UNASSIGNED: Although previous research found that small-sided game (SSG) training was more enjoyable than high-intensity interval training (HIT) in various sports, no data were provided during longer training period in basketball. Furthermore, the comparison of internal loads between the two training approaches needs to be further examined. Thus, this study aimed to examine the acute physiological, perceived exertion and enjoyment responses during 4-week progressive basketball SSG or HIT programs.
UNASSIGNED: Nineteen female collegiate basketball players were randomly assigned to two groups that performed either HIT (n = 10) or SSG (n = 9) 3 times per week for 4 continuous weeks. Average and percentage of maximal heart rate (HRmean and %HRmax), rating of perceived exertion (RPE), and physical activity enjoyment (PACES) were determined during each training session.
UNASSIGNED: There was a main group effect in PACES (p < 0.001; ηp2 = 0.44, moderate), and SSG had higher PACES than HIT in each week (p < 0.05). There were no significant interactions or main group effects in HRmean, %HRmax or RPE, but a main time effect was found in HRmean (p = 0.004; ηp2 = 0.16, minimum), %HRmax (p < 0.001; ηp2 = 0.25, minimum), and RPE (p < 0.001; ηp2 = 0.31, moderate), respectively. In the SSG group, although no significant differences were found in HR responses, %HRmax was below 90% in week 1 and week 2. Accompanied with changes in %HRmax, RPE in week 1 and week 2 was lower than that in week 3 and week 4 (p < 0.05).
UNASSIGNED: Our findings suggest that SSG and HIT elicit similar acute HR response and RPE level, but SSG is perceived as more enjoyable and therefore it is more likely to increase exercise motivation and adherence comparing to HIT. Moreover, it seems that half-court, 2 vs. 2 SS Gtraining format with modified rules and lasting ≥ 7.5 min should be prescribed as an enjoyable training alternative to provide optimal cardiovascular stimuli (> 90% of HRmax) for female basketball players.

In order to study the load-bearing failure characteristics of a RPCCP under internal load, a field prototype test was designed, and a finite element model was established. An internal load was applied up to 2.0 MPa step by step and the force variation law of each part was obtained. During the production of the RPCCP, by wrapping prestressed steel bars around the concrete core with a cylinder, the core was subjected to an initial precompression stress. In the loading process, the protective cover cracked first, from where the concrete core gradually changed from the initial compression state to a tension state, finally cracking from the inner and outer diameter. The stresses of the cylinder and steel bars increased steadily with the internal load and did not yield. The finite element calculation results were in good agreement with the test results, and the influence characteristics of the tension control stress of the steel bar and the concrete strength on the failure of the RPCCP under internal load were discussed. The results showed that the internal load of the protective cover was independent of the tension control stress, but decreases with a decrease in concrete strength, while the load corresponding to the concrete core entering plasticity is related to the tension control stress and the concrete strength, and the relationships were basically linear.

This study demonstrates the utility of internal nutrient loads as an additional parameter to improve the performance of machine learning models in predicting the temporal variations of aqueous TN and TP concentrations in Taihu Lake, a large shallow lake. Internal loads, as a potential input parameter for machine learning models, were estimated using a mass balance calculation. The results showed that between 2011 and 2018 the maximum monthly internal loads of nitrogen and phosphorus in Taihu Lake were 4200 t and 178 t, respectively. Monthly changes in the aqueous TN and TP concentrations of Taihu Lake did not correlate significantly with inflow loads whereas the correlations with estimated internal loads were positive and significant. Long short-term memory (LSTM), random forest (RF), and gradient boosting regression tree (GBRT) models were built, and for all of them the inclusion of internal loads in the input parameters improved their performance. LSTM model III, whose input parameters included both inflow loads and internal loads, had the best performance, based on a testing root mean square error of 0.11 mg TN/L and 0.017 mg TP/L. A 28 % decrease in the annual aqueous TP concentration in Taihu Lake in 2018 simulated by LSTM model III was achieved by lowering the average water level from 3.29 m to 2.99 m, suggesting a possible strategy to control the TP concentration in the lake. In summary, our study showed that aqueous TN and TP concentrations in shallow lakes can be simulated using machine learning, with LSTM models outperforming RF and GBRT models; in these models, internal loads should be included as an input parameter. Additionally, our study identified the water level as an important factor affecting the aqueous TP concentration in Taihu Lake.

The cyclic interaction between myosin crossbridges and actin filaments underlies smooth muscle contraction. Phosphorylation of the 20-kDa myosin light chain (MLC20) is a crucial step in activating the crossbridge cycle. Our current understanding of smooth muscle contraction is based on observed correlations among MLC20 phosphorylation, maximal shortening velocity (Vmax), and isometric force over the time course of contraction. However, during contraction there are changes in the extent of phosphorylation of many additional proteins as well as changes in activation of enzymes associated with the signaling pathways. As a consequence, the mechanical manifestation of muscle contraction is likely to change with time. To simplify the study of these relationships, we measured the mechanical properties of airway smooth muscle at different levels of MLC20 phosphorylation at a fixed time during contraction. A simple correlation emerged when time-dependent variables were fixed. MLC20 phosphorylation was found to be directly and linearly correlated with the active stress, stiffness, and power of the muscle; the observed weak dependence of Vmax on MLC20 phosphorylation could be explained by the presence of an internal load in the muscle preparation. These results can be entirely explained by the Huxley crossbridge model. We conclude that when the influence of time-dependent events during contraction is held constant, the basic crossbridge mechanism in smooth muscle is the same as that in striated muscle.