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142 results on '"Afshari, Afshin"'

1. Typical and extreme weather datasets for studying the resilience of buildings to climate change and heatwaves

Author

Machard, Anaïs, Salvati, Agnese, P. Tootkaboni, Mamak, Gaur, Abhishek, Zou, Jiwei, Wang, Liangzhu Leon, Baba, Fuad, Ge, Hua, Bre, Facundo, Bozonnet, Emmanuel, Corrado, Vincenzo, Luo, Xuan, Levinson, Ronnen, Lee, Sang Hoon, Hong, Tianzhen, Salles Olinger, Marcello, Machado, Rayner Maurício E Silva, da Guarda, Emeli Lalesca Aparecida, Veiga, Rodolfo Kirch, Lamberts, Roberto, Afshari, Afshin, Ramon, Delphine, Ngoc Dung Ngo, Hoang, Sengupta, Abantika, Breesch, Hilde, Heijmans, Nicolas, Deltour, Jade, Kuborn, Xavier, Sayadi, Sana, Qian, Bin, Zhang, Chen, Rahif, Ramin, Attia, Shady, Stern, Philipp, and Holzer, Peter

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Sustainable Cities and Communities
Abstract

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.

Published
2024

24. Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model.

Author

Afshari, Afshin, Vogel, Julian, and Chockalingam, Ganesh

Subjects
*LAND surface temperature, *DOWNSCALING (Climatology), *CONVOLUTIONAL neural networks, *DEEP learning, *URBAN heat islands, *ATMOSPHERIC temperature
Abstract

The analysis of the near-surface air temperature is vital for many applications such as urban heat islands and climate change studies. In particular, extreme weather events are typically localized and so should the corresponding adaptation measures. However, climate scientists are often confronted with the difficulty of providing reliable predictions at high spatial resolutions in the order of 1 km. We propose to train a convolutional neural network model to emulate the hourly high-resolution near-surface air temperature field simulated by the Weather Research and Forecasting (WRF) software over a period of 18 months. The model is driven by current and past lags of coarse SEVIRI land surface temperature fields. This nowcasting application implements a downscaling of the spatial resolution of the input by about a factor of four, while establishing a correlation between current and past land surface temperature maps and the current near-surface air temperature field. The U-Net variant that is proposed in this study uses regularization to prevent over-fitting and implements a novel space-time approach, where multiple time steps are fed into the model through 3D convolution layers. Besides LST, the model also uses urban density as additional static input to be able to predict the temperature more accurately in urban areas and to improve the generalizability of the trained model. The performance of our U-Net model is assessed via comparison to an MLR benchmark (ridge regularization). The model is shown to be superior on all performance metrics. It achieves a mean absolute error of 1.36 ° C versus 1.49 ° C for benchmark (a 9% relative improvement) and a root mean square error of 1.77 ° C versus 1.91 ° C for benchmark (a 7% relative improvement). The absolute error of the model is less than 2 ° C for 77% of the prediction points versus 72% for the benchmark (a 7% relative improvement). The improvement over the benchmark is even more significant during extreme hot periods. We demonstrate the generalizability of the approach by testing the trained model on unseen spatial domains. [ABSTRACT FROM AUTHOR]

Published
2023
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30. The role of humoral mediators in immunological reactions to streptokinase

Author

Afshari, Afshin

Abstract

Streptokinase is now a commonly administered thrombolytic agent in AMI patients. One of the main disadvantages of its administration is that as a foreign antigen it may provoke hypersensitivity reactions due to previous streptococcal infections, and' pre-existing antibodies may reduce efficacy of treatment. The aim of this study was to look at role of humoral immunity to streptokinase. To this end three areas were studied; 1) Specific anti-streptokinase antibodies .2) Role of complement and 3) Role of the immune complex removal. ELISA's were developed to measure the levels of IgG, IgA, IgM, IgE and neutralising anti-streptokinase antibodies in a noraial population. Only a small proportion of the normal individuals had elevated levels of anti-streptokinase antibodies (IgG 1.68%, IgA 2.7%, IgM 9.9% and neutralising anti-streptokinase antibodies 0.4%). The levels of anti-streptokinase antibodies in 20 patients with AMI (10 patients treated with streptokinase and 10 with r-tPA) were compared to the normals. Administration of streptokinase in 10 patients studied, resulted in an immediate fall in level of anti-streptokinase antibodies and developed a specific response. The levels of anti-streptokinase antibodies in patients treated with streptokinase and reperfused early were within the normal ranges, and levels rose in the late reperfusion or non reperfusion groups. Of the 10 patients given streptokinase, one patient with elevated levels of IgG and IgA developed serum sickness. The levels of specific IgM anti-streptokinase antibodies in patients with rheumatoid arthritis were significantly elevated. This did not correlate with IgG anti-mycobacterium heat shock protein 65 (mHSP65). In patients with Henoch Schonlein Purpura there was a correlation between IgG anti-mHSP65 levels and IgG, IgA and neutralising anti-streptokinase antibodies levels. The interactions of complement system and immune complexes was studied by measurement of complement activation products Cls:Cl-INH, C3b-P and C5b-9 using modified techniques introduced by Auda et al 1990. An indirect ELISA for measuring serum C3d levels and a modified ELISA for detecting erythrocyte (E) with bound C3d were developed to evaluate the relationship of bound C3d and the levels of free complement components. Patients treated with r-tPA did not generate complement activation products. All the ten patients administered streptokinase generated Cls:Cl-INH and only one patient had increased levels of C3b-P and C5b-9. The extent of complement activation correlated with the pre-treatment levels of anti-streptokinase antibodies. In vitro effects of the ICs on complement activation showed that the presence of erythrocytes did not modulate significantly C3d or Cls:Cl-INH levels generated by the ICs, however, they exerted an influence on C3b-P through the actions of CRL The kinetics and dose response of streptokinase ICs, C3c and C3d binding to erythrocytes (E's) were studied by flow cytometry and its relationship with free complement activation was determined by measuring the free complement activation products in the accompanying supernatants. This study showed that the binding of ICs to E's depended significantly on the extent of complement activation (p < 0.001) and the dose of ICs (p < 0.001). The clearance of ICs during streptokinase treatment was studied during a period of 30 minutes after streptokinase administration in 4 patients with AMI by detecting E bound ICs using FACscan analysis. Streptokinase ICs were detectable on E's in one of patients. This patient generated high levels of complement activation products which correlated with E's bound ICs (p < 0.001), thus the clearance of streptokinase ICs depends on the extent of complement activation and the observed adverse reaction in patient CD (With elevated levels of IgA) is due to poor complement activation by the formed ICs. Study of anti-streptokinase ICs and IgG interactions with E's revealed that they also bound to E's in absence of NHS. A haemagglutinin test among normal individuals and patient groups revealed that the of binding of rabbit IgG to human erythrocytes is not a non-specific adsorption. In addition, F(ab)2 fragment did not bind to the E's as determined by FACScan analysis. Using flow cytometry human IgG (Fc fragment) inhibited the binding of rabbit IgG to human erythrocytes indicated that both rabbit IgG and Fc fragment of human IgG bind to the same sites on erythrocytes. Using a set of monoclonal anti-human FcyR antibodies, FcyRI (CD64) was detectable on erythrocytes. The pattern of mouse IgG subclasses binding to human E's (IgG3 > IgG2b >IgG2a) was consistent with the pattern of CD 16 (isoforms A and B) and CD64 (isoform A). These findings indicate the presence of FcyR on the erythrocytes, which may play an important role in handling of immune complexes.

Published
1995
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49. Kalman filter-based FDD for an Air Handling Unit (AHU)

Author

Mulumba, Timothy, Afshari, Afshin, and Friedrich, Luiz Augusto

Subjects
FDD, Kalman Filter, HVAC
Abstract

We propose to improve the energy efficiency of commercial HVAC systems by implementing a Kalman filter based fault detection and diagnosis (FDD) scheme to accurately identify certain categories of abnormal conditions that are the most prevalent in hot, humid climates like the UAE. The general approach used to deal with drift in system state includes the following tasks: 1) Determine possible model specifications, 2) Use a Kalman filter to determine time-varying parameters of the specified models, 3) Perform fault detection using a statistical process control and, 4) Perform fault diagnosis by formulating qualitative rules and comparing with those derived from decision trees. The model is built using from normal and faulty data that was generated during ASHRAE Project RP-1312 in which several Air Handling Unit (AHU) faults were artificially introduced and the resulting operational data recorded at 1-minute intervals. The results show that the Kalman filter is well-suited to detecting changes in mean level and the rules derived from the parameter estimates perform as well as those derived from decision trees.

Published
2014

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