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Positron annihilation spectroscopy is often used to analyze the local electronic structure of materials of technological interest. Reliable theoretical tools are crucial to interpret the measured spectra. Here, we propose a parameter-free…
Positron Annihilation Lifetime Spectroscopy (PALS) has shown to be a powerful tool to study the nanostructures of porous materials. Positron Emissions Tomography (PET) are devices allowing imaging of metabolic processes e.g. in human…
Positron Annihilation Lifetime Spectroscopy (PALS) allows examining structure of materials in nano and sub-nanometer scale. This technique is based on the lifetime and intensity of ortho-positronium atoms in free volumes of given…
Dissolution testing is part of the target product quality that is essential in approving new products in the pharmaceutical industry. The prediction of the dissolution profile based on spectroscopic data is an alternative to the current…
The positron-electron annihilation gamma-ray spectra of linear alkanes CnH2n+2 (n=1-12) have been studied systematically. A profile quality (PQ) parameter, is introduced to assess the agreement between the obtained theoretical profiles and…
Positron Annihilation Lifetime Spectroscopy (PALS) is a non-destructive radiological technique widely used in material science studies. PALS typically relies on an analog coincidence measurement setup and allows the estimate of the positron…
The presence of tumor hypoxia is known to correlate with poor patient prognosis. Measurement of tissue oxygen concentration can be challenging, but recent advancements using positron annihilation lifetime spectroscopy (PALS) in…
Single-shot positron annihilation lifetime spectroscopy (SSPALS) is an extremely useful tool for experiments involving the positronium atom (Ps). I examine some of the methods that are typically employed to analyze lifetime spectra, and use…
We present a method for solving the positron diffusion equation in multi-layer systems. Our approach incorporates material-specific implantation profiles, diffusion parameters, and positron affinities. It utilizes a Markov chain approach to…
The interpretation of data from indirect detection experiments searching for dark matter annihilations requires computationally expensive simulations of cosmic-ray propagation. In this work we present a new method based on Recurrent Neural…
Imaging the features of a sample using Positron Annihilation Spectroscopy (PAS) is currently achieved by rastering, i.e. by scanning the sample surface with a sharply focused positron beam. However, a beam of arbitrary shape (sculpted beam)…
Several first-principles calculations of positron-annihilation characteristics in solids have added gradient corrections to the local-density approximation within the theory by Arponen and Pajanne [Ann. Phys. (N.Y.) 121, 343 (1979)] since…
This paper introduces a novel feature extraction technique for the analysis of spectral line Stokes profiles. The procedure is based on the use of an auto-associative artificial neural network containing non-linear hidden layers. The neural…
In recent years, artificial neural networks and their applications for large data sets have became a crucial part of scientific research. In this work, we implement the Multilayer Perceptron (MLP), which is a class of feedforward artificial…
This paper proposes a new methodology to predict and update the residual useful lifetime of a system using a sequence of degradation images. The methodology integrates tensor linear algebra with traditional location-scale regression widely…
Positron Annihilation Lifetime Spectroscopy (PALS) is a powerful technique for detecting microstructural defects in various material classes. In a commonly used 180{\deg} detector configuration equipped with plastic scintillators,…
Various concentrations (8{\div}300 mmol/L) of NaCl, KCl and NaCl + KCl aqueous solutions were investigated using the positron annihilation lifetime spectroscopy (PALS). A strong dependence of the o-Ps intensity as a function of solution…
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find the network needs to be trained on only a small sampling of the data in order to approximate the simulation to high…
Artificial Neural Networks (ANN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions.…
In this paper, a new method was developed for initialising artificial neural networks predicting dynamics of time series. Initial weighting coefficients were determined for neurons analogously to the case of a linear prediction filter.…