Design for the Right to the Smart City in More-than-Human Worlds

Environmental concerns have driven an interest in sustainable smart cities, through the monitoring and optimisation of networked infrastructure processes. At the same time, there are concerns about who these interventions and services are for, and who benefits. HCI researchers and designers interested in civic life have started to call for the democratisation of urban space through resistance and political action to challenge state and corporate claims. This paper aims to add to the growing body of critical and civic led smart city literature in HCI by leveraging concepts from the environmental humanities about more than human worlds, as a way to shift understandings within HCI of smart cities away from the exceptional and human centered, towards a more inclusive understanding that incorporates and designs for other others and other species. We illustrate through a case study that involved codesigning Internet of Things with urban agricultural communities, possibilities for creating more environmentally and socially just smart cities

Spatial targeting of infectious disease control: identifying multiple, unknown sources

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Realizing live sequence charts in SystemVerilog.

The design of an embedded control system starts with an investigation of properties and behaviors of the process evolving within its environment, and an analysis of the requirement for its safety performance. In early stages, system requirements are often specified as scenarios of behavior using sequence charts for different use cases. This specification must be precise, intuitive and expressive enough to capture different aspects of embedded control systems. As a rather rich and useful extension to the classical message sequence charts, live sequence charts (LSC), which provide a rich collection of constructs for specifying both possible and mandatory behaviors, are very suitable for designing an embedded control system. However, it is not a trivial task to realize a high-level design model in executable program codes effectively and correctly. This paper tackles the challenging task by providing a mapping algorithm to automatically synthesize SystemVerilog programs from given LSC specifications

Regionally-structured explanations behind area-level populism: An update to recent ecological analyses

Heavy geographic patterning to the 2016 Brexit vote in UK and Trump vote in US has resulted in numerous ecological analyses of variations in area-level voting behaviours. We extend this work by employing modelling approaches that permit regionally-specific associations between outcome and explanatory variables. We do so by generating a large number of regional models using penalised regression for variable selection and coefficient evaluation. The results reinforce those already published in that we find associations in support of a ‘left-behind’ reading. Multivariate models are dominated by a single variable—levels of degree-education. Net of this effect, ‘secondary’ variables help explain the vote, but do so differently for different regions. For Brexit, variables relating to material disadvantage, and to a lesser extent structural-economic circumstances, are more important for regions with a strong industrial history than for regions that do not share such a history. For Trump, increased material disadvantage reduces the vote both in global models and models built mostly for Southern states, thereby undermining the ‘left-behind’ reading. The reverse is nevertheless true for many other states, particularly those in New England and the Mid-Atlantic, where comparatively high levels of disadvantage assist the Trump vote and where model outputs are more consistent with the UK, especially so for regions with closer economic histories. This pattern of associations is exposed via our regional modelling approach, application of penalised regression and use of carefully designed visualization to reason over 100+ model outputs located within their spatial context. Our analysis, documented in an accompanying github repository, is in response to recent calls in empirical Social and Political Science for fuller exploration of subnational contexts that are often controlled out of analyses, for use of modelling techniques more robust to replication and for greater transparency in research design and methodology

Urban planning, public participation and digital technology: App development as a method of generating citizen involvement in local planning processes

There has been a recent shift in England towards empowering citizens to shape their neighbourhoods. However, current methods of participation are unsuitable or unwieldy for many people. In this paper, we report on ChangeExplorer, a smart watch application to support citizen feedback, to investigate the extent to which digital wearables can address barriers to participation in planning. The research contributes to both technology-mediated citizen involvement and urban planning participation methods. The app leverages in-situ, quick interactions encouraging citizens to reflect and comment on their environment. Taking a case study approach, the paper discusses the design and deployment of the app in a local planning authority through interviews with 19 citizens and three professional planners. The paper discusses the potential of the ChangeExplorer app to address more conceptual issues, and concludes by assessing the degree to which the technology raises awareness of urban change and whether it could serve as a gateway to more meaningful participatory methods

In silico prediction of acute chemical toxicity of biocides in marine crustaceans using machine learning

Biocides are a heterogeneous group of chemical substances intended to control the growth or kill undesired organisms. Due to their extensive use, they enter marine ecosystems via non-point sources and may pose a threat to ecologically important non-target organisms. Consequently, industries and regulatory agencies have recognized the ecotoxicological hazard potential of biocides. However, the prediction of biocide chemical toxicity on marine crustaceans has not been previously evaluated. This study aims to provide in silico models capable of classifying structurally diverse biocidal chemicals into different toxicity categories and predict acute chemical toxicity (LC50) in marine crustaceans using a set of calculated 2D molecular descriptors. The models were built following the guidelines recommended by the OECD (Organization for Economic Cooperation and Development) and validated through stringent processes (internal and external validation). Six machine learning (ML) models were built and compared (linear regression: LR; support vector machine: SVM; random forest: RF; feed-forward backpropagation-based artificial neural network: ANN; decision trees: DT and naïve Bayes: NB) for regression and classification analysis to predict toxicities. All the models displayed encouraging results with high generalisability: the feed-forward-based backpropagation method showed the best results with determination coefficient R2 values of 0.82 and 0.94, respectively, for training set (TS) and validation set (VS). For classification-based modelling, the DT model performed the best with an accuracy (ACC) of 100 % and an area under curve (AUC) value of 1 for both TS and VS. These models showed the potential to replace animal testing for the chemical hazard assessment of untested biocides if they fall within the applicability domain of the proposed models. In general, the models are highly interpretable and robust, with good predictive performance. The models also displayed a trend indicating that toxicity is largely influenced by factors such as lipophilicity, branching, non-polar bonding and saturation of molecules

Determining riverine sediment storage mechanisms of biologically reactive phosphorus in situ using DGT.

Phosphorus (P) is the main reason many surficial water bodies in the UK are currently failing to meet the chemical standards set by the Water Framework Directive (WFD). This work focuses on the role of sediments in the upper reaches of the River Taw in the South West of the UK. Point and diffuse sources of P have been identified as well as a number of mitigation measures applied or planned to address the issues. However, it is unknown what effect these sources have had on the river's sediments and how they will react to diminishing inputs of P into the water column in the future. The diffusive gradient in thin-films (DGT) method is utilised in situ to quantify and identify labile, potentially bioavailable P fluxes and potential storage mechanisms at sites of known diffuse and point P inputs. In the vicinity of a heavily contaminated point source, data present here shows that sediments are still acting as a sink. The proposed mechanism for this is the formation of a 'calcium cap' which provides a geochemical barrier between the sediment and overlying water to prevent loss of labile P. The strong correlation between calcium and total P under most circumstances supports this hypothesis. This conclusion provides some confidence that even though P concentrations in some sediments are significantly elevated, mobility between the sediment and overlying water is restricted. In the context of routine monitoring against WFD targets, the molybdenum blue method generally employed to determine soluble reactive phosphorus was shown to not be equivalent to the DGT labile P pool, especially at pristine or moderately point/diffuse influenced sites. This is likely due to desorption of weakly bound P from colloids, which is unavailable to DGT devices. These results have the potential to be scaled up to the full catchment or other catchments which exhibit similar physical and chemical sediment composition and provide a stronger foundation for management and target setting than current monitoring approaches

Mapping the transcriptome: Realizing the full potential of spatial data analysis

RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics-how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets

Assessing Options for Remediation of Contaminated Mine Site Drainage Entering the River Teign, Southwest England

The river Teign in Devon has come under scrutiny for failing to meet environmental quality standards for ecotoxic metals due to past mining operations. A disused mine known as Bridford Barytes mine, has been found to contribute a significant source of Zn, Cd and Pb to the river. Recently, studies have been focused on the remediation of such mine sites using low-cost treatment methods to help reduce metal loads to the river downstream. This paper explores the metal removal efficiency of red mud, a waste product from the aluminium industry, which has proven to be an attractive low-cost treatment method for adsorbing toxic metals. Adsorption kinetics and capacity experiments reveal metal removal efficiencies of up to 70% within the first 2 h when red mud is applied in pelletized form. Further, it highlights the potential of biochar, another effective adsorbent observed to remove &gt;90% Zn using agricultural feedstock. Compliance of the Teign has been investigated by analysing dissolved metal concentrations and bioavailable fractions of Zn to assess if levels are of environmental concern. By applying a real-world application model, this study reveals that compressed pellets and agricultural biochar offer an effective, low-cost option to reducing metal concentrations and thus improving the quality of the river Teign.</jats:p

The significance of hazardous chemicals in wastewater treatment works effluents

This is the post-print version of the final paper published in Science of The Total Environment. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2012 Elsevier B.V.The advent of increasingly stringent and wider ranging European Union legislation relating to water and the environment has required regulators to assess compliance risk and to respond by formulating appropriate pollution control measures. To support this process the UK Water Industry has completed a national Chemicals Investigation Programme (CIP), to monitor over 160 wastewater treatment works (WwTWs) for 70 determinands. Final effluent concentrations of zinc, polynuclear aromatic hydrocarbons (fluoranthene, benzo(a)pyrene, benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(g,h,i)perylene and indeno(1,2,3-cd)pyrene), “penta” congeners (BDEs) 47 and 99, tributyltin, triclosan, erythromycin, oxytetracycline, ibuprofen, propranolol, fluoxetine, diclofenac, 17β-estradiol and 17α-ethinyl estradiol exceeded existing or proposed Environmental Quality Standards (EQSs) in over 50% of WwTWs. Dilution by receiving water might ensure compliance with EQSs for these chemicals, apart from the BDEs. However, in some cases there will be insufficient dilution to ensure compliance and additional management options may be required