Long Chain-of-Thought (CoT) reasoning has shown great promise in complex reasoning tasks, but its application to medical decision-making presents unique challenges. Unlike structured tasks relying on static verification frameworks, medical decision-making requires dynamic validation through longitudinal clinical outcomes, exhibiting temporal-causal dependencies that complicate the verification of reasoning processes. Therefore, we introduce a novel data construction framework specifically designed for medical decision-making. First, the framework analyzes real-world clinical cases to construct a timeline of medical events and identify critical decision points, including examination, diagnosis, and treatment. Subsequently, it employs a clinical causality-aware strategy to generate decision-making questions at the identified points, along with reasoning traces and corresponding answers. Finally, information drawn from future nodes serves as clinical logic-constrained criteria to re-evaluate and refine the soundness of the generated reasoning and responses. Building on this, we present OncoCoT, an oncologic decision-making dataset derived from clinical records over the past four years across eight common cancer types. Furthermore, we distill a subset of OncoCoT into a dedicated benchmark, OncoEval, to facilitate systematic evaluation of clinical reasoning capabilities in LLMs. Evaluation results show that existing state-of-the-art reasoning models, such as Deepseek-r1 and GPT-o3, exhibit limited capability in addressing clinical problems in OncoEval, highlighting the need for further improvement.

Leveraging breakthroughs in wireless power transfer technology, deploying Mobile Chargers (MCs) to charge sensor nodes can significantly prolong the operational lifetime of wireless sensor networks. Directional Mobile Chargers (DMCs), which integrate directional antennas, can focus radio frequency energy along designated directions, thereby boosting charging efficiency. Most existing works assume that DMCs emit a single energy beam; however, in practice, some DMC platforms can generate multiple sectorial- like energy beams, enabling a novel Multi-Beam DMC (MB-DMC)-based charging paradigm for Wireless Rechargeable Sensor Networks (WRSNs). This paradigm introduces a critical challenge: determining a minimum-size antenna direction set for the DMC while assuring that it is functionally equivalent to the infinite$[0,2\pi )$direction space in maintaining charging schedules with minimal energy loss. This paper systematically investigates the Multi-Sectorial-Beam DMC Charging Scheduling (MSBDCS) problem in MB-DMC-enhanced WRSNs (MB-WRSNs) and proves its NP-hardness. Furthermore, to address this challenge, we propose a Multi-Sectorial Beam Antenna Direction Selection (MSBADS) algorithm by efficiently exploiting geometric properties of local node set distribution, and theoretically prove its optimality in generating a minimum-size charging direction set that is functionally equivalent to the$[0,2\pi )$space. By integrating MSBADS into a four-step charging scheduling framework, we develop our MSBADS-based four-step Charging Scheduling (MSB-4S) algorithm to address the MSBDCS problem in MB-WRSNs. Extensive simulations and testbed experiments demonstrate that MSB-4S outperforms state-of-the-art baseline schemes by over 22% in energy efficiency improvement and more than 14% in scheduling time reduction.