The Dynamic Effects of Perceptions of Dread Risk and Unknown Risk on SNS Sharing Behavior During Emerging Infectious Disease Events: Do Crisis Stages Matter?

In response to the increasing prevalence of emerging infectious disease (EID) threats, individuals are turning to social media platforms to share relevant information in ever greater numbers. In this study, we examine whether risk perceptions related to user-generated content have dynamic impacts on social networking site (SNS) sharing behavior in different crisis stages. To answer this question, we applied psychometric analysis to evaluate how dread risk and unknown risk can characterize EID threats. Drawing broadly on the literature of risk perceptions, self-perception theory, and crisis stages, we relied on microblogs collected from Sina Weibo, utilizing the vector autoregression model to analyze dynamic relationships. We found that perceptions of dread risk have a dominant and immediate impact on SNS sharing behavior in the buildup, breakout, and termination stages of EID events. Perceptions of unknown risk have a dominant and persistent impact on sharing behavior in the abatement stage. The joint effect of these two types of risk perception reveal an antagonism impact on SNS sharing behavior, and perceptions of dread- and unknown risk have interaction effects from the buildup to termination stages of EID events. To check robustness, we analyzed keywords related to perceptions of dread- and unknown risk. The results of this study support the empirical application of Slovic’s risk perception framework for understanding the characteristics of EID threats and provide a picture of how perceptions of dread- and unknown risk exert differential time-varying effects on SNS sharing behavior during EID events. We also discuss theoretical and practical implications for the crisis management of EID threats. This study is among the first that uses user-generated content in social media to investigate dynamic risk perceptions and their relationship to SNS sharing behavior, which may help provide a basis for timely and efficient risk communication

Efficient and Joint Hyperparameter and Architecture Search for Collaborative Filtering

Automated Machine Learning (AutoML) techniques have recently been introduced to design Collaborative Filtering (CF) models in a data-specific manner. However, existing works either search architectures or hyperparameters while ignoring the fact they are intrinsically related and should be considered together. This motivates us to consider a joint hyperparameter and architecture search method to design CF models. However, this is not easy because of the large search space and high evaluation cost. To solve these challenges, we reduce the space by screening out usefulness yperparameter choices through a comprehensive understanding of individual hyperparameters. Next, we propose a two-stage search algorithm to find proper configurations from the reduced space. In the first stage, we leverage knowledge from subsampled datasets to reduce evaluation costs; in the second stage, we efficiently fine-tune top candidate models on the whole dataset. Extensive experiments on real-world datasets show better performance can be achieved compared with both hand-designed and previous searched models. Besides, ablation and case studies demonstrate the effectiveness of our search framework.Comment: Accepted by KDD 202

Tracking Intrinsic Non-Hermitian Skin Effect in Lossy Lattices

Non-Hermitian skin effect (NHSE), characterized by a majority of eigenstates localized at open boundaries, is one of the most iconic phenomena in non-Hermitian lattices. Despite notable experimental studies implemented, most of them witness only certain signs of the NHSE rather than the intrinsic exponential localization inherent in eigenstates, owing to the ubiquitous and inevitable background loss. Even worse, the experimental observation of the NHSE would be completely obscured in highly lossy cases. Here, we theoretically propose a dual test approach to eliminate the destructive loss effect and track the intrinsic NHSE that is essentially irrelevant to background loss. Experimentally, the effectiveness of this approach is precisely validated by one- and two-dimensional non-Hermitian acoustic lattices. Our study sheds new light on the previously untapped intrinsic aspect of the NHSE, which is of particular significance in non-Hermitian topological physics

Moisture self-regulating ionic skins with ultra-long ambient stability for self-healing energy and sensing systems

Dehydration has been a key limiting factor for the operation of conductive hydrogels in practical application. Here, we report self-healable ionic skins that can self-regulate their internal moisture level by capturing extenral moistures via hygroscopic ion-coordinated polymer backbones through antipolyelectrolyte effect. Results show the ionic skin can maintain its mechanical and electrical functions over 16 months in the ambient environment with high stretchability (fracture stretch ∼2216 %) and conductivity (23.5 mS/cm). The moisture self-regulating capability is further demonstrated by repeated exposures to harsh environments such as 200°C heating, freezing, and vacuum drying with recovered conductivity and stretchability. Their reversible ionic and hydrogen bonds also enable self-healing feature as a sample with the fully cut-through damage can restore its conductivity after 24 h at 40 % relative humidity. Utilizing the ionic skin as a building block, self-healing flexible piezoelecret sensors have been constructed to monitor physiological signals. Together with a facile transfer-printing process, a self-powered sensing system with a self-healable supercapacitor and humidity sensor has been successfully demonstrated. These results illustrate broad-ranging possibilities for the ionic skins in applications such as energy storage, wearable sensors, and human-machine interfaces

Novel Natural Inhibitors of CYP1A2 Identified by in Silico and in Vitro Screening

Inhibition of cytochrome P450 (CYP) is a major cause of herb–drug interactions. The CYP1A2 enzyme plays a major role in the metabolism of drugs in humans. Its broad substrate specificity, as well as its inhibition by a vast array of structurally diverse herbal active ingredients, has indicated the possibility of metabolic herb–drug interactions. Therefore nowadays searching inhibitors for CYP1A2 from herbal medicines are drawing much more attention by biological, chemical and pharmological scientists. In our work, a pharmacophore model as well as the docking technology is proposed to screen inhibitors from herbal ingredients data. Firstly different pharmaphore models were constructed and then validated and modified by 202 herbal ingredients. Secondly the best pharmaphore model was chosen to virtually screen the herbal data (a curated database of 989 herbal compounds). Then the hits (147 herbal compounds) were continued to be filtered by a docking process, and were tested in vitro successively. Finally, five of eighteen candidate compounds (272, 284, 300, 616 and 817) were found to have inhibition of CYP1A2 activity. The model developed in our study is efficient for in silico screening of large herbal databases in the identification of CYP1A2 inhibitors. It will play an important role to prevent the risk of herb–drug interactions at an early stage of the drug development process

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines

Room temperature all-solid-state lithium batteries based on a soluble organic cage ionic conductor

All solid-state lithium batteries (SSLBs) are poised to have higher energy density and better safety than current liquid-based Li-ion batteries, but a central requirement is effective ionic conduction pathways throughout the entire cell. Here we develop a catholyte based on an emerging class of porous materials, porous organic cages (POCs). A key feature of these Li(+) conducting POCs is their solution-processibility. They can be dissolved in a cathode slurry, which allows the fabrication of solid-state cathodes using the conventional slurry coating method. These Li(+) conducting cages recrystallize and grow on the surface of the cathode particles during the coating process and are therefore dispersed uniformly in the slurry-coated cathodes to form a highly effective ion-conducting network. This catholyte is shown to be compatible with cathode active materials such as LiFePO(4), LiCoO(2) and LiNi(0.5)Co(0.2)Mn(0.3)O(2), and results in SSLBs with decent electrochemical performance at room temperature

Organochlorine Pesticides in Consumer Fish and Mollusks of Liaoning Province, China: Distribution and Human Exposure Implications

Fish and mollusk samples were collected from markets located in 12 cities in Liaoning province, China, during August and September 2007, and 22 organochlorine pesticides (OCPs) were detected. DDT, HCH, endosulfan, chlordane, and HCB were the dominating OCPs, with mean concentrations and ranges of, respectively, 15.41 and 0.57 to 177.56 ng/g, 0.84 and below detection limit (BDL) to 22.99 ng/g, 1.31 and BDL to 13.1 ng/g, 1.05 and BDL to 15.68 ng/g, and 0.63 and BDL to 9.21 ng/g in all fish and mollusk samples. The concentrations of other OCPs generally were low and were detectable in a minority of samples, reflecting the low levels of these OCPs in the study region. In general, OCP concentrations were obviously higher in fish than in mollusks, and higher in freshwater fish than in marine fish, which indicated, first, that freshwater fish are more easily influenced than seawater fish and mollusks by OCP residues in agricultural areas and, second, that there are different biota accumulation factors for OCPs between fish and mollusk. To learn the consumption of fish and mollusk, 256 questionnaires were sent to families in 12 cities of Liaoning province. Using the contamination data, average estimated daily intakes of OCPs via fish and mollusk consumption were calculated, which were used for exposure assessment. The public health risks caused by exposure to OCPs in the course of fish and mollusk consumption were compared to noncancer benchmarks and cancer benchmarks