Quantitative effects of in-line operations on Campylobacter and Escherichia coli through two Australian broiler processing plants

Campylobacter is an important food borne pathogen, mainly associated with poultry. A lack of through-chain quantitative Campylobacter data has been highlighted within quantitative risk assessments. The aim of this study was to quantitatively and qualitatively measure Campylobacter and Escherichia coli concentration on chicken carcasses through poultry slaughter. Chickens (n = 240) were sampled from each of four flocks along the processing chain, before scald, after scald, before chill, after chill, after packaging and from individual caeca. The overall prevalence of Campylobacter after packaging was 83% with a median concentration of 0.8 log10 CFU/mL. The processing points of scalding and chilling had significant mean reductions of both Campylobacter (1.8 and 2.9 log10 CFU/carcase) and E. coli (1.3 and 2.5 log10 CFU/carcase). The concentration of E. coli and Campylobacter was significantly correlated throughout processing indicating that E. coli may be a useful indicator organism for reductions in Campylobacter concentration. The carriage of species varied between flocks, with two flocks dominated by Campylobacter coli and two flocks dominated by Campylobacter jejuni. Current processing practices can lead to significant reductions in the concentration of Campylobacter on carcasses. Further understanding of the variable effect of processing on Campylobacter and the survival of specific genotypes may enable more targeted interventions to reduce the concentration of this poultry associated pathogen.

Adaptive frequency scaled wavelet packet decomposition for frog call classification

Environmental changes have put great pressure on biological systems leading to the rapid decline of biodiversity. To monitor this change and protect biodiversity, animal vocalizations have been widely explored by the aid of deploying acoustic sensors in the field. Consequently, large volumes of acoustic data are collected. However, traditional manual methods that require ecologists to physically visit sites to collect biodiversity data are both costly and time consuming. Therefore it is essential to develop new semi-automated and automated methods to identify species in automated audio recordings. In this study, a novel feature extraction method based on wavelet packet decomposition is proposed for frog call classification. After syllable segmentation, the advertisement call of each frog syllable is represented by a spectral peak track, from which track duration, dominant frequency and oscillation rate are calculated. Then, a k-means clustering algorithm is applied to the dominant frequency, and the centroids of clustering results are used to generate the frequency scale for wavelet packet decomposition (WPD). Next, a new feature set named adaptive frequency scaled wavelet packet decomposition sub-band cepstral coefficients is extracted by performing WPD on the windowed frog calls. Furthermore, the statistics of all feature vectors over each windowed signal are calculated for producing the final feature set. Finally, two well-known classifiers, a k-nearest neighbour classifier and a support vector machine classifier, are used for classification. In our experiments, we use two different datasets from Queensland, Australia (18 frog species from commercial recordings and field recordings of 8 frog species from James Cook University recordings). The weighted classification accuracy with our proposed method is 99.5% and 97.4% for 18 frog species and 8 frog species respectively, which outperforms all other comparable methods.

Biofuels from food processing wastes

Food processing industry generates substantial high organic wastes along with high energy uses. The recovery of food processing wastes as renewable energy sources represents a sustainable option for the substitution of fossil energy, contributing to the transition of food sector towards a low-carbon economy. This article reviews the latest research progress on biofuel production using food processing wastes. While extensive work on laboratory and pilot-scale biosystems for energy production has been reported, this work presents a review of advances in metabolic pathways, key technical issues and bioengineering outcomes in biofuel production from food processing wastes. Research challenges and further prospects associated with the knowledge advances and technology development of biofuel production are discussed.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.

Downstream processing of reverse osmosis brine: Characterisation of potential scaling compounds

Reverse osmosis (RO) brine produced at a full-scale coal seam gas (CSG) water treatment facility was characterized with spectroscopic and other analytical techniques. A number of potential scalants including silica, calcium, magnesium, sulphates and carbonates, all of which were present in dissolved and non-dissolved forms, were characterized. The presence of spherical particles with a size range of 10–1000 nm and aggregates of 1–10 microns was confirmed by transmission electron microscopy (TEM). Those particulates contained the following metals in decreasing order: K, Si, Sr, Ca, B, Ba, Mg, P, and S. Characterization showed that nearly one-third of the total silicon in the brine was present in the particulates. Further, analysis of the RO brine suggested supersaturation and precipitation of metal carbonates and sulphates during the RO process should take place and could be responsible for subsequently capturing silica in the solid phase. However, the precipitation of crystalline carbonates and sulphates are complex. X-ray diffraction analysis did not confirm the presence of common calcium carbonates or sulphates but instead showed the presence of a suite of complex minerals, to which amorphous silica and/or silica rich compounds could have adhered. A filtration study showed that majority of the siliceous particles were less than 220 nm in size, but could still be potentially captured using a low molecular weight ultrafiltration membrane.

Mapping the carriage of flaA-restriction fragment length polymorphism Campylobacter genotypes on poultry carcasses through the processing chain and comparison to clinical isolates

Poultry are considered a major source for campylobacteriosis in humans. A total of 1866 Campylobacter spp. isolates collected through the poultry processing chain were typed using flaA-restriction fragment length polymorphism to measure the impact of processing on the genotypes present. Temporally related human clinical isolates (n = 497) were also typed. Isolates were obtained from whole chicken carcass rinses of chickens collected before scalding, after scalding, before immersion chilling, after immersion chilling and after packaging as well as from individual caecal samples. A total of 32 genotypes comprising at least four isolates each were recognised. Simpson's Index of Diversity (D) was calculated for each sampling site within each flock, for each flock as a whole and for the clinical isolates. From caecal collection to after packaging samples the D value did not change in two flocks, decreased in one flock and increased in the fourth flock. Dominant genotypes occurred in each flock but their constitutive percentages changed through processing. There were 23 overlapping genotypes between clinical and chicken isolates. The diversity of Campylobacter is flock dependant and may alter through processing. This study confirms that poultry are a source of campylobacteriosis in the Australian population although other sources may contribute.

Material processing and surface reaction studies in free piston driven shock tube

Recently established moderate size free piston driven hypersonic shock tunnel HST3 along with its calibration is described here. The extreme thermodynamic conditions prevalent behind the reflected shock wave have been utilized to study the catalytic and non-catalytic reactions of shock heated test gases like Ar, N2 or O2 with different material like C60 carbon, zirconia and ceria substituted zirconia. The exposed test samples are investigated using different experimental methods. These studies show the formation of carbon nitride due to the non-catalytic interaction of shock heated nitrogen gas with C60 carbon film. On the other hand, the ZrO2 undergoes only phase transformation from cubic to monoclinic structure and Ce0.5Zr0.5O2 in fluorite cubic phase changes to pyrochlore (Ce2Zr2O7±δ) phase by releasing oxygen from the lattice due to heterogeneous catalytic surface reaction.

Middleware for Mobile Sensing Applications in Urban Environments

Sensor networks represent an attractive tool to observe the physical world. Networks of tiny sensors can be used to detect a fire in a forest, to monitor the level of pollution in a river, or to check on the structural integrity of a bridge. Application-specific deployments of static-sensor networks have been widely investigated. Commonly, these networks involve a centralized data-collection point and no sharing of data outside the organization that owns it. Although this approach can accommodate many application scenarios, it significantly deviates from the pervasive computing vision of ubiquitous sensing where user applications seamlessly access anytime, anywhere data produced by sensors embedded in the surroundings. With the ubiquity and ever-increasing capabilities of mobile devices, urban environments can help give substance to the ubiquitous sensing vision through Urbanets, spontaneously created urban networks. Urbanets consist of mobile multi-sensor devices, such as smart phones and vehicular systems, public sensor networks deployed by municipalities, and individual sensors incorporated in buildings, roads, or daily artifacts. My thesis is that "multi-sensor mobile devices can be successfully programmed to become the underpinning elements of an open, infrastructure-less, distributed sensing platform that can bring sensor data out of their traditional close-loop networks into everyday urban applications". Urbanets can support a variety of services ranging from emergency and surveillance to tourist guidance and entertainment. For instance, cars can be used to provide traffic information services to alert drivers to upcoming traffic jams, and phones to provide shopping recommender services to inform users of special offers at the mall. Urbanets cannot be programmed using traditional distributed computing models, which assume underlying networks with functionally homogeneous nodes, stable configurations, and known delays. Conversely, Urbanets have functionally heterogeneous nodes, volatile configurations, and unknown delays. Instead, solutions developed for sensor networks and mobile ad hoc networks can be leveraged to provide novel architectures that address Urbanet-specific requirements, while providing useful abstractions that hide the network complexity from the programmer. This dissertation presents two middleware architectures that can support mobile sensing applications in Urbanets. Contory offers a declarative programming model that views Urbanets as a distributed sensor database and exposes an SQL-like interface to developers. Context-aware Migratory Services provides a client-server paradigm, where services are capable of migrating to different nodes in the network in order to maintain a continuous and semantically correct interaction with clients. Compared to previous approaches to supporting mobile sensing urban applications, our architectures are entirely distributed and do not assume constant availability of Internet connectivity. In addition, they allow on-demand collection of sensor data with the accuracy and at the frequency required by every application. These architectures have been implemented in Java and tested on smart phones. They have proved successful in supporting several prototype applications and experimental results obtained in ad hoc networks of phones have demonstrated their feasibility with reasonable performance in terms of latency, memory, and energy consumption.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.

Using Mathematical Morphology for Geometric Music Retrieval

The usual task in music information retrieval (MIR) is to find occurrences of a monophonic query pattern within a music database, which can contain both monophonic and polyphonic content. The so-called query-by-humming systems are a famous instance of content-based MIR. In such a system, the user's hummed query is converted into symbolic form to perform search operations in a similarly encoded database. The symbolic representation (e.g., textual, MIDI or vector data) is typically a quantized and simplified version of the sampled audio data, yielding to faster search algorithms and space requirements that can be met in real-life situations. In this thesis, we investigate geometric approaches to MIR. We first study some musicological properties often needed in MIR algorithms, and then give a literature review on traditional (e.g., string-matching-based) MIR algorithms and novel techniques based on geometry. We also introduce some concepts from digital image processing, namely the mathematical morphology, which we will use to develop and implement four algorithms for geometric music retrieval. The symbolic representation in the case of our algorithms is a binary 2-D image. We use various morphological pre- and post-processing operations on the query and the database images to perform template matching / pattern recognition for the images. The algorithms are basically extensions to classic image correlation and hit-or-miss transformation techniques used widely in template matching applications. They aim to be a future extension to the retrieval engine of C-BRAHMS, which is a research project of the Department of Computer Science at University of Helsinki.

Insights into students' conceptual understanding using textual analysis: A case study in signal processing

Concept inventory tests are one method to evaluate conceptual understanding and identify possible misconceptions. The multiple-choice question format, offering a choice between a correct selection and common misconceptions, can provide an assessment of students' conceptual understanding in various dimensions. Misconceptions of some engineering concepts exist due to a lack of mental frameworks, or schemas, for these types of concepts or conceptual areas. This study incorporated an open textual response component in a multiple-choice concept inventory test to capture written explanations of students' selections. The study's goal was to identify, through text analysis of student responses, the types and categorizations of concepts in these explanations that had not been uncovered by the distractor selections. The analysis of the textual explanations of a subset of the discrete-time signals and systems concept inventory questions revealed that students have difficulty conceptually explaining several dimensions of signal processing. This contributed to their inability to provide a clear explanation of the underlying concepts, such as mathematical concepts. The methods used in this study evaluate students' understanding of signals and systems concepts through their ability to express understanding in written text. This may present a bias for students with strong written communication skills. This study presents a framework for extracting and identifying the types of concepts students use to express their reasoning when answering conceptual questions.

Perceptions of audio feedback in higher education assessment

The aim of this paper is to present results of research investigating the effectiveness of audio feedback in a third year undergraduate unit. While there is a large and growing body of literature about providing assessment feedback, there is little focussing on the use of audio media. This study employs a mixed method approach, involving semi-structured interviews with academic staff and a survey of students. Analysis of the interview data suggests that there are a number of issues surrounding acceptance of using audio feedback by lecturers. The next stage of the study is to examine the extent to which lecturers change their perceptions as they use audio feedback and to analyse the perceptions of the students (n=120), including the perceived importance of feedback, the ways in which they used the audio feedback and the extent to which they believe they control events that affect them. Ultimately, this study seeks to provide recommendations appropriate to the implementation of audio feedback in higher education.

'Take away from the dry sixties style marking': Lecturer and student perceptions and experiences of audio feedback

Providing audio feedback to assessment is relatively uncommon in higher education. However, published research suggests that it is preferred over written feedback by students but lecturers were less convinced. The aim of this paper is to examine further these findings in the context of a third year business ethics unit. Data was collected from two sources. The first is a series of in-depth, semi-structured interviews conducted with three lecturers providing audio feeback for the first time in Semester One 2011. The second source of data was drawn from the university student evaluation system. A total of 363 responses were used providing 'before' and 'after' perspectives about the effectiveness of audio feedback versus written feedback. Between 2005 and 2009 the survey data provided information about student attitudes to written assessment feedback (n=261). From 2010 onwards the data relates to audio (mp3) feedback (n=102). The analysis of he interview data indicated that introducing audio feedback should be done with care. The perception of the participating lecturers was mixed, ranging from sceptism to outright enthusiasm, but over time the overall approach became positive. It was found that particular attention needs to be paid to small (but important) technical details, and lecturers need to be convinced of its effectieness, especially that it is not necessarily more time consuming than providing written feedback. For students, the analysis revealed a clear preference for audio feedback. It is concluded that there is cause for concern and reason for optimism. It is a cause for concern because there is a possibility that scepticism on the part of academic staff seems to be based on assumptions about what students prefer and a concern about using the technology. There is reason for optimism because the evidence points towards students preferring audio feedback and as academic staff become more familiar with the technology the scepticism tends to evaporate. While this study is limited in scope, questions are raised about tackling negative staff perceptions of audio feedback that are worthy of further research.