Backward neural network (BNN) based multilevel control for enhancing the quality of an islanded RES DC microgrid under variable communication network.

Microgrids (MGs) and energy communities have been widely implemented, leading to the participation of multiple stakeholders in distribution networks. Insufficient information infrastructure, particularly in rural distribution networks, is leading to a growing number of operational blind areas in distribution networks. An optimization challenge is addressed in multi-feeder microgrid systems to handle load sharing and voltage management by implementing a backward neural network (BNN) as a robust control approach. The control technique consists of a neural network that optimizes the control strategy to calculate the operating directions for each distributed generating point. Neural networks improve control during communication connectivity issues to ensure the computation of operational directions. Traditional control of DC microgrids is susceptible to communication link delays. The proposed BNN technique can be expanded to encompass the entire multi-feeder network for precise load distribution and voltage management. The BNN results are achieved through mathematical analysis of different load conditions and uncertain line characteristics in a radial network of a multi-feeder microgrid, demonstrating the effectiveness of the proposed approach. The proposed BNN technique is more effective than conventional control in accurately distributing the load and regulating the feeder voltage, especially during communication failure.

Ascorbic acid and salvianolic acid B enhance the valproic acid and 5-azacytidinemediated cardiac differentiation of mesenchymal stem cells.

BACKGROUND: Cardiovascular diseases remain a major cause of death globally. Cardiac cells once damaged, cannot resume the normal functioning of the heart. Bone marrow derived mesenchymal stem cells (BM-MSCs) have shown the potential to differentiate into cardiac cells. Epigenetic modifications determine cell identity during embryo development via regulation of tissue specific gene expression. The major epigenetic mechanisms that control cell fate and biological functions are DNA methylation and histone modifications. However, epigenetic modifiers alone are not sufficient to generate mature cardiac cells. Various small molecules such as ascorbic acid (AA) and salvianolic acid B (SA) are known for their cardiomyogenic potential. Therefore, this study is aimed to examine the synergistic effects of epigenetic modifiers, valproic acid (VPA) and 5-azacytidine (5-aza) with cardiomyogenic molecules, AA and SA in the cardiac differentiation of MSCs. METHODS AND RESULTS: BM-MSCs were isolated, propagated, characterized, and then treated with an optimized dose of VPA or 5-aza for 24 h. MSCs were maintained in a medium containing AA and SA for 21 days. All groups were assessed for the expression of cardiac genes and proteins through q-PCR and immunocytochemistry, respectively. Results show that epigenetic modifiers VPA or 5-aza in combination with AA and SA significantly upregulate the expression of cardiac genes MEF2C, Nkx2.5, cMHC, Tbx20, and GATA-4. In addition, VPA or 5-aza pretreatment along with AA and SA enhanced the expression of the cardiac proteins connexin-43, GATA-4, cTnI, and Nkx2.5. CONCLUSION: These findings suggest that epigenetic modifiers valproic acid and 5-azacytidine in combination with ascorbic acid and salvianolic acid B promote cardiac differentiation of MSCs. This pretreatment strategy can be exploited for designing future stem cell based therapeutic strategies for cardiovascular diseases.