Multiple Melting Endotherms of Syndiotactic Polystyrene in β Crystalline Form

A series of syndiotactic polystyrene (SPS) samples in β crystalline form were prepared by cooling from the melt at various cooling rates. The effects of cooling rate from the melt, and DSC heating rate on the multiple melting behaviors of β crystals were investigated by differential scanning calorimetry (DSC) and modulated differential scanning calorimetry (MDSC), from which the nature.of the multiple melting behavior was ascribed to the occurring of a recrystallization process.

A mixed stochastic user equilibrium model considering influence of advanced traveller information systems in degradable transport network

Advanced traveler information systems （ATIS） can not only improve drivers＇ accessibility to the more accurate route travel time information, but also can improve drivers＇ adaptability to the stochastic network capacity degradations. In this paper, a mixed stochastic user equilibrium model was proposed to describe the interactive route choice behaviors between ATIS equipped and unequipped drivers on a degradable transport network. In the proposed model the information accessibility of equipped drivers was reflected by lower degree of uncertainty in their stochastic equilibrium flow distributions, and their behavioral adaptability was captured by multiple equilibrium behaviors over the stochastic network state set. The mixed equilibrium model was formulated as a fixed point problem defined in the mixed route flows, and its solution was achieved by executing an iterative algorithm. Numerical experiments were provided to verify the properties of the mixed network equilibrium model and the efficiency of the iterative algorithm.

A Shared Neural Node for Multiple Innate Behaviors in Drosophila

The nervous system orchestrates diverse behaviors such as reproduction, sleep, feeding, and aggression, and selects a single behavior for execution at any given time. This requires neural mechanisms for behavioral selection sensitive to both internal physiological states and external environmental conditions. For example, hungry animals

BA-GNN: Behavior-aware graph neural network for session-based recommendation

Session-based recommendation is a popular research topic that aims to predict users’next possible interactive item by exploiting anonymous sessions.The existing studies mainly focus on making predictions by considering users’single interactive behavior.Some recent efforts have been made to exploit multiple interactive behaviors,but they generally ignore the influences of different interactive behaviors and the noise in interactive sequences.To address these problems,we propose a behavior-aware graph neural network for session-based recommendation.First,different interactive sequences are modeled as directed graphs.Thus,the item representations are learned via graph neural networks.Then,a sparse self-attention module is designed to remove the noise in behavior sequences.Finally,the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session representations.Experimental results on two public datasets show that our proposed method outperforms all competitive baselines.The source code is available at the website of GitHub.

Clustering of Risk Behaviors and their Social Determinants among Primary School Learners in Beijing, China：A Cross-sectional Study

Background：Studies in developed countries reveal that poor lifestyle choices triggering diseases typically cluster among children.However,there is insufficient evidence on the clustering of risk behaviors among children in developing countries.This study aimed to determine the clustering of risk behaviors and their social determinants among 4th-and 5th-grade learners in Beijing,China.Methods：The sample comprised of 967 learners from six primary schools enrolled migrant and resident learners by two-stage stratified cluster sampling.Prevalence denoted the risk behaviors and their clustering.A log-linear model was used to explore the clustering patterns.Ordinal logistic regression determined the influence of demographic characteristics,school environment,and family context on behavioral clustering.Results：The prevalence of none,one,two,and three or more risk factors was 61.2%,20.0%,10.8%,and 8.1% for infectious diseases and 46.0%,30.6%,15.4%,and 8.0% for chronic diseases,respectively.Some behaviors appeared dependent and were more likely to be observed together.The three most influential factors for infectious diseases were school type （odds ratio [OR] =4.47,95% confidence interval [CI] 3.00-6.66）,school located in an inner suburb （OR =0.27,95% CI 0.18-0.38）,and gender （OR =0.56,95% CI 0.42-0.74）.Regarding risk behaviors for chronic diseases,clustering was not associated with household registration status and number of appliances,but was significantly associated with school type （OR =5.36,95% CI 3.72-7.73）,school located in an inner suburb （OR =0.59,95% CI 0.43-0.81）,and gender （OR =0.61,95% CI 0.47-0.78）.School environment variables were the most significant contributor to the number of risk behaviors.Conclusions：The characteristics of schools enrolling migrants and residents influenced the number of risk behaviors.Therefore,improved school conditions and integrated behavioral interventions are particularly recommended for health promotion.

Behavior of reinforced concrete structural walls with various opening locations: experiments and macro model

Two experimental tests of three-storied reinforced concrete structural walls having large openings were performed.Based on an original macro model,a multiple modified macro-model was proposed to develop a simple method to design a reinforced concrete structural wall with large openings and various opening locations.The interaction between reinforcement ties and concrete struts formed along the perimeter of openings was neglected in the original model.However,the strut-and-tie node was proposed to take account of such interaction in the proposed model.The predicted behavior of two specimens using such a proposed model was compared with the experimental results.It is shown that the behavior of structural walls with large openings could be modeled well using the proposed model.Moreover,the study indicates that the proposed model is applicable even in cases of multi-story structural walls having large openings and various opening locations.