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Examining preservice teachers’ bar model representations for solving word problems involving fractions
No full textThe purpose of this mixed methods study is to explore elementary preservice teachers’ (PSTs’) use of bar models for solving problems involving fractions. As part of a larger project, a sample of 17 PSTs’ written work for 10 word problems was analyzed. A mediation analysis showed that bar model correctness had a direct effect on overall solution correctness, and bar model completeness had a direct effect on bar model correctness. However, bar model completeness had only an indirect effect on overall solution correctness. A qualitative analysis of the types of completeness and correctness errors that PSTs made when creating bar models yielded three categories of completeness errors and five categories of correctness errors. Most of these error types were general errors related to creating bar models for problems from any content domain, and the distribution of correct and incorrect overall solutions within these categories was approximately uniform. However, one error category, incorrectly representing a fractional relationship, was specific to the domain of fractions and impeded PSTs’ ability to generate a correct overall solution. This error reflects a key fraction concept, a misunderstanding of the unit whole, which has been well-documented in the literature as a conceptual difficulty for PSTs. Implications for research and teacher preparation are discussed
SPATIOTEMPORAL DYNAMICS OF CARBONATE CHEMISTRY IN RIVER AND GROUNDWATER INPUTS TO THE NORTHEASTERN MEDITERRANEAN COAST
No full textMicrofluidics Made Simple: A Low-Cost, High-Performance Biosensor Platform Using SPEs, MIPs, and Rapid Injection Molding
No full textTracing eastern gene flow and population structure in anatolian sheep: Archaeogenomic insights from the neolithic to the present day
No full textFree will is real—I could not have believed otherwise
No full textThis paper seeks to advance the compatibilist theory of free will, particularly building upon contributions by Christian List (and also Daniel Dennett) regarding the agential capacity to act otherwise. Emphasizing the importance of genuine agential abilities, it aims to conceptualize these abilities in empirically grounded terms, avoiding reliance on modal notions and eschewing an insurmountable divide between physical and psychological levels. To achieve this, the paper conceptualizes agential states as functions of decision-making processes that dissolve the agent’s uncertainty about the consequences of its actions. By modeling the generation and evaluation of multiple potential actions, this approach explicates a form of operational freedom embedded within the physics of information processing. Consequently, it resolves the tension between free will and determinism without isolating psychology from physical processes
ArcNav: Real-Time Curvature Based Map-Free Path Planning in Unstructured Environments for Unmanned Ground Vehicles
No full textAutonomous navigation in unstructured environments remains a fundamental challenge in robotics, particularly for skid-steer unmanned ground vehicles (UGVs), which must operate in unpredictable and dynamically changing terrains. This paper introduces ArcNav, a novel realtime, map-free path planning algorithm that enables UGVs to maneuver efficiently in unstructured environments without relying on precomputed maps. Inspired by Dubins and ReedsShepp curves, ArcNav dynamically adjusts turning radii based on real-time environmental conditions, optimizing both trajectory smoothness and energy efficiency
Task assignment strategies for capacitated agents engaged in lifelong pickup and delivery tasks
No full textIn this study, we tackled the task assignment problem in the capacity-enhanced version of Multi-Agent Pickup and Delivery (MAPD), a lifelong variant of the classical Multi-Agent Path Finding (MAPF) problem. Capacity-enhanced agents can carry multiple items, allowing them to operate several tasks simultaneously by visiting a sequence of pickup and delivery locations (i.e. waypoints) to fulfill their assignments. When determining the next task of the agent from the available options, a method encountered in the literature is to select the task with the nearest pickup location to the agent's current location. In this research, we suggest that improving task assignments of capacitated agents can significantly enhance the solution quality of multi-agent route plans in lifelong pickup and delivery scenarios. We propose novel task assignment strategies that incorporate waypoints as a factor in the task selection process. We devised three groups of task assignment methods based on Closeness Centrality, Hausdorff Distance, and Cost Estimation within the context of the complete Token Passing with Multiple Capacity (TPMC) algorithm. We evaluated the methods in small and large-scale automated warehouse simulations, assessing their effectiveness in terms of makespan against the contemporary task selection method and one another. As a result of our experiments, the Closeness Centrality class of heuristics failed to enhance solution quality in large majority of cases. The Average Hausdorff Distance heuristic achieved good outcomes in scenarios with higher capacity agents. The Cost-Based Estimation method demonstrated significant improvements across all scenarios