The impact of different sentiment in investment decisions: evidence from China’s stock markets IPOs

In this study, we used data on China’s initial public offerings (IPOs), market volatility and macro environment before and after two stock crashes during 2006–2016 to investigate how different investor sentiment affects IPO first-day flipping. The empirical results show that the expected returns of allocated investors are affected by sentiment, with allocated investors having higher psychological expectations of future returns during an optimistic bull market and their optimism discouraging first-day flipping, while higher risk-free interest rate levels and rising broad market indices also discourage first-day flipping and tend to sell in the future. The pessimistic bear market during which allocated investors have lower psychological expectations of future returns, their pessimism will promote first-day flipping, and the increase in the risk-free rate level will also promote first-day flipping, which is the opposite of the optimistic bull market, indicating that their risk aversion has increased and they tend to sell on the same day. We also found an anomaly that the greater the decline in the broad market index during a pessimistic bear market, the more inclined the allocated investors are to sell in the future when the broad market index rises in an attempt to gain higher returns. These findings help explain and understand the impact of market and macro index fluctuations on investor behavior under different investor sentiments

Study on Crosstalk-Free Polarization Splitter Based on Square Lattice Single-Polarization Photonic Crystal Fibers

We propose a novel L-shaped crosstalk-free polarization splitter (PS) based on square lattice single-polarization photonic crystal fibers, which has a wide bandwidth (250 nm) for single-polarization transmission. By employing a vectorial finiteelement method and finite-element beam propagation method, the numerical simulations demonstrate that an incident light polarized in any direction can be split into two orthogonal single-polarization states (xand y-polarizations) without any crosstalk through the proposed PS. Furthermore, a large structural tolerance and a wide transmission bandwidth are also demonstrated, which would lead to more easier fabrication process

Bipartite Heterogeneous Network Method Based on Co-neighbor for MiRNA-Disease Association Prediction

In recent years, miRNA variation and dysregulation have been found to be closely related to human tumors, and identifying miRNA-disease associations is helpful for understanding the mechanisms of disease or tumor development and is greatly significant for the prognosis, diagnosis, and treatment of human diseases. This article proposes a Bipartite Heterogeneous network link prediction method based on co-neighbor to predict miRNA-disease association (BHCN). According to the structural characteristics of the bipartite network, the concept of bipartite network co-neighbors is proposed, and the co-neighbors were used to represent the probability of association between disease and miRNA. To predict the isolated diseases and the new miRNA based on the association probability expressed by co-neighbors, we utilized the similarity between disease nodes and the similarity between miRNA nodes in heterogeneous networks to represent the association probability between disease and miRNA. The model's predictive performance was evaluated by the leave-one-out cross validation (LOOCV) on different datasets. The AUC value of BHCN on the gold benchmark dataset was 0.7973, and the AUC obtained on the prediction dataset was 0.9349, which was better than that of the classic global algorithm. In this case study, we conducted predictive studies on breast neoplasms and colon neoplasms. Most of the top 50 predicted results were confirmed by three databases, namely, HMDD, miR2disease, and dbDEMC, with accuracy rates of 96 and 82%. In addition, BHCN can be used for predicting isolated diseases (without any known associated diseases) and new miRNAs (without any known associated miRNAs). In the isolated disease case study, the top 50 of breast neoplasm and colon neoplasm potentials associated with miRNAs predicted an accuracy of 100 and 96%, respectively, thereby demonstrating the favorable predictive power of BHCN for potentially relevant miRNAs

Spatial-temporal analysis of tuberculosis infections in a rural prefecture in Japan

Background: Japan has remained medium-burden tuberculosis (TB) country for many years. However, a considerable variation was observed in the TB space-time distribution among Japan’s eight regions. This study aimed to investigate the spatial, temporal, and space-time dynamics of TB at the machi-level in Nagasaki prefecture.Methods: Data on the reported TB infections from 2007 to 2018 were collected from the information center for infectious diseases of the Nagasaki Prefectural Institute of Environment and Public Health. The time series, temporal trends, and spatial patterns of TB at the machi-level were explored using Moran’s I and Kulldorff’s space-time scan statistics.Results: A total of 4,364 TB infections were reported between April 2007 and December 2018 in Nagasaki prefecture. The infections were frequently reported in October, June, and January, and they showed spatial clustering with Moran’s I value ranging from 0.07 to 0.17 (p = 0.001). Ten significant clusters were identified, including one most likely cluster and nine secondary clusters, which were mainly concentrated in the densely inhabited districts of the two biggest cities in Nagasaki prefecture (Nagasaki city and Sasebo city), Shimabara peninsula, and Iki island.Conclusion: This study showed significant and unique spatial-temporal characteristics of TB infections in Nagasaki prefecture. Therefore, such information on the prevailing epidemiological situation of TB infections could help develop strategies that could effectively eliminate TB in Japan

Phylogeography and Demographic History of Babina pleuraden (Anura, Ranidae) in Southwestern China

Factors that determine genetic structure of species in southwestern China remain largely unknown. In this study, sequences of two mitochondrial genes (COI and cyt b) were determined to investigate the phylogeography and demography of Babina pleuraden, a pond frog endemic to southwestern China. A total of 262 individuals from 22 populations across the entire range of the species were collected. Our results indicate that B. pleuraden comprises five well-supported mitochondrial lineages roughly corresponding to five geographical areas. The phylogeographic structure of B. pleuraden has been shaped primarily by the unique regional responses of the Yunnan Plateau to the rapid uplift of the Qinghai-Tibetan Plateau occurred c. 2.5 Mya (B phrase of Qingzang Movement) and climatic oscillation during middle Pleistocene (c. 0.64–0.36 Mya), rather than by the paleo-drainage systems. The present wide distribution of the species has resulted from recent population expansion (c. 0.053–0.025 Mya) from multiple refugia prior to the Last Glacial Maximum, corresponding to the scenario of “refugia within refugia”

A Fault Diagnosis Method of Bogie Axle Box Bearing Based on Spectrum Whitening Demodulation

The axle box bearing of bogie is one of the key components of the rail transit train, which can ensure the rotary motion of wheelsets and make the wheelsets adapt to the conditions of uneven railways. At the same time, the axle box bearing also exposes most of the load of the car body. Long-time high-speed rotation and heavy load make the axle box bearing prone to failure. If the bearing failure occurs, it will greatly affect the safety of the train. Therefore, it is extremely important to monitor the health status of the axle box bearing. At present, the health status of the axle box bearing is mainly monitored by vibration information and temperature information. Compared with the temperature data, the vibration data can more easily detect the early fault of the bearing, and early warning of the bearing state can avoid the occurrence of serious fault in time. Therefore, this paper is based on the vibration data of the axle box bearing to carry out adaptive fault diagnosis of bearing. First, the AR model predictive filter is used to denoise the vibration signal of the bearing, and then the signal is whitened in the frequency domain. Finally, the characteristic value of vibration data is extracted by energy operator demodulation, and the fault type is determined by comparing with the theoretical value. Through the analysis of the constructed simulation signal data, the characteristic parameters of the data can be effectively extracted. The experimental data collected from the bearing testbed of high-speed train are analyzed and verified, which further proves the effectiveness of the feature extraction method proposed in this paper. Compared with other axle box bearing fault diagnosis methods, the innovation of the proposed method is that the signal is denoised twice by using AR filter and spectrum whitening, and the adaptive extraction of fault features is realized by using energy operator. At the same time, the steps of setting parameters in the process of feature extraction are avoided in other feature extraction methods, which improves the diagnostic efficiency and is conducive to use in online monitoring system

Poly (3,4-Ethylenedioxythiophene) (PEDOT) Nanofibers Decorated Graphene Oxide (GO) as High-Capacity, Long Cycle Anodes for Sodium Ion Batteries

Conductive Poly (3,4-ethylenedioxythiophene) (PEDOT) nanofibers are uniformly deposited on ultrathin graphene oxide (GO) nanosheets via a simple and effective in situ polymerization process under ambient conditions. The as-prepared samples are characterized by field-emission scanning electron microscopy (FE-SEM), transmission electron microscopy (TEM), Raman spectra, Fourier transforms infrared spectra (FTIR), and electrochemical measurements. The results indicate that the as-obtained PEDOT–GO hybrid (GDOT) achieves excellent sodium storage properties. When explored as a new inorganic/polymeric electrode for sodium ion batteries (SIBs), the GDOT exhibits a high reversible capacity (338 mAh g−1), good cycling stability (234 mAh g−1 after 400 cycles), and excellent rate capabilities (e.g., 62 mAh g−1 at 30 A g−1) due to their ultrathin structure as well as conductive network. This easily scale-up-able and effective strategy shows great potential for large-scale energy applications

DySC : software for greedy clustering of 16S rRNA reads

Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutual information (NMI) criterion show that DySC produces higher quality clusters than UCLUST and CD-HIT at a comparable runtime

A Real-Time Fault Early Warning Method for a High-Speed EMU Axle Box Bearing

An axle box bearing is one of the most important components of high-speed EMUs (electric multiple units), which runs at a very fast speed, suffers a heavy load, and operates under various complex working conditions. Once a bearing fault occurs, it not only has an enormous impact on the railway system, but also poses a threat to personal safety. Therefore, there is significant value in studying a real-time fault early warning of a high-speed EMU axle box bearing. However, to our best knowledge, there are three obvious defects in the existing fault early warning methods used for high-speed EMU axle box bearings: (1) these methods based on vibration are extremely mature, but there are no vibration sensors installed in high-speed EMU axle box because it will greatly increase the manufacturing cost; (2) a TADS (trackside acoustic device system) can effectively detect early failures, but only a portion of railways are equipped with such a facility; and (3) an EMU-ODS (electric multiple unit onboard detection system) has reported numerous untimely warnings, along with warnings of frequent occurrence being missed. Whereupon, a method is proposed to realize the fault early warning of an axle box bearing without installing a vibration sensor on the high-speed EMU in service, namely a MLSTM-iForest (multilayer long short-term memory–isolation forest). First, the time-series data of the temperature-related variables of the axle box bearing is used as the input of MLSTM to predict the axle box bearing temperature in the future. Then, the deviation index of the predicted axle box bearing temperature is calculated. Finally, the deviation index is input into an iForest algorithm for unsupervised classification to realize the fault early warning of an axle box bearing. Experimental results on high-speed EMU operation data sets demonstrated the availability and feasibility of the presented method toward achieving early fault warnings of a high-speed EMU axle box bearing