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Abstract We reveal and justify, both theoretically and experimentally, the existence of a unifying criterion of the boiling crisis. This criterion emerges from an instability in the near-wall interactions of bubbles, which can be described as a percolation process driven by three fundamental boiling parameters: nucleation site density, average bubble footprint radius and product of average bubble growth time and detachment frequency. Our analysis suggests that the boiling crisis occurs on a well-defined critical surface in the multidimensional space of these parameters. Our experiments confirm the existence of this unifying criterion for a wide variety of boiling surface geometries and textures, two boiling regimes (pool and flow boiling) and two fluids (water and liquid nitrogen). This criterion constitutes a simple mechanistic rule to predict the boiling crisis, also providing a guiding principle for designing boiling surfaces that would maximize the nucleate boiling performance.

The optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constraints challenge existing optimizers in balancing between active power and reactive power, along with good trade-offs among many metrics. To address these difficulties, this paper develops a co-evolutionary algorithm to solve the constrained many-objective optimization problem of optimal power flow, which evolves three populations with different selection strategies. These populations are evolved towards different parts of the huge objective space divided by large infeasible regions, and the cooperation between them renders assistance to the search for feasible and Pareto-optimal solutions. According to the experimental results on benchmark problems and the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, the proposed algorithm is superior over peer algorithms in solving constrained many-objective optimization problems, especially the optimal power flow problems.

Abstract Computerized adaptive testing (CAT) targets to accurately assess the student’s proficiency in the required subject/area. The key issue is how to design a question selector that adaptively selects the best-suited questions for each student based on previous performance step by step. Most existing question selectors execute via greedy metric functions (e.g., question information and uncertainty), which can not effectively capture data characteristics. There also exist learning-based question selectors that redefine the CAT problem as a bilevel optimization problem, where the parameter learning of the question selector and the student proficiency estimation model are coupled, which is not flexible enough. To this end, in this paper, we propose a novel CAT framework with Decoupled Learning selector (DL-CAT). Specifically, we first use the currently estimated student ability and question characteristics as input and design a deep learning-based question selector to predict question selection scores. Then, to address the issue that there is no ground truth to measure the quality of the selected question, an approximate ground-truth and a pairwise rank loss function are specially designed to update the parameters of the question selector independently. Extensive experiments on two real datasets demonstrate that our proposed DL-CAT has certain advantages in effectiveness and significant advantages in efficiency.

BackgroundBetter outcome prediction could assist in reliable classification of the illnesses in neurological intensive care unit (ICU) severity to support clinical decision-making. We developed a multifactorial model including quantitative electroencephalography (QEEG) parameters for outcome prediction of patients in neurological ICU.MethodsWe retrospectively analyzed neurological ICU patients from November 2018 to November 2021. We used 3-month mortality as the outcome. Prediction models were created using a linear discriminant analysis (LDA) based on QEEG parameters, APACHEII score, and clinically relevant features. Additionally, we compared our best models with APACHEII score and Glasgow Coma Scale (GCS). The DeLong test was carried out to compare the ROC curves in different models.ResultsA total of 110 patients were included and divided into a training set (n=80) and a validation set (n = 30). The best performing model had an AUC of 0.85 in the training set and an AUC of 0.82 in the validation set, which were better than that of GCS (training set 0.64, validation set 0.61). Models in which we selected only the 4 best QEEG parameters had an AUC of 0.77 in the training set and an AUC of 0.71 in the validation set, which were similar to that of APACHEII (training set 0.75, validation set 0.73). The models also identified the relative importance of each feature.ConclusionMultifactorial machine learning models using QEEG parameters, clinical data, and APACHEII score have a better potential to predict 3-month mortality in non-traumatic patients in neurological ICU.

Submergence impedes photosynthesis and respiration but facilitates aerenchyma formation in bermudagrass. Still, the regulatory genes underlying these physiological responses are unclear in the literature. To identify differentially expressed genes (DEGs) related to these physiological mechanisms, we studied the expression of DEGs in aboveground and underground tissues of bermudagrass after a 7 d treatment under control (CK), shallow submergence (SS), and deep submergence (DS). Results show that compared with CK, 12276 and 12559 DEGs were identified under SS and DS, respectively. Among them, the DEGs closely related to the metabolism of chlorophyll biosynthesis, light-harvesting, protein complex, and carbon fixation were down-regulated in SS and DS. Meanwhile, a large number of DEGs involved in starch and sucrose hydrolase activities, glycolysis/gluconeogenesis, tricarboxylic acid (TCA) cycle, and oxidative phosphorylation were down-regulated in aboveground tissues of bermudagrass in SS and DS. Whereas in underground tissues of bermudagrass these DEGs were all up-regulated under SS, only beta-fructofuranosidase and α-amylase related genes were up-regulated under DS. In addition, we found that DEGs associated with ethylene signaling, Ca2+-ROS signaling, and cell wall modification were also up-regulated during aerenchyma formation in underground tissues of bermudagrass under SS and DS. These results provide the basis for further exploration of the regulatory and functional genes related to the adaptability of bermudagrass to submergence.

Abstract Existing studies have developed different indices based on various approaches including network connectivity, delay time and flow capacity, estimating the traffic reliability states from different angles. However, these indices mainly estimate traffic reliability from single view and rarely consider the combined effect of city traffic dynamics and underlying network structure. Based on percolation theory, Li et al. has developed a traffic reliability index to address this issue (Proc. Natl. Acad. Sci. USA 112(3):669-672, 2015) [1]. Here we compare this percolation-based index with one of the well-known index - congestion delay index (CDI). Using real traffic data of Beijing and Shenzhen (two large cities in China), we compare the two indices in the macroscopic trends and microscopic extreme values. The two indices are found to indicate the state of real-time traffic reliability in different consideration. Our results can be used for better evaluation of traffic system reliability and mitigation measures of traffic jams.

Purpose: To study the influence of miR-497-5p on osteogenic/odontogenic differentiation (OOD) of SCAP, and the signal route involved. Methods: Four groups were set up: miR-497-5p overexpression group (OEG), overexpression control OEC), miR-497-5p inhibition group, and inhibition control group. Alkaline phosphatase (ALP) activity was assayed, and calcified nodules measured. Protein expression levels of dentine salivary phosphoprotein (DSPP), collagen type I, ALP, osteoblast-related factors (Runx2, OSX and OPN) were also assayed. The mRNA expression levels of osteogenesis/dentin-related genes were determined. Results: ALP activity was significantly higher in miR-497-5p overexpression cells than in control, but was reduced, relative to inhibition control group (p < 0.05). The miR-497-5p OEG had significantly more calcified nodules than OEC (p < 0.05). There were markedly up-regulated protein expressions in cells of miR-497-5p OEG than in OCG. Furthermore, protein expressions of Smad2, Smad3 and Smad4 in cells of miR-497-5p OEG were significantly up-regulated, relative to those in OEC, but wer lower in miR-497-5p inhibitory cells than in inhibitory cells. Conclusion: MiR-497-5p enhances the OOD of SCAP via a mechanism involving TGF-β Smad pathway and Smurf2. Thus, Mir-497-5p may be used as a target for OOD-related drugs. [ABSTRACT FROM AUTHOR]

The concept of resilience can be realized in natural and engineering systems, representing the ability of a system to adapt and recover from various disturbances. Although resilience is a critical property needed for understanding and managing the risks and collapses of transportation systems, an accepted and useful definition of resilience for urban traffic as well as its statistical property under perturbations are still missing. Here, we define city traffic resilience based on the spatiotemporal clusters of congestion in real traffic and find that the resilience follows a scale-free distribution in 2D city road networks and 1D highways with different exponents but similar exponents on different days and in different cities. The traffic resilience is also revealed to have a scaling relation between the cluster size of the spatiotemporal jam and its recovery duration independent of microscopic details. Our findings of universal traffic resilience can provide an indication toward better understanding and designing of these complex engineering systems under internal and external disturbances. [ABSTRACT FROM AUTHOR]

We present the first experimental observations of scale-free behavior in the bubble footprint distribution during the boiling crisis of water, in pool and flow boiling conditions. We formulate a continuum percolation model that elucidates how the scale-free behavior emerges from the near-wall stochastic interaction of bubbles and provides a criterion to predict the boiling crisis. It also offers useful insights on how to engineer surfaces that enhance the critical heat flux limit. [ABSTRACT FROM AUTHOR]

Objective: To observe regulatory effect of Naoluoxintong formula (, NLXT) and its split prescriptions on vascular regeneration of rats suffering from cerebral ischemia-reperfusion (IR) syndrome of Qi deficiency with blood stasis (QDBS)., Methods: NLXT is the representative prescription of Yiqi Huoxue Tongluo decoction, and NLXT is divided into Yiqi herbs and Huoxue Tongluo herbs according to their efficacies. One hundred and eight specific-pathogen-free, clean-grade, Sprague-Dawley male rats were selected to prepare the classical rat model with QDBS due to middle artery ischemia-reperfusion using the multi-factor compound simulation approach. The animals were classified into sham operation (S), model (M), Nimodping (NMDP), NLXT, YQ and HXTL groups, each having 18 rats. Cerebral ischemia was reperfused after 2 h, and 24 h later, they were administered traditional Chinese medicine treatment for 14 d twice a day. Angiogenesis changes after NLXT administration to middle cerebral artery occlusion-reperfusion (MCAO/R) rats with QDBS were analyzed using the neurological deficit score and hematoxylin-eosin staining. Cerebral infarct area by 2,3,5-Triphenyltetrazolium chloride was detected, and the ultrastructure of the blood vessel in the ischemic frontoparietal cortex was observed by transmission electron microscopy. Angiopoietin 1 (Ang1), angiopoietin 2 (Ang2), vascular endothelial growth factor A (VEGFA), vascular endothelial growth factor receptor 2 (VEGFR2), platelet endothelial cell adhesion molecule-1 (CD31), angiopoietin receptor 2 (Tie2), and P38 mitogen-activated protein kinase (MAPK) protein levels in the frontal and parietal cortex were quantified by immunofluorescence, reverse transcription-polymerase chain reaction, and Western blotting assays., Results: Relative to the S group, VEGFA and VEGFR2 levels in the frontal and parietal cortex of group M were increased, and Ang1, Ang2, Tie2, CD31, and p38 MAPK levels remarkably increased ( P < 0.05); cerebral infarct area was significant and pathological morphology and ultrastructure damage was obvious. Relative to the group M, VEGFA, VEGFR2, CD31, Ang1, Ang2, and Tie2 expression of group NLXT and NMDP remarkably elevated ( P < 0.05) and infarct focus, pathological morphology and ultrastructure were significantly improved; VEGFA and VEGFR2 levels in the groups YQ and HXTL increased, and Ang1, Ang2, CD31, and Tie2 levels remarkably increased ( P < 0.05); p38 MAPK levels in the three treatment groups decreased ( P < 0.05). Relative to the group NLXT, the expression levels of p38 MAPK in group YQ and group HXTL were significantly increased, and the expression levels of other indicators were significantly decreased ( P < 0.05)., Conclusion: NLXT can promote the angiogenesis of the rat model of MCAO/R with QDBS by activating VEGFA and inhibiting P38 MAPK, and the effect is better than that of split prescription groups.