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Image inpainting is one of the most important problems in the field of image algorithm, and it is also an effective preprocessing method for many other image applications. In this paper, we suggest an image decontamination method which is mainly used to remove mesh stains and also provide a data set for this task. To our knowledge, this work is the first attempt to solve this kind of problem. Specifically, the proposed method is composed of two phases: we first remove the mesh stains with an Improved Conditional Generative Adversarial Network, and then utilize a Semantic Network to fine tune the details. Experiments demonstrated that this two-stage model can remove the mesh stains. Results show that our method significantly out-performs existing methods and achieves superior inpainting results on challenging cases.

Natural gas is a kind of high-quality and low-carbon energy and its demand is increasing rapidly year by year. The natural gas produced from the gas well contains some corrosive impurities like CO2, H2S, and H2O. Therefore, the gas needs to be purified in purification plants before it can be used. In the process of purification, the impurities in the natural gas will cause corrosion to the purification equipment, especially to the industrial pipeline, which will lead to perforation and leakage of the pipeline, and even cause poisoning, fire or explosion, and other serious safety accidents. In order to ensure the safe operation of the industrial pipeline in the purification plant, it is necessary to carry out effective inspection and maintenance measures for the pipelines. The traditional regular inspection method just inspects a certain proportion of the pipelines which are selected by on-site inspection personnel. But limited by technical level and work experience of the inspection personnel, some high-risk pipelines may be neglected. Therefore, some plants try to adopt risk-based inspection (RBI) to carry out a risk assessment and conduct a comprehensive inspection on high-risk pipelines, so as to improve inspection efficiency and reduce inspection cost. However, in practice, it was found that the pipeline risk levels are not in accordance with the actual inspection results. The reason is that the RBI method developed in the background of oil refining and chemical industry is not suitable for natural gas purification plants. In order to solve the problems, this paper analyzed the relationship between the influencing factors and pipeline corrosion behavior. The influencing factors include the impurity contents (CO2, H2S, H2O, and chlorides), pipeline materials (carbon steel and austenitic stainless steel), and service conditions (operating temperature, operating pressure and flow velocity). Meanwhile, the plant management status and maintenance factors were also considered as the influencing factors to pipelines. According to the analysis, a corrosion risk assessment method for the pipelines in the natural gas purification plant was developed based on fault tree and scoring method. Finally, the method was applied to the pipelines in purification plant to verify the accuracy of this method.

Neuropathic pain (NP) is a common complication that negatively affects the lives of patients with spinal cord injury (SCI). The disruption in the balance of excitatory and inhibitory neurons in the spinal cord dorsal horn contributes to the development of SCI and induces NP. The calcium-binding protein (CaBP) calbindin-D 28K (CaBP-28K) is highly expressed in excitatory interneurons, and the CaBP parvalbumin (PV) is present in inhibitory neurons in the dorsal horn. To better define the changes in the CaBPs contributing to the development of SCI-induced NP, we examined the changes in CaBP-28K and PV staining density in the lumbar (L4-6) lamina I and II, and their relationship with NP after mild spinal cord contusion injury in mice. We additionally examined the effects of alternate thermal stimulation (ATS). Compared with sham mice, injured animals developed mechanical allodynia in response to light mechanical stimuli and exhibited mechanical hyporesponsiveness to noxious mechanical stimuli. The decreased response latency to heat stimuli and increased response latency to cold stimuli at 7 days post injury suggested that the injured mice developed heat hyperalgesia and cold hypoalgesia, respectively. Temperature preference tests showed significant warm allodynia after injury. Animals that underwent ATS (15-18 and 35-40°C; +5 minutes/stimulation/day; 5 days/week) displayed significant amelioration of heat hyperalgesia, cold hypoalgesia, and warm allodynia after 2 weeks of ATS. In contrast, mechanical sensitivity was not influenced by ATS. Analysis of the CaBP-28K positive signal in L4-6 lamina I and II indicated an increase in staining density after SCI, which was associated with an increase in the number of CaBP-28K-stained L4-6 dorsal root ganglion (DRG) neurons. ATS decreased the CaBP-28K staining density in L4-6 spinal cord and DRG in injured animals, and was significantly and strongly correlated with ATS alleviation of pain behavior. The expression of PV showed no changes in lamina I and II after ATS in SCI animals. Thus, ATS partially decreases the pain behavior after SCI by modulating the changes in CaBP-associated excitatory-inhibitory neurons.

The internal corrosion of a 90° elbow was found in a natural gas gathering pipeline in Northeast China. The welded joint between the elbow and the downstream pipe was also severely corroded. The 90° elbow was forged of 16Mn steel. The downstream pipe (Φ 76 mm × 9 mm) was made of 20G steel. To determine failure causes, the elbow and the welded joint were taken as a whole and investigated systematically. The influence of the flow disturbance induced by the elbow on the damage at the welded joint was considered. The internal damage at the elbow and that at the welded joint were studied using field investigation, visual examination, scanning electron microscopy (SEM), energy-dispersive spectroscopy (EDS), X-ray powder diffraction (XRD), hardness tests, and computational fluid dynamics (CFD) techniques. The results showed that erosion corrosion from solid particles and corrosive liquid droplets entrained in the natural gas flow was the main cause of the internal damage at the elbow and the welded joint. The welded joint was attacked by the particles with the highest velocity magnitudes and the most dangerous impact angles. The flow disturbance induced by the elbow, the special location of the welded joint, and the angular misalignment due to poor welding quality jointly caused the substantially more severe damage at the welded joint.

Background: Parecoxib is a selective cyclooxygenase (COX)-2 inhibitor widely used as an analgesia technique in perioperative period for its potent anti-inflammatory and analgesic effects. However, litter is known about its effect on postoperative cognitive dysfunction (POCD). The purpose of this meta-analysis of randomized controlled trials (RCTs) was to evaluate the effect of parecoxib in the treatment of postoperative cognitive dysfunction. Methods: We searched PubMed, Cochrane Library and Embase databases for relevant studies up to October 2017. We selected fixed-effect model for analysis of data heterogeneity. Statistical analyses were performed by using Review Manager Version 5.3 for Windows. Results: Four RCTs with 904 patients that underwent surgical operations were included. The meta-analysis demonstrated parecoxib could significantly decrease the incidence of POCD on postoperative day 1, day 3, day 5, and day 7 when compared with control treatment; IL-6 and S100β concentrations were lower up to postoperative day 2. The consumption of morphine, fentanyl and tramadol in parecoxib groups were lower than control groups. Conclusion: Our meta-analysis suggested that the administration of Parecoxib was effective in treating early POCD within 7 days and reducing IL-6 and S100β concentrations within 2 days after operations. Nevertheless, our current study with some limitations such as the small sample size only provided limited quality of evidence, confirmation from further meta-analysis with large-scale, well-designed RCTs is required.

To get the knowledge of local corrosion, thinning is useful for developing targeted inspection plans for pipe components in the oil/gas industry. Aiming at this object, this work presents a computer fluid dynamics (CFD) method to predict CO2 aqueous corrosion in complex fluid domains. The processes involved in CO2 aqueous corrosion, including flow dynamics, mass transfer, chemical reactions, and electrochemical reactions, are modeled and simulated by a commercial CFD software of Fluent V15.0 (Version, manufacturer, city, country). Mass transfer in the straight pipe flow and jet impinging flow are simulated using three low-Reynolds-number turbulent models (Abe&ndash, Kondoh&ndash, Nagano k &minus, &epsilon, model, Change&ndash, Hsieh&ndash, Chenk k &minus, model, and k &minus, shear stress transport model). The flow domains are meshed by grids with the first near-wall node at the position at y+ = 0.1. Comparisons between simulations and experimental data show the Abe&ndash, Nagano model provides the best predictions of near-wall flow and mass transfer. Thus, it is used to predict CO2 aqueous corrosion. Corrosion rates of dissolved CO2 in straight pipes and a jet impinging are predicted. The predicted corrosion rates are compared with experimental data and results derived from commercial software, Multicorp V5.2.105. The results show that predicted corrosion rates are reasonable. The locations of the highest corrosion rate for a jet impinging system are revealed.

Defects of pressure piping, especially cracks, are inevitable during the lifetime of pipe, which will result in the pipe failure. In order to ensure safe operation of pipe, failure assessment for the pressure pipe containing defects needs to be implemented. In fact, the assessment parameters would show two types of uncertainty: randomness and fuzziness, because of the errors of inspection and lack of precise information. In this paper, a fuzzy-probabilistic failure assessment method has been developed based on Failure Assessment Diagram (FAD) from API 579-1/ASME FFS-1 procedure. This method combines the probability theory with possibility theory to deal with fuzzy and random variables. Also, Monte Carlo simulation is used to calculate the failure probability of pipe. This method was applied to a pipe with a surface crack. The effects of variables and their uncertainties on failure probability were analyzed. The results show that the wall thickness and diameter of the pipe has the most important contribution to the pipe failure, followed by the fracture toughness, the yield strength, the operating pressure, the depth and length of the crack.

Natural gas transmission pipeline is prone to internal corrosion due to the combination of corrosive impurities in the pipe (such as CO2, H2S and chlorides) and applied pressure of the pipeline, which seriously affects the safe operation of the pipeline. In this work, the corrosion behavior of a typical X70 pipeline steel was investigated by using potentiodynamic polarization and electrochemical impendence spectroscopy (EIS). The polarization and EIS data under different CO2 partial pressures (0–1 atm), H2S concentrations (0–150 ppm), chloride concentrations (0–3.5 wt%) and tensile stress (0–400 MPa) were obtained. The results show that corrosion rate increases with the increase of CO2 partial pressure and chloride concentration, respectively, while first increases and then decreases with the increase H2S concentrations. The corrosion rate is less affected by elastic tensile stress. In addition, a quantitative prediction model for corrosion rate of natural gas pipeline based on adaptive neuro-fuzzy inference system (ANFIS) was established by fitting the experimental data which maps the relationship between the key influencing factors (i.e. CO2 partial pressure, H2S concentration, chloride concentration and tensile stress) and the corrosion rate. The prediction results show that the relative percentage errors of the predicted and experimental values are relatively small. The prediction accuracy of the model satisfies the engineering application requirement.

Interferons are multifunctional cytokines widely used in clinical settings as an anti-viral drug. In addition, interferon’s exhibit anti-cancer and anti-bacterial effects. Nearly two thousand papers related to interferon are published each year, which illustrates the importance placed by researchers on the study of interferon. This review focuses on recent advances in the study of interferon, particularly in the areas of its mechanism of anti-cancer effect and signal transduction. We also describe the tumor resistance to interferon and the side-effect of interferon-based therapy, which leads to an expectation of future research of interferon.

Components with multiple failure modes may encounter imperfect preventive maintenance triggered by inspections during their two-stage failure. Employing delay-time concepts and accumulative age, a new imperfect-maintenance model for these components is presented under the assumptions that failure modes are independent of each other and all kinds of defects will be dealt with in each maintenance task. Reliability and cost models are derived. Their characteristics are analyzed in numerical simulations. The results show components with more failure modes and worse imperfect inspection maintenance will have lower reliability, lower expected cycle cost and higher average cost per unit time. With an increasing inspection interval, the cycle cost will monotonically decrease while the average cost per unit time may have a local minimum. If inspection maintenance is insufficient, the minimum of the average cost per unit time will disappear, as does the best inspection interval.

Corrosion failures of process equipment have been one of the main sources of risk to refining and petrochemical plants. For reducing failure risk levels and optimizing inspection plans, risk analysis of equipment failures resulting from corrosion need to be implemented. However, due to the complexity and uncertainty of the refining and petrochemical units and risks, effective analyses are hard to accomplish by using conventional risk techniques. With respect to this, a new model for risk analysis of corrosion failures of equipment is developed base on fuzzy set theory. In this model, two essential parts of failure risk (i.e., failure likelihood and severity of failure consequence) are first estimated by using fuzzy synthetic evaluation, and then integrated into a risk index by fuzzy risk graph which is established based on fuzzy logic system. In order to demonstrate the feasibility of this model, an application example in an overhead system of a crude-oil distillation unit was presented. The results show that this model is effective and feasible.

Petrochemical facilities and plants require essential ongoing maintenance to ensure high levels of reliability and safety. A risk-based maintenance (RBM) strategy is a useful tool to design a cost-effective maintenance schedule; its objective is to reduce overall risk in the operating facility. In risk assessment of a failure scenario, consequences often have three key features: personnel safety effect, environmental threat and economic loss. In this paper, to quantify the severity of personnel injury and environmental pollution, a failure modes and effects analysis (FMEA) method is developed using subjective information derived from domain experts. On the basis of failure probability and consequence analysis, the risk is calculated and compared against the known acceptable risk criteria. To facilitate the comparison, a risk index is introduced, and weight factors are determined by an analytic hierarchy process. Finally, the appropriate maintenance tasks are scheduled under the risk constraints. A case study of a continuous catalytic reforming plant is used to illustrate the proposed approach. The results indicate that FMEA is helpful to identify critical facilities; the RBM strategy can increase the reliability of high-risk facilities, and corrective maintenance is the preferred approach for low-risk facilities to reduce maintenance expenditure.

Corrosion is one of the main causes of equipment failure in refining and petrochemical plants. For the safety and normal operation of equipment, it is necessary to identify the corrosion mechanisms resulting in equipment failure and take appropriate mitigation measures. However, there are many corrosion mechanisms in refining and petrochemical plants and identifying them becomes a key issue. In this paper, a knowledge-based reasoning model, which uses a causal table rather than traditional IF–THEN rules to organize and represent domain knowledge, is proposed for the identification of corrosion failure mechanisms. Uncertainty in the reasoning process is addressed by using a hybrid method involving a combination of the Bayesian method and certainty factor theory. An application example for the identification of environmentally assisted cracking mechanisms in petroleum refineries is presented. The results demonstrate that the model is effective and feasible.

The delay time model (DTM) is widely used to model the two-stage failure process and is helpful for developing cost-effective inspection/maintenance plans. Imperfect maintenance is common in practice, but seldom considered in DTM. An improved DTM with imperfect maintenance at inspection has been developed based on the assumption of imperfect inspection maintenance and perfect failure maintenance. The model of the long-run availability for the improved DTM is established. Parameters estimation method and the test for goodness of fit method are given. Numerical simulations are performed to study the influence of imperfect maintenance on the long-run availability and to validate the credibility of the parameters estimation method. The results show that imperfect maintenance will decrease the long-run availability. The existence of the optimal inspection interval regarding the maximum long-run availability is tightly related to the improvement factor, which denotes the maintenance effect. The parameters estimation method proves credible. The maximum likelihood estimations of the reliability parameters can be easily achieved by the Genetic Algorithms (GAs) searching tool.

Risk-based maintenance (RBM) strategy can be used in developing a cost-effective maintenance plan to make financial and safety improvements in a petrochemical system. Most maintenance is imperfect in process industries. Previous works on RBM did not consider imperfect maintenance and thus could hardly meet the engineering requirements of process industries. An improved RBM approach based on the proportional age reduction model is proposed in this paper. The parameters of the failure distribution and maintenance effect of equipment are estimated by maximum likelihood estimation. The financial risk for each subsystem is estimated and compared with the risk criteria. Under the risk constraints, the proper interval of periodic preventive maintenance is determined for the equipment in high-risk subsystems. The case of a reforming reaction system in a petrochemical plant is studied using the improved RBM approach. The results show most equipment in this system is imperfectly repaired. The imperfect nature of the periodic preventive maintenance means it needs to be carried out more frequently.

In Vehicular Ad hoc Network (VANET), the communication modes, i.e., vehicle to vehicle, vehicle to infrastructure, and hybrid communication are expected to be supported. In the case that the communication link between a vehicle and the infrastructure is inaccessible, both the fixed and mobile relay can be employed in VANET for supporting vehicular multi-hop accessing. In this paper, a cross-layer based RSU/mobile relay station (RS) selection scheme for VANET is proposed which jointly considers multiple metrics from multiple protocol layers, including the link status, bandwidth and delay characteristics of access RS and user service requirements, and simple additive weighting (SAW) method is applied to evaluate the performance of candidate RSs. Simulation results demonstrate that comparing to previous algorithms, the novel algorithm offers better average throughput performance.

The rapid growth of mobile telecommunication applications has proposed the requirement on mobility support for both moving single node and moving network. While NEMO protocol is proposed to support the mobility of network, the handoff performance is highly limited. By combining other high layer mobility protocol, such as HIP, the handoff performance can be enhanced significantly. In this paper, the handoff performance, especially the handoff signaling cost is studied for applying NEMO and the combination of NEMO and HIP. A novel micro-mobility scheme based on the combination of NEMO and HIP is also proposed The communication and handoff process are described, and the handoff signaling cost is evaluated. Numerical results demonstrate that for supporting micro-mobility scenario, the handoff signaling cost can be reduced significantly under the proposed scheme comparing to that of NEMO and the combination of HIP and NEMO.

Group mobility in WSN is of particular importance as it is can be found in many practical application scenarios. In this paper, we propose to combine HIP and NEMO protocol for supporting group mobility in WSN. The overview of HIP and NEMO protocol is introduced and the new network architecture for applying HIP and NEMO protocol in WSN is proposed, which enhances mobility management, network security and host multi-homing for WSN. The corresponding group mobility management mechanism and signaling process are discussed.

It is well known that machining induced residual stresses can seriously affect the dimensional accuracy, corrosion and wear resistance, etc., and further influence the longevity and reliability of Micro-Optical Components (MOC). In Ultrasonic Torsional Vibration Assisted Micro-milling (UTVAM), cutting parameters, vibration parameters, mill cutter parameters, the status of wear length of tool flank are the main factors which affect residual stresses. A 2D model of UTVAM was established with FE analysis software ABAQUS. Johnson-Cook's flow stress model and shear failure principle are used as the workpiece material model and failure principle, while friction between tool and workpiece uses modified Coulomb's law whose sliding friction area is combined with sticking friction. By means of FEA, the influence rules of cutting parameters, vibration parameters, mill cutter parameters, the status of wear length of tool flank on residual stresses are obtained, which provides a basis for choosing optimal process parameters and improving the longevity and reliability of MOC.

A novel estimator model, which incorporates DPCA (dynamic principal component analysis), RBF (radial basis function) networks, and MSA (multi-scale analysis), is proposed to infer the properties of manufactured products from real process variables. DPCA is carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis is introduced to acquire much more information and to reduce uncertainty in the system; and RBF networks are used to characterize the nonlinearity of the process. A prediction of the syngas compositions in Texaco coal gasification process is taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy.

Prediction of syngas compositions, the most important parameter in determining the product's grade and quality control of raw syngas produced in coal gasification, was studied. A neural estimator model based on dynamic principal component analysis (DPCA), back-propagation (BP) networks, and multi-scale analysis (MSA) was proposed to infer the syngas compositions from real process variables. DPCA was carried out to select the most relevant process features and to eliminate the correlations of input variables; multi-scale analysis was introduced to acquire much more information and to reduce uncertainly in the system; and BP networks were used to characterize the nonlinearity of the process. A prediction of the syngas compositions in Texaco coal gasification process was taken as a case study. Research results show that the proposed method provides promising prediction reliability and accuracy, and supposed to have extensive application prospects in coal gasification processes.

The AER algorithm testbed has been applied to instruments measuring in spectra from the ultraviolet to the microwave. The sensor simulation component starts with environmental data from numerical weather prediction models, surface property and terrain databases, and imagers, and simulates the detailed sensor spatial and spectral sampling processes, radiative transfer, polarization, and detector characteristics. This simulation is integrated with algorithm execution, to provide end-to-end capability. The tools allow for simulation of specific sensor errors, and tracing of their impact through the algorithm process to the quality of the retrieved environmental products. A critical component of the testbed is its radiative transfer models, which employ state-of-the-science programs for line-by-line optical properties, for radiative transfer in scattering atmospheres, and for a variety of surfaces. Fast and accurate computational methods are incorporated, such as Optimal Spectral Sampling (OSS). Application examples are shown, including characterization of AMSU sensor data errors and retrieval performance evaluation, in which retrievals from the microwave sounder data and infrared image data contribute to the interpretation of remote sensing phenomena in cloudy environments.