Mapping of portable parallel programs

An efficient parallel program designed for a parallel architecture includes a detailed outline of accurate assignments of concurrent computations onto processors, and data transfers onto communication links, such that the overall execution time is minimized. This process may be complex depending on the application task and the target multiprocessor architecture. Furthermore, this process is to be repeated for every different architecture even though the application task may be the same. Consequently, this has a major impact on the ever increasing cost of software development for multiprocessor systems. A remedy for this problem would be to design portable parallel programs which can be mapped efficiently onto any computer system. In this dissertation, we present a portable programming tool called Cluster-M. The three components of Cluster-M are the Specification Module, the Representation Module, and the Mapping Module. In the Specification Module, for a given problem, a machine-independent program is generated and represented in the form of a clustered task graph called Spec graph. Similarly, in the Representation Module, for a given architecture or heterogeneous suite of computers, a clustered system graph called Rep graph is generated. The Mapping Module is responsible for efficient mapping of Spec graphs onto Rep graphs. As part of this module, we present the first algorithm which produces a near-optimal mapping of an arbitrary non-uniform machine-independent task graph with M modules, onto an arbitrary non-uniform task-independent system graph having N processors, in 0(M P) time, where P = max(M, N). Our experimental results indicate that Cluster-M produces better or similar mapping results compared to other leading techniques which work only for restricted task or system graphs

Application of clustering techniques in defining level of service criteria of urban streets

The speed ranges for Level of Service (LOS) categories are not well defined for highly heterogeneous traffic flow on urban streets of India. The LOS analysis procedure followed in India is that developed by HCM 2000. The LOS categories for various urban street classes defined by HCM are apposite for developed countries having homogeneous type of traffic flow. For developing countries like India where the traffic flow is highly heterogeneous, LOS should be defined correctly taking into account the traffic and geometric characteristics. In this study an attempt has been made to define the LOS criteria of urban streets. Mumbai the business capital of India was chosen as the study area comprising of 100 street segments on four north-south and one east-west corridor. Second-wise speed data collected using Global Positioning System (GPS) receiver fitted on mobile vehicles was used for this study. Free-flow speed (FFS) data, average travel speeds during both peak and off peak hours and inventory details were collected and used in this study. These data are obtained from secondary source for this research work. Defining level of service is basically classification problems. Cluster analysis is found to be the most suitable technique for solving these classification problems. Four clustering methods namely Clustering Large Applications (CLARA), Self Organizing Tree Algorithm (SOTA), Hard Competitive Learning (hardcl) and Neural gas (ngas) were used to define LOS criteria in this study. Calinski-Harabasz Index, Homogenity Index, Stability Index, Connectivity Index, Average proportion of non-overlap Index, Average distance Index, Average distance between means Insex, Figure of merit Index, PtBiserial Index, Tau Index, GPlus Index, Ratkowsky Index, Duda Index, McClain Index are used in deriving optimum number of clusters

Level of service criteria of urban walking environment in indian context using cluster analysis

To know how well roadways accommodate pedestrian travel or how they are pedestrian friendly it becomes necessary to assess the walking conditions. It would also help evaluating and prioritizing the needs of existing roadways for sidewalk construction. Estimation of Pedestrian Level of Service (PLOS) is the most common approach to assess the quality of operations of pedestrian facilities. The focus of this study is to identify and access the suitable methodology to evaluate PLOS for off-street pedestrian facilities in Indian context. Defining the level of service criteria for urban off-streets pedestrian facilities are basically classification problems. Cluster analysis is found to be the most suitable technique for solving these classification problems. Cluster analysis groups object based on the information found in the data describing their relationships. K-means, Hierarchical Agglomerative Clustering (HAC), Fuzzy c-means (FCM), Self Organizing MAP (SOM) in Artificial Neural Network (ANN), Affinity Propagation (AP) and Genetic Algorithm Fuzzy (GA Fuzzy) clustering are the six methods are those employed to define PLOS criteria in this study. Four parameters such as pedestrian space, flow rate, speed and volume to capacity (v/c) ratio are considered to classify PLOS categories of off-street pedestrian facilities. And from the analysis six LOS categories i.e. A, B, C, D, E and F which are having different ranges of the four parameters are defined. From the study it found that pedestrian faces a good level of service of “A”, “B” and “C” are more often than at poor levels of service of “D”, “E” and “F”. From all the six clustering methods K-means is found to be the most suitable one to classify PLOS in Indian context

A taxonomy of innovation networks

In this discussion paper we develop a theory-based typology of innovation networks with a special focus on public-private collaboration. This taxonomy is theoretically based on the concept of life cycles which is transferred to the context of innovation networks as well as on the mode of network formation which can occur either spontaneous or planned. The taxonomy distinguishes six different types of networks and incorporates two plausible alternative developments that eventually lead to a similar network structure of the two types of networks. From this, important conclusions and recommendations for network actors and policy makers are drawn. --

Pedestrian perception-based level-of-service model at signalized intersection crosswalks

"jats:p"Pedestrian level of service (PLOS) is an important measure of performance in the analysis of existing pedestrian crosswalk conditions. Many researchers have developed PLOS models based on pedestrian delay, turning vehicle effect, etc., using the conventional regression method. However, these factors may not effectively reflect the pedestrians’ perception of safety while crossing the crosswalk. The conventional regression method has failed to estimate accurate PLOS because of the primary assumption of an arbitrary probability distribution and vagueness in the input data. Moreover, PLOS categories in existing studies are based on rigid threshold values and the boundaries that are not well defined. Therefore, it is an important attempt to develop a PLOS model with respect to pedestrian safety, convenience, and efficiency at signalized intersections. For this purpose, a video-graphic and user perception surveys were conducted at selected nine signalized intersections in Mumbai, India. The data such as pedestrian, traffic, and geometric characteristics were extracted, and significant variables were identified using Pearson correlation analysis. A consistent and statistically calibrated PLOS model was developed using fuzzy linear regression analysis. PLOS was categorized into six levels ("jats:italic"A"/jats:italic"–"jats:italic"F"/jats:italic") based on the predicted user perception score, and threshold values for each level were estimated using the fuzzy "jats:italic"c"/jats:italic"-means clustering technique. The developed PLOS model and threshold values were validated with the field-observed data. Statistical performance tests were conducted and the results provided more accurate and reliable solutions. In conclusion, this study provides a feasible alternative to measure pedestrian perception-based level of service at signalized intersections. The developed PLOS model and threshold values would be useful for planning and designing pedestrian facilities and also in evaluating and improving the existing conditions of pedestrian facilities at signalized intersections. Document type: Articl

Modeling human gut microbiota: from steady states to dynamic systems

Human gut microbes are an essential part of human sub-microscopic systems and involved in many critical biological processes such as Type 2 diabetes (T2D) and osteoporosis. However, the underlying mechanisms are unclear. Several mathematical modeling approaches, such as genome-scale metabolic models (GEMs) and ordinary differential equation (ODE) based models, have been used to simulate the dynamics of human gut microbiota. This thesis aims to explore, simulate, and predict the behavior of gut microbial ecosystems and the relationships between gut microbes and humans by modeling.The importance of the gut microbiome for bone metabolism and T2D has been demonstrated in mice and human cohorts. We first reconstructed a GEM for Limosilactobacillus reuteri ATCC PTA 6475, which is a probiotic that significantly reduces bone loss in older women with lower bone mineral density. To investigate the associations between T2D and the gut microbiota, GEMs for 827 gut microbial species and 1,779 community-level GEMs for T2D cohorts have also been constructed. With these GEMs, we investigated metabolic potentials such as short-chain fatty acids, amino acids, and vitamins that play vital roles in the host metabolism regulation. Furthermore, the integration of the models with machine learning method provides potential insights into the possible roles of gut microbiota in T2D.Cybernetic models, which simulate metabolic rates by integrating the control of enzyme synthesis and enzyme activities, have been applied to explore the dynamic behaviors of small-size metabolic networks. However, only a few studies have applied cybernetic theory to the microbial community so far. The remaining part of this thesis focuses on the use of cybernetic models to explore human gut microbiota\u27s interactions and population dynamics. Considering the high computing burden of the current cybernetic modeling approach for processing the full-size GEMs, we have developed a computing-efficient strategy for model reconstruction and simulation to reveal the metabolic dynamics of human gut microbiota.In this thesis, we explore the human gut microbiota from single L. reuteri species to microbial gut communities, from simple steady state systems by GEMs to complex dynamic systems by cybernetic model

Recent Trends in Computational Research on Diseases

Recent advances in information technology have brought forth a paradigm shift in science, especially in the biology and medical fields. Statistical methodologies based on high-performance computing and big data analysis are now indispensable for the qualitative and quantitative understanding of experimental results. In fact, the last few decades have witnessed drastic improvements in high-throughput experiments in health science, for example, mass spectrometry, DNA microarray, next generation sequencing, etc. Those methods have been providing massive data involving four major branches of omics (genomics, transcriptomics, proteomics, and metabolomics). Information about amino acid sequences, protein structures, and molecular structures are fundamental data for the prediction of bioactivity of chemical compounds when screening drugs. On the other hand, cell imaging, clinical imaging, and personal healthcare devices are also providing important data concerning the human body and disease. In parallel, various methods of mathematical modelling such as machine learning have developed rapidly. All of these types of data can be utilized in computational approaches to understand disease mechanisms, diagnosis, prognosis, drug discovery, drug repositioning, disease biomarkers, driver mutations, copy number variations, disease pathways, and much more. In this Special Issue, we have published 8 excellent papers dedicated to a variety of computational problems in the biomedical field from the genomic level to the whole-person physiological level