Semantics-driven annotation of patient-specific 3D data: a step to assist diagnosis and treatment of rheumatoid arthritis

In the digital era, patient-specific 3D models (3D-PSMs) are becoming increasingly relevant in computer-assisted diagnosis, surgery training on digital models, or implant design. While advanced imaging and reconstruction techniques can create accurate and detailed 3D models of patients’ anatomy, software tools that are able to fully exploit the potential of 3D-PSMs are still far from being satisfactory. In particular, there is still a lack of integrated approaches for extracting, coding, sharing and retrieving medically relevant information from 3D-PSMs and use it concretely as a support to diagnosis and treatment. In this article, we propose the SemAnatomy3D framework, which demonstrates how the ontology-driven annotation of 3D-PSMs and of their anatomically relevant features (parts of relevance) can assist clinicians to document more effectively pathologies and their evolution. We exemplify the idea in the context of the diagnosis of rheumatoid arthritis of the hand district, and show how feature extraction tools and semantic 3D annotation can provide a rich characterization of anatomical landmarks (e.g., articular facets, prominent features, ligament attachments) and pathological markers (erosions, bone loss). The core contributions are an ontology-driven part-based annotation method for the 3D-PSMs and a novel automatic localization of erosion and quantification of the OMERACT RAMRIS erosion score. Finally, our results have been compared against a medical ground truth.     

Blowing Bubbles for Multi-Scale Analysis and Decomposition of Triangle Meshes

Tools for the automatic decomposition of a surface into shape features will facilitate the editing, matching, texturing, morphing, compression and simplification of three-dimensional shapes. Different features, such as flats, limbs, tips, pits and various blending shapes that transition between them, may be characterized in terms of local curvature and other differential properties of the surface or in terms of a global skeletal organization of the volume it encloses. Unfortunately, both solutions are extremely sensitive to small perturbations in surface smoothness and to quantization effects when they operate on triangulated surfaces. Thus, we propose a multi-resolution approach, which not only estimates the curvature of a vertex over neighborhoods of variable size, but also takes into account the topology of the surface in that neighborhood. Our approach is based on blowing a spherical bubble at each vertex and studying how the intersection of that bubble with the surface evolves. We describe an efficient approach for computing these characteristics for a sampled set of bubble radii and for using them to identify features, based on easily formulated filters, that may capture the needs of a particular application.     

Investigation on TG n-FinFET Parameters by Varying Channel Doping Concentration and Gate Length

In this paper, a 3D process of a nanometric n-channel fin field-effect transistor (FinFET) is discussed and the impact of variations of the fin parameter, the gate work function, and doping concentration on device characteristics are studied using the ATLAS Silvaco device simulator. Simulation results for various gate lengths are reported and analyzed. As the quantum effects are pronounced in nanoscale devices, we have included these effects in our study and simulation. We have then compared the achieved results to classical simulations to assess their performance limits. Finally, a comparison of our results with recently published data is presented to confirm our study.     

Use of Salt Baths in the Temperature Range from 175?��C to 540?��C with Uncertainties of Less than 30?m��C

Calibration of thermometers in the temperature range from ?80 °C to 550 °C requires liquid baths; alcohol, water, silicon oil, salt baths and dry block furnaces. In this study, the use of salt baths outside of their usual range of 250 °C to 540 °C for calibrating thermometers in the range between 175 °C and 250 °C is proposed. The calibration range from 150 °C to 250 °C is usually covered by an oil bath, but utilizing a salt bath saves calibration time and resources, improves stability and homogeneity, allows longer term usage of the liquid, and reduces hazardous chemical vapors evaporated at temperatures above 175 °C. This proposal is based on a study of the uncertainty contributions at varying salt bath temperatures in the range from 175 °C to 540 °C which was carried out in this study. Results achieved and analyzed in this study indicate that the implementation of salt baths in this lower temperature range provides opportunities to calibrate reference and/or working thermometers with an uncertainty below 30 m°C, almost the same as the oil-bath uncertainty in the range of 175 °C to 250 °C. The main components of uncertainty contributed by a salt bath over this temperature range are discussed in this study.     

BDN-GWMNN: Internet of Things (IoT) Enabled Secure Smart City Applications

Nowadays, next-generation networks such as the Internet of Things (IoT) and 6G are played a vital role in providing an intelligent environment. The development of technologies helps to create smart city applications like the healthcare system, smart industry, and smart water plan, etc. Any user accesses the developed applications; at the time, security, privacy, and confidentiality arechallenging to manage. So, this paper introduces the blockchain-defined networks with a grey wolf optimized modular neural network approach for managing the smart environment security. During this process, construction, translation, and application layers are created, in which user authenticated based blocks are designed to handle the security and privacy property. Then the optimized neural network is applied to maintain the latency and computational resource utilization in IoT enabled smart applications. Then the efficiency of the system is evaluated using simulation results, in which system ensures low latency, high security (99.12%) compared to the multi-layer perceptron, and deep learning networks.