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This article explores the use of convolutional neural networks (CNNs) in the detection and diagnosis of Attention Deficit Hyperactivity Disorder (ADHD). CNNs, which are adept at image recognition, can analyze brain imaging data to automatically identify relevant brain structures and abnormalities associated with ADHD. The integration of deep learning technology into ADHD diagnosis represents a significant advancement, allowing for more objective and data-driven approaches. The article also highlights specific studies that demonstrate the effectiveness of CNNs in diagnosing ADHD using EEG signals and MRI scans. The authors suggest that future research should focus on integrating different types of data and developing personalized treatment plans while considering ethical considerations and explainable AI models. Overall, CNNs are seen as a promising tool in improving ADHD care and patient outcomes. [Extracted from the article]

The power dispatching network forms the backbone of efforts to automate and modernize power grid dispatching, rendering it an indispensable infrastructure element within the power system. However, accurately forecasting future flows remains a formidable challenge due to the network's intricate nature, variability, and extended periods of missing data resulting from equipment maintenance and anomalies. Vital to enhancing prediction precision is the interpolation of missing values aligned with the data distribution across other time points, facilitating the effective capture of nonlinear patterns within historical flow sequences. To address this, we propose a transfer learning approach leveraging the gated recurrent unit (GRU) for interpolating missing values within the power dispatching network's flow sequence. Subsequently, we decompose the generation of future flow predictions into two stages: first, extracting historical features using the GRU, and then generating robust predictions via eXtreme Gradient Boosting (XGBoost). This integrated process termed the GRU-XGBoost module, is applied in experiments on four flow sequences obtained from a power grid company in southern China. Our experimental findings illustrate that the proposed flow prediction model outperforms both machine learning and neural network models, underscoring its superiority in short-term flow prediction for power-dispatching networks. [ABSTRACT FROM AUTHOR]

Viral variant is one known risk factor associated with post-acute sequelae of COVID-19 (PASC), yet the pathogenesis is largely unknown. Here, we studied SARS-CoV-2 Delta variant-induced PASC in K18-hACE2 mice. The virus replicated productively, induced robust inflammatory responses in lung and brain tissues, and caused weight loss and mortality during the acute infection. Longitudinal behavior studies in surviving mice up to 4 months post-acute infection revealed persistent abnormalities in neuropsychiatric state and motor behaviors, while reflex and sensory functions recovered over time. In the brain, no detectable viral RNA and minimal residential immune cell activation was observed in the surviving mice post-acute infection. Transcriptome analysis revealed persistent activation of immune pathways, including humoral responses, complement, and phagocytosis, and gene expression levels associated with ataxia telangiectasia, impaired cognitive function and memory recall, and neuronal dysfunction and degeneration. Furthermore, surviving mice maintained potent systemic Thelper 1 prone cellular immune responses and strong sera neutralizing antibodies against Delta and Omicron variants months post-acute infection. Overall, our findings suggest that infection in K18-hACE2 mice recapitulates the persistent clinical symptoms reported in long-COVID patients and provides new insights into the role of systemic and brain residential immune factors in PASC pathogenesis. [ABSTRACT FROM AUTHOR]

This paper presents a one-level decomposition Haar Discrete Wavelet Transform (DWT) architecture using a 4:2 compressor and carry propagate adder. In Haar DWT architecture, coefficient multiplication is an essential operation. The Haar coefficient multiplication ((x even ± x odd ) ∗ 0. 7 0 7 1 0 6) is implemented with R a d i x − 2 r multiplier and the generated partial products are represented with sign power of two (SPT) terms. The addition of SPT terms is computed with a 4:2 compressor and the final sum is computed with CPA. A R a d i x − 2 r multiplier with 4:2 compressor technique is used to improve energy and delay. Compared to the previous architectures, the proposed architecture gives reduction in area, power, and delay. The proposed Haar wavelet architecture is implemented in gate-level Verilog HDL and synthesized with UMC 90-nm technology using Cadence RC compiler. When compared to the existing designs, the proposed architecture Haar DWT architecture synthesis results show reduction in latency of 32.32% and 31.46% of circuit area. [ABSTRACT FROM AUTHOR]