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This MATLAB computer program solves the two-dimensional steady state boundary layer equations with general fluid properties for compressible flows in the ideal gas or non-ideal gas (departing from ideal gas) regime, adiabatic or including heat transfer for laminar and/or turbulent (algebraic CS-model) flows.  This computer program was part of the work of the master thesis Simulation of Two-Dimensional Steady State Boundary Layers Applied to Nonideal Gas Flows, (August 10, 2020) by D.D. Dijkshoorn Delft University of Technology, faculty of Mechanical, Maritime and Materials Engineering (3me), The Netherlands. Link: https://repository.tudelft.nl/islandora/object/uuid:433d4c00-e063-4614-9c78-2849870d8d7f

BIIGLE is a web-based software for image and video annotation that enables collaborative research on large datasets. It offers tools for manual and computer-assisted annotation, quality control and the collaboration on custom taxonomies to describe objects. BIIGLE is freely available and can be installed in cloud environments, a local network or on mobile platforms during research expeditions. The public instance on biigle.de is free for non-commercial use.

Global Sensitivity Analysis of an Energy System Optimisation Model The snakemake workflow to conduct a global sensitivity analysis What's Changed Skills Workshop by @willu47 in https://github.com/KTH-dESA/esom_gsa/pull/1 Update readme by @willu47 in https://github.com/KTH-dESA/esom_gsa/pull/2 Exercise updates by @trevorb1 in https://github.com/KTH-dESA/esom_gsa/pull/3 Paper submission by @willu47 in https://github.com/KTH-dESA/esom_gsa/pull/4 New Contributors @willu47 made their first contribution in https://github.com/KTH-dESA/esom_gsa/pull/1 @trevorb1 made their first contribution in https://github.com/KTH-dESA/esom_gsa/pull/3 Full Changelog: https://github.com/KTH-dESA/esom_gsa/commits/v1.0

We developed MINI-EX (Motif-Informed Network Inference based on single-cell EXpression data), an integrative approach to infer cell-type-specific networks in plants. MINI-EX uses single-cell transcriptomic data to define expression-based networks and integrates TF motif information to filter the inferred regulons, resulting in networks with increased accuracy.

The wireless communication used by vehicles in collaborative vehicle platoons (CVPs) is vulnerable to cyber-attacks, which threaten their safe operation. To address this issue, a safety preserving controller is presented. The proposed controller is based on topology-switching coalitional MPC, which utilises a RUIO to detect and isolate the cyber-attacks. Attacked communication links are then disabled to accommodate the attack. Furthermore, the MPC controller is designed to be resilient against undetected attacks and the uncertainty derived from disabling communication links. The uploaded code, illustrates the performance of the proposed scheme on a simulated CVP of four vehicles. The code used to generate results presented in chapter 3 of the PhD dissertation of Twan Keijzer: Advances in Safety and Security of Cyber-Physical Systems - Sliding Mode Observers, Coalitional Control and Homomorphic Encryption

Sliding mode observers (SMOs) have been proposed for exact anomaly estimation for a class of ideal systems without unmatched uncertainties and measurement noise. For such ideal systems anomaly detection is trivial, however for systems with unmatched uncertainties or measurement noise a dedicated detector is needed. In this code two of such robust anomaly detectors are implemented, which extend the anomaly detection capability of a large class of SMOs to include systems with unmatched uncertainties and measurement noise. The first detector is based on the so-called equivalent output injection (EOI), which is closely related to the anomaly estimate. The second detector is directly based on the SMO state estimation error. Doing so, the second detector bypasses the low-pass filter generating the EOI allowing for faster detection of anomalies and making it possible to detect smaller magnitude anomalies. The detectors are applied in this code to detect a man-in-the-middle (MITM) attack on a collaborative vehicle platoon (CVP). The code used to generate results presented in chapter 2 of the PhD dissertation of Twan Keijzer: Advances in Safety and Security of Cyber-Physical Systems - Sliding Mode Observers, Coalitional Control and Homomorphic Encryption

EBI Tools is a project that aims to provide programmatic access to the various databases and retrieval and analysis services that the European Bioinformatics Institute (EBI) provides through Simple Object Access Protocol (SOAP) and other related web service technologies. Example tools include those to compute sequence similarity searches, pairwise/multiple sequence alignment and protein functional analysis.

This code is used for computing the results of the paper "Characterizing residential segregation in cities using intensity, separation, and scale indicators".  This code identifies and characterizes residential segregation patterns from demographic data. It is applied in a Dutch case study. It is written in python, using notebooks. This source code should be stored in a folder named code. The folder code and the folder data (see https://doi.org/10.4121/19597258) should be located in the same directory. FORMAT *.mkd; *.ipynb; *.txt; *.csv RECOMMENDED HARDWARE 1. Processor: Intel® Core™ i5-10210U CPU 2. RAM: 32GiB of RAM (DDR4) 3. GPU: Intel® UHD Graphics GPU RECOMMENDED OPERATING SYSTEM Ubuntu 21.10, 64-bit REQUIRED VERSION OF PYTHON 3.9.7 REQUIRED LIBRARIES USED see requirements.txt EXTRA FILE parameter.csv specifies some parameters used in the analysis. SEQUENCE OF SCRIPTS The scripts should be run in the following order: 1. demographics_preprocess.ipynb 2. extract_city_boundary.ipynb 3. extract_street_network.ipynb 4. extract_zones_in_gemeente.ipynb 5. shortest_path.ipynb 6. adjacency_matrix.ipynb 7. correlation_matrix.ipynb 8. exposure.ipynb 9. cluster_analysis.ipynb 10. descriptive_stats.ipynb 11. combine_zones_into_municipalities.ipynb