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The purpose of this study is to match appropriate friction coefficients for subway operational vehicles, considering the dynamic variations of wheel profile wear.

This study combines experimental testing and numerical simulation to investigate the influence of wheel profile wear coupled with the friction coefficient on the vehicle dynamic response.

For the test route in this paper, it is recommended to control the friction coefficient on straight sections between 0.25 and 0.3, and on curved sections between 0.2 and 0.3. This satisfies the required adhesion coefficient for normal train traction and braking, while also ensuring the straight running performance and curve negotiation performance of the vehicle.

Reasonable friction coefficient ranges are proposed for straight and curved track lines to improve the operational safety and economy of the vehicles. Moreover, this study can provide a theoretical basis and reference direction for developing anti-wear measures for rail vehicles operating on fixed routes.

Considering the wear characteristics of operating vehicles and the dynamic changes in the wear profile, this paper explores the adaptability of different degrees of wheel wear profiles to different friction coefficients. Based on the response characteristics of vehicle dynamics, reasonable lubrication recommendations are proposed for this operating vehicle.

Customer identity and access management (CIAM) is a sub-genre of traditional identity and access management (IAM) that has emerged in the past few years to meet evolving business requirements. CIAM focuses on the connectivity with the customer when accessing any type of systems, on-premises and in the cloud, from registration to track. The purpose of this study is to introduce different dimensions of CIAM toward exploiting them in organizations.

Based on a thorough review of the relevant literature and semi-structured interview with six experts in the field of digital IAM the necessary data were gathered. Then through the use of content analysis technique, analytic codes and also categories and sub-categories of the data were generated.

Results indicate that four categories, namely, customer identity management, customer access management and information technology and business management are the most important factors affecting the identification of CIAM dimensions.

Organizations could avail of the proposed conceptual model toward identification and offering customized products and services solutions to their customers.

The purpose of this paper is to investigate the use of hybrid‐excitation solutions, using contemporaneously permanent magnets and field coils, for DC machines intended to operate as the core of high‐reliability drives in critical applications supplied by batteries (e.g. fire‐extinguishing pumps, smoke blowers, etc.) where a roughly constant speed is required and a minimal use of electronic devices is prescribed to improve overall dependability.

A high‐reliability hybrid‐excitation DC motor, initially designed basing on theoretical considerations, is then analyzed using purposely developed 2D and 3D finite element method (FEM) electromagnetic models under static, dynamic, healthy, and faulty conditions.

The simulation results confirm that properly designed drives employing hybrid‐excitation DC motors may constitute an effective solution for applications requiring a very high reliability under DC supply with limited speed regulation capability.

The methodology employed exhibits the usual limits concerning the accuracy of FEM analysis: hysteresis is neglected, 2D simulations neglect axial component of fields, in 2D dynamic analysis the electrically discontinuous laminated cores are modeled as orthotropic continuous parts, commutator operation is approximated by means of a position‐dependent resistors network, and the excitation current provided by choppers is approximately considered as constant.

Hybrid excitation DC motors, which may be easily manufactured using existing facilities and mature technologies, might provide an interesting solution for emergency drives requiring minimal regulation capabilities and very high reliability under direct DC supply.

Hybrid excitation is not much investigated in the literature especially for DC motors, although such solution may result potentially interesting especially when a limited flux adjustment capability is required.

The aim of this study is to design a guidance method to generate a smoother and feasible gliding reentry trajectory, a highly constrained problem by formalizing the control variables profile.

A novel accelerated fractional-order particle swarm optimization (FAPSO) method is proposed for velocity updates to design the guidance method for gliding reentry flight vehicles with fixed final energy.

By using the common aero vehicle as a test case for the simulation purpose, it is found that during the initial phase of the longitudinal guidance, there are oscillations in the state parameters which cause to violate the path constraints. For the glide phase of the longitudinal guidance, the path constraints have higher values because of the increase in the atmosphere density.

The violation in the path constraints may compromise the flight vehicle safety, whereas the enforcement assures the flight safety by flying it within the reentry corridor.

An oscillation suppression scheme is proposed by using the FAPSO method during the initial phase of the reentry flight, which smooths the trajectory and enforces the path constraints partially. To enforce the path constraints strictly in the glide phase, ultimately, another scheme by using the FAPSO method is proposed. The simulation results show that the proposed algorithm is efficient to achieve better convergence and accuracy for nominal as well as dispersed conditions.

Many off-line retailers have experienced a slump in sales and have the potential risk of overstock or understock. To overcome these problems, retailers have applied data mining techniques, such as association rule mining or sequential association rule mining, to increase sales and predict product demand. However, because these techniques cannot generate shopper-centric rules, many off-line shoppers are often inconvenienced after writing their shopping lists carefully and comprehensively. Therefore, the purpose of this paper is to propose a personalized recommendation methodology for off-line grocery shoppers.

This paper employs a Markov chain model to generate recommendations for the shopper’s next shopping basket. The proposed methodology is based on the knowledge of both purchased products and purchase sequences. This paper compares the proposed methodology with a traditional collaborative filtering (CF)-based system, a bestseller-based system and a Markov-chain-based system as benchmark systems.

The proposed methodology achieves improvements of 15.87, 14.06 and 37.74 percent with respect to the CF-, Markov chain-, and best-seller-based benchmark systems, respectively, meaning that not only the purchased products but also the purchase sequences are important elements in the personalization of grocery recommendations.

Most of the previous studies on this topic have proposed on-line recommendation methodologies. However, because off-line stores collect transaction data from point-of-sale devices, this research proposes a methodology based on purchased products and purchase patterns for off-line grocery recommendations. In practice, this study implies that both purchased products and purchase sequences are viable elements in off-line grocery recommendations.

A novel distributed middleware service platform, called MidGate platform, is presented in this paper. The central contribution is description of the developed MidGate platform and its architecture focusing especially on the adaptation, context‐awareness, and personalization of mobile and pervasive services. The research problem addressed is how to facilitate the development of interoperable applications and services into heterogeneous and distributed service gateway based environments. A requirement analysis of future mobile and pervasive services and key technologies has been carried out to establish a solid base and requirements for the development of the MidGate platform. The key mechanisms supporting adaptation, context‐awareness, and personalization of applications and services are presented. The novel middleware architecture solution of the MidGate platform utilizing these key mechanisms is also described. The MidGate architecture utilizes the emerging Generic Service Elements (GSE) approach, where generic and collectively utilizable services are provided to applications as middleware services that are part of a service platform. The main contribution of this research is the definition of a set of GSEs, the related MidGate platform architecture and its evaluation. The evaluation of the MidGate platform has been carried out in series of laboratory prototypes. The evaluation indicates that the MidGate platform solution is well applicable in various service gateway‐based distributed systems and extends well into resource‐constrained mobile environments.

Research excellency has long been the dominant paradigm in assessing academic quality and hence a prime determinant of academic careers. Lately, this approach to academic performance has come under higher scrutiny for its narrow focus on the individual, promoted an exclusive, performance-oriented talent management and inhibiting collaboration, transparency and societal involvement.

As a response to the limitations of the excellency policy, this chapter examines the emergence of open science as a transformative force in the academic world. Open science represents a paradigm shift, emphasizing the importance of transparency, and increased societal engagement in the academic process. It opens up the possibility to include the context dimension, multiple stakeholders and a more diverse set of development and performance indicators.

This chapter stresses the urgent need to realign our system of recognition and rewards with the premise of open science and with talent management. By highlighting the disconnect between current recognition mechanisms and the values of universities, this chapter emphasizes the necessity of transformative changes at institutional and systemic levels.

To provide concrete insights into the implementation of these changes, this chapter explores a case study of Utrecht University. This specific example showcases how strategic decisions at an institute level allow navigation of the complexities of recognizing and rewarding open science practices. The Utrecht University case study serves as an inspiration for other institutions seeking to embrace open science and adapt their policies and practices accordingly.

Whilst existing literature on real estate risk management focusses almost exclusively on holistic risk management techniques, documented increases in frequency and magnitude of unforeseen, rare and extreme events can throw up sudden, unexpected shocks that can challenge recognised real estate decision-making strategies. The paper aims to discuss this issue.

To advance real estate decision-making practice in this area, this research paper takes the skilfully conceptualised downside risk framework presented by Diebold et al. (2010), being the known (K), the unknown (u) and the unknowable (U) risk categories, to provide a blueprint for effective real estate decision making in a changing global environment.

In recording categories of risk, managing uncertainty can be achieved by an interrelated approach of adaption, robustness and resilience. This is important part of a real estate manager’s decision-making toolkit as risk recognition and knowledge of KuU event categories can augment an effective management strategy.

The mastery of modern real estate risk management can be better served by understanding and managing extreme downside risk events. Creating a comprehensive risk management framework can enhance comparative real estate performance whereby unprepared competitors fail in a world increasingly affected by large, highly improbable and unpredictable events.

The aim of this paper is to describe a project to provide an online web portal that can be used as a front‐end for all university users – students, teachers, staff – and services, library, administration, e‐learning, and e‐mail.

The profile model proposed is mainly inheritable, defined by profile components with transactional templates, security rules and layout templates. This approach is being tested on a campus prototype portal. The prototype is an underway project in development at our university.

This paper explains that, even though different from the overall approaches, the basic profile and the profile management system proposed are solid alternatives when addressing personalization of organization users.

This new approach explains how user modelling applications can be integrated into any organization without the cost of reengineering the entire information system already in place.

Researchers have used many techniques in designing intrusion detection systems (IDS) and yet we still do not have an effective IDS. The interest in this work is to combine techniques of data mining and expert systems in designing an effective anomaly‐based IDS. Combining methods may give better coverage, and make the detection more effective. The idea is to mine system audit data for consistent and useful patterns of user behaviour, and then keep these normal behaviours in profiles. An expert system is used as the detection system that recognizes anomalies and raises an alarm. The evaluation of the intrusion detection system design was carried out to justify the importance of the work.