PubMed İndeksli Yayınlar Koleksiyonu
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Browsing PubMed İndeksli Yayınlar Koleksiyonu by Publisher "CELL PRESS"
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Article Citation - WoS: 8Citation - Scopus: 9A novel stabilized artificial neural network model enhanced by variational mode decomposing(CELL PRESS, 2024) Ali Danandeh Mehr; Sadra Shadkani; Laith Abualigah; Mir Jafar Sadegh Safari; Hazem Migdady; Mehr, Ali Danandeh; Migdady, Hazem; Shadkani, Sadra; Safari, Mir Jafar Sadegh; Abualigah, Laith; Danandeh Mehr, AliExisting artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues we propose a stabilized ANNs called SANN. The SANN efficiently optimizes ANN structure via incorporation of an additional numeric parameter into every layer of the ANN. To exemplify the efficacy and efficiency of the proposed approach we provided two practical case studies involving meteorological drought forecasting at cities of Burdur and Isparta T & uuml,rkiye. To enhance SANN forecasting accuracy we further suggested the hybrid VMD-SANN that integrated variation mode decomposition (VMD) with SANN. To validate the new hybrid model we compared its results with those obtained from hybrid VMD-ANN and VMD-Radial Base Function (VMD-RBF) models. The results showed superiority of the VMD-SANN to its counterparts. Regarding Nash Sutcliffe Efficiency measure the VMD-SANN achieves accurate forecasts as high as 0.945 and 0.980 in Burdur and Isparta cities respectively.Article Citation - WoS: 4Citation - Scopus: 4How does exercise affect energy metabolism? An in silico approach for cardiac muscle(CELL PRESS, 2023) Bahar Hazal Yalcinkaya; Seda Genc; Bayram Yilmaz; Mustafa Ozilgen; Yalçınkaya, Bahar Hazal; Yılmaz, Bayram; Özilgen, Mustafa; Genc, SedaWe explored an in silico model of muscle energy metabolism and demonstrated its theoretical plausibility. Results indicate that energy metabolism triggered by activation can capture the muscle condition rest or exercise and can respond accordingly adjusting the rates of their respiration and energy utilization for efficient use of the nutrients. Our study demonstrated during exercise higher respiratory activity causes a substantial increase in exergy release with an increase in exergy destruction and entropy generation rate. The thermodynamic analysis showed that at the resting state when the exergy destruction rate was 0.66 W/kg and the respiratory metabolism energetic efficiency was 36% and exergetic efficiency was 32%, whereas when the exergy destroyed was 1.24 W/kg the energetic efficiency was 58% and exergetic efficiency was 50% during exercise. The efficiency results suggest the ability of the system to regulate itself in response to higher work demand and become more efficient in terms of converting energy coming from nutrients to useable energy when the circulating medium has sufficient energy precursor.
