Solvent-induced surface hydroxylation of a layered perovskite Sr3FeCoO7−δ for enhanced oxygen evolution catalysis

Efficient oxygen evolution reaction (OER) catalysts made of earth abundant elements are essential for the cost-effective generation of solar fuels. Surface modification is a novel strategy to enhance the activity of OER catalysts, however, it is commonly done under harsh and energy intensive conditions. Herein, we present a facile approach of solvent treatment to hydroxylate the surface of a layered perovskite, Sr3FeCoO7−δ. The catalytic activity correlates with the degree of surface hydroxylation, which influences the absorption energy of intermediates on the surface. The optimized catalyst exhibits higher activity than the current benchmark perovskite OER catalysts. This work introduces a mild and convenient method for surface modification, which may be applicable for the improvement of other nanoscale electrocatalysts

BAT: Behavior-Aware Human-Like Trajectory Prediction for Autonomous Driving

The ability to accurately predict the trajectory of surrounding vehicles is a critical hurdle to overcome on the journey to fully autonomous vehicles. To address this challenge, we pioneer a novel behavior-aware trajectory prediction model (BAT) that incorporates insights and findings from traffic psychology, human behavior, and decision-making. Our model consists of behavior-aware, interaction-aware, priority-aware, and position-aware modules that perceive and understand the underlying interactions and account for uncertainty and variability in prediction, enabling higher-level learning and flexibility without rigid categorization of driving behavior. Importantly, this approach eliminates the need for manual labeling in the training process and addresses the challenges of non-continuous behavior labeling and the selection of appropriate time windows. We evaluate BAT's performance across the Next Generation Simulation (NGSIM), Highway Drone (HighD), Roundabout Drone (RounD), and Macao Connected Autonomous Driving (MoCAD) datasets, showcasing its superiority over prevailing state-of-the-art (SOTA) benchmarks in terms of prediction accuracy and efficiency. Remarkably, even when trained on reduced portions of the training data (25%), our model outperforms most of the baselines, demonstrating its robustness and efficiency in predicting vehicle trajectories, and the potential to reduce the amount of data required to train autonomous vehicles, especially in corner cases. In conclusion, the behavior-aware model represents a significant advancement in the development of autonomous vehicles capable of predicting trajectories with the same level of proficiency as human drivers. The project page is available at https://github.com/Petrichor625/BATraj-Behavior-aware-Model

Graphene oxide mediated self-sacrificial synthesis of LaCO 3 OH-Ni(OH) 2 @graphene hierarchical composite for photocatalytic H 2 evolution and supercapacitor

Abstract(#br)Herein, we designed a one-step lattice-confined etching perovskite nanoparticles and self-sacrificing graphene oxide (GO) induced self-assembly strategy to synthesize novel 3D nest-like LaCO 3 OH and flower-like Ni(OH) 2 @graphene (RGO) hierarchical composite as a high performance photocatalyst and electrode material. The lattice-confined effect regulates the concentration and distribution of nickel ions migrating from perovskite to GO and thus constructs a homogeneous Ni(OH) 2 @RGO nanostructure. La(OH) 3 formed by residual lattice frames react with CO 3 2− from self-sacrificing of GO self-assembly to form nest-like LaCO 3 OH, which is embedded in the Ni(OH) 2 @RGO nanosheets. GO was utilized as both morphology control reagent and self-sacrificed carbon source. Benefit from the extremely rapid transfer of electron on the homogeneous Ni(OH) 2 @RGO nanosheets and high light-harvesting capacity of 3D nest and flower-like composite of LaCO 3 OH-Ni(OH) 2 @RGO, the properties of photocatalysis and supercapacitor are greatly enhanced. The H 2 production rate of 1.3807 mmol h −1 g −1 has been achieved which is 13 times higher than pure LaCO 3 OH. Electrochemical studies showed that a specific capacitance of 572.47 F g −1 was obtained at a scan rate of 10 mv/s with 80% capacitance retention even after 20,000 cycles. This composite synthesized from GO mediated etching solid phase perovskite surface ion migration under lattice-confined action provides a novel technical route for the direct self-assembly of solid nanoparticles and GO to synthesize new functional materials

Fermentation improves flavors, bioactive substances, and antioxidant capacity of Bian-Que Triple-Bean Soup by lactic acid bacteria

The ancient traditional Chinese drink Bian-Que Triple-Bean Soup made by fermentation (FTBS) of Lactococcus lactis subsp. lactis YM313 and Lacticaseibacillus casei YQ336 is a potential functional drink. The effect of fermentation on the flavor and biological activity of FTBS was evaluated by analyzing its chemical composition. Five volatile flavors were detected in modified FTBS. Fermentation decreased the proportion of nonanal (beany flavor substances) but significantly increased the total flavone contents, phenol contents and many bioactive small molecule substances in FTBS. The changes of these substances led to the significant improvement of FTBS sensory evaluation, antioxidant activity and prebiotic potential. This research provides a theoretical basis for the application of Lactic acid bacteria (LAB) in the fermentation of edible plant-based foods and transformation from traditional food to industrial production

A novel insight of sentinel lymph node concept based on 1-3 positive nodes in patients with pT1-2 gastric cancer

<p>Abstract</p> <p>Background</p> <p>Sentinel node (SN) biopsy has been practiced in gastric cancer in recent years, and many studies focused on the distribution of solitary lymph node metastasis (SLM) to assess the pattern of SN. In fact, there is usually more than one SN existing in gastric cancer. The distribution of SNs needs to be further re-evaluated.</p> <p>Methods</p> <p>A total of 289 patients in pT1-2 stage with 1-3 positive nodes confined to same compartment were included in this study with informed consents. The primary lesion was solitary (≤ 5.0 cm in diameter) and D2 or D3 lymph node dissection had been performed. The location of metastatic lymph nodes was analyzed retrospectively.</p> <p>Results</p> <p>Most positive nodes occurred in N1 compartment, with frequency of 79.6% to 85.7% based on site of tumor. In the lower third of stomach, no. 6 was the most common metastatic site and no. 3 was the second; the order was reversed for SLM. With increasing depth of tumor invasion, a progressively augmented nodal involvement was shown. Nearly a half appeared transverse metastasis when the tumor located at the lesser or greater curvature. Among skip metastasis, no. 7, 8a, 9 and 11p were the most common metastatic sites and the prognosis was as similar as that of patients with N1 involved only.</p> <p>Conclusions</p> <p>The 1-3 positive nodes in the same compartment should be possible SNs, and most of which are restricted in N1 in pT1-2 gastric cancer. Transversal and 2 stations lymph node metastasis are common.</p

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

A dual AAV system enables the Cas9-mediated correction of a metabolic liver disease in newborn mice

Many genetic liver diseases present in newborns with repeated, often lethal, metabolic crises. Gene therapy using non-integrating viruses such as AAV is not optimal in this setting because the non-integrating genome is lost as developing hepatocytes proliferate1,2. We reasoned that newborn liver may be an ideal setting for AAV-mediated gene correction using CRISPR/Cas9. Here we intravenously infuse two AAVs, one expressing Cas9 and the other expressing a guide RNA and the donor DNA, into newborn mice with a partial deficiency in the urea cycle disorder enzyme, ornithine transcarbamylase (OTC). This resulted in reversion of the mutation in 10% (6.7% – 20.1%) of hepatocytes and increased survival in mice challenged with a high-protein diet, which exacerbates disease. Gene correction in adult OTC-deficient mice was lower and accompanied by larger deletions that ablated residual expression from the endogenous OTC gene, leading to diminished protein tolerance and lethal hyperammonemia on a chow diet

Three Essays On Big Data Analytics, Traditional Marketing Analytics, Knowledge Discovery, And New Product Performance

In recent years, companies have become aggressive in investing in Big Data Analytics (BDA) for marketing purposes, particularly new product development. One of the basic features of BDA is its promise in delivering automated recommendations or knowledge. For this reason, companies attempt to ascertain if BDA can improve new product performance beyond what Traditional Marketing Analytics (TMA) can. The overarching question is whether different combinations (BDA and TMA in different levels) of analytics capabilities are able to generate different kinds of knowledge for Knowledge and Information Fusion that could improve new product performance. The aim of this study is to build and assess the Knowledge Fusion Taxonomy, and then determine the attributes that are most critical in affecting knowledge generation, Knowledge and Information Fusion, and new product development. Multiple correspondence analysis (MCA), Fuzzy Set QCA, Partial least squares path modeling (PLS-PM), and SEM are the main statistical approaches used in this study to test the model. Heatmaps were also generated to allow users to easily explore trends or dimension patterns of items and latent variables. In general, the study suggests that BDA is an important complementary capability instead of a competing capability with TMA. The results identified by the MCA, Fuzzy Set QCA, and PLS-PM may provide such a roadmap for firms to improve key capabilities in analytics, knowledge discovery and integration, and new product development. The study supports the hypothesized effects of competing analytics capabilities (TMA and BDA) on knowledge generation, and also a positive effect of knowledge generation and Knowledge and Information Fusion on new product performance. In particular, both the Knowledge Fusion Taxonomy and the PLS-PM suggest that when combining information and knowledge in a complex manner, Automated Knowledge is more important than other types of knowledge. Therefore, to capture the pioneer position as shown in the Knowledge Fusion Taxonomy, companies need to build new capabilities on Automated Knowledge generation by synthesizing the unique combination of analytics capabilities. In addition, Heuristic Knowledge was also found to be a moderator when firms adopt high levels of BDA to generate Automated Knowledge. This paper establishes a solid conceptual and data analysis framework for analytics and knowledge capabilities (i.e., discovery and fusion) on new product performance. Additionally, the study provides managers a roadmap to focus on important issues in analytics and knowledge discovery for improving new product performance