Dynamic quantitative risk assessment of accidents induced by leakage on offshore platforms using DEMATEL-BN

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)—using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)—for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms. Keywords: Offshore platform, DEMATEL, Bayesian network, Accident, Leakage, Quantitative risk, Dynami

Sequence Analysis of Insecticide Action and Detoxification-Related Genes in the Insect Pest Natural Enemy Pardosa pseudoannulata.

The pond wolf spider Pardosa pseudoannulata, an important natural predatory enemy of rice planthoppers, is found widely distributed in paddy fields. However, data on the genes involved in insecticide action, detoxification, and response are very limited for P. pseudoannulata, which inhibits the development and appropriate use of selective insecticides to control insect pests on rice. We used transcriptome construction from adult spider cephalothoraxes to analyze and manually identify genes enconding metabolic enzymes and target receptors related to insecticide action and detoxification, including 90 cytochrome P450s, 14 glutathione S-transferases (GSTs), 17 acetylcholinesterases (AChEs), 17 nicotinic acetylcholine receptors (nAChRs), and 17 gamma-aminobutyric acid (GABA) receptors, as well as 12 glutamate-gated chloride channel (GluCl) unigenes. Sequence alignment and phylogenetic analysis revealed the different subclassifications of P450s and GSTs, some important sequence diversities in nAChRs and GABA receptors, polymorphism in AChEs, and high similarities in GluCls. For P450s in P. pseudoannulata, the number of unigenes belonging to the CYP2 clade was much higher than that in CYP3 and CYP4 clades. The results differed from insects in which most P450 genes were in CYP3 and CYP4 clades. For GSTs, most unigenes belonged to the delta and sigma classes, and no epsilon GST class gene was found, which differed from the findings for insects and acarina. Our results will be useful for studies on insecticide action, selectivity, and detoxification in the spider and other related animals, and the sequence differences in target genes between the spider and insects will provide important information for the design of selective insecticides

Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry

Storm disasters are the most common cause of accidents in offshore oil and gas industries. To prevent accidents resulting from storms, it is vital to analyze accident propagation and to learn about accident mechanism from previous accidents. In this paper, a novel risk analysis framework is proposed for systematically identifying and analyzing the evolution of accident causes. First, accident causal factors are identified and coded based on grounded theory (GT). Then, decision making trial and evaluation laboratory (DEMATEL) is integrated with interpretative structural modeling (ISM) to establish accident evolution hierarchy. Finally, complex networks (CN) are developed to analyze the evolution process of accidents. Compared to reported works, the contribution is threefold: (1) the demand for expert knowledge and personnel subjective influence are reduced through the data induction of accident cases; (2) the method of establishing influence matrix and interaction matrix is improved according to the accident frequency analysis; (3) a hybrid algorithm that can calculate multiple shortest paths of accident evolution under the same node pair is proposed. This method provides a new idea for step-by-step assessment of the accident evolution process, which weakens the subjectivity of traditional methods and achieves quantitative assessment of the importance of accident evolution nodes. The proposed method is demonstrated and validated by a case study of major offshore oil and gas industry accidents caused by storm disasters. Results show that there are five key nodes and five critical paths in the process of accident evolution. Through targeted prevention and control of these nodes and paths, the average shortest path length of the accident evolution network is increased by 35.19%, and the maximum global efficiency decreases by 20.12%. This indicates that the proposed method has broad applicability and can effectively reduce operational risk, so that it can guide actual offshore oil and gas operations during storm disasters

Characterization of the Fifth Putative Acetylcholinesterase in the Wolf Spider, Pardosa pseudoannulata

Background: Acetylcholinesterase (AChE) is an important neurotransmitter hydrolase in invertebrate and vertebrate nervous systems. The number of AChEs is various among invertebrate species, with different functions including the ‘classical’ role in terminating synaptic transmission and other ‘non-classical’ roles. Methods: Using rapid amplification of cDNA ends (RACE) technology, a new putative AChE-encoding gene was cloned from Pardosa pseudoannulata, an important predatory natural enemy. Sequence analysis and in vitro expression were employed to determine the structural features and biochemical properties of this putative AChE. Results: The cloned AChE contained the most conserved motifs of AChEs family and was clearly clustered with Arachnida AChEs. Determination of biochemical properties revealed that the recombinant enzyme had the obvious preference for the substrate ATC (acetylthiocholine iodide) versus BTC (butyrylthiocholine iodide). The AChE was highly sensitive to AChE-specific inhibitor BW284C51, but not butyrylcholinesterase-specific inhibitor tetraisopropyl pyrophosphoramide (ISO-OMPA). Based on these results, we concluded that a new AChE was identified from P. pseudoannulata and denoted as PpAChE5. Conclusion: Here we report the identification of a new AChE from P. pseudoannulata and increased the AChE number to five in this species. Although PpAChE5 had the biggest Vmax value among five identified AChEs, it showed relatively low affinity with ATC. Similar sensitivity to test insecticides indicated that this AChE might serve as the target for both organophosphorus and carbamate insecticides

Thermal-Hydraulic Characteristics of Carbon Dioxide in Printed Circuit Heat Exchangers with Staggered Airfoil Fins

Airfoil fin printed circuit heat exchangers (PCHEs) have broad application prospects in the naval, aerospace, electric power, and petrochemical industries. The channel structure is a critical factor affecting their thermal-hydraulic characteristics. In this study, a novel PCHE channel structure with staggered NACA 0025 airfoil-shaped fins was proposed; accordingly, the thermal-hydraulic characteristics of the novel channel structure using carbon dioxide as the working fluid at different fin heights under different operating conditions (trans-, near-, and far-critical) were investigated. The results indicated that the thermal-hydraulic performance of the PCHE under the trans-critical operating condition was better than that under the near-critical and far-critical operating conditions. Compared with conventional airfoil fin channels, the novel airfoil fin channel attained comparable comprehensive performance while reducing the fin volume by 50%, thus achieving a more lightweight PCHE design. The comprehensive performance of the PCHE was the poorest when the fin height was slightly below the channel height, which should be avoided during the design of airfoil fin PCHEs. The results provide theoretical support for the design and optimization of airfoil fin PCHEs

The many roles of cathepsins in restenosis

Drug-eluting stents (DES) and dual antiplatelet regimens have significantly improved the clinical management of ischemic heart disease; however, the drugs loaded with DES in clinical practice are mostly paclitaxel or rapamycin derivatives, which target symptoms of post implantation proliferation and inflammation, leading to delayed re-endothelialization and neo-atherosclerosis. Along with the treatments already in place, there is a need for novel strategies to lessen the negative clinical outcomes of DES delays as well as a need for greater understanding of their pathobiological mechanisms. This review concentrates on the function of cathepsins (Cats) in the inflammatory response and granulation tissue formation that follow Cat-induced damage to the vasculature scaffold, as well as the functions of Cats in intimal hyperplasia, which is characterized by the migration and proliferation of smooth muscle cells, and endothelial denudation, re-endothelialization, and/or neo-endothelialization. Additionally, Cats can alter essential neointima formation and immune response inside scaffolds, and if Cats are properly controlled in vivo, they may improve scaffold biocompatibility. This unique profile of functions could lead to an original concept for a cathepsin-based coronary intervention treatment as an adjunct to stent placement

Multi-layer Long Short-term Memory based Condenser Vacuum Degree Prediction Model on Power Plant

A multi-layer LSTM (Long short-term memory) model is proposed for condenser vacuum degree prediction of power plants. Firstly, Min-max normalization is used to pre-process the input data. Then, the model proposes the two-layer LSTM architecture to identify the time series pattern effectively. ADAM（Adaptive moment）optimizer is selected to find the optimum parameters for the model during training. Under the proposed forecasting framework, experiments illustrates that the two-layer LSTM model can give a more accurate forecast to the condenser vacuum degree compared with other simple RNN (Recurrent Neural Network) and one-layer LSTM model