Two types of El Niño and extratropical sea-level pressure variations

This study applies the nonlinear canonical correlation analysis (NLCCA) to explore the nonlinear relationship between the sea-level pressure (SLP) anomalies over the extratropical North Pacific and sea surface temperature (SST) anomalies in the tropical Pacific during 1985–2009. Our results suggest that the asymmetry between the warm eastern Pacific (EP) El Niño–Aleutian Low mode and the cool EP La Niña–anti-phase of the Aleutian Low mode is exhibited in the first NLCCA mode. Nonlinearity of the first NLCCA SST field is enhanced after 1998, and vice versa for the SLP field. The second NLCCA SST mode reveals weak nonlinearity representing the nonlinear central tropical Pacific (CP) El Niño–CP La Niña modes, while the second SLP field depicts the North Pacific Oscillation and anti-phase with the Aleutian Low phases. The nonlinearity of the second SST and SLP NLCCA modes is found to decrease gradually with time. During 1985–1997, the SST field exhibits linearity, while the SLP field shows weak nonlinearity. During 1997–2009, the SST and SLP fields both display weak linearity. Nonlinearity between the extratropical SLP and SST fields is further weakened from the first period. The Aleutian Low pattern could be excited by both EP and CP El Niños. Moreover, the CP El Niños have more connections with the North Pacific Oscillation state rather than the EP El Niños. Conclusively, this study reveals the asymmetric modes between the SLP and SST by the nonlinear method, and contributes to the understanding of the extratropical SLP variability response to two types El Niño events.     

Robust optimization of supply chain network design in fuzzy decision system

This paper presents a new robust optimization method for supply chain network design problem by employing variable possibility distributions. Due to the variability of market conditions and demands, there exist some impreciseness and ambiguousness in developing procurement and distribution plans. The proposed optimization method incorporates the uncertainties encountered in the manufacturing industry. The main motivation for building this optimization model is to make tools available for producers to develop robust supply chain network design. The modeling approach selected is a fuzzy value-at-risk (VaR) optimization model, in which the uncertain demands and transportation costs are characterized by variable possibility distributions. The variable possibility distributions are obtained by using the method of possibility critical value reduction to the secondary possibility distributions of uncertain demands and costs. We also discuss the equivalent parametric representation of credibility constraints and VaR objective function. Furthermore, we take the advantage of structural characteristics of the equivalent optimization model to design a parameter-based domain decomposition method. Using the proposed method, the original optimization problem is decomposed to two equivalent mixed-integer parametric programming sub-models so that we can solve the original optimization problem indirectly by solving its sub-models. Finally, we present an application example about a food processing company with four suppliers, five plants, five distribution centers and five customer zones. We formulate our application example as parametric optimization models and conduct our numerical experiments in the cases when the input data (demands and costs) are deterministic, have fixed possibility distributions and have variable possibility distributions. Experimental results show that our parametric optimization method can provide an effective and flexible way for decision makers to design a supply chain network.     

On-chip whole blood plasma separator based on microfiltration,sedimentation and wetting contrast

Miniaturized on-chip blood separators have a great value for point-of-care diagnosis. In our work, a combined design strategy—microfiltration, sedimentation in a retarded flow, and wetting contrast—was taken to overcome the known limitations of on-chip blood separators. Our microfluidic chip consists of a polydimethylsiloxane micropillar array and an etched glass with microchannel branches. The red blood cells are significantly slowed and gradually settled down due to micropillars and enlarged dimension of a chamber. An etched glass microchannel allows the extraction of blood plasma exclusively due to the capillary effect. The fabricated microfluidic device can separate blood plasma from a whole blood sample without any external driving force or dilution. The measured plasma separation efficiency was close to 100 % from human whole blood. Autonomous on-chip separation and collection of blood plasma was demonstrated.     

An effective algorithm to detect both smoke and flame using color and wavelet analysis

Fire detection is an important task in many applications. Smoke and flame are two essential symbols of fire in images. In this paper, we propose an algorithm to detect smoke and flame simultaneously for color dynamic video sequences obtained from a stationary camera in open space. Motion is a common feature of smoke and flame and usually has been used at the beginning for extraction from a current frame of candidate areas. The adaptive background subtraction has been utilized at a stage of moving detection. In addition, the optical flow-based movement estimation has been applied to identify a chaotic motion. With the spatial and temporal wavelet analysis, Weber contrast analysis and color segmentation, we achieved moving blobs classification. Real video surveillance sequences from publicly available datasets have been used for smoke detection with the utilization of our algorithm. We also have conducted a set of experiments. Experiments results have shown that our algorithm can achieve higher detection rate of 87% for smoke and 92% for flame.     

PolSAR image compression based on online sparse K-SVD dictionary learning

we present a novel polarimetric synthetic aperture radar (PolSAR) image compression scheme. PolSAR data contains lots of similar redundancies in single-channel and massively correlation between polarimetric channels. So these features make it difficult to represent PolSAR data efficiently. In this paper, discrete cosine transform (DCT) is adopted to remove redundancies between polarimetric channels, simple but quite efficient in improving compressibility. Sparse K-singular value decomposition (K-SVD) dictionary learning algorithm is utilized to remove redundancies within each channel image. Double sparsity scheme will be able to achieve fast convergence and low representation error by using a small number of sparsity dictionary elements, which is beneficial for the task of PolSAR image compression. Experimental results demonstrate that both numerical evaluation indicators and visual effect of reconstructed images outperform other methods, such as SPIHT, JPEG2000, and offline method.     

A heuristic approach to discovering user correlations from organized social stream data

Multimedia Tools and Applications - Recently, with the widespread popularity of SNS (Social Network Service), such as Twitter, Facebook, people are increasingly accustomed to sharing feeling,...     

Social recommendation based on trust and influence in SNS environments

The development of social media provides convenience to people’s lives. People’s social relationship and influence on each other is an important factor in a variety of social activities. It is obviously important for the recommendation, while social relationship and user influence are rarely taken into account in traditional recommendation algorithms. In this paper, we propose a new approach to personalized recommendation on social media in order to make use of such a kind of information, and introduce and define a set of new measures to evaluate trust and influence based on users’ social relationship and rating information. We develop a social recommendation algorithm based on modeling of users’ social trust and influence combined with collaborative filtering. The optimal linear relation between them will be reached by the proposed method, because the importance of users’ social trust and influence varies with the data. Our experimental results show that the proposed algorithm outperforms traditional recommendation in terms of recommendation accuracy and stability.