Climate change has significantly increased adverse effects on human health, and economic growth^1–3. However, few studies have differentiated the impacts of extreme temperatures at the city level, and analysed the future implications for human health under various climate change scenarios.^4–6 Here, data on historical relationship among six kinds of climate-sensitive diseases (CSD) hospitalizations and temperatures across 301 cities (over 90% of all cities) and more than 7,000 hospitals in China are leveraged, and a nonlinear distributed lag model is used. This study projects hospitalization risks associated with extreme temperatures through to the year 2100 and develops the Hospitalization Burden Economic Index to assess the burden under three carbon emission scenarios in cities. Five dimensions including spatial distribution, disease categories, population age groups, future time horizons, and carbon emission development pathways have been evaluated. Historical data specifically indicate more temperature-related risks among the CSDs in northwestern and southwestern China. Notably, gestation-related disease risk is associated with increased vulnerability to extreme heat in specific regions. The projections reveal that, under current thermal conditions with no adaptations, the excess hospitalizations from extreme heat will reach 5.1 million people by 2100 under the high emission scenario.These findings highlight the need for targeted climate change mitigation strategies to reduce uneven climate-related hospitalization risks and economic burdens while accounting for differences in city geography, extreme temperatures, population groups and carbon emission development pathways.

The development of vapor deposition technology will accelerate the process of perovskite solar cells (PSCs) moving from laboratory scale to industrialization. However, the multi‐dimensional and complex parameter space will inevitably increase the cost of trial and error, especially for high‐energy‐consuming and long‐cycle vapor deposition technologies. This study employed an integrated‐feature dataset encompassing macro‐ and micro‐features to enhance the accuracy, robustness, and interpretability of an ETree machine learning (ML) model for power conversion efficiency (PCE) prediction, achieving an impressive coefficient of determination value of 0.9464 and root mean square error value of 1.27%. Through SHAP analysis, Monte Carlo simulations, and parameter space exploration, an optimal FTO/SnO ₂ /Cs _0.04 FA _0.96 PbI ₃ /Spiro‐OMeTAD/Au device architecture and vapor deposition parameters are reverse‐engineered, yielding the highest predicted PCE of 26.21%. Furthermore, the PCEs are enhanced by implementing the universal ML‐derived optimization strategies across six distinct and independent vapor deposition processing, which truly realizing the objective of ML‐guided experiments based on various preparation conditions. This machine learning model is believed to shorten the research and development cycle for breaking through the performance bottleneck of high‐efficiency devices fabricated by vapor deposition technology, providing a potential approach for its commercial application.

Optimal ovarian stimulation (OS) selection is critical for IVF success, but expert-based decisions often lack consistency in outcomes, cost-efficiency, and personalization, highlighting the need for more individualized and data-driven approaches. This study propose an artificial intelligence (AI) system that analyzes extensive IVF-ET cycles to uncover OS-pregnancy outcome relationships, enabling personalized treatment recommendations while improving success rates and minimizing unnecessary costs. This study analyzed anonymized data from 17,791 patients undergoing OS and IVF/ICSI at Tongji Hospital between May 2015 and May 2019. An adaptive AI model was developed to predict key indicators-including progesterone (P), number of oocytes retrieved (NOR), estradiol (E2), and endometrial thickness (EMT) on the hCG day-by integrating personal characteristics, ovarian reserve, and etiological factors. This model facilitated personalized OS selection, pregnancy outcome grading, and the development of an AI-driven clinical decision support system (CDSS). The key indicators-progesterone (P), number of oocytes retrieved (NOR), estradiol (E2), and endometrial thickness (EMT) on the hCG day-were used to establish a pregnancy grading system. Pregnancy rates are stratified as follows: Level IV (Total Score 15-16), 0.55; Level III (Total Score 13-14), 0.44; Level II (Total Score 11-12), 0.24; and Level I (Total Score 4-10), 0.07. After OS optimization, 1,355 patients who were initially at level I were elevated to a better level. Of the 2,341 patients initially in level II, 2,290 improved, and of the 3,839 initially in level III, 1,448 improved. Patients elevated to level IV accounted for 80 percent of all cases. The CDSS prioritized a GnRH antagonist regimen for 54.64 % of patients, resulting in per-patient time savings of 15.39-33.48 days and cost reductions of ¥989-¥2,623 compared to non-optimal to antagonist. Scaled to China's > 1 million ART cycles annually, this corresponds to projected direct savings of approximately ¥0.54-1.43 billion per year. In the new evaluation datasets (n = 4,251), implementation of CDSS recommendations increased the clinical pregnancy rate from 0.452 to 0.512 (p < 0.001) and reduced mean per-cycle cost from ¥7,385 to ¥7,242 (p = 0.018), demonstarting cost-effectiveness dominance with ICER saving of ¥2,383 per additional clinical pregnancy. This AI-assisted CDSS streamlines clinicians' decision-making by enabling efficient and accurate initial judgments on OS, standardizing and personalizing recommendations, and optimizing OS for effectiveness and cost-efficiency.