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Findings emphasize the important focus on graduate nurse support and educational foundation for role transition into professional practice, especially during a pandemic. Participants expressed lack of preparedness for practice but remain excited about being a nurse.Traumatic brain injury (TBI) has traditionally been associated with cognitive and behavioral changes during both the acute and chronic phases of injury. Because of its noninvasive nature, neuroimaging has the potential to provide unique information on underlying macroscopic and microscopic biological mechanisms that may serve as causative agents for these neuropsychiatric sequelae. This broad scoping review identifies at least 4 common macroscopic pathways that exist between TBI and new-onset psychiatric disorders, as well as several examples of how neuroimaging is currently being utilized in clinical research. The review then critically examines the strengths and limitations of neuroimaging for elucidating TBI-related microscopic pathology, such as microstructural changes, neuroinflammation, proteinopathies, blood-brain barrier damage, and disruptions in cellular signaling. A summary is then provided for how neuroimaging is currently being used to investigate TBI-related pathology in new-onset neurocognitive disorders, depression, and posttraumatic stress disorder. Identified gaps in the literature include a lack of prospective studies to definitively associate imaging findings with the development of new-onset psychiatric disorders, as well as antemortem imaging studies subsequently confirmed with postmortem correlates in the same study cohort. Although the spatial resolution and specificity of imaging biomarkers has greatly improved over the last 2 decades, we conclude that neuroimaging biomarkers do not yet exist for the definitive in vivo diagnosis of cellular pathology. This represents a necessary next step for further elucidating causal relationships between TBI and new-onset psychiatric disorders.Metacognition is the ability to reflect on our own cognition and mental states. It is a critical aspect of human subjective experience and operates across many hierarchical levels of abstraction-encompassing local confidence in isolated decisions and global self-beliefs about our abilities and skills. Alterations in metacognition are considered foundational to neurologic and psychiatric disorders, but research has mostly focused on local metacognitive computations, missing out on the role of global aspects of metacognition. Here, we first review current behavioral and neural metrics of local metacognition that lay the foundation for this research. We then address the neurocognitive underpinnings of global metacognition uncovered by recent studies. Finally, we outline a theoretical framework in which higher hierarchical levels of metacognition may help identify the role of maladaptive metacognitive evaluation in mental health conditions, particularly when combined with transdiagnostic methods.

Physical child abuse affects 9 in every 1,000 children in the United States and associated traumatic injuries are often identified by the healthcare system. The COVID-19 pandemic has intensified risk factors for physical child abuse and increased avoidance of the healthcare system. This study examined the effect of the COVID-19 pandemic on presentation and severity of physical child abuse.

A retrospective, cross-sectional study utilizing the Pediatric Health Information System was performed. An interrupted time series analysis estimated the effect of the COVID-19 pandemic on the number of children <15 years old presenting with physical child abuse to children's hospitals from March 1

to June 30

of 2020 by comparing to those presenting during the same period for years 2016-2019. Hierarchical regression models estimated the effect of the pandemic on likelihood of operative intervention, ICU admission, traumatic brain injury, and mortality.

Over the study period, 20,346 physical child abuse encounters were reported by 47 children's hospitals. An interrupted times series model predicted a significant decline in cases due to the effect of the COVID-19 pandemic, representing a deficit of 2,645 cases (p=0.001). Children presenting during the pandemic had increased odds of requiring ICU admission (p=0.03) and having a traumatic brain injury in those under 5 years of age (p=<0.001).

The number of children with physical child abuse presenting to children's hospitals significantly declined during the COVID-19 pandemic, but those that did were more likely to be severe. The pandemic may be a risk factor for worse outcomes associated with physical child abuse.

The number of children with physical child abuse presenting to children's hospitals significantly declined during the COVID-19 pandemic, but those that did were more likely to be severe. The pandemic may be a risk factor for worse outcomes associated with physical child abuse.In the process industry, it is essential to establish a data-driven soft sensor to predict the key variable that is difficult to online measure directly. The accuracy performance of data-driven soft sensors relies heavily on data. Unfortunately, it is hard to acquire sufficient and informative data from the samples with limited number, which is called as the small sample problem. For handling the small sample problem, it is a good solution to generating virtual samples according to the distribution of original data. This paper proposes an enhanced method of virtual sample generation utilizing manifold features to develop soft sensors using small data. First, T-Distribution Stochastic Neighbor Embedding (t-SNE) is utilized to extract the features of input data. The main idea of generating virtual samples is to use the interpolation algorithm to obtain virtual t-SNE input features and then the random forest algorithm is utilized to get the virtual outputs using virtual t-SNE input features. Finally, virtual samples using the proposed t-SNE based virtual sample generation (t-SNE-VSG) can be achieved. For the sake of confirming the effectiveness and feasibility of the presented t-SNE-VSG, a standard data set is first used. What is more, a small data set from an actual industrial process of Purified Terephthalic Acid is used to establish a soft sensor model. The results from simulations show that the accuracy performance of the soft sensor established with small data can be effectively improved by adding the virtual samples generated by t-SNE-VSG. In addition, t-SNE-VSG achieves superior accuracy to state-of-the-art virtual sample generation methods.The mode transition process (MTP) from electric mode to hybrid electric mode (EM-to-HM) will cause the deterioration in occupant comfort of PHEV, to tickle this issue, a torsional oscillation-considered mode transition coordinated control strategy and a novel general evaluation index for MTP are developed in this research, the quality of mode transition and transient torsional oscillation of gears (TTOGs) during MTP are taken into consideration comprehensively. An action dependent heuristic dynamic programming algorithm which takes the vehicle jerk, friction loss and TTOGs as evaluation index is used to optimize the pressure curve of clutch oil and the compensation torque of motor in the entire EM-to-HM process. selleckchem Finally, the simulation results and hardware-in-the-loop tests show that vehicle jerk and TTOGs are suppressed, and the driving comfort can be improved accordingly.Data imbalance is a common problem in rotating machinery fault diagnosis. Traditional data-driven diagnosis methods, which learn fault features based on balance dataset, would be significantly affected by imbalanced data. In this paper, a novel imbalanced data related fault diagnosis method named deep balanced cascade forest is proposed to solve this problem. Deep balanced cascade forest is a multi-channel cascade forest, in which, each of its channels adaptively generates deep cascade structure and is trained on independent data. To enhance the performance of imbalance classification, the deep balanced cascade forest is promoted from both aspects of resampling and algorithm design. A hybrid sampling method, namely Up-down Sampling, is proposed to provide rebalanced data for each cascade forest channel. Meanwhile, a new type of balanced forest with an improved balanced information entropy for attribute selection is designed as the basic classifier of cascade forest. The good synergy of these two methods is the key to the deep balanced cascade forest model. This good synergy makes deep balanced cascade forest achieve the fusion of data-level methods and algorithm-level methods. Comparative experiments on sufficient imbalanced datasets have been designed to verify the performance of the proposed model, and results confirm that deep balanced cascade forest is much more stable and effective in handling imbalance fault diagnosis problem compared to the popular deep learning methods.In the cold tandem rolling process, the product quality and yield are affected by the accuracy of rolling force prediction directly. Fix prediction model is not applicable to the multi-operating conditions rolling environment. In addition, appropriate samples can be hardly selected by a single similarity measure because of the insufficient process knowledge. In order to solve these issues, an ensemble just-in-time-learning modeling method based on multi-weighted similarity measures (MWS-EJITL) is proposed. Firstly, multi-weighted similarity measures is used to select relevant samples. Then, the local model is constructed and the output value of the query data is estimated. Finally, the ensemble learning strategy is adopted to integrate the outputs of each local model. On this basis, the cumulative similarity factor is introduced to optimize the number of samples of local modeling, and the similarity threshold is set to update the local model adaptively. The rolling force prediction experiment verify the effectiveness and accuracy of MWS-EJITL method.In this paper, the tracking control problem of non-minimum phase flexible air-breathing hypersonic vehicles (AHSV) is investigated subject to actuator fault, external disturbances and parameters uncertainties. The study is began with a series of control-oriented manipulations first, the input-output dynamics are derived by using feedback linearization method and the internal dynamics of AHSV are constructed; then, the zero dynamics stability analysis is conducted to verify the non-minimum phase characteristic of AHSV. In order to realize output tracking of the non-minimum phase system with sufficient accuracy, an adaptive fault tolerant controller (FTC) is proposed based on an output-redefinition making the zero-dynamics with respect to the new output stable. Additionally, robust adaptive laws are utilized for the estimation of unknown parameters and actuator failure compensation of the AHSV model. link2 Furthermore, the stability of the closed-loop system is analyzed based on the Lyapunov stability theory. At last, the numerical simulation results are provided to demonstrate the effective tracking performance of the proposed FTC scheme.This paper suggests a methodology for the identification, classification, and evaluation of various types of interactions that may occur in an HVDC link based on modular multi-level converters (MMC). The methodology incorporates the most suitable analytic tools for the frequency-domain study of each interaction category. To do so, a detailed nonlinear model of an MMC-based HVDC link that consists of master and slave MMCs, AC grids, and the DC transmission system is derived. Then, it is linearized to obtain a multi-input multi-output (MIMO) linear model that represents the dynamics of the complete MMC-based HVDC link. link3 Based on the control loops of interest, interactions are classified as (1) state variable interactions, (2) disturbance interactions, (3) control loop interactions, and (4) overall system interactions. Then, through the application examples, the mentioned four categories of interactions are studied in frequency domain via the relevant analytic tools. The results obtained from the frequency-domain analysis are validated by time-domain simulation.