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Under irradiation, Zr and Zr alloys undergo growth in the absence of applied stress. This phenomenon is thought to be associated with the anisotropy of diffusion of either or both radiation-induced point defects and defect clusters. In this work, molecular dynamic simulations are used to study the anisotropy of diffusion of self-interstitial atom clusters. Both near-equilibrium clusters generated by aggregation of self-interstitial atoms and cascade-induced clusters were considered. The cascade-induced clusters display more anisotropy than their counterparts produced by aggregation. In addition to 1-dimensional diffusing clusters, 2-dimensional diffusing clusters were observed. Using our molecular dynamic simulations, the input parameters for the "self-interstitial atom cluster bias" rate-theory model were estimated. The radiation-induced growth strains predicted using this model are largely consistent with experiments, but are highly sensitive to the choice of interatomic interaction potential., Comment: Manuscript and Supplementary Materials included

The methodology discussed in this paper aims to enhance choice models' comprehensiveness and explanatory power for forecasting choice outcomes. To achieve these, we have developed a data-driven method that leverages machine learning procedures for identifying the most effective representation of variables in mode choice empirical probability specifications. The methodology will show its significance, particularly in the face of big data and an abundance of variables where it can search through many candidate models. Furthermore, this study will have potential applications in transportation planning and policy-making, which will be achieved by introducing a sparse identification method that looks for the sparsest specification ( parsimonious model ) in the domain of candidate functions. Finally, this paper applies the method to synthetic choice data as a proof of concept. We perform two experiments and show that if the functional form used to generate the synthetic data lies in the domain of base functions, the methodology can recover that. Otherwise, the method will raise a red flag by outputting small coefficients ( near zero ) for base functions.

Software bots fulfill an important role in collective software development, and their adoption by developers promises increased productivity. Past research has identified that bots that communicate too often can irritate developers, which affects the utility of the bot. However, it is not clear what other properties of human-bot collaboration affect developers' preferences, or what impact these properties might have. The main idea of this paper is to explore characteristics affecting developer preferences for interactions between humans and bots, in the context of GitHub pull requests. We carried out an exploratory sequential study with interviews and a subsequent vignette-based survey. We find developers generally prefer bots that are personable but show little autonomy, however, more experienced developers tend to prefer more autonomous bots. Based on this empirical evidence, we recommend bot developers increase configuration options for bots so that individual developers and projects can configure bots to best align with their own preferences and project cultures., Comment: appears in proceedings of ICSE, 2023

This paper will present a model for pedestrian motion by defining a spacetime metric. This model considers the factors that are effective in the movement of pedestrians (such as obstacles, walls and other pedestrians) by defining a proper metric. In fact, the surrounding environment that affects the motion of pedestrians changes the flat(Euclidean) spacetime metric and, therefore, the shortest possible path to their destination. This change is such that pedestrians have a different feeling of time at any point and moment in the environment. Based on this feeling, they adjust their route to the destination so that they travel the shortest possible route in curved spacetime, which follows a geodesic. The contributions are :1. Defining spacetime metric for pedestrian movement.2. Visualizing the spacetime geometry for several timesteps.3. Introducing a parameter called the time factor and its physical meaning in terms of the pedestrian feeling of time. According to this paper formulation, pedestrians feel time differently at each position and moment, and the time factor measures this feeling of time. 4. Proposing a simple model with differential equations suitable for data assimilation methods while preserving the essential feature of collision avoidance behaviour.

There are different physics-based approaches for analysing pedestrian movement. Physics-based methods like statistical mechanics-based models apply the laws of physics to drive equations for analysing crowd behaviour. This paper will introduce a physics-based approach based on field theory as a new tool for crowd analysis to determine governing differential equations. Formulating the pedestrian movement with differential equations has a primary advantage for data assimilation techniques because some of these methods only work with models with analytical transition functions, which are obtained by incorporating a field approach. Furthermore, the field approach provides more generality since the field could be any scalar field. Several Lagrangians are presented in this work, and the primary purpose was to lay the groundwork for this new type of thinking. Furthermore, as pedestrian movement is mainly unregulated, the approach presented in the paper can be valuable for future development since the Lagrangian could be explicitly obtained for pedestrian movement spaces such as train stations and shopping malls. Finally, we discuss a general approach for predicting the action and how neural networks might play a role, which brings more flexibility and extendibility to our approach.