62 research outputs found

    LLM as A Robotic Brain: Unifying Egocentric Memory and Control

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    Embodied AI focuses on the study and development of intelligent systems that possess a physical or virtual embodiment (i.e. robots) and are able to dynamically interact with their environment. Memory and control are the two essential parts of an embodied system and usually require separate frameworks to model each of them. In this paper, we propose a novel and generalizable framework called LLM-Brain: using Large-scale Language Model as a robotic brain to unify egocentric memory and control. The LLM-Brain framework integrates multiple multimodal language models for robotic tasks, utilizing a zero-shot learning approach. All components within LLM-Brain communicate using natural language in closed-loop multi-round dialogues that encompass perception, planning, control, and memory. The core of the system is an embodied LLM to maintain egocentric memory and control the robot. We demonstrate LLM-Brain by examining two downstream tasks: active exploration and embodied question answering. The active exploration tasks require the robot to extensively explore an unknown environment within a limited number of actions. Meanwhile, the embodied question answering tasks necessitate that the robot answers questions based on observations acquired during prior explorations

    Design and Advanced Manufacturing of NU-1000 Metal–Organic Frameworks with Future Perspectives for Environmental and Renewable Energy Applications

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    Metal–organic frameworks (MOFs) represent a relatively new family of materials that attract lots of attention thanks to their unique features such as hierarchical porosity, active metal centers, versatility of linkers/metal nodes, and large surface area. Among the extended list of MOFs, Zr-based-MOFs demonstrate comparably superior chemical and thermal stabilities, making them ideal candidates for energy and environmental applications. As a Zr-MOF, NU-1000 is first synthesized at Northwestern University. A comprehensive review of various approaches to the synthesis of NU-1000 MOFs for obtaining unique surface properties (e.g., diverse surface morphologies, large surface area, and particular pore size distribution) and their applications in the catalysis (electro-, and photo-catalysis), CO2 reduction, batteries, hydrogen storage, gas storage/separation, and other environmental fields are presented. The review further outlines the current challenges in the development of NU-1000 MOFs and their derivatives in practical applications, revealing areas for future investigation

    Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors

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    We present Magic123, a two-stage coarse-to-fine approach for high-quality, textured 3D meshes generation from a single unposed image in the wild using both2D and 3D priors. In the first stage, we optimize a neural radiance field to produce a coarse geometry. In the second stage, we adopt a memory-efficient differentiable mesh representation to yield a high-resolution mesh with a visually appealing texture. In both stages, the 3D content is learned through reference view supervision and novel views guided by a combination of 2D and 3D diffusion priors. We introduce a single trade-off parameter between the 2D and 3D priors to control exploration (more imaginative) and exploitation (more precise) of the generated geometry. Additionally, we employ textual inversion and monocular depth regularization to encourage consistent appearances across views and to prevent degenerate solutions, respectively. Magic123 demonstrates a significant improvement over previous image-to-3D techniques, as validated through extensive experiments on synthetic benchmarks and diverse real-world images. Our code, models, and generated 3D assets are available at https://github.com/guochengqian/Magic123.Comment: webpage: https://guochengqian.github.io/project/magic123

    Highly graphitized nitrogen-doped porous carbon nanopolyhedra derived from ZIF-8 nanocrystals as efficient electrocatalysts for oxygen reduction reactions

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    Nitrogen-doped graphitic porous carbons (NGPCs) have been synthesized by using a zeolite-type nanoscale metal–organic framework (NMOF) as a self-sacrificing template, which simultaneously acts as both the carbon and nitrogen sources in a facile carbonization process. The NGPCs not only retain the nanopolyhedral morphology of the parent NMOF, but also possess rich nitrogen, high surface area and hierarchical porosity with well-conducting networks. The promising potential of NGPCs as metal-free electrocatalysts for oxygen reduction reactions (ORR) in fuel cells is demonstrated. Compared with commercial Pt/C, the optimized NGPC-1000-10 (carbonized at 1000 °C for 10 h) catalyst exhibits comparable electrocatalytic activity via an efficient four-electron-dominant ORR process coupled with superior methanol tolerance as well as cycling stability in alkaline media. Furthermore, the controlled experiments reveal that the optimum activity of NGPC-1000-10 can be attributed to the synergetic contributions of the abundant active sites with high graphitic-N portion, high surface area and porosity, and the high degree of graphitization. Our findings suggest that solely MOF-derived heteroatom-doped carbon materials can be a promising alternative for Pt-based catalysts in fuel cells

    Traditional Chinese Medicine JianPiHuaTan formula improving quality of life and survival in patients with colorectal cancer through RAS/RAF downstream signaling pathways

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    ObjectiveJianPiHuaTan Formula (JPHTF), a traditional Chinese medicine (TCM), has been utilized as an adjunctive therapy for colorectal cancer (CRC). The study aims to evaluate the potential clinical benefits of JPHTF and its effectiveness in inhibiting tumor growth.Methods300 stage II/III CRC patients and 412 advanced CRC patients were enrolled to verify the clinical value of JPHTF in CRC treatment. Furthermore, CRC patient-derived xenograft (PDX) mice were utilized to investigate the regulatory mechanisms of JPHTF.ResultsJPHTF significantly improved abdominal distension, shortness of breath, drowsiness, loss of appetite, sleep, and tiredness in stage II/III CRC patients, thereby improving their quality of life. Simultaneously, JPHTF served as a supportive therapy in extending the overall survival (OS) of stage IV CRC patients with RAS/RAF mutations undergoing chemotherapy. Additionally, JPHTF effectively impeded tumor progression in CRC PDX models with RAS mutation, accompanied by a reduction in tumor cell content in the JPHTF group. Transcriptomic analysis revealed the involvement of the Hippo and Hedgehog signaling pathways in JPHTF-mediated CRC inhibition. Furthermore, mice in the JPHTF group exhibited increased immune cell infiltration.ConclusionThese findings suggested that JPHTF may inhibits tumor growth in CRC with RAS mutation by modulating RAS/RAF downstream signaling pathways, specifically the Hippo and Hedgehog signaling, leading to increased immune cell infiltration

    Few‐Layer Ag‐Coated Ordered Mesoporous Pt Nanocrystals for Ethanol Oxidation

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    Direct ethanol fuel cells have broad application prospects, but there are still some issues such as slow oxidation kinetics and poisoning effects of intermediates in the anodic ethanol reduction reaction (EOR). Herein, a series of few‐layer Ag‐coated ordered mesoporous Pt nanocrystals (Meso‐Pt@Ag) with different Ag shell thicknesses are prepared by combining hard templating and in situ reduction methods. Benefiting from the ordered mesoporous structure, the lattice strain, and the electronic synergistic effect induced by the core–shell structure, Meso‐Pt@Ag2 nanocrystals exhibit the best EOR performance with a mass activity of 10.01 A mgPt−1, which is 8.40‐fold of commercial Pt/C and 4.45‐fold of Meso‐Pt, respectively. And, Meso‐Pt@Ag2 nanocrystals also show the favorable toxicity resistance to carbonaceous intermediates and stability for EOR. Furthermore, theoretical calculations demonstrate that the Ag‐coated Pt core has more optimized deprotonation reaction kinetics and lower CO adsorption energy, indicating its better resistance to CO toxicity for EOR

    Terbium-Tetracarboxylate Framework as a Luminescent Probe for the Selective Detection of Nitrofurazone

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    A novel terbium-tetracarboxylate framework with the 5,5’-(diazene-1,2-iyl)diisophthalic acid (H4abtc) ligand, formulated as [Tb(Habtc)(DMSO)(H2O)2]n (ZTU-5), has been synthesized and structurally characterized. ZTU-5 features a 2D-layered structure constructed by the binuclear terbium secondary building units (SBUs) and abtc4– ligand, which can be further expanded into a 3D-supramolecular framework by the hydrogen bond interactions. In addition, the magnetic and fluorescence properties of ZTU-5 are investigated and ZTU-5 exhibits highly selective and sensitive detection of nitrofurazone (NZF)

    Research on the Current Situation and Calculation Method of Carbon Emissions Assessment for Building Curtain Walls

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    Curtain wall systems stand out as a pivotal domain within the construction sector’s endeavors towards energy efficiency and carbon mitigation. To refine the evaluation framework for carbon emissions within this industry, this paper explores the calculation and assessment method for building curtain walls. The article first reviews the current research status regarding carbon emissions from materials and the impact of curtain walls on buildings in the operational stage. Based on lifecycle theory, the carbon emissions from building curtain walls are divided into six stages: material acquisition, processing and production, installation and construction, transportation, use and maintenance, and dismantling. On this basis, this paper proposes a method for calculating carbon emissions from building curtain walls. Following that, a case study is conducted using a specific glass curtain wall project for illustrative analysis. The results indicate that the carbon emissions from the material acquisition stage constitute approximately 90% of the total, serving as the primary source of carbon emissions for glass curtain walls. Furthermore, the scientific application of photovoltaics can significantly reduce the carbon emission levels of building curtain walls. Finally, an analysis was conducted on the current issues existing in the evaluation of carbon emissions

    Identification of Prognostic Risk Model Based on DNA Methylation-Driven Genes in Esophageal Adenocarcinoma

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    Background. DNA methylation is an important part of epigenetic modification, and its abnormality is closely related to esophageal adenocarcinoma (EAC). This study was aimed at using bioinformatics analysis to identify methylation-driven genes (MDGs) in EAC patients and establish a risk model as a biological indicator of EAC prognosis. Method. Downloaded EAC DNA methylation, transcriptome, and related clinical data from TCGA database. MethylMix was used to identify MDGs. R package clusterProfiler and the ConsensusPathDB online database were used to analyze the rich functions and pathways of these MDGs. The prognostic risk model was established by univariate Cox regression, Lasso regression, and multivariate Cox regression analysis. Finally each MDG in the model were carried out through the survival R package. Results. A total of 273 MDGs were identified, which were enriched in transcriptional regulation and embryonic organ morphogenesis. Cox regression analysis established a risk model consisting of GPBAR1, OLFM4, FOXI2, and CASP10. In addition, further survival analysis revealed that OLFM4 and its two related sites were significantly related to the EAC patients’ survival. Conclusion. In summary, this study used bioinformatics methods to identify EAC MDGs and established a reliable risk prognosis model. It provided potential biomarkers for the early treatment and prognosis evaluation of EAC
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