Acoustic classification of Australian anurans using syllable features

Acoustic classification of anurans (frogs) has received increasing attention for its promising application in biological and environment studies. In this study, a novel feature extraction method for frog call classification is presented based on the analysis of spectrograms. The frog calls are first automatically segmented into syllables. Then, spectral peak tracks are extracted to separate desired signal (frog calls) from background noise. The spectral peak tracks are used to extract various syllable features, including: syllable duration, dominant frequency, oscillation rate, frequency modulation, and energy modulation. Finally, a k-nearest neighbor classifier is used for classifying frog calls based on the results of principal component analysis. The experiment results show that syllable features can achieve an average classification accuracy of 90.5% which outperforms Mel-frequency cepstral coefficients features (79.0%).

Use of physicochemical signatures to assess the sources of metals in urban road dust

Road deposited dust is a complex mixture of pollutants derived from a wide range of sources. Accurate identification of these sources is seminal for effective source-oriented control measures. A range of techniques such as enrichment factor analysis (EF), principal component analysis (PCA) and hierarchical cluster analysis (HCA) are available for identifying sources of complex mixtures. However, they have multiple deficiencies when applied individually. This study presents an approach for the effective utilisation of EF, PCA and HCA for source identification, so that their specific deficiencies on an individual basis are eliminated. EF analysis confirmed the non-soil origin of metals such as Na, Cu, Cd, Zn, Sn, K, Ca, Sb, Ba, Ti, Ni and Mo providing guidance in the identification of anthropogenic sources. PCA and HCA identified four sources, with soil and asphalt wear in combination being the most prominent sources. Other sources were tyre wear, brake wear and sea salt.

Speeding up AdaBoost Classifier with Random Projection

The development of techniques for scaling up classifiers so that they can be applied to problems with large datasets of training examples is one of the objectives of data mining. Recently, AdaBoost has become popular among machine learning community thanks to its promising results across a variety of applications. However, training AdaBoost on large datasets is a major problem, especially when the dimensionality of the data is very high. This paper discusses the effect of high dimensionality on the training process of AdaBoost. Two preprocessing options to reduce dimensionality, namely the principal component analysis and random projection are briefly examined. Random projection subject to a probabilistic length preserving transformation is explored further as a computationally light preprocessing step. The experimental results obtained demonstrate the effectiveness of the proposed training process for handling high dimensional large datasets.

Cluster analysis of rainfall stations of the Indian peninsula

Principal component analysis is applied to derive patterns of temporal variation of the rainfall at fifty-three stations in peninsular India. The location of the stations in the coordinate space determined by the amplitudes of the two leading eigenvectors is used to delineate them into eight clusters. The clusters obtained seem to be stable with respect to variations in the grid of stations used. Stations within any cluster occur in geographically contiguous areas.

Investigating physical properties of solid dosage forms during pharmaceutical processing : Process analytical applications of vibrational spectroscopy

In order to improve and continuously develop the quality of pharmaceutical products, the process analytical technology (PAT) framework has been adopted by the US Food and Drug Administration. One of the aims of PAT is to identify critical process parameters and their effect on the quality of the final product. Real time analysis of the process data enables better control of the processes to obtain a high quality product. The main purpose of this work was to monitor crucial pharmaceutical unit operations (from blending to coating) and to examine the effect of processing on solid-state transformations and physical properties. The tools used were near-infrared (NIR) and Raman spectroscopy combined with multivariate data analysis, as well as X-ray powder diffraction (XRPD) and terahertz pulsed imaging (TPI). To detect process-induced transformations in active pharmaceutical ingredients (APIs), samples were taken after blending, granulation, extrusion, spheronisation, and drying. These samples were monitored by XRPD, Raman, and NIR spectroscopy showing hydrate formation in the case of theophylline and nitrofurantoin. For erythromycin dihydrate formation of the isomorphic dehydrate was critical. Thus, the main focus was on the drying process. NIR spectroscopy was applied in-line during a fluid-bed drying process. Multivariate data analysis (principal component analysis) enabled detection of the dehydrate formation at temperatures above 45°C. Furthermore, a small-scale rotating plate device was tested to provide an insight into film coating. The process was monitored using NIR spectroscopy. A calibration model, using partial least squares regression, was set up and applied to data obtained by in-line NIR measurements of a coating drum process. The predicted coating thickness agreed with the measured coating thickness. For investigating the quality of film coatings TPI was used to create a 3-D image of a coated tablet. With this technique it was possible to determine coating layer thickness, distribution, reproducibility, and uniformity. In addition, it was possible to localise defects of either the coating or the tablet. It can be concluded from this work that the applied techniques increased the understanding of physico-chemical properties of drugs and drug products during and after processing. They additionally provided useful information to improve and verify the quality of pharmaceutical dosage forms

Volatile components associated with bacterial spoilage of tropical prawns

Analysis of headspace volatiles by gas chromatography/mass spectrometry from king (Penaeus plebejus), banana (P. merguiensis), tiger (P. esculentus/semisulcatus) and greasy (Metapenaeus bennettae) prawns stored in ice or ice slurry, which is effectively an environment of low oxygen tension, indicated the presence of amines at the early stages of storage (less than 8 days) irrespective of the nature of the storage media. Esters were more prevalent in prawns stored on ice (normal oxygen conditions) at the latter stages of storage (more than 8 days) and were only produced by Pseudomonas fragi, whereas sulphides and amines occurred whether the predominant spoilage organism was Ps.fragi or Shewanella putrefaciens. The free amino acid profiles of banana and king prawns were high in arginine (12–14%) and low in cysteine (0.1–0.17%) and methionine (0.1–0.2%). Filter sterilised raw banana prawn broth inoculated with a total of 15 cultures of Ps. fragi and S. putrefaciens and incubated for two weeks at 5°C, showed the presence of 17 major compounds in the headspace volatiles analysed using gas chromatography/mass spectrometry (GC/MS). These were mainly amines, sulphides, ketones and esters. Principal Component Analysis of the results for the comparative levels of the volatiles produced by pure cultures, inoculated into sterile prawn broth, indicated three subgroupings of the organisms; I, Ps. fragi from a particular geographic location; II, S. putrefaciens from another geographic location; and III, a mixture of Ps. fragi and S. putrefaciens from different geographic locations. The sensory impression created by the cultures was strongly related to the chemical profile as determined by GC/MS. Organisms, even within the same subgrouping classified as identical by the usual tests, produced a different range of volatiles in the same uniform substrate.

Assessment of quality defects in macadamia kernels using NIR spectroscopy

Spectral data were collected of intact and ground kernels using 3 instruments (using Si-PbS, Si, and InGaAs detectors), operating over different areas of the spectrum (between 400 and 2500 nm) and employing transmittance, interactance, and reflectance sample presentation strategies. Kernels were assessed on the basis of oil and water content, and with respect to the defect categories of insect damage, rancidity, discoloration, mould growth, germination, and decomposition. Predictive model performance statistics for oil content models were acceptable on all instruments (R2 > 0.98; RMSECV < 2.5%, which is similar to reference analysis error), although that for the instrument employing reflectance optics was inferior to models developed for the instruments employing transmission optics. The spectral positions for calibration coefficients were consistent with absorbance due to the third overtones of CH2 stretching. Calibration models for moisture content in ground samples were acceptable on all instruments (R2 > 0.97; RMSECV < 0.2%), whereas calibration models for intact kernels were relatively poor. Calibration coefficients were more highly weighted around 1360, 740 and 840 nm, consistent with absorbance due to overtones of O-H stretching and combination. Intact kernels with brown centres or rancidity could be discriminated from each other and from sound kernels using principal component analysis. Part kernels affected by insect damage, discoloration, mould growth, germination, and decomposition could be discriminated from sound kernels. However, discrimination among these defect categories was not distinct and could not be validated on an independent set. It is concluded that there is good potential for a low cost Si photodiode array instrument to be employed to identify some quality defects of intact macadamia kernels and to quantify oil and moisture content of kernels in the process laboratory and for oil content in-line. Further work is required to examine the robustness of predictive models across different populations, including growing districts, cultivars and times of harvest.

A Phenomic Scan of the Norfolk Island Genetic Isolate Identifies a Major Pleiotropic Effect Locus Associated with Metabolic and Renal Disorder Markers

Multiphenotype genome-wide association studies (GWAS) may reveal pleiotropic genes, which would remain undetected using single phenotype analyses. Analysis of large pedigrees offers the added advantage of more accurately assessing trait heritability, which can help prioritise genetically influenced phenotypes for GWAS analysis. In this study we performed a principal component analysis (PCA), heritability (h2) estimation and pedigree-based GWAS of 37 cardiovascular disease -related phenotypes in 330 related individuals forming a large pedigree from the Norfolk Island genetic isolate. PCA revealed 13 components explaining >75% of the total variance. Nine components yielded statistically significant h2 values ranging from 0.22 to 0.54 (P<0.05). The most heritable component was loaded with 7 phenotypic measures reflecting metabolic and renal dysfunction. A GWAS of this composite phenotype revealed statistically significant associations for 3 adjacent SNPs on chromosome 1p22.2 (P<1x10-8). These SNPs form a 42kb haplotype block and explain 11% of the genetic variance for this renal function phenotype. Replication analysis of the tagging SNP (rs1396315) in an independent US cohort supports the association (P = 0.000011). Blood transcript analysis showed 35 genes were associated with rs1396315 (P<0.05). Gene set enrichment analysis of these genes revealed the most enriched pathway was purine metabolism (P = 0.0015). Overall, our findings provide convincing evidence for a major pleiotropic effect locus on chromosome 1p22.2 influencing risk of renal dysfunction via purine metabolism pathways in the Norfolk Island population. Further studies are now warranted to interrogate the functional relevance of this locus in terms of renal pathology and cardiovascular disease risk.

Classification and rating of strong-motion earthquake records

Ninety-two strong-motion earthquake records from the California region, U.S.A., have been statistically studied using principal component analysis in terms of twelve important standardized strong-motion characteristics. The first two principal components account for about 57 per cent of the total variance. Based on these two components the earthquake records are classified into nine groups in a two-dimensional principal component plane. Also a unidimensional engineering rating scale is proposed. The procedure can be used as an objective approach for classifying and rating future earthquakes.

Genotypic variation in seedling root architectural traits and implications for drought adaptation in wheat (Triticum aestivum L.)

Root system characteristics are of fundamental importance to soil exploration and below-ground resource acquisition. Root architectural traits determine the in situ space-filling properties of a root system or root architecture. The growth angle of root axes is a principal component of root system architecture that has been strongly associated with acquisition efficiency in many crop species. The aims of this study were to examine the extent of genotypic variability for the growth angle and number of seminal roots in 27 current Australian and 3 CIMMYT wheat (Triticum aestivum L.) genotypes, and to quantify using fractal analysis the root system architecture of a subset of wheat genotypes contrasting in drought tolerance and seminal root characteristics. The growth angle and number of seminal roots showed significant genotypic variation among the wheat genotypes with values ranging from 36 to 56 (degrees) and 3 to 5 (plant-1), respectively. Cluster analysis of wheat genotypes based on similarity in their seminal root characteristics resulted in four groups. The group composition reflected to some extent the genetic background and environmental adaptation of genotypes. Wheat cultivars grown widely in the Mediterranean environments of southern and western Australia generally had wider growth angle and lower number of seminal axes. In contrast, cultivars with superior performance on deep clay soils in the northern cropping region, such as SeriM82, Baxter, Babax, and Dharwar Dry exhibited a narrower angle of seminal axes. The wheat genotypes also showed significant variation in fractal dimension (D). The D values calculated for the individual segments of each root system suggested that, compared to the standard cultivar Hartog, the drought-tolerant genotypes adapted to the northern region tended to distribute relatively more roots in the soil volume directly underneath the plant. These findings suggest that wheat root system architecture is closely linked to the angle of seminal root axes at the seedling stage. The implications of genotypic variation in the seminal root characteristics and fractal dimension for specific adaptation to drought environment types are discussed with emphasis on the possible exploitation of root architectural traits in breeding for improved wheat cultivars for water-limited environments.

Laatutyöhön sitoutumisen edellytykset ammattikorkeakoulussa

The goal of this research was to establish the necessary conditions under which individuals are prepared to commit themselves to quality assurance work in the organisation of a Polytechnic. The conditions were studied using four main concepts: awareness of quality, commitment to the organisation, leadership and work welfare. First, individuals were asked to describe these four concepts. Then, relationships between the concepts were analysed in order to establish the conditions for the commitment of an individual towards quality assurance work (QA). The study group comprised the entire personnel of Helsinki Polytechnic, of which 341 (44.5%) individuals participated. Mixed methods were used as the methodological base. A questionnaire and interviews were used as the research methods. The data from the interviews were used for the validation of the results, as well as for completing the analysis. The results of these interviews and analyses were integrated using the concurrent nested design method. In addition, the questionnaire was used to separately analyse the impressions and meanings of the awareness of quality and leadership, because, according to the pre-understanding, impressions of phenomena expressed in terms of reality have an influence on the commitment to QA. In addition to statistical figures, principal component analysis was used as a description method. For comparisons between groups, one way variance analysis and effect size analysis were used. For explaining the analysis methods, forward regression analysis and structural modelling were applied. As a result of the research it was found that 51% of the conditions necessary for a commitment to QA were explained by an individual’s experience/belief that QA was a method of development, that QA was possible to participate in and that the meaning of quality included both product and process qualities. If analysed separately, other main concepts (commitment to the organisation, leadership and work welfare) played only a small part in explaining an individual’s commitment. In the context of this research, a structural path model of the main concepts was built. In the model, the concepts were interconnected by paths created as a result of a literature search covering the main concepts, as well as a result of an analysis of the empirical material of this thesis work. The path model explained 46% of the necessary conditions under which individuals are prepared to commit themselves to QA. The most important path for achieving a commitment stemmed from product and system quality emanating from the new goals of the Polytechnic, moved through the individual’s experience that QA is a method of the total development of quality and ended in a commitment to QA. The second most important path stemmed from the individual’s experience of belonging to a supportive work community, moved through the supportive value of the job and through affective commitment to the organisation and ended in a commitment to QA. The third path stemmed from an individual’s experiences in participating in QA, moved through collective system quality and through these to the supportive value of the job to affective commitment to the organisation and ended in a commitment to QA. The final path in the path model stemmed from leadership by empowerment, moved through collective system quality, the supportive value of the job and an affective commitment to the organisation, and again, ended in a commitment to QA. As a result of the research, it was found that the individual’s functional department was an important factor in explaining the differences between groups. Therefore, it was found that understanding the processing of part cultures in the organisation is important when developing QA. Likewise, learning-teaching paradigms proved to be a differentiating factor. Individuals thinking according to the humanistic-constructivistic paradigm showed more commitment to QA than technological-rational thinkers. Also, it was proved that the QA training program did not increase commitment, as the path model demonstrated that those who participated in training showed 34% commitment, whereas those who did not showed 55% commitment. As a summary of the results it can be said that the necessary conditions under which individuals are prepared to commit themselves to QA cannot be treated in a reductionistic way. Instead, the conditions must be treated as one totality, with all the main concepts interacting simultaneously. Also, the theoretical framework of quality must include its dynamic aspect, which means the development of the work of the individual and learning through auditing. In addition, this dynamism includes the reflection of the paradigm of the functions of the individual as well as that of all parts of the organisation. It is important to understand and manage the various ways of thinking and the cultural differences produced by the fragmentation of the organisation. Finally, it seems possible that the path model can be generalised for use in any organisation development project where the personnel should be committed.

Estimating crop area using seasonal time series of enhanced vegetation index from MODIS satellite imagery

Cereal grain is one of the main export commodities of Australian agriculture. Over the past decade, crop yield forecasts for wheat and sorghum have shown appreciable utility for industry planning at shire, state, and national scales. There is now an increasing drive from industry for more accurate and cost-effective crop production forecasts. In order to generate production estimates, accurate crop area estimates are needed by the end of the cropping season. Multivariate methods for analysing remotely sensed Enhanced Vegetation Index (EVI) from 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery within the cropping period (i.e. April-November) were investigated to estimate crop area for wheat, barley, chickpea, and total winter cropped area for a case study region in NE Australia. Each pixel classification method was trained on ground truth data collected from the study region. Three approaches to pixel classification were examined: (i) cluster analysis of trajectories of EVI values from consecutive multi-date imagery during the crop growth period; (ii) harmonic analysis of the time series (HANTS) of the EVI values; and (iii) principal component analysis (PCA) of the time series of EVI values. Images classified using these three approaches were compared with each other, and with a classification based on the single MODIS image taken at peak EVI. Imagery for the 2003 and 2004 seasons was used to assess the ability of the methods to determine wheat, barley, chickpea, and total cropped area estimates. The accuracy at pixel scale was determined by the percent correct classification metric by contrasting all pixel scale samples with independent pixel observations. At a shire level, aggregated total crop area estimates were compared with surveyed estimates. All multi-temporal methods showed significant overall capability to estimate total winter crop area. There was high accuracy at pixel scale (>98% correct classification) for identifying overall winter cropping. However, discrimination among crops was less accurate. Although the use of single-date EVI data produced high accuracy for estimates of wheat area at shire scale, the result contradicted the poor pixel-scale accuracy associated with this approach, due to fortuitous compensating errors. Further studies are needed to extrapolate the multi-temporal approaches to other geographical areas and to improve the lead time for deriving cropped-area estimates before harvest.

Non-specific conducting polymer-based array capable of monitoring odour emissions from a biofiltration system in a piggery building

A commercial non-specific gas sensor array system was evaluated in terms of its capability to monitor the odour abatement performance of a biofiltration system developed for treating emissions from a commercial piggery building. The biofiltration system was a modular system comprising an inlet ducting system, humidifier and closed-bed biofilter. It also included a gravimetric moisture monitoring and water application system for precise control of moisture content of an organic woodchip medium. Principal component analysis (PCA) of the sensor array measurements indicated that the biofilter outlet air was significantly different to both inlet air of the system and post-humidifier air. Data pre-processing techniques including normalising and outlier handling were applied to improve the odour discrimination performance of the non-specific gas sensor array. To develop an odour quantification model using the sensor array responses of the non-specific sensor array, PCA regression, artificial neural network (ANN) and partial least squares (PLS) modelling techniques were applied. The correlation coefficient (r(2)) values of the PCA, ANN, and PLS models were 0.44, 0.62 and 0.79, respectively.

Laadukasta yliopisto-opetusta kehittämässä : Laadukkaan yliopisto-opetuksen kehittäminen ja yliopistopedagogisen koulutuksen yhteys opetuksen kehittämiseen Helsingin yliopiston opettajilla

Objectives. The purpose of this study was to examine the development of high-quality university teaching among the teachers of the University of Helsinki. Furthermore, the relation of university pedagogical training to development of teaching was analyzed. This study introduces a new perspective to the research of quality of university teaching by considering quality from the teaching development perspective. The individual level examination was done from teacher's perspective. The development of high-quality university teaching was approached through three factors of teaching development defined by Biggs (2003). These factors are 1) the level of thinking about teaching on which the teaching development is based on (can also be called the quality model), 2) the methods for and 3) the impediments to teaching development. The research of Trigwell and Prosser (1996), Lindblom-Ylänne, Nevgi and Postareff (2004) and Postareff, Lindblom-Ylänne and Nevgi (2007) and the ideas of Ramsden (1992) have been central sources to this study. Methods. This study was a survey study. The data was collected with an electronic questionnaire in the spring of 2007. The sample consisted of 655 person of which some had and some had not university pedagogical training. Total of 251 answered the study. The data was mainly analyzed with SPSS statistical programme. Item analysis, principal component analysis, nonparametric Kruskal-Wallis and Mann-Whitney tests, correlation and crosstabulation were the methods used to analyze the data. Results and conclusions. According to the results it seems that the teachers of the University of Helsinki have good basis for developing high-quality university teaching. The 3rd level of thinking about teaching, which emphasizes student-centred features, could be identified on majority of the teachers. The use of teaching development methods was comprehensive. Most frequently used methods were related to the enhancement of content knowledge. In general the impediments to teaching development were not considered to be very significant. The most significant impediments were the factors related to lack of appreciation of teaching and factors related to lack of time meant for the planning and developing of teaching. Differences were found according to sex, teaching experience, degree, position and faculty. This study also showed that university pedagogical training seems to have a positive relation to the development of high-quality university teaching among the teachers of University of Helsinki. According to the results when the amount of teachers university pedagogical training increased, the 3rd level of thinking about teaching could be identified more often. Teachers also used more often teaching development methods related to cooperation and active participation and enhancement of pedagogical skills. Furthermore, they considered the factors related to lack of pedagogical skills and motivation to be lesser impediments to teaching development.

The potential for consumer segmentation in the Finnish housing market

This article examines whether cluster analysis can be used to identify groups of Finnish residents with similar housing preferences. Because homebuilders in Finland have been providing relatively homogeneous products to an increasingly diverse population, current housing may not represent the occupiers' preferences so a segmentation approach relying on socioeconomic characteristics and expressed preferences may not be sufficient. We use data collected via questionnaire in a principal component analysis followed by a hierarchical cluster analysis to determine whether different combinations of housing attributes are important to groups of residents. We can identify four clusters of housing residents based on important characteristics when looking for a house. The clusters describe Finnish people in different phases of the life cycle and with different preferences based on their recreational activities and financial expenditures. Mass customization of housing could be used to better appeal to these different clusters of consumers who share similar preferences, increasing consumer satisfaction and improving profitability.