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Mortality assessments are conducted for both civil and commercial purposes. Recent advances in epigenetics have resulted in DNA methylation tools to assess risk and aid in this task. However, widely available array-based algorithms are not readily translatable into clinical tools and do not provide a good foundation for clinical recommendations. Further, recent work shows evidence of heritability and possible racial bias in these indices. Using a publicly available array data set, the Framingham Heart Study (FHS), we develop and test a five-locus mortality-risk algorithm using only previously validated methylation biomarkers that have been shown to be free of racial bias, and that provide specific assessments of smoking, alcohol consumption, diabetes and heart disease. We show that a model using age, sex and methylation measurements at these five loci outperforms the 513 probe Levine index and approximates the predictive power of the 1030 probe GrimAge index. We then show each of the five loci in our algorithm can be assessed using a more powerful, reference-free digital PCR approach, further demonstrating that it is readily clinically translatable. Finally, we show the loci do not reflect ethnically specific variation. We conclude that this algorithm is a simple, yet powerful tool for assessing mortality risk. We further suggest that the output from this or similarly derived algorithms using either array or digital PCR can be used to provide powerful feedback to patients, guide recommendations for additional medical assessments, and help monitor the effect of public health prevention interventions. [ABSTRACT FROM AUTHOR]

In this paper the authors present an algorithm that abstracts an arbitrarily shaped set of building volumes into a group of simplified ‘shoebox’ building energy models. It is shown that for generic perimeter and core floorplans the algorithm provides a faster but comparably accurate simulation results of annual load profiles vis-à-vis multi-zone thermal models generated according to ASHRAE90.1 Appendix G guidelines. Envisioned applications range from rapid thermal model generation for urban building energy modelling to schematic architectural design. Following a description of the algorithm, its ability to produce load profiles for a mixed-use neighborhood of 121 fully conditioned buildings for a variety of climates is demonstrated. The comparison yields relative mean square errors in simulated annual building energy use intensity of five to 10 percent compared to ASHRAE 90.1 compliant building energy models while reducing simulation times by a factor of 296. [ABSTRACT FROM AUTHOR]

In this paper, we present a general algorithm to automatically convert arbitrary building massing models into multi-zone building energy models (BEM). The algorithm follows current guidelines for thermal zone discretization of BEMs when actual interior space boundaries are yet undefined. Envisioned applications are for rapid model generation during schematic building design as well as for urban massing studies. We present an argument that current recommendations for separating core from perimeter zones effectively follow a straight-skeleton subdivision. Following a step-by-step explanation of the procedure, a number of example building shapes of varying complexity are shown to demonstrate the algorithm's robustness and suitability for automated multi-zone BEM generation. Going forward, it is recommended that the algorithm is adopted by software developers to ensure more consistent thermal model production within the building simulation community. [ABSTRACT FROM PUBLISHER]

Outdoor thermal comfort simulation simulations rely on the mean radiant temperature (MRT) seen by pedestrians as an important input that remains difficult to compute. Especially for large urban models, computing relevant surface temperatures and radiation fluxes that make up the MRT is a daunting task in terms of simulation setup and the computational overhead. We propose a new algorithm to estimate exterior surface temperatures of building facades, roofs, and ground surfaces in an arbitrary urban 3D model. The algorithm discretizes all model surfaces and clusters them by material properties and sky and sun exposure to reduce computational complexity. The model setup is fully automated, and the algorithm is implemented in the popular Rhino3d CAD environment. We demonstrate the accuracy of the algorithm by comparing both the resulting external surface temperatures against a high-fidelity simulation and the final MRT against real-world measurements. We report an RMSE of 1.8 °C and 2.0 °C, respectively, while reducing simulation times by a factor of ~80. Envisioned applications of the algorithm range from rapid microclimate simulations in fast-paced urban design processes to large scale urban comfort evaluation of existing cities. • New algorithm to predict exterior surface temperatures and MRT in an arbitrarily complex urban 3D model. • The algorithm is utilizing K-Means clustering to reduce simulation times by a factor of 80. • We report an RMSE of 1.8°C for surface temperature predictions of a full year. • Importance of long-wave radiation exchange is analyzed. • Largest heterogeneity in external surface temperature is reported for horizontal clusters close to obstacles and south-facing clusters. [ABSTRACT FROM AUTHOR]