Game Analysis of Government\u27s Response to Network Public Opinion

The network public opinion has brought certain challenges to the authority of the government and the control of social order. Based on the evolutionary game theory, this paper establishes the network public opinion game model of government, netizens, Internet celebrity, network media, and uses the replication dynamic equation to analyze the game process of participants. Finally, according to the evolutionary stabilization strategy, put forward relevant countermeasures to the government in dealing with network public opinion of emergencies

RVD2: an ultra-sensitive variant detection model for low-depth heterogeneous next-generation sequencing data

MOTIVATION: Next-generation sequencing technology is increasingly being used for clinical diagnostic tests. Clinical samples are often genomically heterogeneous due to low sample purity or the presence of genetic subpopulations. Therefore, a variant calling algorithm for calling low-frequency polymorphisms in heterogeneous samples is needed. RESULTS: We present a novel variant calling algorithm that uses a hierarchical Bayesian model to estimate allele frequency and call variants in heterogeneous samples. We show that our algorithm improves upon current classifiers and has higher sensitivity and specificity over a wide range of median read depth and minor allele fraction. We apply our model and identify 15 mutated loci in the PAXP1 gene in a matched clinical breast ductal carcinoma tumor sample; two of which are likely loss-of-heterozygosity events

Improved IEEE 802.11 point coordination function considering fiber-delay difference in distributed antenna systems

In this paper, we present an improved IEEE 802.11 wireless local-area network (WLAN) medium access control (MAC) mechanism for simulcast radio-over-fiber-based distributed antenna systems where multiple remote antenna units (RAUs) are connected to one access point (AP). In the improved mechanism, the fiber delay between RAUs and central unit is taken into account in a modification to the conventional point coordination function (PCF) that achieves coordination by a centralized algorithm. Simulation results show that the improved PCF outperforms the distributed coordination function (DCF) in both the basic-access and request/clear-to-send modes in terms of the total throughput and the fairness among RAU

In the global race over 5G, liberalisation and regulatory independence are key

A global race over 5G is raging, but there has been little systematic exploration of the telecom sector with a global perspective. Rabah Arezki, Vianney Dequiedt, Rachel Yuting Fan, and Carlo Maria Rossotto use a novel ranking in the adoption of telecom technology standards around the world, documenting the complementarity between telecom liberalisation and regulatory independence in driving a sustained pace of technology adoption. They also show a positive and economically significant effect of telecom adoption on stock returns, pointing to significant spillovers of telecom to the rest of the economy

Field Research and Application of Tracking Adjustment Technology While Drilling for a Horizontal Well in Block A

A reservoir in Block A of an oil field is a new layer series in the new area, and a horizontal well has been deployed for the first time around the development of the target layer in the third section of the reservoir. Compared with the oil layers in the old area, the structure of the target layer in Block A is more complex, the reservoir changes rapidly, the prediction of sand-laden mud reservoir is difficult, and the tracking adjustment of horizontal wells while drilling is difficult. In view of the above problems, in order to ensure the drilling effect, we have carried out the basic geological research of the combination of well and earthquake, carried out the study of the correlation between rock and electricity and the combination of well and earthquake, carried out the multi-technology combination, innovatively formed the supporting technical methods of fine fault, micro-amplitude structure identification and reservoir prediction, established the fine three-dimensional geological model, optimized the well location and trajectory optimization design of favorable blocks, According to the technical process of “making plans before drilling, timely adjustment during drilling, and re-recognition after drilling”, 7 horizontal wells have been tracked and adjusted while drilling, with an average oil reservoir drilling rate of 85.3%, which has achieved good drilling results and increased geological reserves and productivity

The Impact of Modifications in Forest Litter Inputs on Soil N2O Fluxes: A Meta-Analysis

Although litter can regulate the global climate by influencing soil N2O fluxes, there is no consensus on the major drivers or their relative importance and how these impact at the global scale. In this paper, we conducted a meta-analysis of 21 global studies to quantify the impact of litter removal and litter doubling on soil N2O fluxes from forests. Overall, our results showed that litter removal significantly reduced soil N2O fluxes (−19.0%), while a doubling of the amount of litter significantly increased soil N2O fluxes (30.3%), based on the results of a small number of studies. Litter removal decreased the N2O fluxes from tropical forest and temperate forest. The warmer the climate, the greater the soil acidity, and the larger the soil C:N ratio, the greater the impact on N2O emissions, which was particularly evident in tropical forest ecosystems. The decreases in soil N2O fluxes associated with litter removal were greater in acid soils (pH 15. Litter removal decreased soil N2O fluxes from coniferous forests (−21.8%) and broad-leaved forests (−17.2%) but had no significant effect in mixed forests. Soil N2O fluxes were significantly reduced in experiments where the duration of litter removal was <1 year. These results showed that modifications in ecosystem N2O fluxes due to changes in the ground litter vary with forest type and need to be considered when evaluating current and future greenhouse gas budgets.Beijing Academy of Agriculture and Forestry Sciences (BAAFS)Natural Science Foundation of Changsh

Deep learning radiomic analysis of DCE-MRI combined with clinical characteristics predicts pathological complete response to neoadjuvant chemotherapy in breast cancer

ObjectiveThe aim of this study was to develop and validate a deep learning-based radiomic (DLR) model combined with clinical characteristics for predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in breast cancer. For early prediction of pCR, the DLR model was based on pre-treatment and early treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.Materials and methodsThis retrospective study included 95 women (mean age, 48.1 years; range, 29–77 years) who underwent DCE-MRI before (pre-treatment) and after two or three cycles of NAC (early treatment) from 2018 to 2021. The patients in this study were randomly divided into a training cohort (n=67) and a validation cohort (n=28) at a ratio of 7:3. Deep learning and handcrafted features were extracted from pre- and early treatment DCE-MRI contoured lesions. These features contribute to the construction of radiomic signature RS1 and RS2 representing information from different periods. Mutual information and least absolute shrinkage and selection operator regression were used for feature selection. A combined model was then developed based on the DCE-MRI features and clinical characteristics. The performance of the models was assessed using the area under the receiver operating characteristic curve (AUC) and compared using the DeLong test.ResultsThe overall pCR rate was 25.3% (24/95). One radiomic feature and three deep learning features in RS1, five radiomic features and 11 deep learning features in RS2, and five clinical characteristics remained in the feature selection. The performance of the DLR model combining pre- and early treatment information (AUC=0.900) was better than that of RS1 (AUC=0.644, P=0.068) and slightly higher that of RS2 (AUC=0.888, P=0.604) in the validation cohort. The combined model including pre- and early treatment information and clinical characteristics showed the best ability with an AUC of 0.925 in the validation cohort.ConclusionThe combined model integrating pre-treatment, early treatment DCE-MRI data, and clinical characteristics showed good performance in predicting pCR to NAC in patients with breast cancer. Early treatment DCE-MRI and clinical characteristics may play an important role in evaluating the outcomes of NAC by predicting pCR