The COVID-19 vaccine is considered to be the most promising approach to alleviate the pandemic. However, in recent surveys, acceptance of the COVID-19 vaccine has been low. To design more effective ...outreach interventions, there is an urgent need to understand public perceptions of COVID-19 vaccines.
Our objective was to analyze the potential of leveraging transfer learning to detect tweets containing opinions, attitudes, and behavioral intentions toward COVID-19 vaccines, and to explore temporal trends as well as automatically extract topics across a large number of tweets.
We developed machine learning and transfer learning models to classify tweets, followed by temporal analysis and topic modeling on a dataset of COVID-19 vaccine-related tweets posted from November 1, 2020 to January 31, 2021. We used the F1 values as the primary outcome to compare the performance of machine learning and transfer learning models. The statistical values and P values from the Augmented Dickey-Fuller test were used to assess whether users' perceptions changed over time. The main topics in tweets were extracted by latent Dirichlet allocation analysis.
We collected 2,678,372 tweets related to COVID-19 vaccines from 841,978 unique users and annotated 5000 tweets. The F1 values of transfer learning models were 0.792 (95% CI 0.789-0.795), 0.578 (95% CI 0.572-0.584), and 0.614 (95% CI 0.606-0.622) for these three tasks, which significantly outperformed the machine learning models (logistic regression, random forest, and support vector machine). The prevalence of tweets containing attitudes and behavioral intentions varied significantly over time. Specifically, tweets containing positive behavioral intentions increased significantly in December 2020. In addition, we selected tweets in the following categories: positive attitudes, negative attitudes, positive behavioral intentions, and negative behavioral intentions. We then identified 10 main topics and relevant terms for each category.
Overall, we provided a method to automatically analyze the public understanding of COVID-19 vaccines from real-time data in social media, which can be used to tailor educational programs and other interventions to effectively promote the public acceptance of COVID-19 vaccines.
PVdF/SiO2 composite nonwoven membranes exhibiting high safety (thermal stability), high ionic conductivity and excellent electrochemical performances are firstly prepared by electrospinning ...poly(vinylidene fluoride) (PVdF) homopolymer and silicon dioxide (SiO2) sol synchronously for the separators of lithium-ion batteries (LIBs). Differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and hot oven tests show that the PVdF/SiO2 composite nonwoven membranes are thermally stable at a high temperature of 400 °C while the commercial Celgard 2400 PP membrane exhibits great shrinkage at 130 °C, indicating a superior thermal stability of PVdF/SiO2 composite nonwoven membranes than that of Celgard membrane. Moreover, the composite membrane exhibits fairly high ionic conductivity (7.47 × 10−3 S cm−1) that significantly improves the performance of LIBs. The PVdF/SiO2 composite membranes are also evaluated to have higher level of porosity (75−85%) and electrolyte uptake (571−646 wt%), lower interfacial resistance compared to the Celgard separator. The lithium-ion cell (using LiFePO4 cathode) assembled with the composite membrane exhibits more stable cycle performance, higher discharge capacity (159 mAh g−1) and excellent capacity retention which proves that they are promising candidates for separators of high performance rechargeable LIBs.
•PVdF/SiO2 composite membranes are first prepared by electrospinning.•Inorganic silicon dioxide sol is added into blended spinning solution directly.•Composite membranes have excellent thermal dimensional stability over a wide range of temperatures.•Composite membranes have superior ionic conductivities.•The electrode electrolyte interfacial resistance is low, indicating good membrane-electrode affinity.
A forest consists of multi-scale branches, tree crowns, and tree clusters. Similar to small tree crowns in shape and scale, branches normally cause over-segmentation of imagery when a watershed ...segmentation approach is used to segment imagery for tree crown delineation. In order to eliminate such over-segmentation, a new method for individual tree crown delineation from optical imagery was proposed based on multi-scale filtering and segmentation in this study. In this method, the dominant sizes of tree crowns are first determined; Gaussian filters are designed to fit the three-dimensional radiometric shapes of multi-scale tree crowns; the grayscale image is smoothed using the Gaussian filters and segmented using the watershed segmentation approach; and finally, the resulting multiple segmentation maps are integrated together to generate a tree crown map. In an experiment on aerial imagery of forests consisting of multi-scale tree crowns, the proposed method yielded high-quality tree crown maps.
Purpose The objective of this paper is to design a protocol for a systematic review and meta-analysis on the effectiveness of self-management interventions in patients with chronic heart failure. ...Methods The protocol is developed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The protocol has been registered in PROSPERO (CRD42021246973). Base on the population, intervention, comparator, and outcome (PICO) framework, our research questions are: 1) What are the effects of eHealth self-management interventions on patients with chronic heart failure? 2) What factors of interventions might affect outcomes? The process includes: 1) search strategy and inclusion criteria; 2) data extraction; 3) risk of bias assessment and 4) data analysis. Searching process and data extraction will be guided by Cochrane Handbook for Systematic Reviews of Interventions. We will use Cochrane Risk of Bias tool to assess the risk of bias. The data analysis will be performed using Metafor package in R. Conclusions This systemic review will synthesize the current evidence and identify gaps. Findings in the meta-analysis will provide guidance for designing a more effective self-management intervention for patients with chronic heart failure in future.
Heart failure (HF) is a common disease and a major public health problem. HF mortality prediction is critical for developing individualized prevention and treatment plans. However, due to their lack ...of interpretability, most HF mortality prediction models have not yet reached clinical practice. We aimed to develop an interpretable model to predict the mortality risk for patients with HF in intensive care units (ICUs) and used the SHapley Additive exPlanation (SHAP) method to explain the extreme gradient boosting (XGBoost) model and explore prognostic factors for HF. In this retrospective cohort study, we achieved model development and performance comparison on the eICU Collaborative Research Database (eICU-CRD). We extracted data during the first 24 hours of each ICU admission, and the data set was randomly divided, with 70% used for model training and 30% used for model validation. The prediction performance of the XGBoost model was compared with three other machine learning models by the area under the curve. We used the SHAP method to explain the XGBoost model. A total of 2798 eligible patients with HF were included in the final cohort for this study. The observed in-hospital mortality of patients with HF was 9.97%. Comparatively, the XGBoost model had the highest predictive performance among four models with an area under the curve (AUC) of 0.824 (95% CI 0.7766-0.8708), whereas support vector machine had the poorest generalization ability (AUC=0.701, 95% CI 0.6433-0.7582). The decision curve showed that the net benefit of the XGBoost model surpassed those of other machine learning models at 10%~28% threshold probabilities. The SHAP method reveals the top 20 predictors of HF according to the importance ranking, and the average of the blood urea nitrogen was recognized as the most important predictor variable. The interpretable predictive model helps physicians more accurately predict the mortality risk in ICU patients with HF, and therefore, provides better treatment plans and optimal resource allocation for their patients. In addition, the interpretable framework can increase the transparency of the model and facilitate understanding the reliability of the predictive model for the physicians.
► Developed novel LiDAR features describing horizontal and vertical tree structures. ► A species classification framework combing segmentation and genetic algorithm. ► Relative degree and scale of ...foliage clustering features were proved to be significant for species classification. ► Individual-tree species classification were achieved with 77.5% overall accuracy. ► Effects of LiDAR point density on the classification accuracy – linear trends.
Automated tree species classification using high density airborne light detection and ranging (LiDAR) data will support more precise forest inventory but further research is required to improve the associated methods. Most existing methods rely on geometric and vertical distribution features, which often do not accurately represent the internal foliage and branch patterns of an individual tree. Our study objective was to develop novel algorithms to characterize internal structures of an individual tree crown and to test their effectiveness for use in classifying tree species. We derived several LiDAR features to describe the three-dimensional texture, foliage clustering degree relative to tree envelop, foliage clustering scale, and gap distribution of an individual tree in both horizontal and vertical directions. Features were selected using a genetic algorithm and then tree species were classified using linear discriminant analysis based on the selected features. The four species, sugar maple (Acer saccharum Marsh.), trembling aspen (Populus tremuloides Michx.), jack pine (Pinus banksiana Lamb.) and eastern white pine (Pinus strobus L.), were classified with an overall accuracy of 77.5% and a Kappa coefficient of 0.7. The results demonstrate the significance of the derived structural features as aids to classify tree species. Our investigation also showed a positive linear correlation (R2=0.88) between LiDAR point density and species classification accuracy.
Electro‐reforming of renewable biomass resources is an alternative technology for sustainable pure H2 production. Herein, we discovered an unconventional cation effect on the concurrent formate and ...H2 production via glycerol electro‐reforming. In stark contrast to the cation effect via forming double layers in cathodic reactions, residual cations at the anode were discovered to interact with the glycerol oxidation intermediates to steer its product selectivity. Through a combination of product analysis, transient kinetics, crown ether trapping experiments, in situ IRRAS and DFT calculations, the aldehyde intermediates were discovered to be stabilized by the Li+ cations to favor the non‐oxidative C−C cleavage for formate production. The maximal formate efficiency could reach 81.3 % under ≈60 mA cm−2 in LiOH. This work emphasizes the significance of engineering the microenvironment at the electrode–electrolyte interface for efficient electrolytic processes.
Glycerol oxidation selectivity can be efficiently steered via cation–intermediate interactions, resulting in highly selective glycerol electro‐reforming into hydrogen and formate. This work emphasizes the significance of engineering the microenvironment at the electrode–electrolyte interface for efficient electrochemical processes.
Heart failure (HF) is a common clinical syndrome associated with substantial morbidity, a heavy economic burden, and high risk of readmission. eHealth self-management interventions may be an ...effective way to improve HF clinical outcomes. The aim of this study was to systematically review the evidence for the effectiveness of eHealth self-management in patients with HF. This study included only randomized controlled trials (RCTs) that compared the effects of eHealth interventions with usual care in adult patients with HF using searches of the EMBASE, PubMed, CENTRAL (Cochrane Central Register of Controlled Trials), and CINAHL databases from January 1, 2011, to July 12, 2022. The Cochrane Risk of Bias tool (RoB 2) was used to assess the risk of bias for each study. The Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were used to rate the certainty of the evidence for each outcome of interest. Meta-analyses were performed using Review Manager (RevMan v.5.4) and R (v.4.1.0 x64) software. In total, 24 RCTs with 9634 participants met the inclusion criteria. Compared with the usual-care group, eHealth self-management interventions could significantly reduce all-cause mortality (odds ratio OR 0.83, 95% CI 0.71-0.98, P=.03; GRADE: low quality) and cardiovascular mortality (OR 0.74, 95% CI 0.59-0.92, P=.008; GRADE: moderate quality), as well as all-cause readmissions (OR 0.82, 95% CI 0.73-0.93, P=.002; GRADE: low quality) and HF-related readmissions (OR 0.77, 95% CI 0.66-0.90, P<.001; GRADE: moderate quality). The meta-analyses also showed that eHealth interventions could increase patients’ knowledge of HF and improve their quality of life, but there were no statistically significant effects. However, eHealth interventions could significantly increase medication adherence (OR 1.82, 95% CI 1.42-2.34, P<.001; GRADE: low quality) and improve self-care behaviors (standardized mean difference –1.34, 95% CI –2.46 to –0.22, P=.02; GRADE: very low quality). A subgroup analysis of primary outcomes regarding the enrolled population setting found that eHealth interventions were more effective in patients with HF after discharge compared with those in the ambulatory clinic setting. eHealth self-management interventions could benefit the health of patients with HF in various ways. However, the clinical effects of eHealth interventions in patients with HF are affected by multiple aspects, and more high-quality studies are needed to demonstrate effectiveness.
Simultaneously achieving abundant and well-defined active sites with high selectivity has been one of the ultimate goals for heterogeneous catalysis. Herein, we construct a class of Ni ...hydroxychloride-based inorganic-organic hybrid electrocatalysts with the inorganic Ni hydroxychloride chains pillared by the bidentate N-N ligands. The precise evacuation of N-N ligands under ultrahigh-vacuum forms ligand vacancies while partially retaining some ligands as structural pillars. The high density of ligand vacancies forms the active vacancy channel with abundant and highly-accessible undercoordinated Ni sites, exhibiting 5-25 fold and 20-400 fold activity enhancement compared to the hybrid pre-catalyst and standard β-Ni(OH)
for the electrochemical oxidation of 25 different organic substrates, respectively. The tunable N-N ligand can also tailor the sizes of the vacancy channels to significantly impact the substrate configuration leading to unprecedented substrate-dependent reactivities on hydroxide/oxide catalysts. This approach bridges heterogenous and homogeneous catalysis for creating efficient and functional catalysis with enzyme-like properties.
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