A comparison of methods for assessing power output in non‐uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm’s power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non‐uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142201/1/we2143-sup-0001-supplementary.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142201/2/we2143.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142201/3/we2143_am.pd

Bullying and Victimization in Elementary Schools: A Comparison of Bullies, Victims, Bully/Victims, and Uninvolved Preadolescents

Research on bullying and victimization largely rests on univariate analyses and on reports from a single informant. Researchers may thus know too little about the simultaneous effects of various independent and dependent variables, and their research may be biased by shared method variance. The database for this Dutch study was large (N = 1,065) and rich enough to allow multivariate analysis and multisource information. In addition, the effect of familial vulnerability for internalizing and externalizing disorders was studied. Gender, aggressiveness, isolation, and dislikability were most strongly related to bullying and victimization. Among the many findings that deviated from or enhanced the univariate knowledge base were that not only victims and bully/victims but bullies as well were disliked and that parenting was unrelated to bullying and victimization once other factors were controlled.

Modeling Age Patterns of Under-5 Mortality: Results From a Log-Quadratic Model Applied to High-Quality Vital Registration Data

Information about how the risk of death varies with age within the 0-5 age range represents critical evidence for guiding health policy. This paper proposes a new model for summarizing regularities about how under-5 mortality is distributed by detailed age. The model is based on a newly compiled database that contains under-5 mortality information by detailed age in countries with high-quality vital registration systems, covering a wide array of mortality levels and patterns. The model uses a log-quadratic approach, predicting a full mortality schedule between age 0 and 5 on the basis of only 1 or 2 parameters. With its larger number of age groups, the proposed model offers greater flexibility than existing models both in terms of entry parameters and model outcomes. We present applications of this model for evaluating and correcting under-5 mortality information by detailed age in countries with problematic mortality data

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Background: Given the negative consequences of early alcohol use for health and social functioning, it is essential to detect children at risk of early drinking. The aim of this study is to determine predictors of early alcohol use that can easily be detected in Preventive Child Healthcare (PCH). Methods: We obtained data from the first two waves on 1261 Dutch adolescents who participated in TRAILS (TRacking Adolescents’ Individual Lives Survey) at ages 10–14 years and from the PCH records regarding ages 4–10 years. Early adolescence alcohol use (age 10–14 years) was defined as alcohol use at least once at ages 10–12 years (wave 1) and at least once in the previous 4 weeks at ages 12–14 years (wave 2). Predictors of early alcohol use concerned parent and teacher reports at wave 1 and PCH registrations, regarding the child’s psychosocial functioning, and parental and socio-demographic characteristics. Results: A total of 17.2% of the adolescents reported early alcohol use. Predictors of early alcohol use were teacher-reported aggressive behaviour [odds ratios (OR); 95% confidence interval (CI): 1.86; 1.11–3.11], being a boy (OR 1.80, 95%-CI 1.31–2.56), being a non-immigrant (OR 2.31, 95%CI 1.05–5.09), and low and middle educational level of the father (OR 1.71, 95%CI 1.12–2.62 and OR 1.77, 95%CI 1.16–2.70, respectively), mutually adjusted. Conclusion: A limited set of factors was predictive for early alcohol use. Use of this set may improve the detection of early adolescence alcohol use in PCH

Estimating the infant mortality rate from DHS birth histories in the presence of age heaping.

BACKGROUND: The infant mortality rate (IMR) is a critical indicator of population health, but its measurement is subject to response bias in countries without complete vital registration systems who rely instead on birth histories collected via sample surveys. One of the most salient bias is the fact that child deaths in these birth histories tend to be reported with a large amount of heaping at age 12 months. Because of this issue, analysts and international agencies do not directly use IMR estimates based on surveys such as Demographic and Health Surveys (DHS); they rely instead on mortality models such as model life tables. The use of model life tables in this context, however, is arbitrary, and the extent to which this approach appropriately addresses bias in DHS-based IMR estimates remains unclear. This hinders our ability to monitor IMR levels and trends in low-and middle-income countries. The objective of this study is to evaluate age heaping bias in DHS-based IMR estimates and propose an improved method for adjusting this bias. METHODS AND FINDINGS: Our method relies on a recently-developed log-quadratic model that can predict age-specific mortality by detailed age between 0 and 5. The model's coefficients were derived from a newly constituted database, the Under-5 Mortality Database (U5MD), that represents the mortality experience of countries with high-quality vital registration data. We applied this model to 204 DHS surveys, and compared unadjusted IMR values to IMR values adjusted with the log-quadratic model as well as with the classic model life table approach. Results show that contrary to existing knowledge, age heaping at age 12 months rarely generates a large amount of bias in IMR estimates. In most cases, the unadjusted IMR values were not deviating by more than +/- 5% from the adjusted values. The model life table approach, by contrast, introduced an unwarranted, downward bias in adjusted IMR values. We also found that two regions, Sub-Saharan Africa and South Asia, present age patterns of under-5 mortality that strongly depart from the experience represented in the U5MD. For these countries, neither the existing model life tables nor the log-quadratic model can produce empirically-supported IMR adjustments. CONCLUSIONS: Age heaping at age 12 months produces a smaller amount of bias in DHS-based IMR estimates than previously thought. If a large amount of age heaping is present in a survey, the log-quadratic model allows users to evaluate, and whenever necessary, adjust IMR estimates in a way that is more informed by the local mortality pattern than existing approaches. Future research should be devoted to understanding why Sub-Saharan African and South Asian countries have such distinct age patterns of under-five mortality

Sharing knowledge: a new frontier for public-private partnerships in medicine

To help overcome the bottlenecks that limit the development of diagnostic and therapeutic products, academic and industrial researchers, patient organizations and charities, and regulatory and funding institutions should redefine the basis for sharing the knowledge collected in large-scale clinical and experimental studies

Understanding smallholder farmers' intention to adopt agricultural apps : the role of mastery approach and innovation hubs in mexico

While several studies have focused on the actual adoption of agricultural apps and the relevance of the apps’ content, very few studies have focused on drivers of the farmer’s intention and initial decision to adopt. Based on a survey of 394 smallholder farmers in 2019, this study investigated willingness to adopt an agricultural advice app in Guanajuato, Mexico. A structural equation modeling approach, based on the unified theory of acceptance and use of technology (UTAUT), was applied. To understand the farmers’ adoption decisions, extended constructs were studied (e.g., mastery-approach goals) along with the farmers’ age and participation in an innovation hub. Results showed that the intention to adopt the app is predicted by how farmers appraise the technical infrastructure and acquire new knowledge by using an app. The multi-group analysis revealed that performance expectancy is a relevant predictor of the intention to adopt, whereas the mastery-approach goal is relevant only for younger farmers and farmers not connected to the innovation hub. This study provides valuable insights about the innovation hubs’ role in the intention to adopt apps, offering precision agriculture advice in developing countries. The findings are useful for practitioners and app developers designing digital-decision support tools