Yong Deng

Bio: Yong Deng is an academic researcher from University of Electronic Science and Technology of China. The author has contributed to research in topics: Complex network & Fuzzy logic. The author has an hindex of 72, co-authored 488 publications receiving 15656 citations. Previous affiliations of Yong Deng include China University of Geosciences (Beijing) & Hangzhou Normal University.

Supplier selection using AHP methodology extended by D numbers

Abstract: Supplier selection is an important issue in supply chain management (SCM), and essentially is a multi-criteria decision-making problem. Supplier selection highly depends on experts' assessments. In the process of that, it inevitably involves various types of uncertainty such as imprecision, fuzziness and incompleteness due to the inability of human being's subjective judgment. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, based on a new effective and feasible representation of uncertain information, called D numbers, a D-AHP method is proposed for the supplier selection problem, which extends the classical analytic hierarchy process (AHP) method. Within the proposed method, D numbers extended fuzzy preference relation has been involved to represent the decision matrix of pairwise comparisons given by experts. An illustrative example is presented to demonstrate the effectiveness of the proposed method.

Short communication: Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment

Abstract: A common algorithm to solve the shortest path problem (SPP) is the Dijkstra algorithm. In this paper, a generalized Dijkstra algorithm is proposed to handle SPP in an uncertain environment. Two key issues need to be addressed in SPP with fuzzy parameters. One is how to determine the addition of two edges. The other is how to compare the distance between two different paths with their edge lengths represented by fuzzy numbers. To solve these problems, the graded mean integration representation of fuzzy numbers is adopted to improve the classical Dijkstra algorithm. A numerical example of a transportation network is used to illustrate the efficiency of the proposed method.

Enhanced thermal conductivity of PEG/diatomite shape-stabilized phase change materials with Ag nanoparticles for thermal energy storage

Abstract: Ag nanoparticles (AgNPs) are a promising additive because they can enhance the thermal conductivity of organic phase change materials. In this paper, a series of high thermally-conductive shape-stabilized phase change materials (ss-PCMs) were tailored by blending PEG with AgNP-decorated diatomite. In order to enlarge its pore size and specific surface area and make it a suitable PEG carrier, the effect of alkali leaching on the microstructure of diatomite was studied. While PEG melted during phase transformation, the maximum load of PEG could reach 63 wt%, which was 31% higher than that of the raw diatomite. Spherical-shaped crystalline AgNPs with a diameter range of 3–10 nm were uniformly decorated onto diatomite. The XPS results for this material proved that the valence state of silver in the PEG/diatomite PCM was mainly zero. The phase change enthalpy of the PEG/diatomite/Ag PCM reached 111.3 J g−1, and the thermal conductivity of the PEG/diatomite PCM containing 7.2 wt% Ag was 0.82 W m−1 K−1, which was 127% higher than that of the PEG/diatomite composite. The reduced melting and freezing periods indirectly proved that heat transfer in the composite material during the heat storage and release process was enhanced through the thermal conductivity improvement. The composite PCM was thermally and chemically stable even after 200 cycles of melting and freezing. This indicated that the resulting composite PCMs were promising candidate materials for building applications due to their large latent heat, suitable phase change temperature, excellent chemical compatibility, improved supercooling extent, and high thermal stability.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Evolution and the Theory of Games

Abstract: In the Hamadryas baboon, males are substantially larger than females. A troop of baboons is subdivided into a number of ‘one-male groups’, consisting of one adult male and one or more females with their young. The male prevents any of ‘his’ females from moving too far from him. Kummer (1971) performed the following experiment. Two males, A and B, previously unknown to each other, were placed in a large enclosure. Male A was free to move about the enclosure, but male B was shut in a small cage, from which he could observe A but not interfere. A female, unknown to both males, was then placed in the enclosure. Within 20 minutes male A had persuaded the female to accept his ownership. Male B was then released into the open enclosure. Instead of challenging male A , B avoided any contact, accepting A’s ownership.