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Melon is an important cash crop grown worldwide, with China leading the world in melon acreage. Melon wilt is becoming more common as the area under melon cultivation gradually increases. Therefore, it is crucial to understand the resistance pattern of Fusarium wilt in melons. In this work, among the four test melon cultivars, YG was highly resistant to Fusarium wilt and 1455 was disease‐resistant. However, 5657 was susceptible, while 5455 was highly susceptible to the disease. We detected 71,852 differentially expressed genes in 16 combinations, including 37,382 with up‐regulation and 3470 with down‐regulation, and we investigated 11 genes associated with resistance to Fusarium wilt through bioinformatics and transcriptome analysis. We identified two differentially expressed cinnamyl alcohol dehydrogenases (CAD) that were down‐regulated in the susceptible melon material in contrast to the resistant variety. Nine chitinase (CHT) genes were differentially expressed, with four showing up‐regulation and four showing down‐regulation in the susceptible compared to the resistant melon material. The MeloCHT genes were located on chromosomes 2, 6, 8, 11 and 12, with four of them on chromosome 8. MeloCAD1 was located on chromosome 1 and MeloCAD2 on chromosome 8. The physicochemical properties of the encoded proteins were predicted by bioinformatics, including theoretical isoelectric point, instability coefficient, relative molecular mass, lipid solubility index and hydrophilicity index. The genes encode proteins with α‐helix, extended strand, random coils and β‐turns. This study provides fundamental knowledge on the transcriptome of melons and establishes a foundation for future research on the detection and application of Fusarium wilt‐resistant genes in melons. [ABSTRACT FROM AUTHOR]

In order to solve the problem of traditional two-dimensional image methods being unable to obtain depth information during surface detection of cigarettes, a new detection method based on rotary scanning of linear structured light sensor was proposed. The cigarette is rotated by the rotation of two rolling wheels in the same direction. The linear structured light vision sensor was used to obtain the axial contour of the object, the random sampling consistency was used to fit the contour, and the distance between the original point and the fitted contour was calculated to judge the defect point. The original data obtained from a certain brand cigarette during the detection process were selected, and the Kappa value of multiple groups of samples reached 0.97, with repeatability reaching 99.3%. The study provides reference for the subsequent detection of cigarette defects. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models are valuable research tools for making accurate predictions. However, ML models often unreliably extrapolate outside their training data. The multiparameter delta method quantifies uncertainty for ML models (and generally for other nonlinear models) with parameters trained by least squares regression. The uncertainty measure requires the gradient of the model prediction and the Hessian of the loss function, both with respect to model parameters. Both the gradient and Hessian can be readily obtained from most ML software frameworks by automatic differentiation. We show examples of the uncertainty method in applications of molecular simulations and neural networks. We further show that the uncertainty measure is larger for input space regions that are not part of the training data. Therefore, this method can be used to identify extrapolation and to aid in selecting training data or assessing model reliability. [ABSTRACT FROM AUTHOR]

One goal in materials simulation is finding relationships between atomic scale and continuum properties. The Stokes–Einstein–Sutherland (SES) equation relates diffusion and viscosity in liquids through the effective diameter d, which is frequently observed to be constant with the ratio T / (D η) where T is the temperature, D is the diffusion coefficient, and η is the viscosity. The SES has a practical use in estimating diffusion from viscosity or vice versa as they can be difficult to measure experimentally. The constant effective diameter observed in the SES holds for many liquids within a temperature range, but at low temperatures, deviations where d is not constant have been observed. This work investigates SES in liquid Al–Si using molecular dynamics. We analyze the local order using Voronoi polyhedrons and agglomerative clustering. Clustering methods from machine learning allowed us to analyze the large amount of data generated from molecular dynamics trajectories in an efficient manner. We found that clusters have minimal effect on diffusion while increasing viscosity, which is a likely origin of the SES deviation for liquid Al–Si at low temperatures near the melting temperature. [ABSTRACT FROM AUTHOR]