In South Africa, the assessment of the suitability and acceptability of water for drinking purposes is done according to the South African National Standards (SANS) 241 (2006) which requires that Cryptosporidium and Giardia in drinking water should be less than 1 oocyst/10l and 1 cyst/10l respectively. Although there is a requirement to monitor for these parasitic protozoans, there is lack of uniformity in the monitoring approach. Therefore, the objective of the study was to develop a protocol/methodology that can be applied by drinking water producers to monitor Cryptosporidium and Giardia to ensure that the risk of exposure to these organisms and the risks of non–compliance to guidelines are reduced. Also, to test the feasibility of the protocol on a small system, the drinking water purification plant at the Vaal River Barrage Reservoir that supplies approximately 350 people with drinking water. The protocol for monitoring of Cryptosporidium and Giardia was developed based on monitoring procedures proposed by the US Environmental Protection Agency, the Drinking Water Inspectorate, Australia, New Zealand, and especially on the risk based procedure followed by Northern Ireland with the intention that it will be applicable to all water supply systems irrespective of size and system complexity of the purification works. It is focused on a preventative approach of monitoring Cryptosporidium and Giardia and it consists of ten steps which are: (i) Assessment of the monitoring requirements, (ii) Description and characterization of the source water types (iii) Abstraction of source water (iv) Assessment of the water purification plant (v) Water quality monitoring (vi) Cryptosporidiosis and Giardiasis outbreak (vii) Risk assessment (viii) Sample collection and Laboratory processing (ix) Data evaluation, interpretation and storage (x) Process evaluation and review. As stated, the developed protocol was tested at a small purification plants situated at the dam wall of the Vaal River Barrage catchment, Gauteng Province . From this assessment it was evident that steps of the protocol were easy to follow and the possible risks in the water value chain i.e. from source water to the supply of purified drinking water could be identified. Some of the challenges encountered during the application of the protocol include difficulty in obtaining detailed information regarding the activities around the catchment and information on the prevalence of cryptosporidiosis and giardiasis in the local community or in South Africa in general. From this study, it could be concluded that the source water from the Vaal River Barrage Reservoir was high risk. However, the use of the multi–barrier approach coupled with advanced treatment of UV rendered the water drinking supplied to the local community within the South African Drinking Water Standards for from Cryptosporidium and Giardia of less than 1 oocyst/10l and 1 cyst/10l. The protocol for the monitoring of Cryptosporidium and Giardia could contribute to the protection of drinking water consumers by identifying high risk source waters, identifying areas that can be improved in the water treatment system and also protecting the catchment areas from further faecal pollution. With respect to this outcome, the developed protocol could be used by water utilities as part of their Water Safety Plans to optimize monitoring. Furthermore, this methodology has a potential to contribute to the blue drop certification as it should for part of the Water Safety Plans. / Thesis (M. Environmental Management)--North-West University, Potchefstroom Campus, 2011.

In South Africa, the assessment of the suitability and acceptability of water for drinking purposes is done according to the South African National Standards (SANS) 241 (2006) which requires that Cryptosporidium and Giardia in drinking water should be less than 1 oocyst/10l and 1 cyst/10l respectively. Although there is a requirement to monitor for these parasitic protozoans, there is lack of uniformity in the monitoring approach. Therefore, the objective of the study was to develop a protocol/methodology that can be applied by drinking water producers to monitor Cryptosporidium and Giardia to ensure that the risk of exposure to these organisms and the risks of non–compliance to guidelines are reduced. Also, to test the feasibility of the protocol on a small system, the drinking water purification plant at the Vaal River Barrage Reservoir that supplies approximately 350 people with drinking water. The protocol for monitoring of Cryptosporidium and Giardia was developed based on monitoring procedures proposed by the US Environmental Protection Agency, the Drinking Water Inspectorate, Australia, New Zealand, and especially on the risk based procedure followed by Northern Ireland with the intention that it will be applicable to all water supply systems irrespective of size and system complexity of the purification works. It is focused on a preventative approach of monitoring Cryptosporidium and Giardia and it consists of ten steps which are: (i) Assessment of the monitoring requirements, (ii) Description and characterization of the source water types (iii) Abstraction of source water (iv) Assessment of the water purification plant (v) Water quality monitoring (vi) Cryptosporidiosis and Giardiasis outbreak (vii) Risk assessment (viii) Sample collection and Laboratory processing (ix) Data evaluation, interpretation and storage (x) Process evaluation and review. As stated, the developed protocol was tested at a small purification plants situated at the dam wall of the Vaal River Barrage catchment, Gauteng Province . From this assessment it was evident that steps of the protocol were easy to follow and the possible risks in the water value chain i.e. from source water to the supply of purified drinking water could be identified. Some of the challenges encountered during the application of the protocol include difficulty in obtaining detailed information regarding the activities around the catchment and information on the prevalence of cryptosporidiosis and giardiasis in the local community or in South Africa in general. From this study, it could be concluded that the source water from the Vaal River Barrage Reservoir was high risk. However, the use of the multi–barrier approach coupled with advanced treatment of UV rendered the water drinking supplied to the local community within the South African Drinking Water Standards for from Cryptosporidium and Giardia of less than 1 oocyst/10l and 1 cyst/10l. The protocol for the monitoring of Cryptosporidium and Giardia could contribute to the protection of drinking water consumers by identifying high risk source waters, identifying areas that can be improved in the water treatment system and also protecting the catchment areas from further faecal pollution. With respect to this outcome, the developed protocol could be used by water utilities as part of their Water Safety Plans to optimize monitoring. Furthermore, this methodology has a potential to contribute to the blue drop certification as it should for part of the Water Safety Plans. / Thesis (M. Environmental Management)--North-West University, Potchefstroom Campus, 2011.

This thesis aimed to apply the causal framework with potential outcomes to examine the causal effect of air temperature on reported violent crimes in Swedish municipalities. The Generalized Estimating Equations method was used on yearly, monthly and also July only data for the time period 2002-2014. One significant causal effect was established but the majority of the results pointed to there being no causal effect between air temperature and reported violent crimes.

No description available.

国立情報学研究所で電子化したコンテンツを使用している。

Identity verification or biometric recognition systems play an important role in our daily lives. Applications include Automatic Teller Machines (ATM), banking and share information retrieval, and personal verification for credit cards. Among the biometric techniques, authentication of speakers by his/her voice is of great importance, since it employs a non-invasive approach and is the only available modality in many applications. However,the performance of Automatic Speaker Verification (ASV) systems degrades significantly under adverse conditions which cause recordings from the same speaker to be different.The objective of this research is to investigate and develop robust techniques for performing automatic speaker recognition over various channel conditions, such as telephony and recorded microphone speech. This research is shown to improve the robustness of ASV systems in three main areas of feature extraction, speaker modelling and score normalization. At the feature level, a new set of dynamic features, termed Delta Cepstral Energy (DCE) is proposed, instead of traditional delta cepstra, which not only greatly reduces thedimensionality of the feature vector compared with delta and delta-delta cepstra, but is also shown to provide the same performance for matched testing and training conditions on TIMIT and a subset of the NIST 2002 dataset. The concept of speaker entropy, which conveys the information contained in a speaker's speech based on the extracted features, facilitates comparative evaluation of the proposed methods. In addition, Frequency Modulation features are combined in a complementary manner with the Mel Frequency CepstralCoefficients (MFCCs) to improve the performance of the ASV system under channel variability of various types. The proposed fused system shows a relative reduction of up to 23% in Equal Error Rate (EER) over the MFCC-based system when evaluated on the NIST 2008 dataset. Currently, the main challenge in speaker modelling is channel variability across different sessions. A recent approach to channel compensation, based on Support Vector Machines (SVM) is Nuisance Attribute Projection (NAP). The proposed multi-component approach to NAP, attempts to compensate for the main sources of inter-session variations through an additional optimization criteria, to allow more accurate estimates of the most dominant channel artefacts and to improve the system performance under mismatched training and test conditions. Another major issue in speaker recognition is that the variability of score distributions due to incompletely modelled regions of the feature space can produce segments of the test speech that are poorly matched to the claimed speaker model. A segment selection technique in score normalization is proposed that relies only on discriminative and reliable segments of the test utterance to verify the speaker. This approach is particularly useful in noisy conditions where using speech activity detection is not reliable at the feature level. Another source of score variability comes from the fact that not all phonemes are equally discriminative. To address this, a new score re-weighting technique is applied to likelihood values based on the discriminative level of each Gaussian component, i.e. each particular region of the feature space. It is found that a limited number of Gaussian mixtures, herein termed discriminative components are responsible for the overall performance, and that inclusion of the other non-discriminative components may only degrade the system performance.

Identity verification or biometric recognition systems play an important role in our daily lives. Applications include Automatic Teller Machines (ATM), banking and share information retrieval, and personal verification for credit cards. Among the biometric techniques, authentication of speakers by his/her voice is of great importance, since it employs a non-invasive approach and is the only available modality in many applications. However,the performance of Automatic Speaker Verification (ASV) systems degrades significantly under adverse conditions which cause recordings from the same speaker to be different.The objective of this research is to investigate and develop robust techniques for performing automatic speaker recognition over various channel conditions, such as telephony and recorded microphone speech. This research is shown to improve the robustness of ASV systems in three main areas of feature extraction, speaker modelling and score normalization. At the feature level, a new set of dynamic features, termed Delta Cepstral Energy (DCE) is proposed, instead of traditional delta cepstra, which not only greatly reduces thedimensionality of the feature vector compared with delta and delta-delta cepstra, but is also shown to provide the same performance for matched testing and training conditions on TIMIT and a subset of the NIST 2002 dataset. The concept of speaker entropy, which conveys the information contained in a speaker's speech based on the extracted features, facilitates comparative evaluation of the proposed methods. In addition, Frequency Modulation features are combined in a complementary manner with the Mel Frequency CepstralCoefficients (MFCCs) to improve the performance of the ASV system under channel variability of various types. The proposed fused system shows a relative reduction of up to 23% in Equal Error Rate (EER) over the MFCC-based system when evaluated on the NIST 2008 dataset. Currently, the main challenge in speaker modelling is channel variability across different sessions. A recent approach to channel compensation, based on Support Vector Machines (SVM) is Nuisance Attribute Projection (NAP). The proposed multi-component approach to NAP, attempts to compensate for the main sources of inter-session variations through an additional optimization criteria, to allow more accurate estimates of the most dominant channel artefacts and to improve the system performance under mismatched training and test conditions. Another major issue in speaker recognition is that the variability of score distributions due to incompletely modelled regions of the feature space can produce segments of the test speech that are poorly matched to the claimed speaker model. A segment selection technique in score normalization is proposed that relies only on discriminative and reliable segments of the test utterance to verify the speaker. This approach is particularly useful in noisy conditions where using speech activity detection is not reliable at the feature level. Another source of score variability comes from the fact that not all phonemes are equally discriminative. To address this, a new score re-weighting technique is applied to likelihood values based on the discriminative level of each Gaussian component, i.e. each particular region of the feature space. It is found that a limited number of Gaussian mixtures, herein termed discriminative components are responsible for the overall performance, and that inclusion of the other non-discriminative components may only degrade the system performance.

Zugl.: Hannover, Tierärztliche Hochsch., Diss., 2009

München, Techn. Univ., Diss., 2004.

[pt] O objetivo da dissertação é apresentar uma nova medida de núcleo de inflação para o Brasil com o intuito de verificar a tendência atual dos preços. Inicialmente são propostos dois núcleos. O primeiro é calculado a partir de métodos tradicionais encontrados na literatura, porém visando a alcançar algumas características desejáveis que um núcleo tenha, algumas alterações são propostas. O segundo método é via Dynamic Conditional Score Models (DCS). Nesse, a inflação é decomposta em componentes não observáveis e o núcleo é definido como a componente de tendência. Fazse uma comparação entre os núcleos propostos e os núcleos divulgados atualmente no Brasil e conclui-se que os núcleos divulgados hoje não contribuem para o entendimento da tendência da inflação e que para isso os dois núcleos propostos são mais indicados. O núcleo via modelo DCS é mais indicado ainda por sua estimação não ser afetada por muita informação defasada como ocorre para o primeiro núcleo. Além disso, algumas análises econômicas sugerem que a utilização do núcleo via modelo DCS pode auxiliar o Banco Central na condução da política monetária, uma vez que este mostra indicativos do desenvolvimento futuro da inflação acumulada no ano (medida alvo para a verificação do controle da meta). As duas metodologias propostas podem ser facilmente recalculadas, uma vez que os códigos de implementação estão disponíveis gratuitamente para qualquer usuário interessado, e também podem ser replicadas para outros índices de inflação. / [en] The objective of this dissertation is to present a new measure of core inflation to brazilian inflation in order to verify the current trend of prices. Initially two core inflation are proposed. The first one is calculated from traditional methods found in the literature with some changes proposed in order to achieve some desirable core inflation characteristics. The second method of estimation is Dynamic Conditional Score Models (DCS), where inflation is decomposed into unobservables components and the core is defined as the trend component. A comparison is made between the proposed cores measures and that ones currently available in Brazil. The conclusion is that the core inflation disclosed today do not contribute to the understanding of the inflation trend. The two proposed core inflation measures are better suited for this purpose. The core by DCS model is further indicated because its estimation is not affected by lagged information as it happens for the first core measure. In addition, some economic analyzes suggest that the use of the core by DCS model can help the Central Bank in the conduct of monetary policy, since this shows indicative of the future development of the accumulated inflation in the year (measure used to verify the inflation target in Brazil). The two proposed methodologies can easily be recalculated, since implementation codes are freely available to any interested user, and it can also be replicated to other inflation indicators.