The role of vegetation in enhancing radon concentration and ion production in the atmosphere

The role of ions in the production of atmospheric particles has gained wide interest due to their profound impact on climate. Away from anthropogenic sources, molecules are ionized by alpha radiation from radon exhaled from the ground and cosmic gamma radiation from space. These molecular ions quickly form into ‘cluster ions’, typically smaller than about 1.5 nm. Using our measurements and the published literature, we present evidence to show that cluster ion concentrations in forest areas are consistently higher than outside. Since alpha radiation cannot penetrate more than a few centimetres of soil, radon present deep in the ground cannot directly contribute to the measured cluster ion concentrations. We propose an additional mechanism whereby radon, which is water soluble, is brought up by trees and plants through the uptake of groundwater and released into the atmosphere by transpiration. We estimate that, in a forest comprising eucalyptus trees spaced 4m apart, approximately 28% of the radon in the air may be released by transpiration. Considering that 24% of the earth’s land area is still covered in forests; these findings have potentially important implications for atmospheric aerosol formation and climate.

Anisotropy of articular cartilage reflects the ECM gradient architecture : Hough-Radon Transform Analysis

Tissue-specific extracellular matrix (ECM) is known to be an ideal bioscaffold to inspire the future of regenerative medicine. It holds the secret of how nature has developed such an organization of molecules into a unique functional complexity. This work exploited an innovative image processing algorithm and high resolution microscopy associated with mechanical analysis to establish a correlation between the gradient organization of cartiligous ECM and its anisotropic biomechanical response. This was hypothesized to be a reliable determinant that can elucidate how microarchitecture interrelates with biomechanical properties. Hough-Radon transform of the ECM cross-section images revealed its conformational variation from tangential interface down to subchondral region. As the orientation varied layer by layer, the anisotropic mechanical response deviated relatively. Although, results were in good agreement (Kendall's tau-b > 90%), there were evidences proposing that alignment of the fibrous network, specifically in middle zone, is not as random as it was previously thought.

Radon-222 activity flux measurement using activated charcoal canisters: Revisiting the methodology

The measurement of radon ((222)Rn) activity flux using activated charcoal canisters was examined to investigate the distribution of the adsorbed (222)Rn in the charcoal bed and the relationship between (222)Rn activity flux and exposure time. The activity flux of (222)Rn from five sources of varying strengths was measured for exposure times of one, two, three, five, seven, 10, and 14 days. The distribution of the adsorbed (222)Rn in the charcoal bed was obtained by dividing the bed into six layers and counting each layer separately after the exposure. (222)Rn activity decreased in the layers that were away from the exposed surface. Nevertheless, the results demonstrated that only a small correction might be required in the actual application of charcoal canisters for activity flux measurement, where calibration standards were often prepared by the uniform mixing of radium ((226)Ra) in the matrix. This was because the diffusion of (222)Rn in the charcoal bed and the detection efficiency as a function of the charcoal depth tended to counterbalance each other. The influence of exposure time on the measured (222)Rn activity flux was observed in two situations of the canister exposure layout: (a) canister sealed to an open bed of the material and (b) canister sealed over a jar containing the material. The measured (222)Rn activity flux decreased as the exposure time increased. The change in the former situation was significant with an exponential decrease as the exposure time increased. In the latter case, lesser reduction was noticed in the observed activity flux with respect to exposure time. This reduction might have been related to certain factors, such as absorption site saturation or the back diffusion of (222)Rn gas occurring at the canister-soil interface.

On the interaction between radon progeny and particles generated by electronic and traditional cigarettes

During their entire lives, people are exposed to the pollutants present in indoor air. Recently, Electronic Nicotine Delivery Systems, mainly known as electronic cigarettes, have been widely commercialized: they deliver particles into the lungs of the users but a “second-hand smoke” has yet to be associated to this indoor source. On the other hand, the naturally-occurring radioactive gas, i.e. radon, represents a significant risk for lung cancer, and the cumulative action of these two agents could be worse than the agents separately would. In order to deepen the interaction between radon progeny and second-hand aerosol from different types of cigarettes, a designed experimental study was carried out by generating aerosol from e-cigarette vaping as well as from second-hand traditional smoke inside a walk-in radon chamber at the National Institute of Ionizing Radiation Metrology (INMRI) of Italy. In this chamber, the radon present in air comes naturally from the floor and ambient conditions are controlled. To characterize the sidestream smoke emitted by cigarettes, condensation particle counters and scanning mobility particle sizer were used. Radon concentration in the air was measured through an Alphaguard ionization chamber, whereas the measurement of radon decay product in the air was performed with the Tracelab BWLM Plus-2S Radon daughter Monitor. It was found an increase of the Potential Alpha-Energy Concentration (PAEC) due to the radon decay products attached to aerosol for higher particle number concentrations. This varied from 7.47 ± 0.34 MeV L−1 to 12.6 ± 0.26 MeV L−1 (69%) for the e-cigarette. In the case of traditional cigarette and at the same radon concentration, the increase was from 14.1 ± 0.43 MeV L−1 to 18.6 ± 0.19 MeV L−1 (31%). The equilibrium factor increases, varying from 23.4% ± 1.11% to 29.5% ± 0.26% and from 30.9% ± 1.0% to 38.1 ± 0.88 for the e-cigarette and traditional cigarette, respectively. These growths still continue for long time after the combustion, by increasing the exposure risk.

Radiological aspects of petroleum exploration and production in the sultanate of Oman

This thesis is a study of naturally occurring radioactive materials (NORM) activity concentration, gamma dose rate and radon (222Rn) exhalation from the waste streams of large-scale onshore petroleum operations. Types of activities covered included; sludge recovery from separation tanks, sludge farming, NORM storage, scaling in oil tubulars, scaling in gas production and sedimentation in produced water evaporation ponds. Field work was conducted in the arid desert terrain of an operational oil exploration and production region in the Sultanate of Oman. The main radionuclides found were 226Ra and 210Pb (238U - series), 228Ra and 228Th (232Th - series), and 227Ac (235U - series), along with 40K. All activity concentrations were higher than the ambient soil level and varied over several orders of magnitude. The range of gamma dose rates at a 1 m height above ground for the farm treated sludge had a range of 0.06 0.43 µSv h 1, and an average close to the ambient soil mean of 0.086 ± 0.014 µSv h 1, whereas the untreated sludge gamma dose rates had a range of 0.07 1.78 µSv h 1, and a mean of 0.456 ± 0.303 µSv h 1. The geometric mean of ambient soil 222Rn exhalation rate for area surrounding the sludge was mBq m 2 s 1. Radon exhalation rates reported in oil waste products were all higher than the ambient soil value and varied over three orders of magnitude. This study resulted in some unique findings including: (i) detection of radiotoxic 227Ac in the oil scales and sludge, (ii) need of a new empirical relation between petroleum sludge activity concentrations and gamma dose rates, and (iii) assessment of exhalation of 222Rn from oil sludge. Additionally the study investigated a method to determine oil scale and sludge age by the use of inherent behaviour of radionuclides as 228Ra:226Ra and 228Th:228Ra activity ratios.

Robust image hashing using higher order spectral features

Robust image hashing seeks to transform a given input image into a shorter hashed version using a key-dependent non-invertible transform. These image hashes can be used for watermarking, image integrity authentication or image indexing for fast retrieval. This paper introduces a new method of generating image hashes based on extracting Higher Order Spectral features from the Radon projection of an input image. The feature extraction process is non-invertible, non-linear and different hashes can be produced from the same image through the use of random permutations of the input. We show that the transform is robust to typical image transformations such as JPEG compression, noise, scaling, rotation, smoothing and cropping. We evaluate our system using a verification-style framework based on calculating false match, false non-match likelihoods using the publicly available Uncompressed Colour Image database (UCID) of 1320 images. We also compare our results to Swaminathan’s Fourier-Mellin based hashing method with at least 1% EER improvement under noise, scaling and sharpening.

Pattern recognition using invariants defined from higher order spectra : 2-D image inputs

A new algorithm for extracting features from images for object recognition is described. The algorithm uses higher order spectra to provide desirable invariance properties, to provide noise immunity, and to incorporate nonlinearity into the feature extraction procedure thereby allowing the use of simple classifiers. An image can be reduced to a set of 1D functions via the Radon transform, or alternatively, the Fourier transform of each 1D projection can be obtained from a radial slice of the 2D Fourier transform of the image according to the Fourier slice theorem. A triple product of Fourier coefficients, referred to as the deterministic bispectrum, is computed for each 1D function and is integrated along radial lines in bifrequency space. Phases of the integrated bispectra are shown to be translation- and scale-invariant. Rotation invariance is achieved by a regrouping of these invariants at a constant radius followed by a second stage of invariant extraction. Rotation invariance is thus converted to translation invariance in the second step. Results using synthetic and actual images show that isolated, compact clusters are formed in feature space. These clusters are linearly separable, indicating that the nonlinearity required in the mapping from the input space to the classification space is incorporated well into the feature extraction stage. The use of higher order spectra results in good noise immunity, as verified with synthetic and real images. Classification of images using the higher order spectra-based algorithm compares favorably to classification using the method of moment invariants

Position, rotation, and scale invariant recognition of images using higher-order spectra

A new approach to recognition of images using invariant features based on higher-order spectra is presented. Higher-order spectra are translation invariant because translation produces linear phase shifts which cancel. Scale and amplification invariance are satisfied by the phase of the integral of a higher-order spectrum along a radial line in higher-order frequency space because the contour of integration maps onto itself and both the real and imaginary parts are affected equally by the transformation. Rotation invariance is introduced by deriving invariants from the Radon transform of the image and using the cyclic-shift invariance property of the discrete Fourier transform magnitude. Results on synthetic and actual images show isolated, compact clusters in feature space and high classification accuracies

Radioactive residues associated with water treatment, use and disposal in Australia

Water resources are known to contain radioactive materials, either from natural or anthropogenic sources. Treatment, including wastewater treatment, of water for drinking, domestic, agricultural and industrial purposes has the potential to concentrate radioactive materials. Inevitably concentrated radioactive material is discharged to the environment as a waste product, reused for soil conditioning, or perhaps recycled as a new potable water supply. This thesis, presented as a collection of peer reviewed scientific papers, explores a number of water / wastewater treatment applications, and the subsequent nature and potential impact of radioactive residues associated with water exploitation processes. The thesis draws together research outcomes for sites predominantly throughout Queensland, Australia, where it is recognised that there is a paucity of published data on the subject. This thesis contributes to current knowledge on the monitoring, assessment and potential for radiation exposure from radioactive residues associated with the water industry.

Breast cancer detection from thermal images using bispectral invariant features

Highly sensitive infrared (IR) cameras provide high-resolution diagnostic images of the temperature and vascular changes of breasts. These images can be processed to emphasize hot spots that exhibit early and subtle changes owing to pathology. The resulting images show clusters that appear random in shape and spatial distribution but carry class dependent information in shape and texture. Automated pattern recognition techniques are challenged because of changes in location, size and orientation of these clusters. Higher order spectral invariant features provide robustness to such transformations and are suited for texture and shape dependent information extraction from noisy images. In this work, the effectiveness of bispectral invariant features in diagnostic classification of breast thermal images into malignant, benign and normal classes is evaluated and a phase-only variant of these features is proposed. High resolution IR images of breasts, captured with measuring accuracy of ±0.4% (full scale) and temperature resolution of 0.1 °C black body, depicting malignant, benign and normal pathologies are used in this study. Breast images are registered using their lower boundaries, automatically extracted using landmark points whose locations are learned during training. Boundaries are extracted using Canny edge detection and elimination of inner edges. Breast images are then segmented using fuzzy c-means clustering and the hottest regions are selected for feature extraction. Bispectral invariant features are extracted from Radon projections of these images. An Adaboost classifier is used to select and fuse the best features during training and then classify unseen test images into malignant, benign and normal classes. A data set comprising 9 malignant, 12 benign and 11 normal cases is used for evaluation of performance. Malignant cases are detected with 95% accuracy. A variant of the features using the normalized bispectrum, which discards all magnitude information, is shown to perform better for classification between benign and normal cases, with 83% accuracy compared to 66% for the original.

Robust image hash functions using higher order spectra

Robust hashing is an emerging field that can be used to hash certain data types in applications unsuitable for traditional cryptographic hashing methods. Traditional hashing functions have been used extensively for data/message integrity, data/message authentication, efficient file identification and password verification. These applications are possible because the hashing process is compressive, allowing for efficient comparisons in the hash domain but non-invertible meaning hashes can be used without revealing the original data. These techniques were developed with deterministic (non-changing) inputs such as files and passwords. For such data types a 1-bit or one character change can be significant, as a result the hashing process is sensitive to any change in the input. Unfortunately, there are certain applications where input data are not perfectly deterministic and minor changes cannot be avoided. Digital images and biometric features are two types of data where such changes exist but do not alter the meaning or appearance of the input. For such data types cryptographic hash functions cannot be usefully applied. In light of this, robust hashing has been developed as an alternative to cryptographic hashing and is designed to be robust to minor changes in the input. Although similar in name, robust hashing is fundamentally different from cryptographic hashing. Current robust hashing techniques are not based on cryptographic methods, but instead on pattern recognition techniques. Modern robust hashing algorithms consist of feature extraction followed by a randomization stage that introduces non-invertibility and compression, followed by quantization and binary encoding to produce a binary hash output. In order to preserve robustness of the extracted features, most randomization methods are linear and this is detrimental to the security aspects required of hash functions. Furthermore, the quantization and encoding stages used to binarize real-valued features requires the learning of appropriate quantization thresholds. How these thresholds are learnt has an important effect on hashing accuracy and the mere presence of such thresholds are a source of information leakage that can reduce hashing security. This dissertation outlines a systematic investigation of the quantization and encoding stages of robust hash functions. While existing literature has focused on the importance of quantization scheme, this research is the first to emphasise the importance of the quantizer training on both hashing accuracy and hashing security. The quantizer training process is presented in a statistical framework which allows a theoretical analysis of the effects of quantizer training on hashing performance. This is experimentally verified using a number of baseline robust image hashing algorithms over a large database of real world images. This dissertation also proposes a new randomization method for robust image hashing based on Higher Order Spectra (HOS) and Radon projections. The method is non-linear and this is an essential requirement for non-invertibility. The method is also designed to produce features more suited for quantization and encoding. The system can operate without the need for quantizer training, is more easily encoded and displays improved hashing performance when compared to existing robust image hashing algorithms. The dissertation also shows how the HOS method can be adapted to work with biometric features obtained from 2D and 3D face images.

Inflammation in lung carcinogenesis

It was Dvorak in 1986 that postulated 'tumours are wounds that do not heal' as they share common cellular and molecular mechanisms, which are active in both wounds and in cancer tissue. Inflammation is a crucial part of the innate immune system that protects against pathogens and initiates adaptive immunity. Acute inflammation is usually a rapid and self-limiting process, however it does not always resolve. This leads to the establishment of a chronic inflammatory state and provides the perfect environment for carcinogenesis. Inflammation and cancer have long had an association, going back as far as Virchow in 1863, when leucocytes were noted in neoplastic tissue. It has been estimated that approximately 25% of all malignancies are initiated or exacerbated by inflammation caused by infectious agents. Furthermore, inflammation is linked to all of the six hallmarks of cancer (evasion of apoptosis, insensitivity to anti-growth signals, unlimited replicative potential, angiogenesis, increase in survival factors and invasion and metastasis). It is thought that inflammation may play a critical role in lung carcinogenesis given that individuals with inflammatory lung conditions have an increased risk of lung cancer development. Cigarette smoking can also induce inflammation in the lung and smokers are at a higher risk of developing lung cancer than non-smokers. However, exposure to a number of environmental agents such as radon, have also been demonstrated as a causative factor in this disease. This chapter will focus on inflammation as a contributory factor in non small cell lung cancer (NSCLC), concentrating primarily on the pathological involvement of the pro-inflammatory cytokines, TNF-α, IL-1β, and the CXC (ELR+) chemokine family. Targeting of inflammatory mediators will also be discussed as a therapeutic strategy in this disease. © 2013 by Nova Science Publishers, Inc. All rights reserved.

Application of higher-order spectra for automated grading of diabetic maculopathy

Diabetic macular edema (DME) is one of the most common causes of visual loss among diabetes mellitus patients. Early detection and successive treatment may improve the visual acuity. DME is mainly graded into non-clinically significant macular edema (NCSME) and clinically significant macular edema according to the location of hard exudates in the macula region. DME can be identified by manual examination of fundus images. It is laborious and resource intensive. Hence, in this work, automated grading of DME is proposed using higher-order spectra (HOS) of Radon transform projections of the fundus images. We have used third-order cumulants and bispectrum magnitude, in this work, as features, and compared their performance. They can capture subtle changes in the fundus image. Spectral regression discriminant analysis (SRDA) reduces feature dimension, and minimum redundancy maximum relevance method is used to rank the significant SRDA components. Ranked features are fed to various supervised classifiers, viz. Naive Bayes, AdaBoost and support vector machine, to discriminate No DME, NCSME and clinically significant macular edema classes. The performance of our system is evaluated using the publicly available MESSIDOR dataset (300 images) and also verified with a local dataset (300 images). Our results show that HOS cumulants and bispectrum magnitude obtained an average accuracy of 95.56 and 94.39 % for MESSIDOR dataset and 95.93 and 93.33 % for local dataset, respectively.