21793 research outputs found
Sort by
Religion and Politics in Post-2003 Iraq: The Destruction Caused by the American Invasion
Get PDFThe spark of the ethnic and religious civil war in Iraq was planned, motivated and fueled by the American strategy to divide the Iraq and turn it into a failed and broken state that would enable the US to control the whole country and prohibit its progress from being a vital and leading player in the Middle East and the world. The US government has tried to secure the continuation of American economic and military supremacy globally, and the US invasion and occupation of Iraq was part of this imperialistic project, which was pursued under the pretext of the war on terrorism and the elimination of weapons of mass destruction in Iraq that have never been found. This thesis explains the integrated relationship between religion and politics and it shows the abuse and manipulation of religion by politics, foreign governments and colonizing countries in order to create cascades of ethnic and religious civil wars that divide the invaded country and turn it into a failed state. Furthermore, the thesis attempts to identify what I – as an Iraqi citizen who lived in Iraq before, during and after the 2003 American invasion – believe are the real causes behind the emergence of radical Islamic groups and to present the massive destruction of the Iraqi state in all aspects after the country’s invasion and occupation. It also explains the extermination of Iraqi Christians and the ongoing terrible consequences of replacing the secular social Baath Party rule with the radical Islamic Shia Coalition which enjoys American support after the country’s invasion
Multi-level Energy Management Framework with Flexibility Provision in Distribution Networks
Get PDFRenewable energy sources are variable and pose new challenges for power systems. A flexible energy management framework is needed for distributed energy resources (DERs) to improve
power system performance. Although home energy management systems (HEMSs) can control household appliances, they can not address the issues that may arise due to high DER penetration
levels on a distribution network. A multi-level energy management system (ML-EMS) is necessary to improve the techno-economic performance of the distribution system and satisfy the objectives
of end-users, aggregators, electricity retailers, and the distribution system operator (DSO). With the rise of DERs, consumers are progressively shifting towards the role of “prosumers,” serving as
flexible energy resources for DSOs. This work proposes a novel ML-EMS coordination framework in which prosumers provide upward and downward flexibility to the DSO. The DSO optimizes the
whole system with the optimal flexibility request sent to the aggregator. The suggested methodology considers the conflicting techno-economic objectives of the DSO and prosumers. To evaluate
the proposed method, we compare two scenarios: without flexibility and with flexibility provision. The results show that our proposed strategy improves the voltage profiles and reduces power losses,
power generation costs, and peak demands from the DSO’s perspective.
To motivate consumers to participate in the proposed coordination framework, an adaptive incentive program is proposed based on the flexibility of the end-user. The prosumer will receive
incentives to provide more flexibility to the DSO. To evaluate the proposed methodology, a comparative analysis is conducted involving five scenarios: ML-Framework (a) without HEMS (base
case), (b) without flexibility and an incentive program, (c) with flexibility and no incentive, (d)with flexibility and a fixed incentive, and (e) with flexibility and an adaptive incentive program.
The results show that our proposed strategy has increased the monetary benefits for prosumers for their flexibility services provided to the DSO compared to other scenarios. Moreover, the proposed
method improves the voltage profiles and reduces the peak load and power losses of a 33-bus radial distribution system.
Taking flexibility to the next level, we propose peer-to-peer (P2P) energy trading to buy and sell energy from neighbors using a smart transformer as an aggregator in our ML-EMS. This part
of the work presents a new coordination framework for HEMS-integrated P2P trading, focusing on the impact of such trading on a distribution transformer. The proposed framework provides a
comprehensive solution to manage power distribution within a smart grid environment by enabling HEMS to engage in P2P trading. This work also examines optimal energy management in a smart
neighborhood to minimize the total cost of energy usage. In addition, to prevent power peaks – that could create overloading and damage the top pole transformer, an adaptive cap within the flexibility
bound of the household is placed on the total power households that can draw/penetrate from/to the power grid. To validate the proposed method, we consider three scenarios: a) HEMS directly with
transformer. b) HEMS with integration of rule-based P2P with transformer, c). HEMS With fixed power limit on transformer. The result shows that the proposed method reduces the electricity cost
of the prosumers and extends the life expectancy of the transformer.
To include the three-phase unbalanced distribution system in the proposed framework, we develop another strategy, which includes four-stage optimization for a three-level coordination framework.
A mixed integer linear programming (MILP)-based HEMS is formulated in the first stage to perform home energy management effectively. At the aggregator level, in the second stage, a MILP-enabled P2P trading mechanism is designed. At the same level, a third-stage loss of life optimization is performed pertaining to the optimal power status of the HEMS and P2P trading. In the last stage, a three-phase optimal power flow-based optimization is proposed to maintain the operational constraints of the unbalanced distribution network. This work compares the proposed P2P-based method with a local energy market community with a HEMS-based smart home neighborhood with a distribution transformer. Optimizing HEMS and P2P trading while addressing transformer limitations, our proposed method reduces peak power and life loss of distribution transformers. Additionally, our method substantially lowers electricity costs for P2P prosumers. Thus, our proposed method outperforms other existing mechanisms from both financial and physical network operation suitability perspectives
Influence of enhanced Amino Acid Compositions on Human Cognitive Functions after COVID-19
Get PDFThis study aimed to demonstrate the effect of enhanced Amino Acid (AA) compositions on human cognitive functioning after Covid-19 damaging effect. The research study design included an open, randomized, placebo-controlled trial for 60 days. Participants in the study were measured initially, randomly divided into three groups: people who had Covid in the previous year. people who never had Covid. and placebo. Experimental and non-Covid groups used amino-acid compositions. while placebo group used sugar pills. All people were measured after 30 and 60 days. 70 healthy people aged 35-65. men and women. divided into three groups: 35 people in experimental Covid group, 15 people in non-covid group, and 20 people in placebo group. People in placebo group all had Covid. People in experimental covid and non-covid groups consumed daily specially prepared aminoacidic composition, charged with the radiation of a cold plasma. In the first test Covid groups demonstrated a statistically significant difference with a non-Covid group. After 30-and 60-days results of the experimental Covid group had no statistical difference with the results of non-Covid group. while results of both these groups demonstrated a statistically significant difference with the results of placebo group. The results showed that using AA compositions during the longitude period significantly affected cognitive functions in particular memory, speed of reactions and attention and allow to overcome negative effects of the Covid inflammation
Quantum Dash Multi-Wavelength Lasers for Next Generation High Capacity Multi-Gb/s Millimeter-Wave Radio-over-Fiber Wireless Communication Networks
Get PDFThe ever-increasing proliferation of mobile users and new technologies with different applications and features, and the demand for reliable high-speed high capacity, pervasive connectivity and low latency have initiated a roadmap for the next generation wireless networks, fifth generation (5G), which is set to revolutionize the existing wireless communications. 5G will use heterogeneous higher carrier frequencies from the plentifully available spectra in the higher microwave and millimeter-wave (MMW) bands, including licensed and unlicensed spectra, for achieving multi-Gb/s wireless connectivity and overcoming the existing wireless spectrum crunch in the sub-6 GHz bands, resulting from the tremendous growth of data-intensive technologies and applications. The use of MMW when complemented by multiple-input-multiple-output (MIMO) technology can significantly increase data capacity through spatial multiplexing, and improve coverage and system reliability through spatial diversity. However, high-frequency MMW signals are prone to extreme propagation path loss and are challenging to generate and process with conventional bandwidth-limiting electronics. In addition, the existing digitized fronthaul for centralized radio access network (C-RAN) architecture is considered inefficient for 5G and beyond. Thus, to fully exploit the promising MMW 5G new radio (NR) resource and to alleviate the electronics and fronthaul bottleneck, microwave photonics with analog radio-over-fiber (A-RoF) technology becomes instrumental for optically synthesizing and processing broadband RF MMW wireless signals over optical links. The generation and distribution of high-frequency MMW signals in the optical domain over A-RoF links facilitate the seamless integration of high-capacity, reliable and transparent optical networks with flexible, mobile and pervasive wireless networks, extending the reach and coverage of high-speed broadband MMW wireless communications. Consequently, this fiber-wireless integration not only overcomes the problem of high bandwidth requirements, transmission capacity and span limitation but also significantly reduces system complexity considering the deployment of ultra-dense small cells with large numbers of 5G remote radio units (RRUs) having massive MIMO antennas with beamforming capabilities connected to the baseband units (BBU) in a C-RAN environment through an optical fiber-based fronthaul network. Nevertheless, photonic generation of spectrally pure RF MMW signals either involves complex circuitry or suffers from frequency fluctuation and phase noise due to uncorrelated optical sources, which can degrade system performance. Thus simple highly integrated and cost-efficient low-noise optical sources are required for next-generation MMW RoF wireless transmission systems.
More recently, well-designed quantum confined nanostructures such as semiconductor quantum dash/dot multi-wavelength lasers (QD-MWLs) have attracted more interest in the photonic generation of RF MMW signals due to their simple compact and integrated design with highly coherent and correlated optical signals having a very low phase and intensity noise attributed to the inherent properties of QD materials. The main theme of this thesis revolves around the experimental investigation of such nanostructures on the device and system level for applications in high-speed high-capacity broadband MMW RoF-based fronthaul and wireless access networks. Several photonic-aided high-capacity long-reach MMW RoF wireless transmission systems are proposed and experimentally demonstrated based on QD-MWLs with the remote distribution and photonic generation of broadband multi-Gb/s MMW wireless signals at 5G NR (FR2) in the K-band, Ka-band and V-band in simplex, full-duplex and MIMO configurations over 10 to 50 km optical fiber and subsequent wireless transmission and detection. The QD-MWLs-based photonic MMW RoF wireless transmission systems’ designs and experimental demonstrations could usher in a new era of ultra-high-speed broadband multi-Gb/s wireless communications at the MMW frequency bands for next-generation wireless networks.
The QD-MWLs investigated in this thesis include a simple monolithically integrated and highly coherent low-noise single-section semiconductor InAs/InP QD buried heterostructure passively mode-locked (PML) laser-based optical coherent frequency comb (CFC) and a novel monolithic highly correlated low-noise semiconductor InAs/InP buried heterostructure common-cavity QD dual-wavelength distributed feedback laser (QD-DW-DFBL). The performance of each device is thoroughly characterized experimentally in terms of optical phase noise, relative intensity noise (RIN), timing jitter and RF phase noise exhibiting promising results. Based on these devices, different long-reach photonic MMW RoF wireless transmission systems, including simplex single-input-single-output (SISO) and multiple-input-multiple-output (MIMO) and bidirectional configurations, are proposed and experimentally demonstrated with real-time remote electrical RF synthesizer-free all-optical frequency up-conversion, wireless transmission and successful reception of wide-bandwidth multi-level quadrature amplitude modulated (M-QAM) RF MMW wireless signals having bit rates ranging from 4 Gb/s to 36 Gb/s over different hybrid fiber-wireless links comprising of standard single mode fiber (SSMF) and indoor wireless channel. The end-to-end links are thoroughly investigated in terms of error-vector-magnitude (EVM), bit-error-rat (BER), constellations and eye diagrams, realizing successful error-free transmission. Finally, novel high-capacity spectrally efficient MIMO and optical beamforming enabled photonic MMW RoF wireless transceivers design and methods based on QD-MWLs with wavelength division multiplexing (WDM) and space division multiplexing (SDM) are proposed and discussed. A proof-of-concept implementation of the proposed photonic MMW RoF wireless transmission system is also simulated in a simple WDM-based configuration with bidirectional 4×4 MIMO MMW carrier streams
Formation Control of Nonlinear Multi-agent Systems Using Neural Networks
Get PDFThis dissertation presents five main contributions to the field of distance-based formation control
and target tracking for multi-agent systems.
The first contribution proposes a neural network-based backstepping controller for distancebased formation control in the presence of disturbance. Agents are modeled as second-order nonlinear systems, and a rigid graph theory is used to develop the controller. The radial basis function neural network (RBFNN) is used to compensate for unknown nonlinearities in the system dynamics, and the neural network (NN) weights tuning law is derived using the Lyapunov stability theory. The uniform ultimate boundedness of the formation distance error and NN weights norm estimation error is proven, and simulation results demonstrate the proposed method’s performance on nonlinear multi-agent systems.
The second contribution establishes the properties of the normalized rigidity matrix in two- and three-dimensional spaces. The upper bounds of the normalized rigidity matrix singular values are derived for minimally and infinitesimally rigid frameworks, and it is proven that transformations of a framework do not affect the normalized rigidity matrix properties. The maximum smallest singular value for a three-agent rigid framework in two-dimensional space is derived, along with the necessary and sufficient conditions to reach that value. The results are applied to stability analysis and control design of distance-based formation control, and numerical simulations are provided to illustrate the theoretical results.
The third contribution proposes an adaptive neural network-based backstepping controller for distance-based formation control and target tracking in the presence of bounded time delay and disturbance. The RBFNN is used to overcome unknown nonlinearities and disturbances, and the control signal is designed based on a Lyapunov function and Young’s inequality to alleviate the effect of state time delay. The adaptive NN weights tuning law is derived using the Lyapunov function, and the uniform ultimate boundedness of the formation distance error is proven. The performance of the proposed method is validated through simulation results and comparisons with an existing displacement-based method.
The fourth contribution addresses the leader-following formation control problem for heterogeneous, uncertain, input-affine, nonlinear multi-agent systems modeled by a directed graph. A tunable three-layer NN is proposed to approximate unknown nonlinearities, and the NN weights tuning laws are derived using the Lyapunov theory. The leader-following and formation control problems are addressed using a robust integral of the sign of the error feedback and NN-based control. The results are rigorously proven using the Lyapunov stability theory, and the performance of the proposed method is compared with two other results.
The fifth contribution is the study of formation control with constant communication delays for second-order, uncertain, nonlinear multi-agent systems with asymmetric control gain matrix and unknown control direction. A three-layer NN is proposed to approximate unknown nonlinearities, and the NN weights tuning law is derived using the Lyapunov stability theory. The leader-following formation control problem with communication delay is addressed using a delayed integral of error variables, NN-based control, and a robustifying term. The semi-globally uniformly ultimately bounded solution of closed-loop signals is rigorously proven using a barrier Lyapunov function, and simulation results are provided to evaluate the efficiency and performance of the proposed method.
The thesis concludes with a summary of the contributions, limitations of the proposed methods, and suggestions for future work
Emotional Discourses of Conservative Opposition to LGBTQ2S+ Rights in the United States
Get PDFLGBTQ2S+ rights in the United States have seen steady rights advancements in the past two decades. However, there has recently been a rise in anti-LGBTQ2S+ laws that focus on parental rights and the well-being of children. These laws are increasingly successful despite favorable public opinion towards LGBTQ2S+ rights and increased protections for LGBTQ2S+ Americans. Following the legalization of same-sex marriage in the United States, conservative opposition to LGBTQ2S+ rights shifted away from emotional discourses of disgust to legal rights-based discourses of religious freedom and individual liberties. This discursive shift seemingly removed emotional discourses from conservative opposition to LGBTQ2S+ rights, but this project finds that this is not entirely true. Through a case study of Florida’s Don’t Say Gay or Trans bill, I ask how emotional discourses are currently being used by conservative opposition to LGBTQ2S+ rights. My findings demonstrate that emotional discourses of fear, security, and disgust are still present in contemporary conservative opposition, and that the shift to legal rights-based discourses represents a sanitization of emotional discourses that are unfavorable to an increasingly LGBTQ2S+ friendly public
Synthetic Data as a Supplement for Training Deep Learning Models
Get PDFTraining supervised machine learning models with suitable data can be challenging due to the expensive and time-consuming process of collection and annotation, particularly when publicly accessible datasets are not available. This work emphasizes the use of synthetic data to reduce the effort spent on data collection. The study is based on an analysis of three widely-used benchmark datasets and two synthetic datasets one of which was created for this specific task. The insights derived from the preliminary analysis identify the required characteristics of the synthetic data that can consistently lead to improvement in performance metric for the task. In this work, the roles of synthetic data as a supplement to real data is investigated. Precisely, the impact of synthetic data with varying proportions and similarities to real data on the performance of Multiple Object Tracking neural networks based on the Convolutional and Transformer architectures is analyzed. Experiments demonstrate the superiority of using a combination of simulated and real data, where the samples of synthetic data are many folds of the real, and the variance of low-level features is high but limited by the low variance of high-level features. The findings can be applied to other
machine learning tasks and provided guidelines can help improve model performance
Data Labeling for Fault Detection in Cloud: A Test Suite-Based Active Learning Approach
Get PDFCloud computing enables ubiquitous on-demand network access to a shared pool of configurable computing resources with minimal management efforts from the user. It has evolved as a key computing paradigm to enable a wide variety of applications such as e-commerce, social networks, high-performance computing, mission-critical applications, and Internet of Things (IoT). Ensuring the quality of service of applications deployed in inherently complex and fault-prone cloud environments is of utmost concern to service providers and end users. Machine learning-based fault management solutions enable proactive identification and mitigation of faults in cloud environments to attain the desired reliability, though they require labeled cloud metrics data for training and evaluation. Moreover, the high dynamicity in cloud environments brings forth emerging data distributions, which necessitate frequent labeling of cloud metrics data stemming from an evolving data distribution for model adaptation. In this thesis, we study the problem of data labeling for fault detection in cloud environments, paying close attention to the phenomenon of evolving cloud metric data distributions. More specifically, we propose a test suite-based active learning framework for automated labeling of cloud metrics data with the corresponding cloud system state while accounting for emerging fault patterns and data or concept drifts. We implemented our solution on a cloud testbed and introduced various emerging data distribution scenarios to evaluate the proposed framework's labeling efficacy over known and emerging data distributions. According to our evaluation results, the proposed framework achieves about 41% higher weighted F1-score and 34% higher average Area Under One-vs-Rest Receiver Operating Characteristic curves (OvR ROC AUC score) than a system without any adaptation for emerging data distributions
Lateral Sway
No full textLATERAL SWAY is a project exploring queer relationality and intimacy at the intersections of Polish-Canadian counterpublics. It is haunted by the nocturnal imaginary, in a collapsed temporality of past and present; not quite in time but in between. There are energetic residues, remainders of, and desire for, what is missing and morphing through memory. Stories of some queer communities are invisible, as many queers are still struggling to appear in public spaces and create their own archives. These stories vibrate with the urgency to materialize, and they find their way into the frame of this project. The frame resonates with the voice of the collection’s young lesbian speaker, recounting her experiences from a different hemisphere, mobilizing the erotic to create spaces of togetherness in the dynamics of recognizing one another, finding common genealogies, and building queer futures. [To be continued…
A humanized yeast model for studying TRAPP complex mutations
No full textRare diseases affect 3.5–5.9% of the global population and mutations in membrane trafficking
proteins are known to cause rare disorders with severe symptoms. The highly conserved transport
protein particle (TRAPP) complexes are key membrane trafficking regulators that are also
involved in autophagy. Pathogenic variants in specific TRAPP subunits are linked to neurological
disorders, muscular dystrophies, and skeletal dysplasias. Characterizing these mutations and
their phenotypes is important for understanding general and specialized roles of TRAPP subunits.
Patient-derived cells are not always available, which poses a limitation for the study of these
diseases. Therefore, other systems, like the yeast Saccharomyces cerevisiae, can be used to
dissect the mechanisms at the intracellular level underlying these disorders. The development of
CRISPR/Cas9 technology in yeast has enabled a scar-less editing method that creates an
efficient humanized yeast model. This project focuses on humanizing the core TRAPP complex
subunits in yeast to generate a platform for studying TRAPP variants associated with disease in
humans. Core yeast subunits were humanized by replacing with their human orthologs and
TRAPPC1, TRAPPC2, TRAPPC2L, TRAPPC6A, and TRAPPC6B were found to successfully
replace their yeast counterparts. This system was used for studying the first reported TRAPPC1
variant identified in humans, which is a compound heterozygous mutation. I show that the
maternal variant (TRAPPC1 p.Val121Alafs*3) is non-functional while the paternal variant
(TRAPPC1 p.His22_Lys24del) is conditional-lethal in yeast. Results show that the paternal
TRAPPC1 variant affects non-selective autophagy in yeast whereas membrane trafficking and
autophagy are defective in patient fibroblasts. This study suggests that humanized yeast can be
an efficient means to study TRAPP subunit variants in the absence of human cells, and it lays the
foundation for characterizing further TRAPP variants through this system