Research on the Evaluation of Green Logistics Based on Cloud Model

Businesses According to the theory of sustainable development, combining with the current development status of the social logistics industry and the characteristics of green logistics, constructing a green logistics evaluation index system. Using cloud model and Delphi method to calculate the cloud weight of green logistics evaluation index, qualitative and quantitative conversion of evaluation index is realized by cloud generator. Take Jiangsu Province as an example to do empirical research, using the cloud model and its algorithm to get the evaluation cloud of green logistics, observing the evaluation result directly and discovering problem easy by comparing the evaluation cloud chart with ruler cloud chart. The evaluation results show that the cloud model is more reasonable, and the credibility of the evaluation results is improved

SMART: A Situation Model for Algebra Story Problems via Attributed Grammar

Solving algebra story problems remains a challenging task in artificial intelligence, which requires a detailed understanding of real-world situations and a strong mathematical reasoning capability. Previous neural solvers of math word problems directly translate problem texts into equations, lacking an explicit interpretation of the situations, and often fail to handle more sophisticated situations. To address such limits of neural solvers, we introduce the concept of a \emph{situation model}, which originates from psychology studies to represent the mental states of humans in problem-solving, and propose \emph{SMART}, which adopts attributed grammar as the representation of situation models for algebra story problems. Specifically, we first train an information extraction module to extract nodes, attributes, and relations from problem texts and then generate a parse graph based on a pre-defined attributed grammar. An iterative learning strategy is also proposed to improve the performance of SMART further. To rigorously study this task, we carefully curate a new dataset named \emph{ASP6.6k}. Experimental results on ASP6.6k show that the proposed model outperforms all previous neural solvers by a large margin while preserving much better interpretability. To test these models' generalization capability, we also design an out-of-distribution (OOD) evaluation, in which problems are more complex than those in the training set. Our model exceeds state-of-the-art models by 17\% in the OOD evaluation, demonstrating its superior generalization ability

A HINT from Arithmetic: On Systematic Generalization of Perception, Syntax, and Semantics

Inspired by humans' remarkable ability to master arithmetic and generalize to unseen problems, we present a new dataset, HINT, to study machines' capability of learning generalizable concepts at three different levels: perception, syntax, and semantics. In particular, concepts in HINT, including both digits and operators, are required to learn in a weakly-supervised fashion: Only the final results of handwriting expressions are provided as supervision. Learning agents need to reckon how concepts are perceived from raw signals such as images (i.e., perception), how multiple concepts are structurally combined to form a valid expression (i.e., syntax), and how concepts are realized to afford various reasoning tasks (i.e., semantics). With a focus on systematic generalization, we carefully design a five-fold test set to evaluate both the interpolation and the extrapolation of learned concepts. To tackle this challenging problem, we propose a neural-symbolic system by integrating neural networks with grammar parsing and program synthesis, learned by a novel deduction--abduction strategy. In experiments, the proposed neural-symbolic system demonstrates strong generalization capability and significantly outperforms end-to-end neural methods like RNN and Transformer. The results also indicate the significance of recursive priors for extrapolation on syntax and semantics.Comment: Preliminary wor

Efficient Neural Neighborhood Search for Pickup and Delivery Problems

We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs). In specific, we design a powerful Synthesis Attention that allows the vanilla self-attention to synthesize various types of features regarding a route solution. We also exploit two customized decoders that automatically learn to perform removal and reinsertion of a pickup-delivery node pair to tackle the precedence constraint. Additionally, a diversity enhancement scheme is leveraged to further ameliorate the performance. Our N2S is generic, and extensive experiments on two canonical PDP variants show that it can produce state-of-the-art results among existing neural methods. Moreover, it even outstrips the well-known LKH3 solver on the more constrained PDP variant. Our implementation for N2S is available online.Comment: Accepted at IJCAI 2022 (short oral

Calculation method for holding prestress of corroded prestressed anchor cable in long-term operation slope

Due to the rich water in the weathered layer of the free section, the prestressed anchor cable of the long-term operating slope is severely corroded and its mechanical properties are deteriorated, affecting the stability of the slope. Based on a certain number of long-term operation highway anchor cable excavation tests, the author found that the free section of the anchor cable orifice was seriously corroded. Currently, there is very little research on the relationship between the holding capacity of anchor cables and the degree of corrosion of the free section of the cable, and the research is mainly focused on the life of the anchor section. Therefore, the constitutive relationship of the cable body is established on the basis of corrosion force coupled statistical damage mechanics, and the relationship between the degree of corrosion of the cable body and the holding prestress of the operating slope anchor cables is derived using the load transfer method. The rationality of prestressed anchor cables on highway slopes during the operation period was verified by actual measurement. This study has positive significance for long-term stability analysis of slopes

Regulation of hepatic autophagy by stress‐sensing transcription factor CREBH

Autophagy, a lysosomal degradative pathway in response to nutrient limitation, plays an important regulatory role in lipid homeostasis upon energy demands. Here, we demonstrated that the endoplasmic reticulum–tethered, stress‐sensing transcription factor cAMP‐responsive element‐binding protein, hepatic‐specific (CREBH) functions as a major transcriptional regulator of hepatic autophagy and lysosomal biogenesis in response to nutritional or circadian signals. CREBH deficiency led to decreased hepatic autophagic activities and increased hepatic lipid accumulation upon starvation. Under unfed or during energy‐demanding phases of the circadian cycle, CREBH is activated to drive expression of the genes encoding the key enzymes or regulators in autophagosome formation or autophagic process, including microtubule‐associated protein IB‐light chain 3, autophagy‐related protein (ATG)7, ATG2b, and autophagosome formation Unc‐51 like kinase 1, and the genes encoding functions in lysosomal biogenesis and homeostasis. Upon nutrient starvation, CREBH regulates and interacts with peroxisome proliferator–activated receptor α (PPARα) and PPARγ coactivator 1α to synergistically drive expression of the key autophagy genes and transcription factor EB, a master regulator of lysosomal biogenesis. Furthermore, CREBH regulates rhythmic expression of the key autophagy genes in the liver in a circadian‐dependent manner. In summary, we identified CREBH as a key transcriptional regulator of hepatic autophagy and lysosomal biogenesis for the purpose of maintaining hepatic lipid homeostasis under nutritional stress or circadian oscillation.—Kim, H., Williams, D., Qiu, Y., Song, Z., Yang, Z., Kimler, V., Goldberg, A., Zhang, R., Yang, Z., Chen, X., Wang, L., Fang, D., Lin, J. D., Zhang, K. Regulation of hepatic autophagy by stress‐sensing transcription factor CREBH. FASEB J. 33, 7896–7914 (2019). www.fasebj.orgPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154423/1/fsb2fj201802528r-sup-0001.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154423/2/fsb2fj201802528r.pd

Characteristics and candidate genes associated with excellent stalk strength in maize (Zea mays L.)

Lodging is a major problem in maize production, which seriously affects yield and hinders mechanized harvesting. Improving stalk strength is an effective way to improve lodging. The maize inbred line Jing2416 (J2416) was an elite germplasm in maize breeding which had strong stalk mechanical strength. To explore the characteristics its stalk strength, we conducted physiological, metabolic and transcriptomic analyses of J2416 and its parents Jing24 (J24) and 5237. At the kernel dent stage, the stalk rind penetrometer strength of J2416 was significantly higher than those of its two parents in multiple environments. The rind thickness, sclerenchyma tissue thickness, and cellulose, hemicellulose, and lignin contents of J2416 were significantly higher than those of its parents. Based on the significant differences between J2416 and 5237, we detected metabolites and gene transcripts showing differences in abundance between these two materials. A total of 212 (68.60%) metabolites and 2287 (43.34%) genes were up-regulated in J2416 compared with 5237. The phenylpropanoid and glycan synthesis/metabolism pathways were enriched in metabolites and genes that were up-regulated in J2416. Twenty-eight of the up-regulated genes in J2416 were involved in lignin, cellulose, and hemicellulose synthesis pathways. These analyses have revealed important physiological characteristics and candidate genes that will be useful for research and breeding of inbred lines with excellent stalk strength