The field of optimization and resource allocation is witnessing significant developments, with a focus on balancing efficiency and fairness in various scenarios. Researchers are exploring novel frameworks and algorithms to address complex problems, such as sensor scheduling, point set constructions, and spectrum sharing. The use of techniques like randomized games, volume arguments, and learning-augmented algorithms is becoming increasingly prevalent. Additionally, there is a growing interest in online optimization problems, including non-clairvoyant scheduling and online knapsack variants. Noteworthy papers in this area include: A Bouquet of Results on Maximum Range Sum, which introduces new techniques for approximating maximum range sum problems. Using Age of Information for Throughput Optimal Spectrum Sharing, which proposes a Whittle index-based scheduling policy for optimizing spectrum sharing. Online Learning for Optimizing AoI-Energy Tradeoff under Unknown Channel Statistics, which develops online learning-based algorithms for optimizing the tradeoff between age of information and energy consumption.