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This paper aims to reveal the changing characteristics of the contribution rates of different production factors in China since the reform and opening up from two dimensions: stage and space. The study used national data from 1978 to 2021 and provincial data from 2000 to 2020, combined with methods such as C-D production function and spatial econometrics for analysis. Research has found that: (1) In terms of stage characteristics, during the structural adjustment stage (1978–1998), economic growth mainly relies on capital and labor input, and the contribution rate of land factors gradually decreases. During the high-speed development stage (1998–2012), the contribution rate of technological factors gradually increased, while the contribution rate of land factors remained relatively stable. In the stage of high-quality development (2012 present), the contribution rate of technological factors continued to rise and become the dominant factor, while the contribution rate of land factors has decreased to a lower level. (2) Regarding spatial characteristics, the spatial econometric model reveals significant spatial agglomeration characteristics of capital and labor, and land factors positively affect local and neighboring economic growth. From 2000 to 2020, the contribution rate of capital factors in various provinces showed a difference of "low in the east and high in the west," which decreased year by year. The contribution rates of labor and land factors have declined to low levels in all provinces; The contribution rate of technological elements has significantly increased, with a higher contribution rate in the southeast region; High-quality development progress in each province can be identified based on the contribution rates of different production factors. The research findings help to understand the impact of varying production factors on economic development at a temporal and spatial scale and provide a scientific basis for achieving a high-quality development pattern of rational allocation of factors and regional coordinated development. [ABSTRACT FROM AUTHOR]

Extracting fragmented cropland is essential for effective cropland management and sustainable agricultural development. However, extracting fragmented cropland presents significant challenges due to its irregular and blurred boundaries, as well as the diversity in crop types and distribution. Deep learning methods are widely used for land cover classification. This paper proposes ConvNeXt-U, a lightweight deep learning network that efficiently extracts fragmented cropland while reducing computational requirements and saving costs. ConvNeXt-U retains the U-shaped structure of U-Net but replaces the encoder with a simplified ConvNeXt architecture. The decoder remains unchanged from U-Net, and the lightweight CBAM (Convolutional Block Attention Module) is integrated. This module adaptively adjusts the channel and spatial dimensions of feature maps, emphasizing key features and suppressing redundant information, which enhances the capture of edge features and improves extraction accuracy. The case study area is Hengyang County, Hunan Province, China, using GF-2 remote sensing imagery. The results show that ConvNeXt-U outperforms existing methods, such as Swin Transformer (Acc = 85.1%, IoU = 79.1%), MobileNetV3 (Acc = 83.4%, IoU = 77.6%), VGG16 (Acc = 80.5%, IoU = 74.6%), and ResUnet (Acc = 81.8%, IoU = 76.1%), achieving an IoU of 79.5% and Acc of 85.2%. Under the same conditions, ConvNeXt-U has a faster inference speed of 37 images/s, compared to 28 images/s for Swin Transformer, 35 images/s for MobileNetV3, and 0.43 and 0.44 images/s for VGG16 and ResUnet, respectively. Moreover, ConvNeXt-U outperforms other methods in processing the boundaries of fragmented cropland, producing clearer and more complete boundaries. The results indicate that the ConvNeXt and CBAM modules significantly enhance the accuracy of fragmented cropland extraction. ConvNeXt-U is also an effective method for extracting fragmented cropland from remote sensing imagery. [ABSTRACT FROM AUTHOR]

Hydrous minerals are significant indicators of the ancient aqueous environment on Mars, and orbital hyperspectral data are one of the most effective tools for obtaining information about the distribution of hydrous minerals on the Martian surface. However, prolonged weathering, erosion, and other external forces result in complex mixing effects, often weakening the spectral absorption features of individual minerals. This study proposes a quantitative inversion method for Martian hydrous minerals by integrating a radiative transfer model with a deep learning network. Based on the physics of the Hapke radiative transfer model, the single-scattering albedo spectra of mineral end members were obtained. Additionally, the Linear Spectral Mixture Model was employed to generate a large number of fully constrained mineral mixture samples, providing theoretical support for experimental data. An LSTM-1DCNN model was trained to establish a data-driven quantitative inversion framework. CRISM data were applied to the Eberswalde Crater region to retrieve the abundances of 21 hydrous minerals, including tremolite, opal, and serpentine. The average abundance of hydrous minerals was calculated to be 0.018, with a total area proportion of approximately 8%. Additionally, by analyzing the distribution areas of hydrous silicates, hydrous sulfates, and hydrous hydroxides, the water activity history of the region was inferred. The results align with findings from related studies and mineral spectral index results. By incorporating deep learning into traditional mixing models, this study identifies the distribution of various low-abundance hydrous minerals, enhancing the accuracy of Martian hydrous mineral inversion. It is expected to provide valuable references for the selection of landing sites for Tianwen-3 and support the smooth implementation of China's Mars exploration mission. [ABSTRACT FROM AUTHOR]

ABSTRACT Traditional physical models and purely data‐driven approaches often struggle with small sample sizes and the complex effects of strain ratios. To overcome these challenges, this study integrates physical principles with machine learning techniques to improve fatigue life predictions for natural rubber (NR). A uniaxial fatigue test on NR was performed, generating data to construct a physical model. A physics‐informed neural network (PINN) model was subsequently developed, utilizing the fatigue life predicted by the physical model, along with engineering strain amplitude and strain ratio as input variables, whereas the experimentally observed fatigue life served as the output variable. The accuracy of the physical model, a data‐driven model, and the proposed PINN model was evaluated by comparing their predictions against measured fatigue life data. The findings demonstrate that the PINN model significantly enhances prediction accuracy, with its fatigue life estimates consistently falling within 1.5 times the measured values. [ABSTRACT FROM AUTHOR]

The hairy roots of Rehmannia glutinosa (Gaertn.) Libosch. ex Fisch. & C. A. Mey. are capable of producing active compounds such as iridoid glycoside, and phenylethanoid glycosides, which have potential applications in the pharmaceutical industry. Silver nanoparticles (AgNPs) have been used as novel elicitors in the induced cultivation of hairy roots, but there is a lack of research regarding their effects on R. glutinosa hairy roots. In the present study, silver nanoparticles (Pp-AgNPs) synthesized by the endophytic fungus Penicillium polandii PG21 were adopted to elicit hairy roots of R. glutinosa, to investigate their influences on the biomass, color, secondary metabolites, antioxidant activity, sucrose metabolism, and phytohormone-related gene expression. The results revealed that the dry weight and fresh weight of R. glutinosa hairy roots were both higher in the treated group than in the control group after addition of 2 mg/L Pp-AgNPs for 20 d. The content of verbascoside, total phenylethanol glycosides and total cycloartenoid in the treatment group reached the highest level at 20 d, which were 1.75, 1.51, 1.44 times more than those in the control group, respectively. Pp-AgNPs significantly stimulated the enzyme activities of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD). The growth-promoting effect of Pp-AgNPs may be accomplished by increasing sucrose metabolism, and regulating the synthesis and signal transduction of gibberellin (GA) and indoleacetic acid (IAA). Moreover, expressed sequence tags-simple sequence repeat (EST-SSR)-based genetic diversity analyses indicated that there was little possibility of genetic variation among samples under different treatment conditions. In conclusion, the appropriate concentration of Pp-AgNPs can be used as an effective elicitor to improve the biomass and secondary metabolites content in R. glutinosa hairy roots. [ABSTRACT FROM AUTHOR]

Analyzing farmland landscape pattern variations induced by human activities can support effective decision making by governments to improve land use efficiency. However, research on long-term and continuous spatial process changes in farmland is scarce, and spatial pattern changes in farmlands remain insufficiently understood. Moreover, studies in which researchers have utilized dynamic process analysis to describe farmlands are relatively limited. This study aimed to apply the state-and-evolution detection model (SEDM), generated from long-term remote sensing data, to characterize farmland spatial pattern variations in Hengyang City, Hunan Province. Annual farmland data from 1990 to 2022, change type samples, and auxiliary data were collected, and six types of spatial pattern variations (perforation, dissection, shrinkage, creation, enlargement, and aggregation) were defined for the study area. Subsequently, the SEDM was applied based on four landscape indices. Finally, spatiotemporal evolution features, namely evolution times, evolution duration, and dominant evolution pattern, were quantified. The farmland in the study area exhibited a generally upward trend with fluctuations. The maximum area was followed by shrinkage (S), perforation (P), and enlargement (E). In over 70% of the study area, fewer than three evolution times occurred over three decades. The dominant evolution patterns were P–S, S–P, and E–P for single evolution events, and P–S–P, S–P–S, and P–S–S for double events. The model achieved an overall accuracy of 85%, thus demonstrating its effectiveness in characterizing landscape pattern variations and providing valuable insights for researchers and policy makers to develop strategies for farmland protection. [ABSTRACT FROM AUTHOR]

Fatigue life of filled natural rubbers used in isolators is very dispersive owing to many uncertainties such as loads and temperature. Conventional crack initiation methods and crack propagation methods to determine fatigue life of rubbers are insufficiently to characterize the phenomenon; thus, probabilistic methods are desired in practical engineering. Based on the test data of uniaxial tensile fatigue experiment of rubbers, the cumulative distribution function and probability density function of residual fatigue life corresponding to two typical probability distribution models of three parameters Weibull distribution（3 PWD） and logarithmic normal distribution（LND） were derived and applied to the prediction of residual life reliability, in order to determine the minimum required under different probability distribution model of sample number and working condition number. The results showed that 3 PWD was more suitable to characterize the probability distribution of rubber fatigue life. The minimum values of the number of rubber fatigue test samples and the number of working conditions are distributed between 4 and 6. The log mean should be preferred for subsequent fatigue analysis when determining the median life. The research can provide guidance for the anti-fatigue design and test of rubber vibration isolators.

Self-healing, stretchable, and moldable hydrogels have a great potential application in tissue engineering and soft robotics. Despite great success in reported hydrogels, it is still a great challenge to construct the moldable hydrogels with an ultrafast self-healing performance. Herein, the composite hydrogels (PBLH) with ultrafast self-healing, stretchable, and moldable properties were successfully constructed by poly (vinyl alcohol) (PVA), borate (B), ε-poly-l-lysine (EPL), and hyaluronic acid (HA) based on an efficient one-pot method. Fourier transform infrared spectroscopy, X-ray diffraction, and rheological measurements confirmed the formation of a dynamic network among PVA, B, EPL, and HA through the cross-linking of dynamic borate bonds, electrostatic interaction, and hydrogen bonding. Having fabricated the dynamic network structure, the damage gap of the composite hydrogels can heal within 1 min, presenting an excellent self-healing ability. Simultaneously, the composite hydrogels can be molded into various shapes, and the length of the composite hydrogels can be stretched to 15 times their original length. In addition, the composite hydrogels exhibited an excellent antibacterial property against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Our results illustrated that the composite hydrogels not only retain the advantages of traditional hydrogels but also possess ultrafast self-healing, outstanding stretchable and antibacterial properties, presenting a prospective candidate for constructing biomedical materials. [ABSTRACT FROM AUTHOR]