Static, Dynamic, and Hybrid Neural Networks in Forecasting Inflation

The back-propagation neural network (BPN) model has been the most popular form of artificial neural network model used for forecasting, particularly in economics and finance. It is a static (feed-forward) model which has a learning process in both hidden and output layers. In this paper we compare the performance of the BPN model with that of two other neural network models, viz., the radial basis function network (RBFN) model and the recurrent neural network (RNN) model, in the context of forecasting inflation. The RBFN model is a hybrid model with a learning process that is much faster than the BPN model and that is able to generate almost the same results as the BPN model. The RNN model is a dynamic model which allows feedback from other layers to the input layer, enabling it to capture the dynamic behavior of the series. The results of the ANN models are also compared with those of the econometric time series models.     

CCFinder: using Spark to find clustering coefficient in big graphs

Networks with billions of vertices introduce new challenges to perform graph analysis in a reasonable time. Clustering coefficient is an important analytical measure of networks such as social networks and biological networks. To compute clustering coefficient in big graphs, existing distributed algorithms suffer from low efficiency such that they may fail due to demanding lots of memory, or even, if they complete successfully, their execution time is not acceptable for real-world applications. We present a distributed MapReduce-based algorithm, called CCFinder, to efficiently compute clustering coefficient in very big graphs. CCFinder is executed on Apache Spark, a scalable data processing platform. It efficiently detects existing triangles through using our proposed data structure, called FONL, which is cached in the distributed memory provided by Spark and reused multiple times. As data items in the FONL are fine-grained and contain the minimum required information, CCFinder requires less storage space and has better parallelism in comparison with its competitors. To find clustering coefficient, our solution to triangle counting is extended to have degree information of the vertices in the appropriate places. We performed several experiments on a Spark cluster with 60 processors. The results show that CCFinder achieves acceptable scalability and outperforms six existing competitor methods. Four competitors are those methods proposed based on graph processing systems, i.e., GraphX, NScale, NScaleSpark, and Pregel frameworks, and two others are the Cohen’s method and NodeIterator++, introduced based on MapReduce.     

Calcium: A comprehensive review on quantification,interaction with milk proteins and implications for processing of dairy products

Calcium (Ca) is a key micronutrient of high relevance for human nutrition that also influences the texture and taste of dairy products and their processability. In bovine milk, Ca is presented in several speciation forms, such as complexed with other milk components or free as ionic calcium while being distributed between colloidal and serum phases of milk. Partitioning of Ca between these phases is highly dynamic and influenced by factors, such as temperature, ionic strength, pH, and milk composition. Processing steps used during the manufacture of dairy products, such as preconditioning, concentration, acidification, salting, cooling, and heating, all contribute to modify Ca speciation and partition, thereby influencing product functionality, product yield, and fouling of equipment. This review aims to provide a comprehensive understanding of the influence of Ca partition on dairy products properties to support the development of kinetics models to reduce product losses and develop added-value products with improved functionality. To achieve this objective, approaches to separate milk phases, analytical approaches to determine Ca partition and speciation, the role of Ca on protein–protein interactions, and their influence on processing of dairy products are discussed.     

Malnutrition in South Asia—A Critical Reappraisal

Malnutrition continues to be a major public health challenge especially in South Asian developing countries. The aim of the present review is to spotlight the magnitude of the prevalence of malnutrition and its dynamics in South Asian region and to suggest potential approaches for the prevention and control of this issue of public health significance. An extensive review of literature, covering malnutrition and its determinants, health and economic consequences and pragmatic preventive strategies was performed on computer based bibliographic databases (PubMed, Google Scholar, Scopus, Medline and Sciencedirect.com) to retrieve abstracts and full texts for India, Pakistan, Bangladesh, Sri Lanka and Nepal. All relevant titles and abstracts were examined and evaluated for malnutrition and its prevalence in South Asia. The results revealed malnutrition to be a major public health problem and a potential cause of high disease burden and mortality in South Asia. Compelling evidence suggests malnutrition to be the leading cause of stunting, wasting and underweight with drastic economic consequences among vulnerable populations. Reduced cognitive performance and low productivity have also been associated with micronutrients malnutrition. Suboptimal breastfeeding, inadequate food supply, micronutrient deficiencies, low household income, poor health care system, increased healthcare costs, illiteracy, unhygienic and substandard living, inappropriate child's care and the caregiver, food insecurity and on top of that vicious cycle of poverty, have been recognized as principal indicators for growing malnutrition prevalence in South Asia. Global organizations, local governments, program managers, NGOs, academia, industry in particular and the society at large need to take up the challenge to completely confiscate malnutrition from the region for economic prosperity and a healthier future.     

Query-oriented topical influential users detection for top-k trending topics in twitter

Applied Intelligence - Online Social Networks (OSNs) have become inevitable for any new methodology both for viral promoting applications and instructing the creation of inciting information and...     

Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzykriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.     

Multimodal fusion for multimedia analysis: a survey

This survey aims at providing multimedia researchers with a state-of-the-art overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.     

IgG from Adult Atopic Dermatitis (AD) Patients Induces Thymic IL-22 Production and CLA Expression on CD4+ T Cells: Possible Epigenetic Implications Mediated by miRNA

Atopic dermatitis (AD) is a common relapsing inflammatory skin disorder characterized by immune-mediated inflammation and epidermal barrier dysfunction. The pathogenesis of AD is multifactorial and has not been fully elucidated to date. This study aimed to evaluate whether serum IgG from adult AD patients could modulate the thymic maturation of IL-22-producing T cells and CLA+ T cells of non-atopic infants. Given that miRNAs regulate immune response genes, we evaluated whether miRNA expression is also altered in cultured thymocytes. Thymocytes were cultured with purified IgG from AD patients or control conditions (mock, Intravenous-IgG (IVIg), non-atopic IgG, or atopic non-AD IgG). Using flow cytometry analysis, we assessed the expression of CLA and intracellular levels of IL-4, IFN-γ, and IL-22 on double-positive T cells (DP T), CD4 T cells, or CD8 T cells. We also investigated the frequency of IgG isotypes and their direct interaction with the thymic T cells membrane. The miRNA profiles were evaluated by the Illumina small RNA-seq approach. MiRNA target gene prediction and enrichment analyses were performed using bioinformatics. Increased frequencies of IL-22 and CLA+ producing CD4+ T cells cultured with IgG of AD patients was seen in non-atopic infant thymocytes compared to all control conditions. No alterations were observed in the frequency of IgG isotypes among evaluated IgG pools. Evidence for a direct interaction between IgG and thymic DP T, CD4 T, and CD8 T cells is presented. The small RNA-seq analysis identified ten mature miRNAs that were modulated by AD IgG compared to mock condition (miR-181b-5p, hsa-miR-130b-3p, hsa-miR-26a-5p, hsa-miR-4497, has-miR-146a, hsa-let-7i-5p, hsa-miR-342-3p, has-miR-148a-3p, has-miR-92a and has-miR-4492). The prediction of the targetome of the seven dysregulated miRNAs between AD and mock control revealed 122 putative targets, and functional and pathway enrichment analyses were performed. Our results enhance our understanding of the mechanism by which IgG can collaborate in thymic T cells in the setting of infant AD.     

Preparation and properties of multiwalled carbon nanotube/polycaprolactone nanocomposites

Multiwalled carbon nanotube/polycaprolactone nanocomposites (MWNT/PCL) were prepared by in situ polymerization, whereby as‐received MWNTs (P‐MWNTs) and purified MWNTs (A‐MWNTs) were used as reinforcing materials. The A‐MWNTs were purified by nitric acid treatment, which introduced the carboxyl groups (COOH) on the MWNT. The micrographs of the fractured surfaces of the nanocomposites showed that the A‐MWNTs in A‐MWNT/PCL were better dispersed than P‐MWNTs in PCL matrix (P‐MWNT/PCL). Percolation thresholds of the P‐MWNT/PCL and A‐MWNT/PCL, which were studied by rheological properties, were found at ～2 wt % of the MWNT. The conductivity of the P‐MWNT/PCL was between 10^?1 and 10^?2 S/cm by loading of 2 wt % of MWNT although that of the A‐MWNT/PCL reached ～10^?2 S/cm by loading of 7 wt % of MWNT. The conductivity of the P‐MWNT/PCL was higher than that of the A‐MWNT/PCL at the entire range of the studied MWNT loading, which might be due to the destruction of π‐network of the MWNT by acid treatment, although the A‐MWNT/PCL was better dispersed than the P‐MWNT/PCL. The amount of the MWNT at which the conductivity of the nanocomposite started to increase was strongly correlated with the percolation threshold. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 1957–1963, 2007