Multiattribute analysis for channel element discrimination in the Taranaki Basin, offshore New Zealand

2019 ◽  
Vol 7 (2) ◽  
pp. SC45-SC61 ◽  
Author(s):  
Phinphorn Amonpantang ◽  
Heather Bedle ◽  
Jonny Wu
Keyword(s):  
Small Scale ◽  
Root Mean Square Amplitude ◽  
Small Individual ◽  
Data Set ◽  
Channel System ◽  
Lithology Identification ◽  
Structure Channel ◽  
Multiattribute Analysis ◽  
Insight Into ◽  
Channel Systems

A detailed study of Pliocene channel systems within the Taranaki Basin was undertaken from the Parihaka 3D seismic volume to improve our understanding of the Plio-Pleistocene channel elements in terms of structure, channel evolution, and lithology. Seismic picking parameters were chosen based on the lateral resolution for optimal mapping of the channels. Individual and multiattribute studies were performed on single, combined, and complex channel systems with the goal of identifying channel features and discriminating between shale- and sand-rich regions of the channels. For this target and data set, the variance attribute provided key insights into channel features, such as the edge of the channel, meander scrolls, and point bars. Root-mean-square amplitude and sweetness performed equally well in lithology identification, and, combined with variance, it aided in identifying sand-rich channels, as well as small individual channels that could provide sediment pathways into the deepwater Taranaki Basin. Depending on the complexity of the channel system, different attribute analyses had varying success with each system. Therefore, it is important to combine various attributes to discriminate channel elements as fully as possible. The lithologies of individual channels and their elements can be determined using seismic attributes, although it becomes increasingly difficult to discriminate small-scale features within the channel as the complexity of the channel system increases. Chronostratigraphic studies using stratal slicing techniques provided insight into the evolution of the channel system through time, demonstrating an overall sand-rich base of the channel, with a shallower shale-rich lithology at the top of the channel fill.

scholarly journals Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

2021 ◽  
Vol 503 (2) ◽  
pp. 2688-2705
Author(s):  
C Doux ◽  
E Baxter ◽  
P Lemos ◽  
C Chang ◽  
A Alarcon ◽  
...  
Keyword(s):  
Dark Energy ◽  
Internal Consistency ◽  
Goodness Of Fit ◽  
Small Scale ◽  
P Value ◽  
Posterior Predictive Distribution ◽  
Data Set ◽  
Dark Energy Survey ◽  
Data Vector ◽  
Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

scholarly journals Combining Multiple Organizational-level Databases: An Empirical Evaluation of Different Matching Methods

2021 ◽  
pp. 004912412098618
Author(s):  
Tim de Leeuw ◽  
Steffen Keijl
Keyword(s):  
Empirical Evaluation ◽  
Organizational Level ◽  
Random Errors ◽  
Data Set ◽  
Future Studies ◽  
Matching Methods ◽  
Matching Process ◽  
Significant Relationships ◽  
Research Findings ◽  
Insight Into

Although multiple organizational-level databases are frequently combined into one data set, there is no overview of the matching methods (MMs) that are utilized because the vast majority of studies does not report how this was done. Furthermore, it is unclear what the differences are between the utilized methods, and it is unclear whether research findings might be influenced by the utilized method. This article describes four commonly used methods for matching databases and potential issues. An empirical comparison of those methods used to combine regularly used organizational-level databases reveals large differences in the number of observations obtained. Furthermore, empirical analyses of these different methods reveal that several of them produce both systematic and random errors. These errors can result in erroneous estimations of regression coefficients in terms of direction and/or size as well as an issue where truly significant relationships might be found to be insignificant. This shows that research findings can be influenced by the MM used, which would argue in favor of the establishment of a preferred method as well as more transparency on the utilized method in future studies. This article provides insight into the matching process and methods, suggests a preferred method, and should aid researchers, reviewers, and editors with both combining multiple databases and describing and assessing them.

scholarly journals Were VCF patients at higher risk of mortality following the 2009 publication of the vertebroplasty “sham” trials?

2017 ◽  
Vol 29 (2) ◽  
pp. 375-383 ◽  
Author(s):  
K. L. Ong ◽  
D. P. Beall ◽  
M. Frohbergh ◽  
E. Lau ◽  
J. A. Hirsch
Keyword(s):  
Mortality Risk ◽  
Cox Regression ◽  
Compression Fracture ◽  
Balloon Kyphoplasty ◽  
Data Set ◽  
Mortality Risks ◽  
Medicare Data ◽  
Kaplan Meier ◽  
Risk Of Mortality ◽  
Insight Into

Abstract Summary The 5-year period following 2009 saw a steep reduction in vertebral augmentation volume and was associated with elevated mortality risk in vertebral compression fracture (VCF) patients. The risk of mortality following a VCF diagnosis was 85.1% at 10 years and was found to be lower for balloon kyphoplasty (BKP) and vertebroplasty (VP) patients. Introduction BKP and VP are associated with lower mortality risks than non-surgical management (NSM) of VCF. VP versus sham trials published in 2009 sparked controversy over its effectiveness, leading to diminished referral volumes. We hypothesized that lower BKP/VP utilization would lead to a greater mortality risk for VCF patients. Methods BKP/VP utilization was evaluated for VCF patients in the 100% US Medicare data set (2005–2014). Survival and morbidity were analyzed by the Kaplan-Meier method and compared between NSM, BKP, and VP using Cox regression with adjustment by propensity score and various factors. Results The cohort included 261,756 BKP (12.6%) and 117,232 VP (5.6%) patients, comprising 20% of the VCF patient population in 2005, peaking at 24% in 2007–2008, and declining to 14% in 2014. The propensity-adjusted mortality risk for VCF patients was 4% (95% CI, 3–4%; p < 0.001) greater in 2010–2014 versus 2005–2009. The 10-year risk of mortality for the overall cohort was 85.1%. BKP and VP cohorts had a 19% (95% CI, 19–19%; p < 0.001) and 7% (95% CI, 7–8%; p < 0.001) lower propensity-adjusted 10-year mortality risk than the NSM cohort, respectively. The BKP cohort had a 13% (95% CI, 12–13%; p < 0.001) lower propensity-adjusted 10-year mortality risk than the VP cohort. Conclusions Changes in treatment patterns following the 2009 VP publications led to fewer augmentation procedures. In turn, the 5-year period following 2009 was associated with elevated mortality risk in VCF patients. This provides insight into the implications of treatment pattern changes and associated mortality risks.

scholarly journals Charting the Rough Journey to ‘Home’: The Contribution of Qualitative Longitudinal Research to Understandings of Homelessness in Austerity

2021 ◽  
pp. 1-17
Author(s):  
Emma Davidson ◽  
Briege Nugent ◽  
Sarah Johnsen
Keyword(s):  
Longitudinal Research ◽  
Support Service ◽  
Added Value ◽  
Small Scale ◽  
Deep Insight ◽  
Policy Makers ◽  
Analytical Potential ◽  
Longitudinal Approach ◽  
The Uk ◽  
Insight Into

This article reflects on the contribution of qualitative longitudinal research (QLR) to understandings of homeless peoples’ experiences of support service interventions in an era of austerity in the UK. It brings into ‘analytic conversation’ data from qualitative longitudinal evaluations of homeless support projects operated by voluntary sector organisations in Scotland. With fieldwork spanning 2014-2019, the analysis expands the analytical potential of pooling small-scale studies through an interrogation of individuals’ ‘journeys’ through homelessness services and their rough path to ‘home’. By reflecting on our substantive findings, the article explores the added value and challenges of a longitudinal approach. It concludes that while QLR can deliver deep insight into lives lived by vulnerable populations and potentially reduce the distance between policy makers and those affected, its benefits must be balanced against pragmatism and the ethical responsibilities associated with the method.

How to foster employee satisfaction by means of coaching, motivation, emotional salary and social media skills in the agri-food value chain

New Medit ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Keyword(s):  
Social Media ◽  
Value Chain ◽  
Employee Satisfaction ◽  
Data Set ◽  
Business Skills ◽  
A Value ◽  
The Social ◽  
Use Of Social Media ◽  
Food Value ◽  
Insight Into

Most employee satisfaction studies do not consider the current digital transformation of the social world. The aim of this research is to provide insight into employee satisfaction in agribusiness by means of coaching, motivation, emotional salary and social media with a value chain methodology. The model is tested empirically by analysing a survey data set of 381 observations in Spanish agribusiness firms of the agri-food value chain. The results show flexible remunerations of emotional salary are determinants of employee satisfaction. Additionally, motivation is relevant in the production within commercialisation link and coaching in the production within transformation link. Whole-of-chain employees showed the greatest satisfaction with the use of social media in personnel management. Findings also confirmed that employees will stay when a job is satisfying. This study contributes to the literature by investigating the effect of current social and digital business skills on employee satisfaction in the agri-food value chain.

scholarly journals TARGETED ADVERTISING IN THE BREAKFAST CEREAL INDUSTRY

2017 ◽  
Vol 49 (3) ◽  
pp. 382-399 ◽  
Author(s):  
JOSHUA BERNING ◽  
ADAM N. RABINOWITZ
Keyword(s):  
Product Differentiation ◽  
Food Product ◽  
Television Advertising ◽  
Breakfast Cereal ◽  
Data Set ◽  
Policy Makers ◽  
Relationship Of ◽  
Cereal Industry ◽  
The Relationship ◽  
Insight Into

AbstractWe examine the relationship of product characteristics of ready-to-eat breakfast cereal and targeted television advertising to specific consumer segments. We compile a unique data set that includes brand-packaging characteristics, including on-box games, nutrition information, and cobranding. We find that the relationship of television advertising and a cereal's brand-packaging characteristics varies by target audience. Our results provide insight into understanding how manufacturers strategically utilize branding, packaging, and television advertising. This can help industry and policy makers develop food product advertising policy. This analysis extends to other product markets where extensive product differentiation and promotion are present as well.

The systematics of Raspailiidae (Demospongiae: Poecilosclerida: Microcionina) re-analysed with a ribosomal marker

2007 ◽  
Vol 87 (6) ◽  
pp. 1571-1576 ◽  
Author(s):  
Dirk Erpenbeck ◽  
Sue List-Armitage ◽  
Belinda Alvarez ◽  
Bernard M. Degnan ◽  
Gert Wörheide ◽  
...  
Keyword(s):  
Gene Tree ◽  
Morphological Characters ◽  
Character Evolution ◽  
28S Rdna ◽  
The Other ◽  
Data Set ◽  
Rdna Gene ◽  
Insight Into

We present a 28S rDNA gene tree of selected Raspailiidae, Axinellidae and other demosponges to obtain insight into raspailiid phylogeny and character evolution. The Raspailiidae in our data set cluster in a well-supported clade, distinguished from Axinellidae, Agelasida and Hadromerida. Raspailia (s.s.), Eurypon, Sollasella, Aulospongus and Ectyoplasia form a Raspailiidae clade. Some Raspailia subgenera, in particular R. (Parasyringella), are not retrieved monophyletically. Trikentrion falls into the Thrinacophorinae, and not the Cyamoninae as earlier hypothesized. The axinellid genera Ptilocaulis and Reniochalina also cluster with Raspailiidae, distant from the other Axinellidae. The suitability of particular morphological characters for raspailiid phylogeny is discussed.

scholarly journals Classification Connection of Twitter Data using K-Means Clustering

2019 ◽  
Vol 8 (6S4) ◽  
pp. 14-22
Keyword(s):  
Social Media ◽  
Real Time ◽  
Clustering Methods ◽  
Time Analysis ◽  
Data Set ◽  
Dynamic Data ◽  
Real Time Analysis ◽  
Twitter Data ◽  
Social Media Platforms ◽  
Insight Into

The rise of social media platforms like Twitter and the increasing adoption by people in order to stay connected provide a large source of data to perform analysis based on the various trends, events and even various personalities. Such analysis also provides insight into a person’s likes and inclinations in real time independent of the data size. Several techniques have been created to retrieve such data however the most efficient technique is clustering. This paper provides an overview of the algorithms of the various clustering methods as well as looking at their efficiency in determining trending information. The clustered data may be further classified by topics for real time analysis on a large dynamic data set. In this paper, data classification is performed and analyzed for flaws followed by another classification on the same data set.

scholarly journals Predicting Anxiety in Routine Palliative Care Using Bayesian-Inspired Association Rule Mining

2021 ◽  
Vol 3 ◽  
Author(s):  
Oliver Haas ◽  
Luis Ignacio Lopera Gonzalez ◽  
Sonja Hofmann ◽  
Christoph Ostgathe ◽  
Andreas Maier ◽  
...  
Keyword(s):  
Palliative Care ◽  
Association Rule ◽  
Association Rule Mining ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Rule Mining ◽  
Data Set ◽  
Routinely Collected Data ◽  
Previous State ◽  
Insight Into

We propose a novel knowledge extraction method based on Bayesian-inspired association rule mining to classify anxiety in heterogeneous, routinely collected data from 9,924 palliative patients. The method extracts association rules mined using lift and local support as selection criteria. The extracted rules are used to assess the maximum evidence supporting and rejecting anxiety for each patient in the test set. We evaluated the predictive accuracy by calculating the area under the receiver operating characteristic curve (AUC). The evaluation produced an AUC of 0.89 and a set of 55 atomic rules with one item in the premise and the conclusion, respectively. The selected rules include variables like pain, nausea, and various medications. Our method outperforms the previous state of the art (AUC = 0.72). We analyzed the relevance and novelty of the mined rules. Palliative experts were asked about the correlation between variables in the data set and anxiety. By comparing expert answers with the retrieved rules, we grouped rules into expected and unexpected ones and found several rules for which experts' opinions and the data-backed rules differ, most notably with the patients' sex. The proposed method offers a novel way to predict anxiety in palliative settings using routinely collected data with an explainable and effective model based on Bayesian-inspired association rule mining. The extracted rules give further insight into potential knowledge gaps in the palliative care field.

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