Search Results (496)

Waveform agility improves anti-reconnaissance and anti-jamming capability in diverse inverse synthetic aperture radar (ISAR) scenarios, but it also breaks the phase variation assumptions used for conventional coherent processing. For cooperative bistatic ISAR radars, the problem is further complicated by the bistatic geometry and phase evolution induced by synchronization. This paper develops a joint coherent integration method for a cooperative bistatic radar with simultaneous pulse width (PW) and pulse repetition interval (PRI) agility. Firstly, we establish and analyze a bistatic geometric model to reveal key integration problems under agile waveforms, and then derive the coherent processing interval (CPI) local polynomial description for bistatic delay, Doppler and acceleration. On this basis, the matched filter response of each agile pulse is analyzed under the fixed-bandwidth assumption with linear frequency modulation (LFM), showing that PW agility produces a compressed peak displacement and an additional deterministic phase term, whereas PRI agility converts slow-time coherent integration into a nonuniformly sampled spectral estimation problem. To solve this problem, a joint fast and slow-time compensation route is derived, together with a bistatic-specific parameter design method that connects coherent integration tolerances with the bistatic angle and the observable projection vector. Finally, we test the performance of the proposed joint integration method in multiple scenarios and verify its effectiveness and robustness, which enhances detection performance and resolution for target localization. Full article

Background/Objectives: Trauma and chronic pain frequently co-occur and mutually reinforce functional impairment, yet few counseling interventions integrate somatic and psychological approaches. This study had two primary objectives: (1) to evaluate the feasibility and acceptability of an interdisciplinary trauma–chronic pain group intervention, and (2) to explore preliminary clinical outcomes related to trauma symptoms, fear of movement, and pain through an interdisciplinary group intervention combining trauma-informed counseling with physical therapy grounded in Postural Restoration Institute (PRI) principles. Methods: A convergent mixed-methods design was used, wherein quantitative and qualitative data were collected concurrently, analyzed separately, and integrated during interpretation to provide a comprehensive understanding of intervention outcomes. Fifteen adults with chronic pain (≥3 months) and clinically significant trauma symptoms (PCL-5 ≥ 31) completed a six-week, 90 min group program co-facilitated by a counselor and a physical therapist. Quantitative measures included weekly pain (BPI), pre/post trauma symptoms (PCL-5), fear of movement (TSK), group climate (GCQ), and postural neutrality. Qualitative data included weekly journals, photographs, open-ended post-surveys, and focus groups, analyzed using inductive thematic analysis. Results: Participants attended most sessions (96.7% overall attendance) with no dropouts and high fidelity (mean 2.89/3). Pre/post analyses indicated statistically significant within-group reductions in trauma symptoms and fear of movement and improvements in pain and postural neutrality; however, findings should be interpreted as preliminary given the pilot design and small sample size. Qualitative themes highlighted the persistent burden of pain/trauma, increased mind–body awareness, emotional regulation, and validation through group support. Conclusions: An interdisciplinary, PRI-informed, trauma-informed group model was obtained. This model demonstrates preliminary feasibility and acceptability and suggests potential benefit; however, findings are derived from a small, non-controlled pilot study and require further validation. Future research should employ larger, controlled, and longitudinal designs. Full article

Salinity stress remains a major bottleneck for agriculture in arid regions. While Opuntia ficus-indica is known for its resilience, its young cladodes maintain a misleadingly healthy visual appearance and stable biomass even under heavy saline pressure, making traditional vegetation indices and standard statistics unreliable for early diagnosis. The objective of this study was to develop a non-destructive phenotyping framework for the early detection of latent salinity stress in young Opuntia cladodes. Controlled experiments were conducted using hyperspectral data cubes (400–1000 nm) acquired from plants exposed to six distinct salinity levels ranging from 2 to 21 dS m⁻¹. Our methodology integrates these high-dimensional spatial–spectral data with a tailor-made 3D Convolutional Neural Network (3D-CNN). Seven physiological vegetation indices—NDVI, PRI, WI, PSRI, MCARI, SIPI, and NDRE were extracted to track sub-clinical shifts and processed as a volumetric depth dimension within the network to preserve spatial–spectral integrity. The optimized 3D-CNN framework achieved a validation accuracy of 99.7% and a weighted F1-score of 99.1%, delivering 100% precision at critical stress thresholds (13 and 21 dS m⁻¹). Spatial confidence maps (Softmax > 0.95) further confirmed the high reliability of the diagnostic output. Requiring a training duration of approximately 8 s, this framework provides a robust basis for precision early-warning irrigation systems to sustain Opuntia cultivation in challenging environments. Full article

In maritime electronic support measures (ESMS), multipath-scattered pulses are often suppressed during pulse sorting, although their delay, amplitude, and angular differences may provide information for passive scatterer localization. This paper investigates a front-end path-classification task positioned after emitter-level clustering and before multipath-assisted passive localization. Pulses produced by the same non-cooperative emitter but received through different propagation paths are classified as direct-path or multipath-scattered pulses. The task is formulated as supervised binary classification over PDW sequences. Five representative solution families are evaluated under a common protocol: FCM, DBSCAN, temporal sequence analysis (TSA), Single-LSTM, and a residual two-layer unidirectional LSTM with residual fusion. The input features are RF, PA, PW, PRI, TOA, DOA, and ΔTOA; the recurrent models use class-weighted training to address the direct/scattered class imbalance. Across 36 coupled scenarios with pulse-loss rates from 0% to 50% and parameter-jitter levels from 0.0 to 1.0, the residual LSTM obtains the highest average macro-F1 score (0.8717), compared with Single-LSTM (0.7726), DBSCAN (0.7686), TSA (0.6511), and FCM (0.5917). Repeated training over four random seeds yields a validation macro-F1 of 0.9821 ± 0.0007 on the original validation set. The ablation results indicate that ΔTOA is the principal temporal cue in this setting, while LayerNorm, residual fusion, class weighting, and augmentation mainly contribute to optimization stability and perturbation robustness. Measured-data verification suggests that the learned temporal representation can provide usable inputs for subsequent scatterer localization. The current validation is limited to a one-emitter simulation and rule-assisted measured-data annotation; mixed-emitter validation and quantitatively calibrated localization evaluation remain subjects for future study. Full article

Rice is one of the world’s most important food crops, feeding over half of the global population and being crucial for food security. Accurate, timely mapping of rice fields is essential for precision agriculture, yet conventional methods relying on static samples fail to capture dynamic farmers’ planting decisions. To address this, we propose the Multi-Source Dynamic Sample Generation and Phenology-Guided Feature Selection Framework for In-Season Rice Identification (MSDF-RiceID) using multi-source remote sensing imagery. It incorporates two key innovations: (i) a rule-based sample updating mechanism based on historical rice maps and a dynamic threshold algorithm, and (ii) phenology-guided feature optimization through exponential weighting. Developed specifically to handle complex cropping patterns and high cloud cover in Hunan Province, MSDF-RiceID integrates these innovations within a grid-search-optimized Random Forest classifier to produce reliable monthly rice distribution maps. In-season samples corresponding to transplanting dates in April (DOY 100, 120), June (DOY 160), and July (DOY 184), differentiated as early-, middle-, and late-rice crops. The optimal feature set combined Sentinel-1 (PRI, VH, VH_VV), Sentinel-2 (NDYI, PSRI, NDBI, NDWI), and MODIS (NDVI, EVI, NDBI, LSWI) indices. Accuracy increased seasonally, with F1-score rising from 0.82 in May to 0.97 at harvest. Cross-region validation in Taishan (Guangdong) and Panjin (Liaoning) showed that the earliest identifiable stage (F1-score > 0.9) occurred earlier than in Hunan due to Hunan’s more complex triple-cropping phenology, highlighting the model’s strong transferability. Furthermore, MSDF-RiceID outperformed existing products (TWDTW-Rice and EARice10), increasing overall accuracy by 0.12–0.18, Kappa by 0.23–0.35, and F1-score by 0.09–0.15. These results demonstrate its effectiveness for in-season, large-scale, and dynamic rice mapping under persistent cloud cover, thereby providing direct support for precision agricultural management in heterogeneous cropping systems. Full article

Abiotic stresses limit crop productivity by disrupting water relations, carbon assimilation, nutrient acquisition, membrane stability, and redox homeostasis. Partial root-zone irrigation (PRI), commonly implemented as partial root-zone drying (PRD), is often viewed as a deficit-irrigation strategy to improve water-use efficiency; however, this view underestimates the biological consequences of spatial root-zone heterogeneity. This review evaluates PRI as a spatially structured, priming-like framework for crop adaptation to abiotic stress. Available evidence indicates that localized drying and wet-side water uptake can coordinate root sensing, hydraulic–chemical signaling, abscisic acid delivery, hormone crosstalk, xylem-mediated regulation, and stomatal control. Beyond gas exchange, PRI is associated with photosynthetic maintenance, osmotic adjustment, antioxidant and redox regulation, root architectural plasticity, nutrient acquisition, and metabolic reprogramming. Evidence is strongest for drought, whereas responses to low temperature, salinity, heat-associated evaporative demand, and combined stresses remain more context-dependent. Emerging work also links PRI to rhizosphere restructuring and microbiome shifts, but the causal mechanisms and field reproducibility remain unresolved. We argue that future progress requires matched PRI–deficit-irrigation comparisons, standardized switching thresholds, shared physiological and molecular readouts across crops, high-resolution root biology, and commercially realistic field validation. This framing distinguishes conserved physiological outcomes from mechanisms that may differ among crops, genotypes, and irrigation designs. Full article

Background/Objectives: MicroRNAs (miRNAs) are key regulators of skeletal muscle adaptation; however, the extent to which exercise modulates the miRNA biogenesis pathway remains poorly understood. To investigate the impact of acute and chronic high-intensity exercise on components of miRNA biogenesis, and whether such changes are reflected in miRNA expression across stages of their biogenesis, we performed secondary analyses of muscle biopsy samples from two previously published studies. Methods: Muscle biopsies were analyzed from the following protocols: nine men and eight women pre- and 3 h post- a bout of high-intensity interval cycling exercise (HIIE), and eleven men and eight women pre- and post- a 6-week period of high-intensity interval training (HIIT) or non-exercise control. mRNA expression of components of miRNA biogenesis including Drosha, Exportin-5, Dicer, and Ago2 were assessed following HIIE using RT-qPCR and their protein abundance was measured following HIIT using Western blotting. Primary (pri-miR-133a1, -133a2, -133b) and mature (miR-133a-3p, -133a-5p, -133b) miRNA expression were quantified following HIIT. Results: An acute bout of HIIE significantly decreased Drosha mRNA (p < 0.05) and resulted in a reduction in Dicer mRNA that approached significance (p < 0.10). Following 6 weeks of HIIT, no significant changes were detected in the protein abundance of Drosha, Exportin-5, Dicer, or Ago2. HIIT did not alter miR-133 expression at either the primary or mature transcript level across all isoforms. Conclusions: This study highlights the complexity of miRNA regulation in skeletal muscle and underscores the need for further research examining the temporal and mechanistic control of miRNA biogenesis in response to exercise. Full article

The antenna scan period is a core technical parameter of radar, and its robust, real-time, and high-precision measurement is of great engineering significance. Based on filed-measured full-pulse radar data, two measurement methods for the antenna scan period are proposed in this paper: the smoothing-fitting method and the smoothing-correlation method. Verified by filed-measured data, the measurement error fluctuation of the smoothing-fitting method is no more than 2 pulse repetition intervals (PRIs), and that of the smoothing-correlation method is no more than 1 PRI, both meeting the requirements of engineering applications. Adopting a point-by-point sliding processing flow without batch fitting, the smoothing-correlation method is more suitable for streaming and real-time data processing compared with the smoothing-fitting method and has a distinct engineering transformation value. Compared with batch processing methods such as the cepstrum method, which need to buffer data of at least one complete scanning period, the smoothing-correlation method avoids full-sequence FFT/IFFT operations. It can significantly reduce memory occupation and processing time and achieve superior real-time performance. Full article

Rising temperatures, driven by global climate change, are profoundly altering high-latitude ecosystems, influencing vegetation phenology and productivity. However, understanding the long-term, nuanced responses of these ecosystems remains a critical challenge. Soil warming experiments have served as useful tools for understanding these shifts. However, many of these studies have relied on a single measure, predominantly the Normalized Difference Vegetation (NDVI), measured at a single level of warming. This approach often fails to separate structural greening from underlying physiological responses. To address these gaps, this study provided a comprehensive snapshot assessment of growing season vegetation dynamics in a subarctic grassland ecosystem in Iceland that had been exposed to continuous geothermal soil warming for over 60 years. Using high-resolution multispectral drone imagery, twelve different vegetation indices (VIs) were derived to assess not only greenness but also physiological stress and photosynthetic efficiency across a range of mean annual soil temperatures (MATs). Using linear regression and redundancy analysis (RDA), the responses of these indices to warming and their relationships with other environmental drivers, such as standing biomass and plant nutrient concentrations (nitrogen and phosphorus), were analyzed. The results revealed significant positive linear relationships between most of the indices and MATs across the 5 to 11 °C range. This indicated that higher MATs led to increased biomass and structural growth, without revealing any significant thresholds or tipping points in vegetation response within the observed warming range. However, the Photochemical Reflectance (PRI) showed a significant negative relationship with warming, suggesting a decoupling between structural greening and photosynthetic light-use efficiency. Furthermore, RDA results indicated that, while most of the VIs were primarily driven by biomass, the decline in PRI was likely a compounding effect of physical canopy self-shading and plant phosphorus constraints. Ultimately, this study demonstrated that, while these subarctic grasslands exhibited local evidence of “Arctic greening” under further warming, multispectral drone remote sensing could detect underlying physiological adjustments and nutrient constraints that traditional greenness indices might overlook, providing a more nuanced understanding of ecosystem response. Full article

The presence of soil hydrocarbon parameters (SHPs), including total petroleum hydrocarbons (TPHs), total organic carbon (TOC; %), and soil toxicity (EC50; mg L⁻¹), can affect vegetation in several ways. This study assessed the impact of SHPs on vegetation in the Niger Delta using field-measured, leaf-scale hyperspectral data acquired across the region. Red-edge position (REP) and four hyperspectral vegetation indices (HVIs)—mND705, photochemical reflectance index (PRI), Normalised Difference Vegetation Vigour Index (NDVVI₈₄₄,₄₄₇; a vegetation vigour index), and modified DATT (MDATT; a chlorophyll-sensitive red-edge index)—were used to quantify chlorophyll content in the vegetation types of Awolowo grass, elephant grass, mango trees, oil palm trees, and mangrove vegetation and to explore their variation with SHPs. The results show that mangrove vegetation was the most impacted by TPHs (R = −0.683), while mango vegetation was the most impacted by TOC (R = −0.725), based on Pearson correlation coefficients derived from the mND705 index. Similarly, mango and mangrove vegetation showed the strongest responses to soil toxicity (EC50; mg L⁻¹), based on Spearman correlation coefficients (r_s = 0.657 and r_s = 0.870, respectively) using the MDATT index. These findings highlight species-specific physiological responses to soil hydrocarbon contamination and demonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress in complex coastal ecosystems such as the Niger Delta. Full article

The nonuniform raw data due to the varying pulse repetition interval (PRI) and the loss of echo pulses inevitably introduce azimuth grating lobes in the low-oversampled staggered synthetic aperture radar (LS-SAR) images, which result in ghost artifacts. In this paper, a deconvolution-based grating lobes reduction method for LS-SAR images is proposed to improve image quality. Firstly, the position-invariant property of azimuth grating lobes is theoretically analyzed and verified, and the LS-SAR image on the same range cell is mathematically modeled as the convolution between the scattering scene and the point spread function (PSF) of the LS-SAR imaging system, accompanied by the additive noise. Then, the PSF is numerically calculated according to the LS-SAR sampling strategy, the measured azimuthal antenna pattern, and the BP (Back Projection) imaging method. Finally, based on the Lucy–Richardson (LR) iterative deconvolution principle, the recovery of observed scenes and grating lobes reduction can be simultaneously achieved by deconvoluting the LS-SAR image with the acquired PSF. Both simulated experiments with point-array targets and real SAR images, as well as validation experiments with airborne measured LS-SAR data, demonstrated the effectiveness of the proposed method. Full article

To further investigate stormwater runoff patterns, pathogenic risks of pollutants on urban roads, and mitigation of urban non-point source pollution, road runoff monitoring and sampling were conducted in selected sections of central Yinchuan, a city in the arid region of northwestern China. Processed data—including rainfall, flow rate, and water quality parameters (conventional five indicators and heavy metals)—were obtained from ten rainfall events in 2024. Through analyses of water quality characteristics, influencing factors, runoff flushing patterns, and stormwater control measures, the current status of road runoff pollution was clarified. The Nemerow pollution index method was applied to evaluate pollutant levels and assess human health risks. Results indicate that pollution levels in Yinchuan are relatively mild, with most pollutant concentrations below the Class IV surface water quality standard. Basic rainfall parameters—peak rainfall intensity (PRI), average rainfall intensity (ARI), and previous sunny days (PSD)—together with urban functional zones, significantly influence pollutants in rainfall runoff, with the antecedent dry period showing the most pronounced effect. Analysis of the runoff scouring effect reveals that scouring of the conventional five water quality indicators (SS, COD, TN, NH₃-N, and TP) is substantially more evident than that of heavy metals. The runoff control depth for roads in central Yinchuan ranges from 0.9 mm to 40 mm, sufficient to manage runoff pollution from small to medium-sized rainfall events. The Nemerow pollution index remains below 8.36, with no severely polluted areas identified, indicating relatively low pollution in Yinchuan’s urban core. Quantitative human health risk assessment suggests that health risks associated with heavy metals on roads are low, with no significant exposure risk, implying that stormwater runoff in Yinchuan poses no substantial threat to human health. This study provides a valuable reference for non-point source pollution control via stormwater runoff management in arid-region cities. Full article

Background: In the paediatric intensive care unit (PICU), certain drugs should be avoided or administered with strict precautions and close monitoring. This is due to their potential for toxicity or adverse effects or a lack of safety data, especially for critically ill children with organ failure. Additionally, practitioners must assess the unique pharmacokinetic and pharmacodynamic properties of drugs when treating critically ill children. In this narrative review, we highlight the risks, advantages, and disadvantages of three exemplary cases of drugs for paediatric patients hospitalised in the PICU: chloramphenicol, acetylsalicylic acid, and propofol. Methods: Apart from key papers on these drugs, a retrospective analysis of the English literature on chloramphenicol, acetylsalicylic acid (ASA), and propofol was performed on PubMed for papers from January 2014 to December 2025. Results: Chloramphenicol should be avoided in neonates due to the risk of grey baby syndrome. Acetylsalicylic acid (ASA) is contraindicated in children ≤18 years with suspected viral illness because of the risk of Reye’s syndrome, but remains essential for Kawasaki disease and post-cardiac surgery antiplatelet therapy. Propofol should be avoided when used for a longer period at high doses. With proper dosing and monitoring, propofol-related infusion syndrome (PRIS) is preventable, but high-risk patients should receive alternative treatment. Conclusions: This narrative review highlights the significant risks associated with the use of chloramphenicol, ASA, and propofol in paediatric intensive care settings. Their potential for life-threatening and severe adverse reactions emphasises the need for cautious and informed use. Clinicians must carefully consider the risks and benefits of these drugs. To minimise adverse events, strict monitoring, dose adjustments, and the use of safer alternatives are essential. However, it appears that their use in well-defined circumstances in acute illness in children is still warranted. The findings of this narrative review underscore the need for further research to focus on identifying high-risk biomarkers, genetic predispositions, and safer alternatives to improve evidence-based guidelines and reduce morbidity and mortality in paediatric intensive care. Full article

Pavement sediments are a significant source of microplastics (MPs) in urban environments and a major contributor to stormwater runoff pollution. In this study, we investigated the abundance and characteristics of microplastics in Beijing’s major road networks and functional zones by collecting road sediment samples, and assessed the ecological risks posed by microplastics in road surface sediments in Beijing. The results showed that the average abundance of microplastics in Beijing pavement sediments was 960.9 items/kg, with the highest abundance observed in commercial areas (1505.7 items/kg). The main characteristics of microplastics were black in color (22.4%), fibrous in shape (55.29%), small to medium in size (10–500 µm, 46.95%), with polyethylene (PE) being the most abundant polymer type (30.69%). The Polymer Risk Index (PRI) method showed clearer differentiation of spatial risk patterns in the Beijing study area, with 5 low-risk sites, 8 medium-risk sites and 15 high-risk sites among the sampling sites. Full article

Background/Objectives: Epirubicin, Olaparib, and Ribociclib are widely used anticancer agents whose serum concentrations exhibit significant inter-individual variability, supporting the need for reliable and robust analytical methods suitable for pharmacokinetic evaluation and therapeutic exposure assessment. Variations in metabolism, drug–drug interactions, organ function, and treatment regimens may substantially influence systemic exposure, highlighting the importance of accurate quantification in clinical practice. This study describes the development and validation of a solid-phase extraction–liquid chromatography–mass spectrometry (SPE–LC–MS) method for the simultaneous quantification of these drugs in human serum. Methods: Sample preparation was performed using Oasis PRiME HLB^® cartridges to ensure efficient clean-up, optimal recovery, and reduced matrix effects. Chromatographic separation was achieved using gradient elution with 0.1% formic acid and acetonitrile on a reversed-phase column, followed by single-quadrupole mass spectrometric (QDa) detection in the selected ion recording mode. The total run time was 13 min, enabling high-throughput analysis. Results: The method demonstrated good linearity (r > 0.997) over the tested concentration ranges, along with adequate selectivity, precision, accuracy, recovery, and stability, fulfilling the ICH M10 guideline validation criteria. No significant carry-over or interference from endogenous compounds was observed. Conclusions: Application to patient samples confirmed reliable performance in real clinical matrices and consistent quantification across different concentration levels. The proposed approach provides a potentially more accessible alternative in laboratories already equipped with LC-MS systems compared to LC-MS/MS platforms and can be applied in pharmacokinetic studies, representing a proof-of-concept for exposure assessment in oncology. Full article