Understanding the effects of electrical interference signals and the environment on the effectiveness of cathodic protection

Cathodic protection (CP) failure due to excursions from safe CP levels is a challenge for the protection and maintenance of buried energy pipelines. Although research shows that stray current is a major factor contributing to CP failure, there is little consensus on how 'big' the excursions (either in magnitude, length or frequency) need to be in order to cause pipeline corrosion problems. This uncertainty has caused difficulties in selecting suitable parameters in relevant industry standards. This paper provides a brief review of past research on different factors affecting CP efficiency. Preliminary results from new electrochemical cells designed to develop an understanding of how CP excursions away from the 'safe' level can lead to corrosion problems are also presented.

All signals are not equal: acoustic signalling of individuality, sex and breeding status in a cooperative breeder

Repeated interactions between individuals in socially living animals select for the evolution of signals that convey information identifying individuals or categories of individuals, which may enable the discrimination of familiar versus unfamiliar individuals. Such information may help animals maximize their inclusive fitness by adjusting their own behaviour, allowing them to avoid conflict, preferentially direct help and/or ignore unreliable individuals. Acoustic signals in birds provide the potential to encode individual-specific information. We examined the degree to which individual identity, sex, breeding status, group membership and genetic relatedness were related to variability in six different call types, which occurred across a variety of different behavioural contexts in the apostlebird, Struthidea cinerea, a socially living and cooperatively breeding Australian passerine. We demonstrated that not all calls reflected the same extent of information. Of the six call types, call variation was related to individual identity in three call types, breeding status in two call types and sex and group relatedness in one call type. Finally, variation in two call types was not related to any of the measured variables. Our results suggest that some, but not all, acoustic signals in apostlebirds may be selected for individual distinctiveness between individuals and categories of individuals (male versus female, breeder versus nonbreeder), and these signals may be important in determining levels of cooperation and interaction between individuals in this cooperatively breeding society. © 2014 The Association for the Study of Animal Behaviour.

Serum levels of brain-derived neurotrophic factor in patients with schizophrenia and bipolar disorder.

There is evidence that major psychiatric discords such as schizophrenia (SZ) and bipolar disorder (BD) are associated with dysregulation of synaptic plasticity with downstream alterations of neurotrophins. Brain-derived neurotrophic factor (BDNF) is the most widely distributed neurotrophin in the central nervous system (CNS), and performs many biological functions such as promoting the survival, differentiation, and plasticity of neurons. Variants in the BDNF gene increase the risk of SZ and bipolar disorder. Chronic administration of drugs used to treat SZ and BD, such as lithium, valproate, quetiapine, clozapine, and olanzapine, increases BDNF expression in rat brain. To examine serum BDNF, three groups of chronically medicated DSM-IV SZ patients, on treatment with clozapine (n=27), typical (n=14), and other atypical antipsychotics (n=19), 30 euthymic BD patients, and 26 healthy control had 5 ml blood samples collected by venipuncture. Serum BDNF levels were significantly higher in SZ patients (p<0.001) when compared to either controls or euthymic BD patients. Increased BDNF in SZ patients might be related to the course of illness or to treatment variables. Prospective studies are warranted.

Separating spatially correlated signals using first-order precoders

This paper deals with blind separation of spatially correlated signals mixed by an instantaneous system. Taking advantage of the fact that the source signals are accessible in some man-made systems such as wireless communication systems, we preprocess the source signals in transmitters by a set of properly designed first-order precoders and then the coded signals are transmitted. At the receiving side, information about the precoders are utilized to perform signal separation. Compared with the existing precoder-based methods, the new method only employs the simplest first-order precoders, which reduces the delay in data transmission and is easier to implement in practical applications.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

Individualized arrhythmia detection with ECG signals from wearable devices

Low cost pervasive electrocardiogram (ECG) monitors is changing how sinus arrhythmia are diagnosed among patients with mild symptoms. With the large amount of data generated from long-term monitoring, come new data science and analytical challenges. Although traditional rule-based detection algorithms still work on relatively short clinical quality ECG, they are not optimal for pervasive signals collected from wearable devices - they don't adapt to individual difference and assume accurate identification of ECG fiducial points. To overcome these short-comings of the rule-based methods, this paper introduces an arrhythmia detection approach for low quality pervasive ECG signals. To achieve the robustness needed, two techniques were applied. First, a set of ECG features with minimal reliance on fiducial point identification were selected. Next, the features were normalized using robust statistics to factors out baseline individual differences and clinically irrelevant temporal drift that is common in pervasive ECG. The proposed method was evaluated using pervasive ECG signals we collected, in combination with clinician validated ECG signals from Physiobank. Empirical evaluation confirms accuracy improvements of the proposed approach over the traditional clinical rules.

Classification of electrocardiogram and auscultatory blood pressure signals using machine learning models

In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets.

Antimanic-like activity of candesartan in mice: possible involvement of antioxidant, anti-inflammatory and neurotrophic mechanisms

Activation of the brain angiotensin II type 1 receptor (AT1R) triggers pro-oxidant and pro-inflammatory mechanisms which are involved in the neurobiology of bipolar disorder (BD). Candesartan (CDS) is an AT1 receptor antagonist with potential neuroprotective properties. Herein we investigated CDS effects against oxidative, neurotrophic inflammatory and cognitive effects of amphetamine (AMPH)-induced mania. In the reversal protocol adult mice were given AMPH 2mg/kg i.p. or saline and between days 8 and 14 received CDS 0.1, 0.3 or 1mg/kg orally, lithium (Li) 47.5mg/kg i.p., or saline. In the prevention treatment, mice were pretreated with CDS, Li or saline prior to AMPH. Locomotor activity and working memory performance were assessed. Glutathione (GSH), thiobarbituric acid-reactive substance (TBARS) and TNF-α levels were evaluated in the hippocampus (HC) and cerebellar vermis (CV). Brain-derived neurotrophic factor (BDNF) and glycogen synthase kinase 3-beta (GSK-3beta) levels were measured in the HC. CDS and Li prevented and reversed the AMPH-induced increases in locomotor activity. Only CDS prevented and reversed AMPH-induced working memory deficits. CDS prevented AMPH-induced alterations in GSH (HC and CV), TBARS (HC and CV), TNF-α (HC and CV) and BDNF (HC) levels. Li prevented alterations in BDNF and phospho-Ser9-GSK3beta. CDS reversed AMPH-induced alterations in GSH (HC and CV), TBARS (HC), TNF-α (CV) and BDNF levels. Li reversed AMPH-induced alterations in TNF-α (HC and CV) and BDNF (HC) levels. CDS is effective in reversing and preventing AMPH-induced behavioral and biochemical alterations, providing a rationale for the design of clinical trials investigating CDS׳s possible therapeutic effects.

Mixed signals among tests for panel cointegration

In this paper, we study the effect that different serial correlation adjustment methods can have on panel cointegration testing. As an example, we consider the very popular tests developed by Pedroni [Pedroni, P. (1999). Critical values for cointegration tests in heterogeneous panels with multiple regressors. Oxford Bulletin of Economics and Statistics 61, 653670., Pedroni, P. (2004). Panel cointegration: asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis. Econometric Theory 20, 597-625.]. Results based on both simulated and real data suggest that different adjustment methods can lead to significant variations in test outcome, and thus also in the conclusions. © 2007 Elsevier B.V. All rights reserved.

The relative importance of spatial proximity, kin selection and potential ‘greenbeard’ signals on provisioning behaviour among helpers in a cooperative bird

© 2015 Springer-Verlag Berlin Heidelberg Many hypotheses have been proposed to account for cooperative behaviour, with those favouring kin selection receiving the greatest support to date. However, the importance of relatedness becomes less clear in complex societies where interactions can involve both kin and non-kin. To help clarify this, we examined the relative effect of indirect versus key direct benefit hypotheses in shaping cooperative decisions. We assessed the relative importance of likely reciprocal aid (as measured by spatial proximity between participants), kin selection (using molecular-based relatedness indices) and putative signals of relatedness (vocal similarity) on helper/helper cooperative provisioning dynamics in bell miners (Manorina melanophrys), a species living in large, complex societies. Using network analysis, we quantified the extent of shared provisioning (helping at the same nests) among individual helpers (excluding breeding pairs) over three seasons and 4290 provisioning visits, and compared these with the location of individuals within a colony and networks built using either genetic molecular relatedness or call similarity indices. Significant levels of clustering were observed in networks; individuals within a cluster were more closely related to each other than other colony members, and cluster membership was stable across years. The probability of a miner helping at another’s nest was not simply a product of spatial proximity and thus the potential for reciprocal aid. Networks constructed using helping data were significantly correlated to those built using molecular data in 5 of 10 comparisons, compared to 8 of 10 comparisons for networks constructed using call similarity. This suggests an important role of kinship in shaping helping dynamics in a complex cooperative society, apparently determined via an acoustic ‘greenbeard’ signal in this system.

Spread spectrum-based high embedding capacity watermarking method for audio signals

Abstract— Audio watermarking is a promising technology for copyright protection of audio data. Built upon the concept of spread spectrum (SS), many SS-based audio watermarking method shave been developed, where a pseudonoise (PN) sequence is usually used to introduce security. A major drawback of the existing SS-based audio watermarking methods is their low embedding capacity. In this paper, we propose a new SS-based audio watermarking method which possesses much higher embedding capacity while ensuring satisfactory imperceptibility and robustness. The high embedding capacity is achieved through a set of mechanisms: embedding multiple watermark bits in one audio segment, reducing host signal interference on watermark extraction, and adaptively adjusting PN sequence amplitude in watermark embedding based on the property of audio segments. The effectiveness of the proposed audio watermarking method is demonstrated by simulation examples.

Nonparametric discovery of movement patterns from accelerometer signals

Monitoring daily physical activity plays an important role in disease prevention and intervention. This paper proposes an approach to monitor the body movement intensity levels from accelerometer data. We collect the data using the accelerometer in a realistic setting without any supervision. The ground-truth of activities is provided by the participants themselves using an experience sampling application running on their mobile phones. We compute a novel feature that has a strong correlation with the movement intensity. We use the hierarchical Dirichlet process (HDP) model to detect the activity levels from this feature. Consisting of Bayesian nonparametric priors over the parameters the model can infer the number of levels automatically. By demonstrating the approach on the publicly available USC-HAD dataset that includes ground-truth activity labels, we show a strong correlation between the discovered activity levels and the movement intensity of the activities. This correlation is further confirmed using our newly collected dataset. We further use the extracted patterns as features for clustering and classifying the activity sequences to improve performance.