A Gaussian Bayesian model to identify spatio-temporal causalities for air pollution based on urban big data

Identifying the causalities for air pollutants and answering questions, such as, where do Beijing's air pollutants come from, are crucial to inform government decision-making. In this paper, we identify the spatio-temporal (ST) causalities among air pollutants at different locations by mining the urban big data. This is challenging for two reasons: 1) since air pollutants can be generated locally or dispersed from the neighborhood, we need to discover the causes in the ST space from many candidate locations with time efficiency; 2) the cause-and-effect relations between air pollutants are further affected by confounding variables like meteorology. To tackle these problems, we propose a coupled Gaussian Bayesian model with two components: 1) a Gaussian Bayesian Network (GBN) to represent the cause-and-effect relations among air pollutants, with an entropy-based algorithm to efficiently locate the causes in the ST space; 2) a coupled model that combines cause-and-effect relations with meteorology to better learn the parameters while eliminating the impact of confounding. The proposed model is verified using air quality and meteorological data from 52 cities over the period Jun 1st 2013 to May 1st 2015. Results show superiority of our model beyond baseline causality learning methods, in both time efficiency and prediction accuracy. © 2016 IEEE.postprintLink_to_subscribed_fulltex

Hahn echo and criticality in spin-chain systems

We establish a relation between Hahn spin-echo of a spin-$\frac 1 2$ particle and quantum phase transition in a spin-chain, which couples to the particle. The Hahn echo is calculated and discussed at zero as well as at finite temperatures. On the example of XY model, we show that the critical points of the chain are marked by the extremal values in the Hahn echo, and influence the Hahn echo in surprising high temperature. An explanation for the relation between the echo and criticality is also presented.Comment: 5 pages, 6 figure

Experimental observation of chiral phonons in monolayer WSe2

Chirality characterizes an object that is not identical to its mirror image. In condensed matter physics, Fermions have been demonstrated to obtain chirality through structural and time-reversal symmetry breaking. These systems display unconventional electronic transport phenomena such as the quantum Hall effect and Weyl semimetals. However, for bosonic collective excitations in atomic lattices, chirality was only theoretically predicted and has never been observed. We experimentally show that phonons can exhibit intrinsic chirality in monolayer tungsten diselenide, whose lattice breaks the inversion symmetry and enables inequivalent electronic K and -K valley states. The time-reversal symmetry is also broken when we selectively excite the valley polarized holes by circularly polarized light. Brillouin-zone-boundary phonons are then optically created by the indirect infrared absorption through the hole-phonon interactions. The unidirectional intervalley transfer of holes ensures that only the phonon modes in one valley are excited. We found that such photons are chiral through the transient infrared circular dichroism, which proves the valley phonons responsible to the indirect absorption has non-zero pseudo-angular momentum. From the spectrum we further deduce the energy transferred to the phonons that agrees with both the first principle calculation and the double-resonance Raman spectroscopy. The chiral phonons have significant implications for electron-phonon coupling in solids, lattice-driven topological states, and energy efficient information processing

Extracting time-frequency feature of single-channel vastus medialis EMG signals for knee exercise pattern recognition

© 2017 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The EMG signal indicates the electrophysiological response to daily living of activities, particularly to lower-limb knee exercises. Literature reports have shown numerous benefits of the Wavelet analysis in EMG feature extraction for pattern recognition. However, its application to typical knee exercises when using only a single EMG channel is limited. In this study, three types of knee exercises, i.e., flexion of the leg up (standing), hip extension from a sitting position (sitting) and gait (walking) are investigated from 14 healthy untrained subjects, while EMG signals from the muscle group of vastus medialis and the goniometer on the knee joint of the detected leg are synchronously monitored and recorded. Four types of lower-limb motions including standing, sitting, stance phase of walking, and swing phase of walking, are segmented. The Wavelet Transform (WT) based Singular Value Decomposition (SVD) approach is proposed for the classification of four lower-limb motions using a single-channel EMG signal from the muscle group of vastus medialis. Based on lower-limb motions from all subjects, the combination of five-level wavelet decomposition and SVD is used to comprise the feature vector. The Support Vector Machine (SVM) is then configured to build a multiple-subject classifier for which the subject independent accuracy will be given across all subjects for the classification of four types of lower-limb motions. In order to effectively indicate the classification performance, EMG features from time-domain (e.g., Mean Absolute Value (MAV), Root-Mean-Square (RMS), integrated EMG (iEMG), Zero Crossing (ZC)) and frequency-domain (e.g., Mean Frequency (MNF) and Median Frequency (MDF)) are also used to classify lower-limb motions. The five-fold cross validation is performed and it repeats fifty times in order to acquire the robust subject independent accuracy. Results show that the proposed WT-based SVD approach has the classification accuracy of 91.85% ±0.88% which outperforms other feature models

Static Synchronous Generator Model: A New Perspective to Investigate Dynamic Characteristics and Stability Issues of Grid-Tied PWM Inverter

With increasing penetration of the renewable energy, the grid-tied PWM inverters need to take corresponding responsibilities for the security and stability of future grid, behaving like conventional rotational synchronous generator (RSG). Therefore, recognizing the inherent relationship and intrinsic differences between inverters and RSGs is essential for such target. By modeling the typical electromechanical transient of grid-tied PWM inverters, this paper first proves that PWM inverters and RSGs are similar in physical mechanism and equivalent in mathematical model, and the concept of static synchronous generator (SSG) is thereby developed. Furthermore, the comprehensive comparison between RSG and SSG is carried out in detail, and their inherent relation is built. Based on these findings, the rationality and feasibility of migrating the concepts, tools, and methods of RSG stability analysis to investigate the dynamic behaviors and stability issues of SSG is therefore confirmed. Taking stability issues as an example, the criteria of small signal and transient stability of a typical grid-tied PWM inverter is put forward to demonstrate the significance of the developed SSG model (including synchronizing coefficient, damping coefficient, inertia constant, and power-angle curve), providing clear physical interpretation on the dynamic characteristics and stability issues. The developed SSG model promotes grid-friendly integration of renewable energy to future grid and stimulates interdisciplinary research between power electronics and power system

A Weak Target Detection Algorithm IAR-STFT Based on Correlated K-distribution Sea Clutter Model

The detection performance of weak target on sea is affected by the special effects of sea clutter amplitude. Aiming at the time and space correlated of sea clutter, the correlated K-distribution sea clutter model is established by the sphere invariant random process algorithm. To solve the problems of range migration (RM) and Doppler frequency migration (DFM) of moving target in the case of long-time coherent accumulation, a novel integration detection algorithm, improved axis rotation short-time Fourier transform (IAR-STFT) is proposed in this paper, which is based on a generalization of traditional Fourier transform (FT) algorithm and combined with improved axis rotation. IAR-STFT not only can eliminate the RM effect by searching for the target motion parameters, but also can divide the non-stationary echo signal without range migration into several blocks. Each block of signal can be regarded as a stationary signal without DFM and FFT is performed on each signal separately. The signals of each block are accumulated to detect the target in the background of the above sea clutter. Finally, the effectiveness of the algorithm is verified by simulation. The results show that the detection ability of this algorithm is better than that of Radon-fractional Fourier transform, generalized Radon Fourier transform and Radon-Lv's distribution in low SNR environment, e.g., when the SNR is -45dB, the detection ability of this algorithm is about 55%, which is higher than that of Radon-fractional Fourier transform, generalized Radon Fourier transform and Radon-Lv's distribution

Extra-hepatic fascioliasis with peritoneal malignancy tumor feature

Fascioliasis is a zoonose parasitic disease caused by Fasciola hepatica and Fasciola gigantica and is widespread in most regions of the world. Ectopic fascioliasis usually caused by juvenile Fasciola spp., but in recent years a few cases of tissue-embedded ova have been reported from different endemic areas. A 79-year-old Iranian man resident in Eird-e-Mousa village from Ardabil Province, north-west of Iran, complained with abdominal pain, nausea, and intestinal obstruction symptoms referred to Ardabil Fatemi hospital. In laparotomy multiple intestinal masses with peritoneal seeding resembling of a malignant lesion were seen. After appendectomy and peritoneal mass biopsy with numerous intraperitoneal adenopathy, paraffin embedded blocks were prepared from each tissues. A blood sample was taken from the patient 5 months later for serological diagnosis. Histopathological examination of sections showed fibrofatty stroma with dense mixed inflammatory cells infiltration and fibrosis in peritoneal masses. Large numbers of ova of Fasciola spp. were noted with typical circumscribed granulomas. Despite of anti-fasciola treatment, IHA test for detecting anti F. hepatica antibodies was positive 5 months after surgery with a titer of 1/128. Due to multiple clinical manifestation of extra-hepatic fascioliasis, its differential diagnosis from intraperitoneal tumors or other similar diseases should be considered