How to Improve the Credibility and Interestingness of Social Media Healthcare Information?

Social media is a widely accepted medium for interaction and communication. A large amount of information about health care springs out through various social medias. We Chat is a multi-function social media as well as an information sharing platform with largest users in China right now. Many We Chat accounts concentrated on showing and spreading healthcare information. They are trying to attract more readers and spread the information among them. Thus ， it is important to find out what changes people ’ s behavior or attitude toward certain kind of information. This research focuses on the influence of the authority of information sources and authors as well as the format and length of information. Those four factors, compared with those in the formal studies are much more specific and much easier to be quantization especially for measurement. Lab experiment study was applied in this paper. The result comes that the authority of subscriptions and information format affect both perceived credibility and interestingness levels, while the authority of authors only makes difference to credibility level. And the length of information shows no significant influence

Promoting Bifunctional Oxygen Catalyst Activity of Double-Perovskite-Type Cubic Nanocrystallites for Aqueous and Quasi-Solid-State Rechargeable Zinc-Air Batteries

Transition metal oxide materials are promising oxygen catalysts that are alternatives to expensive and precious metal-containing catalysts. Integration of transition metal oxides with high activity for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) is an important pathway for good bifunctionality. In contrast to the conventional physical mixing and hybridization strategies, perovskite-type oxide provides an ideal structure for the integration of the transition metal element atoms on an atomic scale. Herein, B-site ordered double-perovskite-type La1.6Sr0.4MnCoO6 nanocrystallites with ultra-small cubic (20–50 nm) morphology and high specific surface areas (25 m2 g−1) were proposed. Rational designs were integrated to promote the ORR-OER catalysis, e.g., introducing oxygen vacancies via A-site cation substitution, further increasing surface oxygen vacancies via integration of a small amount of Pt/C and nanosizing of the material via a facile molten-salt method. The batteries with the La1.6Sr0.4MnCoO6 nanocrystallites and an aqueous alkaline electrolyte demonstrate decent discharge−charge voltage gaps of 0.75 and 1.10 V at 1 and 30 mA cm−2, respectively, and good cycling stability of 250 h (1500 cycles). A coin-type battery with a gel−polymer electrolyte also presents a good performance

Data-Centric Evolution in Autonomous Driving: A Comprehensive Survey of Big Data System, Data Mining, and Closed-Loop Technologies

The aspiration of the next generation's autonomous driving (AD) technology relies on the dedicated integration and interaction among intelligent perception, prediction, planning, and low-level control. There has been a huge bottleneck regarding the upper bound of autonomous driving algorithm performance, a consensus from academia and industry believes that the key to surmount the bottleneck lies in data-centric autonomous driving technology. Recent advancement in AD simulation, closed-loop model training, and AD big data engine have gained some valuable experience. However, there is a lack of systematic knowledge and deep understanding regarding how to build efficient data-centric AD technology for AD algorithm self-evolution and better AD big data accumulation. To fill in the identified research gaps, this article will closely focus on reviewing the state-of-the-art data-driven autonomous driving technologies, with an emphasis on the comprehensive taxonomy of autonomous driving datasets characterized by milestone generations, key features, data acquisition settings, etc. Furthermore, we provide a systematic review of the existing benchmark closed-loop AD big data pipelines from the industrial frontier, including the procedure of closed-loop frameworks, key technologies, and empirical studies. Finally, the future directions, potential applications, limitations and concerns are discussed to arouse efforts from both academia and industry for promoting the further development of autonomous driving. The project repository is available at: https://github.com/LincanLi98/Awesome-Data-Centric-Autonomous-Driving

A porous nano-micro-composite as a high-performance bi-functional air electrode with remarkable stability for rechargeable zinc–air batteries

The development of bi-functional electrocatalyst with high catalytic activity and stable performance for both oxygen evolution/reduction reactions (OER/ORR) in aqueous alkaline solution is key to realize practical application of zinc–air batteries (ZABs). In this study, we reported a new porous nano-micro-composite as a bi-functional electrocatalyst for ZABs, devised by the in situ growth of metal–organic framework (MOF) nanocrystals onto the micrometer-sized Ba0.5Sr0.5Co0.8Fe0.2O3 (BSCF) perovskite oxide. Upon carbonization, MOF was converted to porous nitrogen-doped carbon nanocages and ultrafine cobalt oxides and CoN4 nanoparticles dispersing inside the carbon nanocages, which further anchored on the surface of BSCF oxide. We homogeneously dispersed BSCF perovskite particles in the surfactant; subsequently, ZIF-67 nanocrystals were grown onto the BSCF particles. In this way, leaching of metallic or organic species in MOFs and the aggregation of BSCF were effectively suppressed, thus maximizing the number of active sites for improving OER. The BSCF in turn acted as catalyst to promote the graphitization of carbon during pyrolysis, as well as to optimize the transition metal-to-carbon ratio, thus enhancing the ORR catalytic activity. A ZAB fabricated from such air electrode showed outstanding performance with a potential gap of only 0.83 V at 5 mA cm−2 for OER/ORR. Notably, no obvious performance degradation was observed for the continuous charge–discharge operation for 1800 cycles over an extended period of 300 h

CoNiFe-layered double hydroxide decorated Co-N-C network as a robust bi-functional oxygen electrocatalyst for zinc-air batteries

Rechargeable zinc-air batteries (ZABs) are cost-effective energy storage devices and display high-energy density. To realize high round-trip energy efficiency, it is critical to develop durable bi-functional air electrodes, presenting high catalytic activity towards oxygen evolution/reduction reactions together. Herein, we report a nanocomposite based on ternary CoNiFe-layered double hydroxides (LDH) and cobalt coordinated and N-doped porous carbon (Co-N-C) network, obtained by the in-situ growth of LDH over the surface of ZIF-67-derived 3D porous network. Co-N-C network contributes to the oxygen reduction reaction activity, while CoNiFe-LDH imparts to the oxygen evolution reaction activity. The rich active sites and enhanced electronic and mass transport properties stemmed from their unique architecture, culminated into outstanding bi-functional catalytic activity towards oxygen evolution/reduction in alkaline media. In ZABs, it displays a high peak power density of 228 mW cm−2 and a low voltage gap of 0.77 V over an ultra-long lifespan of 950 h. (Figure presented.)

PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization

In this paper, we present a pure-Python open-source library, called PyPop7, for black-box optimization (BBO). It provides a unified and modular interface for more than 60 versions and variants of different black-box optimization algorithms, particularly population-based optimizers, which can be classified into 12 popular families: Evolution Strategies (ES), Natural Evolution Strategies (NES), Estimation of Distribution Algorithms (EDA), Cross-Entropy Method (CEM), Differential Evolution (DE), Particle Swarm Optimizer (PSO), Cooperative Coevolution (CC), Simulated Annealing (SA), Genetic Algorithms (GA), Evolutionary Programming (EP), Pattern Search (PS), and Random Search (RS). It also provides many examples, interesting tutorials, and full-fledged API documentations. Through this new library, we expect to provide a well-designed platform for benchmarking of optimizers and promote their real-world applications, especially for large-scale BBO. Its source code and documentations are available at https://github.com/Evolutionary-Intelligence/pypop and https://pypop.readthedocs.io/en/latest, respectively.Comment: 5 page

The effect of simulated narratives that leverage EMR data on shared decision-making: a pilot study

BACKGROUND: Shared decision-making can improve patient satisfaction and outcomes. To participate in shared decision-making, patients need information about the potential risks and benefits of treatment options. Our team has developed a novel prototype tool for shared decision-making called hearts like mine (HLM) that leverages EHR data to provide personalized information to patients regarding potential outcomes of different treatments. These potential outcomes are presented through an Icon array and/or simulated narratives for each “person” in the display. In this pilot project we sought to determine whether the inclusion of simulated narratives in the display affects individuals’ decision-making. Thirty subjects participated in this block-randomized study in which they used a version of HLM with simulated narratives and a version without (or in the opposite order) to make a hypothetical therapeutic decision. After each decision, participants completed a questionnaire that measured decisional confidence. We used Chi square tests to compare decisions across conditions and Mann–Whitney U tests to examine the effects of narratives on decisional confidence. Finally, we calculated the mean of subjects’ post-experiment rating of whether narratives were helpful in their decision-making. RESULTS: In this study, there was no effect of simulated narratives on treatment decisions (decision 1: Chi squared = 0, p = 1.0; decision 2: Chi squared = 0.574, p = 0.44) or Decisional confidence (decision 1, w = 105.5, p = 0.78; decision 2, w = 86.5, p = 0.28). Post-experiment, participants reported that narratives helped them to make decisions (mean = 3.3/4). CONCLUSIONS: We found that simulated narratives had no measurable effect on decisional confidence or decisions and most participants felt that the narratives were helpful to them in making therapeutic decisions. The use of simulated stories holds promise for promoting shared decision-making while minimizing their potential biasing effect