Stochastic Efficiency Analysis with a Reliability Consideration

Stochastic Data Envelopment Analysis (DEA) models have been introduced in the literature to assess the performance of operating entities with random input and output data. A stochastic DEA model with a reliability constraint is proposed in this study that maximizes the lower bound of an entity\u27s efficiency score with some pre-selected probability. We define the concept of stochastic efficiency and develop a solution procedure. The economic interpretations of the stochastic efficiency index are presented when the inputs and outputs of each entity follow a multivariate joint normal distribution

Characteristics and drivers of plant C, N, and P stoichiometry in Northern Tibetan Plateau grassland

Understanding vegetation C, N, and P stoichiometry helps us not only to evaluate biogeochemical cycles and ecosystem functions but also to predict the potential impact of environmental change on ecosystem processes. The foliar C, N, and P stoichiometry in Northern Tibetan grasslands, especially the controlling factors, has been highlighted in recent years. In this study, we have collected 340 plant samples and 162 soil samples from 54 plots in three grassland types, with the purpose of evaluating the foliar C, N, and P stoichiometry and underlying control factors in three grassland types along a 1,500-km east-to-west transect in the Northern Tibetan Plateau. Our results indicated that the averaged foliar C, N, and P concentrations were 425.9 ± 15.8, 403.4 ± 22.2, and 420.7 ± 30.7 g kg−1; 21.7 ± 2.9, 19.0 ± 2.3, and 21.7 ± 5.2 g kg−1; and 1.71 ± 0.29, 1.19 ± 0.16, and 1.59 ± 0.6 g kg−1 in the alpine meadow (AM), alpine steppe (AS), and desert steppe (DS) ecosystems, respectively. The foliar C and N ratios were comparable, with values of 19.8 ± 2.8, 20.6 ± 1.9, and 19.9 ± 5.8 in the AM, AS, and DS ecosystems, respectively. Both the C/P and N/P ratios are the lowest in the AM ecosystem, with values of 252.2 ± 32.6 and 12.8 ± 1.3, respectively, whereas the highest values of 347.3 ± 57.0 and 16.2 ± 3.2 were obtained in the AS ecosystem. In contrast, the soil C, N, C/P, and N/P values decreased from the AM to DS ecosystem. Across the whole transects, leaf C, N, and P stoichiometry showed no obvious trend, but soil C and N concentrations showed an increasing trend, and soil P concentrations showed a decreasing trend with the increasing longitude. Based on the general linear model analysis, the vegetation type was the dominant factor controlling the leaf C, N, and P stoichiometry, accounting for 42.8% for leaf C, 45.1% for leaf N, 35.2% for leaf P, 52.9% for leaf C/N, 39.6% for leaf C/P, and 48.0% for leaf N/P; the soil nutrients and climate have relatively low importance. In conclusion, our results supported that vegetation type, rather than climatic variation and soil nutrients, are the major determinants of north Tibet grassland leaf stoichiometry

An Efficient Built-in Temporal Support in MVCC-based Graph Databases

Real-world graphs are often dynamic and evolve over time. To trace the evolving properties of graphs, it is necessary to maintain every change of both vertices and edges in graph databases with the support of temporal features. Existing works either maintain all changes in a single graph or periodically materialize snapshots to maintain the historical states of each vertex and edge and process queries over proper snapshots. The former approach presents poor query performance due to the ever-growing graph size as time goes by, while the latter one suffers from prohibitively high storage overheads due to large redundant copies of graph data across different snapshots. In this paper, we propose a hybrid data storage engine, which is based on the MVCC mechanism, to separately manage current and historical data, which keeps the current graph as small as possible. In our design, changes in each vertex or edge are stored once. To further reduce the storage overhead, we simply store the changes as opposed to storing the complete snapshot. To boost the query performance, we place a few anchors as snapshots to avoid deep historical version traversals. Based on the storage engine, a temporal query engine is proposed to reconstruct subgraphs as needed on the fly. Therefore, our alternative approach can provide fast querying capabilities over subgraphs at a past time point or range with small storage overheads. To provide native support of temporal features, we integrate our approach into Memgraph, and call the extended database system TGDB(Temporal Graph Database). Extensive experiments are conducted on four real and synthetic datasets. The results show TGDB performs better in terms of both storage and performance against state-of-the-art methods and has almost no performance overheads by introducing the temporal features

Effect of YKL-40 RNA Interference on VEGF Gene Polymorphism Expression in Atherosclerotic Mice

Aims: To investigate the effect of YKL-40 RNA interference on VEGF gene polymorphism expression in atherosclerotic mice. Methods: After the atherosclerosis models in mice were built, the mice were divided into three groups including control group, negative control group and observation group, which were separately given to normal saline, negative virus (5 × 107 TU) and YKL-40 RNA interference lentivirus. Then the whole blood DNA was extracted and genotyped in each group of mice and the expression of VEGF in each group of mice was detected by PCR, while the expression level of inflammatory factors in each group of mice was detected by ELISA. Meanwhile, the aortas of mice in each group were pathologically analyzed and the atherosclerosis of mice was detected. Results: Compared with the control group, the VEGF content in both the virus negative control group and the observation group was significantly increased（P<0.05）. The detection rates of CC genotype and C allele at rs699947 of VEGF gene in the observation group were significantly higher than those in the control group and the virus negative control group, and the difference was statistically significant. There were no significant changes for the expression of HDL-C, LDL-C, TC and TG in mice of each group（P>0.05）. Moreover, the levels of Lp-PLA2 and MCP-1 in the negative control group were significantly increased (P < 0.05), while those in the observation group were significantly decreased (P < 0.05) compared to that in control group. What’s more, the histomorphology of the observation group was significantly different from that of the control group and the virus negative control group. The thickness of the fibrous cap of the as plaque was significantly higher than that of the control group and the virus negative control group, but the plaque area and fat content were significantly lower than that of the control group and the virus negative control group and the NC group. Besides, there was no significant difference in lipid content, fiber cap thickness and plaque area between the control group and the virus negative control group. Conclusion: YKL-40 RNAi could improve the VEGF polymorphism, reduce the expression of LPâƒPLA2 and MCPâƒ1, and significantly inhibit the occurrence and development of atherosclerosis, which was expected to provide a new target for the prevention and treatment of atherosclerosis

SmartCiteCon: Implicit Citation Context Extraction from Academic Literature Using Unsupervised Learning

We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers in the ACL Anthology. The model with 19 features achieves F1=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a document on a dedicated server with 6 multithreaded cores. Using SCC, we extracted 11.8 million citation context sentences from ∼33.3k PMC papers in the CORD19 dataset, released on June 13, 2020. The source code is released at https://gitee.com/irlab/SmartCiteCon