Quality and Reliability (Greece)

IntroductionParental mental illness is a major public health issue across the globe with well-known intergenerational impacts on children. There is a wide body of evidence supporting the effectiveness of a range of interventions supporting families, however, their implementation has rarely been sustained across public health systems. Systemic change is an important part of workforce development and known to be crucial to embed and sustain practice, policy and structural initiatives in services for families. While much is known about the barriers to implementing family focused approaches within organizations and systems, less is known about how systems change occurs and what supports systems change to improve outcomes for families.MethodsThis study uses a Delphi method, with 103 experts from 17 countries participating to identify systems change factors from their own experience and to build consensus about key strategies required across the globe to support systems change in health, education, social welfare and mental health services.ResultsThe findings identify that systems change can be defined as any workforce, policy, legislation or other mental health promotion strategy that collectively contributes to improving outcomes for parents with mental illness, their children and their families. A systems approach to improve outcomes for families where a parent has a mental illness requires partnerships and collaboration between services and sectors affecting families (mental health, welfare, primary health, education, social care, public health), social and health policy development, and families themselves. Success in system change requires a focus on change at all levels of the system for momentum building, leadership support, the use of relevant data and reporting mechanisms, establishing practice competency and collaborative care, and being able to reflect and adapt to changing conditions and structural barriers.DiscussionA focus on system change for supporting families where a parent has a mental illness appears to require the combination of many strategies and factors, with international approaches to knowledge sharing imperative to support implementing, resourcing and sustaining change.

Social media text and posts are analyzed through advanced computing and artificial intelligence systems to determine user sentiments. The word intensity and activities involved in their use are analyzed to determine user sentiments. Sentiment140 is used in this study with a corpus of 1.6 million labelled tweets and proposes a systematic way of classifying sentiments. The suggested algorithm entails preprocessing of data, tokenization, min-max normalization, feature extraction through TF-IDF, and class balancing using SMOTE. The processed data is trained with a DistilBERT model to learn contextual dependencies between and semantic structure of textual data. The experimental assessment proves that the suggested model is 98% accurate, 96% precise, with a recall of 96% and an F1-score of 98%, which is far more accurate and precise than the existing models, including Random Forest and Decision Tree and the BERT baseline. The outcomes show that the proposed system works and is efficient, therefore it may be used for social media sentiment analysis on a big scale. This paper forms an excellent basis for future extensions, such as multilingual, multimodal and explainable sentiment classification systems.

Fluorescence spectroscopy is among the most commonly used methods that for analytic studies. The quality of the measured fluorescence data is dependent on various technical parameters. Among them are the excitation intensity, the bandwidths of the slits, the sensitivity of the detector, and the number of scanned wavelengths where the analyte emits. The shortest scanning mode is a single point measurement, which is very short and has the lowest potential to inflict photodamage to the analyte. however, such method may be blind to measurements errors since it does not show the whole peak of the analyte. The most common scanning method is the emission scan, in which λEx is fixed and λEm is variable. A less common method is excitation scan in which λEx is variable and λEm is fixed. Both methods may be restricted by the light scattering of the lamp at wavelengths that are close to the excitation and its double value. A more advanced method is synchronous fluorescence in which λEx and λEm varies during the scan with a constant gap between them. This method is less restricted by the lamp scattering and also display narrower peaks. The most comprehensive scanning method is excitation-emission matrixes that measure the whole fluorescence map. However, while using a system with a photo-multiplier detector, the scan time may be relatively long and cause photodamage to the analyte. In this work, we demonstrate a new scanning method named optical path fluorescence in which the scanning path is conducted from the maxima of the peaks toward their shortest way down to the noise level. We show that upon addition of increasing artificial noise levels, the optimal path method can predict the concentrations of the 3-polyaromatic hydrocarbons in a mixture better than all other scanning methods.

Sex chromosome trisomies (SCTs), caused by the presence of an extra X or Y chromosome, are among the most common chromosomal abnormalities but remain substantially underrecognized. Klinefelter syndrome (47,XXY) is the most frequently diagnosed SCT and, as a result, has been the primary focus of research on associated comorbidities, while individuals with 47,XYY and 47,XXX are typically undiagnosed and understudied. This reliance on clinically identified cases introduces bias and limits understanding of the full spectrum of SCT-associated disease, particularly for physical health outcomes. Large-scale biobanks that link genetic data with medical records offer a unique opportunity to study both diagnosed and undiagnosed SCTs across subtypes. This retrospective cohort study uses biobank data to examine the risk of physical comorbidities across all three SCTs, including both diagnosed and undiagnosed individuals. Data were compiled from three large population-based biobanks: the Million Veteran Program (MVP), FinnGen, and the UK Biobank, each of which includes genotyped blood-derived DNA linked to detailed electronic health records. The MVP cohort was started in 2011 and includes ~650,000 veterans in the VA health care system, while FinnGen includes 500,000 Finns across varied national health registries, with records beginning at or before 1969. The UK Biobank is a prospective cohort of ~500,000 volunteers in the United Kingdom aged 40 to 70 that began recruitment in 2006. To determine the presence of an SCT, the researchers analyzed SNP array data for the relative signal strength of X and Y chromosomes present, from which the copy number was inferred. The number of these patients who were officially diagnosed was abstracted from ICD-9 or ICD-10 codes. Disease outcome rates were captured using phecodes, which are clinically meaningful groups of International Classification of Diseases (ICD) codes, and were grouped into system-based categories. Associations between SCT subtypes and lifetime disease risk were evaluated using a matched case-control design, with each SCT carrier matched to five control individuals by genetic sex, birth year, and genetic ancestry. Among the 1.5 million individuals included, 2769 (0.19%) were found to have an SCT, the large majority of which (86.2%) were undiagnosed. Diagnosis rates differed by subtype, with 26.2% of 47,XXY patients, 1.4% of those with 47,XYY and 6.4% of those with 47,XXX carrying a clinical diagnosis. Phenome-wide association analyses identified hundreds of disease associations spanning nearly all organ systems, with 43 conditions shared across all three SCTs, most of which were categorized as cardiovascular, respiratory, and metabolic diseases. Vascular conditions, including venous thromboembolism, atherosclerosis, and cerebrovascular disease, showed strong associations across SCT subtypes. Metabolic and respiratory conditions, such as obesity, type 2 diabetes, asthma, and sleep apnea, were also more prevalent across SCT subtypes. Subtype-specific associations were also identified, with reproductive and bone disorders concentrated in 47,XXY, select renal and infectious conditions concentrated in 47,XXX, and no distinct phenotype unique to 47,XYY. The findings show that most individuals with SCTs remain undiagnosed into adulthood and experience an increase in a wide range of chronic diseases that are shared across SCT subtypes. Despite the historical attribution of many of these phenotypes to hypogonadism, particularly in cases of 47,XXY, the commonalities found across karyotypes, genetic sexes, and cohorts suggests that increased sex chromosome gene dosage plays a central role in SCT pathophysiology. The vascular phenotypes that were frequently observed across all three SCTs, including venous thromboembolism and chronic venous disease, are consistent with previous studies, and future research should investigate possible underlying etiologies. Limitations of the study include reliance on EHR-based phenotyping, potential homogeneity between biobanks, and limited power to assess phenotype severity or differences across ancestral groups. (Abstracted from Am J Hum Genet. 2025;112:2088–2101. doi:10.1016/j.ajhg.2025.07.017)

SUMMARY & CONCLUSIONSFailure Mode and Effects Analysis (FMEA) is essential in reliability engineering but is often manual and time-consuming. This work implements local version of a semi-automated workflow that integrates Microsoft Excel, Python (using the xlwings package), and ChatGPT to accelerate and standardize FMEA generation while retaining engineering oversight.Engineers begin by entering the system context in an Excel worksheet; a Python back end then queries ChatGPT to generate an initial list of system components and candidate failure modes. Engineers review and refine these suggestions, define system boundaries and each component’s function, and approve or modify the items captured in designated Excel cells. The Python functions prompt ChatGPT with component context and repair/warranty data so that suggested failure modes, effects, and causes are grounded in organization-specific evidence rather than generic model knowledge.The implemented workflow produces FMEA entries that align more closely with observed field failure trends by incorporating repair and warranty data into prompt context. Auto-population of failure modes, causes, and effects substantially reduces routine authoring effort; initial tests indicate markedly faster completion times and broader coverage of plausible failures. Offloading routine content generation to the LLM allows engineers to focus on analysis, validation, and prioritization, improving consistency and the effective use of engineering judgment.Combining a familiar spreadsheet interface with Python automation and ChatGPT produces a practical, deployable semi-automated FMEA process. The approach enables reliability teams to generate richer, data-informed FMEA results more quickly, enhancing productivity and providing deeper insight into failure analysis. Planned Version 2 (cloud) work will include user-upload able, product- and system-specific knowledge bases (with macros to upload and tag documents to the cloud) so that subsequent Design Failure Mode and Effects Analysis (DFMEA) for the same system can limit retrieval-augmented generation (RAG) retrieval to a curated, relevant document set.

OBJECTIVEThis paper aims to highlight the importance of reliability analysis when applied to reparable systems and how it can assist managers, supervisors, and technical professionals in determining the optimal replacement or overhaul time for a fleet of reparable equipment. It also demonstrates how this analysis can aid in forecasting failures, costs, and mean time between failures (MTBF).