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Muhammad Majid

Publications and source records attributed to Muhammad Majid.

At least 19 recordsLinked to original sources

Unique first-forbidden $\beta$-decay transitions in odd-odd and even-even heavy nuclei

The allowed Gamow-Teller (GT) transitions are the most common weak nuclear processes of spin-isospin $(\sigma\tau)$ type. These transitions play a key role in numerous processes in the domain of nuclear physics. Equally important is their contribution in astrophysics, particularly in nuclear synthesis and supernova-explosions. In situations where allowed GT transitions are not favored, first-forbidden transitions become significant, specifically in medium heavy and heavy nuclei. For neutron-rich nuclei, first-forbidden transitions are favored mainly due to the phase-space amplification for these transitions. In this work we calculate the allowed GT as well as unique first-forbidden (U1F) $|\Delta$J$|$ = 2 transitions strength in odd-odd and even-even nuclei in mass range $70\leq A \leq214$. Two different pn-QRPA models were used with a schematic separable interaction to calculate GT and U1F transitions. The inclusion of U1F strength improved the overall comparison of calculated terrestrial $\beta$-decay half-lives in both models. The \textit{ft} values and reduced transition probabilities for the $2^-\longleftrightarrow 0^+$ transitions were also calculated. We compared our calculations with the previously reported correlated RPA calculation and experimental results. Our calculations are in better agreement with measured data. For stellar applications we further calculated the allowed GT and U1F weak rates. These include $\beta^{\pm}$-decay rates and electron/positron capture rates of heavy nuclei in stellar matter. Our study shows that positron and electron capture rates command the total weak rates of these heavy nuclei at high stellar temperatures.

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Allowed and unique first-forbidden stellar electron emission rates of neutron-rich copper isotopes

The allowed charge-changing transitions are the most common weak interaction processes of spin-isospin form that play a crucial role in several nuclear/astrophysical processes. The first-forbidden (FF) transition becomes important, in the circumstances where allowed Gamow-Teller (GT) transitions are unfavored, specifically for neutron-rich nuclei due to phase space considerations. In this paper deformed proton-neutron quasi-particle random phase approximation (pn-QRPA) model is applied, for the first time, for the estimation of allowed GT and unique first-forbidden (U1F) transitions ($|\Delta$J$|$ = 2) of neutron rich copper isotopes in mass range 72 $\leq$ A $\leq$ 82 under stellar conditions. We compared our computed terrestrial $\beta$-decay half-life values with previous calculations and experimental results. It was concluded that the pn-QRPA calculation is in good accordance with measured data. Our study suggests that the addition of rank (0 and 1) operators in FF transitions can further improve the comparison which remain unattended at this stage. The deformed pn-QRPA model was employed for the estimation of GT and U1F stellar electron emission ($\beta$$^{-}$-decay) rates over wide range of stellar temperature (0.01 GK -- 30 GK) and density (10 -- 10$^{11}$ g/cm$^{3}$) domains for astrophysical applications. Our study shows that, in high density and low temperature regions, the contribution of U1F rates to total electron emission rates of neutron-rich copper nuclei is negligible.

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Gamow-Teller strength and lepton captures rates on 66-71Ni in stellar matter

Charge-changing transitions play a significant role in stellar weak-decay processes. The fate of the massive stars is decided by these weak-decay rates including lepton (positron and electron) captures rates, which play a consequential role in the dynamics of core collapse. As per previous simulation results, weak interaction rates on nickel isotopes have significant influence on the stellar core vis-$\grave{a}$-vis controlling the lepton content of stellar matter throughout the silicon shell burning phases of high mass stars up to the presupernova stages. In this paper we perform a microscopic calculation of Gamow-Teller charge-changing transitions, in the $\beta$-decay and electron capture directions, for neutron-rich nickel isotopes ($^{66-71}$Ni). We further compute the associated weak-decay rates for these selected nickel isotopes in stellar environment. The computations are accomplished by employing the deformed proton-neutron quasiparticle random phase approximation (pn-QRPA) model. A recent study showed that the deformed pn-QRPA theory is well suited for the estimation of Gamow-Teller transitions. The astral weak-decay rates are determined over densities in the range of 10 -- 10$^{11}$g/cm$^{3}$ and temperatures in the range of 0.01$\times$10$^{9}$ -- 30$\times$10$^{9}$K. The calculated lepton capture rates are compared with the previous calculation of Pruet and Fuller. The overall comparison demonstrates that, at low stellar densities and high temperatures, our electron captures rates are bigger by as much as two orders of magnitude. Our results show that, at higher temperatures, the lepton capture rates are the dominant mode for the stellar weak rates and the corresponding lepton emission rates may be neglected.

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Study of electron capture rates on chromium isotopes for core-collapse simulations

Electron capture rates on \emph{fp}-shell nuclei play a pivotal role in the dynamics of stellar evolution and core collapse. These rates play a crucial role in the gravitational collapse of the core of a massive star activating the supernova explosion. As per simulation results, capture rates on chromium isotopes have a major impact on controlling the lepton-to-baryon fraction of the stellar core during the late phases of evolution of massive stars. In this paper we calculate the electron capture rates on isotopes of chromium with mass range $42\leq A \leq 65$, including neutron-deficient and neutron-rich isotopes. For the calculation of weak rates in stellar matter, we used the pn-QRPA model with separable Gamow-Teller forces and took deformation of nucleus into consideration. A recent study proved this form of pn-QRPA to be the best for calculation of GT strength distributions amongst the pn-QRPA models. The stellar weak rates are calculated over a broad range of temperature $(0.01 \times 10^{9}-30 \times 10^{9} (K))$ and density $(10-10^{11}(g/cm^{3}))$ domain. We compare our electron capture rates with the pioneering calculation of Fuller, Fowler, and Newman (FFN) and with the large-scale shell model (LSSM) calculation. Our electron capture rates are enhanced compared to the FFN and shell model rates.

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Gamma Ray Heating and Neutrino Cooling Rates due to Weak Interaction Processes on sd-shell Nuclei in Stellar Cores

Gamma ray heating and neutrino cooling rates, due to weak interaction processes, on $sd$-shell nuclei in stellar core are calculated using the proton neutron quasiparticle random phase approximation theory. The recent extensive experimental mass compilation of \citep{Wang12}, other improved model input parameters including nuclear quadrupole deformation \citep{Ram01}, \citep{Mol16} and physical constants are taken into account in the current calculation. The purpose of this work is two fold, one is to improve the earlier calculation of weak rates performed by \citep{Nabi99} using the same theory. We further compare our results with previous calculations. The selected $sd$-shell nuclei, considered in this work, are of special interest for the evolution of O-Ne-Mg core in 8-10 M$_\odot$ stars due to competitive gamma ray heating rates and cooling by URCA processes. The outcome of these competitions is to determine, whether the stars end up as a white dwarf \citep{Nabi08}, an electron-capture supernova \citep{Jones13} or Fe core-collapse supernova \citep{Suz16}. The selected $sd$-shell nuclei for calculation of associated weak-interaction rates include $^{20,23}$O, $^{20,23}$F, $^{20,23,24}$Ne, $^{20,23-25}$Na, and $^{23-25}$Mg. The cooling and heating rates are calculated for density range ($10 \leq \rho($\;g.cm$^{-3}) \leq $ 10$^{11}$) and temperature range ($0.01\times10^{9}$ $\leq$ $\;T(K)$ $\leq$ $30\times10^{9}$). The calculated gamma heating rates are orders of magnitude bigger than the shell model rates (except for $^{25}$Mg at low densities). At high temperatures the gamma heating rates are in reasonable agreement. The calculated cooling rates are up to an order of magnitude bigger for odd-A nuclei.

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Study of Gamow-Teller strength and associated weak-rates on odd-A nuclei in stellar matter

In a recent study by Cole et al., it was concluded that QRPA calculations show larger deviations and overestimate the total experimental Gamow-Teller (GT) strength. It was also concluded that QRPA calculated electron capture rates exhibit larger deviation than those derived from the measured GT strength distributions. The main purpose of this study is to probe the findings of the Cole et al. paper. This study gives useful information on the performance of QRPA-based nuclear models. As per simulation results, the capturing of electrons that occur on medium heavy isotopes have a significant role in decreasing the ratio of electron-to-baryon content of the stellar interior during the late stages of core evolution. We report the calculation of allowed charge-changing transitions strength for odd-A fp-shell nuclei (45Sc and 55Mn) by employing the deformed pn-QRPA approach. The computed GT transition strength is compared with previous theoretical calculations and measured data. For stellar applications the corresponding electron capture rates are computed and compared with rates using previously calculated and measured Gamow-Teller values. Our finding show that our calculated results are in decent accordance with measured data. At higher stellar temperature our calculated electron capture rates are larger than those calculated by Independent Particle Model (IPM) and shell model. It was further concluded that at low temperature and high density regions the positron emission weak-rates from 45Sc and 55Mn may be neglected in simulation codes.

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Neutrino cooling rates due to nickel isotopes for presupernova evolution of massive stars

Simulation studies indicate that weak interaction rates on nickel isotopes play a crucial role in determining the electron-to-baryon ratio within the stellar interior during the late stages of core evolution. (Anti)neutrinos produced through weak decay processes escape from stellar regions with densities below 10^11 g/cm^3, carrying away energy and thereby reducing the core entropy. In this work, we present a microscopic calculation of neutrino and antineutrino cooling rates resulting from weak interactions on nickel isotopes in the mass range 56 <= A <= 71. The calculations are performed using the deformed proton-neutron Quasiparticle Random Phase Approximation (pn-QRPA) model. Recent investigations into the Gamow-Teller (GT) strength distributions of nickel isotopes demonstrate that the deformed pn-QRPA model successfully reproduces experimental charge-changing transition data.

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Ground-state nuclear properties of neutron-rich copper isotopes and lepton capture rates in stellar matter

This study consists of two separate investigations centered on neutron-rich isotopes of copper, utilizing two distinct nuclear models. In the first part, the nuclear ground-state properties of copper isotopes in the mass range 72 <= A <= 82 were analyzed using the relativistic mean field (RMF) model. Quadrupole moment-constrained RMF calculations were carried out with DD-ME2 and DD-PC1 density-dependent interactions to compute the ground-state binding energies, charge radii, proton and neutron radii, quadrupole moments, and deformation parameters for the 71-82Cu isotopes. The results show good agreement with the limited experimental data available and previous theoretical predictions. In addition, potential energy curves were evaluated to investigate the ground-state geometrical configurations of these isotopes. The second part of the study is devoted to calculating lepton capture rates under stellar conditions. While earlier works have provided allowed Gamow-Teller (GT) and unique first-forbidden (U1F) beta-decay rates for selected neutron-rich Cu isotopes in stellar environments, the corresponding lepton capture rates had not yet been computed. This paper presents, for the first time, those lepton capture rates.

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Investigation of weak-rates for odd-A nuclei for presupernova simulations

Calculating weak decay rates under stellar conditions for studying presupernova evolution of massive stars is a challenging task. Here we show the importance of odd A nuclei for presupernova simulations. In order to calculate the required nuclear matrix elements we apply the pn QRPA model in a deformed basis. Nuclear deformation, thought to play an integral role in calculation of associated weak decay rates, is taken into account in our model. We calculate Gamow Teller (GT) strength distributions, emission and positron capture rates for selected odd A nuclei. Our model does not employ the Brink Axel hypothesis as used in previous calculations of weak decay rates and we perform a state by state microscopic calculation of GT strength distributions from all parent excited states. Our calculated decay rates are in good agreement with large scale shell model calculations.

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Human Emotions Analysis and Recognition Using EEG Signals in Response to 360$^\circ$ Videos

Emotion recognition (ER) technology is an integral part for developing innovative applications such as drowsiness detection and health monitoring that plays a pivotal role in contemporary society. This study delves into ER using electroencephalography (EEG), within immersive virtual reality (VR) environments. There are four main stages in our proposed methodology including data acquisition, pre-processing, feature extraction, and emotion classification. Acknowledging the limitations of existing 2D datasets, we introduce a groundbreaking 3D VR dataset to elevate the precision of emotion elicitation. Leveraging the Interaxon Muse headband for EEG recording and Oculus Quest 2 for VR stimuli, we meticulously recorded data from 40 participants, prioritizing subjects without reported mental illnesses. Pre-processing entails rigorous cleaning, uniform truncation, and the application of a Savitzky-Golay filter to the EEG data. Feature extraction encompasses a comprehensive analysis of metrics such as power spectral density, correlation, rational and divisional asymmetry, and power spectrum. To ensure the robustness of our model, we employed a 10-fold cross-validation, revealing an average validation accuracy of 85.54\%, with a noteworthy maximum accuracy of 90.20\% in the best fold. Subsequently, the trained model demonstrated a commendable test accuracy of 82.03\%, promising favorable outcomes.

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Upper Limb Movement Execution Classification using Electroencephalography for Brain Computer Interface

An accurate classification of upper limb movements using electroencephalography (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motion that helps us in our trivial daily tasks. Decoding EEG-based upper limb movements can be of great help to people with spinal cord injury (SCI) or other neuro-muscular diseases such as amyotrophic lateral sclerosis (ALS), primary lateral sclerosis, and periodic paralysis. This can manifest in a loss of sensory and motor function, which could make a person reliant on others to provide care in day-to-day activities. We can detect and classify upper limb movement activities, whether they be executed or imagined using an EEG-based brain-computer interface (BCI). Toward this goal, we focus our attention on decoding movement execution (ME) of the upper limb in this study. For this purpose, we utilize a publicly available EEG dataset that contains EEG signal recordings from fifteen subjects acquired using a 61-channel EEG device. We propose a method to classify four ME classes for different subjects using spectrograms of the EEG data through pre-trained deep learning (DL) models. Our proposed method of using EEG spectrograms for the classification of ME has shown significant results, where the highest average classification accuracy (for four ME classes) obtained is 87.36%, with one subject achieving the best classification accuracy of 97.03%.

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Motor imagery classification using EEG spectrograms

The loss of limb motion arising from damage to the spinal cord is a disability that could effect people while performing their day-to-day activities. The restoration of limb movement would enable people with spinal cord injury to interact with their environment more naturally and this is where a brain-computer interface (BCI) system could be beneficial. The detection of limb movement imagination (MI) could be significant for such a BCI, where the detected MI can guide the computer system. Using MI detection through electroencephalography (EEG), we can recognize the imagination of movement in a user and translate this into a physical movement. In this paper, we utilize pre-trained deep learning (DL) algorithms for the classification of imagined upper limb movements. We use a publicly available EEG dataset with data representing seven classes of limb movements. We compute the spectrograms of the time series EEG signal and use them as an input to the DL model for MI classification. Our novel approach for the classification of upper limb movements using pre-trained DL algorithms and spectrograms has achieved significantly improved results for seven movement classes. When compared with the recently proposed state-of-the-art methods, our algorithm achieved a significant average accuracy of 84.9% for classifying seven movements.

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A Multimodal Perceived Stress Classification Framework using Wearable Physiological Sensors

Mental stress is a largely prevalent condition known to affect many people and could be a serious health concern. The quality of human life can be significantly improved if mental health is properly managed. Towards this, we propose a robust method for perceived stress classification, which is based on using multimodal data, acquired from forty subjects, including three (electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG)) physiological modalities. The data is acquired for three minutes duration in an open eyes condition. A perceived stress scale (PSS) questionnaire is used to record the stress of participants, which is then used to assign stress labels (two- and three classes). Time (four from GSR and PPG signals) and frequency (four from EEG signal) domain features are extracted. Among EEG based features, using a frequency band selection algorithm for selecting the optimum EEG frequency subband, the theta band was selected. Further, a wrapper-based method is used for optimal feature selection. Human stress level classification is performed using three different classifiers, which are fed with a fusion of the selected set of features from three modalities. A significant accuracy (95% for two classes, and 77.5% for three classes) was achieved using the multilayer perceptron classifier.

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Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques

This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral change is induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would provide guidelines for future research enabling effective detection of human stress conditions.

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Deep Convolutional Neural Network based Classification of Alzheimer's Disease using MRI data

Alzheimer's disease (AD) is a progressive and incurable neurodegenerative disease which destroys brain cells and causes loss to patient's memory. An early detection can prevent the patient from further damage of the brain cells and hence avoid permanent memory loss. In past few years, various automatic tools and techniques have been proposed for diagnosis of AD. Several methods focus on fast, accurate and early detection of the disease to minimize the loss to patients mental health. Although machine learning and deep learning techniques have significantly improved medical imaging systems for AD by providing diagnostic performance close to human level. But the main problem faced during multi-class classification is the presence of highly correlated features in the brain structure. In this paper, we have proposed a smart and accurate way of diagnosing AD based on a two-dimensional deep convolutional neural network (2D-DCNN) using imbalanced three-dimensional MRI dataset. Experimental results on Alzheimer Disease Neuroimaging Initiative magnetic resonance imaging (MRI) dataset confirms that the proposed 2D-DCNN model is superior in terms of accuracy, efficiency, and robustness. The model classifies MRI into three categories: AD, mild cognitive impairment, and normal control: and has achieved 99.89% classification accuracy with imbalanced classes. The proposed model exhibits noticeable improvement in accuracy as compared to the state-fo-the-art methods.

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Electroencephalography based Classification of Long-term Stress using Psychological Labeling

Stress research is a rapidly emerging area in thefield of electroencephalography (EEG) based signal processing.The use of EEG as an objective measure for cost effective andpersonalized stress management becomes important in particularsituations such as the non-availability of mental health facilities.In this study, long-term stress is classified using baseline EEGsignal recordings. The labelling for the stress and control groupsis performed using two methods (i) the perceived stress scalescore and (ii) expert evaluation. The frequency domain featuresare extracted from five-channel EEG recordings in addition tothe frontal and temporal alpha and beta asymmetries. The alphaasymmetry is computed from four channels and used as a feature.Feature selection is also performed using a t-test to identifystatistically significant features for both stress and control groups.We found that support vector machine is best suited to classifylong-term human stress when used with alpha asymmetry asa feature. It is observed that expert evaluation based labellingmethod has improved the classification accuracy up to 85.20%.Based on these results, it is concluded that alpha asymmetry maybe used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

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Classification of Perceived Human Stress using Physiological Signals

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of $28$ participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.

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Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals

Tactile enhanced multimedia is generated by synchronizing traditional multimedia clips, to generate hot and cold air effect, with an electric heater and a fan. This objective is to give viewers a more realistic and immersing feel of the multimedia content. The response to this enhanced multimedia content (mulsemedia) is evaluated in terms of the appreciation/emotion by using human brain signals. We observe and record electroencephalography (EEG) data using a commercially available four channel MUSE headband. A total of 21 participants voluntarily participated in this study for EEG recordings. We extract frequency domain features from five different bands of each EEG channel. Four emotions namely: happy, relaxed, sad, and angry are classified using a support vector machine in response to the tactile enhanced multimedia. An increased accuracy of 76:19% is achieved when compared to 63:41% by using the time domain features. Our results show that the selected frequency domain features could be better suited for emotion classification in mulsemedia studies.

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