Quantum Trajectory Analysis of the Two-Mode Three-Level Atom Microlaser

We consider a single atom laser (microlaser) operating on three-level atoms interacting with a two-mode cavity. The quantum statistical properties of the cavity field at steady state are investigated by the quantum trajectory method which is a Monte Carlo simulation applied to open quantum systems. It is found that a steady state solution exists even when the detailed balance condition is not guaranteed. The differences between a single mode microlaser and a two-mode microlaser are highlighted. The second-order correlation function g^2(T) of a single mode is studied and special attention is paid to the one-photon trapping state, for which a simple formula is derived for its correlation function. We show the effects of the velocity spread of the atoms used to pump the microlaser cavity on the second-order correlation function, trapping states, and phase transitions of the cavity field

Using feminist translation theory to increase the compatibility of Saudi Arabia’s personal status judicial decisions with Article 16 of the Convention on the Elimination of all Forms of Discrimination Against Women

This thesis tests whether the application of feminist translation theory (FTT) in the personal status judicial decisions of the Kingdom of Saudi Arabia (KSA) can increase the compatibility of KSA’s judicial decisions with Article 16 of the Convention on the Elimination of all Forms of Discrimination against Women (CEDAW) concerning family relations and marriage. FTT was introduced in the late 1970s with the aim of eliminating discriminatory language against women. Since then, the theory has developed many strategies to challenge gender-inequitable beliefs and convey equality-related concepts to cultures where women’s rights are not fully granted and the concept of equality is unfamiliar (Pas and Zaborowska, 2017; Ergun, 2015). However, prior to this study, no practical framework had been developed to systematically apply FTT strategies to support women’s rights. For the purpose of this study, I developed a context-specific feminist translation framework (FTF) based on my analysis of the report of the Tunisian Individual Freedoms and Equality Committee (IFEC), in which the Committee proposed legislative reforms to some gender-inequitable personal status articles in the Tunisian Personal Status Law through feminist rewriting. The established FTF consists of four elements: (i) legal support of women’s rights: (ii) political support of women’s rights (iii) human rights institutions and civil society support of women’s rights; and (iv) the source of gender-inequitable laws (religion- or custom-based judicial decisions). This thesis used mixed methods to examine these four elements in order to determine the most suitable approaches and strategies to apply FTT in the context of the personal status judicial decisions in KSA. The four elements were examined from two aspects: (i) the degree of support for women’s rights and (ii) the space available for reform. These two aspects were investigated through a questionnaire presented to participants from four categories: (i) religious scholars; (ii) legal practitioners; (iii) legal translators; and (iv) women’s rights advocates, who are representative of the stakeholders involved in the process of reform. The results of the 94 completed questionnaires identified the status of women’s rights in the personal status judicial decisions of KSA. To gain a deeper understanding of the questionnaire’s findings, 10 semi-structured interviews were conducted to identify the reasons for the current status of women’ rights and to find ways to push women’s rights forward. Collected data was analysed using SPSS and ATLAS software. Overall, data indicated that the FTF was successful in identifying key issues hindering women’s rights in the study’s context and the most appropriate FTT strategies to resolve these problems in a systematic manner. Quantitative results revealed that the legal support of women’s rights and its space for reform is the highest among the first three elements of the FTF, while the political support of women’s rights and its space for reform is the lowest. Regarding the fourth element, the questionnaire showed that the space available for reforming custom-based gender-inequitable personal status judicial decisions is higher than the space available for reforming religion-based gender-inequitable personal status judicial decisions. The semi-structured interviews indicated that there is an overlap in the origin of the personal status judicial decisions, as custom-based judicial decisions are often mistaken as religion-based judicial decisions, a situation that needs to be addressed as gender-inequitable custom-based judicial decisions are easier to reform than those based in religion. The qualitative data also showed that a gradual approach of reform is the most suitable for the context of the study. The interviews further showed that a clear national plan that includes input from all stakeholders is needed to make systematic progress towards reforming women’s rights. In conclusion, the thesis results prove that FTT can increase the compatibility of KSA personal status judicial decisions with Article 16 of the CEDAW. Moreover, the thesis provides significant support to the possibility of operationalising FTT in different genres and contexts through modifying a context-specific developed FTF

Measurement of second-order coherence in the microlaser

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2001.Includes bibliographical references.We study the output and the degree of the second-order coherence fmunction for a microlaser in which the average number of atoms inside the cavity mode is larger than one. Two configurations of the microlaser are explored. In the standing-wave configuration, the atom-cavity coupling strength has a large variation depending on where an atom is injected in the cavity standing-wave mode. On the other hand, for the traveling-wave configuration, the atom-cavity coupling is constant along the cavity mode axis. The difference between the behavior of the microlaser for these two configurations can be attributed to the difference between their gain curves. The experimental results from our many-atom microlaser agree well with the predictions of the single-atom microlaser theory. This is anticipated because the average time an atom spends in the cavity mode is much smaller than the lifetime of a photon'in the cavity mode. As byproduct of this research, two experimental techniques are developed: a new velocity selection scheme for the barium atomic beam and a new simple multi-stop time-to- digital converter (MSTDC). Using two .dye lasers, a narrow velocity ground-state barium atomic beam is prepared. It has a velocity width of about 10% and a height of more than 50% of the original effusive atomic beam. The design of the MSTDC is based on a fast first-in-first-out (FIFO) memory. The implemented version provides stop times for any photons separated by more than 20 nsec and its range can be varied from 5 jisec to 0.66 msec.by Abdulaziz M. Aljalal.Ph.D

Selecting EEG channels and features using multi-objective optimization for accurate MCI detection: validation using leave-one-subject-out strategy

Abstract Effective management of dementia requires the timely detection of mild cognitive impairment (MCI). This paper introduces a multi-objective optimization approach for selecting EEG channels (and features) for the purpose of detecting MCI. Firstly, each EEG signal from each channel is decomposed into subbands using either variational mode decomposition (VMD) or discrete wavelet transform (DWT). A feature is then extracted from each subband using one of the following measures: standard deviation, interquartile range, band power, Teager energy, Katz's and Higuchi's fractal dimensions, Shannon entropy, sure entropy, or threshold entropy. Different machine learning techniques are used to classify the features of MCI cases from those of healthy controls. The classifier's performance is validated using leave-one-subject-out (LOSO) cross-validation (CV). The non-dominated sorting genetic algorithm (NSGA)-II is designed with the aim of minimizing the number of EEG channels (or features) and maximizing classification accuracy. The performance is evaluated using a publicly available online dataset containing EEGs from 19 channels recorded from 24 participants. The results demonstrate a significant improvement in performance when utilizing the NSGA-II algorithm. By selecting only a few appropriate EEG channels, the LOSO CV-based results show a significant improvement compared to using all 19 channels. Additionally, the outcomes indicate that accuracy can be further improved by selecting suitable features from different channels. For instance, by combining VMD and Teager energy, the SVM accuracy obtained using all channels is 74.24%. Interestingly, when only five channels are selected using NSGA-II, the accuracy increases to 91.56%. The accuracy is further improved to 95.28% when using only 8 features selected from 7 channels. This demonstrates that by choosing informative features or channels while excluding noisy or irrelevant information, the impact of noise is reduced, resulting in improved accuracy. These promising findings indicate that, with a limited number of channels and features, accurate diagnosis of MCI is achievable, which opens the door for its application in clinical practice

Parkinson’s Disease Detection from Resting-State EEG Signals Using Common Spatial Pattern, Entropy, and Machine Learning Techniques

Parkinson’s disease (PD) is a very common brain abnormality that affects people all over the world. Early detection of such abnormality is critical in clinical diagnosis in order to prevent disease progression. Electroencephalography (EEG) is one of the most important PD diagnostic tools since this disease is linked to the brain. In this study, novel efficient common spatial pattern-based approaches for detecting Parkinson’s disease in two cases, off–medication and on–medication, are proposed. First, the EEG signals are preprocessed to remove major artifacts before spatial filtering using a common spatial pattern. Several features are extracted from spatially filtered signals using different metrics, namely, variance, band power, energy, and several types of entropy. Machine learning techniques, namely, random forest, linear/quadratic discriminant analysis, support vector machine, and k-nearest neighbor, are investigated to classify the extracted features. The impacts of frequency bands, segment length, and reduction number on the results are also investigated in this work. The proposed methods are tested using two EEG datasets: the SanDiego dataset (31 participants, 93 min) and the UNM dataset (54 participants, 54 min). The results show that the proposed methods, particularly the combination of common spatial patterns and log energy entropy, provide competitive results when compared to methods in the literature. The achieved results in terms of classification accuracy, sensitivity, and specificity in the case of off-medication PD detection are around 99%. In the case of on-medication PD, the results range from 95% to 98%. The results also reveal that features extracted from the alpha and beta bands have the highest classification accuracy