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Background: A timely and accurate diagnosis of Alzheimer's disease (AD) can optimize treatment outcomes; however, few people are referred to a specialist for reasons that are not well understood. The objective of this study was to evaluate referral to a specialist by a primary care clinician (PCC) or other healthcare provider (HCP) among people diagnosed with cognitive impairment disorders, including AD (CIDiAD). Method: This retrospective cohort study analyzed claims data from the Optum de‐identified Normative Health Information database between 1/1/2016‐31/12/2021 (study period). Adults were required to have ≥2 ICD‐9/10‐CM diagnosis codes for AD from any provider during the study period. The index event was defined as the first CIDiAD diagnosis (Table 1) from a PCC or other HCP before or on the date of their diagnosis during the identification period (1/1/2017‐31/12/2020) (Table 2). Continuous medical enrollment was required in the pre‐ and postindex periods. No diagnoses of CIDiAD in the pre‐index period were allowed. People with a post‐index diagnosis of AD from a specialist comprised the referral cohort. Time to specialist referral was assessed, and characteristics of people who were or were not referred were summarized. Result: A total of 48,525 people with a CIDiAD diagnosis from a PCC or other HCP were included; of these, 13,074 (26.9%) were referred to a specialist. The overall median (IQR) time to first specialist visit was 10.0 (3.2‐20.7) months. Time to first specialist visit was longer for referrals from PCCs compared with those from other HCPs (10.8 [3.8‐21.6] vs 9.2 [2.8‐19.9] months, respectively; P<0.0001). No notable differences in sex, age, race and ethnicity, or geographic region were observed between people who were referred and those who were not (Table 3). Conclusion: The majority of people with CIDiAD were not referred to a specialist. Among people who were referred to a specialist, time to first visit was long. Approval of disease‐modifying therapies may accelerate referral to a specialist and shorten referral times as early diagnosis of AD becomes more important. Further analyses will inform geographic differences in specialist referral patterns. [ABSTRACT FROM AUTHOR]

Urban Green Infrastructure (UGI) plays a critical role in creating Thermal Resilient Communities (TRCs) by mitigating the adverse climate change impacts and enhancing thermal resilience. In this paper, we consider a range of UGI types, from street trees, urban parks, green spaces, and building-integrated UGI components, such as green roofs and green walls. It is intended that the thermal/aerodynamic effects of UGI be analyzed and the influence of UGI at three spatial scales within urban communities be provided; more specifically: microclimate scale, building scale, and individual scale. The study discusses the findings of 218 papers published since 2010 to elaborate on recent progress in this field. This review delves into three fundamental aspects related to UGI: a) the underlying mechanisms allowing UGI to affect the environment and human health, b) the modeling efforts undertaken to simulate these effects, and c) measurement studies providing empirical data and verifying the models. Additionally, the effects of UGI across the three scales and related aspects are discussed, and the importance of integrative mechanisms and coupled modeling approaches is emphasized. Key methodologies for integrating microclimate and building simulations are summarized, highlighting the data exchange protocols and software tools. In contrast to previous reviews, this study has analyzed major measurement studies, categorizing them into full-scale and sub-scale studies. As a conclusion, several potential directions for future research are identified, including advanced physical and data driven UGI models, enhanced coupled simulations for indoor-outdoor interactions, establishment of measurement datasets for benchmarking both UGI modeling and coupled simulations. • Reviewed 218 post-2010 Urban Green Infrastructure (UGI) studies for Thermal Resilient Communities (TRC). • Analyzed UGI effects on three spatial scales of microclimate, building and individual. • Discussed UGI effects in terms of mechanism, modeling and measurement. • Summarized co-simulation methods for microclimate and buildings with UGI. • Outlined and compared full-scale and sub-scale measurements studies with UGI. [ABSTRACT FROM AUTHOR]

• Nature-based solutions (NBS) are promising extreme heat mitigation strategies. • This study investigates the effectiveness of NBS in three Canadian regions. • NBS is evaluated by albedo cooling (ACE) and vegetation cooling effectiveness (VCE). • NBS is most effective in Vancouver with average ACE of 9.8°C and VCE of 5.1°C. The need to mitigate and adapt to a changing climate is becoming more urgent, especially in highly urbanized areas where a growing portion of the population lives. This study quantifies the effectiveness of two Nature-Based Solutions - cool roofs and urban vegetation - in the moderation of urban temperature during past Extreme Heat Events in the Ottawa-Montreal, Calgary, and Vancouver regions of Canada. The Weather Research and Forecasting model coupled with the multi-layer building effect parameterization and building energy model at a horizontal resolution of 1km is evaluated under diverse climates, and is used to simulate different levels of both NBS applied across each of the aforementioned regions. Increasing implementation of cool roofs and urban vegetation lead to reductions of near-surface air temperature and wind speed, and enhancements in relative humidity. The largest cooling occurred when both cool roofs and increased vegetation are implemented in each of the cities. The largest cooling effect was found in Vancouver, where a maximum reduction of 3.5°C in 2m air temperature could be felt, while the weakest effect was found in Ottawa at 1.2°C across the city. [ABSTRACT FROM AUTHOR]

Abstract The Multiplicative Random Cascade (MRC) disaggregation model has been extensively used to disaggregate precipitation in many regions across the globe. In this study, it is adapted to disaggregate a range of climate variables (CV) relevant for hygrothermal modelling of building envelopes. This generalized MRC-G model is further improved by explicitly modelling the cross-correlation structure between CVs in the MRC-G-CV model. A thorough evaluation of MRC, MRC-G, and MRC-G-CV models is performed for five Canadian cities: Ottawa, Vancouver, Calgary, St. Johns, and Winnipeg. Results indicate that the MRC model is able to simulate grid-level statistics with >90% accuracy. Grid-level extreme magnitudes and spatial cross-correlation structures are also well simulated. Error magnitudes associated with hourly predictions indicate superior performance of the models in respect to thermal variables, followed by wind variables, and then moisture related variables. Finally, the performance of MRC-G model towards modelling cross-correlation among CVs is found to improve by >50% in terms of energy distance by explicitly modelling these relationships in the MRC-G-CV model. Results indicate that the MRC model variants demonstrated in this study have the potential to facilitate effective hygrothermal performance evaluation of building envelopes at locations where observational sub-daily climate records are unavailable. Highlights • Use of Multiple Random Cascade (MRC) for multivariable disaggregation demonstrated. • Both spatial and inter-variable dependence structure explicitly modelled. • Comparable performance of MRC method across variables is obtained. • Modelling of inter-variable dependence structures found to improve MRC skill. • Results highlight utility of MRC model variants in hygrothermal assessments. [ABSTRACT FROM AUTHOR]

Climate change and urbanization have exacerbated concerns about urban overheating, affecting thermal comfort, heat-related mortality, and urban energy consumption, especially during heat waves. Developing climate projections that reliably describe the state of the urban climate, a complex system containing microclimate phenomena on a regional and global scale, is essential for assessing overheating impacts and evaluating mitigation strategies in current and future climates. Using four essential steps for advancing our understanding of overheating and informing future studies, we review climate model projections for urban overheating impact assessments: (1) Acquisition of raw future climate data at the mesoscale through GCM-RCMs; (2) Bias correction and local reference year data generation; (3) Simulations at microscale indoors and outdoors using building performance or CFD models; (4) Evaluation of overheating based on various criteria. A methodology for selecting review articles is presented. The study highlights key future research gaps related to climate data generation, improving data reliability, and addressing climate simulation complexities indoors and outdoors. In addition, a comprehensive overheating assessment also depends upon incorporating social-economic factors into evaluations. Although we focus on urban overheating assessment, the general methodologies of future climate projections may also apply to other building performance simulations considering climate change impacts. • Accurate climate projections are imperative to evaluate urban overheating in extant and forthcoming climates. • Statistical-dynamic methods present a viable balance for high-resolution, multi-decadal urban climate analyses. • Augmenting projected data reliability via bias adjustments and reference year techniques is of great importance. • Addressing challenges in indoor and outdoor climate simulation is essential for studying future overheating. • Overheating evaluation methods should consider social-economic indices to better assess urban overheating risks. [ABSTRACT FROM AUTHOR]

With the expectation of increasing frequency and magnitude of extreme events caused by climate change, one of the most critical yet overlooked aspects of building design in the industry is thermal resilience. An effective way to assess and quantify a building's resilience in the design phase is through modeling and simulation; however, there has been limited effort invested in operationalizing this practice. The purpose of this paper is to provide a critical review of past modeling and simulation research focused on quantifying the thermal resilience of buildings, and to discuss future areas of research that are needed to enhance and standardize practice, as well as building codes and standards. The paper begins by defining a framework for building performance simulation-aided resilience quantification. Next, we review the current state of the art research on a variety of topics such as heat stress and resilience metrics, model assumptions and setup, simulation tool capabilities, weather file type selection, and methods to visualize and communicate resilience. Finally, a discussion on the fundamental research and future tool development needs to accelerate transition from research into practice is presented. • There is urgent need to consider thermal resilience in building design process. • Different methodologies and metrics for analysis are found in existing literature. • Simulation practices to evaluate building thermal resilience are thoroughly analyzed. • Standard practices are needed to better reflect occupant tolerances and reaction. • Practical tools can be developed to facilitate model conversion and results analysis. [ABSTRACT FROM AUTHOR]

Climate change has led to prolonged, more frequent, intense, and severe extreme weather events, such as summertime heatwaves, creating many challenges on the economy and society and human health and energy resources. For example, the 2010 and 2018 heatwave in Quebec, Canada, resulted in about 280 and 93 heat-related deaths, and there were around 500 fatalities due to overheated indoor environments in 2021 around entire Canada. Therefore, it is imperative to understand and evaluate the overheating conditions in buildings, for which selecting suitable future reference weather data under climate change is one of the first critical steps. This study evaluated a reference year selection method in terms of typical and extreme reference years based on future climate datasets to assess both outdoor and indoor overheating in the future. The future climate data were collected from the Coordinated Regional Downscaling Experiment (CORDEX) program. Three Canadian cities (Montreal, Toronto, Vancouver) were selected for the overheating evaluation during three selected periods (2001–2020, 2041–2060, 2081–2100). The CORDEX climate projections were first bias-corrected by the multivariate quantile mapping correction method with the observational data. Then, the typical and extreme reference year data were generated as well as climate data from the design summer year for comparison. The performance of the reference year selection method was evaluated by comparing the maximum, minimum, and average overheating hours for the 20-years data of each period. This study demonstrates that the multivariate quantile mapping bias correction method can improve the reliability of future climate data making it one of the most important steps for any future weather projection study. Besides, the reference year selection method could efficiently capture maximum and minimum monthly overheating hours providing the upper and lower boundary of possible outdoor and indoor overheating conditions.. In contrast, neither the severest nor the typical monthly outdoor and indoor overheating conditions could be predicted by the design summer year method. Finally, owing to the effects of climate change, average monthly overheating hours normally increase by around one time (from 50% to 150%) until the mid-term future (2041–2060) and by around two to three times (even up to nine times for some scenarios) during the long-term future (2081–2100). [ABSTRACT FROM AUTHOR]

Abstract: Diabetic retinopathy is a disorder that accounting as a root cause of uncorrectable vision loss for diabetic patients. It is due to the damaging of blood vessels that nourish the retina. However, earlier detection and treatment through regular screening, blindness can be avoided. In order to lessen the cost of these screenings, modern image processing techniques are used to voluntarily detect the existence of abnormalities in the retinal images acquired during the screenings. The earliest clinical signs of diabetic retinopathy are microaneurysms, small out-pouching from retinal capillaries, and dot intra-retinal Hemorrhages. Exudates are a major indicator of diabetic retinopathy that can possibly be quantified automatically. Patients having these signs are present in type1 stage of diabetic retinopathy also called NPDR (Non-Proliferative Diabetic Retinopathy). This paper implements a method that identifying the feature of exudates from the image using segment based feature extraction. Here, classification into various stages of NPDR is based on their pixels intensity and frequency from the retinal fundus image. A serious of experiments for extracting the feature is performed with the use of effective image processing techniques. To get these feature value from fundus retinal image various techniques like morphological pre-processing, image boundary tracing, adaptive threshold using Otsu methodology, Optic disk localization are implemented. The SVM classifier uses features extracted by combined 2DPCA instead of explicit image features as the input vector Combined 2DPCA is proposed and then for acquiring higher accuracy of classification we can use virtual SVM. Experimental evaluation on the publicly available dataset DRIVE demonstrates the improved performance of the proposed method for automatic detection of Exudates. These automatically detected exudates are validated by comparing with expert ophthalmologists’ hand-drawn ground-truths. The overall sensitivity of proposed method is 97.1% for the classifier and the specificity is of 98.3%. So, by using this tool Specialist gets support in screening a detection of early changes causing Diabetic Retinopathy and hence timely intervention leading to reduced DR related blindness. [Copyright &y& Elsevier]

As a consequence of global warming and rapid urbanization around the globe, the magnitudes and frequencies of extreme heat events (EHEs) are increasing and this trend is expected to continue into the future. In this study, the added benefit of modelling climate at convection-permitting spatial resolutions (grid spacing <4 km) is considered for a set of exterior climate and interior building simulations during EHEs in the urban areas of Ottawa and Montreal, Canada over the summer of 2018. The climate is modelled at two spatial resolutions: i) 25 km – typically considered for regional-scale climate modelling; and ii) 1 km. The results derived from modelling at each of these resolutions is compared in respect to their adequacy in predicting different measures of overheating outdoors as well as those within a typical single-detached home. The results clearly demonstrate that simulations undertaken at a 1 km resolution permit more accurately calculate the magnitude of overheating, whereas simulations completed at a 25 km resolution lead to an underestimation of overheating in about 95% of the urban grids within either of these two cities. These results suggest the necessity of using convection-permitting climate simulations for overheating assessments over the urban scale. • Urban climate of Ottawa and Montreal is simulated at 1 and 25 km spatial resolutions. • Added value of convection-permitting model in overheating studies is demonstrated. • Simulations at a high-resolution are found more accurate in simulating urban temperatures and wind. • Use of regional climate simulations leads to underestimation of overheating. [ABSTRACT FROM AUTHOR]

As a consequence of global warming, buildings in Canada and around the globe are expected to face unprecedented climate over their design lives. The Moisture Index (MI) is a climate-based indicator currently used in the National Building Code of Canada (NBCC) to guide the design of wall assemblies for acceptable durability performance. In this study, future changes in MI are calculated across Canada under 2 and 3.5 °C of future global warming. Results indicate that the coastal and great lakes regions of Canada will have increased MI, whereas prairies and northern regions will in the future have decreased values of MI. Furthermore, it is demonstrated that drastically different projected future MI changes can be obtained with different values of drying potential normalization factor, and it is suggested that this value is chosen by carefully analyzing the historical and future projected drying potential values. Finally, our analysis shows that wall assemblies at an increased number of reference locations given in the NBCC, importantly those falling in the densely populated south-western Ontario region, will need to be designed with additional safety measures such as a capillary break in the future for them to achieve satisfactory moisture performance over their design lives. • Potential moisture risks of building envelopes under a changing climate over Canada. • Projected increases in atmospheric moisture loading requires moisture resilient building design. • Drastic differences in Moisture Index can be obtained with different normalization factors. • Many additional locations will need a capillary break in the future to achieve satisfactory moisture performance. [ABSTRACT FROM AUTHOR]

It is now well accepted that global greenhouse gas emissions will result in drastic changes in climate and patterns of natural disasters such as wildfires in Canada and across the globe. This study quantifies potential future changes in wildfire regimes around 11 Canadian cities with high socio-economic value and historically exposed with low to high wildfire hazard. Changes in extreme wildfire weather and behavior are calculated under 2 and 3.5 °C of global warming with reference to a baseline historical time-period of 1989–2019. Results provide statistically significant evidence of an extended wildfire season and more frequent weather conducive for wildfires around the cities. This will result in more intense wildfires with higher head fire intensities surrounding all cities. The city of Nelson (British Columbia) is found to exhibit highest wildfire magnitudes in both historical and future climates. The city of Thunder Bay (Ontario) is projected with most significant increases in the occurrence frequencies of extreme wildfires where 50-year HFI events are projected to become 18-year and 9-year return period events under 2 and 3.5 °C of global warming respectively. A statistically significant evidence of an increasing numbers of future wildfire events requiring significant suppression resources is also found. Results highlight the need for adapting buildings and infrastructure in the cities in the face of projected future changes in wildfires. • Future wildfire hazard in Wildland Urban Interface areas of 11 Canadian cities projected under 2 and 3.5ºC global warming. • Extended wildfire seasons and frequent weather conducive for wildfires surrounding all cities. • More frequent, intense wildfires requiring significant suppression resources projected around majority of the cities. [ABSTRACT FROM AUTHOR]

In the face of rapid urbanization and global warming, it is important to acquire a better understanding of urban climate and land-atmosphere interactions operating therein. The Weather Research and Forecasting (WRF) model is a limited area model that has been used to study urban microclimate in many cities across the globe. However, such a study is lacking for the Canada's capital city: Ottawa. In this article, the WRF model is set-up at 1 km spatial resolution over the Ottawa region and its sensitivity towards the use of different urban parametrization schemes and land-cover datasets is investigated from 01 June to 31 August 2018 which includes an extreme heat weather event spanning from June 30 to July 6. WRF-simulations are performed using the Noah land surface model with two urban parametrization schemes of different complexity, i.e., a simple bulk urban parametrization and a more advanced multilayer urban canopy model (UCM). Both WRF-simulations used the default land use-land cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) available in WRF. Between these two WRF-simulations, the simulation with the multilayer UCM is found to be more accurate than the bulk scheme simulation in terms of modeled near-surface wind-speed, relative humidity, and total precipitation compared to observations recorded at one urban weather gauging station located within the city limits. Finally, a third WRF-simulation is performed with the multilayer UCM but using a higher resolution 30 m land use-land cover data product for the urban domain. This third WRF-experiment improved further the near-surface wind speed, relative humidity, and total precipitation correspondence to observations within the city. It is worthy to mention that modeled total precipitation is found to be sensitive to both urban parametrization schemes and urban land use-land cover data sets. • This is the first WRF-urban climate modeling study over the City of Ottawa. • The WRF model is evaluated by considering the impact of spatial resolution and climate parameters such as precipitation and relative humidity. • We have found that the use of an advanced urban canopy model results in more accurate description of near surface wind speed, relative humidity, and precipitation. • Modeled precipitation is found to be sensitive to the use of advanced urban parametrization schemes and more accurate depiction of urban areas in the WRF model. [ABSTRACT FROM AUTHOR]

• This research evaluates the environmental impacts by considering a non- stationary changing climate (RCP 8.5) scenarios and prospective electricity mix. • The innovative approach is to measure the relative importance of climate change and energy transition. • A comprehensive uncertainty analysis was undertaken to evaluate the robustness of the impact results. • The results illustrate the importance of dynamic perspectives in environmental impact assessment of building projects. • Electricity-based space and water heating for Ontario and British Columbia along with the gas heating option for Alberta is recommended as a climate friendlier HVAC system. There is a significant share of greenhouse gas (GHG) emissions attributed to the built environment, either for production or the operation of buildings. Various initiatives are being implemented to reduce the release of GHGs into the atmosphere relying on the evaluation, tracking, recording and verification of GHG emissions and removals. The annual accounting of GHG flows associated with buildings should be conducted in a lifecycle context to ensure that policies are effective at mitigating climate change. Buildings operate for decades and both the climate and electricity grid mix are expected to change significantly during such a time frame. This study aims to support the design of resource- as well as energy-efficient buildings using a sound life cycle assessment (LCA) methodology in the preliminary design stage. A straightforward method that can be applied for a detailed understanding of the effects of climate change and prospective electricity grid mix on building energy use is presented. The novelty of this study was to integrate long-term energy projections in a high temporal resolution LCA for buildings and taking into account different future climates and prospective electricity mixes across Canada. The research integrated the dynamic LCA capabilities directly into a Building Information Model (BIM). Such dynamic considerations as climate and energy mix improves the environmental importance and scientific robustness of LCA metrics. The proposed methodology will assist users to apply a clear framework that helps to define an optimized design alternative through a dynamic energy analysis and future weather forecasting simulation. An example of an actual office building is provided to demonstrate the capabilities and usefulness of the developed integrated framework. [ABSTRACT FROM AUTHOR]