We estimated mortality and economic loss attributable to PM2·5 air pollution exposure in 429 counties of Iran in 2018. Ambient PM2.5-related deaths were estimated using the Global Exposure Mortality Model (GEMM). According to the ground-monitored and satellite-based PM2.5 data, the annual mean population-weighted PM2·5 concentrations for Iran were 30.1 and 38.6 μg m−3, respectively. We estimated that long-term exposure to ambient PM2.5 contributed to 49,303 (95% confidence interval (CI) 40,914–57,379) deaths in adults ≥ 25 yr. from all-natural causes based on ground monitored data and 58,873 (95% CI 49,024–68,287) deaths using satellite-based models for PM2.5. The crude death rate and the age-standardized death rate per 100,000 population for age group ≥ 25 year due to ground-monitored PM2.5 data versus satellite-based exposure estimates was 97 (95% CI 81–113) versus 116 (95% CI 97–135) and 125 (95% CI 104–145) versus 149 (95% CI 124–173), respectively. For ground-monitored and satellite-based PM2.5 data, the economic loss attributable to ambient PM2.5-total mortality was approximately 10,713 (95% CI 8890–12,467) and 12,792.1 (95% CI 10,652.0–14,837.6) million USD, equivalent to nearly 3.7% (95% CI 3.06–4.29) and 4.3% (95% CI 3.6–4.5.0) of the total gross domestic product in Iran in 2018.
In this study, a laboratory-scale biotrickling filter (BTF) is used to remove Triethylamine (TEA) from gaseous wastes. The BTF is made of stainless steel with a height of 210 cm and an internal diameter of 21 cm packed with lava rocks. TEA elimination pattern was evaluated by changing empty bed residence times (EBRTs). The maximum elimination capacity (EC) has been determined to be 87 g/m 3 /h. At all EBRTs 52, 31, 20, and 10 s, contaminant transferring from gas phase to liquid was more than the EC. Also, the removal efficiency was 100 % for a mass loading of 100 g/m 3 /h. While the liquid recirculation velocity of 3.466 m 3 /m 2 /h was maintained, the flow rate was adjusted to 60, 100, 156, and 312 L/ min. The results show that due to the high solubility of TEA in water for all the EBRTs, TEA can be solved in the circulated liquid and then be degraded gradually by microorganisms. Therefore, the least EBRT of 10 s is more appropriate.
Deep neural networks can now perform many tasks that were once thought to be only feasible for humans. Unfortunately, while reaching impressive performance under standard settings, such networks are known to be susceptible to adversarial attacks -slight but carefully constructed perturbations of the inputs which drastically decrease the network performance and reduce their trustworthiness. Here we propose to improve network robustness to input perturbations via an adversarial training procedure which we call Adversarial Feature Desensitization (AFD). We augment the normal supervised training with an adversarial game between the embedding network and an additional adversarial decoder which is trained to discriminate between the clean and perturbed inputs from their high-level embeddings. Our theoretical and empirical evidence acknowledges the effectiveness of this approach in learning robust features on MNIST, CIFAR10, and CIFAR100 datasets -substantially improving the state-of-the-art in robust classification against previously observed adversarial attacks. More importantly, we demonstrate that AFD has better generalization ability than previous methods, as the learned features maintain their robustness against a large range of perturbations, including perturbations not seen during training. These results indicate that reducing feature sensitivity using adversarial training is a promising approach for ameliorating the problem of adversarial attacks in deep neural networks.Preprint. Under review.
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