Ryerson University
  • Toronto, Ontario, Canada
Recent publications
The Greater Toronto Area (GTA), located in a province that is lush in natural wonders and geological gems, is actively investing in projects that serve in connecting its residents with stunning landscapes and natural sceneries. A high-profile cable-stayed bridge over a flowing water stream is proposed to accommodate pedestrians and bikers while capturing the picturesque nature and landmark views in the GTA. The cable-stayed system possesses favorable aesthetics, durability, and sustainability. The Cable-bridge consists of cables running directly from A-frame towers to a three-span superstructure, forming a graceful fan cable arrangement. The utilization of corrosion-resistant materials and high-performance components, such as galvanized/sheathed cables and galvanized steel members, is considered for both the superstructure and substructure. Also, the bridge deck incorporates precast concrete deck panels reinforced with corrosion-free glass fibre-reinforced polymers (GFRP) bars. A sophisticated structural analysis is performed, using the finite-element modelling (FEM) to obtain precise figures of the straining actions and design demands as well as representing the bridge construction stages. An optimal design has been achieved while facing various challenges due to the complexity of the structural behaviour during construction and thereafter. For comparison purposes, a conventional concrete semi-integral bridge is investigated where different types of precast/prestressed girders such as CPCI and NU girders are evaluated. Despite having a conventional option as a favorable economic approach, the cable-stayed bridge is the strongly recommended choice as it offers the aesthetics level desired by the client and the public along with high sustainability and outstanding structural integrity.
Plastic pollution is considered one of the most threatening environmental issues of the 21st century. Microplastics (MPs) and nanoplastics (NPs) have significantly contributed to greenhouse gas (GHG) emissions and hence it has an impact on climate change. Methane and ethylene are the two main greenhouse gases that are produced from the most used plastics when exposed to ambient solar radiation. On the one hand, the pollution caused by the MP and NP can impact the gas exchange and CO2 circulation, resulting in higher greenhouse gas emissions. On the other hand, climate change has an impact on MPs and NPs. For example, the terrestrial plastic, windborne plastic, plastic resuspension from sediment, and plastic persistence have been increased because of the impact of the climate change. The interactions between plastic pollution and climate change have yet to be entirely understood as this topic has only recently gained attention. Literature showed that the interactions between plastic pollution and climate change are significant and cannot be overlooked. It has been proven that MPs have deleterious effects on the environment that cannot be ignored. There are some efforts of mitigating the potential impact of MPs on the environment such as recycling the plastics and ocean plastic clean-up.
Micro- and nanoplastics (MPs and NPs) permeate easily through ecosystems and food webs, transporting adsorbed contaminants and impeding metabolic and other essential processes in living organisms. Although not well studied, the cumulation of effects of MPs and NPs indicates potential harm to human health and well-being. The media has been instrumental in bringing plastic pollution awareness to the forefront of societal discussions. Still, the less visible nature of MPs and NPs has resulted in much less coverage and public understanding of these contaminants. By identifying the interrelationships between the impacts of MPs and NPs on the natural environment, ecosystems, and anthropogenic societies, better policy and actions can be taken to mitigate long-term adverse effects. Academic researchers and policymakers need to work closely to develop iterative approaches for MPs and NPs management strategies.
Plastics are an essential component of our day-to-day life; however, their continuously growing production and utilization have inevitably increased plastic waste generation worldwide. Currently, a small portion of plastic wastes is recycled, while a significant portion is disposed. In recent years, the improper management and disposal practices of plastic wastes raised substantial concerns due to potential environmental and health hazards. The potential health risks of microplastics (MPs) and nanoplastics (NPs) exposure to the natural environment have attracted increasing concerns. Specifically, the occurrence and transport of antibiotic resistance genes (ARGs) on MPs/NPs in aquatic and soil environments have been identified. In addition to the adsorption of antibiotics, MPs/NPs act as potential carriers for the growth of antibiotic-resistant bacteria, ultimately driving the spread of ARGs to the environment. This book chapter provides an overview of MPs/NPs sources, emphasizing their interactions with ARGs and exposure pathways.
Thermal dose models are metrics that quantify the thermal effect on tissues based on the temperature and the time of exposure. These models are used to predict and control the outcome of hyperthermia (up to 45°C) treatments, and of thermal coagulation treatments at higher temperatures (>45°C). The validity and accuracy of the commonly used models (CEM43) are questionable when heating above the hyperthermia temperature range occurs, leading to an over-estimation of the accumulation of thermal damage. A new CEM43 dose model based on an Arrhenius-type, Vogel-Tammann-Fulcher, equation using published data, is introduced in this work. The new dose values for the same damage threshold that was produced at different in-vivo skin experiments were in the same order of magnitude, while the current dose values varied by two orders of magnitude. In addition, the dose values obtained using the new model for the same damage threshold in 6 lesions in ex-vivo liver experiments were more consistent than the current model dose values. The contribution of this work is to provide new modeling approaches to inform more robust thermal dosimetry for improved thermal therapy modeling, monitoring, and control.
Soft robots have become important members of the robot community with many potential applications owing to their unique flexibility and security embedded at the material level. An increasing number of researchers are interested in their designing, manufacturing, modeling, and control. However, the dynamic simulation of soft robots is difficult owing to their infinite degrees of freedom and nonlinear characteristics that are associated with soft materials and flexible geometric structures. In this study, a novel multi-flexible body dynamic modeling and simulation technique is introduced for soft robots. Various actuators for soft robots are modeled in a virtual environment, including soft cable-driven, spring actuation, and pneumatic driving. A pneumatic driving simulation was demonstrated by the bending modules with different materials. A cable-driven soft robot arm prototype and a cylindrical soft module actuated by shape memory alley springs inspired by an octopus were manufactured and used to validate the simulation model, and the experimental results demonstrated adequate accuracy. The proposed technique can be widely applied for the modeling and dynamic simulation of other soft robots, including hybrid actuated robots and rigid-flexible coupling robots. This study also provides a fundamental framework for simulating soft mobile robots and soft manipulators in contact with the environment.
Inflammation is a defense mechanism that can protect the host against microbe invasion. A proper inflammatory response can maintain homeostasis, but continuous inflammation can cause many chronic inflammatory diseases. To properly treat inflammatory disorders, the molecular mechanisms underlying the development of inflammation need to be fully elucidated. Pyroptosis is an inflammation-related cell death program, that is different from other types of cell death. Pyroptosis plays crucial roles in host defense against infections through the release of proinflammatory cytokines and cell lysis. Accumulating evidence indicates that pyroptosis is associated with inflammatory diseases, such as arthritis, pneumonia, and colonitis. Furthermore, pyroptosis is also closely involved in cancers that develop as a result of inflammation, such as liver cancer, esophageal cancer, pancreatic cancer, and colon cancer. Here, we review the function and mechanism of pyroptosis in inflammatory disease development and provide a comprehensive description of the potential role of pyroptosis in inflammatory diseases.
The tourism industry is extremely important to the world economy; yet, the industry falls short when it comes to economic, social, and environmental issues. Blockchain as an information technology can be utilized to help solve these issues and establish sustainable tourism globally. However, the challenges to blockchain adoption in the tourism industry have not yet been examined systematically. The goal of this study, therefore, is three-fold: we first identify the challenges to blockchain using literature review and expert opinions. Then, we examine them using the proposed rough Interpretive Structural Modeling - Cross-Impact Matrix Multiplication based on expert judgments. Finally, we link these challenges to diffusion of innovation theory. The results suggest that “lack of technical maturity” and “lack of interoperability” are the most important challenges of blockchain in the tourism industry. The findings of the study support macro- and micro-level decision-making in tourism industry's prospective applications of blockchain.
Introduction Injuries and deaths from motor vehicle collisions are a significant public health issue. As public health researchers and practitioners, we must support the work of municipalities by advocating for effective interventions to reduce this burden. This requires an evidence-based approach; however, many interventions embedded in existing road safety policies in Canada are not supported by evidence. The objective of this work was to review the built environment (BE) interventions in road safety policies in five, urban municipalities in Canada and summarize the peer-reviewed literature to support them. Methods Data were retrieved through an environmental scan of road safety policies across five Canadian urban municipalities, supplemented by a scoping review of articles indexed in MEDLINE and a grey literature search. Inclusion criteria were: 1) BE interventions, 2) collision or collision pathway outcomes (e.g., vehicle speed, vehicle volume), 3) evaluative study designs, and 4) studies published less than 20 years ago (i.e., 1999–2019). We critically appraised the included studies using the TREND checklist. Data were extracted and summarized, grouped by intervention type. Results The environmental scan yielded 42 BE interventions within the existing road safety policies across CHASE regions. The scoping review found a total of 124 studies; the final sample included 45 studies with 29 interventions. The median TREND score [interquartile range (IQR)] was 16 (15, 17) out of 22. Published scientific evidence was not found for 13interventions. Conclusions A low proportion of included studies specific to the existing road safety policies in urban areas in Canada demonstrated a reduction in collisions. Further, significant variability in the level of effectiveness across interventions exists. Information specific to the effectiveness of interventions should be an integral part of the decision making process for BE change; however, more work is needed to better understand critical decision making factors. Mesh and keywords collisions, traffic; injuries and wounds; policy; review, scoping.
We employed the Attraction-Selection-Attribution perspective to examine the effect of gender on crowdfunding. Based on the gender of both project founders and backers, we identified two types of fit, superficial and characteristic. Further, based on characteristic fit, we developed a typology of crowdfunding attractions. We then hypothesized that both superficial and characteristic fits can lead to crowdfunding success. We collected and analyzed data to demonstrate how different characteristics may be important to each attraction. Our findings suggest that similarity is positively related to funding intention for all crowdfunding founders. Male crowdfunding founders should demonstrate their credibility (trustworthiness and expertise) in front of their backers. Female crowdfunding founders should focus on their physical attractiveness, friendliness and warmth to attract more funding from their backers.
Using the theory of social exchange, we investigated the mediating role of a good match between commitment and personal character (independent variables) and achievement of mentorship program objectives (dependent variables). Even though mentorship programs are designed to fulfill their designated objectives, the extent to which they are achieved is often not fully known. The focus of the study is a post-secondary professional mentorship program offered Haskayne School of Business, University of Calgary, which matches undergraduate and graduate students with business professionals. Data were collected primarily through questionnaires. We found that commitment (both the mentees' commitment and the mentees' perception of their mentors' commitment) and the mentors' character are important variables to actuate the exchange mechanism for learning to occur. These input variables are significant in predicting a good match and ultimately in determining whether the mentee's expectations are met, which had not been tested through an empirical analysis in prior literature. Also, our findings suggest that the importance of the mentor's personal character as a role model must be considered in the matching process. Care must be taken to customize the match to the needs of the specific mentor and mentee. Based on the findings, several suggestions are made for improvement of mentorship programs.
Rail transport of hazardous material (RTHM) plays a vital role in the supply chain of raw materials and products. However, RTHM can pose severe risks due to the large quantities of flammable and explosive chemicals transported over rail tracks crossing residential and industrial areas and possible human and technical failures. Among the potential safety issues, the domino effect is one of the most feared events, which can have devastating consequences despite its relatively low probability. As the first study, the present investigation develops a dynamic risk analysis model for analyzing domino effects in RTHM based on Dynamic Bayesian Network. Accident scenarios such as pool fire, flash fire, fire ball, vapor cloud explosion, and BLEVE are considered to analyze domino effects. The model performance is tested on a real RTHM (i.e., gasoline transportation), demonstrating the effectiveness of the proposed model in simulating the domino-driven effects in terms of both consequences and probability escalation and in dealing with the parameter and model uncertainties.
It is of critical importance to understand the relationships between crop yield, soil properties and topographic characteristics for agricultural management. This study’s objective was to compare techniques to quantify the relationship between soil and topographic characteristics for predicting crop yield using high-resolution data and analytical techniques. The study was conducted on a multiple field dataset located in Southwestern Ontario, Canada, where few studies have assessed the impact of applications for precision agriculture and machine learning (ML) to the soil property-yield relationship in this region. The dataset included 145,500 observations of corn and soybean yield, topographic and soil nutrient characteristics. The attributes considered for this study included pH, soil organic matter (OM) content, cation exchange capacity (CEC), soil test phosphorus, zinc (Zn), potassium (K), elevation and topographic wetness index. Multiple linear regression (MLR), artificial neural networks, decision trees and random forests were compared to identify methods able to relate soil properties and crop yields on a subfield scale (2 m). Random forests were the most successful at predicting yield with an R2 value of 0.85 for corn and 0.94 for soybeans. MLR was the least successful with an R2 of 0.40 for corn and 0.45 for soybeans. Cross-validation experiments showed that random forest models in most cases could predict low- and high-yield areas from fields excluded from training datasets, but this was not possible in all cases. Techniques tested the models and identified significant soil and topographic attributes when predicting yield, though the identification was subject to some uncertainty. These results suggest that ML techniques might be used to predict high yield areas of fields without existing yield maps, if those fields have similar relationships of soil properties to yield.
Background Important inequities in child pedestrian-motor vehicle collisions (PMVC) have been observed. The mechanism through which social dimensions influence child PMVC is not well understood, nor is the role of the roadway-built environment. Methods The relationship between area-level social dimensions (material deprivation, proportion recent immigrants, proportion visible minority) and police-reported child PMVC between 2010 and 2018 in Toronto, Canada was examined using multivariable negative binomial regression models, controlling for built environment covariates. Results All social dimensions were significantly associated with child PMVC, including material deprivation (Incidence Rate Ratio (IRR–adjusted): 1.31, 95 % Confidence Interval (CI): 1.22–1.40), recent immigrant proportion (IRR adjusted: 1.58, 95 %CI: 1.30–1.92, per 10 % increase), and visible minority proportion (IRR adjusted: 1.09, 95 %CI: 1.05–1.12, per 10 % increase). Built environment features did not attenuate these associations. Conclusion This study provides evidence of social inequalities in child PMVC, suggesting a need to target traffic safety interventions towards the most socially marginalized areas.
The objective of this work is to identify clinical factors that modulate the risk of progression to lung diseases such as asthma, chronic obstructive pulmonary disease (COPD), emphysema, lung cancer, bronchitis, and allergies among patients using data extracted with assistance from machine learning algorithms. In this work, we have gathered 250 instances along with 14 attributes. These information have been gathered from patients experiencing various lung illnesses alongside different indications. The lung illnesses trait contains two sorts of class which are ‘Positive’ and ‘Negative.’ ‘Positive’ implies that the individual has lung illness. The dataset has been trained using K-fold cross-validation technique. Four machine learning algorithms have been used for analysis which are logistic regression, random forest, KNN, and Bayesian networks.
Diabetes is a condition in which blood glucose, called as blood sugar, is high in an abnormal way. If the prediction of disease is possible at an early stage, then the risk factors associated with diabetes can be considerably lower in severity. The main problem and highly challenging task are to predict diabetes accurately, and the reason of this challenge is the diabetes dataset’s insufficient number of labels data and the existence of outliers. This research paper proposes a strong framework to predict the disease with the help of different types of machine learning (ML) algorithms: K-nearest neighbor (KNN), support vector machine (SVM), decision trees (DTs), Naive Bayes (NB), and logistic regression (LR). For implementation, a dataset has been taken from a PIMA database consisting patient’s health record, and these five machine learning techniques are applied to that dataset. A comparison between all the algorithms is presented in this paper. The motive of the paper is to provide assistance to doctors with their practitioners for the early prediction of diabetes using ML algorithms.KeywordsData miningPredictive analysisMachine learning algorithmsHealthcareDiabetes
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13,952 members
Filippo Arnaldo Salustri
  • Department of Mechanical Engineering
Alexandre Douplik
  • Department of Physics
Frederic Dimanche
  • Ted Rogers School of Hospitality and Tourism Management
Kathryn Church
  • School of Disability Studies
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