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The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

The purpose of this paper is to share the lessons learned in implementing specific design patterns within the “Dr Internet” massive open online course (MOOC).

MOOCs are boasting considerable participant numbers, but also suffer from declining participant activity and low completion rates. Learning analytics results from earlier xMOOCs indicate that this might be alleviated by certain instructional design patterns – critical aspects include shorter course duration, narrative structures with suspense peaks, and a course schedule that is diversified and stimulating. To evaluate their impact on retention, the authors have tried to implement these patterns in the design of the “Dr Internet” MOOC.

Statistical results from the first run of the case study MOOC do not indicate any strong influences of these design patterns on the retention rate.

With inconclusive statistical results from this case study, more research with higher participant numbers is needed to gain insight on the effectiveness of these design patterns in MOOCs. When interpreting retention outcomes, other influencing factors (course content, pacing, timing, etc.) need to be taken into account.

This publication reports about a case study MOOC and gives practical hints for further research.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

This paper aims to address the long-standing problem of suboptimal student team experiences for instructors and students by incorporating the student voice by co-creating a virtual team collaborative environment to improve team collaboration in the online classroom.

This paper presents a novel design science research approach and relates two elaborated action design science research (eADSR) cycles that design, implement and evaluate the student team experience in online courses requiring teamwork.

The outcome is a holistic view of a virtual team classroom environment specified with technologies and practices that may be employed to optimize the student team experience. The eADSR process yields non-obvious diagnoses and actionable steps for continually incorporating the ever-changing social aspects unique to students in addition to the evolving technological landscape.

This paper is valuable to faculty members interested in applying eADSR processes to incorporate the student voice to address pedagogical and learning challenges in the classroom. Additionally, it provides a DSR-based model that can be implemented in the classroom to improve student team collaboration as well as transparency for the instructor and the students in terms of team member contributions with the goal to alleviate student and faculty frustrations. This topic is particularly relevant in light of COVID-19 as students and faculty alike are thrust into new online classroom environments.

Employing eADSR in the classroom is a novel and unique approach to create a replicable model for virtual team collaboration that can be added to the classroom.

Parents whose children have undergone an autism assessment often describe the process as extremely stressful. This affects how parents engage with services post-diagnosis, meaning less likelihood of using subsequent service support despite struggling to cope. Since parents already report many barriers to accessing services, e.g. long waiting lists, lack of co-ordination and limited resources (Sapiets et al. 2023), negative experiences during assessment should not pose another potential barrier to engagement. This study aimed to address how families’ needs can be better met during the assessment process.

In this qualitative study, the author conducted semi-structured interviews with 11 parents whose child had undergone an autism assessment in the last five years. Thematic analysis determined key themes.

The six themes were: clarity and communication, access to support and resources, aftercare, recognition of parent concerns, personalisation of the assessment process and concerns around the use of personal protective equipment/online assessments. These themes led to criteria to assess the quality of autism assessment services in line with parent perspectives.

These parent-informed criteria could facilitate the consideration of parents’ views into service evaluations of autism assessment services across the UK.

Previous research indicates that the autism assessment experience is often extremely stressful and overwhelming for families (Crane et al., 2016). Despite this, guidance to improve autism services rarely prioritises the opinions and experiences of service-users and their families. The criteria presented here were derived from themes identified by interviewing parents on their experiences of the autism assessment process, thus shifting the focus onto service-users.

This paper aims to reflect on the outcomes of a community-based video-conferencing intervention for depression, predating the COVID-19 pandemic. The study investigates the potential implications of its findings for enhancing adherence to digital mental health interventions. The primary objective is to present considerations for researchers aimed at minimising the intention-behaviour gap frequently encountered in digital mental health interventions.

A randomised control feasibility trial design was used to implement a telehealth model adapted from an established face-to-face community-based intervention for individuals clinically diagnosed with depression. In total, 60 participants were initially recruited in association with a local mental health charity offering traditional talking-based therapies with only eight opting to continue through all phases of the project. Modifications aligning with technological advancements were introduced.

However, the study faced challenges, with low uptake observed after an initial surge in recruitment interest. The behaviour-intention gap highlighted technology as a barrier to service accessibility, exacerbated by participant age. Furthermore, the clinical diagnosis of depression, characterised by low mood and reduced interest in activities, emerged as a potential influencing factor.

The limitations of the research include its pre-pandemic execution, during a nascent stage of technological mental health interventions when participants were less familiar with online developments.

Despite these limitations, this study's reflections offer valuable insights for researchers aiming to design and implement telehealth services. Addressing the intention-behaviour gap necessitates a nuanced understanding of participant demographics, diagnosis and technological familiarity.

The study's relevance extends to post-pandemic society, urging researchers to reassess assumptions about technology availability to ensure engagement. This paper contributes to the mental health research landscape by raising awareness of critical considerations in the design and implementation of digital mental health interventions.

Reflections from a pre-pandemic intervention in line with the developments of a post-pandemic society will allow for research to consider that because the technology is available does not necessarily result in engagement.