Molecular mechanism for the influence of yam starch multiscale structure on the sensory texture of cooked yam.

Yam is a dual-purpose crop as both medicine and food. However, the mechanism controlling the eating quality of yam remains to be elucidated. This study explored the influence of starch multiscale structure on the texture of yam. The results indicated that FS and RC yam have higher hardness and chewiness, while BZ, XM, and PL yam possess waxiness, Fineness, and Stickiness. Statistically, high amylose (AM) can increase hardness, chewiness, and compactness; and average molecular size (Rh) is positively correlated with stickiness, fineness, and waxiness. Specifically, medium- and long-chain amylose (1000 < X ≤ 10,000) and amylopectin (24 < X ≤ 100), particularly medium-chain amylose (1000 < X ≤ 5000) and long-chain amylopectin (24 < X ≤ 36), primarily affect sensory and rheological stickiness. The long chains of amylose form a straight chain interspersed in the crystalline and amorphous regions to support the entire lamellar structure. Higher proportion of amylose long chains, promoting the starch's structural rigidity, which in turn enhanced its hardness-related attributes. Moreover, a higher ratio of long chains within amylopectin results in tightly intertwined adjacent outer chains, forming double helix crystalline zones. This consequently augmenting the texture quality linked to stickiness-related attributes.

Influence of Mineral Composition on Initiation Pressure of Waterflood-Induced Fractures in Tight Sandstone Reservoir.

Revealing the impact of core mineral composition on the initiation pressure of waterflood-induced fractures (WIFs) in tight sandstone reservoirs is a crucial aspect of studying the initiation mechanism of WIFs. In this paper, through quantitative characterization of the core mineral composition from six samples of the Chang 6 reservoir in the Wuqi oilfield, western Ordos Basin, and modified experimental cores and displacement equipment for WIF experiments, the influence of the core mineral composition on the initiation pressure of WIFs in tight oil reservoirs is investigated. The conclusions are as follows. (1) The rock mineral composition of the Chang 6 reservoir in the Wuqi oilfield, western Ordos Basin, includes quartz, feldspar calcite, and clay, characterizing it as a typical feldspar sandstone reservoir. Quartz and calcite are considered as brittle minerals, while feldspar and clay are categorized as lithologic minerals. (2) For feldspar sandstone reservoirs, including quartz, feldspar, calcite, and clay minerals, when the combined content of quartz and feldspar exceeds 600% of the total mineral content, the changes of quartz and feldspar content will affect the initiation pressure of WIFs. As the ratio of the quartz content to feldspar content Rqf increases, the initiation pressure of WIFs exhibits a logarithmic function decrease. (3) Considering the contribution of diagenetic minerals to rock brittleness, the calculation method for the brittleness index of feldspar tight sandstone reservoirs is improved. (4) The relationships between Rqf, brittleness index, and initiation pressure of induced fractures suggest that an increase in Rqf leads to a power-law increase in the brittleness index, while the initiation pressure of WIFs relative to the brittleness index shows a power-law decrease. This phenomenon indicates an increased likelihood of WIFs occurring during the long-term water injection process in feldspar sandstone reservoirs. This work contributes to understanding how core minerals affect the initiation pressure of WIFs in tight sandstone reservoirs.

Using crop intercepted solar radiation and vegetation index to estimate dry matter yield of Choy Sum.

An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency (RUE) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination (R2) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation (Aipar) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error (RMSE) of 9.4 and R2 values of 0.82, implying that the Aipar-based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar-based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum.

Droplet spatial distribution of oil-based emulsion spray.

Introduction: Oil-based emulsion solution is a common pesticide formulation in agricultural spraying, and its spray characteristics are different from that of water spraying. The well understanding of its spray characteristics is the theoretical basis to improve the pesticide spraying technology. The objective of the present study is to deepen the understanding of the spray characteristics of oil-based emulsion. Method: In this paper, the spatial distribution characteristics of spray droplets of oil-based emulsion were captured visually using the high-speed photomicrography. On the basis of image processing method, the droplet size and distribution density of spray droplets at different spatial locations were analyzed quantitatively. The effects of nozzle configuration and emulsion concentration on spray structures and droplet spatial distribution were discussed. Results: Oil-based emulsion produced a special perforation atomization mechanism compared with water spray, which led to the increase of spray droplet size and distribution density. Nozzle configuration had a significant effect on oil-based emulsion spray, with the nozzle changed from ST110-01 to ST110-03 and ST110-05; the sheet lengths increased to 18 and 28 mm, respectively, whereas the volumetric median diameters increased to 51.19% and 76.00%, respectively. With emulsion concentration increased from 0.02% to 0.1% and 0.5%, the volumetric median diameters increased to 5.17% and 14.56%, respectively. Discussion: The spray droplet size of oil-based emulsion spray can be scaled by the equivalent diameter of discharge orifice of nozzles. The products of volumetric median diameters and corresponding surface tensions were nearly constant for the oil-based emulsion spray of different emulsion concentrations. It is expected that this research could provide theoretical support for improving the spraying technology of oil-based emulsion and increasing the utilization of pesticide.

Coating peanut shell lignin nanospheres with gelatin via non-covalent adsorption: Key parameters, consequences, and underlying interactions.

In the present work, lignin nanospheres (LNS, average diameter 166.43 nm) were prepared and the affecting parameters, the absorbed types, and mechanisms of their interactions with type-A gelatin (AG) were explored. The findings demonstrated that upon AG coating, the ζ-potential of LNS sharply decreased and concluded a negative-to-positive shift, while the average diameter and polydispersity index increased significantly. AG presented the highest coating capacity (0.32 mg/mg, db) onto LNS (0.5 mg/mL) at an optimum pH of 4.0 and an AG concentration of 1.0 mg/mL. The adsorption of AG onto LNS could be well described by the Hill model (R2 = 0.9895), which was characterized as positive synergistic adsorption by the Hill coefficient (1.32) and physical adsorption by the free energy (3.70 kJ/mg). The spectral analysis revealed that the interactions between AG and LNS were mainly driven by electrostatic forces (ΔG < 0, ΔH < 0, and ΔS > 0) together with the assistance of hydrogen bonds and hydrophobic interactions, which companied a decrease of α-helix (4.04 %) and ß-turn (0.60 %) and an increase of ß-sheet (3.10 %) and random coil (1.53 %) of the secondary structure of AG. The results herein certainly favored the hydrophilic/hydrophobic change of LNS/AG and the quality control of a binary system consisting of lignin and gelatin.

Magnetic Resonance Imaging Features of Cerebral Infarction in Critical Patients Based on Convolutional Neural Network.

The clinical application of the artificial intelligence-assisted system in imaging was investigated by analyzing the magnetic resonance imaging (MRI) influence characteristics of cerebral infarction in critically ill patients based on the convolutional neural network (CNN). Fifty patients with cerebral infarction were enrolled and examined by MRI. Besides, a CNN artificial intelligence system was established for learning and training. The features were extracted from the MRI image results of the patients, and then, the data were calculated by computer technology. The gray-level cooccurrence matrix (GLCM) of T1-weighted images was 0.872 ± 0.069; the reasonable prediction (ALL) result was 0.766 ± 0.112; the gray-level run-length matrix (GLRLM) was 0.812 ± 0.101; the multigray-level area size matrix (MGLSZM) result was 0.713 ± 0.104; and the result of gray-scale area size matrix (GLSZM) was 0.598 ± 0.099. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of enhanced T1-weighted images were 0.710 ± 0.169, 0.742 ± 0.099, 0.778 ± 0.096, 0.801 ± 0.104, and 0.598 ± 0.099, respectively. The GLCM, ALL, GLRLM, MGLSZM, and GLSZM of T2-weighted images were 0.780 ± 0.096, 0.798 ± 0.087, 0.888 ± 0.086, 0.768 ± 0.112, and 0.767 ± 0.100, respectively. In short, the image diagnosis method that could reduce the subjective visual judgment error to a certain extent was found by analyzing the characteristics of MRI images of critically ill patients with cerebral infarction based on CNN.

Roughness measurement of leaf surface based on shape from focus.

BACKGROUND: Surface roughness has a significant effect on leaf wettability. Consequently, it influences the efficiency and effectiveness of pesticide application. Therefore, roughness measurement of leaf surface offers support to the relevant research efforts. To characterize surface roughness, the prevailing methods have drawn support from large equipment that often come with high costs and poor portability, which is not suitable for field measurement. Additionally, such equipment may even suffer from inherent drawbacks like the absence of relationship between pixel intensity and corresponding height for scanning electron microscope (SEM). RESULTS: An imaging system with variable object distance was created to capture images of plant leaves, and a method based on shape from focus (SFF) was proposed. The given space-variantly blurred images were processed with the proposed algorithm to obtain the surface roughness of plant leaves. The algorithm improves the current SFF method through image alignment, focus distortion correction, and the introduction of NaN values that allows it to be applied for precise 3d-reconstruction and small-scale surface roughness measurement. CONCLUSION: Compared with methods that rely on optical three-dimensional interference microscope, the method proposed in this paper preserves the overall topography of leaf surface, and achieves superior cost performance at the same time. It is clear from experiments on standard gauge blocks that the RMSE of step was approximately 4.44 µm. Furthermore, according to the Friedman/Nemenyi test, the focus measure operator SML was expected to demonstrate the best performance.

Estimation of Botanical Composition in Mixed Clover-Grass Fields Using Machine Learning-Based Image Analysis.

This study aims to provide an effective image analysis method for clover detection and botanical composition (BC) estimation in clover-grass mixture fields. Three transfer learning methods, namely, fine-tuned DeepLab V3+, SegNet, and fully convolutional network-8s (FCN-8s), were utilized to detect clover fractions (on an area basis). The detected clover fraction (CF detected ), together with auxiliary variables, viz., measured clover height (H clover ) and grass height (H grass ), were used to build multiple linear regression (MLR) and back propagation neural network (BPNN) models for BC estimation. A total of 347 clover-grass images were used to build the estimation model on clover fraction and BC. Of the 347 samples, 226 images were augmented to 904 images for training, 25 were selected for validation, and the remaining 96 samples were used as an independent dataset for testing. Testing results showed that the intersection-over-union (IoU) values based on the DeepLab V3+, SegNet, and FCN-8s were 0.73, 0.57, and 0.60, respectively. The root mean square error (RMSE) values for the three transfer learning methods were 8.5, 10.6, and 10.0%. Subsequently, models based on BPNN and MLR were built to estimate BC, by using either CF detected only or CF detected , grass height, and clover height all together. Results showed that BPNN was generally superior to MLR in terms of estimating BC. The BPNN model only using CF detected had a RMSE of 8.7%. In contrast, the BPNN model using all three variables (CF detected , H clover , and H grass ) as inputs had an RMSE of 6.6%, implying that DeepLab V3+ together with BPNN can provide good estimation of BC and can offer a promising method for improving forage management.

Ultrasonic measurement of lubricant film thickness distribution of journal bearing.

Most film thickness measurement methods damage the working surface of a bearing and cannot measure the minimum film thickness, making it difficult to reveal the lubrication state and warn of wear. Two non-intrusive ultrasonic methods were proposed for measuring the film thickness distribution of the bearing, i.e., the full circumferential measurement and the prediction based on limited measuring points. The ultrasonic recognition model of film thickness was built. A film thickness measuring device and its calibration device were constructed. A calibration experiment in the range of 1-150 µm and a measurement experiment of the bearing's film thickness distribution were carried out. The results showed that in the calibration range, the relative error of most recognition values was less than ±5%, and some are less than 3%. The identification accuracy of the spring model has a zoned phenomenon. The relative difference between the experimental and the simulated values of the film thickness was less than 8% under most working conditions. The predicted values of eccentricity, attitude angle, and minimum film thickness have a small difference from the simulated values, indicating that the accuracy of the measurement method is high.