United International College
Recent publications
In this study, we isolated starches from non-traditional sources, including quinoa, lentil, arrowhead, gorgon fruit, sorghum, chickpea, proso millet, and purple potato and investigated their morphology, physicochemical, and functional properties. Significant differences in starch particle morphology, swelling power, solubility, syneresis, crystalline pattern, and pasting viscosity were observed among the starches from these non-traditional sources. Further, all these isolated starches had unique properties because of their characteristic distinct granules when seen under scanning electron microscopy (SEM). The amylose content of the isolated starches shown significant difference (P < 0.05), and the values ranged between 11.46 % and 37.61 %. Results demonstrated that the isolated starches contained between 79.82 % to 86.56 % starch, indicating that the isolated starches had high purity. X-ray diffraction (XRD) patterns of starches isolated from sorghum, proso millet, quinoa, purple potato, and gorgon fruit presented A-type diffraction pattern; while lentil seeds, arrowhead, and chickpea starches presented C-type diffraction pattern. Overall, these results will promote the development of products based on starch isolated from non-traditional starches.
Background Investigation on protective effects of Panax notoginseng against obesity and its related mechanisms is incomplete. Present study aimed to investigate the potential anti-obesity effect of the total saponins (PNS) and ethanolic extract of P. notoginseng (PNE). Methods Six-week-old male C57BL/6J mice received 45% kcal fat diet for 12 weeks to induce obesity. Oral administration of PNS and PNE at 20 mg/kg/day was applied for the last 4 weeks in the obese mice. Lipid profile was determined by ELISA. Histological examination was performed in liver and fat tissues. Protein levels were measured by Western blot. Results PNS and PNE did not cause weight loss. PNE but not PNS decreased the mass of epididymal and retroperitoneal white adipose tissue, accompanied by a reduction in adipocyte hypertrophy. PNS and PNE improved lipid profile by reducing the concentrations of triglyceride, total cholesterol and low-density lipoprotein cholesterol in plasma or liver samples. PNS and PNE also relieved fatty liver in obese mice. PNS and PNE inhibited expression and phosphorylation of endoplasmic reticulum (ER) stress-responsive proteins in hypertrophic adipose tissue. Conclusions PNS and PNE can regulate ER stress-mediated apoptosis and inflammation to alleviate obesity.
This paper is concerned with the optimal control problem for networked systems with the round-robin protocol (RRP) under the denial-of-service (DoS) attacker with power interval. In the literature, the control design is studied for the linear networked system subject to the DoS attacks with a known constant power or a known constant probability of data-packet dropouts. In this paper, the objective is to control the unknown nonlinear system in the communication network subject to the DoS attacks with a power interval and a time-varying probability of data-packet dropouts. The effects of the DoS attacks with power interval on a networked system under RRP are modeled accurately. A neural network (NN) -based observer is designed for the nonlinear system under the DoS attacks with power interval, and the relationship between the DoS attacker power interval and state estimation error is obtained. The NN actor–critic policy for the optimal control of the RRP-based networked system under the DoS attacks with power interval is found with adaptive dynamical programming, and stability of the resulting control system is analyzed. The proposed control method is demonstrated by a networked uninterruptible power supply system.
This paper addresses the problem of reconstructing depth and silhouette images of wind turbine from its photos of multiple views using deep learning approaches, which aims for wind turbine blade fault diagnosis. Some previous multi-view based methods have extracted each photo’s silhouette and combined them into separate channels as the input of convolution; others use LSTM to combine a series of views for reconstruction. These approaches inevitably need a fixed number of views and the output result is divergent if the order of the input views is changed. So, we refer to a network, SiDeNet (Wiles and Zisserman, Learning to predict 3d surfaces of sculptures from single and multiple views. Int J Comp Vision, 2018), which has a flexible number of input views and will not be affected by the input order. It integrates both viewpoint and image information from each view to learn a latent 3D shape representation and use it to predict the depth of wind turbine at input views. Also, this representation could generalize to the silhouette of unseen views. We make the following contributions to SiDeNet: improving the resolution of predicted images by deepening network structure, adopting 6D camera pose to increase the degrees of freedom of viewpoint to capture a wider range of views, optimizing the loss function of silhouette by applying weights on edge points, and implementing silhouette refinement with point-wise optimizing. Additionally, we conduct a set of prediction experiments and prove the network’s generalization ability to unseen views. Evaluating predicted results on a realistic wind turbine dataset confirms the high performance of the network on both given views and unseen views.
Background The heterogeneity of Type 2 Diabetes Mellitus (T2DM) complicated with renal diseases has not been fully understood in clinical practice. The purpose of the study was to propose potential predictive factors to identify diabetic kidney disease (DKD), nondiabetic kidney disease (NDKD), and DKD superimposed on NDKD (DKD + NDKD) in T2DM patients noninvasively and accurately. Methods Two hundred forty-one eligible patients confirmed by renal biopsy were enrolled in this retrospective, analytical study. The features composed of clinical and biochemical data prior to renal biopsy were extracted from patients’ electronic medical records. Machine learning algorithms were used to distinguish among different kidney diseases pairwise. Feature variables selected in the developed model were evaluated. Results Logistic regression model achieved an accuracy of 0.8306 ± 0.0057 for DKD and NDKD classification. Hematocrit, diabetic retinopathy (DR), hematuria, platelet distribution width and history of hypertension were identified as important risk factors. Then SVM model allowed us to differentiate NDKD from DKD + NDKD with accuracy 0.8686 ± 0.052 where hematuria, diabetes duration, international normalized ratio (INR), D-Dimer, high-density lipoprotein cholesterol were the top risk factors. Finally, the logistic regression model indicated that dd-dimer, hematuria, INR, systolic pressure, DR were likely to be predictive factors to identify DKD with DKD + NDKD. Conclusion Predictive factors were successfully identified among different renal diseases in type 2 diabetes patients via machine learning methods. More attention should be paid on the coagulation factors in the DKD + NDKD patients, which might indicate a hypercoagulable state and an increased risk of thrombosis.
Prebiotics research in the last decade has come a long way due to the maturation of omics technologies (genomics, transcriptomics, proteomics, metabolomics and foodomics) and bioinformatics tools. Nowadays prebiotics are not only thought of as oligosaccharides, but several classes of compounds which have been proven to have prebiotic characteristics and thousands of new sources of prebiotics are still under exploration. The discovery of novel prebiotics means that further research is needed to understand their roles in the microbiota and the host. The relationship between the gut microbiota and its host is crucial in determining the host well-being and the ability of the microbiota to thrive. A dysbiosis in this relationship can cause severe illnesses. This review discusses how omics technologies can be used in prebiotics research.
Natural polymers, such as polysaccharides, cellulose, and starch, have been widely used in the chemical engineering, medicine, food, and cosmetics industries, which had a great many of biological activities. Natural polysaccharides origin from algae, fungi and plants were components of human diet since antique times. Ultrasonication achieved the breakage the polysaccharides reticulum in an ordered fashion. The factors of temperature, ratio of water/material, sonication frequency, time of exposure, pH of the sonication medium influenced the polysaccharide digestion. Sonication improved the enzyme catalysis over its substrate molecule. Positive health promoting slow digestive starch and resistant starch can be prepared quite easily by the sonication process. The aim of this review is to present the current status and scope of natural polymers as well as some emerging polymers with special characteristic. The physiochemical properties and molecular structure of natural carbohydrates under ultrasonic irradiation were also discussed. Moreover, Polysaccharide based films had industrial applications is formed by ultrasonication. Polysaccharide nanoparticles obtained by sonication had efficient water holding capacity. Sonication is an advanced method to improve the food quality. Hence, this review describes the effects of ultrasonication on physical, chemical, and molecular structure of natural polysaccharides.
With the rapid popularization of mobile devices, the mobile crowdsourcing has become a hot topic in order to make full use of the resources of mobile devices. To achieve this goal, it is necessary to design an excellent incentive mechanism to encourage more mobile users to actively undertake crowdsourcing tasks, so as to achieve maximization of certain economic indicators. However, most of the reported incentive mechanisms in the existing literature adopt a centralized platform, which collects the bidding information from workers and task requesters. There is a risk of privacy exposure. In this paper, we design a decentralized auction framework where mobile workers are sellers and task requesters are buyers. This requires each participant to make its own local and independent decision, thereby avoiding centralized processing of task allocation and pricing. Both of them aim to maximize their utilities under the budget constraint. We theoretically prove that our proposed framework is individual rational, budget balanced, truthful, and computationally efficient, and then we conduct a group of numerical simulations to demonstrate its correctness and effectiveness.
In this paper, we studies the profit maximization problem for multiple kinds of products in social networks. It is formulated as a Profit Maximization Problem for Multiple Products (PMPMP), which aims at selecting a set of seed users within the total budget B such that the total profit for k kinds of products is maximized. We introduce the community structure and assume that different kinds of products are adopted by different groups of people, and different product information spread in different communities under the IC information propagation model. We prove that the objective function satisfies the k-submodularity, and then use the multilinear extension to relax the objective function. A continuous greedy algorithm is put forward for the relaxed function, which can obtain an 12 approximation performance guarantee, respectively. The experimental results on two real world social network datasets show the effectiveness of the proposed continuous greedy algorithm.
We study obnoxious facility location games with facility candidate locations. For obnoxious single facility location games under social utility objective, we present a group strategy-proof mechanism with approximation ratio of 3. Then we prove the ratio is tight by giving a corresponding lower bound instance. This is also proved to be the best possible mechanism. For obnoxious two-facility location games with facility candidate locations, we study the heterogeneous facility case in this paper. We design a group strategy-proof mechanism and prove that the approximation ratio is 2. We also prove that the problem lower bound is \(\frac{3}{2}\). KeywordsObnoxious facility location gameMechanism designStrategyproofApproximation ratio
This paper studies the problem of maximizing a non-negative monotone k-submodular function. A k-submodular function is a generalization of a submodular function, where the input consists of k disjoint subsets, instead of a single subset. For the problem under a knapsack constraint, we consider the algorithm that returns the better solution between the single element of highest value and the result of the fully greedy algorithm, to which we refer as Greedy+Singleton, and prove an approximation ratio 14(1-1e)≈0.158. Though this ratio is strictly smaller than the best known factor for this problem, Greedy+Singleton is simple, fast, and of special interests. Our experiments demonstrates that the algorithm performs well in terms of the solution quality.
In cardiovascular disease studies, a large number of risk factors are measured but it often remains unknown whether all of them are relevant variables and whether the impact of these variables is changing with time or remains constant. In addition, more than one kind of cardiovascular disease events can be observed in the same patient and events of different types are possibly correlated. It is expected that different kinds of events are associated with different covariates and the forms of covariate effects also vary between event types. To tackle these problems, we proposed a multistate modeling framework for the joint analysis of multitype recurrent events and terminal event. Model structure selection is performed to identify covariates with time‐varying coefficients, time‐independent coefficients, and null effects. This helps in understanding the disease process as it can detect relevant covariates and identify the temporal dynamics of the covariate effects. It also provides a more parsimonious model to achieve better risk prediction. The performance of the proposed model and selection method is evaluated in numerical studies and illustrated on a real dataset from the Atherosclerosis Risk in Communities study.
Group Decision Making (GDM) has been well studied in the last two decades. Yet, two challenges exist: (a) how to resolve large-scale groups in GDM and achieve the consensus of preferences and (b) how to conduct GDM under risk and emergency conditions. In this paper, we develop a complete problem-solving approach for GDM that orients twofold settings of the complex large-scale group and the time-sensitive emergency decision scenarios. The crux of the matter is to design a feasible mechanism of group consensus strategies in the environment of time pressure and natural language preferences. To solve this problem, we propose a closed-loop mechanism of feedback recommendation strategies accompanied with a new subgroup identification method. This mechanism is underlain by a fourfold decomposition of complex large-scale groups, which entails multiple thresholds of group consensus, group hesitation, and time-related iteration of loops. Our mechanism and the whole GDM approach thoroughly orient the most intuitive representation of preferences - human natural language, which can be elicited and quantitatively formulated in probability linguistic preference systems. We illustrate the proposed approach through a real case study of China's fight against the COVID-19 epidemic. We verify that our mechanism can perfectly tradeoff between the effectiveness and the efficiency of complex large-scale GDM under risk and emergency. The results of this research provide proposals for mechanisms on large-scale GDM and are expected to contribute to emergency management such as epidemic controls, anti-terrorism, and other man-made or natural hazards.
Phytochemicals have been used as one of the sources for the development of anti-obesity drugs. Plants are rich in a variety of bioactive compounds including polyphenols, saponins and terpenes. Phytochemicals inhibit adipocyte differentiation by inhibiting the transcription and translation of adipogenesis transcription factors such as C/EBPα and PPARγ. It has been proved that phytochemicals inhibit the genes and proteins associated with adipogenesis and lipid accumulation by activating Wnt/β-catenin signaling pathway. The activation of Wnt/β-catenin signaling pathway by phytochemicals is multi-target regulation, including the regulation of pathway critical factor β-catenin and its target gene, the downregulation of destruction complex, and the up-regulation of Wnt ligands, its cell surface receptor and Wnt antagonist. In this review, the literature on the anti-obesity effect of phytochemicals through Wnt/β-catenin signaling pathway is collected from Google Scholar, Scopus, PubMed, and Web of Science, and summarizes the regulation mechanism of phytochemicals in this pathway. As one of the alternative methods of weight loss drugs, Phytochemicals inhibit adipogenesis through Wnt/β-catenin signaling pathway. More progress in relevant fields may pose phytochemicals as the main source of anti-obesity treatment.
The force tracking performance of controllers is seriously degraded by the complex friction behaviors when the electrohydraulic system applies low load. The existing LuGre model is employed in the motion control system with the known trajectory. In this work, a modified LuGre model combined with a zero-velocity crossing window is established to address the lag issue and model oscillation in force control system. Then, the Stribeck curve in the friction model is accurately obtained using a fast measurement method, and the impact of friction parameters on the accuracy of the LuGre model is analyzed. Next, an adaptive friction compensation control scheme is proposed for the electrohydraulic system with low load using the modified LuGre model to compensate for dynamic friction behavior and improve the force tracking performance. The uncertain friction parameters are tuned online to reduce the force error of the low-velocity region via the derived adaptive law and a dual-observer. The disturbance observer is effectively integrated to reduce the negative influence of unconsidered dynamics. Finally, comparative experiments are conducted to verify the effectiveness of the proposed controller in static and dynamic loading modes.
Purpose Hydroxychloroquine (HCQ) is an anti-inflammatory drug in widespread use for the treatment of systemic auto-immune diseases. Vision loss caused by retinal toxicity is a significant risk associated with long term HCQ therapy. Identifying patients at risk of developing retinal toxicity can help prevent vision loss and improve the quality of life for patients. This paper presents updated reference thresholds and examines the diagnostic accuracy of a machine learning approach for identifying retinal toxicity using the multifocal Electroretinogram (mfERG). Methods A retrospective study of patients referred for mfERG testing to detect HCQ retinopathy. A consecutive series of all patients referred to Kensington Vision and Research Centre between August 2017 and July 2020 were considered eligible. Eyes suspect for other ocular pathology including widespread retinal disease and advanced macular pathology unrelated to HCQ or with poor quality mfERG recordings were excluded. All patients received mfERG testing and Ocular Coherence Tomography (OCT) imaging. Presence of HCQ retinopathy was based on ring ratio analysis using clinical reference thresholds established at KVRC coupled with structural features observed on OCT, the clinical reference standard. A Support Vector Machine (SVM) using selected features of the mfERG was trained. Accuracy, sensitivity and specificity are reported. Results 1463 eyes of 748 patients were included in the study. SVM model performance was assessed on 293 eyes from 265 patients. 55 eyes from 54 patients were identified as demonstrating HCQ retinopathy based on the clinical reference standard, 50 eyes from 49 patients were identified by the SVM. Our SVM achieves an accuracy of 85.3% with a sensitivity of 90.9% and specificity of 84.0%. Conclusions Machine learning approaches can be applied to mfERG analysis to identify patients at risk of retinopathy caused by HCQ therapy.
A fully implicit two-step backward differentiation formula (BDF2) scheme with variable time steps is considered for solving the phase field crystal (PFC) model by combining with pseudo-spectral method in space. We show that the BDF2 scheme inherits a modified energy dissipation law under a mild ratio restriction A1, i.e., 0<rk:=τk/τk-1<rmax≈4.8645\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0<r_k:=\tau _k/\tau _{k-1}< r_{\max }\approx 4.8645$$\end{document}, which justifies the thermodynamic consistency of the PFC model numerically. In addition, the optimal second-order convergence of the proposed scheme is also established under the ratio restriction A1. The proof involves the tools of DOC and DCC kernels, and some generalized properties of the DOC kernels. As far as we know, the ratio restriction A1 required in our results is mildest so far for the variable time-step BDF2 scheme for calculating PFC model. Numerical examples are provided to demonstrate our theoretical results.
Commercial sharing services (CSSs) provide consumers with temporary access to products or services. Consumers can use CSSs to communicate an identity by renting products from specific brands. Applying the theory of the extended self, we proposed an attachment-based account of CSS usage. Across four studies, we found consistent evidence that consumers were less likely to rent the products of their strongly attached brands via CSSs because these brands were regarded as part of their extended selves, and thus sharing these products with others would contaminate the self. However, this effect was mitigated when consumers’ psychological ownership of the shared product was augmented. Our findings reveal that psychological ownership can replace the role of actual ownership in the sharing context, rendering profound implications for understanding the relationships among self, brand, and product in sharing services.
This paper studies the dual-role-facility location game with generalized service costs, in which every agent plays a dual role of facility and customer, and is associated with a facility opening cost as his private information. The agents strategically report their opening costs to a mechanism which maps the reports to a set of selected agents and payments to them. Each selected agent opens his facility, incurs his opening cost and receives the payment the mechanism sets for him. Each unselected agent incurs a services cost that is determined by the set of selected agents in a very general way. The mechanism is truthful if under it no agent has an incentive to misreport. We provide a necessary and sufficient condition for mechanisms of the game to be truthful. This characterization particularly requires an invariant service cost for each unselected agent, which is a remarkable difference from related work in literature. As applications of this truthfulness characterization, we focus on the classic metric-space setting, in which agents' service costs equal their distances to closest open facilities. We present truthful mechanisms that minimize or approximately minimize the maximum cost among all agents and the total cost of all agents, respectively. Moreover, when the total payment cannot exceed a given budget, we prove, for both cost-minimization objectives, lower and upper bounds on approximation ratios of truthful mechanisms that satisfy the budget constraint.
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992 members
Baojun Xu
  • Department of Food Science and Technology
Karen Poon
  • Department of Food Science and Technology
Jian zhong Zhang
  • Department of Statistics
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