Flow study on a ducted azimuth thruster

This paper presents the results of the numerical validation and verification studies on an azimuth thruster. The numerical investigations include a grid study as well as an analysis of the simulation results obtained by different isotropic an anisotropic turbulence models, such as k-omega, SST, SAS-SST, BSL-EARSM and DES. The numerical simulation results of selected flow conditions are compared with experimental data. To investigate scale effects on the open water results numerical computations are carried out for a thruster in full- and model scale and the calculated thrust and torque coefficients are compared with model scale simulations and measurements

Metabolic Regulatory Network Kinetic Modeling with Multiple Isotopic Tracers for iPSCs

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway (PPP), tricarboxylic acid (TCA) cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.Comment: 26 pages, 16 figure

Sequential Importance Sampling for Hybrid Model Bayesian Inference to Support Bioprocess Mechanism Learning and Robust Control

Driven by the critical needs of biomanufacturing 4.0, we introduce a probabilistic knowledge graph hybrid model characterizing the risk- and science-based understanding of bioprocess mechanisms. It can faithfully capture the important properties, including nonlinear reactions, partially observed state, and nonstationary dynamics. Given very limited real process observations, we derive a posterior distribution quantifying model estimation uncertainty. To avoid the evaluation of intractable likelihoods, Approximate Bayesian Computation sampling with Sequential Monte Carlo (ABC-SMC) is utilized to approximate the posterior distribution. Under high stochastic and model uncertainties, it is computationally expensive to match output trajectories. Therefore, we create a linear Gaussian dynamic Bayesian network (LG-DBN) auxiliary likelihood-based ABC-SMC approach. Through matching the summary statistics driven through LG-DBN likelihood that can capture critical interactions and variations, the proposed algorithm can accelerate hybrid model inference, support process monitoring, and facilitate mechanism learning and robust control.Comment: 11 pages, 2 figure

A Systematic Approach for Inertial Sensor Calibration of Gravity Recovery Satellites and Its Application to Taiji-1 Mission

High-precision inertial sensors or accelerometers can provide us references of free-falling motions in gravitational field in space. They serve as the key payloads for gravity recovery missions such as the CHAMP, the GRACE-type missions, and the planned Next Generation Gravity Missions. In this work, a systematic method of electrostatic inertial sensor calibrations for gravity recovery satellites is suggested, which is applied to and verified with the Taiji-1 mission. With this method, the complete operating parameters including the scale factors, the center of mass offset vector and the intrinsic biased acceleration can be precisely calibrated with only two sets of short-term in-orbit experiments. Taiji-1 is the first technology demonstration satellite of the "Taiji Program in Space", which, in its final extended phase in 2022, could be viewed as operating in the mode of a high-low satellite-to-satellite tracking gravity mission. Based on the calibration principles, swing maneuvers with time span about 200 s and rolling maneuvers for 19 days were conducted by Taiji-1 in 2022. The inertial sensor's operating parameters are precisely re-calibrated with Kalman filters and are updated to the Taiji-1 science team. Data from one of the sensitive axis is re-processed with the updated operating parameters, and the performance is found to be slightly improved compared with former results. This approach could be of high reference value for the accelerometer or inertial sensor calibrations of the GFO, the Chinese GRACE-type mission, and the Next Generation Gravity Missions. This could also shed some light on the in-orbit calibrations of the ultra-precision inertial sensors for future GW space antennas because of the technological inheritance between these two generations of inertial sensors.Comment: 24 pages, 19 figure

Pendelluft as a predictor of weaning in critically ill patients: An observational cohort study

Objective: Weaning failure is associated with adverse clinical outcomes. This study aimed to evaluate the accuracy of pendelluft during the spontaneous breathing trials (SBT) as a predictor of weaning outcome of patients with mechanical ventilation.Methods: An observational cohort study included 60 critically ill patients who were eligible for extubation. Pendelluft and electrical activity of the diaphragm (Edi) were monitored at baseline and every 10 minutes for the first 30 min of SBT denoted as T0, T1, T2, and T3. The pendelluft was measured using electrical impedance tomography (EIT), and Edi parameters were collected by Edi catheter. Patients were followed up after extubation and were divided into success group and failure group. Pendelluft, Edi parameters, respiratory parameters, and clinical outcomes such as intensive care units (ICU) stay, mortality, and 28-day ventilator-free days were compared between the two groups. Receiver operating characteristic (ROC) curves were constructed to evaluate the ability of pendelluft to predict weaning outcome.Results: Fifty patients (50/60) were successfully weaned from the machine and 10 (10/60) failed, with weaning failure rate of 16.7%. Respiratory parameters such as rapid shallow breathing index (RSBI), respiratory rate (RR) and Edi parameters such as maximum value of Edi (Edimax), Edi variation between a maximum and minimum(ΔEdi) in the failure group were higher than those in the success group. The ICU stay and the 28-day ventilator-free days in the failure group were significantly longer than those in the success group. The 28-day mortality rate was higher in the failure group. The pendelluft mainly occurred in the early stage of SBT. Ventral pendelluft and total pendelluft in the failure group were higher than those in the success group at T1. Edimax and ΔEdi were positively correlated with pendelluft. The area under ROC curve (AUC) showed moderate predictive ability for ventral pendelluft in predicting weaning failure at T1 (AUC 0.76, 95% CI 0.58–0.94, cut-off value > 3% global tidal variation).Conclusion: Pendelluft is one of the factors leading to weaning failure, which may be related to diaphragm function. Measuring pendelluft volume maybe helpful to predict weaning

Fine-Grained Video Retrieval With Scene Sketches

Benefiting from the intuitiveness and naturalness of sketch interaction, sketch-based video retrieval (SBVR) has received considerable attention in the video retrieval research area. However, most existing SBVR research still lacks the capability of accurate video retrieval with fine-grained scene content. To address this problem, in this paper we investigate a new task, which focuses on retrieving the target video by utilizing a fine-grained storyboard sketch depicting the scene layout and major foreground instances’ visual characteristics (e.g., appearance, size, pose, etc.) of video; we call such a task “fine-grained scene-level SBVR”. The most challenging issue in this task is how to perform scene-level cross-modal alignment between sketch and video. Our solution consists of two parts. First, we construct a scene-level sketch-video dataset called SketchVideo, in which sketch-video pairs are provided and each pair contains a clip-level storyboard sketch and several keyframe sketches (corresponding to video frames). Second, we propose a novel deep learning architecture called Sketch Query Graph Convolutional Network (SQ-GCN). In SQ-GCN, we first adaptively sample the video frames to improve video encoding efficiency, and then construct appearance and category graphs to jointly model visual and semantic alignment between sketch and video. Experiments show that our fine-grained scene-level SBVR framework with SQ-GCN architecture outperforms the state-of-the-art fine-grained retrieval methods. The SketchVideo dataset and SQ-GCN code are available in the project webpage https://iscas-mmsketch.github.io/FG-SL-SBVR/

High precision proton beam monitor system concept design on CSNS based on SiC

A high precision beam monitor system based on silicon carbide PIN sensor is designed for China Spallation Neutron Source 1.6 GeV proton beam to monitor the proton beam fluence.The concept design of the beam monitor system is finished together with front-end electronics with silicon carbide PIN sensors, readout system and mechanical system.Several tests are performed to study the performance of each component of the system.The charge collection of the SiC PIN sensors after proton radiation is studied with 80 MeV proton beam for continuous running. Research on the performance of the front-end electronics and readout system is finished for better data acquisition.The uncertainty of proton beam fluence is below 1% in the beam monitor system