Due to the long train marshaling and complex line conditions, the operating modes in heavy haul rail systems frequently change when trains travel. Improper traction or braking operation made by drivers will increase the longitudinal impact force to trains and causes the train decoupling, severely affecting the safe operations of trains. It is quite desirable to replace the manual control with intelligent control in heavy haul rail systems. Traditional machine learning-based intelligent control methods suffer from insufficient data. Due to lacking effective incentives and trust, data from different rail lines or operators cannot be shared directly. In this paper, we propose an approach on blockchain-based federal learning to implement asynchronous collaborative machine learning between distributed agents that own data. This method performs distributed machine learning without a trusted central server. The blockchain smart contract is used to realize the management of the entire federal learning. Using the historical driving data collected from real heavy haul rail systems, the learning agent in the federal learning method adopts a support vector machine (SVM) based intelligent control model. To deal with the imbalanced traction and braking data, we optimize the classic SVM model via assigning different penalty factors to the majority and minority classes. The data set are mapped to a high dimension using kernel functions to make it linearly separable. We construct a mixing kernel function composed of polynomial and radial basis function (RBF) kernel functions, which uses a dynamic weight factor changing with train speeds to improve the model accuracy. The simulation results demonstrate the efficiency and accuracy of our proposed intelligent control method.

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BackgroundMany psychotherapy theories emphasise the importance of self-schema and other-schema, but most previous studies focused on the explicit self-schema in major depressive disorder (MDD). However, the limited studies of implicit self-schema in MDD have shown inconsistencies in their findings. Furthermore, only a few studies have investigated the implicit other-schema, and the pathway illustrating how implicit schemas influence depression remains unclear.AimsThe primary aim of our study was to explore the characteristics of implicit self-schema and other-schema in patients with MDD. We also examine the chain-mediating effect of attachment relationships and interpersonal trust.MethodsThe present study included 88 patients with MDD and 88 healthy controls (HCs). The Hamilton Depression Rating Scale-17, Experiences in Close Relationships Inventory—Revised Questionnaire, Trust Scale and the Extrinsic Affective Simon Task (EAST) were used to assess depressive symptoms, attachment relationships, interpersonal trust and implicit schemas, respectively. Paired sample t-test was used to compare the reaction time (RT) for positive and negative words within the two groups. Analysis of covariance was used to explore the difference between two groups from the perspective of implicit schemas and interpersonal patterns. The chain mediation model was verified by bootstrap.Results(1) For interpersonal patterns, patients with MDD scored significantly higher on attachment anxiety (F=82.150, p<0.001) and attachment avoidance (F=23.192, p<0.001) and scored significantly lower on the predictability (F=30.297, p<0.001), dependence (F=39.728, p<0.001) and faith (F=60.997, p<0.001) dimensions of interpersonal trust. (2) As for implicit schemas, no significant difference was found between the RT for positive self-words and negative self-words in patients with MDD (t=−1.056, p=0.294). However, the HC responded faster to positive self-words than negative self-words (t=−3.286, p=0.001). The RT for positive other-words and negative other-words were significantly different in both patients with MDD (t=2.943, p=0.004) and HCs (t=−2.482, p=0.015), with opposite directions. The EAST effect of other-schema in patients with MDD was significantly different from that in HCs (F=13.051, p<0.001). (3) For the total sample, the EAST effect of other-schema significantly correlated with attachment avoidance, interpersonal trust and depressive symptoms. Attachment avoidance and interpersonal trust were the chain mediators between the EAST effect of other-schema and depressive symptoms (95% CI: −0.090 to −0.008). However, no significant results were found for the EAST effect of other-schema when correlation and mediation analyses were performed for HCs and patients with MDD separately.ConclusionsThis study verified that patients with MDD have abnormal interpersonal patterns and negative implicit schemas. However, no mediating effect of attachment relationships and interpersonal trust was found.

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PSR B1821−24 is a solitary millisecond pulsar (MSP) which radiates multi-wavelength pulsed photons. It has complex radio, X-ray and γ-ray pulse profiles with distinct peak phase-separations that challenge the traditional caustic emission models. Using the single-pole annular gap model with suitable magnetic inclination angle (α = 40 • ) and viewing angle (ζ = 75 • ), we managed to reproduce its pulse profiles of three wavebands. It is found that the middle radio peak is originated from the core gap region at high altitudes, and the other two radio peaks are originated from the annular gap region at relatively low altitudes. Two peaks of both X-ray and γ-ray wavebands are fundamentally originated from annular gap region, while the γ-ray emission generated from the core gap region contributes somewhat to the first γ-ray peak. Precisely reproducing the multi-wavelength pulse profiles of PSR B1821−24 enables us to understand emission regions of distinct wavebands and justify pulsar emission models.