Emulsifiers are added to enhance product stability to obtain a satisfactory shelf-life. For this reason, stable emulsions that do not form peroxides nor change the fatty acid composition of food, as ...well as safe treatments to obtain them, are aspects of utmost importance. High-pressure homogenization is a conventional approach to prepare emulsions because of its high efficiency. In addition, the beneficial effects of ultrasound on the processing efficiency are known. Therefore, the impact of high-pressure homogenization (30 MPa, 50M Pa) or ultrasound power (270 W) on the emulsion stability and emulsifying properties of 5% coconut oil-in-water emulsion were discussed in this study. The complexes (3:7and 4:6, by weight) of propylene glycol alginate and xanthan gum were selected as emulsifier. The apparent viscosity, particle size and distribution, emulsifying properties and ζ-potential of 5% coconut oil-in-water emulsion before and after ultrasound treatment or high-pressure homogenization were investigated and compared. The micro structure of the emulsion was observed under the fluorescence microscope. The experimental results showed that both high-pressure homogenization and ultrasonic treatment effectively reduced the apparent viscosity, average droplet size and narrowed the distribution range of the emulsion, compared with the pre-emulsion. However, aggregation in the emulsion appeared only after being subjected to high-pressure homogenization, while the emulsion made by the ultrasound treatment remained stable during 30 days storage. In conclusion, this study provides valuable information regarding emulsion preparation methods that can be feasible in food and beverage industries, demonstrating a better performance of ultrasound in optimizing and extending food shelf-life in food and beverage industries.
Neural architecture search (NAS) has significant progress in improving the accuracy of image classification. Recently, some works attempt to extend NAS to image segmentation which shows preliminary ...feasibility. However, all of them focus on searching architecture for semantic segmentation in natural scenes. In this paper, we design three types of primitive operation set on search space to automatically find two cell architecture DownSC and UpSC for semantic image segmentation especially medical image segmentation. Inspired by the U-net architecture and its variants successfully applied to various medical image segmentation, we propose NAS-Unet which is stacked by the same number of DownSC and UpSC on a U-like backbone network. The architectures of DownSC and UpSC updated simultaneously by a differential architecture strategy during the search stage. We demonstrate the good segmentation results of the proposed method on Promise12, Chaos, and ultrasound nerve datasets, which collected by magnetic resonance imaging, computed tomography, and ultrasound, respectively. Without any pretraining, our architecture searched on PASCAL VOC2012, attains better performances and much fewer parameters (about 0.8M) than U-net and one of its variants when evaluated on the above three types of medical image datasets.
Currently, advanced communications and networks greatly enhance user experiences and have a major impact on all aspects of people's lifestyles in terms of work, society, and the economy. However ...improving competitiveness and sustainable vehicle network services, such as higher user experience, considerable resource utilization and effective personalized services, is a great challenge. Addressing these issues, this paper proposes a virtual network resource management based on user behavior to further optimize the existing vehicle communications. In particular, ensemble learning is implemented in the proposed scheme to predict the user's voice call duration and traffic usage for supporting user-centric mobile services optimization. Sufficient experiments show that the proposed scheme can significantly improve the quality of services and experiences and that it provides a novel idea for optimizing vehicle networks.
The big challenging issues in photocatalytic H2 evolution are efficient separation of the photoinduced carriers, the stability of the catalyst, enhancing quantum efficiency, and requiring ...photoinduced electrons to enrich on photocatalysts’ surface. Herein, Ti3C2 MXene quantum dots (QDs) possess the activity of Pt as a co-catalyst in promoting the photocatalytic H2 evolution to form heterostructures with g-C3N4 nanosheets (NSs) (denoted g-C3N4@Ti3C2 QDs). The photocatalytic H2 evolution rate of g-C3N4@Ti3C2 QD composites with an optimized Ti3C2 QD loading amounts (100 mL) is nearly 26, 3 and 10 times higher than pristine g-C3N4 NSs, Pt/g-C3N4, and Ti3C2 MXene sheet/g-C3N4, respectively. The Ti3C2 QDs increase the specific surface area of g-C3N4 and boost the density of the active site. Besides, metallic Ti3C2 QDs possess excellent electronic conductivity, causing the improvement of carrier transfer efficiency.
Underwater optical images are usually influenced by low lighting, high turbidity scattering and wavelength absorption. To solve these issues, a great deal of work has been performed to improve the ...quality of underwater images. Most of them use the high-intensity LEDs for lighting to obtain the high contrast images. However, in high turbidity water, high-intensity LEDs cause strong scattering and absorption. In this paper, we propose a light field imaging approach for solving underwater imaging problems in a low-intensity light environment. As a solution, we tackle the problem of de-scattering from light field images by using deep convolutional neural networks with depth estimation. Furthermore, a spectral characteristic-based color correction method is used for recovering the color reduction. Experimental results show the effectiveness of the proposed method by challenging real-world underwater imaging.
•A light field imaging approach for solving underwater imaging problems is proposed.•The problem of de-scattering from light field images by using deep convolutional neural networks is proposed.•A deep learning based depth estimation method is proposed.
Unmanned aerial vehicles (UAVs) are used in many fields including weather observation, farming, infrastructure inspection, and monitoring of disaster areas. However, the currently available UAVs are ...prone to crashing. The goal of this paper is the development of an anomaly detection system to prevent the motor of the drone from operating at abnormal temperatures. In this anomaly detection system, the temperature of the motor is recorded using DS18B20 sensors. Then, using reinforcement learning, the motor is judged to be operating abnormally by a Raspberry Pi processing unit. A specially built user interface allows the activity of the Raspberry Pi to be tracked on a Tablet for observation purposes. The proposed system provides the ability to land a drone when the motor temperature exceeds an automatically generated threshold. The experimental results confirm that the proposed system can safely control the drone using information obtained from temperature sensors attached to the motor.
Getting the point cloud data from sensors and correctly understanding the scene is the core of the intelligent transportation system. Point cloud segmentation can help intelligent transportation ...systems distinguish different objects in the scene. Some methods process the point cloud through a feature extraction network and complete the segmentation task. However, these methods have high requirements on the feature extraction network, and the fineness of the features will directly affect the final segmentation result. In this paper, we propose a new feature extraction network for segmentation by adding an encoder-decoder structure, which can extract the multiscale local feature information from the feature map. In our opinion, the merged multiscale features obtain a better feature matrix, which improves the performance of the segmentation tasks. We report results on the S3DIS dataset, new feature extraction network greatly improves both semantic segmentation and instance segmentation tasks.
Summary
Maize is a globally important food, feed crop and raw material for the food and energy industry. Plant architecture optimization plays important roles in maize yield improvement. PIN‐FORMED ...(PIN) proteins are important for regulating auxin spatiotemporal asymmetric distribution in multiple plant developmental processes. In this study, ZmPIN1a overexpression in maize increased the number of lateral roots and inhibited their elongation, forming a developed root system with longer seminal roots and denser lateral roots. ZmPIN1a overexpression reduced plant height, internode length and ear height. This modification of the maize phenotype increased the yield under high‐density cultivation conditions, and the developed root system improved plant resistance to drought, lodging and a low‐phosphate environment. IAA concentration, transport capacity determination and application of external IAA indicated that ZmPIN1a overexpression led to increased IAA transport from shoot to root. The increase in auxin in the root enabled the plant to allocate more carbohydrates to the roots, enhanced the growth of the root and improved plant resistance to environmental stress. These findings demonstrate that maize plant architecture can be improved by root breeding to create an ideal phenotype for further yield increases.
This study employed eye-tracking technology to investigate how varying dimensions of psychological distance-temporal, probability, and social-affect intertemporal choice. Across three experiments, ...participants were asked to select between two intertemporal options while their eye movements were monitored. Findings revealed inconsistent impacts of different psychological distances on intertemporal decision-making. Increased temporal and social distances led to a preference for larger delayed rewards (Studies 1 and 3), whereas an increase in probability distance did not significantly alter choice preferences (Study 2). The research also highlighted a general pattern in information processing; as psychological distance widened, participants showed a tendency toward dimension-specific processing in making intertemporal decisions.
With the new challenges and crises facing agriculture, digitalization and green transformation have become important ways to solve the problems. This paper uses an international economics perspective ...to chart a new path for sustainable agricultural development. Specifically, it analyzes whether two-way international direct investment (FDI) can facilitate agricultural digital-green fusion(DGF)? Using a sample of 31 provinces (autonomous regions) from 2012 to 2021, this study finds: (1) Two-way FDI can significantly contribute to agriculture's DGF. (2) In the mechanism test, it is proved that two-way FDI can promote agriculture's DGF level by promoting green technology innovation capacity and overall regional technology innovation capacity. (3) The positive effects of two-way FDI are prominent in the eastern and central regions, coastal regions, and economically developed areas. (4) In the spatial Durbin model, the local two-way FDI growth improves agriculture's DGF level in the surrounding areas to a certain extent. The government is advised to prioritize openness, foster an environment for technological innovation, leverage spatial radiation for agricultural DGF, and advance digitally empowered agricultural modernization.
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