Towards a Multimodal Charging Network: Joint Planning of Charging Stations and Battery Swapping Stations for Electrified Ride-Hailing Fleets

This paper considers a multimodal charging network in which charging stations and battery swapping stations are built in tandem to support the electrified ride-hailing fleet in a synergistic manner. Our central thesis is predicated on the observation that charging stations are cost-effective, making them ideal for scaling up electric vehicles in ride-hailing fleets in the beginning, while battery swapping stations offer quick turnaround and can be deployed in tandem with charging stations to improve fleet utilization and reduce operational costs for the ride-hailing platform. To fulfill this vision, we consider a ride-hailing platform that expands the multimodal charging network with a multi-stage investment budget and operates a ride-hailing fleet to maximize its profit. A multi-stage network expansion model is proposed to characterize the coupled planning and operational decisions, which captures demand elasticity, passenger waiting time, charging and swapping waiting times, as well as their dependence on fleet status and charging infrastructure. The overall problem is formulated as a nonconvex program. Instead of pursuing the globally optimal solution, we establish a theoretical upper bound through relaxation, reformulation, and decomposition so that the global optimality of the derived solution to the nonconvex problem is verifiable. In the case study for Manhattan, we find that the two facilities complement each other and play different roles during the expansion of charging infrastructure: at the early stage, the platform always prioritizes building charging stations to electrify the fleet, after which it initiates the deployment of swapping stations to enhance fleet utilization. Compared to the charging-only case, ..

A Genome-Wide Characterization of MicroRNA Genes in Maize

MicroRNAs (miRNAs) are small, non-coding RNAs that play essential roles in plant growth, development, and stress response. We conducted a genome-wide survey of maize miRNA genes, characterizing their structure, expression, and evolution. Computational approaches based on homology and secondary structure modeling identified 150 high-confidence genes within 26 miRNA families. For 25 families, expression was verified by deep-sequencing of small RNA libraries that were prepared from an assortment of maize tissues. PCR–RACE amplification of 68 miRNA transcript precursors, representing 18 families conserved across several plant species, showed that splice variation and the use of alternative transcriptional start and stop sites is common within this class of genes. Comparison of sequence variation data from diverse maize inbred lines versus teosinte accessions suggest that the mature miRNAs are under strong purifying selection while the flanking sequences evolve equivalently to other genes. Since maize is derived from an ancient tetraploid, the effect of whole-genome duplication on miRNA evolution was examined. We found that, like protein-coding genes, duplicated miRNA genes underwent extensive gene-loss, with ∼35% of ancestral sites retained as duplicate homoeologous miRNA genes. This number is higher than that observed with protein-coding genes. A search for putative miRNA targets indicated bias towards genes in regulatory and metabolic pathways. As maize is one of the principal models for plant growth and development, this study will serve as a foundation for future research into the functional roles of miRNA genes

Optimal Spatiotemporal Pricing for Autonomous Mobility-on-Demand Systems: A Decomposition and Dynamic Programming Approach

This paper studies the optimal spatiotemporal pricing of autonomous mobility-on-demand (AMoD) systems. We consider a platform that operates a fleet of autonomous vehicles (AVs) and determines the pricing, rebalancing, and fleet sizing strategies over the transportation network in response to demand fluctuations. A network flow model is formulated to characterize the evolution of system states over space and time. Fundamental elements in AMoD markets are captured, including demand elasticity, passenger waiting time, vehicle-passenger matching, proactive vehicle rebalancing, and dynamic fleet sizing. The platform's profit maximization problem is cast as a constrained optimal control problem, which is highly nonconvex due to the nonlinear demand model and passenger-vehicle matching model. An integrated decomposition and dynamic programming approach is developed to tackle this optimal control problem, where we first relax the problem through a change of variables, then separate the relaxed problem into a few small-scale subproblems via dual decomposition, and finally obtain the exact solution to each relaxed subproblem through dynamic programming. Despite the non-convexity, the proposed method establishes a theoretical upper bound to evaluate the optimality gap of the obtained solution. The proposed approach is validated with numerical studies using real data from New York City. We find that the platform adopts distinct operation strategies in core and non-core areas of the city because of the asymmetric demand pattern. Furthermore, we also find that low-demand areas are less resilient than high-demand ones when demand surges unexpectedly, because the operator prioritizes supporting high-demand areas at the sacrifice of service quality in low-demand areas

Application of Evolutionary Encryption 2D Barcode Generation Technology in Agricultural Product Quality and Safety Traceability System

Two-dimensional (2D) barcode technology is an electronic tagging technology based on combination of computer and optical technology. It is an important way of information collection and input. 2D barcode technology has been widely used in various fields of logistics, production automation, and e-commerce, but it also has brought about a series of safety problems. Based on evolutionary encryption technology, this paper improved algorithm of traditional 2D barcode generation, to improve forgery-proof performance of 2D barcode. This algorithm is applied to agricultural products quality and safety traceability system and the results show that it is effective

Nonnatural protein–protein interaction-pair design by key residues grafting

Protein–protein interface design is one of the most exciting fields in protein science; however, designing nonnatural protein–protein interaction pairs remains difficult. In this article we report a de novo design of a nonnatural protein–protein interaction pair by scanning the Protein Data Bank for suitable scaffold proteins that can be used for grafting key interaction residues and can form stable complexes with the target protein after additional mutations. Using our design algorithm, an unrelated protein, rat PLCδ(1)-PH (pleckstrin homology domain of phospholipase C-δ1), was successfully designed to bind the human erythropoietin receptor (EPOR) after grafting the key interaction residues of human erythropoietin binding to EPOR. The designed mutants of rat PLCδ(1)-PH were expressed and purified to test their binding affinities with EPOR. A designed triple mutation of PLCδ(1)-PH (ERPH1) was found to bind EPOR with high affinity (K(D) of 24 nM and an IC(50) of 5.7 μM) both in vitro and in a cell-based assay, respectively, although the WT PLCδ(1)-PH did not show any detectable binding under the assay conditions. The in vitro binding affinities of the PLCδ(1)-PH mutants correlate qualitatively to the computational binding affinities, validating the design and the protein–protein interaction model. The successful practice of finding a proper protein scaffold and making it bind with EPOR demonstrates a prospective application in protein engineering targeting protein–protein interfaces