New Fe II energy levels from stellar spectra

The spectra of B-type and early A-type stars show numerous unidentified lines in the whole optical range, especially in the 5100 - 5400 A interval. Because Fe II transitions to high energy levels should be observed in this region, we used semiempirical predicted wavelengths and gf-values of Fe II to identify unknown lines. Semiempirical line data for Fe II computed by Kurucz are used to synthesize the spectrum of the slow-rotating, Fe-overabundant CP star HR 6000. We determined a total of 109 new 4f levels for Fe II with energies ranging from 122324 cm^-1 to 128110 cm^-1. They belong to the Fe II subconfigurations 3d^6(^3P)4f (10 levels), 3d^6(^3H)4f (36 levels), 3d^6(^3F)4f (37 levels), and 3d^6(^3G)4f (26 levels). We also found 14 even levels from 4d (3 levels), 5d (7 levels), and 6d (4 levels) configurations. The new levels have allowed us to identify more than 50% of the previously unidentified lines of HR 6000 in the wavelength region 3800-8000 A. Tables listing the new energy levels are given in the paper; tables listing the spectral lines with loggf>/=-1.5 that are transitions to the 4f energy levels are given in the Online Material. These new levels produce 18000 lines throughout the spectrum from the ultraviolet to the infrared.Comment: Paper accepted by A&A for publicatio

Motivating Strategies Leaders Employ to Increase Follower Effort

The purpose of this research was to determine which motivating strategies followers desire from their leaders and what motivating strategies are actually displayed by their leaders to increase followers’ effort. Additionally, this research assessed the followers’ level of self-reported extra effort and the amount of extra effort followers perceive their leaders exert. From this data, conclusions were drawn regarding the relationships between followers’ self-reported extra effort and the followers’ perception of their leaders’ extra effort. This quantitative research study was conducted via LinkedIn using Survey Monk ey and is based on Keller’s 42 item ARCS Model (attention, relevance, confidence, and satisfaction). Regression analysis of the survey responses indicated that: 1) Followers perceive their leaders are not displaying the level of motivating strategies desired; 2) The amount of extra effort that followers perceive that their leaders exert is significant in predicting the amount of extra effort that followers exert; and 3) Followers’ perception is that leaders’ extra effort is less than followers’ extra effort. The findings suggest that leaders should be more aware of the motivating strategies that followers desire and demonstrate those strategies since leaders’ extra effort is a significant predictor of followers’ extra effort. Additionally, leaders should also exert the level of effort that they desire from their followers

Drawing bobbin lace graphs, or, Fundamental cycles for a subclass of periodic graphs

In this paper, we study a class of graph drawings that arise from bobbin lace patterns. The drawings are periodic and require a combinatorial embedding with specific properties which we outline and demonstrate can be verified in linear time. In addition, a lace graph drawing has a topological requirement: it contains a set of non-contractible directed cycles which must be homotopic to $(1,0)$, that is, when drawn on a torus, each cycle wraps once around the minor meridian axis and zero times around the major longitude axis. We provide an algorithm for finding the two fundamental cycles of a canonical rectangular schema in a supergraph that enforces this topological constraint. The polygonal schema is then used to produce a straight-line drawing of the lace graph inside a rectangular frame. We argue that such a polygonal schema always exists for combinatorial embeddings satisfying the conditions of bobbin lace patterns, and that we can therefore create a pattern, given a graph with a fixed combinatorial embedding of genus one.Comment: Appears in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017

New Mn II energy levels from STIS-HST spectrum of the HgMn star HD 175640

The NIST database lists several Mn II lines that were observed in the laboratory but not classified. They cannot be used in spectrum synthesis because their atomic line data are unknown. These lines are concentrated in the 2380-2700 A interval. We aimed to assign energy levels and log gf values to these lines. Semi-empirical line data for Mn II computed by Kurucz were used to synthesize the ultraviolet spectrum of the slow-rotating, HgMn star HD 175640. The spectrum was compared with the high-resolution spectrum observed with the HST-STIS equipment. A UVES spectrum covering the 3050-10000 A region was also examined. We determined a total of 73 new energy levels, 58 from the STIS spectrum of HD 175640 and another 15 from the UVES spectrum. The new energy levels give rise to numerous new computed lines. We have identified more than 50% of the unclassified lines listed in the NIST database and have changed the assignement of another 24 lines. An abundance analysis of the star HD 175640, based on the comparison of observed and computed ultraviolet spectra in the 1250-3040 A interval, is the by-product of this study on Mn II.Comment: Paper accepted by Astronomy & Astrophysic

Engage D5.14 Engage SESAR Summer School 2021

This report describes the third edition of the Engage SESAR summer school, which was held as a virtual event, between 30th August and 2nd September 2021

Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming

Azzali, I., Vanneschi, L., & Giacobini, M. (2020). Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming. In T. Hu, N. Lourenço, E. Medvet, & F. Divina (Eds.), Genetic Programming - 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Proceedings (pp. 52-67). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12101 LNCS). Springer. https://doi.org/10.1007/978-3-030-44094-7_4 ------- This work was partially supported by FCT, Portugal through funding of LASIGE Research Unit (UID/CEC/00408/2019), and projects PREDICT (PTDC/CCI-IF/29877/2017), BINDER (PTDC/CCI-INF/29168/2017), GADgET (DSAIPA/DS/0022/2018) and AICE (DSAIPA/DS/0113/2019).Vectorial Genetic Programming (VE_GP) is a new GP approach for panel data forecasting. Besides permitting the use of vectors as terminal symbols to represent time series and including aggregation functions to extract time series features, it introduces the possibility of evolving the window of aggregation. The local aggregation of data allows the identification of meaningful patterns overcoming the drawback of considering always the previous history of a series of data. In this work, we investigate the use of geometric semantic operators (GSOs) in VE_GP, comparing its performance with traditional GP with GSOs. Experiments are conducted on two real panel data forecasting problems, one allowing the aggregation on moving windows, one not. Results show that classical VE_GP is the best approach in both cases in terms of predictive accuracy, suggesting that GSOs are not able to evolve efficiently individuals when time series are involved. We discuss the possible reasons of this behaviour, to understand how we could design valuable GSOs for time series in the future.authorsversionpublishe