Extracting trust information from security system of a service

The issue of trust is a research problem in emerging open environments, such as ubiquitous networks. Such environments are highly dynamic and they contain diverse number of services and autonomous entities. Entities in open environments have different security needs from services. Trust computations related to the security systems of services necessitate information that meets needs of each entity. Obtaining such information is a challenging issue for entities. In this paper, we propose a model for extracting trust information from the security system of a service based on the needs of an entity. We formally represent security policies and security systems to extract trust information according to needs of an entity. The formal representation ensures an entity to extract trust information about a security property of a service and trust information about whole security system of the service. The proposed model is applied to Dental Clinic Patient Service as a case study with two scenarios. The scenarios are analyzed experimentally with simulations. The experimental evaluation shows that the proposed model provides trust information related to the security system of a service based on the needs of an entity and it is applicable in emerging open environments.     

A new time invariant fuzzy time series forecasting method based on particle swarm optimization

In the analysis of time invariant fuzzy time series, fuzzy logic group relationships tables have been generally preferred for determination of fuzzy logic relationships. The reason of this is that it is not need to perform complex matrix operations when these tables are used. On the other hand, when fuzzy logic group relationships tables are exploited, membership values of fuzzy sets are ignored. Thus, in defiance of fuzzy set theory, fuzzy sets’ elements with the highest membership value are only considered. This situation causes information loss and decrease in the explanation power of the model. To deal with these problems, a novel time invariant fuzzy time series forecasting approach is proposed in this study. In the proposed method, membership values in the fuzzy relationship matrix are computed by using particle swarm optimization technique. The method suggested in this study is the first method proposed in the literature in which particle swarm optimization algorithm is used to determine fuzzy relations. In addition, in order to increase forecasting accuracy and make the proposed approach more systematic, the fuzzy c-means clustering method is used for fuzzification of time series in the proposed method. The proposed method is applied to well-known time series to show the forecasting performance of the method. These time series are also analyzed by using some other forecasting methods available in the literature. Then, the results obtained from the proposed method are compared to those produced by the other methods. It is observed that the proposed method gives the most accurate forecasts.     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Robust multilayer neural network based on median neuron model

Multilayer perceptron has been widely used in time series forecasting for last two decades. However, it is a well-known fact that the forecasting performance of multilayer perceptron is negatively affected when data have outliers and this is an important problem. In recent years, some alternative neuron models such as generalized-mean neuron, geometric mean neuron, and single multiplicative neuron have been also proposed in the literature. However, it is expected that forecasting performance of artificial neural network approaches based on these neuron models can be also negatively affected by outliers since the aggregation function employed in these models is based on mean value. In this study, a new multilayer feed forward neural network, which is called median neuron model multilayer feed forward (MNM-MFF) model, is proposed in order to deal with this problem caused by outliers and to reach high accuracy level. In the proposed model, unlike other models suggested in the literature, MNM which has median-based aggregation function is employed. MNM is also firstly defined in this study. MNM-MFF is a robust neural network method since aggregation functions in MNM-MFF are based on median, which is not affected much by outliers. In addition, to train MNM-MFF model, particle swarm optimization method was utilized. MNM-MFF was applied to two well-known time series in order to evaluate the performance of the proposed approach. As a result of the implementation, it was observed that the proposed MNM-MFF model has high forecasting accuracy and it is not affected by outlier as much as multilayer perceptron model. Proposed method brings improvement in 7 % for data without outlier, in 90 % for data with outlier, in 95 % for data with bigger outlier.     

Analysis of autocatalytic networks in biology

Autocatalytic networks, in particular the glycolytic pathway, constitute an important part of the cell metabolism. Changes in the concentration of metabolites and catalyzing enzymes during the lifetime of the cell can lead to perturbations from its nominal operating condition. We investigate the effects of such perturbations on stability properties, e.g., the extent of regions of attraction, of a particular family of autocatalytic network models. Numerical experiments demonstrate that systems that are robust with respect to perturbations in the parameter space have an easily “verifiable” (in terms of proof complexity) region of attraction properties. Motivated by the computational complexity of optimization-based formulations, we take a compositional approach and exploit a natural decomposition of the system, induced by the underlying biological structure, into a feedback interconnection of two input–output subsystems: a small subsystem with complicating nonlinearities and a large subsystem with simple dynamics. This decomposition simplifies the analysis of large pathways by assembling region of attraction certificates based on the input–output properties of the subsystems. It enables numerical as well as analytical construction of block-diagonal Lyapunov functions for a large family of autocatalytic pathways.     

Functionalized Hybrid Coatings on ABS Surfaces by PLD and Dip Coatings

It is important to give water-repellent and antibacterial properties to the acrylonitrile butadiene styrene (ABS) surfaces of the hearing aids. In this study, the sol–gel Si and sol–gel Ti solutions were prepared from the reactions of silicon ethoxide, titanium butoxide and methacrylic acid. The catalyst and Dynasylan F8815 were added to the sol–gel solutions to give hydrophobic properties onto the ABS surfaces. Additionally, silver nanoparticles were synthesized by nanosecond laser and added to the coating solutions to give extra antibacterial properties. The surfaces of the ABS targets were coated using the sol–gel dip coating and pulsed laser deposition techniques. The coatings with good adhesion between film and substrate and good water-repellent properties were achieved. The average contact angles for the coated ABS surfaces were measured in the range between 120 and 125 degrees. The obtained sol–gel materials and produced thin films onto the ABS surfaces were also analyzed in terms of the antibacterial properties. The highly antibacterial properties were observed in the sol–gel solutions and the thin films.     

Ultrasonic Pulse Velocity in Nondestructive Evaluation of Low Quality and Damaged Concrete and Masonry Construction

This paper discusses applications of ASTM C 597 “Standard Test Method for Pulse Velocity Through Concrete” technique to field detection of damage to concrete in service and field quality assessment of cast-in-place concrete and masonry under construction. Four unique field investigation and validation studies are discussed in this paper. The first part includes field assessments of concrete members under construction with questionable quality. Case studies include detection of zones of high air content and low strength concrete in a cast-in-place, posttensioned structure and detection of voids and honeycombs in poorly consolidated cast-in-place beams. The third case study pertinent to construction involves detection of poorly consolidated collar joints in a masonry rehabilitation project. In addition to assessments during construction and rehabilitation, this paper also discusses assessment of damage to concrete structures in service. The case study included in the paper involves exposure to elevated temperatures during a fire at a precast, double tee concrete parking structure. Nondestructive evaluation (NDE) testing findings were validated by subsequent laboratory testing or selective demolition to confirm NDE findings. This paper should be of value to practicing engineers interested in application of pulse velocity testing technique in field assessments similar to ones discussed. This paper should also be of value to researchers interested in applicability of pulse velocity to research concerning properties of concrete subjected to the damage mechanisms associated with elevated temperatures.     

Knowledge,attitudes and self-reported practices of food service staff regarding food hygiene in Edirne,Turkey

The purpose of this study was to evaluate the knowledge, attitudes and practices among food service staff with regards to food hygiene in hospitals in Edirne, Turkey, and to provide baseline data for implementing HACCP in hospital food services by carrying out a questionnaire by a face-to-face interview. The current study shows that food service staff in Edirne hospitals have insufficient knowledge regarding the basics of food hygiene. And also revealed a discrepancy between attitudes and practices towards food hygiene. There is an immediate need for continuous training among food handlers regarding safe food handling practices.