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BAS-011 IBM SPSS Statistics Level 1 v2

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BAS-011 exam Dumps Source : IBM SPSS Statistics Level 1 v2

Test Code : BAS-011
Test Name : IBM SPSS Statistics Level 1 v2
Vendor Name : IBM
: 55 Real Questions

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IBM IBM SPSS Statistics Level

IBM Cloud is diagnosed at the 2018 Spark Design Awards | killexams.com Real Questions and Pass4sure dumps

The IBM Cloud design group is excited to cap off their 12 months with an additional win for their items. they are proud to announce that IBM Immersive statistics(up to now IBM Immersive Insights) has been awarded the Spark Bronze Award for Digital Design, and IBM Cognos Analytics for Storytelling has been named as a finalist within the Spark Award Digital Design category. The Spark Awards are a global design competitors with a mission to advertise improved living through better design. These awards accept hundreds of submissions worldwide all over the year.

we are so excited to be featured through the Spark Awards for the 2nd yr in a row. Honors and achievements like this permit us to screen the significance of design in their items and how it could make a difference in all commercial enterprise application.

IBM Immersive information

IBM Immersive records is an information exploration tool that uses AR know-how to assist clients explore their statistics and talk findings in new ways. The AR expertise takes information visualizations to a new stage, making records exploration a more multi-faceted, collaborative manner. The AR application allows users to view their records in 3D through a headset, creating effective visuals, and the chance to discover the complete advantage of their statistics in a means that 2nd visuals can also no longer allow. The AR know-how permits for varied clients to use the device at the identical time, permitting for teams to speak less difficult throughout the statistics exploration process.

A person manipulating statistics in the AR space the use of the Immersive records headset.

this is probably the most first projects that explore the way to observe AR to statistics and business use, in addition to how to design for these makes use of. Designing for augmented fact is a different challenge as this technology is in a younger stage and is perpetually evolving. The designers ventured into new territory as they needed to create their personal guidelines and guidelines for the design on account that turned into very little precedent for the category of work they had been doing. This project shows the ability and talent the designers contributed with the intention to create an attractive user journey.

concept images for the Immersive facts design.

“Augmented reality will open actual barriers and should radically change the way they analyze counsel. they are empowering the user to visualize complicated suggestions in an easy method. “ — David Townsend, Design Director, IBM Analytics

IBM Cognos Analytics Storytelling

IBM Cognos Analytics is a data exploration and presentation tool powered by augmented intelligence that enhances the workflow and productiveness of business users. The tool makes it possible for clients to create compelling visualizations and make selections with self-provider analytics, and also offers the scalability of records governance that huge corporations demand.

Storytelling points in IBM Cognos Analytics screen facts the use of eye-catching visuals

The Storytelling component inside IBM Cognos Analytics offers users the tools to existing their statistics in a narrative context. The Storytelling facets include detailed visualizations, infographics, and animations that can be used to display the story behind the numbers. here is a key a part of IBM Cognos Analytics as clients are able to conveniently communicate the big picture that the records incorporates, when this message commonly gets lost or misinterpreted by way of business stakeholders.

“On behalf of the Cognos Design crew, they are basically humbled to have received the nomination and value this as a significant success in their quest in revolutionizing the BI market. “ — Cale Vardy, IBM Cognos Analytics Design main

functions of Design

I’m delighted to peer these two products recognized for his or her achievements in design. These achievements screen how a good deal consumer experience has an have an impact on on shaping the makes use of and skills of emerging technologies. AI and AR are both quickly-evolving technologies and are constantly changing in how involved and impactful they may also be in clients’ lives. person experience design is valuable now not handiest for established utility items like IBM SPSS information as they're updated and redesigned, however also for shaping uses of know-how that are yet to be adopted and surroundings a brand new common of utility.

Award Winners:

IBM Immersive facts:

  • Design Director: David Townsend
  • Design Lead: Alfredo Ruiz
  • Design crew: Ben Resnick, Jenna Goldberg, Christian Fritsche, Dimitri Hoffmann, Jan Hassel
  • IBM Cognos Analytics Storytelling features:

  • Design most important: Cale Vardy
  • Design Lead: Yasmine Taha

  • comparing the leading large facts analytics application options | killexams.com Real Questions and Pass4sure dumps

    there are lots of carriers promoting products labeled as big records analytics software. youngsters, it be challenging to...

    differentiate these items according to functionality on my own, as many of the tools share an identical elements and capabilities. additionally, one of the crucial tools show extremely refined variations.

    That being mentioned, your key differentiating elements will probably center of attention on balancing ease of use, algorithmic sophistication and value in the case of your company's means and stage of maturity in analytics.

    in this article, they assess items from nine huge statistics analytics application vendors: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of these companies give more than one tool. See the "main companies of huge facts analytics utility" sidebar beneath for extra details about their particular product choices.

    These vendors symbolize distinctive aspects of the massive information analytics market. Let's compare and contrast the ways in which these products meet the company needs of user companies.

    Analyst skills and capabilities 

    Some records analytics tools are targeted to amateur clients, some are centered to expert statistics analysts and a few are engineered to appeal to each forms of users.

    items such as IBM SPSS Modeler, RapidMiner's equipment, Oracle superior Analytics and the automated Analytics edition of SAP BusinessObjects Predictive Analytics are commonly designed to enable clients with a constrained background in statistics or statistics evaluation to analyze records, develop analytical models and design analytics workflows with little or no coding.

    whereas each and every supplier wraps its core analytics add-ons with an intuitive consumer interface to e-book the analyst's progress in records practise, analysis, and then model design and validation, the approach taken may range, especially when evaluating a stand-by myself product, corresponding to RapidMiner, with one it is a element of a larger suite, such as the Oracle product.

    tools akin to IBM SPSS statistics, KNIME Analytics Platform, the expert Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform supply the more refined performance that expert users predict. Oracle R superior Analytics for Hadoop (ORAAH), probably the most components in the Oracle massive data utility Connectors suite, offers an R interface for manipulating Hadoop distributed File gadget records and writing mapper and reducer features in R. this pliability could be appealing to extra superior data scientists.

    Alteryx and SAS business Miner present functionality tailored to the person's degree of talents, and nearly fall into each categories. Alteryx has added advancements to facts profiling to assist records scientists superior bear in mind their facts sources. normal, SAS business Miner and IBM's SPSS equipment stand out when it comes to helping more superior analytical recommendations and model scoring, as well as a broader array of analysis functions, together with neural networks, affiliation analysis and visualization capabilities.

    Analytical diversity

    reckoning on the use case and software, your firm's users will be required to aid several types of analytics capabilities with the intention to use particular kinds of modeling, such as regression, clustering, segmentation, habits modeling and choice bushes.

    while this has resulted in huge help for the a lot of kinds of analytical modeling at a high level, some companies have invested decades of labor into tweaking different types of their algorithms and including extra refined functionality. it's important to take into account which models are most imperative to your enterprise issues and to consider the products in terms of how they choicest serve your users' enterprise needs.

    or not it's important to consider which models are most valuable to your enterprise complications and to evaluate the items in terms of how they foremost serve your clients' company wants.

    The extra mature and better-conclusion -- and, as a consequence, greater-priced -- tools will exhibit the most reliable analytical breadth. Oracle information Miner contains an array of commonplace machine gaining knowledge of processes to guide clustering, predictive mining and text mining. each editions of IBM's SPSS product deliver a diverse set of analytical ideas and fashions. And SAS commercial enterprise Miner supports many algorithms and suggestions, including choice bushes, time sequence, neural networks, linear and logistic regression, sequence and net direction analysis, market basket analysis, and link evaluation.

    The newer generation -- and, in some circumstances, lower-priced -- products guide diverse fashions, but possibly with a narrower range of algorithmic sophistication.

    The model inventory in Alteryx Analytics Gallery includes such capabilities as regression analysis, resolution timber, affiliation rule analysis, classification and time series evaluation. KNIME contains methods for textual content mining, graphic mining and time sequence evaluation, and also integrates machine studying algorithms from different open source initiatives, corresponding to Weka and JFreeChart.

    a further factor of analytical diversity is integration with programming languages and statistical equipment, similar to R, for incorporating current libraries, in addition to person-described functionality. basically, integration with R can be regarded an more and more essential differentiator.

    Alteryx designer, Microsoft R, SAS commercial enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and help integration with R. several of the providers, together with IBM, Oracle, Microsoft, RapidMiner and SAP, provide a transforming into library of extensions to R and Python, enabling users to take capabilities of free libraries.

    Scope of the facts to be analyzed

    There are dissimilar sides of the scope of the data to be analyzed, including the challenge of structured vs. unstructured suggestions, as well as access to customary on-premises databases and information warehouses, cloud-primarily based records sources, and information managed in large data systems, similar to Hadoop.

    however, there are various levels of support for records managed within less-normal facts lakes -- both managed within Hadoop or in one other NoSQL information management equipment intended to deliver horizontal scaling. The elements for distinguishing among the many products ought to be in line with your company's certain requirements for gaining access to and processing information volumes and facts range.

    In cognizance of the starting to be variety of enter sources and the diversity of underlying systems used to condo these data sets, a further set of emerging facets that is being adopted through these companies comprises statistics accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all stronger their equipment' statistics import, export and connectivity capabilities. These enhancements should still allow users to entry a more finished listing of statistics sources while simplifying and speeding up the manner of loading information into the items.

    help for scalability and high efficiency

    The need for scalable efficiency is pushed through your firm's facts volumes and appetite for evaluation. Smaller agencies with much less statistics could be in a position to tolerate items that will not have performance features that scale with the accessible supplies, such because the entry-degree types of the reduce-end equipment, together with RapidMiner, KNIME, Microsoft R Open and Alteryx fashion designer, which may run on desktop systems and don't require additional server add-ons.

    higher agencies are more likely to have a more suitable stock of records sets to investigate, in addition to broader communities of clients. This introduces two further requirements -- high efficiency and facilitation of collaboration. The adaptability of a product to excessive-efficiency architectures is a pretty good indication of scalability, and most of the items will also be adapted to the parallelism of Hadoop or make use of every other capability of achieving faster computation.

    the entire products do have some assist for Hadoop, together with IBM SPSS Modeler and SPSS facts; RapidMiner's industrial component Radoop, which connects the Studio entrance end and Server analysis engine to information saved in Hadoop; Oracle's massive facts Discovery and ORAAH tools; and KNIME's big information Extensions and Cluster Execution add-ins.

    IBM SPSS now additionally offers enhanced assist for a couple of multithreaded analytical algorithms that can also pace efficiency. Teradata Aster Analytics addresses excessive-performance necessities through its massively Parallel Processing architecture. SAP's skilled Analytics edition of SAP BusinessObjects Predictive Analytics can execute in-memory information mining for handling tremendous-volume facts analysis efficiently. Microsoft R Server leverages its ScaleR module, a finished library of massive records analytics algorithms that aid parallelization. Scoring algorithms applied using SAS business Miner will also be deployed and executed within a Hadoop environment.

    additionally, integration with Apache Spark looks to be of starting to be magnitude. SPSS, KNIME, Oracle, RapidMiner and SAP all supply entry to Apache Spark libraries to aid analytics functions that should scale with exploding facts volumes. This permits developed applications to take competencies of a excessive-performance cluster platform to distribute the workflow across the cluster.

    Collaboration

    As cited, the higher the firm, the more likely there could be a necessity to share analyses, fashions and applications throughout diverse corporations and amongst many analysts. businesses which have many analysts dispensed throughout the business may also search for increased potential to share models and collaborate regarding the interpretation of effects.

    IBM's SPSS Modeler Gold version gives collaboration capabilities, and RapidMiner's Server product provides aid for sharing and collaboration. Alteryx Analytics Gallery gives a mechanism for sharing refined analytics functions in the cloud with members of a long corporation. KNIME presents industrial extensions to assist group collaboration, in addition to extensions supporting operational collaboration, corresponding to far off-scheduled execution, record technology, shared records space and a workflow repository. SAS commercial enterprise Miner's customer-server architecture permits business users and facts analysts to work collaboratively through sharing models and other work products.

    Alteryx, KNIME and Teradata Aster have added capabilities to aid manipulate analytical workflows. additionally, one of the most carriers have began to study the way to allow their tools to combine with others that may also have complementary practical candy spots. as an example, Teradata Aster now has an extension to combine with KNIME that permits users to leverage the KNIME workflow editor and include Aster Analytics functions into these workflows.

    vendor size and product integration

    vendors may also be compared when it comes to their dimension. One might compare and distinction what can be observed as the mega-carriers, whose huge statistics analytics equipment are just one product among a large portfolio of equipment. in case you work for a bigger firm that usually negotiates site-vast, enterprise licenses for the whole suite of a supplier's tools from a mega-seller corresponding to IBM, SAS, SAP or Oracle may well be a reasonable option.

    The gigantic carriers sell large information analytics tools that are part of a much better tool ecosystem. most likely, the items from a mega-supplier might be at the least a little integrated and meant to work collectively. additionally, some people feel extra relaxed with greater vendors, with an expectation of balance and constant customer carrier. in spite of this, you may additionally most effective be able to purchase these huge facts analytics equipment as a part of a a whole lot better software licensing arrangement.

    Smaller carriers, reminiscent of KNIME, Alteryx and RapidMiner, have revenues that are often according to licensing and aid for a small variety of massive records analytics items. A smaller dealer can also provide closer contact with their product management and innovation teams, and you'll be capable of impact the route of the product roadmap or better performance.

    A smaller vendor might also be greater bendy when it comes to fee and the points protected within the licensing association. You have to realize, besides the fact that children, that working with a smaller seller does existing some possibility when it comes to steadiness, the components attainable for assist and the possibility that the company may be received, which could affect the consumer relationship.

    The bigger vendors are naturally responsive to user wants for integration with different programs, besides the fact that children that frequently facilities on different products within each and every vendor's stock. as an instance, SAP Predictive Analytics has superior integration with SAP HANA and BusinessObjects Cloud. SAS commercial enterprise Miner has added nodes to execute code in a SAS open, cloud-in a position, in-reminiscence Viya atmosphere. Microsoft offers SQL Server R services, an R installation that runs alongside SQL Server and allows users to combine Microsoft R Server information with SQL Server and Microsoft's different business intelligence tools.

    funds for licensing and maintenance

    practically all the carriers sell distinct models or editions of their products, with a number expenses for acquisition and total charge of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft promote variations at distinct tiers, with the license cost proportional to the facets, capabilities and freedom from limitations in terms of the volumes of statistics to be analyzed or the variety of processing nodes the product can use.

    KNIME and RapidMiner supply free and open source types of their products, both charging for help capabilities or for versions assisting enterprise-type functions. KNIME, RapidMiner and Alteryx have rather low licensing fees for a smaller number of clients. if you are on account that SAS or SAP, you ought to contact them for pricing alternate options.

    The industry for big information analytics application can be a difficult vicinity, however expectantly this text has helped you have in mind the advantages big data analytics software can give your corporation, and assisted you in differentiating between the specific equipment examined here.


    How IBM'S project Debater might fix the State of the Union | killexams.com Real Questions and Pass4sure dumps

    final week, like lots of you, I think about, I watched the State of the Union and tried to work out what become genuine and what become fiction. Tied into a couple of of the live-streamed press reality-checking streams, I found that the comments validating or invalidating what the president talked about got here in to this point after the remark changed into made that it will have been stronger to pass the speech and wait except day after today when the speak and the remarks have been improved matched.

    Even then, it wasn't linear, it became simply collections of comments that had been biased both for or against the talk. it really is not reality -- it really is just intellect-numbing pablum for those on both the left or right who basically don't wish to believe. i like to make up my own intellect not have a bunch of folks on either side who basically are commenting from the sidelines drive me into their increasingly delusional world view.

    This week IBM believe takes region in San Francisco, and one of the courses that should be lined is its venture Debater. What makes this AI venture interesting is that it melds human interaction with deep getting to know to create a very diverse kind of answer that could be uniquely helpful to both litigation and politics. It may make the court docket device much fairer, and it also could make it way more complex for a baby-kisser or biased media outlet to confuse or mislead us.

    On this present day before IBM consider, i could clarify how this new hybrid AI platform that IBM is showcasing could make politics and litigation greater advisable after which shut with my fabricated from the week. This week it's the new Dell XPS 13 in Frost White, which makes an astonishing commentary with design, portability, materials and know-how.

    making a Hybrid AI

    one in all IBM's wonderful positions, which in reality isn't pleasing, is that AI know-how may still be used to boost as opposed to replace people. There are lot of causes for this. the most compelling for me is that if robots take over every thing, who is going to purchase stuff? No, wait, in all probability probably the most compelling is that in IBM's world they don't seem to be out of date, and the possibility of becoming out of date as a race is something i would hope more of us may wrap their heads round.

    Now they do have the idea of the singularity, during which man and computing device become inseparable, however it is not going in my lifetime. If it had been to arrive early, i'm pretty sure it could conclusion my existence, so an option route can be drastically more pleasing.

    considering most AIs, even those designed to work with individuals, are created generally as isolated standalone programs that could, with a flip of a change, not want their human operators, a special strategy is required to tackle this human obsolescence difficulty.

    IBM's challenge Debater

    This brings us to IBM's challenge Debater. This modern method uses a mix of crowdsourcing and deep learning AI to come up with whatever thing some distance more desirable than both part can be on my own. project Debater uses the crowdsource mannequin to define the excellent of an argument, which then is used to educate the deep getting to know AI engine and optimize what's at first a debate response.

    This blends the AI's affinity for facts and numbers, the objective a part of the solution, to the human subjective view of high-quality. In an excellent case, what you get is doubtlessly a self-evolving determination system superior in a position to withstand problems like affirmation bias, and make reality-based decisions that are not devoid of feeling and humanity.

    as soon as again, this ideally, it might be a blend of objective and subjective choice criteria, nonetheless according to demonstrated information, this is concentrated on human defined incredible outcomes.

    The groundwork of the film series Terminator could be a show off of an objective AI-driven future devoid of the human facets that task Debater brings to the table. Of course, the area they at the moment are living in is described by using an excess of subjective choice methods which are focused extra on popularity and seeming right than on being right. either excessive would not bode smartly for their survival.

    The State of the Union

    In observing the State of the Union, I followed three problems with trying to evaluate the veracity of the suggestions. One was that the reality checkers couldn't sustain. Two, that they had three classes of response, "correct, wrong, misleading," and probably the most typical, deceptive, changed into all the time nuanced terrible, which made the media insurance appear biased. Three, the response from the Democrats had little to do with the specific speak (not a surprise, when you consider that it turned into written before the adventure and the speaker didn't watch the handle). This last is like watching a debate between two individuals who can not hear every other's positions. It was less a real response and extra a "look, I too will also be on television!"

    What task Debater does is use americans to set up the exceptional of arguments on each side of an argument to get to the strongest arguments. people within the crowdsourced pool vote best on the arguments they believe, in order that they do not compromise the outcomes because of preset biases.

    this might permit a true-time ranking of the arguments on each side just before the tackle. as an instance, let's take the wall. The most powerful argument for it is likely the discount of criminals coming into the U.S. The strongest argument against it's that spending cash on improving latest border security and capabilities like the Coast defend would have a stronger have an effect on on unlawful immigration and raise the probability of catching the criminals on the border. each positions could be backed via validated information to aid the viewers decide upon the more suitable facet.

    So, whereas listening to the president talk, an observer would see both the strongest argument for his position on the wall and the strongest argument against, after which might kind an opinion on which position to support. in contrast to the media, or both facet of the argument, these could be unbiased and truth-based.

    I do consider that this implementation seemingly would force using this tool within the choice-making feeding the State of the Union and that smarter presidents, now not desirous to seem to be foolish on tv, would use it to set their positions within the first vicinity. if they did not, they would have the objective equipment critical to identify their incompetence and the need to remove them from office.

    it's my view that this might greatly repair the present world political process, which is at present more about what the left or appropriate need, and fewer about doing what is correct.

    One other thing that an AI may add is a rating of the arguments in accordance with their interesting cases. as an instance, an AI may, and more and more probably does, understand extra about you than you find out about your self.

    We frequently do not realize that the positions they take are against their own superior interests, as a result of we're satisfied to take their positions by way of charismatic folks who do not even recognize us, let alone have their top-rated pastimes at coronary heart. This could assist gasoline a more robust bipartisan approach in government, because it may still be more exciting to us if whatever thing is to their improvement than if it is a left or right conception.

    This wouldn't enhance government on my own -- it might have a massive wonderful influence on litigation.

    greater effective Litigation

    When i used to be observing the FTC/Qualcomm case a few weeks lower back, i was struck with what a difficult job the choose had, which became made extra problematic due to the fact that the FTC couldn't seem to location its evidence into any variety of choice matrix.

    A choose typically desires to do a great job and reach the appropriate resolution, but here's at cross-functions with the folks presenting their situations, who wish to herd a decide to a choice favoring their side. during this form of a case, either side customarily includes teams of attorneys, and there is only one decide, which makes it essentially unattainable for the choose to see during the smoke. It seems extra seemingly that judges will turn out to be identifying the case on something other than the information in proof.

    What the prison occupation has is miles of documented arguments and positions, together with statistics over which facet prevailed and at what stage. In effect, a lot of the crowdsourcing part of the trouble is cooked -- it just has to be fed into the AI in order that a choose can get a true-time feel of even if what's being argued is each in keeping with the proof and in keeping with case law and priority.

    It not only would force the two criminal groups to up their video game, however additionally would provide the judge with a near superpower when it comes to deciding on the differences between records and fabrications. in brief, it doubtlessly would flip a method it really is regularly more performance art than the rest into whatever thing that could be way more more likely to assess guilt or innocence accurately. it will above all advantage those that are overmatched through their opponents in terms of materials.

    IBM's Watson had the legal occupation as an initial target. This utility no longer best would be extraordinarily effective however also would enhance significantly the integrity of the legal manner. IBM says that its believe conference is ready changing the world. This 12 months, with mission Debater, that looks rather accurate.

    Wrapping Up

    discovering how you can use the subjective abilities we've whereas improving the outcomes by guaranteeing that these decisions have a high-quality purpose foundation would benefit the area. From the proliferation of nuclear weapons, to wars, to what to do about world warming, the appropriate mix of human/machine may make the world an improved area to are living. It might make their items greater focused, and their governments more responsive and fewer prone to get us killed.

    those consequences develop into increasingly feasible as greater groups look at out-of-the-box methods like mission Debater, and the area shifts from being about us vs. them to being almost making things measurably more desirable over time. we've lot of things that want fixing. AI is each a part of the difficulty and a major a part of the solution, and undertaking Debater is on the latter direction. For these going to IBM suppose this week, i may see you there.

    Rob Enderle's Product of the Week

    The Dell XPS line is Dell's similar to GM's Cadillac or Ford's Lincoln. it's a top class line nevertheless at the proper conclusion of cost-efficient, which specializes in design, exceptional and journey over pure efficiency, rate or sturdiness.

    the line took place, a great deal like Microsoft's surface line, as a counter to Apple's MacBook, however as Apple's hobby in PCs waned, it has shifted to extra of a standout product by itself merits and less of a MacBook clone.

    The XPS 13 Dell sent me became most suitable in Frost White. With an Intel Core 7 processor, captivating 4K display, 512-GB NVMe SSD, and home windows 10 professional, it expenses simply over US$2K. Battery lifestyles should still be well over 10 hours, and it has a small pocketable color-matching energy give, so I will not have to lift a backpack and i still have loads of battery lifestyles. (My very own goal is to no longer should lug my heavy backpack everyplace i'm going.)

    one of the most alterations this 12 months is the display bezels are smaller, and the digital camera has been moved from the bottom of the screen to the top, which ability it is never pretty much as good at focusing the individuals you are video conferencing with on the state of your nose hair grooming (some thing i am now not going to leave out).

    With three thunderbolt/charging USB ports, it might probably assist up to 2 further 4K screens (many of us have all started to trip with one or two small moveable displays a good way to enhanced work on the road), and it has a biometric vigour button that fires the computing device up and ensures the person the usage of it's you.

    At round 2.7 pounds, this is a light-weight computing device, and its Frost White color (I typically favor dark colorings like black) is starting to be on me, as it seems like it came from the white science fiction future portrayed in many videos. (it would seem impressively respectable in an advanced weapons or bio lab.)

    With a ninety % recyclable design, it's additionally noticeably green, which is further supported by using a Bronze EPEAT ranking for sustainability. It has Dell's outstanding Cinema answer for looking at tv and films, which might be a long way enhanced if Amazon would at last enable us to down load and watch leading motion pictures on a laptop (that's the place most of my video library is).

    briefly, the XPS 13 is one other show off from Dell that blends design and performance into whatever thing you can be proud to see on your desk, and it is my fabricated from the week.

    The opinions expressed listed here are those of the author and don't always mirror the views of ECT information network.

    Rob Enderle has been an ECT news community columnist when you consider that 2003. His areas of hobby include AI, self sustaining riding, drones, personal expertise, rising expertise, legislation, litigation, M&E, and expertise in politics. He has an MBA in human elements, advertising and laptop science. he is also an authorized management accountant. Enderle at the moment is president and primary analyst of the Enderle group, a consultancy that serves the know-how industry. He formerly served as a senior analysis fellow at Giga advice group and Forrester. email Rob.


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    Technology Infrastructure, Graphics and Visualization, and Adaptive Technologies | killexams.com real questions and Pass4sure dumps

    Technology Infrastructure, Graphics and Visualization, and Adaptive Technologies

    Technology Infrastructure: Servers

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    The PowerEdge Server line has three different models, the PE300, PE2400, and PE4400. The PE300 has up to two Pentium III 800MHz processors and up to 1GB of ECC SDRAM. The PE2400 has up to two Pentium III 1GHz processor, 2 GB of ECC SDRAM and 144 GB of Hot Swap internal disk capacity. The PE 4400 has up to two Pentium III 1GHz processors, 4GB of PC133 SDRAM and 252GB of hot plug ultra-3SCSI internal disk capacity. Contact: Dell, Round Rock, TX; (888) 560-8324; www.dell.com.

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    Criterion validity and test-retest reliability of SED-GIH, a single item question for assessment of daily sitting time | killexams.com real questions and Pass4sure dumps

    The aim of the current study was to investigate the criterion validity and test-retest reliability of the SED-GIH question using activPAL3 micro as the criterion measure. The main findings were a moderate correlation (r = 0.31, CI = 0.20–0.41) and a poor agreement (weighted Kappa 0.12, CI = 0.05–0.18) between SED-GIH and activPAL derived sitting time (activPAL-SIT). Significant differences in activPAL-SIT existed between individuals in the different categorical answer options of SED-GIH. The reliability of SED-GIH was excellent (ICC = 0.86, CI = 0.79–0.90) with a substantial agreement (weighted Kappa 0.77, CI = 0.68–0.86).

    The TASST framework was developed to gain an overview of tools used for assessing sedentary behaviour, and categorized them into four domains: type of assessment, recall period, temporal unit and assessment period. According to TASST, SED-GIH is defined as a single item direct measure of sitting, for an unanchored recall period with a temporal unit of a day, and an non-defined assessment period (taxon 1.1.1/2.4/3.1/4.5) [14]. The moderate correlation between sitting time measured objectively with activPAL and sitting time measured subjectively using the SED-GIH question is in line with other questionnaires. IPAQ (International Physical Activity Questionnaire, (TASST taxon 1.1.1/2.2/3.1/4.3) contains three specific sitting items, which have been validated using activPAL. For sitting time during weekdays, including transportation, correlation was low (r = 0.16, ICC = 0.15) and non-significant (p = 0.2) between the two methods. Here, IPAQ underestimated sitting time by 2.2 h per day [19]. PAST (Past-day Adults Sedentary Time, TASST taxon 1.2.2.1/2.1/3.1/4.5) and PAST-U (modified version of PAST, TASST taxon 1.2.2.1/2.1/3.1/4.5) asks participants to report their time spent sitting or lying during the previous day. When using activPAL (version 3) as criterion measure, the validity for PAST was assessed to be r = 0.57 [20], and PAST-U ICC = 0.64 [21]. When Busschaert and co-workers tested the validity of three different questionnaires measuring context-specific sedentary behaviour (TASST taxon 1.2.2.1/2.2/3.1/4.3, 1.2.2.1/NA/NA/NA, 1.2.2.1/2.4/3.1/4.3) they found weak to acceptable validity for adults (r = 0.06–0.52) and older adults (r = 0.38–0.50) [22]. This implies that the SED-GIH has stronger associations with objective sitting than other single item questionnaires, such as IPAQ, when compared to direct measurement. However, these associations are not as strong as the time-specified PAST and PAST-U, which collect information on sitting during the previous day only.

    Participants who estimated their sitting as ≤3 h using SED-GIH, all underestimated their sitting time as compared to activPAL-SIT (see Table 3). Furthermore, participants who estimated their sitting as ≥13 h almost all overestimated their sitting time. These results are in line with comparisons between PAST and activPAL (version 3) derived sitting times. PAST underestimated sitting times at low levels of sitting, and overestimated sitting time at high levels of sitting [20]. However, a Bland Altman between IPAQ and activPAL indicated that IPAQ underestimated sitting time by up to 2.2 h per day (during a total week including transportation) [19], and both PAST-U and the three different questionnaires measuring context-specific sedentary behaviour overestimated sedentary time, with activPAL as the criterion measure [21, 22]. Dall and colleagues concluded that most sitting questionnaires underestimate sitting time by 2–4 h per day. Single item questionnaires are more likely to underestimate sitting time, while questionnaires assessing sitting during a sum of sedentary behaviours using a composite of several items tend to overestimate sitting time. Questionnaires assessing sitting during a sum of sedentary behaviours over an unanchored or longer period of time tend to report larger underestimations [14]. According to this study, the reasons for sitting time underestimations by the SED-GIH question can be explained by it being based on a single-item question during an unanchored period of time.

    The original seven SED-GIH answer categories were collapsed into five, since there were very few participants choosing “Virtually all day” or “Never”. The intention of including all seven answering options was that “Virtually all day” and “Never” might be easier to relate to instead of < 1 h and > 15 h. They also provide the answer options with some anchorage. When the five categories were analysed, the mean values (displayed in Table 1) of sitting time measured with activPAL did not differ much between the categorical answer options of SED-GIH (varying from 8.7 to 10.3 h per day, mean 9.7 h per day). Thus, the objectively measured average sitting time per day had a narrow distribution, even though the participants subjectively estimated their sitting time with SED-GIH in a wider range. However, the accuracy of SED-GIH changed when only two categories were used (more or less than 10 h of sitting per day). The majority of the participants who rated themselves as sitting for 10 h or more, actually sat for more than 10 h (56.3%). The low sensitivity and specificity of SED-GIH indicates that it would not be useful for identifying hazardous sitters (≥ 10 h per day). Objective measurements may be more useful in detecting sedentary behaviour, possibly in combination with PAST or similar questionnaires. More research is thus needed to develop questionnaires assessing sedentary behaviour and provide better outcomes together with objective methods.

    Test-retest reliability of SED-GIH was excellent (ICC = 0.86, CI = 0.79–0.90), which is better than other reliability tested questionnaires. PAST had fair to good reliability (ICC = 0.50), and three different questionnaires measuring context-specific sedentary behaviour had good reliability for adults (ICC = 0.73–0.77) and older adults (ICC = 0.68–0.80) [20, 22]. However, SED-GIH is a single item questionnaire, whereas PAST and the three different questionnaires measuring context-specific sedentary consist of several questions, which can affect test-retest reliability. With a tool consisting of a single item question, it might be easier to answer the same question twice compared to tools consisting of several questions. Thus, SED-GIH has good repeatability and generates reliable answers among older adults. However, it is not known whether SED-GIH can detect changes of sedentary behaviour over time, such as before and after a behavioural change intervention period. This field needs further research.

    Limitations to the current study have been observed in the methods and the processing of the data. Participants may have become more conscious about their habits regarding sitting time when they answered the web questionnaire prior to the objective measures, which may have affected their sitting habits during the week of measurement with activPAL. Additionally, the measurement period between answering SED-GIH and wearing the activPAL varied (mean 16 days ±14 days), which may have affected the agreement. One impact on internal validity is the accuracy of the participants’ dedication to fill in the diary log correctly, which can affect the whole dataset. In the validity study, participants were employees with an office-based job, which is not representative of a general population. SED-GIH should be validated in other contexts and with different populations. In the reliability part of the current study, all participants were elderly. This may have an effect on the results since some elderly persons can have reduced memory function compared to younger adults.

    Implications

    SED-GIH may be useful as a tool when identifying sitting time as a determinant for health risks on a population level, but would not in itself be sufficiently informative for screening for unhealthy sitting habits in primary care. More studies performed on a broader population are needed.


    Intraoperative Noise Increases Perceived Task Load and Fatigue in Anesthesiology Residents: A Simulation-Based Study | killexams.com real questions and Pass4sure dumps

    During the past 5 decades, the operating room (OR) has acquired the unenviable distinction of being one of the noisiest of clinical environments, with sound pressure levels increasing an average 0.4 dB per year.1 Commonly performed surgeries, such as orthopedic procedures, can have sustained sound levels exceeding 100 dB for 40% of the time,2 far exceeding the limit recommended by the Environmental Protection Agency of 45 dB (day-night average sound level) to avoid annoyance and maintain 100% speech intelligibility3 and the World Health Organization guidelines of ≤55 dB (equivalent sound level during daylight [16] hours).4 There has been an increasing appreciation of the harmful effects noise pollution has on caregiver health, cognition, and performance.5,6

    Two of the noisiest periods during surgery coincide with anesthetic induction and emergence.7 Hypothetically, less frequent but potentially catastrophic intraoperative emergencies such as anaphylaxis, pneumothorax, and hemorrhage also would be associated with elevated noise levels, although this has not been studied. Therefore, anesthesiologists may be particularly susceptible to noise exposure, with 84% subjectively reporting that noise has a negative impact on their work.8 Recently, 2 laboratory studies reported a negative effect of noise on accuracy and response times for detecting audible changes in oxygen saturation9 and on anesthesiology resident performance on mental efficiency and short-term memory tests.10

    OR noise pollution is therefore a significant clinical problem impacting caregiver well-being, and by extension, patient safety. It is difficult, however, to conduct clinical trials to investigate rigorously this problem because of difficulty in controlling for the real-world complexity present in operating suites and because of concern with testing interventions in real patient-care settings. An important study recently reported a lower incidence of postoperative complications after intraoperative noise levels were decreased. However, that study was not randomized nor blinded and was conducted in a pediatric cardiac OR, a specialized perioperative environment.11

    Medical simulators offer a safer and more controlled venue for performing randomized controlled studies investigating the noise problem and the effect of interventions, but the degree to which findings are extrapolatable to the clinical arena depends on simulation realism. The clinical auditory environment (soundscape) is an important simulation component with respect to clinical noise. However, current OR simulators lack the native capability to simulate realistic clinical soundscapes beyond rendering the pulse oximeter auditory display and annunciating medical device alarm sounds.

    Recently, they sought to address this gap by retrofitting their fully functional replica of an OR with a high-fidelity audio reproductive system to add immersive, auditory realism to the simulation experience. They refer to their simulator as NOISE (Noisy OR Immersive Simulation Environment). In a separate study, they investigated the acoustical environments in NOISE and several ORs at their institution. The NOISE has a shorter reverberation time (benefiting intelligibility) relative to the ORs (565 vs 700 milliseconds) likely because the total space (room volume) in NOISE is smaller and construction materials are different between the 2 rooms. However, the equivalent continuous noise levels are similar between the NOISE (76.5 dB) and a typical OR at their institution (76.0 dB).11a

    The primary objective of the current study was to perform randomized and controlled simulation experiments in their NOISE to test the hypothesis that OR noise increases perceived task load and fatigue, which are contributors to workplace stress. A secondary objective of this study was to propose and test the plausibility of a new psychometric model that combines psychometric indicators of task load and fatigue into an instrument for measuring perceived stress. Development of new techniques for measuring the psychological variables in an experimental setting will help increase their basic understanding of the underlying psychological constructs at the interface between environment and caregiver and will augment the effort to characterize and mitigate the harmful effects of clinical noise.

    METHODS

    This study was approved by IRBs at the University of Miami-Miller School of Medicine and the Jackson Health System. Written informed consent was obtained from all subjects. This study was funded by the Anesthesia Patient Safety Foundation. The funder had no role in the study design, study conduct, or writing of the manuscript.

    The NOISE Simulation Setup

    Our OR simulator at the University of Miami-Jackson Memorial Hospital Center for Patient Safety contains an METI human patient simulator (Medical Education Technologies, Inc., Sarasota, FL), an anesthesia workstation (Datex-Ohmeda, GE Healthcare, Little Shalfont, UK), and associated medical alarm equipment. For this study, they installed 4 corner speakers powered by an audio interface (MOTU Traveler, MOTU, Cambridge, MA). Then, quadriphonic soundscapes (Supplemental Digital Content 1, Video 1, http://links.lww.com/AA/B302) were composed by the use of recordings of typical sounds obtained in their clinical ORs (e.g., telephone ringing, suctioning, door closing, shoe skidding, and stepstool and instrument clanging). The desired effect was to create a sound field in which discrete sound sources would be perceived by subjects as coming from distinct areas of the simulator room, similar to where they usually originate in their clinical ORs (Fig. 1). Two 30-minute soundscapes were composed in this fashion with the use of open-source audio editing software (http://audacity.sourceforge.net/).

    A custom multimedia graphical user interface (GUI) was designed with the use of MATLAB® R2010a (The Mathworks, Inc., Natick, MA) to run on a PC laptop (Dell® Inspiron) inconspicuously located on top of the anesthesia workstation (Supplemental Digital Content 1 and 2, Video 1 and 2, http://links.lww.com/AA/B302, http://links.lww.com/AA/B303). The GUI served 3 functions. First, it was capable of displaying simulated patient vital signs and ventilator variables on a 15-inch liquid crystal display (i.e., LCD) monitor connected to the laptop and readily visible to subjects. The GUI continuously updates screen variables by reading XML files pertaining to a custom 30-minute simulation script. Second, the GUI was responsible for rendering the pulse oximeter auditory display and any triggered audible alarms (based on typically used alarm thresholds) to the laptop speaker, which has similar specifications to the workstation speaker. The audible alarms were designed to comply with the International Electrotechnical Commission standard medical audible alarm sounds (60601-1-8). Third, the GUI had a text input/logging feature that allows subjects to enter responses relevant to simulated patient status and answers to distractor task questions via a standard keyboard and mouse. The distractor task questions consisting of a set of 100 questions that were variations of 20 distinct questions related to the practice of anesthesiology were menial and tedious, usually requiring simple calculations to be performed to arrive at the answer (Appendix 1).

    Although their custom GUI logged text entry content and response times relating to simulated patient care and the answering of distractor questions, these variables were not treated as dependent variables under the null hypothesis of the current experiment. They currently are using these preliminary data to guide the development of psychometric instruments for assessing performance in screen-based simulation (Richard R. McNeer, MD, PhD, Roman Dudaryk, MD, Nicholas B. Nedeff, MD, Christopher L. Bennett, MD, unpublished data, 2015).

    The simulated clinical soundscapes consisted of combinations of the quadraphonic soundscapes and the GUI sonification of the script-responsive pulse oximeter display and alarm sounds. Specifically, the noise condition was achieved by combining GUI output with an accompanying quadraphonic soundscape played through the 4 corner speakers. To achieve the Quiet condition, the pulse oximeter and alarm sounds were rendered without an accompanying soundscape. From a position in front of the anesthesia workstation at approximately head level while seated, the sound levels (equivalent [peak]) for the noise and quiet conditions were 76.5 (93.0) and 72 (84), respectively. These levels are comparable with sound levels present in their ORs.11a When they account for the logarithmic basis of the decibel unit, the noise soundscape is 3 times and 8 times louder than the quiet soundscape in terms of equivalent and peak levels, respectively.

    Experimental Procedure

    The experiment was designed to investigate the impact of noise on subject perception of task load and fatigue while subjects cared for a simulated patient and simultaneously answered a set of distractor questions in a simulated OR. There were 2 sessions, spaced approximately 1 week apart (Fig. 2). On the morning of the first scheduled session, each subject was given a sheet with an example of the 20 types of questions to be used as a distractor task (Appendix 1). The purpose of the distractor task was to decrease the likelihood that subject attention would be directed solely and continuously to the GUI. The subject was given enough time to determine that he or she knew how to answer each question type. Questions that could not be answered were explained to the subject by the investigator.

    Each session was composed of 2 consecutive simulated lunch breaks (Fig. 2A). The first lunch break followed a script that was uneventful during which minor fluctuations in vital signs and machine variables occurred, whereas the second lunch break followed an eventful script with 3 intraoperative emergency scenarios. The order of sessions and comprising lunch breaks were constant for all subjects. On the day of the first session, subjects were randomized into 1 of the 2 Soundscape groups (Fig. 2B). Group 1 experienced the quiet condition during the first session and was then crossed-over to the noise condition during the second session 1 week later. Group 2 was exposed to the Soundscape levels in reverse. Both sessions followed the same process flow.

    Sessions began between 12 PM and 1 PM. Each subject was instructed to have eaten lunch, could not have worked the previous night, and had to be on a rotation involving active clinical duty. At the start of each session, subjects completed the Perceived Stress Scale (PSS) 14-item instrument12 (0–100) so their baseline fatigue level could be assessed. The subject also was instructed to use the restroom if needed before the start of each session. To gather preliminary data for another study, a portable, wireless, 4-lead biosensor was attached to the subject to record the electrocardiogram. The electrocardiogram data were recorded throughout all phases of the simulation experiments to obtain preliminary physiologic data not related to the null hypothesis being tested. They are using these data to develop novel methodology to measure physiologic responses including heart rate variability, and the results will be presented in a future manuscript.13,14

    A 15-minute rest period occurred during which the subject was instructed to sit comfortably in a quiet room and to relax by clearing the mind without falling asleep. Next, the subject was brought to the simulated OR, asked to sit at the head of the bed as shown in Figure 1, and to adjust the height of the seat to ensure that the GUI display on the anesthesia workstation could be viewed comfortably. He or she was asked to familiarize himself or herself with the GUI layout and with the location of the text entry box. The subject was instructed that he or she was to be the lunch person and was given 2 simulated lunch breaks and that 2 tasks were to be accomplished: (1) The questions (supplied on a handout) needed to be answered and entered into the GUI; (2) The simulated patient was to be monitored for any changes in patient vital signs or ventilator variables. The subject was instructed to document via the text entry box when problems with patient care were detected, then a differential would need to be generated, and an action or plan for intervention or therapy would need to be formulated. Subjects were instructed to enter the information pertaining to detection (e.g., tachycardia, decreased end-tidal CO2), differential (e.g., hypovolemia), and intervention (e.g., give phenylephrine) into the GUI, pressing the return key between each thought or item. For instance, if the differential consisted of 2 items, each would be entered separately.

    Instructions were reiterated that both completion of the anesthesia-related questions and monitoring/care of the patient needed to be accomplished. If a resident asked which task was more important, he or she was told to take care of the patient and answer the questions. At the start of a lunch break, sign out was brief and the subject was told that “this is an ASA physical status I 20-year-old man who came from home for an elective left inguinal hernia repair under general anesthesia. He has no allergies and good IV access. They are currently in the maintenance period, and everything has been going fine.” The simulation was then started for the first lunch break that lasted 30 minutes. The second lunch break immediately followed, and sign out was similar to the first except that the procedure was a right inguinal hernia repair. At the conclusion of the second lunch break simulation, the NASA Task Load Index (NASA-TLX)15 for assessing perceived task load and the Swedish Occupational Fatigue Inventory (SOFI)16 for assessing fatigue were administered. These validated psychometric instruments are detailed in the next section.

    Study Design and Statistical Methods

    The experiments followed a repeated-measures counterbalanced (mixed) design (Fig. 2). The Soundscape condition consisted of 2 levels (Quiet and Noise) and was treated as the within-subjects variable because each subject was exposed to both levels. Each subject initially was exposed randomly to either Quiet or Noise conditions in the first session and then was crossed-over to the other Soundscape condition in the second session approximately 1 week later. The order of exposure (Order), therefore, was treated as the between-subjects factor. The dependent variables consisted of subject responses from the NASA-TLX and SOFI instruments. Subjects were instructed to complete the instruments separately, NASA-TLX instrument first followed by the SOFI instrument. In addition, subjects were instructed not to discuss instrument responses or any other experimental details with other participants.

    The NASA-TLX instrument (Appendix 2) is composed of 6 items (Mental Demand, Physical Demand, Temporal Demand, Performance, Effort, and Frustration), which subjects respond to on a continuous scale from 0 (very low) to 100 (very high). These items are then individually weighted and combined into an index (total score) that also ranges from 0 to 100. The SOFI instrument (Appendix 3) is composed of 5 items (Lack of Energy, Lack of Motivation, Physical Exertion, Physical Discomfort, and Sleepiness). Subjects select responses from a 7-point Likert scale (0 = not at all and 6 = to a very high degree). Therefore, there were a total of 12 dependent variables: NASA-TLX total score; 6 NASA-TLX items; 5 SOFI items.

    Power Analysis

    The required sample size for this study was calculated with an online power and sample size calculator for general linear multivariate models (GLIMMPSE v2.1.0).17 The set of parameters used for this study is in the form of a JavaScript Object Notation (i.e., JSON) file, which can be uploaded to the GLIMMPSE Web site and easily reviewed (http://fore.miami.edu/downloads/NASA_SOFI_PA.zip). To summarize, the model chosen for power analysis was repeated-measures design; however, after they specified the main effect as Soundscape condition (within-subjects) and predictor as Order (between-subjects), the model is by definition a mixed-design. Because there were 12 response variables, in anticipation of adjusting the significance level because of multiple pairwise comparisons, the type I error was entered as 0.004 (Bonferroni correction for 12 comparisons [0.05/12]).

    Attempts were made to estimate response means and effect size from literature reports for NASA-TLX18 and SOFI.16,19 The effect of Soundscape was predicted to be 15% of predicted mean. In addition to this main effect based on Soundscape grouping, they predicted that subjects would perceive greater stress during the first session relative to the second because of anticipatory cognitive appraisal20,21; they estimated this effect to be 5% of the predicted means. They estimated that within-subject correlation would be 0.6 with a decay rate of 0.05. The within-instrument item correlations for both the NASA-TLX and the SOFI instruments were estimated to be 0.7, whereas between-instrument item correlation (i.e., between the NASA-TLX items and the SOFI items) would be 0.3. With these input parameters and a desired power of 0.90, calculation results indicated that 18 subjects would be required (actual power of 0.935). They decided to enroll 20 subjects for this study to allow for 1 or 2 subject dropouts or exclusions.

    Hypothesis Testing and Strategy to Reduce the Risk of Type I Error

    All statistical analyses were performed using IBM SPSS software suite (version 22, IBM Corp., Armonk, NY). Normality of subject responses to instrument items was determined with the Shapiro-Wilk test, by assessing the degree of skewness and kurtosis and by observing histograms and normal Q-Q and detrended normal Q-Q plots. The Levine test for homogeneity of variance was used to test the assumption that response variance was similar across experimental conditions. For hypothesis testing, a general linear model in the form of a mixed-design (split-plot) analysis was performed to determine main effects of Soundscape condition and any interaction effects between Soundscape and Order (i.e., Soundscape × Order). In addition to lowering the sensitivity level (σ = 0.004) because of multiple pairwise comparisons, a multivariate analysis of variance (MANOVA) was performed on the 12 response variables, and when MANOVA is significant (P < 0.05), likelihood of type 1 error because of multiple comparisons is considered to be decreased.22

    In addition to calculating P values for the univariate comparisons, point estimation was performed, and 95% confidence intervals (CIs) are reported. Effect size is reported as absolute mean difference along each respective psychometric instrument scale. In addition, standardized effect size is reported using eta-squared (η2),23 which ranges from 0 to 1 and can be categorized into small effect (0.02–0.13), medium effect (0.13–0.26), and large effect (>0.26).24 Two additional effect size parameters (partial η2 and generalized η2) are reported in the Supplemental Digital Content section (refer to Results section). The formulas used to calculate the standardized effect size parameters are listed in Appendix 4.

    Presentation of Psychometric Data

    Figure 3 illustrates the format used in this manuscript for presenting all 11 psychometric instrument item responses and the NASA-TLX total score. The within-subjects Soundscape effect is depicted by the use of parallel plots of individual subject responses with point estimates for means and 95% CIs (Fig. 3A). To the right of each parallel plot is a floating axis (green in color), which shows the mean differences and 95% CIs. Interaction plots (Fig. 3B) were used to show Soundscape × Order interactions, and point estimates of the mean differences from the interaction plots are presented in a separate plot (Fig. 3C).

    Development and Feasibility Testing of a Psychometric Model of Stress

    All factor analyses were performed using SPSS software suite (IBM). To determine whether the NASA-TLX and SOFI instruments measured their respective latent constructs (i.e., task load and fatigue), internal consistency was evaluated with the Cronbach α. To test the plausibility of the proposed psychometric model, a partial confirmatory factor analysis (pCFA) was performed.25 Traditionally, psychometric models are proposed based on empirical evidence obtained from exploratory (unrestricted) factor analysis (EFA), wherein the indicators (e.g., NASA-TLX and SOFI items) are allowed to freely load onto extracted factors. Subsequent model confirmation is accomplished by performing a confirmatory (restricted) factor analysis (CFA) on newly acquired data. In contrast to the EFA, the indicators in a CFA are forced to load zero (i.e., not to load) onto some of the latent variables (formerly, the extracted factors from the EFA). A figure highlighting these points can be found in the Supplemental Digital Content section (Supplemental Digital Content 4, http://links.lww.com/AA/B305). A pCFA has been suggested as an intermediary process lying somewhere between EFA and CFA on the pathway to model confirmation.25 Although by itself not confirmatory, a pCFA can supply useful information regarding model fit and help the researcher gauge the likelihood that a future CFA will be successful. For this study, factors were extracted with conventional data reduction techniques that rely partly on calculation of factor eigenvalues and parallel analysis.26,27

    Parallel analysis was performed using an SPSS syntax script available from an online source (https://people.ok.ubc.ca/brioconn/nfactors/nfactors.html). Several indices were calculated to evaluate model fit as part of the pCFA25 and are reported. They are the Bentler-Bonett Normed Fit Index, the Tucker-Lewis Index, the Bentler Comparative Fit Index, root mean square error of approximation, and the standardized root mean square residual. In addition, the pattern matrix nonsalient loadings were used to calculate nonsalient loading distribution. Normality of the nonsalient loading distribution which is another parameter of model fit was assessed by reviewing histograms, Q-Q normal plots, and detrended normal plots, and by performing a Shapiro-Wilk test in SPSS.

    RESULTS Subject Randomization and Demographics

    Twenty Clinical Anesthesia year 1 (CA-1) residents (12 male/8 female) participated in the study. There were an equal number of subjects in the quiet first (n = 10, 5 male/5 female) and noise first (n = 10, 7 male/3 female) groups. NASA-TLX and SOFI data for 1 female subject were excluded from the data set because of observed irregularities while completing the subjective instruments. All subjects were instructed to complete the NASA-TLX instrument before starting the SOFI instrument; however, this subject was observed cross-referencing her responses for the 2 instruments.

    Psychometric Instrument Results

    Baseline perceived fatigue level was assessed with the 14-item PSS before each session. There was no statistical difference in baseline fatigue levels at the starts of session 1 (33.00 [0.69] and session 2 (32.45 [0.40]), as measured by the PSS instrument.

    The NASA-TLX items, NASA-TLX total score, and the SOFI items were all approximately normally distributed and satisfied the Levene test for homogeneity of variance. The MANOVA of the 12-item set (6 NASA-TLX items, NASA-TLX total score, and 5 SOFI items) reached significance (P = 0.003), suggesting that subsequent univariate analysis of variance of individual items are less susceptible to type I error.22

    The NASA-TLX total score was greater in noise than in quiet (Fig. 3A; Table 1) on a scale from 0 to 100 by a mean difference of 13.3 (SE = 4.0, P = 0.004). The standardized effect size η2 was 0.36, indicating that 36% of the variance in the NASA-TLX total score was attributable to Soundscape grouping. A Soundscape × Order interaction was observed, suggesting that the effect of Soundscape was larger in the subjects who were exposed to the Noise condition first (Fig. 3, B and C); however, this interaction did not reach significance (P = 0.131).

    Subject responses to the 5 NASA-TLX items were greater in Noise than in Quiet (Table 1), with Temporal Demand reaching the criterion for significance after Bonferroni adjustment of sensitivity level to 0.004. The largest effect size was observed in Temporal Demand whereas Performance had the smallest (η2 = 0.53 and 0.07, respectively). Except for the Physical Demand item, mean differences based on Soundscape were larger for subjects in the Noise First group, with the largest Soundscape × Order interaction observed for the Mental Demand item (P = 0.045; Fig. 4, A and B; Table 1). Parallel plots and additional calculations of standardized effect size parameters pertaining to the NASA-TLX items can be found in the Supplemental Digital Content section (Supplemental Digital Content 5, http://links.lww.com/AA/B306; Supplemental Digital Content 6, http://links.lww.com/AA/B307).

    Figure 4

    Figure 4

    Table 2

    Table 2

    Figure 5

    Figure 5

    Of the 5 SOFI items, Lack of Energy, Lack of Motivation, and Sleepiness showed an effect of Soundscape, with subjects reporting greater levels in Noise than in Quiet (Table 2). Of these, Lack of Energy reached significance (P =0.001, η2 = 0.467). Modest Soundscape × Order interactions between were observed but did not reach significance (Fig. 5, A and B; Table 2). Parallel plots and additional calculations of standardized effect size parameters pertaining to the SOFI items can be found in the Supplemental Digital Content section (Supplemental Digital Content 7, http://links.lww.com/AA/B308; Supplemental Digital Content 8, http://links.lww.com/AA/B309).

    Proposed Psychometric Model of Stress

    The Cronbach α, performed on the 6-item NASA-TLX, yielded a good internal consistency (α = 0.766), which supports the assumption that the NASA-TLX items measured the same construct (i.e., Task Load). Internal consistency increases substantially after the Performance item is removed from the analysis (Table 3). The 5-item SOFI instrument also had good internal consistency (α = 0.768), which supports the assumption that the SOFI instrument measured the same construct (i.e., Fatigue). Internal consistency increases if either the Physical Exertion or Physical Discomfort items are removed from the analysis (Table 4). When both are removed, the Cronbach α increases to 0.848.

    Table 3

    Table 3

    Table 4

    Table 4

    Partial CFA of the NASA-TLX and SOFI item responses yielded 4 factors with eigenvalues >1, a measure used routinely to indicate factor significance26,27 (Table 5; Supplemental Digital Content 9, http://links.lww.com/AA/B310). Significance of the extracted factors was further verified by performing a Parallel analysis/Monte Carlo simulation. Factor 1 was loaded with the Lack of Energy, Lack of Motivation, and Sleepiness items from the SOFI instrument, whereas factor 2 was loaded with Mental Demand, Temporal Demand, Effort, and Frustration items from the NASA-TLX instrument, although there was cross-loading of the Frustration item with other factors (Table 5). Factor 3 loaded with the NASA-TLX Performance and Physical Demand items and the SOFI Physical Discomfort item. Factor 4 loaded with the SOFI Physical Exertion item. The correlations between factors ranged from negligible between factors 2 and 4 and between factors 3 and 4, to moderate between factors 1 and 2, to strong between factors 2 and 3 (Table 6).

    Table 5

    Table 5

    Table 6

    Table 6

    Table 7

    Table 7

    Figure 6

    Figure 6

    On the basis of these results, a model was proposed to explain the relationship between the psychometric instrument items (observed measures or indicators) and extracted factors (latent variables or constructs; Fig. 6). Global goodness of fit indices were calculated as part of the pCFA (Table 7; Supplemental Digital Content 9, http://links.lww.com/AA/B310). All but one of the fit indices was consistent with good fit (the Normed Fit Index was <0.95). Taken together, the results of the pCFA indicate that the proposed model is plausible, and a future CFA on a new set of data is likely to be successful.

    DISCUSSION

    In this simulation-based study, they observed that intraoperative noise increased the perception of task load and fatigue, which likely contributed to an increase in the stress experienced by the CA-1 resident subjects. This finding is consistent with the previously reported finding that anesthesiologists consider OR noise to have a negative impact on their job.8 Their counterbalanced experimental design controlled for other sources of stress, which could manifest differentially between, for example, sessions 1 and 2. For example, they anticipated and subsequently observed a greater effect of Soundscape on observed stress in residents exposed to noise first and quiet second relative to residents exposed to the conditions in reverse. They attribute this observation to anticipatory cognitive stress appraisal,20 which emphasizes the importance of counterbalancing to control for this effect.

    The authors of the first report investigating noise in the OR compared the problem with air and water pollution.28 Despite the initial lack of understanding and evidence bases for the exact roles of these pollutions in surgery, efforts to provide aseptic surgical environments were implemented early and have been in practice for well over a century. It can be argued that it is time for noise, the third pollution, to be addressed in their ORs with similar urgency, especially considering that numerous, minimally disruptive measures are available such as behavioral modification,11 and use of plastic materials when possible in lieu of clangy metal. The scientific rigor required in modern clinical research is difficult to attain in hard-to-control clinical settings, and findings obtained in approximately realistic clinical simulations like the current study may not be considered completely extrapolatable to the real-world. However, the simulation-based findings reported here should be weighed accordingly using a balance between the desire for definitive scientific results and the need to expeditiously address the clinical noise problem.

    For their secondary objective, they performed factor analyses of the data. The NASA-TLX and SOFI are validated instruments, and reliability analysis of their results indicate that each performed with good internal consistency, supporting the assumption that both Task Load and Fatigue constructs (latent variables) were reliably measured in their experiments. To assess the underlying relationships between the individual NASA-TLX and SOFI items (measured variables) and the Task Load and Fatigue constructs, they performed a pCFA that gives an indication of the likelihood or plausibility that a future CFA (with a new data set) will be successful based on model fit. Their expectation was that pCFA would yield 2 extracted factors from a combined data set of the NASA-TLX and SOFI items, each factor corresponding to the Task Load or Fatigue. Instead, 4 factors were extracted (Fig. 6). The first 2 factors make logical sense by corresponding to the psychological-based NASA-TLX and SOFI items, and they refer to them as Psychological Task Load and Psychological Fatigue constructs. The Physical Exertion item that is described on the SOFI instrument with acute and symptomatic terminology (i.e., palpitations, sweaty, out of breath, and breathing heavily) loaded to the fourth factor and they refer to this construct as Acute Physical Load. The SOFI Physical Discomfort item is defined in more chronic terms (tense muscles, numbness, stiff joints, and aching), whereas the NASA-TLX Physical Demand is defined with the question “How physically demanding was the task?” In their experiment, the task referred to both lunch breaks (over an hour) in a session. The fact that NASA-TLX Performance and Physical Demand items and SOFI Physical Discomfort item loaded to the same factor are therefore understandable because self-appraisal of how one performed in an essentially mental task may correlate with more chronic physical symptoms evoked over the course of an hour in this case. They refer to this construct as Self-Appraisal/Chronic Physical Load.

    Importantly, the pCFA indicates a reasonably good fit of their data with the model presented in Figure 6 and suggests that performance of a future CFA with larger sample size is likely to be successful yielding measures of construct (convergent and discriminant) validity. The model would then represent a new psychometric instrument for specific use in investigations of the noise problem.

    There are several limitations inherent in this study. First, it is simulation-based and uses a screen interface loosely based on existing monitor displays. It is, therefore, difficult to quantify the extent to which these results extrapolate to a real clinical OR. Second, the results were obtained in residents (CA-1s) at their institution because it was logistically easier to enroll and obtain clinical coverage for this group during the conduct of experiments. In addition, restricting enrollment to a single class of anesthesia residents helped ensure homogeneity with regard to subject clinical experience and past simulation exposure. They do not know whether the findings would be reproducible at other institutions or be observed in residents at different levels of training or in fully trained anesthesiologists, anesthesia assistants, nurse anesthetists, surgeons, and other OR staff members.

    A major limitation of the experimental setup is that resident visual attention was directed solely to the GUI, which was part of the anesthesia workstation. A more realistic setup would have resident visual attention necessarily divided (by 90°) between the patient mannequin and GUI. Their NOISE simulator uses a highly realistic OR soundscape, and this immersive condition was crucial in providing the independent variable in these studies. However, because the NOISE soundscape was composed of a heterogeneous group of sound sources that included beepers, equipment noise, clangs, and music, it is not possible to determine which soundscape components were responsible for increasing task load and fatigue levels in their residents.

    Another possible limitation of this study is that it was not adequately powered. Given that it was designed primarily to test the hypothesis that noise increases perceived task load and fatigue, the risk of type 1 error is unlikely for the following reasons. They used an online tool (Glimmpse), which to their knowledge is the most comprehensive resource currently available for calculating power and sample size in mixed-design experiments. Previous reports on the NASA-TLX18 and SOFI16,19 instruments allowed us to input estimates of effect sizes and SDs. The response means, SDs, and effect sizes observed in their data are comparable with those previously published. Socioemotional stress has been shown to increase perceived task load as assessed by NASA-TLX in paramedics while administering advanced life support in a simulated setting; mental demand increased from 39 (18) to 57 (25), temporal demand increased from 25 (21) to 33 (22), effort increased from 40 (26) to 54 (22), and frustration increased from 19 (17) to 42 (25).18 Noise was one factor used to induce socioemotional stress, and the similarity of that study to ours helps to put their NASA-TLX data into context. For example, they observed an increase in NASA-TLX total score from 47.5 (10.2) in quiet to 60.8 (13.5) in noise, and they contend that this statistically significant difference is likely to be clinically relevant. In anticipation of performing Bonferroni corrections, they entered an adjusted type 1 error rate of 0.004 into the calculation. In addition, MANOVA of the data set reached significance, suggesting a decreased risk of type 1 error in the pairwise item comparisons.22 Furthermore, by using a repeated-measures, counterbalanced design, the sample size requirement to achieve a power of 0.90 was greatly reduced. Nonetheless, they cannot completely rule out the possibility of type 1 error or that other unknown and uncontrolled factors influenced their results.

    In summary, they demonstrated that noise increases perceived levels of task load and fatigue in anesthesia CA-1 residents while being given lunch breaks during simulated surgeries. Their NOISE simulator, which faithfully reproduces the auditory environment characteristic in their clinical ORs, was a crucial component in their experimental design. In addition, they used validated psychometric instruments for assessing perceived task load and fatigue. They believe the current findings add significantly to the growing mass of evidence, implicating the negative impact noise has on caregivers and patient safety. In addition, they proposed a psychometric model for stress that combines items from the task load and fatigue instruments. A preliminary pCFA of this model supports further validation with a CFA.

    APPENDIX 1
  • Estimate allowable blood loss to reach the transfusion trigger HCT = 17. Starting HCT = 39, 70 kg.
  • Calculate BMI for an 18-year old female who is 5 foot 6 inches and 125 pounds.
  • What are 3 adverse reactions to sitagliptin? You can use computer/phone (e.g., Epocrates, athenahealth, Watertown, MA).
  • Calculate the size ET tube to use in a 14-year old female patient.
  • Calculate the final concentration of drug X after 1 g is diluted in a 550 mL of NS.
  • Calculate Glasgow Coma Scale: opens eyes to pain, inappropriate verbal responses but words discernible, withdraws to pain.
  • Calculate PaO2 using the alveolar gas equation, when FIO2 = 70%, PaCO2 = 37, RQ = 0.87.
  • Calculate the PaO2/FIO2 ratio when PaO2= 107 mm Hg and FIO2 = 55%.
  • Acid-base interpretation: pH = 7.50, PaCO2 = 31.
  • Calculate MAP when given SBP = 125 and DBP = 78.
  • Convert cm H2O to mm Hg when cm H2O = 17.
  • Calculate tidal volume based on weight: weight = 93 kg.
  • Calculate dead space ratio when MV = 6.2 L/min, RR = 10, and absolute dead space = 180 cc.
  • Calculate MV based on TV = 555 and RR = 11.
  • You want to give 4 μg/kg fentanyl on induction to a 79-kg female. How many μg do you give?
  • Calculate lowest acceptable systolic blood pressure (20% of baseline) when baseline BP = 145/85.
  • Calculate fluid deficit in an 83-kg male who last ate or drank at 11:20 PM. Surgery start at 9:45 AM.
  • Assuming dead space ratio of 0.3, and tidal volume = 570 mm, what is the anatomical dead space? Assume alveolar dead space is negligible.
  • For heart rate = 83, and cardiac output = 5.3 L/min, what is the calculated stroke volume.
  • Assuming the toxic dose of a local anesthetic is 5 μg/kg, how much can be given to an 87-kg male?
  • An IV is flowing at 33 mL/min. How long will it take for 900 mL to be administered?
  • During general anesthesia, a mixture of 55% N2O and 45% O2 is being administered to a patient. Assuming the flow rate of O2 is 1 L/min, what is the flow rate of N2O?
  • Exactly 2.25 L of irrigation is used during a case. Assuming that the suction canister contains only irrigation and blood, what was blood loss if the canister contains 3260 mL of fluid?
  • How many pack years has an 88-year old patient smoked if he started when he was 25 and he has averaged about 1.5 packs per day?
  • Calculate the final concentration of drug Y after 10 g is diluted in a 250 mL of H2O.
  • APPENDIX 2 APPENDIX 3 APPENDIX 4

    Within-subjects

    Between-subjects

    Interaction between within-subjects and between-subjects factors

    = Generalized eta-squared,

    = Partial eta-squared,

    = Eta-squared,

    SSA = Between factor type III sum of squares,

    SSP = Within factor type III sum of squares,

    SSPA = Within × between factor type III sum of squares,

    SSs/A = Between-subjects type III sum of squares error,

    SSPs/A = Within × between factor type III sum of squares error.

    ACKNOWLEDGMENTS

    The authors acknowledge the Anesthesia Patient Safety Foundation (Masimo Foundation Research Award) for funding this study.

    DISCLOSURES

    Name: Richard R. McNeer, MD, PhD.

    Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

    Attestation: Richard R. McNeer has seen the original study data, reviewed the analysis of the data, approved the final manuscript, and is the author responsible for archiving the study files.

    Name: Christopher L. Bennett, PhD.

    Contribution: This author helped design the study and conduct the study.

    Attestation: Christopher L. Bennett has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

    Name: Roman Dudaryk, MD.

    Contribution: This author helped conduct the study and write the manuscript.

    Attestation: Roman Dudaryk has seen the original study data, reviewed the analysis of the data, and approved the final manuscript.

    This manuscript was handled by: Franklin Dexter, MD, PhD.

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    Anesthesia & Analgesia122(2):512-525, February 2016.

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