Data-driven pattern identification and outlier detection in time series

We address the problem of data-driven pattern identification and outlier detection in time series. To this end, we use singular value decomposition (SVD) which is a well-known technique to compute a low-rank approximation for an arbitrary matrix. By recasting the time series as a matrix it becomes possible to use SVD to highlight the underlying patterns and periodicities. This is done without the need for specifying user-defined parameters. From a data mining perspective, this opens up new ways of analyzing time series in a data-driven, bottom-up fashion. However, in order to get correct results, it is important to understand how the SVD-spectrum of a time series is influenced by various characteristics of the underlying signal and noise. In this paper, we have extended the work in earlier papers by initiating a more systematic analysis of these effects. We then illustrate our findings on some real-life data

The correlation between resting EEG power and nonattachment scale

E-Poster: no. 4453INTRODUCTION: Psychological activity is supported by the brain electronic activity, to some extent, recorded by electroencephalography (EEG) (Davidson, et al., 2000). Therefore, it is plausible that some psychological measurements could be correlated with EEG measurements. For example, previous studies have shown that patterns of the frontal and posterior alpha-wave can predict basic dimensions of personality, extraversion and neuroticism (Schmidtke and Heller, 2004). In this paper, we aimed to study the correlations between resting-state EEG and four popular psychological self-reported ...postprin

Large-scale multi-stage constructed wetlands for secondary effluents treatment in northern China: Carbon dynamics

© 2017 Elsevier Ltd Multi-stage constructed wetlands (CWs) have been proved to be a cost-effective alternative in the treatment of various wastewaters for improving the treatment performance as compared with the conventional single-stage CWs. However, few long-term full-scale multi-stage CWs have been performed and evaluated for polishing effluents from domestic wastewater treatment plants (WWTP). This study investigated the seasonal and spatial dynamics of carbon and the effects of the key factors (input loading and temperature) in the large-scale seven-stage Wu River CW polishing domestic WWTP effluents in northern China. The results indicated a significant improvement in water quality. Significant seasonal and spatial variations of organics removal were observed in the Wu River CW with a higher COD removal efficiency of 64–66% in summer and fall. Obvious seasonal and spatial variations of CH4 and CO2 emissions were also found with the average CH4 and CO2 emission rates of 3.78–35.54 mg m−2 d−1 and 610.78–8992.71 mg m−2 d−1, respectively, while the higher CH4 and CO2 emission flux was obtained in spring and summer. Seasonal air temperatures and inflow COD loading rates significantly affected organics removal and CH4 emission, but they appeared to have a weak influence on CO2 emission. Overall, this study suggested that large-scale Wu River CW might be a potential source of GHG, but considering the sustainability of the multi-stage CW, the inflow COD loading rate of 1.8–2.0 g m−2 d−1 and temperature of 15–20 °C may be the suitable condition for achieving the higher organics removal efficiency and lower greenhouse gases (GHG) emission in polishing the domestic WWTP effluent. The obtained knowledge of the carbon dynamics in large-scale Wu River CW will be helpful for understanding the carbon cycles, but also can provide useful field experience for the design, operation and management of multi-stage CW treatments

Intensified organics and nitrogen removal in the intermittent-aerated constructed wetland using a novel sludge-ceramsite as substrate

© 2016 Elsevier Ltd. In this study, a novel sludge-ceramsite was applied as main substrate in intermittent-aerated subsurface flow constructed wetlands (SSF CWs) for treating decentralized domestic wastewater, and intensified organics and nitrogen removal in different SSF CWs (with and without intermittent aeration, with and without sludge-ceramsite substrate) were evaluated. High removal of 97.2% COD, 98.9% NH4+-N and 85.8% TN were obtained simultaneously in the intermittent-aerated CW system using sludge-ceramsite substrate compared with non-aerated CWs. Moreover, results from fluorescence in situ hybridization (FISH) analysis revealed that the growth of ammonia-oxidizing bacteria (AOB) and nitrite-oxidizing bacteria (NOB) in the intermittent-aerated CW system with sludge-ceramsite substrate was enhanced, thus indicating that the application of intermittent aeration and sludge-ceramsite plays an important role in nitrogen transformations. These results suggest that a combination of intermittent aeration and sludge-ceramsite substrate is reliable to enhance the treatment performance in SSF CWs

Sample entropy analysis of EEG signals via artificial neural networks to model patients' consciousness level based on anesthesiologists experience.

Electroencephalogram (EEG) signals, as it can express the human brain's activities and reflect awareness, have been widely used in many research and medical equipment to build a noninvasive monitoring index to the depth of anesthesia (DOA). Bispectral (BIS) index monitor is one of the famous and important indicators for anesthesiologists primarily using EEG signals when assessing the DOA. In this study, an attempt is made to build a new indicator using EEG signals to provide a more valuable reference to the DOA for clinical researchers. The EEG signals are collected from patients under anesthetic surgery which are filtered using multivariate empirical mode decomposition (MEMD) method and analyzed using sample entropy (SampEn) analysis. The calculated signals from SampEn are utilized to train an artificial neural network (ANN) model through using expert assessment of consciousness level (EACL) which is assessed by experienced anesthesiologists as the target to train, validate, and test the ANN. The results that are achieved using the proposed system are compared to BIS index. The proposed system results show that it is not only having similar characteristic to BIS index but also more close to experienced anesthesiologists which illustrates the consciousness level and reflects the DOA successfully.This research is supported by the Center forDynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

The impact of gas slug flow on microfiltration performance in an airlift external loop tubular membrane reactor

© 2016 The Royal Society of Chemistry. This work investigated the impact of gas slug flow on microfiltration in an airlift external loop tubular membrane reactor. A complete description for the characteristics of the slug flow was obtained as the aeration rate increased from 30 to 120 L h-1 with an interval of 30 L h-1. The shear stress of the falling film region could reach 6.37 × 10-3 Pa with the aeration rate of 90 L h-1. Experimental results showed that the growth of transmembrane pressure (TMP) could be controlled effectively by increasing the aeration rate and the optimal aeration rate in a slug flow was around 90 L h-1. However, a subsequent increase in the aeration rate had no significant effect on slowing down the TMP growth rate. Turning the constant air-flow into periodic pulsatile air-flow, low gas-velocity and high gas-velocity led to alternate operation in filtration. When the alternate interval of pulsatile air-flow was 60 s at the alternate aeration rates of 30/90 L h-1 and 60/90 L h-1, it could delay membrane fouling and save a lot of gas compared with implementing a constant air-flow of 90 L h-1. Finally, for different water outlet positions along the membrane tube, membrane fouling gradually slowed down from the bottom to the top

Performance evaluation of powdered activated carbon for removing 28 types of antibiotics from water

© 2016 Elsevier Ltd. Currently, the occurrence and fate of antibiotics in the aquatic environment has become a very serious problem in that they can potentially and irreversibly damage the ecosystem and human health. For this reason, interest has increased in developing strategies to remove antibiotics from water. This study evaluated the performance of powdered activated carbon (PAC) in removing from water 6 representative groups of 28 antibiotics, namely Tetracyclines (TCs), Macrolides (MCs), Chloramphenicols (CPs), Penicillins (PNs), Sulfonamides (SAs) and Quinolones (QNs). Results indicate that PAC demonstrated superior adsorption capacity for all selected antibiotics. The removal efficiency was up to 99.9% in deionized water and 99.6% in surface water at the optimum conditions with PAC dosage of 20 mg/L and contact time of 120 min. According to the Freundlich model's adsorption isotherm, the values of n varied among these antibiotics and most were less than 1, suggesting that the adsorption of antibiotics onto PAC was nonlinear. Adsorption of antibiotics followed well the pseudo-second-order kinetic model (R2 = 0.99). Analysis using the Weber-Morris model revealed that the intra-particle diffusion was not the only rate-controlling step. Overall, the findings in this study confirm that PAC is a feasible and viable option for removing antibiotics from water in terms of water quality improvement and urgent antibiotics pollution control. Further research is essential on the following subjects: (i) removing more types of antibiotics by PAC; (ii) the adsorption process; and (iii) the mechanism of the competitive adsorption existing between natural organic matters (NOMs) and antibiotics

The Importance of the Buddhist Teaching on Three Kinds of Knowing: In a School-based Contemplative Education Program

Includes bibliographical references

Applying a markov chain model in quality function deployment

[[abstract]]The relationships between customer requirements and technical measures are typically resolved by a cross-functional team with the assumption that the relationships are able to be identified objectively. However, due to the limited knowledge and experiences, determining the appropriate relationship could be difficult since the decision makers might not have enough information to evaluate the actual relationship. Moreover, the importance of technical measures is typically expressed in the current time period. It would be of interest to trace the future trends of technical measures since customer needs are fulfilled by technical measures. Under such circumstances, a Markov chain model could be an approach to model the relationship and monitor the trends of technical measures from probabilities viewpoints. With the needed probabilities, the dynamic relationships as well as the trends of technical measures can be performed by different time periods. Finally, the relationships and future trends of technical measures can be updated when the new information is available

The development of a confidence interval-based importance-performance analysis by considering variability in analyzing service quality

[[abstract]]The traditional importance-performance analysis (IPA) uses the mean ratings of importance and performance to construct a two-dimensional grid by identifying improvement opportunities and guiding strategic planning efforts. The point estimates of importance and performance vary from sample to sample such that the numerical analyses are different based upon different samples. Thus, using point estimates for items might lead the management to make false decisions. This study integrates confidence intervals and IPA to reduce the variability which enables the decision maker much easier to identify the strengths and weaknesses based upon the sample of size used. Moreover, the assumptions of equal and unequal population variances for constructing confidence intervals are discussed. (c) 2008 Elsevier Ltd. All rights reserved