Weighted tridiagonal matrix enhanced multivariance products representation (WTMEMPR) for decomposition of multiway arrays: applications on certain chemical system data sets

2016 ◽  
Vol 55 (2) ◽  
pp. 455-476 ◽  
Author(s):  
Evrim Korkmaz Özay ◽  
Metin Demiralp
Keyword(s):  
Tridiagonal Matrix ◽  
Chemical System ◽  
Data Sets ◽  
System Data

scholarly journals Segment-Based Approach for Assessing Hazard Risk of Coastal Highways in Hawai‘i

2019 ◽  
Vol 2673 (1) ◽  
pp. 83-91 ◽  
Author(s):  
Harrison Togia ◽  
Oceana P. Francis ◽  
Karl Kim ◽  
Guohui Zhang
Keyword(s):  
Qualitative Method ◽  
Data Sets ◽  
Regional Environment ◽  
The Road ◽  
Hazard Exposure ◽  
Multiple Indicators ◽  
System Data ◽  
Rural Highway ◽  
Geographic Information System Data

Hazards to roadways and travelers can be drastically different because hazards are largely dependent on the regional environment and climate. This paper describes the development of a qualitative method for assessing infrastructure importance and hazard exposure for rural highway segments in Hawai‘i under different conditions. Multiple indicators of roadway importance are considered, including traffic volume, population served, accessibility, connectivity, reliability, land use, and roadway connection to critical infrastructures, such as hospitals and police stations. The method of evaluating roadway hazards and importance can be tailored to fit different regional hazard scenarios. It assimilates data from diverse sources to estimate risks of disruption. A case study for Highway HI83 in Hawai‘i, which is exposed to multiple hazards, is conducted. Weakening of the road by coastal erosion, inundation from sea level rise, and rockfall hazards require adaptation solutions. By analyzing the risk of disruption to highway segments, adaptation approaches can be prioritized. Using readily available geographic information system data sets for the exposure and impacts of potential hazards, this method could be adapted not only for emergency management but also for planning, design, and engineering of resilient highways.

scholarly journals Global lightning NO<sub>x</sub> production estimated by an assimilation of multiple satellite data sets

2014 ◽  
Vol 14 (7) ◽  
pp. 3277-3305 ◽  
Author(s):  
K. Miyazaki ◽  
H. J. Eskes ◽  
K. Sudo ◽  
C. Zhang
Keyword(s):  
Satellite Data ◽  
Production Efficiency ◽  
Transport Model ◽  
Chemical System ◽  
No Production ◽  
Data Sets ◽  
Chemical Transport Model ◽  
Ozone Monitoring Instrument ◽  
Independent Observations ◽  
Sensitivity Studies

Abstract. The global source of lightning-produced NOx (LNOx) is estimated by assimilating observations of NO2, O3, HNO3, and CO measured by multiple satellite measurements into a chemical transport model. Included are observations from the Ozone Monitoring Instrument (OMI), Microwave Limb Sounder (MLS), Tropospheric Emission Spectrometer (TES), and Measurements of Pollution in the Troposphere (MOPITT) instruments. The assimilation of multiple chemical data sets with different vertical sensitivity profiles provides comprehensive constraints on the global LNOx source while improving the representations of the entire chemical system affecting atmospheric NOx, including surface emissions and inflows from the stratosphere. The annual global LNOx source amount and NO production efficiency are estimated at 6.3 Tg N yr−1 and 310 mol NO flash−1, respectively. Sensitivity studies with perturbed satellite data sets, model and data assimilation settings lead to an error estimate of about 1.4 Tg N yr−1 on this global LNOx source. These estimates are significantly different from those estimated from a parameter inversion that optimizes only the LNOx source from NO2 observations alone, which may lead to an overestimate of the source adjustment. The total LNOx source is predominantly corrected by the assimilation of OMI NO2 observations, while TES and MLS observations add important constraints on the vertical source profile. The results indicate that the widely used lightning parameterization based on the C-shape assumption underestimates the source in the upper troposphere and overestimates the peak source height by up to about 1 km over land and the tropical western Pacific. Adjustments are larger over ocean than over land, suggesting that the cloud height dependence is too weak over the ocean in the Price and Rind (1992) approach. The significantly improved agreement between the analyzed ozone fields and independent observations gives confidence in the performance of the LNOx source estimation.

Exploring the Merit and Feasibility of using Ocean Observing System Data in Education

2005 ◽  
Vol 39 (4) ◽  
pp. 56-63 ◽  
Author(s):  
J. McDonnell ◽  
L. Hotaling ◽  
G.I. Matsumoto ◽  
C. Parsons ◽  
B. Meeson ◽  
...  
Keyword(s):  
Real Time ◽  
Science Inquiry ◽  
Data Sets ◽  
Time Data ◽  
Ocean Science ◽  
Water Sensor ◽  
User Groups ◽  
System Data ◽  
K 12 ◽  
Ocean Observing

Ocean engineers and scientists are transforming the way we experience and understand the ocean through integrated and sustained ocean observations. For the first time, there will be continuous, sustained, near real-time, multi-dimensional data available from the ocean, collected from within the ocean using in-water sensor systems and from above using remote sensing methodologies. These data make inquiry-driven questions concerning the dynamic nature of the ocean's physical, biological and chemical characteristics in both time and space possible. These data will also provide unique and meaningful access to the ocean for a broad range of users. One major anticipated user group is kindergarten through grade 12 (K-12) educators and their students, who will be able to explore and utilize these near realtime data sets and information in their classrooms.The National Science Foundation (NSF)-sponsored Center for Ocean Science Education Excellence–Mid-Atlantic (COSEE-MA) is focused on coastal ocean observing systems and the development of products and services that bring real-time data to a broad range of user groups. COSEE-MA partners with these potential users to develop lesson plans and resources that use these data in meaningful ways to promote science inquiry in the classroom. Within this context, the merit and feasibility of developing a framework for a national ocean observing system education product was explored at a recent community workshop.

Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

2011 ◽  
pp. 243-266 ◽  
Author(s):  
Rosaria Lombardo
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Management System ◽  
Multivariate Data Analysis ◽  
Learning Management System ◽  
Multivariate Data ◽  
Data Sets ◽  
System Data ◽  
Learning Management

By the early 1990s, the term “data mining” had come to mean the process of finding information in large data sets. In the framework of the Total Quality Management, earlier studies have suggested that enterprises could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk customers/consumers and allow for more timely interventions (Macfadyen & Dawson, 2009). The Learning Management System data and the subsequent Customer Interaction System data can help to provide “early warning system data” for risk detection in enterprises. This chapter confirms and extends this proposition by providing data from an international research project investigating on customer satisfaction in services to persons of public utility, like education, training services and health care services, by means of explorative multivariate data analysis tools as Ordered Multiple Correspondence Analysis, Boosting regression, Partial Least Squares regression and its generalizations.

scholarly journals Significance levels of common frequencies extracted from multiple data sets

2020 ◽  
Vol 493 (1) ◽  
pp. 48-54
Author(s):  
Chris Koen
Keyword(s):  
Time Series ◽  
Time Base ◽  
Data Sets ◽  
Frequency Interval ◽  
Data Set ◽  
Frequency Spectra ◽  
Multiple Data ◽  
Significance Levels ◽  
System Data ◽  
Multiple Data Sets

ABSTRACT Large monitoring campaigns, particularly those using multiple filters, have produced replicated time series of observations for literally millions of stars. The search for periodicities in such replicated data can be facilitated by comparing the periodograms of the various time series. In particular, frequency spectra can be searched for common peaks. The sensitivity of this procedure to various parameters (e.g. the time base of the data, length of the frequency interval searched, number of replicate series, etc.) is explored. Two additional statistics that could sharpen results are also discussed: the closeness (in frequency) of peaks identified as common to all data sets, and the sum of the ranks of the peaks. Analytical expressions for the distributions of these two statistics are presented. The method is illustrated by showing that a ‘dubious’ periodicity in an 'Asteroid Terrestrial-impact Last Alert System' data set is highly significant.

scholarly journals Identification of Policy Priorities to Address the Burden of Smokeless Tobacco in Pakistan: A Multimethod Analysis

2019 ◽  
Vol 22 (12) ◽  
pp. 2262-2265 ◽  
Author(s):  
Kamran Siddiqi ◽  
Ziauddin Islam ◽  
Zohaib Khan ◽  
Faraz Siddiqui ◽  
Masuma Mishu ◽  
...  
Keyword(s):  
Smokeless Tobacco ◽  
Policy Instruments ◽  
Data Sets ◽  
Smoking Policies ◽  
Key Policy ◽  
System Data ◽  
Advocacy Campaigns ◽  
Close Cooperation ◽  
Effective Policy ◽  
Interactive Workshop

Abstract Introduction We assessed the magnitude of smokeless tobacco (ST) use in Pakistan and identified policy gaps to help ascertain short-, medium-, and long-term priorities. We then elicited stakeholders’ views as to which of these identified priorities are most important. Methods In a multimethod study, we: analyzed Global Tobacco Surveillance System data sets to estimate ST consumption and disease burden; conducted a documentary review to identify gaps in policies to control ST in comparison with smoking; elicited stakeholders’ views in an interactive workshop to identify a set of policy options available to address ST burden in Pakistan; and ranked policy priorities using a postevent survey. Results Among all tobacco users in Pakistan (n = 24 million), one-third of men and two-thirds of women consume ST. In 2017, its use led to an estimated 18 711 deaths due to cancer and ischemic heart disease. Compared to smoking, policies to control ST lag behind significantly. Priority areas for ST policies included: banning ST sale to and by minors, advocacy campaigns, introduction of licensing, levying taxes on ST, and standardizing ST packaging. A clear commitment to close cooperation between state actors and stakeholder groups is needed to create a climate of support and information for effective policy making. Conclusions Smokeless tobacco control in Pakistan should focus on four key policy instruments: legislation, education, fiscal policies, and quit support. More research into the effectiveness of such policies is also needed. Implications A number of opportunities to improve ST regulation in Pakistan were identified. Among these, immediate priorities include banning ST sale to and by minors, mobilizing advocacy campaign, introduction of licensing through the 1958 Tobacco Vendors Act, levying taxes on ST, and standardizing ST packaging.

Intrusion Detection Over Encrypted Network Data

2019 ◽  
Vol 63 (4) ◽  
pp. 604-619 ◽  
Author(s):  
Leyli Karaçay ◽  
Erkay Savaş ◽  
Halit Alptekin
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Detection System ◽  
Information Leakage ◽  
Cyber Attacks ◽  
Data Sets ◽  
Sensitive Information ◽  
Log Files ◽  
System Data ◽  
Lattice Based Cryptography

Abstract Effective protection against cyber-attacks requires constant monitoring and analysis of system data in an IT infrastructure, such as log files and network packets, which may contain private and sensitive information. Security operation centers (SOC), which are established to detect, analyze and respond to cyber-security incidents, often utilize detection models either for known types of attacks or for anomaly and applies them to the system data for detection. SOC are also motivated to keep their models private to capitalize on the models that are their propriety expertise, and to protect their detection strategies against adversarial machine learning. In this paper, we develop a protocol for privately evaluating detection models on the system data, in which privacy of both the system data and detection models is protected and information leakage is either prevented altogether or quantifiably decreased. Our main approach is to provide an end-to-end encryption for the system data and detection models utilizing lattice-based cryptography that allows homomorphic operations over ciphertext. We employ recent data sets in our experiments which demonstrate that the proposed privacy-preserving intrusion detection system is feasible in terms of execution times and bandwidth requirements and reliable in terms of accuracy.

Data Mining and Explorative Multivariate Data Analysis for Customer Satisfaction Study

Data Mining ◽  
2013 ◽  
pp. 1472-1495
Author(s):  
Rosaria Lombardo
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Customer Satisfaction ◽  
Management System ◽  
Multivariate Data Analysis ◽  
Learning Management System ◽  
Multivariate Data ◽  
Data Sets ◽  
System Data ◽  
Learning Management

By the early 1990s, the term “data mining” had come to mean the process of finding information in large data sets. In the framework of the Total Quality Management, earlier studies have suggested that enterprises could harness the predictive power of Learning Management System (LMS) data to develop reporting tools that identify at-risk customers/consumers and allow for more timely interventions (Macfadyen & Dawson, 2009). The Learning Management System data and the subsequent Customer Interaction System data can help to provide “early warning system data” for risk detection in enterprises. This chapter confirms and extends this proposition by providing data from an international research project investigating on customer satisfaction in services to persons of public utility, like education, training services and health care services, by means of explorative multivariate data analysis tools as Ordered Multiple Correspondence Analysis, Boosting regression, Partial Least Squares regression and its generalizations.

Sign in / Sign up

Export Citation Format

Share Document