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In this study, the effects of adding mixtures of mono- and di-fatty acid glycerides and Tween 80 with different hydrophilic-lipophilic balance (HLB) values on the stability and whipping characteristics of cream were studied, and the hardness, whipping rate, apparent viscosity, stability, and microstructure of the cream samples were detected. The results showed that with the increase in HLB value, the apparent viscosity of the emulsion decreased, and shear thinning occurred in all cream samples. The centrifugal creaming rate and turbiscan stability index (TSI) decreased first and then increased, and so did the absolute value of zeta potential. The results of whipping characteristics analysis showed that with the increase in HLB value, the whipping time and whipping rate of cream decreased, and the hardness after whipping increased first and then decreased, reaching a maximum of 1 150.1 g when the HLB value was 9. At HLB values in the range of 9–11, cream exhibited a shorter whipping time, higher whipping hardness, appropriate whipping rate, and good mounting performance with no collapse or deformation. Meanwhile, the microstructure showed that the size of the cream bubbles was relatively uniform after whipping, and the fat globules around the bubbles were dense and close to each other, thus forming a better network structure to protect the bubbles and make the foam more stable. Therefore, the HLB value of mixed emulsifiers should be controlled in the range of 9–11 to obtain cream with good stability and whipping characteristics.

Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images. However, as more and more remote sensing data is available, it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation. In addition, semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation, but existing methods usually study individual tasks separately, which leads to high computational resource overhead. To this end, we propose a Multi-Task learning framework for Multi-Modal remote sensing images (MM_MT). Specifically, we design a Cross-Modal Feature Fusion (CMFF) method, which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation. Besides, a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation (JSSHE), extracting common features in a shared network to save time and resources, and then learning task-specific features in two task branches. Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently, multi-task learning saves 20% of training time and achieves competitive performance with mIoU of 83.02% for semantic segmentation and accuracy of 95.26% for height estimation.

Due to their low cost, good biocompatibility, and ease of structural modification, organic long-persistent luminescence (LPL) materials have garnered significant attention in organic light-emitting diodes, biological imaging, information encryption, and chemical sensing. Efficient charge separation and carrier migration by the host–guest structure or using polymers and crystal to build rigid environments are effective ways of preparing high-performance materials with long-lasting afterglow. In this study, four types of crystalline materials (MODPA: DDF-O, MODPA: DDF-CHO, MODPA: DDF-Br, and MODPA: DDF-TRC) were prepared by a convenient host–guest doping method at room temperature under ambient conditions, i.e., in the presence of oxygen. The first three types exhibited long-lived charge-separated (CS) states and achieved visible LPL emissions with durations over 7, 4, and 2 s, respectively. More surprisingly, for the DDF-O material prepared with PMMA as the polymer substrate, the afterglow time of DDF-O: PMMA was longer than 10 s. The persistent room-temperature phosphorescence effect caused by different CS state generation efficiencies and rigid environment were the main reason for the difference in LPL duration. The fourth crystalline material was without charge separation and exhibited no LPL because it was not a D-A system. The research results indicate that the CS state generation efficiency and a rigid environment are the key factors affecting the LPL properties. This work provides new understandings in designing organic LPL materials. [ABSTRACT FROM AUTHOR]

Accurately measuring water holdup in horizontal wells is crucial for effectively using heavy oil reservoirs. The capacitance method is among the most widely used and accurate techniques. However, the absence of suitable insulating materials at high temperatures and pressures limits the effectiveness of capacitive water holdup measurement in heavy oil thermal recovery. This study introduces a new composite material based on an aviation-grade, special glass glaze as the insulating medium doped with inorganic components (CaSO4, MgSO4, Ca(OH)2, and SiO2). This new composite material demonstrates outstanding insulating performance under high-temperature and high-pressure conditions in water. A water environment with a high temperature of 350 °C and a pressure of 12 MPa considerably enhances the composite material's insulation. After 72 h of continuous use, the insulation performance remains 0.3 MΩ. The layers exhibit improved insulation and stability, maintaining integrity through five consecutive temperature shocks in 500 °C air and 20 °C water. XRD, IR, SEM, and TEM analyses reveal that the new composite material is amorphous after firing and that the addition of inorganic components improves the bonding between the glass glaze components and contributes to a denser structure. Simultaneously, SEM and TEM analyses indicate that adding inorganic components results in a smoother, crack-free, and more compact surface of the special glass glaze. This enhancement is crucial for the material's long-term stability in high-temperature and high-pressure water environments. [ABSTRACT FROM AUTHOR]

PURPOSE The present study compares the diagnostic performance of unenhanced computed tomography (CT) radiomics-based machine learning (ML) classifiers and a radiologist in cystic renal masses (CRMs). METHODS Patients with pathologically diagnosed CRMs from two hospitals were enrolled in the study. Unenhanced CT radiomic features were extracted for ML modeling in the training set (Guangzhou; 162 CRMs, 85 malignant). Total tumor segmentation was performed by two radiologists. Features with intraclass correlation coefficients of >0.75 were screened using univariate analysis, least absolute shrinkage and selection operator, and bidirectional elimination to construct random forest (RF), decision tree (DT), and k-nearest neighbor (KNN) models. External validation was performed in the Zhuhai set (45 CRMs, 30 malignant). All images were assessed by a radiologist. The ML models were evaluated using calibration curves, decision curves, and receiver operating characteristic (ROC) curves. RESULTS Of the 207 patients (102 women; 59.1 ± 11.5 years), 92 (41 women; 58.0 ± 13.7 years) had benign CRMs, and 115 (61 women; 59.8 ± 11.4 years) had malignant CRMs. The accuracy, sensitivity, and specificity of the radiologist's diagnoses were 85.5%, 84.2%, and 91.1%, respectively [area under the (ROC) curve (AUC), 0.87]. The ML classifiers showed similar sensitivity (94.2%--100%), specificity (94.7%--100%), and accuracy (94.3%--100%) in the training set. In the validation set, KNN showed better sensitivity, accuracy, and AUC than DT and RF but weaker specificity. Calibration and decision curves showed excellent and good results in the training and validation set, respectively. CONCLUSION Unenhanced CT radiomics-based ML classifiers, especially KNN, may aid in screening CRMs. [ABSTRACT FROM AUTHOR]