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As the complexity of corporate financial management increases, conventional financial risk identification methods suffer from limitations in real-time responsiveness, accuracy, and automation, often relying on static data and manual analysis. This study addresses these shortcomings by proposing a real-time financial risk identification and prevention system based on the random forest (RF) algorithm. Unlike prior models, which typically focus on static historical data and lack integration with live enterprise systems, the proposed system enables dynamic risk monitoring by connecting to real-time financial data streams via API interfaces. The system preprocesses and extracts feature from historical financial statements and cash flow records, which are then used to train the RF model using Bagging for sampling and cross-validation for parameter optimization. Upon receiving real-time input, the model generates risk predictions—classified into low, medium, or high levels—along with potential risk source analysis. It then activates a dynamic early warning mechanism if risks exceed preset thresholds and provides tailored prevention strategies based on industry knowledge. Experimental results show that the system achieves an F1 score of 0.8425 and an average accuracy exceeding 80%, demonstrating significant improvements in prediction performance and operational efficiency. This research contributes a robust, real-time, and automated solution for corporate financial risk management.

With the increasing reliance and demand for artificial intelligence in the construction of smart cities, AI has been embedded in various applications and infrastructures. However, uncertainties, concerns, and even fears about AI are constantly emerging. This study based on actor-network theory (ANT) showcases the application practices of artificial intelligence in smart city, including smart transportation, smart water management, smart healthcare, smart grids, and smart city evaluation standards, and analyzes the process of constructing an actor-network involving governments, technology development companies, and urban residents. It demonstrates the interactive network processes such as problem statement, interest alignment, recruitment, and mobilization. It explores the relationships among humans, humans and technology, and the self-challenges faced by technology, such as digital ethics, machine ethics, and relational alienation. In the future, it is necessary to establish explainable, transparent, safe, and responsible AI in the development of smart cities, grant certain rights, and respect to nonhuman actors such as data, AI, and machines, and formulate a series of laws and ethical guidelines to ensure the sustainable development of smart city.

Toward smart building and smart home, floor as one of our most frequently interactive interfaces can be implemented with embedded sensors to extract abundant sensory information without the video-taken concerns. Yet the previously developed floor sensors are normally of small scale, high implementation cost, large power consumption, and complicated device configuration. Here we show a smart floor monitoring system through the integration of self-powered triboelectric floor mats and deep learning-based data analytics. The floor mats are fabricated with unique “identity” electrode patterns using a low-cost and highly scalable screen printing technique, enabling a parallel connection to reduce the system complexity and the deep-learning computational cost. The stepping position, activity status, and identity information can be determined according to the instant sensory data analytics. This developed smart floor technology can establish the foundation using floor as the functional interface for diverse applications in smart building/home, e.g., intelligent automation, healthcare, and security. Designing efficient and fast monitoring and response systems for smart building/home applications remains a challenge. Here, the authors propose a smart floor monitoring system developed through the integration of self-powered triboelectric sensing mechanism and deep learning data analytics.

The advent of the James Webb Space Telescope (JWST) signals a new era in exploring galaxies in the high-z universe. Current and upcoming JWST imaging will potentially detect galaxies at z ∼ 20, creating a new urgency in the quest to infer accurate photometric redshifts (photo-z) for individual galaxies from their spectral energy distributions, as well as masses, ages, and star formation rates. Here we illustrate the utility of informed priors encoding previous observations of galaxies across cosmic time in achieving these goals. We construct three joint priors encoding empirical constraints of redshifts, masses, and star formation histories in the galaxy population within the Prospector Bayesian inference framework. In contrast with uniform priors, our model breaks an age–mass–redshift degeneracy, and thus reduces the mean bias error in masses from 0.3 to 0.1 dex, and in ages from 0.6 to 0.2 dex in tests done on mock JWST observations. Notably, our model recovers redshifts at least as accurately as the state-of-the-art photo-z code EAzY in deep JWST fields, but with two advantages: tailoring a model based on a particular survey is rendered mostly unnecessary given well-motivated priors; obtaining joint posteriors describing stellar, active galactic nuclei, gas, and dust contributions becomes possible. We can now confidently use the joint distribution to propagate full non-Gaussian redshift uncertainties into inferred properties of the galaxy population. This model, “Prospector-β,” is intended for fitting galaxy photometry where the redshift is unknown, and will be instrumental in ensuring the maximum science return from forthcoming photometric surveys with JWST. The code is made publicly available online as a part of Prospector 9 9 The version used in this work corresponds to the state of the Git repository at commit https://github.com/bd-j/prospector/commit/820ad72363a1f9c22cf03610bfe6e361213385cd..

The first JWST data revealed an unexpected population of red galaxies that appear to have redshifts of z ∼ 7–9 and high masses of M * ≳ 1010 M ☉. Here we fit Sérsic profiles to the F200W NIRCam images of the 13 massive galaxy candidates of Labbé et al., to determine their structural parameters. Satisfactory fits were obtained for nine galaxies. We find that their effective radii are extremely small, ranging from r e ∼ 80 pc to r e ∼ 300 pc, with a mean of 〈r e〉 ≈ 150 pc. For their apparent stellar masses, the galaxies are smaller than any other galaxy population that has been observed at any other redshift. We use the fits to derive circularized three-dimensional stellar mass profiles of the galaxies, and compare these to the mass profiles of massive quiescent galaxies at z ∼ 2.3 and nearby elliptical galaxies. Despite the fact that the high-redshift galaxies have 10–20 times smaller half-light radii than their putative descendants, the central stellar densities are very similar. The most straightforward interpretation is that the dense compact inner regions of the most massive ellipticals today were already in place ∼600 Myr after the Big Bang. We caution that the redshifts and masses of the galaxies remain to be confirmed, and that the complex NIRCam point-spread function is not yet fully characterized.

The era of the James Webb Space Telescope ushers stellar population models into uncharted territories, particularly at the high-redshift frontier. In a companion paper, we apply the Prospector Bayesian framework to jointly infer galaxy redshifts and stellar population properties from broadband photometry as part of the UNCOVER survey. Here we present a comprehensive error budget in spectral energy distribution (SED) modeling. Using a sample selected to have photometric redshifts higher than 9, we quantify the systematic shifts stemming from various model choices in inferred stellar mass, star formation rate (SFR), and age. These choices encompass different timescales for changes in the star formation history (SFH), nonuniversal stellar initial mass functions (IMF), and the inclusion of variable nebular abundances, gas density, and ionizing photon budget. We find that the IMF exerts the strongest influence on the inferred properties: the systematic uncertainties can be as much as 1 dex, 2–5 times larger than the formal reported uncertainties in mass and SFR, and importantly, exceed the scatter seen when using different SED fitting codes. Although the assumptions on the lower end of the IMF induce degeneracy, our findings suggest that a common practice in the literature of assessing uncertainties in SED-fitting processes by comparing multiple codes is substantively underestimating the true systematic uncertainty. Highly stochastic SFHs change the inferred SFH by much larger than the formal uncertainties, and introduce ∼0.8 dex systematics in SFR averaged over a short timescale and ∼0.3 dex systematics in average age. Finally, employing a flexible nebular emission model causes ∼0.2 dex systematic increase in mass and SFR, comparable to the formal uncertainty. This paper constitutes an initial step toward a complete uncertainty estimate in SED modeling.

Early JWST studies found an apparent population of massive, compact galaxies at redshifts z ≳ 7. Recently three of these galaxies were shown to have prominent Balmer breaks, demonstrating that their light at λrest ∼ 3500 Å is dominated by a stellar population that is relatively old (∼200 Myr). All three also have broad Hβ emission with σ > 1000 km s−1, a common feature of such “little red dots.” From Sérsic profile fits to the Near Infrared Camera images in F200W we find that the stellar light of galaxies is extremely compact: the galaxies have half-light radii of re ∼ 100 pc, in the regime of ultracompact dwarfs in the nearby Universe. Their masses are uncertain, as they depend on the contribution of possible light from an active galactic nucleus (AGN) to the flux at λrest > 5000 Å. If the AGN contribution is low beyond the Balmer break region, the masses are M* ∼ 1010–1011 M☉, and the central densities are higher than those of any other known galaxy population by 1 order of magnitude. Interestingly, the implied velocity dispersions of ∼1500 km s−1 are in very good agreement with the measured Hβ line widths. We suggest that some of the broad lines in “little red dots” are not due to AGNs, but simply reflect the kinematics of the galaxies, and speculate that the galaxies are observed in a short-lived phase where the central densities are much higher than at later times. We stress, however, that the canonical interpretation of AGNs causing the broad Hβ lines also remains viable.

We report JWST/NIRSpec spectra of three distant T-type brown dwarfs identified in the Ultradeep NIRSpec and NIRCam ObserVations before the Epoch of Reionization (UNCOVER) survey of the Abell 2744 lensing field. One source was previously reported as a candidate T dwarf on the basis of NIRCam photometry, while two sources were initially identified as candidate active galactic nuclei. Low-resolution 1–5 μm spectra confirm the presence of molecular features consistent with T dwarf atmospheres, and comparison to spectral standards infers classifications of sdT1, T6, and T8–T9. The warmest source, UNCOVER-BD-1, shows evidence of subsolar metallicity, and atmosphere model fits indicate T eff = 1300 K and [M/H] ∼ −1.0, making this one of the few spectroscopically confirmed T subdwarfs known. The coldest source, UNCOVER-BD-3, is near the T/Y dwarf boundary with T eff = 550 K, and our analysis indicates the presence of PH3 in the 3–5 μm region, favored over CO2 and a possible indicator of subsolar metallicity. We estimate distances of 0.9–4.5 kpc from the Galactic midplane, making these the most distant brown dwarfs with spectroscopic confirmation. Population simulations indicate high probabilities of membership in the Galactic thick disk for two of these brown dwarfs, and potential halo membership for UNCOVER-BD-1. Our simulations indicate that there are approximately 5 T dwarfs and 1–2 L dwarfs in the Abell 2744 field down to F444W = 30 AB mag, roughly one-third of which are thick disk members. These results highlight the utility of deep JWST/NIRSpec spectroscopy for identifying and characterizing the oldest metal-poor brown dwarfs in the Milky Way.

The identification of red, apparently massive galaxies at z > 7 in early James Webb Space Telescope (JWST) photometry suggests a strongly accelerated time line compared to standard models of galaxy growth. A major uncertainty in the interpretation is whether the red colors are caused by evolved stellar populations, dust, or other effects such as emission lines or active galactic nuclei (AGNs). Here we show that three of the massive galaxy candidates at z = 6.7–8.4 have prominent Balmer breaks in JWST/NIRSpec spectroscopy from the RUBIES program. The Balmer breaks demonstrate unambiguously that stellar emission dominates at λ rest = 0.4 μm and require formation histories extending hundreds of millions of years into the past in galaxies only 600–800 Myr after the big bang. Two of the three galaxies also show broad Balmer lines, with Hβ FWHM > 2500 km s−1, suggesting that dust-reddened AGNs contribute to, or even dominate, the spectral energy distributions of these galaxies at λ rest ≳ 0.6 μm. All three galaxies have relatively narrow [O iii] lines, seemingly ruling out a high-mass interpretation if the lines arise in dynamically relaxed, inclined disks. Yet the inferred masses also remain highly uncertain. We model the high-quality spectra using Prospector to decompose the continuum into stellar and AGN components and explore limiting cases in stellar/AGN contribution. This produces a wide range of possible stellar masses, spanning M ⋆ ∼ 109−1011 M ⊙. Nevertheless, all fits suggest a very early and rapid formation, most of which follow with a truncation in star formation. Potential origins and evolutionary tracks for these objects are discussed, from the cores of massive galaxies to low-mass galaxies with overmassive black holes. Intriguingly, we find all of these explanations to be incomplete; deeper and redder data are needed to understand the physics of these systems.

Galaxies with stellar masses as high as roughly 10 11 solar masses have been identified 1 – 3 out to redshifts z of roughly 6, around 1 billion years after the Big Bang. It has been difficult to find massive galaxies at even earlier times, as the Balmer break region, which is needed for accurate mass estimates, is redshifted to wavelengths beyond 2.5 μm. Here we make use of the 1–5 μm coverage of the James Webb Space Telescope early release observations to search for intrinsically red galaxies in the first roughly 750 million years of cosmic history. In the survey area, we find six candidate massive galaxies (stellar mass more than 10 10 solar masses) at 7.4 ≤  z  ≤ 9.1, 500–700 Myr after the Big Bang, including one galaxy with a possible stellar mass of roughly 10 11 solar masses. If verified with spectroscopy, the stellar mass density in massive galaxies would be much higher than anticipated from previous studies on the basis of rest-frame ultraviolet-selected samples. James Webb Space Telescope early release observations used to search for intrinsically red galaxies from the first 750 million years of cosmic history find six candidate massive galaxies, possibly including one of roughly 10 11 solar masses.