Development of a novel Ba2BiV3O11 photocatalyst with dual functions of both water oxidation and proton reduction performance.

To overcome the limitation of photocatalysts with dual functionality of water oxidation and proton reduction, we proposed a novel bismuth-based Ba2BiV3O11 (BBVO) photocatalyst, which can simultaneously drive the proton reduction reaction under UV light and water oxidation reaction under visible light. After doping with sulfur through an in situ vulcanization strategy, the light absorption and charge separation efficiencies of the sulfur-doped BBVO were significantly improved, thus boosting its oxygen evolution activity (64 µmol h-1) by more than 16 times compared with independent BBVO. The experimental results demonstrate that BBVO can be employed as a very promising bismuth-based photocatalyst for solar energy conversion.

A Deep Neural Network Regularization Measure: The Class-Based Decorrelation Method.

In response to the challenge of overfitting, which may lead to a decline in network generalization performance, this paper proposes a new regularization technique, called the class-based decorrelation method (CDM). Specifically, this method views the neurons in a specific hidden layer as base learners, and aims to boost network generalization as well as model accuracy by minimizing the correlation among individual base learners while simultaneously maximizing their class-conditional correlation. Intuitively, CDM not only promotes diversity among the hidden neurons, but also enhances their cohesiveness among them when processing samples from the same class. Comparative experiments conducted on various datasets using deep models demonstrate that CDM effectively reduces overfitting and improves classification performance.

Driving behavior characterization and traffic emission analysis considering the vehicle trajectory.

Based on the development of the concept of a resource-saving and environmentally friendly society, needing to develop low-carbon and sustainable urban transportation. Most of the pollutants come from the emissions of motor vehicle exhaust. Therefore, this paper analyzes the relationship between driving behavior and traffic emissions, to constrain driver behavior to reduce pollutant emissions. The GPS data are preprocessed by using Navicat for data integration, data screening, data sorting, etc., and then, the speed data are cleaned by using a combination of box-and-line plots and linear interpolation in SPSS. Second, this paper uses principal component analysis (PCA) to downsize 12 indicators such as average speed, average acceleration, and maximum speed and then adopts K-MEANS and K-MEDOIDS methods to cluster the driver's behavioral indicators, selects the aggregation method based on the clustering indexes optimally, and analyzes the driver's driving state by using the symbolic approximation aggregation method; finally, according to the above research results and combined with the MOVES traffic emission model to analyze the relationship between the driver's driving mode, driving state, and traffic emissions, the decision tree can be used to predict the unknown driving mode of the driver to estimate the degree of its emissions.

The impact of working memory capacity on collaborative learning in elementary school students.

Working memory capacity may be a critical factor that influences the effectiveness of collaborative learning; however, no studies have directly explored this effect. Using worked examples as learning tasks, Experiment 1 used a 2 (working memory capacity) × 2 (learning format) factorial design to examine the effects of collaborative learning versus individual learning of 4th-grade Chinese elementary school students with different working memory capacities. High-capacity learners displayed less working memory resource depletion and better transfer performance during collaborative learning than individual learning. In contrast, no differences were found among the low-capacity learners. Collaborative learning benefited high-capacity learners but not low-capacity learners, per our observations. To further optimize collaborative learning for low-capacity learners and expand the findings to heterogeneous collaborative learning, Experiment 2 adopted a 2 (member capacity) × 2 (group capacity) factorial design to explore the effects of member and group working memory capacity on collaborative learning in heterogeneous groups. High-capacity members displayed less working memory resource depletion and better far transfer performance in high-capacity groups compared to low-capacity groups. Simultaneously, all members had better near transfer performance in high-capacity groups compared to low-capacity groups. Both member and group working memory capacities influenced the effect of heterogeneous collaborative learning. However, low-capacity members only partially benefited from collaborative learning in high-capacity heterogeneous groups.

Influence of Nutritional Status on Prognosis of Stroke Patients With Dysphagia.

Context: Stroke is an acute cerebrovascular disease and a neurological disorder that occurs due to a cerebral arterial embolism and rupture. Acute stroke is often accompanied by dysphagia, which reduces patients' intake of food and nutrients, decreases their nutritional status, and affects their quality of life. Objective: The study intended to identify the demographic and clinical characteristics of stroke patients with dysphagia and to explore the relationship of those characteristics to nutritional status and prognosis. Methods: The research team retrospectively collected the clinical data of patients to compare the nutritional status and prognoses of patients with different demographic and clinical characteristics. Setting: The study took place in the Department of Neurology at the First People's Hospital of Shenyang in Shenyang, China. Participants: Participants were 789 stroke patients with dysphagia who had been admitted to the general ward of the neurology departments of hospitals of Grade 3 or higher in Northeast China between January 2019 and September 2020. Based on the results of the Nutrition Risk Screening (NRS-2002) and Subjective Global Assessment (SGA) scales at baseline, participants were enrolled in this study. Outcome Measures: The outcomes were the correlations between participants' demographic and clinical characteristics and their nutritional statuses and prognoses. The Modified Rankin Scale (mRS) was used to evaluate the prognosis of the patients at seven days and three months after participants' enrollment in the study. Using the SPSS 26.0, a t test, chi-square test, and F test were performed to analyze and verify the presence of fundamental differences in baseline characteristics between participants with good nutrition and those with poor nutrition. Also, a statistical correlation analysis was performed. Results: The study showed that participants with different nutritional levels had statistically significant differences in the presence or absence of infections and body temperature and scores on the Standardized Swallowing Assessment (SSA) and National Institutes of Health Stroke Scale (NIHSS), with all P < .001. At baseline seven days after enrollment, the prognoses of participants were significantly different for different previous histories of stroke (P < .001), family history of stroke (P = .005), presence or absence of infections (P < .001), body temperature (P < .001), and SSA (P < .001) and NIHSS (P < .001) scale scores. At three months after enrollment, the prognoses of participants were significantly different for previous history of stroke (P = .003), different body temperatures (P < .001), presence or absence of infections(P < .001), and SSA (P < .001) and NIHSS (P < .001) scale scores. Age, gender, family history of stroke, smoking, alcohol consumption, previous history of stroke, education level, SSA scale score, NIHSS scale score, body mass index (BMI), body temperature, and infection were adjusted in the model. Nutritional status as classified by NRS-2002 and SGA was significantly correlated with prognosis (P < .001). The prognosis of stroke patients with dysphagia was associated with nutritional status by unconditional logistic regression. Conclusion: The prognosis of stroke patients with dysphagia is related to their nutritional status. A better nutritional status indicates the better prognosis, and vice versa. In clinical treatment, attention should be paid to use of a nutritional intervention.

Guiding the behaviors of human umbilical vein endothelial cells with patterned silk fibroin films.

Silk fibroin is an ideal blood vessel substitute due to its advantageous qualities including variable size, good suture retention, low thrombogenicity, non-toxicity, non-immunogenicity, biocompatibility, and controllable biodegradation. In this study, silk fibroin films with a variety of surface patterns (e.g. square wells, round wells plus square pillars, square pillars, and gratings) were prepared for in vitro characterization of human umbilical vein endothelial cell's (HUVEC) response. The affects of biomimetic length-scale topographic cues on the cell orientation/elongation, proliferation, and cell-substrate interactions have been investigated. The density of cells is significantly decreased in response to the grating patterns (70±3nm depth, 600±8nm pitch) and the square pillars (333±42nm gap). Most notably, we observed the contact guidance response of filopodia of cells cultured on the surface of round wells plus square pillars. Overall, our data demonstrates that the patterned silk fibroin films have an impact on the behaviors of human umbilical vein endothelial cells.