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In this study, we investigated the impact of prompting on young students' source consideration when watching videos with conflicting information. 262 French 7th graders were shown a series of videos in which two speakers (varying in credibility) took opposite stances on the topic of organic farming. The students were either given no prompts (control group), an indirect form of prompting (watching an instructional video on the benefits of sourcing before processing the material), a direct form of prompting (filling out source credibility rating scales during the processing of the material) or a combination of direct and indirect prompting. While the impact of the instructional video on students' source consideration proved marginal, students who had to fill in the source credibility rating scales during the processing of the material remembered the identity of the speakers better (notably in delayed posttest), were more inclined to consider the expert interviewee as more convincing and to mention interviewees' expertise to justify their judgement. These results suggest that prompting seventh graders to evaluate the credibility of the sources during the processing of the material through the completion of credibility rating scales is an efficient method to improve their consideration of source information when watching videos.

The purpose of this study was to assess the impact of young students' prior attitude on source consideration when watching videos on controversial topics. Two hundred seventy-one seventh graders watched a series of videos in which two interviewees (one expert in the field, one layperson) expressed divergent positions on a socioscientific issue ("Will organic farming be able to feed the entire world population by 2050?"). After watching the videos, students were asked to recall the identity and arguments of the interviewees and indicate how far they had perceived them to be credible and convincing. If no effect of students' prior attitude was found on source recall, students were prompt to judge the interviewee who provided arguments that were congruent with their prior attitude as more credible and convincing that the interviewee that provided incongruent arguments. These results suggest that young students' beliefs contribute to their assessment of the credibility of an information source when watching videos.

Access to original ortho thioether derivatives was achieved through a [3,3]-rearrangement in a one-pot two-step protocol. Several aryl-SCF3 compounds are reported by variation of the nitrile or of the trifluoroalkyl sulfoxide starting material. The variation of the perfluoroalkyl chain was also possible.

Strontium ions can be removed from wastewater in fixed-bed reactors by adsorption on hierarchical materials (different pore sizes and phases). A detailed understanding of the materials multiscale structure is therefore crucial to optimize their sorption properties. This article presents a multi-technique approach developed to characterize the relationship between microstructure and adsorption in a range of Linde-type A (LTA) zeolite-geopolymer composites,. Two-dimensional scanning electron microscopy and 3D X-ray tomography were used to image the porous and solid phases at different length scales. Advanced numerical methods involving machine learning were applied to segment the images and provide quantitative morphological values describing the porous network geometry and the location and accessibility of the zeolite particles in the geopolymer. These results were then correlated with the materials sorption properties, measured in batch and column processes. Increasing the materials zeolite content (from 0 to 27 wt%) slowed down their adsorption kinetics but increased their maximal adsorption capacities from 20 to 49 mg.g−1 and Sr2+-selectivity at equilibrium. In breakthrough experiments, the material with highest zeolite content had a much higher breakthrough volume passing from 169 to 478 ml in the presented experimental conditions (much higher wastewater decontamination capacity), but a shallower breakthrough curve (lower column efficiency).

Accurately classifying road signs is crucial for autonomous driving due to the high stakes involved in ensuring safety and compliance. As Convolutional Neural Networks (CNNs) have largely replaced traditional Machine Learning models in this domain, the demand for substantial training data has increased. This study aims to compare the performance of classical Machine Learning (ML) models and Deep Learning (DL) models under varying amounts of training data, particularly focusing on altered signs to mimic real-world conditions. We evaluated three classical models: Support Vector Machine (SVM), Random Forest, and Linear Discriminant Analysis (LDA), and one Deep Learning model: Convolutional Neural Network (CNN). Using the German Traffic Sign Recognition Benchmark (GTSRB) dataset, which includes approximately 40,000 German traffic signs, we introduced digital alterations to simulate conditions such as environmental wear or vandalism. Additionally, the Histogram of Oriented Gradients (HOG) descriptor was used to assist classical models. Bayesian optimization and k-fold cross-validation were employed for model fine-tuning and performance assessment. Our findings reveal a threshold in training data beyond which accuracy plateaus. Classical models showed a linear performance decrease under increasing alteration, while CNNs, despite being more robust to alterations, did not significantly outperform classical models in overall accuracy. Ultimately, classical Machine Learning models demonstrated performance comparable to CNNs under certain conditions, suggesting that effective road sign classification can be achieved with less computationally intensive approaches.

The purpose of this study was to better understand how middle school students consider the source of information when processing videos with conflicting information. To this end, we exposed a sample of seventh-graders to a series of videos in which two interviewees expressed divergent positions on a socioscientific issue ('Will organic farming be able to feed the entire world population by 2050?'). After viewing the videos, students were asked to recall the sources they had seen and indicate how far they had perceived the sources to be credible and convincing. Results showed that students paid close attention to the information given about the sources during viewing and rated their credibility accordingly. However, only a minority of students rated the expert sources as the most convincing after viewing the videos, while students' beliefs on the topic contributed to source evaluation. These results suggest that although middle school students may pay attention to the identity of sources when viewing a video, they are unlikely to use this information to assess the reliability of the message. [Spanish translation by Mercè Rius.]

This paper introduces an open-source pixel-wise Digital Image Correlation tool written in Python and targeting graphics processing units (GPUs) with the help of Cupy and Rapids-cuCim libraries. It is capable of computing the kinematic fields that transform an image into another in an efficient and quick way and it allows to treat large images in the GPU. Even if GCPU_OpticalFlow can be easily used by communities concerned by the estimation of displacement, it is particularly tuned to estimate consistent strain (gradient) field. The detection of a crack in a material is presented in this work as a demonstration.

This work presents a novel approach to enhancing iris recognition systems through a two-module approach focusing on low-level image preprocessing techniques and advanced feature extraction. The primary contributions of this paper include: (i) the development of a robust preprocessing module utilizing the Canny algorithm for edge detection and the circle-based Hough transform for precise iris extraction, and (ii) the implementation of Binary Statistical Image Features (BSIF) with domain-specific filters trained on iris-specific data for improved biometric identification. By combining these advanced image preprocessing techniques, the proposed method addresses key challenges in iris recognition, such as occlusions, varying pigmentation, and textural diversity. Experimental results on the Human-inspired Domain-specific Binarized Image Features (HDBIF) Dataset, consisting of 1892 iris images, confirm the significant enhancements achieved. Moreover, this paper offers a comprehensive and reproducible research framework by providing source codes and access to the testing database through the Notre Dame University dataset website, thereby facilitating further application and study. Future research will focus on exploring adaptive algorithms and integrating machine learning techniques to improve performance across diverse and unpredictable real-world scenarios.

Fast X-ray Transients (FXTs) are extragalactic bursts of soft X-rays first identified ≳10 yr ago. Since then, nearly 40 events have been discovered, although almost all of these have been recovered from archival Chandra and XMM-Newton data. To date, optical sky surveys and follow-up searches have not revealed any multiwavelength counterparts. The Einstein Probe, launched in 2024 January, has started surveying the sky in the soft X-ray regime (0.5–4 keV) and will rapidly increase the sample of FXTs discovered in real time. Here we report the first discovery of both an optical and radio counterpart to a distant FXT, the fourth source publicly released by the Einstein Probe. We discovered a fast-fading optical transient within the 3′ localization radius of EP 240315a with the all-sky optical survey ATLAS, and our follow-up Gemini spectrum provides a redshift, z = 4.859 ± 0.002. Furthermore, we uncovered a radio counterpart in the S band (3.0 GHz) with the MeerKAT radio interferometer. The optical (rest-frame UV) and radio luminosities indicate that the FXT most likely originates from either a long gamma-ray burst or a relativistic tidal disruption event. This may be a fortuitous early mission detection by the Einstein Probe or may signpost a mode of discovery for high-redshift, high-energy transients through soft X-ray surveys, combined with locating multiwavelength counterparts.

Background: Lithotripsy with holmium:yttrium-aluminum-garnet (Ho:YAG) laser is the current gold standard for treating stones of the upper urinary tract (UUT). The recently introduced thulium fiber laser (TFL) has the potential to be more efficient and as safe as Ho:YAG. Objective: To compare the performance and complications between Ho:YAG and TFL for UUT lithotripsy. Design, setting, and participants: This was a prospective single-center study of 182 patients treated between February 2021 and February 2022. In a consecutive approach, laser lithotripsy was performed via ureteroscopy with Ho:YAG for 5 mo, and then with TFL for 5 mo. Outcome measurements and statistical analysis: Our primary outcome was stone-free (SF) status at 3 mo after ureteroscopy with Ho:YAG versus TFL lithotripsy. Secondary outcomes were complication rates and results regarding the cumulative stone size. Patients were followed at 3 mo with abdominal imaging (ultrasound or computed tomography). Results and limitations: The study cohort comprised 76 patients treated with Ho:YAG laser and 100 patients treated with TFL. Cumulative stone size was significantly higher in the TFL than in the Ho:YAG group (20.4 vs 14.8 mm; p = 0.01). SF status was similar in both groups (68.4% vs 72%; p = 0.06). Complication rates were comparable. In subgroup analysis, the SF rate was significantly higher (81.6% vs 62.5%; p = 0.04) and the operative time was shorter for stones measuring 1–2 cm, whereas the results were similar for stones 2 cm. The lack of randomization and single-center design are the main limitations of the study. Conclusions: TFL and Ho:YAG lithotripsy are comparable in terms of the SF rate and safety for the treatment of UUT lithiasis. According to our study, for a cumulative stone size of 1–2 cm, TFL is more effective than Ho:YAG. Patient summary: We compared the efficiency and safety of two laser types for the treatment of stones in the upper urinary tract. We found that stone-free status at 3 months did not significantly differ between the holmium and thulium lasers.

Khufu’s Pyramid is one of the largest archaeological monuments in the world, and still contains unexplored voids. Here, the authors use cosmic-ray muon radiography in multiple positions to precisely characterize one of these inner structures called the North Face Corridor.

The objective of this study is to explore the stereotypes that students have about agribusiness students. We seek to determine the nature of these stereotypes and how they compare relative to marketing and accounting students. Our intent is to reveal the degree of fit of the agribusiness major within a college of business, as personality congruence may influence the pursuit of a major within a business school framework. Our results show that stereotypes about agribusiness majors fall in between those of marketing and accounting students, which suggests that stereotypes would not appear to negatively affect enrollment within a business school setting.

Summary: All-solid-state batteries have been developed to increase energy density by replacing the lithiated graphite negative electrode by a lithium metal foil and to increase safety by removing the organic compounds. However, the safety issues of these batteries have received little attention up to now.The behavior of a reassembled all-solid-state battery under thermal stress was recorded by X-ray radiography and a high-speed camera. The thermal runaway (TR) lasted about 5 ms, thus extremely fast reaction kinetics. In comparison, the TR of a lithium-ion battery is about 500 ms. Furthermore, a 188-mbar aerial overpressure was measured using a piezoelectric sensor. Although this cell is not an explosive, 2.7 g TNT equivalent was calculated for it.This atypical behavior could have an impact on the casing or the battery pack. Therefore, it must be studied in greater detail.

In 2017, ScanPyramids project (www.scanpyramids.org) published a paper in Nature (Morishima et al., 2017) revealing the discovery of a big void (ScanPyramids Big Void BV) observed with nuclear emulsion films (Muography), it has a cross-section similar to that of the Grand Gallery and a minimum length of 30 meters situated above the Grand Gallery. In addition, a geometrically non-identified void in the Northern Face of the Pyramid (ScanPyramids Northern Face Corridor SP-NFC) was detected as well in 2017 followed by further detailed and focused muography measurements up to 2022 (Morishima and Procureur et al., 2023). ScanPyramids SP-NFC corridor has been investigated in more detail with a wider set of non-destructive techniques. The result of GPR, Ultrasonic and image fusion detected precisely the location and shape of ScanPyramids SP-NFC (Elkarmoty and Rupfle et al.,2023). In this paper, we present overview on the application of Muography, Ground Penetrating Radar, Ultrasonic Tomography, and Electrical Resistivity Tomography on the Chevron of the Great Pyramid where ScanPyramids SPNFC is located behind. The objective of the NDT measurements is to detect the geometry, location, orientation, and extension of ScanPyramids SP-NFC with more than one NDT method. The paper addresses the validity and limitations of each method used providing the limitations of each technique in this particular case study.

The PYthon module for the CAlibration of cameras by SOloff’s method (PYCASO) provides an open-source Python-based framework for stereoscopic reconstructions from pairs of 2D images. The determination of a matching function between image and physical coordinates is essential to accurately build 3D representations at any scales. Moreover, at the finest scales, this relationship is strongly non-linear, mainly due to optical distortions of the cameras (such as misalignment of the axis of the lens with that of the objective, refraction of optical windows etc.). Therefore, this paper proposes a general and accurate method based on the Soloff method through the PYCASO module. While the basic method is time-consuming, an acceleration of the procedure is proposed by relying on artificial intelligence (AI).

We present Lightcurve Anomaly Identification and Similarity Search ( LAISS ), an automated pipeline to detect anomalous astrophysical transients in real-time data streams. We deploy our anomaly detection model on the nightly Zwicky Transient Facility (ZTF) Alert Stream via the ANTARES broker, identifying a manageable ∼1–5 candidates per night for expert vetting and coordinating follow-up observations. Our method leverages statistical light-curve and contextual host galaxy features within a random forest classifier, tagging transients of rare classes ( spectroscopic anomalies), of uncommon host galaxy environments ( contextual anomalies), and of peculiar or interaction-powered phenomena ( behavioral anomalies). Moreover, we demonstrate the power of a low-latency (∼ms) approximate similarity search method to find transient analogs with similar light-curve evolution and host galaxy environments. We use analogs for data-driven discovery, characterization, (re)classification, and imputation in retrospective and real-time searches. To date, we have identified ∼50 previously known and previously missed rare transients from real-time and retrospective searches, including but not limited to superluminous supernovae (SLSNe), tidal disruption events, SNe IIn, SNe IIb, SNe I-CSM, SNe Ia-91bg-like, SNe Ib, SNe Ic, SNe Ic-BL, and M31 novae. Lastly, we report the discovery of 325 total transients, all observed between 2018 and 2021 and absent from public catalogs (∼1% of all ZTF Astronomical Transient reports to the Transient Name Server through 2021). These methods enable a systematic approach to finding the “needle in the haystack” in large-volume data streams. Because of its integration with the ANTARES broker, LAISS is built to detect exciting transients in Rubin data.

We present the photometric and spectroscopic evolution of SN 2022oqm, a nearby multipeaked hydrogen- and helium-weak calcium-rich transient (CaRT). SN 2022oqm was detected 13.1 kpc from its host galaxy, the face-on spiral galaxy NGC 5875. Extensive spectroscopic coverage reveals an early hot ( T ≥ 40,000 K) continuum and carbon features observed ∼1 day after discovery, SN Ic-like photospheric-phase spectra, and strong forbidden calcium emission starting 38 days after discovery. SN 2022oqm has a relatively high peak luminosity ( M _B = −17 mag) for CaRTs, making it an outlier in the population. We determine that three power sources are necessary to explain the light curve (LC), with each corresponding to a distinct peak. The first peak is powered by an expanding blackbody with a power-law luminosity, suggesting shock cooling by circumstellar material (CSM). Subsequent LC evolution is powered by a double radioactive decay model, consistent with two sources of photons diffusing through optically thick ejecta. From the LC, we derive an ejecta mass and ^56 Ni mass of ∼0.6 M _⊙ and ∼0.09 M _⊙ . Spectroscopic modeling ∼0.6 M _⊙ of ejecta, and with well-mixed Fe-peak elements throughout. We discuss several physical origins for SN 2022oqm and find either a surprisingly massive white dwarf progenitor or a peculiar stripped envelope model could explain SN 2022oqm. A stripped envelope explosion inside a dense, hydrogen- and helium-poor CSM, akin to SNe Icn, but with a large 56Ni mass and small CSM mass could explain SN 2022oqm. Alternatively, helium detonation on an unexpectedly massive white dwarf could also explain SN 2022oqm.

We present ultraviolet/optical/near-infrared observations and modeling of Type II supernovae (SNe II) whose early time ( δ t < 2 days) spectra show transient, narrow emission lines from shock ionization of confined ( r < 10 ^15 cm) circumstellar material (CSM). The observed electron-scattering broadened line profiles (i.e., IIn-like) of H i , He i/ii , C iv , and N iii/iv/v from the CSM persist on a characteristic timescale ( t _IIn ) that marks a transition to a lower-density CSM and the emergence of Doppler-broadened features from the fast-moving SN ejecta. Our sample, the largest to date, consists of 39 SNe with early time IIn-like features in addition to 35 “comparison” SNe with no evidence of early time IIn-like features, all with ultraviolet observations. The total sample includes 50 unpublished objects with a total of 474 previously unpublished spectra and 50 multiband light curves, collected primarily through the Young Supernova Experiment and Global Supernova Project collaborations. For all sample objects, we find a significant correlation between peak ultraviolet brightness and both t _IIn and the rise time, as well as evidence for enhanced peak luminosities in SNe II with IIn-like features. We quantify mass-loss rates and CSM density for the sample through the matching of peak multiband absolute magnitudes, rise times, t _IIn , and optical SN spectra with a grid of radiation hydrodynamics and non-local thermodynamic equilibrium radiative-transfer simulations. For our grid of models, all with the same underlying explosion, there is a trend between the duration of the electron-scattering broadened line profiles and inferred mass-loss rate: ${t}_{\mathrm{IIn}}\approx 3.8[\dot{M}/$ (0.01 M _⊙ yr ^−1 )] days.

We present the most precise constraints to date for the mass and age distributions of single ultracool dwarfs in the solar neighborhood, based on an updated volume-limited sample of 504 L, T, and Y dwarfs within 25 pc. We develop a Monte Carlo approach using the $\langle V/{V}_{\max }\rangle $ statistic to correct for incompleteness and obtain a space density of $({1.83}_{-0.15}^{+0.16})\times {10}^{-2}$ pc ^−3 for spectral types L0–Y2. We calculate bolometric luminosities for our sample, using an updated “super-magnitude” method for the faintest objects. We use our resulting luminosity function and a likelihood-based population synthesis approach to simultaneously constrain the mass and age distributions. We employ the fraction of young L0–L7 dwarfs as a novel input for this analysis that is crucial for constraining the age distribution. For a power-law mass function $\tfrac{{dN}}{{dM}}\propto {M}^{-\alpha }$ , we find $\alpha ={0.58}_{-0.20}^{+0.16}$ , indicating an increase in numbers toward lower masses, consistent with measurements in nearby star-forming regions. For an exponential age distribution b ( t ) ∝ e ^− ^β ^t we find β = −0.44 ± 0.14, i.e., a population with fewer old objects than often assumed, which may reflect dynamical heating of the Galactic plane as much as the historical brown dwarf birthrate. We compare our analysis to that of Kirkpatrick et al., who used a similar volume-limited sample. Although our mass function measurements are numerically consistent, their assumption of a flat age distribution is disfavored by our analysis, and we identify several important methodological differences between our two studies. Our calculation of the age distribution of solar neighborhood brown dwarfs is the first based on a volume-limited sample.

We present high-cadence ultraviolet through near-infrared observations of the Type Ia supernova (SN Ia) 2023bee at D = 32 ± 3 Mpc, finding excess flux in the first days after explosion, particularly in our 10 minutes cadence TESS light curve and Swift UV data. Compared to a few other normal SNe Ia with early excess flux, the excess flux in SN 2023bee is redder in the UV and less luminous. We present optical spectra of SN 2023bee, including two spectra during the period where the flux excess is dominant. At this time, the spectra are similar to those of other SNe Ia but with weaker Si ii , C ii, and Ca ii absorption lines, perhaps because the excess flux creates a stronger continuum. We compare the data to several theoretical models on the origin of early excess flux in SNe Ia. Interaction with either the companion star or close-in circumstellar material is expected to produce a faster evolution than observed. Radioactive material in the outer layers of the ejecta, either from double detonation explosion or from a ^56 Ni clump near the surface, cannot fully reproduce the evolution either, likely due to the sensitivity of early UV observable to the treatment of the outer part of ejecta in simulation. We conclude that no current model can adequately explain the full set of observations. We find that a relatively large fraction of nearby, bright SNe Ia with high-cadence observations have some amount of excess flux within a few days of explosion. Considering potential asymmetric emission, the physical cause of this excess flux may be ubiquitous in normal SNe Ia.

We present the discovery of Ursa Major III/UNIONS 1, the least luminous known satellite of the Milky Way, which is estimated to have an absolute V -band magnitude of $+{2.2}_{-0.3}^{+0.4}$ mag, equivalent to a total stellar mass of ${16}_{-5}^{+6}$ M _⊙ . Ursa Major III/UNIONS 1 was uncovered in the deep, wide-field Ultraviolet Near Infrared Optical Northern Survey (UNIONS) and is consistent with an old ( τ > 11 Gyr), metal-poor ([Fe/H] ∼ −2.2) stellar population at a heliocentric distance of ∼10 kpc. Despite its being compact ( r _h = 3 ± 1 pc) and composed of few stars, we confirm the reality of Ursa Major III/UNIONS 1 with Keck II/DEIMOS follow-up spectroscopy and identify 11 radial velocity members, eight of which have full astrometric data from Gaia and are co-moving based on their proper motions. Based on these 11 radial velocity members, we derive an intrinsic velocity dispersion of ${3.7}_{-1.0}^{+1.4}$ km s ^−1 but some caveats preclude this value from being interpreted as a direct indicator of the underlying gravitational potential at this time. Primarily, the exclusion of the largest velocity outlier from the member list drops the velocity dispersion to ${1.9}_{-1.1}^{+1.4}$ km s ^−1 , and the subsequent removal of an additional outlier star produces an unresolved velocity dispersion. While the presence of binary stars may be inflating the measurement, the possibility of a significant velocity dispersion makes Ursa Major III/UNIONS 1 a high-priority candidate for multi-epoch spectroscopic follow-ups to deduce the true nature of this incredibly faint satellite.

The industrial sintering process used to produce metallic matrix pads has been altered to diminish the amount of copper used. Unfortunately, replacing a large part of the copper with iron seems to have reached a limit. In the high-energy, emergency-type rail braking used in this study, the materials are put to the very limit of their usage capacity, allowing us to observe the evolution of the microstructure and mechanical properties of sintered, metallic matrix pads. After the braking test, their compressive behaviour was assessed using digital image correlation (DIC), and their microstructure with scanning electron microscopy (SEM). The worn material has three flat layers with different microstructures and compressive behaviours. The bottom layer seems unmodified. Macroscopic and microscopic cracks run through the intermediate layer (2–15 mm depth). The top layer has stiffened thanks to resolidification of copper. The temperature reaches 1000 °C during the braking test, which also explains the carbon diffusion into iron that result in the weakening of iron –graphite interfaces in the pad. Finally, submicronic particles are detected at many open interfaces of the worn and compressed pad. Associated with the predominant role of graphite particles, this explains the weak compressive behaviour of the pads.

In the early 1990s, Mehrotra and Nichani developed a filtering-based corner detection method, which, though conceptually intriguing, suffered from limited reliability, leading to minimal references in the literature. Despite its underappreciation, the core concept of this method, rooted in the half-edge concept and directional truncated first derivative of Gaussian, holds significant promise. This article presents a comprehensive assessment of the enhanced corner detection algorithm, combining both qualitative and quantitative evaluations. We thoroughly explore the strengths, limitations, and overall effectiveness of our approach by incorporating visual examples and conducting evaluations. Through experiments conducted on both synthetic and real images, we demonstrate the efficiency and reliability of the proposed algorithm. Collectively, our experimental assessments substantiate that our modifications have transformed the method into one that outperforms established benchmark techniques. Due to its ease of implementation, our improved corner detection process has the potential to become a valuable reference for the computer vision community when dealing with corner detection algorithms. This article thus highlights the quantitative achievements of our refined corner detection algorithm, building upon the groundwork laid by Mehrotra and Nichani, and offers valuable insights for the computer vision community seeking robust corner detection solutions.

Carotid atherosclerotic stenosis of the carotid artery is an important cause of ischemic cerebrovascular disease. The aim of this study was to predict the presence or absence of clinical symptoms in unknown patients by studying the existence or lack of symptoms of patients with carotid atherosclerotic stenosis. First, a deep neural network prediction model based on brain MRI imaging data of patients with multiple modalities is constructed; it uses the multi-modality features extracted from the neural network as inputs and the incidence of diagnosis as output to train the model. Then, a machine learning-based classification algorithm is developed to utilize the clinical features for comparison and evaluation. The experimental results showed that the deep learning model using imaging data could better predict the clinical symptom classification of patients. As part of preventive medicine, this study could help patients with carotid atherosclerosis narrowing to prepare for stroke prevention based on the prediction results.

We present UV and/or optical observations and models of SN 2023ixf, a type II supernova (SN) located in Messier 101 at 6.9 Mpc. Early time ( flash ) spectroscopy of SN 2023ixf, obtained primarily at Lick Observatory, reveals emission lines of H i , He i/ii , C iv , and N iii/iv/v with a narrow core and broad, symmetric wings arising from the photoionization of dense, close-in circumstellar material (CSM) located around the progenitor star prior to shock breakout. These electron-scattering broadened line profiles persist for ∼8 days with respect to first light, at which time Doppler broadened the features from the fastest SN ejecta form, suggesting a reduction in CSM density at r ≳ 10 ^15 cm. The early time light curve of SN 2023ixf shows peak absolute magnitudes (e.g., M _u = −18.6 mag, M _g = −18.4 mag) that are ≳2 mag brighter than typical type II SNe, this photometric boost also being consistent with the shock power supplied from CSM interaction. Comparison of SN 2023ixf to a grid of light-curve and multiepoch spectral models from the non-LTE radiative transfer code CMFGEN and the radiation-hydrodynamics code HERACLES suggests dense, solar-metallicity CSM confined to r = (0.5–1) × 10 ^15 cm, and a progenitor mass-loss rate of $\dot{M}={10}^{-2}\,{M}_{\odot }$ yr ^−1 . For the assumed progenitor wind velocity of v _w = 50 km s ^−1 , this corresponds to enhanced mass loss (i.e., superwind phase) during the last ∼3–6 yr before explosion.

We present the discovery and extensive follow-up of a remarkable fast-evolving optical transient, AT 2022aedm, detected by the Asteroid Terrestrial impact Last Alert Survey (ATLAS). In the ATLAS o band, AT 2022aedm exhibited a rise time of 9 ± 1 days, reaching a luminous peak with M _g ≈ −22 mag. It faded by 2 mag in the g band during the next 15 days. These timescales are consistent with other rapidly evolving transients, though the luminosity is extreme. Most surprisingly, the host galaxy is a massive elliptical with negligible current star formation. Radio and X-ray observations rule out a relativistic AT 2018cow–like explosion. A spectrum in the first few days after explosion showed short-lived He ii emission resembling young core-collapse supernovae, but obvious broad supernova features never developed; later spectra showed only a fast-cooling continuum and narrow, blueshifted absorption lines, possibly arising in a wind with v ≈ 2700 km s ^−1 . We identify two further transients in the literature (Dougie in particular, as well as AT 2020bot) that share similarities in their luminosities, timescales, color evolution, and largely featureless spectra and propose that these may constitute a new class of transients: luminous fast coolers. All three events occurred in passive galaxies at offsets of ∼4–10 kpc from the nucleus, posing a challenge for progenitor models involving massive stars or black holes. The light curves and spectra appear to be consistent with shock breakout emission, though this mechanism is usually associated with core-collapse supernovae. The encounter of a star with a stellar-mass black hole may provide a promising alternative explanation.

We present extensive optical photometry of the afterglow of GRB 221009A. Our data cover 0.9–59.9 days from the time of Swift and Fermi gamma-ray burst (GRB) detections. Photometry in rizy -band filters was collected primarily with Pan-STARRS and supplemented by multiple 1–4 m imaging facilities. We analyzed the Swift X-ray data of the afterglow and found a single decline rate power law f ( t ) ∝ t ^−1.556±0.002 best describes the light curve. In addition to the high foreground Milky Way dust extinction along this line of sight, the data favor additional extinction to consistently model the optical to X-ray flux with optically thin synchrotron emission. We fit the X-ray-derived power law to the optical light curve and find good agreement with the measured data up to 5−6 days. Thereafter we find a flux excess in the riy bands that peaks in the observer frame at ∼20 days. This excess shares similar light-curve profiles to the Type Ic broad-lined supernovae SN 2016jca and SN 2017iuk once corrected for the GRB redshift of z = 0.151 and arbitrarily scaled. This may be representative of an SN emerging from the declining afterglow. We measure rest-frame absolute peak AB magnitudes of M _g = −19.8 ± 0.6 and M _r = − 19.4 ± 0.3 and M _z = −20.1 ± 0.3. If this is an SN component, then Bayesian modeling of the excess flux would imply explosion parameters of ${M}_{\mathrm{ej}}={7.1}_{-1.7}^{+2.4}$ M _⊙ , ${M}_{\mathrm{Ni}}={1.0}_{-0.4}^{+0.6}$ M _⊙ , and ${v}_{\mathrm{ej}}={{\rm{33,900}}}_{-5700}^{+5900}$ km s ^−1 , for the ejecta mass, nickel mass, and ejecta velocity respectively, inferring an explosion energy of E _kin ≃ 2.6–9.0 × 10 ^52 erg.

The electroreduction of SF6 is shown at ambient temperature in acetonitrile using an array of platinum microelectrodes to improve the electrical detection. Its half reduction potential occurs at −2.17 V vs Fc+/Fc. The exact number of electrons for the full consumption of sulfur hexafluoride was determined and this gas further quantitatively transformed into environmentally benign fluoride anion and sulfur by electrochemical reduction.