Showing papers by "University of Windsor published in 2020"

The Effectiveness of Mindfulness-Based Therapies for ADHD A Meta-Analytic Review

Abstract: Objective: Mindfulness-based therapies (MBTs) have been shown to be efficacious in treating internally focused psychological disorders (e.g., depression); however, it is still unclear whether MBTs provide improved functioning and symptom relief for individuals with externalizing disorders, including ADHD. To clarify the literature on the effectiveness of MBTs in treating ADHD and to guide future research, an effect-size analysis was conducted. Method: A systematic review of studies published in PsycINFO, PubMed, and Google Scholar was completed from the earliest available date until December 2014. Results: A total of 10 studies were included in the analysis of inattention and the overall effect size was d = -.66. A total of nine studies were included in the analysis of hyperactivity/impulsivity and the overall effect was calculated at d = -.53. Conclusion: Results of this study highlight the possible benefits of MBTs in reducing symptoms of ADHD.

A 3D CNN-LSTM-Based Image-to-Image Foreground Segmentation

Abstract: The video-based separation of foreground (FG) and background (BG) has been widely studied due to its vital role in many applications, including intelligent transportation and video surveillance. Most of the existing algorithms are based on traditional computer vision techniques that perform pixel-level processing assuming that FG and BG possess distinct visual characteristics. Recently, state-of-the-art solutions exploit deep learning models targeted originally for image classification. Major drawbacks of such a strategy are the lacking delineation of FG regions due to missing temporal information as they segment the FG based on a single frame object detection strategy. To grapple with this issue, we excogitate a 3D convolutional neural network (3D CNN) with long short-term memory (LSTM) pipelines that harness seminal ideas, viz., fully convolutional networking, 3D transpose convolution, and residual feature flows. Thence, an FG-BG segmenter is implemented in an encoder-decoder fashion and trained on representative FG-BG segments. The model devises a strategy called double encoding and slow decoding, which fuses the learned spatio-temporal cues with appropriate feature maps both in the down-sampling and up-sampling paths for achieving well generalized FG object representation. Finally, from the Sigmoid confidence map generated by the 3D CNN-LSTM model, the FG is identified automatically by using Nobuyuki Otsu’s method and an empirical global threshold. The analysis of experimental results via standard quantitative metrics on 16 benchmark datasets including both indoor and outdoor scenes validates that the proposed 3D CNN-LSTM achieves competitive performance in terms of figure of merit evaluated against prior and state-of-the-art methods. Besides, a failure analysis is conducted on 20 video sequences from the DAVIS 2016 dataset.

Chaos and Complexity in Quantum Mechanics

Abstract: We propose a new diagnostic for quantum chaos. We show that the time evolution of complexity for a particular type of target state can provide equivalent information about the classical Lyapunov exponent and scrambling time as out-of-time-order correlators. Moreover, for systems that can be switched from a regular to unstable (chaotic) regime by a tuning of the coupling constant of the interaction Hamiltonian, we find that the complexity defines a new time scale. We interpret this time scale as recording when the system makes the transition from regular to chaotic behavior.

Healthcare avoidance due to anticipated discrimination among transgender people: A call to create trans-affirmative environments

Abstract: Transgender people encounter interpersonal and structural barriers to healthcare access that contribute to their postponement or avoidance of healthcare, which can lead to poor physical and mental health outcomes. Using the 2015 U.S. Transgender Survey, this study examined avoidance of healthcare due to anticipated discrimination among transgender adults aged 25 to 64 (N = 19,157). Multivariable logistic regression analysis was conducted to test whether gender identity/expression, socio-demographic, and transgender-specific factors were associated with healthcare avoidance. Almost one-quarter of the sample (22.8%) avoided healthcare due to anticipated discrimination. Transgender men had increased odds of healthcare avoidance (AOR = 1.32, 95% CI = 1.21–1.45) relative to transgender women. Living in poverty (AOR = 1.52, 95% CI = 1.40–1.65) and visual non-conformity (AOR = 1.48, 95% CI = 1.33–1.66) were significant risk factors. Having health insurance (AOR = 0.87, 95% CI = 0.79–0.96) and disclosure of transgender identity (AOR = 0.77, 95% CI = 0.68–0.87) were protective against healthcare avoidance. A significant interaction of gender identity/expression with health insurance was found; having health insurance moderated the association between gender identity/expression and healthcare avoidance. Providers should consider gender differences, socio-demographic, and transgender-specific factors to improve accessibility of services to transgender communities. A multi-level and multi-faceted approach should be used to create safe, trans-affirmative environments in health systems.

Dynamic Android Malware Category Classification using Semi-Supervised Deep Learning

Abstract: Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is still an open problem. Supervised machine learning has been used to solve this issue. However, it is far to be perfect because it requires a significant amount of malicious and benign code to be identified and labeled beforehand. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique for deep neural networks, namely pseudo-label, which we train using a set of labeled and unlabeled instances. We use dynamic analysis to craft dynamic behavior profiles as feature vectors. Furthermore, we develop a new dataset, namely CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories: Adware, Banking, SMS, Riskware, and Benign. Our offered dataset comprises the most complete captured static and dynamic features among publicly available datasets. We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. The experimental results show that the model can classify Android apps with respect to malware category with F 1 -Score of 97.84 percent and a false positive rate of 2.76 percent, considerably higher than LP. These results demonstrate the robustness of our model despite the small number of labeled instances.

A review and critique of academic lab safety research.

Abstract: Over the past ten years, there have been several high-profile accidents in academic laboratories around the world, resulting in significant injuries and fatalities. The aftermath of these incidents is often characterized by calls for reflection and re-examination of the academic discipline's approach to safety research and policy. However, the study of academic lab safety is still underdeveloped and necessary data about changes in safety attitudes and behaviours has not been gathered. This Review article critically examines the state of academic chemical safety research from a multifactorial stance, including research on the occurrence of lab accidents, contributors to lab accidents, the state of safety training research and the cultural barriers to conducting safety research and implementing safer lab practices. The Review concludes by delineating research questions that must be addressed to minimize future serious academic laboratory incidents as well as stressing the need for committed leadership from our research institutions.

Origins of Boosted Charge Storage on Heteroatom-Doped Carbons.

Abstract: Although tremendous efforts have been devoted to understanding the origin of boosted charge storage on heteroatom-doped carbons, none of the present studies has shown a whole landscape. Herein, by both experimental evidence and theoretical simulation, it is demonstrated that heteroatom doping not only results in a broadened operating voltage, but also successfully promotes the specific capacitance in aqueous supercapacitors. In particular, the electrolyte cations adsorbed on heteroatom-doped carbon can effectively inhibit hydrogen evolution reaction, a key step of water decomposition during the charging process, which broadens the voltage window of aqueous electrolytes even beyond the thermodynamic limit of water (1.23 V). Furthermore, the reduced adsorption energy of heteroatom-doped carbon consequently leads to more stored cations on the heteroatom-doped carbon surface, thus yielding a boosted charge storage performance.

Integrating Sexual Assault Resistance, Bystander, and Men's Social Norms Strategies to Prevent Sexual Violence on College Campuses: A Call to Action.

Abstract: Sexual assault prevention on college campuses often includes programming directed at men, women, and all students as potential bystanders Problematically, specific types of sexual assault prevention are often implemented on campuses in isolation, and sexual assault risk reduction and resistance education programs for women are rarely integrated with other approaches With increasing focus on the problem of sexual assault on college campuses, it is timely to envision a comprehensive and interconnected prevention approach Implementing comprehensive prevention packages that draw upon the strengths of existing approaches is necessary to move toward the common goal of making college campuses safer for all students Toward this goal, this commentary unpacks the models and mechanisms on which current college sexual assault prevention strategies are based with the goal of examining the ways that they can better intersect The authors conclude with suggestions for envisioning a more synthesized approach to campus sexual assault prevention, which includes integrated administration of programs for women, men, and all students as potential bystanders on college campuses

Assessing the Relative Impact of Diverse Stressors among Public Safety Personnel

Abstract: Public Safety Personnel (PSP; e.g., correctional workers and officers, firefighters, paramedics, police officers, and public safety communications officials (e.g., call center operators/dispatchers)) are regularly exposed to potentially psychologically traumatic events (PPTEs). PSP also experience other occupational stressors, including organizational (e.g., staff shortages, inconsistent leadership styles) and operational elements (e.g., shift work, public scrutiny). The current research quantified occupational stressors across PSP categories and assessed for relationships with PPTEs and mental health disorders (e.g., anxiety, depression). The participants were 4820 PSP (31.7% women) responding to established self-report measures for PPTEs, occupational stressors, and mental disorder symptoms. PPTEs and occupational stressors were associated with mental health disorder symptoms (ps < 0.001). PSP reported substantial difficulties with occupational stressors associated with mental health disorder symptoms, even after accounting for diverse PPTE exposures. PPTEs may be inevitable for PSP and are related to mental health; however, leadership style, organizational engagement, stigma, sleep, and social environment are modifiable variables that appear significantly related to mental health.

Critical Wind Turbine Components Prognostics: A Comprehensive Review

Abstract: As wind energy is becoming a significant utility source, minimizing the operation and maintenance (O&M) expenses has raised a crucial issue to make wind energy competitive to fossil fuels. Wind turbines (WTs) are subject to unexpected failures due to operational and environmental conditions, aging, and so on. An accurate estimation of time to failures assures reliable power production and lower maintenance costs. In recent years, a notable amount of research has been undertaken to propose prognosis techniques that can be employed to forecast the remaining useful life (RUL) of wind farm assets. This article provides a recent literature review on modeling developments for the RUL prediction of critical WT components, including physics-based, artificial intelligence (AI)-based, stochastic-based, and hybrid prognostics. In particular, the pros and cons of the prognosis models are investigated to assist researchers in selecting proper models for certain applications of WT RUL forecast. Our comprehensive review has revealed that hybrid methods are now the leading and most accurate tools for WT failure predictions over individual hybrid components. Strong candidates for future research include the consideration of variable operating environments, component interaction, physics-based prognostics, and the Bayesian application in the development of WT prognosis methods.

Ocean acidification does not impair the behaviour of coral reef fishes

Abstract: The partial pressure of CO2 in the oceans has increased rapidly over the past century, driving ocean acidification and raising concern for the stability of marine ecosystems1–3. Coral reef fishes are predicted to be especially susceptible to end-of-century ocean acidification on the basis of several high-profile papers4,5 that have reported profound behavioural and sensory impairments—for example, complete attraction to the chemical cues of predators under conditions of ocean acidification. Here, we comprehensively and transparently show that—in contrast to previous studies—end-of-century ocean acidification levels have negligible effects on important behaviours of coral reef fishes, such as the avoidance of chemical cues from predators, fish activity levels and behavioural lateralization (left–right turning preference). Using data simulations, we additionally show that the large effect sizes and small within-group variances that have been reported in several previous studies are highly improbable. Together, our findings indicate that the reported effects of ocean acidification on the behaviour of coral reef fishes are not reproducible, suggesting that behavioural perturbations will not be a major consequence for coral reef fishes in high CO2 oceans. In contrast to previous studies, analyses now show that ocean acidification does not perturb important behaviours—such as the avoidance of chemical cues from predators—of coral reef fishes.

Recent advances in the concrete industry to reduce its carbon dioxide emissions

Abstract: Increasing sustainability awareness has put the concrete industry in the spotlight to reduce its carbon dioxide emissions. Most of the carbon dioxide emission from the concrete industry is from the production of Portland cement which is the main binder in concrete, and the transportation of materials. Also, the production of other components in concrete such as aggregates, admixtures, and construction processes contribute to the industry's emission. In addition, the concrete industry is one of the major consumers of natural resources, and the increasing production of concrete has posed a huge strain on the natural reserve of these resources. Nevertheless, the last decade has seen several promising initiatives taken by the industry to improve its sustainability in order to achieve a net-zero emission by 2050. These initiatives vary from using alternative materials such as waste materials, optimizing concrete production processes, use of alternative sources of energy, etc. In order to create more awareness within the construction industry and its stakeholders, this paper explored various ways in which the industry is tackling these sustainability issues. The prospects alongside the challenges for these initiatives are discussed.

Fresh, strength and microstructure properties of geopolymer concrete incorporating lime and silica fume as replacement of fly ash

Abstract: The need to cure fly ash-based geopolymer at elevated temperatures has limited the practicability and sustainability of the composite. Hence, there is an imminent need to find ways at which fly ash-based geopolymers can be cured at ambient conditions. This paper presents the results from the experimental investigation of the effect of lime and silica fume on the properties of geopolymer concrete cured at ambient conditions. Lime and silica fume were used as the partial replacement of fly ash as an aluminosilicate precursor and the corresponding effects on the fresh, strength and microstructure of the geopolymer concrete were investigated. The findings from this study showed that the slump and setting times of the geopolymer concrete increases with increasing silica fume content and reduces with increasing lime content. Also, the use of lime and silica fume as 7.5% and 2% respectively, replacement of fly ash yielded the highest compressive strength. Microstructural investigation showed that the combined use of lime as silica fume in GPC resulted in a densified microstructure.

A New Hybrid Fault Detection Method for Wind Turbine Blades Using Recursive PCA and Wavelet-Based PDF

Abstract: This paper introduces a new condition monitoring approach for extracting fault signatures in wind turbine blades by utilizing the data from a real-time Supervisory Control and Data Acquisition (SCADA) system. A hybrid fault detection system based on a combination of Generalized Regression Neural Network Ensemble for Single Imputation (GRNN-ESI) algorithm, Principal Component Analysis (PCA), and wavelet-based Probability Density Function (PDF) approach is proposed in this work. The proposed fault detection strategy accurately detects incipient blade failures and leads to improved maintenance cost and availability of the system. Experimental test results based on data from a wind farm in southwestern Ontario, Canada, illustrate the effectiveness and high accuracy of the proposed monitoring approach.

A Control-Based Solution for Distance Protection of Lines Connected to Converter-Interfaced Sources During Asymmetrical Faults

Abstract: Full-scale converter-interfaced renewable energy sources (CIRESs) can cause misoperation of distance relays installed in their vicinity. Such failure stems from the different fault behavior of CIRESs and synchronous generators (SGs), based on which available relays have been developed. Several measures have been devised to secure the performance of distance protection by modifying existing relays. This paper proposes a new approach to address misoperation of distance relays during asymmetrical faults by regulating the fault currents of CIRESs such that available distance relays function properly without requiring modification. The prime objective of this method is to mimic certain features of the symmetrical components of SGs’ fault currents that affect distance relays. In the meantime, the proposed method satisfies the constraints of a converter, such as its limited phase current magnitude. As a result, correct operation of the distance relays close to CIRESs is ensured regardless of the fault conditions, including its type, resistance, and location. Some salient features of the proposed method are its simplicity, compatibility with off-the-shelf relays, and independence from the voltage and power rating of the CIRES. Moreover, the proposed method uses only local measurements, and so is cost-effective. PSCAD/EMTDC simulation studies verify the performance of this new method.

Tacky Elastomers to Enable Tear-Resistant and Autonomous Self-Healing Semiconductor Composites

Abstract: Mechanical failure of π‐conjugated polymer thin films is unavoidable under cyclic loading conditions, due to intrinsic defects and poor resistance to crack propagation. Here, the first tear‐resistant and room‐temperature self‐healable semiconducting composite is presented, consisting of conjugated polymers and butyl rubber elastomers. This new composite displays both a record‐low elastic modulus (<1 MPa) and ultrahigh deformability with fracture strain above 800%. More importantly, failure behavior is not sensitive to precut notches under deformation. Autonomous self‐healing at room temperature, both mechanical and electronic, is demonstrated through the physical contact of two separate films. The composite film also shows device stability in the ambient environment over 5 months due to much‐improved barrier property to both oxygen and water. Butyl rubber is broadly applicable to various p‐type and n‐type semiconducting polymers for fabricating self‐healable electronics to provide new resilient electronics that mimic the tear resistance and healable property of human skin.

Migrant farmworkers face heightened vulnerabilities during COVID-19

Abstract: First paragraph: The COVID-19 pandemic has dramatically reshaped Canadian society in just a few short weeks. At the same time, its varied impacts shine a light on pre-existing social inequities. Certain populations, including low wage workers, racial minorities, homeless people, and older and disabled residents of long-term care facilities have been disproportionately impacted. One group that is particularly vulnerable to the effects of the crisis, yet has been largely neglected in discussions thus far, is the migrant worker population. . . . See the press release for this article.

Multi-Objective Stochastic Fractal Search: a powerful algorithm for solving complex multi-objective optimization problems

Abstract: Stochastic Fractal Search (SFS) is a novel and powerful metaheuristic algorithm. This paper presents a Multi-Objective Stochastic Fractal Search (MOSFS) for the first time, to solve complex multi-objective optimization problems. The presented algorithm uses an external archive to collect efficient Pareto optimal solutions during the optimization process. Using dominance rules, leader selection and grid mechanisms, MOSFS precisely approximates the true Pareto optimal front. The MOSFS is implemented on nine multi-objective benchmark functions (CEC 2009) with multimodal, convex, discrete and non-convex optimal Pareto fronts. Performance of the proposed algorithm is compared to well-known algorithms. In addition, different performance measures are considered to evaluate the convergence and coverage abilities of the algorithms including Inverted Generational Distance, Maximum Spread and Spacing. Furthermore, statistical analyses are utilized to determine the superior algorithm. The results revealed that the MOSFS performs significantly better than other algorithms in both convergence and coverage and it is able to approximate true Pareto front precisely. In the end, MOSFS is implemented to solve a real-world engineering design problem called welded beam design problem and efficiency of the algorithm is compared to recently developed algorithms. The results of simulations and the Wilcoxon rank-sum test showed that the MOSFS is able to provide the most promising Pareto front for the problem considering various performance metrics at a 95% confidence level.

The Complicated and Confusing Ecology of Microcystis Blooms

Abstract: Blooms of the toxin-producing cyanobacterium Microcystis are increasing globally, leading to the loss of ecosystem services, threats to human health, as well as the deaths of pets and husbandry animals. While nutrient availability is a well-known driver of algal biomass, the factors controlling "who" is present in fresh waters are more complicated. Microcystis possesses multiple strategies to adapt to temperature, light, changes in nutrient chemistry, herbivory, and parasitism that provide a selective advantage over its competitors. Moreover, its ability to alter ecosystem pH provides it a further advantage that helps exclude many of its planktonic competitors. While decades of nutrient monitoring have provided us with the tools to predict the accumulation of phytoplankton biomass, here, we point to factors on the horizon that may inform us why Microcystis is presently the dominant bloom former in freshwaters around the world.