The rapid growth of e-commerce has intensified the demand for efficient urban logistics. Electric Vehicles (EVs), with their eco-friendly and high-efficiency features, have emerged as a promising solution for improving urban logistics efficiency. However, due to their limited battery capacity, EVs often require recharging during operations, and improper charging decisions may lead to delivery delays, resulting in a loss of platform revenue. In this paper, we explore a novel EV Charging-Aware Task Assignment (ECTA) problem in urban logistics scenarios, where the objective is to maximize platform revenue by ensuring timely task completion while meeting the charging needs of EVs. To address this challenge, we present e-Charge, an efficient two-stage framework that enables real-time optimization of two continuous processes: task assignment and charging decision. For task assignment, which focuses on matching tasks to suitable EVs, we construct a hybrid weight model that incorporates charging penalties to calculate matching weights for EVs in both active and charging states, thus improving task assignment quality. Additionally, we implement an effective vehicle selection strategy to expedite the matching process, ensuring the efficiency of task assignment. For charging decision, which focuses on determining when and where EVs should be charged, we propose a multi-agent reinforcement learning (MARL) approach to dynamically select the charging timing for EVs. To further enhance decision-making quality, we devise a hierarchical communication graph that enables better collaboration between EVs and facilitates adaptive charging decisions. Finally, extensive experiments demonstrate thate-Chargesignificantly outperforms compared methods, achieving higher revenue and task completion ratio across a wide range of parameter settings.

Social images shared on social media are often associated with geo-tagged information and text descriptions. Given a set of keywords and a spatial location, geo-tagged social image search can retrieve top-k image objects that best match query parameters in terms of spatial distance and tag similarity of social images. However, due to improper parameter settings, users may notice that some expected images are missing and wonder why these objects do not appear in the query results. This paper studies the why-not top-k social image search question and proposes efficient query refinement algorithms, aiming to minimally modify users' initial queries to reintroduce missing objects. We first develop a baseline algorithm that traverses each possible query parameter sequentially to find the best refinement parameters. Then, we propose a fast search algorithm with two optimization strategies named lower ranking nodes pruning and early stop pruning, which can improve performance by quickly removing low-ranking social images. In addition, we propose an efficient boundary search algorithm that can determine the ranking of missing images at a low time cost. We also extend the proposed techniques to handle multiple missing images. Extensive experimental results demonstrate that the proposed solution is two orders of magnitude faster than baseline and is effective in a wide range of settings.