scholarly journals Procedures for Condition Mapping Using 360° Images

2020 ◽  
Vol 9 (1) ◽  
pp. 34
Author(s):  
Luigi Barazzetti ◽  
Mattia Previtali ◽  
Marco Scaioni
Keyword(s):  
Data Collection ◽  
Data Acquisition ◽  
Virtual Environment ◽  
Temporal Data ◽  
Novel Approach ◽  
Multi Temporal ◽  
Immersive Visualization ◽  
Over Time

The identification of deterioration mechanisms and their monitoring over time is an essential phase for conservation. This work aimed at developing a novel approach for deterioration mapping and monitoring based on 360° images, which allows for simple and rapid data collection. The opportunity to capture the whole scene around a 360° camera reduces the number of images needed in a condition mapping project, resulting in a powerful solution to document small and narrow spaces. The paper will describe the implemented workflow for deterioration mapping based on 360° images, which highlights pathologies on surfaces and quantitatively measures their extension. Such a result will be available as standard outputs as well as an innovative virtual environment for immersive visualization. The case of multi-temporal data acquisition will be considered and discussed as well. Multiple 360° images acquired at different epochs from slightly different points are co-registered to obtain pixel-to-pixel correspondence, providing a solution to quantify and track deterioration effects.

scholarly journals Classification of Multi-temporal Images using Machine Learning

2021 ◽  
Author(s):  
Simranjit Singh Pabla ◽  
Mandeep Singh Mandla ◽  
Hardik Narendra ◽  
Swasti Patel
Keyword(s):  
Supervised Classification ◽  
Kappa Coefficient ◽  
Support Vector ◽  
New Approach ◽  
Object Based Image Analysis ◽  
Novel Approach ◽  
Object Based ◽  
Multi Temporal ◽  
Over Time

In past, there has been a lot of research related to the image-based technique in remote sensing from which object-based classification is giving great results among all the techniques. This paper presents a new approach where we have mixed both OBIA (Object-Based Image Analysis) & supervised classification. And with this novel approach, our team aims to do classification as well as analysis for the change detection over time. The data used in this study is high-resolution Multispectral 4-band images from 2017 to 2019 (i.e. 3.0 m) provided by the PlanetScope satellite of region Chandigarh, India. Here the data has been pre-processed through passing it in a pipeline of steps and used a Multi-resolution segmentation algorithm and classify the 7 classes through supervised learning using 3 algorithms Maximum Likelihood (ML), Support Vector Machine (SVM), Mahalanobis Distance (MD). And out of the three, SVM and ML has given the highest Overall Accuracy of 95.21% & Kappa Coefficient = 0.9159 and Overall Accuracy 91.91% & Kappa Coefficient = 0.8860. Altogether; this is a highly effective approach for classification and detecting the change in Urban area or Rural area or forest area than simply using OBIA or pixel-based approach.

Percutaneous Tracheostomy: A Multidisciplinary Approach

2008 ◽  
Vol 18 (2) ◽  
pp. 87-98 ◽  
Author(s):  
Vinciya Pandian ◽  
Thai Tran Nguyen ◽  
Marek Mirski ◽  
Nasir Islam Bhatti
Keyword(s):  
Health Care ◽  
Quality Assurance ◽  
Data Collection ◽  
Health Care Professionals ◽  
Multidisciplinary Approach ◽  
Percutaneous Tracheostomy ◽  
Johns Hopkins Hospital ◽  
Prospective Data ◽  
Comprehensive Service ◽  
Over Time

Abstract The techniques of performing a tracheostomy has transformed over time. Percutaneous tracheostomy is gaining popularity over open tracheostomy given its advantages and as a result the number of bedside tracheostomies has increased necessitating the need for a Percutaneous Tracheostomy Program. The Percutaneous Tracheostomy Program at the Johns Hopkins Hospital is a comprehensive service that provides care to patients before, during, and after a tracheostomy with a multidisciplinary approach aimed at decreasing complications. Education is provided to patients, families, and health-care professionals who are involved in the management of a tracheostomy. Ongoing prospective data collection serves as a tool for Quality Assurance.

Kajian Interaksi Obat Metformin pada Pasien Diabetes Mellitus

2019 ◽  
Vol 8 (2) ◽  
pp. 55-58
Author(s):  
Havizur Rahman ◽  
Teresia Anggi Octavia
Keyword(s):  
Diabetes Mellitus ◽  
Drug Interaction ◽  
Data Collection ◽  
Drug Interactions ◽  
Degenerative Disease ◽  
Moderate Severity ◽  
Purposive Sampling ◽  
Chronic Degenerative Disease ◽  
Over Time ◽  
Diabetes Melitus

Diabetes melitus merupakan penyakit degeneratif kronis yang apabila tidak ditangani dengan tepat, lama kelamaan bisa timbul berbagai komplikasi, ini cenderung menyebabkan pasien mendapatkan banyak obat dalam satu resep yang dapat menimbulkan interaksi antar obat. Tujuan dari penelitian ini adalah mengetahui persentase terjadinya interaksi obat metformin secara teori serta mengkaji efek yang mungkin timbul dan solusinya. Teknik pengambilan data dengan purpossive sampling, yaitu resep pasien rujuk balik yang menderita diabetes mellitus yang menggunakan metformin. Data yang diperoleh ditemukan bahwa obat yang berinteraksi dengan metformin dengan tingkat keparahan minor ialah sebesar 60%. Kemudian untuk tingkat keparahan moderat ialah sebesar 20%. Sedangkan untuk tingkat keparahan mayor tidak ditemukan. Dari tabel diatas juga dapat diketahui bahwa terdapat 4 obat yang saling berinteraksi dengan metformin, sedangkan untuk obat yang tidak saling berinteraksi dengan metformin terdapat 9 obat. Jumlah obat yang berinteraksi secara teori sebesar 6,85% dan yang tidak berinteraksi 93,15%. Terdapat interaksi obat metformin dengan beberapa obat yaitu furosemid, lisinopril, acarbose dan ramipril.   Kata kunci: interaksi obat, metformin, diabetes mellitus   STUDY OF METFORMIN INTERACTION IN MELLITUS DIABETES PATIENTS   ABSTRACT Mellitus is a chronic degenerative disease which if not handled properly, over time can arise various complications, this tends to cause patients to get many drugs in one recipe that can cause interactions between drugs. The purpose of this study is to determine percentage of metformin drug interactions in theory and examine the effects that may arise and solutions. Data collection techniques using purposive sampling, which is a recipe for reconciliation patients who suffer from diabetes mellitus using metformin. The data obtained it was found that drugs that interact with metformin with minor severity were 60%. Then for moderate severity is 20%. Whereas the major severity was not found. From the table above it can also be seen that there are 4 drugs that interact with metformin, while for drugs that do not interact with metformin there are 9 drugs. The number of drugs that interacted theoretically was 6.85% and 93.15% did not interact. An interaction of the drug metformin with several drugs namely furosemide, lisinopril, acarbose and ramipril.   Keywords: drug interaction, metformin, diabetes mellitus

Developing a sustainable Digital Health Innovation Ecosystem in a rural and resource-poor region: A monitored networking approach (Preprint)

2020 ◽  
Author(s):  
Arunangsu Chatterjee ◽  
Sebastian Stevens ◽  
Sheena Asthana ◽  
Ray B Jones
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Data Collection ◽  
Digital Health ◽  
Resource Poor Setting ◽  
Quadruple Helix ◽  
Innovation Ecosystem ◽  
Resource Poor ◽  
Quadruple Helix Model ◽  
Over Time

BACKGROUND Digital health (DH) innovation ecosystems (IE) are key to the development of new e-health products and services. Within an IE, third parties can help promote innovation by acting as knowledge brokers and the conduits for developing inter-organisational and interpersonal relations, particularly for smaller organisations. Kolehmainen’s quadruple helix model suggests who the critical IE actors are, and their roles. Within an affluent and largely urban setting, such ecosystems evolve and thrive organically with minimal intervention due to favourable economic and geographical conditions. Facilitating and sustaining a thriving DH IE within a resource-poor setting can be far more challenging even though far more important for such peripheral economics and the health and well-being of those communities. OBJECTIVE Taking a rural and remote region in the UK, as an instance of an IE in a peripheral economy, we adapt the quadruple helix model of innovation, apply a monitored social networking approach using McKinsey’s Three Horizons of growth to explore: • What patterns of connectivity between stakeholders develop within an emerging digital health IE? • How do networks develop over time in the DH IE? • In what ways could such networks be nurtured in order to build the capacity, capability and sustainability of the DH IE? METHODS Using an exploratory single case study design for a developing digital health IE, this study adopts a longitudinal social network analysis approach, enabling the authors to observe the development of the innovation ecosystem over time and evaluate the impact of targeted networking interventions on connectivity between stakeholders. Data collection was by an online survey and by a novel method, connection cards. RESULTS Self-reported connections between IE organisations increased between the two waves of data collection, with Small and Medium-sized Enterprises (SMEs) and academic institutions the most connected stakeholder groups. Patients involvement improved over time but still remains rather peripheral to the DH IE network. Connection cards as a monitoring tool worked really well during large events but required significant administrative overheads. Monitored networking information categorised using McKinsey’s Three Horizons proved to be an effective way to organise networking interventions ensuring sustained engagement. CONCLUSIONS The study reinforces the difficulty of developing and sustaining a DH IE in a resource-poor setting. It demonstrates the effective monitored networking approach supported by Social Network Analysis allows to map the networks and provide valuable information to plan future networking interventions (e.g. involving patients or service users). McKinsey’s Three Horizons of growth-based categorisation of the networking assets help ensure continued engagement in the DH IE contributing towards its long-term sustainability. Collecting ongoing data using survey or connection card method will become more labour intensive and ubiquitous ethically driven data collection methods can be used in future to make the process more agile and responsive.

Forecasting Interconnections in International Housing Markets: Evidence from the Dynamic Model Averaging Approach

2020 ◽  
Vol 42 (1) ◽  
pp. 37-103
Author(s):  
Hardik A. Marfatia
Keyword(s):  
Dynamic Model ◽  
Model Averaging ◽  
Housing Markets ◽  
The United States ◽  
Forecasting Model ◽  
Change Over Time ◽  
The United Kingdom ◽  
Novel Approach ◽  
Dynamic Model Averaging ◽  
Over Time

In this paper, I undertake a novel approach to uncover the forecasting interconnections in the international housing markets. Using a dynamic model averaging framework that allows both the coefficients and the entire forecasting model to dynamically change over time, I uncover the intertwined forecasting relationships in 23 leading international housing markets. The evidence suggests significant forecasting interconnections in these markets. However, no country holds a constant forecasting advantage, including the United States and the United Kingdom, although the U.S. housing market's predictive power has increased over time. Evidence also suggests that allowing the forecasting model to change is more important than allowing the coefficients to change over time.

Using computer clusters for processing spatial-temporal data streams in data acquisition systems

2018 ◽  
Vol 935 (5) ◽  
pp. 54-63
Author(s):  
A.A. Maiorov ◽  
A.V. Materuhin ◽  
I.N. Kondaurov
Keyword(s):  
Data Acquisition ◽  
Data Streams ◽  
Spatial Data ◽  
High Performance ◽  
Temporal Data ◽  
Cyberphysical Systems ◽  
Software Environment ◽  
Computer Clusters ◽  
Data Acquisition Systems ◽  
Essential Components

Geoinformation technologies are now becoming “end-to-end” technologies of the new digital economy. There is a need for solutions for efficient processing of spatial and spatio-temporal data that could be applied in various sectors of this new economy. Such solutions are necessary, for example, for cyberphysical systems. Essential components of cyberphysical systems are high-performance and easy-scalable data acquisition systems based on smart geosensor networks. This article discusses the problem of choosing a software environment for this kind of systems, provides a review and a comparative analysis of various open source software environments designed for large spatial data and spatial-temporal data streams processing in computer clusters. It is shown that the software framework STARK can be used to process spatial-temporal data streams in spatial-temporal data streams. An extension of the STARK class system based on the type system for spatial-temporal data streams developed by one of the authors of this article is proposed. The models and data representations obtained as a result of the proposed expansion can be used not only for processing spatial-temporal data streams in data acquisition systems based on smart geosensor networks, but also for processing spatial-temporal data streams in various purposes geoinformation systems that use processing data in computer clusters.

Coping with longitudinal data in research on developmental psychopathology

2006 ◽  
Vol 30 (1) ◽  
pp. 20-25 ◽  
Author(s):  
David A. Cole
Keyword(s):  
Data Collection ◽  
Longitudinal Data ◽  
Developmental Psychopathology ◽  
Careful Consideration ◽  
Two Dimensions ◽  
The Other ◽  
Outcome Variable ◽  
The Third ◽  
Data Analytic ◽  
Over Time

Many outcome variables in developmental psychopathology research are highly stable over time. In conventional longitudinal data analytic approaches such as multiple regression, controlling for prior levels of the outcome variable often yields little (if any) reliable variance in the dependent variable for putative predictors to explain. Three strategies for coping with this problem are described. One involves focusing on developmental periods of transition, in which the outcome of interest may be less stable. A second is to give careful consideration to the amount of time allowed to elapse between waves of data collection. The third is to consider trait-state-occasion models that partition the outcome variable into two dimensions: one entirely stable and trait-like, the other less stable and subject to occasion-specific fluctuations.

scholarly journals Visual4DTracker: a tool to interact with 3D + t image stacks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ermanno Cordelli ◽  
Paolo Soda ◽  
Giulio Iannello
Keyword(s):  
Synthetic Data ◽  
Time Lapse ◽  
Temporal Data ◽  
Image Stack ◽  
Biological Phenomena ◽  
Insulin Granules ◽  
Matlab Package ◽  
User Friendly ◽  
Drosophila Cells ◽  
Over Time

Abstract Background Biological phenomena usually evolves over time and recent advances in high-throughput microscopy have made possible to collect multiple 3D images over time, generating $$3D+t$$ 3 D + t (or 4D) datasets. To extract useful information there is the need to extract spatial and temporal data on the particles that are in the images, but particle tracking and feature extraction need some kind of assistance. Results This manuscript introduces our new freely downloadable toolbox, the Visual4DTracker. It is a MATLAB package implementing several useful functionalities to navigate, analyse and proof-read the track of each particle detected in any $$3D+t$$ 3 D + t stack. Furthermore, it allows users to proof-read and to evaluate the traces with respect to a given gold standard. The Visual4DTracker toolbox permits the users to visualize and save all the generated results through a user-friendly graphical user interface. This tool has been successfully used in three applicative examples. The first processes synthetic data to show all the software functionalities. The second shows how to process a 4D image stack showing the time-lapse growth of Drosophila cells in an embryo. The third example presents the quantitative analysis of insulin granules in living beta-cells, showing that such particles have two main dynamics that coexist inside the cells. Conclusions Visual4DTracker is a software package for MATLAB to visualize, handle and manually track $$3D+t$$ 3 D + t stacks of microscopy images containing objects such cells, granules, etc.. With its unique set of functions, it remarkably permits the user to analyze and proof-read 4D data in a friendly 3D fashion. The tool is freely available at https://drive.google.com/drive/folders/19AEn0TqP-2B8Z10kOavEAopTUxsKUV73?usp=sharing

scholarly journals C3VFC: A Method for Tracing and Quantification of Microglia in 3D Temporal Images

2021 ◽  
Vol 11 (13) ◽  
pp. 6078
Author(s):  
Tiffany T. Ly ◽  
Jie Wang ◽  
Kanchan Bisht ◽  
Ukpong Eyo ◽  
Scott T. Acton
Keyword(s):  
Vector Field ◽  
Critical Points ◽  
State Of The Art ◽  
Temporal Data ◽  
Entire Structure ◽  
Tracing Algorithm ◽  
Vector Field Convolution ◽  
Multiple Cells ◽  
Tracing Method ◽  
Over Time

Automatic glia reconstruction is essential for the dynamic analysis of microglia motility and morphology, notably so in research on neurodegenerative diseases. In this paper, we propose an automatic 3D tracing algorithm called C3VFC that uses vector field convolution to find the critical points along the centerline of an object and trace paths that traverse back to the soma of every cell in an image. The solution provides detection and labeling of multiple cells in an image over time, leading to multi-object reconstruction. The reconstruction results can be used to extract bioinformatics from temporal data in different settings. The C3VFC reconstruction results found up to a 53% improvement on the next best performing state-of-the-art tracing method. C3VFC achieved the highest accuracy scores, in relation to the baseline results, in four of the five different measures: Entire structure average, the average bi-directional entire structure average, the different structure average, and the percentage of different structures.

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