Route Selection and Distribution Cost of Express Delivery: An Urban Metro Network Based Study

Route selection and distribution costs of express delivery based on the urban metro network, referred to as metro express delivery (MeD), is addressed in this study. Considering the characteristics of express delivery transportation and the complexity of the urban metro network, three distribution modes of different time periods are proposed and a strict integrated integer linear programming model is developed to minimize total distribution costs. To effectively solve the optimal problem, a standard genetic algorithm was improved and designed. Finally, the Ningbo subway network is used as an example to confirm the practicability and effectiveness of the model and algorithm. The results show that when the distribution number of express delivery packages is 1980, the three different MeD modes can reduce transportation costs by 40.5%, 62.0%, and 59.0%, respectively. The results of the case analysis will help guide express companies to collaborate with the urban metro network and choose the corresponding delivery mode according to the number of express deliveries required

Prototypical Kernel Learning and Open-set Foreground Perception for Generalized Few-shot Semantic Segmentation

Generalized Few-shot Semantic Segmentation (GFSS) extends Few-shot Semantic Segmentation (FSS) to simultaneously segment unseen classes and seen classes during evaluation. Previous works leverage additional branch or prototypical aggregation to eliminate the constrained setting of FSS. However, representation division and embedding prejudice, which heavily results in poor performance of GFSS, have not been synthetical considered. We address the aforementioned problems by jointing the prototypical kernel learning and open-set foreground perception. Specifically, a group of learnable kernels is proposed to perform segmentation with each kernel in charge of a stuff class. Then, we explore to merge the prototypical learning to the update of base-class kernels, which is consistent with the prototype knowledge aggregation of few-shot novel classes. In addition, a foreground contextual perception module cooperating with conditional bias based inference is adopted to perform class-agnostic as well as open-set foreground detection, thus to mitigate the embedding prejudice and prevent novel targets from being misclassified as background. Moreover, we also adjust our method to the Class Incremental Few-shot Semantic Segmentation (CIFSS) which takes the knowledge of novel classes in a incremental stream. Extensive experiments on PASCAL-5i and COCO-20i datasets demonstrate that our method performs better than previous state-of-the-art.Comment: Accepted by ICCV202

Nitrogen-Doped Ti3_3C2_2Tx_x Coated with a Molecularly Imprinted Polymer as Efficient Cathode Material for Lithium-Sulfur Batteries

Due to their high energy density (2600 Wh/kg), low cost, and low environmental impact, lithium-sulfur batteries are considered a promising alternative to lithium-ion batteries. However, their commercial viability remains a formidable scientific challenge mainly because of the sluggish reaction kinetics at the cathode and the so-called "shuttling effect" of soluble polysulfides, which results in capacity decay and a shortened lifespan. Herein, molecular imprinting with Li$_2$S$_8$ as a target molecule in combination with a two-dimensional material, MXene, is proposed to overcome these issues. Molecularly imprinted polymer-coated nitrogen-doped Ti-based MXene was successfully synthesized and demonstrated to exhibit an appealing electrochemical performance, namely a high specific capacity of 1095 mAh/g at 0.1 C and an extended cycling stability (300 mAh/g at 1.0 C after 300 cycles). X-ray photoelectron spectroscopy was applied to elucidate the underlying mechanisms and proved that Li$_2$S$_8$-imprinted polymer polyacrylamide serves as a polysulfide trap through strong chemical affinity towards the long-chain lithium polysulfides, while N-doped Ti-based MXene promotes the redox kinetics by accelerating the conversion of lithium polysulfides. This distinct interfacial strategy is expected to result in more effective and stable Li-S batteries

Spatially explicit analysis identifies significant potential for bioenergy with carbon capture and storage in China

As China ramped-up coal power capacities rapidly while CO2 emissions need to decline, these capacities would turn into stranded assets. To deal with this risk, a promising option is to retrofit these capacities to co-fire with biomass and eventually upgrade to CCS operation (BECCS), but the feasibility is debated with respect to negative impacts on broader sustainability issues. Here we present a data-rich spatially explicit approach to estimate the marginal cost curve for decarbonizing the power sector in China with BECCS. We identify a potential of 222 GW of power capacities in 2836 counties generated by co-firing 0.9 Gt of biomass from the same county, with half being agricultural residues. Our spatially explicit method helps to reduce uncertainty in the economic costs and emissions of BECCS, identify the best opportunities for bioenergy and show the limitations by logistical challenges to achieve carbon neutrality in the power sector with large-scale BECCS in China

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

The Ninth Visual Object Tracking VOT2021 Challenge Results

acceptedVersionPeer reviewe