Complete gradient expanding Ricci solitons with finite asymptotic scalar curvature ratio

Let $(M^n, g, f)$, $n\geq 5$, be a complete gradient expanding Ricci soliton with nonnegative Ricci curvature $Rc\geq 0$. In this paper, we show that if the asymptotic scalar curvature ratio of $(M^n, g, f)$ is finite (i.e., $\limsup_{r\to \infty} R r^2< \infty$), then the Riemann curvature tensor must have at least sub-quadratic decay, namely, $\limsup_{r\to \infty} |Rm| \ \! r^{\alpha}< \infty$ for any $0<\alpha<2$.Comment: 20 pages. Lemma 3.1 statement simplified; the proof of Lemma 3.2 & Lemma 3.3 streamlined. arXiv admin note: text overlap with arXiv:2111.0984

DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion

Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability. However, diffusion models have not yet received sufficient research in the field of image fusion. In this article, we introduce diffusion model to the image fusion field, treating the image fusion task as image-to-image translation and designing two different conditional injection modulation modules (i.e., style transfer modulation and wavelet modulation) to inject coarse-grained style information and fine-grained high-frequency and low-frequency information into the diffusion UNet, thereby generating fused images. In addition, we also discussed the residual learning and the selection of training objectives of the diffusion model in the image fusion task. Extensive experimental results based on quantitative and qualitative assessments compared with benchmarks demonstrates state-of-the-art results and good generalization performance in image fusion tasks. Finally, it is hoped that our method can inspire other works and gain insight into this field to better apply the diffusion model to image fusion tasks. Code shall be released for better reproducibility

CoAIcoder: Examining the Effectiveness of AI-assisted Collaborative Qualitative Analysis

While the domain of individual-level AI-assisted analysis has been extensively explored in previous studies, the field of AI-assisted collaborative qualitative analysis remains relatively unexplored. After identifying CQA practices and design opportunities through formative interviews, we introduce our collaborative qualitative coding tool, CoAIcoder, and designed the four different collaboration methods. We subsequently implemented a between-subject design involving 32 pairs of users who have undergone training in CQA across three commonly utilized phases under four methods. Our results suggest that CoAIcoder, which employs AI and a Shared Model, could potentially improve the efficiency of the coding process in CQA by fostering a quicker shared understanding and promoting early-stage discussions. However, this may come with the potential downside of reduced code diversity. We also underscored the existence of a trade-off between the level of independence and the coding outcome when humans collaborate during the early coding stages. Lastly, we identify design implications that could inspire and inform the future design of CQA systems

Impact of Human-AI Interaction on User Trust and Reliance in AI-Assisted Qualitative Coding

While AI shows promise for enhancing the efficiency of qualitative analysis, the unique human-AI interaction resulting from varied coding strategies makes it challenging to develop a trustworthy AI-assisted qualitative coding system (AIQCs) that supports coding tasks effectively. We bridge this gap by exploring the impact of varying coding strategies on user trust and reliance on AI. We conducted a mixed-methods split-plot 3x3 study, involving 30 participants, and a follow-up study with 6 participants, exploring varying text selection and code length in the use of our AIQCs system for qualitative analysis. Our results indicate that qualitative open coding should be conceptualized as a series of distinct subtasks, each with differing levels of complexity, and therefore, should be given tailored design considerations. We further observed a discrepancy between perceived and behavioral measures, and emphasized the potential challenges of under- and over-reliance on AIQCs systems. Additional design implications were also proposed for consideration.Comment: 27 pages with references, 9 figures, 5 table

Just add sugar for carbohydrate induced self-assembly of curcumin

In nature, self-assembly processes based on amphiphilic molecules play an integral part in the design of structures of higher order such as cells. Among them, amphiphilic glycoproteins or glycolipids take on a pivotal role due to their bioactivity. Here we show that sugars, in particular, fructose, are capable of directing the self-assembly of highly insoluble curcumin resulting in the formation of well-defined capsules based on non-covalent forces. Simply by mixing an aqueous solution of fructose and curcumin in an open vessel leads to the generation of capsules with sizes ranging between 100 and 150 nm independent of the initial concentrations used. Our results demonstrate that hydrogen bonding displayed by fructose can induce the self-assembly of hydrophobic molecules such as curcumin into well-ordered structures, and serving as a simple and virtually instantaneous way of making nanoparticles from curcumin in water with the potential for template polymerization and nanocarriers.S.W. is grateful for UNSW PhD scholarship. J.H. acknowledges support from the Australian Research Council (DE160100807) and supercomputer resources from the NCI, Pawsey Supercomputing Centre and Intersect Australian Ltd. Finally, M.H.S. and C.J.G. would like to thank the Australian Research Council (ARC DP 160101172) for fundin

Mechanism investigation on the formation of olefins and paraffin from the thermochemical catalytic conversion of triglycerides catalyzed by alkali metal catalysts

Triglycerides are a promising biomass feedstock that can be used for production of organic hydrocarbons including long-chain olefins and paraffin. The challenge for this production process lies on the lack of a clear mechanism of the conversion process. In this work, the conversion mechanism from triglycerides to olefins and paraffin using alkali metal catalysts was investigated adopting both computational calculations using density functional theory and experimental studies. The bond dissociation energies of the main bonds were calculated, especially for the α carbon‑carbon bond, which leads to effective removal of carboxyl groups during the thermochemical conversion process. The dynamic behavior of triglycerides catalyzed by alkali metal catalysts was also investigated using thermogravimetric analysis, which found that Li ion has lowest activation energy below 200 kJ/mol when compared with the other alkali ions studied. The catalytic conversion mechanism was proposed in this work based on the results obtained from TG-IR, GC, GC–MS and XRD analyses. The O atoms are removed in the form of CO, CO2 and H2O, product M + O and M+, which generates M2CO3. A more detailed mechanism has been proposed in this paper, which has significance toward guiding the cleavage of triglycerides to produce long‑carbon-chain terminal olefins and normal paraffin