In China, the number of riders in the on-demand delivery industry has surpassed ten million. Ensuring that these riders earn a decent income can enhance their financial security, reduce poverty, and promote social equity and stability. Due to ease of use, lower-cost maintenance and environmental friendliness, electric bicycles (e-bikes) are the primary mode of transportation for delivery riders. However, these riders frequently encounter depleted batteries due to limited capacity and prolonged charging times, necessitating inconvenient swaps or recharges during deliveries. To address this issue, we propose the e-bike Battery Swap-as-a-Service (eBaaS), an innovative battery-swapping system that leverages an intelligent AIoT network for seamless battery swapping at distributed locations across urban areas. eBaaS integrates edge-cloud collaboration, battery resource allocation, battery anomaly detection, and battery range prediction to minimize downtime and reduce unnecessary mileage. While eBaaS's potential benefits are evident, there has been a lack of robust methods to quantify its impact. Thus, we further developed the eBaaS Impact Evaluation Method (EIEM), the first comprehensive model to address this gap. EIEM analyzes data from approximately 260,000 delivery riders and 5 million riding trajectories. Findings indicate that eBaaS reduces average invalid mileage by 6 km and increases the order volume by an average of over 20% daily per e-bike rider. Meanwhile, the annual electricity savings result in a reduction of 2.74 million kilograms of carbon emissions for 260,000 riders. The eBaaS system is therefore significantly beneficial for environmental conservation and sustainable urban development.

This Paper introduces a novel Enhanced Visual Retrieval and Analysis for Charging Efficiency (EVRACE) system, which presents a novel two-stage framework for visual retrieval and advanced analytics for massive charging curves of electric vehicles. The EVRACE is becoming an important system for enhancing battery performance management, detecting and mitigating charging anomalies, and informing strategic decision-making for stakeholders in the EV ecosystem. EVRACE utilizes deep time-series clustering to categorize charging curves with distinct characteristics and retrieves the most relevant cluster and the top k curves for comprehensive data analysis and risk assessment. This innovative framework substantially enhances retrieval efficiency, mitigates computational complexity, and offers a robust solution for real-time processing of large-scale charging data. Validation using real-world datasets demonstrates that the EVRACE system significantly improves retrieval efficiency and achieves high accuracy in anomaly detection compared to traditional methods.

Hashed Timelock Contract (HTLC) is a widely-used protocol for cross-chain asset swaps. However, it relies on serial asset-locking to guarantee atomicity, which causes high latency and poor fairness. Aiming at the drawbacks of HTLC, we propose Lilac, a cross-chain asset swap protocol that supports parallel asset-locking. Lilac replaces the unique asset-unlocking credential in HTLC with multiple sub-credentials generated by all participating users, and the sequence of sub-credentials is used as the complete asset-unlocking credential. Users obtain the complete credential only when all assets have been locked, and the credential construction process is independent of the order in which assets are locked, so atomicity can be guaranteed when users lock their assets in parallel. Experiments show when a swap involves 2 to 4 blockchains, Lilac reduces the swap latency by 36.75% to 62.20%. Moreover, Lilac reduces the waiting time gap between different users so the fairness of a swap is improved.

On-demand food delivery (OFD) platforms have greatly impacted the food service industry, where OFD recommendation systems play a central role in enhancing user experience and raising revenues. OFD recommendation, compared with existing online e-commerce recommendation systems, needs to put more emphasis on fulfillment time related variables, because the order fulfillment cycle time (OFCT) which refers to the time elapsed between a user placing a food order and receiving the food significantly influences a user's choice from the recommended items. In this paper, we investigate the OFCT related information and propose a Fulfillment-Time-Aware Personalized Ranking (FTAPR) method for recommendation. FTAPR mainly consists of three components. First, Transformers are used to estimate OFCT based on a large amount of user order sequences. Then, the predicted OFCT and other OFCT related features are fused and encoded by a deep & cross network to learn fulfillment time related feature representation. At the last step, the time bias representation from the deep & cross network is integrated into the ranking system to deliver final search results. Extensive offline and online experiments on real-world datasets collected from one of China's largest OFD platforms Ele.me show the superiority of our model, e.g., an online A/B testing shows that FTAPR brings 1.3% and 2.5% gains in CTR and CVR compared with baselines.

A relational database, consisting of multiple tables, provides heterogeneous information across various entities, widely used in real-world services. This paper studies the supervised learning task on multiple tables, aiming to predict one label column with the help of multiple-tabular data. However, classical ML techniques mainly focus on single-tabular data. Multiple-tabular data refers to many-to-many mapping among joinable attributes and n-ary relations, which cannot be utilized directly by classical ML techniques. Besides, current graph techniques, like heterogeneous information network (HIN) and graph neural networks (GNN), are infeasible to be deployed directly and automatically in a multi-table environment, which limits the learning on databases.