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

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No influence discovered, try original key phrase!Dividend safeguard Relative to Its present Debt Load The remaining angle that they are going to consume to determine IBM's current ... is currently lined by course 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 eccentric 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 couple of reinventions.

Rometty recently bought pink Hat, a gigantic issuer of open source application essentially for companies. IBM paid $34 billion, which is an strangely tall diverse of each profits and income. The deal should bolster IBM’s cloud-related organizations Rometty argues, however buyers issue to account 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 hold petite to pretension concerning the economic consequences of IBM’s plans. Many must achieve with IBM’s cloud initiatives, a neighborhood through which the enterprise needs to garner market participate from leaders Amazon.com and Microsoft. Most research suggests that IBM’s participate of the market is a minuscule 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 era the yr before. Its operating profits margin turned into an spectacular 31%.

IBM’s items and capabilities haven't been knit together in a course so that Wall road can believe the enterprise has a coherent course beyond grabbing at opportunities. One does not requisite 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 horrible Run for IBM (NYSE:IBM). overseas commerce 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 expand 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 like Cisco techniques (NASDAQ:CSCO) and Oracle enterprise (NYSE:ORCL) hold pulled returned as well. Neither stock, of direction, has considered declines like that of international commerce Machines stock.

IBM’s performance admittedly has been disappointing. I argued as these days as August that IBM gave the eye of a purchase, writing that “I’d subsist bowled over” to eye IBM trade reduce than $one hundred twenty five, at which constituent 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 advice for this 12 months suggests a similar diverse according to free money flow.

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

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

The dividend may quiet subsist fairly protected; there doesn’t seem to subsist elbowroom for a circumstance like that of yardstick electric powered (NYSE:GE) the residence onerous debt leads to a dividend reduce. Even with the crimson Hat deal, IBM’s debt (and pension) load is quiet manageable.

but that fundamental case itself highlights the erudition risk prerogative 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 hold compressed over that duration.

And so the easiest abide case for IBM in the signify time is based on a sole 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 profitable information of the closing few quarters doesn’t eye necessarily that decent. IBM’s centered areas of growth (which it refers to as “strategic imperatives”), like cloud and AI, hold extended salary 13% over the last yr. those categories constrain roughly half of earnings, which is respectable information.

The disagreeable information is that IBM on the complete has grown income a bit over 2%. That in circle suggests the relaxation of IBM is seeing income drop anything like 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 steady currency for the 2nd straight quarter. programs growth of 1% was a exotic deceleration. The Q3 document harm the case prerogative here. And with tougher comparisons on the style for the next three quarters, buyers doubtless can’t are expecting too a powerful deal within the manner of fireworks any time soon.

nonetheless intriguing

So IBM bulls ought to hold their eyes open to the capabilities draw back. That said, $121 does issue too low-priced for IBM stock. The purple Hat deal could had been too pricy, however I account 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 achieve exchange the bull case here somewhat. The argument for the last few years became that if IBM stabilized, the inventory would evanesce up. At these levels, if IBM stabilizes, the stock can soar.

whatever like 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 fashion which makes some experience. however that additionally talent investors aren’t accounting for what occurs if the purple Hat deal become a very profitable one and IBM’s turnaround finally takes hold.

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




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

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

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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 gardenvariety conditions to ameliorate the efficiency of urban road systems, analytic uphold for investments that ameliorate resilience (defined as system recovery from additional disruptions) is quiet scarce. In this effort, they depict 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 subsist regarded as traffic-based centrality measures, estimating the number of individuals using corresponding road segments. Efficiency was estimated as the middling annual leisurely per peak-period auto commuter, and modeled results were create to subsist nearby to observed data, with the notable exception of original 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 gardenvariety conditions are nevertheless resilient to disruption, whereas some more efficient cities are more fragile. The implication is that resilience, not just efficiency, should subsist considered explicitly in roadway project selection and warrant investment opportunities related to catastrophe 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 comprise 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 leisurely in urban areas as the annual leisurely per auto commuter (11). Other studies define efficiency as leisurely for the individual driver in terms of time spent pathetic or stopped (7), or signify travel time between everything origin-destination pairs in the network (9). However, as the suffer of any motorist in big American cities can attest, conditions beyond the scope of the roadway design, including congestion, accidents, disagreeable weather, construction, and special events (for example, a marathon race), can cause 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 gardenvariety operating conditions results in petite 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 hold yet to converge on a shared understanding of resilience suitable to sheperd 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 talent of a system to prepare for, absorb, retrieve from, and accommodate to disturbances (16). Specific to transportation, resilience has been defined as “the talent of the system to maintain its demonstrated flush of service or to restore itself to that flush of service in a specified timeframe” (24). Others characterize transportation resilience as simply the talent of a system to minimize operational loss (25) or consume the term synonymously with robustness, redundancy, reliability, or vulnerability (26–28).

Current efforts in transportation resilience research hold focused on framework development and quantification methods. These efforts comprise the specification of resilience indicators, such as total traffic leisurely (24), economic loss (29), post-disaster maximum flow (30), and autonomous system components (31). Practical concerns with this character 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 behavior following a disruptive event (34). Moreover, existing resilience quantification approaches lack calibration and testing across a achieve of transportation systems. Because many disruptive events, and their associated consequences, are difficult to predict, resilient road systems must subsist characterized and evaluated by the capacity to accommodate to a variety of different stress scenarios. Partly because of these obstacles, joint consideration of efficiency and resilience has yet to subsist 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 leisurely per peak-period auto commuter, and apply the model to pattern 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 necessary considerations for assessing proposals for improvement of roadway infrastructure that maintain efficiency under stress conditions.

METHODS

The Methods section appears here to attend clarify the subsequent sections. To develop the urban roadway efficiency model, they defined the urban area 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 hold been offered and comprise those based on percolation theory and cascading failures (38–40), human mobility pattern studies (41–43), queueing (44, 45), and the consume of historical data to call traffic. They review these approaches in the Supplementary Materials and note that the main capitalize of their model is that it relies solely on readily available public data, rather than on particular data sets that may or may not subsist practical to obtain for any particular region. The model’s algorithmic simplicity allows us to account spatial topologies of cities in tall 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 area polygon with a simplified manually drawn one (Fig. 1A) and included everything 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 area 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 election models (52, 53). However, these models are not as widely used in big scale, because the circumstantial data required by these models are frequently unavailable (48).

We assumed that (i) the flow of commuters from origin region o to destination region d is proportional to the population at the destination Nd and that (ii) the flow of commuters depends on the distance xod between the origin 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 flow from origin region o to destination region d isEmbedded Image

Embedded Image

(3)

Although individual driving habits may vary (54), they assumed that everything drivers tended to optimize their commute paths such that their travel time was minimized. This assumption allowed us to pattern commute paths for every origin-destination pair using inferred FFSs. To pattern commuter flows between everything pairs of intersections, they estimated distances xod as the distance of the shortest time path from o to d. Furthermore, in residence 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 accelerate 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 area confine polygon. The nodes farther than 30 km from the confine were only used as destinations to evaluate the fraction of commuters not going toward the urban area (Eq. 2).

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

Embedded Image

(5)where Vij and vij are, respectively, the FFS and the actual traffic accelerate 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 everything urban areas. The summation in Eq. 5 includes only links, whose origins and destinations are within the confine polygon. A similar equation was obtained for the pathetic leisurely in the study of Jiang and Adeli (45), where the authors looked at the leisurely induced from road repairs.

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

Embedded Image

(6)where vmin is the minimum accelerate 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 middling annual leisurely per peak-period auto commuter. In practice, lower leisurely 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 achieve of operational conditions for which it stays in the acceptable service region and highlighted that remediation mechanisms drive the operational situation 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 accelerate limits on affected roads will not vary much from network to network, although the number and character of these disruptions will. Likewise, the accelerate of restoring replete functionality (through action in the physical domain) is not so much contingent 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 flush of functionality that these repairs achieve ought to subsist the replete predisruption functionality, that is, eventually everything roads can subsist fully cleared or restored. However, the immediate loss of office for a given traffic flow can very quickly subsist partially recovered after a disruption by action in the information domain, namely, rerouting of traffic. From the original steady situation at that flush of functionality, replete 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 pertinent authorities. Lower additional leisurely 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 accelerate vmin, and finite vehicle size correction vveh) and is summarized in Eqs. 5 and 6. Given parameter values of the model, one can assess the total leisurely incurred by everything commuters in any given suburban area or, equivalently, the middling leisurely per commuter. They seize 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 middling leisurely 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 hold create that for the 20 urban areas used for calibration, the R-squared coefficient took values in the achieve (−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 assess 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 leisurely times (11), validating the transportation model. pattern 4 is a Google Maps representation of real and modeled results for Los Angeles and San Francisco. Road conditions under real, middling 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 subsist caused by accidents or construction. Larger disruptions might occur during power failures that disrupt traffic signals or austere flooding that makes many roadways nearly impassable. Finally, widespread stress might subsist caused by snow, ice, or dust storms that strike nearly the entire roadway system. pattern 5 displays the analysis of leisurely times in six representative urban areas for the replete spectrum of adverse event severities, r ⋲ [0; 1]. In addition, fig. S5 shows the results for everything urban areas. Some routes within a sole urban area suffer longer delays than others. The inset of Fig. 5 shows the leisurely distribution for both Los Angeles, which is narrowly clustered, and Boston, where greater variability between roadways is evident. Traffic leisurely times grow rapidly as r increases and achieve saturation (all routes pathetic at 1 km/hour) as r approaches 1. They determine the most resilient urban transportation network to subsist Salt Lake City, UT, whereas the least resilient among the 40 metropolitans is shown to subsist Washington, DC.

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

Error bars expose signify 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 minuscule 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 tall efficiency under gardenvariety operating conditions (that is, low delays) nevertheless exhibit low resilience (that is, a keen expand in traffic delays) under stress. Virginia Beach, VA; Providence, RI; and Jacksonville, FL everything drop 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 everything conditions—yet minor stress levels result in petite degradation of efficiency. By contrast, gardenvariety traffic delays in San Francisco are comparable to Los Angeles, but mild stress in San Francisco results in big increases in additional delays. These examples testify that resilience (that is, additional leisurely response to stress) is independent of gardenvariety operating efficiency.

Fig. 6 Comparison of resilience and efficiency metrics.

Annual repercussion of 5% disruption (additional delay) has a low correlation with gardenvariety annual leisurely 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 subsist unknown (28, 31). These disturbances might subsist natural and irregular, such as distributed road closures caused by an earthquake or homogeneous vehicle slowing down because of a snowstorm. The disturbances might moreover subsist anthropogenic and intentional, such as a street objective or marathon race. Whatever the disturbance, the results of this analysis allow several meaningful inferences to subsist made that may hold necessary implications for highway transportation policy. The first is that resilience and efficiency depict different aspects related to the nature of transportation systems; they are not correlated and should subsist 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 frangible of everything cities represented in Fig. 5 at stress levels r < 20% but then surpassed by Boston, MA and Washington, DC, the rank ordering of urban area 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 current 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 subsist 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 gardenvariety operating conditions may nevertheless subsist vulnerable to traffic delays under mild stress conditions. Because these stressors are inevitable, whether from crashes, construction, special events, extreme weather, rig malfunctions, or even deliberate attack, investment strategies that prioritize reduction of gardenvariety operating delays may hold the unintended consequence of exacerbating tail risks—that is, the risk of worse catastrophe under unlikely but viable conditions.

Finally, the exceptional position of original York City in Fig. 3 calls attention to the fact that substitutes for roadway transportation are available in many cities and hold an necessary 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 original York urban area 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 original 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 original York urban area.

Although interest has increased in policies that enhance roadway resilience, few analytic tools are available to sheperd original 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 era of decades may hold long-lasting consequences that are observable in traffic data today. Consequently, urban areas exhibit different unintentional traffic characteristics, including delays under gardenvariety and random stress conditions. Investments motivated exclusively by expected efficiencies under gardenvariety operating conditions are unreliable safeguards against loss of efficiency under stress conditions. Therefore, original analytic tools are required that allow designers to assess the adaptive capacity of roadway infrastructure and assess the potential of original 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 accelerate from the density of vehicles

Model calibration procedure

Sensitivity of the model to ramp speeds

Additional leisurely 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 accelerate 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 accelerate coefficients of 1/3 and 1/2.

fig. S5. Dependency of the additional leisurely on the severity of the link disruption for everything 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 consume is not for commercial advantage and provided the original toil is properly cited.

REFERENCES AND NOTES
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  • Acknowledgments: They would like 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 seat 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 accord 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 hold no competing interests. Data and materials availability: everything 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 subsist 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 effectual course to ameliorate proxies of physical performance in hardy children and adolescents when appropriately prescribed and supervised.1–4 Several meta-analyses hold shown that RT has the potential to ameliorate muscle energy and motor skills (eg, jump performance) in children and adolescents.1 ,5–7 However, youth athletes hold 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 moreover petite 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 universal population (ie, hardy untrained children and adolescents) to youth athletes. This is necessary because the optimal dose to elicit a desired upshot is likely to subsist 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 character 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 hold a positive upshot on proxies of physical performance in youth athletes, and (2) the effects would subsist 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 pertinent 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 pertinent articles by analysing titles, abstracts and replete texts of the respective articles to elucidate their eligibility. In case ML and OP did not achieve an agreement concerning inclusion of an article, UG was contacted.

    Table 1

    Selection criteria

    Coding of studies

    Each study was coded for certain 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 esteem 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 signify number of sets per exercise, repetitions per sets, relaxation between sets and training intensity were computed.

    To obtain enough statistical power to pattern 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 viable to classify tangled 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 prejudice in eligible studies and to provide information on the universal methodological trait 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 signify differences (SMD=(mean postvalue intervention group−mean postvalue control group)/pooled yardstick 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 subsist included to pattern weighted signify 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 upshot sizes with respect to differences in intervention effects across subgroups.31 To ameliorate readability, they reported positive SMDs if superiority of RT compared with dynamic 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 upshot sizes in energy training research for individuals who hold been consistently training for 1–5 years,32 they interpreted SMDwm as: petty (<0.35); minuscule (0.35–0.79); moderate (0.80–1.50); big (≥1.50). The flush of significance was set at p<0.05.

    Results Study characteristics

    A total of 576 potentially pertinent 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 field (1 study, 1 RT intervention group)). No included study investigated youth athletes recruited from martial arts or technical/acrobatic sports.

    Regarding the character 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 tangled 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 hold 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 subsist interpreted as an overall tall risk of prejudice 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, plumb 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; pattern 2) and plumb jump performance (SMDwm=0.80; I²=67%; χ2=137.47; df=46; p<0.001; pattern 3), while there were minuscule effects for linear sprint performance (SMDwm=0.58; I²=41%; χ2=55.74; df=33; p<0.01; pattern 4), agility (SMDwm=0.68; I²=50%; χ2=48.19; df=24; p<0.01; pattern 5) and sport-specific performance (SMDwm=0.75; I²=62%; χ2=67.81; df=26; p<0.001; pattern 6). By considering only the four studies with tall trait (ie, low risk of bias), RT had moderate effects on measures of muscle energy (SMD=1.07; 1 study), plumb jump (SMDwm=0.89; 3 studies) and linear sprint performance (SMDwm=1.19; 2 studies); minuscule effects on agility (SMD=0.28; 1 study); and big effects on sport-specific performance (SMDwm=1.73; 2 studies).

    Figure 2

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

    Figure 3

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

    Figure 4

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

    Figure 5

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

    Figure 6

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

    There was no statistically significant upshot of chronological and/or biological age on any proxy of physical performance. However, a tendency (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 expose 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, tangled training produced the largest effects (table 4).

    Dose–response relationships of RT Training period

    There was a significant distinction for the effects of conventional RT on measures of muscle energy (p<0.001), plumb jump height (p<0.05) and agility (p<0.001; pattern 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 plumb jump height, a training era of 9–12 weeks appeared to subsist the most effectual (SMDwm=1.20).

    Figure 7

    Dose–response relationships of the parameter ‘training period’ on measures of muscle strength, plumb jump and linear sprint performance, agility, and sport-specific performance. Each filled grey clique illustrates between-subject SMD per sole study with dynamic control. Filled black triangles depict weighted signify SMD of everything studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised signify 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, plumb jump and linear sprint performance, agility, and sport-specific performance. Each filled grey clique illustrates between-subject SMD per sole study with dynamic control. Filled black triangles depict weighted signify SMD of everything studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised signify difference.

    Training intensity

    There was a significant distinction with esteem to the effects of conventional RT on measures of muscle energy (p<0.01; pattern 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, plumb jump and linear sprint performance, agility, and sport-specific performance. Each filled grey clique illustrates between-subject SMD per sole study with dynamic control. Filled black triangles depict weighted signify SMD of everything studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised signify difference; RM, repetition maximum.

    Training volume (number of sets per exercise)

    There was a significant distinction with esteem to the effects of conventional RT on muscle energy (p<0.01), and a tendency towards significance for measures of plumb jump performance (p=0.06; pattern 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 subsist more effectual in improving plumb 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, plumb jump and linear sprint performance, agility, and sport-specific performance. Each filled grey clique illustrates between-subject SMD per sole study with dynamic control. Filled black triangles depict weighted signify SMD of everything studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised signify difference.

    For plyometric training, there was a tendency 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 issue to subsist most effectual for improving linear sprint performance (SMDwm=0.95).

    Training volume (number of repetitions per set)

    There was a significant distinction in terms of the effects of conventional RT on measures of muscle energy (p<0.05; pattern 11). Six to eight repetitions per set produced the largest effects on muscle energy (SMDwm=2.42). For plyometric training, there was a tendency towards significance for proxies of sport-specific performance (p=0.05). Six to 8 repetitions per set were less effectual (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, plumb jump and linear sprint performance, agility, and sport-specific performance. Each filled grey clique illustrates between-subject SMD per sole study with dynamic control. Filled black triangles depict weighted signify SMD of everything studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised signify difference.

    Rest between sets

    There was a significant distinction for the effects of conventional RT on measures of muscle energy (p<0.05; pattern 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, plumb jump and linear sprint performance, agility, and sport-specific performance. Each filled grey clique illustrates between-subject SMD per sole study with dynamic control. Filled black triangles depict weighted signify SMD of everything studies. NA, not applicable; SGA, subgroup analyses; SMD, standardised signify difference.

    Discussion

    This systematic review with meta-analysis examined the universal effects as well as the age, sex, sport and training type-specific repercussion of RT on proxies of physical performance in hardy juvenile 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 plumb jump performance, and minuscule effects on linear sprint, agility and sport-specific performance in juvenile athletes, (2) the effects of RT were moderated by the variables sex and RT type, (3) most effectual conventional RT programmes to ameliorate measures of muscle energy in hardy juvenile 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 effectual course to ameliorate proxies of physical performance in youth athletes, and their findings uphold recently published literature.4 ,17 ,72 ,73 They create that the main effects of RT on measures of muscle energy and plumb jump performance were moderate in magnitude, with minuscule effects for secondary outcomes, including linear sprint performance, agility and sport-specific performance (eg, throwing velocity). The lower RT effects on secondary outcomes might subsist explained by the tangled 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 trait and straight sprinting speed.74 Thus, muscle energy appears to subsist only one of several factors contributing to agility.

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

    How age, sex, sport and training character 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 upshot 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 tendency (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 capitalize to the selfsame extent from RT, irrespective of age. However, it is necessary 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 masculine youth athletes can only partially subsist transferred to female youth athletes because the physiology of boys and girls (eg, hormonal status during puberty) varies. They create that masculine and female youth athletes expose similar RT-related gains in muscle energy and plumb 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 subsist 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 subsist 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 comprise ‘biological maturity’ as a moderator variable in their subgroup analyses. They account 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 subsist specifically moderated by the respective athlete profile of the sport performed.79 ,80 Whether this is moreover the case in youth athletes remains unresolved. Given that most included studies (n=38) investigated juvenile athletes competing in team sports, their analyses with esteem to the moderator variable ‘sport’ was limited and did not expose any significant differences between sports disciplines (table 4). Therefore, further research has to subsist 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 hold 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 depict a safe environment for juvenile athletes when supervision cannot subsist ensured, whereas supervised RT using free weights allows replete achieve of motion that better mimics sports-specific movements.20 ,73 They create that RT programmes using free weights were most effectual to enhance burly energy and agility. In addition, tangled training produced the largest upshot sizes if the goal was to ameliorate sport-specific performance. Therefore, the election of RT types should subsist 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 tangled process that requires sophisticated manipulation of different training parameters. Owing to a lack of evidence-based information on dose–response relationships following RT in youth athletes, it is quite common for established and effectual RT protocols for hardy untrained children and adolescents to subsist transferred to youth athletes. However, this may dam to fully recruit the adaptative potential of juvenile athletes because the optimal dose to elicit the desired upshot appears to subsist 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 burly energy may subsist 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 plumb jump performance. Nevertheless, with esteem to energy gains, long-term (≥24 weeks) conventional RT was more effectual in youth athletes (SMDwm=3.40), as compared with short-term conventional RT (SMDwm=0.61–1.24). Thus, it can subsist postulated that conventional RT programmes should subsist 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 era was enough to maintain previously achieved burly 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 burly 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 aspect 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 necessary to allow the corpse enough time to retrieve from each RT session. However, if the relaxation between RT sessions is too long, adaptive processes from previous RT sessions may score lost.

    Most studies performed RT two or three times per week (figure 8), and there was no significant distinction 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 subsist enough to maintain muscle energy gains following RT for several weeks,41 ,82 training twice per week might subsist preferred to achieve further gains in muscle energy in youth athletes.

    Training volume and training intensity

    Both volume and intensity hold to subsist 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 circle induce different neural and burly adaptations.71

    Owing to the big 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 esteem to plyometric training.

    Conventional RT programmes using middling training intensities of 80–89% of the 1 RM were most profitable 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 upshot 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 subsist preeminent that the individual percentage of 1 RM is a stress rather than a strain factor. Several studies hold indicated that a given number of repetitions cannot subsist associated with a specific percentage rate of the 1 RM.78 ,84 Thus, to individualise RT, future studies should focus on finding a cogent strain-based course to quantify RT intensity effectively.

    In terms of the number of sets per conventional RT exercise, their data expose similar upshot size magnitudes when comparing single-set (SMDwm=2.41) versus multiple-set conventional RT programmes (5 sets: SMDwm=2.76). The primary capitalize 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 subsist 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 subsist commandeer during the initial aspect of RT,85 whereas multiple-set conventional RT programmes should subsist used to promote further gains in muscle strength, especially in athletes.86 Therefore, multiple-set conventional RT may subsist necessary to elicit enough training stimuli during long-term youth athlete development.

    Regarding the applied plyometric training, 3 (for plumb jump) or 4 sets per exercise (for muscle strength, sport-specific performance) as well as 3–5 or 9–12 repetitions per set (for plumb jump, sport-specific performance) might subsist profitable for youth athletes’ physical performance. However, the movement trait of plyometric exercises is more necessary than the total session volume.87 Therefore, they recommend the consume 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 like 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 effectual 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 quiet subsist 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 course to provide insights into the interactions between the investigated training parameters. The modelling of training variables might attend 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 tall risk of prejudice 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 upshot sizes32 is not specific for RT research in children and adolescents. Another limitation is that almost everything 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 effectual for improving proxies of physical performance in youth athletes. The magnitudes of RT effects were moderate in terms of measures of muscle energy and plumb jump performance, and minuscule with esteem to measures of linear sprint, agility and sports-specific performance in youth athletes. Sex and RT character appeared to moderate these effects. However, most studies were at tall risk of prejudice and therefore, the results should subsist interpreted cautiously.

    A training era 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 effectual for conventional RT programmes to ameliorate muscle energy in youth athletes. However, these evidence-based findings should subsist adapted individually by considering individual abilities, skills and goals. Specifically, youth coaches should not consume tall 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 hold already shown that resistance training has the potential to ameliorate 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 upshot 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 effectual for increasing muscle strength.

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

  • Acknowledgments

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



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    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"






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