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Locally-established marine protected areas (MPAs) have been proven to achieve local-scale fisheries and conservation objectives. However, since many of these MPAs were not designed to form ecologically-connected networks, their contributions to broader-scale goals such as complementarity and connectivity can be limited. In contrast, integrated networks of MPAs designed with systematic conservation planning are assumed to be more effective—ecologically, socially, and economically—than collections of locally-established MPAs. There is, however, little empirical evidence that clearly demonstrates the supposed advantages of systematic MPA networks. A key reason is the poor record of implementation of systematic plans attributable to lack of local buy-in. An intermediate scenario for the expansion of MPAs is scaling up of local decisions, whereby locally-driven MPA initiatives are coordinated through collaborative partnerships among local governments and their communities. Coordination has the potential to extend the benefits of individual MPAs and perhaps to approach the potential benefits offered by systematic MPA networks. We evaluated the benefits of scaling up local MPAs to form networks by simulating seven expansion scenarios for MPAs in the Verde Island Passage, central Philippines. The scenarios were: uncoordinated community-based establishment of MPAs; two scenarios reflecting different levels of coordinated MPA expansion through collaborative partnerships; and four scenarios guided by systematic conservation planning with different contexts for governance. For each scenario, we measured benefits through time in terms of achievement of objectives for representation of marine habitats. We found that: in any governance context, systematic networks were more efficient than non-systematic ones; systematic networks were more efficient in broader governance contexts; and, contrary to expectations but with caveats, the uncoordinated scenario was slightly more efficient than the coordinated scenarios. Overall, however, coordinated MPA networks have the potential to be more efficient than the uncoordinated ones, especially when coordinated planning uses systematic methods. shp - verde_puregion_governanceShapefile presenting the governance areas for each planning unit used in the model simulations and results described in the paper. The file can be used to present the data included in the paper to create maps.shp - verde_puregion_habitatsShapefile describing the area of habitats present within each planning unit. This file was used in the data analysis for the models described in the paper. This area of habitat described in the file are in square metres.DataZip folder containing processed data, model results and source code of the models described in the paper. The excel sheets can be used as attributes in the SHP files provided to create maps. The text file included in the folder is the the source code for the models and can be used and edited in MatLab.

Appendix 14 provides a brief description and other data about 708 of the ASAGE sites documented in the ten Study Areas. It does not provide specific latitude / longitude coordinates, so as not to facilitate looting of these sites, although the spatial data are available to bona fide researchers.

The global internet antiquities market exists in a complex cultural heritage framework, comprised of international law and domestic legislation. In this paper, the questions I seek to answer are the following: how do internet antiquities dealers engage with their legal obligations, and how is this engagement translated to the ethics of their businesses? This paper presents a comparative examination of 45 antiquities dealers split across three categories—internet dealers, eBay dealers and social media dealers—revealing three key insights about the internet antiquities market: firstly, that the level of legal literacy in the market is depicted as being quite poor; secondly, that the performance of legal awareness does not always correspond with ethical dealer practices; and finally, some dealers utilise a suite of justifications for their behaviours, practices and values (known as neutralisation techniques) to undermine their legal obligations. Such results confirm existing claims of the failure of self-regulation in the internet antiquities market and reveal a demand for educational campaigns targeted at raising consumer awareness by challenging misleading market narratives and highlighting the ethical and legal issues involved with the trade of cultural heritage.

The beautiful focus of this film is a quilt made in c.1890 in Swaledale and its journey through the generations of a family and on to the Quilters’ Guild collection in the early twenty-first century. It conveys how textiles hold powerful emotions for their makers and the relatives who have inherited them, and communicates the pleasures of hand quilting in the past and today. It also shows how inherited objects offer insights into our history, reflecting on the way inherited quilts provide insights into changing regional patterns of women’s work and lives. With Deborah McGuire and Joanne Begiato.

Gobbe-Sabz (Green Cupola) is one of the most important and prominent build­ings in Kerman, Iran, dating back to the Qara-khitai dynasty (1222–1306 AD). It was a huge complex of buildings that was almost totally destroyed in an earthquake. Every building during its lifecycle from creation to deterioration acquires values that are important in making any decision about restoration. Brandi’s theory of restoration considers ruins as a work of art, claiming that they are the result of a unique, creative process. He asserts it is necessary to have a creative process for restoration to reclaim its significance in the human consciousness. Although there remains only a small part of Gobbe-Sabz, many remarkable heritage values have been added over time that could be helpful in the creative process for its conservation. The present situation of Gobbe-Sabz precludes rebuilding it as active and complete architecture, determining the process of rehabilitating its original condition. Moreover, some heritage values and meanings have been added to it across the years which are a collection of ideas and concepts that can be useful in deciding the conservation process. In this regard, this research proposes conservation strategies and a conceptual framework for the reha­bilitation of the historic ruins based on their current situation. Conservation Science in Cultural Heritage, Vol 18 (2018)

AbstractVast amounts of clinically relevant text-based variables lie undiscovered and unexploited in electronic medical records (EMR). To exploit this untapped resource, and thus facilitate the discovery of informative covariates from unstructured clinical narratives, we have built a novel computational pipeline termed Text-based Exploratory Pattern Analyser for Prognosticator and Associator discovery (TEPAPA). This pipeline combines semantic-free natural language processing (NLP), regular expression induction, and statistical association testing to identify conserved text patterns associated with outcome variables of clinical interest. When we applied TEPAPA to a cohort of head and neck squamous cell carcinoma patients, plausible concepts known to be correlated with human papilloma virus (HPV) status were identified from the EMR text, including site of primary disease, tumour stage, pathologic characteristics, and treatment modalities. Similarly, correlates of other variables (including gender, nodal status, recurrent disease, smoking and alcohol status) were also reliably recovered. Using highly-associated patterns as covariates, a patient’s HPV status was classifiable using a bootstrap analysis with a mean area under the ROC curve of 0.861, suggesting its predictive utility in supporting EMR-based phenotyping tasks. These data support using this integrative approach to efficiently identify disease-associated factors from unstructured EMR narratives, and thus to efficiently generate testable hypotheses.

Big data analytics can be used by smart cities to improve their citizens’ liveability, health, and wellbeing. Social surveys and also social media can be employed to engage with their communities, and these can require sophisticated analysis techniques. This research was focused on carrying out a sentiment analysis from social surveys. Data analysis techniques using RStudio and Python were applied to several open-source datasets, which included the 2018 Social Indicators Survey dataset published by the City of Melbourne (CoM) and the Casey Next short survey 2016 dataset published by the City of Casey (CoC). The qualitative nature of the CoC dataset responses could produce rich insights using sentiment analysis, unlike the quantitative CoM dataset. RStudio analysis created word cloud visualizations and bar charts for sentiment values. These were then used to inform social media analysis via the Twitter application programming interface. The R codes were all integrated within a Shiny application to create a set of user-friendly interactive web apps that generate sentiment analysis both from the historic survey data and more immediately from the Twitter feeds. The web apps were embedded within a website that provides a customisable solution to estimate sentiment for key issues. Global sentiment was also compared between the social media approach and the 2016 survey dataset analysis and showed some correlation, although there are caveats on the use of social media for sentiment analysis. Further refinement of the methodology is required to improve the social media app and to calibrate it against analysis of recent survey data.

Due to the digitization of fine art collections, pictures of fine art objects stored at museums and art galleries became widely available to the public. It created a demand for efficient software tools that would allow rapid retrieval and semantic categorization of art. This paper introduces a new, two-stage image classification approach aiming to improve the style classification accuracy. At the first stage, the proposed approach divides the input image into five patches and applies a deep convolutional neural network (CNN) to train and classify each patch individually. At the second stage, the outcomes from the individual five patches are fused in the decision-making module, which applies a shallow neural network trained on the probability vectors given by the first-stage classifier. While the first stage categorizes the input image based on the individual patches, the second stage infers the final decision label categorizing the artistic style of the analyzed input image. The key factor in improving the accuracy compared to the baseline techniques is the fact that the second stage is trained independently on the first stage using probability vectors instead of images. This way, the second stage is effectively trained to compensate for the potential mistakes made during the first stage. The proposed method was tested using six different pre-trained CNNs (AlexNet, VGG-16, VGG-19, GoogLeNet, ResNet-50, and Inceptionv3) as the first-stage classifiers, and a shallow neural network as a second-stage classifier. The experiments conducted using three standard art classification datasets indicated that the proposed method presents a significant improvement over the existing baseline techniques.