Objectives:Pareidolia is the interpretation of previously unseen and unrelated objects as familiar due to previous learning. The present study aimed to determine the specific brain areas that exhibited activation during real-face and face-pareidolia processing.Methods:Functional Magnetic Resonance Imaging (fMRI) scans were performed on 20 healthy subjects under real-face and face-pareidolia conditions in National Magnetic Resonance Research Center (UMRAM), Ankara, Turkey from April 2016 to January 2017. FSL software was used to conduct a FEAT higher level (group) analysis to identify the brain areas activated during real-face and face-pareidolia processing.Results:Under both the real-face and face-pareidolia conditions, activation was observed in the Prefrontal Cortex (PFCX), occipital cortex V1, occipital cortex V2, and inferior temporal regions. Also under both conditions, the same degree of activation was observed in the right Fusiform Face Area (FFA) and the right PFCX. On the other hand, PFCX activation was not evident under the real-face versus face scrambled or face-pareidolia versus pareidolia scrambled conditions.Conclusions:The present findings suggest that, as in real-face perception, face-pareidolia requires interaction between top-down and bottom-up brain regions including the FFA and frontal and occipitotemporal areas. Additionally, whole-brain analyses revealed that the right PFCX played an important role in processing real faces and in face pareidolia (illusory face perception), as did the FFA.
Power quality (PQ) problems, including voltage sag, flicker, and harmonics, are the main concerns for the grid operator. Among these disturbances, voltage sag, which affects the sensitive loads in the interconnected system, is a crucial problem in the transmission and distribution systems. The determination of the voltage sag relative location as a downstream (DS) and upstream (US) is an important issue that should be considered when mitigating the sag problem. Therefore, this paper proposes a novel approach to determine the voltage sag relative location based on voltage sag event records of the power quality monitoring system (PQMS) in the real distribution system. By this method, the relative location of voltage sag is defined by Gaussian naive Bayes (Gaussian NB) and K-nearest neighbors (K-NN) algorithms. The proposed methods are compared with support vector machine (SVM) and artificial neural network (ANN). The results indicate that K-NN and Gaussian NB algorithms define the relative location of a voltage sag with 98.75% and 97.34% accuracy, respectively.
A communication system based on unmanned aerial vehicles (UAVs) is a viable alternative for meeting the coverage and capacity needs of future wireless networks. However, because of the limitations of UAV-enabled communications in terms of coverage, energy consumption, and flying laws, the number of studies focused on the sustainability element of UAV-assisted networking in the literature was limited thus far. We present a solution to this problem in this study; specifically, we design a Q-learning-based UAV placement strategy for long-term wireless connectivity while taking into account major constraints such as altitude regulations, nonflight zones, and transmit power. The goal is to determine the best location for the UAV base station (BS) while reducing energy consumption and increasing the number of users covered. Furthermore, a weighting method is devised, allowing energy usage and the number of users served to be prioritized based on network/battery circumstances. The suggested Q-learning-based solution is contrasted to the standard k-means clustering method, in which the UAV BS is positioned at the centroid location with the shortest cumulative distance between it and the users. The results demonstrate that the proposed solution outperforms the baseline k-means clustering-based method in terms of the number of users covered while achieving the desired minimization of the energy consumption.
Surgery is an option for patients with drug-resistant epilepsy, but it requires a comprehensive assessment.Electroencephalography (EEG), magnetic resonance imaging (MRI), and functional MRI (fMRI) are used to localize the epileptogenic zone, which directly affects the surgery outcome. Accessing EEG, MRI, and fMRI results and patient information simultaneously using traditional methods might result in misinformation and increase the workload of clinicians. In this study, we developed a modern web-based repository system for the preoperative evaluation of epilepsy disorder, including multimodal medical images and patient information. Our dedicated system is enriched with clinical metadata that are not currently available and is managed with an online application. It overcomes the identified problem and minimizes possible medical errors. In conclusion, this system has the potential to accelerate surgical procedures, get reliable results, and improve the seizure outcome. It is an extensive solution for epilepsy hospitals and clinical research centers. It may serve as a standard template for archiving multiple imaging techniques.
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