Abstract

Abstract
Purpose - The purpose of this paper is to explore the challenges, interplay and potential directions for future service research to address the first three Sustainable Development Goals (SDGs) of no poverty, zero hunger and good health and well-being. Design/methodology/approach - This commentary examines how service research has addressed these SDGs in the literature, and through the development of a theory of change, the authors propose an agenda for service research going beyond serving, to enabling and transforming service systems, expanding the current focus on individual to community and population well-being through promotion and prevention.Findings - Service research has increasingly advocated human-centered approaches but requires a shift towards an all of humanity perspective. Individual and collective well-being have gained attention in service research, emphasizing the importance of considering collective well-being.Research limitations/implications - The commentary underscores the need for a comprehensive approach to develop services that contribute to the well-being of the human species. It calls for research that transcends dyadic interactions, considers systemic dynamics and broadens the focus from individual to collective and population well-being. Social implications - This paper discusses important societal issues of poverty, hunger and good health and well-being and the need for integrated and ecosystem approaches to develop equitable and sustainable solutions for collective well-being.Originality/value - While SDGs 1, 2 and 3 address individual goals, they collectively underpin the well-being of communities and societies.

Abstract
We study the optimal consumption, investment and life-insurance purchase and selection strategies for a wage-earner with an uncertain lifetime with access to a financial market comprised of one risk-free security and one risky-asset whose prices evolve according to linear diffusions modulated by a continuous-time stochastic process determined by an additional diffusive nonlinear stochastic differential equation. The process modulating the linear diffusions may be regarded as an indicator describing the state of the economy in a given instant of time. Additionally, we allow the Brownian motions driving each of these equations to be correlated. The life-insurance market under consideration herein consists of a fixed number of providers offering pairwise distinct contracts. We use dynamic programming techniques to characterize the solutions to the problem described above for a general family of utility functions, studying the case of discounted constant relative risk aversion utilities with more detail.

Abstract
This article presents the development of an augmented reality (AR) application aimed at promoting events in urban environments. The main goal of the project was to create an immersive experience that enhances user interaction with their surroundings, leveraging AR technology. The application was built using Django Rest Framework (DRF) for backend services and Unity for the AR functionalities and frontend. Key features include user registration and authentication, event viewing, interaction with virtual characters, and feedback on attended events, providing an engaging platform to promote urban events. The development process involved several stages, from requirements analysis and system architecture design to implementation and testing. A series of tests were performed, confirming that the application meets its objectives. These tests highlighted the system's ability to enhance user interaction with urban environments and demonstrated its potential for commercialization. The results suggest that the AR application contributes to innovation in smart cities, offering a new avenue for promoting events and engaging local communities. Future work will focus on refining the user experience and expanding the app's functionality to support more complex event scenarios.

Abstract
METIS, the Mid-infrared ELT Imager and Spectrograph, will be one of the first instruments to be used at ESO's 39m Extremely Large Telescope (ELT), that is currently under construction. With that, a number of firsts are to be addressed in the development of METIS' single-conjugate Adaptive Optics (SCAO) system: the size of the telescope and the associated complexity of the wavefront control tasks, the unique scientific capabilities of METIS, including high contrast imaging, the interaction with the newly established, integrated wavefront control infrastructure of the ELT, the integration of the near-infrared Pyramid Wavefront Sensor and other key Adaptive Optics (AO) hardware embedded within a large, fully cryogenic instrument. METIS and it's AO system have passed the final design review and are now in the manufacturing, assembly, integration and testing phase. The firsts are approached through a compact hard- and software design and an extensive test program to mature METIS SCAO before it is deployed at the telescope. This program includes significant investments in test setups that allow to mimic conditions at the ELT. A dedicated cryo-test facility allows for subsystem testing independent of the METIS infrastructure. A telescope simulator is being set up for end-to-end laboratory tests of the AO control system together with the final SCAO hardware. Specific control algorithm prototypes will be tested on sky. In this contribution, we present the progress of METIS SCAO with an emphasis on the preparation for the test activities foreseen to enable a successful future deployment of METIS SCAO at the ELT.

Abstract
Knowledge models often use hierarchical structures, which help break down complex data into manageable components. This enables better understanding and aids in reasoning and decision-making. Hierarchical structures are effective in organizing, managing, and processing complex information. Traditional Self-Organizing Maps are typically flat, two-dimensional grids for visualizing and grouping data. They can be shaped into hierarchical structures, offering benefits such as improved data representation, scalability, enhanced grouping and visualization, and hierarchical feature extraction while preserving data topology. This paper introduces a self-organizing hierarchical map with an appropriate topology and a suitable learning mechanism for retaining information in an organized way. In this conceptual model, information is selectively absorbed in each layer. These characteristics make the Hierarchical Self-organising Maps a powerful non-linear classifier. Simulations are conducted to test and evaluate the performance of this neural structure as a classifier.