Search Results (7,627)

Electromagnetic implosions of hollow lithium cylinders can be utilized to compress magnetized plasma targets in the context of Magnetized Target Fusion (MTF). Two small-scale experiments were conducted at General Fusion as a stepping stone toward compressing magnetized plasmas on a larger scale. The first experiment is an electromagnetic implosion of a lithium ring, and the second is a compression of toroidal magnetic flux by imploding a hollow lithium cylinder onto an hourglass-shaped central structure. Here we present the methodology and results of modelling these experiments with OpenFOAM. Our in-house axisymmetric compressible MHD multi-phase solver was further extended to incorporate: (i) external RLC circuit model for electromagnetic compression coils and (ii) diffusion of the magnetic field into multiple solid materials. The implementation of the external RLC circuit model for electromagnetic coils was verified by comparison with results obtained with FEMM software and with the analytical solution. The solver was then applied to model both experiments and the main conclusions are as follows: (i) modelling solid lithium as a high-viscosity liquid is an adequate approach for the problems considered; (ii) the magnetic diffusivity of lithium is an important parameter for the accurate prediction of implosion trajectories (for the implosion of the lithium ring, higher values of magnetic diffusivity in the range $0.2\le {\eta }_{\mathrm{ring}}[{\mathrm{m}}^2/\mathrm{s}]\le 0.5$ resulted in a better fit to the experimental data with a relative deviation in the trajectory of $\lesssim 20\%$); (iii) simulation results agree well with experimental data, and in particular, the toroidal field amplification of 2.25 observed in the experiment is reproduced in simulations within a relative error margin of 20%. The solver is proven to be robust and has the potential to be employed in a variety of applications. Full article

With the rapid expansion of multi-platform remote sensing applications, cloud contamination significantly impedes cross-platform data utilization. Current cloud detection methods face critical technical challenges in cross-platform settings, including neglect of atmospheric radiative transfer mechanisms, inadequate multi-scale structural decoupling, high intra-class variability coupled with inter-class similarity, cloud boundary ambiguity, cross-modal feature inconsistency, and noise propagation in pseudo-labels within semi-supervised frameworks. To address these issues, we introduce a Physics-Guided Multi-Representation Network (PGMRN) that adopts a student–teacher architecture and fuses tri-modal representations—Pseudo-NDVI, structural, and textural features—via atmospheric priors and intrinsic image decomposition. Specifically, PGMRN first incorporates an InfoNCE contrastive loss to enhance intra-class compactness and inter-class discrimination while preserving physical consistency; subsequently, a boundary-aware regional adaptive weighted cross-entropy loss integrates PA-CAM confidence with distance transforms to refine edge accuracy; furthermore, an Uncertainty-Aware Quadruple Consistency Propagation (UAQCP) enforces alignment across structural, textural, RGB, and physical modalities; and finally, a dynamic confidence-screening mechanism that couples PA-CAM with information entropy and percentile-based thresholding robustly refines pseudo-labels. Extensive experiments on four benchmark datasets demonstrate that PGMRN achieves state-of-the-art performance, with Mean IoU values of 70.8% on TCDD, 79.0% on HRC_WHU, and 83.8% on SWIMSEG, outperforming existing methods. Full article

Optimizing the regional spatial pattern of land use and high-quality economic development requires an accurate understanding of the multifunctional evolution of land use. Based on remote sensing data and socio-economic data from 2000 to 2023, this study utilizes a land transfer matrix, an evaluation index system, an obstacle degree model, and regression analysis to deeply explore the spatial distribution characteristics and influencing factors of the production–living–ecological functions (PLEF) in the Yangtze River Economic Belt (YREB) over the 23-year period. The results show the following: ① the living function area of the YREB has increased by 22,400 km², while the production function area has decreased by 20,600 km², and the ecological function area has decreased by 1800 km². ② The production and living function spaces are characterized by high values in the eastern region and low values in the western region, and the ecological function space is characterized by high values in the western region and low values in the eastern region. ③ In the YREB, production function was the main obstacle to the PLEF between 2000 and 2023. ④ Population growth, economic development, agricultural technology, and agricultural efficiency are the main factors that influence the spatial and temporal evolution of the PLEF. This study suggests exploring an interactive compensation mechanism of the PLEF that combines the government and the market to form a differentiated development strategy. Full article

The Naozhou stock of large yellow croakers (Larimichthys crocea) exhibits unique phenotypic traits and high genetic diversity, making it a valuable resource for selective breeding and genetic conservation in aquaculture. Despite its importance, simple sequence repeat (SSR) markers have not been developed for this stock, which limits efforts in genetic evaluation, breeding optimization, and sustainable utilization of this commercially important species. In this study, 195,263 SSRs were identified from the genome of the Naozhou stock of large yellow croaker, covering a total length of 16,578,990 bp with a density of 288 bp/Mb. Dinucleotide repeats were the most common, with the AC motif being the most prevalent. The frequency of SSR markers ranged from 245.63 to 346.60 per Mb. A total of 30 primer pairs were synthesized, of which 28 pairs (93.3%) successfully amplified clear and reproducible bands in PCR assays. Among these, 28 SSR markers exhibited distinct and reproducible bands following gel electrophoresis. For eight SSR loci, the number of alleles (Na) ranged from 4 to 22 (mean = 11.375), while the effective number of alleles (Ne) ranged from 1.5401 to 10.4727 (mean = 5.6475). The assembled mitochondrial genome (mtDNA) was 16,467 bp in length and comprised 37 genes, including 13 protein-coding genes (PCGs), 22 tRNA genes, and 2 rRNA genes. The total sequence length of the PCGs was 11,431 bp, accounting for 69.4% of the mtDNA. A large portion of the PCGs (5) used incomplete stop codons (e.g., nad2, nad3, cox2), while others used TAA stop codons (e.g., nad6, nad5, TrnT). The mtDNA encoded a total of 3808 codons, with UAA showing the highest relative synonymous codon usage value. The SSR markers and mtDNA data generated in this study provide valuable tools for future genetic breeding and genomic research on the Naozhou stock of large yellow croakers. Full article

This study investigated the beneficial effect of sea cucumber polysaccharides (SCP) on gut microbiota composition, metabolic profiles, and liver gene expression in mice. Using an integrative approach combining microbiome, metabolome, and transcriptome analyses, we demonstrated that SCP supplementation led to a marked rise in norank_f_Muribaculaceae levels and reduced the Firmicutes-to-Bacteroidota ratio. Metabolomic analysis revealed key alterations in amino acid and lipid metabolism, with L-arginine and 7-dehydrocholesterol identified as potential mediators of SCP’s beneficial effects. Transcriptomics revealed genes expression across nine metabolic pathways, with genes involved in steroid biosynthesis being upregulated, while those related to protein digestion and absorption were downregulated. Spearman’s correlation analysis highlighted strong associations between gut microbiota, lipid metabolism-related genes, and corresponding metabolites. Integration omics data further suggested that SCP primarily supports arginine biosynthesis through gut–liver axis crosstalk. These results provide an important basis for developing SCP-based functional food with prebiotic properties to support metabolic and liver health. Full article

Urban flooding, intensified by climate change, poses significant threats to sustainable development, necessitating accurate precipitation projections for effective risk management. This study utilized Bayesian Model Averaging (BMA) to optimize CMIP6 multi-model ensemble precipitation projections for Shanghai, integrating Delta statistical downscaling with observational data to enhance spatial accuracy and reduce uncertainty. After downscaling, RMSE values of daily precipitation for individual models range from 10.158 to 12.512, with correlation coefficients between −0.009 and 0.0047. The BMA exhibits an RMSE of 8.105 and a correlation coefficient of 0.056, demonstrating better accuracy compared to individual models. The BMA-weighted projections, coupled with Soil Conservation Service Curve Number (SCS-CN) hydrological model and drainage capacity constraints, reveal spatiotemporal flood risk patterns under Shared Socioeconomic Pathway (SSP) 245 and SSP585 scenarios. Key findings indicate that while SSP245 shows stable extreme precipitation intensity, SSP585 drives substantial increases—particularly for 50-year and 100-year return periods, with late 21st century maximums rising by 24.9% and 32.6%, respectively, compared to mid-century. Spatially, flood risk concentrates in peripheral districts due to higher precipitation exposure and average drainage capacity, contrasting with the lower-risk central urban core. This study establishes a watershed-based risk assessment framework linking climate projections directly to urban drainage planning, proposing differentiated strategies: green infrastructure for runoff reduction in high-risk areas, drainage system integration for vulnerable suburbs, and ecological restoration for coastal zones. This integrated methodology provides a replicable approach for climate-resilient urban flood management, demonstrating that effective adaptation requires scenario-specific spatial targeting. Full article

The demand for clean energy to improve waste valorization and enhance resource utilization efficiency has been increasingly recognized in the last few years. In this context, the co-carbonization of different waste streams, aiming at solid fuel production, appears as a potential strategy to address the challenges of the energy transition and divert waste from landfills. In this work, refuse-derived fuel (RDF) samples were subjected to the co-carbonization process with low-quality animal fat waste in different proportions to assess the synergistic effect of the mixture on producing chars with enhanced fuel properties. Dry (DC) and hydrothermal carbonization (HTC) tests were conducted at 425 °C and 300 °C, respectively, with a residence time of 30 min. The RDF sample and produced chars with different animal fat incorporation were analyzed for their physical, chemical, and fuel properties. The results demonstrated that increasing the fat proportion in the samples leads to an increase in mass yield and apparent density of the produced chars. Furthermore, char samples with higher fat addition presented a proportional increase in high heating value (HHV). The highest values for the HHV corresponded to the char samples produced with 30% fat incorporation for both carbonization techniques (27.9 MJ/kg and 32.9 MJ/kg for dry and hydrothermal carbonization, respectively). Fat addition also reduced ash content, improved hydrophobicity in hydrochars, and lowered ignition temperature, although additional washing was necessary to reduce chlorine to acceptable levels. Furthermore, fat incorporation reduced concentrations of elements linked to slagging and fouling. Overall, the results demonstrate that incorporating 30% fat into RDF during DC or HTC is the most effective condition for producing chars with improved physical, chemical, and fuel properties, enhancing their potential as alternative solid fuels. Full article

Background: Young-onset colorectal cancer (YO-CRC) has emerged as a distinct clinical entity, often presenting at advanced stages. Despite the increasing incidence, the molecular and clinical underpinnings of YO-CRC remain underexplored. This study aims to characterize the clinical and molecular features of YO-CRC and to evaluate their impact on OS. Methods: We reviewed 110 patients diagnosed with YO-CRC at our institution who underwent next-generation sequencing. Demographic, clinical, and molecular data, including age, gender, race, tumor location, cancer stage, and mutation status (KRAS, NRAS, BRAF, POLE, ERBB-2/HER2, microsatellite status), were collected by reviewing electronic medical records. For OS analysis, we focused on patients diagnosed with de novo stage IV. Cox proportional hazards regression and Kaplan–Meier survival analysis were utilized to assess the association of these factors with OS, with statistical significance determined by a p-value threshold of <0.05. Results: Among 110 patients, n = 44 (40%) presented with local disease (stage 1–3), while n = 66 (60%) presented with de novo metastatic disease at the time of diagnosis. The median age at diagnosis was 44.5 years. The cohort consisted of 64% males and 36% females, with 84% of patients identified as White. Most tumors were left-sided (77%), including the distal colon/sigmoid (44%) and rectum (33%). KRAS and BRAF mutations were present in 36% and 5.5%, respectively. ERBB-2/HER2 amplification and microsatellite instability were observed in 4.5% and 6.4%, respectively. Tumor mutation burden (TMB) was <10 in 57% of patients, with 14% having TMB > 20. CNV analysis revealed that 14% of patients had copy gains, 12% had concurrent gains/losses, and 31% had copy losses. Among 66 patients with de novo metastatic disease, 44% had died by the time of analysis, with a median overall survival (OS) of 43.6 months (95% CI, 28.7—not reached). KRAS mutations were found to be significantly associated with worse survival outcomes. Cox regression analysis reveals the prognostic significance of KRAS status, with a hazard ratio (HR) of 3.52 (95% CI: 1.59–7.76, p = 0.002), indicating a significantly higher risk of death for KRAS-mutant YO-CRC patients. Conclusions: Patients with YO-CRC are more likely to present with de novo metastatic disease and left-sided tumors with distinct molecular characteristics. KRAS mutations are a key prognostic factor in YO-CRC, highlighting the need for therapeutic interventions to improve outcomes in this high-risk group. Full article

To solve the issue of environmental noise pollution and promote the resource recycling of waste pomelo peel, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), and scanning electron microscopy (SEM) are used to systematically characterize the microstructure and chemical composition of waste pomelo peel. It was found that waste pomelo peel has a porous network structure, which is conducive to the improvement of sound absorption performance. Waste pomelo peel/polycaprolactone (PCL) sound-absorbing composites are prepared by the hot-pressing molding process, and the single-factor analysis method is adopted to explore the effects of seven factors (waste pomelo peel mass fraction, composite density, composite thickness, hot-pressing time, hot-pressing pressure, hot-pressing temperature, and thickness of rear air layer) on the sound absorption performance. Through process optimization, under the optimal conditions, the average sound absorption coefficient (SAC) of the composites reaches 0.54, the noise reduction coefficient (NRC) reaches 0.57, and the maximum SAC reaches 0.99, with the sound absorption performance reaching Grade III. This study not only provides a new idea for the preparation of porous sound-absorbing composites but also opens a new path for the high-value utilization of waste pomelo peel resources. Full article

Object detection in single synthetic aperture radar (SAR) imagery has always been essential for SAR interpretation. Over the years, the saliency-based detection method is considered as a strategy that can overcome some inherent deficiencies in traditional SAR detection and arouses widespread attention. Considering that the conventional saliency method usually suffers performance loss in saliency map generation from lacking specific task priors or highlighted non-object regions, this paper is devoted to achieving excellent salient object detection in single SAR imagery via a two-channel framework integrating Bayesian inference and adaptive iteration. Our algorithm firstly utilizes the two processing channels to calculate the object/background prior without specific task information and extract four typical features that can enhance the object presence, respectively. Then, these two channels are fused to generate an initial saliency map by Bayesian inference, in which object areas are assigned with high saliency values. After that, we develop an adaptive iteration mechanism to further modify the saliency map, during which object saliency is progressively enhanced while the background is continuously suppressed. Thus, in the final saliency map, there will be a distinct difference between object components and the background, allowing object detection to be realized easily by global threshold segmentation. Extensive experiments on real SAR images from the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset and SAR Ship Detection Dataset (SSDD) qualitatively and quantitatively demonstrate that our saliency map is superior to those of four classical benchmark methods, and final detection results of the proposed algorithm present better performance than several comparative methods across both ground and maritime scenarios. Full article

Germplasm resources embody the genetic diversity of plants and form the foundation for breeding and the ongoing improvement of elite cultivars. The establishment of germplasm banks, along with their systematic evaluation, constitutes a critical step toward the conservation, sustainable use, and innovative utilization of these resources. Liriodendron, a rare and endangered tree genus with species distributed in both East Asia and North America, holds considerable ecological, ornamental, and economic significance. However, a standardized evaluation system for Liriodendron germplasm remains unavailable. In this study, 297 Liriodendron germplasm accessions were comprehensively evaluated using 34 phenotypic traits and whole-genome resequencing data. Substantial variation was observed in most phenotypic traits, with significant correlations identified among several characteristics. Cluster analysis based on phenotypic data grouped the accessions into three distinct clusters, each exhibiting unique distribution patterns. This classification was further supported by principal component analysis (PCA), which effectively captured the underlying variation among accessions. These phenotypic groupings demonstrated high consistency with subsequent population structure analysis based on SNP markers (K = 3). Notably, several key traits exhibited significant divergence (p < 0.05) among distinct genetic clusters, thereby validating the coordinated association between phenotypic variation and molecular markers. Genetic diversity and population structure were assessed using 4204 high-quality single-nucleotide polymorphism (SNP) markers obtained through stringent filtering. The results indicated that the Liriodendron sino-americanum displayed the highest genetic diversity, with an expected heterozygosity (He) of 0.18 and a polymorphic information content (PIC) of 0.14. In addition, both hierarchical clustering and PCA revealed clear population differentiation among the accessions. Association analysis between three phenotypic traits (DBH, annual height increment, and branch number) and SNPs identified 25 highly significant SNP loci (p < 0.01). Of particular interest, the branch number-associated locus SNP_17_69375264 (p = 1.03 × 10⁻⁵) demonstrated the strongest association, highlighting distinct genetic regulation patterns among different growth traits. A minimal set of 13 core SNP markers was subsequently used to construct unique DNA fingerprints for all 297 accessions. In conclusion, this study systematically characterized phenotypic traits in Liriodendron, identified high-quality and core SNPs, and established correlations between key phenotypic and molecular markers. These achievements enabled differential analysis and genetic diversity assessment of Liriodendron germplasm, along with the construction of DNA fingerprint profiles. The results provide crucial theoretical basis and technical support for germplasm conservation, accurate identification, and utilization of Liriodendron resources, while offering significant practical value for variety selection, reproduction and commercial applications of this species. Full article

During the last decades, development of novel low-cost sensors commercialized for indoor air quality measurements has gained interest. In this research, three AirVisual Pro air quality monitors were used to monitor PM_2.5 and carbon dioxide concentrations in which two were installed indoors and one outdoors at two residential apartments in Central Europe (Budapest, Hungary). In our research, we present a methodology to support the evaluation of indoor sensors by utilizing official outdoor monitoring data, leveraging the fact that indoor spaces are frequently ventilated and thus influenced by outdoor conditions. We compared six-year measurement data (January 2017–December 2022) with outdoor concentrations provided by the Hungarian Air Quality Monitoring Network (HAQM). However, the well-known low spatial representativeness and high spatio-temporal variability of PM_2.5 in city environments made this evaluation problematic, which needed to be addressed before comparison. Here we quantify the spatial heterogeneity of the HAQM PM_2.5 data for a maximum of eight stations. Then, based on the carbon dioxide readings of the AirVisual Pro units, data filtering was performed for the AirVisual 1 and AirVisual 2 sensors located in indoor environments to identify ventilated periods (nearly 10,000 ventilated events) for the AirVisual 1 and AirVisual 2 sensors, respectively, for the comparison of indoor and outdoor PM_2.5 concentrations. The AirVisual 3 sensor was placed in a garden storage, and the measurements taken there were considered outdoor values throughout. Finally, four heterogeneity criteria were set for the HAQM data to filter conditions that were assumed to be comparable with the indoor sensor data. The results indicate that the spatial heterogeneity was indeed detectable, and in approximately 50–60% of the cases, the readings could be considered as non-representative to single location comparison, but the results depend on the selected homogeneity criteria. The AirVisual and HAQM comparison indicated relatively low sensitivity to heterogeneity criteria, which is a promising result that can be exploited. AirVisual sensors generally overestimated PM_2.5, but this bias could be corrected with a simple linear adjustment. Slopes changed across sensors (0.83–0.85 for AirVisual 1, 0.48–0.53 for AirVisual 2, and 0.70–0.73 for AirVisual 3), indicating general overestimation and correlations from moderate to high (R² = 0.45–0.89) depending on the device. In contrast, when we compared the measurements only with data from the nearest reference station, we obtained a weaker match and slopes that did not match those calculated by taking into account homogeneity criteria. This research contributes to the proliferation of citizen science and supports the application of LCSs in indoor conditions. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

Amaranth is important for the agro-industrial complex. However, when extracting flour and oil from seeds, a lot of waste remains. Waste recycling by co-pyrolysis aims at obtaining new products with high added value. This study examined a combination of A. cruentus (AC) residues and low-density polyethylene (LDPE) waste. The addition of polymer was aimed at obtaining hydrocarbon-rich pyrolysis liquid and biochar. Pyrolysis was performed on an experimental setup, along with thermogravimetry–Fourier infrared spectroscopy–gas chromatography mass spectrometry (TG-FTIR-GC MS), to examine the thermochemical conversion. Experiments were carried out using a thermogravimetric analyzer at heating rates of 5, 10, and 20 °C/min. The average activation energy values for the pyrolysis of the AC/LDPE blend by the Ozawa–Flynn–Wall (OFW) and Kissinger–Akahira–Sunose (KAS) techniques were 301.39 kJ/mol and 287.69 kJ/mol, respectively. A visual examination of the correlations of the kinetic parameters of AC/LDPE was carried out using the Kriging method. The pyrolysis liquid from AC contains 38.14% hydrocarbons, with the main part being aliphatic hydrocarbons. During the pyrolysis of the AC/LDPE mixture, hydrocarbons were found in the resinous and waxy organic fractions of the pyrolysis liquid. The composition and properties of AC and AC/LDPE biochar are similar, and they can both be applied to agriculture. Full article

The traditional radio wave propagation models exhibit several limitations when they are employed to predict the path loss for vehicular ultra-shortwave wireless communication. To addresses these challenges, an optimized approach for Egli model based on the high-resolution remote sensing satellite image is proposed in this study. The optimization process includes three components. First, a method for calculating the actual equivalent antenna height is introduced, utilizing high-precision remote sensing satellite imagery to obtain communication path profiles. This method accounts for the antenna’s physical length, vehicular height, and local terrain characteristics, thereby providing an accurate representation of the antenna’s effective height within its operational environment. Second, an equivalent substitution method for ground loss is developed, utilizing surface information derived from high-precision remote sensing satellite images. This method integrates ground loss directly into the Egli model’s calculation process, eliminating the need for separate computations and simplifying the model. Third, leveraging the Egli model as a foundation, the least squares method (LSM) is employed to fit the relief height, ensuring the model meets the requirements for ultra-short wave communication distances under line-of-sight (LOS) conditions and enhances suitability for real-world vehicular communication systems. Finally, the validity and accuracy of the optimization model are verified by comparing the measured data with the theoretical calculated values. Compared with the Egli model, the Egli model with additional correction factors, and the measured data, the average error of the optimized model is reduced by 8.98%, 2.09%, and the average error is 0.45%. Full article