Local Analysis of Determinism for CSP

Segmentation tasks in medical imaging represent an exhaustive challenge for scientists since the image acquisition nature yields issues that hamper the correct reconstruction and visualization processes. Depending on the specific image modality, we have to consider limitations such as the presence of noise, vanished edges, or high intensity differences, known, in most cases, as inhomogeneities. New algorithms in segmentation are required to provide a better performance. This paper presents a new unified approach to improve traditional segmentation methods as Active Shape Models and Chan-Vese model based on level set. The approach introduces a combination of local analysis implementations with classic segmentation algorithms that incorporates local texture information given by the Hermite transform and Local Binary Patterns. The mixture of both region-based methods and local descriptors highlights relevant regions by considering extra information which is helpful to delimit structures. We performed segmentation experiments on 2D images including midbrain in Magnetic Resonance Imaging and heart’s left ventricle endocardium in Computed Tomography. Quantitative evaluation was obtained with Dice coefficient and Hausdorff distance measures. Results display a substantial advantage over the original methods when we include our characterization schemes. We propose further research validation on different organ structures with promising results.

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Intelligent IoT systems for civil infrastructure health monitoring: a research roadmap

Discover Internet of Things ◽

10.1007/s43926-021-00009-4 ◽

2021 ◽

Vol 1 (1) ◽

Author(s):

E. Bertino ◽

M. R. Jahanshahi ◽

A. Singla ◽

R.-T. Wu

Keyword(s):

Data Acquisition ◽

Real Time ◽

Original Data ◽

Local Analysis ◽

Civil Infrastructure ◽

Time Requirements ◽

Iot Devices ◽

Learning Reinforcement ◽

The Times ◽

Acquisition Processes

AbstractThis paper addresses the problem of efficient and effective data collection and analytics for applications such as civil infrastructure monitoring and emergency management. Such problem requires the development of techniques by which data acquisition devices, such as IoT devices, can: (a) perform local analysis of collected data; and (b) based on the results of such analysis, autonomously decide further data acquisition. The ability to perform local analysis is critical in order to reduce the transmission costs and latency as the results of an analysis are usually smaller in size than the original data. As an example, in case of strict real-time requirements, the analysis results can be transmitted in real-time, whereas the actual collected data can be uploaded later on. The ability to autonomously decide about further data acquisition enhances scalability and reduces the need of real-time human involvement in data acquisition processes, especially in contexts with critical real-time requirements. The paper focuses on deep neural networks and discusses techniques for supporting transfer learning and pruning, so to reduce the times for training the networks and the size of the networks for deployment at IoT devices. We also discuss approaches based on machine learning reinforcement techniques enhancing the autonomy of IoT devices.

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Current Wildland Fire Patterns and Challenges in Europe: A Synthesis of National Perspectives

Air Soil and Water Research ◽

10.1177/11786221211028185 ◽

2021 ◽

Vol 14 ◽

pp. 117862212110281

Author(s):

Nieves Fernandez-Anez ◽

Andrey Krasovskiy ◽

Mortimer Müller ◽

Harald Vacik ◽

Jan Baetens ◽

...

Keyword(s):

Land Use ◽

Large Scale ◽

Wildland Fire ◽

Human Life ◽

Local Analysis ◽

Wildland Fires ◽

Fire Statistics ◽

Quantitative Evidence ◽

Wide Range ◽

Fire Patterns

Changes in climate, land use, and land management impact the occurrence and severity of wildland fires in many parts of the world. This is particularly evident in Europe, where ongoing changes in land use have strongly modified fire patterns over the last decades. Although satellite data by the European Forest Fire Information System provide large-scale wildland fire statistics across European countries, there is still a crucial need to collect and summarize in-depth local analysis and understanding of the wildland fire condition and associated challenges across Europe. This article aims to provide a general overview of the current wildland fire patterns and challenges as perceived by national representatives, supplemented by national fire statistics (2009–2018) across Europe. For each of the 31 countries included, we present a perspective authored by scientists or practitioners from each respective country, representing a wide range of disciplines and cultural backgrounds. The authors were selected from members of the COST Action “Fire and the Earth System: Science & Society” funded by the European Commission with the aim to share knowledge and improve communication about wildland fire. Where relevant, a brief overview of key studies, particular wildland fire challenges a country is facing, and an overview of notable recent fire events are also presented. Key perceived challenges included (1) the lack of consistent and detailed records for wildland fire events, within and across countries, (2) an increase in wildland fires that pose a risk to properties and human life due to high population densities and sprawl into forested regions, and (3) the view that, irrespective of changes in management, climate change is likely to increase the frequency and impact of wildland fires in the coming decades. Addressing challenge (1) will not only be valuable in advancing national and pan-European wildland fire management strategies, but also in evaluating perceptions (2) and (3) against more robust quantitative evidence.

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Local analysis of electrostatic modes in a two-fluid E × B plasma

Physics of Plasmas ◽

10.1063/5.0039341 ◽

2021 ◽

Vol 28 (5) ◽

pp. 052115

Author(s):

J. J. Ramos ◽

E. Bello-Benítez ◽

E. Ahedo

Keyword(s):

Local Analysis ◽

Two Fluid

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Identification and inclusion of gender factors in retrospective cohort studies: the GOING-FWD framework

BMJ Global Health ◽

10.1136/bmjgh-2021-005413 ◽

2021 ◽

Vol 6 (4) ◽

pp. e005413

Author(s):

Valeria Raparelli ◽

Colleen M. Norris ◽

Uri Bender ◽

Maria Trinidad Herrero ◽

Alexandra Kautzky-Willer ◽

...

Keyword(s):

Outcome Measurement ◽

Meta Analysis ◽

Synthetic Data ◽

Well Being ◽

Pooled Analysis ◽

Research Network ◽

Local Analysis ◽

Related Factors ◽

Measurement Framework ◽

Definition Of

Gender refers to the socially constructed roles, behaviours, expressions and identities of girls, women, boys, men and gender diverse people. Gender-related factors are seldom assessed as determinants of health outcomes, despite their powerful contribution. The Gender Outcomes INternational Group: to Further Well-being Development (GOING-FWD) project developed a standard five-step methodology applicable to retrospectively identify gender-related factors and assess their relationship to outcomes across selected cohorts of non-communicable chronic diseases from Austria, Canada, Spain, Sweden. Step 1 (identification of gender-related variables): Based on the gender framework of the Women Health Research Network (ie, identity, role, relations and institutionalised gender), and available literature for a certain disease, an optimal ‘wish-list’ of gender-related variables was created and discussed by experts. Step 2 (definition of outcomes): Data dictionaries were screened for clinical and patient-relevant outcomes, using the International Consortium for Health Outcome Measurement framework. Step 3 (building of feasible final list): a cross-validation between variables per database and the ‘wish-list’ was performed. Step 4 (retrospective data harmonisation): The harmonisation potential of variables was evaluated. Step 5 (definition of data structure and analysis): The following analytic strategies were identified: (1) local analysis of data not transferable followed by a meta-analysis combining study-level estimates; (2) centrally performed federated analysis of data, with the individual-level participant data remaining on local servers; (3) synthesising the data locally and performing a pooled analysis on the synthetic data and (4) central analysis of pooled transferable data. The application of the GOING-FWD multistep approach can help guide investigators to analyse gender and its impact on outcomes in previously collected data.

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An Integrated Geometric and Material Survey for the Conservation of Heritage Masonry Structures

Heritage ◽

10.3390/heritage4020035 ◽

2021 ◽

Vol 4 (2) ◽

pp. 585-611

Author(s):

Michele Betti ◽

Valentina Bonora ◽

Luciano Galano ◽

Eugenio Pellis ◽

Grazia Tucci ◽

...

Keyword(s):

Seismic Behavior ◽

Laser Scanning ◽

Global Analysis ◽

Interdisciplinary Approach ◽

Local Analysis ◽

Masonry Building ◽

In Situ Tests ◽

Knowledge Process ◽

Giorgio Vasari ◽

House Museum

This paper reports the knowledge process and the analyses performed to assess the seismic behavior of a heritage masonry building. The case study is a three-story masonry building that was the house of the Renaissance architect and painter Giorgio Vasari (the Vasari’s House museum). An interdisciplinary approach was adopted, following the Italian “Guidelines for the assessment and mitigation of the seismic risk of the cultural heritage”. This document proposes a methodology of investigation and analysis based on three evaluation levels (EL1, analysis at territorial level; EL2, local analysis and EL3, global analysis), according to an increasing level of knowledge on the building. A comprehensive knowledge process, composed by a 3D survey by Terrestrial Laser Scanning (TLS) and experimental in situ tests, allowed us to identify the basic structural geometry and to assess the value of mechanical parameters subsequently needed to perform a reliable structural assessment. The museum represents a typology of masonry building extremely diffused in the Italian territory, and the assessment of its seismic behavior was performed by investigating its global behavior through the EL1 and the EL3 analyses.

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Local analysis of semiconductor nanoobjects by scanning tunneling atomic force microscopy

St Petersburg Polytechnical University Journal Physics and Mathematics ◽

10.1016/j.spjpm.2015.03.014 ◽

2015 ◽

Vol 1 (1) ◽

pp. 15-23 ◽

Cited By ~ 5

Author(s):

Natalia A. Lashkova ◽

Nikita V. Permiakov ◽

Alexander I. Maximov ◽

Yulia M. Spivak ◽

Vyacheslav A. Moshnikov

Keyword(s):

Atomic Force Microscopy ◽

Local Analysis ◽

Scanning Tunneling ◽

Force Microscopy ◽

Atomic Force

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Comparing sensitivity analysis methods to advance lumped watershed model identification and evaluation

Hydrology and Earth System Sciences ◽

10.5194/hess-11-793-2007 ◽

2007 ◽

Vol 11 (2) ◽

pp. 793-817 ◽

Cited By ~ 195

Author(s):

Y. Tang ◽

P. Reed ◽

T. Wagener ◽

K. van Werkhoven

Keyword(s):

Sensitivity Analysis ◽

Model Identification ◽

Local Analysis ◽

Time Interval ◽

Watershed Model ◽

Soil Moisture Accounting ◽

Analysis Methods ◽

Accounting Model ◽

Sobol’S Method ◽

Comparative Results

Abstract. This study seeks to identify sensitivity tools that will advance our understanding of lumped hydrologic models for the purposes of model improvement, calibration efficiency and improved measurement schemes. Four sensitivity analysis methods were tested: (1) local analysis using parameter estimation software (PEST), (2) regional sensitivity analysis (RSA), (3) analysis of variance (ANOVA), and (4) Sobol's method. The methods' relative efficiencies and effectiveness have been analyzed and compared. These four sensitivity methods were applied to the lumped Sacramento soil moisture accounting model (SAC-SMA) coupled with SNOW-17. Results from this study characterize model sensitivities for two medium sized watersheds within the Juniata River Basin in Pennsylvania, USA. Comparative results for the 4 sensitivity methods are presented for a 3-year time series with 1 h, 6 h, and 24 h time intervals. The results of this study show that model parameter sensitivities are heavily impacted by the choice of analysis method as well as the model time interval. Differences between the two adjacent watersheds also suggest strong influences of local physical characteristics on the sensitivity methods' results. This study also contributes a comprehensive assessment of the repeatability, robustness, efficiency, and ease-of-implementation of the four sensitivity methods. Overall ANOVA and Sobol's method were shown to be superior to RSA and PEST. Relative to one another, ANOVA has reduced computational requirements and Sobol's method yielded more robust sensitivity rankings.

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