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Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin. [ABSTRACT FROM AUTHOR]

Monocular depth estimation is a task aimed at predicting pixel-level distances from a single RGB image. This task holds significance in various applications including autonomous driving and robotics. In particular, the recognition of surrounding environments is important to avoid collisions during autonomous parking. Fisheye cameras are adequate to acquire visual information from a wide field of view, reducing blind spots and preventing potential collisions. While there have been increasing demands for fisheye cameras in visual-recognition systems, existing research on depth estimation has primarily focused on pinhole camera images. Moreover, depth estimation from fisheye images poses additional challenges due to strong distortion and the lack of public datasets. In this work, we propose a novel underground parking lot dataset called JBNU-Depth360, which consists of fisheye camera images and their corresponding LiDAR projections. Our proposed dataset was composed of 4221 pairs of fisheye images and their corresponding LiDAR point clouds, which were obtained from six driving sequences. Furthermore, we employed a knowledge-distillation technique to improve the performance of the state-of-the-art depth-estimation models. The teacher–student learning framework allows the neural network to leverage the information in dense depth predictions and sparse LiDAR projections. Experiments were conducted on the KITTI-360 and JBNU-Depth360 datasets for analyzing the performance of existing depth-estimation models on fisheye camera images. By utilizing the self-distillation technique, the AbsRel and SILog error metrics were reduced by 1.81% and 1.55% on the JBNU-Depth360 dataset. The experimental results demonstrated that the self-distillation technique is beneficial to improve the performance of depth-estimation models. [ABSTRACT FROM AUTHOR]

Studien zu Reaktionen in Lösungen werden oft durch Lösungsmitteleffekte behindert. Zudem ist eine detaillierte Untersuchung der Kinetik auf den kleinen Temperaturbereich beschränkt, in dem das Lösungsmittel flüssig ist. Hier berichten wir über die spektroskopische in situ‐Beobachtung UV‐induzierter Reaktionen von Arylaziden in einer kristallinen Matrix im Vakuum. Die Matrizes werden durch Bindung der reaktiven Teile an ditopische Linker, die dann zu metallorganischen Gerüstverbindungen (MOFs) und oberflächenverankerten MOFs (SURMOFs) zusammengesetzt werden, gebildet. Diese porösen, kristallinen Gerüstverbindungen werden dann als Modellsysteme verwendet, um die chemischen Prozesse im Zusammenhang mit Aziden unter Ultrahochvakuum(UHV)‐Bedingungen – bei denen Lösungsmitteleffekte sicher ausgeschlossen werden können – und in einem großen Temperaturbereich zu untersuchen. Die Infrarot‐Reflexions‐Absorptions‐Spektroskopie (IRRAS) ermöglichte es uns, die Photoreaktion von Aziden in SURMOFs präzise zu überwachen. Die in situ‐IRRAS‐Daten zeigten in Verbindung mit XRD, MS und XPS, dass die Bestrahlung mit UV‐Licht zunächst zur Bildung eines Nitren‐Zwischenprodukts führt. Im zweiten Schritt findet eine intramolekulare Umlagerung statt, die ein Indoloindol‐Derivat ergibt. Diese Ergebnisse eröffnen einen neuartigen Weg für die präzise Untersuchung von chemischen Umwandlungen von Aziden. Referenzexperimente, die für lösungsmittelbeladene SURMOFs durchgeführt wurden, offenbaren eine enorme Vielfalt anderer Reaktionsschemata und unterstreichen daher den Bedarf an Modellsystemen, die unter UHV‐Bedingungen untersucht werden. [ABSTRACT FROM AUTHOR]

Chronic post‐hypoxic myoclonus (PHM) is a rare complication after successful cardiopulmonary resuscitation that causes severe gait disability due to action myoclonus. We report a case of a 69‐year‐old male patient with chronic PHM who showed significant improvement in myoclonus and gait. The diffusion tensor imaging (DTI) was taken at initial admission and long‐term follow‐up (3 years) to find the changes in the diffusion matrices of the related neural correlate region of interest (ROI). There was a change in pedunculopontine nucleus and several subcortical networks. [ABSTRACT FROM AUTHOR]

Identification of the active structure under reaction conditions is of great importance for the rational design of heterogeneous catalysts. However, this is often hampered by their structural complexity. The interplay between the surface structure of Co3O4 and the CO2 hydrogenation is described. Co3O4 with morphology‐dependent crystallographic surfaces presents different reducibility and formation energy of oxygen vacancies, thus resulting in distinct steady‐state composition and product selectivity. Co3O4‐0 h rhombic dodecahedra were completely reduced to Co0 and CoO, which presents circa 85 % CH4 selectivity. In contrast, Co3O4‐2 h nanorods were partially reduced to CoO, which exhibits a circa 95 % CO selectivity. The crucial role of the Co3O4 structure in determining the catalytic performance for higher alcohol synthesis over CuCo‐based catalysts is demonstrated. As expected, Cu/Co3O4‐2 h shows nine‐fold higher ethanol yield than Cu/Co3O4‐0 h owing to the inhibition for methanation. [ABSTRACT FROM AUTHOR]

This paper describes an environmentally friendly and efficient milling-assisted technology to produce soluble potassium from natural K-feldspar with the additive of calcium salts, and capture CO 2 via mineralization. The milling-assisted process could facilitate the extraction of potassium from K-feldspar under mild conditions (e.g. 30–40 °C), and mineralize CO 2 with reacted calcic slag. The milling-assisted and mineralization conditions (e.g. milling time and material ratio) were systematically investigated, reaching a maximum potassium extraction ratio of ∼80% and a CO 2 mineralization ratio of ∼5%. Characterizations indicated that the fragmentation, amorphization, and lattice distortion of the mineral particles during milling-assisted process are responsible for the enhanced K-feldspar decomposition reactions and ion-exchange reaction with calcium salt. Reaction principles of K-extraction and CO 2 mineralization are discussed, and a possible mechanism of milling-assisted process was proposed. [ABSTRACT FROM AUTHOR]

Identification of the active structure under reaction conditions is of great importance for the rational design of heterogeneous catalysts. However, this is often hampered by their structural complexity. The interplay between the surface structure of Co 3 O 4 and the CO 2 hydrogenation is described. Co 3 O 4 with morphology-dependent crystallographic surfaces presents different reducibility and formation energy of oxygen vacancies, thus resulting in distinct steady-state composition and product selectivity. Co 3 O 4 -0 h rhombic dodecahedra were completely reduced to Co 0 and CoO, which presents circa 85 % CH 4 selectivity. In contrast, Co 3 O 4 -2 h nanorods were partially reduced to CoO, which exhibits a circa 95 % CO selectivity. The crucial role of the Co 3 O 4 structure in determining the catalytic performance for higher alcohol synthesis over CuCo-based catalysts is demonstrated. As expected, Cu/Co 3 O 4 -2 h shows nine-fold higher ethanol yield than Cu/Co 3 O 4 -0 h owing to the inhibition for methanation., (© 2019 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Oxide-supported Rh nanoparticles have been widely used for CO 2 hydrogenation, especially for ethanol synthesis. However, this reaction operates under high pressure, up to 8 MPa, and suffers from low CO 2 conversion and alcohol selectivity. This paper describes the crucial role of hydroxyl groups bound on Rh-based catalysts supported on TiO 2 nanorods (NRs). The RhFeLi/TiO 2 NR catalyst shows superior reactivity (≈15% conversion) and ethanol selectivity (32%) for CO 2 hydrogenation. The promoting effect can be attributed to the synergism of high Rh dispersion and high-density hydroxyl groups on TiO 2 NRs. Hydroxyls are proven to stabilize formate species and protonate methanol, which is easily dissociated into *CH x , and then CO obtained from the reverse water-gas shift reaction (RWGS) is inserted into *CH x to form CH 3 CO*, followed by CH 3 CO* hydrogenation to ethanol.