The worse news is that it seems 3D Systems has only itself to blame for the drop. Tesla Inc. Rivian's debut in the public markets has investors buying up shares of other EV sector start-ups. Following the release, an investor conference call was held. Highlights for the year. He exercised 2. AMD stock closed the day at an all-time high as investors cheered the news, which isn't surprising as the new business could significantly boost the chipmaker's growth in the long run.
Let's see why the adoption of AMD's server chips by Meta is going to be a big deal. As it turns out, moreover, these two things are connected. Shares of several related stocks are ripping higher today, suggesting that investors are feeling especially bullish on the prospects of the EV industry. Back in June, Steve Burns resigned as CEO and from Lordstown's board of directors amid accusations of overstating the pre-order data for the company's Endurance electric pickup truck.
Lordstown and electronics manufacturing giant Foxconn officially released details of a partnership that the EV maker believes will transform it into a long-term player in the sector.
A bevy of Wall Street analysts followed up by lowering their price targets for the stock, adding to today's pain. According to The Fly, four analysts lowered their price targets for the stock as a result of third-quarter results. Nearly a week since it reported estimate-crushing earnings for the fiscal third quarter of , shares of rare earth metals miner MP Materials NYSE: MP are marching higher on Thursday, rising 9.
MP Materials may not be the lowest-cost miner of rare earth metals, admitted Jefferies this morning in a note covered by StreetInsider. The recent spin-off of its managed infrastructure business into a company called Kyndryl NYSE: KD removes a noncore business from its balance sheet. Also, management promised that the two companies would maintain the current combined dividend.
Every stock has a backstory, and the backstories offer hints and clues to what lies ahead. A smart investor will learn which clues or signals bode best for the stock. These are the ones to follow. One sound signal is insider buying.
He noted that there are other viable stocks to buy in the lithium recycling space, but reaffirmed that QuantumScape is his top pick. Analysts raise their price targets on Nvidia and reiterate positive ratings ahead of the company's quarterly earnings next week. Blowouts usually occur from trying to stretch the hole too quickly. They often cause sharp pain and inflammation. Overstretching usually causes a blowout. Stretching out your ear should be a slow and gradual process.
If you increase the size of your jewelry too quickly, you can develop blowouts and other complications, like lobe tears and infections. Countries that use the metric system often use millimeters mm instead of gauges. Standard earrings are normally 20 gauge or 18 gauge. As earrings get wider, the gauge size decreases. Many people also recommend waiting 4 to 6 weeks before increasing sizes. The amount of time you need to wait may increase as your jewelry becomes larger.
The development of a blowout causes a ring of skin to form behind the piercing. This ring is usually red, irritated, and painful. A blowout may give the piercing the appearance of turning inside out. Overstretching your ear may also lead to an infection.
This may cause:. You can often treat minor infections at home. You can reduce your chance of developing an infection by regularly cleaning objects that come in contact with your ears often, such as your phone, headphones, and hats. If you think you may be developing a blowout, take action as quickly as possible. Catching a blowout early can help you avoid permanent damage to your ear.
Many people recommend lightly massaging your earlobe for 5 to 10 minutes with an oil to help prevent a blowout from forming. Using oil on your gauged ears helps keep them moisturized, which promotes stronger skin and reduces the likelihood of tears. Many types of oil are effective for keeping your gauged ears moist.
Some of the most common types include:. Most standard earrings are 20 or 18 gauge. Indeed, Figure 3 shows some evidence of a speed-up in mean weekly pace during the early weeks of longer tapers as runners reduce their weekly distance by eliminating slower runs while retaining some speed-work. Only 0. One possible explanation for this is that these runners are not actually training for their completed marathon, but are instead using the race as a stepping-stone to another event, such as an upcoming ultra-race.
For completeness, Figure 4B indicates a strong positive correlation between taper duration and peak weekly distance, as runners with a higher training load in the weeks before their race employ longer tapers. In general, when we control for taper duration, then strict tapers tend to be associated with greater peak weekly distances, which could be due to more experienced runners, with greater training loads, adopting strict tapers.
In Figure 5A strict tapers are associated with faster 10 km paces than relaxed tapers and longer and more strict tapers are associated with faster marathon finish-times in Figure 5B ; there is a strong correlation between the median fastest 10 km paces and marathon finish-times by taper type, which motivates the use of the finish-time efficiency FTE and finish-time benefit FTB metrics, as a way to evaluate marathon performance while controlling for ability differences.
In other words, longer strict tapers are associated with runners who can complete their marathon at a pace that is closer to their fastest 10 km training pace.
The implication is that shorter tapers do not permit runners to perform at this high level on race-day, regardless of their ability. And when we use the FTE of the relaxed 1-week taper as a baseline efficiency level to estimate a runner's expected finish-time—as if they had observed a relaxed 1-week taper—then we find longer and more strict tapers to offer significant finish-time benefits relative to this expected time. These significance results are broadly similar across the three performance metrics with the following observations noted:.
The minimal taper relaxed 1-week is associated with poorer performance than all other types of taper, for FT, FTE, and FTB: the performance associated with the relaxed 1-week taper is significantly worse than all other taper types, except the non-taper , thereby justifying its use as a minimal taper baseline in the calculation of finish-time efficiency and benefits.
Although there is no single taper type with significantly better finish-time benefits than all of the alternatives, the strict 3-week taper offers the best all-round performance, in the sense that its finish-time benefit is significantly better than all other taper types, with the exception of the less common strict 4-week taper, and the finish-time benefits of the strict 4-week taper are not significantly different from those of the strict 3- or 2-week tapers.
Although, there is no material difference in the distribution of male and female runners by taper type, it is relevant to consider whether the sex of a runner influences performance after tapering. The results in Figure 7 indicate that females enjoy a greater median percentage finish-time benefit than males.
For example, females enjoy a 3. Thus, compared to a minimal taper, female runners are associated with greater finish-time benefits than males, for longer tapers up to 3 weeks strict and relaxed. One potential explanation for this is that it is due to pacing differences between male and female marathoners. For example, female runners have been found to be more disciplined even pacers Deaner et al.
This may be related to a tendency among male runners to overestimate their marathon abilities compared to women Hubble and Zhao, , which may lead them to adopt more aggressive or risky pacing strategies on race-day and they are more likely to suffer the greater performance consequences if their pacing cannot be maintained. Table 5 shows the results of an OLS regression based on the speicifcation in Equation The resulting model has an adjusted R 2 of 0.
For example, each unit increase in a runner's fastest 10 km pace, achieved during training, leads to a Notice too how the coefficient associated with male runners 4. This does not imply that male runners are slower than females in general, but rather that, when we control for taper type and ability, then men experience a finish-time cost compared to women.
This is, once again, consistent with the pacing differences that have been observed between male and female runners Deaner et al. More evenly paced races are generally viewed to be more optimal than unevenly paced races—and certainly more desirable than the strong positive splits common among recreational male runners Smyth, —which may explain this finish-time cost for men, and suggests that the pacing decisions made by men are leading to slower finish-times than might otherwise be achieved.
The regression results further clarify the differences between the effect of tapers on finish-time when we control for gender and ability. For all but the non-taper, we can see how tapers are associated with decreases in marathon times, relative to the relaxed 1-week taper used as the baseline in the regression, and when we control for gender and ability. For example, a strict 2-week taper is associated with a mean reduction in marathon time of 3. Moreover, strict tapers and longer tapers are associated with greater finish-time reductions than relaxed tapers or shorter tapers.
For example, the relaxed 2-week taper is associated with a 2. And a strict 3-week taper is associated with a 4. The single exception is the strict 4-week taper whose finish-time reduction 3. This is consistent with the conventional wisdom that tapers should be long enough to allow runners to recover from the accumulated fatigue of training Morgan et al. In many cases this could be as straightforward as re-sequencing their taper weeks to implement a more consistent decrease in training volume.
For example, a switch from a relaxed 2-week taper to a strict 2-week taper is associated with an improvement for males from 1. This reduced improvement for 3-week tapers is likely due to the fact that there are more possible combinations of low quality, relaxed 2-week tapers than there are for 3-week tapers.
By definition, a relaxed 3-week taper can only accommodate a single up week between its down weeks—it must involve at least two consecutive down weeks—whereas some relaxed 2-week tapers will include runners with two consecutive up weeks, perhaps directly before race-day.
Thus, we can expect a greater opportunity for improvement when moving from a 2-week relaxed taper to a 2-week strict taper, than when moving from a 3-week relaxed taper to a 3-week strict taper. The regression analysis also supports the view that recreational male runners tend to make sub-optimal pacing decisions that adversely impact their marathon performance; this is expressed as a finish-time cost for males when we control for taper type and ability.
Thus, male runners should not only consider their tapering strategy but also their race-day pacing if they wish to optimize their race-day performance; men are forgoing 4. As more and more runners routinely track their activities using mobile devices and sensors, it is increasingly feasible to conduct new types of data-driven research to better understand how people train and perform. The scale of the data sets that can be generated may overcome some of the limitations of more traditional, small-scale, selective studies.
We believe that this has the potential to help sports scientists to produce more robust conclusions and could help exercise physiologists to produce more actionable insights for coaches and athletes.
This research is one such example of the type of study that can be conducted at scale, but there are a number of methodological considerations and limitations that need to be acknowledged.
First, the data set used is based on raw activity data collected by a popular fitness application. It has been minimally cleaned, anonymised, and processed to extract m pacing data, as discussed, but it has not been validated for individual runners. While there are sufficient data to be confident about trends observed and the associations implied, it is also true that many of the factors that likely impact marathon training and performance injury, weather, desire, competitiveness etc.
For example, if a runner has a strong desire to achieve a personal-best time or to finish within an important landmark time 3 or 4 h for example , then this may impact performance Allen et al. Similarly, groups of runners who train together might be more likely to taper in a similar way. Certainly, the circumstances of a given race and race day will impact performance: the topology of the course, weather conditions Montain et al.
Unfortunately, it has not been possible to consider these factors in the present study because they are absent from our data set and the anonymisation procedure has further obfuscated features than might be used to single out an individual runner. This means that demographic features, such as age, and even race identifiers have been removed. As a matter of future work it is hoped that some of these limitations may be overcome to accommodate a more in-depth analysis by catering for the fixed effects of runners and races using a regression analysis.
Another consideration is the use of the fastest 10 km pace observed during training as a proxy for a runner's ability and its subsequent use in the estimation of finish-time benefits; a related approach was adopted by Zrenner et al. It is not possible to verify whether an observed fastest 10 km pace is accurate for a given runner because it depends very much on the style of their training.
The 10 km distance was chosen because most runners, while training for a marathon, are likely to participate in some shorter distance time-trials or races, making 10 km a reasonable target distance to used as a proxy for ability. However, the fastest 10 km pace estimate will likely underestimate a runner's true ability if they are disinclined to perform maximal effort training sessions, but since this could also be the case in their marathon, the relationship between their marathon pace and their fastest 10 km pace could still serve as a useful estimate of finish-time efficiency and, ultimately, finish-time benefit.
Regardless, estimating runner ability is one area for improvement in this work. For example, one option may be to use a more robust estimate of runner ability such as the critical speed , which can be estimated using raw training data Smyth and Muniz-Pumares, There is an obvious selection bias in the construction of the data set used because for each runner only their fastest marathon in a given year is included.
The reason for this decision is that while some runners did complete more than one marathon per year, it was usually the case they were targeting a particular marathon as their primary goal-race and, as such, one could expect their training and tapering to differ for their slower races.
Were we to include all of the marathons for a given runner then it could produce overlapping training data sets, if multiple marathons occurred within 23 weeks of each other. One consequence of the decision to focus on the fastest races each year is that it could over estimate the effect of tapering on race performance. In any case, it is more correct to view the analysis as presented as one that compares the tapering strategies of runners for their fastest marathons in a given year.
In this work we have compared different taper types to a 1-week relaxed taper as a nominated control. However, it was not possible to provide a control on a runner-by-runner basis, because many runners have completed only a single marathon within the time-frame of the data set, and those that have completed more than one marathon often do not vary their taper approach across multiple races and years. Thus, although the results indicate that longer and more disciplined tapers are associated with improved race performance, we cannot be certain that this will be case for every runner if they change from a 1-week relaxed taper to a longer or more disciplined taper.
In this study, we used a large data set of raw training and race data from recreational marathon runners to evaluate their different tapering strategies in the weeks before race-day, and their resulting performance on race-day.
We proposed a novel framework for comparing the different types of tapers implemented by recreational runners. We found that longer tapers and more disciplined strict tapers were associated with improved performance benefits for recreational runners and that these benefits were greater for female runners than for male runners.
An important practical implication of this work is that there could be an opportunity for many runners to improve their relative performance by implementing a more disciplined form of taper. This is likely to be of considerable interest to recreational marathoners and coaches. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
BS and AL contributed to conception and design of the study and contributed to manuscript revision, read, and approved the submitted version. AL organized the database and performed the data collection and cleaning. BS performed the statistical analysis and wrote the first draft of the manuscript. All authors contributed to the article and approved the submitted version. Science Foundation Ireland and Strava had no role in study design, data collection and analysis, or the preparation of this manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
The authors would also like to thank the reviewers whose comments and suggestions helped to improve this article. Allen, E. Reference-dependent preferences: Evidence from marathon runners. Banister, E. Training theory and taper: validation in triathlon athletes. Berger, B. Mood and cycling performance in response to three weeks of high-intensity, short-duration overtraining, and a two-week taper.
Sport Psychol. Bosquet, L. Effects of tapering on performance: a meta-analysis. Sports Exerc. Buman, M. Hitting the wall in the marathon: phenomenological characteristics and associations with expectancy, gender, and running history.
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