A decision-making method for reservoir operation schemes based on deep learning and whale optimization algorithm.

Reservoir operation is an important part of basin water resources management. The rational use of reservoir operation scheme can not only enhance the capacity of flood control and disaster reduction in the basin, but also improve the efficiency of water use and give full play to the comprehensive role the reservoir. The conventional decision-making method of reservoir operation scheme is computationally large, subjectivity and difficult to capture the nonlinear relationship. To solve these problems, this paper proposes a reservoir operation scheme decision-making model IWGAN-IWOA-CNN based on artificial intelligence and deep learning technology. In view of the lack of data in the original reservoir operation scheme and the limited improvement of data characteristics by the traditional data augmentation algorithm, an improved generative adversarial network algorithm (IWGAN) is proposed. IWGAN uses the loss function which integrates Wasserstein distance, gradient penalty and difference item, and dynamically adds random noise in the process of model training. The whale optimization algorithm is improved by introducing Logistic chaotic mapping to initialize population, non-linear convergence factor and adaptive weights, and Levy flight perturbation strategy. The improved whale optimization algorithm (IWOA) is used to optimize hyperparameters of convolutional neural networks (CNN), so as to obtain the best parameters for model prediction. The experimental results show that the data generated by IWGAN has certain representation ability and high quality; IWOA has faster convergence speed, higher convergence accuracy and better stability; IWGAN-IWOA-CNN model has higher prediction accuracy and reliability of scheme selection.

The Performances of SNAQ, GLIM, mNICE, and ASPEN for Identification of Neurocritically Ill Patients at High Risk of Developing Refeeding Syndrome.

We previously found that neurocritically ill patients are prone to refeeding syndrome (RFS), a potentially life-threatening complication. However, there is no unified or validated consensus on the screening tool for RFS so far. We aimed to validate and compare the performance of four screening tools for RFS in neurocritically ill patients. We conducted a single-center, observational, retrospective cohort study among neurocritically ill adult patients who were admitted to the neurocritical care unit (NCU), and who received enteral nutrition for 72 h or longer. They were scored on the Short Nutritional Assessment Questionnaire (SNAQ), the Global Leadership Initiative on Malnutrition (GLIM), the modified criteria of the Britain's National Institute for Health and Care Excellence (mNICE), and ASPEN Consensus Recommendations for Refeeding Syndrome (ASPEN) scales to predict RFS risk via admission data. The performance of each scale in predicting RFS was evaluated. Logistic regression analysis was used to identify the independent risk factors for RFS, and they were added to the above scales to strengthen the identification of RFS. Of the 478 patients included, 84 (17.57%) developed RFS. The sensitivity of the SNAQ and GLIM was only 20.2% (12.6-30.7%), although they had excellent specificities of 84.8% (80.8-88.1%) and 86.0% (82.1-89.2%), respectively; mNICE predicted RFS with a sensitivity of 48.8% (37.8-59.9%) and a specificity of 65.0% (60.0-69.9%); ASPEN had the highest Youden index, with a sensitivity and specificity of 53.6% (42.4-64.4%) and 64.7% (59.8-69.4%), respectively. The Area Under the receiver operating characteristic Curves (AUC) of SNAQ, GLIM, mNICE, and ASPEN to predict RFS were 0.516 (0.470-0.561), 0.533 (0.487-0.579), 0.568 (0.522-0.613), and 0.597 (0.551-0.641), respectively. We identified age, Acute Physiology and Chronic Health Evaluation II (APACHE II), Sequential Organ Failure Assessment (SOFA), and Glasgow Coma Scale (GCS) score as independent risk factors of RFS, and the combination of GCS and age can improve the AUC of ASPEN to 0.664 (0.620-0.706) for predicting RFS. SNAQ, GLIM, mNICE, and ASPEN do not perform well in identifying neurocritically ill patients at high risk of RFS, although ASPEN appears to have relatively a good validity among them. Combining GCS and age with ASPEN slightly improves RFS recognition, but it still leaves a lot of room for improvement.

The effectiveness of phenobarbital in patients with refractory status epilepticus undergoing therapeutic plasma exchange.

This study aimed to elucidate the therapeutic concentration range of phenobarbital (PB) for adults, as well as the influence of therapeutic plasma exchange (TPE) on plasma concentration of PB. We retrospectively reviewed consecutive patients diagnosed with refractory status epileptic (RSE) and treated with a bolus injection of PB as well as TPE, admitted to our neurocritical care unit from November 2015 to October 2016. Continuous electroencephalographic monitoring was performed routinely for these patients. TPE was performed using a continuous-flow cell separator. PB concentrations in the plasma and cerebrospinal fluid were measured by gas chromatography-mass spectrometer analysis before and after TPE. A total of seven patients were included; among these, one patient had RSE related to anti-N-methyl-D-aspartate receptor encephalitis, another patient had Hashimoto encephalopathy, and five patients had undetermined etiology. For patients with clinical and electrographic control (n=6), the plasma concentration of PB ranged from 138 to 243.7 µg/ml. In addition, of six paired plasma samples (before and after TPE) obtained from three patients, no significant differences between the concentrations of PB were detected (P=0.6), suggesting that TPE may not significantly affect the plasma concentration of PB. This study confirmed that PB more than 100 µg/ml was effective for adults with RSE. Moreover, TPE may not have an influence on the plasma concentration of PB.Video abstract: http://links.lww.com/WNR/A489.

Glibenclamide enhances the effects of delayed hypothermia after experimental stroke in rats.

In order to evaluate whether glibenclamide can extend the therapeutic window during which induced hypothermia can protect against stroke, we subjected adult male Sprague-Dawley rats to middle cerebral artery occlusion (MCAO). We first verified the protective effects of hypothermia induced at 0, 2, 4 or 6h after MCAO onset, and then we assessed the effects of the combination of glibenclamide and hypothermia at 6, 8 or 10h after MCAO onset. At 24h after MCAO, we assessed brain edema, infarct volume, modified neurological severity score, Evans Blue leakage and expression of Sulfonylurea receptor 1 (SUR1) protein and pro-inflammatory factors. No protective effects were observed when hypothermia was induced too long after MCAO. At 6h after MCAO onset, hypothermia alone failed to decrease cerebral edema and infarct volume, but the combination of glibenclamide and hypothermia decreased both. The combination also improved neurological outcome, ameliorated blood-brain barrier damage and decreased levels of COX-2, TNF-α and IL-1ß. These results suggest that glibenclamide enhances and extends the therapeutic effects of delayed hypothermia against ischemia stroke, potentially by ameliorating blood-brain barrier damage and declining levels of pro-inflammatory factors.