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Showing papers by "Concordia University published in 2022"


Journal ArticleDOI
TL;DR: In this paper, hierarchical loose thin film nanocomposite (TFN) membranes are fabricated via the stacking of polydopamine-modified core-shell-structured CNCs on electrospun nanofiber mats (ENMs), followed by the crosslinking with polyethyleneimine (PEI).

36 citations


Journal ArticleDOI
TL;DR: In this article, a one-pot solvothermal synthesis of carbon dots from glutathione and formamide precursors was performed to obtain the emissive carbon-core and molecular states responsible for the blue and red optical signatures, respectively.

35 citations


Journal ArticleDOI
TL;DR: In this article, the authors present an overview on recent developments in conductive inorganic membranes for water purification and wastewater treatment, and discuss several existing issues and highlights the research needs for the future development and application of the electrochemical membrane filtration (EMF) process.

31 citations


Journal ArticleDOI
TL;DR: In this paper, a polyvinyl pyrrolidone (PVP) coated porous potassium-doped g-C3N4 (PKCN) membrane was fabricated for the first time by vacuum filtration.

31 citations


Journal ArticleDOI
TL;DR: In this article , hierarchical loose thin film nanocomposite (TFN) membranes are fabricated via the stacking of polydopamine-modified core-shell-structured CNCs on electrospun nanofiber mats (ENMs), followed by the crosslinking with polyethyleneimine (PEI).

27 citations


Journal ArticleDOI
TL;DR: In this article , a novel near-infrared dye named ML880 with naphthalimide modified cyanine skeleton is reported, which exhibits remarkable tumor inhibition effects through PDT/PTT/chemotherapy in vivo.

24 citations


Journal ArticleDOI
15 Jan 2022-Energy
TL;DR: A novel DES combining solar photovoltaic and hybrid energy storage, combining the Monte Carlo method and improved K-means clustering, and an optimal design method considering system independence and solar energy utilization scale are proposed, and the novel system is optimized.

20 citations


Journal ArticleDOI
TL;DR: In this paper, a one-dimensional mathematical model by considering axially dispersed plug flow and Langmuir-Hinshelwood (L-H)-based reaction rate as well as linear source spherical emission (LSSE) model for the irradiation distribution on the media surface was developed for the prediction of the byproducts concentration.

18 citations


Journal ArticleDOI
01 Jan 2022-Energy
TL;DR: An innovative Concentrating Glazing system to be adopted in smart building facades: The Concentrating Photovoltaic Glazing System (CoPVG), which consists of a double-glazing panel integrating a series of concentrating lenses.

17 citations


Journal ArticleDOI
TL;DR: A comprehensive review of occupant behavior (OB) modeling approaches, occupant-related input parameters with particular focus on the occupancy schedule, lighting, appliances use schedule, temperature set-point schedule, and domestic hot water usage for urban building energy modeling is presented in this paper .

17 citations


Journal ArticleDOI
TL;DR: In this article, the added benefit of modelling climate at convection-permitting spatial resolutions (grid spacing) was introduced to solve the problem of extreme heat events (EHEs).

Journal ArticleDOI
TL;DR: In this paper, the aging course of polyethylene (PE) in shaking seawater and ultraviolet (UV) radiation conditions was investigated, and the effects of aged MPs on oil behavior in water-sand system were further explored.

Journal ArticleDOI
TL;DR: In this article, a polynomial-based objective model was proposed to find the MTPA angle to maximize the control objective (the ratio of output torque to stator current), which can avoid the timeconsuming search process resulting in fast detection speed in comparison to existing search-based methods.
Abstract: For interior permanent magnet synchronous machines (IPMSMs), maximum torque per ampere (MTPA) control aims to find the MTPA angle to maximize the control objective (the ratio of output torque to stator current). This article proposes a novel online polynomial curve fitting technique for fast and accurate MTPA angle detection, which is motivated by the fact that the objective increases before MTPA angle and decreases after MTPA angle. This article proposes a polynomial-based objective model and identifies the polynomial parameters from a few test data for direct MTPA angle calculation. The proposed approach can avoid the time-consuming search process resulting in fast detection speed in comparison to existing search-based methods. In implementation, the current angle is set to a few test values to obtain the data for online curve fitting and MTPA angle calculation, in which there is no need of machine inductances and PM flux linkage. Moreover, the proposed polynomial model is analyzed to obtain the number of test data required for fast and accurate MTPA angle detection. The proposed approach is validated with extensive experiments and comparisons with existing methods on a laboratory IPMSM.

Journal ArticleDOI
TL;DR: In this article , a one-dimensional mathematical model by considering axially dispersed plug flow and Langmuir-Hinshelwood (L-H)-based reaction rate as well as linear source spherical emission (LSSE) model for the irradiation distribution on the media surface was developed for the prediction of the byproducts concentration.

Journal ArticleDOI
TL;DR: In this article , the authors analyzed the impact of using different types of occupancy prediction models on the performance of occupancy-based heating, ventilation, and air conditioning (HVAC) control systems in residential buildings.

Journal ArticleDOI
TL;DR: In this paper, a review comprehensively summarizes recent advances using green materials for oil spill treatment, namely, oil/water filtration, oil sorption, and surface washing, and the preparation methods, wettability characteristics, oil removal performance, and stability of green biomass-derived materials were introduced.
Abstract: Intensive energy production and consumption are associated with many oil spill accidents which can result in environmental pollution and other socio-economic impacts. Oil/water separation and surface washing strategies have been adopted to remove oil from both water and shorelines. Recently, green biomass-derived materials have attracted tremendous interest from researchers since they are low-cost, non-toxic, widely available, and environmentally friendly. This review comprehensively summarizes recent advances using such green materials for oil spill treatment, namely, oil/water filtration, oil sorption, and surface washing. The preparation methods, wettability characteristics, oil removal performance, and stability of green biomass-derived materials were introduced. The perspectives for future challenges and prospects of green materials in oil spill response are also proposed.

Journal ArticleDOI
TL;DR: In this article , a review comprehensively summarizes recent advances using green materials for oil spill treatment, namely, oil/water filtration, oil sorption , and surface washing, and the preparation methods, wettability characteristics, oil removal performance, and stability of green biomass-derived materials were introduced.
Abstract: Intensive energy production and consumption are associated with many oil spill accidents which can result in environmental pollution and other socio-economic impacts. Oil/water separation and surface washing strategies have been adopted to remove oil from both water and shorelines. Recently, green biomass-derived materials have attracted tremendous interest from researchers since they are low-cost, non-toxic, widely available, and environmentally friendly. This review comprehensively summarizes recent advances using such green materials for oil spill treatment, namely, oil/water filtration, oil sorption , and surface washing. The preparation methods, wettability characteristics, oil removal performance, and stability of green biomass-derived materials were introduced. The perspectives for future challenges and prospects of green materials in oil spill response are also proposed.

Journal ArticleDOI
TL;DR: In this article, a steady-state Gaussian plume model was applied to analyze the concentration distribution of fine particulate matter produced by in-situ burning, as well as to assess the health risks associated with different combustion methods and ambient conditions, in reference to three simulation scenarios.

Journal ArticleDOI
TL;DR: In this article , a steady-state Gaussian plume model was applied to analyze the concentration distribution of fine particulate matter produced by in-situ burning, as well as to assess the health risks associated with different combustion methods and ambient conditions, in reference to three simulation scenarios.

Journal ArticleDOI
TL;DR: This article proposes a dynamic model and a tracking control strategy for a dielectric elastomer (DE) actuator based on the model predictive controller (MPC) that can overcome the influences of the modeling error and uncertainties on the control precision.
Abstract: This article proposes a dynamic model and a tracking control strategy for a dielectric elastomer (DE) actuator based on the model predictive controller (MPC). First, the dynamic model of the DE actuator is established, which can describe its asymmetric hysteresis, creep, and even rate-dependent hysteresis behaviors simultaneously. Then, on the basis of the established dynamic model, an inverse compensation controller (ICC) is designed to compensate the hysteresis and creep nonlinearities of the DE actuator on its tracking control. Moreover, the MPC is designed to cooperate with the ICC, which can overcome the influences of the modeling error and uncertainties on the control precision. Finally, several experiments demonstrate the effectiveness of the proposed dynamic model and control strategy.

Journal ArticleDOI
TL;DR: In this paper, a superwetting and robust polyethersulfone (PES)-PAA-ZrO2 nanofiltration membrane was proposed through surface modification for PAH removal with high efficiency.

Journal ArticleDOI
Jiwei Zou1
TL;DR: In this article , a reference year selection method in terms of typical and extreme reference years based on future climate datasets was evaluated to assess both outdoor and indoor overheating in the future. But neither the severest nor the typical monthly outdoor and interior overheating conditions could be predicted by the design summer year method.

Journal ArticleDOI
TL;DR: In this paper, the authors studied the transmission of a detonation wave across a layer of inert gas via one-and two-dimensional numerical simulations based on the reactive Euler equations.

Journal ArticleDOI
TL;DR: In this paper , an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance.
Abstract: Reverse transcription-polymerase chain reaction is currently the gold standard in COVID-19 diagnosis. It can, however, take days to provide the diagnosis, and false negative rate is relatively high. Imaging, in particular chest computed tomography (CT), can assist with diagnosis and assessment of this disease. Nevertheless, it is shown that standard dose CT scan gives significant radiation burden to patients, especially those in need of multiple scans. In this study, we consider low-dose and ultra-low-dose (LDCT and ULDCT) scan protocols that reduce the radiation exposure close to that of a single X-ray, while maintaining an acceptable resolution for diagnosis purposes. Since thoracic radiology expertise may not be widely available during the pandemic, we develop an Artificial Intelligence (AI)-based framework using a collected dataset of LDCT/ULDCT scans, to study the hypothesis that the AI model can provide human-level performance. The AI model uses a two stage capsule network architecture and can rapidly classify COVID-19, community acquired pneumonia (CAP), and normal cases, using LDCT/ULDCT scans. Based on a cross validation, the AI model achieves COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text], and accuracy of [Formula: see text]. By incorporating clinical data (demographic and symptoms), the performance further improves to COVID-19 sensitivity of [Formula: see text], CAP sensitivity of [Formula: see text], normal cases sensitivity (specificity) of [Formula: see text] , and accuracy of [Formula: see text]. The proposed AI model achieves human-level diagnosis based on the LDCT/ULDCT scans with reduced radiation exposure. We believe that the proposed AI model has the potential to assist the radiologists to accurately and promptly diagnose COVID-19 infection and help control the transmission chain during the pandemic.

Journal ArticleDOI
TL;DR: In this paper, a novel distributed energy system uses a jacket water heat exchanger and a waste heat boiler to recover the waste heat of the internal combustion engine in turn and shares a water tank with the solar thermal collector in parallel.

Journal ArticleDOI
TL;DR: Wang et al. as discussed by the authors proposed a hybrid and parallel deep fusion framework for stock price movement prediction ( COVID19-HPSMP), which integrated different and diversified learning architectures.
Abstract: The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction ( COVID19 PRIMO ) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction ( COVID19-HPSMP ), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.

Journal ArticleDOI
TL;DR: In this paper , a facile route for modulating the photoluminescence and radioluminescent properties of Tb(III)-based MOFs is reported by using Tb-III cluster nodes as X-ray attenuators and organic linkers with varying excited state energies as sensitizers.
Abstract: Luminescent metal–organic frameworks (MOFs) are of interest for sensing, theranostics, dosimetry, and other applications. The use of lanthanoids in MOF nodes allows for intrinsic metal-based luminescence. In this work, a facile route for modulating the photoluminescent and radioluminescent properties of Tb(III)-based MOFs is reported. By using Tb(III) cluster nodes as X-ray attenuators, and organic linkers with varying excited state energies as sensitizers, MOFs with metal-based, linker-based, and metal+linker-based photo- and radioluminescence are reported.

Journal ArticleDOI
TL;DR: In this article, the authors reviewed recent advances in modeling oil biodegradation from aspects of oil phases, reaction kinetics, microbial activities, environmental conditions, and beach hydrodynamics.
Abstract: Modeling oil biodegradation and remediation has become an increasingly important means to predict oil persistence and explore potential in-situ bioremediation strategies for oil-contaminated beaches. Beaches involve complex mixing dynamics between seawater and groundwater. Thus, numerically predicting oil biodegradation within beach systems faces major challenges in merging highly dynamic biogeochemical conditions into microbial degradation models. In this paper, we reviewed recent advances in modeling oil biodegradation from aspects of oil phases, reaction kinetics, microbial activities, environmental conditions, and beach hydrodynamics. We identified key controlling factors of oil biodegradation, highlighted the importance of fate and transport processes on nearshore oil biodegradation, and suggested some advances needed to achieve for developing a robust numerical model to predict oil biodegradation and bioremediation within beaches.

Journal ArticleDOI
TL;DR: The ability to perform gradient-free aerodynamic shape optimization using Large Eddy Simulation (LES) with the Mesh Adaptive Direct Search (MADS) optimization algorithm with a Dynamic Polynomial Approximation (DPA) procedure is demonstrated.

Journal ArticleDOI
TL;DR: In this paper , a novel hybrid and parallel deep fusion framework for stock price movement prediction (COVID19-HPSMP) is proposed, which combines scattered social media news related to COVID-19 with historical mark data.
Abstract: The novel of coronavirus (COVID-19) has suddenly and abruptly changed the world as we knew at the start of the 3rd decade of the 21st century. Particularly, COVID-19 pandemic has negatively affected financial econometrics and stock markets across the globe. Artificial Intelligence (AI) and Machine Learning (ML)-based prediction models, especially Deep Neural Network (DNN) architectures, have the potential to act as a key enabling factor to reduce the adverse effects of the COVID-19 pandemic and future possible ones on financial markets. In this regard, first, a unique COVID-19 related PRIce MOvement prediction (COVID19 PRIMO) dataset is introduced in this paper, which incorporates effects of social media trends related to COVID-19 on stock market price movements. Afterwards, a novel hybrid and parallel DNN-based framework is proposed that integrates different and diversified learning architectures. Referred to as the COVID-19 adopted Hybrid and Parallel deep fusion framework for Stock price Movement Prediction (COVID19-HPSMP), innovative fusion strategies are used to combine scattered social media news related to COVID-19 with historical mark data. The proposed COVID19-HPSMP consists of two parallel paths (hence hybrid), one based on Convolutional Neural Network (CNN) with Local/Global Attention modules, and one integrated CNN and Bi-directional Long Short term Memory (BLSTM) path. The two parallel paths are followed by a multilayer fusion layer acting as a fusion center that combines localized features. Performance evaluations are performed based on the introduced COVID19 PRIMO dataset illustrating superior performance of the proposed framework.