The accelerated evolution of digital technologies has led to their integration across a wide range of domains, from manufacturing and education to medicine, contributing substantially to decision automation, real time data acquisition, and interoperability between systems. In the medical field, digitalization is redefining the traditional paradigms of clinical practice. The integration of emerging technologies such as Digital Twin, Artificial Intelligence, and blockchain facilitates the early identification of pathologies, the personalization of treatments, and the optimization of clinical management processes, thereby laying the foundation for personalized and predictive medicine. Starting from the premise that glaucoma represents one of the leading causes of blindness worldwide, a condition that is initially asymptomatic and progresses gradually, the present study aims to develop a digital representation model, namely a Digital Twin, for e health systems and personalized medicine, with a focus on identifying predispositions to this pathology and outlining a framework for the early detection of its initial stages.

Digital government services have significantly developed over the past two decades, yet citizen adoption and satisfaction remain a problem in this domain. The recent literature demonstrates a growing interest in user-centered factors such as usability, accessibility, feedback mechanisms, trust and public value, but these elements are typically examined individually, without a unifying structure. This paper proposes that User Experience (UX) in E-Government must be reconceptualized as a multi-layer governance model rather than a technical or interface-driven model. Building upon a systematic synthesis of recent literature, this study integrates multiple layers: Functional Experience, Interaction Quality, Institutional Trust and Public Outcomes. By structuring UX across these layered dimensions, the proposed framework centers UX as embedded within the governance dimension. The model introduces a layer-oriented approach integrating technical, empirical and relational perspectives into a unified based framework that will be extended in future research. The framework explicitly integrates AI-driven automation, conversational interfaces and AI transparency as cross-layer constructs.

Digital nudging is increasingly used to promote sustainable behavior, yet its implementation faces substantial challenges. While prior research largely focuses on behavioral aspects, challenges arising from the implementation of nudges in IT systems remain fragmented and underexplored. To address this gap, we conducted a systematic literature review (SLR) and identified 35 peer-reviewed studies. The analysis identifies ten challenge categories, comprising six general and four IT-specific challenges. General challenges include barriers to consumption and behavioral change, general ethical concerns, personal attitudes, design complexity, and varying effectiveness. Critically, we identify a distinct set of IT-related challenges—IT/AI-ethical challenges, legal/privacy challenges, interactional challenges, and technological challenges—that are specific to digital implementations but insufficiently conceptualized in existing literature. This study provides the first structured synthesis of digital nudging challenges in sustainability with an explicit focus on the IT dimension. By distinguishing general from IT-specific challenges, it advances conceptual clarity and can help practitioners implementing digital nudges avoid mistakes that negatively impact the sustainability outcomes of these nudges.

The formulation of multi-objective optimization problems from natural language remains a challenging task, particularly in domains such as maritime voyage planning where decision-makers must balance conflicting objectives under complex operational constraints. As optimization methods become increasingly advanced, a significant barrier to their practical adoption lies not in solving capability, but in the ability of users to express their intent in a form suitable for computational models. This paper addresses this challenge by conceptualizing problem formulation as a human-centered, interactive process. We propose a human-in-the-loop framework that enables users to iteratively construct voyage optimization problem definitions through natural language interaction. The approach leverages a Large Language Model (LLM) to interpret user intent and map it into a structured representation within a predefined feature space. To support this process, the framework employs a state-based representation that enables incremental refinement of user preferences. A hybrid processing pipeline combines semantic interpretation with deterministic validation, ensuring reliable updates while maintaining control over the formulation process. The framework is evaluated through representative maritime scenarios, highlighting its ability to support interpretable and consistent problem formulation, while also revealing limitations in handling ambiguity, multi-intent inputs, and interaction complexity. The results demonstrate the potential of LLM-based interfaces to improve the accessibility and usability of complex optimization processes, enabling more effective human-centered decision support.