Iron nodules in ferric soils of the Fraser Coast, Australia : relicts of laterisation or features of contemporary weathering and pedogenesis?

The genesis of ferruginous nodules and pisoliths in soils and weathering profiles of coastal southern and eastern Australia has long been debated. It is not clear whether iron (Fe) nodules are redox accumulations, residues of Miocene laterite duricrust, or the products of contemporary weathering of Fe-rich sedimentary rocks. This study combines a catchment-wide survey of Fe nodule distribution in Poona Creek catchment (Fraser Coast, Queensland) with detailed investigations of a representative ferric soil profile to show that Fe nodules are derived from Fe-rich sandstones. Where these crop out, they are broken down, transported downslope by colluvial processes, and redeposited. Chemical and physical weathering transforms these eroded rock fragments into non-magnetic Fe nodules. Major features of this transformation include lower hematite/goethite and kaolinite/gibbsite ratios, increased porosity, etching of quartz grains, and development of rounded morphology and a smooth outer cortex. Iron nodules are commonly concentrated in ferric horizons. We show that these horizons form as the result of differential biological mixing of the soil. Bioturbation gradually buries nodules and rock fragments deposited at the surface of the soil, resulting in a largely nodule-free 'biomantle' over a ferric 'stone line'. Maghemite-rich magnetic nodules are a prominent feature of the upper half of the profile. These are most likely formed by the thermal alteration of non-magnetic nodules located at the top of the profile during severe bushfires. They are subsequently redistributed through the soil profile by bioturbation. Iron nodules occurring in the study area are products of contemporary weathering of Fe-rich rock units. They are not laterite duricrust residues nor are they redox accumulations, although redox-controlled dissolution/re-precipitation is an important component of post-depositional modification of these Fe nodules.

Capturing gazelles : features of high potential firms and new venture growth

This paper investigates the characteristics of ventures which have the potential to reach high growth and compares this with ‘everyday’ new ventures. Findings of interest in this paper include: • HP firms are characterised by higher human capital, are more likely to have a team of founders, are more likely to be product based. • HP firms are more likely to achieve more extreme levels of growth (both positive and negative). • HP ventures that make a loss are more likely to do so early in the venture process. Those that do hold on show that there can higher levels of loss made later on in firm development. HP firms have higher resource needs, in terms of seeking external finance, but are no more likely to receive external finance than regular firms.

Separable and non-separable discrete wavelet transform based texture features and image classification of breast thermograms

Highly sensitive infrared cameras can produce high-resolution diagnostic images of the temperature and vascular changes of breasts. Wavelet transform based features are suitable in extracting the texture difference information of these images due to their scale-space decomposition. The objective of this study is to investigate the potential of extracted features in differentiating between breast lesions by comparing the two corresponding pectoral regions of two breast thermograms. The pectoral regions of breastsare important because near 50% of all breast cancer is located in this region. In this study, the pectoral region of the left breast is selected. Then the corresponding pectoral region of the right breast is identified. Texture features based on the first and the second sets of statistics are extracted from wavelet decomposed images of the pectoral regions of two breast thermograms. Principal component analysis is used to reduce dimension and an Adaboost classifier to evaluate classification performance. A number of different wavelet features are compared and it is shown that complex non-separable 2D discrete wavelet transform features perform better than their real separable counterparts.

Surgical resection for non-small cell lung cancer : clinical features and outcomes for a consecutive series at an Irish tertiary referral centre

Background: Few patients diagnosed with lung cancer are still alive 5 years after diagnosis. The aim of the current study was to conduct a 10-year review of a consecutive series of patients undergoing curative-intent surgical resection at the largest tertiary referral centre to identify prognostic factors. Methods: Case records of all patients operated on for lung cancer between 1998 and 2008 were reviewed. The clinical features and outcomes of all patients with non-small cell lung cancer (NSCLC) stage I-IV were recorded. Results: A total of 654 patients underwent surgical resection with curative intent during the study period. Median overall survival for the entire cohort was 37 months. The median age at operation was 66 years, with males accounting for 62.7 %. Squamous cell type was the most common histological subtype, and lobectomies were performed in 76.5 % of surgical resections. Pneumonectomy rates decreased significantly in the latter half of the study (25 vs. 16.3 %), while sub-anatomical resection more than doubled (2 vs. 5 %) (p < 0.005). Clinico-pathological characteristics associated with improved survival by univariate analysis include younger age, female sex, smaller tumour size, smoking status, lobectomy, lower T and N status and less advanced pathological stage. Age, gender, smoking status and tumour size, as well as T and N descriptors have emerged as independent prognostic factors by multivariate analysis. Conclusion: We identified several factors that predicted outcome for NSCLC patients undergoing curative-intent surgical resection. Survival rates in our series are comparable to those reported from other thoracic surgery centres. © 2012 Royal Academy of Medicine in Ireland.

Real-time video event detection in crowded scenes using MPEG derived features : a multiple instance learning approach

This paper presents an investigation into event detection in crowded scenes, where the event of interest co-occurs with other activities and only binary labels at the clip level are available. The proposed approach incorporates a fast feature descriptor from the MPEG domain, and a novel multiple instance learning (MIL) algorithm using sparse approximation and random sensing. MPEG motion vectors are used to build particle trajectories that represent the motion of objects in uniform video clips, and the MPEG DCT coefficients are used to compute a foreground map to remove background particles. Trajectories are transformed into the Fourier domain, and the Fourier representations are quantized into visual words using the K-Means algorithm. The proposed MIL algorithm models the scene as a linear combination of independent events, where each event is a distribution of visual words. Experimental results show that the proposed approaches achieve promising results for event detection compared to the state-of-the-art.

An evaluation of different features and learning models for anomalous event detection

The huge amount of CCTV footage available makes it very burdensome to process these videos manually through human operators. This has made automated processing of video footage through computer vision technologies necessary. During the past several years, there has been a large effort to detect abnormal activities through computer vision techniques. Typically, the problem is formulated as a novelty detection task where the system is trained on normal data and is required to detect events which do not fit the learned ‘normal’ model. There is no precise and exact definition for an abnormal activity; it is dependent on the context of the scene. Hence there is a requirement for different feature sets to detect different kinds of abnormal activities. In this work we evaluate the performance of different state of the art features to detect the presence of the abnormal objects in the scene. These include optical flow vectors to detect motion related anomalies, textures of optical flow and image textures to detect the presence of abnormal objects. These extracted features in different combinations are modeled using different state of the art models such as Gaussian mixture model(GMM) and Semi- 2D Hidden Markov model(HMM) to analyse the performances. Further we apply perspective normalization to the extracted features to compensate for perspective distortion due to the distance between the camera and objects of consideration. The proposed approach is evaluated using the publicly available UCSD datasets and we demonstrate improved performance compared to other state of the art methods.

Semi-binary based video features for activity representation

Efficient and effective feature detection and representation is an important consideration when processing videos, and a large number of applications such as motion analysis, 3D scene understanding, tracking etc. depend on this. Amongst several feature description methods, local features are becoming increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational complexity, their performance is still too limited for real world applications. Furthermore, rapid increases in the uptake of mobile devices has increased the demand for algorithms that can run with reduced memory and computational requirements. In this paper we propose a semi binary based feature detectordescriptor based on the BRISK detector, which can detect and represent videos with significantly reduced computational requirements, while achieving comparable performance to the state of the art spatio-temporal feature descriptors. First, the BRISK feature detector is applied on a frame by frame basis to detect interest points, then the detected key points are compared against consecutive frames for significant motion. Key points with significant motion are encoded with the BRISK descriptor in the spatial domain and Motion Boundary Histogram in the temporal domain. This descriptor is not only lightweight but also has lower memory requirements because of the binary nature of the BRISK descriptor, allowing the possibility of applications using hand held devices.We evaluate the combination of detectordescriptor performance in the context of action classification with a standard, popular bag-of-features with SVM framework. Experiments are carried out on two popular datasets with varying complexity and we demonstrate comparable performance with other descriptors with reduced computational complexity.

Epidemiologic characteristics of cases for influenza A(H7N9) virus infections in China

China's National Health and Family Planning Commission announced 3 deaths caused by avian-origin influenza A(H7N9) virus in March, which was the first time that the H7N9 strain has been found in humans [1]. This is of major public health significance and raises urgent questions and global concerns [2, 3]. To explore epidemic characteristics of human infections with H7N9 virus, data on individual cases from 19 February 2013 (onset date of first case) to 14 April 2013 were collected from the China Information System for Disease Control and Prevention, which included information about sex; age; occupation; residential address; and day of symptom onset, diagnosis, and outcome for each case. The definition of an unconfirmed probable H7N9 case is a patient with epidemiologic evidence of contact …

The impact of social media features on print media firms’ online business models

Many newspapers and magazines have added “social media features” to their web-based information services in order to allow users to participate in the production of content. This study examines the specific impact of the firm’s investment in social media features on their online business models. We make a comparative case study of four Scandinavian print media firms that have added social media features to their online services. We show how social media features lead to online business model innovation, particularly linked to the firms’ value propositions. The paper discusses the repercussions of this transformation on firms’ relationship with consumers and with traditional content contributors. The modified value proposition also requires firms to acquire new competences in order to reap full benefit of their social media investments. We show that the firms have been unable to do so since they have not allowed the social media features to affect their online revenue models.

Mining specific features for acquiring user information needs

Term-based approaches can extract many features in text documents, but most include noise. Many popular text-mining strategies have been adapted to reduce noisy information from extracted features; however, text-mining techniques suffer from low frequency. The key issue is how to discover relevance features in text documents to fulfil user information needs. To address this issue, we propose a new method to extract specific features from user relevance feedback. The proposed approach includes two stages. The first stage extracts topics (or patterns) from text documents to focus on interesting topics. In the second stage, topics are deployed to lower level terms to address the low-frequency problem and find specific terms. The specific terms are determined based on their appearances in relevance feedback and their distribution in topics or high-level patterns. We test our proposed method with extensive experiments in the Reuters Corpus Volume 1 dataset and TREC topics. Results show that our proposed approach significantly outperforms the state-of-the-art models.

Police officer in-vehicle discomfort : appointments carriage method and vehicle seat features

Musculoskeletal pain is commonly reported by police officers. A potential cause of officer discomfort is a mismatch between vehicle seats and the method used for carrying appointments. Twenty-five police officers rated their discomfort while seated in: (1) a standard police vehicle seat, and (2) a vehicle seat custom-designed for police use. Discomfort was recorded in both seats while wearing police appointments on: (1) a traditional appointments belt, and (2) a load-bearing vest / belt combination (LBV). Sitting in the standard vehicle seat and carrying appointments on a traditional appointments belt were both associated with significantly elevated discomfort. Four vehicle seat features were most implicated as contributing to discomfort: back rest bolster prominence; lumbar region support; seat cushion width; and seat cushion bolster depth. Authorising the carriage of appointments using a LBV is a lower cost solution with potential to reduce officer discomfort. Furthermore, the introduction of custom-designed vehicle seats should be considered.

Epidemiological and molecular features of dengue virus type-1 in New Caledonia, South Pacific, 2001–2013

Background The epidemiology of dengue in the South Pacific has been characterized by transmission of a single dominant serotype for 3–5 years, with subsequent replacement by another serotype. From 2001 to 2008 only DENV-1 was reported in the Pacific. In 2008, DENV-4 emerged and quickly displaced DENV-1 in the Pacific, except in New Caledonia (NC) where DENV-1 and DENV-4 co-circulated in 2008–2009. During 2012–2013, another DENV-1 outbreak occurred in NC, the third DENV-1 outbreak in a decade. Given that dengue is a serotype-specific immunizing infection, the recurrent outbreaks of a single serotype within a 10-year period was unexpected. Findings This study aimed to inform this phenomenon by examining the phylogenetic characteristics of the DENV-1 viruses in NC and other Pacific islands between 2001 and 2013. As a result, we have demonstrated that NC experienced introductions of viruses from both the Pacific (genotype IV) and South-east Asia (genotype I). Moreover, whereas genotype IV and I were co-circulating at the beginning of 2012, we observed that from the second half of 2012, i.e. during the major DENV-1 outbreak, all analyzed viruses were genotype I suggesting that a genotype switch occurred. Conclusions Repeated outbreaks of the same dengue serotype, as observed in NC, is uncommon in the Pacific islands. Why the earlier DENV-1 outbreaks did not induce sufficient herd immunity is unclear, and likely multifactorial, but the robust vector control program may have played a role by limiting transmission and thus maintaining a large susceptible pool in the population. Keywords: Dengue; Phylogeny; Genotype; Epidemics; New Caledonia

Features-pooling blind jpeg image steganalysis

In this research, we introduce a new blind steganalysis in detecting grayscale JPEG images. Features-pooling method is employed to extract the steganalytic features and the classification is done by using neural network. Three different steganographic models are tested and classification results are compared to the five state-of-the-art blind steganalysis.

Making YAWL and SmartPM interoperate : managing highly dynamic processes by exploiting automatic adaptation features

In the last years, the trade-o between exibility and sup- port has become a leading issue in work ow technology. In this paper we show how an imperative modeling approach used to de ne stable and well-understood processes can be complemented by a modeling ap- proach that enables automatic process adaptation and exploits planning techniques to deal with environmental changes and exceptions that may occur during process execution. To this end, we designed and imple- mented a Custom Service that allows the Yawl execution environment to delegate the execution of subprocesses and activities to the SmartPM execution environment, which is able to automatically adapt a process to deal with emerging changes and exceptions. We demonstrate the fea- sibility and validity of the approach by showing the design and execution of an emergency management process de ned for train derailments.

Computation of ECG signal features using MCMC modelling in software and FPGA reconfigurable hardware

Computational optimisation of clinically important electrocardiogram signal features, within a single heart beat, using a Markov-chain Monte Carlo (MCMC) method is undertaken. A detailed, efficient data-driven software implementation of an MCMC algorithm has been shown. Initially software parallelisation is explored and has been shown that despite the large amount of model parameter inter-dependency that parallelisation is possible. Also, an initial reconfigurable hardware approach is explored for future applicability to real-time computation on a portable ECG device, under continuous extended use.