Clinical trials are critical for advancing medical treatments but remain prohibitively expensive and time-consuming.Accurate prediction of clinical trial outcomes can significantly reduce research and development costs and accelerate drug discovery.While recent deep learning models have shown promise by leveraging unstructured data, their black-box nature, lack of interpretability, and vulnerability to label leakage limit their practical use in high-stakes biomedical contexts.In this work, we propose AUTOCT 1 , a novel framework that combines the reasoning capabilities of large language models with the explainability of classical machine learning.AUTOCT autonomously generates, evaluates, and refines tabular features based on public information without human input.Our method uses Monte Carlo Tree Search to iteratively optimize predictive performance.Experimental results show that AUTOCT performs on par with or better than state-of-the-art methods on clinical trial prediction tasks within only a limited number of self-refinement iterations, establishing a new paradigm for scalable, interpretable, and cost-efficient clinical trial prediction.

Advancement in wireless technology has promoted the emergence of the Internet of Things (IoT). Specifically, radio- frequency energy harvesting can accelerate the development of IoT technology by enabling the use of miniaturized battery- less sensor nodes. To ensure efficient energy harvesting, the impedance of the antenna and the rectifier should be matched through an impedance matching network. We present RectBoost, which is a dynamically adaptive matching network, for optimum harvesting performance of rectenna. RectBoost monitors the output voltage of the rectifier and accordingly tunes the impedance matching mode. This technique helps the rectenna overcome early state impedance mismatch by dynamically tuning matching networks. RectBoost provides two main advantages over prior works - output voltage and charging speed boost. Simulation results show that the output voltage is increased by 7 times, and the charging speed is improved by 48 times compared with those of the methods mentioned in prior works.

We present the design, implementation, and evaluation of μmedIC, a fully-integrated wireless and batteryless micro-implanted sensor. The sensor powers up by harvesting energy from RF signals and communicates at near-zero power via backscatter. In contrast to prior designs which cannot operate across various in-body environments, our sensor can self-reconfigure to adapt to different tissues and channel conditions. This adaptation is made possible by two key innovations: a reprogrammable antenna that can tune its energy harvesting resonance to surrounding tissues, and a backscatter rate adaptation protocol that closes the feedback loop by tracking circuit-level sensor hints.