Although many previous studies have highlighted the advances in prediction models, instruments for pathological and histological diagnosis and treatment, as well as individualized treatment modalities in mental disorders, these previous syntheses usually study the research outcomes separately and ignore the holistic integration of research regarding artificial intelligence technological approaches, data sets used and applications in mental health research. We used the BioBERT pretrained language model to systematically extract relevant information and develop an extensive knowledge graph that includes 3158 entities connected with 3248 different relationships. Our knowledge graph delineates essential artificial intelligence technological frameworks and explicitly maps out the relationships linking artificial intelligence methods, mental disorders, and diverse data modalities. The synthesis, centered on the analytical axis of "method-disease-data," highlights key research areas where artificial intelligence and neuropsychiatry meet. Specifically, it focuses on key applications in early detection, improved accuracy of diagnosis, and individualized therapeutic interventions. In addition, we summarized the applications derived from basic research findings that extend to ongoing clinical trials, revealing the path toward future clinical application in psychiatric work. Importantly, the research paid special attention to the application of artificial intelligence in identifying key brain regions and neural circuits, providing important clues for elucidating the neural mechanisms of mental disorders and developing targeted interventions. Although artificial intelligence presents great opportunities, there are also significant challenges, including imbalanced data sets, ethical issues, and clinical concerns about trustworthiness and transparency. Strategies to address these challenges are proposed, and a perspective on emerging methods enabled by artificial intelligence is provided, which are expected to greatly change the management and treatment of the future.

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To investigate how task delegation for perioperative anticoagulation management is reported in protocols and daily practice, and how this translates to additional information in work-as-imagined and work-as-done using FRAM to identify improvement possibilities. Existing FRAM visualisations of perioperative anticoagulant management in a Dutch academic hospital were extended to include task delegation in protocols using document analysis and in daily practice using a focus group of eight healthcare professionals, analysed using content analysis. Distinguishing between formal responsibility and task execution revealed additional functions and roles in both work-as-done and work-as-imagined, resulting in more discrepancies between work-as-done and work-as-imagined. Professionals experienced responsibility for delegated tasks, even without formal accountability, and needed to feel qualified and capable as well as complete clear information to accept these tasks. Including task delegation in FRAM models improves the understanding of healthcare processes, communication about distribution of responsibilities and thereby development of more effective improvement strategies.

This work presents a numerical investigation of the applicability of the product of formaldehyde (CH 2 O) and hydroxyl radicals (OH), denoted as CH 2 O ⋅ OH, as a marker for the local heat release rate (HRR) in CH 4 flames over a broad range of boundary conditions. Both laminar freely propagating one-dimensional and premixed counter-flow CH 4 flames were simulated using several detailed reaction mechanisms. The analysis systematically varied the oxidizer composition (from air to pure oxy-fuel), equivalence ratio ( Φ ), inlet temperature, pressure, and strain rate. The correlation between the CH 2 O ⋅ OH profiles and the HRR was quantified using the Pearson correlation coefficient. Overall, the study confirms CH 2 O ⋅ OH as a practical and reliable HRR marker in CH 4 flames, particularly under high-temperature and high-pressure oxy-fuel conditions. The results show that in slightly rich CH 4 -air flames (about Φ = 1.5 ), CH 2 O ⋅ OH provides an accurate estimate of the HRR. In oxygen-enriched and pure oxy-fuel flames, the strongest correlation shifts to ultra-rich conditions (about Φ ≈ 3.0 ). Increasing pressure enhances the correlation at lower equivalence ratios, whereas preheating weakens the correlation locally but expands the overall range of validity. Although increasing strain rate generally degrades the correlation, CH 2 O ⋅ OH remains a robust marker under technically relevant turbulent conditions (strain rates below 10,000 s − 1 ), maintaining Pearson correlation coefficients of approximately R ≈ 0.9 even close to extinction. This demonstrates the strong feasibility of using CH 2 O ⋅ OH for HRR estimation in highly turbulent flames. The demonstrated robustness of CH 2 O ⋅ OH highlights its suitability as an HRR marker, providing valuable insights for experimental diagnostics. • CH 2 O ⋅ OH confirms as robust HRR marker across a wide range of conditions including variations in oxidizer content, equivalence ratio, preheat temperature, pressure, and strain. • For CH 4 -air flames, the highest correlation between CH 2 O ⋅ OH and HRR occurs at slightly rich mixtures ( Φ ≈ 1.5), while in CH 4 /O 2 flames, the strongest correlation shifts to ultra-rich conditions ( Φ ≈ 3.0). • Elevated pressure generally improves the correlation at leaner mixtures but narrows the range of high correlation, whereas preheating shifts the optimal equivalence ratio to richer conditions and broadens the applicability range. • Even under high strain rates (up to 10,000 s − 1 , relevant for turbulent flames), CH 2 O ⋅ OH maintains a strong correlation (R ≈ 0.9) with HRR, highlighting its suitability for turbulent combustion diagnostics. • The results of this study are intended to support and guide experimental diagnostics by identifying reliable marker regimes.