Two socioeconomic transformations, namely, the booms in the sharing economy and retail e-commerce, lead to the prospect where shared mobility of passenger cars prevails throughout urban areas for home delivery services. Logistics service providers as well as local governments are in need of evaluating the potentially substantial impacts of this mode shift, given their economic objectives and environmental concerns. This paper addresses this need by providing new logistics planning models and managerial insights. These models characterize open-loop car routes, car drivers’ wage-response behavior, interplay with the ride-share market, and optimal sizes of service zones within which passenger vehicles pick up goods and fulfill the last-mile delivery. Based on theoretical analysis and empirical estimates in a realistic setting, the findings suggest that crowdsourcing shared mobility is not as scalable as the conventional truck-only system in terms of the operating cost. However, a transition to this paradigm has the potential for creating economic benefits by reducing the truck fleet size and exploiting additional operational flexibilities (e.g., avoiding high-demand areas and peak hours, adjusting vehicle loading capacities, etc.). These insights are insignificantly affected by the dynamic adjustment of wages and prices of the ride-share market. If entering into this paradigm, greenhouse gas emissions may increase because of prolonged car trip distance; on the other hand, even exclusively minimizing operating costs incurs only slightly more emissions than exclusively minimizing emissions. The online appendix is available at https://doi.org/10.1287/msom.2017.0683 .

Aqueous Zn batteries with ideal energy density and absolute safety are deemed the most promising candidates for next-generation energy storage systems. Nevertheless, stubborn dendrite formation and notorious parasitic reactions on the Zn metal anode have significantly compromised the Coulombic efficiency (CE) and cycling stability, severely impeding the Zn metal batteries from being deployed in the proposed applications. Herein, instead of random growth of Zn dendrites, a guided preferential growth of planar Zn layers is accomplished via atomic-scale matching of the surface lattice between the hexagonal close-packed (hcp) Zn(002) and face-centered cubic (fcc) Cu(100) crystal planes, as well as underpotential deposition (UPD)-enabled zincophilicity. The underlying mechanism of uniform Zn plating/stripping on the Cu(100) surface is demonstrated by ab initio molecular dynamics simulations and density functional theory calculations. The results show that each Zn atom layer is driven to grow along the exposed closest packed plane (002) in hcp Zn metal with a low lattice mismatch with Cu(100), leading to compact and planar Zn deposition. In situ optical visualization inspection is adopted to monitor the dynamic morphology evolution of such planar Zn layers. With this surface texture, the Zn anode exhibits exceptional reversibility with an ultrahigh Coulombic efficiency (CE) of 99.9%. The MnO2//Zn@Cu­(100) full battery delivers long cycling stability over 548 cycles and outstanding specific energy and power density (112.5 Wh kg–1 even at 9897.1 W kg–1). This work is expected to address the issues associated with Zn metal anodes and promote the development of high-energy rechargeable Zn metal batteries.

We study how delivery data can be applied to improve the on-time performance of last-mile delivery services. Motivated by the delivery operations and data of a food delivery service provider, we discuss a framework that integrates travel-time predictors with order-assignment optimization. Such integration enables us to capture the driver’s routing behavior in practice as the driver’s decision-making process is often unobservable or intricate to model. Focusing on the order-assignment problem as an example, we discuss the classes of tractable predictors and prediction models that are highly compatible with the existing stochastic and robust optimization tools. We further provide reformulations of the integrated models, which can be efficiently solved with the proposed branch-and-price algorithm. Moreover, we propose two simple heuristics for the multiperiod order-assignment problem, and they are built upon single-period solutions. Using the delivery data, our numerical experiments on a real-world application not only demonstrate the superior performance of our proposed order-assignment models with travel-time predictors, but also highlight the importance of learning behavioral aspects from operational data. We find that a large sample size does not necessarily compensate for the misspecification of the driver’s routing behavior. This paper was accepted by Hamid Nazerzadeh, big data analytics.

Conventional imaging techniques adopt a rectilinear sampling approach, where a finite number of pixels are spread evenly across an entire field of view (FOV). Consequently, their imaging capabilities are limited by an inherent trade-off between the FOV and the resolving power. In contrast, a foveation technique allocates the limited resources (e.g., a finite number of pixels or transmission bandwidth) as a function of foveal eccentricities, which can significantly simplify the optical and electronic designs and reduce the data throughput, while the observer's ability to see fine details is maintained over the whole FOV. We explore an approach to a foveated imaging system design. Our approach approximates the spatially variant properties (i.e., resolution, contrast, and color sensitivities) of the human visual system with multiple low-cost off-the-shelf imaging sensors and maximizes the information throughput and bandwidth savings of the foveated system. We further validate our approach with the design of a compact dual-sensor foveated imaging system. A proof-of-concept bench prototype and experimental results are demonstrated.

Generalized belief propagation (GBP) is a region-based belief propagation algorithm which can get good convergence in Markov random fields. However, the computation time is too heavy to use in practical engineering applications. This paper proposes a method to accelerate the efficiency of GBP. A caching technique and chessboard passing strategy are used to speed up algorithm. Then, the direction set method which is used to reduce the complexity of computing clique messages from quadric to cubic. With such a strategy the processing speed can be greatly increased. Besides, it is the first attempt to apply GBP for solving the stereomatching problem. Experiments show that the proposed algorithm can speed up by 15+ times for typical stereo matching problem and infer a more plausible result.