Automated Intelligent Real-Time System For Aggregate Classification

This research focuses on developing an intelligent real-time classification system called NeuralAgg. Penyelidikan ini memfokuskan untuk membina sistem pengkelasan pintar secara masa nyata dipanggil NeuralAgg

CD4+ T cell subsets in adult allergic rhinitis patients attending Hospital Universiti Sains Malaysia

Memory T cells exert effector function or generate effector cells in response to antigen. The proportions of CD4+ T cell subsets especially memory cells in allergic rhinitis (AR) patients sensitized to common allergens of house dust mites (HDMs) and shellfish have not been extensively studied. This study aimed to compare the mean percentages and absolute counts of CD4+ memory T cell subsets between: (i) non-allergic controls and AR patients; (ii) mild AR patients and moderate-severe AR patients. In addition, sensitization to common allergens, symptom severity scores, and mean absolute counts of leukocyte subsets were also determined. Fifty non-allergic controls and 100 AR patients diagnosed by physicians were recruited in this study. However, only 33 non-allergic controls were included in the analyses as others were excluded due to sensitization to the common allergens (Dermatophagoides farinae (Der f), crab and shrimp) as measured by plasma specific IgE tests. Stratified analyses were done based on two different definitions of AR patients , i.e. (i) non IgE-mediated AR patients; and (ii) IgE-mediated AR patients. Flow cytometry was used to determine the percentage of CD4+ naïve (TN; CD45RA+ CCR7+), central memory (TCM; CD45RA- CCR7+), effector memory (TEM; CD45RA- CCR7-) and terminally differentiated effector memory (TEMRA; CD45RA+ CCR7-) T cells from the peripheral blood. The absolute counts of CD4+ T cell subsets were obtained by dual platform methods from flow cytometer and hematology analyzer. It was observed that AR patients sensitized to common allergens (Dermatophagoides pteronyssinus, Der f, crab and shrimp) measured were predominantly sensitized to HDMs as compared to shellfish allergens. Moderatesevere AR patients had higher nasal and non-nasal symptom scores and reduced quality of life as compared to mild AR patients. Furthermore, the eosinophil count was significantly higher in IgE-mediated AR patients as compared to non-allergic controls. There were no significant differences in the mean percentages and absolute counts of CD4+ T cell subsets between non-allergic controls and IgE-mediated AR patients. However, significant reduction in the mean percentage (p = 0.0287) and absolute count (p = 0.0298) of CD4+ TEMRA cells were found in IgE-mediated moderate-severe AR patients as compared to IgE-mediated mild AR patients and 14/25 (56.0%) IgE-mediated moderate-severe AR patients had persistent symptoms. In conclusion, the mean percentage and absolute count of CD4+ CD45RA+ CCR7- TEMRA cells were siginificantly reduced in IgE-mediated moderate-severe AR patients as compared to IgE-mediated mild AR patients in our population of AR patients predominantly sensitized to HDMs

Intelligent Rock Vertical Shaft Impact Crusher Local Database System

Aggregates are one of the major components in the concrete production. The aggregates output from the Rock Vertical Shaft Impact Crusher (RoR VSI), had been classified to six groups of shapes then divided further into two categories namely the high quality aggregates and the low quality aggregates. The characteristics of the aggregates such as shape, size and color, do play an important roles in the development of high strength concrete. In order to produce high quality aggregates, the system would need to be monitored and maintained continuously by analyzing the past and current data. Presently, there is no database system to store the images for the classified data. The conventional method of the aggregates is done manually which is slow, highly subjective and laborious. Therefore, a local database system is proposed to store information could help to overcome this problem. The images and aggregates’ recognition and classification data will be kept in order and it will have a simple and easy way of storing and retrieving information. The machine performance can be retrieved for any period of time by calculating the output for high quality aggregates out of total of agggregates produced. The shapes break down for all six recognizable shapes also can be displayed. These could help the engineer to monitor the system on output performance with continuous analysis, with shorter time. Other than that, the strength of the concrete can be determined by counting the number and the percentage of good quality of aggregate being used

Automated Intelligent real-time system for aggregate classification

Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective and laborious. This research focuses on developing an intelligent real-time classification system called NeuralAgg which consists of 3 major subsystems namely the real-time machine vision, the intelligent classification and the database system. The image capturing system can send high quality images of moving aggregates to the image processing subsystem, and then to the intelligent system for shape classification using artificial neural network. Finally, the classification information is stored in the database system for data archive, which can be used for post analysis purposes. These 3 subsystems are integrated to work in real-time mode which takes an average of 1.23 s for a complete classification process. The system developed in this study has an accuracy of approximately 87% and has the potential to significantly reduce the processing and/or classification time and workload

Nitrogen (N2) removal in gas separation using polysulfone membrane

The development of membrane separation process in gas separation process has recently gained numerous interests from industries. With low capital and operating cost, operational simplicity, high reliability, space efficiency and environmental friendly, the membrane technology is the best candidate to replace traditional gas separation methods. Polysulfone polymer potentially, can be use as membranes for nitrogen removal in gas separation with very high mechanical and thermal strength which can be applied at wide range of temperature and pressure. The polysulfone membrane was prepared by mixing polysulfone pellet with N,N-dimethylacetamide (DMAc) solvent to dilute the polymer before it is cast using casting knife into smaller samples. Samples of membrane with different polymer composition were produce. Then the already cast membrane was immersed in different coagulation bath and finally the samples undergo permeability test using membrane permeation unit to etermine the permeability and selectivity of each membrane. High polymer composition and coagulation bath temperature will produce high selectivity membrane but low permeability membrane and so vice versa. The most ideal membrane must have high permeability and selectivity. Thus, the olysulfone membrane with 25wt% polymer composition immersed in room temperature tap water, is chosen as the best membrane to separate N 2 from CO 2,02,and C114