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Dr. Lardon

By Dr. Michael Lardon

By Dr. Michael Lardon

Whether you look at the results on the course or the friction surrounding Tom Watson’s captaincy, it’s clear the American Ryder Cup system is in disarray. The 16.5 to 11.5 result at Gleneagles was Europe’s eighth win in the last 10 Ryder Cups—including the last three. Even if Watson had somehow managed to conjure the best out of his team in September, it’s arguable that it wouldn’t made enough of a difference to beat the Europeans—who had the World No. 1 in Rory McIlroy, the winners of the last two U.S. Opens in Martin Kaymer and Justin Rose and two of the greatest Ryder Cup players of all time in Sergio Garcia and Ian Poulter.

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What does that mean for the Americans going forward, and how should they structure the search for the next captain—and, more importantly, an overall strategy that will let them be competitive?

I think it starts with acknowledging the realities of social structure.

The “pod” system Paul Azinger used so successfully in 2008 worked because it worked within these realities. European teams seem to bond well as a group—even if they’re playing for a less-than-popular captain—because the nature of a team event is an extension of what they do week in and week out during the regular season. American players, on the other hand, are much more solitary week-to-week, so the bonding experience is more complicated.

Creating bonds within groups isn’t rocket science. It’s a function of time and size. If you have a large group, like the cast of a Broadway show, it takes some time to create that bond, but after a long series of rehearsals, you have a group that can pull together. By the time the U.S. team is selected and it’s time to get serious, you’re talking about two weeks of bonding time. It isn’t enough for a group that size.

That’s why the pod system worked. You can get four like-minded people to get together and bond in a much shorter time—which is what the Americans did in 2008.

The other piece the next captain has to acknowledge is that playing professional golf at the tour level almost always demands a particular kind of independent personality. That doesn’t mean American players don’t want to be on a team, or that they don’t care about representing their country. It just means they aren’t as used to the team element as a basketball or football player would be.

American tour players are the ultimate “lone wolves” for most of their career. They’re the only ones ultimately responsible for the scores they shoot—and, in fact, that solitary responsibility is probably a significant part of why they gravitated toward golf in the first place.

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, we discussed the role of product managers and business developers in API programmes and concluded that it’s time to engage with techies. One way to do this is to provide API Sandboxes to Fintech Developers where they can play, learn about the bank APIs, and approach the development of their Apps in an agile fashion.

By API sandbox, we mean a simple implementation of an API where Developers can submit API requests and receive realistic responses that reflect the real system behaviour.

But what does ‘good’ look like in an API Sandbox? One characteristic to consider is error handling. We’ll explain the importance of good error handling in the sandbox, with two very simple animations.

This is just one characteristic of a ‘good sandbox’. Watch this space for more discussions on this topic.

Understanding a basic challenge that Fintech developers face

Many Payment Service Providers, such as banks, are exposing APIs for the first time due to the need to comply with PSD2 (https://ec.europa.eu/info/law/payment-services-psd-2-directive-eu-2015-2366_en). At the same time, most Fintechs looking to use these APIs are startups who do not have the luxury of massive resources and time, and need to release new integrations as quickly as possible.

In our experience, many Fintech developers struggle with the API documentation provided by PSPs. The documentation often lacks details and can be hard to understand.

This is not a straightforward issue to resolve. It can be difficult to uncover all the knowledge needed to prepare high quality API documentation. Payment products are often old and there may not be a single source that documents all business rules. But Fintech developers need that information.

Then there’s the question of how best to present the information to developers. Most developers don’t like to read through reams of documentation. Information is always best presented in context, at the point where the developer needs the information. That means, it should be embedded within a development environment, and reported to them as they interact with the API. A ‘good’ sandbox will embed the payments processing business rules and present the relevant information to developers as part of the sandbox ‘error handling’.

We’ll discuss three approaches:

A basic sandbox can be built with open source tooling. For example, REST API sandboxes (server stubs) can be created directly from a swagger file using open source swagger codegen . Many API platforms also provide mechanisms to create basic sandboxes.

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Chinese Physics B

Wang Min (王 敏) , Zeng Jun-Wei (曾俊伟) , Qian Yong-Sheng (钱勇生) , Li Wen-Jun (李文俊) , Yang Fang (杨 芳) and Jia Xin-Xin (贾欣欣)

2012 Chinese Physical Society and IOP Publishing Ltd Chinese Physics B , Removable Hood Down Jacket Very Cheap Online wl39GNdo
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School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, China


Received 26 September 2011 In final form 23 November 2011


Wang Min et al 2012 Chinese Phys. B 21 070502

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The development direction of railways is toward the improvement of capacity and service quality, where the service quality includes safety, schedule, high speed, and comfort. In light of the existing cellular automaton models, in this paper, we develop a model to analyze the mixed running processes of trains with maximal speeds of 500 km/h and 350 km/h respectively in the moving block system. In the proposed model, we establish some sound rules to control the running processes of a train, where the rules include the departure rules in the intermediate stations, the overtaking rules, and the conditions of speed limitation for a train stopping at a station or passing through a station. With the consideration of the mixed ratio and the distance between two adjacent stations, the properties of the train traffic flow (including capacity and average speed) are simulated. The numerical results show that the interactions among different trains will affect the capacity, and a proper increase of the spatial distance between two adjacent stations can enhance the capacity and the average speed under the moving block.

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