Energy-Efficient β

As the first priority of query processing in wireless sensor networks is to save the limited energy of sensor nodes and in many sensing applications a part of skyline result is enough for the user’s requirement, calculating the exact skyline is not energy-efficient relatively. Therefore, a new approximate skyline query, β-approximate skyline query which is limited by a guaranteed error bound, is proposed in this paper. With an objective to reduce the communication cost in evaluating β-approximate skyline queries, we also propose an energy-efficient processing algorithm using mapping and filtering strategies, named Actual Approximate Skyline (AAS). And more than that, an extended algorithm named Hypothetical Approximate Skyline (HAS) which replaces the real tuples with the hypothetical ones is proposed to further reduce the communication cost. Extensive experiments on synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings

Reverse Skyline Computation over Sliding Windows

Reverse skyline queries have been used in many real-world applications such as business planning, market analysis, and environmental monitoring. In this paper, we investigated how to efficiently evaluate continuous reverse skyline queries over sliding windows. We first theoretically analyzed the inherent properties of reverse skyline on data streams and proposed a novel pruning technique to reduce the number of data points preserved for processing continuous reverse skyline queries. Then, an efficient approach, called Semidominance Based Reverse Skyline (SDRS), was proposed to process continuous reverse skyline queries. Moreover, an extension was also proposed to handle n-of-N and (n1,n2)-of-N reverse skyline queries. Our extensive experimental studies have demonstrated the efficiency as well as effectiveness of the proposed approach with various experimental settings

Alternative Tuples Based Probabilistic Skyline Query Processing in Wireless Sensor Networks

As uncertainty is the inherent character of sensing data, the processing and optimization techniques for Probabilistic Skyline (PS) in wireless sensor networks (WSNs) are investigated. It can be proved that PS is not decomposable after analyzing its properties, so in-network aggregation techniques cannot be used directly to improve the performance. In this paper, an efficient algorithm, called Distributed Processing of Probabilistic Skyline (DPPS) query in WSNs, is proposed. The algorithm divides the sensing data into candidate data (CD), irrelevant data (ID), and relevant data (RD). The ID in each sensor node can be filtered directly to reduce data transmissions cost, since, only according to both CD and RD, PS result can be correctly obtained on the base station. Experimental results show that the proposed algorithm can effectively reduce data transmissions by filtering the unnecessary data and greatly prolong the lifetime of WSNs

Using In Vitro Immunomodulatory Properties of Lactic Acid Bacteria for Selection of Probiotics against Salmonella Infection in Broiler Chicks.

Poultry is known to be a major reservoir of Salmonella. The use of lactic acid bacteria has become one of successful strategies to control Salmonella in poultry. The purpose of this study was to select lactic acid bacteria strains by their in vitro immunomodulatory properties for potential use as probiotics against Salmonella infection in broiler chicks. Among 101 isolated lactic acid bacteria strains, 13 strains effectively survived under acidic (pH 2.5) and bile salt (ranging from 0.1% to 1.0%) conditions, effectively inhibited growth of 6 pathogens, and adhered to Caco-2 cells. However, their in vitro immunomodulatory activities differed significantly. Finally, three strains with higher in vitro immunomodulatory properties (Lactobacillus plantarum PZ01, Lactobacillus salivarius JM32 and Pediococcus acidilactici JH231) and three strains with lower in vitro immunomodulatory activities (Enterococcus faecium JS11, Lactobacillus salivarius JK22 and Lactobacillus salivarius JM2A1) were compared for their inhibitory effects on Salmonella adhesion and invasion to Caco-2 cells in vitro and their antimicrobial effects in vivo. The former three strains inhibited Salmonella adhesion and invasion to Caco-2 cells in vitro, reduced the number of Salmonella in intestinal content, spleen and liver, reduced the levels of lipopolysaccharide-induced TNF-α factor (LITAF), IL-1β, IL-6 and IL-12 in serum and increased the level of IL-10 in serum during a challenge study in vivo more efficiently than the latter three strains. These results suggest that in vitro immunomodulatory activities could be used as additional parameters to select more effective probiotics as feed supplements for poultry

Efficient ELM-Based Two Stages Query Processing Optimization for Big Data

MapReduce and its variants have emerged as viable competitors for big data analysis with a commodity cluster of machines. As an extension of MapReduce, ComMapReduce realizes the lightweight communication mechanisms to enhance the performance of query processing applications for big data. However, different communication strategies of ComMapReduce can substantially affect the executions of query processing applications. Although there is already the research work that can identify the communication strategies of ComMapReduce according to the characteristics of the query processing applications, some drawbacks still exist, such as relative simple model, too much user participation, and relative simple query processing execution. Therefore, an efficient ELM-based two stages query processing optimization model is proposed in this paper, named ELM to ELM (E2E) model. Then, we develop an efficient sample training strategy to train our E2E model. Furthermore, two query processing executions based on the E2E model, respectively, Just-in-Time execution and Queue execution, are presented. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the E2E model

Energy-Efficient Reverse Skyline Query Processing over Wireless Sensor Networks

Reverse skyline query plays an important role in many sensing applications, such as environmental monitoring, habitat monitoring, and battlefield monitoring. Due to the limited power supplies of wireless sensor nodes, the existing centralized approaches, which do not consider energy efficiency, cannot be directly applied to the distributed sensor environment. In this paper, we investigate how to process reverse skyline queries energy efficiently in wireless sensor networks. Initially, we theoretically analyzed the properties of reverse skyline query and proposed a skyband-based approach to tackle the problem of reverse skyline query answering over wireless sensor networks. Then, an energy-efficient approach is proposed to minimize the communication cost among sensor nodes of evaluating range reverse skyline query. Moreover, optimization mechanisms to improve the performance of multiple reverse skylines are also discussed. Extensive experiments on both real-world data and synthetic data have demonstrated the efficiency and effectiveness of our proposed approaches with various experimental settings

Efficient ELM-based two stages query processing optimization for big data,”Mathematical Problems in Engineering

MapReduce and its variants have emerged as viable competitors for big data analysis with a commodity cluster of machines. As an extension of MapReduce, ComMapReduce realizes the lightweight communication mechanisms to enhance the performance of query processing applications for big data. However, different communication strategies of ComMapReduce can substantially affect the executions of query processing applications. Although there is already the research work that can identify the communication strategies of ComMapReduce according to the characteristics of the query processing applications, some drawbacks still exist, such as relative simple model, too much user participation, and relative simple query processing execution. Therefore, an efficient ELMbased two stages query processing optimization model is proposed in this paper, named ELM to ELM (E2E) model. Then, we develop an efficient sample training strategy to train our E2E model. Furthermore, two query processing executions based on the E2E model, respectively, Just-in-Time execution and Queue execution, are presented. Finally, extensive experiments are conducted to verify the effectiveness and efficiency of the E2E model

NDCN-Brain: An Extensible Dynamic Functional Brain Network Model

As an extension of the static network, the dynamic functional brain network can show continuous changes in the brain’s connections. Then, limited by the length of the fMRI signal, it is difficult to show every instantaneous moment in the construction of a dynamic network and there is a lack of effective prediction of the dynamic changes of the network after the signal ends. In this paper, an extensible dynamic brain function network model is proposed. The model utilizes the ability of extracting and predicting the instantaneous state of the dynamic network of neural dynamics on complex networks (NDCN) and constructs a dynamic network model structure that can provide more than the original signal range. Experimental results show that every snapshot in the network obtained by the proposed method has a usable network structure and that it also has a good classification result in the diagnosis of cognitive impairment diseases

An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease

In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods

Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject’s brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent