The field of data structures and algorithms is witnessing significant developments, with a focus on improving efficiency, scalability, and performance. Researchers are exploring innovative solutions to address the challenges posed by update-heavy workloads, temporal graph mining, and concurrent data structures. Notably, there is a growing interest in designing adaptive data structures that can dynamically adjust to access patterns, as well as developing algorithms that can efficiently handle temporal information and range queries. These advancements have the potential to impact various applications, including networked and distributed systems, graph mining, and database systems. Some noteworthy papers in this area include: MTASet, which introduces a tree-based set for efficient range queries in update-heavy workloads, achieving up to 2x better performance than existing solutions. TIMEST, which presents a fast and accurate estimation algorithm for counting temporal motifs in temporal networks, exhibiting an average speedup of 28x over state-of-the-art GPU implementations. Bridging Cache-Friendliness and Concurrency, which proposes a locality-optimized in-memory B-skiplist that enhances cache locality and performance while preserving simplicity, achieving between 2x-9x higher throughput compared to state-of-the-art concurrent skiplist implementations.