A two-base encoded DNA sequence alignment problem in computational biology

The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics. The primary objective of the "sequence alignment" problem is to search for a new algorithm that facilitates the use of two-base encoded data for large-scale re-sequencing projects. This algorithm should be able to perform local sequence alignment as well as error detection and correction in a reliable and systematic manner, enabling the direct comparison of encoded DNA sequence reads to a candidate reference DNA sequence. We will first briefly review two well-known sequence alignment approaches and provide a rudimentary improvement for implementation on parallel systems. Then, we carefully examin a unique sequencing technique known as the SOLiDTM System that can be implemented, and follow by the results from the global and local sequence alignment. In this report, the team presents an explanation of the algorithms for color space sequence data from the high-throughput re-sequencing technology and a theoretical parallel approach to the dynamic programming method for global and local alignment. The combination of the di-base approach and dynamic programming provides a possible viewpoint for large-scale re-sequencing projects. We anticipate the use of distributed computing to be the next-generation engine for large-scale problems like such

'Immune System Approaches to Intrusion Detection - A Review'

Abstract. The use of artificial immune systems in intrusion detection is an appealing concept for two reasons. Firstly, the human immune system provides the human body with a high level of protection from invading pathogens, in a robust, self-organised and distributed manner. Secondly, current techniques used in computer security are not able to cope with the dynamic and increasingly complex nature of computer systems and their security. It is hoped that biologically inspired approaches in this area, including the use of immune-based systems will be able to meet this challenge. Here we collate the algorithms used, the development of the systems and the outcome of their implementation. It provides an introduction and review of the key developments within this field, in addition to making suggestions for future research

Contemporary spinal cord protection during thoracic and thoracoabdominal aortic surgery and endovascular aortic repair: a position paper of the vascular domain of the European Association for Cardio-Thoracic Surgery†

Ischaemic spinal cord injury (SCI) remains the Achilles heel of open and endovascular descending thoracic and thoracoabdominal repair. Neurological outcomes have improved coincidentially with the introduction of neuroprotective measures. However, SCI (paraplegia and paraparesis) remains the most devastating complication. The aim of this position paper is to provide physicians with broad information regarding spinal cord blood supply, to share strategies for shortening intraprocedural spinal cord ischaemia and to increase spinal cord tolerance to transitory ischaemia through detection of ischaemia and augmentation of spinal cord blood perfusion. This study is meant to support physicians caring for patients in need of any kind of thoracic or thoracoabdominal aortic repair in decision-making algorithms in order to understand, prevent or reverse ischaemic SCI. Information has been extracted from focused publications available in the PubMed database, which are cohort studies, experimental research reports, case reports, reviews, short series and meta-analyses. Individual chapters of this position paper were assigned and after delivery harmonized by Christian D. Etz, Ernst Weigang and Martin Czerny. Consequently, further writing assignments were distributed within the group and delivered in August 2014. The final version was submitted to the EJCTS for review in September 201

Large Scale Integration of Electric Vehicles into the Power Grid and Its Potential Effects on Power System Reliability

In this thesis, the potential effects of large scale integration of electric vehicles into the power grid are discussed in both the beneficial and detrimental aspects. The literature review gives a comprehensive introduction about the existing smart charging algorithms. According to the system structure and market mechanism, the smart charging algorithms can be divided into centralized and distributed method. With the knowledge of driving patterns and charging characteristics of electric vehicles, both the centralized and decentralized smart charging algorithms are studied in this research. Based on the smart charging pricing and sequential price update mechanism, a multi-agent based distributed smart charging algorithm is used in this research to flatten the load curve and therefore mitigate the potential detrimental effects caused by uncoordinated charging. Each EV agent has some extent of intelligence to solve its own charging scheduling problem. The optimization method used in this research is the binary hybrid GSA-PSO algorithm, which combines the merits of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and has very good exploration and exploitation abilities. A V2G enabled centralized smart charging algorithm is also introduced in this thesis, each EV can earn revenues by discharging power into the grid. The dominant search matrix is used to resolve the \u27\u27curse of dimensionality\u27\u27 problem existing in the centralized optimization problems. Numerical case studies show both the distributed and V2G enabled smart charging algorithms can effectively transfer the charging load from the peak load period to the load valley hours. Because of the limited integration ratio of electric vehicles, most power system reliability methods do not evaluate the charging load of EVs separately in their analytical procedures. However, with a fast increasing integration level, the potential effects of large scale integration of EVs on the power system reliability should be comprehensively evaluated. The effects of EV charging on power system reliability in the planning phase is analyzed in this research based on the RBTS. The results show the uncontrolled charging will deteriorate the reliability level while the smart charging can effectively decrease the detrimental effect. The potential application of aggregated EV providing operating reserve to the grid as a kind of ancillary service is also discussed, and the related effects on power system reliability in operating phase are calculated using the modified PJM method. The case study shows the unit commitment risk of the system can decrease to a very low level with the additional operating reserve capacity provided by aggregated EVs, which can not only improve the system\u27s reliability level but also save the cost

Algon: a framework for supporting comparison of distributed algorithm performance

Programmers often need to use distributed algorithms to add non-functional behaviour such as mutual exclusion, deadlock detection and termination, to a distributed application. They find the selection and implementation of these algorithms daunting. Consequently, they have no idea which algorithm will be best for their particular application. To address this difficulty the Algon framework provides a set of pre-coded distributed algorithms for programmers to choose from, and provides a special performance display tool to support choice between algorithms. The performance tool is discussed. The developer of a distributed application will be able to observe the performance of each of the available algorithms according to a set of of widely accepted and easily-understandable performance metrics and compare and contrast the behaviour of the algorithms to support an informed choice. The strength of the Algon framework is that it does not require a working knowledge of algorithmic theory or functionality in order for the developer to use the algorithms

A survey of parallel algorithms for fractal image compression

This paper presents a short survey of the key research work that has been undertaken in the application of parallel algorithms for Fractal image compression. The interest in fractal image compression techniques stems from their ability to achieve high compression ratios whilst maintaining a very high quality in the reconstructed image. The main drawback of this compression method is the very high computational cost that is associated with the encoding phase. Consequently, there has been significant interest in exploiting parallel computing architectures in order to speed up this phase, whilst still maintaining the advantageous features of the approach. This paper presents a brief introduction to fractal image compression, including the iterated function system theory upon which it is based, and then reviews the different techniques that have been, and can be, applied in order to parallelize the compression algorithm

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial