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We present unprecedented datasets of current and future projected weather files for building simulations in 15 major cities distributed across 10 climate zones worldwide. The datasets include ambient air temperature, relative humidity, atmospheric pressure, direct and diffuse solar irradiance, and wind speed at hourly resolution, which are essential climate elements needed to undertake building simulations. The datasets contain typical and extreme weather years in the EnergyPlus weather file (EPW) format and multiyear projections in comma-separated value (CSV) format for three periods: historical (2001-2020), future mid-term (2041-2060), and future long-term (2081-2100). The datasets were generated from projections of one regional climate model, which were bias-corrected using multiyear observational data for each city. The methodology used makes the datasets among the first to incorporate complex changes in the future climate for the frequency, duration, and magnitude of extreme temperatures. These datasets, created within the IEA EBC Annex 80 "Resilient Cooling for Buildings", are ready to be used for different types of building adaptation and resilience studies to climate change and heatwaves.

BACKGROUND: Prolonged delays between first caregiver concern and autism spectrum disorder (ASD) diagnosis have been reported, but associations between length of time to diagnosis (TTD) and health care resource utilization (HCRU) and costs have not been studied in a large sample of children with ASD. OBJECTIVE: To address these informational gaps in the ASD diagnostic pathway. METHODS: This retrospective, observational, single cohort analysis of Optums administrative claims data from January 1, 2011, to December 31, 2020, included commercially insured children who had 2 or more claims for an ASD diagnosis (earliest diagnosis designated as the index date), were between the ages of older than 1.5 years and 6 years or younger at index date, and were continuously enrolled for up to 48 months before and for 12 months after the index date. Two cohorts (between the ages of older than 1.5 years and 3 years or younger and between the ages of older than 3 years and 6 years or younger at ASD diagnosis) were divided into shorter (less than median) and longer (greater than or equal to median) TTD around each cohort median TTD calculated from the first documented ASD-related concern to the earliest ASD diagnosis, because TTD may vary by age at diagnosis. This exploratory analysis compared all-cause and ASD-related HCRU and costs during a 12-month period preceding ASD diagnosis among children with shorter vs longer TTD. RESULTS: 8,954 children met selection criteria: 4,205 aged 3 years or younger and 4,749 aged older than 3 years at diagnosis, with median TTD of 9.5 and 22.1 months, respectively. In the year preceding ASD diagnosis, children with longer TTD in both age cohorts experienced a greater number of all-cause and ASD-related health care visits compared with those with shorter TTD (mean and median number of office or home visits were approximately 1.5- and 2-fold greater in longer vs shorter TTD groups; P < 0.0001). The mean all-cause medical cost per child in the year preceding ASD diagnosis was approximately 2-fold higher for those with longer vs shorter TTD ($5,268 vs $2,525 in the younger and $5,570 vs $2,265 in the older cohort; P < 0.0001 for both). Mean ASD-related costs were also higher across age cohorts for those with longer vs shorter TTD ($2,355 vs $859 in the younger and $2,351 vs $1,144 in the older cohort; P < 0.0001 for both). CONCLUSIONS: In the year prior to diagnosis, children with longer TTD experienced more frequent health care visits and greater cost burden in their diagnostic journey compared with children with shorter TTD. Novel diagnostic approaches that could accelerate TTD may reduce costs and HCRU for commercially insured children. DISCLOSURES: This study was funded by Cognoa, Inc. Optum received funding from Cognoa to conduct this study. Dr Salomon is an employee and holds stock options of Cognoa, Inc. Dr Campbell was an employee of Cognoa, Inc., at the time this study was conducted. Dr Duhig was an employee of Cognoa, Inc., at the time the study was conducted and holds stock options. Dr Vu, Ms Kruse, Mr Gaur, and Ms Gupta are employees and/or stockholders of Optum. Dr Tibrewal was an employee of Optum at the time the research for this study was conducted. Dr Taraman is an employee and holds stock options of Cognoa, Inc., receives consulting fees from Cognito Therapeutics, volunteers as a board member of the American Academy of Pediatrics California and Orange County Chapter, is a paid advisor for MI10 LLC, and owns stock options of NTX, Inc., and HandzIn.

Rapid urbanization and changing land use dynamics require robust tools for projecting and analyzing future land use scenarios to support sustainable urban development. This study introduces an integrated modeling framework that combines the Patch-generating Land Use Simulation (PLUS) model with Markov Chain (MC) analysis to simulate land use and land cover (LULC) changes for Montreal Island, Canada. This framework leverages historical data, scenario-based adjustments, and spatial drivers, providing urban planners and policymakers with a tool to evaluate the potential impacts of land use policies. Three scenarios—sustainable, industrial, and baseline—are developed to illustrate distinct pathways for Montreal's urban development, each reflecting different policy priorities and economic emphases. The integrated MC-PLUS model achieved a high accuracy level, with an overall accuracy of 0.970 and a Kappa coefficient of 0.963 when validated against actual land use data from 2020. The findings indicate that sustainable policies foster more contiguous green spaces, enhancing ecological connectivity, while industrial-focused policies promote the clustering of commercial and industrial zones, often at the expense of green spaces. This study underscores the model's potential as a valuable decision-support tool in urban planning, allowing for the scenario-driven exploration of LULC dynamics with high spatial precision. Future applications and enhancements could expand its relevance across diverse urban contexts globally. [ABSTRACT FROM AUTHOR]

BACKGROUND: Bonding between metal and ceramic is one of the most important aspects of a successful prosthesis. Various methods have been recommended for preparing the metal surface to enhance the bond between metal and ceramic including the use of a metal bonding agent. OBJECTIVE: This study aims to evaluate and compare the shear bond strength of the metal-ceramic (M-C) interface after combinations of various surface treatments including the application of a metal bonding agent. METHOD: 40 Ni-Cr alloy specimens were made and divided into 4 groups of 10 each based on the combination of surface treatments. Sandblasting, surface grinding, and Oxidation heat treatment (OHT) were performed on specimens from Group 1 (Control). In addition, Group 2 specimens received ultrasonic cleaning, Group 3 steam cleaning, and Group 4 metal bonding agent application. Following surface treatments on all specimens, porcelain build-up was performed, and shear bond strength was tested in a Digital Universal testing machine. The statistical tests used were independent t -test and ANOVA. RESULTS: Results revealed that Group 4 specimens had the highest mean value of shear bond strength of 39.087 MPa while Group 3 specimens showed the least mean shear bond strength of 18.154 MPa with highly statistically significant results (p < 0.001). CONCLUSION: The surface treatments and application of bonding agent to metal prior to porcelain application resulted in increased shear bond strength of the metal-ceramic interface. [ABSTRACT FROM AUTHOR]

The longevity of polymer-based sealant and jointing products, including elastomers, significantly depends on the level of exposure to sunlight and joint movement. These factors are particularly crucial in the application of polymers in construction due to their susceptibility to degradation under environmental conditions. For instance, diurnal cycles of contraction and dilation, arising from daily temperature fluctuations, impose significant stress on sealants and joints, impacting their durability over time. The elastic nature of polymeric sealants enables them to endure these cyclic mechanical loads. Athough there is considerable information on sealant durability obtained from laboratory accelerated aging, there is limited knowledge about the effect of climatic factors using historical and projected weather data on the durability and expected service life of these products. This study employed the Shephard crack growth model to predict the performance of sealants in a Canadian context; the crack growth and time-to-failure of hypothetical silicone sealants were investigated across 564 locations, for which historical climate data were obtained from 1998 to 2017, including gridded reanalysis data for the period of 1836–2015. The historical climate data were classified into four climate categories, and crack growth was estimated based on historical climatic data within the valid range for the Shephard model, revealing that locations in colder climates with lower levels of precipitation typically exhibit higher cumulative crack growth. The impact of climatic variation and environmental stressors on the longevity of sealants in the context of climate change was also investigated using future projected data. [ABSTRACT FROM AUTHOR]

Accurate modeling of snow depth (SD) processes is critical for understanding global energy balance changes, affecting climate change mitigation strategies. This study evaluates the Coupled Model Intercomparison Project Phase 6 (CMIP6) model performance in simulating daily SD across Canada. We assess CMIP6 outputs against observed data, focusing on daily SD averages, snow cover durations, and rates of accumulation and depletion, alongside annual SD peaks for 11 major Canadian catchments. Our findings reveal that CMIP6 simulations generally overestimate daily SD by 57.7% and extend snow cover duration by 30.5 days on average. While three models (CESM2, UKESM1‐0‐LL and MIROC6) notably align with observed annual SD peaks, simulation biases suggest the need for enhanced model parameterization to accurately capture snow physics, particularly in regions with permanent snow cover and complex terrains. This analysis underscores the necessity of refining CMIP6 simulations and incorporating detailed geographical data for better SD predictions. Plain Language Summary: Precise simulations of snow depth (SD) are crucial for understanding changes in the Earth's energy balance and helping efforts to combat climate change. In this research, we evaluate how well the latest phase of the Coupled Model Intercomparison Project (CMIP6) can simulate the daily SD across Canada. By comparing these simulations to various SD data sets, we looked at average daily SD, how long snow remains on the ground, and how quickly it accumulates or melts away, focusing on 11 major catchments in Canada. Our study found that the CMIP6 models tend to simulate more SD, by about 57.7% on average, and suggest that snow cover remains around 30.5 days longer than observed durations. Although three specific models excel in matching the annual peak of daily SD, we noticed biases that point to a need for improving how these models represent various land covers. Our findings highlight the importance of making these models more accurate by using more detailed information about land cover properties, which helps better predict SD and understand its role in climate change. Key Points: Coupled model intercomparison project phase 6 (CMIP6) simulations mostly overestimate the average daily SD values by a median of 57.7% (6.9 cm)The average snow cover duration is overestimated by 30.5 days compared to station data, confirmed by the CMC data setCMIP6 simulations relatively capture extremes well, with a median bias of 12.73% [ABSTRACT FROM AUTHOR]