Chironomid-inferred July-air temperature of Lago Piccolo di Avigliana

Chironomid headcapsules were used to reconstruct late glacial and early-Holocene summer temperatures at Lago Piccolo di Avigliana (LPA). Two training sets (northern Sweden, North America) were used to infer temperatures. The reconstructed patterns of temperature change agreed well with the GRIP and NGRIP d18O records. Inferred temperatures were high during the Bølling (ca 19 °C), slowly decreased to ca 17.5 °C during the Allerød, reached lowest temperatures (ca 16 °C) during the Younger Dryas, and increased to ca. 18.5 °C during the Preboreal. The amplitudes of change at climate transitions (i.e. Oldest Dryas/Bølling: 3 °C, Allerød/Younger Dryas: 1.5 °C, and Younger Dryas/Preboreal: 2.5 °C) were smaller than in the northern Alps but similar to those recorded at another site in northeastern Italy. Our results suggest that (1) Allerød temperatures were higher in the southern Alps and (2) higher during the Preboreal (1 °C) than during the Allerød. These differences might provide an explanation for the different responses of terrestrial-vegetation to late glacial and early-Holocene climatic changes in the two regions. Other sites on both sides of the Alps should be studied to confirm these two hypotheses.

Statistical detection of online drifting twitter spam

Spam has become a critical problem in online social networks. This paper focuses on Twitter spam detection. Recent research works focus on applying machine learning techniques for Twitter spam detection, which make use of the statistical features of tweets. We observe existing machine learning based detection methods suffer from the problem of Twitter spam drift, i.e., the statistical properties of spam tweets vary over time. To avoid this problem, an effective solution is to train one twitter spam classifier every day. However, it faces a challenge of the small number of imbalanced training data because labelling spam samples is time-consuming. This paper proposes a new method to address this challenge. The new method employs two new techniques, fuzzy-based redistribution and asymmetric sampling. We develop a fuzzy-based information decomposition technique to re-distribute the spam class and generate more spam samples. Moreover, an asymmetric sampling technique is proposed to re-balance the sizes of spam samples and non-spam samples in the training data. Finally, we apply the ensemble technique to combine the spam classifiers over two different training sets. A number of experiments are performed on a real-world 10-day ground-truth dataset to evaluate the new method. Experiments results show that the new method can significantly improve the detection performance for drifting Twitter spam.

Inflammation, hepatic enzymes and resistance training in individuals with metabolic risk factors

AIMS: Increases in inflammatory markers, hepatic enzymes and physical inactivity are associated with the development of the metabolic syndrome (MetS). We examined whether inflammatory markers and hepatic enzymes are correlated with traditional risk factors for MetS and studied the effects of resistance training (RT) on these emerging risk factors in individuals with a high number of metabolic risk factors (HiMF, 2.9 +/- 0.8) and those with a low number of metabolic risk factors (LoMF, 0.5 +/- 0.5). METHODS: Twenty-eight men and 27 women aged 50.8 +/- 6.5 years (mean +/- sd) participated in the study. Participants were randomized to four groups, HiMF training (HiMFT), HiMF control (HiMFC), LoMF training (LoMFT) and LoMF control (LoMFC). Before and after 10 weeks of RT [3 days/week, seven exercises, three sets with intensity gradually increased from 40-50% of one repetition maximum (1RM) to 75-85% of 1RM], blood samples were obtained for the measurement of pro-inflammatory cytokines, C-reactive protein (CRP), gamma-glutamyltransferase (GGT) and alanine aminotransferase (ALT). RESULTS: At baseline, HiMF had higher interleukin-6 (33.9%), CRP (57.1%), GGT (45.2%) and ALT (40.6%) levels, compared with LoMF (all P < 0.05). CRP, GGT and ALT correlated with the number of risk factors (r = 0.48, 0.51 and 0.57, respectively, all P < 0.01) and with other anthropometric and clinical measures (r range from 0.26 to 0.60, P < 0.05). RT did not significantly alter inflammatory markers or hepatic enzymes (all P > 0.05). CONCLUSIONS: HiMF was associated with increased inflammatory markers and hepatic enzyme concentrations. RT did not reduce inflammatory markers and hepatic enzymes in individuals with HiMF.

ICT in vocational education and training: a view of information and communication technology in vocational education in New Zealand

This report looks at opportunities in relation to what is either already available or starting to take off in Information and Communication Technology (ICT). ICT focuses on the entire system of information, communication, processes and knowledge within an organisation. It focuses on how technology can be implemented to serve the information and communication needs of people and organisations. An ICT system involves a combination of work practices, information, people and a range of technologies and applications organised to make the business or organisation fully functional and efficient, and to accomplish goals in an organisation. Our focus is on vocational, workbased education in New Zealand. It is not about eLearning, although we briefly touch on the topic. We provide a background on vocational education in New Zealand, cover what we consider to be key trends impacting workbased, vocational education and training (VET), and offer practical suggestions for leveraging better value from ICT initiatives across the main activities of an Industry Training Organisation (ITO). We use a learning value chain approach to demonstrate the main functions ITOs engage in and also use this approach as the basis for developing and prioritising an ICT strategy. Much of what we consider in this report is applicable to the wider tertiary education sector as it relates to life-long learning. We consider ICT as an enabler that: a) connects education businesses (all types including tertiary education institutions) to learners, their career decisions and their learning, and as well, b) enables those same businesses to run more efficiently. We suggest that these two sets of activities are considered as interconnected parts of the same education or training business ICT strategy.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Emergency pre-hospital disaster response training. What's the state of play?

Study/Objective This study examines the current state of disaster response education for Australian paramedics from a national and international perspective and identifies both potential gaps in content and challenges to the sustainability of knowledge acquired through occasional training. Background As demands for domestic and international disaster response increase, experience in the field has begun to challenge traditional assumptions that response to mass casualty events requires little specialist training. The need for a “streamlined process of safe medical team deployment into disaster regions”1 is generally accepted and, in Australia, the emergence of national humanitarian aid training has begun to respond to this gap. However, calls for a national framework for disaster health education2 haven’t received much traction. Methods A critical analysis of the peer reviewed and grey literature on the core components/competencies and training methods required to prepare Australian paramedics to contribute to effective health disaster response has been conducted. Research from the past 10 years has been examined along with federal and state policy with regard to paramedic disaster education. Results The literature shows that education and training for disaster response is variable and that an evidence based study specifically designed to outline sets of core competencies for Australian health care professionals has never been undertaken. While such competencies in disaster response have been developed for the American paradigm it is suggested that disaster response within the Australian context is somewhat different to that of the US, and therefore a gap in the current knowledge base exists. Conclusion Further research is needed to develop core competencies specific to Australian paramedics in order to standardise teaching in the area of health disaster management. Until this occurs the task of evaluating or creating disaster curricula that adequately prepares and maintains paramedics for an effective all hazards disaster response is seen as largely unattainable.

Trainee-therapists are not all equal: Examination of therapeutic efficiency, effectiveness and early client dropout after 12 months of clinical training

Objective Contemporary research demonstrates the feasibility of assessing therapeutic performance of trainee-therapists through the use of objective measures of client treatment outcome. Further, significant variation between individual therapists based on their client treatment outcomes has been demonstrated. This study sets out to determine whether a reliable composite measure of therapeutic efficiency, effectiveness and early dropout can be developed and used to objectively compare trainee-therapists against each other. Design and methods Treatment outcomes of 611 clients receiving treatment from 58 trainee-therapists enrolled in a professional training programme were tracked with the OQ-45.2 over a 6-year period to assess therapeutic efficiency, therapeutic effectiveness and early client dropout. Results Significant variation between trainee-therapists was observed for each index. Findings of a moderately strong correlation between therapeutic efficiency and effectiveness enabled the ranking of trainee-therapists based upon a composite measure of these indexes. A non-significant correlation was found between early client dropout and measures of therapeutic effectiveness and efficiency. Conclusions The findings stress the importance of utilizing objective measures to track the treatment outcomes. Despite all trainee-therapists being enrolled in the same training programme, significant variation between trainee-therapists' therapeutic efficiency and effectiveness was found to exist. Practitioner points Developing of potential benchmarking tools that enable trainee-therapists, supervisors and educational institutions to quickly assess therapeutic performance can become part of a holistic assessment of a trainee-therapist's clinical development. Despite an inherent optimistic belief that therapists do not cause harm, there appears to be a small and significant proportion of trainee-therapists who consistently evidence little therapeutic change. Considerable variability in trainee-therapists' therapeutic efficiency and effectiveness can exist in the one training programme. Early client dropout may not be associated with therapists' therapeutic effectiveness and efficiency.

Cluster based training for scaling non-linear support vector machines

Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space. The data size dependent training time complexity of SVMs usually prohibits its use in applications involving more than a few thousands of data points. In this paper we propose a novel kernel based incremental data clustering approach and its use for scaling Non-linear Support Vector Machines to handle large data sets. The clustering method introduced can find cluster abstractions of the training data in a kernel induced feature space. These cluster abstractions are then used for selective sampling based training of Support Vector Machines to reduce the training time without compromising the generalization performance. Experiments done with real world datasets show that this approach gives good generalization performance at reasonable computational expense.

Impact of Brunswick river mouth training walls on adjacent beaches, Brunswick heads, New South Wales, Australia

River training walls have been built at scores of locations along the NSW coast and their impacts on shoreline change are still not fully understood. In this study, the Brunswick River entrance and adjacent beaches are selected for examination of the impact of the construction of major training walls. Thirteen sets of aerial photographs taken between 1947 and 1994 are used in a CIS approach to accurately determine tire shoreline Position, beach contours and sand volumes, and their changes in both time and space, and then to assess the contribution of both tire structures and natural hydrodynamic conditions to large scale (years-decades and kilometres) beach changes. The impact of the training walls can be divided into four stages: natural conditions prior to their construction (pre 1959), major downdrift erosion and updrift accretion during and. following the construction of the walls in 1959 similar to 1962 and 1966. diminishing impact of the walls between 1966 and 1987, and finally no apparent impact between 1987 similar to 1994. The impact extends horizontally about 8 km updrift and 17 km downdrift, and temporally up to 25 years..

Lessons learned in an information skills training programme for a mental health Trust

Parker, R. & Urquhart, C. (2007). Lessons learned in an information skills training programme for a mental health Trust. Health Information and Libraries Journal, 24(1), 58-61.

Training teachers for further and technical education 1950-1982: staff perceptions of changing demands and policies

This paper aims to create a picture of aspects of the working lives of some trainers of technical and further education teachers in a specialist teacher training college in Bolton, Lancashire, from the 1950s to the 1980's. There is little reference to technical teacher training in the literature on teacher training in the second half of the twentieth century. With this gap in mind, this paper sets out to record some memories and impressions of staff involved during these years. Using data from a series of semi-structured interviews, the discussion centres upon their perceptions of their work: of their students, the working environment, the curriculum and their relationships with the technical colleges for whom they were training teachers. The paper has three sections. It begins with a brief discussion of the issues arising from the choice of research methods. The second section contextualises the study and traces the history of Bolton Technical Teachers' Training College from its establishment through to its merger with the Institute of Technology in 1982. This is followed by the presentation and discussion of the interview data.

Support vector machine classification for large data sets via minimum enclosing ball clustering

Support vector machine (SVM) is a powerful technique for data classification. Despite of its good theoretic foundations and high classification accuracy, normal SVM is not suitable for classification of large data sets, because the training complexity of SVM is highly dependent on the size of data set. This paper presents a novel SVM classification approach for large data sets by using minimum enclosing ball clustering. After the training data are partitioned by the proposed clustering method, the centers of the clusters are used for the first time SVM classification. Then we use the clusters whose centers are support vectors or those clusters which have different classes to perform the second time SVM classification. In this stage most data are removed. Several experimental results show that the approach proposed in this paper has good classification accuracy compared with classic SVM while the training is significantly faster than several other SVM classifiers.

Confounds in pictorial sets: The role of complexity and familiarity in basic-level picture processing

Complexity is conventionally defined as the level of detail or intricacy contained within a picture. The study of complexity has received relatively little attention-in part, because of the absence of an acceptable metric. Traditionally, normative ratings of complexity have been based on human judgments. However, this study demonstrates that published norms for visual complexity are biased. Familiarity and learning influence the subjective complexity scores for nonsense shapes, with a significant training x familiarity interaction [F(1,52) = 17.53, p <.05]. Several image-processing techniques were explored as alternative measures of picture and image complexity. A perimeter detection measure correlates strongly with human judgments of the complexity of line drawings of real-world objects and nonsense shapes and captures some of the processes important in judgments of subjective complexity, while removing the bias due to familiarity effects.

Superimposed vibration confers no additional benefit compared with resistance training alone

Eighteen participants (22-43 years) were randomly allocated to one of two groups: resistance training combined with vibration (VIB; five males, four females) or resistance training alone (CON; five males, four females). Each participant trained three sessions per week (three sets of 10 seated calf raises against a load, which was increased progressively from 75% of one repetition maximum (1RM) to 90% 1RM for 4 weeks. For the VIB group, a vibratory stimulus (30 Hz, 2.5 mm amplitude) was applied to the soles of the feet by a vibration platform. The two groups did not differ significantly with respect to the total amount of work performed during training. Both groups showed a significant increase in maximum voluntary contraction and 1RM (P