Public-Private Collaboration in the Emergence of a National Electronic Identification Policy: The Case of NemID in Denmark

Governments envisioning large-scale national e-government policies increasingly draw on collaboration with private actors, yet the relationship between dynamics and outcomes of public-private partnership (PPP) is still unclear. The involvement of the banking sector in the emergence of a national electronic identification (e-ID) in Denmark is a case in point. Drawing on an analysis of primary and secondary data, we adopt the theoretical lens of collective action to investigate how transformations over time in the convergence of interests, the interdependence of resources, and the alignment of governance models between government and the banking sector shaped the emergence of the Danish national e-ID. We propose a process model to conceptualize paths towards the emergence of public-private collaboration for digital information infrastructure – a common good.

Distributed tuning of boundary resources: the case of Apple's iOS service system

The digital age has seen the rise of service systems involving highly distributed, heterogeneous, and resource-integrating actors whose relationships are governed by shared institutional logics, standards, and digital technology. The cocreation of service within these service systems takes place in the context of a paradoxical tension between the logic of generative and democratic innovations and the logic of infrastructural control. Boundary resources play a critical role in managing the tension as a firm that owns the infrastructure can secure its control over the service system while independent firms can participate in the service system. In this study, we explore the evolution of boundary resources. Drawing on Pickering’s (1993) and Barrett et al.’s (2012) conceptualizations of tuning, the paper seeks to forward our understanding of how heterogeneous actors engage in the tuning of boundary resources within Apple’s iOS service system. We conduct an embedded case study of Apple’s iOS service system with an in-depth analysis of 4,664 blog articles concerned with 30 boundary resources covering 6 distinct themes. Our analysis reveals that boundary resources of service systems enabled by digital technology are shaped and reshaped through distributed tuning, which involves cascading actions of accommodations and rejections of a network of heterogeneous actors and artifacts. Our study also shows the dualistic role of power in the distributed tuning process

Healthy buildings for a healthy city: Is the public health evidence base informing current building policies?

Research has demonstrated that housing quality is a key urban intervention in reducing health risks and improving climate resilience, addressing a key ambition of the United Nations Sustainable Development Goals. Yet housing quality remains a problem even in high income countries such as England. In particular, hazards such as excess cold, excess heat and lack of ventilation leading to damp and mould have been identified as a major issue in homes. Research shows that these hazards can lead to a range of health conditions, such as respiratory and cardiovascular disease, infections and mental health problems. This article explores the use of public health research and evidence in policy to regulate new buildings in England to deliver improved public health, climate resilience and a reduced carbon footprint, in particular exploring the policy drivers and awareness of the public health evidence. Findings show that public health evidence is hardly referenced in policy and that the focus on other evidence bases such as on climate mitigation in building regulations results in both positive and negative impacts on health. This reflects a lack of a systems approach around urban interventions leading to weaknesses in standards regulating the private development sector. In conclusion, this paper recommends: 1. the consideration of health impact in future building regulations; 2. the integration and coordination of key policies covering various scales and phases of the development processes and 3. the better education of residents to understand advances in new energy performance technologies

Power Dependence in the Governance of Public-Private e- Government Infrastructures

National electronic identification systems (e-IDs) are key e-government infrastructures that form the backbone of e-government services. When developed via public-private partnerships (PPP), such e-government infrastructures require appropriate governance arrangements to sustain a delicate balance between governments and the private actors involved. Using the lens of power dependence theory, we investigate the ongoing tender process of the third-generation e-ID in Denmark. The key actors are public agencies and the financial sector. Early findings illustrate how contextual factors related to market, technology, regulations, and social norms affect the distribution of power dependence between the actors; such distribution will eventually shape the governance arrangement resulting from the tender. Through this study, we expect to contribute to research on governance of public-private e-government infrastructures, to research on large scale infrastructure procurement processes and e-ID, and to the theoretical development of power-dependence theory

Stakeholder perspectives of Community Mental Health Forums: a qualitative study in Sierra Leone

Abstract Background Mental health is the leading cause of disability worldwide. In the wake of both a civil war and an Ebola outbreak, Sierra Leone ranks as one of the lowest ranked countries on the Human Development Index (UNDP. Human Development Report 2015, Work for Human Development. The United Nations Development Programme; 2015). The WHO identified Sierra Leone among its priority countries for the piloting of its Mental Health Gap Action Programme (mhGAP). Aligned to these efforts, CBM and their affiliated partners employed the use of Community Mental Health Forums (CMHFs), facilitated by Mental Health Nurses (MHNs), as a sensitive and practical way of engaging key community stakeholders to discuss and address issues of mental health. This study sought firstly, to identify factors that affect the successful implementation of CMHFs, as identified by programme participants. Second, the study sought to identify what changes participants perceived as having taken place as a result of their participation in CMHFs. Methods 10 MHNs and 52 forum participants were purposely selected to take part in key informant interviews and focus group discussions, conducted across eight districts in Sierra Leone. Interview transcripts were analysed across four rounds of coding, using a mixture of deductive and inductive approaches. Results Results identified three themes, Traditional Beliefs and Culture; Health System; and Inclusive Approaches as affecting the implementation of CMHFs in their districts. Participants further perceived that their participation in the Community Mental Health Forums resulted in changes taking place across the themes of Awareness and beliefs, Behaviours towards people experiencing psychological distress, and as leading to greater Collaboration and cooperation between formal and informal mental health practitioners. Conclusions Results are discussed in the context of the extant literature and a novel framework, that incorporates multiple best practice recommendations and factors which influence the successful implementation of CMHFs is put forward. </jats:sec

Modelling production-consumption flows of goods in Europe: the trade model within Transtools3

The paper presents a new model for trade flows in Europe that is integrated with a logistics model for transport chain choice through Logsum variables. Logsums measures accessibility across an entire multi-modal logistical chain, and are calculated from a logistics model that has been estimated on disaggregated micro data and then used as an input variable in the trade model. Using Logsums in a trade model is new in applied large-scale freight models, where previous models have simply relied on the distance (e.g. crow-fly) between zones. This linkage of accessibility to the trade model makes it possible to evaluate how changes in policies on transport costs and changes in multi-modal networks will influence trade patterns. As an example the paper presents outcomes for a European-wide truck tolling scenario, which showcases to which extent trade is influenced by such a policy. The paper discusses how such a complex model can be estimated and considers the choice of mathematical formulation and the link between the trade model and logistics model. In the outcomes for the tolling scenario we decompose the total effects into effects from the trade model and effects from the logistics model

Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods

Within manufacturing there is a growing need for autonomous Tool Condition Monitoring (TCM) systems, with the ability to predict tool wear and failure. This need is increased, when using specialised tools such as Diamond-Coated Burrs (DCBs), in which the random nature of the tool and inconsistent manufacturing methods create large variance in tool life. This unpredictable nature leads to a significant fraction of a DCB tool’s life being underutilised due to premature replacement. Acoustic Emission (AE) in conjunction with Machine Learning (ML) models presents a possible on-machine monitoring technique which could be used as a prediction method for DCB wear. Four wear life tests were conducted with a ∅1.3 mm #1000 DCB until failure, in which AE was continuously acquired during grinding passes, followed by surface measurements of the DCB. Three ML model architectures were trained on AE features to predict DCB mean radius, an indicator of overall tool wear. All architectures showed potential of learning from the dataset, with Long Short-Term Memory (LSTM) models performing the best, resulting in prediction error of MSE = 0.559 μm2 after optimisation. Additionally, links between AE kurtosis and the tool’s run-out/form error were identified during an initial review of the data, showing potential for future work to focus on grinding effectiveness as well as overall wear. This paper has shown that AE contains sufficient information to enable on-machine monitoring of DCBs during the grinding process. ML models have been shown to be sufficiently precise in predicting overall DCB wear and have the potential of interpreting grinding condition

Probabilistic abstract interpretation: From trace semantics to DTMC’s and linear regression

In order to perform probabilistic program analysis we need to consider probabilistic languages or languages with a probabilistic semantics, as well as a corresponding framework for the analysis which is able to accommodate probabilistic properties and properties of probabilistic computations. To this purpose we investigate the relationship between three different types of probabilistic semantics for a core imperative language, namely Kozen’s Fixpoint Semantics, our Linear Operator Semantics and probabilistic versions of Maximal Trace Semantics. We also discuss the relationship between Probabilistic Abstract Interpretation (PAI) and statistical or linear regression analysis. While classical Abstract Interpretation, based on Galois connection, allows only for worst-case analyses, the use of the Moore-Penrose pseudo inverse in PAI opens the possibility of exploiting statistical and noisy observations in order to analyse and identify various system properties