Interpersonal Information Platform Reinforces The Significant Nature of Structure Cost in Latent Terrorist Activities—A Trial of The Biggest IM & Web Portal From China

This paper empirically examines a state to emerge objectively a “structure” characteristic in communicating with each latent terrorist on an Interpersonal Information Platform (IIP), and examines what factors lead to the “structure” characteristic intensified, drawing on two tests that guides the phenomenon of “structure” characteristic in disseminating and sharing of terrorism information through IIP of QQ group and NETEASE web portal from China. The interesting research results are informed of the administering authority could optimize the structure cost and value of posting to adjust the structure characteristic and behavior of posting in order to keep within limits in latent terrorist activities

Investigating rare pathogenic/likely pathogenic exonic variation in bipolar disorder.

Funder: U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)Funder: Dalio Foundation; doi: https://doi.org/10.13039/100009834Funder: Wayne and Gladys Valley Foundation; doi: https://doi.org/10.13039/100001370Funder: Robert Wood Johnson Foundation (RWJF); doi: https://doi.org/10.13039/100000867Funder: U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging)Funder: The Dalio FoundationBipolar disorder (BD) is a serious mental illness with substantial common variant heritability. However, the role of rare coding variation in BD is not well established. We examined the protein-coding (exonic) sequences of 3,987 unrelated individuals with BD and 5,322 controls of predominantly European ancestry across four cohorts from the Bipolar Sequencing Consortium (BSC). We assessed the burden of rare, protein-altering, single nucleotide variants classified as pathogenic or likely pathogenic (P-LP) both exome-wide and within several groups of genes with phenotypic or biologic plausibility in BD. While we observed an increased burden of rare coding P-LP variants within 165 genes identified as BD GWAS regions in 3,987 BD cases (meta-analysis OR = 1.9, 95% CI = 1.3-2.8, one-sided p = 6.0 × 10-4), this enrichment did not replicate in an additional 9,929 BD cases and 14,018 controls (OR = 0.9, one-side p = 0.70). Although BD shares common variant heritability with schizophrenia, in the BSC sample we did not observe a significant enrichment of P-LP variants in SCZ GWAS genes, in two classes of neuronal synaptic genes (RBFOX2 and FMRP) associated with SCZ or in loss-of-function intolerant genes. In this study, the largest analysis of exonic variation in BD, individuals with BD do not carry a replicable enrichment of rare P-LP variants across the exome or in any of several groups of genes with biologic plausibility. Moreover, despite a strong shared susceptibility between BD and SCZ through common genetic variation, we do not observe an association between BD risk and rare P-LP coding variants in genes known to modulate risk for SCZ

The implications of internet-based Chinese language courses on online classes

Interactionist and social-cultural perspectives on second language acquisition suggest that interactions between teachers and students offer promising avenues for acquiring Chinese as a second language, which the vast majority of international students consider difficult. Computer-mediated communication is far safer than face-to-face encounters during the present pandemic. Three aims are being investigated here. It is important to first analyze the differences between traditional classroom and online learning by different modes, then analyze the various ways teachers use computer-mediated communication, and finally analyze the challenges and opportunities presented by online Chinese as a second language courses using qualitative research methods. Three teachers and 84 students are analyzed statistically in terms of their multimodal interactions, and the quality of their weekly classroom exchanges is assessed through an interpretive analysis of questionnaire data, all in the name of a mixed-methods approach. Particular attention was paid to the challenges of online tutoring for students, the discrepancy between instructor and student understandings, and the use of several teaching strategies with international students. The online classroom environment places unique demands on the quality of student-teacher communication. Different strategies must be used when teaching non-native speakers of Chinese as a second language compared to teaching in a traditional classroom setting

Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information

Problem definition: The intelligent transportation system (ITS) plays a vital role in the construction of smart cities. For the past few years, traffic flow prediction has been a hot study topic in the field of transportation. Facing the rapid increase in the amount of traffic information, finding out how to use dynamic traffic information to accurately predict its flow has become a challenge. Methodology: Thus, to figure out this issue, this study put forward a multistep prediction model based on a convolutional neural network and bidirectional long short-term memory (BILSTM) model. The spatial characteristics of traffic data were considered as input of the BILSTM model to extract the time series characteristics of the traffic. Results: The experimental results validated that the BILSTM model improved the prediction accuracy in comparison to the support vector regression and gated recurring unit models. Furthermore, the proposed model was comparatively analyzed in terms of mean absolute error, mean absolute percentage error, and root mean square error, which were reduced by 30.4%, 32.2%, and 39.6%, respectively. Managerial implications: Our study provides useful insights into predicting the short-term traffic flow on highways and will improve the management of traffic flow optimization

Short-Term Traffic Flow Prediction Based on a K-Nearest Neighbor and Bidirectional Long Short-Term Memory Model

In the previous research on traffic flow prediction models, most of the models mainly studied the time series of traffic flow, and the spatial correlation of traffic flow was not fully considered. To solve this problem, this paper proposes a method to predict the spatio-temporal characteristics of short-term traffic flow by combining the k-nearest neighbor algorithm and bidirectional long short-term memory network model. By selecting the real-time traffic flow data observed on high-speed roads in the United Kingdom, the K-nearest neighbor algorithm is used to spatially screen the station data to determine the points with high correlation and then input the BILSTM model for prediction. The experimental results show that compared with SVR, LSTM, GRU, KNN-LSTM, and CNN-LSTM models, the model proposed in this paper has better prediction accuracy, and its performance has been improved by 77%, 19%, 18%, 22%, and 13%, respectively. The proposed K-nearest neighbor-bidirectional long short-time memory model shows better prediction performance

GOVERNANCE OF GLOBAL SUPPLY CHAINS VULNERABILITY BY BUSINESS-BASED INTERORGANIZATIONAL INFORMATION PLATFORM

National Nature Science Foundation of China [G021004, 71171054]; Major Special Project of Fujian Province [2004HZ02]The paper introduces the business-based interorganizational information platform (IOP) and analyzes the feasibility and mechanism of business-based IOP governing global supply chains vulnerability, and then aims to develop a risk evaluation software under reliable algorithm to appraise the capability of an interorganizational information platform resisting to global supply chains risks that supports platform users and providers to make decisions. The paper respectively starts with a basic conceptual model of global supply chains vulnerability and a conceptual model of global supply chains vulnerability in business-based IOP, and then gives the simulation model of governance of global supply chain vulnerability in business-based IOP; then has a discussion with the beneficial model of governing global supply chains vulnerability by using business-based IOP or not. The results of research: (1) If given the ratio of expense per income on global supply chains using business-based IOP, we can estimate the costs to take precautions against risks that decides to the maximum value of the average income of per transaction on global supply chains using business-based IOP. (2) If given total income of transaction on global supply chains using business-based IOP, we can estimate the maximum value of the ratio of expense per income on global supply chains using business-based IOP, which would help to make pricing policy for IOP service provider

Information measurement model based on ordinal utility

Conference Name:2011 International Conference on Future Computer Sciences and Application, ICFCSA 2011. Conference Address: Hong Kong, China. Time:June 18, 2011 - June 19, 2011.Hong Kong Education SocietyInformation measurement is based on cardinal utility whether probability information measurement or grammatical information measurement, or uncertainty information measurement. But for managers, they care about the value of information for decision-making, not what quantity of information itself. The article uses ordinal utility theory to measure information, and to sort of information utility, thereby reduces the complex and cumbersome calculation process of information for decision-making in management. ? 2011 IEEE

Valuation of Land-Use/Land-Cover-Based Ecosystem Services in Afghanistan—An Assessment of the Past and Future

Being one of the weakest economies in the world, livelihoods in Afghanistan remain highly dependent on local ecosystem services. However, the risk of ecosystem services degradation in Afghanistan over the past two decades has significantly increased, mainly due to rapid changes in land-use and land-cover (LULC). As such, policy makers must be able to estimate the impact of LULC changes on various ecosystem services. By utilizing GlobeLand30 land cover products for 2000, 2010 and 2020, and by adopting the value transfer method, this study assessed the ecosystem services value (ESV) changes in response to the changes of LULC in Afghanistan. Additionally, the dynamics of the land system (DLS) model was innovatively coupled with linear programming to predict likely scenarios of ESV changes by 2030. The predicted results were also validated against actual land cover and achieved a Kappa value of 0.78. The results showed that over the 20-year period, ecologically important LULC categories such as forest, water bodies and grassland were severely unstable and rapidly decreasing in scope. These LULC types were being threatened by agricultural, built-up and unused lands. During this period, we estimated a decrease in the total ESV from 161 billion USD in 2000 to 152.27 billion USD in 2020. About 92% of this decrease was shared by supporting and provisioning services. The simulated scenarios also showed that ESV will likely further decrease under Business-As-Usual (BAU), and Rapid Economic Development (RED) scenarios. Positively, an Environmental Protection (ENP) scenario is predicted, with a 4.5% increase in ESV by 2030. However, achieving this scenario requires the enforcement of strict environmental protection measures

Valuation of Land-Use/Land-Cover-Based Ecosystem Services in Afghanistan—An Assessment of the Past and Future

Being one of the weakest economies in the world, livelihoods in Afghanistan remain highly dependent on local ecosystem services. However, the risk of ecosystem services degradation in Afghanistan over the past two decades has significantly increased, mainly due to rapid changes in land-use and land-cover (LULC). As such, policy makers must be able to estimate the impact of LULC changes on various ecosystem services. By utilizing GlobeLand30 land cover products for 2000, 2010 and 2020, and by adopting the value transfer method, this study assessed the ecosystem services value (ESV) changes in response to the changes of LULC in Afghanistan. Additionally, the dynamics of the land system (DLS) model was innovatively coupled with linear programming to predict likely scenarios of ESV changes by 2030. The predicted results were also validated against actual land cover and achieved a Kappa value of 0.78. The results showed that over the 20-year period, ecologically important LULC categories such as forest, water bodies and grassland were severely unstable and rapidly decreasing in scope. These LULC types were being threatened by agricultural, built-up and unused lands. During this period, we estimated a decrease in the total ESV from 161 billion USD in 2000 to 152.27 billion USD in 2020. About 92% of this decrease was shared by supporting and provisioning services. The simulated scenarios also showed that ESV will likely further decrease under Business-As-Usual (BAU), and Rapid Economic Development (RED) scenarios. Positively, an Environmental Protection (ENP) scenario is predicted, with a 4.5% increase in ESV by 2030. However, achieving this scenario requires the enforcement of strict environmental protection measures