Efficient yet realistic ship routing is critical for reducing fuel consumption and greenhouse-gas emissions. However, conventional weather-routing algorithms often produce mathematically optimal routes that conflict with the paths mariners use. This study presents a hybrid approach that constrains physics-based weather routing within an AISderived maritime traffic network (MTN) built from one year of global Automatic Identification System data. The MTN represents common sea lanes as a graph of approximately 10,956 waypoints (nodes) and 17,561 directed edges. Using this network, an optimal low-emission route is computed via graph search and then compared against both a traditional unconstrained route and an advanced weather-routing model (VISIR-2). In a May transitionseason case (Busan–Singapore voyage), the AIS-constrained route reduced fuel consumption and CO₂ emissions by about 1.9% relative to the fastest feasible route, while closely following real traffic corridors (over 90% overlap with actual 2024 AIS tracks). While this 1.9% saving does not reach the high-end potential of an unconstrained, state-of-the-art model like VISIR-2 (which can demonstrate double-digit savings in certain conditions), it is achieved with an increase in transit time of ~6.5 h (≈3.2%). This represents a crucial trade-off, prioritizing operational realism and adherence to real-world traffic corridors over maximum theoretical efficiency.

We estimate the fractal dimension of the ρ Ophiuchus Molecular Cloud Complex, associated with star forming regions. We selected a cube (v, l, b) database, obtained with J = 1−0 transition lines of 12CO and 13CO at a resolution of 22′′ using a multibeam receiver system on the 14-m telescope of the Five College Radio Astronomy Observatory. Using a code developed within IRAF, we identified slice-clouds with two threshold temperatures to estimate the fractal dimension. With threshold temperatures of 2.25 K (3σ) and 3.75 K (5σ), the fractal dimension of the target cloud is estimated to be D = 1.52–1.54, where P / AD/2 , which is larger than previous results. We suggest that the sampling rate (spatial resolution) of observed data must be an important parameter when estimating the fractal dimension, and that narrower or wider dispersion around an arbitrary fit line and the intercepts at NP = 100 should be checked whether they relate to rms noise level or characteristic structure of the target cloud. This issue could be investigated by analysing several high resolution databases with different quality (low or moderate sensitivity).