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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.

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

The most recent wave of the opioid epidemic in the USA has been complicated by a sharp increase in methamphetamine use. In addition, many people classified with opioid use disorder (OUD) and methamphetamine use disorder (MUD) present indications of psychiatric conditions. These diagnoses are also highly prevalent among people who are admitted to jails, but research conducted with this population is limited, due in part to the challenges associated with gaining access to local detention centers. This paper aims to examine the patterns of psychiatric conditions, OUD, and MUD among an understudied population to help inform the development of service delivery systems.

The present study was designed to assess the prevalence of OUD, MUD and common psychiatric conditions in a large sample of adults (n = 846) collected from four local jails. Diagnostic patterns were evaluated according to the current criteria established in the Diagnostic and Statistical Manual of Mental Disorders (5th ed; American Psychiatric Association, 2013).

More than half (57.3%) of the sample met criteria for MUD, one-third (37.2%) exceeded the threshold for an OUD diagnosis and 15.7% were classified with both conditions. Participants who met criteria for both MUD and OUD were significantly more likely to experience symptoms of major depression [adjusted odd ratios (aOR) = 1.76, 9, confidence intervals (CI) = 1.16–2.67], post-traumatic stress disorder (aOR = 2.51, 1.64–3.83), panic attacks (aOR = 3.24, 95% CI = 2.05–5.13), obsessive compulsive disorder (aOR = 2.74, 95% CI = 1.66–4.51) and antisocial personality (aOR = 3.03, 95% CI = 1.97–4.64).

These results, which were derived from an understudied population of adults detained in local jails, indicate the co-–occurrence of MUD and OUD are associated with certain psychiatric conditions.

This study aims to evaluate the dementia assessment and diagnosis care provided to adults with intellectual disability. The authors selected recommendations from the National Institute for Health and Care Excellence (NICE) standards which could be evidenced in clinical notes and aimed to identify characteristics which may be associated with improved adherence to these recommendations.

The study population was adults with an intellectual disability who were diagnosed with dementia between January 2019 and December 2022 by a UK-based intellectual disability service. Data to demonstrate adherence to selected recommendations and demographic and clinical characteristics were extracted from electronic patient records.

The authors identified 41 individuals. A mean of six of the eight recommendations were adhered to. There was low adherence with structural imaging to support dementia subtype diagnosis (9 individuals, 22%). This may be linked with the low percentage of people diagnosed with vascular dementia (1 individual, 2%) despite a national figure of 20%. No demographic or clinical characteristics were associated with level of adherence recorded. The authors found incomplete recording of diagnostic clinical coding in electronic patient records. This may disadvantage this population, as they cannot be readily identified for post diagnostic support or resource allocation.

To the best of the authors’ knowledge, this is the first study to examine adherence to these NICE guidelines in this population.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

This paper makes a conceptual contribution by intersecting two strands of literature (return to work following health issues and industrial relations) to facilitate our understanding of the potential role of social dialogue in supporting return to work (RTW) following the diagnosis of a chronic illness. It conceptualises the levels and channels through which various actors and their interactions may play a role in RTW facilitation within the actor-centred institutional framework.

The paper uses an exploratory design based mainly on desk research but is also informed by roundtable discussions done in six countries as part of a larger project.

The conceptual and analytical framework (CAF) is developed to explain how various actors interact together in ways shaped by the RTW policy framework and the industrial relations systems, resulting in a continuum of RTW facilitation situations.

There is limited research on return-to-work policies following diagnosis of chronic illness from a comprehensive actor-oriented perspective. The existing literature usually focusses on just one stakeholder, overlooking the role of social dialogue actors. By bridging the two streams of literature and incorporating all potential actors and their interactions in a unitary model, the proposed framework provides a valuable tool to further discuss how successful RTW after a diagnosis of chronic illness can be facilitated.

This paper aims to evaluate the quality of co-production between lived experience practitioners (LXPs) and professionals in an interactive National Health Service webinar series aimed at supporting people who were diagnosed or identified with borderline personality disorder.

Transcripts from the webinars were subjected to mixed-method examination combining Foucauldian discourse analysis (FDA) and content analysis (CA).

FDA identified nine discursive objects: diagnosis beyond its medical context, diagnosis as a total explanation, being the other, universality, compassion, hope, faking it, mentalisation and co-production. CA demonstrated those nine discursive objects each corresponded with equalised airtime appropriated by professionals and lived experience practitioners.

The sample was limited and if applied to other mental health settings might reveal different findings. More needs to be understood about the attitudes of professionals and LXPs that support discourse sharing. Although this study has offered evidence of the quality of co-production, it can say very little about whether the co-productive approach offers superior outcomes to other forms of treatment.

Further research could employ FDA and CA to further explore how co-production is being enacted in other situations, with different models, where comparable interventions are delivered. Future research could compare outcomes between co-productive and professional-only interventions.

This study examined naturalistic practice to build new theory in an under-researched area for a substantial mental health population.

This paper aims to investigate the efficacy of teeth flux sensors in detecting, locating and assessing the severity of short-circuit faults in the stator windings of induction machines.

The experimental study involves inducing short-circuit winding turn variations on the induction machine’s stator and continuously measuring the RMS values across teeth flux sensors. Two crucial steps are taken for machine diagnosis: measurements under load operating conditions for fault detection and measurements under no-load conditions to determine fault location and severity.

The experimental results demonstrate that the proposed approach using teeth flux sensors is reliable and effective in detecting, locating and evaluating the severity of stator winding faults.

While this study focuses on short-circuit faults, future research could explore other fault types and alternative sensor configurations to enhance the comprehensiveness of fault diagnosis.

The methodology outlined in this paper holds the potential to significantly reduce maintenance time and costs for induction machines, leading to substantial savings for companies.

This research contributes to the field by presenting an innovative approach that uses teeth flux sensors for a comprehensive fault diagnosis in induction machines. The originality lies in the effectiveness of this approach in providing reliable fault detection, location and severity evaluation.

In the state of Hawaii, it has been shown that certain ethnic minority groups, such as Filipinos and Pacific Islanders, suffer disproportionally high rates of cardiovascular disease, evidence that local health-care systems and governing bodies fail to equally extend the human right to health to all. This study aims to examine whether these ethnic health disparities in cardiovascular disease persist even within an already globally disadvantaged group, the houseless population of Hawaii.

A retrospective chart review of records from Hawaii Houseless Outreach and Medical Education Project clinic sites from 2016 to 2020 was performed to gather patient demographics and reported histories of type II diabetes, obesity, hyperlipidemia, hypertension and other cardiovascular disease diagnoses. Reported disease prevalence rates were compared between larger ethnic categories as well as ethnic subgroups.

Unexpectedly, the data revealed lower reported prevalence rates of most cardiometabolic diseases among the houseless compared to the general population. However, multiple ethnic health disparities were identified, including higher rates of diabetes and obesity among Native Hawaiians and other Pacific Islanders and higher rates of hypertension among Filipinos and Asians overall. The findings suggest that even within a generally disadvantaged houseless population, disparities in health outcomes persist between ethnic groups and that ethnocultural considerations are just as important in caring for this vulnerable population.

To the best of the authors’ knowledge, this is the first comprehensive study focusing on ethnic health disparities in cardiovascular disease and the structural processes that contribute to them, among a houseless population in the ethnically diverse state of Hawaii.

Attention deficit hyperactivity disorder (ADHD) is a neuro-developmental condition that has frequently been pathologised in career research and broader society to date. The study seeks to reframe such assumptions through a qualitative positive-focused exploration of career stories of ADHD adults, elicited through a strength-focused technique with wide applicability for coaching and other career-based development activities.

Situated in a strength-focused coaching psychology paradigm, the authors undertook semi-structured interviews with 17 participants, using an adapted feedforward interview technique (FFI) rooted in positive psychology (PP), to investigate individuals' strengths and successful career experiences.

Narrative thematic analysis of the transcripts identified two core themes: “the paradoxical nature of strengths” and “career success as an evolving narrative”. The participants described how they have achieved career success both “in spite of” and “because of” ADHD. The use of the FFI demonstrated a helpful and easily taught method for eliciting personal narratives of success and strengths, an essential foundation to any coaching process.

This research provides a nuanced overview, and an associated conceptual model, of how adults with ADHD perceive their career-based strengths and experiences of success. Further, the research shows the value of using a positive psychological coaching approach when working with neurominority individuals, using a successful adaptation of the FFI. The authors hope that the documentation of this technique and the resulting insights will offer important guidance for managers as coaches and internal and external career coaches, as well as providing positive and relatable narrative resources for ADHD adults.