Identifying catalyst layer compositions of proton exchange membrane fuel cells through machine-learning-based approach

Membrane electrode assembly (MEA) is considered a key component of a proton exchange membrane fuel cell (PEMFC). However, developing a new MEA to meet desired properties, such as operation under low-humidity conditions without a humidifier, is a time- and cost-consuming process. This study employs a machine-learning-based approach using K-nearest neighbor (KNN) and neural networks (NN) in the MEA development process by identifying a suitable catalyst layer (CL) recipe in MEA. Minimum redundancy maximum relevance and principal component analysis were implemented to specify the most important predictor and reduce the data dimension. The number of predictors was found to play an essential role in the accuracy of the KNN and NN models although the predictors have self-correlations. The KNN model with a K of 7 was found to minimize the model loss with a loss of 11.9%. The NN model constructed by three corresponding hidden layers with nine, eight, and nine nodes can achieve the lowest error of 0.1293 for the Pt catalyst and 0.031 for PVA as a good additive blending in the CL of the MEA. However, even if the error is low, the prediction of PVA seems to be inaccurate, regardless of the model structure. Therefore, the KNN model is more appropriate for CL recipe prediction.     

Current Status of Angiogenic Cell Therapy and Related Strategies Applied in Critical Limb Ischemia

Critical limb ischemia (CLI) constitutes the most severe form of peripheral arterial disease (PAD), it is characterized by progressive blockade of arterial vessels, commonly correlated to atherosclerosis. Currently, revascularization strategies (bypass grafting, angioplasty) remain the first option for CLI patients, although less than 45% of them are eligible for surgical intervention mainly due to associated comorbidities. Moreover, patients usually require amputation in the short-term. Angiogenic cell therapy has arisen as a promising alternative for these “no-option” patients, with many studies demonstrating the potential of stem cells to enhance revascularization by promoting vessel formation and blood flow recovery in ischemic tissues. Herein, we provide an overview of studies focused on the use of angiogenic cell therapies in CLI in the last years, from approaches testing different cell types in animal/pre-clinical models of CLI, to the clinical trials currently under evaluation. Furthermore, recent alternatives related to stem cell therapies such as the use of secretomes, exosomes, or even microRNA, will be also described.     

Comparative analysis of formaldehyde and toluene sorption on indoor floorings and consequence on Indoor Air Quality

Indoor surfaces may be adsorptive sinks with the potential to change Indoor Air Quality. To estimate this effect, the sorption parameters of formaldehyde and toluene were assessed on five floorings by an experimental method using solid-phase microextraction in an airtight emission cell. Adsorption rate constants ranged from 0.003 to 0.075 m·h⁻¹, desorption rate constants from 0.019 to 0.51 h⁻¹, and the partition coefficient from 0.005 to 3.9 m, and these parameters vary greatly from one volatile organic compound/material couple to another indicating contrasted sorption behaviors. A rubber was identified as a sink of formaldehyde characterized by a very low desorption constant close to 0. For these sorbent floorings identified, the adsorption rates of formaldehyde are from 2 to 4 times higher than those of toluene. Two models were used to evaluate the sink effects of floorings on indoor pollutant concentrations in one room from different realistic conditions. The scenarios tested came to the conclusion that the formaldehyde sorption on one rubber (identified as a sink) has a maximum contribution from 15% to 21% for the conditions of low air exchange rate. For other floorings, the sorption has a minor contribution less than or equal to 5%, regardless of the air exchange rate.     

基于神经网络和遗传算法的锭子弹性管性能优化

为得到减振弹性管对下锭胆的支承弹性和锭子高速运动下的稳定性等性能的最优匹配效率,依据减振弹性管的等效抗弯刚度及底部等效刚度系数公式,利用MatLab数值分析软件构建弹性管抗弯刚度和底部挠度数学模型。首先,结合Isight优化软件基于径向基神经网络构建其近似模型,且使精度达到可接受水平,并以模型的关键结构参数弹性模量、螺距、槽宽、壁厚为设计变量,结合遗传算法对弹性管抗弯刚度和底部挠度进行多目标优化设计,得到Pareto最优解集和Pareto前沿图,确定出减振弹性管结构工艺参数的优化方案。通过对优化数据进行分析发现,该方案在保证减振弹性管弹性的同时,其底部振幅明显减弱。     

A review of topic modeling methods

Topic modeling is a popular analytical tool for evaluating data. Numerous methods of topic modeling have been developed which consider many kinds of relationships and restrictions within datasets; however, these methods are not frequently employed. Instead many researchers gravitate to Latent Dirichlet Analysis, which although flexible and adaptive, is not always suited for modeling more complex data relationships. We present different topic modeling approaches capable of dealing with correlation between topics, the changes of topics over time, as well as the ability to handle short texts such as encountered in social media or sparse text data. We also briefly review the algorithms which are used to optimize and infer parameters in topic modeling, which is essential to producing meaningful results regardless of method. We believe this review will encourage more diversity when performing topic modeling and help determine what topic modeling method best suits the user needs.     

Improved error performance for generalised spatial modulation with enhanced spectral efficiency

The existing analytical average bit error rate (ABER) expression of conventional generalised spatial modulation (CGSM) does not agree well with the Monte Carlo simulation results in the low signal‐to‐noise ratio (SNR) region. Hence, the first contribution of this paper is to derive a new and easy way to evaluate analytical ABER expression that improves the validation of the simulation results at low SNRs. Secondly, a novel system termed CGSM with enhanced spectral efficiency (CGSM‐ESE) is presented. This system is realised by applying a rotation angle to one of the two active transmit antennas. As a result, the overall spectral efficiency is increased by 1 bit/s/Hz when compared with the equivalent CGSM system. In order to validate the simulation results of CGSM‐ESE, the third contribution is to derive an analytical ABER expression. Finally, to improve the ABER performance of CGSM‐ESE, three link adaptation algorithms are developed. By assuming full knowledge of the channel at the receiver, the proposed algorithms select a subset of channel gain vector (CGV) pairs based on the Euclidean distance between all CGV pairs, CGV splitting, CGV amplitudes, or a combination of these.     

Off‐grid DoA estimation method of wideband signals

In this paper, an off‐grid direction of arrival (DoA) estimation method is proposed for wideband signals. This method is based on the sparse representation (SR) of the array covariance matrix. Similar to the time domain DoA estimation methods, the correlation function of the sources was assumed to be the same and known. A new measurement vector is obtained using the lower‐left triangular elements of the covariance matrix. The DoAs are estimated by quantizing the entire range of continuous angle space into discrete grid points. However, the exact DoAs may be located between two grid points; therefore, this estimation has errors. The accuracy of DoA estimation is improved by the minimization of the difference between the new measurement vector and its estimated values. Simulation results revealed that the proposed method can enhance the DoA estimation accuracy of wideband signals.