Checking Data-Flow Errors Based on The Guard-Driven Reachability Graph of WFD-Net

In order to guarantee the correctness of workflow systems, it is necessary to check their data-flow errors, e.g., missing data, inconsistent data, lost data and redundant data. The traditional Petri-net-based methods are usually based on the reachability graph. However, these methods have two flaws, i.e., the state space explosion and pseudo states. In order to solve these problems, we use WFD-nets to model workflow systems, and propose an algorithm for checking data-flow errors based on the guard-driven reachability graph (GRG) of WFD-net. Furthermore, a case study and some experiments are given to show the effectiveness and advantage of our method

Guard-Function-Constraint-Based Refinement Method to Generate Dynamic Behaviors of Workflow Net with Table

In order to model complex workflow systems with databases, and detect their data-flow errors such as data inconsistency, we defined Workflow Net with Table model (WFT-net) in our previous work. We used a Petri net to describe control flows and data flows of a workflow system, and labeled some abstract table operation statements on transitions so as to simulate database operations. Meanwhile, we proposed a data refinement method to construct the state reachability graph of WFT-nets, and used it to verify some properties. However, this data refinement method has a defect, i.e., it does not consider the constraint relation between guard functions, and its state reachability graph possibly has some pseudo states. In order to overcome these problems, we propose a new data refinement method that considers some constraint relations, which can guarantee the correctness of our state reachability graph. What is more, we develop the related algorithms and tool. We also illustrate the usefulness and effectiveness of our method through some examples

Fully Probabilistic Analysis of FRP-to-Concrete Bonded Joints Considering Model Uncertainty

This work presents a full reliability-based analysis framework for fiber-reinforced polymer(FRP)-to-concrete bonded joints considering model uncertainty. Eight frequently used bond strength models for FRP-to-concrete bonded joints were calibrated by defining a model factor. A total of 641 well-documented tests were considered. Four of the eight models had model factors that correlated with input design parameters and the systematic part of the model factor was removed by a regression equation f. By doing this type of characterization, all eight model factors could be comparatively uniform and described by lognormally distributed random variables. The merit of the uniform model uncertainties after calibration for the eight models was established by the reliability analysis. This study improves the predictability of concrete strengthened with fiber composites and provides useful suggestions on their model uncertainties in engineering practice

Effects on global warming by microbial methanogenesis in alkaline lakes during the Late Paleozoic Ice Age (LPIA)

This work was jointly funded by the National Natural Science Foundation of China (Grant Nos . 42230808, 42203055 and 41830425) and PetroChina Science and Technology Major project (Grant No. 20 21DJ0108).Methane (CH4) is an important greenhouse gas, but its behavior and influencing factors over geological time scales are not sufficiently clear. This study investigated the Late Paleozoic Ice Age (LPIA), which is thought to have experienced an interval of rapid warming at ca. 304 Ma, that may have been analogous to modern warming. To explore possible causes of this warming event, we investigated ancient alkaline lakes in the Junggar Basin, northwestern China. Results show that microbial CH4 cycling here was strong, as evidenced by carbonate δ13C (δ13Ccarb) values of >5‰, ∼+0.6‰ offsets between pristane δ13C (δ13CPr) and phytane δ13C (δ13CPh) values, a 3β-methylhopane index of 9.5% ± 3.0%, and highly negative δ13C values of hopanes (−44‰ to −61‰). Low sulfate concentrations in the alkaline lakes made methanogenic archaea more competitive than sulfate-reducing bacteria, and the elevated levels of dissolved inorganic carbon promoted methanogenesis. Biogenic CH4 emissions from alkaline lakes, in addition to CO2, may have contributed to rapid climate warming.PostprintPeer reviewe

Single cell atlas for 11 non-model mammals, reptiles and birds.

The availability of viral entry factors is a prerequisite for the cross-species transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Large-scale single-cell screening of animal cells could reveal the expression patterns of viral entry genes in different hosts. However, such exploration for SARS-CoV-2 remains limited. Here, we perform single-nucleus RNA sequencing for 11 non-model species, including pets (cat, dog, hamster, and lizard), livestock (goat and rabbit), poultry (duck and pigeon), and wildlife (pangolin, tiger, and deer), and investigated the co-expression of ACE2 and TMPRSS2. Furthermore, cross-species analysis of the lung cell atlas of the studied mammals, reptiles, and birds reveals core developmental programs, critical connectomes, and conserved regulatory circuits among these evolutionarily distant species. Overall, our work provides a compendium of gene expression profiles for non-model animals, which could be employed to identify potential SARS-CoV-2 target cells and putative zoonotic reservoirs

An Incremental and Backward-Conflict Guided Method for Unfolding Petri Nets

The unfolding technique of Petri net can characterize the real concurrency and alleviate the state space explosion problem. Thus, it is greatly suitable to analyze/check some potential errors in concurrent systems. During the unfolding process of a Petri net, the calculations of configurations, cuts, and cut-off events are the key factors for the unfolding efficiency. However, most of the unfolding methods do not specify a highly efficient calculations on them. In this paper, we reveal some recursive relations and structural properties of these factors. Subsequently, we propose an improved method for computing configurations and cuts. Meanwhile, backward conflicts are used to guide the calculations of cut-off events. Moreover, a case study and a series of experiments are done to illustrate the effectiveness and application scenarios of our methods

Spatiotemporal patterns of land surface temperature and their response to land cover change: A case study in Sichuan Basin

Land surface temperature (LST) is a critical geo-parameter in terrestrial environmental interaction processes, directly related to land cover change (LCC) which modifies surface energy balance. In this study, LST data from 2003 to 2019 were reconstructed in the Sichuan Basin with average R2 of 0.85 (daytime) and 0.91 (nighttime), effectively filling in the missing pixels and reducing the noise components. Emerging hot spot analysis (EHSA) and land cover transfer matrix were utilized to analyze the multi-patterns of LST spatiotemporal evolution and responses to LCC. Results indicate that LST hot spots are concentrated in low-altitude basin floor and are dominated by sporadic hot spots. Cold spots are mainly in marginal high-elevation mountains, but the dominant pattern varies with time scale. The largest proportions of hot and cold spots are found in summer (>46 %) and autumn (>29 %), respectively. Moreover, the significant upward and downward trends of LST cold and hot spots are most prominent in western plain and marginal mountains, respectively, and have the largest coverage in summer and autumn, respectively. In total LCC area, cropland-to-forest (CF), cropland-to-impervious (CI), and forest-to-cropland (FC) account for 93.55 %. Among them, CI significantly promotes the aggregation and upward trend of daytime LST hot spots. CF and FC have the strongest effect of aggregating LST cold spots and cooling LST in daytime, with CF being more effective. The information can serve as a reference for regional planning and climate change mitigation measures

Deep Learning-Based Automatic Defect Detection Method for Sewer Pipelines

To address the issues of low automation, reliance on manual screening by professionals, and long detection cycles in current urban drainage pipeline defect detection, this study proposes an improved object detection algorithm called EFE-SSD (enhanced feature extraction SSD), based on the SSD (Single Shot MultiBox Detector) network. Firstly, the RFB_s module is added to the SSD backbone network to enhance its feature extraction capabilities. Additionally, multiple scale features are fused to improve the detection performance of small target defects to some extent. Then, the improved ECA attention mechanism is used to adjust the channel weights of the output layer, suppressing irrelevant features. Finally, the Focal Loss is employed to replace the cross-entropy loss in the SSD network, effectively addressing the issue of imbalanced positive and negative samples during training. This increases the weight of difficult-to-classify samples during network training, further improving the detection accuracy of the network. Experimental results show that EFE-SSD achieves a detection mAP of 92.2% for four types of pipeline defects: Settled deposits, Displaced joints, Deformations, and Roots. Compared to the SSD algorithm, the model’s mAP was increased by 2.26 percentage points—ensuring the accuracy of pipeline defect detection