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Autism spectrum conditions (ASC) and borderline personality disorder (BPD) have overlapping symptom profiles. Dialectical behaviour therapy (DBT) is an established treatment for self-harm and BPD, but little research has investigated the outcomes of DBT for ASC populations. This exploratory service evaluation aims to investigate the outcomes of a comprehensive DBT programme for adolescents with a diagnosis of emerging BPD and a co-occurring ASC diagnosis as compared to those without an ASC diagnosis.

Differences from the start to end of treatment in the frequency of self-harming behaviours, BPD symptoms, emotion dysregulation, depression, anxiety, the number of A&E attendances and inpatient bed days, education and work status, and treatment non-completion rates were analysed for those with an ASC diagnosis, and compared between those with an ASC diagnosis and those without.

Significant medium to large reductions in self-harming behaviours, BPD symptoms, emotion dysregulation and inpatient bed days were found for those with an ASC diagnosis by the end of treatment. There were no significant differences between those with an ASC and those without in any outcome or in non-completion rates. These findings indicate that DBT may be a useful treatment model for those with an ASC diagnosis, though all results are preliminary and require replication.

To the best of the authors’ knowledge, this is the first study to report the outcomes of a comprehensive DBT programme for adolescents with an ASC diagnosis, and to compare the changes in outcomes between those with a diagnosis and those without.

This study aims to examine the impact that diagnostic factors such as duration of diagnostic period, number of professionals consulted and perceived social support have on parental stress during the diagnostic process of autism spectrum disorder (ASD).

Forty parents of 2–18 year-old children/adolescents with a formal ASD diagnosis recruited from five specialized private clinics in Lisbon completed a survey which included diagnostic questionnaire, parental stress scale and social support scale from April to December 2022.

Data analysis indicated that the mean age of the diagnosis was 5.6 years with a delay of 3.95 years from first concern until final diagnosis. On average, parents consulted a mean of 3.62 professionals. Other findings indicated that as age of parents increased, levels of parental stress decreased. Parents with higher levels of social support had lower levels of parental stress. Furthermore, higher age of child at first concern predicted higher affective social support.

This study reflects on the experience of obtaining the ASD diagnosis in Portugal, raising awareness on the importance of providing early detection and social support for distressed parents.

The purpose of this paper is to explore the historic policy context and social implications of the diagnosis of personality disorder and also consider formulation-based and trauma-informed understandings of distress.

Ongoing changes to (and splits between) medical understandings of what is being labelled as personality disorder have eroded the label’s cultural capital, adding weight to lived-experience-led calls to Drop the Disorder (Watson, 2019). This paper explores the impact and implications of the historic policy and practice context through a lived experience lens.

Such diversity of views in the lived experience and medical communities on personality disorder has allowed alternatives to diagnostically informed understandings of distress (such as formulation-based and trauma-informed approaches) to gain traction with practitioners (Bloom and Farragher, 2013; Johnstone and Boyle, 2020). The broader assimilation of these alternative perspectives into dominant medical ideology is evidenced by the fact that the Royal College of Psychiatrists (RCP) is now also exploring alternatives to diagnosis (2023). This suggests even more change ahead for how we understand people and their relationships with trauma and distress.

This paper discusses UK policy and does not include broader global policies.

This paper would be helpful for any student interested in where the ideas that underpin personality disorder diagnosis stemmed from and why so many lived experience practitioners and experts by profession question the diagnosis' legitimacy.

As the RCP is now considering alternatives to diagnosis, it is even more critical that practitioners are aware of the competing narratives surrounding this contested diagnosis – as the author believes this will promote more compassionate, trauma-informed working practices.

This is the author’s own work and includes not only the RCP position change but also directly quotes Professor Tyrer (who wrote the International Classification of Diseases 11), giving his views on the changed RCP position, as he recently presented at a conference here in Cornwall. The author is a part of Lighthouse peer support group and wrote this paper as preparation for a Participatory Action Research project they are planning, where they will evaluate the Sanctuary Approach with their membership to create a lived experience-designed trauma-informed charter. Before starting that work, the author wanted to better understand the historic policy context and created this paper to fill that need.

This study aims to explore how young people in child and adolescent mental health services (CAMHS) in the UK, Ireland, The Netherlands and Croatia, experienced leaving CAMHS and identified a range of factors impeding optimal discharge or transition to adult mental health services (AMHS).

Interviews about discharge or transition planning, including what information was provided about their ongoing mental health needs, undertaken with 34 young people aged 17–24, all previous or current attendees of CAMHS. Some interviews included accounts by parents or carers. Data were thematically analysed.

A number of previously well-documented barriers to a well-delivered discharge or transition were noted. Two issues less frequently reported on were identified and further discussed; they are the provision of an adequately explained, timely and appropriately used diagnosis and post-CAMHS medication management. Overall, planning processes for discharging or transitioning young people from CAMHS are often sub-optimal. Practice with regard to how and when young people are given a diagnosis and arrangements for the continuation of prescribed medication appear to be areas requiring improvement.

Study participants came from a large cohort involving a wide range of different services and health systems in the first pan-European study exploring the CAMHS to adult service interface. Two novel and infrequently discussed issues in the literature about young people’s mental health transitions, diagnosis and medication management were identified in this cohort and worthy of further study.

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.