Development of a Computational Tool for the Estimation of Alveolar Bone Loss in Oral Radiographic Images

The present study evaluated a newly developed computational tool (CT) to assess the alveolar bone space and the alveolar crest angle and compares it to dentist assessment (GT). The novel tool consisted of a set of processes initiated with image enhancement, points localization, and angle and area calculations. In total, we analyzed 148 sites in 39 radiographic images, and among these, 42 sites were selected and divided into two groups of non-periodontitis and periodontitis. The alveolar space area (ASA) and alveolar crest angle (ACA) were estimated. The agreement between the computer software and the ground truth was analyzed using the Bland–Altman plot. The sensitivity and specificity of the computer tool were measured using the ROC curve. The Bland–Altman plot showed an agreement between the ground truth and the computational tool in all of the parameters assessed. The ROC curve showed 100% sensitivity and 100% specificity for 12.67 mm of the alveolar space area. The maximum percentage of sensitivity and specificity were 80.95% for 13.63 degrees of the alveolar crest angle. Computer tool assessment provides accurate disease severity and treatment monitoring for evaluating the alveolar space area (ASA) and the alveolar crest angle (ACA).

Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation

Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics.

Computational tool for learning electrostatic physics through the development of a disruptive methodology

A computational tool for learning electrostatic physics is presented through the development of a disruptive methodology. The tool allows the analysis of case studies based on Coulomb’s law, Gauss’s law, Poisson’s equation, and Laplace’s equation with boundary value. The tool was tested using reference exercises for each case study, making use of quantitative and qualitative comparative analysis between the traditional mathematical development and the computational tool. Errors were measured using Likert scale. The quantitative results showed errors of less than 1.8% in all the cases studied, concluding that the tool is effective. The qualitative results showed that the methodology allows a better development of the electrostatics learning process, dynamizing the study of complex topics such as electromagnetic physics theories through interactivity and technological resources, in addition to having a theoretical module developed using agile methodologies that provide dynamism and an intuitive environment to the interface.

NETISCE: A Network-Based Tool for Cell Fate Reprogramming

The search for effective therapeutic targets in fields like regenerative medicine and cancer research has generated interest in cell fate reprogramming. This cellular reprogramming paradigm can drive cells to a desired target state from any initial state. However, methods for identifying reprogramming targets remain limited for biological systems that lack large sets of experimental data or a dynamical characterization. We present NETISCE, a novel computational tool for identifying cell fate reprogramming targets in static networks. NETISCE identifies reprogramming targets through the innovative use of control theory within a dynamical systems framework. Through validations in studies of cell fate reprogramming from developmental, stem cell, and cancer biology, we show that NETISCE can predict previously identified cell fate reprogramming targets and identify potentially novel combinations of targets. NETISCE extends cell fate reprogramming studies to larger-scale biological networks without the need for full model parameterization and can be implemented by experimental and computational biologists to identify parts of a biological system that are relevant for the desired reprogramming task.

Computational Tool for Determining the Model in Sequence Components of Overhead Transmission Lines

An indispensable element in addressing the current problem of non-ionizing electromagnetic pollution in the environment is a review of the levels of exposure to the electric and magnetic fields produced by the lines of electric power transmission and distribution systems. In order to establish the exposure levels, it is necessary to determine the model of the lines. Considering that a computational simulation is a helpful tool for power system analysis, this article presents a computational tool developed in Matlab App Designer for the model-in-sequence components of the parameters that make up a transmission line. This tool allows the user to work in a friendly and parameterizable environment according to the performed tests. In order to verify the tool’s performance, two case studies are implemented. The first one is for a transposed transmission line and the second one for a non-transposed transmission line. The results obtained are compared with commercial software, acquiring a maximum error of 0.16402 %.

Lorentz resonance in the homogenization of plasmonic crystals

We explain the Lorentz resonances in plasmonic crystals that consist of two-dimensional nano-dielectric inclusions as the interaction between resonant material properties and geometric resonances of electrostatic nature. One example of such plasmonic crystals are graphene nanosheets that are periodically arranged within a non-magnetic bulk dielectric. We identify local geometric resonances on the length scale of the small-scale period. From a materials perspective, the graphene surface exhibits a dispersive surface conductance captured by the Drude model. Together these phenomena conspire to generate Lorentz resonances at frequencies controlled by the surface geometry and the surface conductance. The Lorentz resonances found in the frequency response of the effective dielectric tensor of the bulk metamaterial are shown to be given by an explicit formula, in which material properties and geometric resonances are decoupled. This formula is rigorous and obtained directly from corrector fields describing local electrostatic fields inside the heterogeneous structure. Our analytical findings can serve as an efficient computational tool to describe the general frequency dependence of periodic optical devices. As a concrete example, we investigate two prototypical geometries composed of nanotubes and nanoribbons.

Parametrisation and Use of a Predictive DFN Model for a High-Energy NCA/Gr-SiOx Battery

We demonstrate the predictive power of a parametrised Doyle-Fuller-Newman (DFN) model of a commercial cylindrical (21700) lithium-ion cell with NCA/Gr-SiOx chemistry. Model parameters result from the deconstruction of a fresh commercial cell to determine/confirm chemistry and microstructure, and also from electrochemical experiments with half-cells built from electrode samples. The simulations predict voltage proles for (i) galvanostatic discharge and (ii) drive-cycles. Predicted voltage responses deviate from measured ones by <1% throughout at least 95% of a full galvanostatic discharge, whilst the drive cycle discharge is matched to a 1-3% error throughout. All simulations are performed using the online computational tool DandeLiion, which rapidly solves the DFN model using only modest computational resource. The DFN results are used to quantify the irreversible energy losses occurring in the cell and deduce their location. In addition to demonstrating the predictive power of a properly validated DFN model, this work provides a novel simplifed parametrisation work that can be used to accurately calibrate an electrochemical model of a cell.

Performance Analysis in the Segmentation of urban asphalted roads in RGB satellite images using K-Means++ and SegNet

The design and manual insertion of new terrestrial roads into geographic databases is a frequent activity in geoprocessing and their demand usually occurs as the most up-to-date satellite imagery of the territory is acquired. Continually, new urban and rural occupations emerge, for which specific vector geometries need to be designed to characterize the cartographic inputs and accommodate the relevant associated data. Therefore, it is convenient to develop a computational tool that, with the help of artificial intelligence, automates what is possible in this respect, since manual editing depends on the limits of user agility, and does it in images that are usually easy and free to access. To test the feasibility of this proposal, a database of RGB images containing asphalted urban roads is presented to the K-Means++ algorithm and the SegNet Convolutional Neural Network, and the performance of each was evaluated and compared for accuracy and IoU of road identification. Under the conditions of the experiment, K-Means++ achieved poor and unviable results for use in a real-life application involving tarmac detection in RGB satellite images, with average accuracy ranging from 41.67% to 64.19% and average IoU of 12.30% to 16.16%, depending on the preprocessing strategy used. On the other hand, the SegNet Convolutional Neural Network proved to be appropriate for precision applications not sensitive to discontinuities, achieving an average accuracy of 87.12% and an average IoU of 71.93%.

Design and Development of a Computational Tool for a Dialyzer by Using Computational Fluid Dynamic (CFD) Model

In order to reduce the hemodialysis cost and duration, an investigation of the effect of dialyzer design and process variables on the solute clearance rate is required. It is not easy to translate the in vivo transfer process with in vitro experiments, as it involves a high cost to produce various designs and membranes for the dialyzer. The primary objective of this study was the design and development of a computational tool for a dialyzer by using a computational fluid dynamic (CFD) model. Due to their complexity, only researchers with expertise in computational analysis can use dialyzer models. Therefore, COMSOL Inc. (Stockholm, Sweden) has made an application on membrane dialysis to study the impact of different design and process parameters on dialyzed liquid concentration. Still, membrane mathematical modeling is not considered in this application. This void hinders an investigation of the impact of membrane characteristics on the solute clearance rate. This study has developed a stand-alone computational tool in COMSOL Multiphysics 5.4 to fill this void. A review of the literature conducted shows that there are no suitable stand-alone computational tools for kidney dialysis. Very little work has been undertaken to validate the stand-alone computational tool. Medical staff in the hospitals require a computational tool that can be installed quickly and provide results with limited knowledge of dialysis. This work aims to construct a user-friendly computational tool to solve this problem. The development of a user-friendly stand-alone computational tool for the dialyzer is described thoroughly. This application simulates a mathematical model with the Finite Element Method using the COMSOL Multiphysics solver. The software tool is converted to a stand-alone version with the COMSOL compiler. The stand-alone computational tool provides the clearance rate of six different toxins and module packing density. Compared with the previous application, the stand-alone computational tool of membrane dialysis enables the user to investigate the impact of membrane characteristics and process parameters on the clearance rate of different solutes. The results are also inconsistent with the literature data, and the differences ranges are 0.09–6.35% and 0.22–2.63% for urea clearance rate and glucose clearance rate, respectively. Statistical analysis of the results is presented as mean with 95% confidence intervals (CIs) and p values 0.9472 and 0.833 of the urea and glucose clearance rates, respectively.