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Background: Chemotherapy related cognitive impairment (CRCI) is a type of memory and cognitive impairment induced by chemotherapy and has become a growing clinical problem. Breast cancer survivors (BCs) refer to patients from the moment of breast cancer diagnosis to the end of their lives. Managing Cancer and Living Meaningfully (CALM) is a convenient and easy‐to‐apply psychological intervention that has been proven to improve quality of life and alleviate CRCI in BCs. However, the underlying neurobiological mechanisms remain unclear. Resting‐state functional magnetic resonance imaging (rs‐fMRI) has become an effective method for understanding the neurobiological mechanisms of brain networks in CRCI. The fractional amplitude of low‐frequency fluctuations (fALFF) and ALFF have often been used in analyzing the power and intensity of spontaneous regional resting state neural activity. Methods: The recruited BCs were randomly divided into the CALM group and the care as usual (CAU) group. All BCs were evaluated by the Functional Assessment of Cancer Therapy Cognitive Function (FACT‐Cog) before and after CALM or CAU. The rs‐fMRI imaging was acquired before and after CALM intervention in CALM group BCs. The BCs were defined as before CALM intervention (BCI) group and after CALM intervention (ACI) group. Results: There were 32 BCs in CALM group and 35 BCs in CAU group completed the overall study. There were significant differences between the BCI group and the ACI group in the FACT‐Cog‐PCI scores. Compared with the BCI group, the ACI group showed lower fALFF signal in the left medial frontal gyrus and right sub‐gyral and higher fALFF in the left occipital_sup and middle occipital gyrus. There was a significant positive correlation between hippocampal ALFF value and FACT‐Cog‐PCI scores. Conclusions: CALM intervention may have an effective function in alleviating CRCI of BCs. The altered local synchronization and regional brain activity may be correlated with the improved cognitive function of BCs who received the CALM intervention. The ALFF value of hippocampus seems to be an important factor in reflect cognitive function in BCs with CRCI and the neural network mechanism of CALM intervention deserves further exploration to promote its application. [ABSTRACT FROM AUTHOR]

Belt conveyors are critical in coal transportation systems; however, they are supported by rollers, which incur high energy costs. Magnetic levitation support is adopted. The measurement of a magnetic levitation air gap is important, and a displacement sensor is usually used in real-time. To address the problems of poor linearity and low measurement stability of single displacement sensors in magnetic levitation systems, a magnetic levitation support system based on multi-sensor data fusion is proposed. First, an industrial camera sensor is used to collect images, and the suspended air gap is obtained through image processing. Second, using an extended Kalman filter, the photoelectric position sensor and industrial camera sensor are fused to the suspension air gap value in the process of suspension. The electromagnet current is controlled by the PID (proportion integration differentiation) control algorithm, and experiments are carried out. Experimental results showed that, compared with an estimation of the suspension state of two sensors before and after EKF fusion, the root mean square error is reduced by 0.0597 and 0.0081, respectively, compared with the measurement value of a single sensor. Moreover, the fusion data were more robust, thereby meeting the requirements of magnetic levitation control. [ABSTRACT FROM AUTHOR]

Dynamic latent variable (DLV) methods have been widely studied for high dimensional time series monitoring by exploiting dynamic relations among process variables. However, explicit extraction of predictable information is rarely emphasized in these DLV methods. In this paper, the graph‐based predictable feature analysis (GPFA) algorithm is introduced for statistical process monitoring due to its explicit predictability, and a novel index, prediction information, is designed to determine the number of its principal components for dimensionality reduction and parameter optimization. A GPFA‐based dynamic process monitoring framework is proposed to differentiate among dynamic faults, normal operating condition changes, and break in relation in the normal data. Case studies on the Tennessee Eastman process and a high‐pressure feedwater heater are conducted to demonstrate the superiority of GPFA over other approaches in terms of fault detection performance. [ABSTRACT FROM AUTHOR]