Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditi...
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Traditional data sources are not sufficient for measuring the United Nations Sustainable Development Goals. New and non-traditional sources of data are required. Citizen science is an emerging example of a non-traditional data source that is already making a contribution. In this Perspective, we present a roadmap that outlines how citizen science can be integrated into the formal Sustainable Development Goals reporting mechanisms. Success will require leadership from the United Nations, innovation from National Statistical Offices and focus from the citizen-science community to identify the indicators for which citizen science can make a real contribution.
Advances in sequencing techniques have led to exponential growth in biological data, demanding the development of large-scale bioinformatics experiments. Because these experiments are computation- and data-intensive, ...
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A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In t...
A system for automatic detection of road damages is essential for logistics management. At the core of the system lies an algorithm for classification of damages. This work intends to establish such an algorithm. In the current proposal, the algorithm receives data of the probe vehicle rate of rotations, extracts statistics descriptive from the data to produce a feature vector, and finally, feeds the vector to a simple-three-layer neural network to classify the damages. The types of damages are limited to four cases: normal, pothole, speed bump, and expansion joint. For the development, a probe vehicle is used to produce 400 empirical data involving those four damage cases. By using Monte Carlo approach, the established neural network model is evaluated. The results show that the approach is able to classify the cases with 85% accuracy. The study also finds that the rates of pitch and roll to be the determining factors.
This paper presents a framework for selecting a combination of existing systems to satisfy new, emerging requirements while reusing existing and proven capabilities to ensure mission success. Decision attributes will ...
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This paper presents a framework for selecting a combination of existing systems to satisfy new, emerging requirements while reusing existing and proven capabilities to ensure mission success. Decision attributes will be considered during the selection process and will be used to measure the networked computer system's effectiveness to accomplish the mission. This approach will enable system stakeholders to make critical, well-informed decisions to address the continuing evolution of missions, threats, budget and technology.
Agile methods have gained wide acceptance over the past several years, to the point that they are now a standard management and execution approach for small-scale software development projects. While conventional Agil...
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ISBN:
(纸本)9781509046249
Agile methods have gained wide acceptance over the past several years, to the point that they are now a standard management and execution approach for small-scale software development projects. While conventional Agile methods are not generally applicable to large multi-year and mission-critical systems, Agile hybrids are now being developed (such as SAFe) to exploit the productivity improvements of Agile while retaining the necessary process rigor and coordination needs of these projects. From the perspective of Independent Verification and Validation (IV&V), however, the adoption of these hybrid Agile frameworks is becoming problematic. Hence, we find it prudent to question the compatibility of conventional IV&V techniques with (hybrid) Agile practices. This paper documents our investigation of (a) relevant literature, (b) the modification and adoption of Agile frameworks to accommodate the development of large scale, mission critical systems, and (c) the compatibility of standard IV&V techniques within hybrid Agile development frameworks. Specific to the latter, we found that the IV&V methods employed within a hybrid Agile process can be divided into three groups: (1) early lifecycle IV&V techniques that are fully compatible with the hybrid lifecycles, (2) IV&V techniques that focus on tracing requirements, test objectives, etc. are somewhat incompatible, but can be tailored with a modest effort, and (3) IV&V techniques involving an assessment requiring artifact completeness that are simply not compatible with hybrid Agile processes, e.g., those that assume complete requirement specification early in the development lifecycle.
In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the...
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ISBN:
(纸本)9781509011735
In order to improve the preclinical diagnose of Alzheimer's disease (AD), there is a great deal of interest in analyzing the AD related brain structural changes with magnetic resonance image (MRI) analyses. As the major features, variation of the structural connectivity and the cortical surface morphometry provide different views of structural changes to determine whether AD is present on presymptomatic patients. However, the large scale tensor-valued information and relatively low imaging resolution in diffusion MRI (dMRI) have created huge challenges for analysis. In this paper, we propose a novel framework that improves dMRI analysis power by fusing cortical surface morphometry features from structural MRI (sMRI). We first compute the hyperbolic harmonic maps between cortical surfaces with the landmark constraints thus to precisely evaluate surface tensor-based morphometry. Meanwhile, the graph-based analysis of structural connectivity derived from dMRI is conducted. Next, we fuse these two features via the optimal mass transportation (OMT) and eventually the Wasserstein distance (WD) based single image index is computed as a potential clinical multimodality imaging score. We apply our framework to brain images of 20 AD patients and 20 matched healthy controls, randomly chosen from the Alzheimer's Disease Neuroimaging Initiative (ADNI2) dataset. Our preliminary experimental results of group classification outperformed those of some other single dMRI-based features, such as regional hippocampal volume, mean scores of fractional anisotropy (FA) and mean axial (MD). The novel image fusion pipeline and simple imaging score of structural changes may benefit the preclinical AD and AD prevention research.
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational ap...
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Having both avian-like receptors (?±2,3-linked sialic acid, SA2,3Gal) and human-like receptors (?±2,6-linked sialic acid, SA2,6Gal), swine are proposed as??úmixing vessel??ùfor generating influenza...
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Having both avian-like receptors (?±2,3-linked sialic acid, SA2,3Gal) and human-like receptors (?±2,6-linked sialic acid, SA2,6Gal), swine are proposed as??úmixing vessel??ùfor generating influenza pandemic strains. Laboratory experiments suggested all HA subtypes of influenza A virus (IAV) can infect swine. However, only sporadic cases of avian
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