New Trends in Computer Sciences

Evolution of regulatory models for public health data ecosystems from a linked democracy perspective

2023-09-27T09:12:56+03:00

Public healthcare is a data-intensive environment that manages ever-increasing volumes of biomedical data resulting from medical data-generating technologies. In this paper, the authors discuss strategies to regulate the collection and use of biomedical data and metadata to build sustainable public health data ecosystems; this can assist citizens to get control of dataflows by defining identity in the public domain and shaping the capacity to use the web of data: get access to healthcare services and receive benefits and appropriate care. The authors suggest that a strategy based on the linked democracy governance model and safeguards, implemented through the meta-rule of law, enables better design of regulatory tools to handle semantically driven data flows. This strategy ties well in with models of deliberative and epistemic democracy, focused on relationships between people, data, and institutions. The authors investigate privacy, security, and data protection issues, applying existing ethical and legal frameworks for public health data and the theory of justice; they discuss the implementation of strategies to articulate the public domain and propose intermediate, anchoring institutions at the meso-level by building ontologies, selecting technical functionalities and algorithms, and embedding protections of the rule of law into specific public health data ecosystems.

Forging connections: the social dynamics of “Death Stranding” as a paradigm shift in gaming

2023-10-05T15:10:47+03:00

This paper explores the video game Death Stranding as a shift in digital interactive media, emphasizing altruism, collaboration, and social connection in its gameplay. The close reading of the game focuses on features that diverge from traditional norms, pivoting towards a more empathetic, community-driven model and identifies five domains of analysis: narrative infrastructure; game mechanics; player-to-player interactions; player-to-NPC interactions; and player interactions as a social agent. The findings are discussed with the possibility of defining a new genre, named by the game’s creator as a “strand game” (Kojima, 2019a, 2019b). The analysis reveals Death Stranding’s ability to create a deeply immersive sense of unity among players, demonstrating the game’s potential as a model for future video games that prioritize positive social interactions and mutual support.

Simulating victim health state evolution from physical and chemical injuries in mass casualty incidents

2023-12-28T09:28:34+02:00

The field of discrete-event simulation for medical disaster management is relatively new. In such simulations, human victims are generated using pre-determined transitions from one health state to the next, based on a set of triggers that correspond to treatment or the clinical progression of untreated injuries or diseases. However, this approach does not account for subtle differences in clinical progression. We propose a parameter-based model to characterize the evolution of symptoms at first for physical and nerve agent chemical injuries. We used a Gompertz function to predict the time of death in trauma based on forensic data. Then we separately considered the effects of the chemical warfare agent sarin (GB) being the origin of the chemical injuries for the purpose of modelling a GB attack in a metro station. We emphasize that our approach can be extended to other CBRN threats pending knowledge of clinical progressions available in the literature for the purpose of casualty estimations. The intent is to provide an estimate of time to death without any treatment and overlay this model with a treatment model, improving the evolution of the health state. A modification for non-life-threatening injuries is included without losing generality. Improvement functions modelling medical treatment are proposed. We argue that the availability of injury scores vs mortality can greatly enhance the validity of the model.

Extending simulation-based assembly planning to include human learning and previous experience: a simulation study

2024-01-18T16:35:39+02:00

When using simulation-based assembly planning in the planning phase of designing modern assembly systems, the prospective system behavior should be predicted as reliably as possible by the simulation. For this purpose, personnel-related adjustment periods, such as those related to learning through task repetition should be considered in the simulation model, if employees are later to be involved in the assembly. The learning effect influences the overall performance of the system and can be described by learning curves. The aim of the approach presented in this paper is to increase the prediction quality of simulation models for assembly planning by taking into account the previous experience of the employees. For this purpose, a learning model is integrated into a discrete-event simulation and subsequently verified. The learning model includes the personnel-related learning curve as well as the previous experience of the employees as dynamic parameters. Simulation experiments with three forms of assembly organization were conducted to investigate the influence of learning and previous experience on the dynamic system behavior of an assembly system. The results indicate that assembly systems organized according to the One Piece Flow principle allow for broader, albeit slower, learning compared to row and group assembly.

First published online 18 January 2024