Search Results (2,098)

As a typical grain base with a dense population and high-level urbanization, the North China Plain (NCP) faces a serious threat to its sustainable development due to water shortage. Surface water area (SWA) is a key indicator for continuously measuring the trends of regional water resources and assessing their current status. Therefore, a deep understanding of its changing patterns and driving forces is essential for achieving the sustainable management of water resources. In this study, we examined the interannual variability and trends of SWA in the NCP from 1990 to 2023 using annual 30 m water body maps generated from all available Landsat imagery, a robust water mapping algorithm, and the cloud computing platform Google Earth Engine (GEE). The results showed that the SWA in the NCP has significantly increased over the past three decades. The continuous emergence of artificial reservoirs and urban lakes, along with the booming aquaculture industry, are the main factors driving the growth of SWA. Consequently, the expansion of artificial water bodies resulted in a significant increase in water evaporation (0.16 km³/yr). Moreover, the proportion of water evaporation to regional evapotranspiration (ET) gradually increased (0–0.7%/yr), indicating that the contribution of water evaporation from artificial water bodies to ET is becoming increasingly prominent. Therefore, it can be concluded that the ever-expanding artificial water bodies have become a new hidden danger affecting the water security of the NCP through evaporative loss and deserve close attention. This study not only provides us with a new perspective for deeply understanding the current status of water resources security in the NCP but also provides a typical case with great reference value for the analysis of water resources changes in other similar regions. Full article

Objective: This study aimed to develop a performance evaluation index system for a district-level public hospital in Chongqing, China, based on Diagnosis-Related Groups (DRGs), to provide a benchmark for performance assessment in similar hospitals. The system was constructed using a literature analysis, the Delphi method, and the Analytic Hierarchy Process (AHP) to identify and weight relevant indicators. Results: The evaluation system consists of three primary indicators and eighteen secondary indicators. Key secondary indicators include the Case Mix Index (CMI), cost consumption index, low-risk group mortality rate, the proportion of patients with three- or four-level surgeries at discharge, and the proportion of medical service revenue to medical income. In 2020, significant improvements were observed in several indicators, such as a decrease in the low-risk group mortality rate to 0% and increases in the proportion of patients with three- or four-level surgeries and CMI by nearly 10% and 13%, respectively. Conclusions: This study successfully developed a comprehensive and scientifically sound performance evaluation index system for a district-level public hospital in Chongqing. The system has proven effective in objectively assessing inpatient medical care performance and providing valuable guidance for improving healthcare services in similar settings. Full article

This study synthesizes and comparatively investigates two squaric acid-based phthalocyanine-like dyes, SNF and its long-chain alkylated derivative LNF, to systematically elucidate the influence of peripheral hydrophobic groups on their third-order nonlinear optical (NLO) properties. The NLO characteristics were comprehensively characterized using femtosecond Z-scan and I-scan techniques at both 800 nm and 900 nm. Both dyes exhibited strong saturable absorption (SA), confirming their potential as saturable absorbers. Critically, the comparative analysis revealed that SNF exhibits a significantly greater nonlinear absorption coefficient (β) compared to LNF under identical conditions. For instance, at 800 nm, the β of SNF was approximately 3–5 times larger than that of LNF. This result conclusively demonstrates that the introduction of long hydrophobic alkyl chains attenuates the NLO response. Furthermore, I-scan measurements revealed excellent SA performance, with high modulation depths (e.g., LNF: 43.0% at 900 nm) and low saturation intensities. This work not only clarifies the structure–property relationship in these D-A-D dyes but also presents a clear strategy for modulating the NLO properties of organic chromophores for applications in near-infrared pulsed lasers. Full article

The overall efficiency of combining denitrification and volatile organic compound (VOC) removal through selective catalytic reduction (SCR) technology is currently mainly limited by the VOC removal aspect. However, existing studies have not studied the microscopic mechanism of the interaction between VOCs and catalysts, failing to provide a theoretical basis for catalysts. Therefore, this work explored the interaction mechanisms between SCR catalysts doped with different additives and typical VOCs (acetone and toluene) in flue gas based on density functional theory (DFT) calculations. The results showed that the VNi-TiO₂ surface exhibited a high adsorption energy of −0.80 eV for acetone and a high adsorption energy of −1.02 eV for toluene on the VMn-TiO₂ surface. Electronic structure analysis revealed the VMn-TiO₂ and VNi-TiO₂ surfaces exhibited more intense orbital hybridization with acetone and toluene, promoting charge transfer between the two and resulting in stronger interactions. The analysis of temperature on adsorption free energy showed that VMn-TiO₂ and VNi-TiO₂ still maintained high activity at high temperatures. This work contributes to clarifying the interaction mechanism between SCR and VOCs and enhancing the VOC removal efficiency. Full article

Neocosmospora (Nectriaceae) is a globally distributed fungal genus, traditionally recognized as a group of plant pathogens, with most members known to cause severe plant diseases. However, recent studies have demonstrated that many of these fungi can also colonize plants endophytically, with certain strains capable of promoting plant growth and stimulating the production of secondary metabolites. In this study, 13 strains of Neocosmospora were isolated from the stems and leaves of Dracaena cambodiana and D. lourei in Yunnan Province, China. To clarify the taxonomic placement of these strains, morphological examination and multi-gene (ITS, nrLSU, tef1, rpb1, and rpb2) phylogenetic analyses were performed. Based on morphological and phylogenetic evidence, four new species are introduced and described here: N. hypertrophia, N. kunmingense, N. rugosa, and N. simplicillium. This study expands our understanding of the fungal diversity associated with Dracaena, provides essential data for the taxonomy of Neocosmospora, and serves as a resource for the future development and utilization of Neocosmospora endophytes. Full article

Increased antimicrobial resistance requires effective ways to overcome the global challenge of bacterial infections, including methicillin-resistant Staphylococcus aureus (MRSA). From the emergence of MRSA to its continued evolution, it is important to explore this pathogen from fresh perspectives and develop corresponding coping strategies to counter its growing threat. New coping strategies are continuously emerging, including but not limited to enhancing penetration capabilities or targeting their virulence. This review summarizes the epidemiological characteristics, drug resistance mechanisms, and therapeutic strategies of MRSA that have emerged over the past fifteen years. The focus of this paper is to explore the promising applications and current limitations of novel MRSA control strategies. This review serves as a key resource for treating MRSA infections and discussing novel strategies to overcome bacterial drug resistance. Full article

The efficient thermal management of cutting tools is critical for ensuring dimensional accuracy, surface integrity, and tool longevity, especially in the high-speed dry machining process. However, conventional cooling methods often fall short in reaching the heat-intensive zones near the cutting inserts. This study proposes a novel internal cooling strategy that integrates triply periodic minimal surface (TPMS) structures into the toolholder, aiming to enhance localized heat removal from the cutting region. The thermal-fluidic behaviors of four TPMS topologies (Gyroid, Diamond, I-WP, and Fischer–Koch S) were systematically analyzed under varying coolant velocities using computational fluid dynamics (CFD). Several key performance indicators, including the convective heat transfer coefficient, Nusselt number, friction factor, and thermal resistance, were evaluated. The Diamond and Gyroid structures exhibited the most favorable balance between heat transfer enhancement and pressure loss. The experimental validation confirmed the CFD prediction accuracy. The results establish a new design paradigm for integrating TPMS structures into toolholders, offering a promising solution for efficient, compact, and sustainable cooling in advanced cutting applications. Full article

Background: During food processing and storage, traditional protein-based delivery systems encounter significant challenges in maintaining the structural and functional integrity of bioactive compounds, primarily due to their temporal instability. Methods: In this study, a nanocomposite hydrogel was prepared through the co-assembly of a self-assembling peptide, 9-Fluorenylmethoxycarbonyl-phenylalanine-arginine-glycine-aspartic acid-phenylalanine (Fmoc-FRGDF), and hyaluronic acid (HA). The stability of this hydrogel as a quercetin (Que) delivery carrier was systematically investigated. Furthermore, the impact of Que co-assembly on the microstructural evolution and physicochemical properties of the hydrogel was characterized. Concurrently, the encapsulation efficiency (EE%) and controlled release kinetics of Que were quantitatively evaluated. Results: The findings indicated that HA significantly reduced the storage modulus (G′) from 256.5 Pa for Fmoc-FRGDF to 21.1 Pa with the addition of 0.1 mg/mL HA. Despite this reduction, HA effectively slowed degradation rates; specifically, residue rates of 5.5% were observed for Fmoc-FRGDF alone compared to 14.1% with 0.5 mg/mL HA present. Notably, Que enhanced G′ within the ternary complex, increasing it from 256.5 Pa in Fmoc-FRGDF to an impressive 7527.0 Pa in the Que/HA/Fmoc-FRGDF hydrogel containing 0.1 mg/mL HA. The interactions among Que, HA, and Fmoc-FRGDF involved hydrogen bonding, electrostatic forces, and hydrophobic interactions; furthermore, the co-assembly process strengthened the β-sheet structure while significantly promoting supramolecular ordering. Interestingly, the release profile of Que adhered to the Korsmeyer–Peppas pharmacokinetic equations. Conclusions: Overall, this study examines the impact of polyphenol on the rheological properties, microstructural features, secondary structure conformation, and supramolecular ordering within peptide–polysaccharide–polyphenol ternary complexes, and the Fmoc-FRGDF/HA hydrogel system demonstrates a superior performance as a delivery vehicle for maintaining quercetin’s bioactivity, thereby establishing a multifunctional platform for bioactive agent encapsulation and controlled release. Full article

Traditional 3D millimeter-wave radars lack target height information, leading to identification failures in complex scenarios. Upgrading to 4D millimeter-wave radars enables four-dimensional information perception, enhancing obstacle detection and improving the safety of autonomous driving. Given the high cost-sensitivity of in-vehicle radar systems, single-chip 4D millimeter-wave radar solutions, despite technical challenges in imaging, are of great research value. This study focuses on developing single-chip 4D automotive millimeter-wave radar, covering system architecture design, antenna optimization, signal processing algorithm creation, and performance validation. The maximum measurement error is approximately ±0.2° for azimuth angles within the range of ±30° and around ±0.4° for elevation angles within the range of ±13°. Extensive road testing has demonstrated that the designed radar is capable of reliably measuring dynamic targets such as vehicles, pedestrians, and bicycles, while also accurately detecting static infrastructure like overpasses and traffic signs. Full article

In the context of intelligent manufacturing, deep learning-based remaining useful life (RUL) prediction has become a research hotspot in the field of Prognostics and Health Management (PHM). The traditional approaches often require strong programming skills and repeated model building, posing a high entry barrier. To address this, in this study, we propose and implement a visualization tool that supports multiple model selections and result visualization and eliminates the need for complex coding and mathematical derivations, helping users to efficiently conduct RUL prediction with lower technical requirements. This study introduces and summarizes various novel neural network models for DL-based RUL prediction. The models are validated using the NASA and HNEI datasets, and among the validated models, the LSTM model best met the requirements for remaining useful life (RUL) prediction. In order to achieve the low-code usage of deep learning for RUL prediction, the following tasks were performed: (1) multiple models were developed using the Python (3.9.18) language and were implemented on the PyTorch (1.12.1) framework, providing users with the freedom to choose their desired model; (2) a user-friendly and low-code RUL prediction interface was built using Streamlit, enabling users to easily make predictions; (3) the visualization of prediction results was implemented using Matplotlib (3.8.2), allowing users to better understand and analyze the results. In addition, the tool offers functionalities such as automatic hyperparameter tuning to optimize the performance of the prediction model and reduce the complexity of operations. Full article

The influence of the copper (Cu) content on the corrosion resistance of 3.5%Ni low-carbon weathering steel was investigated using periodic dry–wet cycle accelerated corrosion tests. The mechanical properties of the steels were assessed via tensile and low-temperature impact tests, while corrosion resistance was evaluated based on weight loss measurements. Surface oxide layers were characterized using three-dimensional laser confocal microscopy, X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and electrochemical methods. Electron probe microanalysis (EPMA) was employed to examine the cross-sectional morphology of the oxide layer after 72 h of accelerated corrosion tests. The results indicate that the solution state of Cu increased the strength of 3.5%Ni steels but significantly damaged the low-temperature toughness. As the Cu content increased from 0.75% to 1.25%, the corrosion rate decreased from 4.65 to 3.74 g/m² h. However, when there was a further increase in the Cu content to 2.15%, there was little decrease in the corrosion rate. With the increase in the Cu content from 0.75% to 2.15%, the surface roughness of 3.5%Ni weathering steel after corrosion decreased from 5.543 to 5.019 μm, and the corrosion behavior was more uniform. Additionally, the α/γ protective factor of the oxide layer of the surface layer increased from 2.58 to 2.84 with an increase in the Cu content from 0.75% to 1.25%, resulting in the oxide layer of the surface layer being more protective. For 1.25%Cu steel, the corrosion current density of rusted samples is lower (ranging from 1.2609 × 10⁻⁴ A/cm² to 3.7376 × 10⁻⁴ A/cm²), and the corrosion potential is higher (ranging from −0.85544 V to −0.40243 V). Therefore, the rusted samples are more corrosion resistant. The Cu in the oxide layer of the surface layer forms CuO and CuFeO₂, which are helpful for increasing corrosion resistance, which inhibits the penetration of Cl⁻. Full article

Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted ×4 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications. Full article

Stones in farmland soil affect the efficiency of agricultural mechanization and the efficient growth of crops. In order to solve the problems of traditional stone pickers, such as large soil disturbance, high soil content and low picking rate, this paper introduces the Archimedean curve with constant radial expansion characteristics into the design of the core working parts of the drum picker and designs a new type of drum stone picker. The key components such as spiral blades, rollers, and scrapers were theoretically analyzed, the structural parameters of the main components were determined, and the reliability of the spiral blades was checked using ANSYS Workbench software. Through the preliminary stone-picking performance test, the forward speed of the stone picker, the rotation speed of the drum, and the starting sliding angle of the spiral blade were determined as the test influencing factors. The picking rate and soil content of the stone picker were determined as the test indicators. The response surface test was carried out in the Design-Expert13.0 software. The results show that, when the forward speed of the stone picker is 0.726 m/s, the drum speed is 30 rpm, and the initial sliding angle of the spiral blade is 26.214°, the picking rate is 91.458% and the soil content is 3.513%. Field tests were carried out with the same parameters, and the picking rate was 91.42% and the soil content was 3.567%, with errors of 0.038% and 0.054% compared with the predicted values, indicating that the stone picker meets the field operation requirements. These research results can provide new ideas and technical paths for improving the performance of pickers and are of great value in promoting the development of advanced harvesting equipment and the efficient use of agricultural resources. Full article

In the contemporary big data era, data-driven prognostic and health management (PHM) methodologies have emerged as indispensable tools for ensuring the secure and reliable operation of complex equipment systems. Central to these methodologies is the accurate prediction of remaining useful life (RUL), which serves as a pivotal cornerstone for effective maintenance and operational decision-making. While significant advancements in computer hardware and artificial intelligence (AI) algorithms have catalyzed substantial progress in AI-based RUL prediction, extant research frequently exhibits a narrow focus on specific algorithms, neglecting a comprehensive and comparative analysis of AI techniques across diverse equipment types and operational scenarios. This study endeavors to bridge this gap through the following contributions: (1) A rigorous analysis and systematic categorization of application scenarios for equipment RUL prediction, elucidating their distinct characteristics and requirements. (2) A comprehensive summary and comparative evaluation of several AI algorithms deemed suitable for RUL prediction, delineating their respective strengths and limitations. (3) An in-depth comparative analysis of the applicability of AI algorithms across varying application contexts, informed by a nuanced understanding of different application scenarios and AI algorithm research. (4) An insightful discussion on the current challenges confronting AI-based RUL prediction technology, coupled with a forward-looking examination of its future prospects. By furnishing a meticulous and holistic understanding of the traits of various AI algorithms and their contextual applicability, this study aspires to facilitate the attainment of optimal application outcomes in the realm of equipment RUL prediction. Full article