Characterisation of microstructure, defect and high-cycle-fatigue behaviour in a stainless steel joint processed by brazing

We report the characterisation of microstructures and high-cycle-fatigue (HCF) properties of Type 304 stainless steel joints processed by brazing. Pure copper was applied as the filler metal for brazing at 1120 °C. A two-phase microstructure was obtained within the joint region: the star-shaped precipitates and copper matrix. The precipitates with an average size of 0.43 μm were rich in iron and chromium. A fixed orientation relationship was found between the precipitates and copper matrix. The joint exhibited much higher tensile strength and HCF life when compared to pure copper. The strength enhancement can be attributed to the presence of precipitates. Furthermore, the effect of joint interface roughness as well as defects was critically investigated. The joint interface roughness showed little influence on the HCF lives. Post-examinations revealed that fatigue crack initiation and propagation occurred entirely within the joint region, hence being consistent with the similar HCF lives regardless of the pre-defined interface roughness conditions. In addition, it was found that the HCF lives decreased exponentially with the increase of initial defect area. Fractography analysis revealed that fatigue striation spacings near the crack initiation zone increased with the increase of defect area, suggesting that the larger defects result in higher crack growth rate, hence shorten the overall fatigue life.</div

A heuristic forecasting model for stock decision

This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting model was found capable of consistently outperforming this benchmark strategy

Review of medical data analysis based on spiking neural networks

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected

Dissipationless Anomalous Hall Current in Fe100−x(SiO2)xFe_{100-x}(SiO_2)_x Films

The observation of dissipationless anomalous Hall current is one of the experimental evidences to confirm the intrinsic origin of anomalous Hall effect. To study the origin of anomalous Hall effect in iron, Fe$_{100-x}$(SiO$_{2}$)$_{x}$ granular films with volume fraction of SiO$_{2}$ 0\le x \le 40.51 were fabricated using co-sputtering. Hall and longitudinal resistivities were measured in the temperature range 5 to 350 K with magnetic fields up to 5 Tesla. As x increased from 0 to 40.51, the anomalous Hall resistivity and longitudinal resistivity increased about 4 and 3 orders in magnitude, respectively. Analysis of the results revealed that the normalized anomalous Hall conductivity is a constant for all the samples, the evidence of dissipationless anomalous Hall current in Fe.Comment: 17 pages, 5 figures; http://link.aps.org/doi/10.1103/PhysRevB.83.20531

Prenatal ketamine exposure causes abnormal development of prefrontal cortex in rat.

Ketamine is commonly used for anesthesia and as a recreational drug. In pregnant users, a potential neurotoxicity in offspring has been noted. Our previous work demonstrated that ketamine exposure of pregnant rats induces affective disorders and cognitive impairments in offspring. As the prefrontal cortex (PFC) is critically involved in emotional and cognitive processes, here we studied whether maternal ketamine exposure influences the development of the PFC in offspring. Pregnant rats on gestational day 14 were treated with ketamine at a sedative dose for 2 hrs, and pups were studied at postnatal day 0 (P0) or P30. We found that maternal ketamine exposure resulted in cell apoptosis and neuronal loss in fetal brain. Upon ketamine exposure in utero, PFC neurons at P30 showed more dendritic branching, while cultured neurons from P0 PFC extended shorter neurites than controls. In addition, maternal ketamine exposure postponed the switch of NR2B/2A expression, and perturbed pre- and postsynaptic protein expression in the PFC. These data suggest that prenatal ketamine exposure impairs neuronal development of the PFC, which may be associated with abnormal behavior in offsprings