An attempt to compare the performance of bioscrubbers and biotrickling filters for degradation of ethyl acetate in gas streams

This study presents a comparison of the efficiency of a bioscrubber and a biotrickling filter (BTF) for the removal of ethyl acetate (EA) vapour from a waste gas stream, under the same operating conditions. The maximum EA elimination capacity achieved in the bioscrubber was 550 g m^?3 h^?1 with removal efficiency higher than 96%. For higher EA loadings the bioscrubber was oxygen limited, which caused incomplete EA biodegradation. When pure oxygen was fed to the bioscrubber at a rate of 0.02 L min^?1, the bioscrubber recovered and could treat higher EA loadings without any oxygen limitation. The BTF achieved EA elimination capacity of 600 g m^?3 h^?1 with removal efficiency higher than 97% and the dissolved oxygen concentration remained substantially higher than in the bioscrubber. However, severe channelling and blockage of the spray nozzle occurred due to the excessive biomass growth. Overall, the bioscrubber system was easier to operate and control than the BTF, while an enhancement of the oxygen mass transfer in the bioscrubber could potentially increase its performance by up to three times. Copyright © 2005 Society of Chemical Industry     

Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors

Traditional ant colony optimization (ACO) algorithms have difficulty in addressing dynamic optimization problems (DOPs). This is because once the algorithm converges to a solution and a dynamic change occurs, it is difficult for the population to adapt to a new environment since high levels of pheromone will be generated to a single trail and force the ants to follow it even after a dynamic change. A good solution to address this problem is to increase the diversity via transferring knowledge from previous environments to the pheromone trails using immigrants schemes. In this paper, an ACO framework for dynamic environments is proposed where different immigrants schemes, including random immigrants, elitism-based immigrants, and memory-based immigrants, are integrated into ACO algorithms for solving DOPs. From this framework, three ACO algorithms, where immigrant ants are generated using the aforementioned immigrants schemes and replace existing ants in the current population, are proposed and investigated. Moreover, two novel types of dynamic travelling salesman problems (DTSPs) with traffic factors, i.e., under random and cyclic dynamic environments, are proposed for the experimental study. The experimental results based on different DTSP test cases show that each proposed algorithm performs well on different environmental cases and that the proposed algorithms outperform several other peer ACO algorithms.     

A memetic ant colony optimization algorithm for the dynamic travelling salesman problem

Ant colony optimization (ACO) has been successfully applied for combinatorial optimization problems, e.g., the travelling salesman problem (TSP), under stationary environments. In this paper, we consider the dynamic TSP (DTSP), where cities are replaced by new ones during the execution of the algorithm. Under such environments, traditional ACO algorithms face a serious challenge: once they converge, they cannot adapt efficiently to environmental changes. To improve the performance of ACO on the DTSP, we investigate a hybridized ACO with local search (LS), called Memetic ACO (M-ACO) algorithm, which is based on the population-based ACO (P-ACO) framework and an adaptive inver-over operator, to solve the DTSP. Moreover, to address premature convergence, we introduce random immigrants to the population of M-ACO when identical ants are stored. The simulation experiments on a series of dynamic environments generated from a set of benchmark TSP instances show that LS is beneficial for ACO algorithms when applied on the DTSP, since it achieves better performance than other traditional ACO and P-ACO algorithms.     

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.     

Efficient search based on content similarity over self-organizing P2P networks

The advent of the World Wide Web has made an enormous amount of information available to everyone and the widespread use of digital equipment enables end-users (peers) to produce their own digital content. This vast amount of information requires scalable data management systems. Peer-to-peer (P2P) systems have so far been well established in several application areas, with file-sharing being the most prominent. The next challenge that needs to be addressed is (more complex) data sharing, management and query processing, thus facilitating the delivery of a wide spectrum of novel data-centric applications to the end-user, while providing high Quality-of-Service. In this paper, we propose a self-organizing P2P system that is capable to identify peers with similar content and intentionally assign them to the same super-peer. During content retrieval, fewer super-peers need to be contacted and therefore efficient similarity search is supported, in terms of reduced network traffic and contacted peers. Our approach increases the responsiveness and reliability of a P2P system and we demonstrate the advantages of our approach using large-scale simulations.     

Bayesian feature and model selection for Gaussian mixture models

We present a Bayesian method for mixture model training that simultaneously treats the feature selection and the model selection problem. The method is based on the integration of a mixture model formulation that takes into account the saliency of the features and a Bayesian approach to mixture learning that can be used to estimate the number of mixture components. The proposed learning algorithm follows the variational framework and can simultaneously optimize over the number of components, the saliency of the features, and the parameters of the mixture model. Experimental results using high-dimensional artificial and real data illustrate the effectiveness of the method.     

Improving the performance of passive network monitoring applications with memory locality enhancements

Passive network monitoring is the basis for a multitude of systems that support the robust, efficient, and secure operation of modern computer networks. Emerging network monitoring applications are more demanding in terms of memory and CPU resources due to the increasingly complex analysis operations that are performed on the inspected traffic. At the same time, as the traffic throughput in modern network links increases, the CPU time that can be devoted for processing each network packet decreases. This leads to a growing demand for more efficient passive network monitoring systems in which runtime performance becomes a critical issue.In this paper we present locality buffering, a novel approach for improving the runtime performance of a large class of CPU and memory intensive passive monitoring applications, such as intrusion detection systems, traffic characterization applications, and NetFlow export probes. Using locality buffering, captured packets are being reordered by clustering packets with the same port number before they are delivered to the monitoring application. This results in improved code and data locality, and consequently, in an overall increase in the packet processing throughput and decrease in the packet loss rate. We have implemented locality buffering within the widely used libpcap packet capturing library, which allows existing monitoring applications to transparently benefit from the reordered packet stream without modifications. Our experimental evaluation shows that locality buffering improves significantly the performance of popular applications, such as the Snort IDS, which exhibits a 21% increase in the packet processing throughput and is able to handle 67% higher traffic rates without dropping any packets.     

Antidiabetic Drugs in the Treatment of Alzheimer’s Disease

The public health burden of type 2 diabetes mellitus and Alzheimer’s disease is steadily increasing worldwide, especially in the population of older adults. Epidemiological and clinical studies suggest a possible shared pathophysiology between the two diseases and an increased risk of AD in patients with type 2 diabetes mellitus. Therefore, in recent years, there has been a substantial interest in identifying the mechanisms of action of antidiabetic drugs and their potential use in Alzheimer’s disease. Human studies in patients with mild cognitive impairment and Alzheimer’s disease have shown that administration of some antidiabetic medications, such as intranasal insulin, metformin, incretins, and thiazolidinediones, can improve cognition and memory. This review aims to examine the latest evidence on antidiabetic medications as a potential candidate for the treatment of Alzheimer’s disease.     

In-depth analysis and evaluation of diffusive glioma models

Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.     

On the investigation of a reliable actuation control method for ohmic RF MEMS switches

Efficient control of RF MEMS switches is a very important issue as it is correlated to main failure mechanisms/modes such as the impact force and bouncing phenomena, which degrade their dynamic performance and longevity. This paper presents the control of specific ohmic RF MEMS switches under three different actuation modes, a tailored pulse optimization method based on Taguchi's technique (voltage mode actuation control), resistive damping (charge mode actuation control) and finally the Hybrid actuation mode, which is a combination of the tailored pulse, the resistive damping and Taguchi's optimization technique. Coventorware simulations indicate that under optimized Tailored pulse and Hybrid actuation modes, the impact velocity is reduced by around 90%, the initial impact force by around 75% and the maximum bouncing displacement during the release phase by around 95%, while the switching speed is increased by around 20% compared with the step pulse control mode. The resistive damping control mode is inappropriate for this type of switch and only partial improvement during the pull-down phase has been achieved. Finally, a comparison between Hybrid and optimized tailored modes shows that Hybrid actuation mode excels with better switching characteristics and most importantly offers immunity to manufacturing and operation tolerances.