The thermal condition of the active layer in the permafrost at Hornsund, Spitsbergen

Ground temperature variations have been analysed to the depth of 160 cm,with respect to meteorological elements and short−wave radiation balance. The database of the ground temperature covers a thirteen month−long period (May 1992 – June 1993), which in− cluded both the seasons of complete freezing of the ground and thaw. Special attention has been given to the development of perennial permafrost and its spatial distribution. In summer, the depth of thawing ground varied in different types of ground—at the Polish Polar Station, this was ca. 130 cm. The ground froze completely in the first week of October. Its thawing started in June. The snow cover restrained heat penetration in the ground, which hindered the ground thawing process. Cross−correlation shows a significant influence of the radiation bal− ance (K*) on the values of near−surface ground temperatures (r2 = 0.62 for summer)

Holographic Path-Integral Optimization

In this work we elaborate on holographic description of the path-integral optimization in conformal field theories (CFT) using Hartle-Hawking wave functions in Anti-de Sitter spacetimes. We argue that the maximization of the Hartle-Hawking wave function is equivalent to the path-integral optimization procedure in CFT. In particular, we show that metrics that maximize gravity wave functions computed in particular holographic geometries, precisely match those derived in the path-integral optimization procedure for their dual CFT states. The present work is a detailed version of \cite{Boruch:2020wax} and contains many new results such as analysis of excited states in various dimensions including JT gravity, and a new way of estimating holographic path-integral complexity from Hartle-Hawking wave functions. Finally, we generalize the analysis to Lorentzian Anti-de Sitter and de Sitter geometries and use it to shed light on path-integral optimization in Lorentzian CFTs.Comment: 74 pages, 6 figures, v2 References added, Published versio

Using simulation to calibrate real data acquisition in veterinary medicine

This paper explores the innovative use of simulation environments to enhance data acquisition and diagnostics in veterinary medicine, focusing specifically on gait analysis in dogs. The study harnesses the power of Blender and the Blenderproc library to generate synthetic datasets that reflect diverse anatomical, environmental, and behavioral conditions. The generated data, represented in graph form and standardized for optimal analysis, is utilized to train machine learning algorithms for identifying normal and abnormal gaits. Two distinct datasets with varying degrees of camera angle granularity are created to further investigate the influence of camera perspective on model accuracy. Preliminary results suggest that this simulation-based approach holds promise for advancing veterinary diagnostics by enabling more precise data acquisition and more effective machine learning models. By integrating synthetic and real-world patient data, the study lays a robust foundation for improving overall effectiveness and efficiency in veterinary medicine