Advancing Interactive Parallelization: iCetus

Outlines the theory behind a CAD (computer-aided design) package for the design of microwave filters based on thick inductive windows in a rectangular waveguide. The authors describe a step-by-step procedure that, based on the use of the code developed, makes it possible to select very accurately the initial geometry of the filter. Finally, they give two filter design examples that demonstrate the effectiveness of the CAD procedure developed. The procedure described is essentially a true CAD technique that can completely replace all of the hardware work and all of the manual tunings in the laboratory that are traditionally required for the manufacture of this class of waveguide filters, thus resulting in a significant reduction in development time and cost. >

Anatomical structure tracing on cephalograms is a significant way to obtain cephalometric analysis. Cephalometric analysis is divided in two categories, manual and automatic approaches. The manual approach is limited in accuracy and repeatability due to differences in inter- and intra-personal marking. In this paper, we have attempted to develop and test a novel method for automatic localization of craniofacial structures based on the detected edges in the region of interest. Before edge detection of the particular region, the region was filtered by adaptive non local filter for noise removal by keeping the edge information undisturbed. According to the gray-scale feature at the different regions of the cephalograms, modified Canny edge detection algorithm for obtaining tissue contour was proposed. With the application of morphological opening and edge linking approaches, an improved bidirectional contour tracing methodology was proposed by an interactive selection of the starting edge pixels, the tracking process searches repetitively for an edge pixel at the neighborhood of previously searched edge pixel to segment images, and then craniofacial structures are obtained. The effectiveness of the algorithm is demonstrated by the preliminary experimental results obtained with the proposed method.

Introduction: An analysis of Heart Rate Variability (HRV) is widely used in clinical and research. To properly calculate HRV parameters, the RR intervals series must be properly preprocessed. There are already automated tools for correcting these artifacts; however, they are not fully transparent and fully customizable. To address these limitations, we introduce CoRRection, a semi-automatic tool for RR interval correction, which integrates both automatic and manual approaches providing greater control over which intervals are corrected. Material and Methods: The application offers detection and correction methods. Additionally, it allows to manually remove an artifact before applying a correction method. It also allows to clean artifacts in shorter segments which enables applying different detection methods in one example, e.g., gathered during intense exercise. The application provides a test to assess signal stationarity (Augmented Dickey-Fuller test). After signal processing the report is created, containing the number of probes deleted and modified. Results: The proposed CoRRection application allows both technical and non-technical users to preprocess RR signals with a better control over the process by enabling the use of semi-automatic approach. A few examples of studies which required Correction’s unique approach of semi-automatic correction were presented. The need for different methods and not only considering different examinations, but also different segments within one recording was emphasized. Conclusions: We have presented a new application designed to preprocess RR intervals signal with a few popular detection and correction methods. This approach enables a good compromise between a precise manual approach and a less time-consuming automatic approach. The semi-automatic method of artifact correction empowers users to explore multiple identification and correction methods (and to choose the one best fitted to the data or measurement conditions), and offers a better understanding of the examination preprocessing tools. This may also result in a better trust in the automatic approach among the users.

The advent of electric propulsion technology has led to a paradigm shift in aircraft design over the past few decades. This shift has expanded the possibilities for design and optimization processes more than at any previous time. To support these advancements, efficient flight dynamics simulation models that can be employed in iterative optimization and design processes are essential. Among the modules of a typical flight dynamics framework—namely, control, flight dynamics, and aerodynamics—the aerodynamics module, which includes the rotor performance model, generally demands the most computational effort, thereby limiting simulation efficiency. In this study, a novel machine learning (ML)-assisted flight dynamics framework is developed, incorporating a Neural Network Blade Element Theory (NN-BET) model as the rotor performance module. The results show a 7- to 8-fold reduction in computational time compared to fast, physics-based frameworks utilizing efficient Blade Element Momentum Theory (BEMT) models, without compromising predictive accuracy. Furthermore, the modular architecture of the framework allows for easy adaptation to a wide range of practical applications by replacing modules with functionally equivalent alternatives. The demonstrated accuracy and computational efficiency of the proposed flight dynamics framework make it a highly promising candidate for optimization and design applications.

There exist a large number of scientific and industrial applications with inherently unstructured parallelism which cannot be solved effectively using a data parallel approach. In this paper we present TGEX, a generic tool for the parallel execution of unstructured problems on distributed memory computers. A general computational problem is first expressed in terms of a computation task graph which serves as a machine-independent representation of the parallel program. TGEX then executes the task graph in a macro data flow style on the target distributed-memory machine. Thus TGEX simplifies parallel programming by freeing the application programmer from the burden of low level task synchronisation and communication. TGEX is implemented using PVM and is therefore portable on a large number of distributed memory systems.KeywordsParallel ProgramParallel ExecutionTask GraphCentral SchedulerUnstructured ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Abstract. The identification of landscapes with similar hydrological behaviour is useful for runoff and flood predictions in small ungauged catchments. An established method for landscape classification is based on the concept of dominant runoff process (DRP). The various DRP-mapping approaches differ with respect to the time and data required for mapping. Manual approaches based on expert knowledge are reliable but time-consuming, whereas automatic GIS-based approaches are easier to implement but rely on simplifications which restrict their application range. To what extent these simplifications are applicable in other catchments is unclear. More information is also needed on how the different complexities of automatic DRP-mapping approaches affect hydrological simulations. In this paper, three automatic approaches were used to map two catchments on the Swiss Plateau. The resulting maps were compared to reference maps obtained with manual mapping. Measures of agreement and association, a class comparison, and a deviation map were derived. The automatically derived DRP maps were used in synthetic runoff simulations with an adapted version of the PREVAH hydrological model, and simulation results compared with those from simulations using the reference maps. The DRP maps derived with the automatic approach with highest complexity and data requirement were the most similar to the reference maps, while those derived with simplified approaches without original soil information differed significantly in terms of both extent and distribution of the DRPs. The runoff simulations derived from the simpler DRP maps were more uncertain due to inaccuracies in the input data and their coarse resolution, but problems were also linked with the use of topography as a proxy for the storage capacity of soils. The perception of the intensity of the DRP classes also seems to vary among the different authors, and a standardised definition of DRPs is still lacking. Furthermore, we argue not to use expert knowledge for only model building and constraining, but also in the phase of landscape classification.

Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians’ experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion’s classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel’s disorder in combination with four supervised classifiers was used to classify CE-NBI images. The results showed that the manual approach’s subjective evaluation could be reduced by using a computer-based approach. Moreover, the automatic approach showed the potential to work as an assistant system in case of disagreements among clinicians and to reduce the manual approach’s misclassification issue.

Development of parallel programming tools has become a mainstream research topic and it has produced many useful tools that reduce the burden of parallel programming. The most important aspect of any such tool is the data dependency analysis, which is very complex and computationally intensive. Fully automatic tools have many demerits such as low efficiency and lack of interaction with the user. In this paper, the authors present our tool called EasyPar having unique features to assist the user at the time of program development as opposed most of the other tools that work on completed programs. Performing parallelization analysis at the time of program development has a potential to exploit more parallelization opportunity by interaction with the programmer. Two most important requirements of such a tool are the accuracy and the performance of the analysis. The authors propose the method that uses database to enable quick and efficient dependency analysis. The proposed method utilizes a database to save program information in structured from and using a query based method for data dependency analysis. The database approach has three benefits. First, it makes the incremental parsing easier and faster; second, query-based approach makes the dependency analysis efficient and third it allows the demand driven program analysis possible. The authors have tested their tool on popular benchmark codes, presented the performance results, and compared it with relevant work.

Integrated Cross-Component Lightweight and Material-Oriented Development Methodology – The Embodiment Design Cycle

Recent advances in large-scale experimental facilities ushered in an era of data-driven science. These large-scale data increase the opportunity to answer many fundamental questions in basic science. However, these data pose new challenges to the scientific community in terms of their optimal processing and transfer. Consequently, scientists are in dire need of robust high performance computing (HPC) solutions that can scale with terabytes of data. In this thesis, I address the challenges in three major aspects of scientific big data processing as follows: 1) Developing scalable software and algorithms for data- and compute-intensive scientific applications. 2) Proposing new cluster architectures that these software tools need for good performance. 3) Transferring the big scientific dataset among clusters situated at geographically disparate locations. In the first part, I develop scalable algorithms to process huge amounts of scientific big data using the power of recent analytic tools such as, Hadoop, Giraph, NoSQL, etc. At a broader level, these algorithms take the advantage of locality-based computing that can scale with increasing amount of data. The thesis mainly addresses the challenges involved in large-scale genome analysis applications such as, genomic error correction and genome assembly which made their way to the forefront of big data challenges recently. In the second part of the thesis, I perform a systematic benchmark study using the above-mentioned algorithms on different distributed cyberinfrastructures to pinpoint the limitations in a traditional HPC cluster to process big data. Then I propose the solution to those limitations by balancing the I/O bandwidth of the solid state drive (SSD) with the computational speed of high-performance CPUs. A theoretical model has been also proposed to help the HPC system designers who are striving for system balance. In the third part of the thesis, I develop a high throughput architecture for transferring these big scientific datasets among geographically disparate clusters. The architecture leverages the power of Ethereum's Blockchain technology and Swarm's peer-to-peer (P2P) storage technology to transfer the data in secure, tamper-proof fashion. Instead of optimizing the computation in a single cluster, in this part, my major motivation is to foster translational research and data interoperability in collaboration with multiple institutions.

Point/Counterpoint: Within the next ten years treatment planning will become fully automated without the need for human intervention.

Optimization methods have been widely applied to the aerodynamic design of gas turbine blades. While applying optimization to high-fidelity computational fluid dynamics (CFD) simulations has proven capable of improving engineering design performance, a challenge has been overcoming the prolonged run-time due to the computationally expensive CFD runs. Reduced-order models and, more recently, machine learning methods have been increasingly used in gas turbine studies to predict performance metrics and operational characteristics, model turbulence, and optimize designs. The application of machine learning methods allows for utilizing existing knowledge and datasets from different sources, such as previous experiments, CFD, low-fidelity simulations, 1D or system-level studies. The present study investigates inserting a machine learning model that utilizes such data into a high-fidelity CFD driven optimization process, and hence effectively reduces the number of required evaluations of the CFD model. Artificial Neural Network (ANN) models were trained on data from over three thousand two-dimensional (2D) CFD analyses of turbine blade cross-sections. The trained ANN models were then used as surrogates in a nested optimization process alongside a full three-dimensional Navier–Stokes CFD simulation. The much lower evaluation cost of the ANN model allows for tens of thousands of design evaluations to guide the search of the best blade profiles to be used in the more expensive, high-fidelity CFD runs, improving the progress of the optimization while reducing the required computation time. It is estimated that the current workflow achieves a five-fold reduction in computational time in comparison to an optimization process that is based on three-dimensional (3D) CFD simulations alone. The methodology is demonstrated on the NASA/General Electric Energy Efficient Engine (E3) high pressure turbine blade and found Pareto front designs with improved blade efficiency and power over the baseline. Quantitative analysis of the optimization data reveals that some design parameters in the present study are more influential than others, such as the lean angle and tip scaling factor. Examining the optimized designs also provides insight into the physics, showing that the optimized designs have a lower amount of pressure drop near the trailing edge, but have an earlier onset of pressure drop on the suction side surface when compared to the baseline design, contributing to the observed improvements in efficiency and power.

Accumulation of heavy metal laden industrial effluent into natural aquatic body leads to disruption of balance of that ecosystem as well as imperils the life cycle of the organisms living thereof. The present work aims to demonstrate the effect of toxic heavy metal containing industrial effluent on several life history traits, adult morphology and global protein expression pattern of insect model Drosophila melanogaster. Flies were reared for five generations in the effluent containing medium. One pair of virgin male and female fly from the stock was transferred to the vials. On the next day the male was removed and the female was transferred to fresh vials for successive days. Daily egg production of the females in different trials was recorded. Developmental time, pupation height, sex-ratio, egg-to-adult viability and mean body and wing length of the emerging flies from both treated and control groups were measured. We noted a drop in mean daily egg production rate, reduction in developmental time, shortening of pupation height, decreased egg-to-adult viability and a reduction in body and wing length in treated group in comparison to the control. However, no significant change was found in the sex-ratio of treated adult flies. Alterations in the expression pattern of body proteins in all the developmental stages were revealed by SDS-PAGE.

Diffusion tensor image analysis along the perivascular space (DTI-ALPS) is a potential non-invasive marker of glymphatic function that typically relies on manual region of interest (ROI) placement. This study compared ALPS indices in treatment-naïve, de novo diagnosed patients with Parkinson’s disease (PD), patients with isolated REM behavior disorder (iRBD), and healthy controls using both manual and automatic approaches to the ROI selection used in ALPS-index calculation. ALPS indices were analyzed bilaterally and correlated with clinical severity (MDS-UPDRS) and nigrostriatal denervation (DAT-SPECT). ANCOVA revealed significant inter-group differences using both manual (p = 0.018) and automatic (p = 0.002) ROI selection methods. The automatic ROI selection approach showed significantly lower ALPS indices in PD compared to controls (p = 0.001) and iRBD (p = 0.009). ALPS indices correlated with symptom severity and nigrostriatal denervation. These findings underscore the reliability of the automatic ROI placement approach for ALPS index calculation and may indicate early glymphatic alterations in Parkinson’s disease.

Significance of host size for a solitary endoparasitoid: A trade-off between fitness parameters