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DESs can swell and sulfate cellulose, greatly reduce the production energy consumption of CNF, and successfully used CNF in food packaging film.

Alibaba Group pioneered integrated online and offline retail models to allow customers to place online orders of e-commerce and grocery products at its participating stores or restaurants for rapid delivery—in some cases, in as little as 30 minutes after an order has been placed. To meet these service commitments, quick online routing decisions must be made to optimize order picking routes at warehouses and delivery routes for drivers. The solutions to these routing problems are complicated by stringent service commitments, uncertainties, and complex operations in warehouses with limited space. Alibaba has developed a set of algorithms for vehicle routing problems (VRPs), which include an open-architecture adaptive large neighborhood search to support the solution of variants of routing problems and a deep learning-based approach that trains neural network models offline to generate almost instantaneous solutions online. These algorithms have been implemented to solve VRPs in several Alibaba subsidiaries, have generated more than $50 million in annual financial savings, and are applicable to the broader logistics industry. The success of these algorithms has fermented an inner-source community of operations researchers within Alibaba, boosted the confidence of the company’s executives in operations research, and made operations research one of the core competencies of Alibaba Group.

Microneedles (MNs) have attracted the interest of researchers. Polymeric MNs offer tremendous promise as drug delivery vehicles for bio-applications because of their high loading capacity, strong patient adherence, excellent biodegradability and biocompatibility, low toxicity, and extremely cheap cost. Incorporating enhanced-property nanomaterials into polymeric MNs matrix increases their features such as better mechanical strength, sustained drug delivery, lower toxicity, and higher therapeutic effects, therefore considerably increasing their biomedical application. This paper discusses polymeric MN fabrication techniques and the present status of polymeric MNs as a delivery method for enhanced drug delivery in cancer therapeutic applications. Furthermore, the opportunities and challenges of polymeric MNs for improved drug delivery in cancer therapy are highlighted.

Clean technology products (e.g., electric vehicles and solar photovoltaic panels) are key to sustainable development of sectors such as transportation and energy. Often, adoption of such products relies on development of supporting service infrastructure, for example, charging networks or energy-storage systems, and is often hindered by the “chicken-and-egg” dilemma: firms are reluctant to make capital-intensive investments before sufficient consumer adoption, and consumers hesitate to adopt without supporting infrastructure. Aiming to overcome this issue, the article examines the efficacy of two forms of government subsidies (as well as their combination), namely service infrastructure subsidy and product subsidy. Although both types of subsidies are generally conducive to clean-technology adoption, the study finds that these subsidies can be detrimental under certain conditions. In particular, the former may cause early adopters to delay their purchase, and the latter may interfere with the firm’s investment incentives and lead to reduced overall adoption. The article identifies the optimal subsidy strategy that follows a “sandwich rule:” it is optimal to provide only the product subsidy when the infrastructure deployment cost is sufficiently high or sufficiently low; however, it is optimal to subsidize both if the cost falls in the moderate range.

We propose two recommendation algorithms for e-commerce with supply limits, a scenario that has not been intensively studied in the literature. One algorithm is a linear programming-based algorithm that uses historical data to approximate customer arrival patterns and generate shadow prices for inventories. The price of inventory can be introduced to existing recommendation algorithms to obtain adjusted rankings for recommendation. The other algorithm balances expected revenue and inventory consumption, and it uses a simple penalty function to reduce the chance of recommending low-inventory-level products. Both algorithms are suitable for online recommendation systems for grocery stores with both online and offline channels, and can incorporate the features of perishable products, which need to be sold within limited time. Both algorithms are tested in a simulation using estimated parameters from Freshippo, a supermarket owned by the Alibaba Group. The numerical results show that both algorithms can generate higher sales volume and higher revenue.

Problem definition: Many cities worldwide are embracing electric vehicle (EV) sharing as a flexible and sustainable means of urban transit. However, it remains challenging for the operators to char...

Problem definition: Many cities worldwide are embracing electric vehicle (EV) sharing as a flexible and sustainable means of urban transit. However, it remains challenging for the operators to charge the fleet due to limited or costly access to charging facilities. In this paper, we focus on answering the core question---how to charge the fleet to make EV sharing viable and profitable. Academic/Practical relevance: Our work is motivated by the setback that struck San Diego, California, where car2go ceased its EV sharing operations. We integrate charging infrastructure planning and vehicle repositioning operations that were often considered separately. More interestingly, our modeling emphasizes the operator-controlled charging operations and customers' EV picking behavior, which are both central to EV sharing but were largely overlooked. Methodology: Supported by the real data of car2go, we develop a queueing network model that characterizes how customers endogenously pick EVs based on energy levels, and how the operator implements a charging up-to policy. The integrated queueing-location model leads to a nonlinear optimization program. We then propose both lower- and upper-bound formulations as mixed-integer second order cone programs, which are computationally tractable and result in a small optimality gap when the fleet size is adequate. Results: We learn lessons from the setback of car2go in San Diego. We find that the viability of EV sharing can be enhanced by concentrating limited charger resources at selected locations. Charging EVs either in a proactive fashion or at the 40% recharge threshold (rather than car2go's policy of charging EVs only when their energy level drops below 20%) can boost the profit by more than 15%. Moreover, sufficient charger availability is crucial when collaborating with a public charger network. Increasing the charging power relieves the charger resource constraint, whereas extending per-charge range or adopting unmanned repositioning improves profitability. Finally, we discuss how EV sharing operations depend on the urban spatial structure, compared with conventional car sharing. Managerial implications: We demonstrate a data-verified and high-granularity modeling approach. Both the high-level planning guidelines and operational policies can be useful for practitioners. We also highlight the value of jointly managing demand fulfillment and EV charging.

The density of the packaging is recently increasing for miniaturization of the electronic system, especially 3D SiP (System in Package). Interposer is a significant research area for 3D packaging which always uses three kinds of materials including silicon, glass and organic. Currently, there are a lot of researches about silicon interposer because the CTE (Coefficient Thermal Expansion) and process is match with semiconductor process. But, from the index of each material, glass interposer is very promising for electronic packaging. Glass interposer has chemical stability, low material cost, high isolation property and low relative dielectric constant. Moreover, the CTE of glass is range value that can be a buffer between IC and substrate. The coefficient of thermal expansion of glass is more similar to silicon's, compared to the organic materials. One of key technology of developing glass interposer for TGV application is metallization especially adhesion layer which can have a good adhesion with glass. This paper presents the adhesion of metallization layer on Pyrex glass. Several metal thin films such as Ti, TiW, Cr, were separately deposited on glass wafer by sputtering process. Scratch test has performed to evaluate adhesion strength of thin film on glass wafer. Effects of metal type, thickness and glass roughness on adhesion strength are studied. Environmental reliability tests are also performed to confirm evolution of the adhesion layer strength during thermal shock experiments. Finally, by a series of experiments a better material as adhesion layer have been found, the relationship between the surface roughness, thickness and adhesion performance have been figured out and the thermal shock aging profile would be found.