1,151 research outputs found

    Extending the DeLone and McLean Information Systems Success Model for e-commerce website success

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    Website evaluation can continuously improve the performance of websites and increase sales. However, the lack of a comprehensive framework as guidance hampered this task. The main purpose of this research is to provide a comprehensive framework for e-commerce websites evaluation by extending the DeLone and McLean Information System Success Model. A new dimension--- Relationship quality is proposed to the model. The study also tries to identify characteristics of e-commerce websites that impact the user\u27s satisfaction; A survey questionnaire was distributed to web users, and a total of 295 responses were obtained. The data was processed through Statistical Package for the Social Science (SPSS); The data analysis result indicates that there are four important factors that impact user satisfaction, and among them, Relationship quality can be clearly defined. This indicates that there\u27s a need to extend the model. The study also yields a list of important characteristics that impact user\u27s satisfaction

    The Construction of a Partially Regular Solution to the Landau-Lifshitz-Gilbert Equation in R2\mathbb{R}^2

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    We establish a framework to construct a global solution in the space of finite energy to a general form of the Landau-Lifshitz-Gilbert equation in R2\mathbb{R}^2. Our characterization yields a partially regular solution, smooth away from a 2-dimensional locally finite Hausdorff measure set. This construction relies on approximation by discretization, using the special geometry to express an equivalent system whose highest order terms are linear and the translation of the machinery of linear estimates on the fundamental solution from the continuous setting into the discrete setting. This method is quite general and accommodates more general geometries involving targets that are compact smooth hypersurfaces.Comment: 43 pages, 2 figure

    What's Left? Concept Grounding with Logic-Enhanced Foundation Models

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    Recent works such as VisProg and ViperGPT have smartly composed foundation models for visual reasoning-using large language models (LLMs) to produce programs that can be executed by pre-trained vision-language models. However, they operate in limited domains, such as 2D images, not fully exploiting the generalization of language: abstract concepts like "left" can also be grounded in 3D, temporal, and action data, as in moving to your left. This limited generalization stems from these inference-only methods' inability to learn or adapt pre-trained models to a new domain. We propose the Logic-Enhanced Foundation Model (LEFT), a unified framework that learns to ground and reason with concepts across domains with a differentiable, domain-independent, first-order logic-based program executor. LEFT has an LLM interpreter that outputs a program represented in a general, logic-based reasoning language, which is shared across all domains and tasks. LEFT's executor then executes the program with trainable domain-specific grounding modules. We show that LEFT flexibly learns concepts in four domains: 2D images, 3D scenes, human motions, and robotic manipulation. It exhibits strong reasoning ability in a wide variety of tasks, including those that are complex and not seen during training, and can be easily applied to new domains.Comment: NeurIPS 2023. First two authors contributed equally. Project page: https://web.stanford.edu/~joycj/projects/left_neurips_202

    Role of the Osteoblast Lineage in the Bone Marrow Hematopoietic Niches

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    Elevated CO2 and Warming Altered Grassland Microbial Communities in Soil Top-Layers.

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    As two central issues of global climate change, the continuous increase of both atmospheric CO2 concentrations and global temperature has profound effects on various terrestrial ecosystems. Microbial communities play pivotal roles in these ecosystems by responding to environmental changes through regulation of soil biogeochemical processes. However, little is known about the effect of elevated CO2 (eCO2) and global warming on soil microbial communities, especially in semiarid zones. We used a functional gene array (GeoChip 3.0) to measure the functional gene composition, structure, and metabolic potential of soil microbial communities under warming, eCO2, and eCO2 + warming conditions in a semiarid grassland. The results showed that the composition and structure of microbial communities was dramatically altered by multiple climate factors, including elevated CO2 and increased temperature. Key functional genes, those involved in carbon (C) degradation and fixation, methane metabolism, nitrogen (N) fixation, denitrification and N mineralization, were all stimulated under eCO2, while those genes involved in denitrification and ammonification were inhibited under warming alone. The interaction effects of eCO2 and warming on soil functional processes were similar to eCO2 alone, whereas some genes involved in recalcitrant C degradation showed no significant changes. In addition, canonical correspondence analysis and Mantel test results suggested that NO3-N and moisture significantly correlated with variations in microbial functional genes. Overall, this study revealed the possible feedback of soil microbial communities to multiple climate change factors by the suppression of N cycling under warming, and enhancement of C and N cycling processes under either eCO2 alone or in interaction with warming. These findings may enhance our understanding of semiarid grassland ecosystem responses to integrated factors of global climate change
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