Fatigue specifically affects the force production capacity of the working muscle, leading to a decline in athletes’ performance. This study investigated the impact of fatigue on ankle flexor muscle activity and ground reaction forces (GRFs) in elite table tennis players, with a focus on the implications for performance and injury risk. Twelve elite male table tennis athletes participated in this study, undergoing a fatigue protocol that simulated intense gameplay conditions. Muscle activity of the soleus (SOL) and gastrocnemius lateralis (GL) muscles, heel height, and GRFs were measured using a combination of wireless electromyography (EMG), motion capture, and force plate systems. Results showed a significant decrease in muscle activity in both legs post-fatigue, with a more pronounced decline in the right leg. This decrease in muscle activity negatively affected ankle joint flexibility, limiting heel lift-off. Interestingly, the maximal anteroposterior GRF generated by the left leg increased in the post-fatigue phase, suggesting the use of compensatory strategies to maintain balance and performance. These findings underscore the importance of managing fatigue, addressing muscle imbalances, and improving ankle flexibility and strength to optimize performance and reduce the risk of injuries.

The precision of centroid location for infrared LED point images is an important factor, which affects the precision of the light-pen Coordinate Measuring Machining (CMM) .In this paper, the error model using the traditional center of mass (COM) algorithm is analyzed and it is shown that there are systematic error and random error. This paper analyzes the systematic error and random error, and then adopts the Gaussian surface fitting (GSF) algorithm and weighted centre-ofgravity (WCOG) algorithm to compensate systematic errors, meanwhile it analyzes how to reduce random error. The simulation results show that the accuracy of the infrared LED point position can be enhanced from 1/ 20 pixel to 1/100 pixel using the new algorithm.

The new material introduced by the ITTF in 2014 for table tennis balls has attracted significant attention from players and coaches. Changes in both material selection and production procedures are likely to have affected the static performance of the ball. However, the raw data regarding the elasticity and hardness of these new material balls, encompassing various brands and structures, often lacks practical information crucial for players’ rapid adaptation and daily training. The static properties tested in this study were provided by the ITTF, covering both hardness and elasticity. Based on computed variables, this study revealed that the hardness of seam balls at the equator was not consistently higher than that at the pole. Additionally, the study confirmed that the hardness and bounce height of new material balls exceeded those of celluloid. Furthermore, correlation analysis was conducted to examine the relationship between these two properties, revealing a significant correlation between the hardness of seamless balls and their elasticity. This study provides an analysis of the static performance of various types of new material balls, aiding players and coaches in better understanding official event balls and offering a theoretical foundation for the formulation of diverse training and game strategies.

Primary urethral non-Hodgkin’s lymphoma (NHL) is uncommon. This case study reports the case of a 52-year-old woman with a light red mass at the urethral orifice. Her clinical manifestations included frequent urination, urgency, dysuria, and occasionally blood in urine. The tumor was surgically removed, and the pathological and immunohistochemistry examination confirmed the presence of a diffuse large B-cell lymphoma (DLBCL). Examination using positron emission tomography/computed tomography (PET/CT) revealed multiple hypermetabolic lymph nodes next to the external iliac vessels and bilateral inguinal regions, and focal hypermetabolic lesions in the vulvar nodules (consistent with the changes caused by lymphoma infiltration). Eight cycles of chemotherapy with rituximab, cyclophosphamide, doxorubicin, vincristine, and dexamethasone (R-CHOP) were prescribed after surgery. Re-examination using PET/CT showed lack of hypermetabolic tumor recurrence signs, and the presence of multiple slightly large lymph nodes in the lateral iliac vessels and inguinal regions, but without significant lymph node metabolism increases. To date, only fifteen cases of primary diffuse large B-cell lymphoma of the urethra (including this one) have been reported in the literature. Therefore, we reviewed the etiology, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of DLBCL based on the existing literature to help characterize this rare disease.

Accurate measurement of excavator working resistance constitutes a critical prerequisite for achieving intelligent autonomous operation and energy-efficient control. However, traditional analytical methods suffer from excessive computational complexity and reliance on prior soil parameters, while existing data-driven approaches are often constrained by singular predictive models, leading to compromised generalization capability and robustness. To address this challenge, a soft-sensing method for excavator working resistance based on Stacking ensemble learning is proposed. The time-varying characteristics of working resistance are first derived from theoretical excavator dynamics modelling to extract physics-informed features. Subsequently, a data-driven Stacking ensemble learning model is established, where the Pearson correlation coefficient is utilized to quantify the diversity among base learners and determine the optimal ensemble architecture. A 5-fold time-series cross-validation strategy is implemented to strictly preserve temporal causality and prevent data leakage. Experiments results based on real-world excavator data demonstrate that the Stacking model achieves a mean absolute error (MAE) of 4.4553, a root mean square error (RMSE) of 6.3949, and a coefficient of determination (R²) of 0.9266, consistently outperforming various individual baseline models. Multi-dimensional error analyses verify the model's superior stability and predictive precision in capturing transient load peaks. The proposed method effectively overcomes the limitations of single models, providing a highly accurate and robust soft-sensing foundation for the intelligent control of construction machinery under complex working conditions.