Fullerenes with the maximum Clar number

The Clar number of a fullerene is the maximum number of independent resonant hexagons in the fullerene. It is known that the Clar number of a fullerene with n vertices is bounded above by [n/6]-2. We find that there are no fullerenes whose order n is congruent to 2 modulo 6 attaining this bound. In other words, the Clar number for a fullerene whose order n is congruent to 2 modulo 6 is bounded above by [n/6]-3. Moreover, we show that two experimentally produced fullerenes C80:1 (D5d) and C80:2 (D2) attain this bound. Finally, we present a graph-theoretical characterization for fullerenes, whose order n is congruent to 2 (respectively, 4) modulo 6, achieving the maximum Clar number [n/6]-3 (respectively, [n/6]-2)

Testing Recognition Memory Models with Forced-choice Testing

People’s ability to call an experienced item “old” and a novel item “new” is recognition memory. Recognition memory is usually studied by first asking participants to learn a list of words and then make judgments of old (studied) or new (not studied) for test words. It has long been debated whether the underlying process of recognition memory is continuous or discrete. Two types of models are compared specifically that assume either discrete or continuous information states: the 2-high threshold (2HT) model and the unequal variance signal detection (UVSD) model, respectively. Researchers have used the receiver operation characteristic (ROC) function and response time (RT) data to test between the two models. However, both methods have provided evidence for 2HT and UVSD, and the debate has not come to consensus. In this study, we used an alternative approach to look into this issue. After studying the words, participants first made “old/new” judgment for each single test item. Then, if there were falsely identified items, each of them was randomly paired with a correctly identified word of the same response. Participants were asked to choose the studied word from the word pair. Simulation and experimental results were able to discriminate the 2HT and UVSD model. Experimental results showed that the UVSD model fitted the data better than the 2HT model. The forced-choice test paradigm provided an effective way to test between the 2HT and UVSD models

Predictors of father involvement : the role of early life events and stressors.

This study utilized the National Longitudinal Survey of Youth 1997 (NLSY97) dataset to examine the effect of men’s early life stressful events and their father involvement with their new biological child(ren). The problems associated with low level of father involvement or even father absence in the country followed by the dearth of studying men who experienced stressful events during childhood were first discussed. A series of factors in the literature that can affect the level of father involvement and various of childhood stressful events were also presented. Following this, the characteristics of study subjects’ demographics, household information, men’s crime history, substance use history, early life stressful events, and men’s father/figure were studied. A logistic regression analysis was used to determine the best predictors of the level of men’s involvement with their new biological child(ren). The best predictors were age when a man became a father and whether he had been arrested in childhood. Future research is needed to evaluate fathering activities representative of the direct and indirect engagement dimensions

Internet-based Distributed Collaborative Engineering Analysis

This paper proposes an engineering analysis environment that allows remote users to conduct three-dimensional finite element analysis collaboratively through the Internet. Java and Java 3D were chosen to develop the working prototype due to their advantages of platform-independence and network supporting. The environment allows remote users to work collaboratively on the same analysis object simultaneously. It reads the geometric data generated by the collaborative geometric modeling environment. The user can interact directly with the geometric model to perform operations, such as applying, editing, and deleting boundary conditions and forces. The operations are propagated among the team members, which creates a distributed shared environment. The commands are transmitted instead of the generated data, and thus the network traffic associated with the collaboration is minimized. Different from classical server/client models,# the environment adopts a strategy in which the client-side application has full analysis capabilities while the server only manages communication. The essential features for distributed collaboration are discussed. The actual design consideration of the working prototype is presented to help illustrate the complexity and development of the collaborative environment. The environment is open to the public at www.vcity.ou.edu.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Prognosis Prediction of Stroke based on Machine Learning and Explanation Model

The prognosis prediction of stroke is of great significance to its prevention and treatment. This paper used machine learning to predict stroke prognosis, and use SHAP method to make feature importance and single sample analysis. Firstly, feature engineering, use Borderline-SMOTE algorithm to deal with data imbalance, use Support Vector Machine(SVM) to build a prognostic prediction model, and use Random Forest(RF), Decision Tree(DT), Logistic Regression(LR) for comparative analysis, and find the performance of SVM after feature engineering better than other models, the accuracy, specificity, F1 score, AUC value reach 0.8306, 0.8356, 0.8415 and 0.9140. Then, the model was further analyzed for explainability, and it was found that the top three causes of the disease were Glasgow Coma Score, NIHSS and atrial fibrillation. Finally, try to analysis a single sample, which is performed to determine that the patient is a low-risk patient, and suffering from atrial fibrillation is the largest potential risk factor for the patient