Generalized Global Solar Radiation Forecasting Model via Cyber-Secure Deep Federated Learning

Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data

A newborn infant with congenital cerebral arteriovenous malformation, and congestive heart failure: case report

Background: Cerebral arteriovenous malformations are rare congenital anomalies presenting as different symptoms depending on their size and the age of patient. Congestive heart failure is a rare condition in neonatal period and is most common due to structural heart defects, but rarely may be a result of peripheral shunts such as cerebral arteriovenous malformation. Case presentation: A term male newborn infant who was delivered by Caesarean Section in Chamran Hospital, Ferdows, South Khorasan Province, June 2016. The infant was admitted to neonatal care unit due to nonreactive nonstress (NST) with normal Apgar score. In first postpartum visit, a systolic heart murmur was detected. Echocardiography showed small atrial septal defect secundum type and patent foramen ovale (PFO). He presented clinical manifestations of heart failure after 72 hours of birth. Antibiotic and treatment of heart failure was started. Following excluding most common etiologies of heart failure such as sepsis, anemia and arrhythmias, for detecting less common conditions such as cerebral vascular aneurism a transfontanelle ultrasonography was performed which showed dilated cerebral venous system. Magnetic resonance imaging (MRI) and Magnetic resonance venography (MRV) revealed a large congenital cerebral arterio-venous malformation (CAVM), in right cerebral hemisphere. Finally, he was expired 9 days after birth due to severe heart failure before any definitive treatment for closing CAVM could be done.  Conclusion: CAVM are extremely rare vascular anomalies in newborns which may present occasionally as congestive heart failure in neonatal period. So after excluding other most common etiologies of heart failure such as structural heart defects, screening CAVMs should be done. Inspite of early diagnosis, usually they have extremely poor prognosis