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In Australia, and internationally, mentoring is proffered as a powerful professional learning experience for both early career teachers (ECTs) and their mentors alike. However, authentically beneficial learning partnerships have proven challenging to achieve. This paper provides a theoretical and practical response to this issue, arguing the criticality of genuine conversations cultivated through balanced, non-hierarchical questioning to position ECTs and mentors as co-learners. Drawing on positioning theory in conjunction with the concept of genuine conversations, the contents of five recorded mentoring conversations involving Australian ECTs and mentors were analysed. These findings have important implications for mentoring practice across educational contexts. • Learning partnerships between early career teachers and mentors are challenging to achieve, regardless of experience. • Innovative use of positioning theory in conjunction with the concept of genuine conversations. • The criticality of genuine conversations cultivated through balanced, non-hierarchical questioning for learning partnerships. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive optimization framework for dynamic rail transit network operations with use of a rollout surrogate-approximate dynamic programming method. The optimization algorithm derives coordinated decisions of service schedules and train unit deployment with respect to prevailing passenger demand. Considering the computational effectiveness needed for real-time applications, a state-dependent surrogate function is incorporated to approximate the costs associated with operational decisions over future stages. The surrogate approximation is updated iteratively via a temporal difference learning process with feeding of observations made from the transit network. The proposed framework is implemented and tested on a real-world scenario in Hong Kong Light Rail Transit (LRT) network. The results reveal that the proposed framework is able to reduce significantly the total passengers' waiting times over existing plans with reasonable computational time via use of the surrogate approximation. This suggests the potential of the proposed optimizer for real time applications in large-scale rail transit networks. • A dynamic transit network optimization model for flexible train unit and schedule assignment. • A novel surrogate approximate dynamic programming with a cross-entropy solution framework. • A novel real-time solution framework with use of cross-entropy method incorporating state-dependent constraints. • A case study demonstrating the efficiency of the proposed framework for passengers in real-time. [ABSTRACT FROM AUTHOR]

This paper presents a Pareto optimization framework for routing and scheduling dynamic rail transit network services. We first develop an event-based dynamic transit network model that can capture the evolution of passenger demand and service operations with incorporation of passengers' transfers and recirculation of limited number of train vehicles over different service lines. A multi-objective optimizer is then built upon the transit model which seeks jointly the settings of service lines and schedules that could minimize passengers' journey times, transfer rates, and operator's cost. The problem is solved by a cross-entropy method (CEM) which samples potential solutions from statistically tractable distribution models with iterative updates via maximum likelihood. The operational constraints are explicitly incorporated in the solution process which enhances the feasibility of the sampled solutions and hence effectiveness of the computational process compared with other metaheuristics used in the literature. A CEM-based ranking algorithm is further developed for deriving the Pareto-frontiers for the multi-objective transit network routing and scheduling. The proposed framework is applied and tested on the Hong Kong Light Rail Transit (LRT) network using real world scenario data. The results reveal new insights on how the existing operational settings could be improved. The present study contributes to urban transit network service planning with advanced computational techniques. • A dynamic transit network model with passengers' transfer and vehicles' circulation. • A novel cross-entropy solution framework incorporating operational constraints. • A novel solution framework that can derive generic Pareto frontiers with multiple conflicting objectives. • A case study demonstrating the efficiency and practicality from both passengers' and operators' perspectives. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive control system for coordinated metro operations with flexible train composition by using a multi-agent deep reinforcement learning (MADRL) approach. The control problem is formulated as a Markov decision process (MDP) with multiple agents regulating different service lines in a metro network with passenger transfer. To ensure the overall computational effectiveness and stability of the control system, we adopt an actor–critic reinforcement learning framework in which each control agent is associated with a critic function for estimating future system states and an actor function deriving local operational decisions. The critics and actors in the MADRL are represented by multi-layer artificial neural networks (ANNs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is developed for training the actor and critic ANNs through successive simulated transitions over the entire metro network. The developed framework is tested with a real-world scenario in Bakerloo and Victoria Lines of London Underground, UK. Experiment results demonstrate that the proposed method can outperform previous centralized optimization and distributed control approaches in terms of solution quality and performance achieved. Further analysis shows the merits of MADRL for coordinated service regulation with flexible train composition. This study contributes to real-time coordinated metro network services with flexible train composition and advanced optimization techniques. • An adaptive rail transit control system with passengers' transfers and flexible train composition. • A novel modeling and optimization framework based on multi-agent deep reinforcement learning. • A computational framework with 'decentralized execution and centralized training' for effectiveness and stability. • Case study demonstrating the system efficiency and computational effectiveness of proposed algorithm over previous methods. [ABSTRACT FROM AUTHOR]