Search Results (20,661)

With the rapid proliferation of unmanned aerial vehicles (UAVs) and the associated rise in security concerns, there is a growing demand for robust detection and identification systems capable of operating reliably in complex electromagnetic environments. To address this challenge, this paper proposes a deep learning-based passive UAV detection and identification system leveraging radio frequency (RF) spectrograms. The system employs a high-resolution RF front-end comprising a multi-beam directional antenna and a wideband spectrum analyzer to scan the target airspace and capture UAV signals with enhanced spatial and spectral granularity. A YOLO-based detection module is then used to extract frequency hopping signal (FHS) regions from the spectrogram, which are subsequently classified by a convolutional neural network (CNN) to identify specific UAV models. Extensive measurements are carried out in both line-of-sight (LoS) and non-line-of-sight (NLoS) urban environments. The proposed system achieves over 96% accuracy in both detection and identification under LoS conditions. In NLoS conditions, it improves the identification accuracy by more than 15% compared with conventional full-spectrum CNN-based methods. These results validate the system’s robustness, real-time responsiveness, and strong practical applicability. Full article

To address the issue of reduced image contrast and visibility caused by turbid media, such as dense fog, this paper proposes a novel polarization-based single-image dehazing model. The model introduces a first-of-its-kind nonlinear joint polarization model for airlight and target light. This model is established within a Cartesian coordinate system, abstracted as an analytical geometric model. Leveraging the structural similarity principle in images, boundary constraints are applied to enhance the accuracy of target light estimation. Finally, image dehazing and enhancement are achieved using the atmospheric scattering model. Experimental results demonstrate that the proposed algorithm does not rely on dataset training, maintains the highest structural consistency, and achieves superior image restoration across various scenarios, producing results that most closely resemble natural observation. Full article

Wind is an important renewable energy source, and even minor variations in wind speed will significantly impact wind power generation. The objective of this study was to systematically assess the impacts of climate change on wind energy resources in the South China Sea (SCS) under future climate projections. To achieve this, we employed a multi-model ensemble approach based on Coupled Model Intercomparison Project Phase 6 (CMIP6) data under three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results demonstrated that, in comparison with scatterometer wind data, the CMIP6 historical results (1995–2014) showed good performance in capturing the spatiotemporal distribution of wind power density (WPD) in the SCS. There were regional discrepancies in the central SCS due to the complex monsoon-driven wind dynamics. Future projections revealed an overall increase in annual mean wind power density (WPD) across the entire SCS by the mid-21st century (2046–2065) and late 21st century (2080–2099). The seasonal analyses indicated significant WPD increases in summer, especially in the northern SCS and the region adjacent to the Kalimantan strait. The increase in summer (>40 × 10⁻⁴ m/s/year under SSP5-8.5) is about triple that in winter. In the late 21st century, an increase in WPD exceeding 10% can be generally anticipated under the SSP2-4.5 and SSP5-8.5 scenarios in all seasons. The extreme wind in the northern and central SCS will further increase by 5% under the three scenarios, which will add an extra extreme load to wind turbines and related marine facilities. These assessments are essential for wind farm planning and long-term energy production evaluations in the SCS. Based on the findings in this study, specific areas of concern can be targeted to conduct localized downscaling analyses and risk assessments. Full article

The four buckets phase demodulation method is a widely used sinusoidal modulation and demodulation technique in interferometry. Strict calibration is essential to minimize nonlinear errors in subsequent measurements. The core of the algorithm calibration lies in determining the initial phase value of the modulation signal that matches the modulation depth while overcoming the influence of system phase delay. Currently, there are few systematic calibration methods specifically designed for optical fiber interferometry. This paper proposes a self-calibration method based on triangular wave mixing for four buckets phase demodulation in fiber optic interferometric probes, which efficiently achieves self-calibration of the phase demodulation while the measured object remains stationary. Simulations and experimental validations were conducted, demonstrating that the optimal initial phase value of 0.62 rad during phase demodulation can be accurately identified under static conditions. The calibrated phase value was then applied to the displacement measurement, where the target displacement was effectively detected, resulting in a root mean square (RMS) error of 3.0337 nm and an average error of 2.4479 nm. Full article

The 2020 Europe Strategy was designed as a comprehensive framework to promote smart, sustainable and inclusive growth in the European Union (EU), particularly emphasising the ‘20/20/20’ targets related to climate protection and energy policy. This study provides an ex post evaluation of the extent to which the strategy’s objectives were achieved in the member states of the EU in the period 2010–2020. The analysis is based on Eurostat data and uses Hellwig’s multidimensional comparative analysis to construct a synthetic indicator of progress. The results show that EU countries have made significant advances in reducing greenhouse gas emissions and increasing the share of renewable energy in gross final energy consumption, with Sweden and Finland identified as leaders, while Malta and Hungary lagged behind. Primary energy consumption overall decreased, although only a minority of the member states reached the planned thresholds. Progress was less evident in research and development (R&D) expenditure, where the average value of the EU remained below the 3% GDP target, and strong disparities persisted between innovation leaders and weaker performers. Improvements in higher education attainment were observed, contributing to the long-term goal of a knowledge-based economy, although labour market difficulties, especially among young people, remained unresolved. The findings suggest that, although the Strategy contributed to tangible progress in several areas, uneven achievements among member states limited its overall effectiveness. The study is limited by the reliance on aggregate statistical data and a single methodological approach. Future research should extend the analysis to longer time horizons, include qualitative assessments of national policies, and address implications for the implementation of the European Green Deal and subsequent EU development strategies. Full article

Ship detection in synthetic aperture radar (SAR) remote sensing imagery is of great significance in military and civilian applications. However, two factors limit detection performance: (1) a high prevalence of small-scale ship targets with limited information content and (2) interference affecting ship detection from speckle noise and land–sea clutter. To address these challenges, we propose a novel end-to-end (E2E) transformer-based SAR ship detection framework, called Flow-Aligned Nested Transformer for SAR Small Ship Detection (FANT-Det). Specifically, in the feature extraction stage, we introduce a Nested Swin Transformer Block (NSTB). The NSTB employs a two-level local self-attention mechanism to enhance fine-grained target representation, thereby enriching features of small ships. For multi-scale feature fusion, we design a Flow-Aligned Depthwise Efficient Channel Attention Network (FADEN). FADEN achieves precise alignment of features across different resolutions via semantic flow and filters background clutter through lightweight channel attention, further enhancing small-target feature quality. Moreover, we propose an Adaptive Multi-scale Contrastive Denoising (AM-CDN) training paradigm. AM-CDN constructs adaptive perturbation thresholds jointly determined by a target scale factor and a clutter factor, generating contrastive denoising samples that better match the physical characteristics of SAR ships. Finally, extensive experiments on three widely used open SAR ship datasets demonstrate that the proposed method achieves superior detection performance, outperforming current state-of-the-art (SOTA) benchmarks. Full article

Background/Objectives: This study aimed to synthesize a novel, high-molecular-weight acyclic heterocyclic compound, compound 5, via a one-pot reaction between Trichloroisocyanuric acid (TCCA) and ethanolamine, and evaluate its anticancer, antioxidant, and antifungal activities. Methods: Its complex tetrameric structure, assembled through N-N linkages, was unequivocally confirmed by a full suite of spectroscopic techniques including IR, ¹H & ¹³C NMR, ²D-NMR, and high-resolution mass spectrometry (LC/Q-TOF/MS). The MTT assay was used to assess the anticancer activity of compound 5 against four different human cancer cell lines. Results: The findings indicate that human colon (HT29) and ovarian (OVCAR3) cancer cells were sensitive to the treatment, whereas brain (glioblastoma) (T98G) cancer cells were resistant. The most pronounced cytotoxic effect was observed in pancreatic (MiaPaCa2) cancer cells. Notably, compound 5 exhibited potent antifungal properties, achieving 100% inhibition of the pathogenic water mould Saprolegnia parasitica zoospores at 100 µM after 10 min. Molecular docking studies corroborated the biological data, revealing a high binding affinity for key cancer and fungal targets (Thymidylate Synthase and CYP51), providing a strong mechanistic basis for its observed activities. Conclusions: These findings establish compound 5 as a promising dual-action agent with significant potential as both a targeted anticancer lead and an eco-friendly antifungal for applications in aquaculture. Full article

To address the challenges of small target detection in UAV aerial images—such as difficulty in feature extraction, complex background interference, high miss rates, and stringent real-time requirements—this paper proposes an innovative model series named SRTSOD-YOLO, based on YOLO11. The backbone network incorporates a Multi-scale Feature Complementary Aggregation Module (MFCAM), designed to mitigate the loss of small target information as network depth increases. By integrating channel and spatial attention mechanisms with multi-scale convolutional feature extraction, MFCAM effectively locates small objects in the image. Furthermore, we introduce a novel neck architecture termed Gated Activation Convolutional Fusion Pyramid Network (GAC-FPN). This module enhances multi-scale feature fusion by emphasizing salient features while suppressing irrelevant background information. GAC-FPN employs three key strategies: adding a detection head with a small receptive field while removing the original largest one, leveraging large-scale features more effectively, and incorporating gated activation convolutional modules. To tackle the issue of positive-negative sample imbalance, we replace the conventional binary cross-entropy loss with an adaptive threshold focal loss in the detection head, accelerating network convergence. Additionally, to accommodate diverse application scenarios, we develop multiple versions of SRTSOD-YOLO by adjusting the width and depth of the network modules: a nano version (SRTSOD-YOLO-n), small (SRTSOD-YOLO-s), medium (SRTSOD-YOLO-m), and large (SRTSOD-YOLO-l). Experimental results on the VisDrone2019 and UAVDT datasets demonstrate that SRTSOD-YOLO-n improves the mAP@0.5 by 3.1% and 1.2% compared to YOLO11n, while SRTSOD-YOLO-l achieves gains of 7.9% and 3.3% over YOLO11l, respectively. Compared to other state-of-the-art methods, SRTSOD-YOLO-l attains the highest detection accuracy while maintaining real-time performance, underscoring the superiority of the proposed approach. Full article

During UAV aerial photography tasks, influenced by flight altitude and imaging mechanisms, the target in images often exhibits characteristics such as small size, complex backgrounds, and small inter-class differences. Under single optical modality, the weak and less discriminative feature representation of targets in drone-captured images makes them easily overwhelmed by complex background noise, leading to low detection accuracy, high missed-detection and false-detection rates in current object detection networks. Moreover, such methods struggle to meet all-weather and all-scenario application requirements. To address these issues, this paper proposes DVIF-Net, a visible-infrared fusion network for small-target detection in UAV aerial images, which leverages the complementary characteristics of visible and infrared images to enhance detection capability in complex environments. Firstly, a dual-branch feature extraction structure is designed based on YOLO architecture to separately extract features from visible and infrared images. Secondly, a P4-level cross-modal fusion strategy is proposed to effectively integrate features from both modalities while reducing computational complexity. Meanwhile, we design a novel dual context-guided fusion module to capture complementary features through channel attention of visible and infrared images during fusion and enhance interaction between modalities via element-wise multiplication. Finally, an edge information enhancement module based on cross stage partial structure is developed to improve sensitivity to small-target edges. Experimental results on two cross-modal datasets, DroneVehicle and VEDAI, demonstrate that DVIF-Net achieves detection accuracies of 85.8% and 62%, respectively. Compared with YOLOv10n, it has improved by 21.7% and 10.5% in visible modality, and by 7.4% and 30.5% in infrared modality, while maintaining a model parameter count of only 2.49 M. Furthermore, compared with 15 other algorithms, the proposed DVIF-Net attains SOTA performance. These results indicate that the method significantly enhances the detection capability for small targets in UAV aerial images, offering a high-precision and lightweight solution for real-time applications in complex aerial scenarios. Full article

Achieving environmentally sustainable growth is a core challenge for developing economies, yet the welfare consequences of green development policies for vulnerable populations remain understudied. This article investigates the distributional impacts of one of the world’s largest development interventions: China’s energy transition. By integrating provincial-level energy metrics with a decade-long household panel survey (CFPS), we employ a fixed-effects model to provide a holistic assessment of the policy’s effects on household well-being. The analysis reveals a stark trade-off: a 10% increase in clean energy adoption generates significant non-monetary well-being gains, equivalent to a 190,000 CNY annual income rise, primarily through improved environmental quality and cleaner cooking fuel access. However, these benefits are partially offset by rising energy costs. Our heterogeneity analysis reveals a clear regressive burden: the transition significantly increases energy expenditures for rural and low-income households, while having a negligible or even cost-reducing effect on their urban and high-income counterparts. Our findings demonstrate that while the energy transition promotes aggregate welfare, its benefits are unevenly distributed, potentially exacerbating energy poverty and inequality. This underscores a critical development challenge: green growth is not automatically inclusive. We argue that for the energy transition to be truly pro-poor, it must be accompanied by robust social protection mechanisms, such as targeted subsidies, to shield the most vulnerable from the adverse economic shocks of the policy. Full article

Conventional separation methods often prove ineffective for complex, refractory high-phosphorus iron ores. Recent advances propose a coal-based direct reduction dephosphorization-magnetic separation process, achieving significant dephosphorization efficiency. This review systematically analyzes phosphorus occurrence states in high-phosphorus oolitic iron ores across global deposits, particularly within iron minerals. We categorize contemporary research and elucidate dephosphorization mechanisms during coal-based direct reduction. Key factors influencing iron mineral phase transformation, iron enrichment, and phosphorus removal are comprehensively evaluated. Phosphorus primarily exists as apatite and collophane gangue m horization agents function by: (1) inhibiting phosphorus-bearing mineral reactions or binding phosphorus into soluble salts to prevent incorporation into metallic iron; (2) enhancing iron oxide reduction and coal gasification; (3) disrupting oolitic structures, promoting metallic iron particle growth, and improving the intergrowth relationship between metallic iron and gangue. Iron mineral phase transformations follow the sequence: Fe₂O₃ → Fe₃O₄ → FeO (FeAl₂O₄, Fe₂SiO₄) → Fe. Critical parameters for effective dephosphorization under non-reductive phosphorus conditions include reduction temperature, duration, reductant/dephosphorization agent types/dosages. Future research should focus on: (1) investigating phosphorus forms in iron minerals for targeted ore utilization; (2) reducing dephosphorization agent consumption and developing sustainable alternatives; (3) refining models for metallic iron growth and improving energy efficiency; (4) optimizing reduction atmosphere control; (5) implementing low-carbon emission strategies. Full article

Background: Appendiceal adenocarcinoma is a rare malignancy, and data guiding its systemic treatment in metastatic settings are limited. This study aimed to determine the clinical outcomes, treatment efficacy, biomarkers, and prognostic factors in patients with metastatic or unresectable appendiceal adenocarcinoma receiving palliative chemotherapy. Methods: We retrospectively reviewed patients with metastatic appendiceal adenocarcinoma who received first-line palliative systemic chemotherapy at the Peter MacCallum Cancer Centre between January 2015 and December 2024. Results: Of the 40 patients included, fluoropyrimidine-based doublet regimens were most commonly used (82.5%) in first-line setting, achieving an objective response rate of 39.4%. Median overall survival (OS) was 21.6 months, and median first-line progression-free survival (PFS) was 8.9 months. 22 patients (55.0%) received second-line treatment. Median OS and PFS were 21.6 and 8.9 months, respectively, among patients treated with oxaliplatin-based doublet regimens, and 66.4 and 10.8 months, respectively, among those treated with irinotecan-based doublet regimens. Molecular biomarker testing was performed in 35 patients (87.5%). KRAS and NRAS mutations were identified in 68.6% and 2.9% of tested patients, respectively. Factors associated with poorer OS included male sex, elevated carcinoembryonic antigen levels, and overweight status. Bevacizumab use was not clearly associated with survival. Conclusions: Palliative systemic chemotherapy, particularly fluoropyrimidine-based doublet regimens, appears to be a reasonable and effective treatment option for patients with advanced appendiceal adenocarcinoma. Although this study was underpowered for formal comparison, the numerically longer OS and PFS of irinotecan-based regimens are hypothesis-generating and support further prospective evaluation. Molecular profiling emphasizes the need for personalized targeted therapeutic strategies. The identified prognostic factors may help guide risk stratification and patient counseling for treatment planning. Full article

This study presents a loop-shaping methodology for the attitude control of a fixed-wing unmanned aerial vehicle (UAV). The proposed controller design focuses on achieving desired frequency–domain characteristics—such as specified phase and gain margins—to ensure stability and robustness. Unlike many existing approaches that rely on oversimplified plant models or involve mathematically intensive robust-control formulations, this work develops controllers directly from a high-fidelity six-degree-of-freedom UAV model that captures realistic aerodynamic and actuator dynamics. The loop-shaping procedure translates multi-objective requirements into a transparent, step-by-step workflow by progressively shaping the plant’s open-loop frequency response to match a target transfer function. This provides an intuitive, visual design process that reduces reliance on empirical PID tuning and makes the method accessible for both hobby-scale UAV applications and commercial platforms. The proposed loop-shaping procedure is demonstrated on the pitch inner rate loop of a fixed-wing UAV, with controllers discretized and validated in nonlinear simulations as well as real flight tests. Experimental results show that the method achieves the intended bandwidth and stability margins on the desired design target closely. Full article

High-resolution CO₂ fossil fuel emission data are critical for developing targeted mitigation policies. As a key approach for estimating spatial distributions of CO₂ emissions, top–down methods typically rely upon spatial proxies to disaggregate administrative-level emission to finer spatial scales. However, conventional linear regression models may fail to capture complex non-linear relationships between proxies and emissions. Furthermore, methods relying on nighttime light data are mostly inadequate in representing emissions for both industrial and rural zones. To address these limitations, this study developed a multiple proxy framework integrating nighttime light, points of interest (POIs), population, road networks, and impervious surface area data. Seven machine learning algorithms—Extra-Trees, Random Forest, XGBoost, CatBoost, Gradient Boosting Decision Trees, LightGBM, and Support Vector Regression—were comprehensively incorporated to estimate high-resolution CO₂ fossil fuel emissions. Comprehensive evaluation revealed that the multiple proxy Extra-Trees model significantly outperformed the single-proxy nighttime light linear regression model at the county scale, achieving R² = 0.96 (RMSE = 0.52 MtCO₂) in cross-validation and R² = 0.92 (RMSE = 0.54 MtCO₂) on the independent test set. Feature importance analysis identified brightness of nighttime light (40.70%) and heavy industrial density (21.11%) as the most critical spatial proxies. The proposed approach also showed strong spatial consistency with the Multi-resolution Emission Inventory for China, exhibiting correlation coefficients of 0.82–0.84. This study demonstrates that integrating local multiple proxy data with machine learning corrects spatial biases inherent in traditional top–down approaches, establishing a transferable framework for high-resolution emissions mapping. Full article

Detecting citrus Huanglongbing (HLB) in orchard environments is particularly challenging due to multi-scale targets and occlusions due to clustering, which manifest as complex and variable backgrounds, targets ranging from distant single leaves to nearby full canopies, and frequent instances where symptomatic leaves are hidden behind others, all significantly hindering accurate detection. To overcome these challenges, this study introduces a novel citrus object detection model, Multi-Scale Huanglongbing DETR (MSHLB-DETR), developed on the basis of an improved Real-Time DEtection TRansformer (RT-DETR). The model significantly enhances detection accuracy and efficiency for HLB under complex orchard conditions. To address the issue of small target feature loss in leaf detection, a new efficient transformer module called Smart Disease Recognition for Citrus Huanglongbing with Multi-scale (SDRM) is introduced. SDRM includes a space-to-depth (SPD) module and inverted residual mobile block (IRMB), which facilitate deep interaction between local and global features and significantly improve the computational efficiency of the transformer. Additionally, the transformer encoder incorporates a Context-Guided Block (CGBlock) for contextual feature learning. To evaluate the proposed model under complex background conditions, a dataset of 4367 images was collected from diverse orchard scenes, preprocessed, and divided into training, validation, and testing subsets. The experimental results demonstrate that the proposed MSHLB-DETR achieved the best detection performance on the test set, with an ${\mathrm{mAP}}_{50}$ of 96.0%, surpassing other state-of-the-art models of similar scale. Compared to the original RT-DETR, the proposed model increased ${\mathrm{mAP}}_{50}$ by 15.8%, reduced Params by 7.5%, and decreased GFLOPs by 5.2%. This study reveals the critical importance of developing efficient multi-scale detection techniques for the accurate identification of citrus Huanglongbing in complex real-time monitoring scenarios. The proposed algorithm is expected to provide valuable references and new insights for the precise and timely detection of citrus Huanglongbing. Full article