A New Bayesian Inference Calibration Platform for Building Energy and Environment Predictions

Buildings account for nearly 40% of total global energy consumption. It is predicted that by 2050 the combined energy consumptions of the residential and commercial sectors will have increased to 22% of the world's total delivered energy. Moreover, requirements for indoor health, safety, thermal comfort, and air quality have become more demanding due to more intensive and frequent extreme climate events, such as heatwaves and cold waves. Such issues have become challenging for the building energy and environment field, especially during the COVID-19 pandemic. Computer simulations play a crucial role in achieving a safe, healthy, comfortable, and sustainable indoor environment. As an integral step in the development of the building models, model calibration can significantly affect simulation results, model accuracy, and model-based decision-making. Conventional calibration methods, however, are often deterministic. As a result, the uncertainties that have been investigated for a building computer model, and those from the inputs have not been given adequate attention and are thus worth studying in more depth. Bayesian Inference is one of the most effective approaches to calibrating computer models with uncertainties. Several studies have explored its application in building energy modeling, but a comprehensive application in the general field of building energy and environment has not been adequate. This thesis started with a comprehensive literature review of Bayesian Inference calibration focusing on building energy modeling. Then, a systematic Bayesian calibration workflow and a new platform were developed. As well as a general study of its application for the predictions of building energy performance, the thesis investigated how to use the platform to calibrate thermal models of buildings and indoor air quality models. To solve the issue of the computing cost of Bayesian Inference, another calibration and prediction method, Ensemble Kalman Filter (EnKF), was proposed and applied to the estimation of ventilation performance and predictions of free cooling load. The conclusion includes a summary of the contributions of this thesis and suggestions for future work

Molecular and functional characterization of Schistosoma japonicum annexin A13

Abstract Schistosomiasis is a neglected tropical disease that affects humans and animals in tropical and subtropical regions worldwide. Schistosome eggs are responsible for the pathogenesis and transmission of schistosomiasis, thus reducing egg production is vital for prevention and control of schistosomiasis. However, the mechanisms underlying schistosome reproduction remain unclear. Annexin proteins (ANXs) are involved in the physiological and pathological functions of schistosomes, but the specific regulatory mechanisms and roles of ANX A13 in the development of Schistosoma japonicum and host–parasite interactions remain poorly understood. Therefore, in this study, the expression profiles of SjANX A13 at different life cycle stages of S. japonicum were assessed using quantitative PCR. In addition, the expression profiles of the homolog in S. mansoni were analyzed in reference to public datasets. The results of RNA interference showed that knockdown of SjANX A13 significantly affected the development and egg production of female worms in vivo. The results of an immune protection assay showed that recombinant SjANX A13 increased production of immunoglobulin G-specific antibodies. Finally, co-culture of S. japonicum exosomes with LX-2 cells using a transwell system demonstrated that SjANX A13 is involved in host–parasite interactions via exosomes. Collectively, these results will help to clarify the roles of SjANX A13 in the development of S. japonicum and host–parasite interactions as a potential vaccine candidate

Building energy simulation and its application for building performance optimization: A review of methods, tools, and case studies

As one of the most important and advanced technology for carbon-mitigation in the building sector, building performance simulation (BPS) has played an increasingly important role with the powerful support of building energy modelling (BEM) technology for energy-efficient designs, operations, and retrofitting of buildings. Owing to its deep integration of multi-disciplinary approaches, the researchers, as well as tool developers and practitioners, are facing opportunities and challenges during the application of BEM at multiple scales and stages, e.g., building/system/community levels and planning/design/operation stages. By reviewing recent studies, this paper aims to provide a clear picture of how BEM performs in solving different research questions on varied scales of building phase and spatial resolution, with a focus on the objectives and frameworks, modelling methods and tools, applicability and transferability. To guide future applications of BEM for performance-driven building energy management, we classified the current research trends and future research opportunities into five topics that span through different stages and levels: (1) Simulation for performance-driven design for new building and retrofit design, (2) Model-based operational performance optimization, (3) Integrated simulation using data measurements for digital twin, (4) Building simulation supporting urban energy planning, and (5) Modelling of building-to-grid interaction for demand response. Additionally, future research recommendations are discussed, covering potential applications of BEM through integration with occupancy and behaviour modelling, integration with machine learning, quantification of model uncertainties, and linking to building monitoring systems