Impact of Informativeness and Social Cues of Medical Crowdfunding Projects on Cognitive Trust and Willingness to Donate

With the rapid development of the Internet and mobile payments, medical crowdfunding is becoming more and more popular. However, the success rate of crowdfunding projects is low, and many patients are unable to raise the money they need to pay for their medical treatment in a timely manner, so how to increase user donation Willingness is a worthwhile research problem. In this paper, from the perspective of website interface design, we take project informativeness as the central route and social cues as the peripheral route to create a research model based on the Elaboration Likelihood Model (ELM). Around this model we explore how different website design factors on healthcare crowdfunding platforms affect users\u27 perceived trust in the platform and project , which in turn influenced users\u27 willingness to donate. Laboratory experiments were used to obtain data and the data were analyzed by SPSS24.0 and AMOS23.0 software. The results showed that the richer the project informativeness and the presence of social cues positively influenced potential donors\u27 intention to donate,and cognitive trust has a mediating effect on the relationship between them. The results of this study are instructive for fundraisers to conduct efficient fundraising campaigns and for medical crowdfunding platform managers to better manage platforms

The Impact of Beneficiary Facial Expressions on Donation Intention in Medical Crowdfunding

In recent years, medical crowdfunding has become an emerging and effective way to raise funds for patients with severe illness and their families, and has solved huge economic problems for many families. This study studies the information expression of medical crowdfunding projects. This study combines the S-O-R model, considered the model of altruistic and egoistic motives for helping, adopted laboratory research methods, studied the effect of the facial expressions of beneficiary on individual donate intention. The results showed that individual altruism and guilt can positively influence individual donate intention. The facial expressions of beneficiaries affected both egoistic motivation and altruism motivation at the same time, and there were significant differences in the two types of motivation. In addition, research has found that individual guilt has a moderating effect on altruism. This study enriched the research of the SOR model and the altruistic and self-interest motivation model in the context of medical crowdfunding, at the same time studied the impact of facial expressions on personal motivation to provide recommendations for medical crowdfunding content writing

Quantum oscillations in adsorption energetics of atomic oxygen on Pb(111) ultrathin films: A density-functional theory study

Using first-principles calculations, we have systematically studied the quantum size effects of ultrathin Pb(111) films on the adsorption energies and diffusion energy barriers of oxygen atoms. For the on-surface adsorption of oxygen atoms at different coverages, all the adsorption energies are found to show bilayer oscillation behaviors. It is also found that the work function of Pb(111) films still keeps the bilayer-oscillation behavior after the adsorption of oxygen atoms, with the values being enlarged by 2.10 to 2.62 eV. For the diffusion and penetration of the adsorbed oxygen atoms, it is found that the most energetically favored paths are the same on different Pb(111) films. And because of the modulation of quantum size effects, the corresponding energy barriers are all oscillating with a bilayer period on different Pb(111) films. Our studies indicate that the quantum size effect in ultrathin metal films can modulate a lot of processes during surface oxidation

Convolutional Sequence to Sequence Non-intrusive Load Monitoring

A convolutional sequence to sequence non-intrusive load monitoring model is proposed in this paper. Gated linear unit convolutional layers are used to extract information from the sequences of aggregate electricity consumption. Residual blocks are also introduced to refine the output of the neural network. The partially overlapped output sequences of the network are averaged to produce the final output of the model. We apply the proposed model to the REDD dataset and compare it with the convolutional sequence to point model in the literature. Results show that the proposed model is able to give satisfactory disaggregation performance for appliances with varied characteristics.Comment: This paper is submitted to IET-The Journal of Engineerin

TwinTex: Geometry-aware Texture Generation for Abstracted 3D Architectural Models

Coarse architectural models are often generated at scales ranging from individual buildings to scenes for downstream applications such as Digital Twin City, Metaverse, LODs, etc. Such piece-wise planar models can be abstracted as twins from 3D dense reconstructions. However, these models typically lack realistic texture relative to the real building or scene, making them unsuitable for vivid display or direct reference. In this paper, we present TwinTex, the first automatic texture mapping framework to generate a photo-realistic texture for a piece-wise planar proxy. Our method addresses most challenges occurring in such twin texture generation. Specifically, for each primitive plane, we first select a small set of photos with greedy heuristics considering photometric quality, perspective quality and facade texture completeness. Then, different levels of line features (LoLs) are extracted from the set of selected photos to generate guidance for later steps. With LoLs, we employ optimization algorithms to align texture with geometry from local to global. Finally, we fine-tune a diffusion model with a multi-mask initialization component and a new dataset to inpaint the missing region. Experimental results on many buildings, indoor scenes and man-made objects of varying complexity demonstrate the generalization ability of our algorithm. Our approach surpasses state-of-the-art texture mapping methods in terms of high-fidelity quality and reaches a human-expert production level with much less effort. Project page: https://vcc.tech/research/2023/TwinTex.Comment: Accepted to SIGGRAPH ASIA 202

Design of new drugs for medullary thyroid carcinoma

Medullary thyroid carcinoma (MTC) is one of the common malignant endocrine tumors, which seriously affects human health. Although surgical resection offers a potentially curative therapeutic option to some MTC patients, most patients do not benefit from it due to the difficulty to access the tumors and tumor metastasis. The survival rate of MTC patients has improved with the recent advances in the research, which has improved our understanding of the molecular mechanism underlying MTC and enabled the development and approval of novel targeted drugs. In this article, we reviewed the molecular mechanisms related to MTC progression and the principle for the design of molecular targeted drugs, and proposed some future directions for prospective studies exploring targeted drugs for MTC

Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos

The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has inspired numerous attempts on dynamic scenes. Current techniques that utilize neural rendering for facilitating free-view videos (FVVs) are restricted to either offline rendering or are capable of processing only brief sequences with minimal motion. In this paper, we present a novel technique, Residual Radiance Field or ReRF, as a highly compact neural representation to achieve real-time FVV rendering on long-duration dynamic scenes. ReRF explicitly models the residual information between adjacent timestamps in the spatial-temporal feature space, with a global coordinate-based tiny MLP as the feature decoder. Specifically, ReRF employs a compact motion grid along with a residual feature grid to exploit inter-frame feature similarities. We show such a strategy can handle large motions without sacrificing quality. We further present a sequential training scheme to maintain the smoothness and the sparsity of the motion/residual grids. Based on ReRF, we design a special FVV codec that achieves three orders of magnitudes compression rate and provides a companion ReRF player to support online streaming of long-duration FVVs of dynamic scenes. Extensive experiments demonstrate the effectiveness of ReRF for compactly representing dynamic radiance fields, enabling an unprecedented free-viewpoint viewing experience in speed and quality.Comment: Accepted by CVPR 2023. Project page, see https://aoliao12138.github.io/ReRF