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Social media has become an indispensable resource in disaster response, providing real-time crowdsourced data on public experiences, needs, and conditions during crises. This user-generated content enables government agencies and emergency responders to identify emerging threats, prioritize resource allocation, and optimize relief operations through data-driven insights. We present an AI-powered framework that combines natural language processing with geospatial visualization to analyze disaster-related social media content. Our solution features a text analysis model that achieved an 81.4% F1 score in classifying Twitter/X posts, integrated with an interactive web platform that maps emotional trends and crisis situations across geographic regions. The system’s dynamic visualization capabilities allow authorities to monitor situational developments through an interactive map, facilitating targeted response coordination. The experimental results show the model’s effectiveness in extracting actionable intelligence from Twitter/X posts during natural disasters. 

Abstract Many sub-Neptune and super-Earth exoplanets are expected to develop metal-enriched atmospheres due to atmospheric loss processes such as photoevaporation or core-powered mass loss. Thermochemical equilibrium calculations predict that at high metallicity and a temperature range of 300–700 K, CO₂becomes the dominant carbon species, and graphite may be the thermodynamically favored condensate under low-pressure conditions. Building on prior laboratory findings that such environments yield organic haze rather than graphite, we measured the transmittance spectra of organic haze analogs and graphite samples and computed their optical constants across the measured wavelength range from 0.4 to 25μm. The organic haze exhibits strong vibrational absorption bands, notably at 3.0, 4.5, and 6.0μm, while graphite shows featureless broadband absorption. The derived optical constants of haze and graphite provide the first data set for organic haze analogs formed in CO₂-rich atmospheres and offer improved applicability over prior graphite data derived from bulk reflectance or ellipsometry. We implemented these optical constants into the Virga and PICASO cloud and radiative transfer models to simulate transit spectra for GJ 1214b. The synthetic spectra with organic hazes reproduce the muted spectral features in the near-infrared observed by Hubble and general trends observed by JWST for GJ 1214b, while graphite models yield flat spectra across the observed wavelengths. This suggests haze features may serve as observational markers of carbon-rich atmospheres, whereas graphite’s opacity could lead to radius overestimation, offering a possible explanation for superpuff exoplanets. Our work supplies essential optical to infrared data for interpreting observations of CO₂-rich exoplanet atmospheres. 

Abstract Super-Earths and sub-Neptunes are the most common exoplanets, with a “radius valley” suggesting that super-Earths may form by shedding sub-Neptunes’ gaseous envelopes. Exoplanets that lie closer to the super-Earth side of the valley are more likely to have lost a significant fraction of their original H/He envelopes and become enriched in heavier elements, with CO₂gaining in abundance. It remains unclear which types of haze would form in such atmospheres, potentially significantly affecting spectroscopic observations. To investigate this, we performed laboratory simulations of two CO₂-rich gas mixtures (with 2000 times solar metallicity at 300 and 500 K). We found that under plasma irradiation organic hazes were produced at both temperatures, with a higher haze production rate at 300 K, probably because condensation occurs more readily at lower temperature. Gas-phase analysis demonstrates the formation of various hydrocarbons, oxygen- and nitrogen-containing species, including reactive gas precursors like C₂H₄, CH₂O, and HCN, for haze formation. The compositional analysis of the haze particles reveals various functional groups and molecular formulas in both samples. The 500 K haze sample has larger average molecular sizes, a higher degree of unsaturation with more double or triple bonds present, and higher nitrogen content incorporated as N–H and C=N bonds, indicating different haze formation pathways. These findings not only improve the haze formation theories in CO₂-rich exoplanet atmospheres but also offer important implications for the interpretation of future observational data. 

Silicon monoxide (SiO) is classified as a key precursor and fundamental molecular building block to interstellar silicate nanoparticles, which play an essential role in the synthesis of molecular building blocks connected to the Origins of Life. In the cold interstellar medium, silicon monoxide is of critical importance in initiating a series of elementary chemical reactions leading to larger silicon oxides and eventually to silicates. To date, the fundamental formation mechanisms and chemical dynamics leading to gas phase silicon monoxide have remained largely elusive. Here, through a concerted effort between crossed molecular beam experiments and electronic structure calculations, it is revealed that instead of forming highly-stable silicon dioxide (SiO 2 ), silicon monoxide can be formed via a barrierless, exoergic, single-collision event between ground state molecular oxygen and atomic silicon involving non-adiabatic reaction dynamics through various intersystem crossings. Our research affords persuasive evidence for a likely source of highly rovibrationally excited silicon monoxide in cold molecular clouds thus initiating the complex chain of exoergic reactions leading ultimately to a population of silicates at low temperatures in our Galaxy. 

Abstract Through the diagnosis of 29 Atmospheric Model Inter-comparison Project (AMIP) experiments from the CMIP5 inter-comparison project, we investigate the impact of the mean state on simulated western North Pacific anomalous anticyclone (WNPAC) during El Niño decaying summer. The result indicates that the inter-model difference of the JJA mean precipitation in the Indo-western Pacific warm pool is responsible for the difference of the WNPAC. During the decaying summer of an Eastern Pacific (EP) type El Niño, a model that simulates excessive mean rainfall over the western North Pacific (WNP) reproduces a stronger WNPAC response, through an enhanced local convection-circulation-moisture feedback. The intensity of the simulated WNPAC during the decay summer of a Central Pacific (CP) type El Niño, on the other hand, depends on the mean precipitation over the tropical Indian Ocean. The distinctive WNPAC-mean precipitation relationships between the EP and CP El Niño result from different anomalous SST patterns in the WNP. While the local SST anomaly plays an active role in maintaining the WNPAC during the EP El Niño, it plays a passive role during the CP El Niño. As a result, only the mean-state precipitation/moisture field in the tropical Indian Ocean modulates the circulation anomaly in the WNP in the latter case.