Relaxed constraints support vector machines for noisy data

Real-world data collected for computer-based applications are frequently impure. Differentiation of outliers and noisy data from normal ones is a major task in data mining applications. On the other hand, elimination of noisy and outlier data from training samples of a dataset may lead to over-fitting or information loss. A fuzzy support vector machine (FSVM) provides an effective means to deal with this problem. It reduces the effect of the noisy data and outliers by using a fuzzy membership functions. In this paper, a new formation for SVMs is introduced that considers importance degrees for training samples. The constraints of the SVM are converted to fuzzy inequalities. The proposed method, RSVM, shows better efficiency in the classification of data in different domains. Especially, using the proposed RSVM for multi-class classification of arrhythmia disease is presented at the end of this paper as a practical case study to show the effectiveness of the proposed system.     

Hamshahri: A standard Persian text collection

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.     

Multi-objective optimization of laminated composite shells for minimum mass/cost and maximum buckling pressure with failure criteria under external hydrostatic pressure

In the present study, Multi-objective optimization of composite cylindrical shell under external hydrostatic pressure was investigated. Parameters of mass, cost and buckling pressure as fitness functions and failure criteria as optimization criterion were considered. The objective function of buckling has been used by performing the analytical energy equations and Tsai-Wu and Hashin failure criteria have been considered. Multi-objective optimization was performed by improving the evolutionary algorithm of NSGA-II. Also the kind of material, quantity of layers and fiber orientations have been considered as design variables. After optimizing, Pareto front and corresponding points to Pareto front are presented. Trade of points which have optimized mass and cost were selected by determining the specified pressure as design criteria. Finally, an optimized model of composite cylindrical shell with the optimum pattern of fiber orientations having appropriate cost and mass is presented which can tolerate the maximum external hydrostatic pressure.

     

RBC-OLSR: Reputation-based clustering OLSR protocol for wireless ad hoc networks

We consider the problem of assuring the trustworthiness (i.e. reliability and robustness) and prolonging the lifetime of wireless ad hoc networks, using the OLSR routing protocol, in the presence of selfish nodes. Assuring the trustworthiness of these networks can be achieved by selecting the most trusted paths, while prolonging the lifetime can be achieved by (1) reducing the number of relay nodes (MPR) propagating the topology control (TC) messages and (2) considering the residual energy levels of these relay nodes in the selection process. In this paper, we propose a novel clustering algorithm and a relay node selection algorithm based on the residual energy level and connectivity index of the nodes. This hybrid model is referred to as H-OLSR. The OLSR messages are adapted to handle the cluster heads election and the MPR nodes selection algorithms. These algorithms are designed to cope with selfish nodes that are getting benefits from others without cooperating with them. Hence, we propose an incentive compatible mechanism that motivates nodes to behave truthfully during the selection and election processes. Incentive retributions increase the reputation of the nodes. Since network services are granted according to nodes’ accumulated reputation, the nodes should cooperate. Finally, based on nodes’ reputation, the most trusted forwarding paths are determined. This reputation-based hybrid model is referred to as RH-OLSR. Simulation results show that the novel H-OLSR model based on energy and connectivity can efficiently prolong the network lifetime, while the RH-OLSR model improves the trustworthiness of the network through the selection of the most trusted paths based on nodes’ reputations. These are the two different processes used to define the reputation-based clustering OLSR (RBC-OLSR) routing protocol.     

Classification of non-alcoholic beer based on aftertaste sensory evaluation by chemometric tools

Sensory evaluation is the application of knowledge and skills derived from several different scientific and technical disciplines, physiology, chemistry, mathematics and statistics, human behavior, and knowledge about product preparation practices. This research was aimed to evaluate aftertaste sensory attributes of commercial non-alcoholic beer brands (P1, P2, P3, P4, P5, P6, P7) by several chemometric tools. These attributes were bitter, sour, sweet, fruity, liquorice, artificial, body, intensity and duration. The results showed that the data are in a good consistency. Therefore, the brands were statistically classified in several categories. Linear techniques as Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were performed over the data that revealed all types of beer are well separated except a partial overlapping between zones corresponding to P4, P6 and P7. In this research, for the confirmation of the groups observed in PCA and in order to calculate the errors in calibration and in validation, PLS-DA technique was used. Based on the quantitative data of PLS-DA, the classification accuracy values were ranked within 49-86%. Moreover, it was found that the classification accuracy of LDA was much better than PCA. It shows that this trained sensory panel can discriminate among the samples except an overlapping between two types of beer. Also, two types of artificial networks were used: Probabilistic Neural Networks (PNN) with Radial Basis Functions (RBF) and FeedForward Networks with Back Propagation (BP) learning method. The highest classification success rate (correct predicted number over total number of measurements) of about 97% was obtained for RBF followed by 94% for BP. The results obtained in this study could be used as a reference for electronic nose and electronic tongue in beer quality control.     

Adaptation of Oxidative Phosphorylation Machinery Compensates for Hepatic Lipotoxicity in Early Stages of MAFLD

Alterations in mitochondrial function are an important control variable in the progression of metabolic dysfunction-associated fatty liver disease (MAFLD), while also noted by increased de novo lipogenesis (DNL) and hepatic insulin resistance. We hypothesized that the organization and function of a mitochondrial electron transport chain (ETC) in this pathologic condition is a consequence of shifted substrate availability. We addressed this question using a transgenic mouse model with increased hepatic insulin resistance and DNL due to constitutively active human SREBP-1c. The abundance of ETC complex subunits and components of key metabolic pathways are regulated in the liver of these animals. Further omics approaches combined with functional assays in isolated liver mitochondria and primary hepatocytes revealed that the SREBP-1c-forced fatty liver induced a substrate limitation for oxidative phosphorylation, inducing enhanced complex II activity. The observed increased expression of mitochondrial genes may have indicated a counteraction. In conclusion, a shift of available substrates directed toward activated DNL results in increased electron flows, mainly through complex II, to compensate for the increased energy demand of the cell. The reorganization of key compounds in energy metabolism observed in the SREBP-1c animal model might explain the initial increase in mitochondrial function observed in the early stages of human MAFLD.     

A dynamic search pattern motion estimation algorithm using prioritized motion vectors

Motion estimation is one of the critical parts in video compression standards with a high computational load. Many motion estimation algorithms have been developed to reduce the number of search points compared to a full-search algorithm without losing the quality considerably. Most of them use fixed search patterns in their first step which may suffer from trapping into local minima or searching unnecessary blocks due to inappropriate size and type of search patterns. In this paper, a new dynamic search pattern using motion vectors of spatial and temporal neighboring blocks is proposed. The motion vectors of neighboring blocks are prioritized, in order to efficiently use of halfway stop technique. The simulation results indicate that proposed algorithm is very close to the full-search algorithm in quality, compared to other rivals. Moreover, the average number of searches is often less than other algorithms.     

Hybrid Paper–Plastic Microchip for Flexible and High‐Performance Point‐of‐Care Diagnostics

A low‐cost and easy‐to‐fabricate microchip remains a key challenge for the development of true point‐of‐care (POC) diagnostics. Cellulose paper and plastic are thin, light, flexible, and abundant raw materials, which make them excellent substrates for mass production of POC devices. Herein, a hybrid paper–plastic microchip (PPMC) is developed, which can be used for both single and multiplexed detection of different targets, providing flexibility in the design and fabrication of the microchip. The developed PPMC with printed electronics is evaluated for sensitive and reliable detection of a broad range of targets, such as liver and colon cancer protein biomarkers, intact Zika virus, and human papillomavirus nucleic acid amplicons. The presented approach allows a highly specific detection of the tested targets with detection limits as low as 10² ng mL^?1 for protein biomarkers, 10³ particle per milliliter for virus particles, and 10² copies per microliter for a target nucleic acid. This approach can potentially be considered for the development of inexpensive and stable POC microchip diagnostics and is suitable for the detection of a wide range of microbial infections and cancer biomarkers.