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The rotational cutting method has been used in needle biopsy technologies to sample tough tissues, such as calcifications in the breast. The rotational motion of the needle introduces shear forces to the cutting surface such that the cutting force in the axial direction is reduced. As a result, tissue samples with large volume and better quality can be obtained. In order to comprehensively understand the effect of the needle rotation to the axial cutting force under a wide range of the needle insertion speed, this paper demonstrates a computational approach that incorporates the surface-based cohesive behavior to simulate a rotating needle cutting soft tissue. The computational model is validated by comparing with a cutting test dataset reported in the literature. The validated model is then used to generate response surfaces of the axial cutting force and torque in a large parameter space of needle rotation and insertion speeds. The results provide guidelines for selecting optimal speed configurations under different design situations. [ABSTRACT FROM AUTHOR]

We present an interface for exploring large design spaces as encountered in simulation-based engineering, design of visual effects, and other tasks that require tuning parameters of computationally-intensive simulations and visually evaluating results. The goal is to enable a style of design with simulations that feels as-direct-as-possible so users can concentrate on creative design tasks. The approach integrates forward design via direct manipulation of simulation inputs (e.g., geometric properties, applied forces) in the same visual space with inverse design via 'tugging' and reshaping simulation outputs (e.g., scalar fields from finite element analysis (FEA) or computational fluid dynamics (CFD)). The interface includes algorithms for interpreting the intent of users' drag operations relative to parameterized models, morphing arbitrary scalar fields output from FEA and CFD simulations, and in-place interactive ensemble visualization. The inverse design strategy can be extended to use multi-touch input in combination with an as-rigid-as-possible shape manipulation to support rich visual queries. The potential of this new design approach is confirmed via two applications: medical device engineering of a vacuum-assisted biopsy device and visual effects design using a physically based flame simulation. [ABSTRACT FROM AUTHOR]

Statement of Problem: The complete arch implant-supported treatment concept with 2 angled implants has been widely used for the prosthetic rehabilitation of edentulous patients. While mechanical analysis plays a pivotal role in minimizing suboptimal outcomes or premature failure, it is notably time-consuming. Consequently, clinical treatment planning relies heavily on dentists' subjective judgment and an optimization process is needed., Purpose: The purpose of this study was to develop an optimization process for providing immediate recommendations to support decision-making in configuring complete arch implant-supported prostheses., Material and Methods: This research was carried out in 2 phases. The first consisted of collecting a dataset from a total of 2800 finite element simulations by randomly configuring 10 implant design variables with 4 types of mandibles. The dataset was used to train an artificial neural network to predict the biomechanical performance of a given complete arch implant-supported prosthesis design configuration. In the second phase, the artificial neural network was used as the objective function predictor in a particle swarm optimization process to enable immediate recommendations for the implant placement. The optimization process was evaluated for accuracy, computing performance, and adaptability for unseen mandibles., Results: Within the specified design space, the optimization process was able to find an optimal design based on an imported mandible model in 30 seconds. The optimized designs were found to improve peri-implant stress by 11.08 ±6.43%. When verified through finite element analysis, the prediction error was found to be 10.4 ±8.1%. Furthermore, the prediction of the optimal design was highly accurate when tested on 2 unseen mandibles, yielding an error of less than 1.7%., Conclusions: The suggested approach can quickly provide an optimal implant configuration for each individual and effectively reduce the average peri-implant stress in the mandible., (Copyright © 2024 Editorial Council for The Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.)

Parkinson's disease (PD) affects 1% of the population over the age of 60, and its prevalence increases with age. The disease progresses over time, and the condition can vary significantly in a day, which makes it difficult for precise diagnosis and medication based on short clinical sessions. Therefore, home health monitoring can play an important role in improving the healthcare of the PD patients. In this study, we proposed a method to detect, classify, and quantify daily movements and motor symptoms of PD by using the wireless sensing technology. With the presence of human movements in a space with the Wi-Fi coverage, the channel state information (CSI) of the wireless signal was transformed into images. The images were used to train a deep learning model to distinguish between different daily movements and simulated tremor. The results showed that our method obtained 99.59% and 100% accuracy of recognizing the tremor with modified VGG19 and modified Resnet152, respectively. In addition, the tremor movement was then successfully segmented out and quantified for the frequency and duration.