Find us on Facebook Follow us on Twitter





























Killexams.com A2010-578 braindumps with exact Questions | brain dumps | 3D Visualization

Download Pass4sure A2010-578 examcollection - Prepare our Pass4sure A2010-578 Questions and Answers and exam prep and you will pass A2010-578 exam4sure - brain dumps - 3D Visualization

Pass4sure A2010-578 dumps | Killexams.com A2010-578 real questions | http://morganstudioonline.com/

A2010-578 Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Study steer Prepared by Killexams.com IBM Dumps Experts


Killexams.com A2010-578 Dumps and real Questions

100% real Questions - Exam Pass Guarantee with towering Marks - Just Memorize the Answers



A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Test Code : A2010-578
Test denomination : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Vendor denomination : IBM
: 120 real Questions

afraid of failing A2010-578 examination!
word of mouth is a totally robust route of advertising for a product. I say, whilst something is so desirable, why no longerdo some towering attribute publicity for it I would really dote to unfold the phrase about this one of a ilk and truly high-quality killexams.com which helped me in acting outstandingly properly in my A2010-578 examination and exceeding All expectancies. i would speak that this killexams.com is one of the maximum admirable on line coaching ventures ive ever stumble upon and it merits quite a few recognition.


A2010-578 actual test questions and solutions!
I got a excellent give up result with this bundle. Extremely respectable fine, questions are correct and that i got most of them on theexamination. After ive handed it, I recommended killexams.com to my colleagues, and actually each person passed their exams, too (a number of them took Cisco assessments, others did Microsoft, VMware, and lots of others). I acquire no longer heard a lousy assessment of killexams.com, so this necessity to live the remarkable IT schooling you may presently ascertain online.


Where can I find A2010-578 Latest dumps questions?
It clarified the subjects in a rearranged way. In the undoubted examination, I scored a 81% with out plenty hassle, finishing the A2010-578 examination in seventy five minutes I additionally read a incredible deal of captivating books and it served to pass well. My success inside the examination become the determination of the killexams.Com dumps. I must with out an dreadful lot of a stretch give up its decently prepared purport inner 2 week time. Lots obliged to you.


need real exam questions of A2010-578 exam? download here.
Every topic and vicinity, each situation, killexams.com A2010-578 substances acquire been wonderful help for me while getting ready for this examination and in reality doing it! I was worried, however going lower back to this A2010-578 and wondering that I understand the all thing due to the fact the A2010-578 examination changed into very smooth after the killexams.com stuff, I got an awesome result. Now, doing the next degree of IBM certifications.


genuinely first-firstexcellent enjoy!
After trying several books, I was quite disappointed not getting the right materials. I was looking for a guideline for exam A2010-578 with simple language and well-organized content. killexams.com fulfilled my need, as it explained the complicated topics in the simplest way. In the real exam I got 89%, which was beyond my expectation. Thank you killexams.com, for your much guide-line!


Take a ingenious flux to skip A2010-578
Yes, the question pecuniary institution could live very useful and i insinuate it to All people who wants to choose those checks. Congrats on a process nicely notion out and completed. I cleared my A2010-578 tests.


updated and real examination pecuniary institution today's A2010-578.
I dont experience by myself a mid tests any longer in light of the fact that ive a elegant examine colleague as this killexams.Com dumps. Im quite appreciative to the educators right right here for being so extraordinary and properly disposed and assisting me in clearing my distinctly exam A2010-578. I solved All questions in exam. This equal course turned into given to me amid my exams and it didnt develop a dissimilarity whether or not or no longer it acquire become day or night, All my inquiries acquire been spoke back.


worked difficult on A2010-578 books, however the entire component acquire become on this test manual.
The acquire a gape at material of A2010-578 exam is printed well for bag prepared internal a brief age of time. killexams.com Questions & answers made me score 88% in the wake of answering All questions ninety mins of time. The examinationpaper A2010-578 has numerous celebrate substances in commercial enterprise zone. but it got to live extraordinarily tough for me to select the exceptional one. live that as it can after my brother asked that I used killexams.com Questions & solutions, I didnt acquire a gape at for other books. an dreadful lot obliged for helping me.


it's far exquisite to acquire A2010-578 dumps.
killexams.com has top products for college students because those are designed for those students whore interested by the education of A2010-578 certification. It become brilliant selection due to the fact A2010-578 exam engine has top notch celebrate contents which can live smooth to understand in short time period. im thankful to the grotesque group because this helped me in my career improvement. It helped me to understand the route to solution All famous inquiries to bag most rankings. It was brilliant determination that made me fan of killexams. ive decided to compass back returned one greater time.


Belive me or now not! This aid latest A2010-578 questions is actual.
I got this p.c. and exceeded the A2010-578 examination with 97% marks after 10 days. Im extraordinarily fulfilled via the result. There can live notable stuff for associate stage confirmations, yet regarding the professional stage, I suppose this is the main sturdy scheme of action for fine stuff, specifically with the exam simulator that offers you a risk to exercise with the gape and experience of a undoubted exam. this is a totally sizeable brain sell off, actual gape at manual. that is elusive for reducing edge exams.


IBM IBM Assess: Fundamentals of

How safe Is IBM's Dividend? | killexams.com real Questions and Pass4sure dumps

No influence discovered, try fresh key phrase!Dividend safeguard Relative to Its present Debt Load The remaining angle that they are going to expend to determine IBM's current ... is currently lined by route of its fundamentals. IBM's dividend looks protected for the ...

IBM Shares Drop 22% This 12 months as Hope of Turnaround Dims | killexams.com real Questions and Pass4sure dumps

Shares of alien company Machines Corp. (NYSE: IBM) are down 22% this year as hopes of a turnaround promised by means of CEO Ginni Rometty dissolve. She has been at the stint given that 2012 and has taken IBM through a yoke of reinventions.

Rometty recently bought pink Hat, a Big issuer of open source application essentially for companies. IBM paid $34 billion, which is an strangely towering diverse of each profits and income. The deal should bolster IBM’s cloud-related organizations Rometty argues, however buyers loom to deem IBM made that case poorly.

Most weeks, IBM continues to punch out a few press releases, which is extraordinary for publicly held companies. among the most fresh:

Ilusión, Fiorentina & David’s Bridal open the door for digital transformation in vogue with IBM

Most acquire microscopic to title concerning the economic consequences of IBM’s plans. Many must consequence with IBM’s cloud initiatives, a neighborhood through which the enterprise needs to garner market share from leaders Amazon.com and Microsoft. Most research suggests that IBM’s share of the market is a diminutive fraction of its fundamental competitors. IBM has now not made a believable case this may change, most likely as a result of there is none.

The largest knock against IBM is that it has not grown in years, in line with earnings, while its fundamental competition has grown at double digits quarter after quarter. The market remains stinging from another drop in IBM’s profits remaining quarter, down 2% to $18.8 billion. IBM said its cloud features brought in $19 million over the 365 days that led to the fresh quarter. it is challenging to tease that number out from what IBM calls “strategic imperatives” so a assessment to consequences from different agencies is challenging to make.

What isn't complicated to determine is that salary from market leader Amazon net services hit $6.7 billion closing quarter, up 48% from the selfsame age the yr before. Its operating profits margin turned into an spectacular 31%.

IBM’s items and capabilities haven't been knit together in a route so that Wall road can believe the enterprise has a coherent mode beyond grabbing at opportunities. One does not necessity to examine simply the year-to-date stock expense for proof. As an aside, IBM’s shares are down 32% over five years, whereas the Nasdaq is better via seventy four%.

i am drawn to the publication Get Newsletterterms and stipulations  

After a Disastrous Run, IBM inventory is too low priced to ignore | killexams.com real Questions and Pass4sure dumps

It’s in fact been an hideous elope for IBM (NYSE:IBM). overseas traffic Machines inventory has been hammered considering that early October, falling 25% at one factor. IBM inventory touched a nine-12 months low at one factor earlier than a modest rebound.

Admittedly, there are some explanations for the pullback. in spite of the fact that IBM inventory looked tremendously low-cost earlier than the declines, Q3 revenue disappointed, with income extend again turning poor after three quarters of raises.

on the conclusion of October, IBM agreed to acquire purple Hat (NYSE:RHT) for $34 billion in cash, an acquisition the market looks to dislike.

average weak spot in tech stocks doubtless added to the power. Mature, low-growth tech performs dote Cisco techniques (NASDAQ:CSCO) and Oracle enterprise (NYSE:ORCL) acquire pulled returned as well. Neither stock, of direction, has considered declines dote that of international traffic Machines stock.

IBM’s performance admittedly has been disappointing. I argued as these days as August that IBM gave the gape of a purchase, writing that “I’d live bowled over” to gape IBM trade abate than $one hundred twenty five, at which factor it could present a 5%+ dividend yield.

IBM is beneath $125, and that i am slightly bowled over. and i feel the sell-off in IBM stock has gone too far.

Is IBM a expense trap, or a value Play?

fundamentally, IBM is inexpensive. It trades at lower than 9x consensus 2019 EPS estimates. Free cash stream recommendation for this 12 months suggests a similar diverse according to free money flow.

With IBM administration guiding for red Hat to live accretive to the ~$12 billion FCF figure, that assorted should descend even extra next yr. And it leaves ample latitude for IBM to pay out its present ~$6 billion in dividends.

So the simple argument right here seems reasonably smooth to make. pink Hat itself adds roughly two points of salary boom a yr, helping to stabilize the traffic going ahead. IBM inventory is priced for a decline when it comes to each profits and free money move.

The dividend may silent live fairly protected; there doesn’t seem to live latitude for a circumstance dote that of benchmark electric powered (NYSE:GE) the set onerous debt leads to a dividend reduce. Even with the crimson Hat deal, IBM’s debt (and pension) load is silent manageable.

but that fundamental case itself highlights the erudition risk right here. The market in customary, and this market exceptionally, isn’t leaving pleasant organizations sitting around with a 9x P/E and a 5%+ dividend yield, even after some recent weakness. IBM stock didn’t hit a nine-yr low because the market wasn’t paying attention. The market turned into.

The risks to IBM inventory

the fundamentals here imply that buyers are pricing overseas company Machines as if it had been a declining company. looking backward, it is. revenue fell year-over-year for 23 consecutive quarters earlier than closing year’s this fall. operating margins acquire compressed over that duration.

And so the easiest stand case for IBM in the weigh in time is based on a unique question: even with purple Hat, what’s distinct? The argument for buying IBM going again to 2012 has been, essentially, that the inventory is just too low-cost if it may stabilize revenue and margins. That bull case has been suitable, but IBM hasn’t been able to obtain that stabilization.

Even the respectable information of the closing few quarters doesn’t gape necessarily that decent. IBM’s centered areas of growth (which it refers to as “strategic imperatives”), dote cloud and AI, acquire extended salary 13% over the terminal yr. those categories compel roughly half of earnings, which is respectable information.

The defective information is that IBM on the complete has grown income a bit over 2%. That in swirl suggests the relaxation of IBM is seeing income descend anything dote eight%. And the modest margin drive on the enterprise suggests that IBM is relocating from stronger revenue to weaker sales. It’s trying to trap up in cloud  while seeing its mainframe business, for example, wither away.

And on that front, Q3 in fact was disappointing. Cognitive options (which homes the established Watson) earnings declined in uniform currency for the 2nd straight quarter. programs growth of 1% was a grotesque deceleration. The Q3 document harm the case right here. And with tougher comparisons on the style for the next three quarters, buyers doubtless can’t are expecting too a much deal within the manner of fireworks any time soon.

nonetheless intriguing

So IBM bulls ought to acquire their eyes open to the capabilities draw back. That said, $121 does loom too low-priced for IBM stock. The purple Hat deal could had been too pricy, however I deem Luke Lango, who made a forceful case for the strategic expense of the acquisition. And with IBM having misplaced about $30 billion in market cap for the intuition that early October, IBM inventory has more than priced within the expense tag.

The dividend appears secure in the mid-term. The equilibrium sheet is safe. And eight-9x profits and free cash movement gives the company loads of flexibility to either pay off the crimson Hat-connected debt or ramp up shareholder returns.

extra vital, these multiples consequence exchange the bull case here somewhat. The argument for the terminal few years became that if IBM stabilized, the inventory would ebb up. At these levels, if IBM stabilizes, the stock can soar.

whatever dote 13x $13 in 2019 EPS receives the stock to ~$170 – about 40% upside even before the dividend. The market now's pricing within the fresh vogue which makes some experience. however that additionally faculty investors aren’t accounting for what occurs if the purple Hat deal become a very respectable one and IBM’s turnaround finally takes hold.

As of this writing, Vince Martin has no positions in any securities mentioned.


A2010-578 Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Study steer Prepared by Killexams.com IBM Dumps Experts


Killexams.com A2010-578 Dumps and real Questions

100% real Questions - Exam Pass Guarantee with towering Marks - Just Memorize the Answers



A2010-578 exam Dumps Source : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Test Code : A2010-578
Test denomination : Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma
Vendor denomination : IBM
: 120 real Questions

afraid of failing A2010-578 examination!
word of mouth is a totally robust route of advertising for a product. I say, whilst something is so desirable, why no longerdo some towering attribute publicity for it I would really dote to unfold the phrase about this one of a ilk and truly high-quality killexams.com which helped me in acting outstandingly properly in my A2010-578 examination and exceeding All expectancies. i would speak that this killexams.com is one of the maximum admirable on line coaching ventures ive ever stumble upon and it merits quite a few recognition.


A2010-578 actual test questions and solutions!
I got a excellent give up result with this bundle. Extremely respectable fine, questions are correct and that i got most of them on theexamination. After ive handed it, I recommended killexams.com to my colleagues, and actually each person passed their exams, too (a number of them took Cisco assessments, others did Microsoft, VMware, and lots of others). I acquire no longer heard a lousy assessment of killexams.com, so this necessity to live the remarkable IT schooling you may presently ascertain online.


Where can I find A2010-578 Latest dumps questions?
It clarified the subjects in a rearranged way. In the undoubted examination, I scored a 81% with out plenty hassle, finishing the A2010-578 examination in seventy five minutes I additionally read a incredible deal of captivating books and it served to pass well. My success inside the examination become the determination of the killexams.Com dumps. I must with out an dreadful lot of a stretch give up its decently prepared purport inner 2 week time. Lots obliged to you.


need real exam questions of A2010-578 exam? download here.
Every topic and vicinity, each situation, killexams.com A2010-578 substances acquire been wonderful help for me while getting ready for this examination and in reality doing it! I was worried, however going lower back to this A2010-578 and wondering that I understand the all thing due to the fact the A2010-578 examination changed into very smooth after the killexams.com stuff, I got an awesome result. Now, doing the next degree of IBM certifications.


genuinely first-firstexcellent enjoy!
After trying several books, I was quite disappointed not getting the right materials. I was looking for a guideline for exam A2010-578 with simple language and well-organized content. killexams.com fulfilled my need, as it explained the complicated topics in the simplest way. In the real exam I got 89%, which was beyond my expectation. Thank you killexams.com, for your much guide-line!


Take a ingenious flux to skip A2010-578
Yes, the question pecuniary institution could live very useful and i insinuate it to All people who wants to choose those checks. Congrats on a process nicely notion out and completed. I cleared my A2010-578 tests.


updated and real examination pecuniary institution today's A2010-578.
I dont experience by myself a mid tests any longer in light of the fact that ive a elegant examine colleague as this killexams.Com dumps. Im quite appreciative to the educators right right here for being so extraordinary and properly disposed and assisting me in clearing my distinctly exam A2010-578. I solved All questions in exam. This equal course turned into given to me amid my exams and it didnt develop a dissimilarity whether or not or no longer it acquire become day or night, All my inquiries acquire been spoke back.


worked difficult on A2010-578 books, however the entire component acquire become on this test manual.
The acquire a gape at material of A2010-578 exam is printed well for bag prepared internal a brief age of time. killexams.com Questions & answers made me score 88% in the wake of answering All questions ninety mins of time. The examinationpaper A2010-578 has numerous celebrate substances in commercial enterprise zone. but it got to live extraordinarily tough for me to select the exceptional one. live that as it can after my brother asked that I used killexams.com Questions & solutions, I didnt acquire a gape at for other books. an dreadful lot obliged for helping me.


it's far exquisite to acquire A2010-578 dumps.
killexams.com has top products for college students because those are designed for those students whore interested by the education of A2010-578 certification. It become brilliant selection due to the fact A2010-578 exam engine has top notch celebrate contents which can live smooth to understand in short time period. im thankful to the grotesque group because this helped me in my career improvement. It helped me to understand the route to solution All famous inquiries to bag most rankings. It was brilliant determination that made me fan of killexams. ive decided to compass back returned one greater time.


Belive me or now not! This aid latest A2010-578 questions is actual.
I got this p.c. and exceeded the A2010-578 examination with 97% marks after 10 days. Im extraordinarily fulfilled via the result. There can live notable stuff for associate stage confirmations, yet regarding the professional stage, I suppose this is the main sturdy scheme of action for fine stuff, specifically with the exam simulator that offers you a risk to exercise with the gape and experience of a undoubted exam. this is a totally sizeable brain sell off, actual gape at manual. that is elusive for reducing edge exams.


Unquestionably it is hard assignment to pick dependable certification questions/answers assets regarding review, reputation and validity since individuals bag sham because of picking incorrectly benefit. Killexams.com ensure to serve its customers best to its assets concerning exam dumps update and validity. The vast majority of other's sham report dissension customers compass to us for the brain dumps and pass their exams joyfully and effortlessly. They never trade off on their review, reputation and attribute on the grounds that killexams review, killexams reputation and killexams customer certitude is imperative to us. Uniquely they deal with killexams.com review, killexams.com reputation, killexams.com sham report objection, killexams.com trust, killexams.com validity, killexams.com report and killexams.com scam. On the off haphazard that you behold any fake report posted by their rivals with the denomination killexams sham report grievance web, killexams.com sham report, killexams.com scam, killexams.com protest or something dote this, simply bethink there are constantly dreadful individuals harming reputation of respectable administrations because of their advantages. There are a huge number of fulfilled clients that pass their exams utilizing killexams.com brain dumps, killexams PDF questions, killexams hone questions, killexams exam simulator. Visit Killexams.com, their specimen questions and test brain dumps, their exam simulator and you will realize that killexams.com is the best brain dumps site.


Vk Profile
Vk Details
Tumbler
linkedin
Killexams Reddit
digg
Slashdot
Facebook
Twitter
dzone
Instagram
Google Album
Google About me
Youtube



C2180-278 questions answers | 9A0-802 study guide | UM0-411 exam prep | CCA-505 study guide | 000-733 dumps questions | C9560-023 free pdf download | HP5-K01D mock exam | LOT-956 exercise test | 000-388 exercise exam | 1Z0-533 test prep | 000-N05 sample test | PGCES-02 exam questions | EX0-112 free pdf | 70-740 cheat sheets | BH0-011 exercise questions | HP0-S17 questions and answers | CAT-220 dumps | 000-215 real questions | HP0-891 brain dumps | 412-79 examcollection |


A2010-578 exam questions | A2010-578 free pdf | A2010-578 pdf download | A2010-578 test questions | A2010-578 real questions | A2010-578 practice questions

Memorize these A2010-578 dumps and register for the test
killexams.com give most recent and updated exercise Test with Actual Exam Questions and Answers for fresh syllabus of IBM A2010-578 Exam. exercise their real Questions and Answers to help your erudition and pass your exam with towering Marks. They guarantee your success in the Test Center, covering every one of the points of exam and construct your erudition of the A2010-578 exam. Pass beyond any doubt with their actual questions.

We are excited with their supporting people pass the A2010-578 exam in their first attempt. Their prosperity quotes within the preceding 2 years had been utterly glorious, as a consequence of their cheerful shoppers presently able to impel their professions within the speedy tune. killexams.com is the principle muster amongst IT specialists, notably people who hoping to scale the chain of command stages speedier in their respective associations. killexams.com Discount Coupons and Promo Codes are as below; WC2017 : 60% Discount Coupon for All tests on web site PROF17 : 10% Discount Coupon for Orders over $69 DEAL17 : 15% Discount Coupon for Orders additional than $99 SEPSPECIAL : 10% Special Discount Coupon for All Orders You ought to bag the foremost updated IBM A2010-578 Braindumps with the proper answers, that are ready by killexams.com professionals, permitting the candidates to understand information regarding their A2010-578 exam course within the most, you will not realize A2010-578 product of such attribute anyplace within the market. Their IBM A2010-578 brain Dumps are given to candidates to bag 100% in their test. Their IBM A2010-578 exam dumps are latest within the market, providing you with an occasion to organize for your A2010-578 exam within the right means.

On the off haphazard that you are scanning for A2010-578 exercise Test containing real Test Questions, you're at precise locale. killexams.com acquire accumulated database of inquiries from Actual Exams keeping up at the top of the priority list the halt objective to empower you to devise and pass your exam on the essential endeavor. All instructing materials at the site are Up To Date and verified by mode for their masters.

killexams.com give latest and updated Pass4sure exercise Test with Actual Exam Questions and Answers for fresh syllabus of IBM A2010-578 Exam. exercise their real Questions and Answers to help your insight and pass your exam with towering Marks. They ensure your prosperity inside the Test Center, securing each one of the subjects of exam and enhance your erudition of the A2010-578 exam. ebb with no skepticism with their real issues.

Our A2010-578 Exam PDF consolidates Complete Pool of Questions and Answers and Dumps verified and certified together with references and clarifications (inmaterial). Their target to collect the Questions and Answers isn't basically to pass the exam at first endeavor yet Really help Your erudition roughly the A2010-578 exam references.

A2010-578 exam Questions and Answers are Printable in towering attribute Study steer that you can download for your Computer or some extraordinary machine and commence putting in your A2010-578 exam. Print Complete A2010-578 Study Guide, pass on with you while you are at Vacations or Traveling and savor your Exam Prep. You can bag to updated A2010-578 Exam out of your online record at whatever point.

killexams.com Huge Discount Coupons and Promo Codes are as under;
WC2017: 60% Discount Coupon for All exams on website
PROF17: 10% Discount Coupon for Orders greater than $69
DEAL17: 15% Discount Coupon for Orders greater than $99
OCTSPECIAL: 10% Special Discount Coupon for All Orders


Download your Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma Study steer specifically after purchasing and Start Preparing Your Exam Prep right Now!

A2010-578 Practice Test | A2010-578 examcollection | A2010-578 VCE | A2010-578 study guide | A2010-578 practice exam | A2010-578 cram


Killexams 920-138 free pdf | Killexams 1Z0-510 free pdf download | Killexams 2V0-731 bootcamp | Killexams C2140-819 free pdf | Killexams MA0-100 study guide | Killexams HP0-J53 exam prep | Killexams E20-533 cheat sheets | Killexams 310-043 test prep | Killexams 102-400 brain dumps | Killexams ST0-025 real questions | Killexams HP2-Z07 dumps questions | Killexams P2020-007 dump | Killexams CLOUDF cram | Killexams JN0-680 free pdf | Killexams 1Z0-418 dumps | Killexams 650-377 braindumps | Killexams HPE6-A42 mock exam | Killexams JN0-101 exercise Test | Killexams 9A0-313 test prep | Killexams 000-771 test questions |


killexams.com huge List of Exam Braindumps

View Complete list of Killexams.com Brain dumps


Killexams 650-297 real questions | Killexams BCP-420 braindumps | Killexams 000-578 bootcamp | Killexams 000-080 study guide | Killexams DC0-200 real questions | Killexams 3303 real questions | Killexams 9L0-622 questions and answers | Killexams 0B0-410 pdf download | Killexams 000-970 exercise exam | Killexams 9A0-054 mock exam | Killexams 70-698 exercise questions | Killexams 000-X01 free pdf | Killexams 500-005 exercise test | Killexams ST0-029 exercise questions | Killexams 9L0-509 examcollection | Killexams COG-142 free pdf | Killexams HP0-058 questions and answers | Killexams 1T6-510 exercise test | Killexams VCS-275 VCE | Killexams HP0-M45 cheat sheets |


Assess: Fundamentals of Applying Tivoli Service Availability/Performance Ma

Pass 4 certain A2010-578 dumps | Killexams.com A2010-578 real questions | http://morganstudioonline.com/

Mastercard (MA) Q1 2017 Results - Earnings muster Transcript | killexams.com real questions and Pass4sure dumps

No result found, try fresh keyword!Mastercard, Inc. (NYSE:MA) Q1 2017 Earnings ... up to domestic assessment, and that was a 3-ppt impact. And the other impact, which was furthermore a 3-ppt impact, was where they actually provided in a custome...

Resilience and efficiency in transportation networks | killexams.com real questions and Pass4sure dumps

Abstract

Urban transportation systems are vulnerable to congestion, accidents, weather, special events, and other costly delays. Whereas typical policy responses prioritize reduction of delays under proper conditions to help the efficiency of urban road systems, analytic champion for investments that help resilience (defined as system recovery from additional disruptions) is silent scarce. In this effort, they represent paved roads as a transportation network by mapping intersections to nodes and road segments between the intersections to links. They built road networks for 40 of the urban areas defined by the U.S. Census Bureau. They developed and calibrated a model to evaluate traffic delays using link loads. The loads may live regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the medium annual retard per peak-period auto commuter, and modeled results were create to live close to observed data, with the notable exception of fresh York City. Resilience was estimated as the change in efficiency resulting from roadway disruptions and was create to vary between cities, with increased delays due to a 5% random loss of road linkages ranging from 9.5% in Los Angeles to 56.0% in San Francisco. The results demonstrate that many urban road systems that operate inefficiently under proper conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should live considered explicitly in roadway project selection and justify investment opportunities related to cataclysm and other disruptions.

INTRODUCTION

Existing roadway design standards emphasize the efficient movement of vehicles through a transportation network (1–4). Efficiency in this context may embrace identification of the shortest or fastest route (1, 5–7), or the route that minimizes congestion (8). It is the primary criterion on which road networks are modeled and design alternatives are considered (6, 7, 9, 10). The Texas A&M Transportation Institute defines and reports traffic retard in urban areas as the annual retard per auto commuter (11). Other studies define efficiency as retard for the individual driver in terms of time spent poignant or stopped (7), or weigh in travel time between All origin-destination pairs in the network (9). However, as the experience of any motorist in large American cities can attest, conditions beyond the scope of the roadway design, including congestion, accidents, defective weather, construction, and special events (for example, a marathon race), can intuition costly delays and frustrating inefficiencies that result in fuel waste, infrastructure deterioration, and increased pollution (12, 13). Evaluating road networks based only on efficiency under proper operating conditions results in microscopic to no information about how the system performs under suboptimal or disrupted conditions.

Infrastructure systems that exhibit adaptive response to stress are typically characterized as resilient (14–21). Given the essential role of transportation in emergency response, provision of essential services, and economic well-being, the resilience of roadway networks has received increasing policy attention. Nonetheless, scholars acquire yet to converge on a shared understanding of resilience suitable to steer design, operation, and reconstruction of roadway networks. Although resilience in infrastructure systems is characterized as a multidimensional concept (22, 23), in many engineering and civil infrastructure implementations, resilience is defined as the faculty of a system to prepare for, absorb, regain from, and adapt to disturbances (16). Specific to transportation, resilience has been defined as “the faculty of the system to maintain its demonstrated flat of service or to restore itself to that flat of service in a specified timeframe” (24). Others relate transportation resilience as simply the faculty of a system to minimize operational loss (25) or expend the term synonymously with robustness, redundancy, reliability, or vulnerability (26–28).

Current efforts in transportation resilience research acquire focused on framework development and quantification methods. These efforts embrace the specification of resilience indicators, such as total traffic retard (24), economic loss (29), post-disaster maximum flux (30), and autonomous system components (31). Practical concerns with this ilk of resilience evaluation are that it relies on uncertain performance data and often omits indicators that are unquantifiable (19). Other resilience approaches apply traffic network modeling to identify locations for censorious buildings (for example, hospitals and fire stations) (32), minimize trip distance for individual passengers (33), and minimize travel time across the system (12). One drawback of existing network resilience methods is that they are data-intensive, often requiring limited information about resources for unusual road system repair (26, 28) or network deportment following a disruptive event (34). Moreover, existing resilience quantification approaches necessity calibration and testing across a orbit of transportation systems. Because many disruptive events, and their associated consequences, are difficult to predict, resilient road systems must live characterized and evaluated by the capacity to adapt to a variety of different stress scenarios. Partly because of these obstacles, joint consideration of efficiency and resilience has yet to live implemented for transportation networks.

Here, they study the interconnections between resilience and efficiency (20) among road transportation networks in 40 major U.S. cities. They develop an urban roadway efficiency model, calibrate it on the basis of the observed data (11) of annual retard per peak-period auto commuter, and apply the model to compute efficiency in 40 cities. Then, they model traffic response to random roadway disruptions and recalculate expected delays to determine the sensitivity of each city to loss of roadway linkages. The results may expose famous considerations for assessing proposals for improvement of roadway infrastructure that maintain efficiency under stress conditions.

METHODS

The Methods section appears here to help clarify the subsequent sections. To develop the urban roadway efficiency model, they defined the urban belt boundaries, constructed the road networks, and evaluated the population density within cities using the Census Bureau data sets (35, 36) and OpenStreetMap (OSM) data sets (37). They relied on these data to assess commuter patterns, which they used to measure efficiency and resilience of road networks.

Alternative approaches to transportation acquire been offered and embrace those based on percolation theory and cascading failures (38–40), human mobility pattern studies (41–43), queueing (44, 45), and the expend of historical data to forecast traffic. They review these approaches in the Supplementary Materials and note that the main profit of their model is that it relies solely on readily available public data, rather than on particular data sets that may or may not live practical to obtain for any particular region. The model’s algorithmic simplicity allows us to deem spatial topologies of cities in towering resolution including tens of thousands of nodes and links. They did not create a more accurate transportation model than the existing ones, but they were able to obtain measurable characteristics of transportation systems (average delays) using their model.

Geospatial boundaries and population density

To define geospatial boundaries for the transportation infrastructure networks, they used the U.S. Census Bureau geospatial data set (35) for urban areas—densely developed residential, commercial, and other nonresidential areas (46). They approximated the exact urban belt polygon with a simplified manually drawn one (Fig. 1A) and included All roadways within 40 km (25 miles) of it in the network. For each of the links, they calculated its length on the basis of the polyline defining the link and assigned a number of lanes m and the FFSs (see the Supplementary Materials).

Fig. 1 Definition of urban areas and assignment of nodes’ population.

(A) Boston, MA-NH-RI urban belt as defined by the U.S. Census Bureau shapefiles (gray background). To simplify the model and the algorithms calculating the distance from network nodes to the city boundary, they approximate each of the urban areas shapefiles with a indelicate manually drawn polygon (pink outline). (B) Assignment of the number of people departing from each of the network nodes. Population distribution (color polygons; red corresponds to higher population density), Voronoi polygons (black outline), and network nodes (dots) in Downtown Boston.

We next estimated population in vicinity of each intersection i using the Census Tract data (36). To this end, they split the map into Voronoi cells centered at intersections and then evaluated the population of each cell Ni asEmbedded Image

Embedded Image

(1)

Above, Nt is the population of Census Tract t, and Pi and Pt are the polygons of the cell and the tract, respectively (Fig. 1B and table S2).

Transportation model

We built on the gravity model to generate commuting patterns. The gravity model (47) is a classical model for trip distribution assignment and is extensively adopted in most metropolitan planning and statewide travel demand models in the United States (48–51). Other trip distribution models include, for example, destination selection models (52, 53). However, these models are not as widely used in large scale, because the circumstantial data required by these models are frequently unavailable (48).

We assumed that (i) the flux of commuters from root region o to destination region d is proportional to the population at the destination Nd and that (ii) the flux of commuters depends on the distance xod between the root and destination and is given by a distance factor, P(xod). Using these assumptions, they assessed the fraction of individuals commuting from region o to destination region d, fod, asEmbedded Image

Embedded Image

(2)

Then, the commuter flux from root region o to destination region d isEmbedded Image

Embedded Image

(3)

Although individual driving habits may vary (54), they assumed that All drivers tended to optimize their commute paths such that their travel time was minimized. This assumption allowed us to compute commute paths for every origin-destination pair using inferred FFSs. To compute commuter flows between All pairs of intersections, they estimated distances xod as the distance of the shortest time path from o to d. Furthermore, in set of the distance factor P(xod), they used the distribution of trip lengths from the U.S. Federal Highway Administration National Household Travel Survey (55, 56), which they approximated with the exponential office (Fig. 2A and table S3).

Fig. 2 Model details.

(A) Distance factor P(xod) (Eq. 2) of trips given the distance between nodes (solid line) and the statistical data (bars). (B) Dependency of precipitate on density for V = 100 km/hour.

Next, they defined the commuter load on each road segment asEmbedded Image

Embedded Image

(4)where θod(ij) is a binary variable equal to 0 when the link ij is not on the shortest path connecting nodes o and d, and 1 otherwise. Note that in Eq. 4, they only considered origins that were not farther than 30 km from the urban belt boundary polygon. The nodes farther than 30 km from the boundary were only used as destinations to evaluate the fraction of commuters not going toward the urban belt (Eq. 2).

Because most commuters travel during peak periods, commuter loads Lij can live regarded as traffic-based centrality measures estimating the number of individuals using corresponding road segments. Then, the cumulative time lost by All commuters isEmbedded Image

Embedded Image

(5)where Vij and vij are, respectively, the FFS and the actual traffic precipitate along the ij road segment, lij is its length, l0 is the length correction due to traffic signals, and β is the proportionality coefficient selfsame for All urban areas. The summation in Eq. 5 includes only links, whose origins and destinations are within the boundary polygon. A similar equation was obtained for the poignant retard in the study of Jiang and Adeli (45), where the authors looked at the retard induced from road repairs.

The actual traffic precipitate vij depends on many factors including the precipitate limit, the number of drivers on the road, and road conditions. Although there exist a number of approaches to evaluate actual traffic precipitate (57, 58), they chose to expend the Daganzo model (59) to derive the traffic speed, as shown in the Supplementary MaterialsEmbedded Image

Embedded Image

(6)where vmin is the minimum precipitate in the traffic, vveh is the correction for the finite size of the car, and α is the proportionality coefficient (Fig. 2B).

Efficiency and resilience metrics

We measured efficiency as the medium annual retard per peak-period auto commuter. In practice, lower retard means higher efficiency. There are multiple ways to map from delays to efficiency, such as taking the inverse values of delays, taking negative values of delays, etc. To avoid ambiguity and facilitate the interpretation of results, they used the delays themselves to quantify the transportation efficiency of urban areas.

We operationalized resilience through the change in traffic delays relative to stress, which is modeled as loss or impairment of roadway linkages. Looking at resilience from the network science perspective, they focused on topological features of cities, rather than on recovery resources available. Sterbenz et al. (60) evaluated a network’s resilience as a orbit of operational conditions for which it stays in the acceptable service region and highlighted that remediation mechanisms drive the operational state toward improvement. They are studying how availability of alternate routes helps remediate the consequences of the initial disruption to the network. In the traffic context, the immediate repercussion of a given physical disruption (and the time for it to unfold) in terms of closing lanes or reducing precipitate limits on affected roads will not vary much from network to network, although the number and ilk of these disruptions will. Likewise, the precipitate of restoring plenary functionality (through action in the physical domain) is not so much relative on the road network as it is on the nature of the disruption (snow versus earthquake versus flood) and the resources that the city allocates to such repair. The flat of functionality that these repairs achieve ought to live the plenary predisruption functionality, that is, eventually All roads can live fully cleared or restored. However, the immediate loss of office for a given traffic flux can very quickly live partially recovered after a disruption by action in the information domain, namely, rerouting of traffic. From the fresh uniform state at that flat of functionality, plenary functionality is gradually restored. Thus, their model proxies for resilience and is calibrated against the data that proxy for efficiency. At the selfsame time, they note that to fully capture resilience characteristics of a transportation system, it is required to resolve recovery resources available and the effectiveness of coordination between the material authorities. Lower additional retard corresponds to higher resilience, but using the selfsame reasoning that they had for efficiency, they quantified resilience through additional delays.

RESULTS Efficiency

Together, their traffic model has three parameters (proportionality coefficient α, minimum precipitate vmin, and finite vehicle size correction vveh) and is summarized in Eqs. 5 and 6. Given parameter values of the model, one can evaluate the total retard incurred by All commuters in any given suburban belt or, equivalently, the medium retard per commuter. They choose vveh = 9 km/hour and vmin = 5 km/hour and calibrate the model to determine the value of α to match the real data on the annual medium retard per peak-period auto commuter provided by the Urban Mobility Scorecard (11).

We divide the 40 urban areas into two equally sized groups for model calibration and validation, respectively. They acquire create that for the 20 urban areas used for calibration, the R-squared coefficient took values in the orbit (−0.01 to 0.83) (Fig. 3 and Supplementary Materials). This allows us to set model parameters α and β (see Methods) as follows: α = 4.30 × 104 hour−1 and β = 10.59. These values correspond to the Pearson coefficient of 0.91 (P = 2.17 × 10−8).

Fig. 3 Modeled and observed delays in 40 urban areas.

Pearson correlation coefficients and P values between observed and modeled delays are (0.91, 2.17 × 10−8) for the 20 cities used to calibrate the model and (0.63, 3.00 × 10−3) for the 20 cities used to validate the model. Observed delays were taken from the Texas A&M Transportation Institute Urban Mobility Scorecard (11).

To validate the model, they evaluate travel delays in 20 different urban areas. As seen from Fig. 3, the estimated travel delays are significantly correlated (R = 0.63, P = 3.00 × 10−3) with actual retard times (11), validating the transportation model. design 4 is a Google Maps representation of real and modeled results for Los Angeles and San Francisco. Road conditions under real, medium traffic patterns at 8 a.m. provided by Google Maps are in Fig. 4 (A and D). Modeled conditions are given for comparison in Fig. 4 (B and E). Finally, Fig. 4 (C and F) shows the new, modeled traffic patterns that result from redistribution of travel in response to a disruption of 5% of the links.

Fig. 4 Traffic distributions.

Typical congestion at 8 a.m. for Los Angeles (top) and San Francisco (bottom) as given by Google Maps (A and D), modeled with no disruptions (B and E), and modeled with a 5% link disruption (C and F). Notably, in Los Angeles, the disruption results in traffic redistribution to smaller roads, whereas in San Francisco, it results in increased congestion along the major highways.

Resilience

Our approach to model stress is inspired by percolation theory. For every independent simulation of stress, they select a finite fraction of affected road segments r at random, with the probability of failure proportional to segment length. They collect statistics for 20 realizations of the percolation. On failed segments, free-flow speeds (FFSs) are reduced to 1 km/hour (representing near-total loss), and loads L and traffic delays are then recalculated using the updated FFSs. Low-stress scenarios (r < 0.1) might live caused by accidents or construction. Larger disruptions might occur during power failures that disrupt traffic signals or severe flooding that makes many roadways nearly impassable. Finally, widespread stress might live caused by snow, ice, or dust storms that affect nearly the entire roadway system. design 5 displays the analysis of retard times in six representative urban areas for the plenary spectrum of adverse event severities, r ⋲ [0; 1]. In addition, fig. S5 shows the results for All urban areas. Some routes within a unique urban belt experience longer delays than others. The inset of Fig. 5 shows the retard distribution for both Los Angeles, which is narrowly clustered, and Boston, where greater variability between roadways is evident. Traffic retard times grow rapidly as r increases and compass saturation (all routes poignant at 1 km/hour) as r approaches 1. They determine the most resilient urban transportation network to live Salt Lake City, UT, whereas the least resilient among the 40 metropolitans is shown to live Washington, DC.

Fig. 5 Dependency of the additional retard on the severity of the links disruption for six representative urban areas.

Error bars parade weigh in values ± SD. The inset shows distribution densities for two selected urban areas for 1000 realizations of 5% disruption. Note that San Francisco’s unique topology makes it susceptible to failures of a diminutive number of discrete roadways, and this produces an anomalous repercussion at 5 to 15% disruption.

Figure 6 shows both the efficiency (in blue) and resilience response (additional delays due to 5% link disruption, in orange) for the 40 urban areas modeled. Some cities with towering efficiency under proper operating conditions (that is, low delays) nevertheless exhibit low resilience (that is, a keen extend in traffic delays) under stress. Virginia Beach, VA; Providence, RI; and Jacksonville, FL All descend into this category of urban areas in which traffic operates well under ordinary circumstances but rapidly become snarled under mild stress. On the other hand, Los Angeles is notorious for traffic delays under All conditions—yet minor stress levels result in microscopic degradation of efficiency. By contrast, proper traffic delays in San Francisco are comparable to Los Angeles, but mild stress in San Francisco results in large increases in additional delays. These examples argue that resilience (that is, additional retard response to stress) is independent of proper operating efficiency.

Fig. 6 Comparison of resilience and efficiency metrics.

Annual repercussion of 5% disruption (additional delay) has a low correlation with proper annual retard per peak-period auto commuter (delay). Pearson R = 0.49, P = 1.18 × 10−3.

DISCUSSION

The disturbances affecting the road infrastructure are often complex, and their repercussion on the structure and office of roadway systems may live unknown (28, 31). These disturbances might live natural and irregular, such as distributed road closures caused by an earthquake or homogeneous vehicle slowing down because of a snowstorm. The disturbances might furthermore live anthropogenic and intentional, such as a street objective or marathon race. Whatever the disturbance, the results of this analysis allow several meaningful inferences to live made that may acquire famous implications for highway transportation policy. The first is that resilience and efficiency represent different aspects related to the nature of transportation systems; they are not correlated and should live considered jointly as complementary characteristics of roadway networks.

Second, there are characteristic differences in the resilience of different urban areas, and these differences are persistent at mild, medium, or widespread levels of stress (Fig. 5). Except for San Francisco, CA, which is the most breakable of All cities represented in Fig. 5 at stress levels r < 20% but then surpassed by Boston, MA and Washington, DC, the rank ordering of urban belt resilience is insensitive to stress levels. That is, cities that exhibit relatively low resilience under mild stress are the selfsame cities that exhibit low levels of resilience (relative to peers) under widespread roadway impairment. This suggests that the characteristics that impart resilience (such as availability or alternate routes through redundancy of links) are protective against both the intermittent outages caused by occasional car crashes and those caused by snow and ice storms. For cities without resilience, a widespread hazard such as snow may lead to a cascade of conditions (for example, crashes) that rapidly deteriorate into gridlock. This was exactly the case for Washington, DC 20 January 2016 under only 2.5 × 10−2 m or 2.5 cm of snow (61), and for Atlanta, GA 2 years earlier, which experienced 5.1 × 10−2 m or 5.1 cm of snow in the middle of the day that resulted in traffic jams that took days to disentangle (62). Whereas Popular explanations of these traffic catastrophes focus on the failure of roadway managers to prepare plows and emergency response equipment, Fig. 5 suggests that cities with similar climates (Memphis, TN and Richmond, VA) are less likely to live affected, regardless of the availability of plow or sand trucks.

The third inference follows from Fig. 6, which suggests that urban areas that develop capital investments to reduce traffic delays under proper operating conditions may nevertheless live vulnerable to traffic delays under mild stress conditions. Because these stressors are inevitable, whether from crashes, construction, special events, extreme weather, tackle malfunctions, or even deliberate attack, investment strategies that prioritize reduction of proper operating delays may acquire the unintended consequence of exacerbating tail risks—that is, the risk of worse catastrophe under unlikely but practicable conditions.

Finally, the exceptional position of fresh York City in Fig. 3 calls attention to the fact that substitutes for roadway transportation are available in many cities and acquire an famous role to play in relieving traffic congestion. According to the Texas A&M Institute (63, 64), public transit reduces delays per peak-period auto commuter in the fresh York urban belt by 63 hours, in Chicago by 23 hours, and by less than 20 hours in other urban areas. Because their model considers only roadway transit, and fresh York City contains a myriad of nonroad-based options to avoid roadway congestion, it is unlikely that their model can provide informative results for the fresh York urban area.

Although interest has increased in policies that enhance roadway resilience, few analytic tools are available to steer fresh investments in achieving resilience goals. It is widely understood that roadway infrastructure is expensive, both in acquiring land for rights-of-way and in construction of improvements, and thus, decisions regarding alignment, crossing, and access made over a age of decades may acquire long-lasting consequences that are observable in traffic data today. Consequently, urban areas exhibit different unintentional traffic characteristics, including delays under proper and random stress conditions. Investments motivated exclusively by expected efficiencies under proper operating conditions are unreliable safeguards against loss of efficiency under stress conditions. Therefore, fresh analytic tools are required that allow designers to assess the adaptive capacity of roadway infrastructure and assess the potential of fresh investments to provide enhanced resilience. The adaptive network-based model described herein is one such approach.

SUPPLEMENTARY MATERIALS

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/12/e1701079/DC1

Alternative approaches to model transportation

Mapping from OSM Foundation shapefiles to network nodes and links

Population assignment algorithm

Distance factor of the likelihood of travel between nodes

Estimation of the traffic precipitate from the density of vehicles

Model calibration procedure

Sensitivity of the model to ramp speeds

Additional retard as a office of the severity of link disruption

table S1. Mapping original OSM types to network link types and assignment of the number of lanes.

table S2. The algorithm of the node population assignment.

table S3. Distance factor P(xod) of the likelihood of travel between nodes.

table S4. Model sensitivity to ramp precipitate coefficient.

fig. S1. Effects of the removal of nodes of degree 2.

fig. S2. Density-flow relationship in the Daganzo traffic model.

fig. S3. Model calibration.

fig. S4. Modeled delays for ramp precipitate coefficients of 1/3 and 1/2.

fig. S5. Dependency of the additional retard on the severity of the link disruption for All 40 urban areas.

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant expend is not for commercial handicap and provided the original drudgery is properly cited.

REFERENCES AND NOTES
  • K. Beverly, Efficient expend of Highway Capacity (FHWA-HOP-10-023, Texas Transportation Institute, 2010), p. 100.

  • T. Yamashita, K. Izumi, K. Kurumatani, Car navigation with route information sharing for improvement of traffic efficiency, in Proceedings of the 7th International IEEE Conference on bright Transportation Systems (IEEE, 2004), pp. 465–470.

  • K. Turnbull, Technical Activities Division, Transportation Research Board, National Academies of Sciences, Engineering, and Medicine, Transportation Resilience: Adaptation to Climate Change (Transportation Research Board, 2016).

  • C. S. Holling, Engineering Resilience versus Ecological Resilience, in Engineering within Ecological Constraints, P. C. Schulze, Ed. (The National Academies Press, 1996), pp. 31–44.

  • S. E. Flynn, S. P. Burke, censorious Transportation Infrastructure and Societal Resilience (Center for National Policy, 2012).

  • T. P. Seager, S. Spierre Clark, D. A. Eisenberg, J. E. Thomas, M. M. Hinrichs, R. Kofron, C. N. Jensen, L. R. McBurnett, M. Snell, D. L. Alderson, Redesigning resilient infrastructure research, in Resilience and Risk, I. Linkov, J. M. Palma-Oliveira, Eds. (Springer, 2017).

  • D. Freckleton, K. Heaslip, W. Louisell, J. Collura, Evaluation of transportation network resiliency with consideration for cataclysm magnitude, paper presented at the 91st Annual Meeting of the Transportation Research Board, Washington, DC, 2012).

  • S. B. Pant, Transportation network resiliency: A study of self-annealing, thesis, Utah state University (2012).

  • D. King, A. Shalaby, Performance metrics and analysis of transit network resilience in Toronto, paper presented at the 95th Annual Meeting of the Transportation Research Board, Washington, DC, 10 to 14 January 2016.

  • D. Li, Resilience of spatial networks, in complicated Systems and Networks, J. Lü, X. Yu, G. Chen, W. Yu, Eds. (Springer Berlin Heidelberg, 2016), pp. 79–106.

  • P. M. Murray-Tuite, A Comparison of Transportation Network Resilience under Simulated System Optimum and User Equilibrium Conditions, in Proceedings of the Winter Simulation Conference WSC 06, 3 to 6 December 2006, pp. 1398–1405.

  • A. Thiagarajan, L. Ravindranath, K. LaCurts, S. Madden, H. Balakrishnan, S. Toledo, J. Eriksson, VTrack: Accurate, energy-aware road traffic retard estimation using mobile phones, in Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems (ACM Press, 2009), p. 85.

  • E. Cho, S. A. Myers, J. Leskovec, Friendship and mobility: User movement in location-based companionable networks, in Proceedings of the 17th ACM SIGKDD International Conference on erudition Discovery and Data Mining (ACM Press, 2011), p. 1082.

  • D. Gross, J. F. Shortie, J. M. Thompson, C. M. Harris, Fundamentals of Queueing Theory (Wiley chain in Probability and Statistics, Wiley, Hoboken, NJ, ed. 4, 2008).

  • M. Sabyasachee, Y. Wang, X. Zhu, R. Moeckel, S. Mahapatra, Comparison between gravity and destination selection models for trip distribution in Maryland, paper presented at the TRB 92nd Annual Meeting of Compendium of Papers, 13 to 17 January 2013.

  • J. de Dios Ortúzar, L. G. Willumsen, Modelling Transport (John Wiley & Sons, ed. 4, 2011).

  • National Research Council (U.S.), Metropolitan Travel Forecasting: Current exercise and Future Direction (Transportation Research Board, 2007).

  • R. Van Haaren, Assessment of Electric Cars’ orbit Requirements and Usage Patterns based on Driving deportment recorded in the National Household Travel Survey of 2009 (Solar Journey, 2012), p. 25.

  • B. D. Greenshields, J. R. Bibbins, W. S. Channing, H. H. Miller, R. W. Crum, A study of traffic capacity, in Proceedings of the 14th Annual Meeting of the Highway Research Board, 6 to 7 December 1934, vol. 1.

  • Acknowledgments: They would dote to thank S. Buldyrev (Yeshiva University) and J. Palma-Oliveira (University of Lisbon) for their insightful comments. Funding: This study was supported by the U.S. Army Engineer Research and development headquarters and by the Defense Threat Reduction Agency, Basic Research Program (P. Tandy, program manager). A.A.G. was additionally supported by the Virginia Transportation Research Council and Virginia Department of Transportation. T.S. was supported by the NSF under concede no. 1441352. Author contributions: A.A.G., M.K., and I.L. conceived the model and designed the simulations. A.A.G. developed software and performed data retrieval and simulations. A.A.G. and M.K. analyzed results. I.L. provided senior guidance. A.A.G., M.K., J.M.K., T.S., and I.L. wrote the paper and contributed to the interpretation of the results. Competing interests: The authors declare that they acquire no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may live requested from the authors. Map data were copyrighted by OSM contributors and are available at www.openstreetmap.org.

    Effects and dose–response relationships of resistance training on physical performance in youth athletes: a systematic review and meta-analysis | killexams.com real questions and Pass4sure dumps

    Introduction

    Resistance training (RT) is a safe and effective route to help proxies of physical performance in well children and adolescents when appropriately prescribed and supervised.1–4 Several meta-analyses acquire shown that RT has the potential to help muscle energy and motor skills (eg, jump performance) in children and adolescents.1 ,5–7 However, youth athletes acquire different training capacities, adherence, physical demands of activities, physical conditions and injury risks compared with their non-athlete peers; so the generalisability of previous research on youth athletes is uncertain.8–10

    To the best of their knowledge, there is only one meta-analysis available that examined the effects of RT on one specific proxy of physical performance (ie, jump performance) and in one age group (ie, youth aged 13–18 years).11 It is reasonable to hypothesise that factors such as age, sex and sport may influence the effects of RT. Therefore, a systematic review with meta-analysis is needed to aggregate findings from the literature in terms of age, sex and sport-specific effects of RT on additional physical performance measures (eg, muscle strength, linear sprint performance, agility, sport-specific performance) in youth athletes.

    There is furthermore microscopic evidence-based information available regarding how to appropriately prescribe exercise to optimise training effects and avoid overprescription or underprescription of RT in youth athletes.12 The available guidelines for RT prescription are primarily based on expert opinion, and usually transfer study findings from the generic population (ie, well untrained children and adolescents) to youth athletes. This is famous because the optimal dose to elicit a desired consequence is likely to live different for trained and untrained youth.13

    Therefore, the objectives of this systematic literature review and meta-analysis were (1) to analyse the effectiveness of RT on proxies of physical performance in youth athletes by considering potential moderator variables, including age, sex, sport and the ilk of RT, and (2) to characterise dose–response relationships of RT parameters (eg, training period, training frequency) by quantitative analyses of intervention studies in youth athletes. They hypothesised that (1) RT would acquire a positive consequence on proxies of physical performance in youth athletes, and (2) the effects would live moderated by age, sex, sport and RT type.

    Methods

    Our meta-analysis was conducted in accordance with the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).14

    Literature search

    We performed a computerised systematic literature search in the databases PubMed and Web of Science.

    The following Boolean search syntax was used: (‘strength training’ OR ‘resistance training’ OR ‘weight training’ OR ‘power training’ OR ‘plyometric training’ OR ‘complex training’ OR ‘weight-bearing exercise’) AND (athlete OR elite OR trained OR sport) AND (children OR adolescent OR youth OR puberty OR kids OR teens OR girls OR boys). The search was limited to: full-text availability, publication dates: 01/01/1975 to 07/31/2015, ages: 6–13; 13–18 years, and languages: English, German. The reference list of each included study and material review article1 ,4–6 ,11 ,15–19 was screened for title to identify any additional suitable studies for inclusion in their review.

    Selection criteria

    Based on the defined inclusion and exclusion criteria (table 1), two independent reviewers (ML and OP) screened potentially material articles by analysing titles, abstracts and plenary texts of the respective articles to elucidate their eligibility. In case ML and OP did not compass an agreement concerning inclusion of an article, UG was contacted.

    Table 1

    Selection criteria

    Coding of studies

    Each study was coded for inescapable variables listed in table 2. Their analyses focused on different outcome categories. If studies reported multiple variables within one of these outcome categories, only one representative outcome variable was included in the analyses. The variable with the highest priority for each outcome is mentioned in table 2.

    If a study solely used other tests, they included those tests in their quantitative analyses that were most similar with respect to the ones described above in terms of their temporal/ spatial structure.

    Further, they coded RT according to the following training parameters: training period, training frequency, and training volume (ie, number of sets per exercise, number of repetitions per set), training intensity, temporal distribution of muscle action modes per repetition, and relaxation (ie, relaxation between sets and repetitions). Training parameters were categorised according to common classifications of RT protocols.21 If a study reported exercise progression over the training period, the weigh in number of sets per exercise, repetitions per sets, relaxation between sets and training intensity were computed.

    To obtain sufficient statistical power to compute dose–response relationships, they summarised RT types as conventional RT (ie, machine based, free weights, combined machine based and free weights, functional training) and plyometric training (ie, jumping). As it is not practicable to classify complicated training as either conventional RT nor plyometric training,22 they excluded these studies23–27 from dose–response analyses. Their dose–response analyses were computed independent of age, sex and sport.

    Assessment of risk of bias

    The Physiotherapy Evidence Database (PEDro) scale was used to quantify the risk of warp in eligible studies and to provide information on the generic methodological attribute of studies. The PEDro scale rates internal study validity and the presence of statistical replicable information on a scale from 0 (high risk of bias) to 10 (low risk of bias) with ≥6 representing a cut-off score for studies with low risk of bias.28

    Statistical analyses

    To determine the effectiveness of RT on proxies of physical performance and to establish dose–response relationships of RT in youth athletes, they computed between-subject standardised weigh in differences (SMD=(mean postvalue intervention group−mean postvalue control group)/pooled benchmark deviation). They adjusted the SMD for the respective sample size by using the term (1−(3/(4N-9))).29 Their meta-analysis on categoric variables was computed using Review Manager V.5.3.4 (Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2008). Included studies were weighted according to the magnitude of the respective SE using a random-effects model.

    At least two RT intervention groups had to live included to compute weighted weigh in SMDs, hereafter refered to as SMDwm, for each performance category.30 They used Review Manager for subgroup analyses: computing a weight for each subgroup, aggregating SMDwm values of specific subgroups, comparing subgroup consequence sizes with respect to differences in intervention effects across subgroups.31 To help readability, they reported positive SMDs if superiority of RT compared with active control was found. Heterogeneity was assessed using I² and χ2 statistics.

    Owing to a low number of studies in each physical performance outcome category that completely reported information on the applied RT parameters, metaregression was precluded.30 According to a scale for determining the magnitude of consequence sizes in energy training research for individuals who acquire been consistently training for 1–5 years,32 they interpreted SMDwm as: trifling (<0.35); diminutive (0.35–0.79); moderate (0.80–1.50); large (≥1.50). The flat of significance was set at p<0.05.

    Results Study characteristics

    A total of 576 potentially material studies were identified in the electronic database search (figure 1). Finally, 43 studies remained for the quantitative analyses. A total of 1558 youth athletes participated, and of these, 891 received RT in 62 RT intervention groups. The sample size of the RT intervention groups ranged from 5 to 54 participants (table 3).

    Table 3

    Included studies examining the effects of resistance training in youth athletes

    Figure 1

    Flow chart illustrating the different phases of the search and study selection.

    There were 13 studies (21 RT intervention groups) that included children, and 29 studies (36 RT intervention groups) that included adolescents. In terms of biological maturation, only 15 studies reported Tanner stages. Three (5 RT intervention groups) of those studies examined prepubertal and 12 (15 RT intervention groups) postpubertal/pubertal youth athletes. Thirty studies (44 RT intervention groups) included boys only, whereas 4 studies (4 RT intervention groups) included girls only.

    Youth athletes were recruited from team sports (soccer (20 studies; 34 RT intervention groups), basketball (9 studies; 11 RT intervention groups), baseball (3 studies; 5 RT intervention groups), handball (3 studies; 3 RT intervention groups), tennis (2 studies; 3 RT intervention groups), volleyball (1 study; 1 RT intervention group)), and strength-dominated sports (swimming (3 studies; 3 RT intervention groups), track and sphere (1 study, 1 RT intervention group)). No included study investigated youth athletes recruited from martial arts or technical/acrobatic sports.

    Regarding the ilk of RT, 4 studies performed RT using machines, 4 studies using free weights, 4 studies using both machines and free weights, 5 studies performed functional RT, 5 studies performed complicated training, and 19 studies applied plyometric training. Classification of studies was not always feasible due to missing information or group heterogeneity.

    The RT interventions lasted between 4 and 80 weeks, with training frequencies ranging from 1 to 3 sessions per week, 1–8 sets per exercise, 4–15 repetitions per set, and 20–220 s of relaxation between sets. Training intensity ranged from 35% to 88% of the 1 repetition maximum (RM). Training parameters (eg, temporal distribution of muscle action modes per repetition, and relaxation in-between repetitions) which acquire gained attention in the literature71 were not quantified due to insufficient data.

    A median PEDro score of 4 (95% CI 4 to 5) was detected and only 4 out of 43 studies reached the predetermined cut-off value of ≥6, which can live interpreted as an overall towering risk of warp of the included studies (table 3).

    Effectiveness of RT

    Table 4 shows the overall as well as age, sex, sport and training type-specific effects of RT on measures of muscle strength, perpendicular jump and linear sprint performance, agility and sport-specific performance.

    Table 4

    Overall as well as age, sex, sport and training type-specific effects of resistance training in youth athletes

    There were moderate effects of RT on measures of muscle energy (SMDwm=1.09; I²=81%; χ2=114.24; df=22; p<0.001; design 2) and perpendicular jump performance (SMDwm=0.80; I²=67%; χ2=137.47; df=46; p<0.001; design 3), while there were diminutive effects for linear sprint performance (SMDwm=0.58; I²=41%; χ2=55.74; df=33; p<0.01; design 4), agility (SMDwm=0.68; I²=50%; χ2=48.19; df=24; p<0.01; design 5) and sport-specific performance (SMDwm=0.75; I²=62%; χ2=67.81; df=26; p<0.001; design 6). By considering only the four studies with towering attribute (ie, low risk of bias), RT had moderate effects on measures of muscle energy (SMD=1.07; 1 study), perpendicular jump (SMDwm=0.89; 3 studies) and linear sprint performance (SMDwm=1.19; 2 studies); diminutive effects on agility (SMD=0.28; 1 study); and large effects on sport-specific performance (SMDwm=1.73; 2 studies).

    Figure 2

    Effects of resistance training (experimental) versus active control on measures of muscle energy (IV, inverse variance).

    Figure 3

    Effects of resistance training (experimental) versus active control on measures of perpendicular jump performance (IV, inverse variance).

    Figure 4

    Effects of resistance training (experimental) versus active control on measures of linear sprint performance (IV, inverse variance).

    Figure 5

    Effects of resistance training (experimental) versus active control on agility (IV, inverse variance).

    Figure 6

    Effects of resistance training (experimental) versus active control on proxies of sport-specific performance (IV, inverse variance).

    There was no statistically significant consequence of chronological and/or biological age on any proxy of physical performance. However, a current (p=0.05) towards larger RT effects were create for proxies of sport-specific performance in adolescents (SMDwm=1.03) compared with children (SMDwm=0.50; table 4). Subgroup analyses indicated that RT produced significantly larger effects (p<0.05) on proxies of sport-specific performance in girls (SMDwm=1.81) compared with boys (SMDwm=0.72; table 4). Given that most included studies (n=38) examined participants competing in team sports, their subgroup analyses regarding the moderator variable ‘sport’ is limited and did not parade any significant subgroup differences (table 4). Subgroup analyses demonstrated that different training types of RT produced significantly different gains in muscle energy (p<0.001), agility (p<0.05) and sport-specific performance (p<0.05). Free weight RT showed the largest effects on muscle energy and agility, while for sport-specific performance, complicated training produced the largest effects (table 4).

    Dose–response relationships of RT Training period

    There was a significant dissimilarity for the effects of conventional RT on measures of muscle energy (p<0.001), perpendicular jump height (p<0.05) and agility (p<0.001; design 7). The dose–response curves indicated that long lasting conventional RT (>23 training weeks) resulted in more pronounced improvements in measures of muscle energy (SMDwm=3.40) and agility (SMDwm=1.31), as compared with shorter training periods (<23 weeks). In terms of perpendicular jump height, a training age of 9–12 weeks appeared to live the most effective (SMDwm=1.20).

    Figure 7

    Dose–response relationships of the parameter ‘training period’ on measures of muscle strength, perpendicular jump and linear sprint performance, agility, and sport-specific performance. Each filled grey coterie illustrates between-subject SMD per unique study with active control. Filled black triangles represent weighted weigh in SMD of All studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised weigh in difference.

    Training frequency

    There were no significant differences between the observed training frequencies (ie, 1, 2, 3 times per week) for RT as well as plyometric training (figure 8).

    Figure 8

    Dose–response relationships of the parameter ‘training frequency’ on measures of muscle strength, perpendicular jump and linear sprint performance, agility, and sport-specific performance. Each filled grey coterie illustrates between-subject SMD per unique study with active control. Filled black triangles represent weighted weigh in SMD of All studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised weigh in difference.

    Training intensity

    There was a significant dissimilarity with respect to the effects of conventional RT on measures of muscle energy (p<0.01; design 9). High-intensity conventional RT (ie, 80–89% of 1 RM) resulted in more pronounced improvements in muscle energy (SMDwm=2.52) compared with lower training intensities (ie, 30–39%, 40–49%, 50–59%, 60–69%, 70–79% of the 1 RM).

    Figure 9

    Dose–response relationships of the parameter ‘training intensity’ on measures of muscle strength, perpendicular jump and linear sprint performance, agility, and sport-specific performance. Each filled grey coterie illustrates between-subject SMD per unique study with active control. Filled black triangles represent weighted weigh in SMD of All studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised weigh in difference; RM, repetition maximum.

    Training volume (number of sets per exercise)

    There was a significant dissimilarity with respect to the effects of conventional RT on muscle energy (p<0.01), and a current towards significance for measures of perpendicular jump performance (p=0.06; design 10). Five sets per exercise resulted in more pronounced improvements in muscle energy (SMDwm=2.76) compared with fewer sets. Three sets per exercise tended to live more effective in improving perpendicular jump performance (SMDwm=1.19), as compared with four or five sets per exercise.

    Figure 10

    Dose–response relationships of the parameter ‘sets per exercise’ on measures of muscle strength, perpendicular jump and linear sprint performance, agility, and sport-specific performance. Each filled grey coterie illustrates between-subject SMD per unique study with active control. Filled black triangles represent weighted weigh in SMD of All studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised weigh in difference.

    For plyometric training, there was a current towards larger training-related effects on measures of muscle energy (p=0.09), linear sprint performance (p=0.07), as well as sport-specific performance (p=0.05) depending on the number of sets per exercise. Four sets per exercise revealed the largest effects for measures of muscle energy (SMDwm=0.79) and sport-specific performance (SMDwm=1.84), while three or four sets loom to live most effective for improving linear sprint performance (SMDwm=0.95).

    Training volume (number of repetitions per set)

    There was a significant dissimilarity in terms of the effects of conventional RT on measures of muscle energy (p<0.05; design 11). Six to eight repetitions per set produced the largest effects on muscle energy (SMDwm=2.42). For plyometric training, there was a current towards significance for proxies of sport-specific performance (p=0.05). Six to 8 repetitions per set were less effective (SMDwm=0.15), while 3–5 and 9–12 repetitions per set produced similar effects (SMDwm=0.89 and 0.93).

    Figure 11

    Dose–response relationships of the parameter ‘repetitions per set’ on measures of muscle strength, perpendicular jump and linear sprint performance, agility, and sport-specific performance. Each filled grey coterie illustrates between-subject SMD per unique study with active control. Filled black triangles represent weighted weigh in SMD of All studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised weigh in difference.

    Rest between sets

    There was a significant dissimilarity for the effects of conventional RT on measures of muscle energy (p<0.05; design 12). Three to 4 min of relaxation between sets resulted in more pronounced improvements in measures of muscle energy (SMDwm=2.09), as compared with shorter durations of rest.

    Figure 12

    Dose–response relationships of the parameter ‘rest between sets’ on measures of muscle strength, perpendicular jump and linear sprint performance, agility, and sport-specific performance. Each filled grey coterie illustrates between-subject SMD per unique study with active control. Filled black triangles represent weighted weigh in SMD of All studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised weigh in difference.

    Discussion

    This systematic review with meta-analysis examined the generic effects as well as the age, sex, sport and training type-specific repercussion of RT on proxies of physical performance in well youthful athletes. In addition, dose–response relationships of RT parameters were independently computed. The main findings were: (1) RT has moderate effects on muscle energy as well as on perpendicular jump performance, and diminutive effects on linear sprint, agility and sport-specific performance in youthful athletes, (2) the effects of RT were moderated by the variables sex and RT type, (3) most effective conventional RT programmes to help measures of muscle energy in well youthful athletes comprised training periods of more than 23 weeks, 5 sets per exercise, 6–8 repetition per set, a training intensity of 80–89% of the 1 RM, and 3–4 min of relaxation between sets.

    Effects of RT on physical performance in youth athletes

    In general, RT is an effective route to help proxies of physical performance in youth athletes, and their findings champion recently published literature.4 ,17 ,72 ,73 They create that the main effects of RT on measures of muscle energy and perpendicular jump performance were moderate in magnitude, with diminutive effects for secondary outcomes, including linear sprint performance, agility and sport-specific performance (eg, throwing velocity). The lower RT effects on secondary outcomes might live explained by the complicated nature of these qualities, with various determinants contributing to the performance level. For instance, agility depends on perceptual factors and decision-making as well as on changes in direction of speed, which is again influenced by movement technique, leg muscle attribute and straight sprinting speed.74 Thus, muscle energy appears to live only one of several factors contributing to agility.

    We recommend the incorporation of RT as an famous piece of youth athletes’ regular training routine to enhance muscle energy and jump performance.

    How age, sex, sport and training ilk moderate RT effects Age-specific effects of RT in youth athletes

    Biological maturity is related to chronological age, and has a major repercussion on physical performance in youth athletes.75 However, unlike age, growth and maturation are not linear factors.76 ,77 There is often a discrepancy between chronological age and biological maturity among youth athletes.4 ,16 ,78

    We create no significant differences in consequence sizes for any proxy of physical performance between prepubertal and postpubertal athletes. Similarly, they did not find significant differences for the effects of RT on any physical performance measure with respect to the moderator variable ‘chronological age’ (table 4). Merely, a current (p=0.05) towards higher sport-specific performance gains following RT in adolescents, compared with children, was identified.

    Although a minimum age has been defined at which children are mentally and physically ready to comply with coaching instructions,4 their subgroup analyses regarding biological and chronological age insinuate that youth athletes may profit to the selfsame extent from RT, irrespective of age. However, it is famous to note that most studies did not report the biological maturity status of the participants. Therefore, more research is needed to elucidate biological age-specific RT effects on physical performance in youth athletes and to verify their preparatory findings.

    Sex-specific effects of RT in youth athletes

    Previous research on the effects of RT on proxies of physical performance in youth athletes has primarily focused on boys. However, findings from male youth athletes can only partially live transferred to female youth athletes because the physiology of boys and girls (eg, hormonal status during puberty) varies. They create that male and female youth athletes parade similar RT-related gains in muscle energy and perpendicular jump performance, but girls had significantly larger training-induced improvements in sport-specific performance (SMDwm=1.81) compared with boys (SMDwm=0.72). This suggests preparatory evidence that the RT trainability of female adolescent athletes may live at least similar or even higher compared with males. Given that girls’ and boys’ physiology changes differently with age and maturation,76 ,77 sex-specific effects of RT in youth athletes should live investigated with respect to biological maturity. Owing to an insufficient number of studies that examined female youth athletes and reported their biological maturity status, they were not able to embrace ‘biological maturity’ as a moderator variable in their subgroup analyses. They deem their sex-specific findings preparatory because these are based on five studies only investigating female youth athletes. More research is needed to elucidate sex-specific RT effects on physical performance in youth athletes and to verify their preparatory findings.

    Sport-specific effects of RT in youth athletes

    The effects of RT in elite adult athletes may live specifically moderated by the respective athlete profile of the sport performed.79 ,80 Whether this is furthermore the case in youth athletes remains unresolved. Given that most included studies (n=38) investigated youthful athletes competing in team sports, their analyses with respect to the moderator variable ‘sport’ was limited and did not expose any significant differences between sports disciplines (table 4). Therefore, further research has to live conducted to examine if youth athletes respond differently to RT programmes as per the sport practiced.

    Training type-specific effects of RT in youth athletes

    Various types of RT acquire been reported (eg, machine-based RT, free weight RT and functional RT). Each of these types has specific benefits and limitations.20 ,73 Machine-based RT may represent a safe environment for youthful athletes when supervision cannot live ensured, whereas supervised RT using free weights allows plenary orbit of motion that better mimics sports-specific movements.20 ,73 They create that RT programmes using free weights were most effective to enhance sinewy energy and agility. In addition, complicated training produced the largest consequence sizes if the goal was to help sport-specific performance. Therefore, the selection of RT types should live variable and based on the exercise goal (eg, enhancing muscle energy or sport-specific performance).

    Dose–response relationships of RT in youth athletes

    Planning and designing RT programmes is a complicated process that requires sophisticated manipulation of different training parameters. Owing to a necessity of evidence-based information on dose–response relationships following RT in youth athletes, it is quite common for established and effective RT protocols for well untrained children and adolescents to live transferred to youth athletes. However, this may bar to fully recruit the adaptative potential of youthful athletes because the optimal dose to elicit the desired consequence appears to live different in trained compared with untrained youth.13 Owing to the observed limitations regarding female youth athletes and biological maturation status in the present meta-analysis, the dose–response relationships of RT in youth athletes were determined irrespective of sex and maturity.

    In general, the specific configuration of RT parameters determines the underlying training stimulus and thus, the desired physiological adaptations. However, significant effects were predominantly identified for conventional RT parameters for measures of muscle strength. Therefore, it appears that gains in sinewy energy may live more sensitive to the applied training parameters of the conventional RT programmes, as compared with the secondary performance outcomes (eg, linear sprint performance, agility, sport-specific performance).

    Training period

    The effects of short-term (<24 weeks) RT peaked almost consistently with training periods of 9–12 weeks for both conventional RT and plyometric training. However, their subgroup analyses indicated significant differences only for conventional RT for measures of muscle energy and perpendicular jump performance. Nevertheless, with respect to energy gains, long-term (≥24 weeks) conventional RT was more effective in youth athletes (SMDwm=3.40), as compared with short-term conventional RT (SMDwm=0.61–1.24). Thus, it can live postulated that conventional RT programmes should live incorporated on a regular basis in long-term athlete development.66 Given that continuous performance improvements are difficult to achieve particularly over long time periods, properly varying RT programmes may avert training plateaus, maximise performance gains and reduce the likelihood of overtraining.

    Regular basketball exercise during a detraining/reduced training age was sufficient to maintain previously achieved sinewy power gains due to its predominantly power-type training drills.81 Therefore, it is reasonable to hypothesise that regular training can maintain RT-based gains in sinewy energy for several weeks if similar physical demands are addressed during regular training. Coaches may reduce the time spent on RT for several weeks without impairing previously achieved energy gains during competition periods when the training must emphasise motor skills and competition demands.

    Training frequency

    The angle of periodisation, projected exercise loads and the dose of additional physical training (ie, overall amount of physical stress) may influence training frequency.21 In order to avoid overtraining and achieve maximal benefits of RT, it is famous to allow the corpse sufficient time to regain from each RT session. However, if the relaxation between RT sessions is too long, adaptive processes from previous RT sessions may bag lost.

    Most studies performed RT two or three times per week (figure 8), and there was no significant dissimilarity between the observed training frequencies. To their knowledge, there is no study available that directly compared the effects of two RT sessions per week as opposed to three sessions for youth athletes. Although a reduced RT frequency of one session per week may live sufficient to maintain muscle energy gains following RT for several weeks,41 ,82 training twice per week might live preferred to achieve further gains in muscle energy in youth athletes.

    Training volume and training intensity

    Both volume and intensity acquire to live considered when prescribing RT to maximise physiological adaptations and minimise injury risk.4 Different configurations of training volume and intensity result in different forms of physiological stress, which in swirl induce different neural and sinewy adaptations.71

    Owing to the large methodological variety in dealing with training intensity during plyometric training, they were not able to consistently quantify the dose–response relationship for training intensity with respect to plyometric training.

    Conventional RT programmes using medium training intensities of 80–89% of the 1 RM were most beneficial in terms of improving muscle energy in youth athletes. These findings are in accordance with the position stand of the American College of Sports Medicine for energy training in adults.83 The largest consequence sizes for muscle energy gains in adults, trained individuals and athletes were achieved at 80–85% of the 1 RM.8 ,12 However, it should live renowned that the individual percentage of 1 RM is a stress rather than a strain factor. Several studies acquire indicated that a given number of repetitions cannot live associated with a specific percentage rate of the 1 RM.78 ,84 Thus, to individualise RT, future studies should focus on finding a valid strain-based mode to quantify RT intensity effectively.

    In terms of the number of sets per conventional RT exercise, their data parade similar consequence size magnitudes when comparing single-set (SMDwm=2.41) versus multiple-set conventional RT programmes (5 sets: SMDwm=2.76). The primary profit of a single-set conventional RT is time efficiency. Nevertheless, since their results for single-set conventional RT are based on two intervention groups from one study, this finding has to live interpreted with caution. Although there was no study that directly compared the effects of single-set versus multiple-set conventional RT in youth athletes, there is evidence from adult athletes that single-set conventional RT may live preempt during the initial angle of RT,85 whereas multiple-set conventional RT programmes should live used to promote further gains in muscle strength, especially in athletes.86 Therefore, multiple-set conventional RT may live necessary to elicit sufficient training stimuli during long-term youth athlete development.

    Regarding the applied plyometric training, 3 (for perpendicular jump) or 4 sets per exercise (for muscle strength, sport-specific performance) as well as 3–5 or 9–12 repetitions per set (for perpendicular jump, sport-specific performance) might live beneficial for youth athletes’ physical performance. However, the movement attribute of plyometric exercises is more famous than the total session volume.87 Therefore, they recommend the expend of thresholds for performance variables, such as ground contact time or performance indices, to determine individualised training volume.87

    Rest between sets

    The duration of relaxation between sets and repetitions depends on parameters dote training intensity and volume. The relaxation interval significantly affects the biochemical responses following RT.71 Owing to an insufficient number of studies that reported the duration of relaxation between repetitions, they focused on dose–response relationships for relaxation between sets. Long relaxation periods (ie, 3–4 min of relaxation between sets) were most effective for improving muscle energy following conventional RT in youth athletes. This is most likely because long relaxation periods allow athletes to withstand higher volumes and intensities during training.

    Limitations of this meta-analysis

    A major limitation is that they could not provide insights into the interactions between the reported training parameters. Their analyses are based on a variety of studies using different combinations of training parameters magnitudes (eg, training frequency, number of sets, intensity). It remains unclear if performance gains would silent live maximal if, according to the present dose–response relationships, the optimum of each parameter was implemented in RT programmes.81 Thus, further research is necessary to find an analytical mode to provide insights into the interactions between the investigated training parameters. The modelling of training variables might help to address this limitation. Holding a set of RT variables constant while changing the effects of one specific variable could determine the unique effects of each training variable.

    Further limitations of this systematic review and meta-analysis are the towering risk of warp of the included studies (only 4 out of 43 studies reached a PEDro score of ≥6), the considerable heterogeneity between studies (ie, I²=41–81%), and the uneven distribution of SMDs calculated for the respective training parameters. In addition, the scale for determining the magnitude of consequence sizes32 is not specific for RT research in children and adolescents. Another limitation is that almost All studies failed to report RT parameters which had got recent research attention (eg, temporal distribution of muscle action modes per repetition).71 Further, studies used traditional stress-based (ie, RM) instead of recent strain-based (eg, OMNI resistance exercise scale of perceived exertion88) methods to quantify RT intensity.89 They were not able to aggregate the effects of moderator variables, such as sex and maturation, for the dose–response relationships due to an insufficient number of studies that specifically addressed these issues.

    Summary

    RT was effective for improving proxies of physical performance in youth athletes. The magnitudes of RT effects were moderate in terms of measures of muscle energy and perpendicular jump performance, and diminutive with respect to measures of linear sprint, agility and sports-specific performance in youth athletes. Sex and RT ilk appeared to moderate these effects. However, most studies were at towering risk of warp and therefore, the results should live interpreted cautiously.

    A training age of more than 23 weeks, 5 sets per exercise, 6–8 repetitions per set, a training intensity of 80–89% of 1 RM, and 3–4 min relaxation between sets were most effective for conventional RT programmes to help muscle energy in youth athletes. However, these evidence-based findings should live adapted individually by considering individual abilities, skills and goals. Specifically, youth coaches should not expend towering RT intensities before the youth athlete developed technical skills to adequately accomplish the RT exercises.

    What is already known on this topic?
  • Resistance training is safe for children and adolescents if appropriately prescribed and supervised.

  • Several meta-analyses acquire already shown that resistance training has the potential to help muscle energy and motor skills (eg, jump performance) in healthy, untrained children and adolescents.

  • What this study adds
  • This is the first systematic review and meta-analysis to examine age, sex, sport and training type-specific effects of resistance training on physical performance measures in youth athletes.

  • The consequence of resistance training was moderated by sex and resistance training type. Girls had greater training-related sport-specific performance gains compared with boys, and resistance training programmes with free weights were most effective for increasing muscle strength.

  • Dose–response relationships for key training parameters argue that youth coaches should flat for resistance training programmes with fewer repetitions and higher intensities to help physical performance measures.

  • Acknowledgments

    The authors would dote to thank Dr Andrea Horn for her champion during the course of the research project.



    Direct Download of over 5500 Certification Exams

    3COM [8 Certification Exam(s) ]
    AccessData [1 Certification Exam(s) ]
    ACFE [1 Certification Exam(s) ]
    ACI [3 Certification Exam(s) ]
    Acme-Packet [1 Certification Exam(s) ]
    ACSM [4 Certification Exam(s) ]
    ACT [1 Certification Exam(s) ]
    Admission-Tests [13 Certification Exam(s) ]
    ADOBE [93 Certification Exam(s) ]
    AFP [1 Certification Exam(s) ]
    AICPA [2 Certification Exam(s) ]
    AIIM [1 Certification Exam(s) ]
    Alcatel-Lucent [13 Certification Exam(s) ]
    Alfresco [1 Certification Exam(s) ]
    Altiris [3 Certification Exam(s) ]
    Amazon [2 Certification Exam(s) ]
    American-College [2 Certification Exam(s) ]
    Android [4 Certification Exam(s) ]
    APA [1 Certification Exam(s) ]
    APC [2 Certification Exam(s) ]
    APICS [2 Certification Exam(s) ]
    Apple [69 Certification Exam(s) ]
    AppSense [1 Certification Exam(s) ]
    APTUSC [1 Certification Exam(s) ]
    Arizona-Education [1 Certification Exam(s) ]
    ARM [1 Certification Exam(s) ]
    Aruba [6 Certification Exam(s) ]
    ASIS [2 Certification Exam(s) ]
    ASQ [3 Certification Exam(s) ]
    ASTQB [8 Certification Exam(s) ]
    Autodesk [2 Certification Exam(s) ]
    Avaya [96 Certification Exam(s) ]
    AXELOS [1 Certification Exam(s) ]
    Axis [1 Certification Exam(s) ]
    Banking [1 Certification Exam(s) ]
    BEA [5 Certification Exam(s) ]
    BICSI [2 Certification Exam(s) ]
    BlackBerry [17 Certification Exam(s) ]
    BlueCoat [2 Certification Exam(s) ]
    Brocade [4 Certification Exam(s) ]
    Business-Objects [11 Certification Exam(s) ]
    Business-Tests [4 Certification Exam(s) ]
    CA-Technologies [21 Certification Exam(s) ]
    Certification-Board [10 Certification Exam(s) ]
    Certiport [3 Certification Exam(s) ]
    CheckPoint [41 Certification Exam(s) ]
    CIDQ [1 Certification Exam(s) ]
    CIPS [4 Certification Exam(s) ]
    Cisco [318 Certification Exam(s) ]
    Citrix [47 Certification Exam(s) ]
    CIW [18 Certification Exam(s) ]
    Cloudera [10 Certification Exam(s) ]
    Cognos [19 Certification Exam(s) ]
    College-Board [2 Certification Exam(s) ]
    CompTIA [76 Certification Exam(s) ]
    ComputerAssociates [6 Certification Exam(s) ]
    Consultant [2 Certification Exam(s) ]
    Counselor [4 Certification Exam(s) ]
    CPP-Institue [2 Certification Exam(s) ]
    CPP-Institute [1 Certification Exam(s) ]
    CSP [1 Certification Exam(s) ]
    CWNA [1 Certification Exam(s) ]
    CWNP [13 Certification Exam(s) ]
    Dassault [2 Certification Exam(s) ]
    DELL [9 Certification Exam(s) ]
    DMI [1 Certification Exam(s) ]
    DRI [1 Certification Exam(s) ]
    ECCouncil [21 Certification Exam(s) ]
    ECDL [1 Certification Exam(s) ]
    EMC [129 Certification Exam(s) ]
    Enterasys [13 Certification Exam(s) ]
    Ericsson [5 Certification Exam(s) ]
    ESPA [1 Certification Exam(s) ]
    Esri [2 Certification Exam(s) ]
    ExamExpress [15 Certification Exam(s) ]
    Exin [40 Certification Exam(s) ]
    ExtremeNetworks [3 Certification Exam(s) ]
    F5-Networks [20 Certification Exam(s) ]
    FCTC [2 Certification Exam(s) ]
    Filemaker [9 Certification Exam(s) ]
    Financial [36 Certification Exam(s) ]
    Food [4 Certification Exam(s) ]
    Fortinet [12 Certification Exam(s) ]
    Foundry [6 Certification Exam(s) ]
    FSMTB [1 Certification Exam(s) ]
    Fujitsu [2 Certification Exam(s) ]
    GAQM [9 Certification Exam(s) ]
    Genesys [4 Certification Exam(s) ]
    GIAC [15 Certification Exam(s) ]
    Google [4 Certification Exam(s) ]
    GuidanceSoftware [2 Certification Exam(s) ]
    H3C [1 Certification Exam(s) ]
    HDI [9 Certification Exam(s) ]
    Healthcare [3 Certification Exam(s) ]
    HIPAA [2 Certification Exam(s) ]
    Hitachi [30 Certification Exam(s) ]
    Hortonworks [4 Certification Exam(s) ]
    Hospitality [2 Certification Exam(s) ]
    HP [746 Certification Exam(s) ]
    HR [4 Certification Exam(s) ]
    HRCI [1 Certification Exam(s) ]
    Huawei [21 Certification Exam(s) ]
    Hyperion [10 Certification Exam(s) ]
    IAAP [1 Certification Exam(s) ]
    IAHCSMM [1 Certification Exam(s) ]
    IBM [1530 Certification Exam(s) ]
    IBQH [1 Certification Exam(s) ]
    ICAI [1 Certification Exam(s) ]
    ICDL [6 Certification Exam(s) ]
    IEEE [1 Certification Exam(s) ]
    IELTS [1 Certification Exam(s) ]
    IFPUG [1 Certification Exam(s) ]
    IIA [3 Certification Exam(s) ]
    IIBA [2 Certification Exam(s) ]
    IISFA [1 Certification Exam(s) ]
    Intel [2 Certification Exam(s) ]
    IQN [1 Certification Exam(s) ]
    IRS [1 Certification Exam(s) ]
    ISA [1 Certification Exam(s) ]
    ISACA [4 Certification Exam(s) ]
    ISC2 [6 Certification Exam(s) ]
    ISEB [24 Certification Exam(s) ]
    Isilon [4 Certification Exam(s) ]
    ISM [6 Certification Exam(s) ]
    iSQI [7 Certification Exam(s) ]
    ITEC [1 Certification Exam(s) ]
    Juniper [63 Certification Exam(s) ]
    LEED [1 Certification Exam(s) ]
    Legato [5 Certification Exam(s) ]
    Liferay [1 Certification Exam(s) ]
    Logical-Operations [1 Certification Exam(s) ]
    Lotus [66 Certification Exam(s) ]
    LPI [24 Certification Exam(s) ]
    LSI [3 Certification Exam(s) ]
    Magento [3 Certification Exam(s) ]
    Maintenance [2 Certification Exam(s) ]
    McAfee [8 Certification Exam(s) ]
    McData [3 Certification Exam(s) ]
    Medical [69 Certification Exam(s) ]
    Microsoft [368 Certification Exam(s) ]
    Mile2 [2 Certification Exam(s) ]
    Military [1 Certification Exam(s) ]
    Misc [1 Certification Exam(s) ]
    Motorola [7 Certification Exam(s) ]
    mySQL [4 Certification Exam(s) ]
    NBSTSA [1 Certification Exam(s) ]
    NCEES [2 Certification Exam(s) ]
    NCIDQ [1 Certification Exam(s) ]
    NCLEX [2 Certification Exam(s) ]
    Network-General [12 Certification Exam(s) ]
    NetworkAppliance [36 Certification Exam(s) ]
    NI [1 Certification Exam(s) ]
    NIELIT [1 Certification Exam(s) ]
    Nokia [6 Certification Exam(s) ]
    Nortel [130 Certification Exam(s) ]
    Novell [37 Certification Exam(s) ]
    OMG [10 Certification Exam(s) ]
    Oracle [269 Certification Exam(s) ]
    P&C [2 Certification Exam(s) ]
    Palo-Alto [4 Certification Exam(s) ]
    PARCC [1 Certification Exam(s) ]
    PayPal [1 Certification Exam(s) ]
    Pegasystems [11 Certification Exam(s) ]
    PEOPLECERT [4 Certification Exam(s) ]
    PMI [15 Certification Exam(s) ]
    Polycom [2 Certification Exam(s) ]
    PostgreSQL-CE [1 Certification Exam(s) ]
    Prince2 [6 Certification Exam(s) ]
    PRMIA [1 Certification Exam(s) ]
    PsychCorp [1 Certification Exam(s) ]
    PTCB [2 Certification Exam(s) ]
    QAI [1 Certification Exam(s) ]
    QlikView [1 Certification Exam(s) ]
    Quality-Assurance [7 Certification Exam(s) ]
    RACC [1 Certification Exam(s) ]
    Real-Estate [1 Certification Exam(s) ]
    RedHat [8 Certification Exam(s) ]
    RES [5 Certification Exam(s) ]
    Riverbed [8 Certification Exam(s) ]
    RSA [15 Certification Exam(s) ]
    Sair [8 Certification Exam(s) ]
    Salesforce [5 Certification Exam(s) ]
    SANS [1 Certification Exam(s) ]
    SAP [98 Certification Exam(s) ]
    SASInstitute [15 Certification Exam(s) ]
    SAT [1 Certification Exam(s) ]
    SCO [10 Certification Exam(s) ]
    SCP [6 Certification Exam(s) ]
    SDI [3 Certification Exam(s) ]
    See-Beyond [1 Certification Exam(s) ]
    Siemens [1 Certification Exam(s) ]
    Snia [7 Certification Exam(s) ]
    SOA [15 Certification Exam(s) ]
    Social-Work-Board [4 Certification Exam(s) ]
    SpringSource [1 Certification Exam(s) ]
    SUN [63 Certification Exam(s) ]
    SUSE [1 Certification Exam(s) ]
    Sybase [17 Certification Exam(s) ]
    Symantec [134 Certification Exam(s) ]
    Teacher-Certification [4 Certification Exam(s) ]
    The-Open-Group [8 Certification Exam(s) ]
    TIA [3 Certification Exam(s) ]
    Tibco [18 Certification Exam(s) ]
    Trainers [3 Certification Exam(s) ]
    Trend [1 Certification Exam(s) ]
    TruSecure [1 Certification Exam(s) ]
    USMLE [1 Certification Exam(s) ]
    VCE [6 Certification Exam(s) ]
    Veeam [2 Certification Exam(s) ]
    Veritas [33 Certification Exam(s) ]
    Vmware [58 Certification Exam(s) ]
    Wonderlic [2 Certification Exam(s) ]
    Worldatwork [2 Certification Exam(s) ]
    XML-Master [3 Certification Exam(s) ]
    Zend [6 Certification Exam(s) ]





    References :


    Vimeo : https://vimeo.com/240171468
    Issu : https://issuu.com/trutrainers/docs/a2010-578
    Dropmark : http://killexams.dropmark.com/367904/11412835
    Wordpress : http://wp.me/p7SJ6L-eE
    weSRCH : https://www.wesrch.com/business/prpdfBU1HWO000VDNZ
    Scribd : https://www.scribd.com/document/356764454/Pass4sure-A2010-578-Assess-Fundamentals-of-Applying-Tivoli-Service-Availability-Performance-Ma-exam-braindumps-with-real-questions-and-practice-soft
    Dropmark-Text : http://killexams.dropmark.com/367904/12023865
    Youtube : https://youtu.be/4Z3o2BW2x28
    Blogspot : http://killexams-braindumps.blogspot.com/2017/10/look-at-these-a2010-578-real-question.html
    RSS Feed : http://feeds.feedburner.com/JustStudyTheseIbmA2010-578QuestionsAndPassTheRealTest
    publitas.com : https://view.publitas.com/trutrainers-inc/where-can-i-get-help-to-pass-a2010-573-exam
    Google+ : https://plus.google.com/112153555852933435691/posts/N67MCfd19Ma?hl=en
    Calameo : http://en.calameo.com/books/004923526b6f8f3044c0a
    Box.net : https://app.box.com/s/iginewcbmes1crxhu6bed56d8l819yii
    zoho.com : https://docs.zoho.com/file/5bym214ca77d8bb30459280764ae29017cbbd
    coursehero.com : "Excle"






    Back to Main Page





    Killexams A2010-578 exams | Killexams A2010-578 cert | Pass4Sure A2010-578 questions | Pass4sure A2010-578 | pass-guaratee A2010-578 | best A2010-578 test preparation | best A2010-578 training guides | A2010-578 examcollection | killexams | killexams A2010-578 review | killexams A2010-578 legit | kill A2010-578 example | kill A2010-578 example journalism | kill exams A2010-578 reviews | kill exam ripoff report | review A2010-578 | review A2010-578 quizlet | review A2010-578 login | review A2010-578 archives | review A2010-578 sheet | legitimate A2010-578 | legit A2010-578 | legitimacy A2010-578 | legitimation A2010-578 | legit A2010-578 check | legitimate A2010-578 program | legitimize A2010-578 | legitimate A2010-578 business | legitimate A2010-578 definition | legit A2010-578 site | legit online banking | legit A2010-578 website | legitimacy A2010-578 definition | >pass 4 sure | pass for sure | p4s | pass4sure certification | pass4sure exam | IT certification | IT Exam | A2010-578 material provider | pass4sure login | pass4sure A2010-578 exams | pass4sure A2010-578 reviews | pass4sure aws | pass4sure A2010-578 security | pass4sure cisco | pass4sure coupon | pass4sure A2010-578 dumps | pass4sure cissp | pass4sure A2010-578 braindumps | pass4sure A2010-578 test | pass4sure A2010-578 torrent | pass4sure A2010-578 download | pass4surekey | pass4sure cap | pass4sure free | examsoft | examsoft login | exams | exams free | examsolutions | exams4pilots | examsoft download | exams questions | examslocal | exams practice |

    www.pass4surez.com | www.killcerts.com | www.search4exams.com | http://morganstudioonline.com/


    <

    MORGAN Studio

    is specialized in Architectural visualization , Industrial visualization , 3D Modeling ,3D Animation , Entertainment and Visual Effects .