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This paper used the micro panel data from 2016 to 2019 of 2031 registered poor households in B Town, W County, Lu’an City of Anhui Province in China to analyze the diversified patterns and poverty alleviation effect of paired assistance based on the PSM-DID model. The empirical results show that paired assistance provided by social forces can significantly contribute to the poverty alleviation of poor households, promoting the poverty alleviation rate by 7.8%, which can be concluded through sample matching and control of relevant variables. Furthermore, based on the subsample of poor households with social assistance, we found that external social assistance subject to paired assistance can significantly improve the poverty alleviation rate of poor households by 14.26%, mainly hung on their economic base and strength of poverty alleviation.

Using panel data from 182 county-level cotton-growing regions in the Middle and Lower Reaches of the Yangtze River (2000–2022), this study investigates the drivers of cotton planting area contraction, focusing on the synergistic impacts of non-farm employment, agricultural policies, and their synergies, while verifying mechanisms via rural labor outflow and cotton economic returns. From a sustainability perspective, cotton planting area and output were relatively stable with fluctuations in 2000–2010, but plummeted by 80.6% and 82.8%, respectively, by 2022 (a “cliff-like” decline). Empirical results from the Spatial Durbin Model (SDM) show: (1) Non-farm employment significantly reduces local cotton cultivation and exhibits spatial spillover effects—counties neighboring or economically similar to regions with higher non-farm employment experience greater pressure for contraction; (2) This contraction is more pronounced in counties with smaller rural populations and lower cotton returns, confirming that labor scarcity and low profitability are key channels; (3) Agricultural policies exacerbate the decline: the 2005 Reward Policy for Major Grain-Producing Counties triggers cotton-to-grain substitution, while the 2014 shift from cotton temporary stockpiling to target price subsidies further accelerated the contraction of cotton cultivation in inland regions. This study contributes to understanding agricultural system transitions in the Yangtze River Basin, offering insights for optimizing sustainable planting structure adjustment and balancing food security with cash crop development under rural economic transformation.

Nickel-based single-crystal superalloys are widely used in aeroengine hot-end components, owing to their unique crystal structure and outstanding high-temperature mechanical properties. In the present study, round rod specimens of different sizes were subjected to high temperature creep tests at 980 °C/300 MPa of a second-generation nickel-based single crystal superalloy. The effect of size on the creep behavior of nickel-based single-crystal superalloys was studied with reference to the creep curves and microstructure morphologies. Creep interruption tests of 3-mm micro-round rod specimens were performed for 30, 60, and 90 h until creep fracture occurred. It was found that for nickel-based single crystal superalloys, the smaller the diameter of the specimen, the longer its creep life. Furthermore, the creep fracture morphology showed obvious creep cavitation in the fracture region. The law of organization evolution was used to analyze the rafting phenomena during the creep process. A typical “N”-type drifting strip structure was found during the creep process. Meanwhile, the width of the γ-phase channel increases continuously with creep, and the rate of change of the width of the matrix phase was fastest at the earliest stage of creep, slowing significantly during the middle and late stages of creep with the completion and appearance the rafting phenomenon.

This study analyzes the effect of digital rural construction on farmers’ income growth and the underlying mechanism using a 2SLS instrumental variable approach based on the county digital village index developed by Peking University and AliResearch, as well as micro-survey data of farmers in China. After fully correcting for endogeneity and verifying the robustness of the models, we found that digital rural construction has a significant positive impact on farmers’ total household income, wage income, and property income, while also inhibiting the growth of net agricultural income. Furthermore, we found that digital rural construction increases farmers’ income mainly by promoting non-agricultural employment and asset transformation. In terms of heterogeneity analysis, digital rural construction has a greater effect on increasing farmers’ income with high physical and human capital, but it is not beneficial to farmers with moderate social capital. It also has a greater effect on increasing farmers’ income in villages with better infrastructure. In addition, digital rural construction more significantly increases farmers’ income in the eastern, central, and southern regions of China compared with the western and northern regions. These findings provide new empirical evidence of the effect of digital rural construction on farmers’ income growth in China and other developing countries.

A new method for the determination of glutathione was established based on the principle of fluorescence quenching. The reaction mechanism has been studied by the measurement of fluorescence lifetime and based on the Stern–Volmer plot. The binding constant, K  = 9.86 × 10 5  J mol −1 and the number of binding sites n  = 1.12 were obtained against this reaction. The thermodynamic parameters were estimated. The data, ΔG = −34.19 KJ mol −1 , ΔH = −182.2 KJ mol −1 , and ΔS = −496.8 J K −1  mol −1 showed that the reaction was spontaneous and exothermic. The calibration curve was found to be linear between the fluorescence quenching ( F 0 / F ) and the concentration of glutathione with the range of 3.0 × 10 −5 –3.6 × 10 −3  g/L. The detection limit was 4.5 μg/L and the relative standard derivative was 3.32% for 11 replicate determination of 6.0 × 10 −4  g/L glutathione. This method can be used for the determination of glutathione in fresh vegetables with satisfactory results.

Yanshanian magmatisms are intensive in the southern Anhui Province and can be divided into early （152-137 Ma） and late （136-122 Ma） stages. A Yanshanian granitic zone was found to crop out along Qingshan to Changgai areas in the Ttmxi district in Field investigation which has a genetic link with molybdenum multiple metal mineralization. To be a representative syenitic granite in the southern Anhui Province, the Huangshan pluton has not been found so far to have any genetic link with mineralization. Zircon LA-ICP-MS dating indicate that the four granitic bodies from the Qingshan-Changgai zone have concurrent formed ages from 140~：4 to 141~2 Ma, belonging to the Yanshanian early stage magmatism. However, the Huangshan granite is dated to be 12912 Ma, belonging to the Yanshanian late stage magmatism. The Qingshan-Changgai granites show high SiO2 and K20 contents, low P205 contents and middle A12O3 contents and are high-K calc-alkaline series metaluminum I-type granite. These rocks are characterized by enrichments in the large ion lithophile elements and light rare earth elements （REE）, depletions in the high field-strength elements, and middle degree negative anomalies of Eu, geochemical features of arc or continent crustal derived magma affinities. These rocks have 87Sr/StSr（t） ratios from 0.7120 to 0.7125,εNd（t） values from -7.24 to -4.38 and zircon εHf（t） values of -4.4 to 6.7, similar to that of the coeval ore-bearing granodiorites in the southern Anhui Province. Integrated geochemical studies indicate that the Yanshanian ore-bearing granodiorites were formed by partial melting of the Meso-Neoproterozoic accreted thickened low crust. Meanwhile, the Qingshan-Changgai granites were formed through a AFC process of plagioclase＋amphibole＋Shangxi Group of magmas that formed the ore-bearing granodiorites. The Huangshan granites are characterized by high SiOz and K2O contents, moderate Al2O3 contents, seagull shape REE distributed pattern and distinct Eu negative abnormities. Comparing with the Qingshan-Changgai granites, the Huangshan granites show more Ba, Sr, P, and Ti negative abnormities with no Nb and Ta depletions and are high-K calc-alkaline series metaluminum A-type granite, εHr（t） values of the Huangshan granites are from -6.6 to -1.2, similar to that of the early stage ore-bearing granodiorites, indicating that they were also formed by anatexis of the Meso-Neoproterozoic accreted crust, but their magma sources might be residual granulitic crust which ever underwent Yanshanian early stage I-type intermediate-acid magma extraction. Comparing studies on the two stages granites indicate that the early stage granites derived from a relative thickened low crust under a lower temperature condition. Their magma sources were Meso-Neoproterozoic accreted crust which enriched in ore-forming materials and further became more enriched through processes of magma AFC evolution. However, the late stage A-type granites originated from relative shallow crust under a higher temperature condition. Their magma source was depleted in ore-forming materials due to the early stage magma extraction and thus had weak ore-forming capacity. From early to late stage, the magmatisms tectonic setting translated from post-orogenic to anorogenic and the later corresponded to a back-arc extensional setting as increase of the slab subducted angle of the Paleo-Pacific plate.

Background Cell type prediction is crucial to cell type identification of genomics, cancer diagnosis and drug development, and it can solve the time-consuming and difficult problem of cell classification in biological experiments. Therefore, a computational method is urgently needed to classify and predict cell types using single-cell Hi-C data. In previous studies, there is a lack of convenient and accurate method to predict cell types based on single-cell Hi-C data. Deep neural networks can form complex representations of single-cell Hi-C data and make it possible to handle the multidimensional and sparse biological datasets. Results We compare the performance of SCANN with existing methods and analyze the model by using five different evaluation metrics. When using only ML1 and ML3 datasets, the ARI and NMI values of SCANN increase by 14% and 11% over those of scHiCluster respectively. However, when using all six libraries of data, the ARI and NMI values of SCANN increase by 63% and 88% over those of scHiCluster respectively. These findings show that SCANN is highly accurate in predicting the type of independent cell samples using single-cell Hi-C data. Conclusions SCANN enhances the training speed and requires fewer resources for predicting cell types. In addition, when the number of cells in different cell types was extremely unbalanced, SCANN has higher stability and flexibility in solving cell classification and cell type prediction using the single-cell Hi-C data. This predication method can assist biologists to study the differences in the chromosome structure of cells between different cell types.

Elementary tellurium is currently of great interest as an element with potential promise in nano-technology applications because of the recent discovery regarding its three two-dimensional phases and the existence of Weyl nodes around its Femi level. Here, we report on the unique nano-photonic properties of elemental tellurium particles [Te(0)], as harvest from a culture of a tellurium-oxyanion respiring bacteria. The bacterially-formed nano-crystals prove effective in the photonic applications tested compared to the chemically-formed nano-materials, suggesting a unique and environmentally friendly route of synthesis. Nonlinear optical measurements of this material reveal the strong saturable absorption and nonlinear optical extinctions induced by Mie scattering over broad temporal and wavelength ranges. In both cases, Te-nanoparticles exhibit superior optical nonlinearity compared to graphene. We demonstrate that biological tellurium can be used for a variety of photonic applications which include their proof-of-concept for employment as ultrafast mode-lockers and all-optical switches. Tellurium has two-dimensional phases and some Weyl nodes around its Femi level that make it a suitable candidate for the study and application in nonlinear optics. Here the authors show the synthesis and use of bio-grown optical Te(0) nanoparticles for optical modulation and thermo-optic switching.

This paper designed a mouldboard plough device, and performs performance analysis on the share-type plow body and frame based on numerical simulation methods. Firstly, the effects of forward speed, lugs angle, and the radius of the raised structure on the mouldboard plough’s performance were investigated, utilizing the discrete element method. The effects of these variables are analyzed through the response surface method. Furthermore, the significance of each factor is examined, and optimal parameter combinations are identified. The degree of influence on soil penetration resistance, ranked from largest to smallest, is as follows: forward speed, lugs angle, and mouldboard plough surface convex radius. Specifically, resistance to soil penetration increases with an increase in forward speed, lugs angle, and the radius of the convex structure. The degree of influence on the number of soil disturbance particles is ranked as follows: forward speed, the radius of the convex structure, and lugs angle. The number of soil disturbance particles decreases with an increase in forward speed, increases with a larger radius of the convex structure, and slightly decreases with an increase in the lugs angle. By establishing a regression model, the optimal parameter combination for the mouldboard plough was determined to be a forward speed of 0.8 m/s, a lugs angle of 45°, and a convex structure radius of 9 mm. Then, based on finite element analysis, both static and modal analyses were conducted on the frame of the plow device. The results indicated that, during stable operation of the mouldboard plough, the maximum stress occurs at the hinge joint between the frame and the tractor. The maximum stress value recorded is 24.7 MPa, with a corresponding maximum deformation of 0.02 mm, demonstrating that the designed frame satisfies the static requirements. Furthermore, the first-order natural frequency of the frame is 92 Hz, which is significantly higher than the external excitation frequency, thereby preventing the occurrence of resonance. Finally, the harmonic response analysis was performed on the frame, and the results showed that under the excitation conditions of 90 Hz and 500 Hz, the maximum displacement of the rack was 16.5 mm, and the results showed that it would not affect the working performance of the machine.

Structural variations (SVs) are diverse forms of genetic alterations and drive a wide range of human diseases. Accurately genotyping SVs, particularly occurring at repetitive genomic regions, from short-read sequencing data remains challenging. Here, we introduce SVLearn, a machine-learning approach for genotyping bi-allelic SVs. It exploits a dual-reference strategy to engineer a curated set of genomic, alignment, and genotyping features based on a reference genome in concert with an allele-based alternative genome. Using 38,613 human-derived SVs, we show that SVLearn significantly outperforms four state-of-the-art tools, with precision improvements of up to 15.61% for insertions and 13.75% for deletions in repetitive regions. On two additional sets of 121,435 cattle SVs and 113,042 sheep SVs, SVLearn demonstrates a strong generalizability to cross-species genotype SVs with a weighted genotype concordance score of up to 90%. Notably, SVLearn enables accurate genotyping of SVs at low sequencing coverage, which is comparable to the accuracy at 30× coverage. Our studies suggest that SVLearn can accelerate the understanding of associations between the genome-scale, high-quality genotyped SVs and diseases across multiple species. Accurately genotyping structural variations (SVs) from short-read sequencing data is challenging. Here, the authors introduce SVLearn for precise genotyping of bi-allelic SVs, demonstrating robust cross-species generalizability across multiple coverage levels.