This article discusses the problems and possible solutions for HFT data in empirical asset price models. The Capital Asset Pricing Model (CAPM) and Fama-French three-factor model are validated with high-frequency data before and after cleaning the data using advanced outlier detection methods. It use the compute tools like Isolation Forest, DBSCAN, and RPCA to spot and correct the inaccurate data points that often distort financial models. Models accuracy and robustness were improved dramatically following the data cleaning, with CAPM and Fama-French models receiving an accuracy enhancement of 0.75 to 0.89 and 0.78 to 0.85, respectively. The paper also examines classical data cleaning processes versus computational methods and the efficiency of the latter. The impact for financial modeling and asset management is far-reaching, with a message that better data means better choices and more predictable models. These results provide evidence of the importance of advanced data cleaning in high frequency trading and their ability to enhance decision making in financial markets.

The emergence of gravitational-wave astronomy has enabled us to unveil events that were previously concealed: compact binary coalescences. Neutron stars have been recognized as significant sources of gravitational radiation, thus triggering a timely exploration of their deformations. Through gravitational waves, scientists are presented with a unique opportunity to study the interiors of neutron stars and deepen the understanding of the equation of state of ultra-dense nuclear matter. In this article, reflects the necessary condition for the generation of gravitational radiation, namely the time-varying quadrupole moment, and calculate the ellipticity resulting from a neutron star’s simplistic model. Subsequently, this article examine Ushomirsky’s research on the maximum quadrupole moment, in which this term does not show any explicit dependence on mass. Finally, the author makes a comparison between simplistic model and Ushomirsky, Haskell, gives a estimation on ellipticity and maximum quadrupole moment, expecting a potential improvement on the model would be incoporating dynamic terms.,such as accretion and star quakes.

Our research examines how Natural Language Processing and Deep Learning methods help learners better understand foreign languages and cultures. Our research showed that advanced language technology tools including machine translation speech recognition and sentiment analysis help students learn both language skills and cultural understanding better. Our research team tested 100 participants across English, Chinese, and Spanish with 50 participants in each group. The experimental group studied with NLP and DL platform-based language learning platforms but the control group used traditional education methods. Researchers tested cultural knowledge and language skills for 12 weeks by giving tests before and after the experiment. The experimental group made better progress in speaking English (35%) and understanding Chinese culture (45%) compared to the control group. The learning system used NLP and DL tools to provide real-time feedback and context analysis which improved both the personalization and depth of the educational experience. These technologies show how they can enhance language and cultural learning in modern education so students benefit from them.

With the growing interest of the public in the mysteries of the universe and the continuous achievements in the field of aerospace, it has become essential to introduce the enigmatic celestial bodies of the cosmos—the black holes. This paper will outline the historical journey of human understanding of black holes, their classification, the possible mechanisms of their formation, and some cutting-edge findings aimed at stimulating the readers' interest. This review article is designed to provide readers with a basic understanding of black holes from a historical and categorical perspective. This thesis finds that, although the development of the concept of black holes involves numerous complex theories and a protracted process of exploration, the historical trajectory of their discovery and the detailed classification thereof conform to the logical progression of human cognition. Moreover, this process enables the integration of black holes into the existing astrophysical framework, thereby rendering it more complete.

In the wake of modern technology's continuous evolution, thermal transfer compositions have emerged as crucial materials for optimizing heat management across diverse fields, including electronics, energy, and automotive sectors. This review zeroes in on the research progress of thermal transfer compositions, methodically exploring aspects such as material selection, performance optimization, and application prospects. Grounded on this, it analyzes the selection of heat conductive fillers, the requisites for matrix materials, and the impact of interface effects from the perspective of materials science while also introducing advanced design and preparation technologies. Furthermore, through specific case analyses, it vividly showcases the practical application effects of thermal transfer compositions in areas like electronic product heat dissipation heat management of new energy vehicle batteries, and efficiency enhancement of solar photovoltaic panels. Eventually, it pinpoints the current challenges, namely interface compatibility, long-term stability, and cost-effectiveness, and peers into future research directions, emphasizing the importance of new material development, advanced manufacturing technologies, and interdisciplinary integration. Research findings suggest that thermal transfer compositions not only significantly improve the heat management performance of systems but also promote sustainable development and technological innovation, thus harboring extensive application potential and development prospects.

With the development of computer technology, various disciplines are committed to promoting the combination of disciplines and computer AI technology, mathematics is no exception, as one of the most important basic disciplines in the world, mathematicians are also actively trying the integration of mathematics and AI technology, one of the very popular directions is to promote the automatic proof of mathematics, with the help of AI to promote the further development of mathematics discipline, and inject new blood into mathematics discipline. But there exist some difficulties in the mathematical proving because of the differences between the Mathematical Logic and Computer Logic. To overcome the difficulties, Mathematician and computer scientist created many tools, like lean and Lean Copilot. Lean has a powerful type system and efficient proof capabilities. Lean Copilot, on the other hand, offers intelligent code suggestions and supports a variety of tasks, among many other advantages. In this paper, we will introduce the tool of mathematical formalization, LEAN, as well as the development process and future direction of mathematical automated proofs.

The Drift Tube Linac (DTL) is an important accelerator structure, primarily used to accelerate the beam from low-energy to medium-high energy stages. It is widely applied in scientific research, medicine, materials science, and other fields. This paper provides a detailed design of an 81.25MHz DTL. the dynamic design process of the Drift Tube Linac based on the LORASR software, including the optimization of drift tube geometric parameters, acceleration gap voltage, and Synchronous phase. The final design includes a 2-period KONUS structure with 19 acceleration gaps, supporting 100% transmission efficiency. It ensures stable high-energy particle transport while controlling the emittance growth below the design target of 25%.

Wildfires not only pose a danger to humans and animals, they are also harmful to the climate and air quality by producing vast quantities of CO2. Early wildfire warning systems are crucial for minimizing environmental damage, economic losses, and threats to human life. Current unmanned aerial system for wildfire detection is limited by its low-capacity power source, which significantly reduce its flight stability and travel distance. This work designs wind-dispersed microflyer system for early wildfire warning, inspired by the glider-shaped Alsomitra Macrocarpa seeds. Famous for remarkable aerodynamic stability and exceptionally low terminal velocity, the seeds of this plant have large and papery wings and can glide long distances without reliance on gusts or updrafts. The aerodynamic analysis and wind tunnel experiment were conducted utilizing the shapes of these seeds. The analysis revealed that the Alsomitra Macrocarpa seed has an excellent aerodynamic shape with weak flow separation, which is a key reason for its superior lift-to-drag ratio. The designed microflyer with a sensor will be deployed from the air and monitor wildfire-related parameters like temperature effectively and reliably, demonstrating its potential for long-distance wind-assisted fire warning system.

Bayesian methods, rooted in Bayes' theorem, offer a robust framework for statistical inference and decision-making by integrating prior knowledge with new evidence. This paper examines the theoretical foundations, learning paradigms, and applications of Bayesian methods in fields such as data mining, credit risk assessment, and actuarial science. Parametric and theoretical learning are highlighted as essential methodologies that enhance Bayesian inference’s adaptability and reliability in complex environments. The study highlights recent progress, particularly enhancements in Bayesian network architectures and computational methodologies like Markov Chain Monte Carlo (MCMC) and variational inference, which have broadened the applicability of Bayesian techniques to high-dimensional datasets. However, significant challenges remain, particularly in managing computational complexity and addressing ethical considerations, such as data privacy and fairness, in sensitive domains like healthcare. The paper concludes by advocating for continued innovation to overcome these barriers, focusing on scalable algorithms and interdisciplinary collaboration to ensure ethical and efficient implementations. By addressing these challenges, Bayesian methods can sustain their transformative potential across diverse fields, driving progress in data-driven research and decision-making practices.

This paper delves into the evolution of airfoils and the various design methods that have been utilized over time. The application of airfoils, which is crucial to aircraft design, pertains to the shaping of wings, compressors, and turbine blades, significantly impacting lift and drag forces. Historical developments in airfoil design have seen transitions from basic shapes to refined, mathematically defined profiles. This study particularly emphasizes the CST (Class function/Shape function Thickness distribution) Method as an advanced and efficient approach for airfoil generation and optimization. Using Python3 and the application Profili, different airfoil shapes are computed and evaluated through numerical simulations in COMSOL Multiphysics. The simulations aimed to optimize several performance characteristics such as the lift coefficient, pressure distribution, and maximum angle of attack. The results highlight the enhanced performance of the newly generated airfoil compared to traditional designs, with a noticeable increase in maximum lift coefficient and a smoother pressure profile. This research illustrates the potential of the CST method coupled with computational tools to innovate airfoil designs, offering significant improvements for future applications. Future work will focus on optimizing CST parameters for specific performance criteria, extending the versatility and efficiency of the airfoil design process.