The Social/HMO Demonstration evaluates the feasibility of expanding Medicare Supplemental Insurance benefits to cover a limited amount of ICF and community based long-term care (LTC) services provided under a comprehensive HMO benefit package for capitated Medicare beneficiaries. The policy research question addressed by this study is whether adding an Expanded Care Benefit (ECB) to the capitated HMO benefit package offered by Kaiser Permanente (KP) changes utilization patterns and costs of ICF services, and the probability of becoming Medicaid eligible. This study provides descriptive information regarding this policy research question. The research goal of this study is to measure the extent to which collective ICF use rates and expenditure patterns for S/HMO members are consistently the same, greater or less than baseline data of Risk HMO Medicare members who do not have the S/HMO ECB. The purpose of such measurement is to determine if an empirical basis exists for postulating an ICF utilization and expenditures outcome effect which is influenced by the S/HMO ECB. Utilization and financial data are collected from all SNF and ICF level nursing homes in Multnomah County for all Medicare beneficiaries enrolled in KP between June 1, 1986 and July 31, 1988. Eligibility data are assembled on all Medicare beneficiaries enrolled in KP during the same time period who were residents of Multnomah County. Nursing home use rates and rates for related expenditures are determined for all nursing home residents (1, 331) by their eligibility status in KP during the time of each nursing home stay. Days in an ICF are censored by transfers between Cost, Risk and S/HMO enrollment status. Rates are standardized by the age and gender distribution of research population members (19, 261) to adjust use rates for differences in age cohort distribution of Risk members and S/HMO members. Risk rates and S/HMO rates are compared and differences in utilization and expenditures are evaluated. Conclusions about such patterns are used to formulate hypotheses for testing and confirming descriptive observations. Findings show that overall S/HMO member rates are less than Risk member rates for five of the six Research Questions addressed in this study. Specifically, the probability of admission to an ICF is substantially greater for S/HMO members than for Risk members. However, S/HMO members remained in ICFs fewer days than Risk members, over the two year study period, as measured by age adjusted rates for ICF days per member year of eligibility during the study period. Difference in the mean length of ICF stay is statistically significant between Risk and S/HMO. The rate of total payments received by nursing homes for S/HMO ICF residents per 1000 S/HMO members was substantially less than that for Risk members. The rate of spend-down to welfare status was substantially lower for S/HMO members than for Risk members who became ICF residents. Higher proportions of S/HMO members were discharged from ICFs to home than were Risk members, which is consistent with S/HMO Expanded Care Benefit objectives.

Dem nationalsozialistschen Unrechtsstaat fielen zahlreiche Menschen zum Opfer, an denen Menschenversuche durchgeführt wurden. Zu ihnen zählen auch jene, an deren Hirngewebeproben in den Kaiser-Wilhelm-Instituten geforscht wurde. Diese Gewebeproben verblieben oft auch nach 1945 in Institutssammlungen und wurden zum Teil weiter für Forschung und Lehre verwendet. Das Forschungsprojekt zielt darauf ab, die verbliebenen Präparate zu erfassen, die Opfer zu identifizieren und wo immer möglich ihre Lebensgeschichte zu rekonstruieren. Ein zentrales Ergebnis des Projektes wird eine Datenbank mit Daten zur Biografie der Opfer, zum Weg der Gewebeproben durch die Institutionen und zu den entsprechenden Archivquellen sein. Diese Datenbank wird hier vorgestellt.

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Es ist eine geschichtsträchtige Zeit. Napoleon Bonaparte, Zar Alexander I. von Russland, König Friedrich Wilhelm III. von Preußen, Graf zu Sayn-Wittgenstein, Ludwig Graf Yorck von Wartenburg und viele andere höchste Politiker und Militärs waren 1813, nach Napoleons Rückzug aus Russland, in Radeberg zu Lage-Sondierungen, Gelände-Besichtigungen und hochrangigen Gesprächen. Nur knapp sind Radeberg und Lotzdorf direkten militärischen Kämpfen entgangen, trotzdem waren die Schäden durch Belagerungen, Requirierungen, Plünderungen u. ä. unvorstellbar.... Dabei spielte der Sandberg, gelegen zwischen Radeberg und Lotzdorf am „Lotzdorfer Zacken“ und die höchste Erhebung im Radeberger Gebiet, eine besondere Rolle, denn Napoleon kam mit seinem Stab von Dresden, um von hier aus das Terrain für die Vorbereitung einer Schlacht zu sondieren.

Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics)

In the early 20th century, there were few therapeutic options for mental illness and asylum numbers were rising. This pessimistic outlook favoured the rise of the eugenics movement. Heredity was assumed to be the principal cause of mental illness. Politicians, scientists and clinicians in North America and Europe called for compulsory sterilisation of the mentally ill. Psychiatric genetic research aimed to prove a Mendelian mode of inheritance as a scientific justification for these measures. Ernst Rüdin’s seminal 1916 epidemiological study on inheritance of dementia praecox featured large, systematically ascertained samples and statistical analyses. Rüdin’s 1922–1925 study on the inheritance of “manic-depressive insanity” was completed in manuscript form, but never published. It failed to prove a pattern of Mendelian inheritance, counter to the tenets of eugenics of which Rüdin was a prominent proponent. It appears he withheld the study from publication, unable to reconcile this contradiction, thus subordinating his carefully derived scientific findings to his ideological preoccupations. Instead, Rüdin continued to promote prevention of assumed hereditary mental illnesses by prohibition of marriage or sterilisation and was influential in the introduction by the National Socialist regime of the 1933 “Law for the Prevention of Hereditarily Diseased Offspring” (Gesetz zur Verhütung erbkranken Nachwuchses).

Return on Capital Employed (ROCE) is a popular financial instrument and communication tool for the appraisal of companies. Often, companies management and other practitioners use untested rules and behavioural approach when investigating the key determinants of ROCE, instead of the scientific statistical paradigm. The aim of this dissertation was to identify and quantify key determinants of ROCE of individual companies listed on the Johannesburg Stock Exchange (JSE), by comparing classical multiple linear regression, principal components regression, generalized least squares regression, and robust maximum likelihood regression approaches in order to improve companies decision making. Performance indicators used to arrive at the best approach were coefficient of determination ( ), adjusted ( , and Mean Square Residual (MSE). Since the ROCE variable had positive and negative values two separate analyses were done. The classical multiple linear regression models were constructed using stepwise directed search for dependent variable log ROCE for the two data sets. Assumptions were satisfied and problem of multicollinearity was addressed. For the positive ROCE data set, the classical multiple linear regression model had a of 0.928, an of 0.927, a MSE of 0.013, and the lead key determinant was Return on Equity (ROE),with positive elasticity, followed by Debt to Equity (D/E) and Capital Employed (CE), both with negative elasticities. The model showed good validation performance. For the negative ROCE data set, the classical multiple linear regression model had a of 0.666, an of 0.652, a MSE of 0.149, and the lead key determinant was Assets per Capital Employed (APCE) with positive effect, followed by Return on Assets (ROA) and Market Capitalization (MC), both with negative effects. The model showed poor validation performance. The results indicated more and less precision than those found by previous studies. This suggested that the key determinants are also important sources of variability in ROCE of individual companies that management need to work with. To handle the problem of multicollinearity in the data, principal components were selected using Kaiser-Guttman criterion. The principal components regression model was constructed using dependent variable log ROCE for the two data sets. Assumptions were satisfied. For the positive ROCE data set, the principal components regression model had a of 0.929, an of 0.929, a MSE of 0.069, and the lead key determinant was PC4 (log ROA, log ROE, log Operating Profit Margin (OPM)) and followed by PC2 (log Earnings Yield (EY), log Price to Earnings (P/E)), both with positive effects. The model resulted in a satisfactory validation performance. For the negative ROCE data set, the principal components regression model had a of 0.544, an of 0.532, a MSE of 0.167, and the lead key determinant was PC3 (ROA, EY, APCE) and followed by PC1 (MC, CE), both with negative effects. The model indicated an accurate validation performance. The results showed that the use of principal components as independent variables did not improve classical multiple linear regression model prediction in our data. This implied that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Generalized least square regression was used to assess heteroscedasticity and dependences in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the weighted generalized least squares regression model had a of 0.920, an of 0.919, a MSE of 0.044, and the lead key determinant was ROE with positive effect, followed by D/E with negative effect, Dividend Yield (DY) with positive effect and lastly CE with negative effect. The model indicated an accurate validation performance. For the negative ROCE data set, the weighted generalized least squares regression model had a of 0.559, an of 0.548, a MSE of 57.125, and the lead key determinant was APCE and followed by ROA, both with positive effects.The model showed a weak validation performance. The results suggested that the key determinants are less important sources of variability in ROCE of individual companies that management need to work with. Robust maximum likelihood regression was employed to handle the problem of contamination in the data. It was constructed using stepwise directed search for dependent variable ROCE for the two data sets. For the positive ROCE data set, the robust maximum likelihood regression model had a of 0.998, an of 0.997, a MSE of 6.739, and the lead key determinant was ROE with positive effect, followed by DY and lastly D/E, both with negative effects. The model showed a strong validation performance. For the negative ROCE data set, the robust maximum likelihood regression model had a of 0.990, an of 0.984, a MSE of 98.883, and the lead key determinant was APCE with positive effect and followed by ROA with negative effect. The model also showed a strong validation performance. The results reflected that the key determinants are major sources of variability in ROCE of individual companies that management need to work with. Overall, the findings showed that the use of robust maximum likelihood regression provided more precise results compared to those obtained using the three competing approaches, because it is more consistent, sufficient and efficient; has a higher breakdown point and no conditions. Companies management can establish and control proper marketing strategies using the key determinants, and results of these strategies can see an improvement in ROCE. / Mathematical Sciences / M. Sc. (Statistics)

Time domain analysis in vector network analyzers (VNAs) is a method to represent the frequency response, stated by the S-parameters, in time domain with apparent high resolution. Among other utilities time domain option from Agilent allows to measure microwave devices into a specific frequency range and down till DC as well with the two time domain mode: band-pass and low-pass mode. A special feature named gating is of important as it allows representing a portion of the time domain representation in frequency domain.   This thesis studies the time domain option 010 from Agilent; its uncertainties and sensitivity. The task is to find the best method to measure the antenna element matching taking care to reduce the influence of measurement errors on the results.   The Agilent 8753ES is the instrument used in the thesis. A specific matching problem in the antenna electric down-tilt (AEDT) previously designed by Powerwave Technologies is the task to be solved. This is because it can not be measured directly with 2-port VNAs. It requires adapters, extra coaxial cables and N-connectors, all of which influences the accuracy. The AEDT connects to the array antenna through cable-board-connectors (CBCs). The AEDT and the CBCs were designed before being put into the antenna-system. Their S-parameters do not coincide with the ones measured after these devices were put in the antenna block.   Time domain gating and de-embedding algorithms are two methods proposed in this thesis to measure the S-parameters of the desired antenna element while reducing the influence of measurement errors due to cables CBCs and other connectors. The aim is to find a method which causes less error and gives high confidence measurements.   For the time domain analysis, reverse engineering of the time domain option used in the Agilent VNA 8753ES is implemented in a PC for full control of the process. The results using time-domain are not sufficiently reliable to be used due to the multiple approximations done in the design. The methodology that Agilent uses to compensate the gating effects is not reliable when the gate is not centered on the analyzed response. Big errors are considered due to truncation and masking effects in the frequency response.   The de-embedding method using LRL is implemented in the AEDT measurements, taking away the influences of the CBCs, coaxial cables and N-connector. It is found to have sufficient performance, comparable to the mathematical model. Error analysis of both methods has been done to explaine the different in measurements and design.

The purpose of this community needs assessment was to explore the unmet health needs in some of the communities of Riverside County, to discover which populations are most adversely affected by these unmet needs, and to determine what barriers hinder individuals from getting their needs met. United Way of the Inland Valleys, in cooperation with Kaiser Permanente of Riverside conducted this study as part of their community based needs assessment.