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Organic light-emitting transistors are photonic devices combining the function of an electrical switch with the capability of generating light under appropriate bias conditions. Achieving high-performance light-emitting transistors requires high-mobility organic semiconductors, optimized device structures, and highly efficient emissive layers. In this work, we studied the optoelectronic response of green blends (TCTA:Ir(ppy)3) with varying doping concentrations in the limit of field-effect within a transistor device configuration. Increasing the dye concentration within the blend leads to a quenching of the photoluminescence signal; however, when implemented in a multilayer stack in a transistor, we observed an approximately 5-fold improvement in the light output for a 10% Ir(ppy)3 doping blend. We analyzed our results in terms of balanced charge transport in the emissive layer, which, in the limit of field-effect (horizontal component), leads to an improved exciton formation and decay process. While the performances of our devices are yet to achieve the state-of-the-art diode counterpart, this work demonstrates that engineering the emissive layer is a promising approach to enhance the light emission in field-effect devices. This opens the way for a broader exploitation of organic light-emitting transistors as alternative photonic devices in several fields, ranging from display technology to flexible and wearable electronics.

Buildings contribute to the urban heat island (UHI) phenomenon, but also have vast “un-used” surfaces that can be employed to incorporate innovative nature-based solutions (NBS) to mitigate UHI effects. NBS on buildings can be achieved by increasing surface reflectivity (ISR) on flat building envelop and increasing surface greenery/vegetation (ISG) on both vertical and horizontal building envelope components. This paper presents a snapshot of the current understanding of NBS and how they affect building performance, the simulations conducted to evaluate NBS and UHI effects and modeling approaches used, the primary knowledge gaps as well as the future steps needed to reduce the effects of UHI in urban agglomerations across Canada. The road map consists of; I) providing approaches to lessen the UHI influences with respect to the implications of warming/changing climate, urban canopy/landscape characteristics and building design; II) development of the best management practices (BMP) for NBS – UHI for Canadian communities; and III) guideline for the design of NBS–UHI solutions to mitigate UHI in accordance with Canadian design climatic load., COBEE 2022, 5th International Conference on Building Energy and Environment, July 25-29, 2022, Montreal, QC, Canada

Overheating in built environments during climate extreme heat events is a major concern to human health, particularly for people vulnerable to prolonged exposure to heat and humidity. However, currently available methods for assessing the risk of overheating lack robust procedures to evaluate the effects of overheating on the comfort and health of vulnerable occupants residing in the various different dwellings. This paper developed a general methodology to define and characterise overheating events and evaluate the risk to reduced comfort and undesirable health effects of building occupants exposed to extreme heat events. Criteria to declare overheating were developed based on heat-related health outcomes related to limits for body dehydration and core temperature of healthy average-age and older adults. The methodology was then applied to a case study of a residential building with typical local construction practice for cold climates and compared with existing methods for assessing risk from exposure to extreme heat events. The results showed that all the methods predicted that highly insulated and airtight buildings are more prone to overheating than older buildings that are less well insulated and airtight. However, only the proposed methodology described in this paper predicted that natural ventilation through opening windows significantly reduced the overheating risk to below the threshold value. Furthermore, multi criteria methods might be difficult to apply in practice in that it is not guaranteed to violate all the criteria to declare a building space is not overheated. The proposed methodology sets the groundwork for establishing a benchmark model from which different overheating metrics can be compared.

This publication reports on how the Covid-19 pandemic affected Indigenous Peoples and Local Communities (IPLCs) in 2020-1, particularly those who govern, manage and conserve their lands and waters. This research complements preliminary findings from a global survey conducted by a group of 16 authors from various research institutes and civil society organisations, including the University of Lausanne and the ICCA Consortium. The report presents the results of qualitative research based on the analysis of stories and online questionnaires through the use of SenseMaker® software. This tool allows for the analysis of multiple perspectives of complex situations and enables a meta-analysis of qualitative data. The objective of this report is to identify how the Covid-19 pandemic impacted Indigenous Peoples and local communities and how was being managed at that time. The report 1) reflects the importance of traditional and local knowledge to IPLCs, 2) how COVID-19 affects their rights, 3) how it impacts their access to natural resources that constitute their livelihoods, 4) increases understanding about their resilience, and 5) provides lessons for future pandemics.

In this study, the spatial distribution of the urban climate is evaluated and its impact on indoor overheating conditions is assessed. This was done by modelling the near-field climate of Ottawa and Montreal at 1 km resolution for the summer of 2018 during which an extreme heat event occurred causing nearly 100 deaths in this area. The climate data obtained from Weather Research Forecasting simulations were used for assessing the extent of overheating within a prototype model of a single-detached home using EnergyPlus. The overheating conditions were evaluated using the mean temperature, the number of hours with the temperature above 28°C, and the number of hours cooling from a base temperature of 28°C. A workflow for selecting representative locations within the city for building overheating assessments was established by considering five (5) different quantiles, including 0%, 5%, 50%, 95%, and 100%, of the three calculated overheating metrics over the urban and the rural area. The degree of indoor overheating in homes was quantified and differences in overheating of homes in urban and rural settings as well as those arising within different urban areas (intra-urban) were determined. The most significant intra-urban indoor mean temperature differences of buildings at different locations were 1.8°C for Montreal and 1.6°C for Ottawa. For the number of hours with a temperature above 28°C, the intra-urban difference can be up to 829 hours for both cities. It was also found that the overheating conditions between different locations may be affected by the external air temperature and other variables, such as the local wind speed, which greatly varied the natural ventilation air change rate of buildings. The overheating conditions in buildings of different locations were also compared by analyzing the time-series variation in temperature. It was determined that there always exists a difference between the duration and intensity of indoor overheating in single-family homes of different locations., CLIMA 2022 conference, 2022: CLIMA 2022 The 14th REHVA HVAC World Congress

Building models that can accurately predict hourly indoor air temperatures in free-running situations are key to understanding overheating conditions and the resilience of passive cooling strategies under a changing climate. To accurately predict indoor temperatures it is necessary to properly model pressure-driven infiltration and natural ventilation. This can be achieved by coupling a building thermal model to an airflow network model. In this paper, the development of coupled building thermal and airflow network models is described to calibrate building models using field measurements of indoor air temperature. Building models of three types of buildings were configured: long-term care building, primary school and multi-unit social housing. The building models were developed in Design BuilderTM and exported for use in an EnergyPlus simulation package. From information obtained from building surveys, site visits and architecture drawings, building parameters and operation schedules were collected. The unknown parameters, which included envelope thermal properties, shading devices, internal heat gains, envelope air leakage, window and door openings, were then calibrated based on measured values of indoor temperature. Reasonable ranges in value of the unknown parameters were first retrieved from applicable building construction practice documents and building energy standards. Two rounds of calibration were conducted through parametric simulations using the Monte-Carlo sampling method. A sensitivity analysis was also conducted for ranking the importance of all building parameters. The values for indoor air temperature as obtained through simulation were compared with measurements and the RMSE (root mean square error) was calculated for all values. The parameter value combinations corresponding to the minimum RMSE were adopted for the building models. The calibration process ended when the value for RMSE was, CLIMA 2022 conference, 2022: CLIMA 2022 The 14th REHVA HVAC World Congress

NRC-CU Seminar-Overheating and Mitigation of UHI, Sept. 29, 2021, NRC Construction Research Centre, Ottawa, Ontario

BC Step Code Part 3 - Overheating Working Group, September 15, 202

This study demonstrates the importance of high-resolution climate simulations when conducting city-scale outdoor heat wave alert and indoor overheating assessments. This is done by modelling urban climate of the Ottawa and Montreal cities at 1 km and 25 km, typical for regional climate modelling respectively, over the summer of 2018 when an extreme heat event caused around 100 deaths in these cities. It is shown that urban climate characteristics (higher temperatures, lower wind speeds, lower relative humidity in the urban core than surroundings) are better simulated at 1 km than at 25 km spatial resolution. Indoor conditions are simulated for an archetype model of a single detached house using EnergyPlus software for two locations within the cities: a) city center and b) airport location. It is shown that the simulated indoor air temperature in the building is highly correlated with the outdoor air temperature. Furthermore, it is found that the maximum indoor air temperature difference of the city center and the airport can be as high as 8°C in Montreal and 9°C in Ottawa. Such intra-urban differences in overheating in buildings will be ignored if microscale simulations are not performed, highlighting their importance for building overheating assessments in cities., 8th International Building Physics Conference (IBPC 2021) 25-27 August 2021, Copenhagen, Denmark

The two-node bioheat model is widely used in thermal comfort standards and design tools. In recent years, there have been many new experimental studies and thermoregulatory models developed under stressful heat or cold conditions, but those have not been tested under the two-node model structure. Furthermore, limited validation studies of the two-node model revealed significant discrepancies in the prediction of skin temperature. This study collects relevant thermoregulatory models (six for sweating, three for skin blood flow and shivering, and four for sweat evaporation efficiency) and devises a methodology to compare the accuracy of various model combinations against experimental data. An improved model is developed and validated under heat and cold exposure conditions. The RMSE method is used to compare the accuracy of various model combinations and to optimize the proposed thermoregulatory model constants. The results reveal that only several model combinations can be considered as accurate for the core and skin temperature predictions, amongst which are the proposed models at the first rank.

This report is the final deliverable of the project A1-017320.1. The main purpose of this report is to investigate the feasibility of using one of the emerging data sources, i.e. smart thermostats, along with machine learning approaches to detect and quantify overheating in buildings. This report is organized in two sections. Section A represents the results of the indoor overheating analysis in residential buildings based on smart thermostat data. In section B, a data-driven indoor conditions warning framework for residential buildings is developed and assessed. In section A, indoor temperature and humidity measurements from more than 3,000 connected thermostats, during summers of 2016-2019 and from homes located in five major metropolitan areas in Canada were used. Furthermore, a robust procedure to evaluate the frequency and severity of indoor overheating based on the heat-related health outcomes for older people was utilized. In particular, the overheating occurrence in homes with and without central air conditioning (AC) units was comparatively evaluated. The results of the first section showed that 12% of houses under study experienced at least one overheating event. Furthermore, the extreme indoor overheating events with potential health risks to the vulnerable occupants were more common amongst houses without central air conditioning units across the selected cities. In section B, a feasibility study was done on more than 180 houses located in Ontario that do not use central AC units. In this approach, two gray box models were developed based on the hourly indoor and outdoor temperatures. The gray box models were implemented in a recursive forecasting strategy using sliding training and forecast time periods to provide the forecasted hourly indoor temperatures. The proposed models were able to forecast 12-hour ahead hourly indoor conditions in 92% of houses with less than 5% mean absolute percentage error. This indicated the potential for leveraging data from IoT devices and machine learning, as part of an overheating detection and warning system in buildings. Although this work was based on data from residential buildings, the methodologies developed here can be used for other buildings.

In cold climates having temperate summers, over the past decades overheating has not been an issue in respect to older and leakier buildings. However, with the ever-increasing threat of global warming and aggressive measures to reduce winter heating energy use to improve the energy efficiency of homes, overheating has become a real concern to the comfort and health of building occupants. Overheating is the result of excessive heat accumulation in building interiors combined with limited means to effectively dissipate this heat to the outdoor. The outdoor environment is the principle cause for this excessive heat, particularly during extreme heat events as occur in the summertime. However, buildings can exacerbate the situation by generating additional internal heat from equipment, lighting, occupants (density) and as well, from the trapping of heat given high levels of insulation, more effective airtightness of envelopes and inadequate space ventilation.

Overheating in buildings arising from global warming and extreme heat events (EHEs) is a growing health concern in urban areas of many countries. Overheating is the condition of the indoor environment that results in thermal discomfort or heat-related health stress to building occupants. Overheating is found in naturally ventilated buildings, buildings with limited cooling capacity or intermittent use of air conditioning, and buildings that experience extended periods of power outages or HVAC failure. Despite the extensive studies on this topic, there is a lack of a standard approach to analyse the overheating risk. This paper develops a framework to analyse the risk of overheating in buildings from the perspective of comfort and health of occupants through the use of building simulation. The framework includes four steps: (1) Generation of reference climate data for the historical period and future projections to extract various types of EHEs; (2) Development of heat stress metric to quantity the effect of heat on the comfort and health of occupants; (3) Generation of reference summer weather years for building simulation; and (4) Development of assessment criteria for overheating risk., 2nd International Conference on New Horizons in Green Civil Engineering (NHICE‐02)- Held Online, August 24-26, 2020, Victoria, B.C.

In this study, we examined the feasibility of using cold ice as a medium to store the cold energy in the cold season and use it during the hot season in locations which experience both cold and hot seasons. We studied a cylindrical storage tank, two meters in diameter and two meters in length, wrapped with conventional insulation providing R-50. We assumed that the contained ice reaches -25 ℃ during the winter time and it was assumed to be exposed to the warm temperature of 18 ℃ for six months prior to being required for cooling. We used COMSOL Multiphysics to model the heat transfer in a three dimensional configuration. The results showed that 40 percent of the stored cold energy would be depleted during the six month period prior to the hot season. It would take 43 days for the ice in the tank to reach the melting point after consuming the sensible stored cold, and the melting process begins after that. The remaining energy is able to provide the cooling energy for almost full 24 days for an R-2000 2-story detached house located in Ottawa, Ontario., 2nd International Conference on New Horizons in Green Civil Engineering (NHICE-02) - Held Online, August 24-26, 2020, Victoria, B.C.

This study describes the welfare and animals used for scientific and educational purposes in the field of laboratory animal sciences in Algeria. The aim of this study is to provide an overview of the status of the care and use of animals and to improve implementing plans and animal welfare measures. A literature review was performed using online databases and reference lists of the US National Library of Medicine to assess the prevalence of animal use for research in Algeria between 2013 and 2017. Also a retrospective study was conducted using the Pasteur Institute of Algeria report for 2015 to assess the prevalence of animal use in both teaching and research. The first workshop on animal experimentation was organized in 2013 in collaboration with international animal laboratory organizations (ICLAS and OIE) and involving the participation of universities, research centers, veterinary schools and the Pasteur Institute of Algeria. In addition, after accreditation of the Algerian Association of Experimental Animal Sciences, a number of training workshops and courses relating to laboratory animal sciences were organized. In Algeria the use of laboratory animals in research and education is a subject of debate regarding the need to establish regulations and to propose an appropriate ethical framework for the use of animals. Finally, some actions have been already taken in Algeria to promote the ethical use of animals but many more sustainable actions are needed and require cooperation, harmonization of policies and establishment of regional and international networks for experience exchange., Scandinavian Journal of Laboratory Animal Sciences, Vol. 46 No. 1 (2020)

Exposure to hot and humid conditions results in physiological changes in metabolism, cardiac output and thermoregulation of the young adult and these changes deviate with elderly due to aging. The elderly population is more vulnerable than the healthy and young population due to age-weakened physiology and thermoregulatory functions. There are, however, limited bioheat models addressing such changes due to hot exposure in the young and the elderly. This paper develops robust bioheat models for young and elderly while incorporating the physiological changes under exposure to heat-stressful conditions for both age groups the age-related changes in physiology and thermoregulation to an elderly human. However, due to a large variability of thermoregulation among the elderly population, a sensitivity analysis revealed that the average elderly is characterized by metabolic rate and cardiac output, which are lower than those of the young by 21% and 14.4%, respectively. Moreover, the thresholds of the onset of vasodilation and sweating are delayed from those of young adults by 0.5 °C and 0.21 °C, respectively. The elderly and young bioheat models were validated with number of independent published experimental studies under hot exposures in steady and transient conditions. Model predictions of core and mean skin temperatures showed good agreement with published experimental data with a discrepancy of 0.1 °C and 0.5 °C, respectively.

Canada’s buildings, bridges, roads, rail transit, potable water systems and wastewater systems provide shelter and safe drinking water, enable transport of people and goods, and collect and treat wastewater. They provide basic and core services for Canadians and are critical to Canada’s economy and the nation’s quality of life. These buildings and core public infrastructure (B&CPI) are subject to time-varying and uncertain effects due to climate change, as well as more frequent extreme weather events, such as flooding. These climate changeinduced pressures could potentially reduce the safety, serviceability, functionality, and service life of Canada’s B&CPI. In addition, Canadian B&CPI are aging and deteriorating due to different degradation mechanisms, as well as inadequate inspection and maintenance practices – issues potentially made worse by the effects of climate change. The Climate-Resilient Buildings and Core Public Infrastructure (CRBCPI) initiative was established as a research and development partnership between the National Research Council of Canada (NRC) and Infrastructure Canada to develop new and revised codes, standards, specifications, guidelines and decision support tools to ensure that Canada’s new and existing B&CPI are more resilient to the effects of climate change and extreme weather events. Funded by Infrastructure Canada and managed by NRC, the effort brings together experts from across Canada. Initiative priority areas include buildings, bridges, roads, potable water, storm water, and wastewater systems, and rail transit guideways. The initiative addresses resilience to climate change impacts on temperature, precipitation, wind and extreme weather events, including flooding, wildfires and extreme wind, which can affect these types of B&CPI across the country.