The proton exchange membrane fuel cell systems (PEMFC)s are interesting devices for energy conversion. Recent researches are aimed at developing new monitoring and diagnosis techniques; a good ...management of these systems would allow optimizing the performance and reducing their degradation. The objective of a suitable diagnostic tool is to identify and isolate the different faults that may occur in the system being monitored in real time. Therefore, the main features of computational methods are accuracy, reliability and high computational speed. In order to perform the diagnosis, it is necessary to evaluate different approaches. In this work different model-based approaches are investigated as well as their validation and applications. An overview of different methodologies available in the literature is proposed, which is oriented to help in developing suitable diagnostic tool for PEMFC monitoring and fault detection and isolation (FDI).
•Model-based methodologies for PEMFC diagnosis are summarized based on 59 articles.•The main approaches are introduced and discussed separately.•Parameter identification, observer-based and parity space methods are presented.•Models are classified in white-, grey- and black-box categories.•Application areas, advantages and limitations are underlined for each approach.
A review of non-model based methodologies applied to diagnosis of Proton Exchange Membrane Fuel Cell (PEMFC) system is presented. Three types of non-model based methods including artificial ...intelligence, statistical method and signal processing method are discussed and compared. The artificial intelligence one, divided into Neural Network (NN), Fuzzy Logic (FL) and neural-fuzzy method, is applied as a fault classifier which is quite different from its role in model-based method. Linear feature reduction methods including Principle Component Analysis (PCA) and Fisher Discriminant Analysis (FDA), and nonlinear ones such as Kernel PCA (KPCA) and Kernel FDA (KFDA) are demonstrated as part of statistical methods. Additionally, a statistical theory based classifier- Bayesian Network (BN) is also introduced in this part. As for signal processing method, both Fast Fourier Transform (FFT) for stationary signals and short-time Fourier Transform (STFT), as well as Wavelet Transform (WT) for non-stationary signals are introduced. Since each method has its advantages and limitations, a comparison is made finally and hybrid approaches resulting from integration of different methods are believed to be promising.
•Non-model based methodologies applied to diagnosis of PEMFC system are summarized.•AI method, statistical method and signal processing one are discussed.•Hybrid approaches resulting from the above methods are believed to be promising.•A general structure of the hybrid approach composed of four steps is given.
Spinal muscular atrophy is a rare, autosomal recessive, neuromuscular disease caused by biallelic loss of the survival motor neuron 1 (SMN1) gene, resulting in motor neuron dysfunction. In this ...STR1VE-EU study, we aimed to evaluate the safety and efficacy of onasemnogene abeparvovec gene replacement therapy in infants with spinal muscular atrophy type 1, using broader eligibility criteria than those used in STR1VE-US.
STR1VE-EU was a multicentre, single-arm, single-dose, open-label phase 3 trial done at nine sites (hospitals and universities) in Italy (n=4), the UK (n=2), Belgium (n=2), and France (n=1). We enrolled patients younger than 6 months (180 days) with spinal muscular atrophy type 1 and the common biallelic pathogenic SMN1 exon 7–8 deletion or point mutations, and one or two copies of SMN2. Patients received a one-time intravenous infusion of onasemnogene abeparvovec (1·1 × 1014 vector genomes vg/kg). The outpatient follow-up consisted of assessments once per week starting at day 7 post-infusion for 4 weeks and then once per month until the end of the study (at age 18 months or early termination). The primary outcome was independent sitting for at least 10 s, as defined by the WHO Multicentre Growth Reference Study, at any visit up to the 18 months of age study visit, measured in the intention-to-treat population. Efficacy was compared with the Pediatric Neuromuscular Clinical Research (PNCR) natural history cohort. This trial is registered with ClinicalTrials.gov, NCT03461289 (completed).
From Aug 16, 2018, to Sept 11, 2020, 41 patients with spinal muscular atrophy were assessed for eligibility. The median age at onasemnogene abeparvovec dosing was 4·1 months (IQR 3·0–5·2). 32 (97%) of 33 patients completed the study and were included in the ITT population (one patient was excluded despite completing the study because of dosing at 181 days). 14 (44%, 97·5% CI 26–100) of 32 patients achieved the primary endpoint of functional independent sitting for at least 10 s at any visit up to the 18 months of age study visit (vs 0 of 23 untreated patients in the PNCR cohort; p<0·0001). 31 (97%, 95% CI 91–100) of 32 patients in the ITT population survived free from permanent ventilatory support at 14 months compared with six (26%, 8–44) of 23 patients in the PNCR natural history cohort (p<0·0001). 32 (97%) of 33 patients had at least one adverse event and six (18%) had adverse events that were considered serious and related to onasemnogene abeparvovec. The most common adverse events were pyrexia (22 67% of 33), upper respiratory infection (11 33%), and increased alanine aminotransferase (nine 27%). One death, unrelated to the study drug, occurred from hypoxic-ischaemic brain damage because of a respiratory tract infection during the study.
STR1VE-EU showed efficacy of onasemnogene abeparvovec in infants with symptomatic spinal muscular atrophy type 1. No new safety signals were identified, but further studies are needed to show long-term safety. The benefit–risk profile of onasemnogene abeparvovec seems favourable for this patient population, including those with severe disease at baseline.
Novartis Gene Therapies.
A Review on solid oxide fuel cell models Wang, K.; Hissel, D.; Péra, M.C. ...
International journal of hydrogen energy,
06/2011, Letnik:
36, Številka:
12
Journal Article
Recenzirano
Since the model plays an important role in diagnosing solid oxide fuel cell (SOFC) system, this paper proposes a review of existing SOFC models for model-based diagnosis of SOFC stack and system. ...Three categories of modelling based on the white-, the black- and the grey-box approaches are introduced. The white-box model includes two types, i.e. physical model and equivalent circuit model based on EIS technique. The black-box model is based on artificial intelligence and its realisation relies mainly on experimental data. The grey-box model is more flexible: it is a physical representation but with some parts being modelled empirically. Validation of models is discussed and a hierarchical modelling approach involving all of three modelling methods is briefly mentioned, which gives an overview of the design for implementing a generic diagnostic tool on SOFC system.
Aims.
We present pyUPMASK, an unsupervised clustering method for stellar clusters that builds upon the original UPMASK package. The general approach of this method makes it plausible to be applied to ...analyses that deal with binary classes of any kind as long as the fundamental hypotheses are met. The code is written entirely in Python and is made available through a public repository.
Methods.
The core of the algorithm follows the method developed in UPMASK but introduces several key enhancements. These enhancements not only make pyUPMASK more general, they also improve its performance considerably.
Results.
We thoroughly tested the performance of pyUPMASK on 600 synthetic clusters affected by varying degrees of contamination by field stars. To assess the performance, we employed six different statistical metrics that measure the accuracy of probabilistic classification.
Conclusions.
Our results show that pyUPMASK is better performant than UPMASK for every statistical performance metric, while still managing to be many times faster.
BMP receptors determine the intensity of BMP signals via Smad1 C-terminal phosphorylations. Here we show that a finely controlled cell biological pathway terminates this activity. The duration of the ...activated pSmad1
Cter signal was regulated by sequential Smad1 linker region phosphorylations at conserved MAPK and GSK3 sites required for its polyubiquitinylation and transport to the centrosome. Proteasomal degradation of activated Smad1 and total polyubiquitinated proteins took place in the centrosome. Inhibitors of the Erk, p38, and JNK MAPKs, as well as GSK3 inhibitors, prolonged the duration of a pulse of BMP7. Wnt signaling decreased pSmad1
GSK3 antigen levels and redistributed it from the centrosome to cytoplasmic LRP6 signalosomes. In
Xenopus embryos, it was found that Wnts induce epidermis and that this required an active BMP-Smad pathway. Epistatic experiments suggested that the dorsoventral (BMP) and anteroposterior (Wnt/GSK3) patterning gradients are integrated at the level of Smad1 phosphorylations during embryonic pattern formation.
How do very diverse signaling pathways induce neural differentiation in Xenopus? Anti-BMP (Chordin), FGF8, and IGF2 signals are integrated in the embryo via the regulation of Smad1 phosphorylation. ...Neural induction results from the combined inhibition of BMP receptor serine/threonine kinases and activation of receptor tyrosine kinases that signal through MAPK and phosphorylate Smad1 in the linker region, further inhibiting Smad1 transcriptional activity. This hard-wired molecular mechanism at the level of the Smad1 transcription factor may help explain the opposing activities of IGF, FGF, and BMP signals not only in neural induction, but also in other aspects of vertebrate development.
Abstract Background Malnutrition is common in patients undergoing gastric cancer resection, leading to weight loss, although little is known about how this impacts on health related quality of life ...(HRQL) This study aimed to explore the association between HRQL and weight loss in patients 2 years after curative gastric cancer resection. Methods Consecutive patients undergoing curative gastric cancer resection and surviving at least 2 years without disease recurrence were recruited. Patients completed the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and the specific module for gastric cancer (STO22) before and 2 years postoperatively and associations between HRQL scores and patients with and without ≥ 10% body weight loss (BWL) were examined. Results A total of 76 patients were included, of whom 51 (67%) had BWL ≥ 10%. At 2 years postoperatively, BWL ≥ 10% was associated with deterioration of all functional aspects of quality of life, with persistent pain (21.6%), diarrhoea (13.7%) and nausea/vomiting (13.7%). By contrast, none of the patients with BWL < 10% experienced severe nausea/vomiting, pain or diarrhoea. Conclusions Disabling symptoms occurred more frequently in patients with ≥ 10% BWL than in those with < 10% BWL, with a relevant negative impact on HRQL. A cause-effect relationship between weight loss and postoperative outcome remains unsolved.
In this paper, a graphical model of Proton Exchange Membrane (PEM) electrolyser is presented. The modelling is performed with respect to a generic approach then it is tuned regarding the electrolyser ...considered for the experimental validation.
The graphical modelling based on Energetic Macroscopic Representation (EMR) has advantages such as readability, modularity, structural and functional characteristics. The EMR modelling presented here allows the modelling of multi-physics components and highlights the interactions of the electrochemical, thermodynamical, thermal and fluidic phenomena that occur simultaneously in an electrolyser.
Generally, in electrolyser models, the temperature is considered as a parameter with different constant values and its influence on other variables of the model is observed. In this paper, the dynamic evolution of the temperature in the stack and the supply water tank is described. The static behaviour of the electrical variables is also studied. To validate the model, a small-scale laboratory electrolyser is used as an experimental tool. The electrical parameters are identified using Matlab/Simulink curves fitting tools. Then, the whole model is simulated. The simulation results fit very well the experimental data.
Furthermore, the parameters values of this model are compared to those of the literature and their relevance is pointed out. Using Energetic Macroscopic Representation, the proposed model describes accurately the experimental electrolyser; moreover it can be easily adapted to other electrolysers.
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