Optimizing inferior vena cava filter design: A computational fluid dynamics study on strut configuration for enhanced hemodynamic performance and thrombosis reduction.

Background and objective: Inferior vena cava filters have been shown to be effective in preventing deep vein thrombosis and its secondary complication, pulmonary embolism, thereby reducing the high mortality rate. Although inferior vena cava filters have evolved, specific complications like inferior vena cava thrombosis-induced deep vein thrombosis worsening and recurrent pulmonary embolism continue to pose challenges. This study analyzes the effects of geometric parameter variations of inferior vena cava filters, which have a significant impact on the thrombus formation inside the filter, the capture, dissolution, and hemodynamic flow of thrombus, as well as the shear stress on the filter and vascular wall. Methods: This study used computational fluid dynamic simulations with the carreau model to investigate the impact of varying inferior vena cava filter design parameters (number of struts, strut arm length, and tilt angle) on hemodynamics. Results: Recirculation and stagnation areas due to flow velocity and pressure, along with wall shear stress values, were identified as key factors. It is important to find a balance between wall shear stress high enough to aid thrombolysis and low enough to prevent platelet activation. The results of this paper show that the risk of platelet activation and thrombus filtration may be lowest when the wall shear stress of the filter ranges from 0 to 4 [Pa], minimizing stress concentration within the filter. Conclusion: 16 arm struts with a length of 20 mm and a tilt angle of 0° provide the best balance between thrombus capture and minimization of hemodynamic disturbance. This configuration minimizes the size of the stagnation and recirculation zones while maintaining sufficient wall shear stress for thrombus dissolution.

Reinforcement Learning with Task Decomposition and Task-Specific Reward System for Automation of High-Level Tasks.

This paper introduces a reinforcement learning method that leverages task decomposition and a task-specific reward system to address complex high-level tasks, such as door opening, block stacking, and nut assembly. These tasks are decomposed into various subtasks, with the grasping and putting tasks executed through single joint and gripper actions, while other tasks are trained using the SAC algorithm alongside the task-specific reward system. The task-specific reward system aims to increase the learning speed, enhance the success rate, and enable more efficient task execution. The experimental results demonstrate the efficacy of the proposed method, achieving success rates of 99.9% for door opening, 95.25% for block stacking, 80.8% for square-nut assembly, and 90.9% for round-nut assembly. Overall, this method presents a promising solution to address the challenges associated with complex tasks, offering improvements over the traditional end-to-end approach.

A novel computer modeling and simulation technique for bronchi motion tracking in human lungs under respiration.

In this work, we proposed a novel computer modeling and simulation technique for motion tracking of lung bronchi (or tumors) under respiration using 9 cases of computed tomography (CT)-based patient-specific finite element (FE) models and Ogden's hyperelastic model. In the fabrication of patient-specific FE models for the respiratory system, various organs such as the mediastinum, diaphragm, and thorax that could affect the lung motions during breathing were considered. To describe the nonlinear material behavior of lung parenchyma, the comparative simulation for biaxial tension-compression of lung parenchyma was carried out using several hyperelastic models in ABAQUS, and then, Ogden's model was adopted as an optimal model. Based on the aforementioned FE models and Ogden's material model, the 9 cases of respiration simulation were carried out from exhalation to inhalation, and the motion of lung bronchi (or tumors) was tracked. In addition, the changes in lung volume, lung cross-sectional area on the axial plane during breathing were calculated. Finally, the simulation results were quantitatively compared to the inhalation/exhalation CT images of 9 subjects to validate the proposed technique. Through the simulation, it was confirmed that the average relative errors of simulation to clinical data regarding to the displacement of 258 landmarks in the lung bronchi branches of total subjects were 1.10%~2.67%. In addition, the average relative errors of those with respect to the lung cross-sectional area changes and the volume changes in the superior-inferior direction were 0.20%~5.00% and 1.29 ~ 9.23%, respectively. Hence, it was considered that the simulation results were coincided well with the clinical data. The novelty of the present study is as follows: (1) The framework from fabrication of the human respiratory system to validation of the bronchi motion tracking is provided step by step. (2) The comparative simulation study for nonlinear material behavior of lung parenchyma was carried out to describe the realistic lung motion. (3) Various organs surrounding the lung parenchyma and restricting its motion were considered in respiration simulation. (4) The simulation results such as landmark displacement, lung cross-sectional area/volume changes were quantitatively compared to the clinical data of 9 subjects.

The Task Decomposition and Dedicated Reward-System-Based Reinforcement Learning Algorithm for Pick-and-Place.

This paper proposes a task decomposition and dedicated reward-system-based reinforcement learning algorithm for the Pick-and-Place task, which is one of the high-level tasks of robot manipulators. The proposed method decomposes the Pick-and-Place task into three subtasks: two reaching tasks and one grasping task. One of the two reaching tasks is approaching the object, and the other is reaching the place position. These two reaching tasks are carried out using each optimal policy of the agents which are trained using Soft Actor-Critic (SAC). Different from the two reaching tasks, the grasping is implemented via simple logic which is easily designable but may result in improper gripping. To assist the grasping task properly, a dedicated reward system for approaching the object is designed through using individual axis-based weights. To verify the validity of the proposed method, wecarry out various experiments in the MuJoCo physics engine with the Robosuite framework. According to the simulation results of four trials, the robot manipulator picked up and released the object in the goal position with an average success rate of 93.2%.

Age Group Classification of Dental Radiography without Precise Age Information Using Convolutional Neural Networks.

Automatic age estimation using panoramic dental radiographic images is an important procedure for forensics and personal oral healthcare. The accuracies of the age estimation have increased recently with the advances in deep neural networks (DNN), but DNN requires large sizes of the labeled dataset which is not always available. This study examined whether a deep neural network is able to estimate tooth ages when precise age information is not given. A deep neural network model was developed and applied to age estimation using an image augmentation technique. A total of 10,023 original images were classified according to age groups (in decades, from the 10s to the 70s). The proposed model was validated using a 10-fold cross-validation technique for precise evaluation, and the accuracies of the predicted tooth ages were calculated by varying the tolerance. The accuracies were 53.846% with a tolerance of ±5 years, 95.121% with ±15 years, and 99.581% with ±25 years, which means the probability for the estimation error to be larger than one age group is 0.419%. The results indicate that artificial intelligence has potential not only in the forensic aspect but also in the clinical aspect of oral care.

Prediction of respiratory complications by quantifying lung contusion volume using chest computed tomography in patients with chest trauma.

Pulmonary contusion is an important risk factor for respiratory complications in trauma patients. Hence, we aimed to determine the relationship between the ratio of pulmonary contusion volume to the total lung volume and patient outcomes and the predictability of respiratory complications. We retrospectively included 73 patients with a pulmonary contusion on chest computed tomography (CT) from 800 patients with chest trauma admitted to our facility between January 2019 and January 2020. Chest injury severity was expressed as the ratio of pulmonary contusion volume to total lung volume by quantifying pulmonary contusion volume on chest CT. The cut-off value was 80%. Among the 73 patients with pulmonary contusion (77% males, mean age: 45.3 years), 28 patients had pneumonia, and five had acute respiratory distress syndrome. The number of patients in the severe risk group with > 20% of pulmonary contusion volume was 38, among whom 23 had pneumonia. For predicting pneumonia, the area under the receiver operating characteristic curves for the ratio of pulmonary contusion volume was 0.85 (95% confidence interval 0.76-0.95, p = 0.008); the optimal threshold was 70.4%. Quantifying pulmonary contusion volume using initial CT enables identifying patients with chest trauma at high risk of delayed respiratory complications.

Characterization of Nickel Oxide Nanoparticles Synthesized under Low Temperature.

In this study, ultrafine nickel oxide nanoparticles (NiO NPs) were well synthesized using a simple wet chemical method under low temperature, 300 °C. An Ni(OH)2 precursor was well precipitated by dropping NH4OH into an Ni(Ac)2 solution. TG-DTA showed that the weight of the precipitate decreases until 300 °C; therefore, the precursor was heat-treated at 300 °C. X-ray diffraction (XRD) patterns indicated that hexagonal-structured NiO NPs with (200) preferred orientation was synthesized. In addition, BET specific surface area (SSA) and HRTEM analyses revealed that spherical NiO NPs were formed with SSA and particle size of 60.14 m2/g and ca. 5-15 nm by using the low temperature method. FT-IR spectra of the NiO NPs showed only a sharp vibrating absorption peak at around 550 cm-1 owing to the Ni-O bond. Additionally, in UV-vis absorption spectra, the wavelength for absorption edge and energy band gap of the ultrafine NiO NPs was 290 nm and 3.44 eV.

Three-dimensional image acquisition and reconstruction system on a mobile device based on computer-generated integral imaging.

A mobile three-dimensional image acquisition and reconstruction system using a computer-generated integral imaging technique is proposed. A depth camera connected to the mobile device acquires the color and depth data of a real object simultaneously, and an elemental image array is generated based on the original three-dimensional information for the object, with lens array specifications input into the mobile device. The three-dimensional visualization of the real object is reconstructed on the mobile display through optical or digital reconstruction methods. The proposed system is implemented successfully and the experimental results certify that the system is an effective and interesting method of displaying real three-dimensional content on a mobile device.

An Investigation of Gate Pulse Induced Degradation in a-InGaZnO Thin Film Transistors.

We investigated the effects of pulsed gate bias on degradation of amorphous indium gallium zinc oxide (a-InGaZnO) thin film transistors (TFTs). The waveform composed of 0 V and 20 V produced little degradation, but the waveform composed of -20 V and 0 V produced a considerable degradation on the turn-on current in the transfer characteristics. Those instabilities were found mostly in TFTs of which the concentration of Zn is higher than the other metallic components (In, Ga). In order to explain the anomalous degradation behaviors, we propose a possible degradation model which is different from the conventional model of charge trapping. Our proposed model is related to an increase of acceptor-like states in a-InGaZnO near the source and drain electrodes. More electrons can be trapped there, and the increased potential barrier hinders current flow in the channel. The proposed model can also account for the increased frequency dispersion in C-V characteristics of our a-InGaZnO TFTs after the waveform stress.