Search Results (65,579)

The global demand for Salvia miltiorrhiza Bunge in the botanical medicine market is steadily increasing. However, its production has long relied on asexual root propagation, making it highly susceptible to germplasm degradation. Transitioning to seed reproduction offers the advantage of genetic renewal, yet it is constrained by unstable seed vigor and slow seedling growth. In the present study, comprehensive physiological and microbiome analyses of S. miltiorrhiza seeds from 14 regions across 7 provinces in China were conducted to elucidate the association between the seed microbiome and vigor, and to identify plant growth-promoting (PGP) strains. The results demonstrated: (1) Seed physical traits and germination characteristics varied significantly across geographic origins. Seed vigor, exhibiting the highest coefficient of variation, served as a key parameter reflecting germination quality. (2) High-vigor seeds harbored distinct microbial communities characterized by higher diversity indices, greater network complexity, and the significant enrichment of potentially beneficial bacteria (e.g., Pseudomonas). (3) Through correlation-directed screening of isolated pure cultures, Pseudomonas mendocina P-6 and Enterobacter ludwigii BM-12 were identified as exhibiting robust, multi-trait PGP capacity. In planta validation showed that these two strains significantly promoted the growth of 1-month-old S. miltiorrhiza seedlings, increasing total fresh weight by 33.9–71.3%. This study reveals the microecological drivers of seed vigor and provides candidate strains for inoculant development, thereby supporting the sustainable, seed-based propagation of S. miltiorrhiza. Full article

Heavy metal contamination of foods remains a persistent global challenge for food safety and public health, driven by industrialization, mining activities, intensive agriculture, and ongoing environmental degradation. This scoping review synthesizes peer-reviewed literature on the occurrence of priority toxic metals—arsenic, cadmium, lead, mercury, and nickel—in food matrices, with emphasis on contamination pathways, analytical detection strategies, and documented human health effects. The reviewed studies reveal widespread accumulation of heavy metals in staple foods, including cereals, vegetables, seafood, and processed products, with concentrations frequently approaching or exceeding international regulatory limits, particularly in regions exposed to strong anthropogenic pressure. Conventional laboratory-based techniques, such as atomic absorption spectrometry and inductively coupled plasma methods, remain the reference standards for quantitative determination and regulatory compliance; however, their application to large-scale or continuous monitoring is often constrained by cost, infrastructure, and operational complexity. Consequently, increasing attention has been directed toward emerging detection approaches, including portable X-Ray fluorescence, Raman/SERS spectroscopy, electrochemical biosensors, electronic tongues, and in situ magnetic measurements, as complementary tools for rapid screening and field-based surveillance. Among these, environmental magnetism and in situ magnetic techniques stand out as non-destructive, low-cost proxies capable of identifying metal-associated particulate contamination linked to food production systems. Chronic dietary exposure to heavy metals is consistently associated with neurotoxicity, nephrotoxicity, carcinogenicity, and oxidative stress, underscoring the need for integrated, multi-tiered monitoring frameworks to support early detection, risk assessment, and prevention. Full article

Tunnels excavated in non-coal oil- and gas-bearing strata may experience the seepage and intermittent ingress of an oil–gas–water mixture during construction, creating aggressive corrosive conditions that can compromise the integrity of primary support and the safety margin of the final lining. However, the coupled degradation mechanism of primary support and its cascading effect on lining safety under such conditions remain poorly understood. Based on the Huaying Mountain Tunnel project, this study investigates the corrosion-driven damage evolution of primary support and its implications for the structural safety of the secondary lining under wet–dry cycling exposure. Accelerated wet–dry cycling tests were performed on concrete specimens using an on-site crude-oil–formation-water mixture collected during tunnelling, with exposure levels ranging from 0 to 120 cycles. Laboratory observations were then combined with inverse identification of degradation-dependent material parameters to establish a corrosion-informed mechanical description, which was implemented in numerical simulations for structural response assessment. Results show a staged evolution of mechanical properties, with an initial increase followed by progressive deterioration. After 120 cycles, compressive strength, tensile strength, and elastic modulus decreased by approximately 18.9%, 23.1%, and 17.4%, respectively. Degradation is more pronounced in the corroded zone, with tensile capacity and stiffness deteriorating earlier than compressive resistance. Numerical results indicate that corrosion leads to significant stress redistribution and damage development. The sidewall tensile stress reaches 2.80 MPa after 120 cycles, exceeding the post-corrosion capacity, while the safety factor drops below the code threshold at 90 cycles. The overall safety probability decreases from 1.0 to 0.4, accompanied by a degradation in safety grade from Level I to Level IV. These findings provide a quantitative basis for deterioration assessment, safety verification, and maintenance planning for tunnels subjected to oil–gas corrosive environments. Full article

As demand for energy-efficient buildings grows, the use of expanded polystyrene (EPS) insulation is expected to increase, intensifying the need for material-efficient strategies such as recycling and reuse. This study investigates the technical feasibility, chemical safety, and climate implications of reusing EPS insulation recovered from building and infrastructure applications. EPS boards with service lives exceeding 20 years were collected from demolition sites and characterised for density, compressive strength, thermal conductivity, and hazardous substance content. Measured material properties were compared with historical test reports from 1976 to 2009 to assess long-term performance. The thermal conductivity and compressive strength of the used EPS samples fell within or close to the 95% prediction intervals for the corresponding products at the time of production, indicating limited long-term degradation. No brominated flame retardants or other substances of concern were detected above the detection limits. Life cycle assessment (LCA) results showed that reuse provides greater greenhouse gas (GHG) emission reduction potential than improved recycling alone, primarily through avoided virgin EPS production and reduced processing needs. An important insight from this study is that key material properties of used EPS can be reliably estimated from simple measurements of density, dimensions, and weight, and that direct reuse is feasible for less demanding applications. Additionally, further work is needed to test additional samples from diverse demolition sites across various applications and climates to establish a consistent basis for reuse. Full article

Crude fidaxomicin contains difficult-to-separate impurities, and conventional dual-step purification usually requires intermediate concentration and transfer, which increases process complexity and may aggravate product loss or degradation. To address this challenge, this study exploits the complementary selectivity of methanol/water (80/20, v/v) and acetonitrile/water (70/30, v/v) binary mobile phases and proposes two purification processes based on step-gradient twin-column recycling chromatography, namely spatial integration and system integration. In the spatial integration strategy, dual-stage separations that are conventionally performed in separate chromatographic systems are sequentially integrated into a single twin-column recycling system in combination with on-line heart-cutting, thereby eliminating intermediate off-line processing steps. In contrast, the system integration strategy merges the two binary mobile phases in defined proportions to construct a single ternary mobile phase composed of methanol/acetonitrile/water (37.5/37.5/25, v/v/v), enabling one-step complete separation. The results demonstrate that the spatial integration strategy, employing binary mobile-phase switching, produces fidaxomicin with a purity of 99.9%, recoveries ranging from 75.27% to 78.77%, and productivities ranging from 307.22 to 328.82 g·L⁻¹·day⁻¹, regardless of the switching sequence. The system integration strategy, based on one-step elution with the ternary mobile phase, achieves the same product purity of 99.9% without mobile-phase switching, with a recovery of 70.41% and a productivity of 246.33 g·L⁻¹·day⁻¹. These results confirm the applicability and flexibility of both integrated strategies for fidaxomicin purification, while indicating that the spatial integration strategy provides better overall preparative performance and the system integration strategy offers a simpler one-step operation. Full article

This work presents the first in-depth investigation of Theodor Aman’s paintings that focuses on three of his heritage artworks: “Hora de peste Olt” (1866), “Teleleice în Harem” (1879), and “Regimul vechi” (1881), and that relies on both elemental and spectroscopic analytical techniques. Non-destructive Raman spectroscopy was employed on all three works of art to identify the pigments used by the Romanian master. In addition, micro-samples were available from “Hora de peste Olt” and “Teleleice în Harem”, which were further analyzed using XRD, micro-Raman, ATR-FTIR, and SEM-EDS techniques to provide complementary information on the pigments. SEM-EDS was also applied to study the structure of the preparation layers. The analyses revealed significant differences between the artworks in terms of both the pigments employed and the preparation of the canvas, suggesting that the earlier artwork belongs to one creative phase, while the newer pieces can be attributed to a later phase in the artist’s career. Full article

Sunset Yellow is a water-soluble synthetic dye resistant to degradation and stable under various conditions, posing an environmental challenge. In the present study pure chitosan hydrogel (PCH) films were synthesized, followed by the assessment of sorption capacity and recyclability compared to chitosan-based films doped with niobium oxide (CHN) or activated carbon (CHC). The aim was to promote the application of sorption methods for Sunset Yellow dye using these films as a treatment option for the pollutant, with the analysis of the effectiveness of the method and its behavior using adsorption kinetic models and thermodynamic analysis. Equilibrium was reached at 240 min for all films tested, with the adsorbed amounts ranging from 18.58 to 18.79 mg g⁻¹ at 30 °C, when the highest kinetic rate constants were observed. The pseudo-first-order kinetic model best described the experimental data, with the lowest Bayesian information criterion, Akaike information criterion, and mean absolute error values. Thermodynamic analysis indicated a spontaneous, exothermic process, with interactions ranging from electrostatic interactions in CHC and PCH to physisorption in CHN. Recycling tests showed 80% efficiency after the third cycle for all three films. These findings highlight the potential of chitosan-based films as an efficient option for removing Sunset Yellow dye from water, thus improving water quality and enhancing wastewater treatment. Full article

Circular RNAs (circRNAs) have been reported to be closely associated with tumor progression in multiple malignancies. However, the specific mechanism by which circPRKCA influences tumor progression has not been fully elucidated. CircPRKCA is highly expressed in non-small cell lung cancer (NSCLC) tissues and cells. Knockdown of circPRKCA inhibits the malignant behaviors of NSCLC cells. RNA sequencing results revealed that FRMD6 and SNAI2 mRNAs are positively correlated with circPRKCA. Subsequently, we proved that circPRKCA acts as a molecular sponge for miR-200b-3p. Additionally, miR-200b-3p binds to the 3′ untranslated regions (3′UTRs) of FRMD6 and SNAI2 mRNAs to promote their degradation. Overexpression of circPRKCA thereby suppresses this degradation process and coun-teracts the tumor-suppressive effects induced by miR-200b-3p. CircPRKCA functions as the sponge of miR-200b-3p, suppressing the SNAI2/FRMD6 mRNA degradation driven by miR-200b-3p and accelerating NSCLC progression. Full article

The inherent non-uniformity of Infrared Focal Plane Arrays (IRFPA) inevitably results in stripe noise, which severely degrades image quality and hinders downstream applications. Existing deep learning methods often struggle to strike a balance between effective denoising and the preservation of fine thermal textures. To address this issue, we propose a Bottleneck-free Channel Dual-path Aggregation Network (BCDA-Net) based on a “Perception-Reconstruction” design principle. In the perception stage, the network jointly employs the Dual-Path Channel Down-sampling (DCD) module and the Context-Guided Stripe Attention Block (CGSAB). The DCD module utilizes a channel split strategy to simultaneously extract semantic features and preserve high-frequency textures, while the CGSAB performs global context modeling on these features to precisely perceive and locate global stripe noise patterns. In the reconstruction stage, we integrate the Cascaded Dense Feature Aggregation (CDFA) module with a Bottleneck-Free Aggregation Strategy (BFAS). The CDFA utilizes the perceived information to densely aggregate features and progressively reconstruct clean image details, whereas the BFAS structurally blocks the propagation of low-resolution noise during decoding, effectively mitigating aliasing artifacts induced by deep feature upsampling. Together, these components form a complete closed loop from accurate noise perception to high-fidelity reconstruction. Extensive experiments on public and real-world datasets demonstrate that BCDA-Net maximally preserves image details while removing non-uniform stripe noise. Both objective metrics and subjective visual quality outperform existing state-of-the-art methods. Full article

Piezo-enhanced photocatalysis is progressively considered an eco-friendly technology for contaminant removal, harvesting not only solar energy but also mechanical vibrations found in nature. Multiferroic materials present a coupled effect of various properties and can potentially increase the applicability of this process. In this study, Cr- doped bismuth ferrite thin film was deposited on SrTiO₃ substrate by HiPIMS, and its photo-, piezo-, and piezo-photocatalytic efficiencies in Rhodamine B (RhB) degradation were analyzed. The highest removal percentage was found under the simultaneous exposure of visible light and mechanical vibrations, reaching 86.2% after 180 min. The calculated efficiencies for photo- and piezocatalysis were 12.2% and 83.7%, respectively. The rate constant (k) for piezo-photocatalysis was 16.1 times higher than that found during photocatalytic experiments. To assess the contribution of each reactive species to the decomposition process, different reagents were added to the Rhodamine B contaminated solution. The results revealed that when p-benzoquinone was used, the degradation efficiency declined significantly from 86.2% to 37.6%, suggesting that superoxide radicals (O₂^•−) play a key role in decomposing RhB molecules. The structural, chemical, optical, and ferroelectric changes caused by the catalytic processes were analyzed and linked to the proposed degradation mechanisms. The poor photocatalytic efficiency was linked to an improper band structure and an improper polarization orientation of the ferroelectric domains in the as-deposited film. The degradation mechanisms in piezo-photocatalysis were driven partly by the band bending caused by mechanical vibrations and partly by the reorientation of the induced polarization of the domains in the unstrained film. Full article

For power-grid applications such as transmission corridor inspection, substation asset inspection, and post-disaster emergency repair, reliable UAV self-localization under GNSS-degraded or GNSS-denied conditions is critical to ensuring operational safety and accurate defect geotagging. Due to substantial discrepancies in viewpoint, scale, and geometric structure between oblique UAV images and nadir satellite images, conventional RGB-based cross-view retrieval methods often suffer from unstable alignment and insufficient geometric modeling, particularly in scenarios with repetitive textures and partial overlap. To address these challenges, we propose a cross-view visual geo-localization model that integrates RGBD multimodal inputs with multi-scale attention enhancement. Specifically, MiDaS is used to estimate relative depth from UAV imagery, which is concatenated with RGB to form a four-channel input, while satellite images are padded with an additional zero channel to maintain dimensional consistency. A shared-weight ViTAdapter is adopted to learn joint semantic–geometric representations, and a lightweight Efficient Multi-scale Attention (EMA) module is adopted on spatial feature maps to strengthen multi-scale spatial consistency. In addition, an IoU-weighted InfoNCE loss is employed to accommodate partial matching during training, thereby improving the robustness of feature alignment. Experiments on the GTA-UAV dataset under the cross-area protocol show stable performance across both retrieval and localization metrics. Specifically, Recall@1, Recall@5, and Recall@10 reach 18.12%, 38.83%, and 49.47%, respectively; AP is 28.01 and SDM@3 is 0.53; meanwhile, the top-1 geodesic distance error Dis@1 is 1052.73 m. These results indicate that explicit geometric priors combined with multi-scale spatial enhancement can effectively improve cross-view feature alignment, leading to enhanced robustness and accuracy for localization in challenging power inspection scenarios. Full article

Laser fiber-based collimation straightness measurement can eliminate the intrinsic drift of the laser source while offering a simple configuration and simultaneous measurement of straightness in two orthogonal directions. As a high-precision optoelectronic sensing method, it has been widely used for the measurement of straightness, parallelism, perpendicularity, and multi-degree-of-freedom geometric errors. However, two common issues remain in practical applications. One is the nonlinear response of the four-quadrant detector, the core position-sensitive sensor, which is caused by detector nonuniformity and the quasi-Gaussian distribution of the spot. The other is the degradation of measurement performance by atmospheric inhomogeneity and air turbulence along the optical path, particularly in long-distance measurements. To address these issues, a two-dimensional planar calibration method is first proposed to replace conventional one-dimensional linear calibration. A polynomial surface-fitting model is introduced to correct the nonlinear response and inter-axis coupling errors of the four-quadrant photoelectric sensor. Simulation and experimental results show that the proposed method significantly reduces the standard deviation of calibration residuals and improves measurement accuracy. In addition, based on our previously developed common-path beam-drift digital compensation method, comparative experiments were carried out on double-pass common-path and single-pass optical configurations employing corner-cube retroreflectors, and theoretical simulations were performed to analyze the influence of air-turbulence disturbances on measurement stability. Both theoretical and experimental results show that the double-pass common-path configuration exhibits more pronounced temporal drift. Therefore, a real-time digital compensation method for beam drift in long-distance single-pass common-path measurements is proposed. Experimental results demonstrate that the proposed method effectively suppresses drift induced by environmental air turbulence and thereby improving the accuracy and stability of long-travel geometric-error and related straightness measurement for machine-tool linear axes. Full article

Light Detection and Ranging (LiDAR) three-dimensional (3D) object detection degrades under point sparsity, outliers, coordinate noise, and calibration drift, yet detector evaluation remains largely limited to clean benchmarks. This study focuses on sensing robustness rather than detector redesign. We introduce Bounded Graph Conditioning (BGC)—a deterministic pre-voxelization front-end that applies k-nearest-neighbor (kNN) neighborhood averaging with bounded residual correction upstream of an unchanged detector backbone. BGC is evaluated together with a reproducible sensor-degradation stress protocol and a risk-constrained operating-boundary analysis. Experiments on KITTI with PointPillars, SECOND, and Voxel R-CNN show that BGC most clearly improves retained detection quality and feasible operating coverage under strong noise and strong outlier stress; gains under other degradation types are smaller and backbone-dependent. In the primary score-level box-disjoint calibration/test evaluation on SECOND, maximum feasible coverage at a target risk bound of $0.2$ improves from $0.0754$ to $0.1374$ under strong noise ($\sigma =0.10$ m) and from $0.1323$ to $0.1591$ under strong outliers ($p=0.10$); a cross-backbone check on Voxel R-CNN confirms the same direction ($0.1860\to 0.2864$). Comparison with traditional filtering (SOR and ROR) reveals complementary strengths across fault types. A range-adaptive BGC variant that adjusts parameters per distance bin further improves performance under mixed unknown faults, spherical-coordinate noise, and on a dataset-matched nuScenes validation (adaptive BGC mAP/NDS: $0.2687/0.4493$ vs. baseline $0.2471/0.3846$ under strong noise). Severe translation drift collapses all configurations to full rejection, exposing an explicit sensing boundary beyond the reach of local conditioning. These results support BGC as a practical sensor-side robustness enhancement under the studied degradation protocol, with conditional rather than universal applicability across backbones and fault types. Full article

Effect-size estimation for Mood’s median test has received relatively little methodological attention despite the test’s widespread use in robust and nonparametric analysis. This study evaluates four candidate effect-size estimators: the median absolute deviation-based estimator (Delta–MAD), the probability of superiority (PS), Cramér’s V, and a newly proposed bootstrap-standardized median difference (Delta-Boot) across simulation settings involving normal data with equal variances, log-normal skewness, and heteroscedasticity with a twofold variance difference. Under equal variances, PS achieved the highest classification accuracy for moderate and large effects, with Delta–MAD and Delta–Boot close behind and Cramér’s V performing worst. Performance under log-normal skewness was nearly unchanged, demonstrating the robustness of median- and rank-based methods to heavy right-tailed distributions. Notably, Delta–Boot began to show improved performance for moderate effect sizes in the log-normal setting. Under heteroscedasticity, estimator behaviour diverged sharply: PS remained highly effective for distinguishing no and large effects but showed reduced accuracy for moderate effects due to its sensitivity to spread differences; Cramér’s V degraded substantially across all effect sizes; and the two median-standardized estimators—especially Delta–Boot—were more resilient, stabilizing more rapidly with increasing sample size and achieving the highest accuracy for moderate and large shifts at larger n. These patterns indicate that PS (or Delta–MAD) is most appropriate when variances are equal or nearly so, whereas Delta–Boot provides the most reliable performance in settings where variance imbalance is likely. Finally, a real-world application to fasting glucose data from the 2024 WHO STEPS survey in Trinidad and Tobago illustrates the practical utility of these approaches. Full article

High-frequency surface wave radar (HFSWR) often suffers from highly non-uniform, striped ionospheric clutter, which significantly degrades sea-surface target detection performance. To address this challenge, this paper proposes a reduced-dimension space-time adaptive processing (STAP) algorithm based on sparse representation. In this method, a dictionary is first constructed using the Doppler resolution and an appropriate angle interval as the frequency and angle grids, aiming to obtain fully orthogonal clutter atoms. Both the training sample and the cell under test are then sparsely represented over this dictionary to extract consistent clutter atoms. Due to discrepancies between the dictionary atoms and the actual clutter, low-power atoms are deemed unreliable and are discarded via a thresholding procedure. The remaining reliable atoms are used to construct a dimensionality-reduction matrix, thereby obtaining an accurate local clutter-plus-noise covariance matrix. Experimental results on measured data demonstrate that the proposed method effectively suppresses striped ionospheric clutter and enhances target detection performance. Full article