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There has been a decrease in the number of patients admitted for urgent, potentially surgical, abdominal pathology during the period of the COVID-19 epidemic in our center.<br /><br />There has been a decrease in the number of patients admitted for urgent, potentially surgical, abdominal pathology during the period of the COVID-19 epidemic in our center.<br /><br /> Self-care is a fundamental element of treatment for patients with a chronic condition and a major focus of many interventions. A large body of research exists describing different types of self-care interventions, but these studies have never been compared across conditions. Examination of heterogeneous interventions could provide insights into effective approaches that should be used in diverse patient populations.<br /><br /> To provide a comprehensive and standardized cross-condition overview of interventions to enhance self-care in patients with a chronic condition. Specific aims were to 1) identify what self-care concepts and behaviors are evaluated in self-care interventions; 2) classify and quantify heterogeneity in mode and type of delivery; 3) quantify the behavior change techniques used to enhance self-care behavior; and 4) assess the dose of self-care interventions delivered.<br /><br /> Scoping review DATA SOURCES Four electronic databases - PubMed, EMBASE, PsychINFO and CINAHL - were searched from January 2008 thrajor deficits found in self-care interventions included a lack of attention to the psychological consequences of chronic illness, technology and behavior change techniques were rarely used, few studies focused on helping patients manage signs and symptoms, and the interventions were rarely innovative. Research reporting was generally poor.<br /><br /> Major gaps in targeted areas of self-care were identified. Opportunities exist to improve the quality and reporting of future self-care intervention research. Registration The study was registered in the PROSPERO database (#123,719).<br /><br />Major gaps in targeted areas of self-care were identified. Opportunities exist to improve the quality and reporting of future self-care intervention research. Registration The study was registered in the PROSPERO database (#123,719).When judging what caused an event, people do not treat all factors equally - for instance, they will say that a forest fire was caused by a lit match, and not mention the oxygen in the air which helped fuel the fire. We develop a computational model formalizing the idea that causal judgment is designed to identify "portable" causes - causes that are likely to generalize across a variety of background circumstances. Under minimal assumptions, the model is surprisingly simple a factor is regarded as a cause of an outcome to the extent that it is, across counterfactual worlds, correlated with that outcome. The model explains why causal judgment is influenced by the normality of candidate causes, and outperforms other known computational models when tested against an existing fine-grained dataset of human graded causal judgments (Morris, A., Phillips, J., Gerstenberg, T., &amp; Cushman, F. (2019). Quantitative causal selection patterns in token causation. PloS one, 14(8).).This paper presents a novel vibration signal fusion algorithm using improved empirical wavelet transform and variance contribution rate to fuse three-channel vibration signals for weak fault detection of hydraulic pumps. Firstly, empirical wavelet transform (EWT) is utilized to decompose the three-channel signals into several AM-FM components. Then in accordance with the statistical characteristics of these component data, variance contribution rate is defined to measure the weight of component data points. A series of fusion coefficients are computed and assigned to every component point. Finally, these component points are fused into one single signal and Hilbert transform is conducted to demodulate the fault characteristic frequency for weak fault detection. Moreover, to address the issue of improper EWT spectrum segmentation, we introduce Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to improve EWT in the full space and the frequencies corresponding to outlier points are taken as the boundaries of spectrum segmentation. Therefore, the number of boundaries is more reasonable and the AM-FM components are more consistent with inherent components existing in the vibration signals of pumps. Results of simulation and experiment analysis demonstrate the good performance of the exhibited fusion algorithm in weak fault detection of hydraulic pumps.In this paper, we consider a fixed-time autonomous ship landing control design for helicopters subject to asymmetric output constraints, model uncertainties and external perturbations. [https://www.selleckchem.com/products/atuveciclib-bay-1143572.html selleckchem] By incorporating a universal barrier function into the backstepping design, an output-constrained fixed-time control algorithm is proposed, where a new adaptive estimation method is introduced to compensate the effects resulting from uncertainties and disturbances. To avoid crash and overcome the limitation of the helicopter's under-actuated property, the whole landing operation is completed in a dual-phase landing sequence with two controllers, which are both designed based on the output-constrained fixed-time control algorithm. The proposed control strategy guarantees that the tracking and landing errors converge into a small neighborhood of zero in a fixed settling time without violating the constraint requirement. Numerical comparative simulations are executed to further verify the prominent control performance.Correlated representation learning has found wide usage in process monitoring. However, slow and normal changes frequently occur in practical production processes, which may lead to model mismatch and degrade monitoring performance. Therefore, updating the monitoring model online and involving recently processed data information are important. This study proposes a recursive correlated representation learning (RCRL) incorporating an approach for online model updating for adaptive monitoring of slowly varying processes. First, an initial canonical correlation analysis-based monitoring model is established using historical process data. Second, an online model updating criterion is developed, and updating procedures are provided to reflect online data information and update monitoring model in a timely manner. Then, monitoring statistics are established and decision making logic is established to identify process status. The fitness of the monitoring scheme is increased because the online process information is considered to update the model.