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- Gavrilov, Nemanja, et al.
(författare)
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A study of ordered mesoporous carbon doped with Co and Ni as a catalyst of oxygen reduction reaction in both alkaline and acidic media
- 2018
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Ingår i: Surface & Coatings Technology. - : Elsevier. - 0257-8972 .- 1879-3347. ; 349, s. 511-521
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Tidskriftsartikel (refereegranskat)abstract
- The incorporation of trace amounts (< 0.2%) of Co and Ni noticeably enhanced the catalytic activity of nitrogen free ordered mesoporous carbon (OMC) towards oxygen reduction reaction (ORR). (Co,Ni)-doped OMCs were characterized by N-2-adsorption measurements, X-ray powder diffraction, field emission scanning electron microscopy and Raman spectroscopy methods, and their ORR activity was estimated by voltammetry on rotating disk electrode in acidic and alkaline media. (Co,Ni)-doped OMCs show modest activities in acidic media, while the catalytic activity in alkaline media is rather high. The measured activities are compared to the Pt-based and Pt-free ORR catalysts reported in the literature. The number of electrons consumed per O-2 in metal-doped OMCs was found to vary between 2 and 4, which is advantageous in comparison to metal-free OMC. Also, the mass activities of metal-doped OMCs were found to be up to 2.5 times higher compared to that of metal-free OMC. We suggest that the ORR activity is governed by a balance between (i) textural properties, which determine the electrochemically accessible surface of the catalyst and which are influenced by the addition of a metal precursor, and (ii) novel active sites formed upon the introduction of metals into the carbon structure. In particular, our Density Functional Theory calculations suggest that Co and Ni atoms embedded into the single vacancies of graphene can activate the O-2 molecule and contribute to the decomposition of peroxide.
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- Potpara, Tatjana S., et al.
(författare)
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Cardiac Arrhythmias in Patients with Chronic Kidney Disease : Implications of Renal Failure for Antiarrhythmic Drug Therapy
- 2016
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Ingår i: Current Medicinal Chemistry. - : Bentham Science Publishers Ltd.. - 0929-8673 .- 1875-533X. ; 23:19, s. 2070-2083
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Forskningsöversikt (refereegranskat)abstract
- The kidney has numerous complex interactions with the heart, including shared risk factors (e.g., hypertension, dyslipidemia, etc.) and mutual amplification of morbidity and mortality. Both cardiovascular diseases and chronic kidney disease (CKD) may cause various alterations in cardiovascular system, metabolic homeostasis and autonomic nervous system that may facilitate the occurrence of cardiac arrhythmias. Also, pre-existent or incident cardiac arrhythmias such as atrial fibrillation (AF) may accelerate the progression of CKD. Patients with CKD may experience various cardiac rhythm disturbances including sudden cardiac death. Contemporary management of cardiac arrhythmias includes the use of antiarrhythmic drugs (AADs), catheter ablation and cardiac implantable electronic devices (CIEDs). Importantly, AADs are not used only as the principal treatment strategy, but also as an adjunct therapy in combination with CIEDs, to facilitate their effects or to minimize inappropriate device activation in selected patients. Along with their principal antiarrhythmic effect, AADs may also induce cardiac arrhythmias and the risk for such proarrhythmic effect(s) is particularly increased in patients with reduced left ventricular systolic function or in the setting of electrolyte imbalance. Moreover, CKD itself can induce profound alterations in the pharmacokinetics and pharmacodynamics of many drugs including AADs, thus facilitating the drug accumulation and increased exposure. Hence, the use of AADs in patients with CKD may be challenging. In this review article, we provide an overview of the characteristics of arrhythmogenesis in patients with CKD with special emphasis on the complexity of pharmacokinetics and risk for proarrhythmias when using AADs in patients with cardiac arrhythmias and CKD.
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- Sarić, R., et al.
(författare)
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FPGA-based real-time epileptic seizure classification using Artificial Neural Network
- 2020
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Ingår i: Biomedical Signal Processing and Control. - : Elsevier BV. - 1746-8094 .- 1746-8108. ; 62
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Tidskriftsartikel (refereegranskat)abstract
- Epilepsy is a neurological disorder characterised by unusual brain activity widely known as seizure affecting 4-7% of the world's population. The diagnosis of this disorder is currently based on analysis of the electroencephalography (EEG) signals in the time-frequency domain. The analysis is performed applying various algorithms that yield high performance, however the challenge of effective real-time epilepsy diagnosis persists. To address this, we have developed a Field Programmable Gate Array (FPGA) based solution for the classification of generalized and focal epileptic seizure types using a feed-forward multi-layer neural network architecture (MLP ANN). The neural network algorithm is trained, validated and tested on 822 captured signals from Temple University Hospital Seizure Detection Corpus (TUH EEG Corpus) database. Inputs into the system were five main features obtained from EEG signals by time-frequency analysis followed by Continuous Wavelet Transform (CWT) and subsequent statistical analysis. Out of the total number of samples, 583 (70 %) of them were utilised during the system development in MATLAB and TensorFlow and 239 (30 %) samples were further used for subsequent testing of the model performance on the FPGA. Subsequently, the adequate parameters of the ANN model were determined by using k-Fold Cross-Validation. Finally, the best performing ANN model in terms of average validation data accuracy achieved during cross-validation was implemented on the FPGA for real-time seizure classification. The digital ANN solution was coded in Very High-Speed Integrated Circuit Hardware Description Language (VHDL) and tested on the FPGA using 30 % reaming data. The results of this research demonstrate that epilepsy diagnosis with quite high accuracy (95.14 %) can be achieved with (5-12-3) MLP ANN implemented on FPGA. Also, the results show the steps towards appropriate implementation of ANN on the FPGA. These results can be utilised as the basis for the design of an application-specific integrated circuit (ASIC) allowing large serial production.
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- Glasbey, JC, et al.
(författare)
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- 2021
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swepub:Mat__t
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