Dataset Information

A dataset of visualization methods to assessing soil profile using RES2DINV and VOXLER software.

ABSTRACT: This data illustration the similarity and accuracy of two subsurface profile analysis software which is RES2DINV and VOXLER. Electrical resistivity imaging methods was conducted as a geophysical technique to get subsurface profile were borehole had previously been made in the same locations. The General Department of Geoscience (JMG) conducted the drilling of the borehole in three locations which is Kampung Bangkahulu, Gemas, Kampung Semerbok, Rembau and Felda Bukit Rokan Utara. The 2D resistivity image from RES2DINV and the 3D image from VOXLER was highly matching the subsurface profile compared with borehole data log. The depth of the resistivity was 76.8, 87.2 and 39.4 respectively for the sites. This two software gave more clearly interpreted result for investigate the sub ground and geological formations.

SUBMITTER: Yasir SF

PROVIDER: S-EPMC6441720 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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A dataset of visualization methods to assessing soil profile using RES2DINV and VOXLER software.

Yasir Safaa F SF Jani Janmaizatulriah J Mukri Mazidah M

Data in brief 20190321

This data illustration the similarity and accuracy of two subsurface profile analysis software which is RES2DINV and VOXLER. Electrical resistivity imaging methods was conducted as a geophysical technique to get subsurface profile were borehole had previously been made in the same locations. The General Department of Geoscience (JMG) conducted the drilling of the borehole in three locations which is Kampung Bangkahulu, Gemas, Kampung Semerbok, Rembau and Felda Bukit Rokan Utara. The 2D resistivi ...[more]

PMID: 30976635

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Project description:Intensive care clinicians use several sources of data in order to inform decision-making. We set out to evaluate a new interactive data integration platform called T3™ made available for pediatric intensive care. Three primary functions are supported: tracking of physiologic signals, displaying trajectory, and triggering decisions, by highlighting data or estimating risk of patient instability. We designed a human factors study to identify interface usability issues, to measure ease of use, and to describe interface features that may enable or hinder clinical tasks.Twenty-two participants, consisting of bedside intensive care physicians, nurses, and respiratory therapists, tested the T3™ interface in a simulation laboratory setting. Twenty tasks were performed with a true-to-setting, fully functional, prototype, populated with physiological and therapeutic intervention patient data. Primary data visualization was time series and secondary visualizations were: 1) shading out-of-target values, 2) mini-trends with exaggerated maxima and minima (sparklines), and 3) bar graph of a 16-parameter indicator. Task completion was video recorded and assessed using a use error rating scale. Usability issues were classified in the context of task and type of clinician. A severity rating scale was used to rate potential clinical impact of usability issues.Time series supported tracking a single parameter but partially supported determining patient trajectory using multiple parameters. Visual pattern overload was observed with multiple parameter data streams. Automated data processing using shading and sparklines was often ignored but the 16-parameter data reduction algorithm, displayed as a persistent bar graph, was visually intuitive. However, by selecting or automatically processing data, triggering aids distorted the raw data that clinicians use regularly. Consequently, clinicians could not rely on new data representations because they did not know how they were established or derived.Usability issues, observed through contextual use, provided directions for tangible design improvements of data integration software that may lessen use errors and promote safe use. Data-driven decision making can benefit from iterative interface redesign involving clinician-users in simulated environments. This study is a first step in understanding how software can support clinicians' decision making with integrated continuous monitoring data. Importantly, testing of similar platforms by all the different disciplines who may become clinician users is a fundamental step necessary to understand the impact on clinical outcomes of decision aids.

Project description:BackgroundRepair of complete atrioventricular canal (CAVC) is often complicated by residual left atrioventricular valve regurgitation. The structure of the mitral and tricuspid valves in biventricular hearts has previously been shown to be associated with valve dysfunction. However, the three-dimensional (3D) structure of the entire unrepaired CAVC valve has not been quantified. Understanding the 3D structure of the CAVC may inform optimized repair.MethodsNovel open-source work flows were created in SlicerHeart for the modeling and quantification of CAVC valves on the basis of 3D echocardiographic images. These methods were applied to model the annulus, leaflets, and papillary muscle (PM) structure of 35 patients (29 with trisomy 21) with CAVC using transthoracic 3D echocardiography. The mean leaflet and annular shapes were calculated and visualized using shape analysis. Metrics of the complete native CAVC valve structure were compared with those of normal mitral valves using the Mann-Whitney U test. Associations between CAVC structure and atrioventricular valve regurgitation were analyzed.ResultsCAVC leaflet metrics varied throughout systole. Compared with normal mitral valves, the left CAVC PMs were more acutely angled in relation to the annular plane (P < .001). In addition, the anterolateral PM was laterally and inferiorly rotated in CAVC, while the posteromedial PM was more superiorly and laterally rotated, relative to normal mitral valves (P < .001). Lower native CAVC atrioventricular valve annular height and annular height-to-valve width ratio before repair were both associated with moderate or greater left atrioventricular valve regurgitation after repair (P < .01).ConclusionsIt is feasible to model and quantify 3D CAVC structure using 3D echocardiographic images. The results demonstrate significant variation in CAVC structure across the cohort and differences in annular, leaflet, and PM structure compared with the mitral valve. These tools may be used in future studies to catalyze future research intended to identify structural associations of valve dysfunction and to optimize repair in this vulnerable and complex population.

Dataset Information

A dataset of visualization methods to assessing soil profile using RES2DINV and VOXLER software.

Publications

A dataset of visualization methods to assessing soil profile using RES2DINV and VOXLER software.

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