Climate-related information is increasingly integrated with financial, macroeconomic, and market-sentiment data, creating a heterogeneous scientific data management problem in which noisy environmental series, high-frequency market observations, and policy indicators must be aligned, transformed, and analyzed reproducibly. This paper develops and evaluates a reproducible threshold vector autoregression (TVAR) workflow to detect state-dependent climate-risk effects on U.S. equity market volatility. Using monthly data from February 1990 to February 2026, the workflow integrates climate variables, realized volatility constructed from daily S&P 500 returns, VIX-based market-stress regimes, macro-financial controls, stationarity transformations, threshold validation, impulse-response analysis, forecast-error variance decomposition, and robustness checks. The results show that climate risk has no statistically meaningful effect on volatility in low-VIX regimes. At the same time, its first two lags become positive and larger in magnitude in high-VIX regimes. The baseline high-VIX coefficient at the second lag is 0.003152 (p = 0.018), and impulse responses peak around the second month after a climate-risk shock. Although the forecast-error variance share remains modest, it rises to about 1.5% in stressed markets and is nearly zero in calm markets. Beyond the substantive climate-finance result, the paper contributes an auditable scientific data-analysis pipeline for heterogeneous time-series integration, regime-aware uncertainty handling, and reproducible nonlinear modeling, aligning climate-finance analytics with the data-intensive research concerns of scalable scientific data management.

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With the development of the low-altitude economy, unmanned aerial vehicle (UAV) systems have gradually become one of the important tools for environmental monitoring and field observation. Compared with fixed-wing aircraft and quadcopters, flapping-wing robots have significant advantages in maneuverability, environmental adaptability, and low-interference flight. However, most existing flapping-wing platforms face three major challenges: difficulty in scaling up, weak amphibious flight capability, and poor hovering stability. This project proposes a novel hummingbird-like flapping-wing robot that employs reinforcement learning control to achieve stable cross-medium flight and reliable hovering. The design utilizes biomimetic figure-eight wing motion and a simple three-degree-of-freedom mechanical structure to improve aerodynamic efficiency. The robot uses a crank-rocker transmission system, brushless motors, gearboxes, and micro servo motors to achieve wing flapping and attitude adjustment. Its fuselage is made of lightweight carbon fiber tubing, while the wings are made of waterproof and airtight fabric. A specially designed support leg structure enables the robot to float stably on water. To achieve control, we constructed a reinforcement learning framework that takes altitude, pitch angle, roll angle, and vertical velocity as input states and outputs flapping frequency, flapping amplitude, and tail angle. The reward function penalizes unstable altitude and attitude while encouraging efficient lift generation. Test results show that the reinforcement learning model converges well during training. The robot can accurately track the target trajectory in three-dimensional position and attitude, and stably complete takeoff, landing, and stable floating on water. This work demonstrates that biomimetic design combined with reinforcement learning can effectively improve the performance of flapping-wing robots and support cross-medium practical applications, providing a reference for future environmental monitoring and field observation.

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Nowadays, wireless charging technology for electric vehicles has reached a high level both at home and abroad, whose foundational framework has already been established, while its application in daily life still cannot reach the expected ideal outcomes. At an attempt to better foster the commercialization and civilian application of this technology, this paper provides some optimization strategies on the base of electromagnetic induction principle to address the drawbacks in the field of transmission efficiency, transmission distance and thermal management and improve their charging efficiency. In terms of transmission efficiency, researchers proposed to remove the transmitting coil and the receiving coil relatively to achieve a larger overlap and then reduce transmission losses. As for transmission distance, the magnetic field frequency can be improved to enhance power transmission capacity and distance. Moreover, the method of strengthening heat dissipation and decreasing heat generation can significantly reduce thermal losses. In a nutshell, with the optimization in these three aspects, the researcher hopes to address some shortcomings of wireless charging and enhance the feasibility of wireless charging and facilitate people's daily life.

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Supernovae connect stellar evolution, high-energy transient astronomy, and observational cosmology. This review examines how supernova physics and survey design together determine the cosmological value of supernova observations. It distinguishes Type Ia thermonuclear supernovae, which are standardizable luminosity-distance indicators, from Type II and other core-collapse supernovae, which probe massive-star evolution, circumstellar interaction, compact-remnant formation, and multi-messenger signals. The discussion first reviews the physical mechanisms of Type Ia and Type II explosions, emphasizing that spectral classification and explosion mechanism should not be conflated. It then surveys representative low-, intermediate-, and high-redshift programs, including the Nearby Supernova Factory, Foundation Supernova Survey, Sloan Digital Sky Survey-II Supernova Survey, Pan-STARRS1, Zwicky Transient Facility, High-z Supernova Search Team, Supernova Legacy Survey, and Dark Energy Survey. Core-collapse follow-up programs, X-ray monitoring, and neutrino observatories are also discussed because they clarify the astrophysical diversity that must be separated from the Type Ia cosmology program. Finally, the review explains how Type Ia Hubble diagrams support late-time cosmic acceleration and why future progress depends less on sample size alone than on calibration, selection effects, dust, host-galaxy correlations, population evolution, and cross-probe consistency. The central conclusion is that supernova cosmology remains powerful precisely because it joins empirical standardization with explicit treatment of astrophysical and observational systematics.

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