Observation of multiple doubly degenerate bands in ¹⁹⁵Tl

The High-spin states in 195 Tl, populated through the 185,187 Re( 13 C, xn) fusion evaporation reaction at the beam energy of 75 MeV, were studied using the Indian National Gamma Array (INGA). More than 50 new γ transitions have been placed in the proposed level scheme which is extended up to the excitation energy of ≈ 5.6 MeV and spin =22.5ħ . Two pairs of degenerate bands based on two different quasi-particle configurations have been identified in this nucleus indicating the first observation of such bands in an odd- A nucleus in A∼190 region and signify the first evidence of multiple chiral bands in a nucleus in this region. The total Routhian surface calculations predict triaxial shapes for both the configurations and thereby, support the experimental observation. The importance of multiple neutron holes in the i13/2 orbital and the stability of shapes for these two configurations have been discussed.Financial support of Department of Science & Technology, Govt. of India for clover detectors of INGA (Grant No. IR/S2/PF-03/2003-II) is greatfully acknowledged. One of the authors (S. Bhattacharya) acknowledges with thanks the financial support received as Raja Ramanna Fellowship from the Department of Atomic Energy, Govt. of India. T.R and Md. A.A acknowledge with thanks the financial support received as research fellows from the Department of Atomic Energy (DAE), Govt. of India

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours

Level Crossings of a Class of Random Algebraic Polynomial

We know the expected number of times that a polynomial of degree n with independent normally distributed rando