The diet of the dingo (Canis lupus dingo and hybrids) in north-eastern Australia: a supplement to the paper of Brook and Kutt (2011)

Dingoes and other wild dogs (Canis lupus dingo and hybrids) are generalist predators that consume a wide variety of different prey species within their range. Little is known, however, of the diets of dingoes in north-eastern Australia where the potential for impacts by dingoes exists. Recently new information has been provided on the diets of dingoes from several sites in Queensland, Australia, significantly adding to the body of published knowledge on ecosystems within this region. Further information on the diet of dingoes in north-eastern Australia is added from 1460 scats collected from five sites, representing tropical savannahs, tropical offshore islands (and a matched mainland area), dry sclerophyll forests and peri-urban areas on the fringe of Townsville. Macropods, possums and bandicoots were found to be common prey for dingoes in these areas. Evidence suggested that the frequency of prey remains in scats can be an unreliable indicator of predation risk to potential prey and it was found that novel and unexpected prey species appear in dingo diets as preferred prey become unavailable. The results support the generalisation that dingoes prefer medium- to large-sized native prey species when available but also highlight the capacity for dingoes to exploit populations of both large and small prey species that might not initially be considered at risk from predation based solely on data on scats.

The neutron-antineutron transition amplitude in the MIT Bag Model

The neutron-antineutron transition amplitude caused by an effective six fermion interaction with strength λeff is calculated within the context of the MIT Bag Model. The transition mass δm is found to have the value λeff×3×10−4(GeV6).

Experimental investigation of the effects of oxygenated fuels on exhaust emissions in a heavy duty diesel engine

Physical and chemical properties of biofuels vary among various feedstocks and their subsequent conversions to fuels. The biofuels contain various amounts of oxygen, and this has a significant influence on exhaust emission. This oxygen content has been considered in order to investigate its effect on diesel engine exhaust emissions. The experiments have been conducted with a heavy duty diesel engine and various oxygenated fuels. It is found that the amount of oxygen in the fuel has a high level of influence on its exhaust emissions, and this provides agreement with diesel emissions results such as PN reduction. By increasing the amount of oxygen in the blend (by adding more biofuel), the particulate number (PN) is reduced and NOx increases gradually. However, the variation of PN and NOx are not similar for waste cooking biodiesel (WCBD) and butanol blend, even though their oxygen content are the same in the blends. This is due to the source of the biofuel and their internal chemistry.

Microbial life and deposits in paper machine circuits

Väitöskirjani käsittele mikrobien ja erilaisten kemikaalien rooleja saostumien ja biofilmien muodostumisessa paperi- ja kartonkikoneilla. "Saostuma" tässä työssä tarkoittaa kiinteän aineen kertymää konepinnoille tai rajapinnoille konekierroissa, jotka on tarkoitettu massasulppujen, lietteiden, vesien tai ilman kuljetukseen. Saostumasta tulee "biofilmi" silloin kun sen oleellinen rakennekomponentti on mikrobisolut tai niiden tuotteet. Väitöstyöni työhypoteesina oli, että i. tietämys saostumien koostumuksesta, sekä ii. niiden rakenteesta, biologisista, fysikaalis-kemiallisista ja teknisistä ominaisuuksista ohjaavat tutkijaa löytämään ympäristöä säästäviä keinoja estää epätoivottujen saostumien muodostus tai purkaa jo muodostuneita saostumia. Selvittääkseni saostumien koostumista ja rakennetta käytin monia erilaisia analytiikan työkaluja, kuten elektronimikroskopiaa, konfokaali-laser mikroskopiaa (CLSM), energiadispersiivistä röntgenanalyysiä (EDX), pyrolyysi kaasukromatografiaa yhdistettynä massaspektrometriaan (Py-GCMS), joninvaihtokromatografiaa, kaasukromatografiaa ja mikrobiologisia analyysejä. Osallistuin aktiivisesti innovatiivisen, valon takaisinsirontaan perustuvan sensorin kehittämistyöhön, käytettäväksi biofilmin kasvun mittaukseen suoraan koneen vesikierroista ja säiliöistä. Työni osoitti, että monet paperinvalmistuksessa käytetyistä kemikaaleista reagoivat keskenään tuottaen orgaanisia tahmakerroksia konekiertojen teräspinnoille. Löysin myös kerrostumia, jotka valomikroskooppisessa tarkastelussa oli tulkittu mikrobeiksi, mutta jotka elektronimikroskopia paljasti alunasta syntyneiksi, alumiinihydroksidiksi joka saostui pH:ssa 6,8 kiertokuitua käyttävän koneen viiravesistä. Monet paperintekijät käyttävät vieläkin alunaa kiinnitysaineena vaikka prosessiolot ovat muuttuneet happamista neutraaleiksi. Sitä pidetään paperitekijän "aspiriinina", mutta väitöstutkimukseni osoitti sen riskit. Löysin myös orgaanisia saostumia, joiden alkuperä oli aineiden, kuten pihkan, saippuoituminen (kalsium saippuat) niin että muodostui tahmankasvua ylläpitävä alusta monilla paperi- ja kartonkikoneilla. Näin solumuodoiltaan Deinococcus geothermalista muistuttavia bakteereita kasvamassa lujasti teräskoepalojen pintaan kiinnittyneinä pesäkkeinä, kun koepaloja upotettiin paperikoneiden vesikiertoihin. Nämä deinokokkimaiset pesäkkeet voivat toimia jalustana, tarttumisalustana muiden mikrobien massoille, joka selittäisi miksi saostumat yleisesti sisältävät deinokokkeja pienenä, muttei koskaan pääasiallisena rakenneosana. Kun paperikoneiden käyttämien vesien (raakavedet, lämminvesi, biologisesti puhdistettu jätevesi) laatua tutkitaan, mittausmenetelmällä on suuri merkitys. Koepalan upotusmenetelmällä todettu biofilmikasvu ja viljelmenetelmällä mitattu bakteerisaastuneisuus korreloivat toisiinsa huonosti etenkin silloin kun likaantumisessa oli mukana rihmamaiseti kasvavia bakteereja. Huoli ympäristöstä on pakottanut paperi- ja kartonkikoneiden vesikiertojen sulkemiseen. Vesien kierrätys ja prosessivesien uudelleenkäyttö nostavat prosessilämpötilaa ja lisäävät koneella kiertävien kolloidisten ja liuenneiden aineiden määriä. Tutkin kiertovesien pitoisuuksia kolmessa eriasteisesti suljetussa tehtaassa, joiden päästöt olivat 0 m3, 0,5 m3 ja 4 m3 jätevettä tuotetonnia kohden, perustuen puhdistetun jäteveden uudelleen käyttöön. Nollapäästöisellä tehtaalla kiertovesiin kertyi paljon orgaanisesti sidottua hiiltä (> 10 g L-1), etenkin haihtuvina happoina (maito-, etikka-, propioni- ja voi-). Myös sulfaatteja, klorideja, natriumia ja kalsiumia kertyi paljon, > 1 g L-1 kutakin. Pääosa (>40%) kaikista bakteereista oli 16S rRNA geenisekvenssianalyysien tulosten perusteella sukua, joskin etäistä (< 96%) ainoastaan Enterococcus cecorum bakteerille. 4 m3 päästävältä tehtaalta löytyi lisäksi Bacillus thermoamylovorans ja Bacillus coagulans. Tehtaiden saostumat sisälsivät arkkeja suurina pitoisuuksina, ≥ 108 g-1, mutta tunnistukseen riittävää sekvenssisamanlaisuutta löytyi vain yhteen arkkisukuun, Methanothrix. Tutkimustulokset osoittivat että tehtaan vesikiertojen sulkeminen vähensi rajusti mikrobiston monimuotoisuutta, muttei estänyt liuenneen aineen ja kiintoaineen mineralisoitumista.

Phytochemicals in Australian papaya cultivars: the effect of maturity and cooking

To measure the effect of maturity and cooking on phytochemical composition and antioxidant capacity of fruit and leaves of four commercially available Australian papaya cultivars (RB1, RB2, RB4 and YB1).

A Colony Paper

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Towards automated content analysis of discussion transcripts: A cognitive presence case

In this paper, we present the results of an exploratory study that examined the problem of automating content analysis of student online discussion transcripts. We looked at the problem of coding discussion transcripts for the levels of cognitive presence, one of the three main constructs in the Community of Inquiry (CoI) model of distance education. Using Coh-Metrix and LIWC features, together with a set of custom features developed to capture discussion context, we developed a random forest classification system that achieved 70.3% classification accuracy and 0.63 Cohen's kappa, which is significantly higher than values reported in the previous studies. Besides improvement in classification accuracy, the developed system is also less sensitive to overfitting as it uses only 205 classification features, which is around 100 times less features than in similar systems based on bag-of-words features. We also provide an overview of the classification features most indicative of the different phases of cognitive presence that gives an additional insights into the nature of cognitive presence learning cycle. Overall, our results show great potential of the proposed approach, with an added benefit of providing further characterization of the cognitive presence coding scheme.

Activity recognition using binary tree SVM

This paper presents an effective classification method based on Support Vector Machines (SVM) in the context of activity recognition. Local features that capture both spatial and temporal information in activity videos have made significant progress recently. Efficient and effective features, feature representation and classification plays a crucial role in activity recognition. For classification, SVMs are popularly used because of their simplicity and efficiency; however the common multi-class SVM approaches applied suffer from limitations including having easily confused classes and been computationally inefficient. We propose using a binary tree SVM to address the shortcomings of multi-class SVMs in activity recognition. We proposed constructing a binary tree using Gaussian Mixture Models (GMM), where activities are repeatedly allocated to subnodes until every new created node contains only one activity. Then, for each internal node a separate SVM is learned to classify activities, which significantly reduces the training time and increases the speed of testing compared to popular the `one-against-the-rest' multi-class SVM classifier. Experiments carried out on the challenging and complex Hollywood dataset demonstrates comparable performance over the baseline bag-of-features method.

Class-specific sparse codes for representing activities

In this paper we investigate the effectiveness of class specific sparse codes in the context of discriminative action classification. The bag-of-words representation is widely used in activity recognition to encode features, and although it yields state-of-the art performance with several feature descriptors it still suffers from large quantization errors and reduces the overall performance. Recently proposed sparse representation methods have been shown to effectively represent features as a linear combination of an over complete dictionary by minimizing the reconstruction error. In contrast to most of the sparse representation methods which focus on Sparse-Reconstruction based Classification (SRC), this paper focuses on a discriminative classification using a SVM by constructing class-specific sparse codes for motion and appearance separately. Experimental results demonstrates that separate motion and appearance specific sparse coefficients provide the most effective and discriminative representation for each class compared to a single class-specific sparse coefficients.

Multiple instance dictionary learning for activity representation

This paper presents an effective feature representation method in the context of activity recognition. Efficient and effective feature representation plays a crucial role not only in activity recognition, but also in a wide range of applications such as motion analysis, tracking, 3D scene understanding etc. In the context of activity recognition, local features are increasingly popular for representing videos because of their simplicity and efficiency. While they achieve state-of-the-art performance with low computational requirements, their performance is still limited for real world applications due to a lack of contextual information and models not being tailored to specific activities. We propose a new activity representation framework to address the shortcomings of the popular, but simple bag-of-words approach. In our framework, first multiple instance SVM (mi-SVM) is used to identify positive features for each action category and the k-means algorithm is used to generate a codebook. Then locality-constrained linear coding is used to encode the features into the generated codebook, followed by spatio-temporal pyramid pooling to convey the spatio-temporal statistics. Finally, an SVM is used to classify the videos. Experiments carried out on two popular datasets with varying complexity demonstrate significant performance improvement over the base-line bag-of-feature method.

Separation β-apocarotenals and related compounds by reversed-phase paper and thin-layer chromatography

Abstract is not available.