Pipeline Failure Cause Theory: A New Accident Characteristics, Quantification, and Cause Theory

Based on the accident research and management practices of oil and gas pipelines, the characteristics and the quantitative description of the accident/failure are set up. Several characteristics are summarized which clearly describe the essential prosperities of the accident. Fragility, anti-fragility, and integrity are used as an index to describe the state of accident, which provides a new way of evaluating and describing accident, different from the traditional accident assessment. The understanding and the evaluation of the nature of accident become clearer. Accident cause theory is the basic theory of cognition and prevention of failure. In this chapter, based on the analysis of characteristics and limitations of some accident cause theories, and comprehension of characteristics of failure and systematic statistics, a new systematic accident cause theory is proposed, named by analogy with “tree-type.” This theory provides a systematical supplement of accident cause theories

Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification

This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. Besides, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with several state-of-the-art methods, including the CNN framework, on three widely used HSIs. The obtained results show that Bi-CLSTM can improve the classification performance as compared to other methods

Doping dependence of the electron-doped cuprate superconductors from the antiferromagnetic properties of the Hubbard model

Within the Kotliar-Ruckenstein slave-boson approach, we have studied the antiferromagnetic (AF) properties for the $t$-$t'$-$t''$-$U$ model applied to electron-doped cuprate superconductors. Due to inclusion of spin fluctuations the AF order decreases with doping much faster than obtained in the Hartree-Fock theory. Under an intermediate {\it constant} $U$ the calculated doping evolution of the spectral intensity has satisfactorily reproduced the experimental results, without need of a strongly doping-dependent $U$ as argued earlier. This may reconcile a discrepancy suggested in recent studies on photoemission and optical conductivity.Comment: 5 pages, 4 eps figures, minor improvement, references added, to appear in Phys. Rev.

Study of gossamer superconductivity and antiferromagnetism in the t-J-U model

The d-wave superconductivity (dSC) and antiferromagnetism are analytically studied in a renormalized mean field theory for a two dimensional t-J model plus an on-site repulsive Hubbard interaction $U$. The purpose of introducing the $U$ term is to partially impose the no double occupancy constraint by employing the Gutzwiller approximation. The phase diagrams as functions of doping $\delta$ and $U$ are studied. Using the standard value of $t/J=3.0$ and in the large $U$ limit, we show that the antiferromagnetic (AF) order emerges and coexists with the dSC in the underdoped region below the doping $\delta\sim0.1$. The dSC order parameter increases from zero as the doping increases and reaches a maximum near the optimal doping $\delta\sim0.15$. In the small $U$ limit, only the dSC order survives while the AF order disappears. As $U$ increased to a critical value, the AF order shows up and coexists with the dSC in the underdoped regime. At half filing, the system is in the dSC state for small $U$ and becomes an AF insulator for large $U$. Within the present mean field approach, We show that the ground state energy of the coexistent state is always lower than that of the pure dSC state.Comment: 7 pages, 8 figure

A Parameter-Free Hybrid Clustering algorithm used for Malware Categorization

Nowadays, numerous attacks made by the malware, such as viruses, backdoors, spyware, trojans and worms, have presented a major security threat to computer users. The most significant line of defense against malware is anti-virus products which detects, removes, and characterizes these threats. The ability of these AV products to successfully characterize these threats greatly depends on the method for categorizing these profiles of malware into groups. Therefore, clustering malware into different families is one of the computer security topics that are of great interest. In this paper, resting on the analysis of the extracted instruction of malware samples, we propose a novel parameter-free hybrid clustering algorithm (PFHC) which combines the merits of hierarchical clustering and K-means algorithms for malware clustering. It can not only generate stable initial division, but also give the best K. PFHC first utilizes agglomerative hierarchical clustering algorithm as the frame, starting with N singleton clusters, each of which exactly includes one sample, then reuses the centroids of upper level in every level and merges the two nearest clusters, finally adopts K-means algorithm for iteration to achieve an approximate global optimal division. PFHC evaluates clustering validity of each iteration procedure and generates the best K by comparing the values. The promising studies on real daily data collection illustrate that, compared with popular existing K-means and hierarchical clustering approaches, our proposed PFHC algorithm always generates much higher quality clusters and it can be well used for malware categorization

Pipeline Bending Strain Measurement and Compensation Technology Based on Wavelet Neural Network

The bending strain of long distance oil and gas pipelines may lead to instability of the pipeline and failure of materials, which seriously deteriorates the transportation security of oil and gas. To locate the position of the bending strain for maintenance, an Inertial Measurement Unit (IMU) is usually adopted in a Pipeline Inspection Gauge (PIG). The attitude data of the IMU is usually acquired to calculate the bending strain in the pipe. However, because of the vibrations in the pipeline and other system noises, the resulting bending strain calculations may be incorrect. To improve the measurement precision, a method, based on wavelet neural network, was proposed. To test the proposed method experimentally, a PIG with the proposed method is used to detect a straight pipeline. It can be obtained that the proposed method has a better repeatability and convergence than the original method. Furthermore, the new method is more accurate than the original method and the accuracy of bending strain is raised by about 23% compared to original method. This paper provides a novel method for precisely inspecting bending strain of long distance oil and gas pipelines and lays a foundation for improving the precision of inspection of bending strain of long distance oil and gas pipelines

Electrocaloric effect in ferroelectric ceramics with point defects

The electrocaloric effect has drawn much attention due to its potential application in cooling devices. A negative electrocaloric effect is predicted to be induced in defect-doped ferroelectrics by computational results [A. Grunebohm and T. Nishimatsu, Phys. Rev. B 93, 134101 (2016) and Ma et al., Phys. Rev. B 94, 094113 (2016)], but it need to be confirmed by experimental results. In this work, we prepared a 1mol. % Mn-doped Pb(Zr0.2,Ti0.8)O3 ceramics (Pb((Zr0.2,Ti0.8)0.99,Mn0.01)O3), and the electrocaloric effect of the defect-containing ferroelectric ceramics has been investigated by both direct and indirect methods. The indirect method shows a similar negative electrocaloric effect signal as the computational results predicted, while the direct method gives a positive electrocaloric effect. The absence of the negative electrocaloric effect obtained by the direct method may originate from: (a) the unavailability and the improper prediction of the Maxwell relation, (b) an improper assumption of fixed defects in the computational models, and (c) the offset of heat loss due to the application of a large electric field. In addition, we find a giant positive electrocaloric effect of 0.55K at room temperature in the aged ceramics where no phase transition takes place. We attribute this abnormal electrocaloric effect to the restoration force of the defect dipoles. Our results not only provide insights into the origin of the negative electrocaloric effect, but also offer opportunities for the design of electrocaloric materials

Molecular Targets and Associated Potential Pathways of Danlu Capsules in Hyperplasia of Mammary Glands Based on Systems Pharmacology

Hyperplasia of mammary glands (HMG) is common in middle-aged women. Danlu capsules (DLCs) can effectively relieve pain and improve clinical symptoms and are safe for treating HMG. However, the active substances in DLCs and the molecular mechanisms of DLCs in HMG remain unclear. This study identified the bioactive compounds and delineated the molecular targets and potential pathways of DLCs by using a systems pharmacology approach. The candidate compounds were retrieved from the traditional Chinese medicine systems pharmacology (TCMSP) database and analysis platform. Each candidate’s druggability was analyzed according to its oral bioavailability and drug-likeness indices. The candidate proteins and genes were extracted in the TCMSP and UniProt Knowledgebase, respectively. The potential pathways associated with the genes were identified by performing gene enrichment analysis with DAVID Bioinformatics Resources 6.7. A total of 603 compounds were obtained from DLCs, and 39 compounds and 66 targets associated with HMG were obtained. Gene enrichment analysis yielded 10 significant pathways with 34 targets. The integrated HMG pathway revealed that DLCs probably act in patients with HMG through multiple mechanisms of anti-inflammation, analgesic effects, and hormonal regulation. This study provides novel insights into the mechanisms of DLCs in HMG, from the molecular level to the pathway level

Extinction and recurrence of multi-group SEIR epidemic

In this paper, we consider a class of multi-group SEIR epidemic models with stochastic perturbations. By the method of stochastic Lyapunov functions, we study their asymptotic behavior in terms of the intensity of the stochastic perturbations and the reproductive number R0R0. When the perturbations are sufficiently large, the exposed and infective components decay exponentially to zero whilst the susceptible components converge weakly to a class of explicit stationary distributions regardless of the magnitude of R0R0. An interesting result is that, if the perturbations are sufficiently small and R0≤1R0≤1, then the exposed, infective and susceptible components have similar behaviors, respectively, as in the case of large perturbations. When the perturbations are small and R0>1R0>1, we construct a new class of stochastic Lyapunov functions to show the ergodic property and the positive recurrence, and our results reveal some cycling phenomena of recurrent diseases. Computer simulations are carried out to illustrate our analytical results

Cellphone-Based Hand-Held Microplate Reader for Point-of-Care Testing of Enzyme-Linked Immunosorbent Assays

Standard microplate based enzyme-linked immunosorbent assays (ELISA) are widely utilized for various nanomedicine, molecular sensing, and disease screening applications, and this multiwell plate batched analysis dramatically reduces diagnosis costs per patient compared to nonbatched or nonstandard tests. However, their use in resource-limited and field-settings is inhibited by the necessity for relatively large and expensive readout instruments. To mitigate this problem, we created a hand-held and cost-effective cellphone-based colorimetric microplate reader, which uses a 3D-printed optomechanical attachment to hold and illuminate a 96-well plate using a light-emitting-diode (LED) array. This LED light is transmitted through each well, and is then collected via 96 individual optical fibers. Captured images of this fiber-bundle are transmitted to our servers through a custom-designed app for processing using a machine learning algorithm, yielding diagnostic results, which are delivered to the user within ∼1 min per 96-well plate, and are visualized using the same app. We successfully tested this mobile platform in a clinical microbiology laboratory using FDA-approved mumps IgG, measles IgG, and herpes simplex virus IgG (HSV-1 and HSV-2) ELISA tests using a total of 567 and 571 patient samples for training and blind testing, respectively, and achieved an accuracy of 99.6%, 98.6%, 99.4%, and 99.4% for mumps, measles, HSV-1, and HSV-2 tests, respectively. This cost-effective and hand-held platform could assist health-care professionals to perform high-throughput disease screening or tracking of vaccination campaigns at the point-of-care, even in resource-poor and field-settings. Also, its intrinsic wireless connectivity can serve epidemiological studies, generating spatiotemporal maps of disease prevalence and immunity