  This paper addresses the challenge of representing complex human action (HA)
in a nuclear power plant (NPP) digital twin (DT) and minimizing latency in
partial computation offloading (PCO) in sixth-generation-enabled computing in
the network (COIN) assisted multiaccess edge computing (MEC). Accurate HA
representation in the DT-HA model is vital for modeling human interventions
that are crucial for the safe and efficient operation of NPPs. In this context,
DT-enabled COIN-assisted MEC harnesses DT (known as a cybertwin) capabilities
to optimize resource allocation and reduce latency effectively. A two-stage
approach is employed to address system complexity. First, a probabilistic
graphical model (PGM) is introduced to capture HAs in the DT abstraction. In
the PGM, HA and NPP asset-twin abstractions form coupled systems that evolve
and interact through observable data and control input. Next, the underlying
PCO problem is formulated as a multiuser game, where NPP assets can partially
offload tasks to COIN and MEC. We propose a decentralized algorithm to optimize
offloading decisions, offloading ratios, and resource allocation. The
simulation results demonstrate the effectiveness of the proposed method in
capturing complex HAs and optimal resource allocation in DT-enabled NPPs.
