Detection of rain in acoustic recordings of the environment

Environmental monitoring has become increasingly important due to the significant impact of human activities and climate change on biodiversity. Environmental sound sources such as rain and insect vocalizations are a rich and underexploited source of information in environmental audio recordings. This paper is concerned with the classification of rain within acoustic sensor re-cordings. We present the novel application of a set of features for classifying environmental acoustics: acoustic entropy, the acoustic complexity index, spectral cover, and background noise. In order to improve the performance of the rain classification system we automatically classify segments of environmental recordings into the classes of heavy rain or non-rain. A decision tree classifier is experientially compared with other classifiers. The experimental results show that our system is effective in classifying segments of environmental audio recordings with an accuracy of 93% for the binary classification of heavy rain/non-rain.

Locality-sensitive hashing for protein classification

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successive waves of innovation in sequencing technologies – so-called Next Generation Sequencing (NGS) approaches – have led to an explosion in data availability, challenging existing methods and motivating novel approaches to sequence representation and similarity scoring, including adaptation of existing methods from other domains such as information retrieval. In this work, we investigate locality-sensitive hashing of sequences through binary document signatures, applying the method to a bacterial protein classification task. Here, the goal is to predict the gene family to which a given query protein belongs. Experiments carried out on a pair of small but biologically realistic datasets (the full protein repertoires of families of Chlamydia and Staphylococcus aureus genomes respectively) show that a measure of similarity obtained by locality sensitive hashing gives highly accurate results while offering a number of avenues which will lead to substantial performance improvements over BLAST..

Synthesis, characterization and activity of an immobilized photocatalyst : natural porous diatomite supported titania nanoparticles

Diatomite, a porous non-metal mineral, was used as support to prepare TiO2/diatomite composites by a modified sol–gel method. The as-prepared composites were calcined at temperatures ranging from 450 to 950 _C. The characterization tests included X-ray powder diffraction (XRD), scanning electron microscopy (SEM) with an energy-dispersive X-ray spectrometer (EDS), high-resolution transmission electron microscopy (HRTEM), X-ray photoelectron spectroscopy (XPS), and nitrogen adsorption/desorption measurements. The XRD analysis indicated that the binary mixtures of anatase and rutile exist in the composites. The morphology analysis confirmed the TiO2 particles were uniformly immobilized on the surface of diatom with a strong interfacial anchoring strength, which leads to few drain of photocatalytic components during practical applications. In further XPS studies of hybrid catalyst, we found the evidence of the presence of Ti–O–Si bond and increased percentage of surface hydroxyl. In addition, the adsorption capacity and photocatalytic activity of synthesized TiO2/diatomite composites were evaluated by studying the degradation kinetics of aqueous Rhodamine B under UV-light irradiation. The photocatalytic degradation was found to follow pseudo-first order kinetics according to the Langmuir–Hinshelwood model. The preferable removal efficiency was observed in composites by 750 _C calcination, which is attributed to a relatively appropriate anatase/rutile mixing ratio of 90/10.

Injury narrative text classification : a preliminary study

Description of a patient's injuries is recorded in narrative text form by hospital emergency departments. For statistical reporting, this text data needs to be mapped to pre-defined codes. Existing research in this field uses the Naïve Bayes probabilistic method to build classifiers for mapping. In this paper, we focus on providing guidance on the selection of a classification method. We build a number of classifiers belonging to different classification families such as decision tree, probabilistic, neural networks, and instance-based, ensemble-based and kernel-based linear classifiers. An extensive pre-processing is carried out to ensure the quality of data and, in hence, the quality classification outcome. The records with a null entry in injury description are removed. The misspelling correction process is carried out by finding and replacing the misspelt word with a soundlike word. Meaningful phrases have been identified and kept, instead of removing the part of phrase as a stop word. The abbreviations appearing in many forms of entry are manually identified and only one form of abbreviations is used. Clustering is utilised to discriminate between non-frequent and frequent terms. This process reduced the number of text features dramatically from about 28,000 to 5000. The medical narrative text injury dataset, under consideration, is composed of many short documents. The data can be characterized as high-dimensional and sparse, i.e., few features are irrelevant but features are correlated with one another. Therefore, Matrix factorization techniques such as Singular Value Decomposition (SVD) and Non Negative Matrix Factorization (NNMF) have been used to map the processed feature space to a lower-dimensional feature space. Classifiers with these reduced feature space have been built. In experiments, a set of tests are conducted to reflect which classification method is best for the medical text classification. The Non Negative Matrix Factorization with Support Vector Machine method can achieve 93% precision which is higher than all the tested traditional classifiers. We also found that TF/IDF weighting which works well for long text classification is inferior to binary weighting in short document classification. Another finding is that the Top-n terms should be removed in consultation with medical experts, as it affects the classification performance.

Governing prostitution: Differentiating the bad from the bad

Accounts of the governance of prostitution have typically argued that prostitutes are, in one way or another, stigmatised social outcasts. There is a persistent claim that power has operated to dislocate or banish the prostitute from the community in order to silence, isolate, hide, restrict, or punish. I argue that another position may be tenable; that is, power has operated to locate prostitution within the social. Power does not operate to 'desocialise' prostitution, but has in recent times operated increasingly to normalise it. Power does not demarcate prostitutes from the social according to some binary mechanics of difference, but works instead according to a principle of differentiation which seeks to connect, include, circulate and enable specific prostitute populations within the social. In this paper I examine how prostitution has been singled out for public attention as a sociopolitical problem and governed accordingly. The concept of governmentality is used to think through such issues, providing, as it does, a non-totalising and non-reductionist account of rule. It is argued that a combination of self-regulatory and punitive practices developed during modernity to manage socially problematic prostitute populations.

Multiphase porous media model for heat and mass transfer during drying of agricultural products

Modelling of food processing is complex because it involves sophisticated material and transport phenomena. Most of the agricultural products such fruits and vegetables are hygroscopic porous media containing free water, bound water, gas and solid matrix. Considering all phase in modelling is still not developed. In this article, a comprehensive porous media model for drying has been developed considering bound water, free water separately, as well as water vapour and air. Free water transport was considered as diffusion, pressure driven and evaporation. Bound water assumed to be converted to free water due to concentration difference and also can diffuse. Binary diffusion between water vapour and air was considered. Since, the model is fundamental physics based it can be applied to any drying applications and other food processing where heat and mass transfer takes place in porous media with significant evaporation and other phase change.

A Gaussian pseudolikelihood approach for quantile regression with repeated measurements

To enhance the efficiency of regression parameter estimation by modeling the correlation structure of correlated binary error terms in quantile regression with repeated measurements, we propose a Gaussian pseudolikelihood approach for estimating correlation parameters and selecting the most appropriate working correlation matrix simultaneously. The induced smoothing method is applied to estimate the covariance of the regression parameter estimates, which can bypass density estimation of the errors. Extensive numerical studies indicate that the proposed method performs well in selecting an accurate correlation structure and improving regression parameter estimation efficiency. The proposed method is further illustrated by analyzing a dental dataset.

Sustainability Non-Price Incentives and Rewards: A Collaborative Procurement Perspective

Construction scholars suggest that procurement processes can be used as mechanisms to change construction industry practices. This paper discusses industry changes as a response to the calls for integration of sustainability ideals into construction practices. Because major infrastructure construction has been identified as a key producer of greenhouse gas emissions (GHGE), this study explores collaborative procurement models that have been used to facilitate mitigation of GHGE. The study focuses on the application of non-price incentives and rewards that work together as a binary mechanism. Data were collected using mixed-methods: government document content analysis was complemented with data collected through focus groups and individual interviews with both clients and contractors. This report includes examples of greening procurement agendas for three Australian road authorities relating to collaborative procurement project delivery models. Three collaborative procurement models, Alliance Consortium, Early Contractor Involvement and Public Private Partnerships provide evidence of construction projects that were completed early. It can also be argued that both clients and contractors are rewarded through collaborative project delivery. The incentive of early completion is rewarded with reduction of GHGE. This positive environmental outcome, based on a dual benefit and non-price sustainability criteria, suggests a step towards changed industry practices though the use of green procurement models.

Do dissonants in transit oriented development adjust commuting travel behaviour?

Residential dissonance signifies a mismatch between an individual’s preferred and actual proximal land use patterns in residential neighbourhoods, whereas residential consonance signifies agreement between actual and preferred proximal land uses. Residential dissonance is a relatively unexplored theme in the literature, yet it acts as a barrier to the development of sustainable transport and land use policy. This research identifies mode choice behaviour of four groups living in transit oriented development (TOD) and non-TOD areas in Brisbane, Australia using panel data from 2675 commuters: TOD consonants, TOD dissonants, non-TOD consonants, and non-TOD dissonants. The research investigates a hypothetical understanding that dissonants adjust their travel attitudes and perceptions according to their surrounding land uses over time. The adjustment process was examined by comparing the commuting mode choice behaviour of dissonants between 2009 and 2011. Six binary logistic regression models were estimated, one for each of the three modes considered (e.g. public transport, active transport, and car) and one for each of the 2009 and 2011 waves. Results indicate that TOD dissonants and non-TOD consonants were less likely to use the public transport and active transport; and more likely to use the car compared with TOD consonants. Non-TOD dissonants use public transport and active transport equally to TOD consonants. The results suggest that commuting mode choice behaviour is largely determined by travel attitudes than built environment factors; however, the latter influence public transport and car use propensity. This research also supports the view that dissonants adjust their attitudes to surrounding land uses, but very slowly. Both place (e.g. TOD development) and people-based (e.g. motivational) policies are needed for an effective travel behavioural shift.

Centroid training to achieve effective text classification

Traditional text classification technology based on machine learning and data mining techniques has made a big progress. However, it is still a big problem on how to draw an exact decision boundary between relevant and irrelevant objects in binary classification due to much uncertainty produced in the process of the traditional algorithms. The proposed model CTTC (Centroid Training for Text Classification) aims to build an uncertainty boundary to absorb as many indeterminate objects as possible so as to elevate the certainty of the relevant and irrelevant groups through the centroid clustering and training process. The clustering starts from the two training subsets labelled as relevant or irrelevant respectively to create two principal centroid vectors by which all the training samples are further separated into three groups: POS, NEG and BND, with all the indeterminate objects absorbed into the uncertain decision boundary BND. Two pairs of centroid vectors are proposed to be trained and optimized through the subsequent iterative multi-learning process, all of which are proposed to collaboratively help predict the polarities of the incoming objects thereafter. For the assessment of the proposed model, F1 and Accuracy have been chosen as the key evaluation measures. We stress the F1 measure because it can display the overall performance improvement of the final classifier better than Accuracy. A large number of experiments have been completed using the proposed model on the Reuters Corpus Volume 1 (RCV1) which is important standard dataset in the field. The experiment results show that the proposed model has significantly improved the binary text classification performance in both F1 and Accuracy compared with three other influential baseline models.

A smooth transition logit model of the effects of deregulation in the electricity market

This paper introduces the smooth transition logit (STL) model that is designed to detect and model situations in which there is structural change in the behaviour underlying the latent index from which the binary dependent variable is constructed. The maximum likelihood estimators of the parameters of the model are derived along with their asymptotic properties, together with a Lagrange multiplier test of the null hypothesis of linearity in the underlying latent index. The development of the STL model is motivated by the desire to assess the impact of deregulation in the Queensland electricity market and ascertain whether increased competition has resulted in significant changes in the behaviour of the spot price of electricity, specifically with respect to the occurrence of periodic abnormally high prices. The model allows the timing of any change to be endogenously determined and also market participants' behaviour to change gradually over time. The main results provide clear evidence in support of a structural change in the nature of price events, and the endogenously determined timing of the change is consistent with the process of deregulation in Queensland.

Saudi Arabian adult intensive care unit pressure ulcer incidence and risk factors: A prospective cohort study

The purpose of this study was to identify pressure ulcer (PU) incidence and risk factors that are associated with PU development in patients in two adult intensive care units (ICU) in Saudi Arabia. A prospective cohort study design was used. A total of 84 participants were screened second daily basis until discharge or death, over a consecutive 30-day period, out of which 33 participants with new PUs were identified giving a cumulative hospital-acquired PU incidence of 39·3% (33/84 participants). The incidence of medical devices-related PUs was 8·3% (7/84). Age, length of stay in the ICU, history of cardiovascular disease and kidney disease, infrequent repositioning, time of operation, emergency admission, mechanical ventilation and lower Braden Scale scores independently predicted the development of a PU. According to binary logistic regression analyses, age, longer stay in ICU and infrequent repositioning were significant predictors of all stages of PUs, while the length of stay in the ICU and infrequent repositioning were associated with the development of stages II-IV PUs. In conclusion, PU incidence rate was higher than that reported in other international studies. This indicates that urgent attention is required for PU prevention strategies in this setting.

MATLAB/Simulink simulation and C2000 code generation model for a grid-interactive 3-phase 4-wire inverter

This work is a MATLAB/Simulink model of a controller for a three-phase, four-wire, grid-interactive inverter. The model provides capacity for simulating the performance of power electroinic hardware, as well as code generation for an embedded controller. The implemented hardware topology is a three-leg bridge with a neutral connection to the centre-tap of the DC bus. An LQR-based current controller and MAF-based phase detector are implemented. The model is configured for code generation for a Texas Instruments TMS320F28335 Digital Signal Processor (DSP).

Commuting mode choice in transit oriented development: Disentangling the effects of competitive neighbourhoods, travel attitudes, and self-selection

This research identifies the commuting mode choice behaviour of 3537 adults living in different types of transit oriented development (TOD) in Brisbane by disentangling the effects of their “evil twin” transit adjacent developments (TADs), and by also controlling for residential self-selection, travel attitudes and preferences, and socio-demographic effects. A TwoStep cluster analysis was conducted to identify the natural groupings of respondents’ living environment based on six built environment indicators. The analysis resulted in five types of neighbourhoods: urban TODs, activity centre TODs, potential TODs, TADs, and traditional suburbs. HABITAT survey data were used to derive the commute mode choice behaviour of people living in these neighbourhoods. In addition, statements reflecting both respondents’ travel attitudes and living preferences were also collected as part of the survey. Factor analyses were conducted based on these statements and these derived factors were then used to control for residential self-selection. Four binary logistic regression models were estimated, one for each of the travel modes used (e.g. public transport, active transport, less sustainable transport such as the car/taxi, and other), to differentiate between the commuting behaviour of people living in the five types of neighbourhoods. The findings verify that urban TODs enhance the use of public transport and reduce car usage. No significant difference was found in the commuting behaviour between respondents living in traditional suburbs and TADs. The results confirm the hypothesis that TADs are the “evil twin” of TODs. The data indicates that TADs and the mode choices of residents in these neighbourhoods is a missed transport policy opportunity. Further policy efforts are required for a successive transition of TADs into TODs in order to realise the full benefits of these. TOD policy should also be integrated with context specific TOD design principles.

The influence of family history of hypertension on disease prevalence and associated metabolic risk factors among Sri Lankan adults

Background Hypertension is a major contributor to the global non-communicable disease burden. Family history is an important non-modifiable risk factor for hypertension. The present study aims to describe the influence of family history (FH) on hypertension prevalence and associated metabolic risk factors in a large cohort of South Asian adults, from a nationally representative sample from Sri Lanka. Methods A cross-sectional survey among 5,000 Sri Lankan adults, evaluating FH at the levels of parents, grandparents, siblings and children. A binary logistic regression analysis was performed in all patients with ‘presence of hypertension’ as dichotomous dependent variable and using family history in parents, grandparents, siblings and children as binary independent variables. The adjusted odds ratio controlling for confounders (age, gender, body mass index, diabetes, hyperlipidemia and physical activity) are presented below. Results In all adults the prevalence of hypertension was significantly higher in patients with a FH (29.3 %, n = 572/1951) than those without (24.4 %, n = 616/2530) (p < 0.001). Presence of a FH significantly increased the risk of hypertension (OR:1.29; 95 % CI:1.13-1.47), obesity (OR:1.36; 95 % CI: 1.27–1.45), central obesity (OR:1.30; 95 % CI 1.22–1.40) and metabolic syndrome (OR:1.19; 95 % CI: 1.08–1.30). In all adults presence of family history in parents (OR:1.28; 95 % CI: 1.12–1.48), grandparents (OR:1.34; 95 % CI: 1.20–1.50) and siblings (OR:1.27; 95 % CI: 1.21–1.33) all were associated with significantly increased risk of developing hypertension. Conclusions Our results show that the prevalence of hypertension was significantly higher in those with a FH of hypertension. FH of hypertension was also associated with the prevalence of obesity, central obesity and metabolic syndrome. Individuals with a FH of hypertension form an easily identifiable group who may benefit from targeted interventions.