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Purpose: This study aimed to investigate the effect of PMS on aerobic, anaerobic, and cognitive performance in female combat athletes. Method: In this semi-experimental study with a repeated measures design, 20 female combat athletes (mean age: 24.5±2.1 years) were assessed during two phases of their menstrual cycle: the PMS phase (7-10 days before menstruation) and the non-PMS phase (days 7-14 of the cycle). Aerobic performance was measured using the Bruce treadmill test, anaerobic performance was assessed by the Wingate test, and cognitive performance was evaluated through simple and choice reaction time tests. Statistical analyses were performed using paired t-tests and Pearson correlation (p<0.05). Results: The findings showed a significant reduction in VO₂ max (6.2%), time to exhaustion, and a significant increase in maximal heart rate and Rate of Perceived Exertion (RPE) during the PMS phase compared to the non-PMS phase (p<0.05). Anaerobic performance also declined, with a reduction in peak power (6.2%) and mean power (7.5%), an increase in fatigue index (16.2%), and lower blood lactate levels (p<0.01). Cognitive performance deteriorated during PMS, demonstrated by increased simple (16.7%) and choice (16.6%) reaction times, more errors, and decreased accuracy (p<0.01). Furthermore, there was a significant positive correlation between PMS symptom severity and VO₂ max reduction (r=0.72), as well as between blood lactate levels and reaction time (r=0.65) (p<0.01). Conclusion: PMS negatively affects aerobic, anaerobic, and cognitive performance in female combat athletes. These findings highlight the importance of considering the menstrual cycle phases in designing training and competition schedules for female athletes to optimize performance and prevent potential declines associated with PMS.

Ecological cities seek to create a sustainable balance between human activities and the environment. Based on the objective, this is applied-developmental research. In the qualitative section, grounded theory and MAXQDA software were used for qualitative data analysis. Interpretive-structural modeling and a structural self-interaction matrix were used to provide the initial model. In the quantitative analysis section, the partial least squares technique, structural equation modeling with PLS software, was used. The location and case study are the city of Qazvin. The characteristics and dimensions related to the ecological city in the community, geographical dimension, physical boundary, and sphere of influence with eco-urban dimensions have been studied and analyzed. The results seek to raise awareness and assist in generalizing to similar societies and expanding its application. The statistical population in the qualitative section includes experts, university professors, and urban management specialists. The sampling method combines two methods: purposive non-probability sampling and theoretical sampling. The questionnaire includes 15 main factors and 60 items. Using the power analysis rule at a 95% confidence level, with an effect size of 0.15 and a statistical power of 80%, the minimum sample size was estimated to be 145 people. For greater certainty, 150 questionnaires were collected. The relationships of the constructs affecting the model were investigated and identified. After identifying the sequence of constructs, they were classified in the MICMAC diagram based on the power of influence and the degree of dependence.