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Autism Spectrum Disorder (ASD) is presented with significant challenges in diagnosis and intervention due to its multifaceted nature, varied symptomatology, and the high cost and time demands of behavior-based assessments. Traditional behavior-based tests, while effective, have been noted for being time-consuming and expensive. This study investigates an Electroencephalography (EEG)-based approach as a cost-effective, non-invasive alternative, utilizing minimal EEG channels to capture ASD-related abnormalities in brain oscillations with high temporal resolution. To enhance diagnostic accuracy, channel selection and feature extraction were optimized using filtering methods and particle swarm optimization. EEG data from individuals with ASD and control groups were analyzed, employing various visibility graph types and machine learning classifiers to assess coherence, causality, and time lag metrics. Results show that visibility graphs effectively capture brain connectivity differences in ASD, and machine learning classifiers trained on these features achieve higher classification accuracy compared to traditional methods. The efficacy of different visibility graph types and machine learning classifiers in ASD classification was analyzed, focusing on coherence, causality, and time lag methods. It is widely accepted that EEG signal patterns reliably reflect ASD-related abnormalities and that visibility graphs effectively represent brain connectivity. Limitations include the variability in EEG signal quality and the need for larger, more diverse datasets for validation. Comparative analysis has highlighted the pros and cons of other available methods, such as MRI and behavior-based assessments, emphasizing the superior cost-efficiency and accessibility of EEG approaches. The proposed method has been shown to outperform the existing methodologies quantitatively, achieving higher classification accuracy and performance by integrating horizontal and natural visibility graphs with machine learning classifiers. This study is presented as a significant step forward in ASD diagnosis and intervention, underscoring the potential of EEG-based technologies to revolutionize clinical practices and improve outcomes for individuals with ASD.

Seizure prediction using intracranial electroencephalogram (iEEG) has attracted an increasing attention during recent years. iEEG signals are commonly recorded in the form of multiple channels. Many previous studies generally used the iEEG signals of all channels to predict seizures, ignoring the consideration of channel selection. In this study, a method of one-dimensional convolutional neural networks (1D-CNN) combined with channel selection strategy was proposed for seizure prediction. First, we used 30-s sliding windows to segment the raw iEEG signals. Then, the 30-s iEEG segments, which were in three channel forms (single channel, channels only from seizure onset or free zone and all channels from seizure onset and free zones), were used as the inputs of 1D-CNN for classification, and the patient-specific model was trained. Finally, the channel form with the best classification was selected for each patient. The proposed method was evaluated on the Freiburg Hospital iEEG dataset. In the situation of seizure occurrence period (SOP) of 30min and seizure prediction horizon (SPH) of 5min, 98.60$\%$ accuracy, 98.85$\%$ sensitivity and 0.01/h false prediction rate (FPR) were achieved. In the situation of SOP of 60min and SPH of 5min, 98.32$\%$ accuracy, 98.48$\%$ sensitivity and 0.01/h FPR were attained. Compared with the many existing methods using the same iEEG dataset, our method showed a better performance.

Some prominent e-commerce giants, such as Amazon and JD.com, have established their own logistics service and shared it with other merchants. In this paper, we study the channel (reselling channel or agency channel) and logistics service (platform’s logistics or third-party logistics) selection strategies in an e-commerce supply chain composed of a limited capital supplier and a platform within the consideration of the supply chain finance. We show that the supplier’s channel and logistics service selection strategies depend on the cost coefficient of the platform’s logistics service and the production cost. Interestingly, the supplier prefers the reselling channel rather than the agency channel with the platform’s logistics service. This explains why the merchants in JD.com rarely use JD.com logistics in the agency channel. From the perspective of the platform, the platform may prefer the supplier to use third-party logistics service to deliver goods in the agency channel particularly when the cost coefficient of the platform’s logistics service is relatively large. Furthermore, the platform will put in more effort level aimed at enhancing the logistics service in the reselling channel.

This paper presents a cognitive state estimation system focused on some effective feature extraction based on temporal and spectral analysis of electroencephalogram (EEG) signal and the proper channel selection of the BIOPAC automated EEG analysis system. In the proposed approach, different frequency components (i) real value; (ii) imaginary value; (iii) magnitude; (iv) phase angle and (v) power spectral density of the EEG data samples during different mental task performed to assess seven types of human cognitive states — relax, mental task, memory related task, motor action, pleasant, fear and enjoying music on the three channels of BIOPAC EEG data acquisition system — EEG, Alpha and Alpha RMS signal. Also the time and time-frequency-based features were extracted to compare the performance of the system. After feature extraction, the channel efficacy is evaluated by support vector machine (SVM) based on the classification rate in different cognitive states. From the experimental results and classification accuracy, it is determined that the overall accuracy for alpha channel shows much improved result for power spectral density than the other frequency based features and other channels. The classification rate is 69.17% for alpha channel whereas for EEG and alpha RMS channel it is found 47.22% and 32.21%, respectively. For statistical analysis standard deviation shows better result for alpha channel and it is found 65.4%. The time-frequency analysis shows much improved result for alpha channel also. For the mean value of DWT coefficients the accuracy is highest and it is 81.3%. Besides the classification accuracy, SVM shows better performance in compare with kNN classifier.

The degrees of freedom for signal (DFS) is a useful figure of merit to estimate the observation impact of a high-spectral-resolution instrument. We calculate DFS within the ensemble-based assimilation system as a preliminary test of channel selection for the Geostationary Environmental Monitoring Spectrometer (GEMS). In an ensemble-based assimilation system, DFS is calculated in ensemble subspace and relatively small dimensions. We employ the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) interfaced with an ensemble data assimilation scheme — the Maximum Likelihood Ensemble Filter (MLEF). Our results indicate that the observation impact of GEMS is dependent on wavelength. Therefore, channel selection is useful to efficient data transmission, storage, and assimilation.