Smoking in film in New Zealand: measuring risk exposure

BACKGROUND: Smoking in film is a risk factor for smoking uptake in adolescence. This study aimed to quantify exposure to smoking in film received by New Zealand audiences, and evaluate potential interventions to reduce the quantity and impact of this exposure. METHODS: The ten highest-grossing films in New Zealand for 2003 were each analysed independently by two viewers for smoking, smoking references and related imagery. Potential interventions were explored by reviewing relevant New Zealand legislation, and scientific literature. RESULTS: Seven of the ten films contained at least one tobacco reference, similar to larger film samples. The majority of the 38 tobacco references involved characters smoking, most of whom were male. Smoking was associated with positive character traits, notably rebellion (which may appeal to adolescents). There appeared to be a low threshold for including smoking in film. Legislative or censorship approaches to smoking in film are currently unlikely to succeed. Anti-smoking advertising before films has promise, but experimental research is required to demonstrate cost effectiveness. CONCLUSION: Smoking in film warrants concern from public health advocates. In New Zealand, pre-film anti-smoking advertising appears to be the most promising immediate policy response

Proceedings of Abstracts, School of Physics, Engineering and Computer Science Research Conference 2022

© 2022 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Plenary by Prof. Timothy Foat, ‘Indoor dispersion at Dstl and its recent application to COVID-19 transmission’ is © Crown copyright (2022), Dstl. This material is licensed under the terms of the Open Government Licence except where otherwise stated. To view this licence, visit http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3 or write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email: [email protected] present proceedings record the abstracts submitted and accepted for presentation at SPECS 2022, the second edition of the School of Physics, Engineering and Computer Science Research Conference that took place online, the 12th April 2022

How Solutions Chase Problems: Instrument Constituencies in the Policy Process

Public policies are composed of complex arrangements of policy goals and policy means matched through some decision-making process. Exactly how this process works and which comes first—problem or solution—is an outstanding research question in the policy sciences. This article argues the emerging concept of an “instrument constituency”—a subsystem component dedicated to the articulation and promotion of particular kinds of solutions regardless of problem context—can help policy scholars answer this critical question and better understand policymaking. At present, however, there is only limited empirical evidence of the existence, accuracy, and relevance of the instrument constituency concept. This article clarifies and refines the concept through cross-sectoral and cross-national case studies, demonstrating its utility in aiding our understanding of policy processes and their dynamics, including the issue of how prob- lems and solutions are proposed and matched in the course of policy adoption

Local inter-session variability modelling for object classification

Object classification is plagued by the issue of session variation. Session variation describes any variation that makes one instance of an object look different to another, for instance due to pose or illumination variation. Recent work in the challenging task of face verification has shown that session variability modelling provides a mechanism to overcome some of these limitations. However, for computer vision purposes, it has only been applied in the limited setting of face verification. In this paper we propose a local region based intersession variability (ISV) modelling approach, and apply it to challenging real-world data. We propose a region based session variability modelling approach so that local session variations can be modelled, termed Local ISV. We then demonstrate the efficacy of this technique on a challenging real-world fish image database which includes images taken underwater, providing significant real-world session variations. This Local ISV approach provides a relative performance improvement of, on average, 23% on the challenging MOBIO, Multi-PIE and SCface face databases. It also provides a relative performance improvement of 35% on our challenging fish image dataset

Fruit Detection in the Wild : The Impact of Varying Conditions and Cultivar

Agricultural robotics is a rapidly evolving research field due to advances in computer vision, machine learning, robotics, and increased agricultural demand. However, there is still a considerable gap between farming requirements and available technology due to the large differences between cropping environments. This creates a pressing need for models with greater generalisability. We explore the issue of generalisability by considering a fruit (sweet pepper) that is grown using different cultivar (sub-species) and in different environments (field vs glasshouse). To investigate these differences, we publicly release three novel datasets captured with different domains, cultivar, cameras, and geographic locations. We exploit these new datasets in a singular and combined (to promote generalisation) manner to evaluate sweet pepper (fruit) detection and classification in the wild. For evaluation, we employ Faster-RCNN for detection due to the ease in which it can be expanded to incorporate multitask learning by utilising the Mask-RCNN framework (instance-based segmentation). This multi-task learning technique is shown to increase the cross dataset detection F1-Score from 0.323 to 0.700, demonstrating the potential to reduce the requirements of new annotations through improved generalisation of the model. We further exploit the Faster-RCNN architecture to include both super-and sub-classes, fruit and ripeness respectively, by incorporating a parallel classification layer. For sub-class classification considering the percentage of correct detections, we are able to achieve an accuracy score of 0.900 in a cross domain evaluation. In our experiments, we find that intra-environmental inference is generally inferior, however, diversifying the data by using a combination of datasets increases performance through greater diversity in the training data. Overall, the introduction of these three novel and diverse datasets demonstrates the potential for multi-task learning to improve cross-dataset generalisability while also highlighting the importance of diverse data to adequately train and evaluate real-world systems.</p

Fruit quantity and ripeness estimation using a robotic vision system

Accurate localization of crop remains highly challenging in unstructured environments, such as farms. Many developed systems still rely on the use of hand selected features for crop identification and often neglect the estimation of crop quantity and ripeness, which is a key to assigning labor during farming processes. To alleviate these limitations, we present a robotic vision system that can accurately estimate the quantity and ripeness of sweet pepper (Capsicum annuum L), a key horticultural crop. This system consists of three parts: detection, ripeness estimation, and tracking. Efficient detection is achieved using the FasterRCNN (FRCNN) framework. Ripeness is then estimated in the same framework by learning a parallel layer which we experimentally show results in superior performance than treating ripeness as extra classes in the traditional FRCNN framework. Evaluation of these two techniques outlines the improved performance of the parallel layer, where we achieve an F 1 score of 77.3 for the parallel technique yet only 72.5 for the best scoring (red) of the multiclass implementation. To track the crop, we present a vision only tracking via detection approach, which uses the FRCNN with parallel layers as input. Being a vision only solution, this approach is cheap to implement as it only requires a camera and in experiments, using field data, we show that our proposed system can accurately estimate the number of sweet pepper present, to within 4.1% of the visual ground truth

New Insight into the Role of the PhaP Phasin of Ralstonia eutropha in Promoting Synthesis of Polyhydroxybutyrate

Phasins are proteins that are proposed to play important roles in polyhydroxyalkanoate synthesis and granule formation. Here the phasin PhaP of Ralstonia eutropha has been analyzed with regard to its role in the synthesis of polyhydroxybutyrate (PHB). Purified recombinant PhaP, antibodies against PhaP, and an R. eutropha phaP deletion strain have been generated for this analysis. Studies with the phaP deletion strain show that PhaP must accumulate to high levels in order to play its normal role in PHB synthesis and that the accumulation of PhaP to low levels is functionally equivalent to the absence of PhaP. PhaP positively affects PHB synthesis under growth conditions which promote production of PHB to low, intermediate, or high levels. The levels of PhaP generally parallel levels of PHB in cells. The results are consistent with models whereby PhaP promotes PHB synthesis by regulating the surface/volume ratio of PHB granules or by interacting with polyhydroxyalkanoate synthase and indicate that PhaP plays an important role in PHB synthesis from the early stages in PHB production and across a range of growth conditions