Assessment of seed germination is an essential task for seed researchers to measure the quality and performance of seeds. Usually, seed assessments are done mainly manual, which is a cumbersome, time consuming and error-prone process. Classical image analyses methods are not well suited for large-scale germination experiments, because they often rely on manual adjustments of color-based thresholds. We here propose a machine learning approach using modern artificial neural networks for accurate seed germination detection for high-throughput seed germination experiments.
This dataset contains captures of Germination Experiments of 824 Zea mays seeds, 811 Secale cereale seeds and 814 Pennisetum glaucum seeds. Approximately 10 seeds were placed in one petri dish and captured by a low-cost Raspberry Pi Camera Module (v2.1) in intervals of 30 minutes for ~ 2 days. All Images were annotated by Bounding Boxes containing their germination state (germinated/non-germinated). The code for running the models that are built with this data can be found on GitHub (https://github.com/grimmlab/GerminationPrediction).
• Project name: ANN4EEG
• Project home page: https://cmi.to/ann4eeg
Rats were randomized and administered various anticonvulsants at the maximum single therapeutic dose (conversion factor from humans to rats used in this study was 5.9 as recommended) or subconvulsive dose for substances with proconvulsant activity. After reaching the peak concentration (depending on the pharmacokinetic properties of the drug) under control of the operator, brain activity was recorded for 10 min.
Diazepam (6 mg/kg, po)
Phenazepam (1 mg/kg, po)
Chloral hydrate (100 mg/kg, ip)
Pregabalin (60 mg/kg, po)
Gabapentin (360 mg/kg, po)
Carbamazepine (200 mg/kg, po)
Eslicarbazepine (160 mg/kg, po)
Corazol (pentylenetetrazole; 20 mg/kg, ip)
Picrotoxin (2 mg/kg, ip)
Pilocarpine (60 mg/kg, ip)
Arecoline (40 mg/kg, ip)
A laboratory electroencephalograph (NVX-36; MKS, Moscow, Russian Federation) was used to record bioelectrical activity. Intracranial EEG signals were recorded at a sampling rate of 500 Hz, in a bipolar montage. Electrode impedance < 5 kΩ.
i-EEG montage: Olfactory bulbs (OB) (ground); P3-A1 (channel 1); O1-A1 (channel 2); P4-A2 (channel 3); O2-A2 (channel 4).
Contributors:Stefan Mijin, Antony A., Militello F., Kingham R.J.
Here we present a new code for modelling electron kinetics in the tokamak Scrape-Off Layer (SOL). SOL-KiT (Scrape-Off Layer Kinetic Transport) is a fully implicit 1D code with kinetic (or fluid) electrons, fluid (or stationary) ions, and diffusive neutrals. The code is designed for fundamental exploration of non-local physics in the SOL and utilizes an arbitrary degree Legendre polynomial decomposition of the electron distribution function, treating both electron–ion and electron–atom collisions. We present a novel method for ensuring particle and energy conservation in inelastic and superelastic collisions, as well as the first full treatment of the logical boundary condition in the Legendre polynomial formalism. To our knowledge, SOL-KiT is the first fully implicit arbitrary degree harmonic kinetic code, offering a conservative and self-consistent approach to fluid–kinetic comparison with its integrated fluid electron mode. In this paper we give the model equations and their discretizations, as well as showing the results of a number of verification/benchmarking simulations.
A general program to fit global adiabatic potential energy surfaces of up to tetratomic molecules to ab initio points and available spectroscopic data for simple diatomics is reported. It is based on the Combined-Hyperbolic-Inverse-Power-Representation (CHIPR) method. The final form describes all dissociating fragments and long-range/valence interactions, while obeying the system permutational symmetry. The code yields as output a Fortran 90 subroutine that readily evaluates the potential and gradient at any arbitrary geometry.
Contributors:Mehdikhani Mahoor, Breite Christian, Swolfs Yentl, Wevers Martine, Lomov Stepan et al
We have performed synchrotron computed tomography on two different fiber-reinforced composites while they were being continuously in-situ loaded in tension. One material is a glass/epoxy laminate and the other is a carbon/epoxy laminate. The voxel size is 1.1 µm, which allows clear recognition of the glass fibers, but not distinct individual carbon fibers. For each material, four loading steps are selected with approximately 0, 40, 73, and 95% of the failure load, and the 3D images of the four volumes from each material are overlaid. A volume of interest in the middle 0° ply is chosen and located in the 3D image of each loading step. The cropped volumes of interest for each material are presented in this dataset. As examples of two frequently-used type of unidirectional fiber-reinforced composites, the presented data can be used for different microstructural analyses, including investigation of the 3D variability in fiber distribution and orientation, and their evolution during tensile loading. Moreover, real-time formation of fiber breaks with tensile loading can be investigated in the data.
Contributors:Al-Khatib Ra'ed M., Barhoush Malek, Aya migdadi, Al-Madi Mohammad
The HFRD (v2020) dataset includes 4835 images of masked human faces. Originally, the faces were taken from three different publicly available datasets: MUCT, FASSEG, and AT&T datasets. The collected subsets of the HFRD dataset contain standard images that are previously used in the field of facial recognition, and then we added masks to cover the nose, mouth, and chin area, as for what remains visible from the face are the eyes, forehead, and hair. Thus, we have introduced new standard images for use in the field of partial facial recognition (PFR) domain. The new structure of the current HFRD store inherits the structure of each original dataset, which means there are three main datasets: MaskedMUCT contains five folders, MaskedFASSEG contains four folders, and MaskedAT&T has organized in 40 folders. This store of HFRD masked face datasets is not only inheriting the structure of the original datasets but also inheriting the varsity of them. Finally, all folders consist of facial images surrounded and covered by various shapes of masks. Consequently, the proposed new enhanced HFRD datasets have been developed based on the impact of the pandemic COVID-19 stage. Our enhanced improved Human Face Recognition Datasets (HFRD) datasets could be used to test studies of FR algorithms for human identification. The obtained outcomes from this enhanced HFRD data can also be useful in providing more knowledge for the Artificial Intelligence (AI) tools, and decision support system for predicting the spread of COVID-19.