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Particulate matter emission is one of the major air pollution problems of coal fired power plants. Fine particulates constitute a smaller fraction by weight of the total suspended particle matter in a typical particulate emission, but they are considered potentially hazardous to health because of the high probability of deposition in deeper parts of the respiratory tract. Electrostatic precipitators (ESP) are the most widely used devices that are capable of controlling particulate emission effectively from power plants and other process industries. Although the dust collection efficiency of the industrial precipitator is reported as about 99.5%, an anticipation of future stricter environmental protection agency (EPA) regulations have led the local power station seeking new technologies to achieve the new requirements at minimum cost and thus control their fine particulate emissions to a much greater degree than ever before. This study aims to identify the options for controlling fine particle emission through improvement of the ESP performance efficiency. An ESP system consists of flow field, electrostatic field and particle dynamics. The performance of an ESP is significantly affected by its complex flow distribution arising as a result of its complex internal geometry, hence the aerodynamic characteristics of the flow inside an ESP always need considerable attention to improve the efficiency of an ESP. Therefore, a laboratory scale ESP model, geometrically similar to an industrial ESP, was designed and fabricated at the Thermodynamics Laboratory of CQUniversity, Australia to examine the flow behaviour inside the ESP. Particle size and shape morphology analyses were conducted to reveal the properties of the fly ash particles which were used for developing numerical models of the ESP. Numerical simulations were carried out using Computational Fluid Dynamics (CFD) code FLUENT and comparisons were made with the experimental results. The ESP was modelled in two steps. Firstly, a novel 3D fluid (air) flow was modelled considering the detailed geometrical configuration inside the ESP. A novel boundary condition was applied at the inlet boundary of this model to overcome all previous assumptions on uniform velocity at the inlet boundary. Numerically predicted velocity profiles inside the ESP model are compared with the measured data obtained from the laboratory experiment. The model with a novel boundary condition predicted the flow distribution more accurately. In the second step, as the complete ESP system consists of an electric field and a particle phase in addition to the fluid flow field, a two dimensional ESP model was developed. The electrostatic force was applied to the flow equations using User Defined Functions (UDF). A discrete phase model was incorporated with this two dimensional model to study the effect of particle size, electric field and flue gas flow on the collection efficiency of particles inside the ESP. The simulated results revealed that the collection efficiency cannot be improved by the increased electric force only unless the flow velocity is optimized. The CFD model was successfully applied to a prototype ESP at the power plant and used to recommend options for improving the efficiency of the ESP. The aerodynamic behaviour of the flow was improved by geometrical modifications in the existing 3D numerical model. In particular, the simulation was performed to improve and optimize the flow in order to achieve uniform flow and to increase particle collection inside the ESP. The particles injected in the improved flow condition were collected with higher efficiency after increasing the electrostatic force inside the 2D model. The approach adopted in this study to optimize flow and electrostatic field properties is a novel approach for improving the performance of an electrostatic precipitator.

Cyber-physical system (CPS) is an integration of physical processes with computation and communication. It has the ability to add more intelligence to social life. Wireless sensor networks (WSN) can be a vital part of CPS as strong sensing capability is one of the major driving factors for CPS applications. CPS is still considered to be a nascent technology, and there are many challenges yet to be addressed. A few CPS applications in healthcare have been proposed to date, and they lack the flexibility of technology integration, such as integration of computing resources with sensor networks. This paper presents a survey of CPS in healthcare applications that have been proposed to date by academia as well as industry. A comprehensive taxonomy is also provided that characterizes and classifies different components and methods that are required for the application of CPS in healthcare. The taxonomy not only highlights the similarities and differences of the state-of-the-art technologies utilized in CPS for healthcare from the perspective of WSN and Cloud Computing but also identifies the areas that require further research. It is expected that this taxonomy and its mapping to relevant systems will be highly useful for further development of CPS for healthcare.

High-performance liquid chromatography ( HPLC ) has been used to complement size-exclusion (gel permeation) chromatography ( SEC ) for the characterization of functional polymers. Whereas SEC is unable to detect compositional changes, HPLC in an appropriate interacting medium can provide detailed information on compositional changes occurring during chemical modification of a polymer. The method has been demonstrated using a normal-phase column consisting of porous monodisperse 10 μm poly( 2,3-dihydroxypropyl methacrylate-co-ethylene dimethacrylate) beads that have a homogeneous coverage of aliphatic hydroxyl groups for the analysis of brominated poly( isobutylene-co-4-methylstyrene ). Differences of well below 1 mol % of bromomethylstyrene units are easily detected and quantified.

A newly synthesized iniferter, N,N′-dimethyl-N,N′-bis(phenethyl)-thiuram disulfide, has been used in the free-radical living polymerization of styrene by a photochemical method. The low molecular weight (M w = 6000) difunctionalized polystyrene was used as a macroiniferter to photopolymerize methyl methacrylate, and was fractionated to obtain an A-B-A type block copolymer containing two poly(methyl methacrylate) units and one polystyrene unit in each block. The glass transition temperature, thermal stability, and 13C NMR of the block copolymer are discussed.

Modification of carbon black N220, ISAF-HM, by aprotonic and protonic acids uniquely improves its catalytic activity in relation to the polymerization of N-vinylcarbazole. The polymerization is first order in monomer and directly proportional to the weight of the catalyst. However, the molecular weights of the polymer do not depend significantly on the nature of the modification. Heat treatment of the catalysts at 500°C reduces the yield of polymer drastically. The activation energy of the polymerization by unmodified carbon black is 83 kJ/mol as compared to 71 kJ/mol for the modified catalyst. Modification by dialkylzinc (0.1 to 0.4 M) produces stereoregularity as indicated by the MEK insolubility of the polymer.

The polymerization of N-vinylcarbazole has been carried out at 60°C under nitrogen with different grades of carbon black, N220, N110, N330, and N660 as heterogeneous catalysts. The order of efficiency of the catalysts in regard to the polymerization is: N220 > N110 > N660 > N330. Effect of solvent polarity on the polymerization has been studied with acetonitrile, 1,2-dichloroethane, and toluene-nitrobenzene mixtures. Nitrobenzene, with highest polarity, affords the highest rate, which decreases with decreasing nitrobenzene percentage in the solvent mixture. These features suggest the polymerization to be cationic in nature. The relative viscosities of the polymers are low, but tend to increase only slightly with increasing nitrobenzene content. The rate of polymerization can be expressed by the relation A suitable kinetic model has been presented in conformity with the experimental findings.

Wireless Sensor Networks (WSN) are vulnerable to various sensor faults and faulty measurements. This vulnerability hinders efficient and timely response in various WSN applications, such as healthcare. For example, faulty measurements can create false alarms which may require unnecessary intervention from healthcare personnel. Therefore, an approach to differentiate between real medical conditions and false alarms will improve remote patient monitoring systems and quality of healthcare service afforded by WSN. In this paper, a novel approach is proposed to detect sensor anomaly by analyzing collected physiological data from medical sensors. The objective of this method is to effectively distinguish false alarms from true alarms. It predicts a sensor value from historic values and compares it with the actual sensed value for a particular instance. The difference is compared against a threshold value, which is dynamically adjusted, to ascertain whether the sensor value is anomalous. The proposed approach has been applied to real healthcare datasets and compared with existing approaches. Experimental results demonstrate the effectiveness of the proposed system, providing high Detection Rate (DR) and low False Positive Rate (FPR). Refereed/Peer-reviewed

Thesis (PhD(Electrical and Information Engineering))--University of South Australia, 2016. Includes bibliographical references (pages 148-154) This thesis proposes a prediction-based anomaly detection technique which is reliant on three underlying algorithms: Sequential Minimal Optimization for Support Vector Regression (SMO regression) for the prediction of sensor values, Dynamic Threshold (DT) calculation for error computation, and Majority Voting (MV) for making decision on alarm generation. The proposed approach has been applied to real healthcare datasets and compared with existing approaches.

Cyber-physical system (CPS) is an integration of physical processes with computation and communication. It has the ability to add more intelligence to social life. Wireless sensor networks (WSN) can be a vital part of CPS as strong sensing capability is one of the major driving factors for CPS applications. CPS is still considered to be a nascent technology, and there are many challenges yet to be addressed. A few CPS applications in healthcare have been proposed to date, and they lack the flexibility of technology integration, such as integration of computing resources with sensor networks. This paper presents a survey of CPS in healthcare applications that have been proposed to date by academia as well as industry. A comprehensive taxonomy is also provided that characterizes and classifies different components and methods that are required for the application of CPS in healthcare. The taxonomy not only highlights the similarities and differences of the state-of-the-art technologies utilized in CPS for healthcare from the perspective of WSN and Cloud Computing but also identifies the areas that require further research. It is expected that this taxonomy and its mapping to relevant systems will be highly useful for further development of CPS for healthcare. Refereed/Peer-reviewed

Efficient routing is a key concern for the execution of high performance query processing in Wireless Sensor Networks (WSN). Existing routing protocols lack the efficiency in terms of energy consumption, accuracy and timing response. Data storage and processing constraints in sensor nodes also hinder the efficient and timely response in various applications, especially in healthcare, where, many vital data are collected from the patients using wireless devices. These data can be crucial for saving the patient's life. Thus, an efficient, accurate and fast routing protocol is of utmost importance in healthcare. Present routing protocols in hierarchical network protocols, the cluster heads are selected from general sensor nodes, however the storage and processing constraints perceive in the usual wireless biomedical sensors as the processing requires more energy consumption and the storage capacity is limited. In this paper we propose a novel routing protocol by utilizing Storage nodes. We evaluate and prove the feasibility of the proposed protocol through extensive simulations. Simulation results show that the protocol is more energy efficient, faster and data resilient compared to other techniques. Refereed/Peer-reviewed

Cyber-Physical System (CPS) is an integration of physical processes with computation and communication. CPS connects the virtual world with physical world. It has the ability to add more intelligence to social life. A CPS integrates physical devices, such as sensors and cameras, with cyber (or informational) components to form a situation-integrated analytical system that responds intelligently to dynamic changes in the real-world scenarios. Wireless Sensor Networks (WSN) can be a vital part of CPS as strong sensing capabilities can be one of the major driving factors for CPS applications. Still CPS is considered as a nascent technology and there are many challenges yet to be addressed. These challenges are often posed due to the complexity of communication, computation and the uncertainty of physical processes. CPS can have wide ranging applications, such as smart medical technology, assisted living, environmental control and traffic management. Unfortunately, no unified architecture for CPS has yet been developed, primarily due to variations among the applications. In CPS healthcare application, a wide range of medical sensors are used, however these sensors generate a large number of false alarms. These false alarms add confusion and reduce the efficiency of overall healthcare service. Although a few CPS healthcare architectures have been proposed for healthcare application they all lack of efficient false alarm detection. In this paper, a novel false alarm detection architecture is proposed in CPS for healthcare applications. Refereed/Peer-reviewed