Since the advent of LLM's like GPT4 everyone in various industries has been trying to harness their power. Healthcare is an industry where this is a specifically challenging problem due to the high accuracy requirements. Prompt Engineering is a common technique used to design instructions for model responses, however, its challenges lie in the fact that the generic models may not be trained to accurately execute these specific tasks. We will present our journey of developing a cost-effective medical LLM, surpassing GPT4 in medical note-writing tasks. We'll touch upon our trials with medical prompt engineering, GPT4's limitations, and training an optimized LLM for specific medical tasks. We'll showcase multiple comparisons on model sizes, training data, and pipeline designs that enabled us to outperform GPT4 with smaller models, maintaining precision, reducing biases, preventing hallucinations, and enhancing note-writing style.

Capture effect can improve the throughput of random multiple access protocols. An evaluation model of the capture effect is proposed for coherent BPSK demodulation. The model introduces the concept of carrier synchronisation probability conditioned upon the carrier-to-interference ratio into the evaluation of the capture probability. Moreover, the effect of bit-sync offset of interfering packets on the capture probability is also taken into account. An attempt is made by means of the proposed model to investigate the capture effect in the LEO satellite channel. The result shows that a relatively large link margin can result in a remarkable capture probability, and consequently benefit slotted ALOHA protocols with a substantial throughput improvement.