Linkage measures of the construction sector using the hypothetical extraction method

The hypothetical extraction method (HEM) is used to extract a sector hypothetically from an economic system and examine the influence of this extraction on other sectors in the economy. Linkage measures based on the HEM become increasingly prominent. However, little construction linkage research applies the HEM. Using the recently published Organisation for Economic Co-operation and Development input-output database at constant prices, this research applies the HEM to the construction sector in order to explore the role of this sector in national economies and the quantitative interdependence between the construction sector and the remaining sectors. The output differences before and after the hypothetical extraction reflect the linkages of the construction sector. Empirical results show a declining trend of the total, backward and forward linkages, which confirms the decreasing role of the construction sector with economic maturity over the examined period from a new angle. Analytical results reveal that the unique nature of the construction sector and multifold external factors are the main reasons for the linkage difference between countries. Moreover, hypothesis-testing results consider statistically that the extraction structures employed in this research are appropriate to analyse the linkages of the construction sector.

K-Complex Detection Using a Hybrid-Synergic Machine Learning Method

Sleep stage identification is the first step in modern sleep disorder diagnostics process. K-complex is an indicator for the sleep stage 2. However, due to the ambiguity of the translation of the medical standards into a computer-based procedure, reliability of automated K-complex detection from the EEG wave is still far from expectation. More specifically, there are some significant barriers to the research of automatic K-complex detection. First, there is no adequate description of K-complex that makes it difficult to develop automatic detection algorithm. Second, human experts only provided the label for whether a whole EEG segment contains K-complex or not, rather than individual labels for each subsegment. These barriers render most pattern recognition algorithms inapplicable in detecting K-complex. In this paper, we attempt to address these two challenges, by designing a new feature extraction method that can transform visual features of the EEG wave with any length into mathematical representation and proposing a hybrid-synergic machine learning method to build a K-complex classifier. The tenfold cross-validation results indicate that both the accuracy and the precision of this proposed model are at least as good as a human expert in K-complex detection.

A linkage measure framework for the real estate sector

Linkage is one of the most important factors for gaining competitive advantage. Information on linkages is essential to understanding the structure of an economy, which is in turn important in formulating industry policies and business strategies. The hypothetical extraction method is used to measure the linkages by extracting a sector hypothetically from an economic system in the literature. In the previous research, however, the internal linkage (linkage within a sector) and sectoral linkages (linkage between two specific sectors) are ignored, and there is not a comprehensive framework to measure the linkages of a specific sector. Using the recently published Organisation for Economic Co-operation and Development input-output database at constant prices, this paper aims to resolve these two shortcomings and thereby propose a linkage measure framework to explore the linkages between the real estate sector and other sectors from a new angle. The relative and absolute linkages are termed and the total, backward, forward, internal and sectoral linkage indicators are formulated to investigate the linkages of the real estate sector from all directions. Empirical results show an increasing trend of these linkages, which confirms the increasing role of the real estate sector with economic maturity over the examined period. This framework also can be employed in other sectors.

An input-output approach for measuring real estate sector linkages

This research aims to measure and compare the total, backward, forward, internal and sectoral linkages of the real estate sector using the hypothetical extraction method over 30 years and explore the role of this sector in national economies and the quantitative interdependence between the real estate sector and the remaining sectors from a new angle. Empirical results show an increasing trend of these linkages, which confirms the increasing role of the real estate sector with economic maturity over the examined period. On the other hand, the significant rank correlations in the linkages imply that the importance of real estate remained fairly stable among highly developed economies over the examined period. This may supply a tool to signal the maturity of an entire economy. Furthermore, the findings can aid both governments making relative policies and businesses choosing strategic partners and location strategies.

Linkage measures of the real estate sector considering the effect of capital

A significant limitation in previous linkage relevant research is that the flow of capital goods is not addressed. Using the OECD input-output tables, this research first generates a new input-output model considering capital as an intermediate factor. Using the new model, the real estate linkages are re-calculated and investigated in order to evaluate appropriately the impact of the real estate sector on national economies. The findings verify that the linkages of the real estate sector were extremely underestimated in previous research. A correct linkage measure of the real estate sector can contribute to produce correct information corresponding to the sectors responsible for the economic growth during the period under study and provide substantial contributions towards guiding the appropriate strategies for future economic development.

Exploring intersectoral linkages between real estate and construction

Even though linkages have attracted a lot of research interest, few researchers focus on the intersectoral linkages between two specific sectors. This research therefore proposes an indirect intersectoral linkage measure model to explore linkages between the real estate and construction sectors using the Hypothetical Extraction Method (HEM). Using the OECD input-output tables, the direct, total intersectoral linkages and the proposed indirect intersectoral linkages are explored and tested respectively for seven OECD countries over twenty years. The findings describe that the intersectoral linkages from construction to real estate are larger than those from real estate to construction. The statistical testing results imply that the proposed indirect intersectoral linkage measure method seems to be appropriate to analyse the intersectoral linkage between the construction and real estate sectors.

Endocrine disrupting chemicals accumulate in earthworms exposed to sewage effluent

Endocrine disrupting chemicals (EDCs) can alter endocrine function in exposed animals. Such critical effects, combined with the ubiquity of EDCs in sewage effluent and potentially in tapwater, have led to concerns that they could be major physiological disruptors for wildlife and more controversially for humans. Although sewage effluent is known to be a rich source of EDCs, there is as yet no evidence for EDC uptake by invertebrates that live within the sewage treatment system. Here, we describe the use of an extraction method and GC–MS for the first time to determine levels of EDCs (e.g., dibutylphthalate, dioctylphthalate, bisphenol-A and 17β-estradiol) in tissue samples from earthworms (Eisenia fetida) living in sewage percolating filter beds and garden soil. To the best of our knowledge, this is the first such use of these techniques to determine EDCs in tissue samples in any organism. We found significantly higher concentrations of these chemicals in the animals from sewage percolating filter beds. Our data suggest that earthworms can be used as bioindicators for EDCs in these substrates and that the animals accumulate these compounds to levels well above those reported for waste water. The potential transfer into the terrestrial food chain and effects on wildlife are discussed.

Extracting fingerprint features using textures

Personal identification of individuals is becoming increasingly adopted in society today. Due to the large number of electronic systems that require human identification, faster and more secure identification systems are pursued. Biometrics is based upon the physical characteristics of individuals; of these the fingerprint is the most common as used within law enforcement. Fingerprint-based systems have been introduced into the society but have not been well received due to relatively high rejection rates and false acceptance rates. This limited acceptance of fingerprint identification systems requires new techniques to be investigated to improve this identification method and the acceptance of the technology within society. Electronic fingerprint identification provides a method of identifying an individual within seconds quickly and easily. The fingerprint must be captured instantly to allow the system to identify the individual without any technical user interaction to simplify system operation. The performance of the entire system relies heavily on the quality of the original fingerprint image that is captured digitally. A single fingerprint scan for verification makes it easier for users accessing the system as it replaces the need to remember passwords or authorisation codes. The identification system comprises of several components to perform this function, which includes a fingerprint sensor, processor, feature extraction and verification algorithms. A compact texture feature extraction method will be implemented within an embedded microprocessor-based system for security, performance and cost effective production over currently available commercial fingerprint identification systems. To perform these functions various software packages are available for developing programs for windows-based operating systems but must not constrain to a graphical user interface alone. MATLAB was the software package chosen for this thesis due to its strong mathematical library, data analysis and image analysis libraries and capability. MATLAB enables the complete fingerprint identification system to be developed and implemented within a PC environment and also to be exported at a later date directly to an embedded processing environment. The nucleus of the fingerprint identification system is the feature extraction approach presented in this thesis that uses global texture information unlike traditional local information in minutiae-based identification methods. Commercial solid-state sensors such as the type selected for use in this thesis have a limited contact area with the fingertip and therefore only sample a limited portion of the fingerprint. This limits the number of minutiae that can be extracted from the fingerprint and as such limits the number of common singular points between two impressions of the same fingerprint. The application of texture feature extraction will be tested using variety of fingerprint images to determine the most appropriate format for use within the embedded system. This thesis has focused on designing a fingerprint-based identification system that is highly expandable using the MATLAB environment. The main components that are defined within this thesis are the hardware design, image capture, image processing and feature extraction methods. Selection of the final system components for this electronic fingerprint identification system was determined by using specific criteria to yield the highest performance from an embedded processing environment. These platforms are very cost effective and will allow fingerprint-based identification technology to be implemented in more commercial products that can benefit from the security and simplicity of a fingerprint identification system.

Anti-amnesic effect of enzyme extracted stevioside from Stevia rebaudiana

The increasing consumption of sucrose has resulted in several nutritional and medicinal problems, including obesity. There is an alarming rise in the prevalence of obesity, type 2 diabetes mellitus, and metabolic syndrome in children and adults around the world, partly related to increasing availability of energy-dense, high-calorie foods, and perhaps to increased consumption of sugar and particularly fructose sweetened beverages. Therefore, low calorie sweeteners are urgently required to substitute table sugar.

Stevioside, a diterpene glycoside, is well known for its intense sweetness and is used as a non-caloric sweetener. Its potential widespread use requires an easy and effective extraction method. Enzymatic extraction of stevioside from Stevia rebaudiana leaves with cellulase, pectinase and hemicellulase using various parameters such as concentration of enzyme, incubation time and temperature was optimized. The extraction conditions were further optimized using response surface methodology (RSM). Under the optimized conditions, the experimental values were in close agreement with predicted model and resulted in a three times yield enhancement of stevioside.

Various studies have revealed that in addition to sweetening nature of stevisoide, it exerts beneficial effects including antihypertensive, anti-hyperglycemic, anti-human rotavirus, antioxidant, anti-inflammatory and antitumor actions. Its anti-amnesic potential remains to be explored, therefore the present study has been undertaken to investigate the beneficial effect of stevioside in memory deficit of rats employing scopolamine induced amnesia as an animal model.

Significance: Stevia is gaining significance in different parts of the world and is expected to develop into a major source of high potency sweetener for the growing natural food market. There is a strong possibility that Stevia sweeteners could replace aspartame in some diet variants. In addition, Stevia is expected to be used as a part substitute for sugar and also used in combination with other artificial sweeteners in the emerging phase of life cycle.

Using a circular grid for offline handwritten character recognition

Offline handwritten recognition is more challenging as indicated by the recognition technologies. This study demonstrates significantly higher rates recognition when compared with other comparable studies. In this paper, we present a circular grid zoning method applied on Polar transformation recognition system. It compares the circular grid zoning (CGZ) and standard zoning (SZ) feature extraction method on Polar and Cartesian coordinate system. We report recognition rates of 92.3%, which are considerably higher than previous studies of zoning based Polar transformation system (86.6%) and zoning based Cartesian recognition system (80.6%). Based on the finding, we propose that our circular grid zoning based Polar transformation system may provide improved classification rates for complex offline handwritten recognition.

Mass spectrometry cancer data classification using wavelets and genetic algorithm

This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

An integrated method of feature extraction and objective evaluation of fabric pilling

A pilled fabric image consists of sub-images of different frequency components, and the fabric texture and the pilling information are in different frequency bands. Interference from fabric background texture affects the accuracy of computer-aided pilling ratings. A new approach for pilling evaluation based on the multi-scale two-dimensional dualtree complex wavelet transform (CWT) is presented in this paper to extract the pilling information from pilled fabric images. The CWT method can effectively decompose the pilled fabric image with six orientations at different scales and reconstruct fabric background texture and pilling sub-images. This study used an energy analysis method to search for an optimum image decomposition scale and dynamically discriminate pilling image from noise, fabric texture, fabric surface unevenness, and illuminative variation in the pilled fabric image. For pilling objective rating, six parameters were extracted from the pilling image to describe pill properties. A Levenberg-Marquardt backpropagation neural rule was used as a classifier to classify the pilling grade. The proposed method was evaluated using knitted, woven, and nonwoven pilled fabric images photographed with a digital camera.

A novel polymer membrane for extraction applications

In this study, a new type of Aliquat 336/PVC membrane has been made for extraction experiments. This new membrane is capable of holding more Aliquat 336 than previously developed extraction membranes, hence overcoming a major problem that has confronted many researchers for a long time. The new membrane has been used to investigate the rate of extraction for the Cd(II) ion in 2.0 M HCl solution and the effect of membrane thickness on the rate of extraction. The experimental results have shown this new membrane has a promising future in relevant industrial applications. A new method is also used in this study to qualitatively identify the oily substance on the surface of membrane after the extraction experiment was completed. This oily substance has been found to be Aliquat 336.