An Empirical Investigation of Culture’s Influence in Online Service Ratings: From the Perspective of Uncertainty Avoidance

In order to figure out the influence of consumers’ cultural background on their online review generation behavior, this study aims to investigate how consumers’ uncertainty avoidance values influence their online ratings. Utilizing data collected from a major travel review website, TripAdvisor, we find a negative relationship between uncertainty avoidance degree and online review rating. Consumers’ travel type and hotel star are found to have a moderating effect between consumers’ uncertainty avoidance and their online ratings. Moreover, the negative effect of uncertainty avoidance value on review rating is weaker for consumers on business travel, and this effect also decreases for upscale hotels. The results are further confirmed by a robustness check using another method. From a theoretical perspective, our study enriches existing literature dealing with online reviews. From a practical perspective, our research findings provide helpful insights to hotel practitioners

Unveiling the mechanism of controllable CO2 hydrogenation by group VIB metal single atom anchored on N-doped graphite: A density functional theory study

CO2 hydrogenation has raised considerable interest due to concerns about climate change. Realizing low-temperature reverse water gas shift (rWGS) reaction remains a significant challenge in the context of coupling it with the C–C growth reactions to convert CO2 to C2+ fuels. We carried out systematic DFT simulations to unveil the underlying low-temperature mechanism for the selective hydrogenation of CO2 to produce CO, over a variety of metal-based single atom catalysts (SACs) supported on the nitrogen-doped graphite. Group VIB metal-based SACs outperformed other 15 metal candidates in terms of versatile capacities in both selective activation of CO2 molecule and facilitating escaping of CO and H2O. Mo1/N3-Gt was especially outstanding by giving rise to spontaneous production of CO and O∗ through an effective electron injection into the CO2 molecule. Water formation has been identified as the potential rate-controlling step in such a catalytic reaction over Mo1/N3-Gt with an energy barrier of 1.10 eV. Herein, the H migration played a pivotal role and had tight affinity to the charge of H∗ on the active site of catalyst. The dynamic coordination environment of Moδ+ was revealed to be the dominant factor affecting the surface H∗ charge, leading to a variety of hydrogenation behaviors. The electron-deficient ligands of CO2 and O∗ on Mo1/N3-Gt, as well as additional adsorbed H2, were effective in adjusting the 4d and 5s electronic structure of central Mo and consequently resulted in nearly electric neutral surface H∗s, thus most benefiting the hydrogenation process. The optimal charge of the coordinated Mo for an outstanding selective hydrogenation performance in this scenario was found to be no less than +1.7e

Tree-Structured Shading Decomposition

We study inferring a tree-structured representation from a single image for object shading. Prior work typically uses the parametric or measured representation to model shading, which is neither interpretable nor easily editable. We propose using the shade tree representation, which combines basic shading nodes and compositing methods to factorize object surface shading. The shade tree representation enables novice users who are unfamiliar with the physical shading process to edit object shading in an efficient and intuitive manner. A main challenge in inferring the shade tree is that the inference problem involves both the discrete tree structure and the continuous parameters of the tree nodes. We propose a hybrid approach to address this issue. We introduce an auto-regressive inference model to generate a rough estimation of the tree structure and node parameters, and then we fine-tune the inferred shade tree through an optimization algorithm. We show experiments on synthetic images, captured reflectance, real images, and non-realistic vector drawings, allowing downstream applications such as material editing, vectorized shading, and relighting. Project website: https://chen-geng.com/inv-shade-treesComment: Accepted at ICCV 2023. Project website: https://chen-geng.com/inv-shade-tree

Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark

We introduce Stanford-ORB, a new real-world 3D Object inverse Rendering Benchmark. Recent advances in inverse rendering have enabled a wide range of real-world applications in 3D content generation, moving rapidly from research and commercial use cases to consumer devices. While the results continue to improve, there is no real-world benchmark that can quantitatively assess and compare the performance of various inverse rendering methods. Existing real-world datasets typically only consist of the shape and multi-view images of objects, which are not sufficient for evaluating the quality of material recovery and object relighting. Methods capable of recovering material and lighting often resort to synthetic data for quantitative evaluation, which on the other hand does not guarantee generalization to complex real-world environments. We introduce a new dataset of real-world objects captured under a variety of natural scenes with ground-truth 3D scans, multi-view images, and environment lighting. Using this dataset, we establish the first comprehensive real-world evaluation benchmark for object inverse rendering tasks from in-the-wild scenes, and compare the performance of various existing methods.Comment: NeurIPS 2023 Datasets and Benchmarks Track. The first two authors contributed equally to this work. Project page: https://stanfordorb.github.io

Defect-Driven Efficient Selective CO2 Hydrogenation with Mo-Based Clusters

Synthetic fuels produced from CO2 show promise in combating climate change. The reverse water gas shift (RWGS) reaction is the key to opening the CO2 molecule, and CO serves as a versatile intermediate for creating various hydrocarbons. Mo-based catalysts are of great interest for RWGS reactions featured for their stability and strong metal–oxygen interactions. Our study identified Mo defects as the intrinsic origin of the high activity of cluster Mo2C for CO2-selective hydrogenation. Specifically, we found that defected Mo2C clusters supported on nitrogen-doped graphene exhibited exceptional catalytic performance, attaining a reaction rate of 6.3 gCO/gcat/h at 400 °C with over 99% CO selectivity and good stability. Such a catalyst outperformed other Mo-based catalysts and noble metal-based catalysts in terms of facile dissociation of CO2, highly selective hydrogenation, and nonbarrier liberation of CO. Our study revealed that as a potential descriptor, the atomic magnetism linearly correlates to the liberation capacity of CO, and Mo defects facilitated product desorption by reducing the magnetization of the adsorption site. On the other hand, the defects were effective in neutralizing the negative charges of surface hydrogen, which is crucial for selective hydrogenation. Finally, we have successfully demonstrated that the combination of a carbon support and the carbonization process synergistically serves as a feasible strategy for creating rich Mo defects, and biochar can be a low-cost alternative option for large-scale applications

A Comprehensive Exploration of Personalized Learning in Smart Education: From Student Modeling to Personalized Recommendations

With the development of artificial intelligence, personalized learning has attracted much attention as an integral part of intelligent education. China, the United States, the European Union, and others have put forward the importance of personalized learning in recent years, emphasizing the realization of the organic combination of large-scale education and personalized training. The development of a personalized learning system oriented to learners' preferences and suited to learners' needs should be accelerated. This review provides a comprehensive analysis of the current situation of personalized learning and its key role in education. It discusses the research on personalized learning from multiple perspectives, combining definitions, goals, and related educational theories to provide an in-depth understanding of personalized learning from an educational perspective, analyzing the implications of different theories on personalized learning, and highlighting the potential of personalized learning to meet the needs of individuals and to enhance their abilities. Data applications and assessment indicators in personalized learning are described in detail, providing a solid data foundation and evaluation system for subsequent research. Meanwhile, we start from both student modeling and recommendation algorithms and deeply analyze the cognitive and non-cognitive perspectives and the contribution of personalized recommendations to personalized learning. Finally, we explore the challenges and future trajectories of personalized learning. This review provides a multidimensional analysis of personalized learning through a more comprehensive study, providing academics and practitioners with cutting-edge explorations to promote continuous progress in the field of personalized learning.Comment: 82 pages,5 figure

Accidental Light Probes

Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes are generally unavailable in everyday images. In this work, we study recovering lighting from accidental light probes (ALPs) -- common, shiny objects like Coke cans, which often accidentally appear in daily scenes. We propose a physically-based approach to model ALPs and estimate lighting from their appearances in single images. The main idea is to model the appearance of ALPs by photogrammetrically principled shading and to invert this process via differentiable rendering to recover incidental illumination. We demonstrate that we can put an ALP into a scene to allow high-fidelity lighting estimation. Our model can also recover lighting for existing images that happen to contain an ALP.Comment: CVPR2023. Project website: https://kovenyu.com/ALP

Techno-economic and environmental evaluation of the production of biodiesel from rice-straw in China

Rice straw (RS) is the residue obtained during the rice processing process, and is recognized as one of the most abundant biomass resources in the world. Approximately 800 million to 1 billion tons of rice straw are produced globally every year, and most of them are considered general waste and typically end up in landfills or incineration. This approach wastes resources and can also lead to environmental pollution. In the current study, the RS was used as the source of biodiesel production and a comprehensive process model of the RS valorization process was developed to evaluate the energy flow, production efficiency, production costs, and greenhouse gas emissions in Hunan Province, China. The evaluation results showed that the energy efficiency of biodiesel production from rice straw and the overall energy efficiency of the rice straw valorization process are reported as 52.1% and 56.1%, respectively. The minimum selling price of biodiesel, which is CNY 3.03/kg, is considerably lower than the current market prices for similar products in China. The largest proportion of the production cost of biodiesel is the cost of natural gas, followed by utilities, capital, transportation, plant maintenance and overheads, consumables, labor, and waste disposal. For the current RS valorization plant with a 5000 kg/h RS feed rate, the investment payback times are 8.9 yr and 7.1 yr when the biodiesel is sold at the lowest (CNY 4/kg) and highest (CNY 4.6/kg) market price, respectively. Environmental analysis shows that the greenhouse gas emissions intensity of biodiesel production is 75.8 g CO2eq/MJ, which is only about 52% of traditional fossil diesel and indicating that biodiesel is an environmentally friendly energy source

Exogenous Testosterone Increases Decoy Effect in Healthy Males

There is increasing interest in the role played by testosterone in economic decision-making and social cognition. However, despite the growing body of findings in this field of research, no empirical study to date has tested whether testosterone modulates decision-making when an asymmetrically dominated decoy option is introduced in a choice set. Within a choice set that comprises two options, an asymmetrically dominated decoy option is a third option that, when introduced in the choice set, is much worse than one of the existing options, but comparable to the other existing option. Introduction of a decoy option leads to a preference toward the dominating option (i.e., decoy effect). Healthy male participants (n = 63) received a single-dose of 150 mg testosterone gel in a double-blind, placebo-controlled, between-subjects design. At 180 min post-administration, participants took part in a decision-making task to elicit decoy effect. Results showed that participants in the testosterone group made less consistent choices and more target choices (i.e., decoy effect) than participants in the placebo group. These findings are interpreted in light of the dual-process theory and are in line with existing evidence suggesting that testosterone promotes more intuitive and automatic judgments in human decision-making