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25 results on '"Kiiveri, H."'

1. Väärinkäytösten havaitseminen ja ehkäisy tilintarkastajan näkökulmasta

Author

Kiiveri, H-R. (Henna-Riikka)

Subjects
Laskentatoimi
Abstract

Tiivistelmä. Talousrikollisuus ja sen vaikutukset on paljon puhuttu aihe maailmanlaajuisesti sekä merkittävä ilmiö myös Suomessa. Lisäksi viime aikoina on nostettu esille myös tilintarkastajien velvollisuudet havaita sekä ennaltaehkäistä väärinkäytöksiä. Talousrikollisuutta on käsitelty paljon pro gradu -tutkielmissa muun muassa haastattelu- ja kyselytutkimusten avulla. Aikaisemmat tutkimustulokset osoittavat, että väärinkäytöksiä on mahdollista havaita jo aikaisessa vaiheessa ja tilintarkastajilla on näkemys niistä seikoista, joita voidaan pitää varoitusmerkkeinä talousrikollisuudesta. Tämän tutkimuksen tavoitteena on esittää konkreettisia keinoja väärinkäytösten havaitsemiseksi ja ehkäisemiseksi sekä selvittää väärinkäytöksen jälkeisiä toimenpiteitä. Lisäksi tavoitteena on selvittää, mihin asioihin tilintarkastaja voi tarkastustyössään kiinnittää huomiota havaitakseen mahdollisia väärinkäytöksiä. Tutkimusmenetelmänä toimii kvalitatiivinen eli laadullinen tutkimus. Aineistonkeruu menetelmänä on käytetty puolistrukturoituja haastatteluita eli teemahaastatteluita. Tutkimusta varten on haastateltu kolme auktorisoitua tilintarkastajaa. Menetelmänä kerätyn aineiston analysoinnissa käytetään sisällönanalyysia, jonka avulla aineistoista etsitään oleelliset ja tutkimuksen kannalta merkittävät seikat. Tutkimuksen tulokset osoittavat, että väärinkäytösten havaitsemisen ja ehkäisemisen kannalta on tärkeää toteuttaa suunnitteluvaihe huolellisesti ja luoda riskiarviointi. Vaikka väärinkäytösten aktiivinen etsintä ei kuulu varsinaisesti tilintarkastajan työtehtäviin, on tilintarkastajan kuitenkin mahdollista kiinnittää huomiota varsin selviin väärinkäytösten varoitusmerkkeihin. Myös toimialan ja sen erityispiirteiden tunteminen nähdään hyödyllisenä ominaisuutena. Tulokset osoittavat, että väärinkäytös epäilyn havaittaessa ensimmäiseksi tulee selvittää todenperäisyys ja luoda kokonaiskuva väärinkäytöksen laajuudesta. Epäilystä tulee ilmoittaa välittömästi johdolle tai hallintoelimille. Myös digitalisaation kehitys nähdään tilintarkastustyötä helpottavana tekijänä, jonka avulla voidaan vähentää väärinkäytöksiä. Tutkimus rajautuu käsittelemään yrityksiä Suomessa, jotka ovat velvollisia valitsemaan tilintarkastajan. Lisäksi tutkimuksessa käsitellään tilintarkastajan näkökulmasta oleellisia seikkoja ja tulokset rajautuvatkin tilintarkastajien omiin kokemuksiin. Tutkimustuloksia voidaan hyödyntää tilintarkastustyössä etenkin aloittelevien tilintarkastajien osalta. Tutkimus tuo esille konkreettisia keinoja ja toimintatapoja väärinkäytösten ennaltaehkäisyyn sekä niiden torjuntaan.

Published
2023

8. An Incomplete Data Approach to the Analysis of Covariance Structures.

Author

Kiiveri, H. T.

Abstract

Covariance structures associated with linear structural equation models are discussed. Algorithms for computing maximum likelihood estimates (namely, the EM algorithm) are reviewed. An example of using likelihood ratio tests based on complete and incomplete data to improve the fit of a model is given. (SLD)

Published
1987

10. NetRaVE: Constructing dependency networks using sparse linear regression

Author

Phatak, Aloke, Kiiveri, H., Clemmensen, L., Wilson, W., Phatak, Aloke, Kiiveri, H., Clemmensen, L., and Wilson, W.

Abstract

Summary: NetRaVE is a small suite of R functions for generating dependency networks using sparse regression methods. Such networks provide an alternative to interpreting 'top n lists' of genes arising out of an analysis of microarray data, and they provide a means of organizing and visualizing the resulting information in a manner that may suggest relationships between genes. Availability: NetRaVE is freely available for academic use and has been tested in R 2.10.1 under Windows XP, Linux and Mac OS X. Contact: harri.kiiveri@csiro.au. Supplementary information: Supplementary data are available at Bioinformatics online. © The Author 2010. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org.

Published
2010

12. Environmental monitoring using a time series of satellite images and other spatial data sets

Author

Kiiveri, H., Caccetta, P., Campbell, N., Evans, F., Furby, S., Wallace, J., Kiiveri, H., Caccetta, P., Campbell, N., Evans, F., Furby, S., and Wallace, J.

Abstract

As a result of extensive farmland clearing over the last hundred years or so, dry-land salinity is a major problem in Western Australia. In fact, in some parts of the state, over 20 percent of Agricultural land is no longer productive. Prior to the work to be described in this chapter, no reliable large scale estimates of the extent or progression of salinity were available. This chapter describes a methodology for monitoring the historical extent of salinity, using a time series of satellite imagery, landform information derived from digital elevation models and ground truth data collected by experts with local knowledge. This work has served to highlight the salinity problem to decision makers in government and to provide input into the process of developing and applying remedial measures to arrest the spread of salinity.

Published
2003

14. The Land Monitor Project

Author

Caccetta, P., Allen, A., Watson, I., Beetson, B., Behn, G., Campbell, N., Eddy, P., Evans, F., Furby, S., Kiiveri, H., Mauger, G., McFarlane, D., Goh, J., Pearce, C., Smith, R., Wallace, J., Wallis, R., Caccetta, P., Allen, A., Watson, I., Beetson, B., Behn, G., Campbell, N., Eddy, P., Evans, F., Furby, S., Kiiveri, H., Mauger, G., McFarlane, D., Goh, J., Pearce, C., Smith, R., Wallace, J., and Wallis, R.

Abstract

The Land Monitor Project is providing information over the southwest agricultural region of WA. It is assembling and processing sequences of Landsat TM data, a new highresolution digital elevation model (DEM) and other spatial data to provide monitoring information on the area of salt-affected land, and on changes in the area and status of perennial vegetation over the period 1988-2000. Land Monitor is a multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust. The Project will also providing estimates of areas at risk from secondary or future salinisation, based on the historical salinity maps and a set of landform variables derived from the high resolution DEM. Sequences of calibrated Landsat Thematic Mapper satellite images integrated with landform information derived from height data, ground truthing and other existing mapped data are used as the basis for monitoring changes in salinity and woody vegetation. Procedures for accurate registration and calibration were developed by CSIRO Mathematical and Information Sciences (CMIS), as were the data integration procedures for salinity mapping and prediction. For the DEM, heights are derived on a 10m grid from stereo aerial photography flown at 1:40,000 scale, using soft-copy automatic terrain extraction (image correlation) techniques. Land Monitor products include: high resolution DEMs; calibrated sequences of Landsat imgery; present and historical salinity maps; predicted salinity maps; maps of change in vegetation status and spectral/temporal statistics. These products are available in a range of formats and scales, from paddock to catchment and shire scales to suit customer needs.

Published
2000

15. A multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust

Author

Allen, A., Beetson, B., Behn, G., Caccetta, P., Campbell, N., Eddy, P., Evans, F., Furby, S., Kiiveri, H., Mauger, G., McFarlane, D., Napier, A., Pearce, C., Smith, R., Wallace, J., Wallis, R., Watson, I., Allen, A., Beetson, B., Behn, G., Caccetta, P., Campbell, N., Eddy, P., Evans, F., Furby, S., Kiiveri, H., Mauger, G., McFarlane, D., Napier, A., Pearce, C., Smith, R., Wallace, J., Wallis, R., and Watson, I.

Abstract

Land Monitor is a multi-agency project of the Western Australian Salinity Action Plan supported by the Natural Heritage Trust. It will provide land managers and administrators with baseline salinity and vegetation data for monitoring changes over time, and land height data from which contours accurate to two metre intervals can be produced. The Project will also provide estimates of areas at risk from secondary or future salinisation. Land Monitor will cover the 18 million hectares of agricultural area of south-west, Western Australia. Sequences of calibrated Landsat Thematic Mapper satellite images integrated with landform information derived from height data, ground truthing and other existing mapped data sets are used as the basis for monitoring changes in salinity and woody vegetation. Heights are derived on a 10m grid from stereo aerial photography flown at 1:40,000 scale, using soft-copy automatic terrain extraction (image correlation) techniques. Proposed Land Monitor products include salinity maps, predicted salinity maps, enhanced imagery, vegetation status maps and spectral / temporal statistics. These products will be available in a range of formats and scales, from paddock, farm to catchment and shire scales to suit customer needs.

Published
1999

24. A blood-based predictor for neocortical Aβ burden in Alzheimer’s disease: results from the AIBL study

Author

C.L. Masters, Ralph N. Martins, James D. Doecke, David Ames, Richard Head, Olivier Salvado, Alan Rembach, Ashley I. Bush, Gareth Jones, Noel G. Faux, Samantha C. Burnham, Kathryn A. Ellis, S L Macaulay, Harri Kiiveri, Simon M. Laws, Christopher C. Rowe, Victor L. Villemagne, Justin Bedo, William Wilson, Burnham, SC, Faux, N G, Wilson, W, Laws, S M, Ames, D, Bedo, J, Bush, A I, Doecke, J D, Ellis, K A, Head, R, Jones, G, Kiiveri, H, Martins, R N, Rembach, A, Rowe, C C, Salvado, O, Macaulay, S L, Masters, C L, and Villemagne, V L

Subjects
Male, Oncology, medicine.medical_specialty, Pathology, Population, Neocortex, Pancreatic Polypeptide, Cohort Studies, Cellular and Molecular Neuroscience, chemistry.chemical_compound, Apolipoproteins E, Neuroimaging, Alzheimer Disease, Predictive Value of Tests, Internal medicine, medicine, Humans, Dementia, education, Molecular Biology, Aged, Chemokine CCL3, Aged, 80 and over, education.field_of_study, Amyloid beta-Peptides, Aniline Compounds, business.industry, Interleukin-17, blood biomarkers, Cullin Proteins, medicine.disease, amyloid beta, Thiazoles, Psychiatry and Mental health, ROC Curve, chemistry, Positron-Emission Tomography, Cohort, alzheimer's disease, Biomarker (medicine), Female, Alzheimer's disease, business, Pittsburgh compound B, Alzheimer's Disease Neuroimaging Initiative
Abstract

Dementia is a global epidemic with Alzheimer's disease (AD) being the leading cause. Early identification of patients at risk of developing AD is now becoming an international priority. Neocortical Aβ (extracellular β-amyloid) burden (NAB), as assessed by positron emission tomography (PET), represents one such marker for early identification. These scans are expensive and are not widely available, thus, there is a need for cheaper and more widely accessible alternatives. Addressing this need, a blood biomarker-based signature having efficacy for the prediction of NAB and which can be easily adapted for population screening is described. Blood data (176 analytes measured in plasma) and Pittsburgh Compound B (PiB)-PET measurements from 273 participants from the Australian Imaging, Biomarkers and Lifestyle (AIBL) study were utilised. Univariate analysis was conducted to assess the difference of plasma measures between high and low NAB groups, and cross-validated machine-learning models were generated for predicting NAB. These models were applied to 817 non-imaged AIBL subjects and 82 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) for validation. Five analytes showed significant difference between subjects with high compared to low NAB. A machine-learning model (based on nine markers) achieved sensitivity and specificity of 80 and 82%, respectively, for predicting NAB. Validation using the ADNI cohort yielded similar results (sensitivity 79% and specificity 76%). These results show that a panel of blood-based biomarkers is able to accurately predict NAB, supporting the hypothesis for a relationship between a blood-based signature and Aβ accumulation, therefore, providing a platform for developing a population-based screen.

Published
2013

25. High-throughput fabrication and screening improves gold nanoparticle chemiresistor sensor performance.

Author

Hubble LJ, Cooper JS, Sosa-Pintos A, Kiiveri H, Chow E, Webster MS, Wieczorek L, and Raguse B

Subjects
Benzene analysis, Benzene Derivatives analysis, Electrodes, Metal Nanoparticles analysis, Naphthalenes analysis, Sulfhydryl Compounds chemistry, Toluene analysis, Xylenes analysis, Electrochemical Techniques methods, Gold chemistry, High-Throughput Screening Assays methods, Metal Nanoparticles chemistry
Abstract

Chemiresistor sensor arrays are a promising technology to replace current laboratory-based analysis instrumentation, with the advantage of facile integration into portable, low-cost devices for in-field use. To increase the performance of chemiresistor sensor arrays a high-throughput fabrication and screening methodology was developed to assess different organothiol-functionalized gold nanoparticle chemiresistors. This high-throughput fabrication and testing methodology was implemented to screen a library consisting of 132 different organothiol compounds as capping agents for functionalized gold nanoparticle chemiresistor sensors. The methodology utilized an automated liquid handling workstation for the in situ functionalization of gold nanoparticle films and subsequent automated analyte testing of sensor arrays using a flow-injection analysis system. To test the methodology we focused on the discrimination and quantitation of benzene, toluene, ethylbenzene, p-xylene, and naphthalene (BTEXN) mixtures in water at low microgram per liter concentration levels. The high-throughput methodology identified a sensor array configuration consisting of a subset of organothiol-functionalized chemiresistors which in combination with random forests analysis was able to predict individual analyte concentrations with overall root-mean-square errors ranging between 8-17 μg/L for mixtures of BTEXN in water at the 100 μg/L concentration. The ability to use a simple sensor array system to quantitate BTEXN mixtures in water at the low μg/L concentration range has direct and significant implications to future environmental monitoring and reporting strategies. In addition, these results demonstrate the advantages of high-throughput screening to improve the performance of gold nanoparticle based chemiresistors for both new and existing applications.

Published
2015
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