The study provides a statistical trend analysis of different air pollutants using Mann-Kendall and Sen’s slope estimator approach on past pollutants statistics from air quality index station of Varanasi, India. Further, using autoregressive integrated moving average model, future values of air pollutant levels are predicted. Carbon monoxide, nitrogen dioxide, sulphur dioxide, particu...

Close to 40% of the primary energy consumption in the U.S. comes from commercial and residential buildings [1]. Therefore, reducing this energy consumption is very important, both economically, and environmentally (due to the amount of CO2 emitted in the process of generating the electricity). Predicting the energy consumption of buildings (energy modeling) is a key component in reducing the bu...

the present research focuses on the changes of urmia lake level. for this purpose, two timescales have been considered. the trend changes of temperature, precipitation rate and quantitative values of climate type for the observational period from 1968 to 2011 (past scale) and from 2011 to 2100 (future scale) have been analyzed. general circulation model (gcm) is considered for simulating the va...

Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...

viscosity (µ) and density (ρ) are important physical roperties for analysis of membrane processes performance and for designing a new membrane process. in addition, the energy requirement for fluid pumping is depend on these two physical properties magnitiude. in this study, firstly, the effects of different process factors such as transmembrane pressure (51, 101, 152, 203 and 253 kpa), tempera...

Many impact studies require climate change information at a finer resolution than that provided by Global Climate Models (GCMs). In the last 10 years, downscaling techniques, both dynamical (i.e. Regional Climate Model) and statistical methods, have been developed to obtain fine resolution climate change scenarios. In this study, an automated statistical downscaling (ASD) regression-based appro...

in this study, mathematical modeling of hot air-drying of thin-layer papaya (carica papaya l.) slices with 5±1 mm thickness pretreated in osmotic solution (50% sucrose) was investigated. thin-layer drying was conducted under three different drying temperatures of 40, 50 and 60 °c at a constant air velocity of 0.9±0.1 m/s and absolute humidity of 0.6 ± 0.02 g of water/kg of dry air. it was found...

Statistical methods, and especially machine learning, have been increasingly used in nanofluid modeling. This paper presents some of the interesting and applicable methods for thermal conductivity prediction and compares them with each other according to results and errors that are defined. The thermal conductivity of nanofluids increases with the volume fraction and temperature. Machine learni...