A morphospace of functional configuration to assess configural breadth based on brain functional networks

The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience. We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology and integration of information within and between a reference set of FNs. In this study, we use this framework to quantify the Network Configural Breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information: 11 pages, 5 figure

Theorizing #Girlboss Culture: Mediated Neoliberal Feminisms from Influencers to Multi-level Marketing Schemes

I define girlboss feminism as emergent, mediated formations of neoliberal feminism that equate feminist empowerment with financial success, market competition, individualized work-life balance, and curated digital and physical presences driven by self-monetization. I look toward how the mediation of girlboss feminism utilizes branded and affective engagements with representational politics, discourses of authenticity and rebellion, as well as meritocratic aspiration to promote cultural interest in conceptualizing feminism in ways that are divorced from collective, intersectional struggle. I question the stakes involved in reducing feminist interrogations and commitments to discourses of representation, visibility, and meritocracy. I argue that while girlboss feminism may facilitate individual opportunities for stability and advancement under neoliberal constraints, the proliferation of girlboss feminism as an emergent and mediated thread of neoliberal feminism plays a vital role in perpetuating the severe inequalities required to sustain racial capitalism as an oppressive political-economic and socio-cultural framework. I look to three key spaces: wellness culture, self-help coaching, and multi-level marketing to understand how feminism and racial capitalism grow intertwined via mediated formations of girlboss culture. In charting these formations, I initiate conversations that investigate the nuances and complications of feminist movement work under racial capitalism. I hope that identifying these emergent threads of neoliberal feminism provides insight on how intersectional and liberatory modes of collective struggle might remain more nimble, and generate more political power, than incarnations of feminism that reinforce an oppressive status quo

Increasing the Capacity of Wireless Networks Using Beamforming

Wireless mobile communications are growing in an exponential manner, especially in terms of the number of users. Also, the demand for high Quality of Service (QoS) has become essential. Nowadays, subscribers are using more applications such as the internet, video conferencing, and high quality TV. These applications require high data rates. The Space Division Multiple Access (SDMA) is the key element that can enable reusing of the same channels among different users in the same cell to meet this demand. For the application of SDMA in an efficient way, it is required to identify the users’ positions and directions in the cell. The Direction of Arrival (DOA) algorithms can estimate the incident angles of all the received signals impinging on the array antenna. These algorithms give the DOAs of all relevant signals of the user sources and interference sources. However, they are not capable of distinguishing and identifying which one is the direction of the desired user. In this thesis, we have proposed to use a Reference Signal (RFS) known by the transmitter and the receiver to identify which one of the estimated DOAs is the DOA of the desired user in the cell. Using a RFS and applying the correlation concept, we can distinguish the desired signal from the others. Moreover, we have considered the Affine Projection Algorithm (APA) to enhance the accuracy of the estimated direction and to form a beam towards the desired user and nulls towards the interferers. Our simulation results assure that, in the presence of the RFS, the DOA algorithms can identify the direction of the desired user with high accuracy and resolution. We have investigated this concept on different DOA algorithms such as MUltiple Signal Classification (MUSIC), ROOT MUSIC, and Estimate the direction of arrival of Signals Parameters via Rotational Invariance Technique (ESPRIT) algorithms. Moreover , we have introduced an approach for using the smart antennas (SA) to exploit the space diversity for the next generations of mobile communication systems. We have applied a combination of the MUSIC and the Least Mean Squares (LMS) algorithms. We have proposed the MUSIC algorithm for finding the directions of the users in the cell. In addition, we have considered the LMS algorithm for enhancing the accuracy of the DOA, performing the beam generation process, and keeping track of the users in the cell. Furthermore, we have proposed a scheduling algorithm that performs the scheduling in terms of the generated beams. The space diversity, together with the time and frequency diversities of LTE (Long Term Evolution) results in a large capacity increase in the next generations of wireless mobile communication systems. Simulation results show that the proposed algorithm called MUltiple Signal Classification and Least Mean Squares (MLMS), has the capability to converge and completely follow the desired user signal with a very high resolution. The convergence and the accurate tracking of the desired signal user take place after 13 iterations while in the traditional LMS, the convergence needs 85 iterations to take place. This means an 84.7% improvement over the traditional LMS algorithm for the same number of calculations in each iteration. In contrast to the traditional LMS algorithm, the proposed algorithm can work in the presence of high level of interference. Furthermore, the proposed scheduling scheme based on beamforming shows a gain of 15% in the total aggregated throughput for each 10o decrease in the beam size. The proposed model provides an optimum, complete, and practical design for the next generations of the mobile communication systems. In this model, we have proposed a mechanism to find the direction of each user in the cell, enhance the accuracy of the obtained DOAs, and perform scheduling based on the generated beams. In addition, we have presented an approach for Frequency Reuse (FR) based on beamforming for 5G. We have implemented a synthesizer in order to smartly form the desired beam shape and make the nulls deeper. We have taken the advantage of the SAs, beamforming capabilities, and the radiation pattern (RP) synthesizing techniques to build up a FR scheme for 5G. Also, we have developed a formula for calculating the Signal to Interference and Noise Ratio (SINR) in terms of the desired and the interferers directions. The objective is to maintain the SINR at the minimum acceptable levels required by the LTE while reducing the beam sizes, and hence increase the FR factor. The simulation results show that with a Uniform Linear Antenna (ULA) of 11 elements, we can achieve the desirable SINR levels using beams of 100 width, which improves the FR factor from 1 to 18, and subsequently increases the number of mobile users

Optimizing energy performance of building renovation using traditional and machine learning approaches

International Energy Agency (IEA) studies show that buildings are responsible for more than 30% of the total energy consumption and an equally large amount of related greenhouse gas emissions. Improving the energy performance of buildings is a critical element of building energy conservation. Furthermore, renovating existing buildings envelopes and systems offers significant opportunities for reducing Life-Cycle cost (LCC) and minimizing negative environmental impacts. This approach can be considered as one of the key strategies for achieving sustainable development goals at a relatively low cost, especially when compared with the demolition and reconstruction of new buildings. One of the main methodological and technical issues of this approach is selecting a desirable renovation strategy among a wide range of available options. The main motivation behind this research relies on trying to bridge the gap between building simulation, optimization algorithms, and Artificial Intelligence (AI) techniques, to take full advantage of the value of their couplings. Furthermore, for a whole building simulation and optimization, current simulation-based optimization models, often need thousands of simulation evaluations. Therefore, the optimization becomes unfeasible because of the computation time and complexity of the dependent parameters. To this end, one feasible technique to solve this problem is to implement surrogate models to computationally imitate expensive real building simulation models. The aim of this research is three-fold: (1) to propose a Simulation-Based Multi-Objective Optimization (SBMO) model for optimizing the selection of renovation scenarios for existing buildings by minimizing Total Energy Consumption (TEC), LCC and negative environmental impacts considering Life-Cycle Assessment (LCA); (2) to develop surrogate Artificial Neural Networks (ANNs) for selecting near-optimal building energy renovation methods; and (3) to develop generative deep Machine Learning Models (MLMs) to generate renovation scenarios considering TEC and LCC. This study considers three main areas of building renovation, which are the building envelope, Heating, Ventilation and Air-Conditioning (HVAC) system, and lighting system; each of which has a significant impact on building energy performance. On this premise, this research initially develops a framework for data collection and preparation to define the renovation strategies and proposes a comprehensive database including different renovation methods. Using this database, different renovation scenarios can be compared to find the near-optimal scenario based on the renovation strategy. Each scenario is created from the combination of several methods within the applicable strategy. The SBMO model simulates the process of renovating buildings by using the renovation data in energy analysis software to analyze TEC, LCC, and LCA and identifies the near-optimal renovation scenarios based on the selected renovation methods. Furthermore, an LCA tool is used to evaluate the environmental sustainability of the final decision. It is found that, although the proposed SBMO is accurate, the process of simulation is time consuming. To this end, the second objective focuses on developing robust MLMs to explore vast and complex data generated from the SBMO model and develop a surrogate building energy model to predict TEC, LCC, and LCA for all building renovation scenarios. The main advantage of these MLMs is improving the computing time while achieving acceptable accuracy. More specifically, the second developed model integrates the optimization power of SBMO with the modeling capability of ANNs. While, the proposed ANNs are found to provide satisfactory approximation to the SBMO model in a very short period of time, they do not have the capability to generate renovation scenarios. Finally, the third objective focuses on developing a generative deep learning building energy model using Variational Autoencoders (VAEs). The proposed semi-supervised VAEs extract deep features from a whole building renovation dataset and generate renovation scenarios considering TEC and LCC of existing institutional buildings. The proposed model also has the generalization ability due to its potential to reuse the dataset from a specific case in similar situations. The proposed models will potentially offer new venues in two directions: (1) to predict TEC, LCC, and LCA for different renovation scenarios, and select the near-optimal scenario, and (2) to generate renovation scenarios considering TEC and LCC. Architects and engineers can see the effects of different materials, HVAC systems, etc., on the energy consumption, and make necessary changes to increase the energy performance of the building. The proposed models encourage the implementation of sustainable materials and components to decrease negative environmental impacts. The ultimate impact of the practical implementation of this research is significant savings in buildings’ energy consumption and having more environmentally friendly buildings within the predefined renovation budget

Structural Health Monitoring of Pipelines in Radioactive Environments Through Acoustic Sensing and Machine Learning

Structural health monitoring (SHM) comprises multiple methodologies for the detection and characterization of stress, damage, and aberrations in engineering structures and equipment. Although, standard commercial engineering operations may freely adopt new technology into everyday operations, the nuclear industry is slowed down by tight governmental regulations and extremely harsh environments. This work aims to investigate and evaluate different sensor systems for real-time structural health monitoring of piping systems and develop a novel machine learning model to detect anomalies from the sensor data. The novelty of the current work lies in the development of an LSTM-autoencoder neural network to automate anomaly detection on pipelines based on a fiber optic acoustic transducer sensor system. Results show that pipeline events and faults can be detected by the MLM developed, with a high degree of accuracy and low rate of false positives even in a noisy environment near pumps and machinery