We have characterized fractionated magnetic nanoparticles (MNPs) for magnetic particle imaging. Original Ferucarbotran particles were magnetically divided into three fractionated MNPs called MS1, MS2, and MS3. Harmonic spectra from the three fractionated MNPs were measured at excitation fields of 2.8 and 28 mT with a frequency of 10 kHz. MS1 showed a 2.5-fold increase in the harmonic spectrum over that of the original MNPs. To understand the origin of the enhancement in the harmonic spectrum from MS1, we explored the magnetic properties of the MS series, such as distributions of effective core size and anisotropy energy barrier, and the correlation between them. Using these results, we performed numerical simulations of the harmonic spectra based on the Langevin equation. The simulation results quantitatively explained the experimental results of the fractionated MS series. It was also clarified that MS1 includes a large portion of the MNPs that are responsible for the harmonic spectrum. V C 2013 AIP Publishing LLC.
The agricultural and food processing industries generate a significant portion of residues, refuse and waste. Conversion of these wastes into useful end product would be beneficial not only to the economy but also the environment as it reducing the solid waste disposal problem. The present study was aimed to investigate the performance of cassava peel starch (CPS) extracted from cassava peel waste in combination with alum to act as dual coagulant for turbidity removal in raw water from Sembrong dam. Comparative studies by employing both alum and CPS as primary coagulant using several series of Jar test were also conducted. Results showed that the usage of alum-CPS as dual coagulant not only enhanced the turbidity removal with maximum achievement up to 91.47%, but also significantly improve the coagulation process by reducing both alum dosage and settling time up to 50% which indicates broad prospects to be further developed as emerging green coagulant.
Water pollution is a severe health concern. Several studies have recently demonstrated the efficacy of various approaches for treating wastewater from anthropogenic activities. Wastewater treatment is an artificial procedure that removes contaminants and impurities from wastewater or sewage before discharging the effluent back into the environment. It can also be recycled by being further treated or polished to provide safe quality water for use, such as potable water. Municipal and industrial wastewater treatment systems are designed to create effluent discharged to the surrounding environments and must comply with various authorities’ environmental discharge quality rules. An effective, low-cost, environmentally friendly, and long-term wastewater treatment system is critical to protecting our unique and finite water supplies. Moreover, this paper discusses water pollution classification and the three traditional treatment methods of precipitation/encapsulation, adsorption, and membrane technologies, such as electrodialysis, nanofiltration, reverse osmosis, and other artificial intelligence technology. The treatment performances in terms of application and variables have been fully addressed. The ultimate purpose of wastewater treatment is to protect the environment that is compatible with public health and socioeconomic considerations. Realization of the nature of wastewater is the guiding concept for designing a practical and advanced treatment technology to assure the treated wastewater’s productivity, safety, and quality.
Abstract. In white blood cell (WBC) diagnosis, the most crucial measurement parameter is the WBC counting. Such information is widely used to evaluate the effectiveness of cancer therapy and to diagnose several hidden infection within human body. The current practice of manual WBC counting is laborious and a very subjective assessment which leads to the invention of computer aided system (CAS) with rigorous image processing solution. In the CAS counting work, segmentation is the crucial step to ensure the accuracy of the counted cell. The optimal segmentation strategy that can work under various blood smeared image acquisition conditions is remain a great challenge. In this paper, a comparison between different segmentation methods based on color space analysis to get the best counting outcome is elaborated. Initially, color space correction is applied to the original blood smeared image to standardize the image color intensity level. Next, white blood cell segmentation is performed by using combination of several color analysis subtraction which are RGB, CMYK and HSV, and Otsu thresholding. Noises and unwanted regions that present after the segmentation process is eliminated by applying a combination of morphological and Connected Component Labelling (CCL) filter. Eventually, Circle Hough Transform (CHT) method is applied to the segmented image to estimate the number of WBC including the one under the clump region. From the experiment, it is found that G-S yields the best performance.
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