Changes in quality and safety attributes throughout long term storage of tomato juice processed by high pressure.

Tomato is the second most widely grown vegetable crop across the globe and it is one of widely cultivated crops in Sri Lanka. However, tomato industry in Sri Lanka facing a problem of high postharvest loss (54%) during the glut coupled with heavy revenue loss to the country by importing processed products. The aim of this work is to develop shelf-stable tomato product with maximum quality characteristics using high pressure processing (HPP). Tomato juice with altered and unaltered pH was processed using HPP at 600 MPa for 1 min after blanching (90 oC/2 min). As a control tomato juice was subjected to thermal processing (TP) at 95 oC /20 min. Processed samples were stored under 20oC and 28oC for 9 month period and analysed for total viable count (TVC) and instrumental colour (L, a, b) value at 0,1,2 3, and 4 week and 2, 3, 6 and 9 months interval. The raw juice sample had initial 6.69 log10 CFU/ml and both TP and HPP caused a more than 4.69 log10 reduction in the TVC of juice and microbial numbers remained low throughout the storage period even at 3 months after storage irrespective of the storage temperature. Both TP and HPP treated samples had the redness ⤘a value’ of 14.44-17.15 just after processing and showed non-significant reduction with storage in all the treatments after 3 months. The storage study results and discussed in relation to the end goal and compared with the literature.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

Graduate Attitudes towards Vocational Training

Introduction: Vocational training (VT) is a mandatory requirement for all UK dental graduates prior to entering NHS practice. The VT period provides structured, supervised experience supported by study days and interaction with peers. It is not compulsory for Irish dental graduates working in either Ireland or the UK to undertake VT but yet a proportion voluntarily do so each year.

Objectives: This study was designed to explore the choices made by Irish dental graduates. It aimed to record any benefits of VT and its impact upon future career choices.

Method: A self-completion questionnaire was developed and piloted before being circulated electronically to recent dental graduates from University College Cork. After collecting demographic information respondents were asked to indicate if they pursued vocational training on graduation, give their perception of their post-graduation experience, describe their current work profile and detail any formal postgraduate studies.

Results: 35% of respondents opted to undertake VT and 79% did so in the UK. Those who completed VT regarded it as a very positive experience with benefits including: working in a positive learning environment, help on demand and interaction with peers. Of those who chose VT, 49% have pursued some form of further formal postgraduate study as compared to 40% of those who did not. All of the respondents who completed VT indicated they would recommend it to current Irish graduates. The majority of those who took up an associate position immediately after graduation reported that this was beneficial but up to three quarters would recommend current graduates undertake VT and 45% would now chose to do so themselves.

Conclusions: Increasing numbers of Irish graduates are moving to the UK to undertake VT and they find it a beneficial experience. In addition, those who undertook VT were more likely to undertake formal postgraduate study.

Understanding Graduate Recruitment, Development and Retention for the Enhancement of Talent Management: Sharpening “the edge” of Graduate Talent

Graduates are deemed to be a key source of talent within many organisations and thus recruiting, developing and retaining them is viewed as a logical talent management (TM) strategy. However, there has been little attention paid to university graduates as part of an organisation’s TM strategy. Such a specific focus addresses the need for further research into the segmentation of talent pools and the specific challenges different talent pools are likely to create. This research, which utilised a qualitative data collection strategy, examined the experiences and practices of six large UK organisations in relation to graduate TM. Drawing from Gallardo-Gallardo, Dries and González-Cruz’s (2013. What is the meaning of ‘talent’ in the world of work? Human Resource Management Review, 23, 290–300.) framework for the conceptualisation of talent, the findings from this research indicate and explain why graduate employers are frequently compelled to use the object approach (talent as characteristics of people) due to the unique characteristics that recent graduates possess, even though other studies have found that a subject approach (talent as people and what they do) is preferred by most employers. Ultimately, employers conceptualise graduate talent by what they describe as ‘the edge’ which needs to be ‘sharpened’ to fully realise the potential that graduates offer.

Anomaly Detection for Data with Spatial Attributes

The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan Statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statstics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood.In this chapter,we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to well-studied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds eye view of the work on anomalous region detection;we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.