21793 research outputs found
Sort by
Electrically Conductive Polymer Nanocomposite Platforms for Routing: Modeling, Fabrication, and Verification
Get PDFCreating an electrically conductive composite that can be used in a wide variety of circumstances is getting much attention. Making a flexible, easy to manufacture, and cost-effective electrical route is the challenge the research. Electrically conductive polymer composites have a wide range of applications in electronics and nanotechnology industries. They have unique features that allow them to be employed as smart materials in applications such as sensors, wearable devices, optoelectronics, and printed electronics. Thermal and electrical characterization was performed for two polymer nanocomposites to investigate their properties. Several fillers can be added to the host polymer to make a conductive nanocomposite. The filler used in this study is multi-wall carbon nanotube (MWCNT) which also attracted attention in the literature. Moreover, silver nanoparticles were also used to compare the effect of multiple fillers on conductivity. Polydimethylsiloxane (PDMS) and poly(3,4-ethylene dioxythiophene): poly(4-styrene-sulfonate), (PEDOT:PSS) were the two different host polymers that have been used to see the effect of the base polymer on conductivity. PDMS is a non-conductive polymer but more flexible while on the other hand PEDOT:PSS is a highly conductive polymer but less flexible.
Results show that using conductive polymer (PEDOT:PSS) as the host could increase the conductivity up to 378.97 S/m. The nanocomposite investigated was found to have excellent flexibility and well adhesion to the PDMS substrate. Also, based on the experiments, these conductive lines are sensitive to temperature, which is an attractive feature
A Novel Safe Deep Reinforcement Learning Approach for Optimal Dispatch of Energy Hubs with Compressed Air Energy Storage
Get PDFThe development of renewable energy and energy storage technologies has resulted in the emergence of Energy Hubs (EHs) in recent years. Due to the uncertainty associated with energy supply and load, scheduling EH presents a challenging task. Current model-based optimization approaches have limitations in terms of solution accuracy and computational efficiency, which hamper their application. Deep Reinforcement Learning (DRL) is a model-free approach that has demonstrated superior performance over model-based approaches. The current DRL algorithms, however, perform poorly in terms of constraint handling and global optimality. The purpose of this study is to propose a model-free, safe deep reinforcement learning approach, combining primal-dual optimization and imitation learning, for the optimal scheduling of an EH with an Advanced Adiabatic Compressed Air Energy Storage (AA-CAES). First, the operation of an AA-CAES under off-design conditions is modeled and linearized using Mixed Integer Linear Programming (MILP). Then, a safe DRL approach is proposed with training and testing steps considering a case study. The performance of the proposed approach in reducing operational cost and satisfying constraints is compared to state-of-the-art DRL algorithms as well as a deterministic MILP approach. Additionally, a test set is used to examine the generalizability of the proposed approach. Finally, the effect of off-design conditions of a tri-generative AA-CAES on the optimal dispatch strategy is investigated. Furthermore, a sensitivity analysis indicates that the proposed approach is reproducible and reliable. The results indicate that the proposed approach can effectively reduce the operational cost and satisfy the operational constraints
Procurement Circuit under Machine Learning Political Order: Governance of, through, and for AI
Get PDFIn a landscape where governments are shaped by and depend on private AI providers, what does it mean to govern artificial intelligence (AI)? Public procurement is a point of intervention where the entrepreneurial pull of states to integrate digital expertise and reformulate its problem within machine-learning logics can be halted, questioned and examined. This thesis examines public procurement of AI as a crucial site where governments and AI providers engage in a complex co-shaping process, which I term the procurement circuit. Specifically, the thesis examines Canada’s procurement of AI as part of its national Responsible AI Strategy.
Through situational analysis, this thesis maps and explains how this co-shaping occurs and considers how the procurement circuit distributes authority and legitimacy over normative questions on AI between AI providers and government. I argue that Canada's regulatory architecture is built under what Louise Amoore coined Machine Learning (ML) political order. Chapter 3 maps the regulatory architecture Canada built to enforce Responsible AI and evaluate suppliers. Chapter 4 considers 11 suppliers’ responses to these requirements and outlines their normative views on both AI and its governance. In the conclusion, I suggest recommendations on how Canada might reformulate the procurement circuit as a space where legitimacy and authority is negotiated to resist ML political order
How does Major Adjustment Affect Chinese College Admissions?
Get PDFDuring the annual college application season, popular college majors often attract a large number of applicants, while less popular majors rarely receive many applications. To fulfill the government’s enrollment targets, Chinese colleges may accept additional applicants through the major adjustment process and place them in majors with vacancies. This poses a dilemma for applicants: should they allow colleges to place them in an adjusted major? In this paper, we examine the impact of the adjustment process on Chinese college admissions from the perspective of mechanism design. Based on the Application-Rejection mechanisms, we introduce the Major-Adjusted Parallel Mechanisms. The latter considers both the regular admissions process and the adjustment process. We find that, under the same conditions, the Major-Adjusted Parallel Mechanisms allocate at least as many college seats as the Application-Rejection Mechanisms, implying that the major adjustment process can boost enrollment. Furthermore, we show that truth-telling is a weakly dominant strategy for every student with a preference ordering that can be recognized by the system. However, there is no weakly dominant strategy for other students
Photoactive TiO2 coatings by suspension plasma spraying
Get PDFRecently the advantages of titanium dioxide (TiO2), as one of the most recognized photocatalysts for the degradation of organic compounds in wastewater, have prompted a great deal of research. However, its unsatisfactory efficiency caused by the large bandgap of TiO2 and high recombination rate of photo-generated e-/h+ has limited its application. Thermal spray processes as environmentally friendly technologies appear as a versatile and rapid processing approach compatible with industrial production. Suspension plasma spray (SPS) deposition could be used to obtain TiO2 deposits with an effective photoactive performance to decompose organic compounds and antiviral applications. Furthermore, TiO2 coatings with various degrees of sub-stoichiometric oxidation can be achieved with plasma spray deposition.
In this project, coatings with different anatase phase contents were produced. The results show no direct correlation between anatase content and the photocatalytic activity of as-sprayed coatings due to their unique microstructure and the presence of oxygen vacancies. In comparison, anatase phase positively enhanced the photocatalytic activity in the post-treated coatings with similar coating characteristics and oxygen contents.
Moreover, oxygen vacancy presented an influential role in improving the photocatalytic activity of sub-stoichiometric TiO2-x coatings produced by SPS. The energy levels introduced by oxygen vacancies and Ti3+ ions in TiO2 lattice sites decreased the bandgap energy and shifted the absorption edge to visible light. The energy levels can also improve the charge carriers’ lifetime by acting as traps for electrons and holes. Results showed that the photocatalytic activity of as-sprayed sub-stoichiometric TiO2-x coatings was 2-3 times higher than that of post-treated stoichiometric TiO2 coatings.
Further studies on the cerium-doped TiO2 showed SPS-SPPS technique as a promising method for doping TiO2. However, results revealed the importance of adding an optimum amount of dopant to reach the highest photoactivity. Moreover, a nanocomposite of TiO2- CeO2 can help photocatalytic activity. Otherwise, sub-micron CeO2 particles in the composite matrix could cover the active surface of TiO2 particles and decrease the photocatalytic efficiency.
Finally, the antiviral performance of the coatings was assessed. The results show that thermally sprayed coatings can introduce a potentially cost-effective solution to produce efficient antiviral high-touch coatings for indoor/ outdoor applications
Autonomous Virtual Cognitive Assessment through Conversational Agents Leveraging Natural Language Processing Techniques
Get PDFThe COVID-19 pandemic has abruptly and undoubtedly changed the world we knew at the end of the second decade of the 21st century. To be prepared for future possible pandemics, special attention should be devoted to the fact that seniors, aged 60 and over, are more vulnerable during the pandemic era. In addition to a higher risk of infections, seniors are also at higher risk of suffering from mental and cognitive issues. Given an expected and alarming population ageing in near future, it is crucial and of significant importance to developing innovative and advanced autonomous cognitive screening systems. Of particular interest to this thesis is the development of autonomous cognitive screening systems via the integration of Signal Processing (SP), Artificial Intelligence (AI), and Machine Learning (ML) models. In particular, the focus is on the development of an AI-empowered avatar that autonomously performs the Neurobehavioral Cognitive Status Examination (Cognistat) assessments. Results obtained from Cognitive screening tests can be used in conjunction with other data sources to perform differential diagnosis of dementia or other cognitive disorders. Although Cognistat is widely utilized in clinical applications, its administration and interpretation of the results solely rely on a well trained physiologist, which is highly restrictive during a pandemic. Towards addressing this issue, in the thesis, an Automated Virtual Cognitive Assessment (AVCA) framework [1, 2] is proposed that integrates Natural Language Processing (NLP) and hand gesture recognition techniques. The AVCA framework is an autonomous cognitive assessment system that receives audio and video signals in a real-time fashion and performs semantic and synthetic analysis using NLP techniques and DNN models. The proposed framework provides individual scores in the seven major cognitive domains, i.e., orientation, attention, language, contractual ability, memory, calculation, and reasoning. Additionally, we propose an efficient model to facilitate human-machine interactions from speech recognition to text classification. In particular, an unsupervised contrastive learning framework is proposed using Bidirectional Encoder Representations from Transformers (BERT) that outperforms its state-of-the-art unsupervised counterparts
The COVID-19 Experience for the Family and Children: A Study of Iranian Immigrant Families in Montreal, Canada
Get PDFThe COVID-19 pandemic has had a significant impact on children and families around the world. Iranian immigrant families in Montreal, Canada, have faced unique challenges related to social isolation, economic pressures, and difficulties accessing public services during this time. Pre-existing stressors, such as language barriers, cultural differences, and the integration process in the new country, compounded these challenges. Eight Iranian immigrant parents (seven mothers and one father) were interviewed regarding their experiences coping with the COVID-19 pandemic, in particular, the effects on the children and the strategies families used to cope with the pandemic. Finally, parents were interviewed regarding their perceptions and beliefs regarding exposing children to nature, its opportunities and challenges and one coping strategy. Parents reported feeling overwhelmed by the demands of managing their children's education at home while also trying to work and manage their own stress. Parents also stated that children, in turn, experienced feeling lonely and disconnected from their peers and struggling with the abrupt changes in their daily routines. Despite these challenges, the study also found that this population was resilient and resourceful, relying on their own networks of support and seeking out community resources to cope with the pandemic. Overall, the study highlights the need for policymakers and service providers to understand better the unique needs of immigrant families, including access to resources in multiple languages, addressing financial challenges, and mental health support during the pandemic
A Tight Coupling Context-Based Framework for Dataset Discovery
Get PDFDiscovering datasets of relevance to meet research goals is at the core of different analysis tasks in order to prove proposed hypothesis and theories. In particular, researchers
in Artificial Intelligence (AI) and Machine Learning (ML) research domains where relevant
datasets are essential for precise predictions have identified how the absence of methods to
discover quality datasets are leading to delay and in many cases failure, of ML projects.
Many research reports have brought out the absence of dataset discovery methods that fills
the gap between analysis requirements and available datasets, and have given statistics to
show how it hinders the process of analysis, with completion rate less than 2%. To the
best of our knowledge, removing the above inadequacies remains “an open problem of great
importance”. It is in this context that the thesis is making a contribution on context-based
tightly coupled framework that will tightly couple dataset providers and data analytics
teams. Through this framework, dataset providers publish the metadata descriptions of
their datasets and analysts formulate and submit rich queries with goal specifications and
quality requirements. The dataset search engine component tightly couples the query specification
with metadata specifications datasets through a formal contextualized semantic
matching and quality-based ranking and discover all datasets that are relevant to analyst
requirements. The thesis gives a proof of concept prototype implementation and reports on
its performance and efficiency through a case study
Evaluation of Citation Graph Thematic Dataset Construction and Paper Filtering Methods for Research Literature Recommendation
Get PDFOne of the main challenges faced by new researchers is immersing themselves in the existing literature relevant to their field of interest. The vastness and continuous growth of knowledge in their field can be overwhelming, making it difficult to identify the most pertinent research papers within their research themes. To address this issue, research paper recommender systems have emerged as valuable tools. These systems allow researchers to find relevant papers based on their specific interests or research themes by analyzing various aspects such as titles, abstracts, and full texts. The quality of the dataset used is crucial for the development, testing, and refinement of these systems to ensure optimal results. Dataset quality directly impacts the accuracy and reliability of a recommender system. In this thesis, I propose a novel approach for constructing datasets using citation graph networks. These networks consist of nodes representing research papers and edges representing citations between them. By leveraging citation graph networks, we gain a more comprehensive understanding of the relationships and influences among different papers compared to traditional methods that rely solely on keyword searches. To evaluate the effectiveness of the citation graph network method, I compared it with the traditional keyword search approach for dataset construction. Additionally, I assessed the effectiveness of three recommender system algorithms: user-based collaborative filtering, combined with PageRank and personalized PageRank algorithms. The experimental findings provide clear evidence that utilizing citation graph network datasets significantly enhances the efficacy of research paper recommender systems. This improvement simplifies the process of finding relevant literature for researchers, potentially accelerating scientific discovery
Evaluating the Relation of BIM and 3D City Models for Energy Demand Assessment
Get PDFClimate change awareness and growing urban density drive worldwide demand for higher-performing buildings, prompting designers to pay greater attention to building efficiency.
Determining the effects of a design change on a building’s overall performance and analyzing how it can be affected by other buildings, is beneficial in the urban building modeling domain. This study intends to address a gap in the literature by establishing a way to measure a building’s energy performance that considers the morphology of its surrounding network of buildings.
In this research, a single building is introduced as a case study among 3 different urban density contexts. Each scenario’s effect on the target building’s energy consumption is estimated and compared with the stand-alone condition. Moreover, several urban morphology indicators are measured for each scenario. The impact of each alternative on building performance was assessed by calculating monthly total energy consumption. The results
show that shading due to the nearby buildings plays an essential role in energy demand throughout the year. Increasing the height of the surrounding buildings in winter increases the heating consumption by up to 11%, and a reduction in cooling by up to 37% is seen during summertime.
Therefore, building energy behavior analysis in urban planning can be the theoretical foundation for logical architecture design and energy consumption reduction when efficient cities are constructed. Moreover, the influence of other urban environmental factors, such
as meteorological loads, Urban Heat Island (UHI) effects, or urban morphology, could be investigated for future studies