55 research outputs found

    Operational performance of a PV generator feeding DC shunt and induction motors with MPPT

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    This paper presented the transient and operational behavior of a parallel Combination of DC Shunt Motor and IM fed by a photovoltaic generator at different solar irradiance levels. The maximum power point of current/voltage (I/V) characteristic of the PV generator was achieved for different solar intensities, by utilizing an open circuit voltage method. The nonlinear operational behavior of (I/V) characteristics of the PV generator at various solar intensities and the magnetization curve of the ferromagnetic material of the DC shunt motor were both modeled by high order polynomial mathematical expressions. The study investigated the response of the system at different solar irradiance levels and changing the torque loads for both motors and then following step change in solar intensity levels with fixed loading torques for both motors. All numerical simulations were executed using MATLAB software

    Safety out of control: dopamine and defence

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    A novel approach for detecting deep fake videos using graph neural network

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    Abstract Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs

    Deep brain stimulation for obsessive compulsive disorder: A review of results by anatomical target.

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    Studies suggest deep brain stimulation (DBS) as a treatment modality for the refractory obsessive-compulsive disorder (OCD). It is unclear where to place the DBS. Various sites are proposed for placement with the ventral capsule/ventral striatum (VC/VS) among the most studied. Herein, we aim to summarize both quantitative Yale-Brown Obsessive-Compulsive Scale (YBOCS) data and qualitative descriptions of the participants' symptoms when given. A literature search conducted via PubMed yielded 32 articles. We sought to apply a standard based on the utilization of YBOCS. This yielded 153 distinct patients. The outcome measure we focused on in this review is the latest YBOCS score reported for each patient/cohort in comparison to the location of the DBS. A total of 32 articles were found in the search results. In total, 153 distinct patients' results were reported in these studies. Across this collection of papers, a total of 9 anatomic structures were targeted. The majority of studies showed a better response at the last time point as compared to the first time point. Most patients had DBS at nucleus accumbens followed by VC/VS and the least patients had DBS at the bilateral superolateral branch of the median forebrain bundle and the bilateral basolateral amygdala. The average YBOCS improvement did not seem to directly correlate with the percentile of patients responding to the intervention. Well-controlled, randomized studies with larger sample sizes with close follow up are needed to provide a more accurate determination for placement of DBS for OCD
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