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Soil-rock mixtures are widely used in engineering for filling and subgrade applications, with their mechanical behavior playing a critical role in the stability and safety of engineering structures. This study utilized two types of fine-grained soils, non-cohesive and cohesive, to construct the soil-rock mixtures with similar stone content; indoor triaxial tests were conducted to investigate the effects of fine-grained soil type and loading rate on the mechanical behavior of soil-rock mixtures. The results show that under drained conditions, the strength of the soil-rock mixture containing non-cohesive soil is higher than that of cohesive soil, while under undrained conditions, the strength of the soil-rock mixture containing cohesive soil is higher. Variations in loading rate significantly impacted the mechanical behavior of soil-rock mixtures, with faster loading rates causing more intense movement, rotation, and frequent damage to internal stone particles, ultimately influencing their macroscopic mechanical properties. This study provides valuable insights for the design and construction of soil-rock mixture engineering.

Potato is the fourth-most important food crop around the world, and most of the potatoes are used for foodstuffs and starch products. The aim of this paper is to identify the volatile compounds and metabolites in potatoes with different processing properties. The results showed large differences of volatile and metabolite compounds such as 2,4-Heptadienal and rhoifolin in potatoes and indicated the potential regulations between volatile compounds and metabolites. Moreover, the differences in volatile and metabolite compounds were compared between fresh eating and processing type potatoes. Compared to process type potatoes, fresh eating potatoes contained a higher proportion of aldehyde and alcohol compounds, but being lower in hydrocarbon, furan, and ketone compounds. Moreover, the different expressed metabolites were involved in the metabolism of amino acids, flavone and flavanol biosynthesis, and tryptophan metabolism. The Random forest showed that the fresh eating and processing type potatoes could be distinguished by the content of amino acids and phenols.

Abstract In the era of smart agriculture, the precise labeling and recording of growth information in plants pose challenges for modern agricultural production. This study introduces strontium aluminate particles coated with H3PO4 as luminescent labels capable of spatial embedding within plants for information encoding and storage during growth. The encapsulation with H3PO4 imparts stability and enhanced luminescence to SrAl2O4:Eu2+,Dy3+ (SAO). Using SAO@H3PO4 as a low-damage luminescent label, we implement its delivery into plants through microneedles (MNs) patches. The embedded SAO@H3PO4 within plants exhibits sustained and unaltered high signal-to-noise afterglow emission, with luminous intensity remaining at approximately 78% of the original for 27 days. To cater to diverse information recording needs, MNs of various geometric shapes are designed for loading SAO@H3PO4, and the luminescent signals in different shapes can be accurately identified through a designed program, the corresponding information can be conveniently viewed on a computer. Additionally, inspired by binary information concepts, MNs patches with specific arrangements of luminescent and non-luminescent points are created, resulting in varied luminescent MNs arrays on leaves. An advanced camera system with a tailored program accurately identifies and maps the labels to the corresponding recorded information. These findings showcase the potential of low-damage luminescent labels within plants, paving the way for convenient and widespread storage of plant growth information.

Abstract Asparaginyl ligases have been extensively utilized as valuable tools for site-specific bioconjugation or surface-modification. However, the application is hindered by the laborious and poorly reproducible preparation processes, unstable activity and ambiguous substrate requirements. To address these limitations, this study employed a structure-based rational approach to obtain a high-yield and high-activity protein ligase called OaAEP1-C247A-aa55-351. It was observed that OaAEP1-C247A-aa55-351 exhibits appreciable catalytic activities across a wide pH range, and the addition of the Fe3+ metal ion effectively enhances the catalytic power. Importantly, this study provides insight into the recognition and nucleophile peptide profiles of OaAEP1-C247A-aa55-351. The ligase demonstrates a higher recognition ability for the “Asn-Ala-Leu” motif and an N-terminus “Arg-Leu” as nucleophiles, which significantly increases the reaction yield. Consequently, the catalytic activity of OaAEP1-C247A-aa55-351 with highly efficient recognition and nucleophile motif, “Asn-Ala-Leu” and “Arg-Leu” under the buffer containing Fe3+ is 70-fold and 2-fold higher than previously reported OaAEP1-C247A and the most efficient butelase-1, respectively. Thus, the designed OaAEP1-C247A-aa55-351, with its highly efficient recognition and alternative nucleophile options, holds promising potential for applications in protein engineering, chemo-enzymatic modification, and the development of drugs.

Abstract The prediction of hydrological time series is of great significance for developing flood and drought prevention approaches and is an important component in research on smart water resources. The nonlinear characteristics of hydrological time series are important factors affecting the accuracy of predictions. To enhance the prediction of the nonlinear component in hydrological time series, we employed an improved whale optimisation algorithm (IWOA) to optimise an attention-based long short-term memory (ALSTM) network. The proposed model is termed IWOA-ALSTM. Specifically, we introduced an attention mechanism between two LSTM layers, enabling adaptive focus on distinct features within each time unit to gather information pertaining to a hydrological time series. Furthermore, given the critical impact of the model hyperparameter configuration on the prediction accuracy and operational efficiency, the proposed improved whale optimisation algorithm facilitates the discovery of optimal hyperparameters for the ALSTM model. In this work, we used nonlinear water level information obtained from Hankou station as experimental data. The results of this model were compared with those of genetic algorithms, particle swarm optimisation algorithms and whale optimisation algorithms. The experiments were conducted using five evaluation metrics, namely, the RMSE, MAE, NSE, SI and DR. The results show that the IWOA is effective at optimising the ALSTM and significantly improves the prediction accuracy of nonlinear hydrological time series.