Internal structure of coalitions in competitive and altruistic graphical coalitional games

This paper introduces a certain graphical coalitional game where the internal topology of the coalition depends on a prescribed communication graph structure among the agents. The game Value Function is required to satisfy four Axioms of Value. These axioms make it possible to provide a refined study of coalition structures on graphs by defining a formal graphical game and by assigning a Positional Advantage, based on the Shapley value, to each agent in a coalition based on its connectivity properties within the graph. Using the Axioms of Value the graphical coalitional game can be shown to satisfy properties such as convexity, fairness, cohesiveness, and full cooperativeness. Three measures of the contributions of agents to a coalition are introduced: marginal contribution, competitive contribution, and altruistic contribution. The mathematical framework given here is used to establish results regarding the dependence of these three types of contributions on the graph topology, and changes in these contributions due to changes in graph topology. Based on these different contributions, three online sequential decision games are defined on top of the graphical coalitional game, and the stable graphs under each of these sequential decision games are studied. It is shown that the stable graphs under the objective of maximizing the marginal contribution are any connected graph. The stable graphs under the objective of maximizing the competitive contribution are the complete graph. The stable graphs under the objective of maximizing the altruistic contribution are any tree.     

以系统为中心的全层次纳米级SoC设计方法学

在SoC的开发期间,一种以系统为中心的、从架构设计到流片的全层次设计方法学被用于加速设计进度。本文详细介绍了新的电子/物理设计方法学以及在芯片的开发过程中得到的结论。     

Analysis for superior radiation resistance of InP-based solar cells

Better radiation resistance of InP-based solar cells such as InGaP and InGaAsP cells, and minority-carrier-injection-enhanced annealing phenomena of radiation-induced defects in those materials previously found by the authors has been analyzed by considering introduction rates and annealing behavior of radiation-induced defects in InP-related materials. Radiation resistance of InP-related materials is found to be explained by lower damage coefficients and band-gap energy effects on solar cell degradation compared to other materials.     

Velocity-image model for online signature verification.

In general, online signature capturing devices provide outputs in the form of shape and velocity signals. In the past, strokes have been extracted while tracking velocity signal minimas. However, the resulting strokes are larger and complicated in shape and thus make the subsequent job of generating a discriminative template difficult. We propose a new stroke-based algorithm that splits velocity signal into various bands. Based on these bands, strokes are extracted which are smaller and more simpler in nature. Training of our proposed system revealed that low- and high-velocity bands of the signal are unstable, whereas the medium-velocity band can be used for discrimination purposes. Euclidean distances of strokes extracted on the basis of medium velocity band are used for verification purpose. The experiments conducted show improvement in discriminative capability of the proposed stroke-based system.     

Characterization of dissolved compounds in submerged anaerobic membrane bioreactors (SAMBRs)

Two submerged anaerobic membrane bioreactors (SAMBRs) with essentially 100% cell recycle (150 days retention time, SRT), one with powdered activated carbon addition (PAC 1.7 g L^?1) and one without, were continuously fed a low‐strength feed (450 mg COD L^?1) in order to investigate membrane fouling and to characterize the foulants. The SAMBR which did not receive PAC experienced more fouling, and the molecular weight (MW) distribution showed that there was a greater amount of high‐MW compounds in this reactor when compared with the reactor with PAC. Size exclusion chromatography showed that although extracellular polymeric substances (EPS) seemed to contribute to the soluble chemical oxygen demand (COD) inside the reactor, it was mainly rejected by the membrane. High‐MW protein and carbohydrate material originating mainly from cell lysis and EPS seemed to be the main organics that contributed to the internal fouling of the membrane. Copyright © 2006 Society of Chemical Industry     

Influence of oxygen vacancies on the structural,dielectric, and magnetic properties of (Mn,Co) co-doped ZnO nanostructures

We analysed the variation and effect of oxygen vacancies on the structural, dielectric and magnetic properties in case of Mn (4%) and Co (1, 2 and 4%) co-doped ZnO nanoparticles (NPs), synthesized by chemical precipitation route and annealed at 750 °C for 2 h. From the XRD, the calculated average crystallite size increased from15.30?±?0.73 nm to 16.71?±?012 nm, when Co content is increased from 1 to 4%. Enhancement of dopants (Mn, Co) introduced more and more oxygen vacancies to ZnO lattice confirmed from EDX and XPS. The high-temperature annealing leads to reduction of the dielectric properties due to enhancement in grain growth (large grain volume and lesser number of grain boundaries) with the incorporation of Co and Mn ions into the ZnO lattice. The electrical conductivity of the Mn doped and (Mn, Co) co-doped ZnO samples were enhanced due to increase in the volume of conducting grains and charge density (liberation of trapped charge carriers in oxygen vacancies and free charge carriers at higher frequencies). The Mn-doped and (Mn, Co) co-doped ZnO NPs show ferromagnetic (FM) behaviour. The saturation and remnant magnetizations (M_s and M_r) elevates from (0.235 to 1.489)?×?10^?2 and (0.12 to 0.27)?×?10^?2 emu/g while Coercivity (H_c) reduced from 97 to 36 Oe with enhancement in the concentration of dopants in ZnO matrix. Oxygen vacancies were found to be the main reason for room-temperature ferromagnetism (RTFM) in the doped and co-doped ZnO NPs. The results show that the enhanced dielectric and magnetic properties of Mn doped and (Mn, Co) co-doped ZnO is strongly correlated with the concentration of oxygen vacancies. The observed enhanced RTFM, dielectric properties and electrical conductivity makes TM doped ZnO nanoparticles suitable for spintronics, microelectronics and optoelectronics based applications.     

Modeling of Effective Thermal Conductivity of Dunite Rocks as a Function of Temperature

The thermal conductivity, thermal diffusivity, and heat capacity per unit volume of dunite rocks taken from Chillas near Gilgit, Pakistan, have been measured simultaneously using the transient plane source technique. The temperature dependence of the thermal transport properties was studied in the temperature range from 303 K to 483 K. Different relations for the estimation of the thermal conductivity are applied. A proposed model for the prediction of the thermal conductivity as a function of temperature is also given. It is observed that the values of the effective thermal conductivity predicted by the proposed model are in agreement with the experimental thermal conductivity data within 9%.     

Local temperature determination of optically excited nanoparticles and nanodots

A thin film of Al(0.94)Ga(0.06)N embedded with Er(3+) ions is used as an optical temperature sensor to image the temperature profile around optically excited gold nanostructures of 40 nm gold nanoparticles and lithographically prepared gold nanodots. The sensor is calibrated to give the local temperature of a hot nanostructure by comparing the measured temperature change of a spherical 40 nm gold NP to the theoretical temperature change calculated from the absorption cross section. The calibration allows us to measure the temperature where a lithographically prepared gold nanodot melts, in agreement with the bulk melting point of gold, and the size of the nanodot, in agreement with SEM and AFM results. Also, we measure an enhancement in the Er(3+) photoluminescence due to an interaction of the NP and Er(3+). We use this enhancement to determine the laser intensity that melts the NP and find that there is a positive discontinuous temperature of 833 K. We use this discontinuous temperature to obtain an interface conductance of ～10 MW/m(2)-K for the gold NP on our thermal sensor surface.     

Gastric Tract Disease Recognition Using Optimized Deep Learning Features

Artificial intelligence aids for healthcare have received a great deal of attention. Approximately one million patients with gastrointestinal diseases have been diagnosed via wireless capsule endoscopy (WCE). Early diagnosis facilitates appropriate treatment and saves lives. Deep learning-based techniques have been used to identify gastrointestinal ulcers, bleeding sites, and polyps. However, small lesions may be misclassified. We developed a deep learning-based best-feature method to classify various stomach diseases evident in WCE images. Initially, we use hybrid contrast enhancement to distinguish diseased from normal regions. Then, a pretrained model is fine-tuned, and further training is done via transfer learning. Deep features are extracted from the last two layers and fused using a vector length-based approach. We improve the genetic algorithm using a fitness function and kurtosis to select optimal features that are graded by a classifier. We evaluate a database containing 24,000 WCE images of ulcers, bleeding sites, polyps, and healthy tissue. The cubic support vector machine classifier was optimal; the average accuracy was 99%.     

Can end-user feedback in social media be trusted for software evolution: Exploring and analyzing fake reviews

End-user feedback in social media platforms, particularly in the app stores, is increasing exponentially with each passing day. Software researchers and vendors started to mine end-user feedback by proposing text analytics methods and tools to extract useful information for software evolution and maintenance. In addition, research shows that positive feedback and high-star app ratings attract more users and increase downloads. However, it emerged in the fake review market, where software vendors started incorporating fake reviews against their corresponding applications to improve overall software ratings. For this purpose, we conducted an exploratory study to understand how end-users register and write fake reviews in the Google Play Store. We curated a research data set containing 68,000 end-user comments from the Google Play Store and a fake review generator, that is, the Testimonial generator (TG). Its purpose is to understand fake reviews on these platforms and identify the common patterns potential end-users and professionals use to report fake reviews by critically analyzing the end-user feedback. We conducted a detailed survey at the University of Science and Technology Bannu, Pakistan, to identify the intelligence and accuracy of crowd-users in manually identifying fake reviews. In addition, we developed a ground truth to be compared with the results obtained from the automated machine and deep learning (M&DL) classifier experiment. In the survey, 512 end-users participated and recorded their responses in identifying fake reviews. Finally, various M&DL classifiers are employed to classify and identify end-user reviews into real and fake to automate the process. Unlike humans, the M&DL classifiers performed well in automatically classifying reviews into real and fake by obtaining much higher accuracy, precision, recall, and f-measures. The accuracy of manually identifying fake reviews by the crowd-users is 44.4%. In contrast, the M&DL classifiers obtained an average accuracy of 96%. The experimental results obtained with various M&DL classifiers are encouraging. It is the first step towards identifying fake reviews in the app store by studying its implications in software and requirements engineering.