Using Support Vector Machines and Acoustic Noise Signal for Degradation Analysis of Rotating Machinery

An automated approach to degradation analysis is proposed that uses a rotating machine’s acoustic signal to determine Remaining Useful Life (RUL). High resolution spectral features are extracted from the acoustic data collected over the entire lifetime of the machine. A novel approach to the computation of Mutual Information based Feature Subset Selection is applied, to remove redundant and irrelevant features, that does not require class label boundaries of the dataset or spectral locations of developing defect to be known or pre-estimated. Using subsets of the feature space, multi-class linear and Radial Basis Function (RBF) Support Vector Machine (SVM) classifiers are developed and a comparison of their performance is provided. Performance of all classifiers is found to be very high, 85 to 98%, with RBF SVMs outperforming linear SVMs when a smaller number of features are used. As larger numbers of features are used for classification, the problem space becomes more linearly separable and the linear SVMs are shown to have comparable performance. A detailed analysis of the misclassifications is provided and an approach to better understand and interpret costly misclassifications is discussed. While defining class label boundaries using an automated k-means clustering algorithm improves performance with an accuracy of approximately 99%, further analysis shows that in 88% of all misclassifications the actual class of failure had the next highest probability of occurring. Thus, a system that incorporates probability distributions as a measure of confidence for the predicted RUL would provide additional valuable information for scheduling preventative maintenance

Using Broad Phonetic Group Experts for Improved Speech Recognition

In phoneme recognition experiments, it was found that approximately 75% of misclassified frames were assigned labels within the same broad phonetic group (BPG). While the phoneme can be described as the smallest distinguishable unit of speech, phonemes within BPGs contain very similar characteristics and can be easily confused. However, different BPGs, such as vowels and stops, possess very different spectral and temporal characteristics. In order to accommodate the full range of phonemes, acoustic models of speech recognition systems calculate input features from all frequencies over a large temporal context window. A new phoneme classifier is proposed consisting of a modular arrangement of experts, with one expert assigned to each BPG and focused on discriminating between phonemes within that BPG. Due to the different temporal and spectral structure of each BPG, novel feature sets are extracted using mutual information, to select a relevant time-frequency (TF) feature set for each expert. To construct a phone recognition system, the output of each expert is combined with a baseline classifier under the guidance of a separate BPG detector. Considering phoneme recognition experiments using the TIMIT continuous speech corpus, the proposed architecture afforded significant error rate reductions up to 5% relative

Collaboration in Primary Science Classrooms: Learning about Evaporation

We have been studying collaboration in the context of children conducting science investigations in British primary classrooms. The classroom is the site of action where learning occurs and it is the teacher who plays the key role in manipulating the learning environment and selecting and structuring tasks to achieve the best learning effect for all children. In this paper we describe our general approach and focus in particular on the data we collect to explore how children's conceptual understanding of evaporation progresses. The paper highlights some of the messages emerging about how collaboration can sometimes enhance learning, and sometimes thwart it

Real-time head nod and shake detection for continuous human affect recognition

Human affect recognition is the field of study associated with using automatic techniques to identify human emotion or human affective state. A person’s affective states is often communicated non-verbally through body language. A large part of human body language communication is the use of head gestures. Almost all cultures use subtle head movements to convey meaning. Two of the most common and distinct head gestures are the head nod and the head shake gestures. In this paper we present a robust system to automatically detect head nod and shakes. We employ the Microsoft Kinect and utilise discrete Hidden Markov Models (HMMs) as the backbone to a to a machine learning based classifier within the system. The system achieves 86% accuracy on test datasets and results are provided

Keeping them honest: Promises reduce cheating in adolescents

People frequently engage in dishonest behavior at a cost to others, and it is therefore beneficial to study interventions promoting honest behavior. We implemented a novel intervention that gave participants a choice to promise to be truthful or not to promise. To measure cheating behavior, we developed a novel variant of the mind game—the dice‐box game—as well as a child‐friendly sender–receiver game. Across three studies with adolescents aged 10 to 14 years (N = 640) from schools in India, we found that promises systematically lowered cheating rates compared with no‐promise control conditions. Adolescents who sent truthful messages in the sender–receiver game cheated less in the dice‐box game and promises reduced cheating in both tasks (Study 1). Promises in the dice‐box game remained effective when negative externalities (Study 2) or incentives for competition (Study 3) were added. A joint analysis of data from all three studies revealed demographic variables that influenced cheating. Our findings confirm that promises have a strong, binding effect on behavior and can be an effective intervention to reduce cheating

The Effector Domain of MARCKS Is a Nuclear Localization Signal that Regulates Cellular PIP2 Levels and Nuclear PIP2 Localization

Translocation to the nucleus of diacylglycerol kinase (DGK)– ζ is dependent on a sequence homologous to the effector domain of Myristoylated Alanine Rich C-Kinase Substrate (MARCKS). These data would suggest that MARCKS could also localize to the nucleus. A single report demonstrated immunofluorescence staining of MARCKS in the nucleus; however, further experimental evidence confirming the specific domain responsible for this localization has not been reported. Here, we report that MARCKS is present in the nucleus in GBM cell lines. We then over-expressed wild-type MARCKS (WT) and MARCKS with the effector domain deleted (ΔED), both tagged with V5-epitope in a GBM cell line with low endogenous MARCKS expression (U87). We found that MARCKS-WT localized to the nucleus, while the MARCKS construct without the effector domain remained in the cytoplasm. We also found that over-expression of MARCKS-WT resulted in a significant increase in total cellular phosphatidyl-inositol (4,5) bisphosphate (PIP2) levels, consistent with prior evidence that MARCKS can regulate PIP2 levels. We also found increased staining for PIP2 in the nucleus with MARCKS-WT over-expression compared to MARCKS ΔED by immunofluorescence. Interestingly, we observed MARCKS and PIP2 co-localization in the nucleus. Lastly, we found changes in gene expression when MARCKS was not present in the nucleus (MARCKS ΔED). These data indicate that the MARCKS effector domain can function as a nuclear localization signal and that this sequence is critical for the ability of MARCKS to regulate PIP2 levels, nuclear localization, and gene expression. These data suggests a novel role for MARCKS in regulating nuclear functions such as gene expression

Barriers to breast cancer screening among diverse cultural groups in Melbourne, Australia

This study explored the association between health literacy, barriers to breast cancer screening, and breast screening participation for women from culturally and linguistically diverse (CALD) backgrounds. English-, Arabic- and Italian-speaking women (n = 317) between the ages of 50 to 74 in North West Melbourne, Australia were recruited to complete a survey exploring health literacy, barriers to breast cancer screening, and self-reported screening participation. A total of 219 women (69%) reported having a breast screen within the past two years. Results revealed that health literacy was not associated with screening participation. Instead, emotional barriers were a significant factor in the self-reported uptake of screening. Three health literacy domains were related to lower emotional breast screening barriers, feeling understood and supported by healthcare providers, social support for health and understanding health information well enough to know what to do. Compared with English- and Italian-speaking women, Arabic-speaking women reported more emotional barriers to screening and greater challenges in understanding health information well enough to know what to do. Interventions that can improve breast screening participation rates should aim to reduce emotional barriers to breast screening, particularly for Arabic-speaking women

Variations in hydrological connectivity of Australian semiarid landscapes indicate abrupt changes in rainfall-use efficiency of vegetation

[1] Dryland vegetation frequently shows self‐organized spatial patterns as mosaic‐like structures of sources (bare areas) and sinks (vegetation patches) of water runoff and sediments with variable interconnection. Good examples are banded landscapes displayed by Mulga in semiarid Australia, where the spatial organization of vegetation optimizes the redistribution and use of water (and other scarce resources) at the landscape scale. Disturbances can disrupt the spatial distribution of vegetation causing a substantial loss of water by increasing landscape hydrological connectivity and consequently, affecting ecosystem function (e.g., decreasing the rainfall‐use efficiency of the landscape). We analyze (i) connectivity trends obtained from coupled analysis of remotely sensed vegetation patterns and terrain elevations in several Mulga landscapes subjected to different levels of disturbance, and (ii) the rainfall‐use efficiency of these landscapes, exploring the relationship between rainfall and remotely sensed Normalized Difference Vegetation Index. Our analyses indicate that small reductions in the fractional cover of vegetation near a particular threshold can cause abrupt changes in ecosystem function, driven by large nonlinear increases in the length of the connected flowpaths. In addition, simulations with simple vegetation‐thinning algorithms show that these nonlinear changes are especially sensitive to the type of disturbance, suggesting that the amount of alterations that an ecosystem can absorb and still remain functional largely depends on disturbance type. In fact, selective thinning of the vegetation patches from their edges can cause a higher impact on the landscape hydrological connectivity than spatially random disturbances. These results highlight surface connectivity patterns as practical indicators for monitoring landscape health

Longitudinal assessment of daily activity patterns on weight change after involuntary job loss: the ADAPT study protocol

Background: The World Health Organization has identified obesity as one of the most visible and neglected public health problems worldwide. Meta-analytic studies suggest that insufficient sleep increases the risk of developing obesity and related serious medical conditions. Unfortunately, the nationwide average sleep duration has steadily declined over the last two decades with 25% of U.S. adults reporting insufficient sleep. Stress is also an important indirect factor in obesity, and chronic stress and laboratory-induced stress negatively impact sleep. Despite what we know from basic sciences about (a) stress and sleep and (b) sleep and obesity, we know very little about how these factors actually manifest in a natural environment. The Assessing Daily Activity Patterns Through Occupational Transitions (ADAPT) study tests whether sleep disruption plays a key role in the development of obesity for individuals exposed to involuntary job loss, a life event that is often stressful and disrupting to an individual’s daily routine. Methods: This is an 18-month closed, cohort research design examining social rhythms, sleep, dietary intake, energy expenditure, waist circumference, and weight gain over 18 months in individuals who have sustained involuntary job loss. Approximately 332 participants who lost their job within the last 3 months are recruited from flyers within the Arizona Department of Economic Security (AZDES) Unemployment Insurance Administration application packets and other related postings. Multivariate growth curve modeling will be used to investigate the temporal precedence of changes in social rhythms, sleep, and weight gain. Discussion It is hypothesized that: (1) unemployed individuals with less consistent social rhythms and worse sleep will have steeper weight gain trajectories over 18 months than unemployed individuals with stable social rhythms and better sleep; (2) disrupted sleep will mediate the relationship between social rhythm disruption and weight gain; and (3) reemployment will be associated with a reversal in the negative trajectories outlined above. Positive findings will provide support for the development of obesity prevention campaigns targeting sleep and social rhythms in an accessible subgroup of vulnerable individuals

The 1.2 A resolution crystal structure of TcpG, the Vibrio cholerae DsbA disulfide-forming protein required for pilus and cholera-toxin production

The enzyme TcpG is a periplasmic protein produced by the Gram-negative pathogen Vibrio cholerae. TcpG is essential for the production of ToxR-regulated proteins, including virulence-factor pilus proteins and cholera toxin, and is therefore a target for the development of a new class of anti-virulence drugs. Here, the 1.2 Å resolution crystal structure of TcpG is reported using a cryocooled crystal. This structure is compared with a previous crystal structure determined at 2.1 Å resolution from data measured at room temperature. The new crystal structure is the first DsbA crystal structure to be solved at a sufficiently high resolution to allow the inclusion of refined H atoms in the model. The redox properties of TcpG are also reported, allowing comparison of its oxidoreductase activity with those of other DSB proteins. One of the defining features of the Escherichia coli DsbA enzyme is its destabilizing disulfide, and this is also present in TcpG. The data presented here provide new insights into the structure and redox properties of this enzyme, showing that the binding mode identified between E. coli DsbB and DsbA is likely to be conserved in TcpG and that the [beta]5-[alpha]7 loop near the proposed DsbB binding site is flexible, and suggesting that the tense oxidized conformation of TcpG may be the consequence of a short contact at the active site that is induced by disulfide formation and is relieved by reduction