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Rice stands as the paramount food crop worldwide, catering to more than half of the global populace as staple sustenance. Accurately and non-destructively predicting rice yield on a large scale assumes paramount importance for assessing rice growth, market planning and food security monitoring. Nonetheless, the pivotal factors that influence the final yield remain inadequately understood. In this study, we evaluated the variation patterns of Normalized Difference Vegetation Index, Enhanced Vegetation Index, Ratio Vegetation Index, Red Edge Ratio Vegetation Index and Normalized Difference Red Edge during crucial growth stages of long, medium and short-grain rice cultivars (YX054, DF018 and LF203) from 2019 to 2021. We investigated the correlation between vegetation index (VI) combinations at different growth stages and rice yield for these three cultivars. To establish predictive models, we deployed multi-seasonal VIs from multi-year dataset and three regression algorithms: partial least squares regression (PLSR), random forest regression (RFR) and support vector regression (SVR). The outcomes evinced a lack of significant correlation between single-season VIs and rice yield. The PLSR algorithm was deemed optimal for YX054, while the RFR was adjudged most suitable for DF018 and LF203. Moreover, the triple-growth and quadruple-growth period VIs models evinced superior robustness compared to the penta-growth period VIs models for all three cultivars, attaining the highest R2 value of 0.86 and the lowest RMSE of 88.17 kg/ha. This paper underscores the criticality of multi-seasonal VIs in bolstering the performance of rice yield prediction. [ABSTRACT FROM AUTHOR]

It is important to explore the characteristics and rules of atmospheric aerosol in the East Asian Sea for monitoring and evaluating atmospheric environmental quality. Based on Aerosol Robot Network (AERONET), Visible Infrared Imaging Radiometer (VIIRS), and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data, the temporal and spatial variation characteristics and differences of aerosol parameters and types in the East Asian Sea were studied by using figure classification method (FIGCM), aerosol optical depth (AOD)440-Angstrom exponent (AE)440–870 method (AA1M), and AOD550-AE490-670 method (AA2M). The results show that the seasonal variation trend of aerosol characteristics and types is obvious in East Asia Sea. AOD, volume concentration (Cv), and aerosol effective radius (reff) in the Bohai-Yellow Sea and the Sea of Japan in autumn are lower than those in other seasons, and the occurrence frequency of ocean-type aerosols is high. Different from the Bohai-Yellow Sea and Sea of Japan, human activities in winter, summer, and autumn seriously affect the air quality in the East China Sea and South China Sea. Especially at the Taipei CWB site, from aerosol parameters and high biomass burning/urban industrial (BB/UI) aerosol, human activity is an important factor for high pollution at the Taipei CWB site. Aerosol types of AA1M, FIGCM, AA2M, and CALIPSO were compared at Anmyon and Yonsei University sites in the Bohai-Yellow Sea in March 2020. The results show that aerosol types based on threshold classification methods generally have higher mixed aerosol results, and the marine (MA) results of AA1M, FIGCM, and AA2M are close to the clean marine aerosol results of CALIPSO. Comparing the results of AA 2 M and CALIPSO on a spatial scale, it is found that the clean marine aerosol proportion identified by CALIPSO (0.38, 0.48, 0.82) is consistent with the MA proportion identified by AA 2 M (0.43, 0.46, 0.97) in the East China Sea, South China Sea, and Western Pacific Ocean. [ABSTRACT FROM AUTHOR]

Analyzing blood lipid and bile acid profile changes in colorectal cancer (CRC) patients. Evaluating the integrated model's diagnostic significance for CRC. Ninety-one individuals with colorectal cancer (CRC group) and 120 healthy volunteers (HC group) were selected for comparison. Serum levels of total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and apolipoproteins (Apo) A1, ApoA2, ApoB, ApoC2, and ApoC3 were measured using immunoturbidimetric and colorimetric methods. Additionally, LC–MS/MS was employed to detect fifteen bile acids in the serum, along with six tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigens (CA) 125, CA19-9, CA242, CA50, and CA72-4. Group comparisons utilized independent sample t-tests and Mann–Whitney U tests. A binary logistic regression algorithm was applied to fit the indicators and establish a screening model; the diagnostic accuracy of individual Indicators and the model was analyzed using receiver operating characteristic (ROC) curves. The CRC group showed significantly lower levels in eight serum lipid indicators and eleven bile acids compared to the HC group (P < 0.05). Conversely, serum levels of TG, CA19-9, and CEA were elevated (P < 0.05). Among the measured parameters, ApoA2 stands out for its strong correlation with the presence of CRC, showcasing exceptional screening efficacy with an area under the curve (AUC) of 0.957, a sensitivity of 85.71%, and a specificity of 93.33%. The screening model, integrating ApoA1, ApoA2, lithocholic acid (LCA), and CEA, attained an impressive AUC of 0.995, surpassing the diagnostic accuracy of individual lipids, bile acids, and tumor markers. CRC patients manifest noteworthy alterations in both blood lipids and bile acid profiles. A screening model incorporating ApoA1, ApoA2, LCA, and CEA provides valuable insights for detecting CRC. [ABSTRACT FROM AUTHOR]

Geometric error is one of the important factors for evaluating the performance of a machine tool, which should be seriously regulated through appropriate accuracy design. This paper proposes a rapid precision design method for horizontal machining center based on Sobol sensitivity analysis method and improved particle swarm optimization (IPSO). Firstly, the geometric error models are established using multi-body system theory and homogenous transformation matrix method. Subsequently, the Sobol method is taken to analyze the sensitivity of geometric errors and their coupling effects, and the sensitivity coefficients are substituted into the objective function of cost-tolerance model. Finally, a multi-objective optimization approach with nonlinear constraints is proposed by comprehensively considering the trade-off between cost and accuracy. An improved particle swarm optimization is applied for the optimization, which can effectively and rapidly obtain the optimal allocation of geometric errors by varying parameters in each iteration and judging the constrains after iteration process. This method provides valuable guidance for accuracy design of machine tools in the manufacturing industry. [ABSTRACT FROM AUTHOR]

In this study, we collected 56 species of fishery organisms (including fish, crustaceans, cephalopods, gastropods, and bivalves) from four seasonal survey cruises at the Dachen fishery grounds. We measured the concentrations of seven heavy metals (Cd, Zn, Cu, Pb, Cr, As, and Hg) in these fisheries organisms. We determined their trophic levels using carbon and nitrogen stable isotope techniques. We analyzed the characteristics of heavy metal transfer in the food chain. The results showed significant differences in heavy metal concentrations among different species. Among all biological groups, bivalves and gastropods exhibited higher levels of heavy metal enrichment than other biological groups, while fish had the lowest levels of heavy metal enrichment. Heavy metals exhibited different patterns of nutritional transfer in the food chain. While Hg showed a biomagnification phenomenon in the food chain, it was not significant. Cd, Zn, Cu, Pb, Cr, and As exhibited a trend of biodilution with increasing nutritional levels, except for As, which showed no significant correlation with δ15N. [ABSTRACT FROM AUTHOR]

The double-layer NiCr-Cr3C2/Ni-Zn-Al2O3 coatings with sufficient corrosion and wear resistance were prepared on low carbon steel substrates. The intermediate layers Ni-Zn-Al2O3 were fabricated by using low-pressure cold spray (LPCS) method to improve the salt fog corrosion resistance properties of the supersonic plasma spray (SPS) NiCr-Cr3C2 coatings. The friction and wear performance for the double-layer and single-layer NiCr-Cr3C2 coatings were carried out by line-contact reciprocating sliding, respectively. Combined with the coating surface analysis techniques, the effect of the salt fog corrosion on the tribological properties of the double-layer coatings was studied. The results showed that the double-layer coatings exhibited better wear resistance than that of the single-layer coatings, due to the better corrosion resistance of the intermediate layer; the wear mass losses of the double-layer coatings was reduced by 70% than that of the single layer coatings and the wear mechanism of coatings after salt fog corrosion conditions is mainly corrosion wear. [ABSTRACT FROM AUTHOR]

The potential applications of magnetoelectric composites in capacitors, sensors, memory electronic devices, and energy harvesting devices have aroused great interest due to their mutual coupling between ferroelectric and ferromagnetic phases. In this paper, BFO/CFO composite films were prepared by the sol–gel method, and the regulation of magneto-photoelectric and coupling properties of BFO/CFO films with different compositions was studied. The results show that with the increase of BFO composition, the interface between BFO layer and BFO layer also appears, the dielectric constant of the film decreases with increasing the content of BFO, and the photoelectric conversion efficiency of the film increases at first and then decreases with the increase of BFO content. When the composition is 4BFO/6CFO, the photoelectric conversion efficiency of the composite film is the highest, which is 2.02 × 10−3%. With the increase of CFO content, the remanent magnetization and saturation magnetization of the film increase gradually. However, when the compositions are 4BFO/6CFO and 3BFO/7CFO, their leakage current is high, resulting in the degradation of ferroelectric properties. The composition 5BFO/5CFO has the highest magnetodielectric and photo induced magnetodielectric response, the corresponding values are 2.21% and 3.88%, respectively. The magnetoelectric coupling coefficient of the film is greatly increased under the action of light, reaching 115%, which could plausibly relate to the photovoltaic effect and the photostriction effects of the film. These results may provide good guidance on designing new photoelectromagnetic detector. [ABSTRACT FROM AUTHOR]

Thin-walled parts with low stiffness and poor machinability are prone to chatter during milling. Chatter can adversely affect machined surface quality and workpiece performance. Most existing chatter monitoring models require training and test data from workpieces with the same stiffness as well as large amounts of training data. In this paper, force signals from workpieces with different stiffnesses are obtained through a series of experiments as source and target domain data. Autoencoders are utilized for feature extraction and support vector machines for chatter recognition. Model-agnostic meta-learning (MAML) optimizes the global model parameters, and transfer learning reduces the demand for target domain samples. The model can monitor the chatter of two types of workpieces at the same time through one training. Compared with common transfer learning models visual geometry group network-16 (VGG-16), this paper decomposes force signals using ensemble empirical mode decomposition (EEMD) and extracts intrinsic mode function (IMF) components as features. VGG-16 model is optimized by adding fully-connected layers, dropout, L2 regularization for chatter recognition. The proposed method achieves 99% accuracy on source domain and 98% on target domain. The target domain accuracy is 6% higher than without using MAML optimization, and 8% higher than the VGG-16 method. [ABSTRACT FROM AUTHOR]

Background and Aims: Litter decomposition is a major determinant of carbon (C) and nutrient cycling in ecosystems, and contributes to soil organic carbon (SOC) formation. Ongoing global changes are exacerbating biodiversity loss, potentially elevating foliar fungal pathogen infections and consequently impacting litter quality and quantity. However, the potential interplay between biodiversity loss and fungal pathogen infection on litter decomposition and SOC formation remains largely unknown.We collected leaf litter with different fungal pathogen infection levels across various tree species richness (TSR) stands within a subtropical forest biodiversity-ecosystem functioning experiment in China. We conducted a 383-day incubation experiment using these litter samples and measured initial litter quality, litter mass loss, and incubation-induced changes in mineral-associated soil C and nitrogen (N).We found that litter from higher richness plots exhibited lower N concentration and higher carbon to nitrogen ratio (C:N). Moreover, TSR exerted control over the effects of fungal pathogen infection on litter quality, decomposition, and N turnover. Under higher richness, litter with higher fungal infection levels tended to have higher N concentration and lower C:N, thus leading to faster decomposition rates and more soil N loss. Meanwhile, litter with elevated fungal infection levels contributed more to litter C retained in soil.Our findings indicate that changes in litter chemistry and fungal pathogen infection rates induced by biodiversity loss could affect decomposition and the extent of C stabilized in soil, highlighting the significance of considering fungal pathogen infection in studies related to biodiversity and biogeochemical cycles.Methods: Litter decomposition is a major determinant of carbon (C) and nutrient cycling in ecosystems, and contributes to soil organic carbon (SOC) formation. Ongoing global changes are exacerbating biodiversity loss, potentially elevating foliar fungal pathogen infections and consequently impacting litter quality and quantity. However, the potential interplay between biodiversity loss and fungal pathogen infection on litter decomposition and SOC formation remains largely unknown.We collected leaf litter with different fungal pathogen infection levels across various tree species richness (TSR) stands within a subtropical forest biodiversity-ecosystem functioning experiment in China. We conducted a 383-day incubation experiment using these litter samples and measured initial litter quality, litter mass loss, and incubation-induced changes in mineral-associated soil C and nitrogen (N).We found that litter from higher richness plots exhibited lower N concentration and higher carbon to nitrogen ratio (C:N). Moreover, TSR exerted control over the effects of fungal pathogen infection on litter quality, decomposition, and N turnover. Under higher richness, litter with higher fungal infection levels tended to have higher N concentration and lower C:N, thus leading to faster decomposition rates and more soil N loss. Meanwhile, litter with elevated fungal infection levels contributed more to litter C retained in soil.Our findings indicate that changes in litter chemistry and fungal pathogen infection rates induced by biodiversity loss could affect decomposition and the extent of C stabilized in soil, highlighting the significance of considering fungal pathogen infection in studies related to biodiversity and biogeochemical cycles.Results: Litter decomposition is a major determinant of carbon (C) and nutrient cycling in ecosystems, and contributes to soil organic carbon (SOC) formation. Ongoing global changes are exacerbating biodiversity loss, potentially elevating foliar fungal pathogen infections and consequently impacting litter quality and quantity. However, the potential interplay between biodiversity loss and fungal pathogen infection on litter decomposition and SOC formation remains largely unknown.We collected leaf litter with different fungal pathogen infection levels across various tree species richness (TSR) stands within a subtropical forest biodiversity-ecosystem functioning experiment in China. We conducted a 383-day incubation experiment using these litter samples and measured initial litter quality, litter mass loss, and incubation-induced changes in mineral-associated soil C and nitrogen (N).We found that litter from higher richness plots exhibited lower N concentration and higher carbon to nitrogen ratio (C:N). Moreover, TSR exerted control over the effects of fungal pathogen infection on litter quality, decomposition, and N turnover. Under higher richness, litter with higher fungal infection levels tended to have higher N concentration and lower C:N, thus leading to faster decomposition rates and more soil N loss. Meanwhile, litter with elevated fungal infection levels contributed more to litter C retained in soil.Our findings indicate that changes in litter chemistry and fungal pathogen infection rates induced by biodiversity loss could affect decomposition and the extent of C stabilized in soil, highlighting the significance of considering fungal pathogen infection in studies related to biodiversity and biogeochemical cycles.Conclusions: Litter decomposition is a major determinant of carbon (C) and nutrient cycling in ecosystems, and contributes to soil organic carbon (SOC) formation. Ongoing global changes are exacerbating biodiversity loss, potentially elevating foliar fungal pathogen infections and consequently impacting litter quality and quantity. However, the potential interplay between biodiversity loss and fungal pathogen infection on litter decomposition and SOC formation remains largely unknown.We collected leaf litter with different fungal pathogen infection levels across various tree species richness (TSR) stands within a subtropical forest biodiversity-ecosystem functioning experiment in China. We conducted a 383-day incubation experiment using these litter samples and measured initial litter quality, litter mass loss, and incubation-induced changes in mineral-associated soil C and nitrogen (N).We found that litter from higher richness plots exhibited lower N concentration and higher carbon to nitrogen ratio (C:N). Moreover, TSR exerted control over the effects of fungal pathogen infection on litter quality, decomposition, and N turnover. Under higher richness, litter with higher fungal infection levels tended to have higher N concentration and lower C:N, thus leading to faster decomposition rates and more soil N loss. Meanwhile, litter with elevated fungal infection levels contributed more to litter C retained in soil.Our findings indicate that changes in litter chemistry and fungal pathogen infection rates induced by biodiversity loss could affect decomposition and the extent of C stabilized in soil, highlighting the significance of considering fungal pathogen infection in studies related to biodiversity and biogeochemical cycles. [ABSTRACT FROM AUTHOR]