Adequate sleep is essential for adolescents' cognitive function and academic performance. This study examines the relationship between sleep duration and academic outcomes among senior high school students. The data in this study were collected from 10th and 11th-grade students in international schools in Qingdao and Dalian. A one-sample z-test for proportion assessed whether a significant number of students slept less than six hours per night. Results indicated that a substantial proportion of students experienced insufficient sleep. However, no significant correlation was found between sleep duration and academic performance, suggesting that other factors may influence academic outcomes. Instead, the results suggest that factors beyond school type—such as time management, extracurricular commitments, and individual lifestyle choices—may play a more crucial role in determining students' sleep duration. These findings underscore the need for further research to explore additional variables affecting students' academic achievements.

In laser welding, the plasma plume often shields the input energy, causing laser beam defocusing and significantly impacting welding efficiency and quality. This paper elucidates experimental facts concerning the attenuation and dispersion of laser energy at varying ambient pressures, and derives the associated absorption and refraction mechanisms of the plasma plume. By analyzing experimental and simulation results from scholars, the mechanism by which laser-plasma interaction affects weld formation is illustrated. A comprehensive analysis is conducted on the impact of ambient pressure and welding speed on weld morphology. The conclusion drawn is that there is a good correspondence between the influence of ambient pressure on melting depth and plasma plume, with a material-dependent critical pressure value. Furthermore, higher welding speeds decouple ambient pressure from weld depth but contribute to reducing porosity defects. Therefore, appropriate welding speeds and ambient pressures should be selected based on actual industrial application scenarios. Finally, the paper suggests future research on LWSP should focus on real-time monitoring with multi-sensor fusion, optimizing machine learning models, exploring novel imaging techniques, and combining LWSP with hybrid welding.

Galaxy mergers are a crucial pathway for the growth and evolution of galaxies at the cosmic center. Supermassive black holes (SMBHs) are commonly present at the centers of galaxies. During galaxy mergers, the central SMBHs may form binary black hole systems and, in some cases, merge. Detecting and studying supermassive binary black hole systems within galaxies is a significant aspect of galaxy evolution research. Active black holes produce prominent broad emission lines, which serve as effective probes of such activity. The presence of two distinct sets of broad emission lines with velocity differences has traditionally been considered a key indicator for identifying binary black holes. However, the probability of both black holes in a binary system being active is extremely low. More commonly, binary systems consist of one active and one quiescent black hole. The spectral signature of such systems is characterized by a significant velocity offset between the broad emission lines and the system's narrow emission lines. This velocity difference has also led to the discovery of "recoiling" black holes, driven by gravitational wave radiation from closely bound binary systems. Recoiling black holes are crucial observational targets for studying black hole mergers, binary orbital evolution, and galaxy mergers.We have identified a class of recoiling black holes with partially obscured nuclear regions. Dust in the nuclear region attenuates the intense radiation from the active black hole, allowing the host galaxy's emission to become visible alongside the nuclear radiation and broad-line features. This discovery provides a unique perspective for exploring the physical connection between the evolution of binary black hole systems and galaxy evolution. Further studies using multi-wavelength photometry, high-resolution spectroscopic analysis, and long-term spectral monitoring of partially obscured supermassive binary black hole systems are expected to reveal the physical processes underlying black hole mergers and galaxy mergers, as well as their significant role in galaxy evolution.

With the economic development, environmental regulating equipment such as air conditioners have improved indoor temperature while also bringing about problems such as dry air and deteriorated air quality. This paper takes a rectangular room as the research object, based on the Navier-Stokes equation and Fourier's law of heat conduction, designs the optimal shape and size, and simulates the temperature changes in summer and winter under different influencing factors. Through random search algorithm and finite difference method for solution, the optimal shape and size of the air conditioner are determined. Then, using the Lagrangian CFD method, the influence of the position, quantity, wind direction, angle, wind speed and air volume of the air conditioner on temperature changes is deeply studied. The results show that the summer regulation effect of the air conditioner is better than that of winter. The research in this paper provides theoretical basis and optimization schemes for air conditioner design, and helps to improve the energy efficiency and user experience of air conditioners.

This study evaluates the applicability of ERA5-Land and NCEP/NCAR reanalysis precipitation data in the Sichuan-Yunnan region by comparing them with GPM-IMERG satellite observations from 1998 to 2023. The analysis focuses on seasonal variations in precipitation across Sichuan-Yunnan Region and its surrounding areas. Results indicate that both datasets effectively capture the seasonal precipitation patterns observed in IMERG, with precipitation peaking in summer, followed by autumn and spring, and reaching its lowest levels in winter. On average, summer precipitation is approximately seven times higher than in winter. Over the full study period (1998–2023), both datasets tend to overestimate precipitation compared to satellite observations. However, from 2010 to 2023, ERA5-Land exhibited smaller biases than NCEP/NCAR, whereas from 1998 to 2010, their biases were relatively similar. In terms of spatial distribution, ERA5-Land generally overestimates precipitation, particularly in the western Hengduan Mountains and in parts of Sichuan and Yunnan, where summer and autumn precipitation exceed 1000 mm. Meanwhile, NCEP/NCAR consistently identifies a high-precipitation center (peaking at around 500 mm) in Sichuan Province, with significant precipitation extending eastward into Sichuan and western Yunnan. During winter and spring, this high-precipitation center shifts further east, reaching southern Yunnan. Notably, in summer and autumn, NCEP/NCAR underestimates precipitation by approximately 650 mm in areas west of Sichuan and east of Yunnan.

Large Language Models (LLMs) have demonstrated powerful capabilities in the field of code generation, with a deep understanding of the semantics and functionality of code. Building websites is one of the most important tasks in software development, as it utilizes rich frontend displays and backend processing to achieve various service functions. It is one of the most widely used interactive software models. Although there have been some efforts in Web website generation, these efforts have been limited to the automation of generating Web pages. The advent of LLMs provides a new approach to Web site generation tasks. However, there is currently a lack of comprehensive evaluation of the generation performance of LLMs in this context, making it difficult to optimize and improve the generated results in a targeted manner. To address this issue, this paper conducts a multi-angle investigation and analysis of the performance of LLMs in Web site generation tasks. Firstly, Web generation requirements are collected, and effective prompt engineering is designed. These prompts are then input into different LLMs to initiate the self-iteration process. Next, the generated code is fed back into the LLM for security self-iteration, where the model performs vulnerability detection and repair on the code it has generated. The security-enhanced code is subsequently subjected to manual review, where it is evaluated using predefined quantitative metrics to generate indicator values. Finally, through testing, the quality, security, and code defects of the generated front-end and back-end Web code across different LLMs are analyzed, providing a comprehensive evaluation of the generation results. The experiments demonstrate that LLM systems perform well in completing and implementing the functions and layouts of pages in prompts for Web generation tasks, but there remains room for improvement in the security of the Web code.

This paper investigates black hole spin using the relativistic reflection method, which analyses distorted X-ray spectra to understand accretion dynamics near the event horizon. The RELXILL series of models are very popular in anaylising refletion features. These Models enable a detailed and consistent understanding of the interaction between the accretion disk and relativistic effects. The paper shows the spin distribution of 33 black holes, including 14 stellar-mass black holes and 19 supermassive black holes. [1] High-spin (≥0.8) systems dominate, highlighting their association with intense relativistic effects near the innermost stable circular orbit. The high spin in stellar-mass black holes is driven by binary accretion, while the high spin in suppermassive black holes is driven by prolonged disk accretion and aligned mergers. [2] These results confirm the reliability of the method and provide insights into black hole evolution and accretion physics. Future research should explore how evolving models can refine spin predictions for complex systems.

As a commonly used symmetric encryption method, AES (Advanced Encryption Standard) still has some steps that cannot fully ensure the effectiveness of encryption, and Symmetric Key Encryption has the risk of key leakage in common. This paper mainly researches an optimization model of AES encryption method, which is based on the original AES encryption method. This model adds another password in the SubByte and MixColumns step, in order to ensure the effectiveness of encryption. The optimization model is achieved with programs on python. Experimental data shows that the encryption process is too regular due to the methodologies of the model itself: the location of those bytes after mapping can be separated into some sets, and the transforms of this model can’t exchange the mapping of those bytes from different sets. After analyzing and optimizing the model, another improved model is worked out, which can simply confuse the bytes with their locations. The experiments show that the next model has a better encrypting effect than the first one.

Large-scale deep learning weather prediction models are revolutionizing the field of weather forecasting. GraphCast is the current state-of-the-art model, but its training is not explicitly designed for predicting extreme weather events. However, extreme weather prediction is more critical because it directly impacts public safety, potentially saving a lot of lives and resources. This paper improves large-scale weather models such as GraphCast by introducing an uncertainty estimation module to differentiate the importance of extreme weather data. We hypothesize and demonstrate that regions with higher uncertainty are more prone to cause prediction errors. By fine-tuning large-scale weather prediction models such as GraphCast with our uncertainty-aware weighting method, we enhance extreme weather forecasting in extreme cases where predictions were previously poor. Our approach provides a pathway for more accurate extreme weather forecasts and a pipeline for future model fine-tuning efforts.

This report explores methods to enhance the sound quality of drums and the potential to generate two distinct tones from a single strike through modifications to drum head structure. The study begins with a theoretical analysis of drum sound production, focusing on the vibrational behavior of the drum head and how factors such as membrane diameter, density, and thickness influence the frequencies generated. Experiments were conducted to assess the impact of drum size, drum head thickness, pretension, and material properties on sound generation and repeatability. While none of these factors alone produced dual-tone effects, further exploration into structural modifications, specifically partial thickness changes in the drum head, revealed improvements in sound quality. Although the goal of generating two distinct tones was not achieved, the findings demonstrate that targeted modifications can enhance the purity and richness of the drum’s sound. Future research will focus on combining selective stiffness adjustments and varied striking positions to achieve the desired tonal outcomes.