A Dynamic Anomaly Detection Approach Based on Permutation Entropy for Predicting Aging-Related Failures

Software aging is a phenomenon referring to the performance degradation of a long-running software system. This phenomenon is an accumulative process during execution, which will gradually lead the system from a normal state to a failure-prone state. It is a crucial challenge for system reliability to predict the Aging-Related Failures (ARFs) accurately. In this paper, permutation entropy (PE) is modified to Multidimensional Multi-scale Permutation Entropy (MMPE) as a novel aging indicator to detect performance anomalies, since MMPE is sensitive to dynamic state changes. An experiment is set on the distributed database system Voldemort, and MMPE is calculated based on the collected performance metrics during execution. Finally, based on MMPE, a failure prediction model using the machine learning method to reveal the anomalies is presented, which can predict failures with high accuracy.     

Convolutional Neural Network Approach for Multispectral Facial Presentation Attack Detection in Automated Border Control Systems

Automated border control systems are the first critical infrastructure point when crossing a border country. Crossing border lines for unauthorized passengers is a high security risk to any country. This paper presents a multispectral analysis of presentation attack detection for facial biometrics using the learned features from a convolutional neural network. Three sensors are considered to design and develop a new database that is composed of visible (VIS), near-infrared (NIR), and thermal images. Most studies are based on laboratory or ideal conditions-controlled environments. However, in a real scenario, a subject’s situation is completely modified due to diverse physiological conditions, such as stress, temperature changes, sweating, and increased blood pressure. For this reason, the added value of this study is that this database was acquired in situ. The attacks considered were printed, masked, and displayed images. In addition, five classifiers were used to detect the presentation attack. Note that thermal sensors provide better performance than other solutions. The results present better outputs when all sensors are used together, regardless of whether classifier or feature-level fusion is considered. Finally, classifiers such as KNN or SVM show high performance and low computational level.     

On the Motion of Substance in a Channel of a Network: Extended Model and New Classes of Probability Distributions

We discuss the motion of substance in a channel containing nodes of a network. Each node of the channel can exchange substance with: (i) neighboring nodes of the channel, (ii) network nodes which do not belong to the channel, and (iii) environment of the network. The new point in this study is that we assume possibility for exchange of substance among flows of substance between nodes of the channel and: (i) nodes that belong to the network but do not belong to the channel and (ii) environment of the network. This leads to an extension of the model of motion of substance and the extended model contains previous models as particular cases. We use a discrete-time model of motion of substance and consider a stationary regime of motion of substance in a channel containing a finite number of nodes. As results of the study, we obtain a class of probability distributions connected to the amount of substance in nodes of the channel. We prove that the obtained class of distributions contains all truncated discrete probability distributions of discrete random variable ω which can take values 0,1,⋯,N. Theory for the case of a channel containing infinite number of nodes is presented in Appendix A. The continuous version of the discussed discrete probability distributions is described in Appendix B. The discussed extended model and obtained results can be used for the study of phenomena that can be modeled by flows in networks: motion of resources, traffic flows, motion of migrants, etc.     

Nanoscale detection of faint machinery vibration using the NV center in diamond

We propose a novel method for detection of the faint machinery vibration at the nanometer resolution based on the spin magnetic resonant effect. A suspension magnet acts as a vibration sensor to transfer the vibration signals as the magnetic field fluctuation to excite the spin magnetic resonance. Due to the high sensitivity of the magnetic field of the nitrogen-vacancy center in diamond, the theoretical detection limit of mechanical vibration is as high as 5.7 nm, and the actual measurement resolution reached 12.8 nm, and proved the potential for further improvement to ∼pm resolution. The feasibility of this method is verified by dynamic tests. This method provides a novel approach for the detection of micro-mechanical vibrations.     

Superhydrophobic MoS2-based multifunctional sponge for recovery and detection of spilled oil

Oil spills are a major threat to the marine ecosystem, requiring immediate solutions to remove spilled oil from oceanic environments. In this study, we report a superhydrophobic molybdenum disulfide (MoS₂) coated polydimethylsiloxane (PDMS) sponge and demonstrate its high proficiency in spilled oil recovery and oil spill detection based on oil-water separation ability. This novel oil sorbent is fabricated by a simple dip-coating to incorporate MoS₂ flakes into PDMS sponge. The optimized MoS₂-sponge displays a water contact angle of >152°, demonstrating excellent superhydrophobicity and high oil absorption (>97 wt%) for a variety of oils, including vegetable oil and fuel waste. Moreover, the material retains excellent oil absorption capability upon repetitive compression cycles. The versatility of this novel sorbent has been extended for the real-time spontaneous detection of oils by taking advantage of electrically conductive MoS₂ layers.     

A solvent-governed surface state strategy for rational synthesis of N and S co-doped carbon dots with multicolour fluorescence

ABSTRACT

Multicolour emissive carbon dots (CDs) are widely investigated by virtue of their merits on fluorescent properties. Method on heteroatom doping assisted with various solvents has been proved efficient in achieving multiple-colour-emissive CDs, especially long-wavelength emission. Herein, a synthesis of multicolour-emissive CDs by controlled surface function is reported. By tuning the thermal-pyrolysis temperature and molar ratio of reactants, optimal emission of the resulted CDs gradually shifts from blue to yellow light with the assistance of different solvents. According to the emissive relationship dependent on excitation, fluorescence lifetimes, and FT-IR of these CDs, the different surface states participated with S and N elements on the surface of carbogenic core govern fluorescent colours of the CDs. In terms of the applications, blue CDs (B-CDs) exhibits high sensitivity for ion detections of Ag⁺ and Fe³⁺, which is further illustrated to have different quenching mechanisms each other because that these ions have the affinity interaction with different surface groups of the CDs. Moreover, blue and yellow CDs solutions are mixed with PVP water solution to fabricate white-light CDs/PVP film, which exhibits stable fluorescence with a CIE coordinate of (0.32, 0.33) and endows these CDs as potentially fluorescent nanomaterial in the solid state lighting field.     

A rapid,spatially dispersive frequency comb spectrograph aimed at gas phase chemical reaction kinetics

This New Views article will highlight some recent advances in high sensitivity gas detection using direct infrared absorption frequency comb laser spectroscopy, with a focus on frequency comb use in chemical reaction kinetics and our own contribution to this field. Our recently implemented detection technique uses a combination of a 12.9?GHz free spectral range virtually imaged phased array and diffraction grating to spatially disperse the mid-infrared frequency comb onto a camera. Individual frequencies or ‘comb teeth’ of a 250?MHz repetition-rate frequency comb are able to be resolved. High molecular sensitivity is achieved by increasing the interaction path length using a Herriott multipass cell. High spectral resolution, broadband spectral coverage, and high molecular sensitivity are all achieved on an adjustable 1–50 µs timescale, making this frequency comb apparatus ideal for measuring chemical reaction kinetics where multiple absorbing species can be monitored simultaneously. This New Views article will also discuss some of the challenges and decisions that chemists might face in implementing this advanced physics technology in their own laboratory.

Spatially dispersed 250 MHz mid-infrared frequency comb laser, with absorption of some frequencies by a dilute sample of methane.     

噪声对滑动移除近似熵的影响

受外强迫和仪器本身的测量误差等因素的影响, 观测数据中经常包含噪声和扰动等一些虚假的信息. 针对这一问题, 本文研究了各种不同噪声对滑动移除近似熵的影响. 研究结果表明, 滑动移除近似熵的检测结果受尖峰噪声和高斯白噪声的影响较小, 意味着其具有很强的抗噪能力, 这为该方法在实际观测资料中的广泛应用提供了坚实的实验基础.     

具连续偏差变元的向量抛物型方程的H-振动性

考虑一类具连续偏差变元的向量抛物型偏微分方程的振动性,利用Domslak引进的H-振动的概念及内积降维的方法,将多维振动问题化为一维泛函微分不等式不存在最终正解的问题,给出了该类方程在Robin边值条件下所有解H-振动的若干充分条件,这里H是R^M中的单位向量．     

聚乙撑二氧噻吩导电聚合物上吗啡的电化学检测

在水相中电沉积制备得到了聚乙撑二氧噻吩（PEDOT）导电聚合物膜,研究了沉积电量、沉积电位等因素对聚合物膜的电化学活性及其在水溶液中检测吗啡的电化学响应的影-向．结果表明．沉积电量为20～40mC,沉积电位为1．2V（相对于Ag／AgCl电极）时所得的聚合物膜对吗啡具有最高的电化学响应．在此基础上,研究了PEDOT膜修饰电极在不同浓度的吗啡水溶液中的电化学检测,发现在0．05～6mmol·L^-1浓度范围内具有很好的线性响应．最低检测限为0．05mmol·L^-1,相关系数达0．995．