Conflict types, resolution, and relational satisfaction: A U.S.-China investigation

The purpose of this study is to examine the relationships of conflict types with conflict resolution and relational satisfaction in the U.S. and Chinese cultures, and to explore the moderating effects of culture in the relationships of conflict types and resolution with relational satisfaction. Four main findings are reported. First, task conflict is more likely to be resolved than relationship conflict in both cultures. Second, Chinese experience less relational satisfaction than Americans during conflict. Third, task conflict is associated with a higher level of relational satisfaction than relationship conflict in both cultures; similarly, resolved conflict is associated with a higher level of relational satisfaction than unresolved conflict. Fourth, culture mediates the effects of both conflict types and conflict resolution on relational satisfaction

Determining E-Bike Drivers’ Decision-Making Mechanisms during Signal Change Interval Using the Hidden Markov Driving Model

Rapidly increasing e-bike use in China has resulted in new traffic problems including rising accident rates at intersections related to e-bike drivers’ decision-making during multiple signal phases. Traditional one-step decision models (such as GHM) lack randomness and cannot adequately model e-bike drivers’ complex behavior. Therefore, this study used a Hidden Markov Driving Model (HMDM) to analyze e-bike drivers’ decision-making process based on high-resolution trajectory data. Video data were collected at three intersections in Shanghai and processed for use in the HMDM model. Five decision types (pass, stop, stop-pass, pass-stop, and multiple) composed of speed and acceleration/deceleration information were defined and used to analyze the impact of flashing green signals on e-bike drivers’ behavior and decision-making processes. Approximately 40% of drivers made multiple decisions during the flashing green and yellow signal phases, in contrast to the traditional GHM model assumption that drivers only make one decision. Distance from stop-line had the most obvious influence on the number of decisions. The use of flashing green signals nearly eliminated the dilemma zone for e-bike drivers but enlarged the option zone, inducing more stop/pass decisions. HMDM can be applied to improve the accuracy of traffic simulation, the fine design of traffic signals, the stability analysis of traffic control schemes, and so on. Document type: Articl

Prediction of recurrence of ischemic stroke within 1 year of discharge based on machine learning MRI radiomics

PurposeThis study aimed to investigate the value of a machine learning-based magnetic resonance imaging (MRI) radiomics model in predicting the risk of recurrence within 1 year following an acute ischemic stroke (AIS).MethodsThe MRI and clinical data of 612 patients diagnosed with AIS at the Second Affiliated Hospital of Nanchang University from March 1, 2019, to March 5, 2021, were obtained. The patients were divided into recurrence and non-recurrence groups according to whether they had a recurrent stroke within 1 year after discharge. Randomized splitting was used to divide the data into training and validation sets using a ratio of 7:3. Two radiologists used the 3D-slicer software to label the lesions on brain diffusion-weighted (DWI) MRI sequences. Radiomics features were extracted from the annotated images using the pyradiomics software package, and the features were filtered using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis. Four machine learning algorithms, logistic regression (LR), Support Vector Classification (SVC), LightGBM, and Random forest (RF), were used to construct a recurrence prediction model. For each algorithm, three models were constructed based on the MRI radiomics features, clinical features, and combined MRI radiomics and clinical features. The sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were used to compare the predictive efficacy of the models.ResultsTwenty features were selected from 1,037 radiomics features extracted from DWI images. The LightGBM model based on data with three different features achieved the best prediction accuracy from all 4 models in the validation set. The LightGBM model based solely on radiomics features achieved a sensitivity, specificity, and AUC of 0.65, 0.671, and 0.647, respectively, and the model based on clinical data achieved a sensitivity, specificity, and AUC of 0.7, 0.799, 0.735, respectively. The sensitivity, specificity, and AUC of the LightGBM model base on both radiomics and clinical features achieved the best performance with a sensitivity, specificity, and AUC of 0.85, 0.805, 0.789, respectively.ConclusionThe ischemic stroke recurrence prediction model based on LightGBM achieved the best prediction of recurrence within 1 year following an AIS. The combination of MRI radiomics features and clinical data improved the prediction performance of the model

The oyster genome reveals stress adaptation and complexity of shell formation

The Pacific oyster Crassostrea gigas belongs to one of the most species-rich but genomically poorly explored phyla, the Mollusca. Here we report the sequencing and assembly of the oyster genome using short reads and a fosmid-pooling strategy, along with transcriptomes of development and stress response and the proteome of the shell. The oyster genome is highly polymorphic and rich in repetitive sequences, with some transposable elements still actively shaping variation. Transcriptome studies reveal an extensive set of genes responding to environmental stress. The expansion of genes coding for heat shock protein 70 and inhibitors of apoptosis is probably central to the oyster's adaptation to sessile life in the highly stressful intertidal zone. Our analyses also show that shell formation in molluscs is more complex than currently understood and involves extensive participation of cells and their exosomes. The oyster genome sequence fills a void in our understanding of the Lophotrochozoa. © 2012 Macmillan Publishers Limited. All rights reserved

Unravelling Nutrients and Carbon Interactions in an Urban Coastal Water during Algal Bloom Period in Zhanjiang Bay, China

Nutrients and carbon play important roles in algal bloom and development. However, nutrients and carbon interactions in the period of the spring algal bloom are not well understood. The aim of this study is to explore the nutrients and carbon interactions in the period of the spring algal bloom covering an urban Jinsha Bay (JSB) coastal water in Zhanjiang Bay (South China Sea) using in situ multidiscipline observation. The results showed that the average concentration of total nitrogen (TN), total phosphorus (TP), and dissolved silicon (DSi) was 97.79 ± 26.31 μmol/L, 12.84 ± 4.48 μmol/L, and 16.29 ± 4.00 μmol/L in coastal water, respectively. Moreover, the average concentration of total dissolved carbon (TDC), dissolved inorganic carbon (DIC) and organic carbon (DOC) in JSB was 2187.43 ± 195.92 μmol/L, 1516.25 ± 133.24 μmol/L, and 671.13 ± 150.81 μmol/L, respectively. Furthermore, the main dominant species were Phaeocystis globosa and Nitzschia closterium during the spring algal bloom. Additionally, the correlation analysis showed salinity (S) was significantly negatively correlated with nutrients, indicating that nutrients derived from land-based sources sustained spring algal bloom development. However, as the major fraction of TDC, DIC was significantly positively correlated with S, which was mainly derived from marine sources. Besides, the algal density showed a significant positive correlation with temperature (T) (p < 0.001) and dissolved oxygen (DO) (p < 0.001), but a significant negative correlation with DIC (p < 0.05), suggesting that spring algal blooms may be simulated by water T increase, and then large amounts of DIC and nutrients were adsorbed, accompanying DO release through photosynthesis in coastal water. This study revealed nutrients and carbon interactions in the spring algal bloom of urban eutrophic coastal water, which has implications for understanding the nutrients and carbon biogeochemical cycle and algal bloom mitigation under climate change and anthropogenic pressures in the future

Teacher clarity: Effects on classroom communication apprehension, student motivation, and learning in Chinese college classrooms

Teacher clarity is central to overall teaching effectiveness and student learning. The purpose of this study is to extend the line of research on teacher clarity from U. S. classrooms to Chinese classrooms. Specifically, it investigates the effects of teacher clarity on classroom communication apprehension, student motivation, and affective and cognitive learning in Chinese college classrooms. Pearson correlation suggests that teacher clarity is associated negatively with classroom communication apprehension, but positively with student motivation to learn and affective and cognitive learning in Chinese classrooms

Teacher Burnout and Turnover Intention in a Chinese Sample: The Mediating Role of Teacher Satisfaction

The purpose of this study is to examine the differences in burnout and turnover intention between Chinese college instructors and pre-college teachers, the relationship of teacher burnout with turnover intention, and the mediating role of teacher satisfaction in the effects of teacher burnout on turnover intention. Three major findings are reported: (a) Chinese pre-college teachers report a higher level of emotional exhaustion and depersonalization than college instructors, but there are no significant differences in reduced accomplishments; (b) there is a positive correlation between teacher burnout and turnover intention; and (c) teacher satisfaction mediates the effects of teacher burnout on turnover intention