Search Results (3,901)

This paper presents a wideband multi-linear polarization reconfigurable antenna featuring five linear polarization states. We use the semi-ellipsoidal dipoles as the main radiators to broaden the operating bandwidth; the states of linear polarizations are switched by controlling the ON/OFF of PIN diodes between feeding pads and dipoles to excite a specific pair of dipoles. A 7 × 7 AMC array is added below the antenna to obtain a small height of 0.14 λ₀ (λ₀ is the free space wavelength at the operating frequency). Prototypes of the designed antenna are fabricated, and experimental results illustrate that the proposed antenna yields an impedance bandwidth of 50% (from 2.25 GHz to 3.75 GHz) for all polarization states, stable radiation patterns, and low cross-polarization within the operating band. In addition, the maximum gain reaches 8.1 dBi. The proposed five linear-polarized switching antenna with wide band and low-profile features can be applied in reconfigurable conformal array antennas, thus flexibly realizing linear polarization reconfiguration of conformal arrays in radar and military platforms. Full article

Electrocatalytic conversion of CO₂ to high-energy-density multicarbon products (C₂₊) offers a sustainable route for renewable energy storage and carbon neutrality. Precisely modulating Cu-based catalysts to enhance C₂₊ selectivity remains challenging due to uncontrollable reduction of Cu^δ+ active sites. Here, an efficient and stable Ge/Cu catalyst was developed for CO₂ reduction to ethanol via Ge modification. A Cu₂O/GeO₂/Cu core–shell composite was constructed by controlling Ge doping. The structure–performance relationship was elucidated through in situ characterization and theoretical calculations. Ge⁴⁺ stabilized Cu¹⁺ active sites and regulated the surface microenvironment via electronic effects. Ge modification simultaneously altered CO intermediate adsorption to promote asymmetric CO–CHO coupling, optimized water structure at the electrode/electrolyte interface, and inhibited over-reduction of Cu^δ+. This multi-scale synergistic effect enabled a significant ethanol Faradaic efficiency enhancement (11–20%) over a wide potential range, demonstrating promising applicability for renewable energy conversion. This study provides a strategy for designing efficient ECR catalysts and offers mechanistic insights into interfacial engineering for C–C coupling in sustainable fuel production. Full article

Effective management of municipal solid waste is crucial for achieving sustainable development and maintaining a healthy ecological environment. Municipal solid waste incineration (MSWI) processes are highly nonlinear and exhibit strong coupling characteristics, which makes long-term stable control challenging. Accurate prediction of the various toxic and harmful acidic gases that will be generated during this process is crucial for supporting optimization and control research. This study proposes a predictive model for acidic gases using Random Forest (RF) and Inverted Transformer (ITransformer). First, the RF algorithm is used to identify feature variables that strongly correlate with the target variables, thereby facilitating the shared feature selection process for multiple acidic gases. These selected features are then fed into a multi-output ITransformer model, which predicts the target variables and generates multiple evaluation metrics. Finally, the model’s hyperparameters are optimized based on these metrics and the threshold ranges of the acidic gases. The experimental results using real data from a specific incineration plant show that 13 features remain after the shared feature selection process. Compared to other models, the proposed approach uses the fewest shared features while reducing computational costs. Moreover, the R² values for NOx, SO₂, and HCl are 0.9791, 0.9793, and 0.9838, respectively. Full article

As semiconductor technologies advance, SRAM cells deployed in space systems face heightened sensitivity to radiation-induced soft errors. In conventional 6T SRAM, when high-energy particles strike sensitive nodes, single-event upsets (SEUs) may occur, flipping stored bits. Furthermore, with aggressive scaling, charge sharing among adjacent devices can trigger single-event multi-node upsets (SEMNU). To address these reliability concerns, this study presents a radiation-hardened SRAM design, SHWA18T, tailored for space applications. The proposed architecture is evaluated against IASE16T, PRO14T, PRO16T, QCCS, SIRI, and SEA14T. Simulation analysis demonstrates that SHWA18T achieves improved performance, particularly in terms of critical charge and write capability. The design was implemented in 90 nm CMOS technology at a 1 V supply. With enhanced robustness, the cell withstands both SEUs and SEMNUs, thereby guaranteeing stable data retention in space environments. Full article

The growing threat of organic pollution to surface waters necessitates the development of rapid and scalable monitoring tools that transcend the limitations of the standard 5-day biochemical oxygen demand (BOD₅) test. This study presents a novel approach by developing a highly stable and rapid BOD biosensor based on the microorganism Paracoccus yeei, immobilized within a sol–gel-derived xerogel matrix synthesized on a polyhydroxybutyrate (PHB) substrate. The PHB-supported xerogel significantly enhanced microbial viability and sensor stability. This biosensor demonstrated a correlation (R² = 0.93) with the standard BOD₅ method across 53 diverse water samples from the Tula region, Russia, providing precise results in just 5 min. The second pillar of our methodology involved analyzing multi-year Landsat satellite imagery via the Global Surface Water Explorer to map hydrological changes and identify zones of potential anthropogenic impact. The synergy of rapid ground-truth biosensor measurements and remote sensing analysis enabled a comprehensive spatial assessment of water quality, successfully identifying and ranking pollution sources, with wastewater discharges and agro-industrial facilities constituting the most significant factors. This work underscores the high potential of PHB–xerogel composites as efficient immobilization matrices and establishes a powerful, scalable framework for regional environmental monitoring by integrating advanced biosensor technology with satellite observation. Full article

Nitric acid-treated multi-walled carbon nanotubes (CNTs) have been extensively utilized for removing dissolved heavy metals from aqueous systems; however, their use as a capping material to immobilize heavy metals in sediments has rarely been investigated. Consequently, the impact of CNTs on millimeter-scale variations in pore-water heavy metal concentrations along sediment profiles remains poorly understood. In this study, CNTs were applied as a capping agent, and microelectrodes combined with high-resolution diffusive equilibrium in thin-film (HR-Peeper) samplers were employed to simultaneously obtain vertical profiles of pH, soluble copper (Cu) and lead (Pb), and dissolved oxygen (DO) in sediments in order to assess the effectiveness of CNTs in controlling the mobility of Cu and Pb. The results revealed that CNTs application markedly reduced the concentrations of soluble Cu and Pb, with maximum reduction rates of 58.69% and 64.97%, respectively. Compared with the control treatment, CNTs capping decreased the maximum release fluxes of soluble Cu and Pb by 3.78 and 1.91 µg·m⁻²·d⁻¹, respectively. Moreover, CNTs treatment enhanced the stable fractions of Cu and Pb within sediments, thereby improving the sediment’s capacity to retain these metals. Overall, this study demonstrates that CNTs can serve as an effective capping material to inhibit the leaching of Cu and Pb from sediments, offering a promising strategy for the in situ remediation of heavy metal-contaminated sediments. Full article

Currently, the PPP-B2b service faces challenges such as long convergence times and re-convergence issues after signal interruptions due to the lack of high-precision atmospheric enhancement. To address this, this study develops a multi-frequency uncombined Precise Point Positioning (PPP) model that accounts for Clock Constant Bias (CCB) based on PPP-B2b products, extracting atmospheric delays from reference stations and performing regional modeling. Considering the spatiotemporal characteristics of the ionosphere, a stochastic model for enhancement information that varies with time and satellite elevation is established. The performance of atmospheric-enhanced PPP-B2b is validated on the user end. Results demonstrate that zenith wet delay (ZWD) and ionospheric modeling generally achieve centimeter-level accuracy. However, during certain periods, ionospheric modeling errors are significant. By adjusting the stochastic model, approximately 98% of modeling errors can be enveloped. With atmospheric constraints, both convergence speed and positioning accuracy of PPP-B2b are significantly improved. Using thresholds of 30 cm horizontally and 40 cm vertically, the convergence times for horizontal and vertical components are approximately (16.7, 21.3) min for single BDS-3 and (3.8, 5.0) min for the dual-system combination, respectively. In contrast, with atmospheric constraints applied, convergence thresholds are met almost at the first epoch. Within one minute, single BDS-3 and the dual-system combination achieve accuracies better than (0.15, 0.3) m and (0.1, 0.2) m horizontally and vertically, respectively. Furthermore, even under high-elevation cutoff conditions, stable and rapid high-precision positioning remains achievable through atmospheric enhancement. Full article

Laboratories are complex and dynamic environments where diverse hazards—including toxic gas leakage, volatile solvent combustion, and unexpected fire ignition—pose serious threats to personnel safety and property. Traditional monitoring systems relying on single-type sensors or manual inspections often fail to provide timely warnings or comprehensive hazard perception, resulting in delayed response and potential escalation of incidents. To address these limitations, this study proposes a multi-node laboratory safety monitoring and early warning system integrating multi-source sensing, heterogeneous communication, and cloud–edge collaboration. The system employs a LoRa-based star-topology network to connect distributed sensing and actuation nodes, ensuring long-range, low-power communication. A Raspberry Pi-based module performs real-time facial recognition for intelligent access control, while an OpenMV module conducts lightweight flame detection using color-space blob analysis for early fire identification. These edge-intelligent components are optimized for embedded operation under resource constraints. The cloud–edge–app collaborative architecture supports real-time data visualization, remote control, and adaptive threshold configuration, forming a closed-loop safety management cycle from perception to decision and execution. Experimental results show that the facial recognition module achieves 95.2% accuracy at the optimal threshold, and the flame detection algorithm attains the best balance of precision, recall, and F1-score at an area threshold of around 60. The LoRa network maintains stable communication up to 0.8 km, and the system’s emergency actuation latency ranges from 0.3 s to 5.5 s, meeting real-time safety requirements. Overall, the proposed system significantly enhances early fire warning, multi-source environmental monitoring, and rapid hazard response, demonstrating strong applicability and scalability in modern laboratory safety management. Full article

Urban green walking systems have emerged as a pivotal strategy to alleviate urban challenges, enhance public health, and promote sustainable urban development, garnering increasing attention across multiple disciplines. This study presents a comprehensive bibliometric review of literature published in the Web of Science Core Collection between 1998 and 2023, employing the R-based tool Bibliometrix (version 4.5.0) to analyze research output, scholarly contributions, and thematic evolution. Findings reveal an overall growth in publications, with the field progressing through nascent (1998–2016), rapid growth (2017–2020), and stable maturity (2021–2023) phases. Urban Forestry & Urban Greening recorded the highest number of publications, while Landscape and Urban Planning exhibited strong academic influence. China and the United States are leading contributors, though international collaboration rates suggest opportunities for broader global engagement. Core research themes center on health promotion, spatial planning, and social equity, reflecting interdisciplinary integration across environmental science, urban planning, and public health. Despite the formation of an emerging theoretical framework, gaps persist regarding research trajectory clarity, interdisciplinary depth, thematic synthesis, and methodological diversity. Future research should expand multilingual and multi-source datasets, integrate field-based investigations with quantitative modeling, and advance understanding of the mechanisms underpinning urban green walking systems, thereby informing evidence-based urban planning and the development of livable cities. Full article

Land surface temperature (LST) is a critical parameter in agricultural drought monitoring, crop growth analysis, and climate change research. However, the challenge of acquiring high-resolution LST data with both fine spatial and temporal scales remains a significant obstacle in remote sensing applications. Despite the high temporal resolution afforded by daily MODIS LST observations, the coarse (1 km) spatial scale of these data restricts their applicability for studies demanding finer spatial resolution. To address this challenge, a novel deep learning-based approach is proposed for LST downscaling: the spatial resolution downscaling attention network (SRDA-Net). The model is designed to upscale the resolution of MODIS LST from 1000 m to 250 m, overcoming the shortcomings of traditional interpolation techniques in reconstructing spatial details, as well as reducing the reliance on linear models and multi-source high-temporal LST data typical of conventional fusion approaches. SRDA-Net captures the feature interaction between MODIS LST and auxiliary data through global resolution attention to address spatial heterogeneity. It further enhances the feature representation ability under heterogeneous surface conditions by optimizing multi-source features to handle heterogeneous data. Additionally, it strengthens the model of spatial dependency relationships through a multi-level feature refinement module. Moreover, this study constructs a composite loss function system that integrates physical mechanisms and data characteristics, ensuring the improvement of reconstruction details while maintaining numerical accuracy and model interpret-ability through a triple collaborative constraint mechanism. Experimental results show that the proposed model performs excellently in the simulation experiment (from 2000 m to 1000 m), with an MAE of 0.928 K and an R² of 0.95. In farmland areas, the model performs particularly well (MAE = 0.615 K, R² = 0.96, RMSE = 0.823 K), effectively supporting irrigation scheduling and crop health monitoring. It also maintains good vegetation heterogeneity expression ability in grassland areas, making it suitable for drought monitoring tasks. In the target downscaling experiment (from 1000 m to 500 m and 250 m), the model achieved an RMSE of 1.804 K, an MAE of 1.587 K, and an R² of 0.915, confirming its stable generalization ability across multiple scales. This study supports agricultural drought warning and precise irrigation and provides data support for interdisciplinary applications such as climate change research and ecological monitoring, while offering a new approach to generating high spatio-temporal resolution LST. Full article

An integrated QR-based identification and multi-participant quiz system is developed for classroom and competition scenarios. It reduces the check-in latency, removes fixed buzz-in timing, and lifts hardware-imposed limits on the participant count. On the software side, a MATLAB-R2022b-based module integrates the generation and recognition of linear barcodes and QR Codes, enabling fast, accurate acquisition of contestant information while reducing the latency and error risk of manual entry. On the hardware side, control circuits for compulsory and buzz-in modules are designed and simulated in Multisim-14.3. To accommodate diverse scenarios, the team-versus-team buzz-in mode is extended to support two- or three-member teams. Functional tests demonstrate the stable display of key states—including contestant identity, buzz-in priority group ID, and response duration. Compared with typical MCU-channel-based designs, the proposed system relaxes hardware-channel constraints, decoupling the participant count from fixed input channels. It also overcomes fixed-timing limitations by supporting scenario-dependent configuration. The Participant Information Registration subsystem achieved a mean accuracy of 86.7% and a mean per-sample computation time of 14 ms. The 0–99 s configurable timing aligns with question difficulty and instructional procedures. It enhances fairness, adaptability, and usability in formative assessments and competition-based learning. Full article

This study proposes a hybrid model that integrates Wavelet frequency decomposition, convolutional neural networks (CNNs), and Transformers to predict correlation structures among eight major cryptocurrencies. The Wavelet module decomposes asset time series into short-, medium-, and long-term components, enabling multi-scale trend analysis. CNNs capture localized correlation patterns across frequency bands, while the Transformer models long-term temporal dependencies and global relationships. Ablation studies with three baselines (Wavelet–CNN, Wavelet–Transformer, and CNN–Transformer) confirm that the proposed Wavelet–CNN–Transformer (WCT) consistently outperforms all alternatives across regression metrics (MSE, MAE, RMSE) and matrix similarity measures (Cosine Similarity and Frobenius Norm). The performance gap with the Wavelet–Transformer highlights CNN’s critical role in processing frequency-decomposed features, and WCT demonstrates stable accuracy even during periods of high market volatility. By improving correlation forecasts, the model enhances portfolio diversification and enables more effective risk-hedging strategies than volatility-based approaches. Moreover, it is capable of capturing the impact of major events such as policy announcements, geopolitical conflicts, and corporate earnings releases on market networks. This capability provides a powerful framework for monitoring structural transformations that are often overlooked by traditional price prediction models. Full article

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

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

Using a Donsker-type construction, we prove the existence of a new class of processes, which we call the self-stabilizing processes. These processes have a particular property: the “local intensities of jumps” vary with the values. Moreover, we also show that the self-stabilizing processes have many other good properties, such as stochastic Hölder continuity and strong localizability. Such a self-stabilizing process is simultaneously a Markov process, a martingale (when the local index of stability is greater than 1), a self-scaling process and a self-regulating process. Full article