Showing papers by "Zhejiang Gongshang University published in 2017"

Development of international terminology and definitions for texture-modified foods and thickened fluids used in dysphagia management: the IDDSI framework

Abstract: Dysphagia is estimated to affect ~8% of the world’s population (~590 million people). Texture-modified foods and thickened drinks are commonly used to reduce the risks of choking and aspiration. The International Dysphagia Diet Standardisation Initiative (IDDSI) was founded with the goal of developing globally standardized terminology and definitions for texture-modified foods and liquids applicable to individuals with dysphagia of all ages, in all care settings, and all cultures. A multi-professional volunteer committee developed a dysphagia diet framework through systematic review and stakeholder consultation. First, a survey of existing national terminologies and current practice was conducted, receiving 2050 responses from 33 countries. Respondents included individuals with dysphagia; their caregivers; organizations supporting individuals with dysphagia; healthcare professionals; food service providers; researchers; and industry. The results revealed common use of 3–4 levels of food texture (54 different names) and ≥3 levels of liquid thickness (27 different names). Substantial support was expressed for international standardization. Next, a systematic review regarding the impact of food texture and liquid consistency on swallowing was completed. A meeting was then convened to review data from previous phases, and develop a draft framework. A further international stakeholder survey sought feedback to guide framework refinement; 3190 responses were received from 57 countries. The IDDSI Framework (released in November, 2015) involves a continuum of 8 levels (0–7) identified by numbers, text labels, color codes, definitions, and measurement methods. The IDDSI Framework is recommended for implementation throughout the world.

Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection

Abstract: In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of ${k}$ -nearest neighbor classification performance.

DeepDefense: Identifying DDoS Attack via Deep Learning

Abstract: Distributed Denial of Service (DDoS) attacks grow rapidly and become one of the fatal threats to the Internet. Automatically detecting DDoS attack packets is one of the main defense mechanisms. Conventional solutions monitor network traffic and identify attack activities from legitimate network traffic based on statistical divergence. Machine learning is another method to improve identifying performance based on statistical features. However, conventional machine learning techniques are limited by the shallow representation models. In this paper, we propose a deep learning based DDoS attack detection approach (DeepDefense). Deep learning approach can automatically extract high-level features from low-level ones and gain powerful representation and inference. We design a recurrent deep neural network to learn patterns from sequences of network traffic and trace network attack activities. The experimental results demonstrate a better performance of our model compared with conventional machine learning models. We reduce the error rate from 7.517% to 2.103% compared with conventional machine learning method in the larger data set.

Antidiabetic Micro-/Nanoaggregates from Ge-Gen-Qin-Lian-Tang Decoction Increase Absorption of Baicalin and Cellular Antioxidant Activity In Vitro

Abstract: The antidiabetic effects of Ge-Gen-Qin-Lian-Tang decoction (GQD) have been proven clinically. In a pharmacological study conducted on STZ-induced diabetic rats, the constitutive aggregates/sediments of Ge-Gen-Qin-Lian-Tang decoction exhibited stronger hypoglycemic and antioxidant activities compared to the soluble compositions. This study aims to demonstrate the pharmacological properties of aggregates derived from GQD by measuring permeability of the active monomer phytochemicals (e.g., baicalin) in a Caco-2 cell monolayer and determine the cellular viability, intracellular redox status (MDA and SOD), and insulin secretion of pancreatic β-cell line, INS-1, following STZ-induced oxidative stress. The aggregates were separated into three fractions, namely, “MA (microaggregates),” “400 g supernatant,” and “MNA (micro-/nanoaggregates),” by centrifugation at 400 ×g and 15000 ×g, respectively. Aggregates in the sediment increased baicalin absorption, showed little toxicity to β-cells, elevated intracellular SOD levels, and significantly suppressed oxidative damage effects on cellular viability and functions. The “MA” fraction had a larger particle size and provided higher antioxidant cellular protection than “MNA” in vitro, implying that the sediments may be the active components in the herbal decoction. The actions of these micro-/nanoaggregates may provide a new perspective for understanding the antidiabetic effects of herbal decoctions and aid in interpretation of synergistic actions between the multiple components.

REPLACE: A Reliable Trust-Based Platoon Service Recommendation Scheme in VANET

Abstract: The fast development of intelligent transportation has paved the way for innovative techniques for highways, and an entirely new driving pattern of highway vehicular platooning might offer a solution to the persistent problem of road congestion, travel comfort, and road safety. In this vehicular platooning system, a platoon head vehicle provides platoon service to its user vehicles. However, some badly behaved platoon head vehicles may put the platoon in danger, which makes it crucial for user vehicles to distinguish and avoid them. In this paper, we propose a reliable trust-based platoon service recommendation scheme, which is called REPLACE, to help the user vehicles avoid choosing badly behaved platoon head vehicles. Specifically, at the core of REPLACE, a reputation system is designed for the platoon head vehicles by collecting and modeling their user vehicle's feedback. Then, an iterative filtering algorithm is designed to deal with the untruthful feedback from user vehicles. A detailed security analysis is given to show that our proposed REPLACE scheme is secure and robust against badmouth, ballot-stuffing, newcomers, and on–off attacks that exist in vehicular ad hoc networks (VANETs). In addition, we conduct extensive experiments to demonstrate the correctness, accuracy, and robustness of our proposed scheme.

Role of biochar on composting of organic wastes and remediation of contaminated soils-a review.

Abstract: Biochar is produced by pyrolysis of biomass residues under limited oxygen conditions. In recent years, biochar as an amendment has received increasing attention on composting and soil remediation, due to its unique properties such as chemical recalcitrance, high porosity and sorption capacity, and large surface area. This paper provides an overview on the impact of biochar on the chemical characteristics (greenhouse gas emissions, nitrogen loss, decomposition and humification of organic matter) and microbial community structure during composting of organic wastes. This review also discusses the use of biochar for remediation of soils contaminated with organic pollutants and heavy metals as well as related mechanisms. Besides its aging, the effects of biochar on the environment fate and efficacy of pesticides deserve special attention. Moreover, the combined application of biochar and compost affects synergistically on soil remediation and plant growth. Future research needs are identified to ensure a wide application of biochar in composting and soil remediation.

Food-saliva interactions: Mechanisms and implications

Abstract: Saliva is a complex fluid with multifunctional roles in the oral cavity. Even though its importance on oral health maintenance has long been recognized, the critical functions of saliva on food oral processing and perception have only been acknowledged recently.This review will present saliva as an unavoidable ingredient that has a profound impact on our eating experience. We aim to emphasize that what is perceived in-mouth is a food-saliva mixture rather than the food on the plate. The mechanisms through which saliva interacts with food components will be discussed in detail together with the implications of food-saliva interactions to food oral processing and perception.At the structural level, saliva facilitates the breakdown of food structures, contributes to the formation of a cohesive bolus and increases the degree of lubrication for a safe swallow. At the molecular level, saliva interacts with food components, leading to the formation of new compounds, complexes and microstructures. The mechanisms underlying food-saliva interactions include surface coating and clustering, colloidal interactions, complexation, enzymatic breakdown and binding of aroma compounds.

SuFEx-Based Polysulfonate Formation from Ethenesulfonyl Fluoride-Amine Adducts.

Abstract: The SuFEx-based polycondensation between bisalkylsulfonyl fluorides (AA monomers) and bisphenol bis(t-butyldimethylsilyl) ethers (BB monomers) using [Ph3 P=N-PPh3 ]+ [HF2 ]- as the catalyst is described. The AA monomers were prepared via the highly reliable Michael addition of ethenesulfonyl fluoride and amines/anilines while the BB monomers were obtained from silylation of bisphenols by t-butyldimethylsilyl chloride. With these reactions, a remarkable diversity of monomeric building blocks was achieved by exploiting readily available amines, anilines, and bisphenols as starting materials. The SuFEx-based polysulfonate formation reaction exhibited excellent efficiency and functional group tolerance, producing polysulfonates with a variety of side chain functionalities in >99 % conversion within 10 min to 1 h. When bearing an orthogonal group on the side chain, the polysulfonates can be further functionalized via click-chemistry-based post-polymerization modification.

A Necessary and Sufficient Condition for a Resource Subset to Generate a Strict Minimal Siphon in S 4PR

Abstract: Systems of sequential systems with shared resources (S4PR) represent a class of Petri nets that have powerful modeling capability for resource allocation systems. Their efficient siphon computation is important. An open issue is how to determine whether a resource subset can generate a strict minimal siphon (SMS). This paper presents the answer. In particular, we propose a new concept called characteristic implicit resource-transition nets. By charactering such nets, we successfully establish a necessary and sufficient condition for a resource subset to generate an SMS.

Characterizing Neural Entrainment to Hierarchical Linguistic Units using Electroencephalography (EEG).

Abstract: To understand speech, listeners have to combine the words they hear into phrases and sentences. Recent magnetoencephalography (MEG) and electrocorticography (ECoG) studies show that cortical activity is concurrently entrained/synchronized to the rhythms of multiple levels of linguistic units including words, phrases, and sentences. Here we investigate whether this phenomenon can be observed using electroencephalography (EEG), a technique that is more widely available than MEG and ECoG. We show that the EEG responses concurrently track the rhythms of hierarchical linguistic units such as syllables/words, phrases, and sentences. The strength of the sentential-rate response correlates with how well each subject can detect random words embedded in a sequence of sentences. In contrast, only a syllabic-rate response is observed for an unintelligible control stimulus. In sum, EEG provides a useful tool to characterize neural encoding of hierarchical linguistic units, potentially even in individual participants.

Angle and Delay Estimation for 3-D Massive MIMO/FD-MIMO Systems Based on Parametric Channel Modeling

Abstract: In order to meet the challenge of increasing data-rate demand as well as the form factor limitation of the base station (BS), 3-D massive multiple-input multiple-output (MIMO) technology has been introduced as one of the enabling technologies for fifth generation mobile cellular systems In 3-D massive MIMO systems, a BS will rely on the uplink sounding signals from mobile stations to figure out the spatial information for downlink MIMO operations Accordingly, multi-dimensional parameter estimation of a MIMO channel becomes crucial for such systems to realize the predicted capacity gains In this paper, we study the angle and delay estimation for 3-D massive MIMO systems under a parametric channel modeling To be specific, we first introduce separate low complexity time delay and angle estimation algorithms based on unitary transformation, and analytically characterize the mean squared errors (MSEs) of these estimations for massive MIMO systems Then, a matrix-based estimation of signal parameters via rotational invariance technique algorithm is applied to jointly estimate the delay and the angles where the MSEs are also analytically characterized Our results show that the antenna array configuration at the BS plays a critical role in determining the underlying channel estimation performance Simulation results suggest that the characterized MSEs match well with the simulated ones