In mobile edge computing (MEC), edge caching (EC), i.e., deploying data closer to end users at the network edge layer, has become one of the most active research areas for mitigating the high latency caused by massive data in centralized storage systems. However, when caching data that are sensitive to freshness, distributed EC systems often face severe strong content consistency challenges, as such data undergo frequent updates and modifications over long usage cycles. To address the strong consistency issue faced by freshness-sensitive data in resource-constrained MEC scenarios, we innovatively reformulate this challenge as a content replacement problem. The objective is to minimize long-term content transmission costs while maintaining strong content consistency. More specifically, we propose the Content Strong Consistency Decision Transformer (CSC-DT) algorithm to maintain strong consistency. The cache replacement problem is modeled as a Markov Decision Process (MDP), with decisions constructed as trajectories. These trajectories are processed using the Transformer architecture to capture the long-term dependencies between the temporal dynamics of content requests and updates, optimizing cache replacement decisions. Validated with railway engineering data, which undergo frequent updates during the design phase, our method effectively maintains strong content consistency and reduces content transmission costs.

In rail trains, the prognostics and health management system will make use of data from sensors deployed in key components. These data are typically non-stationary multi-variable time series with abrupt variations. The challenges of multivariate time series forecasting include how to identify the interactions between various variables, and how to assess the influence of historical data on present data. This paper presents the nature of the air brake system and its physical models. By analyzing the observed data obtained from the freight train air brake system, a Memory network-based Time series Multi-step fault Forecasting model (MTMF) is proposed. MTMF consists of a dual-encoder and a memory network module for feature extraction. The two encoders are used to extract features of short-term and long-term historical data respectively. The memory network is used to further mine the different weights of the long-term historical data. MTMF also designs a joint multi-step forecasting loss function, which is composed of shape, time and mean square error losses between the forecasting series and the real series. The improved loss function is no longer restricted to the differences in evaluation points. MTMF is evaluated on the real freight train braking system time series dataset. The results show that MTMF outperforms other forecasting methods on multivariate time series. The forecasting accuracy is increased by 5%-45%, demonstrating that the model is effective for non-stationary series and can better mine the dependence patterns for the train braking system.

Mutation analysis is a powerful dynamic approach that has many applications, such as measuring the quality of test suites or automatically locating faults. However, the inherent low scalability hampers its practical use. To accelerate mutation analysis, researchers propose approaches to reduce redundant executions. A family of fork-based approaches tries to share identical executions among mutants. Fork-based approaches carry all mutants in one process and decide whether to fork new child processes when reaching a mutated statement. The mutants carried by the parent process are split into groups and distributed to different processes to finish the remaining executions. However, existing fork-based approaches have two limitations: (1) the limited analysis scope on a single statement to compare and cluster mutants prevents their systems from detecting more equivalent mutants, and (2) the interpretation of the mutants and the runtime equivalence analysis introduce significant overhead.In this paper, we present a novel fork-based mutation analysis approach WinMut, which (1) groups mutants in a scope of mutated statements and, (2) removes redundant computations inside interpreters. WinMut not only reduces the number of invoked processes but also has a lower cost for executing a single process. Our experiments show that our approach can further accelerate mutation analysis with an average speedup of 5.57x on top of the state-of-the-art fork-based approach, AccMut.

Electronically controlled pneumatic (ECP) brake is widely used in heavy-haul train. Although the latest data-driven fault diagnosis can exploit the collection data from the braking system, it still has challenges for effective fault diagnosis model because of industrial data noise and insufficient fault samples. This article proposes a fault diagnosis model based on multidimensional feature fusion and ensemble learning for braking system of heavy-haul train (MFF-GBFD). First, the multidimensional features are extracted. By principal component analysis and feature fusion, the redundant features are eliminated. Then, the model is trained under ensemble learning framework with boosting strategy. Experiments are carried out on the data from the ECP braking system of DK-2 locomotive. The efforts show that the proposed MFF-GBFD model presents better performances as a result from the early-stage feature extraction, feature selection, and feature fusion. It also has higher accuracy and $F_1$ values compared with the traditional classification algorithms.