TEHRAN AIR POLLUTION MODELING USING LONG-SHORT TERM MEMORY ALGORITHM: AN UNCERTAINTY ANALYSIS

Air pollution is a major environmental issue in urban areas, and accurate forecasting of particles 10 μm or smaller (PM10) level is essential for smart public health policies and environmental management in Tehran, Iran. In this study, we evaluated the performance and uncertainty of long short-term memory (LSTM) model, along with two spatial interpolation methods including ordinary kriging (OK) and inverse distance weighting (IDW) for mapping the forecasted daily air pollution in Tehran. We used root mean square error (RMSE) and mean square error (MSE) to evaluate the prediction power of the LSTM model. In addition, prediction intervals (PIs), and Mean and standard deviation (STD) were employed to assess the uncertainty of the process. For this research, the air pollution data in 19 Tehran air pollution monitoring stations and temperature, humidity, wind speed and direction as influential factors were taken into account. The results showed that the OK had better RMSE and STD in the test (32.48 ± 9.8 μg/m3) and predicted data (56.6 ± 13.3 μg/m3) compared with those of the IDW in the test (47.7 ± 22.43 μg/m3) and predicted set (62.18 ± 26.1 μg/m3). However, in PIs, IDW ([0, 0.7] μg/m3) compared with the OK ([0, 0.5] μg/m3) had better performance. The LSTM model achieved in the predicted values an RMSE of 8.6 μg/m3 and a standard deviation of 9.8 μg/m3 and PIs between [2.7 ± 4.8, 14.9 ± 15] μg/m3

Machine Learning for Mathematical Software

While there has been some discussion on how Symbolic Computation could be used for AI there is little literature on applications in the other direction. However, recent results for quantifier elimination suggest that, given enough example problems, there is scope for machine learning tools like Support Vector Machines to improve the performance of Computer Algebra Systems. We survey the authors own work and similar applications for other mathematical software. It may seem that the inherently probabilistic nature of machine learning tools would invalidate the exact results prized by mathematical software. However, algorithms and implementations often come with a range of choices which have no effect on the mathematical correctness of the end result but a great effect on the resources required to find it, and thus here, machine learning can have a significant impact.Comment: To appear in Proc. ICMS 201

Assembly and Cleaning of CSPs for High, Low, and UltraLow Volume Applications

ABSTRACT A JPL-led CSP Consortium of enterprises, composed of representing government agencies and private companies, recently joined together to pool in-kind resources for developing the quality and reliability of chip scale packages (CSPs) for a variety of projects. Since last year, more than 150 test vehicles, single-and double-sided multilayer PWBs, have been assembled and are presently being subjected to various environmental tests. Recent reliability data, specifically the impact of assembly underfill on reliability, is being presented in another paper in this conference. This paper presents lessons learned on assemblies at three facilities with high, low, and ultralow volume production

Synthesis and characterization of ZnO nanostructures using palm olein as biotemplate

Background: A green approach to synthesize nanomaterials using biotemplates has been subjected to intense research due to several advantages. Palm olein as a biotemplate offers the benefits of eco-friendliness, low-cost and scale-up for large scale production. Therefore, the effect of palm olein on morphology and surface properties of ZnO nanostructures were investigated. Results: The results indicate that palm olein as a biotemplate can be used to modify the shape and size of ZnO particles synthesized by hydrothermal method. Different morphology including flake-, flower- and three dimensional star-like structures were obtained. FTIR study indicated the reaction between carboxyl group of palm olein and zinc species had taken place. Specific surface area enhanced while no considerable change were observed in optical properties. Conclusion: Phase-pure ZnO particles were successfully synthesized using palm olein as soft biotemplating agent by hydrothermal method. The physico-chemical properties of the resulting ZnO particles can be tuned using the ratio of palm olein to Zn cation

Removal of hydrogen sulfide by zinc oxide nanoparticles in drilling fluid

Hydrogen sulfide is a very dangerous, toxic and corrosive gas. It can diffuse into drilling fluid from formations during drilling of gas and oil wells. Hydrogen sulfide should be removed from this fluid to reduce the environmental pollution, protect the health of drilling workers and prevent corrosion of pipelines and equipments. In this research nano zinc oxide with 14-25 nm particle size and 44-56 m2/g specific surface area was synthesized by spray pyrolysis method. The synthesized nanoparticles were used to remove hydrogen sulfide from water based drilling fluid. The efficiency of these nanoparticles in the removal of hydrogen sulfide from drilling mud were evaluated and compared with that of bulk zinc oxide. The obtained results show that synthesized zinc oxide nanoparticles are completely able to remove hydrogen sulfide from water based drilling mud in just 15 min., whereas bulk zinc oxide is able to remove 2.5% of hydrogen sulfide in as long as 90 min. under the same operating conditions

The association of postpartum maternal mental health with breastfeeding status of mothers: A case-control study

Objectives: This study was conducted to assess the association between maternal mental health and breastfeeding status of mothers in Kashan province. Patients and Methods: This case-control study was conducted on 458 mothers in two groups of unsuccessful breastfeeding (case) and successful breastfeeding (control) attending Kashan province health clinics. In this study, the GHQ questionnaire and clinical interview were employed to collect data. The data were statistically analyzed using Chi-square and Fisher's exact tests. Results: It was found that mothers of the case group had a greater susceptibility to depression than those of the control group, that is, breastfeeding status was directly associated with susceptibility to depression (P = 0.001, OR = 5.48). Furthermore, there was a significant association between basic characteristics such as maternal occupational status (P = 0.04) or their educations (P = 0.006) with breastfeeding. Besides, clinical interview revealed that mixed depression and anxiety disorder was the most prevalent type of psychological disorder in the case group. Conclusions: Screening depression during pregnancy and postpartum period appeared to be necessary and it should be incorporated into prenatal and postnatal care due to its influence on mothers' successful breastfeeding. © 2014, Iranian Red Crescent Medical Journal; Published by Kowsar Corp