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42% and the best accuracy was 98.33%, while for the mild AD dataset, the average accuracy was 98.54% and the best accuracy was 100%. To determine the timing of early treatment and discovering the susceptible patients, and to slow the progression of the disease, we assume that the occurrence of MCI and mild AD and their progression to more serious AD and dementia could be inferred by analyzing the topological structure of the brain network generated by EEG. Transmembrane Transporters inhibitor Our findings provide a novel solution for connectome-based biomarker analysis to improve personalized medicine.This article investigates a closed-loop brain stimulation method based on model predictive control strategy to suppress epileptic seizures. A neural mass model (NMM), exhibiting the normal and various epileptic seizures by changing physiologically meaningful parameters, is used as a black-box model of the brain. Based on system identification, an auto-regressive moving-average Volterra model is established to reveal the relationship between stimulation and neuronal responses. Then, the model predictive control strategy is implemented based the Volterra model, which can generate an optimal stimulation waveform to eliminate epileptiform waves. The computational simulation results indicate the proposed closed-loop control strategy can optimize the stimulation waveform without particular knowledge of the physiological properties of the system. The robustness of the proposed control strategy to system disturbances makes it more appropriate for future clinical application.Active Lamb-wave-based structural health monitoring techniques have been widely studied to inspect large structures using permanently installed arrays of sensors and actuators. Most of these methods depend on comparing baseline signals recorded from the structure before going into service and test signals acquired during inspection. Temperature changes affect the propagation of the wave in a nonlinear and mode-dependent manner. As a result, baseline comparison methods fail when the test and baseline signals are acquired at vastly different temperatures. Approximate methods that compensate for the effects of temperature on the waves using signal stretch models have been introduced in the literature. These methods are effective when the temperature changes are small and the propagation distances are short. However, they perform poorly when these conditions are not satisfied. Consequently, there is a need for better temperature compensation algorithms than presently available. This article presents a data-driven approach that separately compensates for the effects of temperature on different mode components of the sensor signals. The performance of the temperature compensation algorithm of this article is compared with that of a commonly used baseline signal stretch (BSS) algorithm using experimental signals measured from an aluminum panel and a unidirectional composite panel. Analysis results indicate that the method of this article outperforms the BSS algorithm for large temperature differences. The usefulness of the temperature compensation algorithm is further validated by demonstrating the ability of compensated signals to accurately reconstruct anomaly maps associated with damaged composite structures.One way of resolving the problem of scarce and expensive data in deep learning for medical applications is using transfer learning and fine-tuning a network which has been trained on a large data set. The common practice in transfer learning is to keep the shallow layers unchanged and to modify deeper layers according to the new data set. This approach may not work when using a U-Net and when moving from a different domain to ultrasound (US) images due to their drastically different appearance. In this study, we investigated the effect of fine-tuning different sets of layers of a pretrained U-Net for US image segmentation. Two different schemes were analyzed, based on two different definitions of shallow and deep layers. We studied simulated US images, as well as two human US data sets. We also included a chest X-ray data set. The results showed that choosing which layers to fine-tune is a critical task. In particular, they demonstrated that fine-tuning the last layers of the network, which is the common practice for classification networks, is often the worst strategy. It may therefore be more appropriate to fine-tune the shallow layers rather than deep layers in US image segmentation when using a U-Net. Shallow layers learn lower level features which are critical in automatic segmentation of medical images. Even when a large US data set is available, we observed that fine-tuning shallow layers is a faster approach compared to fine-tuning the whole network.The frequency-temperature ( f – T ) characteristic of quartz crystal resonators is an important topic closely related to the frequency-deviation compensation in the design of cost-effective oscillators. Traditional studies depict the f – T characteristic as a polynomial function (usually a cubic function). However, this omits the thermal hysteresis phenomenon and cannot provide a very accurate frequency compensation. To handle this issue, this article is to propose two modified f – T characteristic modeling methods with considering the thermal hysteresis. First, to reflect the thermal hysteresis property of quartz crystal resonators, a two-directional f – T model is designed by introducing two individual submodels for describing the increasing and decreasing temperature stages, respectively. Furthermore, to integrate the submodels and provide the more accurate frequency-deviation estimation, a holistic f – T characteristic model based on double-hidden layer extreme learning machine (DHL-ELM) is presented. Different from the basic ELM model, two hidden layers, including one deterministic nonlinear mapping and one random nonlinear activation layer, are constructed for a better description of f – T characteristic. To validate our studies, an experiment system is applied to obtain the testing data of the real crystal resonators, and the applications demonstrate the effectiveness of the proposed methods.