Tungsten(VI) oxide supported rhodium nanoparticles: Highly active catalysts in hydrogen generation from ammonia borane

Herein, we report the use of tungsten(VI) oxide (WO₃) as support for Rh⁰ nanoparticles. The resulting Rh⁰/WO₃ nanoparticles are highly active and stable catalysts in H₂ generation from the hydrolysis of ammonia borane (AB). We present the results of our investigation on the particle size distribution, catalytic activity and stability of Rh⁰/WO₃ catalysts with 0.5%, 1.0%, 2.0% wt. Rh loadings in the hydrolysis reaction. The results reveal that Rh⁰/WO₃ (0.5% wt. Rh) is very promising catalyst providing a turnover frequency of 749 min^?1 in releasing 3.0 equivalent H₂ per mole of AB from the hydrolysis at 25.0 °C. The high catalytic activity of Rh⁰/WO₃ catalyst is attributed to the reducible nature of support. The report covers the results of kinetics study as well as comparative investigation of activity, recyclability, and reusability of colloidal(0) nanoparticles and Rh⁰/WO₃ (0.5 % wt. Rh) catalyst in the hydrolysis reaction.     

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.     

An Analytical Approach to the Concept of Counter-UA Operations (CUAOPS)

Unmanned Aircraft (UA) have become an integral part of the present-day joint air operations. UA have potential to be employed across the full spectrum of Air Force functions. On the other hand, UA technology could be a subject of an asymmetric use by state actors in high or low density conflict, and/or by non-state actors in many ways including terrorism, drug smuggling, and limited attack with unconventional payloads i.e. biological or chemical agents. Therefore countering the threat associated with the hostile UA use could be necessary in the future. First part of the research revealed that UA can be a threat in the future. In the second part of the research, the SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis supplemented with the Tree analysis (SWOT+Tree) provided a broader look for investigating the factors related to hostile UA use analytically. In literature, there is little information about the concepts for CUAOPS. The final goal of the paper is to find possible solutions and means for a better understanding of the nature of CUAOPS.     

Towards occupant-centric simulation-aided building design: a case study

ABSTRACT

This paper aims at illustrating the importance of occupant modelling for decision-making during the building design process. It identifies the energy implications of conventional ways of modelling occupants for the design of office buildings. Furthermore, it presents a step towards bridging the gap between the research efforts and conventional practices within the field of building performance simulation (BPS) aided design. The paper first describes occupant-related assumptions that were made during the design process of a case study office building, obtained via stakeholder interviews. Then, the impact of these assumptions on the design decisions is examined through a simulation-based investigation. The stakeholder interviews revealed that professionals from each design discipline made significantly different assumptions about occupants. The simulation results showed that assumptions about occupants and their behaviour impacted predicted energy savings of some design decisions by a factor of five or more.     

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.