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Order acceptance under uncertainty is a critical decision-making problem at the interface between customer relationship management and production planning of order-driven manufacturing systems. In this work, a novel appr...

"Very few learning systems applied to problem solving have focused on learning operator definitions from the interaction with a completely unknown environment. In order to achieve better learning convergence, several age...

With the continuous innovation in household appliance technology and the improvement of living standards, the production of discarded household appliances has rapidly increased, making their recycling increasingly signif...

To bridge the gap in visualization and interactivity found in traditional chemistry tools, this study developed an AR-powered comic using differentiated instruction to help students navigate the spatial complexities of m...

This classroom action research examined whether the Teaching at the Right Level (TaRL) approach could improve fourth-grade students' learning outcomes in IPAS, especially on the topic of ethnic and cultural diversity, at...

Abstract The rapid adoption of encrypted communication protocols has raised privacy levels but has also dealt a significant blow to traditional intrusion detection systems that rely on payload inspection for anomaly dete...

Federated learning (FL) is a pivotal paradigm for financial institutions to collaboratively train models while preserving data privacy, yet standard frameworks are hampered by a triad of challenges: vulnerability to pois...

On-demand ride services or ride-sourcing services have been experiencing fast development and steadily reshaping the way people travel in the past decade. Various optimization algorithms, including reinforcement learning...

IntroductionWe address moral uncertainty in reinforcement learning (RL) by proposing a framework that integrates multiple ethical theories into decision-making. Existing approaches rely on single moral frameworks or hand...

The next generation of tactical networks (TNs) is poised to further leverage the key enablers of 5G and beyond 5G (B5G) technology, such as radio access network (RAN) slicing and the open RAN (O-RAN) paradigm, to unlock ...

Abstract To address the challenge of low adaptability in distribution network planning caused by significant regional differences in electricity consumption, this paper proposes a distribution network planning method bas...

Solving optimal control problems serves as the basic demand of industrial control tasks. Existing methods like model predictive control often suffer from heavy online computational burdens. Reinforcement learning has sho...

Mandatory lane changes (MLCs) pose significant challenges to trajectory planning at intersections, where vehicles are required to change lanes mid-block to reach designated turn lanes before the stop bar. MLCs often gene...

Abstract Cable-driven parallel robots (CDPRs) use cables instead of the rigid limbs of traditional parallel robots, thus processing a large potential workspace, easy to assemble and disassemble characteristics, and with ...

This study examines the role of artificial intelligence in improving the efficiency of photovoltaic (PV) systems through maximum power point tracking (MPPT) under dynamic operating conditions. A simulation-based model wa...

Abstract Accurate estimation of the shear capacity of steel fiber reinforced concrete (SFRC) beams without stirrups remains challenging because the response is governed by strongly coupled geometric, material, and reinfo...

Abstract Modern broadband access networks increasingly carry heterogeneous, delay-sensitive traffic generated by cloud-assisted applications, real-time communication services, and short-form multimedia exchanges. These w...

ABSTRACT Efficient exploration is critical in handling sparse rewards and partial observability in deep reinforcement learning. However, most existing intrinsic reward methods based on novelty rely on single‐step obser...

Background: Interprofessional collaborative practice (IPCP) is vital for perfecting patient safety and quality of care through effective teamwork, communication, and role clarity. Aims and Objectives: This study was d...

Abstract Background: Marker-based training is a widely used type of positive reinforcement training believed to improve learning outcomes by providing a precise and consistent marker for desired behavior. However, resear...