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Model copes with chaos to deliver relief Computer program helps responders transport supplies in tough conditions By Rachel Ehrenberg Science News, Web edition: Monday, February 21
Interpretação de Textos (Understanding)
CESGRANRIO · 2011
Model copes with chaos to deliver relief
Computer program helps responders transport
supplies in tough conditions
By Rachel Ehrenberg
Science News, Web edition: Monday, February 21st, 2011
WASHINGTON — Getting blood or other perishable supplies to an area that’s been struck by an earthquake or hurricane isn’t as simple as asking what brown can do for you. But a new model quickly determines the best routes and means for delivering humanitarian aid, even in situations where bridges are out or airport tarmacs are clogged with planes.
The research, presented February 18 at the annual meeting of the American Association for the Advancement of Science, could help get supplies to areas which have experienced natural disasters or help prepare for efficient distribution of vaccines when the flu hits.
Efficient supply chains have long been a goal of manufacturers, but transport in fragile networks — where supply, demand and delivery routes may be in extremely rapid flux — requires a different approach, said Anna Nagurney of the University of Massachusetts Amherst, who presented the new work. Rather than considering the shortest path from one place to another to maximize profit, her system aims for the cleanest path at minimum cost, while capturing factors such as the perishability of the product and the uncertainty of supply routes. ‘You don’t know where demand is, so it’s tricky,’ said Nagurney. ‘It’s a multicriteria decision-making problem.’
By calculating the total cost associated with each link in a network, accounting for congestion and incorporating penalties for time and products that are lost, the computer model calculates the best supply chain in situations where standard routes may be disrupted.
‘Mathematical tools are essential to develop formal means to predict, and to respond to, such critical perturbations,’ said Iain Couzin of Princeton University, who uses similar computational tools to study collective animal behavior. ‘This is particularly important where response must be rapid and effective, such as during disaster scenarios … or during epidemics or breaches of national security.’
The work can be applied to immediate, pressing situations, such as getting blood, food or medication to a disaster site, or to longer-term problems such as determining the best locations for manufacturing flu vaccines.
<http://www.sciencenews.org/view/generic/id/70083/title/Model_ copes_with_chaos_to_deliver_relief>. Retrieved April 7th, 2011.
The expression in boldface introduces the idea of conclusion in
- “But a new model quickly determines the best routes and means for delivering humanitarian aid,”
- “Rather than considering the shortest path from one place to another to maximize profit,”
- “her system aims for the cleanest path at minimum cost, while capturing factors such as the perishability of the product…”
- “‘You don’t know where demand is, so it’s tricky,’”
- “ ‘This is particularly important where response must be rapid and effective, such as during disaster scenarios...’”
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“You don’t know where demand is, so it’s tricky,”
Nesse enunciado, o termo so funciona como conector de consequência: a primeira oração apresenta a causa (não saber onde está a demanda) e a oração seguinte expressa o efeito (a situação torna‑se complicada).
Gramaticalmente, so é uma conjunção coordenativa que liga duas orações independentes para indicar resultado; pode ser substituída por therefore, thus ou, em português, por portanto/por isso. Note o uso da vírgula antes de so quando as orações são independentes.
Paráfrase: “Como você não sabe onde está a demanda, por isso é complicado.” Assim, so introduz claramente a ideia de conclusão/resultante do que foi dito antes.
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