Differences in perception of dysentery and enteric fever and willingness to receive vaccines among rural residents in China.

BACKGROUND: Enteric diseases including dysentery and enteric fever remain significant public health problems in China. While vaccines offer great potential in controlling these diseases, greater understanding of factors influencing acceptance of vaccines is needed to create effective enteric disease control programs in rural China. DESIGN: Cross-sectional quantitative study with randomly sampled households from two sites in China, one experiencing high rates of shigellosis (Zengding) and the other of typhoid/paratyphoid (Lingchuan). METHODS: Sociobehavioral survey data were collected through face-to-face interviews from 501 respondents (56% female) in Zhengding regarding dysentery and 624 in Lingchuan (51% female) regarding enteric fever. Vaccine acceptability was measured by expressed need for vaccination and willingness to pay. Comparative and associative analyses were conducted to assess disease perception, vaccination service satisfaction, likelihood of improvements in water and sanitation, and vaccine acceptability. RESULTS: Nearly all respondents in Lingchuan considered enteric fever to be prevalent in the community, while only one half of the respondents in Zhengding considered dysentery to be problematic (p < 0.01). Nevertheless, more respondents in Zhengding were fearful that a household member would acquire dysentery than were Lingchuan respondents worried that a household member would acquire enteric fever (p < 0.01). Perceived vulnerability of specific subgroups (odds ratios ranging from 1.6 to 8.1), knowing someone who died of the disease (odds ratio reached infinity) and satisfaction with past vaccination services (odds ratios reached infinity) were consistently associated with perceived need for vaccines of target populations of all age groups while the association between perception of sanitary improvement and vaccine need was limited. Perceived need for a vaccine was associated with willingness to pay for the vaccine. CONCLUSIONS: Perceptions of enhanced vulnerability of specific subgroups to a disease and satisfactory experiences with vaccination services may increase the perceived need for a vaccine, leading to increased willingness to pay for vaccine. Vaccines are not perceived as important for the elderly

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. However, we do observe that the learned graph structure demonstrates a strong generalization ability across different GNN backbones, despite its high computational and space requirements. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in the field of GSL. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.Comment: 9 pages, 4 figure

Fabrication of nanowire network AAO and its application in SERS

In this paper, nanowire network anodized aluminum oxide (AAO) was fabricated by just adding a simple film-eroding process after the production of porous AAO. After depositing 50 nm of Au onto the surface, nanowire network AAO can be used as ultrasensitive and high reproducibility surface-enhanced Raman scattering (SERS) substrate. The average Raman enhancement factor of the nanowire network AAO SERS substrate can reach 5.93 × 10(6), which is about 14% larger than that of commercial Klarite® substrates. Simultaneously, the relative standard deviations in the SERS intensities are limited to approximately 7%. All of the results indicate that our large-area low-cost high-performance nanowire structure AAO SERS substrates have a great advantage in chemical/biological sensing applications

Symbolic Discovery of Optimization Algorithms

We present a method to formulate algorithm discovery as program search, and apply it to discover optimization algorithms for deep neural network training. We leverage efficient search techniques to explore an infinite and sparse program space. To bridge the large generalization gap between proxy and target tasks, we also introduce program selection and simplification strategies. Our method discovers a simple and effective optimization algorithm, $\textbf{Lion}$ (\textit{Evo\textbf{L}$ved S$\textbf{i}$gn M$\textbf{o}$me$\textbf{n}tum}). It is more memory-efficient than Adam as it only keeps track of the momentum. Different from adaptive optimizers, its update has the same magnitude for each parameter calculated through the sign operation. We compare Lion with widely used optimizers, such as Adam and Adafactor, for training a variety of models on different tasks. On image classification, Lion boosts the accuracy of ViT by up to 2% on ImageNet and saves up to 5x the pre-training compute on JFT. On vision-language contrastive learning, we achieve 88.3% $\textit{zero-shot}$ and 91.1% $\textit{fine-tuning}$ accuracy on ImageNet, surpassing the previous best results by 2% and 0.1%, respectively. On diffusion models, Lion outperforms Adam by achieving a better FID score and reducing the training compute by up to 2.3x. For autoregressive, masked language modeling, and fine-tuning, Lion exhibits a similar or better performance compared to Adam. Our analysis of Lion reveals that its performance gain grows with the training batch size. It also requires a smaller learning rate than Adam due to the larger norm of the update produced by the sign function. Additionally, we examine the limitations of Lion and identify scenarios where its improvements are small or not statistically significant. The implementation of Lion is publicly available.Comment: 30 pages, update the tuning instruction

Thermally enhanced photoluminescence and temperature sensing properties of Sc2_2W3_3O12_{12}:Eu3+^{3+} phosphors

Currently,lanthanide ions doped luminescence materials applying as optical thermometers have arose much concern. Basing on the different responses of two emissions to temperature, the fluorescence intensity ratio (FIR) technique can be executed and further estimate the sensitivities to assess the optical thermometry performances. In this study, we introduce different doping concentrations of Eu$^{3+}$ ions into negative expansion material Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$, accessing to the thermal enhanced luminescence from 373 to 548 K, and investigate the temperature sensing properties in detail. All samples exhibit good thermally enhanced luminescence behavior. The emission intensity of Sc$_2$W$_3$O$_{12}$: 6 mol% Eu$^{3+}$ phosphors reaches at 147.81% of initial intensity at 473 K. As the Eu doping concentration increases, the resistance of the samples to thermal quenching decreases. The FIR technique based on the transitions 5D0-7F1 (592 nm) and 5D0-7F2 (613 nm) of Eu$^{3+}$ ions demonstrate a maximum relative temperature sensitivity of 3.063% K-1 at 298 K for Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: 6 mol% Eu$^{3+}$ phosphors. The sensitivity of sample decreases with the increase of Eu$^{3+}$ concentration. Benefiting from the thermal enhanced luminescence performance and good temperature sensing properties, the Sc$_2$W$_3$O$_{12}$:Eu$^{3+}$: Eu$^{3+}$ phosphors can be applies as optical thermometers