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See also. References. Butterfly effect in popular culture. Butterfly effect image. The butterfly effect describes a phenomenon in chaos theory whereby a minor change in circumstances can cause a large change in outcome. The scientific concept is attributed to Edward Lorenz, a mathematician and meteorologist who used the metaphor to describe his ...
1 day ago · The ads emphasised that her signature was on each box of pills and warned that many of the other compounds sold were ‘worthless if not injurious’. Her rivals Madame Costello and Mrs Bird tended to use euphemisms in their marketing material: Costello’s ‘female monthly pill’, for example, helped suppress women’s ‘natural illness’.
3 days ago · Social Determinants: Factors like income, education, and social support significantly influence health outcomes. Higher income and education often lead to better health and well-being. Physical Environment: Quality housing, safe neighbourhoods, and low pollution levels contribute positively to health, while poor conditions can lead to various ...
1 day ago · Best practices. Subtlety and restraint: make sure animations blend naturally into the design rather than becoming the center of attention. A simple, understated animation is often more effective than something that’s too flashy or distracting. Animation hierarchy: not all animations should have the same importance.
3 days ago · Don’t trust the charlatans Narrator: In past times of crisis, we depended more on who we considered to be experts. These days, rightly or wrongly, we have become more cynical about experts and their expert analysis and advice.
2 days ago · The task and challenge within the coming decade should be to draw frontiers between acceptable CAMs and charlatanism, ie, to clearly differentiate truly inefficacious CAMs from the few that have, or might have, useful therapeutic or preventive effects at least slightly above placebo effects.
1 day ago · Underfitting is when a machine learning model fails to capture the underlying patterns in the training data, leading to poor performance on both the training and test data. When this occurs it means that the model is too simple and doesn’t do a good job of representing the data’s most important relationships. As a result, the model ...
