Accurate Estimates Without Local Data?

The article of record as published may be found at http://dx.doi.org/10.1002/spip.414Models of software projects input project details and output predictions via their internal tunings. The output predictions, therefore, are affected by variance in the project details P and variance in the internal tunings T. Local data is often used to constrain the internal tunings (reducing T). While constraining internal tunings with local data is always the preferred option, there exist some models for which constraining tuning is optional. We show empirically that, for the USC COCOMO family of models, the effects of P dominate the effects of T i.e. the output variance of these models can be controlled without using local data to constrain the tuning variance (in ten case studies, we show that the estimates generated by only constraining P are very similar to those produced by constraining T with historical data). We conclude that, if possible, models should be designed such that the effects of the project options dominate the effects of the tuning options. Such models can be used for the purposes of decision making without elaborate, tedious, and time‐consuming data collection from the local domain. Copyright © 2009 John Wiley & Sons, Ltd

The Role of Education for Intergenerational Income Mobility: A comparison of the United States, Great Britain, and Sweden

Previous studies have found that intergenerational income persistence is relatively high in the United States and Britain, especially as compared to Nordic countries. We compare the association between family income and sons’ earnings in the United States (National Longitudinal Study of Youth 1979), Britain (British Cohort Study 1970), and Sweden (Population Register Data, 1965 cohort), and find that both income elasticities and rank-order correlations are highest in the United States, followed by Britain, with Sweden being clearly more equal. We ask whether differences in educational inequality and in return to qualifications can explain these cross-country differences. Surprisingly, we find that this is not the case, even though returns to education are higher in the United States. Instead, the low income mobility in the United States and Britain is almost entirely due to the part of the parent-son association that is not mediated by educational attainment. In the United States and especially Britain, parental income is far more important for earnings at a given level of education than in Sweden, a result that holds also when controlling for cognitive ability. This goes against widespread ideas of the United States as a country where the role of ascription is limited and meritocratic stratification prevails

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Marqueurs osseux d'activités physiques : étude du squelette appendiculaire d'une population nabatéo-romaine (Syrie du Sud)

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Accurate Estimates Without Calibration?

Most process models calibrate their internal settings using local data. Collecting this data is expensive, tedious, and often an incomplete process. Is it possible to make accurate process decisions without historical data? Variability in model output arises from (a) uncertainty in model inputs and (b) uncertainty in the internal parameters that control the conversion of inputs to outputs. We find that, for USC family process models such as COCOMO and COQUALMO, we can control model outputs by using an AI search engine to adjust the controllable project choices without requiring local tuning. For example, in ten case studies, we show that the estimates generated in this manner are very similar to those produced by traditional methods (local calibration). Our conclusion is that, (a) while local tuning is always the preferred option, there exist some process models for which local tuning is optional; and (b) when building a process model, we should design it such that it is possible to use it without tuning

Revision of morphological changes observed in the Kocabaş frontal bone (Denizli, Turkey)

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Revision of morphological changes observed in the Kocabaş frontal bone (Denizli, Turkey)

International audienc

Revision of morphological changes observed in the Kocabaş frontal bone (Denizli, Turkey)

International audienc