Determining trust in media-rich websites using semantic similarity

Significant growth of multimedia content on the World Wide Web (or simply ??Web??) has made it an essential part of peoples lives. The web provides enormous amount of information, however, it is very important for the users to be able to gauge the trustworthiness of web information. Users normally access content from the first few links provided to them by search engines such as Google or Yahoo!. This is assuming that these search engines provide factual information, which may be popular due to criteria such as page rank but may not always be trustworthy from the factual aspects. This paper presents a mechanism to determine trust of websites based on the semantic similarity of their multimedia content with already established and trusted websites. The proposed method allows for dynamic computation of the trust level of websites of different domains and hence overcomes the dependency on traditional user feedback methods for determining trust. In fact, our method attempts to emulate the evolving process of trust that takes place in a user??s mind. The experimental results have been provided to demonstrate the utility and practicality of the proposed method.     

Impact of Zinc oxide nanoparticles on eggplant (S. melongena): studies on growth and the accumulation of nanoparticles

The increasing use of nanoparticles and their occurrence in the environment has made it imperative to elucidate their impact on the environment. Although several studies have advanced the authors’ understanding of nanoparticle–plant interactions, their knowledge of the exposure of plants to nanoparticles and their effects on edible crop plants remain meager and is often paradoxical. The aim of this study was to increase their knowledge on the effect of zinc oxide (ZnO) nanoparticles on eggplant seed germination and seedling growth. ZnO nanoparticles had a negative effect on the growth of eggplant in plant tissue‐culture conditions, as the growth of seedlings decreased with the increase in the concentration of ZnO nanoparticles. In contrast, ZnO nanoparticles enhanced eggplant growth under greenhouse conditions. The accumulation of ZnO nanoparticles in various parts of eggplant was observed through scanning electron microscopy of both plant tissue‐culture and greenhouse‐raised eggplant seedlings. To the best of their knowledge, this is the first study to report on ZnO nanoparticle accumulation in eggplant and its effect on seed germination and seedling growth.Inspec keywords: crops, zinc compounds, scanning electron microscopy, II‐VI semiconductors, nanoparticles, agriculture, cellular biophysics, nanofabricationOther keywords: plant tissue‐culture, greenhouse‐raised eggplant seedlings, ZnO nanoparticle accumulation, seedling growth, ZnO nanoparticles, nanoparticle–plant interactions, zinc oxide nanoparticles, eggplant seed germination, eggplant growth, ZnO     

An application of the technology acceptance model to the level of Internet usage by older adults

In this cross-sectional study, the principles of a technology acceptance model were used to identify variables related to the level of Internet usage by older adults. Community-dwelling older adults aged 60–88 years completed a postal questionnaire survey that elicited responses on the use of the Internet. Out of a sample of 592 older adults (236 males and 356 females), 50.7% used the Internet. A multiple linear regression analysis was carried out on the Internet users sample using the self-reported number of hours of Internet usage per week as the dependent variable. The results indicated that attitude toward using the Internet and good health status were statistically significant predictors of the level of Internet usage. A second multiple regression analysis using Internet activity as the dependent variable showed that attitude, usefulness, good health, and gender (males) were significant predictor variables.     

Discovering excitatory relationships using dynamic Bayesian networks

Mining temporal network models from discrete event streams is an important problem with applications in computational neuroscience, physical plant diagnostics, and human–computer interaction modeling. In this paper, we introduce the notion of excitatory networks which are essentially temporal models where all connections are stimulative, rather than inhibitive. The emphasis on excitatory connections facilitates learning of network models by creating bridges to frequent episode mining. Specifically, we show that frequent episodes help identify nodes with high mutual information relationships and that such relationships can be summarized into a dynamic Bayesian network (DBN). This leads to an algorithm that is significantly faster than state-of-the-art methods for inferring DBNs, while simultaneously providing theoretical guarantees on network optimality. We demonstrate the advantages of our approach through an application in neuroscience, where we show how strong excitatory networks can be efficiently inferred from both mathematical models of spiking neurons and several real neuroscience datasets.     

Rough Neural Computing in Signal Analysis

This paper introduces an application of a particular form of rough neural computing in signal analysis. The form of rough neural network used in this study is based on rough sets, rough membership functions, and decision rules. Two forms of neurons are found in such a network: rough membership function neurons and decider neurons. Each rough membership function neuron constructs upper and lower approximation equivalence classes in response to input signals as an aid to classifying inputs. In this paper, the output of a rough membership function neuron results from the computation performed by a rough membership function in determining degree of overlap between an upper approximation set representing approximate knowledge about inputs and a set of measurements representing certain knowledge about a particular class of objects. Decider neurons implement granules derived from decision rules extracted from data sets using rough set theory. A decider neuron instantiates approximate reasoning in assessing rough membership function values gleaned from input data. An introduction to the basic concepts underlying rough membership neural networks is briefly given. An application of rough neural computing in classifying the power system faults is considered.     

The interaction between lid diameter,height and shape on wrist torque exertion in younger and older adults

The following study investigated wrist torque strength measurements of a group of younger and older adults. The aim of the study was to examine the impact of shape, diameter and height of lid on wrist torque opening strength. Forty participants took part in the study in four groups, younger males and females and older males and females. Data were collected for 12 test pieces. Anthropometric data were also obtained for stature, weight, hand breadth, hand length, chuck grip force, grip force, lateral grip force and pinch grip force. The analysis of the wrist torque strength measurements found that participants could exert greater force with square test pieces compared to circular test pieces of the same diameter. Examination of the circular test pieces found that as diameter and height increased, so did torque exertion data for the test pieces between 20 mm and 50 mm diameter. The surface area of the test pieces was found to be highly correlated with the level of torque exertion, thus a linear model was developed to describe this relationship. The model could be used to predict maximal torque closure levels for use in the packaging industry. The anthropometric data revealed that as height, weight, hand length and hand breadth increased, there was a correlation with the levels of torque exerted. Future research needs to further examine the relationship between surface area and torque exerted and the design of spherical lids to increase the contact area between the hand and the lid.     

Systematic hardness measurements on some rare earth garnet crystal

Microhardness measurements were undertaken on twelve rare earth garnet crystals. In yttrium aluminium garnet and gadolinium gallium garnet, there was no measurable difference in the hardness values of pure and nominally Nd-doped crystals. The hardness values were correlated with the lattice and elastic constants. An analysis of hardness data in terms of the interatomic binding indicated a high degree of covalency. Dedicated to the memory of Dr R Thyagarajan, eminent Solid State Physicist.     

Trouble Call Analysis in Radial Distribution Feeders Using an Idiotypic Immune System

Utilities typically depend on customers to call and inform them of the service interruptions due to outages by entering their addresses. After sufficient calls are received, the utility is able to pinpoint the location of outages. It is essential for the utility to have a good idea on the location of outages before sending crew for restoration work to optimally utilize their resources. This article uses an idiotypic immune system to model and simulate radial distribution system to identify the operated protective devices due to outages. The incoming calls are modeled as antigens, and the protective devices are modeled as antibodies. Using a differential equation describing dynamics of the immune system, the operated protective devices in the network are identified. A pre-processing technique is presented to handle cases with multiple outages within the same period. Different threshold values for call grouping are tested to determine the most suitable values. Several real-life examples are considered to demonstrate that the proposed method is able to identify the correct protective devices.