Metal meshes are commonly used as reflective elements in far infrared astronomical instruments, where spectrally uniform reflectivity is important for stable Fabry–Perot interferometer performance and reliable calibration. In this work, we quantitatively compare the spectral reflectivity uniformity of representative metal mesh geometries using full wave electromagnetic simulations. Square and circular meshes with inductive and capacitive topologies are analyzed over the 200 to 400 GHz band. A figure of merit based on the normalized root mean square deviation of reflectance from a target value is used to evaluate spectral flatness. Parameter sweeps over lattice pitch and normalized feature size reveal well defined regions that minimize spectral variation. All four geometries can achieve comparable reflectivity uniformity when properly optimized, with square inductive meshes yielding the lowest figure of merit within the explored design space. The results provide practical guidance for selecting metal mesh geometries with flat reflectance profiles in far infrared Fabry–Perot interferometers and related optical systems.

Study of low Reynolds number flow environments are very practical in microfluidic systems, biological fluid scenarios, and micro underwater vehicles. Due to the influence of fluid viscosity, traditional propeller propulsion mechanisms completely fail in this environment. The spiral flagellar structure inspired by microorganisms can achieve stable propulsion in low Reynolds number fluids. However, the verification of its related theoretical models still lacks sufficient experimental support, and existing research mostly focuses on numerical theoretical derivation. Therefore, this article takes the mathematical model proposed by Lauga and Magariyama etc. of the spiral flagellar propulsion effect of micro robots in low Reynolds number fluids as the research object. Using both CFD simulation and real built robot to verify the model. The research results indicate that the result obtained from CFD simulation is highly consistent with the mathematical model proposed by Lauga and Magariyama. And the physical model further verified the rationality of the theoretical model. The research results can provide direct experimental basis for the design and optimization of biomimetic spiral flagellar propulsed robot in low Reynolds number environment.

This paper provides a comprehensive review of the research on Pluto, covering its physical properties, orbital dynamics, and its role in the evolution of the Kuiper Belt. In particular, it examines the key characteristics of Pluto's orbit, including its high eccentricity and pronounced inclination, and discusses prevailing formation theories, particularly its location within the Kuiper Belt and its complex interactions with Neptune, governed by mean-motion resonance mechanisms. In addition, this study further explores the practical implications of Pluto's unique orbital resonance, illustrating how this mechanism can be harnessed for gravity-assist maneuvers to substantially reduce fuel consumption in deep space missions. The results reveal that Pluto's orbital characteristics not only significantly influence the dynamics of the Kuiper Belt but also provide valuable guidance for optimizing trajectories, offering important references for the more efficient planning of future missions to Pluto and other outer Solar System bodies.

The theory of cosmic inflation has resolved the horizon and flatness dilemmas in standard Big Bang cosmology and successfully predicted the existence of primordial density perturbations and primordial gravitational waves. This paper systematically reviews three theoretical frameworks of inflation: classical slow-roll inflation, bounce cosmology, and the unified model (Quintessential Inflation). In the theoretical section, this paper introduces the dynamic equations of the inflationary field, the slow-roll condition, the power spectrum of primordial perturbations, and the mechanism for breaking the zero-energy condition in bounce cosmology. In the applied section, this paper analyzes the constraints of the cosmic microwave background radiation on the inflationary model using the latest observational data from the Planck satellite and the BICEP/Keck experiment, and discusses the enhancement effect of the kination phase on the primordial gravitational wave spectrum. The results show that the bounce model can explain the suppressed anomaly of the large-scale power spectrum of the CMB, while the unified model not only predicts gravitational wave signals that can be detected by future detectors (such as LISA), but also provides a natural solution to the problem of fine-tuning dark energy.

The global gambling industry has maintained a long-term stable profit growth trend, yet the public generally attribute its profitability to random luck. In fact, the industry's sustained earnings are not driven by chance, but by the inherent mathematical and economic logic supported by probability theory. This study takes casinos as the typical representative of the gambling industry, with expected return and the gambler's ruin model as the core analytical tools, and explores the intrinsic profit mechanism of the gambling industry from the perspectives of probability theory and economics. Through theoretical deduction and logical analysis, this research reveals that casinos obtain a stable mathematical edge by rationally designing game rules and setting odds, which inherently leads to negative expected returns for individual gamblers. Meanwhile, relying on the law of large numbers, casinos effectively smooth out short-term profit and loss fluctuations, ensuring the stability of operational income. In addition, the industry's substantial financial strength, mature capital operation modes and relevant regulatory advantages further consolidate its profit foundation, laying a solid guarantee for long-term and high profitability. This study clarifies the essential logic of the gambling industry's profitability, makes up for the deficiency of in-depth analysis on the industry's profit mechanism in existing research, and provides a theoretical reference for the standardized supervision of the gambling industry, as well as rational decision-making and risk avoidance for individual participants. The research findings help break the public's misunderstanding of gambling profitability and reveal the objective regularity behind the seemingly random gambling industry.

College students' entrepreneurship has always been given high concern by the entire society and has overtime become a research topic of central concern in the area of entrepreneurship. Nevertheless, for university students who could be potential entrepreneurs, entrepreneurial self-efficacy serves as an important variable that influences the incidence of entrepreneurship behavior. Current literature is not extensive enough to penetrate the depths of explaining the process of self-efficacy in shaping entrepreneurial behavior. Thus, this paper will use final-year college students as the research participants, according to the survey data collected on 310 valid questionnaires. This paper incorporates the social cognitive theory to examine the process by which self-efficacy influences entrepreneurial behavior, develops a mediation model and examines how the behavior of university students is formed through hierarchical regression. Findings indicate that a strong positive predictive value of entrepreneurial self-efficacy on entrepreneurial intention exists, and entrepreneurial intention connects the relation between self-efficacy and entrepreneurial behavior. The findings of this paper can be used to examine how entrepreneurial behavior can be encouraged amongst college students and how an entrepreneurial and innovation friendly environment can be created.

This study analyzes urban housing prices using multiple linear regression and Random Forest models based on 50 simulated observations. The predictors include floor area, house age, distance to the city center, number of rooms, and number of toilets. The results show that Random Forest achieves higher prediction accuracy, with an R² of about 0.90, indicating strong ability to capture complex patterns in the data. In contrast, multiple linear regression provides clearer interpretation, allowing each factor's impact to be directly understood through coefficients. Both models consistently identify floor area, house age, and distance to the city center as the most important factors affecting housing prices. Floor area has a positive effect, while house age and distance show negative effects. The findings highlight a key trade-off: Random Forest offers better predictive performance, while linear regression provides better transparency. Therefore, the choice of model should depend on whether prediction accuracy or interpretability is more important in practical applications.

In the world of quantitative finance, pinpointing the directional drift of asset returns often proves more reliable than chasing exact price points, a reality that has positioned trend forecasting as a cornerstone of modern trading strategies. This study moves beyond simple observation to build a comprehensive evaluative system focused on the Guotai Nasdaq 100 ETF, where four distinct analytical heavyweights—Logistic Regression, Random Forest, LightGBM, and XGBoost—are pitted against each other in a controlled head-to-head comparison. To maintain the sanctity of predictive signals, a feature space was meticulously woven together from simulated execution logs and external market variables while a zero-tolerance policy was enforced for any look-ahead bias that might skew the results. A deep dive into the performance metrics indicates that Random Forest consistently held the high ground, particularly when measuring overall accuracy and the harmonic balance of precision and recall, a success that essentially validates how ensemble-based bagging frameworks excel. While it was noticed that Logistic Regression still holds its own when it comes to flagging downside risks, the more complex boosting models seemed to hit a performance ceiling, likely because they were starved for the massive sample sizes they usually crave. By bridging this gap, this work offers a gritty, empirical look at model selection in noisy environments, highlighting that the real secret to success lies in matching the complexity of the algorithm with the actual statistical pulse of the market being traded.

The aerodynamic progress of the motor car reflects the progress in materials, structure design, and calculation methods. The present article researches this development via the BMW E92 M3, one automobile that was released at the 2007 Geneva Motor Show, which occupies a transition position between experience-oriented engineering and design directed by simulation. Three topics are researched: material development in aerodynamic use cases, structure change from passive additional components to integrated active systems, and method change from wind tunnel experiments to AI-enhanced calculation fluid dynamics. The analysis puts the E92 M3's carbon fiber roof, which is an early production standard for carbon fiber reinforced polymer usage, into bigger industry development paths. The thermal management that uses the hood vents is being researched together with the current studies on cooling drag. The passive aerodynamic shape arrangement is compared with later progresses in active aerodynamic systems. This article follows the material development process from steel being in the leading position to CFRP single-shell structures and mixed combination structures, the structure change direction to active and bionic designs, and the method change from wind tunnel experience methods to artificial-intelligence-driven optimization which uses multi-fidelity neural networks and GPU-native solvers. Research results show that material selection has developed from passive restriction to active helper, structure design from fixed balance to requirement-based arrangement, and research method from resource-costly physical repeated test to calculation-based easy optimization. The E92 M3 contains the engineering thought of the middle 2000s yet acts as a platform for the modern analysis use. Future development directions include the wide spread of active aerodynamics, the growing mature of bionic design principles, and the progress of hybrid material solutions.

Atmospheric stability is a critical meteorological factor affecting flow characteristics, wake development, and power generation performance in wind farms. This paper provides a review of current research on wind farm engineering optimisation under the influence of atmospheric stability. It systematically collates quantitative characterisation methods for atmospheric stability and analyses the patterns of influence of different stable stratifications on wind speed profiles, turbulence intensity, wind turbine wakes, and energy output. Existing engineering optimisation strategies and research progress are summarised across four aspects: wind farm layout and siting, wind turbine blade design improvement, wind turbine control and regulatory systems, and operation and maintenance optimisation. On this basis, a comprehensive wind farm optimisation framework integrating multi-module and multi-objective development is introduced, elaborating on its design objectives, core components, implementation schemes, and application prospects. Finally, the limitations of current research are highlighted, and future development directions, such as high-precision stability forecasting, automatic application across all operating conditions, and integrated optimisation of offshore wind farms, are discussed. This paper provides a reference for improving the operational efficiency and safety of wind farms under complex atmospheric conditions.