Critical Evaluation of Resistive Force Theory using a Machine Learning Assisted Model

The hydrodynamic forces on a slender rod in a fluid medium at low Reynolds number can be modeled using resistive force theories (RFTs) or slender body theories (SBTs). The former represent the forces by local drag coefficients and are computationally cheap; however, they are physically inaccurate when long-range hydrodynamic interaction is involved. The later are physically accurate but require solving integral equations and, therefore, are computationally expensive. This paper investigates RFTs in comparison with state-of-the art SBT methods. During the process, a neural network-based hydrodynamic model that -- similar to RFTs -- relies on local drag coefficients for computational efficiency was developed. However, the network is trained using data from an SBT (regularized stokeslet segments method).The $R^2$ value of the trained coefficients were $\sim 0.99$ with mean absolute error of $1.6\times10^{-2}$. The machine learning resistive force theory (MLRFT) accounts for local hydrodynamic forces distribution, the dependence on rotational and translational speeds and directions, and geometric parameters of the slender object. We show that, when classical RFT fails to accurately predict the forces, torques, and drags on slender rods under low Reynolds number flows, MLRFT exhibits good agreement with physically accurate SBT simulations. In terms of computational speed, MLRFT forgoes the need of solving an inverse problem and, therefore, requires negligible computation time in comparison with SBT. MLRFT presents a computationally inexpensive hydrodynamic model for flagellar propulsion can be used in the design and optimization of biomimetic flagellated robots and analysis of bacterial locomotion.Comment: 9 pages, 10 figure

Bacteria-inspired Robotic Propulsion from Bundling of Soft Helical Filaments at Low Reynolds Number

The bundling of flagella is known to create a "run" phase, where the bacteria moves in a nearly straight line rather than making changes in direction. Historically, mechanical explanations for the bundling phenomenon intrigued many researchers, and significant advances were made in physical models and experimental methods. Contributing to the field of research, we present a bacteria-inspired centimeter-scale soft robotic hardware platform and a computational framework for a physically plausible simulation model of the multi-flagellated robot under low Reynolds number (~0.1). The fluid-structure interaction simulation couples the Discrete Elastic Rods algorithm with the method of Regularized Stokeslet Segments. Contact between two flagella is handled by a penalty-based method. We present a comparison between our experimental and simulation results and verify that the simulation tool can capture the essential physics of this problem. Preliminary findings on robustness to buckling provided by the bundling phenomenon and the efficiency of a multi-flagellated soft robot are compared with the single-flagellated counterparts. Observations were made on the coupling between geometry and elasticity, which manifests itself in the propulsion of the robot by nonlinear dependency on the rotational speed of the flagella.Comment: Supplementary Video: https://youtu.be/qevN1NovCZ

Can We Utilize Pre-trained Language Models within Causal Discovery Algorithms?

Scaling laws have allowed Pre-trained Language Models (PLMs) into the field of causal reasoning. Causal reasoning of PLM relies solely on text-based descriptions, in contrast to causal discovery which aims to determine the causal relationships between variables utilizing data. Recently, there has been current research regarding a method that mimics causal discovery by aggregating the outcomes of repetitive causal reasoning, achieved through specifically designed prompts. It highlights the usefulness of PLMs in discovering cause and effect, which is often limited by a lack of data, especially when dealing with multiple variables. Conversely, the characteristics of PLMs which are that PLMs do not analyze data and they are highly dependent on prompt design leads to a crucial limitation for directly using PLMs in causal discovery. Accordingly, PLM-based causal reasoning deeply depends on the prompt design and carries out the risk of overconfidence and false predictions in determining causal relationships. In this paper, we empirically demonstrate the aforementioned limitations of PLM-based causal reasoning through experiments on physics-inspired synthetic data. Then, we propose a new framework that integrates prior knowledge obtained from PLM with a causal discovery algorithm. This is accomplished by initializing an adjacency matrix for causal discovery and incorporating regularization using prior knowledge. Our proposed framework not only demonstrates improved performance through the integration of PLM and causal discovery but also suggests how to leverage PLM-extracted prior knowledge with existing causal discovery algorithms

Dynamic left ventricular outflow tract obstruction in living donor liver transplantation recipients -A report of two cases-

We present two cases of dynamic left ventricular outflow tract obstruction in 2 patients who were undergoing living donor liver transplantation. On the preoperative transthoracic echocardiography, the first patient showed normal ventricular function and a normal wall thickness, but severe hemodynamic deterioration developed during the anhepatic period and this was further aggravated after reperfusion in spite of volume resuscitation and catecholamine therapy. Intraoperative transesophageal echocardiography revealed the systolic anterior motion of the mitral valve leaflet together with left ventricular outflow tract obstruction. The second patient showed left ventricular hypertrophy with left ventricular outflow tract obstruction on the preoperative echocardiography. Intraoperative transesophageal echocardiography was used to guide fluid administration and the hemodynamic management throughout the procedure and a temporary portocaval shunt was established to mitigate the venous pooling during the anhepatic period. The purpose of this report is to emphasize the clinical significance of dynamic left ventricular outflow tract obstruction in patients who are undergoing living donor liver transplantation and the role of intraoperative echocardiography to detect and manage it

Augmented Mechanical Forces of the Surface-Modified Nanoporous Acupuncture Needles Elicit Enhanced Analgesic Effects

Over the past several decades, clinical studies have shown significant analgesic effects of acupuncture. The efficacy of acupuncture treatment has improved with the recent development of nanoporous needles (PN), which are produced by modifying the needle surface using nanotechnology. Herein, we showed that PN at acupoint ST36 produces prolonged analgesic effects in an inflammatory pain model; the analgesic effects of PN acupuncture were sustained over 2 h, while those using a conventional needle (CN) lasted only 30 min. In addition, the PN showed greater therapeutic effects than CN after 10 acupuncture treatments once per day for 10 days. We explored how the porous surface of the PN contributes to changes in local tissue, which may in turn result in enhanced analgesic effects. We showed that the PN has greater rotational torque and pulling force than the CN, particularly at acupoints ST36 and LI11, situated on thick muscle layers. Additionally, in ex vivo experiments, the PN showed greater winding of subcutaneous connective tissues and muscle layers. Our results suggest that local mechanical forces are augmented by the PN and its nanoporous surface, contributing to the enhanced and prolonged analgesic effects of PN acupuncture.1

Protective Effects of Gabapentin on Allodynia and α2δ1-Subunit of Voltage-dependent Calcium Channel in Spinal Nerve-Ligated Rats

This study was designed to determine whether early gabapentin treatment has a protective analgesic effect on neuropathic pain and compared its effect to the late treatment in a rat neuropathic model, and as the potential mechanism of protective action, the α2δ1-subunit of the voltage-dependent calcium channel (α2δ1-subunit) was evaluated in both sides of the L5 dorsal root ganglia (DRG). Neuropathic pain was induced in male Sprague-Dawley rats by a surgical ligation of left L5 nerve. For the early treatment group, rats were injected with gabapentin (100 mg/kg) intraperitoneally 15 min prior to surgery and then every 24 hr during postoperative day (POD) 1-4. For the late treatment group, the same dose of gabapentin was injected every 24 hr during POD 8-12. For the control group, L5 nerve was ligated but no gabapentin was administered. In the early treatment group, the development of allodynia was delayed up to POD 10, whereas allodynia was developed on POD 2 in the control and the late treatment group (p<0.05). The α2δ1-subunit was up-regulated in all groups, however, there was no difference in the level of the α2δ1-subunit among the three groups. These results suggest that early treatment with gabapentin offers some protection against neuropathic pain but it is unlikely that this action is mediated through modulation of the α2δ1-subunit in DRG

Discrete Differential Geometry-Based Modeling of Robots at Low Reynolds Number

Robots on a microscopic scale, especially soft robots, are actively being developed for their potential in in-vivo therapeutic usage. Initially inspired by nature displayed through microscopes (e.g. bacteria, respiratory cilia), the physics of the robots at a low Reynolds number differentiates itself from the physics of the human scale. Traditional modeling and experimental methods for microbots require advanced microfabrication facilities and computationally expensive finite element methods due to innate objectives to interact with bodily fluids. Moreover, precise control and conclusive verification on a microscopic scale are often unfeasible due to technological limitations in manufacturing, control, and modeling. We resolve these challenges by combining discrete differential geometry-based modeling with embedded external force models and active material properties. We verify our modeling methods through experiments in various scales including desktop scale experiments, as well as millimeter and micrometer robot experiments, and exploit our model’s operability independent of the scale. The prominence of scale-independence of our model and desktop experiments enables us to analyze the characteristics of the behavior of the robots operating in a low Reynolds number at a low-cost viable scale with less challenge in microfabrication and control. The form factor choices for our robots are bacterial flagella, cilia, and functional ferromagnetic soft robots, which share a common ultrastructure of rod and beam. A collection of modeling problems of ferromagnetic soft robots and bio-inspired locomotion at disparate length scales are explored.\\First, we conduct a desktop-scale experiment, which resembles the propulsion mechanism of bacteria, using two artificial elastic flagella. The locomotion of the near straight-line motion of bacteria called bundling occurs when both flagella are rotating in the same direction. A 3D-printed robotic prototype with a palm-sized body was submerged in glycerine for the experiment. We implemented the discrete elastic rod (DER) method with Regularized Stokeslet Segments (RSS) method for hydrodynamics and a constraint-based contact model to verify our model against the experiment. Dimensionless analysis was conducted for the results to enable adaptation for robots of the same form factor with different scales. Furthermore, a comparison of propulsion due to single flagellum and preliminary findings on cyclic bundling and unbundling sequence is reported.We then investigate the directional change mechanism of bacteria referred to as tumbling. Using a rigid robot experiment with RSS method and numerical simulation accounting for the righting moment of the head due to center of gravity, the tumbling behavior could be analyzed for controllability. The reults on attitude control of the robot inside a viscous medium using tumbling mechanism is reported. Furthermore, a non dimensional analysis enabled generalization of our results to microscale as well as help optimizing the design space of flagella for the best turn over ability.Next using the programmable ferromagnetic soft robot of a rod, cilium, and functional robot configuration, we introduced a ferromagnetic coupling into the DER method. Our model shows excellent quantitative agreement with the experiment. The model could capture the dynamic buckling of the soft robot, the motion of an mm-scale ciliary robot in glycerol, and the microscale functional robot with walking, jumping, and rolling gait modes. Our modeling method is the fastest for ferromagnetic soft robots with frictional contact known thus far. With the rod model with fixed boundary conditions, faster than real-time simulation was achieved. Lastly, a coordinate invariant, machine learning (ML)-based, reduced-order hydrodynamics model suited for helical form factors was developed. The ML-based hydrodynamics model shows the accuracy of a high-fidelity hydrodynamics model (RSS) with the speed of an empirical coefficient-based local hydrodynamics model for a low Reynolds number flow

2012 경제발전경험모듈화사업 : 대중교통체계 개선

Summary Chapter 1 Background to Public Transport Reform 　1. Social and Economic Conditions of Recent Decades 　2. Transport Policy Conditions of Recent Decades Chapter 2 Building a Public Transport System: Urban Rail 　1. Process of Introducing Urban Railways in Seoul 　2. Phase 1 and 2 of Seoul Metropolitan Urban Rail 　3. Urban Rail Operation System 　4. Roles of the Central and Local Governments in Urban Rail Construction 　5. Achievements of Seoul Metropolitan Urban Rail Projects and Their Implications Chapter 3 Building a Public Transport System: Buses 　1. Background to Bus Reform 　2. Overview of the Bus Operation System 　3. Contents of Bus Reform 　4. Bus Transport Operational Management System 　5. Achievement and Implications of Bus Reform Chapter 4 Achievements of Public Transport Reform and Policy Suggestions 　1. Achievements of Public Transport Reform 　2. Policy Suggestions for Improving Public Transport System Reference

Influence of Light-Intensity-Dependent Droplet Directionality on Dimensions of Structures Constructed Using an In Situ Light-Guided 3D Printing Method

As an alternative to conventional 3D printing methods that require supports, a new 3D printing strategy that utilizes guided light in situ has been developed for fabricating freestanding overhanging structures without supports. Light intensity has been found to be a crucial factor in modifying the dimensions of structures printed using this method; however, the underlying mechanism has not been clearly identified. Therefore, the light-intensity-dependent changes in the structure dimensions were analyzed in this study to elucidate the associated mechanism. Essentially, the entire process of deposition was monitored by assessing the behavior of photocurable droplets prior to their collision with the structure using imaging analysis tools such as a high-speed camera and MATLAB®. With increasing light intensity, the instability of the ejected falling droplets increased, and the droplet directionality deteriorated. This increased the dispersion of the droplet midpoints, which caused the average midpoints of the deposited single layers to shift further away from the center of the structure. Consequently, the diameter of the structure formed by successive stacking of single layers increased, and the layer thickness per droplet decreased. These led to light-intensity-dependent differences in the diameter and height of structures that were created from the same number of droplets