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Most incidents in complex systems such as process plants are not-chance events and weak signals emerge for a long time before incidents occur. It is necessary to identify and respond to the weak signals as early as possible for preventing incidents. However, in the era of Industry 4.0, recognizing weak signals from the abundance of data is challenging. Since the terminology “weak signal” was not precisely defined, the study first proposed a formal definition of “weak signal” and discussed its characteristics. Additionally, a framework was developed to address the challenges of observing, evaluating, and responding to weak signals in complex systems. The framework first utilized Function Resonance Analysis Method (FRAM) to determine the information to be collected for observing weak signals. Then, the relevance of weak signals and the corresponding responses were evaluated by utilizing Balanced Random Forest (BRF), Weighted Random Forest (WRF) and Decision Tree (DT) classification models. The case study of a hypothetical batch process showed great potentials for applying the framework in real operations. Based on the potential weak signals that were indicated by FRAM, probabilities of temperature deviations in the process were predicted with high accuracy by the optimal BRF model. Underlying weak signals and the corresponding responses to reduce the probabilities were identified from the DT.

The key to learning from the past incidents is to identify the underlying causes and contributory factors of the incidents. A large amount of text data on incident narratives has been accumulated over the years and can be a good learning source, if properly utilized. However, the vast amount and unstructured nature of the text data impedes generating insights on occurring patterns of incidents. This research sets upon applying natural language processing (NLP) and text mining techniques to utilize the resource for understanding contributing factors and causations behind the incidents with pipeline industry as an illustrative example. The 3587 records of incident narratives of the ‘comment’ section in the incident database of Pipeline and Hazardous Materials Safety Administration (PHMSA) are exploited. Two methods of text analytics, K-means clustering and co-occurrence network, are employed to infer latent causality of incidents. The results demonstrate that both methods are capable of identifying contributing factors under specific failure types. The co-occurrence network approach exhibits advantages on extracting dependency among the contributory factors, while K-means clustering is only able to indicate general correlations. The workflow proposed in this paper provides new perspectives of identifying contributing factors and their causal dependency from incident text data for promising applications in risk analysis and accident modeling.

The linear dichroism (LD) conversion shows promising applications for polarized detectors, optical transition and light propagation. However, polarity reversal always occurs at a certain wavelength in LD materials, which can only distinguish two wavelength bands as wavelength-selective photodetectors. In this study, the multi-degree-of-freedom of optical anisotropy based on 2D PdPS flakes is carefully described, in which four critical switching wavelengths are observed. Remarkably, the quadruple LD conversion shows a significant wavelength-dependent behavior, allowing us to pinpoint five wavelength bands, 200-239 nm, 239-259 nm, 259-469 nm, 469-546 nm, and 546-700 nm, for a wavelength-selective approach to photodetectors. In addition, the polarized photoresponse under 532 nm was realized with an anisotropy factor of ∼1.51 and further illustrated the in-plane anisotropy. Raman spectroscopy of PdPS flakes also shows strong phonon anisotropy. The unique wavelength-selective property shows great potential for the miniaturization and integration of photodetectors.

Hazardous scenarios emerging from complex system where number of functions are large and corresponding function coupling are humongous, are very difficult if not impossible to identify humanly. Today’s complex systems generate a very large dataset every minute and dynamic nature of the generated data makes it difficult to track such couplings. The Functional Resonance Analysis Method (FRAM) got success in recent years to understand hazards emerging from function couplings in complex systems, however, challenges remain to estimate aggregated couplings appropriately without quantitative analysis. The current study developed a data-driven approach to quantify function couplings using lift confidence intervals of association rules. Later, association rules were merged to identify the paths leading to a potential hazardous scenario. The paths were presented graphically and equipped with quantified coupling information and capable of providing guidance to prevent the emerging hazard scenario. The approach has been demonstrated with a case study of a polymerization process in process industry for which function couplings are represented by a very large dataset.

Pipeline network facilitates transportation of hazardous materials over long distances and is considered a relatively safer mode of transportation. However, a significant number of incidents occurred over the last few years that has increased the need for development of an accurate predictive model for incidents. Pipeline incident databases have been collecting a wide range of data regarding the incidents including the background operating conditions, the causes contributed to the incident, and the severity of consequences. Most academic studies analyzing these databases have developed predictive failure models focusing only on a narrow range of factors that played a role behind certain categories of incidents. They fail to provide a holistic understanding of how interaction of multiple factors contributing to an incident or how their combined effect eventually leads to failure. The amount of data collected in the databases are however huge. For example, the current reporting format of Pipeline and Hazardous Materials Safety Administration (PHMSA) collects over six hundred datapoints from each incident. Given the large amount of information available and current technologies, it is now possible to look into ways to understand the holistic effect of all contributing factors. The current study looks into how the databases can be utilized to derive such a complete understanding. The study compares the databases of US PHMSA, Canada National Energy Board (NEB), and European Gas Pipeline Incident Data Group (EGIG) to examine the frameworks used to classify the causal factors of the incident data and shows that the background factors, underlying factors and the causal factors identified in the various databases are inter-related and show varying degrees of dependency. This curves the way forward to development of a causal model that will help identify the important factors that needs to be addressed and a prediction of future failure given existing conditions of a pipeline system.

Propane is used in various products regularly in day to day life. Often the hazards due to consumer propane products are overlooked. The events due to consumer propane are usually medium- to high-f...

With the rapid development of chemical process plants worldwide, the safe storage of hazardous chemicals continues to be an important topic. Chemical warehouse incidents related to fire and explosi...

Forkhead box protein M1 (FoxM1) is an important transcription factor involved in various pathological processes including tumor metastasis. The changes of adhesive, migratory, and invasive abilities are considered as crucial events in tumor metastasis progression. In this study, we aimed to investigate the correlation between FoxM1 and retinoblastoma (Rb) metastasis and to explore the detailed mechanism. Wound healing, cell adhesion, and invasion assays showed that FoxM1 overexpression induced epithelial-mesenchymal transition in Y-79 cells and inhibited adhesion and subsequently promoted metastasis of Y-79 cells, while FoxM1 knockdown showed the opposite effects. A luciferase reporter assay and chromatin immunoprecipitation assay provided evidence that FoxM1 promoted matrix metalloproteinase 2 (MMP2) transcription by directly binding to and promoting MMP2 promoter. MMP2 knockdown by siRNA transfection attenuated cell metastasis of Y-79 cells induced by FoxM1 overexpression. Furthermore, the FoxM1-binding site mapped between -1167 and -1161 bp of the MMP2 promoter was identified. Our results suggested that the FoxM1-MMP2 axis plays an important role in Rb metastasis, which may be a novel target for designing therapeutic regimen to control Rb metastasis.

The edge detection algorithm based on sub-pixel can effectively improve the positioning accuracy of contour edges, but the quantitative evaluation method of sub-pixel positioning accuracy needs to be further studied. In this paper, a subpixel edge detection methodology supported by Zernike moment is initial analyzed. A triangle-based simulation method and a calibration-plate–based experiment method are proposed to judge the accuracy of the subpixel algorithm. The findings indicate that the proposed simulation method can quickly and quantitatively evaluate the accuracy of the sub-pixel detection algorithm. The effectiveness of the sub-pixel detection algorithm in improving the accuracy of edge detection is also verified.

Interleukin 38 (IL-38) is a new member of the IL-1 family, and it has anti-inflammatory activity. However, its role in type 2 diabetes mellitus (T2DM) has not been reported.The study included 40 T2DM patients and 42 healthy control subjects. The anthropometric and biochemical measurements were performed using an automatic biochemical analyser, high-performance liquid chromatography, and electrochemiluminescence immunoassay. Circulating IL-38 levels were determined by enzyme-linked immunosorbent assay.Serum IL-38 levels in T2DM patients were significantly lower than those in controls. Correlation analysis showed that serum IL-38 was negatively correlated with systolic blood pressure and interleukin 17 (IL-17).The results suggest that IL-38 may be a new biomarker of T2DM.

Foodborne pathogens and microbial toxins are the main causes of foodborne illness. However, trace pathogens and toxins in foods are difficult to detect. Thus, techniques for their rapid and sensitive identification and quantification are urgently needed. Phages can specifically recognize and adhere to certain species of microbes or toxins due to molecular complementation between capsid proteins of phages and receptors on the host cell wall or toxins, and thus they have been successfully developed into a detection platform for pathogens and toxins. This review presents an update on phage-based luminescent detection technologies as well as their working principles and characteristics. Based on phage display techniques of temperate phages, reporter gene detection assays have been designed to sensitively detect trace pathogens by luminous intensity. By the host-specific lytic effects of virulent phages, enzyme-catalyzed chemiluminescent detection technologies for pathogens have been exploited. Notably, these phage-based luminescent detection technologies can discriminate viable versus dead microbes. Further, highly selective and sensitive immune-based assays have been developed to detect trace toxins qualitatively and quantitatively via antibody analogs displayed by phages, such as phage-ELISA (enzyme-linked immunosorbent assay) and phage-IPCR (immuno-polymerase chain reaction). This literature research may lead to novel and innocuous phage-based rapid detection technologies to ensure food safety.

The present study aims to determine hydrogen sulfide (H2S) concentrations of the aqueous humor from patients with diabetic retinopathy (DR) to compare its levels in the anterior segments, also to investigate its effect on the retinal microvascular endothelial cells under high glucose condition. AH samples were collected from patients with proliferative diabetic retinopathy (n = 11), non-proliferative diabetic retinopathy (n = 12) and diabetic patients without DR as controls (n = 12). There were 5 patients with PDR received intraocular anti-VEGF injection (Lucentis). Cultured RF/6A cells were grouped into control group, mannitol group, high glucose group and NaHS co-administrated high glucose group. Concentrations of H2S were detected by chemical assay. Cell apoptosis was evaluated by flow cytometry. A significantly higher H2S level was observed in AH samples of PDR patients among other groups. The H2S level of DR group was higher than that of control group. Decreased H2S levels in the AH of post-injected PDR patients were observed compared with their AH samples before the anti-VEGF injection. In cell culture, low concentration of NaHS can reverse high-glucose-induced apoptosis of RF/6A cells. Our study revealed increased H2S levels in the anterior segments of different DR patients. The anti-VEGF injection reduced the H2S level in AH from PDR patients. The study suggested that H2S may serve as a biomarker in the progression of PDR. On the other hand, the H2S donor exerted a protective effect on retinal vascular endothelial cells against high-glucose-induced apoptosis.

This paper proposes a clustering technology based on K-means big data to design an online shop order recognition algorithm applied in the field of e-commerce. This algorithm mainly analyzes user consumption behavior based on user consumption data and finds out a user who is single suspected or who has behavior of click farming. This algorithm improves the unsupervised learning characteristics of the original K-means algorithm and is equipped with an outlier detection algorithm based on business experience, which greatly improves the recognition performance of online shop users.

Aqueous humor (AH) proteins are involved in many physiological and pathological processes of the eye. The proteome analysis of AH is important to understand its physiological and pathophysiological functions. In the present study, AH samples obtained from 21 cataract volunteers were pooled together. After high-pH RPLC offline separation, the pooled sample was analyzed by LC-MS/MS to provide a comprehensive profile of AH proteome. The function analysis was provided by the GO and IPA annotation. In order to determine whether the AH proteome can reflect the pathophysiological changes of the disease, DIA technology was used to analyze the AH samples obtained from three neovascular glaucoma (NVG) patients (six samples) before and after drug treatment. The differential proteins were validated by PRM technology in an independent group (14 samples). In the AH proteome database, 802 proteins were identified, and 318 proteins were identified for the first time. Furthermore, 480 proteins were quantified based on the peak intensity-based semiquantification (iBAQ), which ranged by approximately 7 orders of magnitude. These proteins are primarily involved in immunity- and inflammation-related pathways. The differential AH proteomic analysis in NVG treatment revealed that the AH proteome can reflect the pathophysiological changes of drug treatment. Angiogenesis and thrombus coagulation progression are deeply involved in NVG treatment. The present experiment provided a comprehensive AH proteome analysis and expanded the profile of human AH proteome. The differential AH proteomic analysis of NVG treatment indicated that AH proteome can reflect the pathophysiological changes in drug intervention.

Mooring line tension monitoring is required for permanently moored floating offshore platforms by some regional regulators and classification societies. This requirement is typically satisfied by installing physical sensors that directly measure the line tension. Experience shows these sensors have relatively short life compared to the platform operational life and consequently they need to be changed several times thereby increasing the operational expenses. It is also possible that changing the sensors in the field may not be feasible due to access and safety issues or it may be prohibitively expensive, which could lead to the platform operating without meeting the regulations. This paper presents a machine learning based model, which we call ‘virtual sensor’, for predicting the mooring line tensions based on the platform’s heading, horizontal position and six-degrees-of-freedom (6-dof) rigid body motions. The model’s development and testing are demonstrated with the help of data generated through numerical simulations of a permanently moored semi-submersible. When deployed in field, the inputs to the virtual sensor would be obtained from the global position system (GPS) and accelerometers. Both the GPS and accelerometer are cheaper to install and maintain, reliable and easy to replace. The neural network model is pre-trained using a dataset of 5000 static simulations and further fine-tuned with 48 dynamic simulation cases. Model performance on four mooring lines are presented in the study. The accuracy of the model was assessed by determining the percentage of predictions with errors within ±5% of the simulated mooring line tensions. Three of the mooring lines achieved accuracy greater than 90% and one mooring line achieved 77% accuracy. The relevant limitations of the study and future work are discussed in the paper.

Workers of oil and gas extraction industries are exposed to multiple safety hazards, resulting in high fatality rates. Texas has the highest employment and fatality of oil and gas industries. Current regulations that apply to the industries in Texas require no safety management system (SMS) and inadequately address the safety hazards associated with drilling and servicing operations. The objective of the study is to develop a SMS to improve safety performances of Texas drilling and servicing operations. First, current SMSs in the United States, i.e., Process Safety Management (PSM), and Safety and Environment Management Systems (SEMS) were studied to determine if they can be directly extended. According to analysis of Occupational Safety and Health Administration (OSHA) incident data, development of a new SMS is a better solution rather than extension of the two SMSs. A new SMS has been proposed by identifying and categorizing causal factors of incidents. The SMS has been developed to integrate both process and personnel safety and was designed right for Texas drilling and servicing operations. Industries can use the management system as a framework to establish their safety programs.

Background: In this study, we have successfully developed a simvastatin (SMV) and miR-21i-loaded poly (D,L-lactide-co-glycolide)/polyethylenimine (PLGA/PEI) nanoparticles (NP) to enhance the therapeutic efficacy in gastric cancers. Methods: The PLGA/PEI nanoparticles by sonication method. The nanoparticles were characterized for in vitro physicochemical and biological assays and pharmacokinetic study was performed in rats. Results: CLSM/FACS results clearly showed the ability of SMV/miR-21i-loaded PLGA/PEI NP (PPN-S21i) to deliver the combinational therapeutics of SMV and miR-21i to the cancer cells. Combination of SMV+miR-21i showed significantly lower cell viability compared to that of free SMV. Our results clearly highlight the importance of simultaneous interaction of SMV+miR-21i and that it could significantly decrease the cell proliferation in BGC-823, SGC-7901 and HGC-27 gastric cancer cells while it was significantly less cytotoxic to normal gastric mucosa cells (GES-1). Cell apoptosis of SMV+miR-21i was significantly higher compared to that of individual drug or miRNA. Finally, pharmacokinetic analysis revealed that PPN-S21i significantly prolonged the blood circulation time of SMV compared to that of free SMV indicating the potential of carrier system. Conclusion: Overall, results clearly indicate that the combination of SMV+miR-21i (gene + drug therapy) might provide a valuable strategy for the clinical management of gastric cancers.

WGCNA (weighted gene co-expression network analysis) can provide a system-level insight into molecular interaction. To understand the co-expression pattern of miRNA in the progress of colon cancer development, this study constructed the WGCNA co-expression network to find the key microRNA of clinical significance and therefor provide possible personalized treatment targets. The miRNA expression data and clinical follow-up information of 442 valid colon cancer samples were obtained from TCGA. After the co-expression network was constructed, we found the turquoise module has the strongest correlation with stage IV. The miRNAs of the key module are enriched by GO and analyzed by KEGG, and the HUB-miRNAs are screened out through the network node degree. For clinical significance validation, data of 65 cases of colon cancer in GES29623 were analyzed by Cox regression and Kaplan-Meier, in 9 HUB-miRNAs, 2 miRNAs associated with overall survival rate were screened out. They can divide patients into high-risk group and low-risk group. Conclusion: has-miR-126-3p and has-miR-137 are the key miRNAs of COAD metastasis, they are of prognostic value in COAD.

Based on the integrated RISC-V hardware platform architecture, this paper implements the monitoring and counting of some SEU in the on-chip RAM area by software, and performs single-particle irradiation test and software simulation in the real heavy ion environment. The SEU event that occurs during the operation of the analog device is implemented in two ways. At the same time, according to the test results, the software is further tested and analyzed, and the feasibility and effectiveness of the proposed method are verified. It provides a new measurement method for improving the reliability of software operation.

This article is based on a comprehensive analysis of application bearing work methods and specific business models according to various types of power grid transmission services. For different application methods of power grid transmission services on each service transmission network and data transmission to the service network channel bearing work the way of analysis is different. Based on the security level of the power grid transmission service, the method of carrying the service transmission channel network, the method of carrying the service transmission network, the direction and flow of the service, this paper analyzes the way of carrying the channel on the business network.

Aiming at the problems of poor maintenance effect and large measurement error of traditional communication power supply monitoring system, a redundant communication power supply monitoring system based on ZigBee is designed. The sensor and power supply cable are installed, the battery working state parameters are collected and cleaned, compressed and fused. The MSP430F149 single chip microcomputer is used to realize the high-speed processing of field information and optimize the configuration of hardware structure. The software maintenance process of the complete redundant communication power supply monitoring system is designed. The logic editing technology is used to correct, adjust and improve the system software. The mathematical model of hardware maintenance is established. The most reasonable monitoring and measurement of redundant communication power supply is calculated by ZigBee method to realize the effective monitoring of communication power supply. The experimental results show that the hardware operation effect of the design system is good, and the measurement error can be effectively reduced.

Numerous studies have detected choroidal thickness abnormalities and changes in open-angle glaucoma (OAG), as measured by enhanced depth imaging optical coherence tomography technologies, but the results have not always been consistent. Therefore, a meta-analysis and systematic review was performed to evaluate the choroidal thickness in OAG.A comprehensive literature search was performed on Medline, Embase, ISI Web of Science, Cochrane Central Register of Controlled Trials, Google Scholar, and Chinese databases including Wangfang and CNKI (China National Knowledge Infrastructure). Eligible articles were identified by reviewing the retrieved results. For continuous outcomes, we calculated the weighted mean difference (WMD) and 95% confidence interval (CI). Statistical analysis was performed using STATA 12.0 software.Twenty-two case-control or cross-sectional studies were included in the present meta-analysis. The results of our study showed that there was no significant difference in subfoveal choroidal thickness between patients with OAG and controls (WMD=-7.94; 95% CI, -26.01 to 10.13; P=0.389). Similar findings were obtained for the average peripapillary choroidal thickness (WMD=-14.24; 95% CI, -30.20 to 1.73; P=0.08).Our meta-analysis found no significant difference in the choroidal thickness both under the fovea and around the optic nerve head between OAG patients and controls. On the basis of the anatomic features of blood supply in optic nerve head, it is plausible that the choroidal thickness is not an appropriate parameter to evaluate the damage of OAG, and choroidal thinning may not be an important component of glaucomatous optic neuropathy.

Glaucoma is the leading cause of irreversible blindness worldwide. The proteome characterization of glaucoma is not clearly understood. A total of 175 subjects, including 57 primary acute angle-closure glaucoma (PAACG), 50 primary chronic angle-closure glaucoma (PCACG), 35 neovascular glaucoma (NVG), and 33 cataract patients, were enrolled and comparison proteomic analysis was provided. The samples were randomly divided into discovery group or validation group, whose aqueous humor proteome was analyzed by data-independent acquisition or by parallel reaction monitoring. The common proteome features of three types of glaucoma were immune response, lipid metabolism, and cell death. Three proteins, VTN, SERPIND1, and CD14, showed significant upregulation in glaucoma and could discriminate glaucoma from cataract. Mutual differential proteomic analysis of PAACG, PCACG, and NVG showed different proteome characterization of the three types of glaucoma. NVG was characterized with activated angiogenesis. PAACG was characterized with activation of inflammation response. SERPIND1 was discovered to play vital role in glaucoma occurrences, which is associated with eye transparency decrease and glucose metabolism. This study would provide insights in understanding proteome characterization of glaucoma and benefit the clinical application of AH proteome., Graphical Abstract, Highlights • Aqueous humor proteome of different glaucoma (PACG, NVG) was profiled. • Potential protein biomarkers for glaucoma were proposed. • Potential mechanism of glaucoma was described. • SERPIND1 was discovered to have potential value for glaucoma diagnosis., In Brief Liu et al. characterized the proteome of aqueous humor from three types of glaucoma. Fifty-seven primary acute angle-closure glaucoma (PAACG), 50 primary chronic angle-closure glaucoma (PCACG), 35 neovascular glaucoma (NVG), and 33 cataract patient samples were analyzed using data-independent analysis and parallel reaction monitoring. Lipid metabolism, immune response, and cell death pathways showed different degrees of activation among the three types of glaucoma but were all higher relative to cataract. SERPIND1 was discovered as a vital protein in glaucoma.

In recent years, remote sensing imaging technology has developed rapidly. A growing number of high resolution remote sensing images become available, which largely facilitates the research and applications of remote sensing images. Landcover classification is one of the most important tasks of remote sensing image applications [1]. However, traditional classification methods rely on manual feature design, which is time-consuming and requires expertise. It is difficult to apply to massive data. Compared with the traditional classification methods, deep learning [2] can automatically acquire the most intrinsic and discriminative features of the image. Based on the deep learning image classification, this paper designs a high-level semantic information extraction system with high efficiency and robustness. A deep fully convolutional networks (FCN) is designed to extract the features from remote sensing images and to predict the landcover classes of each image, which include building, tree, road, and grass. On the basis of the classification results, we use binarization to highlight the building objects. Then the noise of the binarized image is removed by Gaussian filtering and morphological image processing. After that we set a threshold to delete small misdiagnosis areas. At last the connected domain algorithm is applied to detect the buildings and calculate the building number in each image. The forest coverage is then obtained by computing the proportion of the pixels with ‘tree’ class label to the total number of the pixels in each image. Different from the traditional image interpretation method, this systematic high-level semantic information extraction framework not only detects the number of buildings in the scene but also extracts forest coverage. Moreover, more high-level information extraction can be easily supplemented to this framework, such as road localization or interested object detection.

System-based approaches such as Functional Resonance Analysis Model (FRAM) are developed to model the complex interactions of system variables and their performance variabilities that may lead to a hazardous scenario in a complex system. However, they have limitations to be applied in process industries for hazard identification since they are heavily based on qualitative analysis and expert elicitations. To overcome the limitations of the system-based hazard identification, the study developed a FRAM-based framework to integrate a human performance model, an equipment performance model, and a first-principle based chemical process model into a hybrid simulator, which will be able to aid hazard analysis in the process industries. The simulator is capable of simulating the performance variabilities of the functions through the aggregation of mathematical models within a complex system, which can be used to simulate potential hazard situations and identify the corresponding interactions. Interaction analysis is conducted by applying association rule mining to the simulated data. The impact of the interactions among upstream functions on the performance of downstream functions can be identified by interpreting the rules, whose antecedents contain upstream functions and consequents contain downstream functions.

Rhegmatogenous retinal detachment associated with choroidal detachment (RRDCD) is a complicated and serious type of rhegmatogenous retinal detachment (RRD). In this study, we identified differentially expressed proteins in the vitreous humors of RRDCD and RRD using isobaric tags for relative and absolute quantitation (iTRAQ) combined with nano-liquid chromatography-electrospray ion trap-mass spectrometry-mass spectrometry (nano-LC-ESI-MS/MS) and bioinformatic analysis. Our result shows that 103 differentially expressed proteins, including 54 up-regulated and 49 down-regulated proteins were identified in RRDCD. Gene ontology (GO) analysis suggested that most of the differentially expressed proteins were extracellular．The Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis suggested that proteins related to complement and coagulation cascades were significantly enriched. iTRAQ-based proteomic profiling reveals that complement and coagulation cascades and inflammation may play important roles in the pathogenesis of RRDCD. This study may provide novel insights into the pathogenesis of RRDCD and offer potential opportunities for the diagnosis and treatment of RRDCD.

In the AC/DC hybrid system consisting MMC-HVDC, it needs the distance relay zone 2 or stage III of higher AC line action to remove fault when the protection for lower level AC line which is directly connected with the MMC-HVDC cannot correctly remove fault. The changes of MMC-HVDC operation mode will result that the scope of higher line distance relay zone 2 is narrowed. It has a great probability to cause the miss operation of protection. At this point, it can only rely on distance relay zone 3 to operate which may lead to fault clearing time extended. This paper carries out research on this issue, deeply analyzes the effects of MMC-HVDC operation mode on AC line distance relay. An adaptive distance relay based on the measurement information on MMC-HVDC common connection points is developed. This adaptive distance relay can reduce the adverse impact on the AC line distance relay and ensure the correct operation of the protection device. In PSCAD/EMTDC, we establish an AC/DC hybrid system consisting MMC-HVDC to verify the proposed method and analysis.

DC fault is the main fault type of MMC-HVDC (modular multilevel converter based HVDC), the research of DC fault of MMC-HVDC is mainly focused on pseudo bipolar system, while the research of DC fault in bipolar system is still in its infancy. For the above, in this paper, the topology structure and working principle of MMC in bipolar system is introduced and the effects of different types of DC faults to running state in AC/DC hybrid power system is analyzed. The equivalent circuit of converter under fault condition is built according to the change of the AC/DC electrical parameters and the bridge arm sub-module capacitance discharging mechanism and different DC fault mechanisms of bridge arm valve group is revealed, furthermore, the propagation path of the fault disturbance energy in the hybrid system is given. Finally, a 51-level two ends bipolar HVDC model is set up based in the software RT-LAB and the simulation results validate the accuracy of the theoretical analysis of fault mechanism.

Along with laser countermeasure technology is widely used in the field of photoelectric detector, how the interference and damage degree of laser to detector, when the damage threshold is reached and how to make objective evaluation is a very concern of the people. And therefore, it has important significance to carry out the laser interference and damage experiment of photoelectric detector and propose an objective and reasonable evaluation of Ajamming effect. Combined with typical image evaluation methods such as structural similarity, correlation measurement, feature point analysis and cross entropy, this paper systematically studies the interference effect evaluation technology of laser interfered CCD detector, and finally summarizes the development trend of the interference effect evaluation technology of laser interfered CCD detector.

This study aimed to identify what information triggered social media users' responses regarding infectious diseases. Chinese microblogs in 2012 regarding 42 infectious diseases were obtained through a keyword search in the Weiboscope database. Qualitative content analysis was performed for the posts pertinent to each keyword of the day of the year with the highest daily count. Similar posts were grouped and coded. We identified five categories of information that increased microblog traffic pertaining to infectious diseases: news of an outbreak or a case; health education / information; alternative health information / Traditional Chinese Medicine; commercial advertisement / entertainment; and social issues. News unrelated to the specified infectious diseases also led to elevated microblog traffic. Our study showcases the diverse contexts from which increased social media traffic occur. Our results will facilitate better health communication as causes underlying increased social media traffic are revealed., published_or_final_version

A compact MCU for automotive body applications is introduced with built-in CAN/LIN controller and high reliability, high performance, low power consumption, and low noise features. Equipped with rich elements such as multi-channel timers and PWM modules, 16-channel A/D converter, standard SPI and I2C interface, the proposed MCU can be applied to various automotive body control applications. A code banking scheme is offered to enrich program memory up to 128KB, which realizes the increase in programs in concurrence with the expansion of system functions.

Purpose Our aim was to identify obvious different metabolites and metabolic pathways to elucidate the etiology of rhegmatogenous retinal detachment associated with choroidal detachment (RRDCD) so as to provide direction toward diagnosis and treatment of RRDCD. Methods We used a liquid chromatography-quadrupole-time-of-flight/mass spectrometer (LC-Q-TOF/MS) to obtain the metabolome from the vitreous tissue of patients with rhegmatogenous retinal detachment (RRD) and RRDCD. The metabolomes from 29 samples (14 from RRD patients and 15 from RRDCD patients) were analyzed by principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). A one-way analysis of variance with a Bonferroni correction was used to test significance. The Biofluid Metabolites Database and Human Metabolome Database were used to identify ions. Results The PLS-DA identified 265 (variable importance in the project >1) ions whose levels were significantly different in vitreous from patients with RRD and RRDCD. Among the 265 ions, 24 (23 observed in the positive mode and 1 observed in the negative mode) were identified by searching MS and MS/MS fragments in the Biofluid Metabolites Database and Human Metabolome Database. Metabolites found were associated with pathways related to proliferation, inflammatory reactions, and hemodynamic changes. Conclusions Metabolites have been identified that were present in vitreous at significantly different levels between RRDCD and RRD. These metabolites are likely to be involved in the pathology of each disease and may potentially be used to diagnose and treat RRDCD and RRD.