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This paper explores parallelism performance for C, C++, Go, Java, Julia, and Rust on N-body simulations. We begin with a basic O(N2) simulation for each language based on the n-body benchmark in the Benchmark Game. The original benchmark is adjusted to include a larger number of particles and run in parallel. We also add parallelism to the force calculations using a kD-tree. This work builds on previous work by including parallelism and adding the Julia programming language to our survey. We find that for straight number-crunching, all of these languages provide similar performance, and all have sufficient support for parallelism that runtimes scale well with thread counts. On the other hand, when a spatial data structure, such as the kD-tree, is introduced, the runtimes vary dramatically between languages. In that situation, Julia’s performance looks more like Python, taking over 100 times as long as Rust/C/C++ to finish. Rust comes out on top with an impressive 50% lead over C and C++. 

Abstract The discovery of two thin rings around the ∼ 250 km sized Centaur Chariklo was the first of its kind, and their formation and evolutionary mechanisms are not well understood. Here, we explore a single shepherd satellite as a mechanism to confine Chariklo’s rings. We also investigate the impact of such a perturber on reaccretion, which is a likely process for material located outside the Roche limit. We have modifiedN-body code that was developed for Saturn’s rings to model the Chariklo system. Exploration of a reasonable parameter space indicates that rings like those observed could be stable as the result of a single satellite with a mass of a few ×10¹³kg that is in orbital resonance with the rings. There is a roughly linear relationship between the model optical depth and the mass of the satellite required to confine a ring. Ring particles do not accrete into moonlets during hard-sphere simulations. However, a reasonable fraction of the ring material forms into moonlets after a few tens of orbits for soft-sphere collisions. The ring-particle properties are thus key parameters in terms of moonlet accretion or destruction in this system. 

Abstract A stellar occultation of Gaia DR3 2646598228351156352 by the Centaur (2060) Chiron was observed from the South African Astronomical Observatory on 2018 November 28 UT. Here we present a positive detection of material surrounding Chiron from the 74-inch telescope for this event. Additionally, a global atmosphere is ruled out at the tens of microbars level for several possible atmospheric compositions. There are multiple 3σdrops in the 74-inch light curve: three during immersion and two during emersion. Occulting material is located between 242 and 270 km from the center of the nucleus in the sky plane. Assuming the ring-plane orientation proposed for Chiron from the 2011 occultation, the flux drops are located at 352, 344, and 316 km (immersion) and 357 and 364 km (emersion) from the center, with normal optical depths of 0.26, 0.36, and 0.22 (immersion) and 0.26 and 0.18 (emersion) and equivalent widths between 0.7 and 1.3 km. This detection is similar to the previously proposed two-ring system and is located within the error bars of that ring-pole plane; however, the normal optical depths are less than half of the previous values, and three features are detected on immersion. These results suggest that the properties of the surrounding material have evolved between the 2011, 2018, and 2022 observations. 

Integrating diverse concepts from animal behavior, movement ecology, and machine learning, we develop an overview of the ecology of learning and animal movement. Learning-based movement is clearly relevant to ecological problems, but the subject is rooted firmly in psychology, including a distinct terminology. We contrast this psychological origin of learning with the task-oriented perspective on learning that has emerged from the field of machine learning. We review conceptual frameworks that characterize the role of learning in movement, discuss emerging trends, and summarize recent developments in the analysis of movement data. We also discuss the relative advantages of different modeling approaches for exploring the learning-movement interface. We explore in depth how individual and social modalities of learning can matter to the ecology of animal movement, and highlight how diverse kinds of field studies, ranging from translocation efforts to manipulative experiments, can provide critical insight into the learning process in animal movement. 

Abstract: Morgan and Parker proved that if G is a group with Z(G)=1, then the connected components of the commuting graph of G have diameter at most 10. Parker proved that if, in addition, G is solvable, then the commuting graph of G is disconnected if and only if G is a Frobenius group or a 2-Frobenius group, and if the commuting graph of G is connected, then its diameter is at most 8. We prove that the hypothesis Z (G) = 1 in these results can be replaced with G' \cap Z(G)=1. We also prove that if G is solvable and G/Z(G) is either a Frobenius group or a 2-Frobenius group, then the commuting graph of G is disconnected. 

Abstract: For a group G, we define a graph Delta (G) by letting G^#=G\{1} be the set of vertices and by drawing an edge between distinct elements x,y in G^# if and only if the subgroup is cyclic. Recall that a Z-group is a group where every Sylow subgroup is cyclic. In this short note, we investigate Delta (G) for a Z-group G. 

Abstract Understanding the movement of species’ ranges is a classic ecological problem that takes on urgency in this era of global change. Historically treated as a purely ecological process, range expansion is now understood to involve eco‐evolutionary feedbacks due to spatial genetic structure that emerges as populations spread. We synthesize empirical and theoretical work on the eco‐evolutionary dynamics of range expansion, with emphasis on bridging directional, deterministic processes that favor evolved increases in dispersal and demographic traits with stochastic processes that lead to the random fixation of alleles and traits. We develop a framework for understanding the joint influence of these processes in changing the mean and variance of expansion speed and its underlying traits. Our synthesis of recent laboratory experiments supports the consistent role of evolution in accelerating expansion speed on average, and highlights unexpected diversity in how evolution can influence variability in speed: results not well predicted by current theory. We discuss and evaluate support for three classes of modifiers of eco‐evolutionary range dynamics (landscape context, trait genetics, and biotic interactions), identify emerging themes, and suggest new directions for future work in a field that stands to increase in relevance as populations move in response to global change.