Travelling Salesman Problem using Prim Algorithm in High Performance Computing

Thanks to the advances in wide area network technology and the low cost of computing resources, High Performance Computing came into being and currently research area. One incentive of High Performance Computing is to summative the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal an efficient job scheduling algorithm system is an essential part of the High Performance Computing. This preliminary report emphasizes on the basic terms of the efficient job scheduling algorithm for traveling salesman problem in high performance computing. Job scheduling algorithm will reduce the traffic between the processors and can help improve resource utilization and quality of service. Traveling salesman problem is finding is the shortest path connecting number of locations such as cities, visited by a traveling salesman on his sales route. TSP has been used in The Two-Period Travelling Salesman Problem Applied to Milk Collection in Ireland and Usefulness of Solution Algorithms of the Travelling Salesman Problem in the typing of Biological Sequences in a Clinical Laboratory Setting

Travelling Salesman Problem using Prim Algorithm in High Performance Computing

Thanks to the advances in wide area network technology and the low cost of computing resources, High Performance Computing came into being and currently research area. One incentive of High Performance Computing is to summative the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal an efficient job scheduling algorithm system is an essential part of the High Performance Computing. This preliminary report emphasizes on the basic terms of the efficient job scheduling algorithm for traveling salesman problem in high performance computing. Job scheduling algorithm will reduce the traffic between the processors and can help improve resource utilization and quality of service. Traveling salesman problem is finding is the shortest path connecting number of locations such as cities, visited by a traveling salesman on his sales route. TSP has been used in The Two-Period Travelling Salesman Problem Applied to Milk Collection in Ireland and Usefulness of Solution Algorithms of the Travelling Salesman Problem in the typing of Biological Sequences in a Clinical Laboratory Setting

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Computational Methods towards Personalized Cancer Vaccines and their Application through a Web-based Platform

Cancer immunotherapy is a treatment option that involves or uses components of a patient’s immune system. Today, it is heading towards becoming an integral part of treatment plans together with chemotherapy, surgery, and radiotherapy. Personalized epitope-based vaccines (EVs) serve as one strategy that is truly personalized. Each patient possesses a distinct immune system, and each tumor is unique, rendering the design of a potent vaccine challenging and dependent on the patient and the tumor. The potency of a vaccine is reliant on the ability of its constituent epitopes – short, immunogenic antigen fragments – to trigger an immune response. To assess this ability, one has to take into account the individuality of the immune system, among others conditioned by the variability of the human leukocyte antigen (HLA) gene cluster. Determining the HLA genotype with traditional experimental techniques can be time- and cost-intensive. We proposed a novel HLA genotyping algorithm based on integer linear programming that is independent of dedicated data generation for the sole purpose of HLA typing. On publicly available next-generation sequencing (NGS) data, our method outperformed previously published approaches. HLA binding is a prerequisite for T-cell recognition, and precise prediction algorithms exist. However, this information is not sufficient to assess the immunogenic potential of a peptide. To induce an immune response, reactive T-cell clones with receptors specific for a peptide-HLA complex have to be present. We suggested a method for the prediction of immunogenicity that includes peripheral tolerance models, based on gut microbiome data, in addition to central tolerance, previously shown to increase performance. The comparison to a previously published method suggests that the incorporation of gut microbiome data and HLA-binding stability estimates do not enhance prediction performance. High-throughput sequencing provides the basis for the design of personalized EVs. Through genome and transcriptome sequencing of tumor and matched non-malignant tissue samples, cancer-specific mutations can be identified, which can be further validated using other technologies such as mass spectrometry (MS). Multi-omics approaches can result in the acquisition of several hundreds of gigabytes of data. Handling and analysis of such data usually require data management solutions and high-performance computing (HPC) infrastructures. We developed the web-based platform qPortal for data-driven biomedical research that allows users to manage and analyze quantitative biological data intuitively. To emphasize the advantages of our data-driven approach with an integrated workflow system, we conducted a comparison to Galaxy. Building on qPortal, we implemented the web-based platform iVacPortal for the design of personalized EVs to facilitate data management and data analysis in such projects. Further, we applied the implemented methods through iVacPortal in two studies of two distinct cancer entities, indicating the added value of our platform for the assessment of personalized EV candidates and alternative targets for cancer immunotherapy

Tracking the Temporal-Evolution of Supernova Bubbles in Numerical Simulations

The study of low-dimensional, noisy manifolds embedded in a higher dimensional space has been extremely useful in many applications, from the chemical analysis of multi-phase flows to simulations of galactic mergers. Building a probabilistic model of the manifolds has helped in describing their essential properties and how they vary in space. However, when the manifold is evolving through time, a joint spatio-temporal modelling is needed, in order to fully comprehend its nature. We propose a first-order Markovian process that propagates the spatial probabilistic model of a manifold at fixed time, to its adjacent temporal stages. The proposed methodology is demonstrated using a particle simulation of an interacting dwarf galaxy to describe the evolution of a cavity generated by a Supernov

Dynamics of Macrosystems; Proceedings of a Workshop, September 3-7, 1984

There is an increasing awareness of the important and persuasive role that instability and random, chaotic motion play in the dynamics of macrosystems. Further research in the field should aim at providing useful tools, and therefore the motivation should come from important questions arising in specific macrosystems. Such systems include biochemical networks, genetic mechanisms, biological communities, neutral networks, cognitive processes and economic structures. This list may seem heterogeneous, but there are similarities between evolution in the different fields. It is not surprising that mathematical methods devised in one field can also be used to describe the dynamics of another. IIASA is attempting to make progress in this direction. With this aim in view this workshop was held at Laxenburg over the period 3-7 September 1984. These Proceedings cover a broad canvas, ranging from specific biological and economic problems to general aspects of dynamical systems and evolutionary theory

Complexity, Emergent Systems and Complex Biological Systems:\ud Complex Systems Theory and Biodynamics. [Edited book by I.C. Baianu, with listed contributors (2011)]

An overview is presented of System dynamics, the study of the behaviour of complex systems, Dynamical system in mathematics Dynamic programming in computer science and control theory, Complex systems biology, Neurodynamics and Psychodynamics.\u