This paper presents a literature review on the shear behaviour of steel fiber reinforced concrete (SFRC) and proposes equations for predicting the shear resistance of SFRC beams. First a large database of test results is used to evaluate the effect of various parameters on the shear response of SFRC beams. In addition, the paper reviews various equations proposed in the literature to predict the shear capacity of SFRC beams. The paper then presents equations which can be used to predict the shear resistance of SFRC beams. The shear resistance equations modify the general method of the 2004 CSA A23.3 Standard to account for the effect of steel fibers on shear capacity. The method proposed in this paper and equations proposed in the literature are used to predict a large database of SFRC beam test results. The results show that the method presented in the paper provides reasonably accurate predictions of shear capacity for beams having a wide range of properties.
Aluminum vehicular bridge decks figuhave been increasingly used for the replacement of deficient bridge slabs made from reinforced concrete or steel. The design of these decks has been under increasing development since the late 90s and different types of vehicular bridge deck profiles have been industrialized. Aluminum vehicular bridge decks are mainly manufactured through joining several extrusions together by means of welding. Initially, traditional fusion welding techniques were used in the fabrication process. However, more recently, a relatively new technology, friction stir welding (FSW), was used in certain bridge projects. FSW is proving to enhance the welding quality and the overall mechanical performance of the joint compared to traditional welding techniques. However, unlike fusion welding techniques, FSW standards and specifications still lack important quality control criteria and tolerance levels for common FSW defects. In this context, this paper aims at defining tolerance levels for common FSW fit-up defects in aluminium bridge deck application. First, FSW trials of typical vehicular bridge deck extrusions were conducted to determine the welding parameters yielding a sound welding quality. Then, fit-up defects such as gap and tool offset were investigated experimentally, and tolerance levels were determined following a concise stage prequalification process. It was found that a gap of 1.5 mm and a tool offset in the retreating side (RS) of 1.5 mm were acceptable limits. It was also shown metallographically that the level of the tool offset in the advancing side (AS) of 5 mm presented a sound weld quality with a continuous remnant oxide layer within the weld nugget.
Masons are aided by ergonomic inventions like tools, processes, and equipment, yet they are still subjected to performing physically demanding and hazardous tasks at the worksite. With advances in materials, design, and automation, the masonry work system may be modified to minimize the bodily harm associated with the industry. Analysis of the extensive motion data collected on masonry work enables us to understand the ergonomic risks of masonry tasks. Previous studies found that expert masons adopted ergonomically safer and more productive work methods than less experienced masons, as from analyzing the experts’ body kinematics and biomechanical force levels during masonry activities, we can provide training for a safer and more productive generation of masons. To achieve these goals, we investigated the expert masons’ work methods during four masonry activities to determine the associated risk. Specifically, eight expert masons with over 20 years of experience, laid out 16.6 kg concrete masonry units (CMUs) to construct (1) a standard wall, (2) a reinforced wall, (3) a wall in constraint space (under ceiling), and (4) a lead (first) course. Motion capture suits captured their motions, and a biomechanical analysis determined the load experienced by major body joints in each activity. The study found that the most critical body joints were in the lower back and upper limbs, as strain to these joints may lead to days away from work or, in severe cases, retirement. Furthermore, the results provided insights into expert masons’ distinctive work techniques through ergonomic evaluation for various masonry tasks.
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.
Mass timber products have shown tremendous potential as sustainable structural components in large building systems. However, challenges occur when open floor plan structures are desired due to conventional flat slab floor systems having difficulties achieving the longer floor span expectations (i.e., exceeding 9 m). This paper investigates the potential of an all-wood solution, namely, hollowcore mass timber (HMT) panels, in meeting the demands of longer spans while also minimizing the use of wood material when compared to a solid slab. A 400 mm deep, 9 m long HMT panel composed of 3-layer cross-laminated timber (CLT) panels as flanges, and glulam beams as webs, is compared to the maximum commonly available CLT and dowel-laminated timber (DLT) alternatives. Two analytical methods and a finite element model are used to determine the effective bending stiffness of the HMT panel, while CSA O86 design procedures are used for the CLT and DLT panels. The effective bending stiffness of the HMT panel between the finite element model and analytical methods ranged from 1.71–1.94 and 1.14–1.29 times greater, despite being 18% and 24% lighter, than the CLT and DLT panels, respectively. Although slightly deeper, the HMT section provided a more efficient use of materials when compared to the solid slab options. The vibration-controlled span limit of the HMT panel was on average 9.8 m, which was 1.8 m and 0.9 m longer than the CLT and DLT panels, respectively. Further areas of study were also identified and will be investigated as part of future work in the broader HMT panel research program.
Background Most North American temperate forests are plantation or regrowth forests, which are actively managed. These forests are in different stages of their growth cycles and their ability to sequester atmospheric carbon is affected by extreme weather events. In this study, the impact of heat and drought events on carbon sequestration in an age-sequence (80, 45, and 17 years as of 2019) of eastern white pine ( Pinus strobus L.) forests in southern Ontario, Canada was examined using eddy covariance flux measurements from 2003 to 2019. Results Over the 17-year study period, the mean annual values of net ecosystem productivity (NEP) were 180 ± 96, 538 ± 177 and 64 ± 165 g C m –2 yr –1 in the 80-, 45- and 17-year-old stands, respectively, with the highest annual carbon sequestration rate observed in the 45-year-old stand. We found that air temperature (Ta) was the dominant control on NEP in all three different-aged stands and drought, which was a limiting factor for both gross ecosystem productivity (GEP) and ecosystems respiration (RE), had a smaller impact on NEP. However, the simultaneous occurrence of heat and drought events during the early growing seasons or over the consecutive years had a significant negative impact on annual NEP in all three forests. We observed a similar trend of NEP decline in all three stands over three consecutive years that experienced extreme weather events, with 2016 being a hot and dry, 2017 being a dry, and 2018 being a hot year. The youngest stand became a net source of carbon for all three of these years and the oldest stand became a small source of carbon for the first time in 2018 since observations started in 2003. However, in 2019, all three stands reverted to annual net carbon sinks. Conclusions Our study results indicate that the timing, frequency and concurrent or consecutive occurrence of extreme weather events may have significant implications for carbon sequestration in temperate conifer forests in Eastern North America. This study is one of few globally available to provide long-term observational data on carbon exchanges in different-aged temperate plantation forests. It highlights interannual variability in carbon fluxes and enhances our understanding of the responses of these forest ecosystems to extreme weather events. Study results will help in developing climate resilient and sustainable forestry practices to offset atmospheric greenhouse gas emissions and improving simulation of carbon exchange processes in terrestrial ecosystem models.
Noncommuting conserved quantities have recently launched a subfield of quantum thermodynamics. In conventional thermodynamics, a system of interest and an environment exchange quantities—energy, particles, electric charge, etc.—that are globally conserved and are represented by Hermitian operators. These operators were implicitly assumed to commute with each other, until a few years ago. Freeing the operators to fail to commute has enabled many theoretical discoveries—about reference frames, entropy production, resource-theory models, etc. Little work has bridged these results from abstract theory to experimental reality. This paper provides a methodology for building this bridge systematically: we present a prescription for constructing Hamiltonians that conserve noncommuting quantities globally while transporting the quantities locally. The Hamiltonians can couple arbitrarily many subsystems together and can be integrable or nonintegrable. Our Hamiltonians may be realized physically with superconducting qudits, with ultracold atoms, and with trapped ions.
Muons are particles with a spin of ½ that can be implanted into a wide range of condensed matter materials to act as a local probe of the surrounding atomic environment. Measurement of the muon’s precession and relaxation provides an insight into how it interacts with its local environment. From this, unique information is obtained about the static and dynamic properties of the material of interest. This has enabled muon spin spectroscopy, more commonly known as muon spin rotation/relaxation/resonance (μSR), to develop into a powerful tool to investigate material properties such as fundamental magnetism, superconductivity and functional materials. Alongside this, μSR may be used to study, for example, energy storage materials, ionic diffusion in potential batteries, the dynamics of soft matter, free radical chemistry, reaction kinetics, semiconductors, advanced manufacturing and cultural artefacts. This Primer is intended as an introductory article and introduces the μSR technique, the typical results obtained and some recent advances across various fields. Data reproducibility and limitations are also discussed, before highlighting promising future developments. Muon spin spectroscopy examines how muons interact with their local environment through measurement of the muon’s precession and relaxation. This provides unique information about the static and dynamic properties of a material. This Primer gives an introductory overview to muon spin spectroscopy, describing how muons are produced and used experimentally in various applications.
The prevalence of self-reported and DXA-confirmed osteoporosis was 7.8% (males 2.2%; females 12.7%), and 3.6% (males 1.2%; females 5.9%), respectively. We found that most community-dwelling older adults at high fracture risk are not taking osteoporosis medication, particularly males. There is a major opportunity for improved primary fracture prevention in the community. Purpose To provide an up-to-date prevalence estimate of osteoporosis, fracture risk factors, fracture risk, and the proportion of older Canadians at high fracture risk who are not taking an osteoporosis medication. Methods We included Canadian Longitudinal Study on Aging (CLSA) participants: a community-dwelling cohort aged 45 to 85 years who completed the baseline (2015) comprehensive interview and had dual-energy X-ray absorptiometry (DXA) scans ( N = 30,097). We describe the age- and sex-stratified prevalence of (1) self-reported osteoporosis; (2) DXA-confirmed osteoporosis; (3) fracture risk factors and people who are at high risk (FRAX® major osteoporotic fracture probability ≥ 20%); and (4) people who are at high fracture risk not taking osteoporosis medications. Sampling weights, as defined by the CLSA, were applied. Results The mean age of participants was 70.0 (SD 10.3). Overall, 7.8% had self-reported osteoporosis (males 2.2%; females 12.7%) while 3.6% had DXA-confirmed osteoporosis (males 1.2%; females 5.9%), and 2.8% were at high fracture risk (males 0.3%; females 5.1%). Of people who had osteoporosis and were at high risk, 77.3% were not taking an osteoporosis medication (males 92.3%; females 76.8%). Conclusions Our study provides an up-to-date prevalence estimate of osteoporosis for community-dwelling older Canadians. We found that most community-dwelling older adults at high fracture risk are not taking an osteoporosis medication, particularly males. There is a major opportunity for improved primary fracture prevention in the community.
There are many claims that gamification (i.e., using game elements outside games) impact decreases over time (i.e., the novelty effect). Most studies analyzing this effect focused on extrinsic game elements, while fictional and collaborative competition have been recently recommended. Additionally, to the best of our knowledge, no long-term research has been carried out with STEM learners from introductory programming courses (CS1), a context that demands encouraging practice and mitigating motivation throughout the semester. Therefore, the main goal of this work is to better understand how the impact of a gamification design, featuring fictional and competitive-collaborative elements, changes over a 14-week period of time, when applied to CS1 courses taken by STEM students (N = 756). In an ecological setting, we followed a 2x7 quasi-experimental design, where Brazilian STEM students completed assignments in either a gamified or non-gamified version of the same system, which provided the measures (number of attempts, usage time, and system access) to assess user behavior at seven points in time. Results indicate changes in gamification’s impact that appear to follow a U-shaped pattern. Supporting the novelty effect, the gamification’s effect started to decrease after four weeks, decrease that lasted between two to six weeks. Interestingly, the gamification’s impact shifted to an uptrend between six and 10 weeks after the start of the intervention, partially recovering its contribution naturally. Thus, we found empirical evidence supporting that gamification likely suffers from the novelty effect, but also benefits from the familiarization effect, which contributes to an overall positive impact on students. These findings may provide some guidelines to inform practitioners about how long the initial contributions of gamification last, and how long they take to recover after some reduction in benefits. It can also help researchers to realize when to apply/evaluate interventions that use gamification by taking into consideration the novelty effect and, thereby, better understand the real impact of gamification on students’ behavior in the long run.
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.
Aims: With increased liberalization of cannabis policies in North America, there is growing interest in the use of cannabis to manage pain instead of opioids. The objectives of the study were to (1) examine the use of cannabis for pain relief in Canada and the United States (US) in 2018 and 2019; (2) examine the association between recreational cannabis laws and changes in the use of cannabis for pain relief, instead of opioids or prescription pain medication. Methods: Repeat cross-sectional survey data were used from Wave 1 and Wave 2 of the International Cannabis Policy Study conducted in 2018 and 2019 in Canada and the US. Respondents were recruited through commercial panels, aged 16-65, and had ever tried cannabis (N = 44,119). Weighted binary logistic regression models examined the association between the legal status of recreational cannabis and cannabis use for pain relief instead of opioids or prescription pain medication (n = 15,092). Results: Between 14-33% of cannabis consumers in Canada and the US reported using cannabis to manage headaches or pain. Of these consumers, 79% and 78% respondents in Canada; 80% and 83% in US illegal states; and 83% and 84% in US legal states, in 2018 and 2019, respectively, reported cannabis use for pain relief instead of opioids or prescription pain medication. There was little evidence of an association between the legal status of recreational cannabis and cannabis use for pain relief instead of opioids or prescription pain medication, among Canadian (AOR = 0.98, 95% CI: 0.78, 1.22) and US respondents (AOR = 1.11, 95% CI: 0.96, 1.28). Conclusions: Although substitution of cannabis for opioids or prescription pain medication is common among those who use cannabis for pain, there does not seem to be a significant difference according to cannabis legality. Future research should examine cannabis and opioid substitution using different research designs and time frames.
The current concept of clinical high-risk(CHR) of psychosis relies heavily on “below-threshold” (i.e. attenuated or limited and intermittent) psychotic positive phenomena as predictors of the risk for future progression to “above-threshold” positive symptoms (aka “transition” or “conversion”). Positive symptoms, even at attenuated levels are often treated with antipsychotics (AP) to achieve clinical stabilization and mitigate the psychopathological severity. The goal of this study is to contextually examine clinicians’ decision to prescribe AP, CHR individuals’ decision to take AP and psychosis conversion risk in relation to prodromal symptoms profiles. CHR individuals ( n = 600) were recruited and followed up for 2 years between 2016 and 2021. CHR individuals were referred to the participating the naturalistic follow-up study, which research procedure was independent of the routine clinical treatment. Clinical factors from the Structured Interview for Prodromal Syndromes (SIPS) and global assessment of function (GAF) were profiled via exploratory factor analysis (EFA), then the extracted factor structure was used to investigate the relationship of prodromal psychopathology with clinicians’ decisions to AP-prescription, CHR individuals’ decisions to AP-taking and conversion to psychosis. A total of 427(71.2%) CHR individuals were prescribed AP at baseline, 532(88.7%) completed the 2-year follow-up, 377(377/532, 70.9%) were taken AP at least for 2 weeks during the follow-up. EFA identified six factors (Factor-1-Negative symptoms, Factor-2-Global functions, Factor-3-Disorganized communication & behavior, Factor-4-General symptoms, Factor-5-Odd thoughts, and Factor-6-Distorted cognition & perception). Positive symptoms (Factor-5 and 6) and global functions (Factor-2) factors were significant predictors for clinicians’ decisions to AP-prescription and CHR individuals’ decisions to assume AP, whereas negative symptoms (Factor-1) and global functions (Factor-2) factors predicted conversion. While decisions to AP-prescription, decisions to AP-taking were associated to the same factors (positive symptoms and global functions), only one of those was predictive of conversion, i.e. global functions. The other predictor of conversion, i.e. negative symptoms, did not seem to be contemplated both on the clinician and patients’ sides. Overall, the findings indicated that a realignment in the understanding of AP usage is warranted.
Bio-n-butanol is widely recognized as a renewable alternative biomass fuel, it is gradually used in internal combustion engines for scientific research to alleviate the energy crisis and environmental pollution. However, due to the difference of physical and chemical properties between n-butanol and gasoline, if n-butanol is directly applied to gasoline engines, it will certainly cause changes in engine performance. Therefore, it is of great significance to explore the optimal energy conservation potential of n-butanol application in gasoline engines. Driven by this fact, an experiment was conducted in a high speed, spark ignition (SI) engine with n-butanol blended ratio of 0% and 35% by volume to gasoline, and simulation model of GT-Power coupling with MATLAB/Simulink is built and calibrated based on the tested data. The synergistic optimization of multiple operating variables with the corresponding genetic algorithms (GA) optimization methodologies is carried out to reveal the optimal energy conservation potential of engine fueled with butanol–gasoline blends. Results show that, when the torque remains the same with respect to that of the original engine, the optimized brake specific energy consumption (BSEC) at the corresponding engine speed under full load are significantly lower than the original BSEC, the average improvement in percentage of BSEC is approximately 7.11%. By adopting the method of multi-objective genetic algorithm (MOGA), the power and economy of the engine fueled with butanol–gasoline blends can be balanced simultaneously. The engine operating parameters can be chosen that the BSEC may be small while the engine is optimally powered. Besides, it is found that the pumping loss is affected by the combined effect of intake timing and exhaust timing. The optimization procedure can be applied to calibrate the universal characteristics of engines, and the findings obtained from this study can provide guidance for better application of alternative energy in traditional thermal machines.
The analysis of gaze behaviour during complex tasks provides a promising non-invasive method to examine how specific eye movement patterns relate to various aspects of cognition and action. Notably, the association between aspects of gaze behaviour and subsequent goal-directed action during high-level visuospatial problem solving remains elusive. Therefore, the current study comprehensively examined gaze behaviour using traditional and entropy-based gaze analyses in healthy adults (N = 27) while they performed the Freiburg version of the Tower of London task. Results demonstrated that both gaze analyses provided crucial temporal and spatial information related to planning, solution elaboration and execution. Specifically, gaze biases toward task-relevant areas (i.e., the work space) and an increase in gaze complexity (i.e., gaze transition entropy) during optimal performance reflected changes in cognitive demands as task difficulty increased. A comparison between optimal and non-optimal performance revealed sub-optimal gaze patterns that occurred in the early stages of planning, which were taken to reflect poor information extraction from the task environment and impaired maintenance of information in visuospatial working memory. Gaze behaviour during movement execution indicated an increased need to extract and process information from the goal space. Consequently, movement execution time increased in order to reverse erroneous movements and re-sequence the problem solution. Taken together, the traditional and entropy-based gaze analyses applied in the present study provide a promising approach to identify eye movement patterns that support neurocognitive performance on tasks relying on visuospatial planning and problem solving.
An occupational fatigue risk management standard is a timely initiative for thousands of first responders in Canada. Fatigue is a pervasive problem in paramedic, police, and firefighting occupations, where personnel experience significant levels of physical fatigue, cognitive task- and sleep-related fatigue, burnout, and emotional fatigue. A fatigue risk management standard is being developed and is informed from several sources, including a scoping review of first responder fatigue literature. The objective of this paper is to report on findings from the scoping review to (1) identify measures and tools used by researchers to assess fatigue-related risks, and (2) organize these measures and tools into adapted SOBANE risk management categories to facilitate their selection by organizations and occupational health and safety practitioners. We identified 60 unique measures and tools for the five different dimensions of fatigue across all first response occupations. Several fatigue measurements found in occupational fatigue literature and critical industry fatigue management were not reported; future research should investigate the development of predictive models and the reliability and validity of these tools in first response. The results of this scoping review provide a starting point for organizations to assess fatigue, of any dimension, but the psychometric properties of the identified measures and tools should be considered.
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