scholarly journals A description of the first open source community release of the 1D atmospheric chemistry model MISTRA-v9.0

2021 ◽  
Author(s):  
Josué Bock ◽  
Jan Kaiser ◽  
Max Thomas ◽  
Andreas Bott ◽  
Roland von Glasow
Keyword(s):  
Open Source ◽  
Atmospheric Chemistry ◽  
Particle Interactions ◽  
User Friendliness ◽  
Community Model ◽  
Wide Range ◽  
Platform Independence ◽  
Major Review ◽  
Phase Chemistry ◽  
Scientific Questions

Abstract. We present MISTRA-v9.0, a one dimensional (1D) atmospheric chemistry model. The model includes a detailed particle description with regards to the microphysics, gas-particle interactions, and liquid phase chemistry within particles. Version 9.0 is the first release of MISTRA as an open-source community model. A major review of the code has been performed along with this public version release to improve the user-friendliness and platform-independence of the model. In the past 20 years, MISTRA has been used in over 25 studies to address a wide range of scientific questions. The purpose of this public release is to maximise the benefit of MISTRA to the community by making the model freely available and easier to use and develop. This paper presents a thorough description of the model characteristics and components. We show some examples of simulations reproducing previous studies with MISTRA, finding that version 9.0 is consistent with previous versions.

Investigating the Efficacy and Cost-Effectiveness of a Web-based Self-help Program for People With Adjustment Problems After an Accident (SelFIT): Protocol for a Randomized Controlled Trial (Preprint)

2020 ◽  
Author(s):  
Julia Hegy ◽  
Noemi Anja Brog ◽  
Thomas Berger ◽  
Hansjoerg Znoj
Keyword(s):  
Health Care ◽  
Psychological Distress ◽  
Cost Effectiveness ◽  
Well Being ◽  
User Friendliness ◽  
Adjustment Problems ◽  
Health Care Consumption ◽  
Web Based ◽  
Self Help ◽  
Wide Range

BACKGROUND Accidents and the resulting injuries are one of the world’s biggest health care issues often causing long-term effects on psychological and physical health. With regard to psychological consequences, accidents can cause a wide range of burdens including adjustment problems. Although adjustment problems are among the most frequent mental health problems, there are few specific interventions available. The newly developed program SelFIT aims to remedy this situation by offering a low-threshold web-based self-help intervention for psychological distress after an accident. OBJECTIVE The overall aim is to evaluate the efficacy and cost-effectiveness of the SelFIT program plus care as usual (CAU) compared to only care as usual. Furthermore, the program’s user friendliness, acceptance and adherence are assessed. We expect that the use of SelFIT is associated with a greater reduction in psychological distress, greater improvement in mental and physical well-being, and greater cost-effectiveness compared to CAU. METHODS Adults (n=240) showing adjustment problems due to an accident they experienced between 2 weeks and 2 years before entering the study will be randomized. Participants in the intervention group receive direct access to SelFIT. The control group receives access to the program after 12 weeks. There are 6 measurement points for both groups (baseline as well as after 4, 8, 12, 24 and 36 weeks). The main outcome is a reduction in anxiety, depression and stress symptoms that indicate adjustment problems. Secondary outcomes include well-being, optimism, embitterment, self-esteem, self-efficacy, emotion regulation, pain, costs of health care consumption and productivity loss as well as the program’s adherence, acceptance and user-friendliness. RESULTS Recruitment started in December 2019 and is ongoing. CONCLUSIONS To the best of our knowledge, this is the first study examining a web-based self-help program designed to treat adjustment problems resulting from an accident. If effective, the program could complement the still limited offer of secondary and tertiary psychological prevention after an accident. CLINICALTRIAL ClinicalTrials.gov NCT03785912; https://clinicaltrials.gov/ct2/show/NCT03785912?cond=NCT03785912&draw=2&rank=1

scholarly journals Line-of-Sight Winds and Doppler Effect Smearing in ACE-FTS Solar Occultation Measurements

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 680
Author(s):  
Chris D. Boone ◽  
Johnathan Steffen ◽  
Jeff Crouse ◽  
Peter F. Bernath
Keyword(s):  
Atmospheric Chemistry ◽  
Doppler Effect ◽  
Line Of Sight ◽  
Measured Signal ◽  
Mixing Ratio ◽  
Model Calculations ◽  
Doppler Shifts ◽  
Solar Occultation ◽  
Wide Range ◽  
Wind Profiles

Line-of-sight wind profiles are derived from Doppler shifts in infrared solar occultation measurements from the Atmospheric Chemistry Experiment Fourier transform spectrometers (ACE-FTS), the primary instrument on SCISAT, a satellite-based mission for monitoring the Earth’s atmosphere. Comparisons suggest a possible eastward bias from 20 m/s to 30 m/s in ACE-FTS results above 80 km relative to some datasets but no persistent bias relative to other datasets. For instruments operating in a limb geometry, looking through a wide range of altitudes, smearing of the Doppler effect along the line of sight can impact the measured signal, particularly for saturated absorption lines. Implications of Doppler effect smearing are investigated for forward model calculations and volume mixing ratio retrievals. Effects are generally small enough to be safely ignored, except for molecules having a large overhang in their volume mixing ratio profile, such as carbon monoxide.

scholarly journals Coupling between the JULES land-surface scheme and the CCATT-BRAMS atmospheric chemistry model (JULES-CCATT-BRAMS1.0): applications to numerical weather forecasting and the CO<sub>2</sub> budget in South America

2013 ◽  
Vol 6 (1) ◽  
pp. 453-494 ◽  
Author(s):  
D. S. Moreira ◽  
S. R. Freitas ◽  
J. P. Bonatti ◽  
L. M. Mercado ◽  
N. M. É. Rosário ◽  
...  
Keyword(s):  
South America ◽  
Atmospheric Chemistry ◽  
Land Surface ◽  
Amazon Basin ◽  
Mean Squared Error ◽  
Weather Forecasting ◽  
Dew Point ◽  
Surface Model ◽  
Co2 Concentrations ◽  
Wide Range

Abstract. This article presents the development of a new numerical system denominated JULES-CCATT-BRAMS, which resulted from the coupling of the JULES surface model to the CCATT-BRAMS atmospheric chemistry model. The performance of this system in relation to several meteorological variables (wind speed at 10 m, air temperature at 2 m, dew point temperature at 2 m, pressure reduced to mean sea level and 6 h accumulated precipitation) and the CO2 concentration above an extensive area of South America is also presented, focusing on the Amazon basin. The evaluations were conducted for two periods, the wet (March) and dry (September) seasons of 2010. The statistics used to perform the evaluation included bias (BIAS) and root mean squared error (RMSE). The errors were calculated in relation to observations at conventional stations in airports and automatic stations. In addition, CO2 concentrations in the first model level were compared with meteorological tower measurements and vertical CO2 profiles were compared with aircraft data. The results of this study show that the JULES model coupled to CCATT-BRAMS provided a significant gain in performance in the evaluated atmospheric fields relative to those simulated by the LEAF (version 3) surface model originally utilized by CCATT-BRAMS. Simulations of CO2 concentrations in Amazonia and a comparison with observations are also discussed and show that the system presents a gain in performance relative to previous studies. Finally, we discuss a wide range of numerical studies integrating coupled atmospheric, land surface and chemistry processes that could be produced with the system described here. Therefore, this work presents to the scientific community a free tool, with good performance in relation to the observed data and re-analyses, able to produce atmospheric simulations/forecasts at different resolutions, for any period of time and in any region of the globe.

scholarly journals INSPECTOR: free software for magnetic resonance spectroscopy data inspection, processing, simulation and analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Martin Gajdošík ◽  
Karl Landheer ◽  
Kelley M. Swanberg ◽  
Christoph Juchem
Keyword(s):  
Magnetic Resonance ◽  
Quality Management ◽  
Magnetic Resonance Spectroscopy ◽  
Data Processing ◽  
Biomedical Research ◽  
Clinical Diagnostics ◽  
Resonance Spectroscopy ◽  
User Friendliness ◽  
Wide Range

AbstractIn vivo magnetic resonance spectroscopy (MRS) is a powerful tool for biomedical research and clinical diagnostics, allowing for non-invasive measurement and analysis of small molecules from living tissues. However, currently available MRS processing and analytical software tools are limited in their potential for in-depth quality management, access to details of the processing stream, and user friendliness. Moreover, available MRS software focuses on selected aspects of MRS such as simulation, signal processing or analysis, necessitating the use of multiple packages and interfacing among them for biomedical applications. The freeware INSPECTOR comprises enhanced MRS data processing, simulation and analytical capabilities in a one-stop-shop solution for a wide range of biomedical research and diagnostic applications. Extensive data handling, quality management and visualization options are built in, enabling the assessment of every step of the processing chain with maximum transparency. The parameters of the processing can be flexibly chosen and tailored for the specific research problem, and extended confidence information is provided with the analysis. The INSPECTOR software stands out in its user-friendly workflow and potential for automation. In addition to convenience, the functionalities of INSPECTOR ensure rigorous and consistent data processing throughout multi-experiment and multi-center studies.

scholarly journals Application of random forest regression to the calculation of gas-phase chemistry within the GEOS-Chem chemistry model v10

2019 ◽  
Vol 12 (3) ◽  
pp. 1209-1225 ◽  
Author(s):  
Christoph A. Keller ◽  
Mat J. Evans
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Gas Phase ◽  
Atmospheric Chemistry ◽  
Random Forest Regression ◽  
Data Set ◽  
Gas Phase Chemistry ◽  
Chemical Conditions ◽  
Phase Chemistry ◽  
The Impact

Abstract. Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models are numerically intense, and previous attempts to reduce the numerical cost of chemistry solvers have not delivered transformative change. We show here the potential of a machine learning (in this case random forest regression) replacement for the gas-phase chemistry in atmospheric chemistry transport models. Our training data consist of 1 month (July 2013) of output of chemical conditions together with the model physical state, produced from the GEOS-Chem chemistry model v10. From this data set we train random forest regression models to predict the concentration of each transported species after the integrator, based on the physical and chemical conditions before the integrator. The choice of prediction type has a strong impact on the skill of the regression model. We find best results from predicting the change in concentration for long-lived species and the absolute concentration for short-lived species. We also find improvements from a simple implementation of chemical families (NOx = NO + NO2). We then implement the trained random forest predictors back into GEOS-Chem to replace the numerical integrator. The machine-learning-driven GEOS-Chem model compares well to the standard simulation. For ozone (O3), errors from using the random forests (compared to the reference simulation) grow slowly and after 5 days the normalized mean bias (NMB), root mean square error (RMSE) and R2 are 4.2 %, 35 % and 0.9, respectively; after 30 days the errors increase to 13 %, 67 % and 0.75, respectively. The biases become largest in remote areas such as the tropical Pacific where errors in the chemistry can accumulate with little balancing influence from emissions or deposition. Over polluted regions the model error is less than 10 % and has significant fidelity in following the time series of the full model. Modelled NOx shows similar features, with the most significant errors occurring in remote locations far from recent emissions. For other species such as inorganic bromine species and short-lived nitrogen species, errors become large, with NMB, RMSE and R2 reaching >2100 % >400 % and <0.1, respectively. This proof-of-concept implementation takes 1.8 times more time than the direct integration of the differential equations, but optimization and software engineering should allow substantial increases in speed. We discuss potential improvements in the implementation, some of its advantages from both a software and hardware perspective, its limitations, and its applicability to operational air quality activities.

Implementation of Shape Memory Alloys as Active Elements in Injuries Recuperative Equipment

2014 ◽  
Vol 657 ◽  
pp. 392-396
Author(s):  
Adela Ursanu Dragoş ◽  
Sergiu Stanciu ◽  
Nicanor Cimpoeşu ◽  
Mihai Dumitru ◽  
Ciprian Paraschiv
Keyword(s):  
Shape Memory Alloys ◽  
Shape Memory ◽  
Spinal Cord Injuries ◽  
Good Alternative ◽  
Careful Consideration ◽  
Damping Capacity ◽  
Loss Of Function ◽  
User Friendliness ◽  
Wide Range ◽  
Constructive Models

Entire or partial loss of function in the shoulder, elbow or wrist represent an increasingly common ailment connected to a wide range of injuries or other conditions including sports, occupational, spinal cord injuries or strokes. A general treatment of these problems relies on physiotherapy procedures. An increasing number of metallic materials are continuously being developed to expect the requirements for different engineering applications including biomedical field. Few constructive models that can involve intelligent materials are analyzed to establish the advantages in usage of shape memory elements mechanical implementation. The shape memory effect, superelasticity and damping capacity are unique characteristics at metallic alloys which demand careful consideration in both design and manufacturing processes. The actual rehabilitation systems can be improved using smart elements in motorized equipments like robotic systems. Shape memory alloys, especially NiTi (nitinol), represent a very good alternative for actuation in equipments with moving dispositive based on very good actuation properties, low mass, small size, safety and user friendliness. In this article the actuation and the force characteristics were analyzed to investigate a relationship between the bending angle and the actuation real value.

An open-source R-package and web application for high-quality probabilistic predictions in hydrology

2021 ◽  
Author(s):  
Jason Hunter ◽  
Mark Thyer ◽  
Dmitri Kavetski ◽  
David McInerney
Keyword(s):  
Open Source ◽  
Web Application ◽  
R Package ◽  
Error Model ◽  
Objective Functions ◽  
High Quality ◽  
Wide Range ◽  
Probabilistic Error

&lt;p&gt;Probabilistic predictions provide crucial information regarding the uncertainty of hydrological predictions, which are a key input for risk-based decision-making. However, they are often excluded from hydrological modelling applications because suitable probabilistic error models can be both challenging to construct and interpret, and the quality of results are often reliant on the objective function used to calibrate the hydrological model.&lt;/p&gt;&lt;p&gt;We present an open-source R-package and an online web application that achieves the following two aims. Firstly, these resources are easy-to-use and accessible, so that users need not have specialised knowledge in probabilistic modelling to apply them. Secondly, the probabilistic error model that we describe provides high-quality probabilistic predictions for a wide range of commonly-used hydrological objective functions, which it is only able to do by including a new innovation that resolves a long-standing issue relating to model assumptions that previously prevented this broad application. &amp;#160;&lt;/p&gt;&lt;p&gt;We demonstrate our methods by comparing our new probabilistic error model with an existing reference error model in an empirical case study that uses 54 perennial Australian catchments, the hydrological model GR4J, 8 common objective functions and 4 performance metrics (reliability, precision, volumetric bias and errors in the flow duration curve). The existing reference error model introduces additional flow dependencies into the residual error structure when it is used with most of the study objective functions, which in turn leads to poor-quality probabilistic predictions. In contrast, the new probabilistic error model achieves high-quality probabilistic predictions for all objective functions used in this case study.&lt;/p&gt;&lt;p&gt;The new probabilistic error model and the open-source software and web application aims to facilitate the adoption of probabilistic predictions in the hydrological modelling community, and to improve the quality of predictions and decisions that are made using those predictions. In particular, our methods can be used to achieve high-quality probabilistic predictions from hydrological models that are calibrated with a wide range of common objective functions.&lt;/p&gt;

scholarly journals The Open Global Glacier Model (OGGM) v1.0

2018 ◽  
Author(s):  
Fabien Maussion ◽  
Anton Butenko ◽  
Julia Eis ◽  
Kévin Fourteau ◽  
Alexander H. Jarosch ◽  
...  
Keyword(s):  
Mass Balance ◽  
Open Source ◽  
Sea Level ◽  
Computational Cost ◽  
Balance Model ◽  
Climate Data ◽  
Estimation Model ◽  
Model Framework ◽  
Validation Data ◽  
Wide Range

Abstract. Despite of their importance for sea-level rise, seasonal water availability, and as source of geohazards, mountain glaciers are one of the few remaining sub-systems of the global climate system for which no globally applicable, open source, community-driven model exists. Here we present the Open Global Glacier Model (OGGM, http://www.oggm.org), developed to provide a modular and open source numerical model framework for simulating past and future change of any glacier in the world. The modelling chain comprises data downloading tools (glacier outlines, topography, climate, validation data), a preprocessing module, a mass-balance model, a distributed ice thickness estimation model, and an ice flow model. The monthly mass-balance is obtained from gridded climate data and a temperature index melt model. To our knowledge, OGGM is the first global model explicitly simulating glacier dynamics: the model relies on the shallow ice approximation to compute the depth-integrated flux of ice along multiple connected flowlines. In this paper, we describe and illustrate each processing step by applying the model to a selection of glaciers before running global simulations under idealized climate forcings. Even without an in-depth calibration, the model shows a very realistic behaviour. We are able to reproduce earlier estimates of global glacier volume by varying the ice dynamical parameters within a range of plausible values. At the same time, the increased complexity of OGGM compared to other prevalent global glacier models comes at a reasonable computational cost: several dozens of glaciers can be simulated on a personal computer, while global simulations realized in a supercomputing environment take up to a few hours per century. Thanks to the modular framework, modules of various complexity can be added to the codebase, allowing to run new kinds of model intercomparisons in a controlled environment. Future developments will add new physical processes to the model as well as tools to calibrate the model in a more comprehensive way. OGGM spans a wide range of applications, from ice-climate interaction studies at millenial time scales to estimates of the contribution of glaciers to past and future sea-level change. It has the potential to become a self-sustained, community driven model for global and regional glacier evolution.

scholarly journals micrIO: An Open-Source Autosampler and Fraction Collector for Automated Microfluidic Input-Output

2019 ◽  
Author(s):  
Scott A. Longwell ◽  
Polly M. Fordyce
Keyword(s):  
Open Source ◽  
Positive Pressure ◽  
Cross Contamination ◽  
Wide Spread ◽  
Fraction Collector ◽  
Input And Output ◽  
Wide Range ◽  
Molecular Separations ◽  
Multiwell Plates

Microfluidic devices are an empowering technology for many labs, enabling a wide range of applications spanning high-throughput encapsulation, molecular separations, and long-term cell culture. In many cases, however, their utility is limited by a ‘world-to-chip’ barrier that makes it difficult to serially interface samples with these devices. As a result, many researchers are forced to rely on low-throughput, manual approaches for managing device input and output (IO) of samples, reagents, and effluent. Here, we present a hardware-software platform for automated microfluidic IO (micrIO). The platform, which is uniquely compatible with positive-pressure microfluidics, comprises an ‘AutoSipper’ for input and a Fraction Collector for output. To facilitate wide-spread adoption, both are open-source builds constructed from components that are readily purchased online or fabricated from included design files. The software control library, written in Python, allows the platform to be integrated with existing experimental setups and to coordinate IO with other functions such as valve actuation and assay imaging. We demonstrate these capabilities by coupling both the AutoSipper and Fraction Collector to a microfluidic device that produces beads with distinct spectral codes, and an analysis of the collected bead fractions establishes the ability of the platform to draw from and output to specific wells of multiwell plates with no detectable cross-contamination between samples.

scholarly journals Usage and Scaling of an Open-Source Spiking Multi-Area Model of Monkey Cortex

2021 ◽  
pp. 47-59
Author(s):  
Sacha J. van Albada ◽  
Jari Pronold ◽  
Alexander van Meegen ◽  
Markus Diesmann
Keyword(s):  
Open Source ◽  
Large Scale ◽  
Network Models ◽  
Macaque Monkey ◽  
Source Model ◽  
Model Specification ◽  
Data Sets ◽  
Neural Network Models ◽  
Wide Range ◽  
Ict Infrastructure

AbstractWe are entering an age of ‘big’ computational neuroscience, in which neural network models are increasing in size and in numbers of underlying data sets. Consolidating the zoo of models into large-scale models simultaneously consistent with a wide range of data is only possible through the effort of large teams, which can be spread across multiple research institutions. To ensure that computational neuroscientists can build on each other’s work, it is important to make models publicly available as well-documented code. This chapter describes such an open-source model, which relates the connectivity structure of all vision-related cortical areas of the macaque monkey with their resting-state dynamics. We give a brief overview of how to use the executable model specification, which employs NEST as simulation engine, and show its runtime scaling. The solutions found serve as an example for organizing the workflow of future models from the raw experimental data to the visualization of the results, expose the challenges, and give guidance for the construction of an ICT infrastructure for neuroscience.

Sign in / Sign up

Export Citation Format

Share Document