Bangladesh University of Engineering and Technology

About: Bangladesh University of Engineering and Technology is a education organization based out in Dhaka, Bangladesh. It is known for research contribution in the topics: Heat transfer & Nusselt number. The organization has 5341 authors who have published 7675 publications receiving 83969 citations. The organization is also known as: BUET & Bangladesh University of Engineering & Technology.

Arsenic mobility and groundwater extraction in Bangladesh.

Abstract: High levels of arsenic in well water are causing widespread poisoning in Bangladesh. In a typical aquifer in southern Bangladesh, chemical data imply that arsenic mobilization is associated with recent inflow of carbon. High concentrations of radiocarbon-young methane indicate that young carbon has driven recent biogeochemical processes, and irrigation pumping is sufficient to have drawn water to the depth where dissolved arsenic is at a maximum. The results of field injection of molasses, nitrate, and low-arsenic water show that organic carbon or its degradation products may quickly mobilize arsenic, oxidants may lower arsenic concentrations, and sorption of arsenic is limited by saturation of aquifer materials.

MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning

Abstract: Imbalanced learning problems contain an unequal distribution of data samples among different classes and pose a challenge to any classifier as it becomes hard to learn the minority class samples. Synthetic oversampling methods address this problem by generating the synthetic minority class samples to balance the distribution between the samples of the majority and minority classes. This paper identifies that most of the existing oversampling methods may generate the wrong synthetic minority samples in some scenarios and make learning tasks harder. To this end, a new method, called Majority Weighted Minority Oversampling TEchnique (MWMOTE), is presented for efficiently handling imbalanced learning problems. MWMOTE first identifies the hard-to-learn informative minority class samples and assigns them weights according to their euclidean distance from the nearest majority class samples. It then generates the synthetic samples from the weighted informative minority class samples using a clustering approach. This is done in such a way that all the generated samples lie inside some minority class cluster. MWMOTE has been evaluated extensively on four artificial and 20 real-world data sets. The simulation results show that our method is better than or comparable with some other existing methods in terms of various assessment metrics, such as geometric mean (G-mean) and area under the receiver operating curve (ROC), usually known as area under curve (AUC).

Parameters affecting the photocatalytic degradation of dyes using TiO2: a review

Abstract: Traditional chemical, physical and biological processes for treating wastewater containing textile dye have such disadvantages as high cost, high energy requirement and generation of secondary pollution during treatment process. The advanced oxidation processes technology has been attracting growing attention for the decomposition of organic dyes. Such processes are based on the light-enhanced generation of highly reactive hydroxyl radicals, which oxidize the organic matter in solution and convert it completely into water, CO2 and inorganic compounds. In this presentation, the photocatalytic degradation of dyes in aqueous solution using TiO2 as photocatalyst under solar and UV irradiation has been reviewed. It is observed that the degradation of dyes depends on several parameters such as pH, catalyst concentration, substrate concentration and the presence of oxidants. Reaction temperature and the intensity of light also affect the degradation of dyes. Particle size, BET-surface area and different mineral forms of TiO2 also have influence on the degradation rate.