The Public Health Responsibility Deal (RD) in England is a public-private partnership involving voluntary pledges between government, industry and other organisations in the areas of food, alcohol, physical activity, and health at work, and is designed to improve public health. The RD is currently being evaluated in terms of its process and likely impact on the health of the English population. This paper analyses the RD food pledges in terms of (i) the evidence of the effectiveness of the specific interventions in the pledges and (ii) the likelihood that the pledges have brought about actions among organisations that would not otherwise have taken place. We systematically reviewed evidence of the effectiveness of the interventions proposed in six food pledges of the RD, namely nutrition labelling (including out-of-home calorie labelling and front-of-pack nutrition labelling), salt reduction, calorie reduction, fruit and vegetable consumption, and reduction of saturated fats. We then analysed publically available data on organisations' plans and progress towards achieving the pledges, and assessed the extent to which activities among organisations could be brought about by the RD. Based on seventeen evidence reviews, some of the RD food interventions could be effective, if fully implemented. However the most effective strategies to improve diet, such as food pricing strategies, restrictions on marketing, and reducing sugar intake, are not reflected in the RD food pledges. Moreover it was difficult to establish the quality and extent of implementation of RD pledge interventions due to the paucity and heterogeneity of organisations' progress reports. Finally, most interventions reported by organisations seemed either clearly (37%) or possibly (37%) already underway, regardless of the RD. Irrespective of the nature of a public health policy to improve nutritional health, pledges or proposed actions need to be evidence-based, well-defined, and measurable, pushing actors to go beyond 'business as usual' and setting out clear penalties for not demonstrating progress.

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Around 40% of the current world conventional oil production comes from carbonate reservoirs, dominantly mature and declining giant oilfields. Tertiary oil production methods as part of an Enhanced Oil Recovery (EOR) scheme are inevitable after primary and secondary oil production. The goal of surfactant flooding is to reduce the mobility ratio by lowering the interfacial tension between oil and water and mobilizing the residual oil. This paper highlights adsorption kinetics and equilibrium of Glycyrrhiza Glabra, a novel surfactant, in aqueous solutions for EOR and reservoir stimulation purposes. A conductivity technique was used to assess adsorption of the surfactant in the aqueous phase. Batch experimental runs were also performed at various temperatures to understand the effect of adsorbate dose on the sorption efficiency. The adsorption kinetics was experimentally investigated at room temperature (27 °C) by monitoring the uptake of the Glycyrrhiza Glabra as a function of time. The adsorption data were examined using different adsorption equilibrium and kinetic models. The Langmuir isotherm suits the equilibrium data very well. A pseudo-second order kinetic model can satisfactorily estimate the kinetics of the surfactant adsorption on carbonates. Results obtained from this research can help in selecting appropriate surfactants for design of EOR schemes and reservoir stimulation plans for carbonate reservoirs.

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Condensate-to-gas ratio (CGR) plays an important role in sales potential assessment of both gas and liquid, design of required surface processing facilities, reservoir characterization, and modeling of gas condensate reservoirs. Field work and laboratory determination of CGR is both time consuming and resource intensive. Developing a rapid and inexpensive technique to accurately estimate CGR is of great interest. An intelligent model is proposed in this paper based on a feed-forward artificial neural network (ANN) optimized by particle swarm optimization (PSO) technique. The PSO-ANN model was evaluated using experimental data and some PVT data available in the literature. The model predictions were compared with field data, experimental data, and the CGR obtained from an empirical correlation. A good agreement was observed between the predicted CGR values and the experimental and field data. Results of this study indicate that mixture molecular weight among input parameters selected for PSO-ANN has the greatest impact on CGR value, and the PSO-ANN is superior over conventional neural networks and empirical correlations. The developed model has the ability to predict the CGR with high precision in a wide range of thermodynamic conditions. The proposed model can serve as a reliable tool for quick and inexpensive but effective assessment of CGR in the absence of adequate experimental or field data.

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With the emergence of industry 4.0, the oil and gas (O&G) industry is now considering a range of digital technologies to enhance productivity, efficiency, and safety of their operations while minimizing capital and operating costs, health and environment risks, and variability in the O&G project life cycles. The deployment of emerging technologies allows O&G companies to construct digital twins (DT) of their assets. Considering DT adoption, the O&G industry is still at an early stage with implementations limited to isolated and selective applications instead of industry-wide implementation, limiting the benefits from DT implementation. To gain the full potential of DT and related technological adoption, a comprehensive understanding of DT technology, the current status of O&G-related DT research activities, and the opportunities and challenges associated with the deployment of DT in the O&G industry are of paramount importance. In order to develop this understanding, this paper presents a literature review of DT within the context of the O&G industry. The paper follows a systematic approach to select articles for the literature review. First, a keywords-based publication search was performed on the scientific databases such as Elsevier, IEEE Xplore, OnePetro, Scopus, and Springer. The filtered articles were then analyzed using online text analytic software (Voyant Tools) followed by a manual review of the abstract, introduction and conclusion sections to select the most relevant articles for our study. These articles and the industrial publications cited by them were thoroughly reviewed to present a comprehensive overview of DT technology and to identify current research status, opportunities and challenges of DT deployment in the O&G industry. From this literature review, it was found that asset integrity monitoring, project planning, and life cycle management are the key application areas of digital twin in the O&G industry while cyber security, lack of standardization, and uncertainty in scope and focus are the key challenges of DT deployment in the O&G industry. When considering the geographical distribution for the DT related research in the O&G industry, the United States (US) is the leading country, followed by Norway,

The RD is unlikely to have contributed significantly to reducing alcohol consumption, as most alcohol pledge signatories appear to have committed to actions that they would have undertaken anyway, regardless of the RD. Irrespective of this, there is considerable scope to improve the clarity of progress reports and reduce the variability of metrics provided by RD pledge signatories.