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The aim of this work is to research and design an expert diagnosis system for rail vehicle driven by data mechanism models.

The expert diagnosis system utilizes statistical and deep learning methods to model the real-time status and historical data features of rail vehicle. Based on data mechanism models, it predicts the lifespan of key components, evaluates the health status of the vehicle and achieves intelligent monitoring and diagnosis of rail vehicle.

The actual operation effect of this system shows that it has improved the intelligent level of the rail vehicle monitoring system, which helps operators to monitor the operation of vehicle online, predict potential risks and faults of vehicle and ensure the smooth and safe operation of vehicle.

This system improves the efficiency of rail vehicle operation, scheduling and maintenance through intelligent monitoring and diagnosis of rail vehicle.

In the field of heritage science, especially applied to buildings and artefacts made by organic hygroscopic materials, analyzing the microclimate has always been of extreme importance. In particular, in many cases, the knowledge of the outdoor/indoor microclimate may support the decision process in conservation and preservation matters of historic buildings. This knowledge is often gained by implementing long and time-consuming monitoring campaigns that allow collecting atmospheric and climatic data.

Sometimes the collected time series may be corrupted, incomplete and/or subjected to the sensors' errors because of the remoteness of the historic building location, the natural aging of the sensor or the lack of a continuous check of the data downloading process. For this reason, in this work, an innovative approach about reconstructing the indoor microclimate into heritage buildings, just knowing the outdoor one, is proposed. This methodology is based on using machine learning tools known as variational auto encoders (VAEs), that are able to reconstruct time series and/or to fill data gaps.

The proposed approach is implemented using data collected in Ringebu Stave Church, a Norwegian medieval wooden heritage building. Reconstructing a realistic time series, for the vast majority of the year period, of the natural internal climate of the Church has been successfully implemented.

The novelty of this work is discussed in the framework of the existing literature. The work explores the potentials of machine learning tools compared to traditional ones, providing a method that is able to reliably fill missing data in time series.

The social change model of leadership (SCM) is a widely used leadership model in higher education. StrengthsQuest is conceptually similar to the individual values of the SCM in its aim to identify and grow individual talents. This model is based on the idea that individuals perform at higher levels when they build upon their identified talents (Clifton & Harter, 2003). Prior studies have not examined the relationship of hope or one’s belief in their identified StrengthsQuest talents to the individual values of the SCM. This study examines that relationship using the adult-trait hope and strengths self-efficacy scales. The relationship between these constructs along and other predictors of social change capacity were explored using canonical correlation analysis. Strengths self-efficacy, hope, and student engagement were statistically significant (73% of the variability among the individual values of the SCM). Gender, race, and community service were not statistically significant in this study.

High-speed turnouts are more complex in structure and thus may cause abnormal vibration of high-speed train car body, affecting driving safety and passenger riding experience. Therefore, it is necessary to analyze the data characteristics of continuous hunting of high-speed trains passing through turnouts and propose a diagnostic method for engineering applications.

First, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) is performed to determine the first characteristic component of the car body’s lateral acceleration. Then, the Short-Time Fourier Transform (STFT) is performed to calculate the marginal spectra. Finally, the presence of a continuous hunting problem is determined based on the results of the comparison calculations and diagnostic thresholds. To improve computational efficiency, permutation entropy (PE) is used as a fast indicator to identify turnouts with potential problems.

Under continuous hunting conditions, the PE is less than 0.90; the ratio of the maximum peak value of the signal component to the original signal peak value exceeded 0.7, and there is an energy band in the STFT time-frequency map, which corresponds to a frequency distribution range of 1–2 Hz.

The research results have revealed the lateral vibration characteristics of the high-speed train’s car body during continuous hunting when passing through turnouts. On this basis, an effective diagnostic method has been proposed. With a focus on practical engineering applications, a rapid screening index for identifying potential issues has been proposed, significantly enhancing the efficiency of diagnostic processes.

Since the 1980s the economic development of countries in East Asia has had a marked impact on the world port community, particularly in container transport. This paper analyses changes in the competitive environment of the world container port sector using some standard tools of market concentration. Initially, this paper reviews the competitive position of world container port system and then examines the East Asian economic environment. Both ordinal and cardinal measures of port system inequality are used to demonstrate both the rankings and levels of container throughput have been diverging in the world's major economic blocs. Conversely, East Asian countries during the 1990s have shown a trend towards convergence. Measures of dispersion suggest that ports in East Asian countries have become more competitive in their levels of container throughput.

This paper aims to introduce vehicular network platform, routing and broadcasting methods and vehicular positioning enhancement technology, which are three aspects of the applications of intelligent computing in vehicular networks. From this paper, the role of intelligent algorithm in the field of transportation and the vehicular networks can be understood.

In this paper, the authors introduce three different methods in three layers of vehicle networking, which are data cleaning based on machine learning, routing algorithm based on epidemic model and cooperative localization algorithm based on the connect vehicles.

In Section 2, a novel classification-based framework is proposed to efficiently assess the data quality and screen out the abnormal vehicles in database. In Section 3, the authors can find when traffic conditions varied from free flow to congestion, the number of message copies increased dramatically and the reachability also improved. The error of vehicle positioning is reduced by 35.39% based on the CV-IMM-EKF in Section 4. Finally, it can be concluded that the intelligent computing in the vehicle network system is effective, and it will improve the development of the car networking system.

This paper reviews the research of intelligent algorithms in three related areas of vehicle networking. In the field of vehicle networking, these research results are conducive to promoting data processing and algorithm optimization, and it may lay the foundation for the new methods.

Numerous metals can be processed using the additive manufacturing process laser-based powder bed fusion of metals (PBF-LB/M, ISO/ASTM 52900). The main advantages of additive manufacturing technologies are the high degree of design freedom and the cost-effective implementation of lightweight structures. This could be profitable for gears with increased power density, combining reduced mass with considerable material strength. Current research on additively manufactured gears is focused on developing lightweight structures but is seldom accompanied by simulations and even less by mechanical testing. There has been very little research into the mechanical and material properties of additively manufactured gears. The purpose of this study is to investigate the behavior of lightweight structures in additively manufactured gears under static loads.

This research identifies the static load-carrying capacity of helical gears with different lightweight structures produced by PBF-LB/M with the case hardening steel 16MnCr5. A static gear loading test rig with a maximum torque at the pinion of T₁ = 1200 Nm is used. Further focus is set on analyzing material properties such as the relative density, microstructure, hardness depth profile and chemical composition.

All additively manufactured gear variants show no failure or plastic deformation at the maximum test load. The shaft hub connection, the lightweight hub designs and the gearing itself are stable and intact regarding their form and function. The identified material characteristics are comparable to conventionally manufactured gears (wrought and machined), but also some particularities were observed.

This research demonstrates the mechanical strength of lightweight structures in gears. Future research needs to consider the dynamic load-carrying capacity of additively manufactured gears.

This study aims to conduct performance and clustering analyses with the help of Digital Government Reference Library (DGRL) v16.6 database examining the role of emerging technologies (ETs) in public services delivery.

VOSviewer and SciMAT techniques were used for clustering and mapping the use of ETs in the public services delivery. Collecting documents from the DGRL v16.6 database, the paper uses text mining analysis for identifying key terms and trends in e-Government research regarding ETs and public services.

The analysis indicates that all ETs are strongly linked to each other, except for blockchain technologies (due to its disruptive nature), which indicate that ETs can be, therefore, seen as accumulative knowledge. In addition, on the whole, findings identify four stages in the evolution of ETs and their application to public services: the “electronic administration” stage, the “technological baseline” stage, the “managerial” stage and the “disruptive technological” stage.

The output of the present research will help to orient policymakers in the implementation and use of ETs, evaluating the influence of these technologies on public services.

The research helps researchers to track research trends and uncover new paths on ETs and its implementation in public services.

Recent research has focused on the need of implementing ETs for improving public services, which could help cities to improve the citizens’ quality of life in urban areas. This paper contributes to expanding the knowledge about ETs and its implementation in public services, identifying trends and networks in the research about these issues.

Foodbank use in the UK is rising but, despite high levels of poverty, Pakistani women are less likely to use food banks than white British women. The purpose of this paper is to understand the lived experience of food in the context of poverty amongst Pakistani and white British women in Bradford, including perspectives on food aid.

A total of 16 Pakistani and white British women, recruited through community initiatives, participated in three focus groups (one interview was also held as a consequence of recruitment difficulties). Each group met for two hours aided by a moderator and professional interpreter. The transcripts were analysed thematically using a three-stage process.

Women in low-income households employed dual strategies to reconcile caring responsibilities and financial obligations: the first sought to make ends meet within household income; the second looked to outside sources of support. There was a reported near absence of food insecurity amongst Pakistani women which could be attributed to support from social/familial networks, resource management within the household, and cultural and religious frameworks. A minority of participants and no Pakistani respondents accessed charitable food aid. There were three reasons for the non-use of food aid: it was not required because of resource management strategies within the household and assistance from familial/social networks; it was avoided out of shame; and knowledge about its existence was poor.

This case study is the first examination of varying experiences of food insecurity amongst UK white British and Pakistani women. Whilst the sample size is small, it presents new evidence on perceptions of food insecurity amongst Pakistani households and on why households of varying ethnicities do not use food aid.

In recent years, increased awareness on global warming effects led to a renewed interest in all kinds of green technologies. Among them, some attention has been devoted to hybrid-electric aircraft – aircraft where the propulsion system contains power systems driven by electricity and power systems driven by hydrocarbon-based fuel. Examples of these systems include electric motors and gas turbines, respectively. Despite the fact that several research groups have tried to design such aircraft, in a way, it can actually save fuel with respect to conventional designs, the results hardly approach the required fuel savings to justify a new design. One possible path to improve these designs is to optimize the onboard energy management, in other words, when to use fuel and when to use stored electricity during a mission. The purpose of this paper is to address the topic of energy management applied to hybrid-electric aircraft, including its relevance for the conceptual design of aircraft and present a practical example of optimal energy management.

To address this problem the dynamic programming (DP) method for optimal control problems was used and, together with an aircraft performance model, an optimal energy management was obtained for a given aircraft flying a given trajectory.

The results show how the energy onboard a hybrid fuel-battery aircraft can be optimally managed during the mission. The optimal results were compared with non-optimal result, and small differences were found. A large sensitivity of the results to the battery charging efficiency was also found.

The novelty of this work comes from the application of DP for energy management to a variable weight system which includes energy recovery via a propeller.