scholarly journals Multidimensional characterisation of time-dependent image data: A case study for the peripheral nervous system in ageing mice

2021 ◽  
Author(s):  
Matthias Weber ◽  
Thomas Wilhelm ◽  
Volker Schmidt
Keyword(s):  
Joint Probability ◽  
Image Data ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Myelin Sheaths ◽  
Local Thresholding ◽  
Segmentation Algorithms ◽  
Copula Approach ◽  
The Given

Segmentation of µm-resolution image data of irregularly shaped objects poses challenges to existing segmentation algorithms. This is especially true, when imperfections like noise, uneven lightning or traces of sample preparation are present in the image data. In this paper, considering electron micrographs of femoral quadriceps nerve sections of mice, a segmentation method to extract single axons surrounded by myelin sheaths is developed which is able to cope with various imperfections and artefacts. This approach successfully combines established methods like local thresholding and marker-based watershed transform to achieve a reliable segmentation of the given data. Indeed, the resulting segmentation map can be used to quantitatively determine geometrical characteristics of the axons and myelin sheaths. This is exemplified by modelling the joint probability distribution of axon area and myelin sphericity using a parametric copula approach, and by analysing the evolution of the model parameters for image data obtained from mice of different ages.

Regional Multiseverity Casualty Estimation Due to Building Damage following a Mw 8.8 Earthquake Scenario in Lima, Peru

2018 ◽  
Vol 34 (4) ◽  
pp. 1739-1761 ◽  
Author(s):  
Luis Ceferino ◽  
Anne Kiremidjian ◽  
Gregory Deierlein
Keyword(s):  
Probability Distribution ◽  
Joint Probability ◽  
Building Damage ◽  
Patient Treatment ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Probabilistic Framework ◽  
Earthquake Scenario ◽  
Selection Of

This paper presents the application of a rigorous probabilistic framework that estimates the number, severity, and distribution of casualties over a region. A brief summary of the model is included in this paper. The application is for casualties resulting from a Mw 8.8 earthquake scenario occurring on the sub-duction fault along the coastline of Lima, Peru. The case study demonstrates an application of the casualty model, including the procedures for acquiring the required information, the selection of model parameters, and a step-by-step explanation of the model-solving algorithms. The model provides an estimate of the joint probability distribution of multiseverity casualties, including spatial and across-severity correlations. This paper also shows how the model can be useful for (1) estimating 90th-percentile casualties, (2) identifying unsafe communities and structural typologies, and (3) providing evidence to support hospital collaboration policies across different districts to increase the patient treatment reliability. Additionally, the results demonstrate that empirical fatality prediction methodologies can underestimate fatality rates in countries with scarce and outdated fatality data.

Copula Approach to Multivariate Energy Efficiency Analysis

2021 ◽  
Author(s):  
Mervenur Sözen ◽  
Mehmet Ali Cengiz
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Data Envelopment ◽  
Network Dea ◽  
Dependency Structure ◽  
Optimal Weights ◽  
Joint Efficiency ◽  
Copula Approach

Data envelopment analysis (DEA) is a method that finds the effectiveness of an existing system using a number of input and output variables. In this study, we obtained energy efficiencies of construction, industrial, power, and transportation sectors in OECD countries for 2011 using DEA. It is possible to achieve the efficiencies in different sectors. However, we aim to find joint energy efficiency scores for all sectors. One of the methods proposed in the literature to obtain joint efficiency is network data envelopment analysis (network DEA). Network DEA treats sectors as sub-processes and obtains system and process efficiencies through optimal weights. Alternatively, we used a novel copula-based approach to achieve common efficiency scores. In this approach, it is possible to demonstrate the dependency structure between the efficiency scores of similar qualities obtained with DEA by copula families. New efficiency scores are obtained with the help of joint probability distribution. Then, we obtained joint efficiency scores through the copula approach using these efficiency scores. Finally, we obtained the joint efficiency scores of the same sectors through network DEA. As a result, we compared network DEA with the copula approach and interpreted the efficiencies of each energy sector and joint efficiencies.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2015 ◽  
Vol 12 (6) ◽  
pp. 5389-5426 ◽  
Author(s):  
S. Almeida ◽  
N. Le Vine ◽  
N. McIntyre ◽  
T. Wagener ◽  
W. Buytaert
Keyword(s):  
Joint Probability ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Independent Information ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall-runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information, and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall-runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be disinformative.

Non-Uniqueness in Model Parameterization and its Potential Contribution to Uncertainty

2020 ◽  
Author(s):  
Jun Guo ◽  
Daniel Segalman
Keyword(s):  
Parameter Uncertainty ◽  
Joint Probability ◽  
Peak Stress ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Potential Contribution ◽  
First Case ◽  
Model Predictions ◽  
Different Temperatures ◽  
Best Parameter

Abstract In the ordinary process of estimating uncertainty in model predictions one usually looks to some set of calibration experiments from which the model can be parameterized and then the resulting discrete set of model parameters are used to approximate the joint probability distribution of parameter vectors. That parameter uncertainty is propagated through the model to obtain predictive uncertainty. A key observation here is that usually, the modeler will attempt to find a unique “best” vector of parameters to match each calibration experiment and these “best” parameter vectors are used to estimate parameter uncertainty. In the work presented here, it is shown how for complex models — having more than a few parameters — it can happen that each experiment can befit equally well by a multitude of parameter vectors. It is also shown that when these large numbers of candidate parameter vectors are compiled the resulting model predictions may manifest substantially more variance than would be the case without consideration of the non-uniqueness issue. The contribution of non-uniqueness to prediction uncertainty is illustrated on two very different sorts of model. In the first case Johnson-Cook models for a titanium alloy are parameterized to match calibration experiments on three different alloy samples at different temperatures and strain rates. The resulting ensemble of parameter vectors are used to predict peak stress in a different experiment. In the second case, an epidemiological model is calibrated to history data and the parameter vectors are used to calculate a quantity of interest and uncertainty of that quantity.

scholarly journals Accounting for dependencies in regionalized signatures for predictions in ungauged catchments

2016 ◽  
Vol 20 (2) ◽  
pp. 887-901 ◽  
Author(s):  
Susana Almeida ◽  
Nataliya Le Vine ◽  
Neil McIntyre ◽  
Thorsten Wagener ◽  
Wouter Buytaert
Keyword(s):  
Joint Probability ◽  
Joint Probability Distribution ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Ungauged Catchments ◽  
Independent Information ◽  
Streamflow Data ◽  
Rainfall Runoff Model ◽  
Runoff Model ◽  
Selection Of

Abstract. A recurrent problem in hydrology is the absence of streamflow data to calibrate rainfall–runoff models. A commonly used approach in such circumstances conditions model parameters on regionalized response signatures. While several different signatures are often available to be included in this process, an outstanding challenge is the selection of signatures that provide useful and complementary information. Different signatures do not necessarily provide independent information and this has led to signatures being omitted or included on a subjective basis. This paper presents a method that accounts for the inter-signature error correlation structure so that regional information is neither neglected nor double-counted when multiple signatures are included. Using 84 catchments from the MOPEX database, observed signatures are regressed against physical and climatic catchment attributes. The derived relationships are then utilized to assess the joint probability distribution of the signature regionalization errors that is subsequently used in a Bayesian procedure to condition a rainfall–runoff model. The results show that the consideration of the inter-signature error structure may improve predictions when the error correlations are strong. However, other uncertainties such as model structure and observational error may outweigh the importance of these correlations. Further, these other uncertainties cause some signatures to appear repeatedly to be misinformative.

scholarly journals Managing uncertainty in flood protection planning with climate projections

2018 ◽  
Vol 22 (4) ◽  
pp. 2511-2526 ◽  
Author(s):  
Beatrice Dittes ◽  
Olga Špačková ◽  
Lukas Schoppa ◽  
Daniel Straub
Keyword(s):  
Joint Probability ◽  
Decision Makers ◽  
Flood Protection ◽  
Joint Probability Distribution ◽  
Climate Projections ◽  
Damage Potential ◽  
Protection Measures ◽  
Practical Applications ◽  
The Impact

Abstract. Technical flood protection is a necessary part of integrated strategies to protect riverine settlements from extreme floods. Many technical flood protection measures, such as dikes and protection walls, are costly to adapt after their initial construction. This poses a challenge to decision makers as there is large uncertainty in how the required protection level will change during the measure lifetime, which is typically many decades long. Flood protection requirements should account for multiple future uncertain factors: socioeconomic, e.g., whether the population and with it the damage potential grows or falls; technological, e.g., possible advancements in flood protection; and climatic, e.g., whether extreme discharge will become more frequent or not. This paper focuses on climatic uncertainty. Specifically, we devise methodology to account for uncertainty associated with the use of discharge projections, ultimately leading to planning implications. For planning purposes, we categorize uncertainties as either “visible”, if they can be quantified from available catchment data, or “hidden”, if they cannot be quantified from catchment data and must be estimated, e.g., from the literature. It is vital to consider the “hidden uncertainty”, since in practical applications only a limited amount of information (e.g., a finite projection ensemble) is available. We use a Bayesian approach to quantify the “visible uncertainties” and combine them with an estimate of the hidden uncertainties to learn a joint probability distribution of the parameters of extreme discharge. The methodology is integrated into an optimization framework and applied to a pre-alpine case study to give a quantitative, cost-optimal recommendation on the required amount of flood protection. The results show that hidden uncertainty ought to be considered in planning, but the larger the uncertainty already present, the smaller the impact of adding more. The recommended planning is robust to moderate changes in uncertainty as well as in trend. In contrast, planning without consideration of bias and dependencies in and between uncertainty components leads to strongly suboptimal planning recommendations.

scholarly journals SIMULATION OF SEA STORMS INCLUDING MULTIVARIATE STORM EVOLUTION

2018 ◽  
pp. 35
Author(s):  
Sebastián Solari ◽  
Miguel A. Losada
Keyword(s):  
Probability Distribution ◽  
Autoregressive Model ◽  
Joint Probability ◽  
New Method ◽  
Joint Probability Distribution ◽  
Northern Coast ◽  
Vector Autoregressive Model ◽  
Vector Autoregressive ◽  
Storm Evolution

A new method for the simulation of storms is proposed which takes into account the multivariate evolution of the storms, allowing to innovate in the form of each simulated storm, for all the variables involved. The method is based on two novel aspects: (a) measured storms are grouped using clusters techniques and a set of average evolution forms is defined for each cluster, one for each of the variables involved, and (b) a Vector Autoregressive model is fitted to the differences between the average evolution of each variable and the actual measured evolutions. The ability of the methodology to properly reproduce the joint probability distribution of all the variables involved is demonstrated for a case study at the mid Rio de la Plata northern coast.

scholarly journals Climate uncertainty in flood protection planning

2017 ◽  
Author(s):  
Beatrice Dittes ◽  
Olga Špačková ◽  
Lukas Schoppa ◽  
Daniel Straub
Keyword(s):  
Joint Probability ◽  
Life Time ◽  
Decision Makers ◽  
Flood Protection ◽  
Joint Probability Distribution ◽  
Damage Potential ◽  
Protection Measures ◽  
Practical Applications ◽  
Historic Record

Abstract. Technical flood protection is a necessary part of integrated strategies to protect riverine settlements from extreme floods. Many technical flood protection measures, such as dikes and protection walls, are costly to adapt after their initial construction. This poses a challenge to decision makers as there is large uncertainty in how the required protection level will change during the measure life time, which is typically many decades long. Flood protection requirements should account for multiple future uncertain factors: socio-economic, e.g. whether the population and with it the damage potential grows or falls; technological, e.g. possible advancements in flood protection; and climatic, e.g. whether extreme discharge will become more frequent or not. We focus here on the planning implications of the uncertainty in extreme discharge. We account for the sequential nature of the decision process, in which the adequacy of the protection is regularly revised in the future based on the discharges that have been observed by that point and that reduce uncertainty. For planning purposes, we categorize uncertainties as either visible, if they can be quantified from available catchment data, or hidden, if they cannot be quantified from catchment data and must be estimated, e.g. from literature. It is vital to consider the hidden uncertainty, since in practical applications only a limited amount of information (e.g. through projections, historic record) is available. We use a Bayesian approach to quantify the visible uncertainties and combine them with an estimate of the hidden uncertainties to learn a joint probability distribution of the parameters of extreme discharge. The methodology is integrated into an optimization framework and applied to a pre-alpine case study to give a quantitative, cost-optimal recommendation on the required amount of flood protection.

Literary Representations of Female Identity

2002 ◽  
Vol 19 (4) ◽  
pp. 23-41
Author(s):  
Safoi Babana-Hampton
Keyword(s):  
Related Problem ◽  
Female Identity ◽  
Literary Representations ◽  
Main Interest ◽  
Female Relationships ◽  
Arab Muslim ◽  
Feminine Writing ◽  
The Given ◽  
Female Experience

The essay examines the texts of the two women writers - Leila Abouzeid (from Morocco) and Nawal El Saadawi (from Egypt) - as offering two female perspectives within what is commonly referred to as "feminine" writing in the Arab Muslim world. My main interest is to explore the various discursive articulations of female identity that are challenged or foregrounded as a positive model. The essay points to the serious pitfalls of some feminist narratives in Arab-Muslim societies by dealing with a related problem: the author's setting up of convenient conceptual dichotomies, which account for the female experience, that reduce male-female relationships in the given social context to a fundamentally antagonistic one. Abouzeid's novel will be a case study of a more positive but also realistic and complex perspec­tive on female experience ...

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