scholarly journals A data-driven framework for paleomagnetic Euler pole analysis

2021 ◽  
Author(s):  
Leandro Gallo ◽  
Facundo Sapienza ◽  
Mathew Domeier
Keyword(s):  
Synthetic Data ◽  
Data Driven ◽  
Web Browser ◽  
Deep Time ◽  
Polar Wander ◽  
The Past ◽  
Apparent Polar Wander Path ◽  
Euler Pole ◽  
Data Driven Approach ◽  
Pole Analysis

Owing to the inherent axial symmetry of the Earth’s magnetic field, paleomagnetic data only directly record the latitudinal and azimuthal positions of crustal blocks in the past, but paleolongitude cannot be constrained. An ability to overcome this obstacle is fundamental to paleogeographic reconstruction. The paleomagnetic Euler pole (PEP) analysis presents a unique means to recover such information in deep-time. However, prior applications of the PEP method have invariably incorporated subjective decisions into its execution, undercutting its fidelity and rigor. Here we present a data-driven approach to PEP analysis that addresses some of these deficiencies---namely the objective identification of change-points and small-circle arcs that together approximate an apparent polar wander path. We elaborate on our novel methodology and conduct some experiments with synthetic data to demonstrate its performance. We furthermore present implementations of our methods both as adaptable, stand-alone scripts and as a streamlined interactive workflow that can be operated through a web browser.

The Vanderbilt PICC Service: Program, Procedural, and Patient Outcomes Successes

2005 ◽  
Vol 10 (4) ◽  
pp. 183-192 ◽  
Author(s):  
Doug Burns
Keyword(s):  
Patient Outcomes ◽  
Medical Center ◽  
Nursing Program ◽  
Data Driven ◽  
Complication Rates ◽  
Advanced Practice ◽  
The Past ◽  
Data Driven Approach ◽  
High Level ◽  
Vanderbilt University

Abstract Since its inception in early 2000, Vanderbilt University's Peripherally Inserted Central Catheter (PICC) Service has experienced a high level of success as measured by high proficiency rates and increasing patient procedures each year, low complication rates during and after PICC placements, and an increasing scope of influence within the Vanderbilt University Medical Center and Children's Hospital, the surrounding community, and in the Southeastern United States. Primary drivers of the PICC Service's continuing success include consistent applications of technique and technology, a data-driven approach to assessing the program's progress, and appropriately managing customers' expectations and needs. Over the past five years, data were collected on more than 12,500 PICC placements performed in this specialized nursing program. Retrospective analyses of the data demonstrate an increasing rate of successful placements (from 87.2% to 92.4%) since the program's inception in 2000 to late 2004. Furthermore, the choice of PICC technology has had a significant impact on the odds for occlusion or infection. The Vanderbilt PICC Service provides a model by which other programs can be established, maintained, and expanded into advanced practice.

scholarly journals Identifying the best questions for rapid screening of secondhand smoke exposure among children

2020 ◽  
Author(s):  
Albert J Ksinan ◽  
Yaou Sheng ◽  
Elizabeth K Do ◽  
Julia C Schechter ◽  
Junfeng (Jim) Zhang ◽  
...  
Keyword(s):  
At Risk ◽  
Screening Tool ◽  
Smoke Exposure ◽  
Data Driven ◽  
The Past ◽  
Smoking Exposure ◽  
Data Driven Approach ◽  
Shs Exposure ◽  
Child Age ◽  
Cotinine Concentration

Abstract Introduction Many children suffer from secondhand smoke exposure (SHSe), which leads to a variety of negative health consequences. However, there is no consensus on how clinicians can best query parents for possible SHSe among children. We employed a data-driven approach to create an efficient screening tool for clinicians to quickly and correctly identify children at risk for SHSe. Methods Survey data from mothers and biospecimens from children were ascertained from the Neurodevelopment and Improving Children’s Health following Environmental Tobacco Smoke Exposure (NICHES) study. Included were mothers and their children whose saliva were assayed for cotinine (n = 351 pairs, mean child age = 5.6 years). Elastic net regression predicting SHSe, as indicated from cotinine concentration, was conducted on available smoking-related questions and cross-validated with 2015-2016 National Health and Nutrition Examination Survey (NHANES) data to select the most predictive items of SHSe among children (n = 1,670, mean child age = 8.4 years). Results Answering positively to at least one of the two final items (“During the past 30 days, did you smoke cigarettes at all?” and “Has anyone, including yourself, smoked tobacco in your home in the past 7 days?”) showed AUC = .82, and good specificity (.88) and sensitivity (.74). These results were validated with similar items in the nationally-representative NHANES sample, AUC = .82, specificity = .78, and sensitivity = .77. Conclusions Our data-driven approach identified and validated two items that may be useful as a screening tool for a speedy and accurate assessment of SHSe among children. Implications The current study used a rigorous data-driven approach to identify questions that could reliably predict secondhand smoking exposure (SHS) among children.Using saliva cotinine concentration levels as a gold standard for determining SHS exposure, our analysis employing elastic net regression identified two questions that served as good classifier for distinguishing children who might be at risk for SHS exposure. The two items that we validated in the current study can be readily used by clinicians, such as pediatricians, as part of screening procedures to quickly identify whether children might be at risk for secondhand smoking exposure.

Pliocene–Pleistocene genesis for the Murray Brook and Heath Steele Au–Ag gossan ore deposits, New Brunswick, from paleomagnetism

1996 ◽  
Vol 33 (1) ◽  
pp. 1-11 ◽  
Author(s):  
D. T. A. Symons ◽  
M. T. Lewchuk ◽  
D. R. Boyle
Keyword(s):  
New Brunswick ◽  
Ore Deposits ◽  
Age Dating ◽  
Rotational Axis ◽  
Polar Wander ◽  
The Past ◽  
The North ◽  
Apparent Polar Wander Path ◽  
Last Glaciation ◽  
Host Rocks

Several Au–Ag gossans occur over massive sulphide deposits in the Ordovician Tetagouche Group near Bathurst, New Brunswick. The Murray Brook and Heath Steele B zone goethite gossans were about 45 and 15 m thick, respectively, prior to mining. They contain no minerals suitable for radiometric age dating. Geologically they must be younger than the Devonian Acadian orogeny and older than the last glaciation. Paleomagnetic methods were used to analyse specimens from 29 sites, mostly from ore on the pit walls. Host rocks and sulphide mineralization retain characteristic remanent magnetization directions in magnetite and pyrrhotite with a variety of directions that include possible Devonian overprints. Fertile and barren gossan specimens at 21 sites retain antiparallel normal and reversed A characteristic remanence components in goethite and (or) hematite. The A direction is D = 357.7°, I = 61.7 °(α95 = 4.5°, k = 31). Its pole of 134.2°E, 85.2°N (dp = 5.4°, dm = 7.0°) falls on the North American apparent polar wander path and circumscribes the Earth's present rotational axis, indicating that the gossans formed during the Pliocene–Pleistocene. Examination of the site locations in the pits, along with the remanence polarity of their goethite and hematite A components, suggests the gossans formed during Chrons 1 and 2 only, or in the past 2.3 ± 0.3 Ma.

Failure Prognostics for In-Tank Fuel Pumps of the Returnless Fuel Systems

2016 ◽  
Author(s):  
Ehsan Taheri ◽  
Oleg Gusikhin ◽  
Ilya Kolmanovsky
Keyword(s):  
Electrical Signal ◽  
Data Driven ◽  
Connected Vehicle ◽  
Reliability Models ◽  
The Past ◽  
Degradation Modeling ◽  
Fuel Delivery ◽  
Data Driven Approach ◽  
Alternative Approaches ◽  
Type Fuel

With the motivation to develop Condition Based Maintenance (CBM) strategies for the automotive vehicles, this paper considers a data-driven approach to the prognostics of the automotive fuel pumps. Focusing on the returnless type fuel delivery systems, our approach is based on estimating the fuel pump workload based on the model learned from the past driving history. Statistical reliability models are then exploited to estimate failure probability. These models are formulated in terms of the workload and updated from data available from vehicles in the field. Numerical examples which illustrate the proposed methodology are reported. Compared to alternative approaches, which are based on detailed physics-based degradation modeling and/or electrical signal analysis, our approach is data-driven, exploits connected vehicle analytics and reliability-based modeling, and has a potential to lead to simpler implementations.

Engineering the Microbiome to Prevent Adverse Events: Challenges and Opportunities

2021 ◽  
Vol 61 (1) ◽  
pp. 159-179 ◽  
Author(s):  
Saad Khan ◽  
Ruth Hauptman ◽  
Libusha Kelly
Keyword(s):  
Radiation Therapy ◽  
Adverse Events ◽  
Data Driven ◽  
Medical Interventions ◽  
The Past ◽  
Challenges And Opportunities ◽  
Microbiome Research ◽  
Data Driven Approach ◽  
Difficult Challenge ◽  
The Many

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

scholarly journals A Comparison Study of Data-Driven Anomaly Detection Approaches for Industrial Chillers

2021 ◽  
Vol 1 ◽  
Author(s):  
Constantin Falk ◽  
Ron Van de Sand ◽  
Sandra Corasaniti ◽  
Jörg Reiff-Stephan
Keyword(s):  
Operating Conditions ◽  
Data Driven ◽  
Sensor Data ◽  
Comparison Study ◽  
True Negative ◽  
The Past ◽  
Use Of Data ◽  
Data Driven Approach ◽  
System States ◽  
System Operating

Faults in industrial chiller systems can lead to higher energy consumption, increasing wear of system components and shorten equipment life. While they gradually cause anomalous system operating conditions, modern automatic fault detection models aim to detect them at low severity by using real-time sensor data. Many scientific contributions addressed this topic in the past and presented data-driven approaches to detect faulty system states. Although many promising results were presented to date, there is lack of suitable comparison studies that show the effectiveness of the proposed models by use of data stemming from different chiller systems. Therefore this study aims at detecting a suitable data-driven approach to detect faults reliable in different domains of industrial chillers. Thus, a unified procedure is developed, to train all algorithms in an identical way with same data-basis. Since most of the reviewed papers used only one dataset for training and testing, the selected approaches are trained and validated on two different datasets from real refrigeration systems. The data-driven approaches are evaluated based on their accuracy and true negative rate, from which the most suitable approach is derived as a conclusion.

scholarly journals A data-driven approach for assessing biking safety in cities

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Sara Daraei ◽  
Konstantinos Pelechrinis ◽  
Daniele Quercia
Keyword(s):  
Local Governments ◽  
Policy Decision ◽  
Sustainable Transportation ◽  
Data Driven ◽  
Critical Factors ◽  
The Past ◽  
The World ◽  
Accident Data ◽  
Data Driven Approach ◽  
Policy Decision Making

AbstractWith the focus that cities around the world have put on sustainable transportation during the past few years, biking has become one of the foci for local governments globally. Cities all over the world invest in biking infrastructure, including bike lanes, bike parking racks, shared (dockless) bike systems etc. However, one of the critical factors in converting city-dwellers to (regular) bike users/commuters is safety. In this work, we utilize bike accident data from different cities to model the biking safety based on street-level (geographical and infrastructural) features. Our evaluations indicate that our model provides well-calibrated probabilities that accurately capture the risk of a biking accident. We further perform cross-city comparisons in order to explore whether there are universal features that relate to cycling safety. Finally, we discuss and showcase how our model can be utilized to explore “what-if” scenarios and facilitate policy decision making.

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