Influence of annealing and bulk hydrogenation on lifetime-limiting defects in nitrogen-doped floating zone silicon

A recombination active defect is found in as-grown high-purity floating zone n-type silicon wafers containing grown-in nitrogen. In order to identify the properties of the defect, injection dependent minority carrier lifetime measurements, secondary ion mass spectroscopy measurements, and photoluminescence lifetime imaging are performed. The lateral recombination center distribution varies greatly in a radially symmetric way, while the nitrogen concentration remains constant. The defect is shown to be deactivated through high temperature annealing and hydrogenation. We suggest that a nitrogen-intrinsic point defect complex may be responsible for the observed recombination

A NLP Approach to Automating the Generation of Surveys for Market Research

Market Research is vital but includes activities that are often laborious and time consuming. Survey questionnaires are one possible output of the process and market researchers spend a lot of time manually developing questions for focus groups. The proposed research aims to develop a software prototype that utilizes Natural Language Processing (NLP) to automate the process of generating survey questions for market research. The software uses a pre-trained Open AI language model to generate multiple choice survey questions based on a given product prompt, send it to a targeted email list, and also provides a real-time analysis of the responses stored in a SQL database. The idea is for market researchers to provide minimal information and have the system propose to them options for potential questions for the focus group. The project involves creating a web-based software prototype that features user-friendly interfaces for seamless interaction between companies and users. The expected outcome of the project is to provide a more efficient and effective method for companies to generate meaningful survey questions for market research

Classification of signal events for CPT symmetry studies with J-PET using machine learning techniques

The deviations from combined Charge, Parity, and Time (CPT) symmetry could indicate the presence of new physics beyond the current theoretical framework. The positronium (Ps), the lightest bound state of an electron–positron pair, offers a unique probe for such investigations because it is an eigenstate of charge conjugation (C) and parity (P). This work explores the potential of the Jagiellonian Positron Emission Tomography (J-PET) detector for sensitive tests of CPT symmetry in the three-photon decay of ortho-Ps (o-Ps) atom. The CPT symmetry invariance in o-Ps decays has been previously tested using the J-PET detector, measuring the CPT-violating angular correlation between the o-Ps spin and its annihilation photon momenta, achieving a precision of 0.00067 $\pm$ 0.00095. However, a range of five orders of magnitude is still unexplored to test its exactness. A Monte Carlo simulation study is presented to distinguish between the o-Ps signal and background events using Multivariate Data Analysis (TMVA). We discuss the impact of improving the sample purity in enhancing the sensitivity of the CPT symmetry test

An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction.

Software Maintainability is an indispensable factor to acclaim for the quality of particular software. It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment. The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability. However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet. Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets. Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25), Pred(0.30), & Pred(0.75) as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs. Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82. Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE. Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms. This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs

Modular J-PET with improved o-Ps detection efficiency for CPT tests

J-PET is a photon detector built of plastic scintillators, which already has been commissioned for CPT studies in the decays of positronium. In the first experiment, J-PET has achieved a sensitivity to CPT violation at a level of 10−4, and now it aims to reach a level of 10−5. This will be done by enhancing the three-photon registration efficiency for ortho-positronium decays using a new layer of densely packed plastic scintillators termed Modular J-PET. We present the simulation studies performed for different experimental detection setups to be used for the next CPT test with the Modular J-PET detector

An Extensive Analysis of Machine Learning Based Boosting Algorithms for Software Maintainability Prediction

Software Maintainability is an indispensable factor to acclaim for the quality of particular software. It describes the ease to perform several maintenance activities to make a software adaptable to the modified environment. The availability & growing popularity of a wide range of Machine Learning (ML) algorithms for data analysis further provides the motivation for predicting this maintainability. However, an extensive analysis & comparison of various ML based Boosting Algorithms (BAs) for Software Maintainability Prediction (SMP) has not been made yet. Therefore, the current study analyzes and compares five different BAs, i.e., AdaBoost, GBM, XGB, LightGBM, and CatBoost, for SMP using open-source datasets. Performance of the propounded prediction models has been evaluated using Root Mean Square Error (RMSE), Mean Magnitude of Relative Error (MMRE), Pred(0.25), Pred(0.30), & Pred(0.75) as prediction accuracy measures followed by a non-parametric statistical test and a post hoc analysis to account for the differences in the performances of various BAs. Based on the residual errors obtained, it was observed that GBM is the best performer, followed by LightGBM for RMSE, whereas, in the case of MMRE, XGB performed the best for six out of the seven datasets, i.e., for 85.71% of the total datasets by providing minimum values for MMRE, ranging from 0.90 to 3.82. Further, on applying the statistical test and on performing the post hoc analysis, it was found that significant differences exist in the performance of different BAs and, XGB and CatBoost outperformed all other BAs for MMRE. Lastly, a comparison of BAs with four other ML algorithms has also been made to bring out BAs superiority over other algorithms. This study would open new doors for the software developers for carrying out comparatively more precise predictions well in time and hence reduce the overall maintenance costs

CPT symmetry test in positronium annihilations with the J-PET detector

Discrete symmetry under combined transformation of charge, parity and time reversal (CPT) can be tested in the decays of positronium atom, the lightest bound system built of charged leptons. Jagiellonian Positron Emission Tomograph (J-PET) device constructed from plastic scintillators, detects the photons originating from electron positron annihilation. This feature enables J-PET to study CPT symmetry in the three photon annihilations of the triplet state of positronium. Signs of violation of the CPT symmetry can be sought as a non-vanishing expectation value of an angular correlation operator that is odd under CPT transformation. A technique to estimate the spin of ortho-positronium and momenta of annihilation photons for single recorded ortho-positronium annihilation events allows J-PET to measure the expectation value of a CPT symmetry odd angular correlation operator. J-PET measures a broad range of kinematical configurations of ortho-positronium annihilation to three photons and is the first experiment to determine the full range of the CPT-odd angular correlation

Towards improving the sensitivity of the CPT symmetry in positronium decays with the modular J-PET detector

The Jagiellonian Positron Emission Tomograph (J-PET) is the first plastic scintillator-based tomographic device used to test discrete symmetries in the charged leptonic sector. One of such tests is for the CPT symmetry, under the combined transformation of charge, parity, and time reversal in the decays of positronium atoms. J-PET performed its first measurement for the CPT symmetry test by searching for non-vanishing CPT-violating angular correlations between the spin and orientation of the decay plane of ortho-positronium (o-Ps) atoms, which is the triplet state of positronium. The sensitivity of testing CPT symmetry with the J-PET detector reaches the precision level of 10$^{-4}$. Here, we will discuss the prospects of improving the sensitivity of this test beyond the level of 10$^{-4}$ by enhancing the photon registration efficiency using a new layer of densely packed plastic scintillators and a spherical annihilation chamber as a positronium production medium