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This definition is part of our Essential Guide: A comprehensive guide to enterprise IoT project success
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The industrial internet of things, or IIoT, is the use of internet of things technologies to enhance manufacturing and industrial processes.

Also known as the industrial internet or Industrie 4.0 , IIoT incorporates machine learning and big data technologies to harness the sensor data, machine-to-machine () communication and automation technologies that have existed in industrial settings for years.

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The driving philosophy behind IIoT is that smart machines are better than humans at accurately and consistently capturing and communicating real-time data. This data enables companies to pick up on inefficiencies and problems sooner, saving time and money and supporting business intelligence (BI) efforts.

In manufacturing specifically, IIoT holds great potential for quality control, sustainable and green practices, supply chain traceability and overall supply chain efficiency.

In an industrial setting, IIoT is key to processes such as predictive maintenance () , enhanced field service, energy management and asset tracking.

IIoT is a network of devices connected via communications technologies to form systems that monitor, collect, exchange and analyze data, delivering valuable insights that enable industrial companies to make smarter business decisions faster.

An industrial IoT system consists of:

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and intelligent assets transmit information directly to the data communications infrastructure, where it is converted into actionable information on how a certain piece of machinery is operating, for instance. This information can then be used for predictive maintenance, as well as to optimize business processes.

One of the top touted benefits the industrial internet of things affords businesses is predictive maintenance. This involves organizations using real-time data generated from IIoT systems to predict defects in machinery, for example, before they occur, enabling companies to take action to address those issues before a part fails or a machine goes down.

Another common benefit is improved field service. IIoT technologies help field service technicians identify potential issues in customer equipment before they become major issues, enabling techs to fix the problems before they inconvenience customers.

5.5 Once again: randomization vs. comparability

Apart from the rather explicit rhetoric of a “valid framework”, there is also always the implicit logic of the experiment. Thus, although the received theory emphasizes that “actual balance has nothing to do with validity of statistical inference; it is an issue of efficiency only” [ 41 ]; comparability turns out to be crucial:

Many, if not most, of those supporting randomization rush to mention that it promotes similar groups. Nowadays, only a small minority bases its inferences on the known permutation distribution created by the process of randomization; but an overwhelming majority checks for comparability. Reviewers of experimental studies routinely request that authors provide randomization checks, that is, statistical tests designed to substantiate the equivalence of and . At least, in almost every article a list of covariates—with their groupwise means and standard errors—can be found.

A narrow, restricted framework is only able to support weak conclusions; there is no such thing as a “free lunch.” Therefore, upon reaching a strong conclusion, there must be implicit, hidden assumptions (cf. [ 19 ], pp. 139, 155). In particular, a second look at the “little-assumption” argument reveals that it is the hidden assumption of comparability that carries much of the burden of evidence: It is no coincidence that in Pawitan’s example an eye drug was tested. Suppose one had tested a liver drug instead. The same numerical result would be almost as convincing if such a drug had been applied to twins. However, if the liver drug had been administered to a heterogenous set of persons or if it had been given to a different biological species (mice instead of men, say), exactly the same formal result would not be convincing at all; since, rather obviously, a certain discrepancy a priori may cause a remarkable difference a posteriori.

Savage’s example is quite similar. No matter how one splits a small heterogenous group into two, the latter groups will always be systematically different. Randomization does not help: If you assign randomly and detect a large effect in the end, still, your experimental intervention the initial difference between and may have caused it. All “valid” inferential statistics is, in a sense, an illusion, since it cannot exclude the straightforward second explanation. Instead, it’s the initial exchangeability of the groups that turns out to be decisive; similarity of and rules out the second explanation and leaves the experimental intervention as the only cause.

In conclusion, comparability, much more than randomization, keeps alternative explanations at bay. Since it is our endeavour to achieve similar groups, minimization is not just some supplementary technique to improve efficiency. Rather, it is a straightforward and elaborate device to enhance comparability, i.e., to consciously construct similar groups. (The influence of unknown factors is discussed in Section 4.) Though, at times, we fail, e.g. “it does not seem possible to base a meaningful experiment on a small heterogenous group” [ 23 ], there can hardly be any doubt that “the purpose of randomization is to achieve homogeneity in the sample units. [Thus] it should be spelled out that stability and homogeneity are the foundation of the statistical solution, the other way around” [ 52 ], p. 70 (my emphasis).

In a nutshell, nobody, not even Fisher, follows “pure Frequentist logic”, in particular the distribution that randomization generates. In a strict sense, there is no logic at all, rather a certain kind of mathematical reasoning plus—since the formal framework is restricted to sampling—a fairly large set of conventions; rigid “pure” arguments being readily complemented by applied “flexibility”, consisting of time-honored informal reasoning and shibboleth, but also outright concessions. Bayesians noticed long ago [ 28 , 31 ] that “Frequentist theory is shot full of contradictions” [ 53 ], and during the last few decades, efforts to overcome the received framework have gained momentum.

In the 20th century, R.A. Fisher (1890–1962) was the most influential statistician. However, while his early work on mathematical statistics is highly respected in all quarters, hardly anybody relies on his later ideas, in particular fiducial inference [ 24 ]. Randomization lies in-between, and, quite fittingly, public opinion on this formal technique has remained divided.

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Targeting your email

On the second create page you can target your email to your entire list, or a specific subset of your list.

First, choose the email list you'd like to send to from the drop down menu at the top. Your choices will be your personal email list (which includes all of the activists you collected through forms you created and/or your referral codes) and any group email lists that you are an administrator of. (Groups are explained more .)

Choosing a list targets your email to that entire list. You then have the option of cutting down your targeted universe further by including or excluding different sets of activists on your list below.

Standard Mode and Query Mode

In standard mode you have many options for targeting your list further and each appears as either an include or an exclude, which will include or exclude that set of activists from this email respectively.

You can also target with query mode, activated with the query mode toggle on top of the page. When in query mode you can target using the same filters as standard mode, but you get to choose the logical ANDs and ORs that go between each filter, allowing you to do more complex targeting. This mode uses the same options as our query feature. You can read more about how that works here.

In query mode, you have the option to import existing saved queries to your current targeting. Select a query from the import query section and click "Import" to add that saved query's parameters at the end of your current query.

You can switch between query mode and standard mode at any time, though switching between them will clear any targeting currently on your email. Once the email is saved, it will always load its targeting in that mode unless switched. You can choose which mode is the default for you when making new emails or reports by editing your profile, Thom Browne Classic wingtip ankle boots xq8uRo6

Targeting Filters

You can target activists based on geography -- whether they live in one or many states, one or many US counties, one or many legislative districts (congress or state level), one or many countries, whether they live within a certain number of miles of a ZIP/postal code (or ZIP/postal codes, enter multiple separated by commas), or if you're sending to a group's list, whether they live within a certain number of miles of events that are part of a specific event campaign. (Event campaigns are explained more .)

You can target activists based on their preferred language.

You can target activists based on the email list of child groups they are on if the group is part of a network. For example, you can email activists on your group's list who are not on a child group's list. (Networks are explained more .)

You can target activists who live in places with targets on a certain letter campaign. For example, if you've set up a letter campaign targeting Representatives John and Jane Doe, you can choose that letter campaign as a target and only send your email to activists on your list who live in those Representatives' districts, ensuring that when your activists go to write a letter they won't see a "No targets found for your address" message.

You can target activists based on action or email history -- if they've taken action on one or multiple pages (including actions made by child groups if this group is part of a network, explained more ), if they are targeted by one or multiple emails you've made in the past, or if they've opened or clicked emails you've sent in the past.

You can target activists based on donation history -- all donors, active recurring donors, donors with failing recurring donations, donors with cancelled recurring donations, donors eligible for one-click donations (have saved their card information with us), and donors who have donated more than a certain amount in the last certain amount of days.

You can target activists based on source codes they've used to take action by filling in one or many source codes, separated by commas.

And you can target activists based on whether they've filled out a customfieldwith a certain name using a certain value. (ex: All activists who have the value of "yes" under the "unemployed" custom field.)You can also target based on core fields like email address or name, allowing you to target activists who have .edu in their email addresses for example.

% is the wildcard character for field targeting, so on the email field matches everyone who's email address ends in .edu. You can use multiple % characters, so %1% matches anyone who has a 1 in any part of the form field you're targeting.

You can also interpret custom fields as numbers or dates and target with greater than or less than operations using << or >>. For example, <<1 will target every activist with a custom field value numberless than 1. >>2017/12/01 14:00:15 will target everyone with a custom field value date after December 1st, 2017, 2:00pm and 15 seconds. The format for dates is yyyy/mm/dd hh:mm:ss. Time is optional.

You can also use regular expressions, so[0-9] will target any activistwith any of the characters 0-9 in that form field. The wildcard character can be combined with regular expressions. (For more detail on our regular expression parser and syntax, see .)

You can target activists based on tags they have on their record.

And you can target activists based on queries you've previously set up.

You can target activists based on whether they are targeted with a report you've previously made.

You can target activists based on their action activity level -- for example whether they've taken at least 3 actions in the last 90 days on your email list. This does not include uploads.

You can also target based on their email open or click activity level -- for example whether they've clicked at least 2 emails in the last 7 days. (If you're in a network, you can optionally include opens or clicks from child groups in this calculation as well, just select them from the dropdown menu.)

And you can target activists based on the date theysubscribed or resubscribedto your list-- for example subscribed 3 days ago or less.

Includes and Excludes

In standard mode, on the left side is your options for includes. Between each section is a logical AND operation and inside each section is a logical OR. (ex: If you select New York and Vermont from the State section and an action titled "Petition to Walmart" from the Action list, this email would target users who live in New York OR Vermont, AND have taken action on "Petition to Walmart.")

On the right side is your options for excludes. Between and inside each section is a logical OR operation. (ex: If you select New York and Vermont from the State section and an action titled "Petition to Walmart" from the Action list, this email would target all users on your list except those living in New York OR Vermont OR have taken action on "Petition to Walmart.")

You can use both includes and excludes at the same time. To do ANDs between a section, for example, you would target two emails by duplicating the first -- one targeted to the first group and one to the second. The lists of states, actions, emails, and reports are scrollable, and you can select more than one option by command-clicking each item on Mac or control-clicking on Windows.

In query mode, you get to choose the ANDs and ORs you want to use.

Saving and Re-Targeting

When you're ready, click "Save and Preview Send Your Email" to trigger the targeting process to calculate the number of people the email will be sent to, and to move on to the next step.

Your targeting will be calculated and saved. It will not update when the underlying data updates.

To do that, after you've saved your targeting once, if you come back to the targeting page you can update your targeting. Click the save and re-calculate button to re-run targeting and update it (including saving any changes in filter options you make). Otherwise, you can choose not to re-calculate, which can often save you time if your targeting or the underlying data has not really changed since the last time you saved.

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