Constructing the Seismograms of Future Earthquakes in Yunnan, China, Using Compressed Sensing

2020 ◽  
Vol 92 (1) ◽  
pp. 261-274
Author(s):  
Jie Zhang ◽  
Huiyu Zhu ◽  
Siwei Yu ◽  
Jianwei Ma
Keyword(s):  
Compressed Sensing ◽  
Active Fault ◽  
Regional Scale ◽  
Focal Mechanism Solution ◽  
Total Output ◽  
Detailed Knowledge ◽  
Large Dataset ◽  
Sensing Technology ◽  
Data Driven Approach ◽  
Using Data

Abstract The ability to calculate the seismogram of an earthquake at a local or regional scale is critical but challenging for many seismological studies because detailed knowledge about the 3D heterogeneities in the Earth’s subsurface, although essential, is often insufficient. Here, we present an application of compressed sensing technology that can help predict the seismograms of earthquakes at any position using data from past events randomly distributed in the same area in Jinggu County, Yunnan, China. This first data-driven approach for calculating seismograms generates a large dataset in 3D with a volume encompassing an active fault zone. The input number of earthquakes comprises only 1.27% of the total output events. We use the output data to create a database intended to find the best-matching waveform of a new event by applying an earthquake search engine, which instantly reveals the hypocenter and focal-mechanism solution.

scholarly journals Semantic transparency effects in German compounds: A large dataset and multiple-task investigation

2019 ◽  
Author(s):  
Fritz Guenther ◽  
Marco Marelli ◽  
Jens Bölte
Keyword(s):  
Comprehensive Analysis ◽  
Distributional Semantics ◽  
Semantic Model ◽  
Semantic Transparency ◽  
Dimensional Approach ◽  
Compound Words ◽  
Behavioral Experiments ◽  
Large Dataset ◽  
Using Data ◽  
Multiple Task

In the present study, we provide a comprehensive analysis and a multi-dimensional dataset of semantic transparency measures for 1,810 German compound words. Compound words are considered semantically transparent when the contribution of the constituents’ meaning to the compound meaning is clear (as in airport), but the degree of semantic transparency varies between compounds (compare strawberry or sandman). Our dataset includes both compositional and relatedness-based semantic transparency measures, also differentiated by constituents. The measures are obtained from a computational and fully implemented semantic model based on distributional semantics. We validate the measures using data from four behavioral experiments: Explicit transparency ratings, two different lexical decision tasks using different nonwords, and an eye-tracking study. We demonstrate that different semantic effects emerge in different behavioral tasks, which can only be capturedusing a multi-dimensional approach to semantic transparency. We further provide the semantic transparency measures derived from the model for a dataset of 40,475 additional German compounds, as well as for 2,061 novel German compounds.

scholarly journals Data-Driven Approach for Predicting and Explaining the Risk of Long-Term Unemployment

2020 ◽  
Vol 214 ◽  
pp. 01023
Author(s):  
Linan (Frank) Zhao
Keyword(s):  
Machine Learning ◽  
Age Groups ◽  
Learning Models ◽  
Public Authorities ◽  
Ensemble Machine Learning ◽  
European Public ◽  
Data Driven Approach ◽  
Using Data ◽  
Machine Learning Models

Long-term unemployment has significant societal impact and is of particular concerns for policymakers with regard to economic growth and public finances. This paper constructs advanced ensemble machine learning models to predict citizens’ risks of becoming long-term unemployed using data collected from European public authorities for employment service. The proposed model achieves 81.2% accuracy on identifying citizens with high risks of long-term unemployment. This paper also examines how to dissect black-box machine learning models by offering explanations at both a local and global level using SHAP, a state-of-the-art model-agnostic approach to explain factors that contribute to long-term unemployment. Lastly, this paper addresses an under-explored question when applying machine learning in the public domain, that is, the inherent bias in model predictions. The results show that popular models such as gradient boosted trees may produce unfair predictions against senior age groups and immigrants. Overall, this paper sheds light on the recent increasing shift for governments to adopt machine learning models to profile and prioritize employment resources to reduce the detrimental effects of long-term unemployment and improve public welfare.

scholarly journals The current COVID-19 wave will likely be mitigated in the second-line European countries

2020 ◽  
Author(s):  
S. Soubeyrand ◽  
M. Ribaud ◽  
V. Baudrot ◽  
D. Allard ◽  
D. Pommeret ◽  
...  
Keyword(s):  
United Kingdom ◽  
Mixture Model ◽  
European Countries ◽  
Systems Science ◽  
Second Line ◽  
University Center ◽  
The Future ◽  
Population Structures ◽  
Data Driven Approach ◽  
Using Data

AbstractObjectiveCountries presently apply different strategies to control the COVID-19 outbreak. Differences in population structures, decision making, health systems and numerous other factors result in various trajectories in terms of mortality at country scale. Our objective in this manuscript is to disentangle the future of second-line European countries (i.e. countries that present, today, a moderate death rate) with respect to the current COVID-19 wave.MethodWe propose a data-driven approach, grounded on a mixture model, to forecast the dynamics of the number of deaths from COVID-19 in a given focal country using data from countries that are ahead in time in terms of COVID-19-induced mortality. In this approach, the mortality curves of ahead-in-time countries are used to build predictors, which are then used as the components of the mixture model. This approach was applied to eight second-line European countries (Austria, Denmark, Germany, Ireland, Poland, Portugal, Romania and Sweden), using Belgium, France, Italy, Netherlands, Spain, Switzerland, United Kingdom as well as the Hubei province in China to build predictors. For this analysis, we used data pooled by the Johns Hopkins University Center for Systems Science and Engineering.ResultsIn general, the second-line European countries tend to follow relatively mild mortality curves (typically, those of Switzerland and Hubei) rather than fast and severe ones (typically, those of Spain, Italy, Belgium, France and the United Kingdom). From a methodological viewpoint, the performance of our forecasting approach is about 80% up to 8 days in the future, as soon as the focal country has accumulated at least two hundreds of deaths.DiscussionOur results suggest that the continuation of the current COVID-19 wave across Europe will likely be mitigated, and not as strong as it was in most of the front-line countries first impacted by the wave.

Modeling the Gas Exchange Processes of a Modern Diesel Engine With an Integrated Physics-Based and Data-Driven Approach

2019 ◽  
Author(s):  
Jorge Pulpeiro Gonzalez ◽  
King Ankobea-Ansah ◽  
Elena Escuder Milian ◽  
Carrie M. Hall
Keyword(s):  
Diesel Engine ◽  
Gas Exchange ◽  
High Efficiency ◽  
Direct Injection ◽  
Data Driven ◽  
Model Framework ◽  
Variable Valve Actuation ◽  
Exchange Processes ◽  
Data Driven Approach ◽  
Using Data

Abstract The gas exchange processes of engines are becoming increasingly complex since modern engines leverage technologies including variable valve actuation, turbochargers, and exhaust gas recirculation. Control of these many devices and the underlying gas flows is essential for high efficiency engine concepts. If these processes are to be controlled and estimated using model-based techniques, accurate models are required. This work explores a model framework that leverages a data-driven model of the turbocharger along with submodels of the intercooler, intake and exhaust manifolds and engine processes to provide cylinder-specific predictions of the pressure and temperatures of the gases across the system. This model is developed and validated using data from a 2.0 liter VW turbocharged, direct-injection diesel engine and shown to provide accurate prediction of critical gas properties.

Metamorphic amphiboles in the Ironwood Iron-Formation, Gogebic Iron Range, Wisconsin: Implications for potential resource development

2020 ◽  
Vol 105 (8) ◽  
pp. 1259-1269
Author(s):  
Carlin J. Green ◽  
Robert R. Seal ◽  
Nadine M. Piatak ◽  
William F. Cannon ◽  
Ryan J. McAleer ◽  
...  
Keyword(s):  
Objective Assessment ◽  
Regional Scale ◽  
Resource Development ◽  
The United States ◽  
Contact Metamorphism ◽  
Iron Formation ◽  
Detailed Knowledge ◽  
Lower Grade ◽  
Iron Range ◽  
Wide Range

Abstract The Paleoproterozoic Ironwood Iron-Formation, a Superior-type banded iron formation located in the western Gogebic Iron Range in Wisconsin, is one of the largest undeveloped iron ore resources in the United States. Interest in the development of this resource is complicated by potential environmental and health effects related to the presence of amphibole minerals in the Ironwood, a consequence of Mesoproterozoic contact metamorphism. The presence of these amphiboles and their contact metamorphic origin have long been recognized; however, recent interest in this resource has highlighted the lack of detailed knowledge on their distribution, mineral chemistry, and morphology. Optical microscopy, X-ray diffraction, scanning electron microscopy, and electron microprobe analysis were utilized to investigate the origin, distribution, morphology, and chemistry of amphiboles in the Ironwood. Amphibole is present in the western portion of the study area due to regional-scale contact meta-morphism associated with the intrusion of the 1.1 Ga Mellen Intrusive Complex. Locally amphibole is also present, adjacent to diabase and/or gabbro dikes and sills in the lower-grade Ironwood in the eastern portion of the study area. In both localities, amphiboles in the Ironwood most commonly developed in massive and prismatic habits, and locally assumed a fibrous habit. Fibrous amphiboles were recognized locally in the two potential ore zones of the Ironwood but were not observed in the portion likely to be waste rock. Massive and prismatic amphiboles show a wide range of Mg# [molar Mg/(Mg+Fe2+)] values (0.06 to 0.87), whereas Mg# values of fibrous amphiboles are restricted from 0.14 to 0.35. Factors that influenced the compositional variability of amphiboles in the Ironwood may have included temperature of formation, morphology, bulk chemistry of the iron formation, and variations in prograde and retrograde metamorphism. The presence of amphiboles in the Ironwood is a known issue that will need to be factored into any future mine plans. This study provides an objective assessment of the distribution and character of amphiboles in the Ironwood to aid all decision-makers in any future resource development scenarios.

Using data-driven approach for wind power prediction: A comparative study

2016 ◽  
Vol 118 ◽  
pp. 193-203 ◽  
Author(s):  
Ehsan Taslimi Renani ◽  
Mohamad Fathi Mohamad Elias ◽  
Nasrudin Abd. Rahim
Keyword(s):  
Comparative Study ◽  
Wind Power ◽  
Data Driven ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Data Driven Approach ◽  
Using Data

Terrestrial avifauna of the Gippsland Plain and Strzelecki Ranges, Victoria, Australia: insights from Atlas data

2005 ◽  
Vol 32 (6) ◽  
pp. 531 ◽  
Author(s):  
James Q. Radford ◽  
Andrew F. Bennett
Keyword(s):  
Regional Scale ◽  
Bird Species ◽  
Common Species ◽  
Reporting Rate ◽  
Natural Environments ◽  
Study Region ◽  
Typical Species ◽  
The Status ◽  
Using Data ◽  
Landscape Units

The rate and spatial scale at which natural environments are being modified by human land-uses mean that a regional or national perspective is necessary to understand the status of the native biota. Here, we outline a landscape-based approach for using data from the ‘New Atlas of Australian Birds’ to examine the distribution and status of avifauna at a regional scale. We use data from two bioregions in south-east Australia – the Gippsland Plain and the Strzelecki Ranges (collectively termed the greater Gippsland Plains) – to demonstrate this approach. Records were compiled for 57 landscape units, each 10′ latitude by 10′ longitude (~270 km2) across the study region. A total of 165 terrestrial bird species was recorded from 1870 ‘area searches’, with a further 24 species added from incidental observations and other surveys. Of these, 108 species were considered ‘typical’ of the greater Gippsland Plain in that they currently or historically occur regularly in the study region. An index of species ‘occurrence’, combining reporting rate and breadth of distribution, was used to identify rare, common, widespread and restricted species. Ordination of the dataset highlighted assemblages of birds that had similar spatial distributions. A complementarity analysis identified a subset of 14 landscape units that together contained records from at least three different landscape units for each of the 108 ‘typical’ species. When compared with the 40 most common ‘typical’ species, the 40 least common species were more likely to be forest specialists, nest on the ground and, owing to the prevalence of raptors in the least common group, take prey on the wing. The future status of the terrestrial avifauna of the greater Gippsland Plains will depend on the extent to which effective restoration actions can be undertaken to ensure adequate representation of habitats for all species, especially for the large number of species of conservation concern.

An economic model comparison of EMSI and IMPLAN

2016 ◽  
Vol 23 (5) ◽  
pp. 1124-1130 ◽  
Author(s):  
Sungsoo Kim ◽  
Chad R. Miller
Keyword(s):  
Model Comparison ◽  
Impact Analysis ◽  
Economic Modeling ◽  
Total Output ◽  
Type I ◽  
Type Ii ◽  
Development Research ◽  
Tourism Research ◽  
Multiplier Effects ◽  
Using Data

Economic Modeling Specialist International (EMSI) model is a common economic development research tool that has begun to be utilized for tourism research. Therefore, it is important to examine the differences between the EMSI model and the commonly used Impact Analysis for Planning (IMPLAN) model. The multiplier effects of the default version of EMSI and IMPLAN were compared using data obtained from a visitor expenditure survey of the Jackson Mississippi Mistletoe Marketplace. The results revealed that IMPLAN estimated larger multiplier effects (both type I and type II) than EMSI for the total output and employment (job supports).

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