the low-frequency components of source and target domain images are exchanged via Fourier transform to simulate cross-domain style transfer. The original target images and their style-transferred counterparts are then processed by a dual-path segmentation network to extract their respective features, and a confidence-based pseudo-label fusion strategy is employed to generate more reliable pseudo-labels for self-supervised learning. In addition, we employ adversarial training and cross-domain contrastive learning to further reduce style discrepancies between domains. The former aligns feature distributions across domains using a feature discriminator, effectively mitigating the adverse effects of style inconsistency, while the latter minimizes the feature distance between original and style-transferred images, thereby ensuring structural consistency. Experimental results demonstrate that our method achieves more accurate OD and OC segmentation in the target domain during testing, thereby confirming its efficacy in cross-domain adaptation tasks. Full article

As an economically active region, the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) faces dual challenges of carbon and air pollution reduction. Existing studies predominantly focus on single pollutants or engineering pathways, lacking systematic analyses of multi-scale synergistic effects. This study investigates the spatiotemporal distributions, driving factors, and synergistic effects of CO₂ and volatile organic compounds (VOCs) at the multi-scale of urban agglomerations, cities, and industries, using global Moran’s index, standard deviational ellipse, logarithmic mean divisa index decomposition model, and Tapio decoupling model. The results show that the average annual growth rate of CO₂ (7.4%) was significantly higher than that of VOCs (4.5%) from 2000 to 2020, and the industrial sector contributed more than 70% of CO₂ and VOC emissions, with the center of gravity of emissions migrating to Dongguan. Industrial energy intensity improvement emerged as the primary mitigation driver, with Guangzhou and Shenzhen demonstrating the highest contribution rates. Additionally, CO₂ and VOC reduction show two-way positive synergy, and the path of “energy intensity enhancement–carbon and pollution reduction” in the industrial sector is effective. Notably, the number of strong decouplings of the economy from CO₂ (11 times) is much higher than the number of strong decouplings of VOCs (3 times), suggesting that the synergy between VOC management and economic transformation needs to be strengthened. This study provides scientific foundations for phased co-reduction targets and energy–industrial structure optimization, proposing regional joint prevention and control policy frameworks. Full article

Surface texturing is vital for enhancing light absorption and optimizing the optoelectronic properties of multicrystalline silicon (mc-Si) samples. Texturing significantly improves light absorption by minimizing reflectance and extending the effective path length of incident light. Furthermore, porous silicon treatment on textured mc-Si surfaces offers additional advantages, including enhanced carrier generation, reduced surface recombination, and improved light emission. In this study, a dual treatment combining porous silicon and texturing was employed as an effective approach to enhance the optical and optoelectronic properties of mc-Si. Both porous silicon and texturing were achieved through a chemical etching process. After these surface modifications, the morphology and structure of mc-Si were examined using Atomic Force Microscopy (AFM), Scanning Electron Microscopy (SEM), UV-Vis-IR spectroscopy, photoluminescence (PL), WCT-120 photo-conductance lifetime measurements, and Two-Internal Quantum Efficiency (IQE) analysis. The results reveal a substantial improvement in the material’s properties. The total reflectivity dropped from 35% to approximately 5%, while the effective minority carrier lifetime increased from 2 µs for bare mc-Si to 36 µs after treatment. Additionally, the two-dimensional IQE value rose from 35% for the untreated sample to 66% after treatment, representing an enhancement of around 31%. These findings highlight the potential of surface engineering techniques in optimizing mc-Si for photovoltaic applications. Full article

With the accelerated transition of the global energy structure towards decarbonization, the share of PV power generation in the power system continues to rise. IEA predicts PV will account for 80% of new global renewable installations during 2025–2030. However, latent faults emerging from the long-term operation of photovoltaic (PV) power plants significantly compromise their operational efficiency. The existing EL detection methods in PV plants face challenges including grain boundary interference, probe band artifacts, non-uniform luminescence, and complex backgrounds, which elevate the risk of missing small defects. In this paper, we propose a high-precision defect detection method based on BiFDRep-YOLOv8n for small target defects in photovoltaic (PV) power plants, aiming to improve the detection accuracy and real-time performance and to provide an efficient solution for the intelligent detection of PV power plants. Firstly, the visual transformer RepViT is constructed as the backbone network, based on the dual-path mechanism of Token Mixer and Channel Mixer, to achieve local feature extraction and global information modeling, and combined with the structural reparameterization technique, to enhance the sensitivity of detecting small defects. Secondly, for the multi-scale characteristics of defects, the neck network is optimized by introducing a bidirectional weighted feature pyramid network (BiFPN), which adopts an adaptive weight allocation strategy to enhance feature fusion and improve the characterization of defects at different scales. Finally, the detection head part uses DyHead-DCNv3, which combines the triple attention mechanism of scale, space, and task awareness, and introduces deformable convolution (DCNv3) to improve the modeling capability and detection accuracy of irregular defects. Full article

The construction industry is facing a dual challenge: an increasing demand for new buildings on the one hand and the urgent need to drastically reduce emissions and waste on the other. One promising field of research to face these challenges comprises additive manufacturing (AM) technologies. Through these advanced methods, digital workflows between design and fabrication can be implemented to optimise the form and structure, unlocking new architectural freedom while ensuring sustainability and efficiency. However, to drive this transformation in construction, the new technologies must be investigated in large-scale applications. One of these fast-emerging AM techniques is Shotcrete 3D Printing (SC3DP). The present research documents the 1:1 scale manufacturing process, from digital to real, of a building section utilising SC3DP. A workflow and production steps, spanning from design over manufacturing to assembly, are introduced. The architectural design, reinforced by computational methods, was iteratively refined to adapt to manufacturing constraints. The paper also emphasises the importance of a digital twin in ensuring seamless data integration and real-time adjustments during construction. By incorporating reinforcement techniques such as short rebar insertion and robotic fibre winding, this study demonstrates the structural capabilities achievable with SC3DP. In summary, the implementation of comprehensive digital workflows utilising computational design, automated data acquisition and data flow, as well as robotic fabrication is presented to demonstrate the potential of AM methods in construction. Furthermore, this paper provides a perspective on potential future research paths and opportunities inherent in leveraging the innovative SC3DP technique. Full article

The southern slope of the Tianshan Mountains features complex terrain and an arid climate, yet paradoxically experiences frequent extreme precipitation events (EPEs), which pose significant challenges for weather forecasting. This study investigates an EPE that occurred from 20 to 21 August 2019 using multi-source data to examine circulation patterns, mesoscale characteristics, moisture dynamics, and energy-instability mechanisms. The results reveal distinct spatiotemporal variability in precipitation, prompting a two-stage analytical framework: stage 1 (western plains), dominated by localized convective cells, and stage 2 (northeastern mountains), characterized by orographically enhanced precipitation clusters. The event was associated with a “two ridges and one trough” circulation pattern at 500 hPa and a dual-core structure of the South Asian high at 200 hPa. Dynamic forcing stemmed from cyclonic convergence, vertical wind shear, low-level convergence lines, water vapor (WV) transport, and jet-induced upper-level divergence. A stronger vorticity, divergence, and vertical velocity in stage 1 resulted in more intense precipitation. The thermodynamic analysis showed enhanced low-level cold advection in the plains before the event. Sounding data revealed increases in precipitable water and convective available potential energy (CAPE) in both stages. WV tracing showed vertical differences in moisture sources: at 3000 m, ~70% originated from Central Asia via the Caspian and Black Seas; at 5000 m, source and path differences emerged between stages. In stage 1, specific humidity along each vapor track was higher than in stage 2 during the EPE, with a 12 h pre-event enhancement. Both stages featured rapid convective cloud growth, with decreases in total black body temperature (TBB) associated with precipitation intensification. During stage 1, the EPE center aligned with a large TBB gradient at the edge of a cold cloud zone, where vigorous convection occurred. In contrast to typical northern events, which are linked to colder cloud tops and vigorous convection, the afternoon EPE in stage 2 formed near cloud edges with lesser negative TBB values. These findings advance the understanding of multi-scale extreme precipitation mechanisms in arid mountains, aiding improved forecasting in complex terrains. Full article

In this study, cyclic polarization (CP) measurements were conducted on the molybdenum-based titanium–zirconium–molybdenum (TZM) alloy in 3.5% NaCl solutions under varying pH conditions, and the results were compared with those of pure molybdenum. No passivity breakdown was observed during cyclic polarization in acidic and neutral chloride solutions. The surface film formed on the TZM, and pure Mo samples displayed a dual-layered structure, comprising an inner layer of p-type semiconductivity and an outer layer of n-type semiconductivity. The defect density of the n-type layer ranged from 7.5 × 10¹⁷ to 7.5 × 10¹⁹ cm⁻³, while the p-type layer had a carrier density ranging from 2 × 10¹⁸ to 9 × 10¹⁹ cm⁻³. The pure molybdenum samples demonstrated lower passive current densities, lower charge carrier densities, and higher impedance than the TZM alloy. The lower corrosion resistance of TZM alloy could be attributed to the higher dislocation density, which acted as short-circuit paths for Mo diffusion, and the presence of carbides that exhibited a microgalvanic effect. Overall, this study clarified that the localized corrosion reported in the literature was not due to the breakdown of the passive layer but may be linked to the heterogeneous microstructure. Full article

Cloud removal is a vital preprocessing step in optical remote sensing images (RSIs), directly enhancing image quality and providing a high-quality data foundation for downstream tasks, such as water body extraction and land cover classification. Existing methods attempt to combine spatial and frequency features for cloud removal, but they rely on shallow feature concatenation or simplistic addition operations, which fail to establish effective cross-domain synergistic mechanisms. These approaches lead to edge blurring and noticeable color distortions. To address this issue, we propose a spatial–frequency collaborative enhancement Transformer network named SFCRFormer, which significantly improves cloud removal performance. The core of SFCRFormer is the spatial–frequency combined Transformer (SFCT) block, which implements cross-domain feature reinforcement through a dual-branch spatial attention (DBSA) module and frequency self-attention (FreSA) module to effectively capture global context information. The DBSA module enhances the representation of spatial features by decoupling spatial-channel dependencies via parallelized feature refinement paths, surpassing the performance of traditional single-branch attention mechanisms in maintaining the overall structure of the image. FreSA leverages fast Fourier transform to convert features into the frequency domain, using frequency differences between object and cloud regions to achieve precise cloud detection and fine-grained removal. In order to further enhance the features extracted by DBSA and FreSA, we design the dual-domain feed-forward network (DDFFN), which effectively improves the detail fidelity of the restored image by multi-scale convolution for local refinement and frequency transformation for global structural optimization. A composite loss function, incorporating Charbonnier loss and Structural Similarity Index (SSIM) loss, is employed to optimize model training and balance pixel-level accuracy with structural fidelity. Experimental evaluations on the public datasets demonstrate that SFCRFormer outperforms state-of-the-art methods across various quantitative metrics, including PSNR and SSIM, while delivering superior visual results. Full article

The detrimental effects of low-frequency vibrations on the measurement accuracy of commercial high-precision instrumentation demand urgent resolution, particularly for instruments requiring <1 μm positioning stability. Conventional base-mounted active damping systems exhibit limitations in suppressing the structural resonance induced by passive isolators—especially when the environmental vibration intensity surpasses the standard thresholds. Therefore, in this study, we developed an innovative multi-mode control architecture to substantially enhance the vibration-damping capabilities of the DCSMS. The proposed methodology synergistically integrates foundation vibration isolators with embedded passive modules through a dual-stage spring-mass system optimization framework. Experimental validation combining ADAMS–MATLAB multi-physics co-simulation, complemented by a decoupling analytical control model based on the vibrational transmission characteristics of the source propagation path, substantiated the efficacy of the proposed control methodology. Full article

This study examines the complex effects of empathic concern on mental health and behavioral manifestations and the potential indirect paths through excessive adaptation. A cross-sectional design with 1355 participants was employed. Empathic concern, excessive adaptation, prosocial behaviors, reactive aggression, depression, and positive mental health were assessed using established scales. Structural equation modeling and Bayesian linear regression were applied to analyze the paths. For direct paths, empathic concern positively predicted prosocial behaviors and positive mental health, whereas it was negatively related to depression and reactive aggression. For indirect paths, excessive adaptation was found to mediate the relationship between empathic concern and the outcome variables with the exception of positive mental health. By elucidating the mediating role of excessive adaptation, the results herein not only deepen our understanding of the dual effect of empathic concern on mental health and behavioral manifestations but also offer important insights for the medical and educational fields. Full article

China’s coal industry is reckoned as one of the topmost contributors to global carbon emissions, and as a result, poses severe challenges both to human health and climate change mitigation efforts. Achieving carbon neutrality requires thorough analyses of mechanisms driving the coal sector’s transition. This study employs a structural model to investigate the transmission pathways through/by which the “dual carbon” goals influence the coal industry, using a policy text quantification approach to assess specific carbon reduction measures. Findings reveal that the impact of the “dual carbon” target on the coal industry operates through multiple pathways. Carbon reduction policies significantly enhance technical advancements, social and economic factors, energy-saving measures, and alternative energy development, all of which indirectly affect coal supply. Notably, the pathway from coal demand to coal supply shows a high path coefficient of 1.121, far surpassing the path coefficient from factor input to coal supply, measured at 0.169. This highlights coal demand as the pivotal intermediary variable in determining the “dual carbon” target’s impact on the coal industry. While current technologies and alternative energy sources have limited immediate effects on coal supply, they hold significant potential as transformative factors in the future. Full article

The demand for large-scale components continues to grow with the development of frontier technologies. Traditionally, these components are machined using machine tools, which are costly and have functional limitations. High-flexibility robots provide a cost-effective solution for machining large-scale components. This research proposes a dual-robot fabrication system for producing a 2.4 m × 4.58 m carbon fiber reinforced polymer (CFRP) antenna reflector. First, the kinematic model of the in-house developed robot was established to compute its theoretical workspace, which was subsequently used to partition the machining regions. Based on laser tracker measurements and theoretical calculations, a method and procedure for calibrating the Tool Center Point and Tool Control Frame of the robot were proposed. Subsequently, the dual-robot fabrication system was configured based on the determined machining regions for each robot. To further improve the figuring accuracy of the system, the support structure and figuring path were investigated and determined. Finally, processing experiments were conducted, and the material removal function for the flexible processing tool was computed to shape the reflector surface. The final results achieved the required surface figure accuracies for areas ≤ φ1750 mm, ≤φ2400 mm, and the whole surface were improved to 13.5 μm RMS, 23.4 μm RMS, and 45.8 μm RMS, respectively. This validates the processing capability and demonstrates the potential application of the dual-robot fabrication system in producing large-scale components with high accuracy. Full article

The growing sophistication of cyber-attacks demands encryption processes that go beyond the confines of conventional cryptographic methods. Traditional cryptographic systems based on numerical algorithms or standard graph theory are still open to structural and computational attacks, particularly in light of advances in computation power. Fuzzy logic’s in-built ability to manage uncertainty together with the representation ability of fuzzy hypergraphs for describing complex interrelations offers an exciting avenue in the direction of developing highly evolved and secure cryptosystems. This paper lays out a new framework for cryptography using fuzzy hypergraph networks in which a hidden value is converted into a complex structure of dual fuzzy hypergraphs that remains completely connected. This technique not only increases the complexity of the encryption process, but also significantly enhances security, thus making it highly resistant to modern-day cryptographic attacks and appropriate for high security application. This approach improves security through enhanced entropy and the introduction of intricate multi-path data exchange through simulated nodes, rendering it highly resistant to contemporary cryptographic attacks. It ensures effective key distribution, accelerated encryption–decryption processes, and enhanced fault tolerance through dynamic path switching and redundancy. The adaptability of the framework to high-security, large-scale applications further enhances its robustness and performance. Full article

Transition metal selenides are considered one of the most promising materials for sodium-ion battery anodes due to their excellent theoretical capacity. However, it remains challenging to suppress the volume variation and the resulted capacity decay during the charge–discharge process. Herein, hollow-structured CoNiSe₂ dual transition metal selenides wrapped in a carbon shell (HS-Co_xNi_ySe₂@C) were deliberately designed and prepared through sequential coating of polyacrylonitrile (PAN), ion exchange of ZIF-67 with Ni²⁺ metal ions, and carbonization/selenization. The hollow structure was evidenced by transmission electron microscopy, and the crystalline structure was confirmed by X-ray diffraction. The ample internal space of HS-Co_xNi_ySe₂@C effectively accommodated volume expansion during the charge and discharge processes, and the large surface area enabled sufficient contact between the electrode and electrolyte and shortened the diffusion path of sodium ions for a feasible electrochemical reaction. The surface area and ionic conductivity of HS-Co_xNi_ySe₂@C were strongly dependent on the ratio of Co to Ni. The synergistic effect between Co and Ni enhanced the conductivity and electron mobility of HS-Co_xNi_ySe₂@C, thereby improving charge transfer efficiency. By taking into account the structural advantages and rational metal selenide ratios, significant improvements can be achieved in the cycling performance, rate performance, and overall electrochemical stability of sodium-ion batteries. The optimized HS-Co_xNi_ySe₂@C demonstrated excellent performance, and the reversible capacity remained at 334 mAh g⁻¹ after 1000 cycles at a high current of 5.0 A g⁻¹. Full article