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Deteriorating air quality and uncontrolled air pollution can affect climate change as well as human health. The Air Pollutant Index (API) has been used as an indicator to indicate the status of air quality. The objective of this study is to determine the level of air pollution based on the calculation of API as well as to identify clusters for air quality monitoring stations based on the concentration of air pollutants and API values. This study involved 10 air quality monitoring stations from the urban, sub-urban and industrial areas. The data used in this study are the daily concentration of air pollutants namely carbon monoxide (CO), ozone (O3), fine particles (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) from January until December 2011. Median of the pollutant concentrations were used to represent the monthly API for each station. Urban areas had the highest percentage of moderate air quality levels compared to suburban and industrial areas. O3 was found to be the dominant pollutant in urban areas while PM10 is the dominant pollutant in suburban and industrial areas. Fuzzy k-medoid clustering (FKM) is one of the fuzzy clustering algorithms that has a cluster center known as the medoid and can produce soft clusters. The air quality monitoring stations are assigned to the clusters according to the membership degrees ranging in the interval [0,1] and the data points of the station can belong to more than one cluster at the same time with different membership degrees. The degree of memberships indicate the level of confidence that the data points are in a particular group. Two clusters were obtained in which cluster 1 is characterized as a cluster with higher pollutant concentrations and API values compared to cluster 2. [ABSTRACT FROM AUTHOR]

Time series clustering is a data mining technique where it involves grouping of time series data into a few separate clusters. In this paper, a shape-based clustering is applied on time series of PM10 data collected from 60 air quality monitoring stations in Malaysia from 5th July 2017 to 31st December 2018 obtained from Malaysian Department of Environment. k-shape clustering method along with shape-based distance (SBD) were used to cluster the PM10 time series data. Air quality patterns were analysed from the clusters formed. The results show that the clusters formed using the method used are very well separated and mainly influenced by the region and geographical locations instead of the station's categories or man activities at the locations. [ABSTRACT FROM AUTHOR]

Hierarchical clustering is a common method used for monitoring air quality stations. This method groups homogeneous stations and subsequently helps in making decision whether to increase or reduce the number of air monitoring stations in an area. There are numerous dissimilarity measures available to be applied in hierarchical clustering and choosing a suitable dissimilarity measure is crucial. The aim of this study is to compare dissimilarity measures of hierarchical clustering such as Euclidean, Manhattan, Chebyshev, Minkowski and Pearson correlation distance. The hierarchical clustering using the five different dissimilarity measures were performed on the daily concentrations of CO, O2, SO2, O3 and PM10 from January to November 2011 in Peninsular Malaysia. For each cluster obtained in the analysis, the average air quality is observed by calculating the air pollution index (API) of the cluster. The hierarchical clustering found using Euclidean, Manhattan, Chebyshev and Minkowski distance produce similar clusters. Meanwhile, the Pearson correlation distance produce different clusters for almost all of the air pollutants under study. This is because the Pearson correlation distance depend on the strength of relationships between variables while the other dissimilarity measures depend on the standard distance between variables from different stations to form the clusters. Hence, it is sensible to use a dissimilarity measure from the Minkowski family with the Pearson correlation distance to extract information from two different angles in clustering air pollutants. [ABSTRACT FROM AUTHOR]

This study compares three different deep learning approaches for forecasting the air quality time series data. All the approaches were based on the convolutional neural network (CNN), which is one of the deep learning algorithms. The first approach, known as the univariate CNN (UCNN), is based solely on the time series past values. Meanwhile, the second approach, known as the multi-parallel CNN (MPCNN), will use the same variable time series from different observations as the input for the model. The last approach is the multivariate CNN (MCNN), where the forecast will incorporate other variables' time series as the input for the model. The time series data used in this study are the hourly particulate matter, PM10 and PM2.5, concentrations from eight air quality monitoring stations in Peninsular Malaysia. The duration of the time series is from July 2017 until June 2019. The results show that the best air quality predictive model is a model which is built using UCNN since it shows the highest accuracy for both time series PM10 and PM2.5. It is also shown that the forecast will have better accuracy if shorter past values are used as the input for the model. [ABSTRACT FROM AUTHOR]

Air is an essential asset in daily life and air pollution is a global problem that concerns society. Apart from obvious factors of air pollution, this study aims to investigate the influence of meteorological parameters such as temperature, humidity, and wind speed on air pollution whether they affect the concentration of pollutants like CO, NO2, SO2, PM10 and O3. Pearson correlation analysis is conducted to determine the significance and strength of relationships between the concentrations of each pollutant with meteorological parameters from January to November 2011 in Malaysia. The results of Pearson correlation analysis show that there is a significant correlation between the concentrations of pollutants with temperature at 5% level of significance. PM10 has a significant negative correlation with humidity while CO, NO2 and O3 negatively correlated at 5% level of significance with wind speed. Then, simple linear, polynomial, exponential, and multiple linear regression models were built to further explore the relationship between meteorological factors and the concentration of air pollutants. The regression models obtained show that CO, NO2, and O3 pollutants are dependent on temperature, humidity, and wind speed where the dependence of these three pollutants on wind velocity is negative while the dependence on temperature and humidity are positive. As for PM10 pollutants, it was found that it has a relationship with both temperature and humidity. Meanwhile, for SO2, it only showed a significant relationship with temperature. Overall, the model and results obtained in this study can be used for air quality prediction and management in Malaysia. [ABSTRACT FROM AUTHOR]

In this study, the Support Vector Regression (SVR) model was used to forecast air quality time series data which is the particulate matter 10 micrometers or less in diameter (PM10) in Malaysia. Several SVR kernel functions, which are the Linear, Polynomial and Radial Basis Function (RBF) kernels, were considered in this study to determine the most suitable kernel function for forecasting the PM10 time series. The period of the data is from 5th July 2017 to 31st January 2019 consists of five air quality monitoring stations which are Kangar station in Perlis, Tasek Ipoh station in Perak, Shah Alam station in Selangor, Pasir Gudang station in Johor and Kuala Terengganu station in Terengganu. Model performance was compared based on the testing dataset's mean squared error (MSE) values. The results show that the SVR model with Radial Basis Function kernel is more suitable for forecasting the PM10 time series compared to the Linear and Polynomial kernels. [ABSTRACT FROM AUTHOR]

Moore's Law state that, the number of transistors in silicon chip will be doubled every 2 years. The size of Metal Oxide Semiconductor Field Effect Transistor (MOSFET) will be scaled down. MOSFET is a semiconductor device that used for switching and amplifying. As the MOSFET shrunk down, there will be Short Channel Effect (SCE) occur which can affect the performance of chi p. Therefore, Silicon on Insulator (SOI) technology have been introduced as a solution to the problem. The aim of this research is to investigate the effect of channel doping concentration of SOI MOSFET using SILVACO TCAD Simulator. By adding a layer of buried oxide (BOX) on the top of silicon substrate, the electrical characteristic of MOSFET can be improved and the performance will increase. Simulation results were observed using TCAD tools, SILVACO (ATHENA, ATLAS and Tony-Plot). Based on the observation, increasing dose of doping concentration leads to increasing on threshold voltage (Vt) and decrease the leakage current (Ioff). [ABSTRACT FROM AUTHOR]

This study aims to produce cassava starch (CS) based low density polyethylene (LDPE) biofilms with the addition of glycerol and palm olein as processing aids. Prior to blown film extrusion the CS-LDPE composite was prepared via blown film extrusion process. The composites were formulated with the addition of polyethylene-graftedmaleic anhydride (PE-g-MA) as a compatibilizer. The flow ability analysis of the produced CS-LDPE biofilms was conducted using melt flow indexer. The biofilms with addition of fixed amounts of palm oil based olein displayed excellent film blowing ability compared to glycerol. In addition, incorporating of CS to the biofilms significantly lower the physical-mechanical properties of the final products. This is due to the poor interfacial adhesion between starch and synthetic polymer. The melt index of CS-LDPE biofilms noted a significant decreasing trend caused by the incorporation of starch and plasticizers in the blends for both, glycerol and palm oil based glycerin. Scanning electron micrograph (SEM) of this biofilm with starch composition up to 30 wt% showed good dispersion of starch granular in the polymer matrix. SEM also found the images of starch particles seem to be embedded in the LDPE matrix. [ABSTRACT FROM AUTHOR]

For high-temperature superconductors to be used in engineering applications, the compounds must be fabricated into composite elements with the required microstructure to provide mechanical strength. Titanium oxide having very high melting point with low heat capacity, is an excellent candidate for reinforcement of brittle materials such as superconductor ceramics. In addition to high melting point, TiO2 is also capable of establishing flux pinning centers in Bi-2212 superconductor compounds. As such, TiO2 has the characteristics to reinforce composite materials without compromising their superconducting properties. To further enhance the flux pinning properties, irradiation is one of the techniques that can be used to create the required point defects. Besides, irradiation is able to enhance the mechanical properties of ceramics materials. In this study, the effect of electron irradiation on TiO2 added Bi-2212 superconductor ceramics was studied through phase and microstructure characteristics of irradiated and non-irradiated samples. Characterizations were done by X-ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray (EDX) analysis. The XRD patterns for both irradiated and non-irradiated samples show well-defined peaks all of which could be indexed on the basis of the Bi-2212 structure. In addition, the XRD patterns indicate that electron irradiation of 100 kGray enhanced the Bi-2212 superconducting phase in the compounds. Results of SEM micrographs show improvement in the texture of the microstructure for samples that are subjected to electron irradiation of 100 kGray. The grains are seen to align in much lower degree of orientation. [ABSTRACT FROM AUTHOR]

Single layer OLEDs by using poly(9,9-di-n-hexylfluorenyl-2,7-diyl) (PHF) as the electroluminescent (EL) layer were fabricated. This paper reports the effect of doping ratios of organic salt, tetrabutylammonium hexafluorophosphate (TBAPF6) on the turn-on voltage, brightness and the EL spectra of the OLEDs. The PHF:TBAPF6 EL layers with weight ratios of 100:x (x = 5, x = 10 and x =20) were prepared by spin coating technique using chloroform as solvent. The reduction of turn-on voltage and increment of EL intensity were achieved by increasing the TBAPF6 doping ratio. The ITO/PHF:TBAPF6(100:20)/Al device showed the best performance with turn-on voltage as low as 7 V and the highest brightness at 0.36 cd/m2. [ABSTRACT FROM AUTHOR]

In this work, the modeling strategy of 32 V asymmetric High Voltage MOSFETs fabricated in 180 nm High Voltage CMOS process technology using Aurora and Hspice level 66 is presented. The model is validated on the measured characteristics of asymmetric HV MOSFET and implemented on commercial circuit simulator HSPICE. The model shows excellent DC IV characteristics and good behavior for capacitances. The model also exhibits excellent scalability with transistor width and length where the model accuracy of the extracted VTH and IDSAT are within 0 to 5% at T = 25° C. [ABSTRACT FROM AUTHOR]

Zinc oxide is an important chemical substance that is widely used in the production of various industrial products. However, to produce nano particle ZnO remains a challenge. This paper describes the synthesis of zinc oxide nanoparticles via precipitation and self-combustion methods. By precipitation method, the dissolved zinc nitrate solution is drop wisely into aqueous sodium hydroxide, NaOH as precipitating agent. Self-combustion technique is a new method to obtain nanoparticles by heating the materials until it combust at 110° C. Experiment has been done by dissolving Zn(NO3)2.6H2O and HNO3. The dissolved solution was then stirred for 2 and 4 days followed by heating process until 110° C. By the data analysis, it demonstrated that ZnO prepared via self-combustion gave better morphology, which can be referred to single-phase with nanorods structure at 2θ of 13.05° with average crystallite size of 563.10 nm. Raman and XRD result for self-combustion show higher purity single-phase structure compared to those samples prepared using precipitation technique. [ABSTRACT FROM AUTHOR]

Single layer OLED devices made of poly(9,9-di-n-hexylfluorenyl-2,7-diyl) (PHF) as electroluminescent layer were fabricated. The effect of organic salt, tetrabutylammonium hexafluorophosphate (TBAPF6) doping on these devices were studied. The original device with structure of ITO/PHF/Al has the brightness of 0.16 cd/m2 and turn-on voltage of 23 V. After doping process, the device with the structure of ITO/PHF:TBAPF6/Al has the brightness of 0.63 cd/m2 and the turn on voltage of 15 V. The doping process of the luminescent material with organic salt not only enhanced the performance of the OLED devices in terms of turn-on voltage and the brightness, but also changed the color of light emission where the doped device has emitted broader visible spectrum. [ABSTRACT FROM AUTHOR]