Modeling of Wind Turbine Gearbox Mounting

In this paper three bushing models are evaluated to find a best practice in modeling the mounting of wind turbine gearboxes. Parameter identification on measurements has been used to determine the bushing parameters for dynamic simulation of a gearbox including main shaft. The stiffness of the main components of the gearbox has been calculated. The torsional stiffness of the main shaft, gearbox and the mounting of the gearbox are of same order of magnitude, and eigenfrequency analysis clearly reveals that the stiffness of the gearbox mounting is of importance when modeling full wind turbine drivetrains

Equalities in Freefall? Ontological Insecurity and the long-term Impact of COVID-19 in the Academy

This intervention focuses on the impact of the global crisis resulting from the COVID‐19 pandemic on existing racialized and gendered inequalities within the academy and in particular our discipline of Politics and International Relations. We argue that responses to recent crises within the academy have exacerbated ontological insecurity among minoritized groups, including women. When coupled with increased caring responsibilities the current crises call into question who can be creative and innovative, necessary conditions for knowledge production. While University managers seek to reassure University staff of the temporary nature of COVID‐19 interventions, we argue that the possibilities for progressive leaps at a later state of institutional regeneration is unlikely when efforts to address structural inequalities are sidelined and crisis responses are undertaken which run counter to such work

Profitability Level of Fish Farming In Ife-Ijesa Agricultural Development Programme, Osun State, Nigeria

The objective of this paper is to estimate the profitability level of fish farmers in Ife-ijesa zone of Osun State Agricultural Development Programme (OSADEP), Nigeria using standard procedures of structured questionnaires administered to 100 respondents that were randomly selected. The study revealed that fish farming in the Area was male dominated with 89%. For the respondents using earthen 41.3% were range between50-60% and 31.6% of the respondents using concrete were within the range of 50-60years. 70.3% of the respondents using concrete were married and 56.6% of the respondents using earthen were married. 40.5% of the respondents using concrete had tertiary education and 45.2% of the respondents using earthen had tertiary education. 95.7% of the respondents using earthen operate by self-savings and 98.1% of the respondents using concrete also operate by self-saving and the average pond size used was 0.5 hectares. Findings also showed that the estimated average fixed cost and variable cost incurred for all fish farmers were N109.296.43 for the respondents using concrete and N173,473.38 for the respondents using earthen, while gross margin and average farm profit were N108, 718,43 for the respondents using concrete and N172,734.88 for the respondents using earthen per annum respectively

A 20-year prospective study of mortality and causes of death among hospitalized opioid addicts in Oslo

<p>Abstract</p> <p>Background</p> <p>To study mortality rate and causes of death among all hospitalized opioid addicts treated for self-poisoning or admitted for voluntary detoxification in Oslo between 1980 and 1981, and to compare their mortality to that of the general population.</p> <p>Methods</p> <p>A prospective cohort study was conducted on 185 opioid addicts from all medical departments in Oslo who were treated for either self-poisoning (<it>n </it>= 93, 1980), voluntary detoxification (<it>n </it>= 75, 1980/1981) or both (<it>n </it>= 17). Their median age was 24 years; with a range from 16 to 41, and 53% were males. All deaths that had occurred by the end of 2000 were identified from the Central Population Register. Causes of death were obtained from Statistics Norway. Standardized mortality ratios (SMRs) were computed for mortality, in general, and in particular, for different causes of death.</p> <p>Results</p> <p>During a period of 20 years, 70 opioid addicts died (37.8%), with a standardized mortality ratio (SMR) equal to 23.6 (95% CI, 18.7–29.9). The SMR remained high during the whole period, ranging from 32.4 in the first five-year period, to 13.4 in the last five-year period. There were no significant differences in SMR between self-poisonings and those admitted for voluntarily detoxification. The registered causes of death were accidents (11.4%), suicide (7.1%), cancer (4.3%), cardiovascular disease (2.9%), other violent deaths (2.9%), other diseases (71.4%). Among the 50 deaths classified as other diseases, the category "drug dependence" was listed in the vast majority of cases (37 deaths, 52.9% of the total). SMRs increased significantly for all causes of death, with the other diseases group having the highest SMR; 65.8 (95% CI, 49.9–86.9). The SMR was 5.4 (95% CI, 1.3–21.5) for cardiovascular diseases, and 4.3 (95% CI, 1.4–13.5) for cancer. The SMR was 13.2 (95% CI, 6.6–26.4) for accidents, 10.7 (95% CI, 4.5–25.8) for suicides, and 28.6 (95% CI, 7.1–114.4) for other violent deaths.</p> <p>Conclusion</p> <p>The risk of death among opioid addicts was significantly higher for all causes of death compared with the general population, implying a poor prognosis over a 20-year period for this young patient group.</p

Prescriptive variability of drugs by general practitioners

<div><p>Prescription drug spending is growing faster than any other sector of healthcare. However, very little is known about patterns of prescribing and cost of prescribing between general practices. In this study, we examined variation in prescription rates and prescription costs through time for 55 GP surgeries in Northern Ireland Western Health and Social Care Trust. Temporal changes in variability of prescribing rates and costs were assessed using the Mann–Kendall test. Outlier practices contributing to between practice variation in prescribing rates were identified with the interquartile range outlier detection method. The relationship between rates and cost of prescribing was explored with Spearman's statistics. The differences in variability and mean number of prescribing rates associated with the practice setting and socioeconomic deprivation were tested using t-test and <i>F</i>-test respectively. The largest between-practice difference in prescribing rates was observed for Apr-Jun 2015, with the number of prescriptions ranging from 3.34 to 8.36 per patient. We showed that practices with outlier prescribing rates greatly contributed to between-practice variability. The largest difference in prescribing costs was reported for Apr-Jun 2014, with the prescription cost per patient ranging from £26.4 to £64.5. In addition, the temporal changes in variability of prescribing rates and costs were shown to undergo an upward trend. We demonstrated that practice setting and socio-economic deprivation accounted for some of the between-practice variation in prescribing. Rural practices had higher between practice variability than urban practices at all time points. Practices situated in more deprived areas had higher prescribing rates but lower variability than those located in less deprived areas. Further analysis is recommended to assess if variation in prescribing can be explained by demographic characteristics of patient population and practice features. Identification of other factors contributing to prescribing variability can help us better address potential inappropriateness of prescribing.</p></div

Research trends in combinatorial optimization

Acknowledgments This work has been partially funded by the Spanish Ministry of Science, Innovation, and Universities through the project COGDRIVE (DPI2017-86915-C3-3-R). In this context, we would also like to thank the Karlsruhe Institute of Technology. Open access funding enabled and organized by Projekt DEAL.Peer reviewedPublisher PD

Expression quantitative trait loci of genes predicting outcome are associated with survival of multiple myeloma patients

Canadian Institutes of Health Research, Grant/ Award Number: 81274; Huntsman Cancer Institute Pilot Funds; Leukemia Lymphoma Society, Grant/Award Number: 6067-09; the National Institute of Health/National Cancer Institute, Grant/Award Numbers: P30 CA016672, P30 CA042014, P30 CA13148, P50 CA186781, R01 CA107476, R01 CA134674, R01 CA168762, R01 CA186646, R01 CA235026, R21 CA155951, R25 CA092049, R25 CA47888, U54 CA118948; Utah Population Database, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah State Department of Health, University of Utah; VicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council, Grant/Award Numbers: 1074383, 209057, 396414; Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer Database; Mayo Clinic Cancer Center; University of Pisa and DKFZThe authors thank all site investigators that contributed to the studies within the Multiple Myeloma Working Group (Interlymph Consortium), staff involved at each site and, most importantly, the study participants for their contributions that made our study possible. This work was partially supported by intramural funds of University of Pisa and DKFZ. This work was supported in part by the National Institute of Health/National Cancer Institute (R25 CA092049, P30 CA016672, R01 CA134674, P30 CA042014, R01 CA186646, R21 CA155951, U54 CA118948, P30 CA13148, R25 CA47888, R01 CA235026, R01 CA107476, R01 CA168762, P50 CA186781 and the NCI Intramural Research Program), Leukemia Lymphoma Society (6067-09), Huntsman Cancer Institute Pilot Funds, Utah PopulationDatabase, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah StateDepartment of Health, University of Utah, Canadian Institutes of Health Research (Grant number 81274), VicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council (Grants 209057, 396414, 1074383), Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer Database and the Mayo Clinic Cancer Center.Open Access funding enabled and organized by ProjektDEAL.The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.Gene expression profiling can be used for predicting survival in multiple myeloma (MM) and identifying patients who will benefit from particular types of therapy. Some germline single nucleotide polymorphisms (SNPs) act as expression quantitative trait loci (eQTLs) showing strong associations with gene expression levels. We performed an association study to test whether eQTLs of genes reported to be associated with prognosis of MM patients are directly associated with measures of adverse outcome. Using the genotype-tissue expression portal, we identified a total of 16 candidate genes with at least one eQTL SNP associated with their expression with P < 10(-7) either in EBV-transformed B-lymphocytes or whole blood. We genotyped the resulting 22 SNPs in 1327 MM cases from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and examined their association with overall survival (OS) and progression-free survival (PFS), adjusting for age, sex, country of origin and disease stage. Three polymorphisms in two genes (TBRG4-rs1992292, TBRG4-rs2287535 and ENTPD1-rs2153913) showed associations with OS at P < .05, with the former two also associated with PFS. The associations of two polymorphisms in TBRG4 with OS were replicated in 1277 MM cases from the International Lymphoma Epidemiology (InterLymph) Consortium. A meta-analysis of the data from IMMEnSE and InterLymph (2579 cases) showed that TBRG4-rs1992292 is associated with OS (hazard ratio = 1.14, 95% confidence interval 1.04-1.26, P = .007). In conclusion, we found biologically a plausible association between a SNP in TBRG4 and OS of MM patients.Canadian Institutes of Health Research (CIHR) 81274Huntsman Cancer Institute Pilot FundsLeukemia and Lymphoma Society 6067-09United States Department of Health & Human Services National Institutes of Health (NIH) - USA NIH National Cancer Institute (NCI) P30 CA016672 P30 CA042014 P30 CA13148 P50 CA186781 R01 CA107476 R01 CA134674 R01 CA168762 R01 CA186646 R01 CA235026 R21 CA155951 R25 CA092049 R25 CA47888 U54 CA118948Utah Population Database, Utah Cancer Registry, Huntsman Cancer Center Support Grant, Utah State Department of Health, University of UtahVicHealth, Cancer Council Victoria, Australian National Health and Medical Research Council 1074383 209057 396414Victorian Cancer Registry, Australian Institute of Health and Welfare, Australian National Death Index, Australian Cancer DatabaseMayo Clinic Cancer CenterUniversity of PisaHelmholtz Associatio