Cowpea flour, whey protein fortification of rice starches: Effects on antioxidant and starch digestibility and starch pasting properties : A dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Food Innovation at Lincoln University

Rice contains more starch, less protein and dietary fibre compared with other cereal. Cowpea is one of the important legumes with high nutrition content. It is rich in proteins, complex carbohydrates, dietary fibres, bioactive compounds, vitamins and minerals. Generally, rice flour has a high glycemic index (GI), while legume flour is considered as low GI food due to the high dietary fiber and slowly digestible starch content. Therefore, it is an excellent way to improve the nutrition of the rice starch product and manipulate the starch digestibility by incorporating protein and legume flour (such as cowpea flour) to rice flour. However, the fortification of protein and legume flour also might affect the pasting property of the blended flour due to the synergistic effect of protein, starch and dietary fiber. The objective of this study is to examine the effect of incorporating both legume flour (cowpea flour) and whey protein to rice flour on the antioxidant properties, pasting attributes and starch digestibility of the blended flour composed of different ratio of cowpea flour, whey protein concentrate (WPC) and rice flour. Five formulations were studied. There is a significant positive correlation between mean total phenolic content (TPC) of the samples and the proportion of cowpea flour incorporated (P≤0.05). Also, there is a significant positive correlation between TPC with ABTS radical scavenging capacity (P≤0.05) of the samples. According to the analysis of RVA results, the addition of cowpea flour and whey protein has a significant effect on the pasting properties of the blended flour. The peak, breakdown and final viscosity of samples decreased gradually with the increasing proportion of cowpea four and whey protein concentrate. However, according to ANOVA analysis and Tukey’s comparison test of RVA results, the peak viscosity of Formulation 1 to formulation 3 and cowpea flour, rice flour is significantly different (P≤0.05) while there is no significant difference between Formulation4, 5 samples and cowpea flour in peak viscosity(P>0.05). This means the peak viscosity increased significantly by the incorporation of cowpea flour and whey protein at a low level, while the influence on peak viscosity became not significant at high-level addition. Similarly, the breakdown values also did not significantly differ among Formulation 2-5 samples, which means a low concentration of cowpea flour 10% has a significant effect on breakdown viscosity(P≤0.05), while the effect of higher-level incorporation was not significant(P>0.05). The final viscosity differed significantly among all samples (P≤0.05). Based on the in vitro starch digestion analysis, the incorporation of whey protein and cowpea flour affected the starch digestibility of samples. Overall, the amount of reducing sugar released of the samples decreased during in vitro starch digestion with the increased proportion of whey protein and cowpea flour in the formulations due to the decrease in starch and increasing of slowly digestible starch from cowpea flour, and the synergistic effect of protein, starch and dietary fiber. The effect of cowpea flour added in rice flour on the pasting property and starch digestibility needs to be further studied using a higher proportion of cowpea flour

Measuring the Angular Velocity of a Propeller with Video Camera Using Electronic Rolling Shutter

Noncontact measurement for rotational motion has advantages over the traditional method which measures rotational motion by means of installing some devices on the object, such as a rotary encoder. Cameras can be employed as remote monitoring or inspecting sensors to measure the angular velocity of a propeller because of their commonplace availability, simplicity, and potentially low cost. A defect of the measurement with cameras is to process the massive data generated by cameras. In order to reduce the collected data from the camera, a camera using ERS (electronic rolling shutter) is applied to measure angular velocities which are higher than the speed of the camera. The effect of rolling shutter can induce geometric distortion in the image, when the propeller rotates during capturing an image. In order to reveal the relationship between the angular velocity and the image distortion, a rotation model has been established. The proposed method was applied to measure the angular velocities of the two-blade propeller and the multiblade propeller. The experimental results showed that this method could detect the angular velocities which were higher than the camera speed, and the accuracy was acceptable

Ozonation of trace organic compounds in different municipal and industrial wastewaters : kinetic-based prediction of removal efficiency and ozone dose requirements

For the wide application of ozonation in (industrial and municipal) wastewater treatment, prediction of trace organic compounds (TrOCs) removal and evaluation of energy requirements are essential for its design and operation. In this study, a kinetics approach, based on the correlation between the second order reaction rate constants of TrOCs with ozone and hydroxyl radicals ((OH)-O-center dot) and the ozone and (OH)-O-center dot exposure (i.e., integral (sic)O-3(sic)dt and integral [(OH)-O-center dot]dt, which are defined as the time integral concentration of O-3 and (OH)-O-center dot for a given reaction time), was validated to predict the elimination efficiency in not only municipal wastewaters but also industrial wastewaters. Two municipal wastewater treatment plant effluents from Belgium (HB-effluent) and China (QG-effluent) and two industrial wastewater treatment plant effluents respectively from a China printing and dyeing factory (PD-effluent) and a China lithium-ion battery factory (LZ-effluent) were used for this purpose. The (OH)-O-center dot scavenging rate from the major scavengers (namely alkalinity, effluent organic matter (EfOM) and NO2-) and the total (OH)-O-center dot scavenging rate of each effluent were calculated. The various water matrices and the (OH)-O-center dot scavenging rates resulted in a difference in the requirement for ozone dose and energy for the same level of TrOCs elimination. For example, for more than 90% atrazine (ATZ) abatement in HB-effluent (with a total (OH)-O-center dot scavenging rate of 1.9 x 10(5) s(-1)) the energy requirement was 12.3 x 10(-2) kWh/m(3), which was lower than 30.1 x 10(-2) kWh/m(3) for PD-effluent (with the highest total (OH)-O-center dot scavenging rate of 4.7 x 10(5) s(-1)). Even though the water characteristics of selected wastewater effluents are quite different, the results of measured and predicted TrOCs abatement efficiency demonstrate that the kinetics approach is applicability for the prediction of target TrOCs elimination by ozonation in both municipal and industrial wastewater treatment plant effluents

A Novel Clustering Tree-based Video lookup Strategy for Supporting VCR-like Operations in MANETs

Mobile Peer-to-Peer (MP2P) network is a promising avenue for large-scale deployment of Video-on-Demand (VoD) applications over mobile ad-hoc networks (MANETs). In P2P VoD systems, fast search for resources is key determinants for improving the Quality of Service (QoS) due to the low delay of seeking resources caused by streaming interactivity. In this paper, we propose a novel Clustering Tree-based Video Lookup strategy for supporting VCR-like operations in MANETs (CTVL) CTVL selects the chunks with the high popularity as "overlay router" chunks to build the "virtual connection" with other chunks in terms of the popularities and external connection of video chunks. CTVL designs a new clustering strategy to group nodes in P2P networks and a maintenance mechanism of cluster structure, which achieves the high system scalability and fast resource search performance. Thorough simulation results also show how CTVL achieves higher average lookup success rate, lower maintenance cost, lower average end-to-end delay and lower packet loss ratio (PLR) in comparison with other state of the art solutions

Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling

High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work in image rescaling formulates downscaling and upscaling as a unified task and learns a bijective mapping between HR and LR via invertible networks. However, in real-world applications (e.g., social media), most images are compressed for transmission. Lossy compression will lead to irreversible information loss on LR images, hence damaging the inverse upscaling procedure and degrading the reconstruction accuracy. In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling. To tackle the distribution shift, we first develop an end-to-end asymmetric framework with two separate bijective mappings for high-quality and compressed LR images, respectively. Then, based on empirical analysis of this framework, we model the distribution of the lost information (including downscaling and compression) using isotropic Gaussian mixtures and propose the Enhanced Invertible Block to derive high-quality/compressed LR images in one forward pass. Besides, we design a set of losses to regularize the learned LR images and enhance the invertibility. Extensive experiments demonstrate the consistent improvements of SAIN across various image rescaling datasets in terms of both quantitative and qualitative evaluation under standard image compression formats (i.e., JPEG and WebP).Comment: Accepted by AAAI 2023. Code is available at https://github.com/yang-jin-hai/SAI

Relative Status Determination for Spacecraft Relative Motion Based on Dual Quaternion

For the two-satellite formation, the relative motion and attitude determination algorithm is a key component that affects the flight quality and mission efficiency. The relative status determination algorithm is proposed based on the Extended Kalman Filter (EKF) and the system state optimal estimate linearization. Aiming at the relative motion of the spacecraft formation navigation problem, the spacecraft relative kinematics and dynamics model are derived from the dual quaternion in the algorithm. Then taking advantage of EKF technique, combining with the dual quaternion integrated dynamic models, considering the navigation algorithm using the fusion measurement by the gyroscope and star sensors, the relative status determination algorithm is designed. At last the simulation is done to verify the feasibility of the algorithm. The simulation results show that the EKF algorithm has faster convergence speed and higher accuracy

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

Link prediction is an important task that has wide applications in various domains. However, the majority of existing link prediction approaches assume the given graph follows homophily assumption, and designs similarity-based heuristics or representation learning approaches to predict links. However, many real-world graphs are heterophilic graphs, where the homophily assumption does not hold, which challenges existing link prediction methods. Generally, in heterophilic graphs, there are many latent factors causing the link formation, and two linked nodes tend to be similar in one or two factors but might be dissimilar in other factors, leading to low overall similarity. Thus, one way is to learn disentangled representation for each node with each vector capturing the latent representation of a node on one factor, which paves a way to model the link formation in heterophilic graphs, resulting in better node representation learning and link prediction performance. However, the work on this is rather limited. Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs. We propose a novel framework DisenLink which can learn disentangled representations by modeling the link formation and perform factor-aware message-passing to facilitate link prediction. Extensive experiments on 13 real-world datasets demonstrate the effectiveness of DisenLink for link prediction on both heterophilic and hemophiliac graphs. Our codes are available at https://github.com/sjz5202/DisenLin