Search Results (3,798)

Relict fern species, confined to microhabitats with stable historical conditions, are especially vulnerable to climate change. The Alboran Arc hosts a unique relict fern flora, including Culcita macrocarpa, Diplazium caudatum, and Pteris incompleta, and functions as a major Pleistocene refuge. This study assesses the population trends and climate sensitivity of these species in Los Alcornocales Natural Park using annual abundance time series for a decade, empirical survival projections, and principal component analysis to identify key climatic drivers. Results reveal distinct climate response clusters among populations, though intra-specific variation highlights the importance of local conditions. Climate change is already impacting population viability, especially for P. incompleta, which shows high sensitivity to rising maximum temperatures and prolonged heatwaves. Climate-driven models forecast more severe declines than empirical ones, particularly for C. macrocarpa and P. incompleta, with the latter showing a projected collapse by the mid-century. In contrast, D. caudatum exhibits moderate vulnerability. Crucially, the divergence between models underscores the impact of conservation efforts: without reinforcement and reintroduction actions, projected declines would likely be more severe. These results project a decline in the populations of the studied ferns, highlighting the urgent need to continue implementing both in situ and ex situ conservation measures. Full article

This paper presents a novel harmonic-selective clocking scheme that facilitates concurrent downconversion of spectrally distant radio frequency (RF) signals using a single low-frequency local oscillator (LO) in an N-path receiver architecture. The proposed scheme selectively generates LO harmonics aligned with multiple RF bands, enabling simultaneous downconversion without modification of the passive mixer topology. The receiver employs a 4-path passive mixer configuration to enhance harmonic selectivity and provide flexible frequency planning.The architecture is implemented on a printed circuit board (PCB) and validated through comprehensive simulation and experimental measurements under continuous wave and modulated signal conditions. Measured results demonstrate a sensitivity of $-55\mathrm{dBm}$ and a conversion gain varying from $-2.5\mathrm{dB}$ to $-9\mathrm{dB}$ depending on the selected harmonic pair. The receiver’s performance is further corroborated by concurrent (dual band) reception of real-world signals, including a GPS signal centered at 1575 MHz and an LTE signal at 1179 MHz, both downconverted using a single 393 MHz LO. Signal fidelity is assessed via Normalized Mean Square Error (NMSE) and Error Vector Magnitude (EVM), confirming the proposed architecture’s effectiveness in maintaining high-quality signal reception under concurrent multiband operation. The results highlight the potential of harmonic-selective clocking to simplify multiband receiver design for wireless communication and global navigation satellite system (GNSS) applications. Full article

Accurate and robust spatial perception is fundamental for dynamic 3D city modeling and urban environmental sensing. High-resolution remote sensing data, particularly LiDAR point clouds, are pivotal for these tasks due to their lighting invariance and precise geometric information. However, processing and aligning sequential LiDAR point clouds in complex urban environments presents significant challenges: traditional point-based or feature-matching methods are often sensitive to urban dynamics (e.g., moving vehicles and pedestrians) and struggle to establish reliable correspondences. While deep learning offers solutions, current approaches for point cloud alignment exhibit key limitations: self-supervised losses often neglect inherent alignment uncertainties, and supervised methods require costly pixel-level correspondence annotations. To address these challenges, we propose UnMinkLO-Net, an end-to-end self-supervised LiDAR odometry framework. Our method is as follows: (1) we efficiently encode 3D point cloud structures using voxel-based sparse convolution, and (2) we model inherent alignment uncertainty via covariance matrices, enabling novel self-supervised loss based on uncertainty modeling. Extensive evaluations on the KITTI urban dataset demonstrate UnMinkLO-Net’s effectiveness in achieving highly accurate point cloud registration. Our self-supervised approach, eliminating the need for manual annotations, provides a powerful foundation for processing and analyzing LiDAR data within multi-sensor urban sensing frameworks. Full article

Building height information plays an important role in many urban-related applications, such as urban planning, disaster management, and environmental studies. With the rapid development of real scene maps, street view images are becoming a new data source for building height estimation, considering their easy collection and low cost. However, existing studies on building height estimation primarily utilize remote sensing images, with little exploration of height estimation from street-view images. In this study, we proposed a deep learning-based method for estimating the height of a single building in Baidu panoramic street view imagery. Firstly, the Segment Anything Model was used to extract the region of interest image and location features of individual buildings from the panorama. Subsequently, a cross-view matching algorithm was proposed by combining Baidu panorama and building footprint data with height information to generate building height samples. Finally, a Two-Branch feature fusion model (TBFF) was constructed to combine building location features and visual features, enabling accurate height estimation for individual buildings. The experimental results showed that the TBFF model had the best performance, with an RMSE of 5.69 m, MAE of 3.97 m, and MAPE of 0.11. Compared with two state-of-the-art methods, the TBFF model exhibited robustness and higher accuracy. The Random Forest model had an RMSE of 11.83 m, MAE of 4.76 m, and MAPE of 0.32, and the Pano2Geo model had an RMSE of 10.51 m, MAE of 6.52 m, and MAPE of 0.22. The ablation analysis demonstrated that fusing building location and visual features can improve the accuracy of height estimation by 14.98% to 69.99%. Moreover, the accuracy of the proposed method meets the LOD1 level 3D modeling requirements defined by the OGC (height error ≤ 5 m), which can provide data support for urban research. Full article

Detecting pharmaceuticals in environmental matrices, particularly in wastewater, is crucial due to their potential environmental occurrence and unpredictable ecological and health-related consequences. These substances, often present in trace amounts, require highly sensitive and selective analytical methods for effective monitoring. A modified version of the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method was evaluated to evaluate 18 pharmaceuticals and 2 metabolites in wastewater samples using liquid chromatography with tandem mass spectrometry (LC-MS/MS). The method’s performance was assessed using linearity, recovery, precision, limits of quantification (LOQ) and detection (LOD), and the matrix effect (ME). The final method was based on acetonitrile, Na₂EDTA, citrate buffer, and graphene oxide (GO). Finally, the calibration curves prepared in acetonitrile and the matrix extract showed a correlation coefficient of 0.99. Most of the compounds had LOQ values lower than 0.5 μg⋅mL⁻¹. Recoveries were achieved in the 70–98% range, with RSD lower than 13%. GO allowed the elimination of the ME, which occurred in the range of −11% to 15%. The results indicate that a low-cost and straightforward method is suitable for routinely monitoring pharmaceuticals in wastewater, which is crucial for minimizing the impact of pollutants on aquatic ecosystems. Full article

Birch (Betula sp.) bark is a well-known natural source of betulin (Bet) and betulinic acid (BAc), both of which have several bioactive properties. The evaluation of the extraction performance, relative to these lupane-type triterpenoids, provided by different biosolvents requires the development of a high-resolution and high-sensitivity liquid chromatography-mass spectrometry (LC-MS) approach that is also compatible with challenging extractants such as natural deep eutectic solvents (NADESs). In this work, an LC-MS method was developed and analytically characterized prior to its application for the quantitation of Bet and BAc in birch bark extracts obtained using conventional solvents (methanol and acetone) and biosolvents (limonene and NADESs). High precision (RSD < 3.3%), sensitivity (LOD: 23 ng mL⁻¹ and 29 ng mL⁻¹ for Bet and BAc, respectively), and accuracy (95–102% recovery) were found for this optimized method, using an acidulated water–methanol mixture as the mobile phase and sodium acetate as an additive. Extraction experiments conducted at 55 °C revealed that the NADESs, particularly thymol:1-octanol (1:1 molar ratio), outperformed the other solvents and were highly effective for the recovery of both triterpenoids (17.50 mg g⁻¹ and 0.92 mg g⁻¹ of Bet and BAc, respectively). This method can also be applied to similar extracts obtained from other biomasses. Full article

As a crucial reactive oxygen species, hydrogen peroxide (H₂O₂) serves as both a physiological regulator and a pathological indicator in human systems. Its urinary concentration has emerged as a valuable biomarker for assessing metabolic disorders and renal function. While conventional colorimetric determination methods predominantly employ enzymatic or nanozyme catalysts, we present an innovative non-catalytic approach utilizing the redox-responsive properties of organic neutral radicals. Specifically, we designed and synthesized a novel radical TTM-DMODPA based on the tris (2,4,6-trichlorophenyl) methyl (TTM) scaffold, which exhibits remarkable optical tunability and oxidative sensitivity. This system enables dual-mode H₂O₂ quantification: (1) UV-vis spectrophotometry (linear range: 2.5–250 μmol/L, LOD: 1.275 μmol/L) and (2) smartphone-based visual analysis (linear range: 2.5–250 μmol/L, LOD: 3.633 μmol/L), the latter being particularly suitable for point-of-care testing. Validation studies using urine samples demonstrated excellent recovery rates (96–104%), confirming the method’s reliability for real-sample applications. Our work establishes a portable, instrument-free platform for urinary H₂O₂ determination, with significant potential in clinical diagnostics and environmental monitoring. Full article

Urban environments present substantial obstacles to GPS positioning accuracy, primarily due to multipath interference and limited satellite visibility. To address these challenges, we propose a novel weighting approach, referred to as the HK model, that enhances real-time GPS positioning performance by leveraging the variability of the signal-to-noise ratio (SNR), without requiring auxiliary sensors. Analysis of 24 h observational datasets collected across diverse environments, including open-sky (OS), city streets (CS), and urban canyons (UC), demonstrates that multipath-affected non-line-of-sight (NLOS) signals exhibit significantly greater SNR variability than direct line-of-sight (LOS) signals. The HK model classifies received signals based on the standard deviation of their SNR and assigns corresponding weights during position estimation. Comparative performance evaluation indicates that relative to existing weighting models, the HK model improves 3D positioning accuracy by up to 22.4 m in urban canyon scenarios, reducing horizontal RMSE from 13.0 m to 4.7 m and vertical RMSE from 19.5 m to 6.9 m. In city street environments, horizontal RMSE is reduced from 11.6 m to 3.8 m. Furthermore, a time-sequential analysis at the TEHE site confirms consistent improvements in vertical positioning accuracy across all 24-hourly datasets, and in terms of horizontal accuracy, in 22 out of 24 cases. These results demonstrate that the HK model substantially surpasses conventional SNR- or elevation-based weighting techniques, particularly under severe multipath conditions frequently encountered in dense urban settings. Full article

Background: Cell-free DNA (cfDNA) has become a cornerstone of liquid biopsy applications, offering promise for early disease detection and monitoring. However, its widespread clinical adoption is limited by variability in pre-analytical processing, especially during isolation. Current extraction methods face challenges in yield, purity, and reproducibility. Methods: We developed and optimized SafeCAP 2.0, a novel magnetic bead-based cfDNA extraction kit, focusing on efficient recovery, minimal genomic DNA contamination, and PCR compatibility. Optimization involved systematic evaluation of magnetic bead chemistry, buffer composition, and reagent volumes. Performance was benchmarked against a commercial reference kit (Apostle MiniMax) using spiked oligonucleotides and plasma from patients with stage IV NSCLC. Results: The optimized protocol demonstrated superior recovery with a limit of detection (LoD) as low as 0.3 pg/µL and a limit of quantification (LoQ) of 1 pg/μL with no detectable PCR inhibition. In comparative studies, SafeCAP 2.0 showed equivalent or improved performance over the commercial kit. Clinical validation using 47 patient plasma samples confirmed robust cfDNA recovery and fragment integrity. Conclusions: SafeCAP 2.0 offers a cost-effective, high-performance solution for cfDNA extraction in both research and clinical workflows. Its design and validation address key pre-analytical barriers, supporting integration into routine diagnostics and precision medicine platforms. Full article

Background/Objective: Smoking is known to impair fracture healing and worsen surgical outcomes, but its effect on psychological recovery in spine trauma patients remains unclear. The purpose of this study is to assess how smoking affects pain and anxiety in patients with spine fractures treated either conservatively or surgically. Methods: We conducted a retrospective analysis looking at spine fracture patients > 18 years old seen at a single institution between 11/2015 and 9/2019. Patient variables such as age, sex, race, ethnicity, mechanism of injury, fracture location, presence of spinal cord injury, surgical intervention, hospital and ICU LOS, disposition, and EQ-5D-3L at 3 and 12 months were collected and analyzed. Results: Non-operative management was selected for 403 patients, of which 304 never smoked and 99 were smokers. Surgical management was utilized for 126 patients, of which 90 never smoked and 36 were smokers. Studying non-smokers and current smokers, higher levels of extreme pain and anxiety at 3 and 12 months were reported in smokers managed conservatively. Smokers managed surgically reported higher levels of pain and anxiety than non-smokers at 3 months but not at 12 months. No significant differences were seen with regards to changes in pain or anxiety between the 3- and 12-month follow-up. Conclusions: Smoking is independently associated with higher levels of pain and anxiety in conservatively managed spine fracture patients. These findings suggest a need for early intervention and cessation efforts in the trauma setting. Further investigation is warranted to clarify whether underlying psychological or physiological phenomena are impacting patient outcomes. Full article

Selenium (Se) is an essential trace element involved in antioxidant redox regulation, thyroid hormone metabolism, and cancer prevention. Among its different forms, elemental selenium (Se⁰), particularly at the nanoscale, has gained growing attention in food, feed, and biomedical applications due to its lower toxicity and higher bioavailability compared to inorganic selenium species. However, the detection of Se⁰ in real samples remains challenging as current analytical methods are time-consuming, labour-intensive, and often unsuitable for rapid analysis. In this study, we developed a method for rapidly measuring Se⁰ using carbon nanodots (CNDs) produced from the Maillard reaction between glucose and glycine. The fabricated CNDs were water-dispersible and strongly fluorescent, with an average particle size of 3.90 ± 1.36 nm. Comprehensive characterisation by transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), fluorescence spectroscopy, and Raman spectroscopy confirmed their structural and optical properties. The CNDs were employed as fluorescent probes for the selective detection of Se⁰. The sensor showed a wide linear detection range (0–12.665 mmol L⁻¹), with a low detection limit (LOD) of 0.381 mmol L⁻¹ and a quantification limit (LOQ) of 0.465 mmol L⁻¹. Validation with spiked real samples—including ultra-pure water, tap water, and soft drinks—yielded high recoveries (98.6–108.1%) and low relative standard deviations (<3.4%). These results highlight the potential of CNDs as a simple, reliable, and environmentally friendly sensing platform for trace-level Se⁰ detection in complex food and beverage matrices. Full article

In this paper, we propose an optoelectronic feed-forward millimeter-wave generator based on the Mach–Zehnder interferometer (MZI) structure. The phase noise of the local oscillation (LO) input is extracted by loop design and used for phase noise suppression of the output, thereby optimizing the phase noise performance of the generator output. The scheme achieves separation of the phase noise by using an MZI structure and a mixing-frequency oscillator to realize the differential and integration process of the phase noise from the LO input source, respectively. Then, it is combined with a feed-forward operation to skillfully realize phase noise rejection of the resulting high-frequency output. The proposed scheme has been demonstrated to facilitate millimeter-wave generation at 40 GHz and 50 GHz. The measured phase noise is as low as −120 dBc/Hz at a 10 kHz offset, and the experimental setup achieves phase noise suppression of up to 36 dB at this frequency offset. Through systematic theoretical analysis and experimental verification, the excellent capabilities of the proposed scheme in high-frequency signal generation and phase noise suppression are fully demonstrated, which provides a new technological path for high-performance millimeter-wave generation, avoiding the deterioration of the phase noise introduced using high-frequency optoelectronic devices other than photodetectors (PDs) to process the signals. Full article

Reservoir-induced landslides threaten infrastructures and downstream communities, making continuous deformation monitoring vital. Time-series InSAR, notably the SBAS algorithm, provides high-precision surface-displacement mapping but suffers from voids due to layover/shadow effects and temporal decorrelation. Existing deep-learning approaches often operate on fixed-size patches or ignore irregular spatio-temporal dependencies, limiting their ability to recover missing pixels. With this objective, a hybrid spatio-temporal Graph Attention (ST-GAT) framework was developed and trained on SBAS-InSAR values using 24 influential features. A unified spatio-temporal graph is constructed, where each node represents a pixel at a specific acquisition time. The nodes are connected via inverse distance spatial edges to their K-nearest neighbors, and they have bidirectional temporal edges to themselves in adjacent acquisitions. The two spatial GAT layers capture terrain-driven influences, while the two temporal GAT layers model annual deformation trends. A compact MLP with per-map bias converts the fused node embeddings into normalized LOS estimates. The SBAS-InSAR results reveal LOS deformation, with 48% of missing pixels and 20% located near the Dasu dam. ST D-GAT reconstructed fully continuous spatio-temporal displacement fields, filling voids at critical sites. The model was validated and achieved an overall R² (0.907), ρ (0.947), per-map R² ≥ 0.807 with RMSE ≤ 9.99, and a ROC-AUC of 0.91. It also outperformed the six compared baseline models (IDW, KNN, RF, XGBoost, MLP, simple-NN) in both RMSE and R². By combining observed LOS values with 24 covariates in the proposed model, it delivers physically consistent gap-filling and enables continuous, high-resolution landslide monitoring in radar-challenged mountainous terrain. Full article

Ascorbic acid (AA) plays a multidimensional role in human physiological and pathological processes, and the detection of its urinary concentration facilitates the diagnosis of metabolic or kidney diseases. Visual detection exhibits minimal reliance on instrumentation and is suitable for on-site analysis in routine settings. Current visual colorimetric detection methods typically rely on enzymatic or nanozyme-based catalysis. Organic neutral radicals bearing unpaired electrons represent a class of materials exhibiting intrinsic responsiveness to redox stimuli. The tris (2,4,6-trichlorophenyl) methyl (TTM) radical has attracted widespread attention for its adjustable optical properties and sensitive response to external redox stimuli. We synthesized a novel radical TTM-DMODPA and applied it for non-catalytic colorimetric detection of AA. It not only enables quantitative AA measurement via UV-vis spectroscopy (linear range: 1.25–75 μmol/L, LOD: 0.288 μmol/L) but also facilitates instrument-free visual detection using smartphone cameras (linear range: 0–65 μmol/L, LOD: 1.46 μmol/L). This method demonstrated satisfactory performance in the measurement of AA in actual urine samples. Recovery rates ranged from 97.8% to 104.1%. Consequently, this work provides a portable and effective method for assessing AA levels in actual urine samples. Full article

In this study, a magnet-integrated fabric phase sorptive extraction (MI-FPSE) protocol was developed in combination with high pressure liquid chromatography—diode array detection (HPLC-DAD) for the simultaneous determination of five phenylurea pesticides (i.e., chlorbromuron, diuron, linuron, metoxuron, monuron) in environmental water samples. To produce the MI-FPSE device, two individual sol-gel coated carbowax 20 M (CW 20 M) cellulose membranes were fabricated and stitched to each other, while a magnetic rod was inserted between them to give the resulting device the ability to spin and serve as a stand-alone microextraction platform. The adsorption and desorption step of the MI-FPSE protocol was optimized to achieve high extraction efficiency and the MI-FPSE-HPLC-DAD method was validated in terms of linearity, sensitivity, selectivity, accuracy, and precision. The limits of detection (LODs) were found to be 0.3 μg L⁻¹. The relative recoveries were 85.2–110.0% for the intra-day and 87.7–103.2% for the inter-day study. The relative standard deviations were better than 13% in all cases. The green character and the practicality of the developed procedure were assessed using ComplexGAPI and Blue Analytical Grade Index metric tools, showing good method performance. Finally, the developed method was successfully used for the analysis of tap, river, and lake water samples. Full article