Removing 65 Years of Approximation in Rotating Ring Disk Electrode Theory with Physics-Informed Neural Networks

The rotating Ring Disk Electrode (RRDE), since its introduction in 1959 by Frumkin and Nekrasov, has become indispensable with diverse applications in electrochemistry, catalysis, and material science. The collection efficiency ( N ) is an important parameter extracted from the ring and disk currents of the RRDE, providing valuable information about reaction mechanism, kinetics, and pathways. The theoretical prediction of N is a challenging task: requiring solution of the complete convective diffusion mass transport equation with complex velocity profiles. Previous efforts, including by Albery and Bruckenstein who developed the most widely used analytical equations, heavily relied on approximations by removing radial diffusion and using approximate velocity profiles. 65 years after the introduction of RRDE, we employ a physics-informed neural network to solve the complete convective diffusion mass transport equation, to reveal the formerly neglected edge effects and velocity corrections on N , and to provide a guideline where conventional approximation is applicable

Efficacy and safety of inclisiran in stroke or cerebrovascular disease prevention: a systematic review and meta-analysis of randomized controlled trials

Aims: As the impact of inclisiran in stroke prevention in atherosclerotic cardiovascular disease (ASCVD) patients or those at high risk of ASCVD is still unclear, we conducted a systematic review and meta-analysis of randomized controlled trials (RCT) to quantify the effectiveness of inclisiran in stroke prevention in these patients.Methods: Literature research was conducted in four electronic databases (PubMed, EMBASE, Web of Science, CENTRAL) and two clinical trials registers (ClinicalTrials.gov, WHO ICTRP) from the inception of the study to 17 October 2022, and was updated by the end of the study on 5 January 2023. Two authors independently screened the studies, extracted the data, and assessed the bias. The risk of bias was assessed using the Cochrane risk-of-bias tool for randomized trials (RoB 2). The intervention effect was estimated by calculating risk ratio (RR), weighted mean difference (WMD), and 95% confidence interval (CI) with R 4.0.5. Sensitivity analysis by changing meta-analysis model was also performed to test the robustness of the pooled results. If this was not possible, a descriptive analysis was conducted.Results: Four RCTs (n = 3,713 patients) were rated as high-risk bias. Meta-analysis of three RCTs (ORION-9, ORION-10, and ORION-11) showed that inclisiran reduced myocardial infarction (MI) risk by 32% (RR = 0.68, 95%CI = 0.48–0.96) but did not reduce stroke (RR = 0.92, 95%CI = 0.54–1.58) and major cardiovascular events (MACE) (RR = 0.81, 95%CI = 0.65–1.02) risk. Sensitivity analysis results were stable. Safety was similar to the placebo group but had frequent injection-site reactions (RR = 6.56, 95%CI = 3.83–11.25), which were predominantly mild or moderate. A descriptive analysis of one RCT (ORION-5) was conducted due to different study designs, and suggested that inclisiran might be given semiannually from the beginning.Conclusion: Inclisiran is not beneficial for stroke or MACE prevention in ASCVD or patients at high risk of ASCVD but is associated with the reduction of MI. Given the limited number and quality of the available studies and the lack of a standardized definition for cardiovascular events, further studies are essential for confirming the results

Direct and indirect effects of climate on richness drive the latitudinal diversity gradient in forest trees

Data accessibility statement: Full census data are available upon reasonable request from the ForestGEO data portal, http://ctfs.si.edu/datarequest/ We thank Margie Mayfield, three anonymous reviewers and Jacob Weiner for constructive comments on the manuscript. This study was financially supported by the National Key R&D Program of China (2017YFC0506100), the National Natural Science Foundation of China (31622014 and 31570426), and the Fundamental Research Funds for the Central Universities (17lgzd24) to CC. XW was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB3103). DS was supported by the Czech Science Foundation (grant no. 16-26369S). Yves Rosseel provided us valuable suggestions on using the lavaan package conducting SEM analyses. Funding and citation information for each forest plot is available in the Supplementary Information Text 1.Peer reviewedPostprin

Diffuse Map Guiding Unsupervised Generative Adversarial Network for SVBRDF Estimation

Reconstructing materials in the real world has always been a difficult problem in computer graphics. Accurately reconstructing the material in the real world is critical in the field of realistic rendering. Traditionally, materials in computer graphics are mapped by an artist, then mapped onto a geometric model by coordinate transformation, and finally rendered with a rendering engine to get realistic materials. For opaque objects, the industry commonly uses physical-based bidirectional reflectance distribution function (BRDF) rendering models for material modeling. The commonly used physical-based rendering models are Cook-Torrance BRDF, Disney BRDF. In this paper, we use the Cook-Torrance model to reconstruct the materials. The SVBRDF material parameters include Normal, Diffuse, Specular and Roughness. This paper presents a Diffuse map guiding material estimation method based on the Generative Adversarial Network(GAN). This method can predict plausible SVBRDF maps with global features using only a few pictures taken by the mobile phone. The main contributions of this paper are: 1) We preprocess a small number of input pictures to produce a large number of non-repeating pictures for training to reduce over-fitting. 2) We use a novel method to directly obtain the guessed diffuse map with global characteristics, which provides more prior information for the training process. 3) We improve the network architecture of the generator so that it can generate fine details of normal maps and reduce the possibility to generate over-flat normal maps. The method used in this paper can obtain prior knowledge without using dataset training, which greatly reduces the difficulty of material reconstruction and saves a lot of time to generate and calibrate datasets

A detailed evaluation of surface, thermal, and flammable properties of polyamide 12/glass beads composites fabricated by multi jet fusion

Multi jet fusion (MJF) is an emerging powder three-dimensional (3D) printing technology with an ultrafast printing speed, in which polyamide 12 (PA12) is the main material currently utilised. Although structurally sophisticated 3D components have been printed by MJF, there are limitations due to the high cost of printing materials and inferior performance of the printed parts. Here, PA12/glass beads (PA12/GBs) composites with improved performance were manufactured via the MJF technique. Incorporating GBs effectively regulated the dimensional accuracy. Furthermore, the composite possessed lower surface roughness and higher surface hardness than neat PA12. Thermal analysis showed that the composite exhibited an enhanced decomposition temperature (390 °C) and char yield (38.4 wt.%) compared to neat PA12. Remarkably, the incorporation of GBs did not improve the flame retardance of neat PA12. We identified the improved functionalities of PA12/GBs composites in terms of dimensional accuracy, surface, and thermal properties and analysed possible mechanisms for the observed increased flammability

Air-quality prediction based on the ARIMA-CNN-LSTM combination model optimized by dung beetle optimizer

Abstract Air pollution is a serious problem that affects economic development and people’s health, so an efficient and accurate air quality prediction model would help to manage the air pollution problem. In this paper, we build a combined model to accurately predict the AQI based on real AQI data from four cities. First, we use an ARIMA model to fit the linear part of the data and a CNN-LSTM model to fit the non-linear part of the data to avoid the problem of blinding in the CNN-LSTM hyperparameter setting. Then, to avoid the blinding dilemma in the CNN-LSTM hyperparameter setting, we use the Dung Beetle Optimizer algorithm to find the hyperparameters of the CNN-LSTM model, determine the optimal hyperparameters, and check the accuracy of the model. Finally, we compare the proposed model with nine other widely used models. The experimental results show that the model proposed in this paper outperforms the comparison models in terms of root mean square error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The RMSE values for the four cities were 7.594, 14.94, 7.841 and 5.496; the MAE values were 5.285, 10.839, 5.12 and 3.77; and the R2 values were 0.989, 0.962, 0.953 and 0.953 respectively

Resorption efficiency of leaf nutrients in woody plants on Mt. Dongling of Beijing, North China

Aims To explore resorption efficiency of nitrogen (NRE) and phosphorus (PRE) of woody plants in relation to soil nutrient availability, climate and evolutionary history, in North China. Methods We measured concentrations of nitrogen ([N]) and phosphorus ([P]) in both full expanded mature green and senescent leaves of the same individuals for 88 woody species from 10 sites of Mt. Dongling, Beijing, China. We built a phylogenetic tree for all these species and compared NRE and PRE among life forms (trees, shrubs and woody lianas) and between functional groups (N-fixers and non-N-fixers). We then explored patterns of NRE and PRE along gradients of mean annual temperature (MAT), soil inorganic N and available P, and phylogeny using a general linear model. Important Findings Mass-based NRE (NREm) and PRE (PREm) averaged 57.4 and 61.4%, respectively, with no significant difference among life forms or functional groups. Neither NREm nor PREm exhibited significant phylogenetic signals, indicating that NREm and PREm were not phylogenetically conserved. NREm was not related to [N] in green leaves; PREm was positively correlated with [P] in green leaves; however, this relationship disappeared for different groups. NREm decreased with [N] in senescent leaves, PREm decreased with [P] in senescent leaves, for all species combined and for trees and shrubs. NREm decreased with soil inorganic N for all species and for shrubs; PREm did not exhibit a significant trend with soil available P for all species or for different plant groups. Neither NREm nor PREm was significantly related to MAT for overall species and for species of different groups

Clinical nurses’ compassion fatigue psychological experience process: a constructivist grounded theory study

Abstract Background Clinical nurses are susceptible to compassion fatigue when exposed to various types of traumatic events in patients for extended periods of time. However, the developmental process, staging, and psychological responses distinct to each stage of compassion fatigue in nurses are not fully clarified. This study aimed to explore the processes of compassion fatigue and the psychological experiences specific to each phase of compassion fatigue among clinical nurses. Methods Charmaz’s Constructivist Grounded Theory methodology was used in this qualitative research. Semi-structured interviews were conducted with 13 clinical nurses with varying degrees of compassion fatigue from December 2020 to January 2021. Interview data were analyzed using grounded theory processes. Results The data were categorized into five separate categories and 22 sub-categories. This study found that the process of compassion fatigue is dynamic and cumulative, which was classified into five phases: compassion experience period, compassion decrement period, compassion discomfort period, compassion distress period, and compassion fatigue period. Conclusion Clinical nurses who experience compassion fatigue may go through five stages that are stage-specific and predictable. The findings can shed light on local and global applications to better understand the problem of nurses’ compassion fatigue. The interventions for addressing compassion fatigue in clinical nurses should be stage-specific, targeted, and individualized