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Automated CAD model simplification plays an important role in effectively utilizing physicsbased simulation during the product realization process. Currently a rich body of literature exists that describe many successful techniques for fully-automatic or semi-automatic simplification of CAD models for a wide variety of applications. The purpose of this paper is to compile a list of the techniques that are relevant for physics-based simulations problems and to characterize them based on their attributes. We have classified them into the following four categories: techniques based on surface entity based operators, volume entity based operators, explicit feature based operators, and dimension reduction operators. This paper also presents the necessary background information in the CAD model representation to assist the new readers. We conclude the paper by outlining open research directions in this field.

Underwater robot designs inspired by the behavior, physiology, and anatomy of fishes can provide enhanced maneuverability, stealth, and energy efficiency. Over the last two decades, robotics researchers have developed and reported a large variety of fish-inspired robot designs. The purpose of this review is to report different types of fish-inspired robot designs based upon their intended locomotion patterns. We present a detailed comparison of various design features like sensing, actuation, autonomy, waterproofing, and morphological structure of fish-inspired robots reported in the past decade. We believe that by studying the existing robots, future designers will be able to create new designs by adopting features from the successful robots. The review also summarizes the open research issues that need to be taken up for the further advancement of the field and also for the deployment of fish-inspired robots in practice.

The capability of noninvasive and precise micromanipulation of sensitive, living cells is necessary for understanding their underlying biological processes. Optical tweezers (OT) is an effective tool that uses highly focused laser beams for accurate manipulation of cells and dielectric beads at micro-scale. However, direct exposure of the laser beams on the cells can negatively influence their behavior or even cause a photo-damage. In this paper, we introduce a control and planning approach for automated, indirect manipulation of cells using silica beads arranged into gripper formations. The developed approach employs path planning and feedback control for efficient, collision-free transport of a cell between two specified locations. The planning component of the approach computes a path that explicitly respects the nonholonomic constraints of the gripper formations. The feedback control component ensures stable tracking of the path by manipulating the cell using a set of predefined maneuvers. We demonstrate the effectiveness of the approach by transporting a yeast cell using four different types of gripper formations along collision-free paths on our OT setup. We analyzed the performance of the proposed gripper formations with respect to their maximum transport speed that depends on the laser power used.Note to Practitioners -This paper presents computational tools necessary for automated cell micromanipulation using Optical Tweezers. Autonomy in optical manipulation enables us to realize a method for fast and accurate placement of micro-particles. The method presented in the paper is especially useful for accurately placing cells in an array for biological experiments.

In this paper, we introduce an indirect pushing based technique for automated micromanipulation of biological cells. In indirect pushing, an optically trapped glass bead pushes a freely diffusing intermediate bead that in turn pushes a freely diffusing target cell towards a desired goal. Some cells can undergo significant changes in their behaviors as a result of direct exposure to a laser beam. Indirect pushing eliminates this problem by minimizing the exposure of the cell to the laser beam. We report an automated feedback planning algorithm that combines three motion maneuvers, namely, push, align, and backup for micromanipulation of cells. We have developed a dynamics based simulation model of indirect pushing dynamics and also identified parameters of measurement noise using physical experiments. We present an optimization-based approach for automated tuning of planner parameters to enhance its robustness. Finally, we have tested the developed planner using our optical tweezers physical setup and carried out a detailed analysis of the experimental results. The developed approach can be utilized in biological experiments for studying collective cell migration by accurately arranging the cells in arrays without exposing them to a laser beam.

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Objectives To describe the epidemiological and clinical characteristics and outcome of hospitalized children with COVID-19 during the initial phase of the pandemic. Methods This was a cross-sectional descriptive study conducted at the dedicated COVID-19 hospital of a tertiary care referral center in North India. Consecutive children aged 14 y or younger who tested positive for SARS-CoV-2 by RT-PCR from nasopharyngeal swab between 1 April 2020 and 15 July 2020 were included. Results Of 31 children with median (IQR) age of 33 (9-96) mo, 9 (29%) were infants. About 74% (n = 23) had history of household contact. Comorbidities were noted in 6 (19%) children. More than half (58%) were asymptomatic. Of 13 symptomatic children, median (IQR) duration of symptoms was 2 (1-5.5) d. Fever (32%) was most common followed by cough (19%), rapid breathing (13%), diarrhea (10%) and vomiting (10%). Severe [n = 4, 13%] and critical [n = 1, 3%] illnesses were noted more commonly in infants with comorbidities. Three (10%) children required PICU admission and invasive ventilation; one died. Median (IQR) length of hospital stay was 15 (11-20) d. Follow up RT-PCR before discharge was performed in 17 children and the median (IQR) duration to RT-PCR negativity was 16 (12-19) d. Conclusions In the early pandemic, most children with COVID-19 had a household contact and presented with asymptomatic or mild illness. Severe and critical illness were observed in young infants and those with comorbidities.

This paper describes GPU based algorithms to compute state transition model for unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations of vehicle-wave interaction. State transition model is a key component of Markov Decision Process (MDP), which is a natural framework to formulate the problem of trajectory planning under motion uncertainty. USV trajectory planning problem is characterized by the presence of large and somewhat stochastic forces due to ocean waves, which can cause significant deviations in their motion. Feedback controllers are often employed to reject disturbances and get back on the desired trajectory. However, the motion uncertainty can be significant and must be considered in the trajectory planning to avoid collisions with the surrounding obstacles. In case of USV missions, state transition probabilities need to be generated on-board, to compute trajectory plans that can handle dynamically changing USV parameters and environment (e.g., changing boat inertia tensor due to fuel consumption, variations in damping due to changes in water density, variations in sea-state, etc.). The 6 DOF dynamics simulations reported in this paper are based on potential flow theory. We also present a model simplification algorithm based on temporal coherence and its GPU implementation to accelerate simulation computation performance. Using the techniques discussed in this paper we were able to compute state transition probabilities in less than 10 minutes. Computed transition probabilities are subsequently used in a stochastic dynamic programming based approach to solve the MDP to obtain trajectory plan. Using this approach, we are able to generate dynamically feasible trajectories for USVs that exhibit safe behaviors in high sea-states in the vicinity of static obstacles.