Passive concentration dynamics incorporated into the library IB2d, a two-dimensional implementation of the immersed boundary method

In this paper, we present an open-source software library that can be used to numerically simulate the advection and diffusion of a chemical concentration or heat density in a viscous fluid where a moving, elastic boundary drives the fluid and acts as a source or sink. The fully- coupled fluid-structure interaction problem of an elastic boundary in a viscous fluid is solved using Peskin’s immersed boundary method. The addition or removal of the concentration or heat density from the boundary is solved using an immersed boundary-like approach in which the concentration is spread from the immersed boundary to the fluid using a regularized delta function. The concentration or density over time is then described by the advection-diffusion equation and numerically solved. This functionality has been added to our software library, IB2d, which provides an easy-to-use immersed boundary method in two dimensions with full implementations in MATLAB and Python. We provide four examples that illustrate the usefulness of the method. A simple rubber band that resists stretching and absorbs and releases a chemical concentration is simulated as a first example. Complete convergence results are presented for this benchmark case. Three more biological examples are presented: (1) an oscillating row of cylinders, representative of an idealized appendage used for filter-feeding or sniffing, (2) an oscillating plate in a background flow is considered to study the case of heat dissipation in a vibrating leaf, and (3) a simplified model of a pulsing soft coral where carbon dioxide is taken up and oxygen is released as a byproduct from the moving tentacles. This method is applicable to a broad range of problems in the life sciences, including chemical sensing by antennae, heat dissipation in plants and other structures, the advection-diffusion of morphogens during development, filter-feeding by marine organisms, and the release of waste products from organisms in flows.

Technology of AIRS/AMSU/ATMS satellite data processing

The Atmospheric Infrared Sounder (AIRS) is a hyperspectral instrument with 2378 channels. It is a part of the Aqua space platform equipment. It registers outgoing longwave radiation in the IR-band from 3.74 to 15.4 microns. To correctly retrieve the atmospheric profiles in the presence of cloud structures, AIRS measurement processing scheme uses data from the 15-channel AMSU-A microwave instrument, which is also installed on Aqua. The paper proposes a technology for synthesizing the readings of AMSU-A, that failed in the fall of 2015, by using observations from the 22-channel radiometer of the Advanced Technology Microwave Sounder (ATMS) installed on Suomi-NPP and NOAA-20 satellites. These platforms were launched in 2011 and 2018, respectively. The transition between the coordinate grids of the two instruments was implemented by the “resample” software library, which transferred the radiance temperature values obtained by ATMS radiometer to the AMSU-A irregular measurement grid by means of a Gaussian function. The method was tested for the Aqua and Suomi-NPP neighboring orbits of 2015, when AMSU-A was still operating normally. It is established that the root-mean-square deviation of the radiance temperature values during the transferring of ATMS data to the AMSU-A coordinate grid does not exceed 1%, and the correlation coefficient is 0.98. Using the synthesized AMSU-A readings, reconstructions of the parameters of the atmosphere and the underlying surface were carried out. The analysis of the obtained results showed the suitability of the proposed method of replacing the microwave data from AMSU by the data from ATMS instruments. It should be noted that in the case of a rapidly changing atmosphere, for example, with a strong wind, the use of ATMS observations is possible only if the difference in the passage time of the two satellites does not exceed 10–15 minutes.

Open Source Software Library for Thermo-Hydro-Mechanical Coupled Processes in Python

This contributed poster shows the current state of development of a finite element implementation as part of an open source software library (OSSL) for the simulation of thermo-hydro-mechanical (THM) coupled processes. The reliable handling of numerical methods is fundamental for the understanding of scientific interrelationships and thus, a crucial prerequisite for modeling THM scenarios, as well as for the understanding and evaluation of preliminary safety investigations during the site selection process for the storage of high-level radioactive waste. There are several motivations for developing an in-house OSSL, which will allow us to: Build capacity and maintenance within BASE (Federal Office for the Safety of Nuclear Waste Management) regarding issues of the numerical modeling of safety-relevant aspects on the long-term safety analyses specified by the German legislator in the site selection process. Develop a collection of known benchmarks and evaluation examples for the comparison of different software tools, applying a uniform interface to simplify the use of the available highly specialized open source codes. Diversify the testing possibilities regarding the preliminary safety investigations by means of our own, independent modeling software. Document basic THM scenarios for internal or, if necessary, public technical training, e.g., density-driven fluid flow (Fig. 1), convergence in salt, temperature propagation in the repository area, crack development, diffusive or advective mass transport. Ensure transparency and, in principle, might allow for appropriately proven-quality (validated) and documented simulation tools for the public regarding questions about the preliminary safety investigations during the site selection process. The development of the OSSL is mainly based on the scripting language Python, which allows the necessary flexibility for the diverse fields of application and at the same time enables maximum transparency for all aspects of the source code. To ensure the high quality of the software, state of the art development tools are used (e.g., version control, automated tests, and documentation generation). Figure 1 shows our preliminary simulation results of the so-called Elder problem (Elder, 1967), a popular standard benchmark for thermo-hydrogeological coupling in which fluid motion in a porous medium is driven by buoyancy forces.

Algorithmic Approaches for Assessing Irreversibility in Time Series: Review and Comparison

The assessment of time irreversibility, i.e., of the lack of invariance of the statistical properties of a system under the operation of time reversal, is a topic steadily gaining attention within the research community. Irreversible dynamics have been found in many real-world systems, with alterations being connected to, for instance, pathologies in the human brain, heart and gait, or to inefficiencies in financial markets. Assessing irreversibility in time series is not an easy task, due to its many aetiologies and to the different ways it manifests in data. It is thus not surprising that several numerical methods have been proposed in the last decades, based on different principles and with different applications in mind. In this contribution we review the most important algorithmic solutions that have been proposed to test the irreversibility of time series, their underlying hypotheses, computational and practical limitations, and their comparative performance. We further provide an open-source software library that includes all tests here considered. As a final point, we show that “one size does not fit all”, as tests yield complementary, and sometimes conflicting views to the problem; and discuss some future research avenues.

The Realization and Optimization Technology of Recognition Algorithm Based on Tensorflow Deep Learning Mechanism

With the rapid development of today’s technological society, recognition algorithms have received more and more attention. In addition, in recent years, deep learning algorithms have developed rapidly at the theoretical level, and related new technologies have also been applied to various industries. TensorFlow is a deep learning framework that performs well in all aspects. The purpose of this article is to study the realization of recognition algorithms based on TensorFlow’s deep learning mechanism and their optimization techniques. The target detection algorithm used in the system in this paper combines deep learning technology to replace the traditional method based on convolutional filtering. The paper is based on the TensorFlow deep learning framework. TensorFlow is an open source software library for machine intelligence. The learning software library of the network learning framework. This article uses a semi-automatic labeling method combined with an incremental learning algorithm to label the data set. After labeling the data, the parameters are set, the model is trained, and the model is finally trained and applied to the detection system. Studies have shown that: in the recognition algorithm, only the single sub-analysis stream is considered, and the short video sequence analysis stream can get the most excellent accuracy. Compared with the second best long video sequence analysis stream, it can also increase by about 3%.

Open-Source Software Library For Real-Time Inertial Measurement Unit Data-Based Inverse Kinematics Using OpenSim

An open-source software library for multithreaded real-time inverse kinematical (IK) analysis of inertial measurement unit (IMU) data using OpenSim was developed. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. Full-body inverse kinematics with data from 12 IMUs could be calculated in real-time with a mean delay below 100 ms and at more than 900 samples per second. Live visualization of IK is an option but results in limited IK throughput. The effect of this limitation was assessed by comparing the range of motion (ROM) of each joint from visualized real-time IK to the ROM from offline IK at IMU sampling frequency, resulting in mean ROM differences below 0.3 degrees. The software library enables real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models, making it possible to do subject-specific full-body motion analysis outside the motion laboratory in real-time.

Pout2Prot: an efficient tool to create protein (sub)groups from Percolator output files

The protein inference problem is complicated in metaproteomics due to the presence of homologous proteins from closely related species. Nevertheless, this process is vital to assign taxonomy and functions to identified proteins of microbial species, a task for which specialized tools such as Prophane have been developed. We here present Pout2Prot, which takes Percolator Output (.pout) files from multiple experiments and creates protein (sub)group output files (.tsv) that can be used directly with Prophane. Pout2Prot offers different grouping strategies, allows distinction between sample categories and replicates for multiple files, and uses a weighted spectral count for protein (sub)groups to reflect (sub)group abundance. Pout2Prot is available as a web application at https://pout2prot.ugent.be and is installable via pip as a standalone command line tool and reusable software library. All code is open source under the Apache License 2.0 and is available at https://github.com/compomics/pout2prot.

Open-Source Software Library for Real-Time Inertial Measurement Unit Data-Based Inverse Kinematics using OpenSim

Background: An open-source software library for multithreaded real-time inverse kinematical (IK) analysis of inertial measurement unit (IMU) data using OpenSim was developed. Its operation delays and throughputs were measured with a varying number of IMUs and parallel computing IK threads using two different musculoskeletal models, one a lower-body and torso model and the other a full-body model. Results: Full-body inverse kinematics with data from 12 IMUs could be calculated in real-time with a mean delay below 100 ms and at more than 900 samples per second. Live visualization of IK is an option but results in limited IK throughput. The effect of this limitation was assessed by comparing the range of motion (ROM) of each joint from visualized real-time IK to the ROM from offline IK at IMU sampling frequency, resulting in mean ROM differences below 0.3 degrees. Conclusions: The software library enables real-time inverse kinematical analysis with different numbers of IMUs and customizable musculoskeletal models, making it possible to do subject-specific full-body motion analysis outside the motion laboratory in real-time.