Evaporation and Evolution of Suspended Solution Droplets at Atmospheric and Reduced Pressures

Thermal history and solute precipitation behavior of suspended solution droplets of sodium chloride (NaCl), magnesium sulphate (MgSO₄), and zirconium hydroxychloride (ZrO(OH)Cl) evaporating at atmospheric and reduced pressures are studied. Experimental measurements on the variation of droplet diameter, solution concentration, and temperature during the evaporation period are presented and discussed. The results of solute precipitation behavior in solution droplets observed under an optical microscope are displayed and discussed. Results indicate that reducing the pressure (∼ 33 kPa) results in a change in the solution droplet evaporation rate, but the thermal histories of a particular solution droplet are similar at the atmospheric and reduced pressures. At atmospheric and reduced pressures used in this study, the d² law for solution droplets is valid at early stages of the evaporation and before the solute precipitation initiates. Drying of MgSO₄ and ZrO(OH)Cl solution droplets results in the formation of spherical particles, whereas drying of spherical NaCl solution droplets results in the formation of cubic particles.     

Effect of Reactor Ambient Pressure on the Morphology of Spray Dried Magnesium Sulphate Powders

The effect of reducing the reactor air pressure on the morphology of spray dried magnesium sulphate powders is investigated, experimentally. A reactor, capable of drying and pyrolyzing solution sprays at low pressures, is designed and manufactured. A vibrating mesh nebulizer is employed to generate the spray. Four different pressures, starting from 60 Torr to the atmospheric pressure, and two different reactor air temperatures of 130°C and 420°C, are considered. In addition, two different concentrations of magnesium sulphate solutions are tested. The results are explained based on the effect of reactor air pressure on the droplet evaporation rate.     

Upstream Cutoff and Downstream Filters to Control of Seepage in Dams

The finite element method was used in this study to investigate cutoff walls and downstream filters to control seepage, the exit hydraulic gradient, and uplift forces for dams. Experimental data was used for validating the numerical modelling. The effective parameters are the length of filter and its distance downstream from the dam, the depth of the cutoff walls, the upstream dam head, and the thickness of alluvial foundation. The results show that by increasing filter length, the exit hydraulic gradient, uplift force, and seepage are reduced. The optimum relative length of the filter is L/H?=?0.028 which results in a decrease of about 65% in the exit hydraulic gradient, a 35% decrease in seepage and 10% reduction in the uplift force at the upstream foundation and a 60% decrease in the uplift force at the downstream foundation. Increase of cutoff wall depth reduces the exit hydraulic gradient, uplift force, and seepage. Using two cutoff walls both upstream and downstream of the dam decreases seepage, hydraulic gradient, and uplift force 132%, 450% and 11% respectively. However, using an upstream cutoff and downstream filter decreases seepage, hydraulic gradient, and uplift force by 180%, 490%, and 119% respectively. Thus, based on this study, recommendations for suitable combinations of upstream cutoff and downstream filter are provided.

     

A brain–computer interface for the Dasher alternative text entry system

To investigate whether the alternative text entry system, Dasher, is useful to physically and intellectually disabled students when controlled by a brain–computer interface (BCI) a new software tool was developed to allow subjects to type words onto a computer screen via Dasher using their thoughts. A case study approach was adopted. Subjects were selected based on their suitability for the experiment, and the potential benefit to them of this system, by their head teacher. Subjects entered literacy level-matched phrases onto a computer using QWERTY keyboard, Dasher-mouse and Dasher-BCI. A researcher recorded qualitative and quantitative data, including characters entered per minute and their system preferences. Informed written consent was given for seven subjects to participate (aged 14–19 years, five male, with a range of physical and intellectual disabilities). After a short training period, all subjects had some degree of control over the Dasher-BCI system. With regard to typing speed, Dasher-BCI performed relatively poorly (3.9 ± 1.5 characters per minute), and QWERTY keyboard performed the best (31.9 ± 21.9 characters per minute). Dasher-BCI was the most preferred method. Areas of weakness in Dasher and the BCI hardware were highlighted and suggestions for improvement given. BCI-based text entry is not yet ready to compete with more established methods for students with combined cognitive and physical disabilities. Although underpowered, this study suggests that for people whose predominant disability is physical (cerebral palsy), BCI technology shows great potential as a viable text entry alternative. Suggestions for further research are discussed.     

Congestion prevention in broadband wireless access systems: An economic approach

While the demand for mobile broadband wireless services continues to increase, radio resources remain scarce. Even with the substantial increase in the supported bandwidth in the next generation broadband wireless access systems (BWASs), it is expected that these systems will severely suffer from congestion, due to the rapid increase in demand of bandwidth-intensive multimedia services. Without efficient bandwidth management and congestion control schemes, network operators may not be able to meet the increasing demand of users for multimedia services, and hence they may suffer an immense revenue loss. In this paper, we propose an admission-level bandwidth management scheme consisting of call admission control (CAC) and dynamic pricing. The main aim of our proposed scheme is to provide monetary incentives to users to use the wireless resources efficiently and rationally, hence, allowing efficient bandwidth management at the admission level. By dynamically determining the prices of units of bandwidth, the proposed scheme can guarantee that the number of connection requests to the system are less than or equal to certain optimal values computed dynamically, hence, ensuring a congestion-free system. The proposed scheme is general and can accommodate different objective functions for the admission control as well as different pricing functions. Comprehensive simulation results with accurate and inaccurate demand modeling are provided to show the effectiveness and strengths of our proposed approach.     

Parallel Iterator for Parallelizing Object-Oriented Applications

With the advent of multi-core processors, desktop application developers must finally face parallel computing and its challenges. A large portion of the computational load in a program rests within iterative computations. In object-oriented languages these are commonly handled using iterators which are inadequate for parallel programming. This paper presents a powerful Parallel Iterator concept to be used in object-oriented programs for the parallel traversal of a collection of elements. The Parallel Iterator may be used with any collection type (even those inherently sequential) and it supports several scheduling schemes which may even be decided dynamically at run-time. Some additional features are provided to allow early termination of parallel loops, exception handling and a solution for performing reductions. With a slight contract modification, the Parallel Iterator interface imitates that of the Java-style sequential iterator. All these features combine together to promote minimal, if any, code restructuring. Along with the ease of use, the results reveal negligible overhead and the expected inherent speedup.     

A novel ACO–GA hybrid algorithm for feature selection in protein function prediction

Protein function prediction is an important problem in functional genomics. Typically, protein sequences are represented by feature vectors. A major problem of protein datasets that increase the complexity of classification models is their large number of features. Feature selection (FS) techniques are used to deal with this high dimensional space of features. In this paper, we propose a novel feature selection algorithm that combines genetic algorithms (GA) and ant colony optimization (ACO) for faster and better search capability. The hybrid algorithm makes use of advantages of both ACO and GA methods. Proposed algorithm is easily implemented and because of use of a simple classifier in that, its computational complexity is very low. The performance of proposed algorithm is compared to the performance of two prominent population-based algorithms, ACO and genetic algorithms. Experimentation is carried out using two challenging biological datasets, involving the hierarchical functional classification of GPCRs and enzymes. The criteria used for comparison are maximizing predictive accuracy, and finding the smallest subset of features. The results of experiments indicate the superiority of proposed algorithm.     

Stochastic flow-shop scheduling with minimizing the expected number of tardy jobs

In this research, minimizing the expected number of tardy jobs in a dynamic m machine flow-shop scheduling problem, i.e., $ {F_m}\left| {{r_j}\left| {{\text{E}}\left[ {\sum {{U_j}} } \right]} \right.} \right. $ is investigated. It is assumed that the jobs with deterministic processing times and stochastic due dates arrive randomly to the flow-shop cell. The due date of each job is assumed to be normally distributed with known mean and variance. A dynamic method is proposed for this problem by which the m machine stochastic flow-shop problem is decomposed into m stochastic single-machine sub-problems. Then, each sub-problem is solved as an independent stochastic single-machine scheduling problem by a mathematical programming model. Comparison of the proposed method with the most effective rule of thumb for the proposed problem, i.e., shortest processing time first rule shows that the proposed method performs 23.9 % better than the SPT rule on average for industry-size scheduling problems.