Ignition Time Delay in Swirling Supersonic Flow Combustion

Supersonic hydrogen-air cylindrical mixing layer is numerically analyzed to investigate the effect of inlet swirl on ignition time delay in scramjets. Combustion is treated using detail chemical kinetics. One-equation turbulence model of Spalart and Allmaras is chosen to study the problem and advection upstream splitting method is used as computational scheme. The results show that swirling both fuel and oxidizer streams may drastically decrease the ignition distance in supersonic combustion, unlike using the swirl just in fuel stream which has no helpful effect.

Induction of Alternative Oxidase Activity in Candida albicans by Oxidising Conditions

Candida albicans ATCC 10231 had low endogenous activity of the alternative oxidase compared with that of C. albicans ATCC 10261. In C. albicans ATCC 10231 the endogenous activity declined as the cultures aged. Alternative oxidase activity could be induced in C. albicans ATCC 10231 by treatment with cyanide, but the induction of this activity required the presence of oxygen which could be replaced, at least in part, with high concentrations of potassium ferricyanide. We infer from this that the expression of the gene encoding the alternative oxidase is under the control of a redoxsensitive transcription factor.

Effects of Beak Trimming on Behavior and Agonistic Activity of Thai Native Pullets Raised in Floor Pens

The effect of beak trimming on behavior of two strains of Thai native pullets kept in floor pens was studied. Six general activities (standing, crouching, moving, comforting, roosting, and nesting), 6 beak related activities (preening, feeding, drinking, pecking at inedible object, feather pecking, and litter pecking), and 4 agonistic activities (head pecking, threatening, avoiding, and fighting) were measured twice a for 15 consecutive days, started when the pullets were 19 wk old. It was found that beak trimmed pullets drank more frequent (P

A Semantic Assistant Agent for Digital Libraries

In this paper we present semantic assistant agent (SAA), an open source digital library agent which takes user query for finding information in the digital library and takes resources- metadata and stores it semantically. SAA uses Semantic Web to improve browsing and searching for resources in digital library. All metadata stored in the library are available in RDF format for querying and processing by SemanSreach which is a part of SAA architecture. The architecture includes a generic RDF-based model that represents relationships among objects and their components. Queries against these relationships are supported by an RDF triple store.

Quality-Driven Business Process Refactoring

Appropriate description of business processes through standard notations has become one of the most important assets for organizations. Organizations must therefore deal with quality faults in business process models such as the lack of understandability and modifiability. These quality faults may be exacerbated if business process models are mined by reverse engineering, e.g., from existing information systems that support those business processes. Hence, business process refactoring is often used, which change the internal structure of business processes whilst its external behavior is preserved. This paper aims to choose the most appropriate set of refactoring operators through the quality assessment concerning understandability and modifiability. These quality features are assessed through well-proven measures proposed in the literature. Additionally, a set of measure thresholds are heuristically established for applying the most promising refactoring operators, i.e., those that achieve the highest quality improvement according to the selected measures in each case.

Developing a Simple and an Accurate Formula for the Conduction Angle of a Single Phase Rectifier with RL Load

The paper presents a simple and an accurate formula that has been developed for the conduction angle (δ) of a single phase half-wave or full-wave controlled rectifier with RL load. This formula can be also used for calculating the conduction angle (δ) in case of A.C. voltage regulator with inductive load under discontinuous current mode. The simulation results shows that the conduction angle calculated from the developed formula agree very well with that obtained from the exact solution arrived from the iterative method. Applying the developed formula can reduce the computational time and reduce the time for manual classroom calculation. In addition, the proposed formula is attractive for real time implementations.

The Role of Ga to Improve AlN-Nucleation Layer for Al0.1Ga0.9N/Si(111)

Group-III nitride material as particularly AlxGa1-xN is one of promising optoelectronic materials to require for shortwavelength devices. To achieve the high-quality AlxGa1-xN films for a high performance of such devices, AlN-nucleation layers are the important factor. To improve the AlN-nucleation layers with a variation of Ga-addition, XRD measurements were conducted to analyze the crystalline quality of the subsequent Al0.1Ga0.9N with the minimum ω-FWHMs of (0002) and (10-10) reflections of 425 arcsec and 750 arcsec, respectively. SEM and AFM measurements were performed to observe the surface morphology and TEM measurements to identify the microstructures and orientations. Results showed that the optimized Ga-atoms in the Al(Ga)Nnucleation layers improved the surface diffusion to form moreuniform crystallites in structure and size, better alignment of each crystallite, and better homogeneity of island distribution. This, hence, improves the orientation of epilayers on the Si-surface and finally improves the crystalline quality and reduces the residual strain of subsequent Al0.1Ga0.9N layers.

Improved Back Propagation Algorithm to Avoid Local Minima in Multiplicative Neuron Model

The back propagation algorithm calculates the weight changes of artificial neural networks, and a common approach is to use a training algorithm consisting of a learning rate and a momentum factor. The major drawbacks of above learning algorithm are the problems of local minima and slow convergence speeds. The addition of an extra term, called a proportional factor reduces the convergence of the back propagation algorithm. We have applied the three term back propagation to multiplicative neural network learning. The algorithm is tested on XOR and parity problem and compared with the standard back propagation training algorithm.

Low-Cost Pre-Treatment of Pharmaceutical Wastewater

Pharmaceutical industries and effluents of sewage treatment plants are the main sources of residual pharmaceuticals in water resources. These emergent pollutants may adversely impact the biophysical environment. Pharmaceutical industries often generate wastewater that changes in characteristics and quantity depending on the used manufacturing processes. Carbamazepine (CBZ), {5Hdibenzo [b,f]azepine-5-carboxamide, (C15H12N2O)}, is a significant non-biodegradable pharmaceutical contaminant in the Jordanian pharmaceutical wastewater, which is not removed by the activated sludge processes in treatment plants. Activated carbon may potentially remove that pollutant from effluents, but the high cost involved suggests that more attention should be given to the potential use of low-cost materials in order to reduce cost and environmental contamination. Powders of Jordanian non-metallic raw materials namely, Azraq Bentonite (AB), Kaolinite (K), and Zeolite (Zeo) were activated (acid and thermal treatment) and evaluated by removing CBZ. The results of batch and column techniques experiments showed around 46% and 67% removal of CBZ respectively.

Correlation of Viscosity in Nanofluids using Genetic Algorithm-neural Network (GA-NN)

An accurate and proficient artificial neural network (ANN) based genetic algorithm (GA) is developed for predicting of nanofluids viscosity. A genetic algorithm (GA) is used to optimize the neural network parameters for minimizing the error between the predictive viscosity and the experimental one. The experimental viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15 to 343.15 K and volume fraction up to 15% were used from literature. The result of this study reveals that GA-NN model is outperform to the conventional neural nets in predicting the viscosity of nanofluids with mean absolute relative error of 1.22% and 1.77% for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results of this work have also been compared with others models. The findings of this work demonstrate that the GA-NN model is an effective method for prediction viscosity of nanofluids and have better accuracy and simplicity compared with the others models.

Selection Initial modes for Belief K-modes Method

The belief K-modes method (BKM) approach is a new clustering technique handling uncertainty in the attribute values of objects in both the cluster construction task and the classification one. Like the standard version of this method, the BKM results depend on the chosen initial modes. So, one selection method of initial modes is developed, in this paper, aiming at improving the performances of the BKM approach. Experiments with several sets of real data show that by considered the developed selection initial modes method, the clustering algorithm produces more accurate results.

Solving the Flexible Job Shop Scheduling Problem with Uniform Processing Time Uncertainty

The performance of schedules released to a shop floor may greatly be affected by unexpected disruptions. Thus, this paper considers the flexible job shop scheduling problem when processing times of some operations are represented by a uniform distribution with given lower and upper bounds. The objective is to find a predictive schedule that can deal with this uncertainty. The paper compares two genetic approaches to obtain predictive schedule. To determine the performance of the predictive schedules obtained by both approaches, an experimental study is conducted on a number of benchmark problems.

Role-play Gaming Simulation for Flood Management on Cultural Heritage: A Case Study of Ayutthaya Historic City

The main aim of this research is to develop a methodology to encourage people's awareness, knowledge and understanding on the participation of flood management for cultural heritage, as the cooperation and interaction among government section, private section, and public section through role-play gaming simulation theory. The format of this research is to develop Role-play gaming simulation from existing documents, game or role-playing from several sources and existing data of the research site. We found that role-play gaming simulation can be implemented to help improving the understanding of the existing problem and the impact of the flood on cultural heritage, and the role-play game can be developed into the tool to improve people's knowledge, understanding and awareness about people's participation for flood management on cultural heritage, moreover the cooperation among the government, private section and public section will be improved through the theory of role-play gaming simulation.

Visualising Energy Efficiency Landscape

This paper discusses the landscape design that could increase energy efficiency in a house. By planting trees in a house compound, the tree shades prevent direct sunlight from heating up the building, and it enables cooling off the surrounding air. The requirement for air-conditioning could be minimized and the air quality could be improved. During the life time of a tree, the saving cost from the mentioned benefits could be up to US $ 200 for each tree. The project intends to visually describe the landscape design in a house compound that could enhance energy efficiency and consequently lead to energy saving. The house compound model was developed in three dimensions by using AutoCAD 2005, the animation was programmed by using LightWave 3D softwares i.e. Modeler and Layout to display the tree shadings in the wall. The visualization was executed on a VRML Pad platform and implemented on a web environment.

Seasonal Prevalence of Aedes aegypti and Ae.albopictus in Three Topographical Areas of Southern Thailand

This study investigated the seasonal prevalence of Aedes aegypti and Ae. albopictus larvae in three topographical areas (i.e. mangrove, rice paddy and mountainous areas). Samples were collected from 300 households in both wet and dry seasons in nine districts in Nakhon Si Thammarat province. Ae. aegypti and Ae. albopictus were found in 21 out of 29 types of water containers in mangrove, rice paddy and mountainous areas. Ae. aegypti and Ae. albopictus laid eggs in different container types depending on season and topographical areas. Ae. aegypti larvae were found most in metal box in mangrove and mountainous areas in wet season. Ae. albopictus larvae were also found most in metal box in mangrove and mountainous areas in both wet and dry seasons. All Ae. albopictus larval indices were higher than Ae. aegypti larval indices in all three topographical areas and both seasons. HI and BI did not differ in three topographical areas but differed between Aedes sp. HI for both Ae. aegypti and Ae. albopictus in all three topographical areas in both seasons were greater than 10 %, except Aedes aegypti in rice paddy area in wet season. This indicated high risks of DHF transmission in these areas.

Current Status and Energy Savings Potential of Solar Shading in Ningbo

To investigate the energy performance of solar shading devices, this paper carried out a survey on the current status of solar shading utilization in buildings in Ningbo and performed building simulations to evaluate the energy savings potential by adopting different solar shading devices. Results show that solar shading utilization in this area is not popular and effective, and should be considered firstly in the design stage since the potential for energy savings is up to 6.8% for residential buildings and 9.4% for commercial buildings.

Ensembling Adaptively Constructed Polynomial Regression Models

The approach of subset selection in polynomial regression model building assumes that the chosen fixed full set of predefined basis functions contains a subset that is sufficient to describe the target relation sufficiently well. However, in most cases the necessary set of basis functions is not known and needs to be guessed – a potentially non-trivial (and long) trial and error process. In our research we consider a potentially more efficient approach – Adaptive Basis Function Construction (ABFC). It lets the model building method itself construct the basis functions necessary for creating a model of arbitrary complexity with adequate predictive performance. However, there are two issues that to some extent plague the methods of both the subset selection and the ABFC, especially when working with relatively small data samples: the selection bias and the selection instability. We try to correct these issues by model post-evaluation using Cross-Validation and model ensembling. To evaluate the proposed method, we empirically compare it to ABFC methods without ensembling, to a widely used method of subset selection, as well as to some other well-known regression modeling methods, using publicly available data sets.

The Sequestration of Heavy Metals Contaminating the Wonderfonteinspruit Catchment Area using Natural Zeolite

For more than 120 years, gold mining formed the backbone the South Africa-s economy. The consequence of mine closure was observed in large-scale land degradation and widespread pollution of surface water and groundwater. This paper investigates the feasibility of using natural zeolite in removing heavy metals contaminating the Wonderfonteinspruit Catchment Area (WCA), a water stream with high levels of heavy metals and radionuclide pollution. Batch experiments were conducted to study the adsorption behavior of natural zeolite with respect to Fe2+, Mn2+, Ni2+, and Zn2+. The data was analysed using the Langmuir and Freudlich isotherms. Langmuir was found to correlate the adsorption of Fe2+, Mn2+, Ni2+, and Zn2+ better, with the adsorption capacity of 11.9 mg/g, 1.2 mg/g, 1.3 mg/g, and 14.7 mg/g, respectively. Two kinetic models namely, pseudo-first order and pseudo second order were also tested to fit the data. Pseudo-second order equation was found to be the best fit for the adsorption of heavy metals by natural zeolite. Zeolite functionalization with humic acid increased its uptake ability.

Three Dimensional Modeling of Mixture Formation and Combustion in a Direct Injection Heavy-Duty Diesel Engine

Due to the stringent legislation for emission of diesel engines and also increasing demand on fuel consumption, the importance of detailed 3D simulation of fuel injection, mixing and combustion have been increased in the recent years. In the present work, FIRE code has been used to study the detailed modeling of spray and mixture formation in a Caterpillar heavy-duty diesel engine. The paper provides an overview of the submodels implemented, which account for liquid spray atomization, droplet secondary break-up, droplet collision, impingement, turbulent dispersion and evaporation. The simulation was performed from intake valve closing (IVC) to exhaust valve opening (EVO). The predicted in-cylinder pressure is validated by comparing with existing experimental data. A good agreement between the predicted and experimental values ensures the accuracy of the numerical predictions collected with the present work. Predictions of engine emissions were also performed and a good quantitative agreement between measured and predicted NOx and soot emission data were obtained with the use of the present Zeldowich mechanism and Hiroyasu model. In addition, the results reported in this paper illustrate that the numerical simulation can be one of the most powerful and beneficial tools for the internal combustion engine design, optimization and performance analysis.