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A bstract The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of s NN = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover | η | < 1 . 1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of $\\sqrt{{\\textrm{s}}_{\\textrm{NN}}}$ = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover |η| < 1.1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of $\\sqrt{{\\textrm{s}}_{\\textrm{NN}}}$ = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover |η| < 1.1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au+Au collisions at a center-of-mass energy of$$ \\sqrt{{\\textrm{s}}_{\\textrm{NN}}} $$s NN = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover | η | < 1 . 1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

The pseudorapidity distribution of charged hadrons produced in Au + Au collisions at a center-of-mass energy of √s̅_̅(̅N̅N̅)̅ = 200 GeV is measured using data collected by the sPHENIX detector. Charged hadron yields are extracted by counting cluster pairs in the inner and outer layers of the Intermediate Silicon Tracker, with corrections applied for detector acceptance, reconstruction efficiency, combinatorial pairs, and contributions from secondary decays. The measured distributions cover |η| < 1.1 across various centralities, and the average pseudorapidity density of charged hadrons at mid-rapidity is compared to predictions from Monte Carlo heavy-ion event generators. This result, featuring full azimuthal coverage at mid-rapidity, is consistent with previous experimental measurements at the Relativistic Heavy Ion Collider, thereby supporting the broader sPHENIX physics program.

Background: A field test which can be easily integrated into the training routine of cyclists to monitor performance changes is valuable. It has been demonstrated that when performing a 20-min outdoor time trial (TT), cyclists produce approximately 5.4% higher mean power output during uphill than flat routes (Nimmerichter et al., 2012: European Journal of Applied Physiology, 112(1), 69-78). Therefore, this discrepancy raises questions the relationship between uphill TT performance and physiological parameters obtained during laboratory graded exercise tests (GXT), as previously demonstrated on flat courses. Methods: Separated by at least 48 hours, eleven male and one female moderately trained cyclists (30±5 years; 78.7±16.2 kg; 175±8 cm; mean±s) undertook a 30-s Wingate test on a mechanically braked cycle ergometer (Biotec2100, Cefise, Nova Odessa, Brazil) fitted with a power-measuring crank (SRM, Jülich, Germany), a GXT to exhaustion (Computrainer ProLab, RacerMate, Seattle, USA) and a 20-min outdoor uphill TT (2.8% mean gradient). GXT pulmonary gas exchanges were measured using breath-by-breath analyses (K4b2, Cosmed, Rome, Italy). During the TT, power output was measured using a mobile power-meter (PowerTap, Saris, Madison, USA). Results: Multiple linear regressions demonstrated that 95% of the variation in TT mean power output (PTT) was predicted by GXT VO2max and the respiratory compensation point (RCP), with standardized beta coefficients of 0.68 and 0.37 respectively. Moderate intraclass correlation coefficients were demonstrated for 94.6% PTT and RCP power (r = 0.87; 95%CI: 0.47-0.96). Bland Altman plot showed a bias ± random error of 4.4±51.6 W or 1.2±21.1 %. Mean values for Wingate 5-s peak power, Wingate 30-s mean power, PTT and 94.6% PTT were 899±163 W; 668±108 W; 295±53 W and 279±50 W, respectively. Mean values for GXT peak power output (Pmax), VO2max, RCP power and VT power were 341±46 W; 4.44±0.73 L.min-1; 274±45 W and 173±31 W, respectively. Discussion: The results of this study demonstrate that 95% uphill PTT can be explained by physiological laboratory parameters of VO2max and RCP. Moreover, when PTT is adjusted according to the findings of Nimmerichter et al. (2012), RCP power output agrees better with it. Consequently, this finding adds more experimental evidence for the predictive validity of the 20-min outdoor TT to estimate power output at RCP during GXT, provided that the uphill course is taken to account and a 5.4% subtraction is made to PTT. Conclusion: A 20-min outdoor TT performed on an uphill course can be utilized to predict with reasonable accuracy, power output at the respiratory compensation point and to monitor performance changes on moderately trained cyclists.

Introduction Background: Performance-assessment tests are often used to verify the efficacy of cycling training programs or experimental interventions in scientific studies. Previous research has shown high reliability of mean power output (POmean) during field time-trials of different courses, such as 36- and 40-km flat, 1.4-km uphill and 4- and 20-min flat. However, the reliability of uphill time-trial performance during long-duration efforts is yet to be determined. As fluctuations in gradient and wind can affect power distribution, it is important to analyse reliability of pacing strategy when investigating reliability of performance during field time-trials. Purpose: To assess the reliability of POmean and pacing strategy during field-based uphill time-trials. Methods Eighteen trained cyclists volunteered (age 31.8 ± 7.6 years, body mass 71.6 ± 8.3 kg and height 1.74 ± 0.08 m). Participants performed an incremental test firstly, and 4 field-based 20-min time-trials then (7 days apart). Different courses were utilised (6.5 and 5.6% mean gradients for courses 1 and 2, respectively), but each participant performed all of their time-trials on the same (course 1, n = 8; course 2, n = 10). Data were log-transformed and analysed using Excel spreadsheets to describe POmean reliability by intraclass correlation coefficients (ICC), typical errors (TE) and coefficients of variation (CV)-along with 90% confidence limits (CL90%). Within-participant differences in POmean were verified using one-way repeated measures ANOVA. To analyse pacing strategy, POmean from each 2-min interval was percentage normalised to the whole time-trial POmean, with statistical interactions assessed via two-way repeated measures ANOVA. Three-way mixed ANOVAs were performed to analyse whether pacing strategy would interact with performance level (cyclists split into 2 groups based upon POmean) and course. Statistical significance was set at P ≤ 0.05. Results Peak power output from the incremental test was 350 ± 36 W. ICC, TE and CV of POmean between trials 2-1, 3-2 and 4-3 are presented in Table 1. Power output was not different (F = 0.150, P = 0.855, ƞp2 = 0.009) between time-trials (287 ± 30, 287 ± 29, 286 ± 32 and 286 ± 34 W for time-trials 1, 2, 3, and 4, respectively). Pacing strategy and TE of POmean at each 2-min interval along with CL90% are presented in Figure 1. We found higher variability in pacing strategy at the start and end of time-trials (TE = 7.57%, 6.29% and 6.08%; 7.01%, 6.34% and 6.24% for 0-2 and 18-20 min intervals, comparisons 2-1, 3-2 and 4-3, respectively) and a significant main effect of time (F = 96.134, P < 0.001, ƞp2 = 0.850). Pairwise comparisons revealed differences between intervals 0-2 and 2-4 only (P < 0.001). Pacing strategy adopted by cyclists did not differ between time-trials (F = 1.970, P = 0.060, ƞp2 = 0.104) and performance groups (F = 1.052, P = 0.399, ƞp2 = 0.062), but differed between courses (F = 4.861, P = 0.006, ƞp2 = 0.233). Discussion As shown in flat courses (Nimmerichter et al., 2010, International Journal of Sports Medicine, 31, 160-166), we demonstrated high reliability of performance during 20-min uphill time-trials, both overall and after splitting cyclists into groups. They adopted positive pacing strategies in all time-trials, with higher variability at the start and end of time-trials, similar to results reported from laboratory tests (Thomas et al., 2012, European Journal of Applied Physiology, 112, 223-229). Cyclists’ performance level does not seem to influence pacing strategy, but course selection does, suggesting future studies should address POmean comparisons among different courses. Conclusions POmean during 20-min uphill time-trials is reliable and cyclists do not seem to adjust pacing strategy after consecutive performances, making this protocol robust for performance evaluations in the field.

Introduction Functional threshold power (FTP60) is the highest power that a cyclist can maintain in a quasi-steady state for approximately one hour without fatiguing (Allen; Coggan, 2010). In an attempt to reduce the effort time for FTP determination, the authors proposed a shorter 20-min time-trial. In this case, FTP corresponds to 95% of the power output averaged (FTP20) (Allen; Coggan, 2010). Anecdotally, FTP20 is widely used to estimate FTP60 and consequently used to determine aerobic training zones. In a recent study, FTP20 correlated fairly with mountain bike cross-country performance (Miller et al., 2014). However, no study has investigated FTP20 comparison with individual anaerobic threshold (IAT), FTP60 and its physiological significance. Purpose: To determine the agreement between FTP20 with IAT and FTP60 and physiological responses at FTP20. Methods Twenty-three trained male cyclists were recruited (age: 33 ± 6 years; body mass: 76.4 ± 8.3 kg; height: 179 ± 5 cm; PPO: 327 ± 34 W). Cyclists performed an incremental exercise test to exhaustion, two randomised time-trials (20-min and 60-min), and a time to exhaustion (TTE) at FTP20 (indoor tests). During the tests, power output, HR, VO2, [la] and RPE were measured. During time-trials and TTE, participants were able to view their progress over the course on a computer screen and were provided with information on completed distance and gear selected only. One-way ANOVA with repeated measures were used for mean comparisons between tests. The bias and limits of agreement (LoA) between performance measures and IAT were defined using the method of Bland and Altman. The confidence intervals (CI) were fixed at 95%. Statistical significance was accepted at P < 0.05. Results The main findings of this study are presented in Table 1 and Figure 1. The mean power output, HR and VO2 of the FTP20 was not significantly different than FTP60 and IAT. Discussion The most direct determination of FTP60 is by simply doing a one-hour time trial. Due to the impact of pacing strategy on performance during such a long time-trial, Allen & Coggan (2010) suggested that FTP20 could be more reliable for FTP60 determination. Accordingly, this study showed no significant differences between 95% FTP20 (236 ± 38 W), IAT (237 ± 29 W; P>0.05) and FTP60 (231 ± 33 W; P>0.05). Moreover, we found low bias between FTP20 and both IAT and FTP60 (Figure 2). The TTE at FTP20 was ~51 min, although we found a high inter-individual variability. Collectively, the results of this study support that FTP20 might be used as a method of FTP60 determination in trained cyclists. Conclusions The FTP60 is similar and showed good agreement with the power output of IAT and FTP20, although some random error might be found. In addition, TTE at FTP20 was close to one hour. Therefore, this study support the concept of using FTP20 to estimate IAT and the power output sustained by a cyclist for approximately one hour. However, due to high LoA caution needs to be addressed if the FTP20 is used interchangeably with IAT and FTP60.

This White Paper presents the community inputs and scientific conclusions from the Hot and Cold QCD Town Meeting that took place September 23-25, 2022 at MIT, as part of the Nuclear Science Advisory Committee (NSAC) 2023 Long Range Planning process. A total of 424 physicists registered for the meeting. The meeting highlighted progress in Quantum Chromodynamics (QCD) nuclear physics since the 2015 LRP (LRP15) and identified key questions and plausible paths to obtaining answers to those questions, defining priorities for our research over the coming decade. In defining the priority of outstanding physics opportunities for the future, both prospects for the short (~ 5 years) and longer term (5-10 years and beyond) are identified together with the facilities, personnel and other resources needed to maximize the discovery potential and maintain United States leadership in QCD physics worldwide. This White Paper is organized as follows: In the Executive Summary, we detail the Recommendations and Initiatives that were presented and discussed at the Town Meeting, and their supporting rationales. Section 2 highlights major progress and accomplishments of the past seven years. It is followed, in Section 3, by an overview of the physics opportunities for the immediate future, and in relation with the next QCD frontier: the EIC. Section 4 provides an overview of the physics motivations and goals associated with the EIC. Section 5 is devoted to the workforce development and support of diversity, equity and inclusion. This is followed by a dedicated section on computing in Section 6. Section 7 describes the national need for nuclear data science and the relevance to QCD research.

This R\\&D project, initiated by the DOE Nuclear Physics AI-Machine Learning initiative in 2022, leverages AI to address data processing challenges in high-energy nuclear experiments (RHIC, LHC, and future EIC). Our focus is on developing a demonstrator for real-time processing of high-rate data streams from sPHENIX experiment tracking detectors. The limitations of a 15 kHz maximum trigger rate imposed by the calorimeters can be negated by intelligent use of streaming technology in the tracking system. The approach efficiently identifies low momentum rare heavy flavor events in high-rate p+p collisions (3MHz), using Graph Neural Network (GNN) and High Level Synthesis for Machine Learning (hls4ml). Success at sPHENIX promises immediate benefits, minimizing resources and accelerating the heavy-flavor measurements. The approach is transferable to other fields. For the EIC, we develop a DIS-electron tagger using Artificial Intelligence - Machine Learning (AI-ML) algorithms for real-time identification, showcasing the transformative potential of AI and FPGA technologies in high-energy nuclear and particle experiments real-time data processing pipelines.