Characterization of manufacturing-induced surface scratches and their effect on laser damage resistance performance of diamond fly-cut KDP crystal

Manufacturing-induced defects have drawn more and more attentions to improve the laser damage resistance performance of KDP crystal applied in high-power laser systems. Here, the morphology of surface scratches on diamond fly-cut KDP crystal is characterized and their effect on the laser damage resistance is theoretically and experimentally investigated. The results indicate that surface scratches could lower laser-induced damage threshold (LIDT) by modulating incident lasers and producing resultant local light intensifications. The induced maximum light intensity enhancement factors (LIEFs) are dependent on scratch shapes and dimensions. The diffraction effects originating from scratch edges are responsible for the strongest light intensification. Even for ultra-precision finished KDP surface with scratches that well satisfy the currently applied scratch/dig specification, the induced LIEFs are quite high, indicating that the actual defect dimension allowance should be amended and specified according to the defect-induced LIEFs. The effect of scratches on laser damage resistance is experimentally verified by the tested LIDT, which is approximately consistent with the simulation one. The morphologies of laser damage sites further confirm the role of scratches in lowering LIDT. This work could offer new perspective and guidance for fully evaluating the performance of ultra-precision manufactured optical materials applied in high-power laser facilities

Towards Applying Powerful Large AI Models in Classroom Teaching: Opportunities, Challenges and Prospects

This perspective paper proposes a series of interactive scenarios that utilize Artificial Intelligence (AI) to enhance classroom teaching, such as dialogue auto-completion, knowledge and style transfer, and assessment of AI-generated content. By leveraging recent developments in Large Language Models (LLMs), we explore the potential of AI to augment and enrich teacher-student dialogues and improve the quality of teaching. Our goal is to produce innovative and meaningful conversations between teachers and students, create standards for evaluation, and improve the efficacy of AI-for-Education initiatives. In Section 3, we discuss the challenges of utilizing existing LLMs to effectively complete the educated tasks and present a unified framework for addressing diverse education dataset, processing lengthy conversations, and condensing information to better accomplish more downstream tasks. In Section 4, we summarize the pivoting tasks including Teacher-Student Dialogue Auto-Completion, Expert Teaching Knowledge and Style Transfer, and Assessment of AI-Generated Content (AIGC), providing a clear path for future research. In Section 5, we also explore the use of external and adjustable LLMs to improve the generated content through human-in-the-loop supervision and reinforcement learning. Ultimately, this paper seeks to highlight the potential for AI to aid the field of education and promote its further exploration.Comment: 16 pages, 2 figure

Carbon Price Forecasting with Quantile Regression and Feature Selection

Carbon futures has recently emerged as a novel financial asset in the trading markets such as the European Union and China. Monitoring the trend of the carbon price has become critical for both national policy-making as well as industrial manufacturing planning. However, various geopolitical, social, and economic factors can impose substantial influence on the carbon price. Due to its volatility and non-linearity, predicting accurate carbon prices is generally a difficult task. In this study, we propose to improve carbon price forecasting with several novel practices. First, we collect various influencing factors, including commodity prices, export volumes such as oil and natural gas, and prosperity indices. Then we select the most significant factors and disclose their optimal grouping for explainability. Finally, we use the Sparse Quantile Group Lasso and Adaptive Sparse Quantile Group Lasso for robust price predictions. We demonstrate through extensive experimental studies that our proposed methods outperform existing ones. Also, our quantile predictions provide a complete profile of future prices at different levels, which better describes the distributions of the carbon market

Do Asian women do as well as their Caucasian counterparts in IVF treatment : cohort study

Aim: To evaluate if there is a difference in pregnancy rate between Asian and Caucasian women when they undergo in vitro fertilization (IVF). Methods: This was a retrospective cohort study set in a private reproductive medicine clinic. The study consisted of a total of 2594 patients (Asian, n = 522; Caucasian, n = 2072) undergoing IVF managed by a single doctor over a 10 year period. The main outcome measures were clinical pregnancy rate and live birth rate. Logistic regression was used to control for confounding factors. Results: Asian women achieved a significantly lower clinical pregnancy and live birth rate than their Caucasian counterparts, despite replacement of more embryos. This difference was not significant after controlling for age and duration of infertility. Despite higher doses of gonadotrophin, they achieved fewer oocytes and had resultant fewer embryos for transfer or cryopreservation. Conclusions: In a study designed to reduce the effect of confounding factors by looking at a large number of patients from a single IVF unit under the care of a single doctor, there does not appear to be a difference in IVF pregnancy rate as a result of race. Asian women tend to present for IVF treatment at a later age after having tried for a longer period of time and this contributes significantly to their lower pregnancy rate.6 page(s