McMaster University
  • Hamilton, Ontario, Canada
Recent publications
Advancements in Microelectromechanical systems (MEMS) have enabled the manufacture of affordable and efficient wearable devices. In sensor-based gait analysis, motion and biofeedback sensor devices are easily attached to different parts of the body. Instrumentation of gait using different sensor technologies enables researchers and clinicians to capture high-resolution quantitative motion data within and beyond the lab. Integration of advanced sensor technologies provides objective and rater-independent multimodal outcomes that complement established clinical examination. Multi-modal data capture in ecologically valid, patient-relevant habitual settings opens new possibilities to monitor fluctuating and rare incidents by informing different aspects of impaired gait. Interconnected device communication and the Internet of Things (IoT) provide the infrastructural platform to enable remote gait assessment. However, an extended period of motion data recorded by different sensor technologies results in a vast amount of unlabelled data. Computational methods and artificial intelligence techniques (e.g., data mining) provide opportunities to manage data collected in unsupervised environments. Although technological advancement and algorithms promote remote gait assessment, more work needs to be done in terms of analytical and clinical validation to achieve robust and reliable gait analysis tools that contribute to better rehabilitation and treatment.
Background Some studies suggest that patients with pulmonary hypertension (PH) may be at higher risk of complications and death after noncardiac surgery. However, the magnitude of these associations is unclear. Objectives To determine the associations between PH and adverse outcomes after noncardiac surgery. Methods We searched PUBMED and EMBASE for studies published from January 1970 to April 2022. We included studies that reported the association between PH and one or more outcomes of interest occurring after noncardiac surgery. Data were pooled using random-effects models and reported as summary odds ratios (ORs) with 95% confidence intervals (CIs). Results Eighteen studies met eligibility criteria (n=18,214,760). PH was independently associated with mortality (adjusted odds ratio [OR] 2.09; 95% CI, 1.51-2.90; I²=98%; 8 studies). PH was associated with a higher unadjusted risk of deep venous thrombosis (OR 4.02; 95% CI, 2.14-7.54; I²=85%; 3 studies), pulmonary embolism (OR 4.16; 95% CI, 3.23-5.36; I²=69%; 7 studies), myocardial infarction (OR 1.49; 95% CI, 1.44-1.54; I²=0%; 5 studies), congestive heart failure or cardiogenic shock (OR 3.37; 95% CI, 1.73–6.60; I²=34%; 5 studies), length of hospital stay (mean difference 1.97 days; 95% CI, 0.81–3.12; I²=99%; 5 studies), and delayed extubation (OR 5.98; 95% CI, 1.70–21.02; I²=3%; 3 studies). PH was associated with lower unadjusted risk of postoperative stroke (OR 0.93; 95% CI, 0.88–0.98; I²=0%; 3 studies). Conclusion PH is a predictor of morbidity and mortality after noncardiac surgery. High quality studies are needed to determine effective strategies for reducing postoperative complications in this population.
Individuals differ in the extent to which they believe that their emotions are controllable or not, and these beliefs have significant impacts on emotional functioning. A strong belief that your emotions are uncontrollable (fixed mindset) is a vulnerability for emotional dysfunction, such as internalizing symptoms; however, the proximal mechanisms that might explain how emotional beliefs manifest as symptoms remain unclear. Across two studies, we examined whether mindset was indirectly related to internalizing symptoms through use of avoidance-based strategies and whether perceptions of stress function to amplify this relationship. In Study 1, (N = 163; mean age = 17.9), a fixed mindset was associated with a greater presence of depression and anxiety symptoms in undergraduates indirectly through the use of avoidance for anxiety, but not depression. Perceived stress did not moderate the indirect effect. In Study 2 (N = 183; mean age = 18.74) we replicated this model and extended this finding by examining social-avoidance. There was an indirect effect of mindset on anxiety and on depression via the use of avoidance-based strategies; however, there was no moderating effect of perceived stress. These findings extend the previous literature by demonstrating for role of avoidance in understanding the relationship between fixed mindsets and internalizing symptoms.
The purpose of this study is to improve the quality of the multi-track laser cladding coating. The Taguchi-Grey relation method was selected to realize process parameter optimization. The Taguchi method is used to design an L16 orthogonal experiment. The influence of three important laser cladding parameters (laser power, powder feeding rate, and scanning speed) on the micro-hardness, maximum load value, yield strength, UTS, and elongation had been analyzed based on the analysis of variance (ANOVA) and signal to noise ratio (SNR) methods. The results showed that the yield strength, UTS, and elongation were significantly affected by the laser power; the powder feeding rate denoted a highly significant influence on the microhardness and yield strength; and the scanning speed was a highly significant factor that had an influence on the maximum load value. Then, grey relational analysis (GRA) was used to convert five response targets into a single grey relational grade (GRG) that could be quantified in order to optimize the parameters for maximum micro-hardness, maximum load value, yield strength, UTS, and elongation. Finally, the optimum cladding process parameters were obtained. Through analysis of microstructure, the reduction of the Laves phase might be the main reason for the improvement of coating performance after optimization.
Background Collecting duct carcinoma (CDC) is biologically more aggressive than clear cell renal cell carcinoma (ccRCC). We tested for differences in cancer specific mortality (CSM) rates according to CDC vs. ISUP (International Society of Urological Pathology) 4 ccRCC histological subtype. We hypothesized that the survival disadvantage still applies, even after most detailed adjustments. Methods Within Surveillance, Epidemiology, and End Results database (2004–2018), we identified 380 CDC vs. 6273 ISUP 4 ccRCC patients of all stages. Propensity score matching (age, sex, race/ethnicity, T, N, and M stages, nephrectomy, and systemic therapy status), Kaplan-Meier plots and multivariable Cox regression models were used. Results All 380 CDC were matched (1:2) with 760 ISUP4 ccRCC patients. Prior to matching CDC patients exhibited higher rates of lymph node invasion (37.6 % vs. 14.7 %, p < 0.001), and of distant metastases (40.8 % vs. 30.4 %, p < 0.001). Systemic therapy rates were higher in CDC (29.5 % vs. 20.5 %, p < 0.001). However, nephrectomy rates were higher in ISUP4 ccRCC patients (97.5 % vs. 84.7 %, p < 0.001). After matching, in multivariable Cox regression models addressing CSM, CDC was associated with a HR of 1.5 (p < 0.001) in the overall population vs. 1.9 (p = 0.014) in stage I-II vs. 1.4 (p = 0.022) in stage III vs. 1.6 in stage IV (p < 0.001), relative to ISUP4 ccRCC. Conclusion CDC patients exhibited 40–90 % higher CSM than their ISUP4 ccRCC counterparts in the overall analysis, as well as in stage specific analyses. The CSM disadvantage applies despite higher rates of systemic therapy in CDC patients.
Much research suggests democracies invest more in human capital formation than dictatorships. In particular, scholars have suggested that democracies outspend autocracies on education, due to electoral and interest group pressures. However, some democracies spend no more on education - and some spend much less - than autocracies. What explains this variation within democracies? The answer is the influence of landed agricultural elites. Urban industrial elites support human capital investment because it leads to higher rates of return even if wages increase. Yet greater education spending encourages out-migration from the countryside, reducing the supply and increasing the price of agricultural labor. Given the differential impact of education spending across economic sectors, the effect of democracy on education spending may be conditional on the power of landed elites. We test this argument in two ways. First, we run a series of time series cross-sectional regressions on data from 107 countries for the period 1970 to 2000. Second, we conduct a difference-in-difference analysis, comparing countries that democratize at high versus low levels of land inequality, for 73 countries for the same time period. Results confirm a negative relationship between the power of landed elites and investment in public education under democracy, adding important and novel insight into the sources of differences in public-goods spending and human capital investment both within across political regimes.
Calcium phosphates (CaP) represent an important class of osteoconductive and osteoinductive biomaterials. As proof-of-concept, we show how a multi-component CaP formulation (monetite, beta-tricalcium phosphate, and calcium pyrophosphate) guides osteogenesis beyond the physiological envelope. In a sheep model, hollow dome-shaped constructs were placed directly over the occipital bone. At 12 months, large amounts of bone (∼75%) occupy the hollow space with strong evidence of ongoing remodelling. Features of both compact bone (osteonal/osteon-like arrangements) and spongy bone (trabeculae separated by marrow cavities) reveal insights into function/need-driven microstructural adaptation. Pores within the CaP also contain both woven bone and vascularised lamellar bone. Osteoclasts actively contribute to CaP degradation/removal. Of the constituent phases, only calcium pyrophosphate persists within osseous (cutting cones) and non-osseous (macrophages) sites. From a translational perspective, this multi-component CaP opens up exciting new avenues for osteotomy-free and minimally-invasive repair of large bone defects and augmentation of the dental alveolar ridge.
Asphalt concrete is among the materials which are most widely used for roads and airport pavements. These pavements over time suffer failure due to passing traffic loads and exposure to different environmental conditions. Typically, when designing a road at the project level, a homogenous pavement design is considered for the entire road segment meaning similar pavement materials and thicknesses are applied throughout a road segment. However, locations such as approach intersections undergo different loading scenarios which make these areas more vulnerable to pavement premature failures such as pavement permanent deformation/rutting and shoving during its service life. The difference in loading scenario is due to high shear stresses associated with vehicle’s stopping and accelerating and also slow traffic movement. As a result, sections such as approach intersections required more frequent treatments for addressing the pavement distresses which makes it both costly and time consuming. Annually, millions of dollars have been spent to compensate rutting failures in the pavement. Therefore, it is critical to agencies to select proper asphalt mixes such as high-performance asphalt mixes for the approach intersections to ensure adequate durability, quality, and safety. The proper mixes also save time and money and minimize environmental impact throughout the road’s lifecycle. With more people coming to York Region’s community every year, the number of vehicles and percentage of trucks transporting goods and services has increased significantly. Therefore, with an increase in temperature pattern in recent years in addition to this traffic increase, York Region is experiencing premature pavement failure, commonly rutting and some shoving, at some of its high-volume intersections. To study the in-service performance and root cause of the rutting and other distress at York Region’s approach intersection, six (6) approach intersections are selected for this study. The study consists of conducting rut depth measurement and geotechnical investigation such as ground generation radar (GPR) testing and collecting cores and borehole samples on the selected sites. This paper presents the field investigation results along with ranking methods to compare the susceptibility of the asphalt surface layer mix to rutting for the tested locations. This paper also explores ideas on how to extrapolate this project level information to the network level for the asset management purposes.
Elastomeric bridge bearings are widely used in bridges to accommodate the deformations produced by mechanical and environmental loads. As their acceptable performance is critical for the bridge performance, finite element analysis (FEA) can be applied to supplement test results on the performance of elastomeric bearings. However, uncertainties are present in both the material properties of their components and the boundary conditions. Therefore, this study provides an initial exploration on how these uncertainties will affect the performance of the bearings under compression. The elastomeric bridge bearing is first modeled using the finite element (FE) method, and then probabilistic analysis is applied using the Monte Carlo simulation (MCS). Material properties of the elastomer and steel components of the bearing and the friction coefficient at the bearing–support interfaces are treated as random variables, and a probabilistic analysis is performed that shows how specific parameters will influence the output response, including the vertical stiffness, and induced stresses and strains. In addition, the study also provides an initial exploration into the sensitivity of the bearing’s response to epistemic uncertainties in these input parameters. The probabilistic FEA results can ease the development of numerical models of elastomeric bridge bearings, and they can be used to improve the code provisions associated with the design of these bearings.
One-shot devices analysis involves an extreme case of interval censoring, wherein one can only know whether the failure time is before the test time. Some kind of one-shot units do not get destroyed when tested, and then survival units can continue within the test providing extra information for inference. This not-destructiveness is a great advantage when the number of units under test are few. On the other hand, one-shot devices may last for long times under normal operating conditions and so accelerated life tests (ALTs), which increases the stress levels at which units are tested, may be needed. ALTs relate the lifetime distribution of an unit with the stress level at which it is tested via log-linear relationship, so inference results can be easily extrapolated to normal operating conditions. In particular, the step-stress model, which allows the experimenter to increase the stress level at pre-fixed times gradually during the life-testing experiment is specially advantageous for non-destructive one-shot devices. In this paper, we develop robust Rao-type test statistics based on the density power divergence (DPD) for testing linear null hypothesis for non-destructive one-shot devices under the step-stress ALTs with exponential lifetime distributions. We theoretically study their asymptotic and robustness properties, and empirically illustrates such properties through a simulation study.
Brain-computer interface systems aim to facilitate human–computer interactions by directly translating brain signals for computers. Recently, using many electrodes has led to better performance in these systems. However, increasing the number of recorded electrodes causes additional time, hardware, and computational costs besides undesired complications of the recording process. Channel selection decreases data dimension and eliminates irrelevant channels while reducing the noise effects. Furthermore, the technique lowers the time and computational costs in the test phase. We present a channel selection method, which combines a sequential search method with a genetic algorithm called Deep Genetic Algorithm Fitness Formation (DGAFF). The proposed method accelerates the convergence of the genetic algorithm and increases the system’s performance. The system evaluation is based on a lightweight deep neural network that automates the whole model training process. Our method, compared to other channel selection methods, outperforms the tradeoff between the classification accuracy and the number of selected channels.
Medical events can affect space crew health and compromise the success of deep space missions. To successfully manage such events, crew members must be sufficiently prepared to manage certain medical conditions for which they are not technically trained. Extended Reality (XR) can provide an immersive, realistic user experience that, when integrated with augmented clinical tools (ACT), can improve training outcomes and provide real-time guidance during non-routine tasks, diagnostic, and therapeutic procedures. The goal of this study was to develop a framework to guide XR platform development using astronaut medical training and guidance as the domain for illustration. We conducted a mixed-methods study—using video conference meetings (45 subject-matter experts), Delphi panel surveys, and a web-based card sorting application—to develop a standard taxonomy of essential XR capabilities. We augmented this by identifying additional models and taxonomies from related fields. Together, this “taxonomy of taxonomies,” and the essential XR capabilities identified, serve as an initial framework to structure the development of XR-based medical training and guidance for use during deep space exploration missions. We provide a schematic approach, illustrated with a use case, for how this framework and materials generated through this study might be employed.
The experimental lengthening kinetics of bainitic ferrite in steels have been consistently shown in the literature to be slower than those predicted using the diffusional Zener-Hillert model. Reconciliation of the experimental and calculated kinetics has required the introduction of a ‘barrier energy’ which until now has not had a clear physical meaning. A modified diffusional growth model based on the Zener-Hillert model for plate-like ferrite growth is introduced with a physics-based barrier energy. The model assumes diffusion controlled growth and takes into account a barrier energy arising from interfacial disconnections motion and their interaction with defects in the matrix. The model is able to successfully describe the C-curve growth rate of Widmanstätten ferrite and bainitic ferrite in a wide range of steels.
Background The development of digital technologies and the evolution of open innovation approaches have enabled the creation of diverse virtual organizations and enterprises coordinating their activities primarily online. The open innovation platform titled “International Natural Product Sciences Taskforce” (INPST) was established in 2018, to bring together in collaborative environment individuals and organizations interested in natural product scientific research, and to empower their interactions by using digital communication tools. Methods In this work, we present a general overview of INPST activities and showcase the specific use of Twitter as a powerful networking tool that was used to host a one-week “2021 INPST Twitter Networking Event” (spanning from 31st May 2021 to 6th June 2021) based on the application of the Twitter hashtag #INPST. Results and Conclusion The use of this hashtag during the networking event period was analyzed with Symplur Signals (, revealing a total of 6,036 tweets, shared by 686 users, which generated a total of 65,004,773 impressions (views of the respective tweets). This networking event's achieved high visibility and participation rate showcases a convincing example of how this social media platform can be used as a highly effective tool to host virtual Twitter-based international biomedical research events.
In our publication [Nucl. Phys. A 1027 (2022) 122511,] we have detected typographical errors in Table 4 [List of 21+ States in Even-Even Nuclei]. The misprints of decimals with one or more trailing zeros are not central to our findings, however, they may confuse the journal readers and require corrections. In addition, we have included recent results for the 02+ and 21+ first excited states that are not available in the ENSDF library as of April, 2021 or B(E2) tables.
Reinforced concrete block shear walls (RCBSWs) have been used as an effective seismic force resisting system in low- and medium-rise buildings for many decades. However, attributed to their complex nonlinear behavior and the composite nature of their constituent materials, accurate prediction of their seismic performance, relying solely on mechanics, has been challenging. This study adopts multi-gene genetic programming (MGGP)— a class of bio-inspired artificial intelligence, to uncover the complexity of RCBSW behaviors and develop simplified procedures for predicting the full backbone curve of flexure-dominated RCBSWs under cyclic loading. A piecewise linear backbone curve was developed using five secant stiffness expressions associated with: cracking; yielding; 80% ultimate; ultimate; and 20% strength degradation (i.e., post-peak stage) derived through mechanics-controlled MGGP. Based on the experimental results of large-scale cyclically loaded fully-grouted RCBSWs, compiled from previously reported studies, a variable selection procedure was performed to identify the most influential variable subset governing wall behaviors. Subsequently, the MGGP stiffness expressions were trained and tested, and their accuracy was compared to that of existing models employing various statistical measures. In addition, the predictability of the developed backbone model was assessed at the system-level against experimental results of two two-story buildings available in the literature. This study demonstrates the power of the MGGP approach in addressing the complexity of the cyclic behavior of RCBSWs at both component- and system-level—offering an efficient prediction tool that can be adopted by relevant seismic design standards pertaining to RCBSW buildings.
Pregnancy and the early postpartum signify a period of high stress. Perinatal stress can include psychological distress (PD), such as anxiety, depression, and stress, as well as neuroendocrine stress, indexed by activation of the hypothalamic-pituitary-adrenal (HPA) axis and the production of the hormone cortisol. Elevated PD and cortisol levels during the perinatal period can have long-term implications for the mother and child. Methodological advances have enabled the sampling of cortisol from hair, to provide a retrospective marker of HPA axis activity over several months. Despite knowing that maternal PD and HPA activity during the perinatal period independently impact health and development, research to date is unclear as to the association between maternal PD and hair cortisol. The present meta-analysis included 29 studies to assess the strength of the relation between maternal PD and hair cortisol levels during pregnancy and the early postpartum period. Several sample and methodological factors were assessed as moderators of this effect. Analyses were conducted using multilevel meta-analysis. Results of the multilevel meta-analysis indicated that the overall effect size between PD and HCC was small but not significant z = 0.039, 95% CI [− 0.001, 0.079]. Moderator analyses indicated that the strength of the association between PD and hair cortisol was moderated by pregnancy status (i.e., effects were stronger in pregnant compared to postpartum samples), timing of HCC and PD measurements (i.e., effects were larger when PD was measured before HCC) and geographic location (i.e., effects were larger in North American studies). The findings advance our understanding of the link between PD and HPA activity during the perinatal period, a time of critical impact to child development.
Single fiber pull-out and fiber-matrix interfacial interaction play an essential role in understanding the mechanical behavior of fiber-reinforced cementitious composites. The present study introduces a computational model for predicting the maximum fiber pull-out force and corresponding bond slip. An extensive literature survey was performed to create a pertinent comprehensive experimental database. A total of 382 experimental data were utilized to develop and train the Artificial Neural Network (ANN) models. The model input parameters included the fiber embedded length, fiber inclination angle, fiber tensile strength, fiber length-to-diameter ratio, loading rate, water-to-cement ratio, concrete compressive strength, and fiber geometry. The model output consisted of the maximum pull-out force and corresponding slip. The results indicate that ANN with two hidden layers and 12 neurons was adequate for predicting the outputs with a mean absolute percentage error (MAPE) of less than 10%. To obtain the importance of the inputs on the outputs (the maximum fiber pull-out force and the corresponding slip), a sensitivity analysis was done based on the Milne formula on the proposed ANN. According to the results, it was found that among the eight inputs, the parameters of the geometric shape of the fibers (straight, hooked-end and spiral fibers) and fiber tensile strength have the highest effect on the outputs, with an impact percentage of 16.1 and 15.1, respectively. The mean square error (MSE) was 0.9 for the maximum pull-out force and 0.14 for slip, respectively. Overall, the proposed executed model attained reasonable predictions and could offer a data driven approach to optimizing fiber-reinforced cementitious composites.
The solid–liquid interfacial free energy, γ, and its associated anisotropy were computed for the Al-Mg binary alloy system using Molecular Dynamics (MD) simulations in conjunction with the capillary fluctuation method (CFM). Interactions between atoms were modeled based on a second nearest neighbor modified embedded atom method (MEAM) potential, a successor to the established embedded atom method (EAM) potential. The MEAM potential predicts a melting temperature of 938 K ± 17 K for pure Al and the solid–liquid phase equilibria as a function of temperature and composition was found using Monte Carlo technique implemented in Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). The average interfacial free energy, γ0, for pure Al was found to be 135.94 ± 3.62 mJ/m² which agrees well with experimental values and an interfacial free energy relationship of γ100>γ110>γ111 was found across the majority of the temperatures which is consistent with FCC metals. The anisotropy values were found to fall within the 100 growth direction along the orientation selection map suggesting the addition of Mg stabilizes this growth direction. The results from the MEAM potential compares qualitatively well with other EAM potentials, however, experiences larger errors at lower temperatures.
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17,894 members
Behnam Sadeghirad
  • Departments of Anethesia and Clinical Epidemiology & Biostatistics
Omar M. Bdair
  • Department of Mathematics and Statistics
Matiar Howlader
  • Department of Electrical and Computer Engineering
Carlos Alberto Cuello-Garcia
  • Health Research Methods Evidence and Impact
Loubna Akhabir
  • Department of Medicine
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