Public value creation is traditionally considered as the citizens' collective expectations with respect to government and public services. Recent e-government literature indicates that what exactly constitutes public value in digital government is still debated. Whereas previous research acknowledges aspects such as co-production and the orchestration role of government in the context of public value creation, there is only a limited understanding of how public value is created by the interactions between government and business actors, and the role digital technologies play in that process. Furthermore, so far, research into public value creation processes is limited to specific services that aim to meet a specific goal; for a more complete view, an integrative perspective is required to address the multiplicity of goals. Societal challenges including climate change, sustainability, and the transition towards circularity will require governments to play a crucial role. Businesses are also transforming their vision by adding societal goals to their economic objectives and contributing to these societal challenges. This necessitates even more the need to explicitly consider the role of business in public value creation processes. In this paper we argue that there is a need to understand public value creation as an interactive process, involving both government and business actors. In this process, voluntary information sharing enabled by digital infrastructures has the potential to contribute to the value creation processes, but the increased complexity of digital technologies obscures the effects they can have on value creation. Therefore, we develop a framework that allows to reason about public value creation as an interactive process, involving government and businesses, facilitated by voluntary information sharing. The framework also allows to reason about how the technological design choices of the underlying digital infrastructure influence this value creation process. For the framework development, we use an in-depth case study from the domain of international trade. We analyze the interactions between customs authorities and supply chain actors for jointly creating public value related to revenue collection, as well as safety and security of goods entering the European Union, using business data made available via a global blockchain-enabled infrastructure. In future research, the framework that we developed can be used to analyze more complex cases with additional public value aspects, such as sustainability and circularity.@en

Vital biological processes, such as trafficking, sensing, and motility, are facilitated by cellular lipid membranes, which interact mechanically with surrounding fluids. Such lipid membranes are only a few nanometers thick and composed of a liquid crystalline structure known as the lipid bilayer. Here, we introduce an active, noncontact, two-point microrheology technique combining multiple optical tweezers probes with planar freestanding lipid bilayers accessible on both sides. We use the method to quantify both fluid slip close to the bilayer surface and transmission of fluid flow across the structure, and we use numerical simulations to determine the monolayer viscosity and the intermonolayer friction. We find that these physical properties are highly dependent on the molecular structure of the lipids in the bilayer. We compare ordered-phase with liquid disordered-phase lipid bilayers, and we find the ordered-phase bilayers to be 10 to 100 times more viscous but with 100 times less intermonolayer friction. When a local shear is applied by the optical tweezers, the ultralow intermonolayer friction results in full slip of the two leaflets relative to each other and as a consequence, no shear transmission across the membrane. Our study sheds light on the physical principles governing the transfer of shear forces by and through lipid membranes, which underpin cell behavior and homeostasis.@en

As an increasing volume of international trade activities around the world, the amount of cross-boarder import declarations grows rapidly, resulting in an unprecedented scale of potentially fraudulent transactions, in particular false commodity code (e.g., HS Code). The incorrect HS Code will cause duty risk and adversely impact the revenue collection. Physical investigation by the customs administrations is impractical due to the substantial quantity of declarations. This paper provides an automatic approach by harnessing the power of machine learning techniques to relief the burden of customs targeting officers. We introduced a novel model based on the off-the-shelf embedding encoder to identify the correctness of HS Code without any human effort. Determining whether the HS Code is correctly matched with commodity description is a classification task, so the labelled data is typically required. However, the lack of gold standard labelled data sets in customs domain limits the development of supervised-based approach. Our model is developed by the unsupervised mechanism and trained on the unlabelled historical declaration records, which is robust and able to be smoothly adapted by the different customs administrations. Rather than typically classifying whether the HS Code is correct or not, our model predicts the score to indicate the degree of the HS Code being correct. We have evaluated our proposed model on the ground-truth data set provided by Dutch customs officers. Results show promising performance of 71% overall accuracy.@en