Standing Out from the Crowd: The Real Effects of Outliers

We study the impact of outlier opinions – extreme views voiced by individuals – in financial markets. Using analyst forecasts as a laboratory, we show that market participants respond to the arrival of extremely optimistic forecasts, instead of ignoring them as noise. An outlier forecast subsequently moves group consensus and begets more extreme forecasts by peers. Outlier forecasts also generate stronger market reactions from investors, more media coverage, and more conservative management guidance. Further analyses reveal that issuing outlier forecasts increases an analyst’s chance to cover more important clients of his employer. Outlier forecasts are also more likely to take place when an analyst’s reputation cost is lower and information uncertainty is high. These findings suggest that the propensity for expressing extreme views is situational and that personal incentives are the likely cause at play

Identification of multi-fault in rotor-bearing system using spectral kurtosis and EEMD

Condition monitoring and fault diagnosis via vibration signal processing play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback, the characteristic frequency of relevant fault could not be determined effectively with traditional method. The Spectral Kurtosis (SK) is useful for the bearing fault detection. Nevertheless, the simulation of experiment in this paper shows that the SK is unable to identify multi-fault of rotor-bearing system fully when different faults excite different resonance frequencies. A new multi-fault detection method based on EEMD and spectral kurtosis (SK) is proposed in order to overcoming the shortcoming. The proposed method is applied to multi-faults of rotor imbalance and faulty bearings. The superiority of the proposed method based on spectral kurtosis (SK) and EEMD is demonstrated in extracting fault characteristic information of rotating machinery

Blind source separation of rolling element bearing’ single channel compound fault based on Shift Invariant Sparse Coding

The mechanical vibration source signal collected by sensor often includes a variety of internal vibration source of contributions such as gears, bearings, shaft and so on. It is often hoped to achieve effective separation of the source signal in order to obtain better fault diagnosis result. Blind source separation of the failure signal of rolling element bearing is a challenging task due to the above reasons, especially in the case of single channel compound fault. A method of blind source separation of rolling element bearing’s single channel compound fault based on Shift-Invariant Sparse Coding (SISC) is proposed in the paper. The waveform characteristic of different fault signal has some difference in the structure even that the same impulse characteristics of signals are produced by different parts, and the difference can be captured by the SISC method with the following reasons: Firstly, a set of basis functions is trained and obtained by SISC feature self-study method (The number of the basis functions is big necessarily). Then the potential components are constructed using the corresponding obtained basis functions. At last, the clustering operation is carried out using the structural similarity of the potential components, and the clustering signals represent the different vibration source signals. Apply the traditional vibration signal handling method such as envelope demodulation to the obtained clustering signals respectively and better fault diagnosis results are obtained at last

Diagnosis of rolling element bearing fault arising in gearbox based on sparse morphological component analysis

It is hard to diagnose the rolling element bearing fault occurring in gearbox due to the complexity and the probable mutual coupling among the kinds of signals. A novel diagnosis method of rolling element bearing fault arising in gearbox based on morphological component analysis (MCA) originating from sparse representation theory is proposed in the paper. By selecting proper dictionaries, different morphological components can be separated successfully from the complex rolling fault signal arising in gearbox, which helps to improve the efficiency and accuracy of diagnosis result. The effectiveness of the proposed method is verified through simulations firstly. Then the proposed method is used in fault feature extracting of complex vibration signals collected from rotating machinery, and the effectiveness of the proposed method is further verified. Besides, the advantage of the proposed method over other relative method is presented

What Really Matters for Loneliness Among Left-Behind Children in Rural China: A Meta-Analytic Review

In rural China, left-behind children are likely to suffer chronic loneliness. Research has identified a variety of factors that may be associated with loneliness among these children. A meta-analysis is needed to address the empirical inconsistencies and examine the strength of relations between different factors and loneliness. The current meta-analysis included 51 studies on predictors of loneliness published from 2008 to 2017. Results showed that one individual factor (social anxiety) is a key risk factor for loneliness, whereas eight individual (older age, self-esteem, resilience, extroversion) and contextual factors (family functioning, parent–child relationship, peer relationship, social support) serve as protective factors in predicting loneliness. In addition, boys were more likely to feel lonely than girls. Findings and implications of this study were discussed

Vibration performance prediction and reliability analysis for rolling bearing

The bearing vibration signal is a rich dynamic symptom of bearing wear, and the vibration signal of rolling bearing presents chaotic characteristics. Input and output variables of vibration signal can be constructed through phase space reconstruction, the Input and output variables can be imported into the prediction model for prediction. The prediction accuracy of the extreme learning machine (ELM) model, Kriging model and RBF model are compared, the results show that ELM has higher accuracy, so ELM chaos model is used to predict the future vibration time series data, and the forecasting error can be obtained by comparing the prediction value with the actual values so as to verity the feasibility of the ELM model. The prediction results of the future state of the bearing are processed as the grey-bootstrap method, and the performance reliability prediction of the bearing is realized by the Poisson counting process. The experimental data show that with the deepening of the fault degree, the reliability performance decreases gradually. The reliability performance of the bearing without fault is 100 %, and the reliability performance is 47.56 % when the inner ring faulty size is 0.72 mm

Abnormal expression of an ADAR2 alternative splicing variant in gliomas downregulates adenosine-to-inosine RNA editing

BACKGROUND: RNA editing is catalyzed by adenosine deaminases acting on RNA (ADARs). ADAR2 is the main enzyme responsible for recoding editing in humans. Adenosine-to-inosine (A-to-I) editing at the Q/R site is reported to be decreased in gliomas; however, the expression of ADAR2 mRNA was not greatly affected. METHODS: We determined ADAR2 mRNA expression in human glioblastoma cell lines and in normal human glial cells by real-time RT-PCR. We also determined ADAR2 mRNA expression in 44 glioma tissues and normal white matter. After identifying an alternative splicing variant (ASV) of ADAR2 in gliomas, we performed sequencing. We then classified glioblastomas based on the presence (+) or absence (–) of the ASV to determine the correlations between ASV + and malignant features of glioblastomas, such as invasion, peritumoral brain edema, and survival time. RESULTS: There were no significant differences in ADAR2 mRNA expression among human glioblastoma cell lines or in gliomas compared with normal white matter (all p > 0.05). The ASV, which contained a 47-nucleotide insertion in the ADAR2 mRNA transcript, was detected in the U251 and BT325 cell lines, and in some glioma tissues. The expression rate of ASV differed among gliomas of different grades. ASV + glioblastomas were more malignant than ASV – glioblastomas. CONCLUSIONS: ADAR2 is a family of enzymes in which ASVs result in differences in enzymatic activity. The ADAR2 ASV may be correlated with the invasiveness of gliomas. Identification of the mechanistic characterization of ADAR2 ASV may have future potential for individualized molecular targeted-therapy for glioma

Prompt-enhanced Hierarchical Transformer Elevating Cardiopulmonary Resuscitation Instruction via Temporal Action Segmentation

The vast majority of people who suffer unexpected cardiac arrest are performed cardiopulmonary resuscitation (CPR) by passersby in a desperate attempt to restore life, but endeavors turn out to be fruitless on account of disqualification. Fortunately, many pieces of research manifest that disciplined training will help to elevate the success rate of resuscitation, which constantly desires a seamless combination of novel techniques to yield further advancement. To this end, we collect a custom CPR video dataset in which trainees make efforts to behave resuscitation on mannequins independently in adherence to approved guidelines, thereby devising an auxiliary toolbox to assist supervision and rectification of intermediate potential issues via modern deep learning methodologies. Our research empirically views this problem as a temporal action segmentation (TAS) task in computer vision, which aims to segment an untrimmed video at a frame-wise level. Here, we propose a Prompt-enhanced hierarchical Transformer (PhiTrans) that integrates three indispensable modules, including a textual prompt-based Video Features Extractor (VFE), a transformer-based Action Segmentation Executor (ASE), and a regression-based Prediction Refinement Calibrator (PRC). The backbone of the model preferentially derives from applications in three approved public datasets (GTEA, 50Salads, and Breakfast) collected for TAS tasks, which accounts for the excavation of the segmentation pipeline on the CPR dataset. In general, we unprecedentedly probe into a feasible pipeline that genuinely elevates the CPR instruction qualification via action segmentation in conjunction with cutting-edge deep learning techniques. Associated experiments advocate our implementation with multiple metrics surpassing 91.0%.Comment: Transformer for Cardiopulmonary Resuscitatio

Bioinformatics analysis of CUL2/4A/9 and its function in head and neck squamous cell carcinoma

Introduction: Several previous studies have shown that differential expression of cullin (CUL) family proteins may be involved in mediation of the signal transduction pathways associated with cancer. However, the function of CULs is still unclear in head and neck squamous cell carcinoma (HNSCC). Material and methods: We used The Cancer Genome Atlas (TCGA) database, cBioPortal, Metascape, STRING, Cytoscape, Tumor Immune Estimation Resource (TIMER), Kaplan-Meier plotter, and Tumor Immune System Interaction Database (TISIDB) to access the expression of CULs and the possible correlation with the tumourigenesis, development, prognosis, immunity, and transcriptional level of CULs in HNSCC. Furthermore, real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect messenger ribonucleid acid (mRNA) levels in HNSCC tissues and cell samples. We also explored the cell proliferation and migration separately by CCK8 assay and wound healing assay. Results: The results showed that the expressions of CUL2/4A were upregulated and CUL9 was downregulated in HNSCC patients as compared with normal patients. CUL2/4A/9 were also linked to the clinicopathological features and overall survival of HNSCC in bioinformatics analysis. Moreover, we noticed that CUL2/4A/9 may take part in tumour-specific immune response by modulating the tumour-infiltrating lymphocytes (TILs) and immunomodulators. Lastly, we found that CUL2/4A/9 could promote cellular proliferation and migration. Conclusion: These results suggest that the transcriptional levels of CUL2/4A/9 were upregulated and these genes could affect proliferation and migration of HNSCC cells. Therefore, CUL2/4A/9 could potentially function as novel independent biomarkers in HNSCC patients

Suppression of Jasmonic Acid-Dependent Defense in Cotton Plant by the Mealybug Phenacoccus solenopsis

The solenopsis mealybug, Phenacoccus solenopsis, has been recently recognized as an aggressively invasive pest in China, and is now becoming a serious threat to the cotton industry in the country. Thus, it is necessary to investigate the molecular mechanisms employed by cotton for defending against P. solenopsis before the pest populations reach epidemic levels. Here, we examined the effects of exogenous jasmonic acid (JA), salicylic acid (SA), and herbivory treatments on feeding behavior and on development of female P. solenopsis. Further, we compared the volatile emissions of cotton plants upon JA, SA, and herbivory treatments, as well as the time-related changes in gossypol production and defense-related genes. Female adult P. solenopsis were repelled by leaves from JA-treated plant, but were not repelled by leaves from SA-treated plants. In contrast, females were attracted by leaves from plants pre-infested by P. solenopsis. The diverse feeding responses by P. solenopsis were due to the difference in volatile emission of plants from different treatments. Furthermore, we show that JA-treated plants slowed P. solenopsis development, but plants pre-infested by P. solenopsis accelerated its development. We also show that P. solenopsis feeding inhibited the JA-regulated gossypol production, and prevented the induction of JA-related genes. We conclude that P. solenopsis is able to prevent the activation of JA-dependent defenses associated with basal resistance to mealybugs