Развој Друштва за ЕТРАН од оснивања до данас и његових 70
конференција.

Graded Logic (GL) is a propositional logic of human
commonsense reasoning and decision making. GL is fully
continuum-valued, i.e., everything is a matter of degree.
It is based on continuum-valued logic variables (graded
truth), continuum-valued simultaneity (graded conjunction),
continuum-valued substitutability (graded disjunction), and
continuum-valued importance of logic variables. The graded
conjunction and the graded disjunction are dualized,
complementary, and unified in a single continuum-valued,
andness-directed, importance-weighted,
idempotence-selectable, and annihilator-selectable
fundamental logic function called Graded
Conjunction/Disjunction (GCD). Graded Logic is the
mathematical infrastructure of the Logic Scoring of
Preference (LSP) decision method which has applicability in
solving a variety of complex evaluations and decision
problems Our goal is to present the following typical
applications in healthcare:
• Machine learning methods for development of the LSP
medical diagnostic models
• Explainability of graded logic models for disease
diagnosis (explainability of diagnostic models and
explainability of diagnostic results)
• Evaluation of disease severity, patient disability, and
effects of a therapy
• Vaccination priority evaluation (COVID-19)
• Organ transplantation priority evaluation (liver
transplantations)
• Optimum timing of risky therapy
• Evaluation and selection of medical equipment and medical
software tools
• Suitability maps for optimum location of medical services

The transition towards 6G networks necessitates a
fundamental paradigm shift from reactive network management
towards proactive and autonomous optimization strategies.
At the center of this evolution lies the "Digital Twin"
(DT), which serves as a high-fidelity virtual
representation of the physical radio environment. In this
talk, we present recent research findings on the
construction of differentiable network twins, supported by
extensive empirical measurements from real-world datasets
in Vienna. In contrast to conventional "black-box"
approaches, we propose a framework where network
abstractions are rendered fully differentiable. This allows
for the direct and scalable optimization of critical
network parameters, such as transmit power and
load-balancing, utilizing gradient-based Artificial
Intelligence.
Furthermore, we discuss the integration of
uncertainty-aware Bayesian learning to enhance the
prediction reliability of signal parameters (RSRP),
particularly within complex urban environments and railway
corridors. In the context of 6G, these digital twins evolve
beyond simple monitoring tools to become the core engine
for Integrated Sensing and Communication (ISAC),
facilitating high-precision localization and context-aware
connectivity. By addressing the "sim-to-real" gap as a
structured AI challenge, this work outlines a practical
roadmap for sustainable, zero-touch network management.
These insights are intended to provide a solid basis for
future communication and collaboration with researchers
working on digital-twin-based network evolution and
intelligent infrastructure.