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To investigate the control effect of Bacillus amyloliquefaciens YN201728 on tobacco powdery mildew, and to clarify the influences of YN201728 on the activities of defense enzymes (peroxidase [POD], phenylalnine ammonialyase [PAL] and polyphenol oxidase [PPO])in tobacco leaves infected by pathogen of tobacco powdery mildew (Erysiphe cichoracearum), pot experiments were conducted to analyze the control effects of YN201728 fermentation broth, fermentation filtrate, bacterial cell precipitation resuspension, bacterial cell crushing liquid on tobacco powdery mildew. The water agar slide method was used to investigate the inhibitory effect of the fermentation filtrate on spore germination of Erysiphe cichoracearum, and the changes in the activities of POD, PAL and PPO in tobacco leaves inoculated with YN201728 fermentation broth and Erysiphe cichoracearum were analyzed through pot experiments. The results showed that there was no significant difference in the relative control efficacies on tobacco powdery mildew between fermentation broth treatment and the fermentation filtrate treatment under pot conditions, and the control efficacies reached 61.64% and 56.95% respectively after 7-day treatments, and the highest relative control efficacies of the treatments of bacterial cell precipitation resuspension and bacterial cell crushing liquid were 18.70% and 10.80%, respectively. Tested fermentation filtrate had a significant inhibitory effect on spore germination, and the inhibition rate of the fermentation filtrate with the content (volume fraction) of 20.0% on spore germination of Erysiphe cichoracearum was 92.07% . When tobacco was infected with Erysiphe cichoracearum, the activities of POD and PAL in tobacco leaves treated with the fermentation broth were not significantly higher than those treated with aseptic water, the strain did not improve the resistance to tobacco powdery mildew by inducing increases in the activities of POD and PAL defense enzyme. [ABSTRACT FROM AUTHOR]

Soil visible and near–infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development of spectroscopic technology has increased the application of spectral libraries for SOC research. However, the direct application of spectral libraries for SOC prediction remains challenging due to the high variability in soil types and soil–forming factors. This study aims to address this challenge by improving SOC prediction accuracy through spectral classification. We utilized the European Land Use and Cover Area frame Survey (LUCAS) large–scale spectral library and employed a geographically weighted principal component analysis (GWPCA) combined with a fuzzy c–means (FCM) clustering algorithm to classify the spectra. Subsequently, we used partial least squares regression (PLSR) and the Cubist model for SOC prediction. Additionally, we classified the soil data by land cover types and compared the classification prediction results with those obtained from spectral classification. The results showed that (1) the GWPCA–FCM–Cubist model yielded the best predictions, with an average accuracy of R2 = 0.83 and RPIQ = 2.95, representing improvements of 10.33% and 18.00% in R2 and RPIQ, respectively, compared to unclassified full sample modeling. (2) The accuracy of spectral classification modeling based on GWPCA–FCM was significantly superior to that of land cover type classification modeling. Specifically, there was a 7.64% and 14.22% improvement in R2 and RPIQ, respectively, under PLSR, and a 13.36% and 29.10% improvement in R2 and RPIQ, respectively, under Cubist. (3) Overall, the prediction accuracy of Cubist models was better than that of PLSR models. These findings indicate that the application of GWPCA and FCM clustering in conjunction with the Cubist modeling technique can significantly enhance the prediction accuracy of SOC from large–scale spectral libraries. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: The organic cocrystal strategy has provided a convenient and efficient platform for preparing organic photothermal materials. However, the rapidly directional preparation of cocrystals with desirable photothermal properties remains challenging due to a lack of suitable design ideas. Here, two new photothermal cocrystals, MTC and MFC, based on acceptor molecules (TCNQ and F4TCNQ) with different electron‐withdrawing capacities were quickly prepared by the coprecipitation method, aiming to explore the effect of charge transfer (CT) interaction on photothermal properties. Compared with MTC, the stronger intermolecular CT interaction in MFC facilitates extending the absorption range (from the NIR‐I to the NIR‐II region) and enhancing the non‐radiative transition process. Under the 808 nm laser irradiation, the photothermal conversion efficiency (PCE) of MFC is 54.6%, whereas MTC displays a mere 36.8%. The MFC cocrystal was further combined with a flexible polymer substrate (HPDMS) to prepare a flexible wearable heater (HPDMS@MFC), which exhibits excellent NIR‐II photothermal performance. This work points out a research direction for the rapid assembly of efficient photothermal cocrystals and additionally provides an extensive application prospect for organic photothermal cocrystals in the field of wearable devices. [ABSTRACT FROM AUTHOR]

The stand basal area, closely related to age, site quality, and stand density, is an important factor for predicting forest growth and yield. The accurate estimation of site quality is especially a key component in the stand basal area model. We utilized sample plots with Picea asperata Mast. as the dominant species in the multi-period National Forest Inventory (NFI) dataset to establish a site index (SI) model including climate effects through the difference form of theoretical growth equations and mixed-effects models. We combined the SI calculated from the SI model, stand age, and stand density index to construct a basal area growth model for Picea asperata Mast. stands. The results show that the Korf model is the best SI base model for Picea asperata Mast. The mean temperatures in summer and winter precipitation were used as the fixed parameters to construct a nonlinear model. Ultimately, elevation, origin, and region, as random effects, were incorporated into the mixed-effects model. The coefficients (R2) of determination of the base model, the nonlinear model including climate, and the nonlinear mixed-effects model are 0.869, 0.899, and 0.921, with root-mean-square errors (RMSEs) of 1.320, 1.315, and 1.301, respectively. Among the basal area models, the Richards model has higher precision. And the basal area model including an SI incorporating climatic factors had a higher determination coefficient (R2) of 0.918 than that of the model including an SI without considering climatic effects. The mixed-effects model incorporating climatic and topographic factors shows a better fitting performance of SI, resulting in a higher precision of the basal area model. This indicates that in the development of forest growth models, both biophysical and climatic factors should be comprehensively considered. [ABSTRACT FROM AUTHOR]

Directly using aluminum (Al) foil as anode material offers a streamlined manufacturing process by eliminating the need for conductive additives, binders, and casting procedures. Nonetheless, monolithic Al foil anodes often suffer from mechanical failure and poor cyclability, posing challenges for practical adoption. In this study, a high‐concentration ether‐based electrolyte is employed to boost the durability of the Al foil anode. In contrast to traditional low‐concentration electrolytes, the use of 5 m lithium bis(fluorosulfonyl)imide in 1,2‐dimethoxyethane promotes the priority decomposition of anions, leading to the creation of a fluoride‐rich solid electrolyte interphase (SEI) layer with exceptional structural modulus and high ion conductivity. These outcomes, coupled with Al's superior compatibility in the chosen electrolyte, enable a record‐breaking cycle life of up to 400 cycles for Li//Al half‐cells, when operated at a high areal capacity of 1 mAh cm−2. In full‐cell configurations, an outstanding capacity retention is also observed, with 96.9% after 150 cycles even under practical conditions involving a 40 µm thin Al foil and a 7.4 mg cm−2 LiFePO4 cathode. These results not only mark the pioneering use of high‐concentration ether‐based electrolyte systems in Al foil anodes but also showcase the high potential for developing low‐cost and high‐energy Al foil‐based LIBs. [ABSTRACT FROM AUTHOR]

Currently, complex scene classification strategies are limited to high-definition image scene sets, and low-quality scene sets are overlooked. Although a few studies have focused on artificially noisy images or specific image sets, none have involved actual low-resolution scene images. Therefore, designing classification models around practicality is of paramount importance. To solve the above problems, this paper proposes a two-stage classification optimization algorithm model based on MPSO, thus achieving high-precision classification of low-quality scene images. Firstly, to verify the rationality of the proposed model, three groups of internationally recognized scene datasets were used to conduct comparative experiments with the proposed model and 21 existing methods. It was found that the proposed model performs better, especially in the 15-scene dataset, with 1.54% higher accuracy than the best existing method ResNet-ELM. Secondly, to prove the necessity of the pre-reconstruction stage of the proposed model, the same classification architecture was used to conduct comparative experiments between the proposed reconstruction method and six existing preprocessing methods on the seven self-built low-quality news scene frames. The results show that the proposed model has a higher improvement rate for outdoor scenes. Finally, to test the application potential of the proposed model in outdoor environments, an adaptive test experiment was conducted on the two self-built scene sets affected by lighting and weather. The results indicate that the proposed model is suitable for weather-affected scene classification, with an average accuracy improvement of 1.42%. [ABSTRACT FROM AUTHOR]