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Regions make Europe fly

Regions make Europe fly

The Airport Regions Conference (ARC) is an association of regional and local authorities across Europe with an international airport situated within or near their territories. The ARC brings together a wide range of expertise at the interface of air transport and local and regional policies. A common concern is to balance the economic benefits generated by the airports with their environmental impact.

Find out more about the ARC


Member Regions


EU Countries


Million Residents


Million Passengers

Latest News

July 6, 2018 - This extract from “Air cargo in airport regions” presents what can local communities do to support air cargo. It covers the following topics: Location criteria for the air cargo industry as well as new trends around airports and what can be done.

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July 4, 2018 - Presentation of Liam Henderson on the passenger experience through the air-rail interchange signage. It is an extract from the proceedings compiled as a follow-up of the event on airport access in November 2017 organised by Global AirRail Alliance and Airport Regions Conference.

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June 27, 2018 - During the European Parliament breakfast meeting, dedicated to the ANIMA Project, stakeholders explained the main goals of the project, the main issues on the noise management andthe importance of ANIMA for the future implementation of European policies, related to noise emissions.

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June 27, 2018 - November 2015 These proceedings were compiled as a follow-up of the event organised by ARC on the 20th of November 2015. The purpose of the conference was to analyse the impact of the EU Regulation No 598/2014 on cities and regions.

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ANIMA stands for Aviation Noise Impact Management through Novel Approaches. The ANIMA project is a people-oriented research project. It aims at identifying and diffusing best practices to lower the noise annoyance endured by communities around airports.

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The ARC is the legitimate voice of regions and municipalities in aviation related debates. Join us and be part of an interregional platform of cooperation.

Normal functional imaging results are linked with excellent prognosis while documented ischaemia is associated with increased risk for MACE. Prognostic information obtained from MDCT imaging is becoming available.

The prognosis of patients with chronic ischaemic systolic LV dysfunction is poor, despite advances in various therapies. Non-invasive assessment of myocardial viability should guide patient management. Multiple imaging techniques including PET, SPECT, and dobutamine stress echocardiography have been extensively evaluated for assessment of viability and prediction of clinical outcome after myocardial revascularization. In general, nuclear imaging techniques have a high sensitivity, whereas techniques evaluating contractile reserve have somewhat lower sensitivity but higher specificity. MRI has a high diagnostic accuracy to assess transmural extent of myocardial scar tissue, but its ability to detect viability and predict recovery of wall motion is not superior to other imaging techniques [ 16 ]. The differences in performance of the various imaging techniques are small, and experience and availability commonly determine which technique is used. Current evidence is mostly based on observational studies or meta-analyses, with the exception of two RCTs, both relating to PET imaging [ 17 ]. Patients with a substantial amount of dysfunctional but viable myocardium are likely to benefit from myocardial revascularization and may show improvements in regional and global contractile function, symptoms, exercise capacity, and long-term prognosis [ 16 ].

Depending on its symptomatic, functional, and anatomical complexity, stable CAD can be treated by OMT only or combined with revascularization using PCI or CABG. The main indications for revascularization are persistence of symptoms despite OMT and/or prognosis. Over the last two decades significant advances in all three treatment modalities have reduced many previous trials to historic value.

The evidence basis for CABG and PCI is derived from RCTs and large propensity-matched observational registries; both have important strengths, but also limitations.

By eliminating bias, individual RCTs and their subsequent meta-analyses [ 29–31 ] constitute the highest hierarchical form of evidence-based medicine. However, their extrapolation to routine clinical practice is complicated by the fact that their patient populations are often not representative of those encountered in normal clinical practice (e.g. most RCTs of PCI and CABG in ‘multivessel’ CAD enrolled ≪10% of potentially eligible patients, most of whom actually had single or double vessel CAD). Analysis on an intention-to-treat basis is problematic when many patients cross over from medical therapy to revascularization or from PCI to CABG. Limited duration of follow-up (usually ≪5 years) incompletely depicts the advantages of CABG, which initially accrue with time but which may also eventually be eroded by progressive vein graft failure.

The URGE study aimed to evaluate the effectiveness of a guideline-based open access ‘fast-track’ investigation service for two common urological problems, benign prostatic hyperplasia (BPH) and microscopic haematuria. General practices were allocated randomly to two groups; one group received guidelines for the appropriate referral of BPH patients for the open access ‘fast-track’ system whilst the other group acted as a control for BPH patients (but did receive guidelines for microscopic haematuria).

Data were collected on two cohorts of patients, one referred before (an indicator of baseline performance) and another referred after the introduction of the fast-track service. Data were collected on pre-referral general practice management, hospital and general practice care following referral, and patient outcome.

For the purposes of this article, we focus on the evaluation of the effectiveness of the intervention for BPH patients only. Data for a single outcome are used: waiting time from the date of patient referral to first appointment at hospital. Waiting time was measured in days and was found to have a skewed distribution that was log transformed to normality. Therefore, geometric means are quoted throughout; the effect sizes and the corresponding 95% confidence intervals (CIs) relate to the ratio of mean waiting time in the intervention group compared with the control group. Data were available on 513 patients (211 before and 312 after the introduction of the fast-track service) referred from 54 general practices from the North East of Scotland.

The traditional approach to the analysis of cluster randomized trials has been to calculate a summary measure for each cluster, such as a cluster mean or proportion. Because each cluster then provides only one data point, the data can be considered to be independent, allowing standard statistical tests to be used.

For example, within the URGE trial, the mean waiting times post-intervention for each general practice could be calculated (when different patients are included pre- and post-, only post data comparisons can be made using simple analyses) (see Table 1). The overall group means can then be compared using a standard t -test resulting in a significance of t 48 = 3.99, P = 0.0003. This results in an effect size of 0.65 (95% CI: 0.53–0.81); in other words, the waiting time was on average 35% less in the guideline group (Table 2). When the size of the clusters varies widely, it is preferable to carry out a weighted t -test, using cluster sizes as the weights. 11 This weighted analysis returns an effect size of 0.65 (95% CI: 0.54–0.78), with a significance of t 48 = 4.72, P = 0.00003.

Standard statistical techniques such as multiple regression can also be used when data have been summarized at a cluster level. These analyses, however, can only adjust for cluster level covariates directly, but can incorporate patient level covariates through a two-stage process. 12

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These tools will get you a list of possible email addresses in seconds. Now you need one more tool which will check these suggestions: LinkedIn Sales Navigator Lite for Chrome (ex. Rapportive ). Install it now if you have not yet.

Now click “Compose” button in your Gmail and paste all the email permutations into the “To” field. Then move the cursor over the email address one by one and observe.

Gmail will show you if the email address is associated with a Google+ account, while the LinkedIn Sales Navigatorextension will reveal if this email address belongs to a LinkedIn profile (you must have a Linkedin account).

If both Sales Navigator and Gmail remain silent, you can run another check by searching for the exact match of your best guess on Google to see if it was mentioned anywhere on the Web.

I don’t recommend relying on email verification services because they are often wrong.

Result from

In reality, this email address does not exist. Unfortunately.

Now you might already be thinking that there’s a problem with this tip.

And if so, you are (kind of) right!

Googleuses the “@” symbol for social tags. And if you put “” (exact match search) in Google, you won’t find any email addresses.

But don’t forget that Google is not the only search engine!

I discovered this hack not so long ago: you can use Bing search instead.

Use exact match search for “,” and Bing will reveal email addresses related to this domain if they are publicly available.

As you can see, this little-known trick does work.


Did you know that a user’s email address is always visible on LinkedIn for your direct connections?

Check one of your connection’s profiles, and their email address will be there for you to grab in the ‘Contact Info’ section.


You can also export all your LinkedIn connections to a single file on your computer.

Here’s the official manual from LinkedIn:

You will then get a CSV file. And your connections’ email addresses will all be there.

That’s handy if you have a VA collecting email addresses for you. Just send the exported file over to your VA .

And here goes a warning message for everyone—don’t blindly connect with everyone on LinkedIn. You risk leaking your personal email address into improper hands!!!

I’ve seen plenty of contact pages where people say that the best way to reach them is to drop them a message on Twitter.

But generally the message you want to send is more than 280 characters long, so you’d prefer an email address.

If that’s the case, don’t hesitate to find that person on Twitter and ask for their email address.

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