Search Results (900)

Artificial intelligence (AI) is a new paradigm in software engineering that automates key phases of the development cycle. The methods of creating test cases and designing requirements are still mostly manual and prone to error. Unclear requirements can result in expensive rework and undiscovered defects in the development process. Scalability and dependability are crucial concerns in complex systems. These shortcomings highlight the need for improved methods to enhance accuracy and consistency throughout these critical phases. To generate well-organized system requirements, this article outlines a clear strategy that leverages Extended Finite State Machine models as formal inputs for large language models (LLMs). Five system models are used to assess the suggested framework. The comparison analysis evaluates the accuracy, completeness, test coverage, and runtime efficiency of the artifacts. Along with a comparison with a human-made reference standard, the study evaluates the performance of LLMs such as ChatGPT-5, Claude Sonnet 4.5, and DeepSeek V3.2. The findings demonstrate that AI models can achieve human-comparable accuracy by exceeding 90% with EFSM-based prompting. Claude Sonnet generated the most reliable findings, ChatGPT demonstrated exceptional flexibility, and DeepSeek demonstrated exceptional runtime economy. These findings show that human–AI workflows provide a new paradigm in scalable, traceable, and reproducible system engineering. Full article

Solitary living is an evolutionarily widespread yet comparatively under-theorized social system, despite its occurrence across diverse animal taxa. Shrikes (family Laniidae) are small predatory passerines that combine raptorial behavior, strong territoriality, and predominantly solitary space use, making them a powerful model for examining the ecology and evolution of solitary living. Here, I synthesize published work on shrike behavioral ecology and explicitly link these traits to the costs and benefits of a solitary lifestyle. I argue that shrikes exemplify how solitary species can offset the absence of social buffering through cognitive innovation, finetuned habitat selection, and flexible yet tightly bounded sociality. I then compare shrike ecology to solitary mammals and reptiles, highlighting convergent patterns in resource dispersion, spatial memory, risk management, and juvenile dispersal. I further examine how anthropogenic pressures, such as habitat fragmentation, climatic instability, and urbanization, interact with solitary life histories and review evidence from management interventions in both European farmland and North American systems that demographic recovery is achievable but remains contingent on addressing broader land-use conflicts and sources of adult mortality. Finally, I outline five interconnected research priorities—spanning cognitive ecology, trophic interactions, movement ecology, genomics, and formal comparative analyses—that would move shrike research from its current observational foundation toward a more experimental, mechanistic, and phylogenetically informed programme. By reframing shrikes as a model taxon for solitary living, this review aims to integrate avian behavioral ecology into broader comparative frameworks of social organization, cognition, and resilience under global change. Full article

Efficient and approximate evaluation of urban coverage is important for wireless network planning. While standard statistical propagation models are fast, they do not directly describe the physical environment of a specific urban scene and consequently often fail to accurately capture local blockage and site-specific propagation effects. Ray tracing can model these effects more directly, but becomes costly when testing many tiles, frequencies, and transmitter heights simultaneously. To address this problem, the present study investigates the use of an RT-supervised simulation-to-simulation tile-based learning framework for path loss prediction based on geographic environmental information. This methodology first builds realistic 3D city scenes from geographic data, then uses offline ray tracing to generate supervision labels across multiple carrier frequencies and base-station heights. Each city region is divided into 500 m by 500 m tiles, which are then further discretized into 125 by 125 grids. For each tile, raster priors, such as occupancy, normalized height, and a valid-ground mask, are prepared. During training and inference, the model input is organized as an 8-channel raster tensor together with a 2D condition vector for frequency and transmitter height. The raster tensor combines three stored environment priors and five online-generated transmitter-related feature maps. By utilizing masked supervision, the network learns the excess loss residual exclusively on valid outdoor pixels, and the final path loss map is reconstructed by combining the residual prediction with the FSPL prior. The final model in this work was trained on 134,317 samples and validated on 33,589 samples. In the in-city setting, used as a preliminary verification before subsequent cross-city experiments, it achieved an MAE of 5.0116 dB and an RMSE of 9.3182 dB. On the formal cross-city test with a completely unseen target city, it achieved an MAE of 4.8536 dB and an RMSE of 9.3504 dB. These results demonstrate that the proposed framework can provide a stable tile-level approximation of RT-generated path loss maps under multiple conditions. Because both training labels and evaluation references are generated by RT rather than drive-test measurements, the present study should be understood as a simulation-to-simulation surrogate framework rather than a direct validation of real-world propagation accuracy. Full article

Information security compliance has become critical for organizations worldwide, with the ISO/IEC 27000 family serving as the most widely adopted framework for establishing information security management systems. Despite their global acceptance, these standards present significant interpretation challenges due to their formal language, abstract structure, and extensive cross-referencing across 97 documents. Traditional retrieval-augmented generation (RAG) systems, which rely on independent text chunking and dense vector retrieval, prove inadequate for such highly interconnected regulatory materials, often fragmenting contextual relationships and reducing accuracy. This study introduces a privacy-preserving RAG framework that integrates LightRAG, a knowledge graph-based retrieval system, with locally hosted open-source language models. Unlike chunk-based RAG systems that treat document segments independently, the system in this study constructs a semantic knowledge graph that explicitly models relationships between clauses through typed edges representing cross-references, semantic similarity, and hierarchical dependencies. To enable rigorous evaluation, we developed a curated benchmark dataset of 222 multiple-choice questions with authoritative ground-truth answers, systematically constructed from official ISO standards, certification preparation materials, and academic sources. Through systematic evaluation on this benchmark, we show that knowledge graph-based retrieval achieves higher accuracy than chunk-based RAG and non-retrieval LLM baselines within the evaluated setup. The analysis indicates that embedding model quality is strongly associated with system performance, that hybrid retrieval modes combining local and global graph traversal tend to yield better accuracy, and that mid-sized open-source models paired with strong retrievers can approach the performance of larger proprietary systems. The best configuration achieves 90.54% accuracy, demonstrating the promising effectiveness of graph-structured retrieval for multiple-choice regulatory questions. Full article

Background: Long-bone non-unions complicated by osteomyelitis remain a major reconstructive and healthcare challenge, particularly in resource-limited settings with a high prevalence of multidrug-resistant (MDR) pathogens. Conventional staged management is associated with a prolonged treatment burden, repeated procedures, and delayed functional recovery. This study evaluated the clinical, radiological, functional, and short-term safety outcomes of a single-stage approach using custom-threaded antibiotic-coated locking nails (TACLNs) in a high-resistance cohort. Methods: This prospective single-center cohort study enrolled 30 adults with osteomyelitis-associated femoral or tibial nonunion at a tertiary hospital in Peshawar, Pakistan. All patients underwent radical debridement and single-stage stabilization with a chest tube mold TACLN loaded with vancomycin and gentamicin, with culture-directed adjunctive antibiotics for resistant organisms. Outcomes were assessed at baseline, Weeks 3 and 6, and Month 6 using inflammatory markers, RUST score, VAS pain, EQ-5D-5L, ASAMI criteria, and return to work or usual activity. No formal sample size calculation was performed, and this study was exploratory in nature. Results: The cohort (mean age 44.9 ± 9.9 years) had a challenging microbiological profile, with 40.0% MDR and 13.3% extensively drug-resistant (XDR) infections. By Month 6, short-term infection control was achieved in 96.7% of patients, with significant reductions in ESR and CRP (both p < 0.001). Radiographic union was achieved in 90.0% of cases at a mean of 18.6 weeks, and the mean RUST score improved from 4.87 to 10.43 at the final follow-up. The VAS pain decreased from 5.23 at week 3 to 0.73 at month 6, EQ-5D-5L improved from 0.39 to 0.84, and 90.0% returned to work or usual activity by month 6. No cement debonding, implant failure, or nephrotoxicity was noted. Conclusions: In this single-arm exploratory cohort, TACLNs were associated with favorable short-term infection control, radiographic union, and functional recovery in osteomyelitis-associated long-bone nonunion, including in an MDR/XDR setting. The independent contribution of the threaded core design cannot be established. Larger multicenter comparative studies with longer follow-ups are needed to confirm the durability and implementation feasibility. Full article

Background: Post-cardiac arrest syndrome (PCAS) following out-of-hospital cardiac arrest (OHCA) is driven by global ischemia–reperfusion injury, endothelial dysfunction, and a dysregulated inflammatory response. This cascade frequently culminates in profound vasoplegia and multiorgan failure, even when guideline-directed post-resuscitation management is applied. Hemoadsorption using the CytoSorb device may attenuate hyperinflammation and vasoplegia by removing circulating inflammatory and injury-related mediators. Methods: This single-centre, retrospective cohort study compared adults with PCAS following OHCA who received hemoadsorption with propensity score-matched controls (1:1 matching; n = 50 per group). For patients treated with hemoadsorption, data were analyzed within predefined intervals covering the 24 h preceding therapy initiation (T1) and the 24 h following the completion of the hemoadsorption treatment period (T2). Controls were evaluated at time points aligned to those of their matched hemoadsorption counterparts. Hemodynamic, metabolic, respiratory, and organ injury markers were assessed. Results: Formal between-group comparisons of temporal change between T1 and T2 showed no statistically significant differences between hemoadsorption-treated patients and matched controls across key parameters, including VIS (Δ −18.7 vs. −7.7; p = 0.183) and lactate (Δ −1.8 vs. −1.25 mmol/L; p = 0.780), as well as markers of organ injury, pH, and oxygenation. In exploratory ANCOVA models, only base excess was associated with treatment group (p = 0.035). Survival to hospital discharge was comparable (48% vs. 40%; p = 0.423), with similar neurological outcomes. Within the hemoadsorption group, pre–post comparisons around hemoadsorption initiation (T1–T2) demonstrated marked improvements, including reduced vasoactive support (VIS 70.0 to 12.1; p = 0.039), substantial lactate clearance (4.1 to 1.1 mmol/L; p < 0.001), and declines in organ injury markers (AST, ALT, LDH, myoglobin), alongside more pronounced platelet reduction compared with controls (129 to 57 × 10³/µL vs. 189 to 123 × 10³/µL). However, adjusted analyses indicated that these changes were primarily driven by baseline shock severity rather than a treatment-specific effect. Conclusions: In this propensity score-matched cohort of PCAS patients after OHCA, hemoadsorption was associated with within-group physiological changes but showed no detectable advantage over matched controls, with similar survival. These findings are hypothesis-generating and warrant prospective studies with standardized timing and phenotype-guided patient selection. Full article

Multimodal biomedical machine learning increasingly integrates heterogeneous data sources (including medical imaging, multi-omics profiles, electronic health records, and wearable sensor signals) to support clinical diagnosis, prognosis, and treatment response prediction. Despite strong empirical performance, most existing multimodal systems lack a principled theoretical foundation for understanding why fusion improves prediction, how information is distributed across modalities, and when models can be trusted under incomplete or shifting data. This paper develops a unified information-theoretic framework that formalizes multimodal biomedical learning as an information optimization problem. We formulate multimodal representation learning through the information bottleneck principle, deriving a variational objective that balances predictive sufficiency against informational compression in an architecture-agnostic manner. Building on this foundation, we introduce information-theoretic tools for decomposing modality contributions via conditional mutual information, quantifying redundancy and synergy, and diagnosing fusion collapse. We further show that robustness to missing modalities can be cast as an information consistency problem and extend the framework to longitudinal disease modeling through transfer entropy and sequential information bottleneck objectives. Applications to multimodal foundation models, uncertainty quantification, calibration, and out-of-distribution detection are developed. Empirical case studies across three biomedical datasets (TCGA breast cancer multi-omics, TCGA glioma clinical-plus-molecular data, and OASIS-2 longitudinal Alzheimer’s data) show that the framework’s key quantities are computable and interpretable on real data: MI decomposition identifies modality dominance and redundancy; the VMIB traces a compression–prediction tradeoff in the information plane; entropy-based selective prediction raises accuracy from 0.787 to 0.939 at 50% coverage; transfer entropy reveals stage-dependent modality influence in disease progression; and pretraining/adaptation diagnostics distinguish efficient from wasteful fine-tuning strategies. Together, these results develop entropy and mutual information as organizing principles for the design, analysis, and evaluation of multimodal biomedical AI systems. Full article

Background: The landscape of paediatric oncology has undergone a remarkable transformation over recent decades. Advances in both oncological and supportive therapies have dramatically improved survival in children with haematological malignancies and solid tumours, with current survival rates exceeding 80% for many childhood cancers. However, this therapeutic success has brought with it an unexpected consequence: the intensification of treatment protocols has led to a parallel increase in life-threatening complications requiring intensive care support. Current evidence indicates that up to 40% of paediatric oncology patients will require admission to a Paediatric Intensive Care Unit (PICU) at some point during their disease trajectory. Objectives: This comprehensive review synthesises current evidence to provide an updated framework for PICU admission decision-making in oncology haematology patients. We have integrated the most recently published international guidelines, including the groundbreaking Phoenix 2024 sepsis criteria and the updated PALICC-2 2023 recommendations for paediatric acute respiratory distress syndrome. Beyond establishing admission criteria, we critically analyse the efficacy of advanced support strategies and examine emerging therapeutic approaches in this uniquely vulnerable population. Methods: Our methodology encompassed a systematic review of the literature published between 2011 and 2024, complemented by a detailed analysis of current international guidelines and expert consensus statements. We included randomised controlled trials, observational studies, meta-analyses, and consensus conference proceedings specifically addressing the intensive care management of paediatric patients with oncological or haematological conditions. Main Results: Several key findings emerge from our analysis. The Phoenix 2024 criteria represent a fundamental reconceptualisation of paediatric sepsis diagnosis, validated through an unprecedented dataset encompassing more than 3 million paediatric encounters. In the realm of respiratory support, early implementation of non-invasive ventilation (NIV) or continuous positive airway pressure (CPAP) has demonstrated remarkable efficacy, reducing the need for invasive mechanical ventilation by 45% (RR 0.45, 95% CI 0.26–0.78) when applied to appropriately selected patients. Extracorporeal membrane oxygenation (ECMO), whilst increasingly utilised, shows survival to decannulation ranging from 52% to 64%, though survival to hospital discharge remains less encouraging at 36–42%. Continuous renal replacement therapy (CRRT) has proven highly effective for tumour lysis syndrome, achieving metabolic correction in 90% of severe cases. Perhaps most promisingly, emerging biomarkers—particularly interleukin-6, interleukin-10, and procalcitonin—have substantially enhanced our ability to stratify infection risk, demonstrating sensitivity exceeding 85% for bacteraemia detection. Conclusions: The evidence unequivocally supports several core principles for optimising outcomes in this population. Early identification of deterioration through validated scoring systems enables timely intervention before irreversible organ failure develops. Prompt implementation of non-invasive respiratory support, when appropriately applied, can obviate the need for mechanical ventilation with its attendant complications. Perhaps most critically, centralisation of care in centres with dedicated expertise and comprehensive support capabilities fundamentally improves survival. These findings argue compellingly for the establishment of a formal national network of reference centres, implementing standardised protocols and structured care pathways specifically designed for critically ill paediatric oncology haematology patients. Full article

Graph-based spatial analysis formalizes relations among spatial units, but the formation of these units and their boundary correspondences remains under-specified. This study defines the structural stage preceding relational abstraction and establishes the conditions under which spatial units and boundary correspondences become analytically determinate. It then develops a layered spatial articulation procedure that derives spatial objects from plan-encoded architectural information by differentiating topographic substrate, building frame, spatial enclosure, and relational boundary conditions. These are organized into a base spatial graph. The topology of this graph is fixed by articulation, and its edges encode admissible relational mode combinations. Using traditional Korean housing (hanok) as an illustrative reference for the proposed methodology, the study shows that heterogeneous spatial conditions can be consistently articulated into a unified structural domain prior to relational abstraction. The resulting base spatial graph defines a finite but combinatorially extensive space of admissible relational configurations. Within this domain, graph-domain operations act without expanding the articulated structure, while certain operations may reduce it through structural transformation. The study shows that spatial units cannot be treated as pre-given entities but must be structurally constituted. By formalizing this prior stage, the study establishes explicit structural preconditions for graph-based spatial analysis and provides a consistent analytical domain for subsequent spatial interpretation. Full article

Differential operators often admit multiple algebraically equivalent symbolic formulations, yet those formulations can differ in the organization of their internal structure prior to solution analysis. A reproducible symbolic framework is introduced to compare such formulations at the level of operator expressions. Within a declared symbolic specification consisting of a fixed grammar, an admissible weight class, canonical compression rules, and an admissible family of reformulations, we define four encoding-relative structural descriptors: structural strain $\tau$, structural curvature $\kappa$, compressibility $\sigma$, and the balance ratio $\Gamma =\kappa /\tau$. Structural strain compares an encoding to a designated reference representation, while compressibility measures reduction under canonical symbolic compression. These quantities are deterministic descriptors within the declared encoding class rather than coordinate-free invariants of the underlying operator. The structural length functional underlying these descriptors is developed, canonical compression is formalized, and finite symbolic comparison is distinguished from pathwise symbolic deformation. A robustness theorem shows that, away from the threshold surface $\Gamma =\sigma$, sufficiently small admissible perturbations preserve the induced diagnostic label. A supporting weight-robustness result further shows that qualitative labels persist across a local admissible family of weight choices under corresponding nondegeneracy conditions. The framework serves as a reproducible diagnostic for operator representations alongside Lyapunov, spectral, pseudospectral, and energy-based stability theories. Examples of representative ordinary and partial differential operators illustrate how the descriptors are computed and how they behave under admissible re-expression, while the appendices provide the technical backbone of the paper: formal definitions, reproducibility protocol, extended perturbation arguments, and explicit failure-mode analysis. Additional sensitivity checks regarding encoding, weights, and threshold variation clarify the method’s scope, and explicit failure modes delineate the boundary cases in which the descriptors cease to apply. The main contribution of this study is a formally delimited and reproducible symbolic framework for comparing differential operators under a fixed, declared specification, together with robustness results and worked examples that clarify the method’s scope. Full article

Experience-based co-design emphasizes understanding service-users’ experiences to inform service improvement, yet little research has explored domestic abuse survivors’ perspectives within this framework. This study examined survivors’ accounts of their interactions with the police and organizations that support domestic abuse survivors. We aimed to identify aspects of practice experienced as either helpful or in need of improvement. Semi-structured interviews with six survivors in one area of England were analyzed using reflexive thematic analysis. Survivors described obstructive and supportive responses from formal services. Four interrelated themes were developed: The Importance of Being Understood, Believed, and Cared For; It Is Important That There Is Good Communication Between the Survivor and Formal Services; Survivors Want a Victim-Centered, Rapid, and Meaningful Response; and Specific Circumstances Sometimes Influence Opportunities for Help-Seeking. Survivors described being dismissed and disbelieved, which contributed to negative help-seeking experiences and heightened feelings of vulnerability. In contrast, empathic and timely responses validated survivors’ experiences and supported their sense of safety. The findings highlighted the importance of practice that recognizes the different forms abuse can take, provides timely, victim-centered support, and responds equitably to survivors in diverse circumstances. This study demonstrates the valuable insights gained through applying an experience-based co-design approach in this setting. Full article

Background/Objectives: Joubert syndrome (JS) is a rare neurodevelopmental disorder characterized by cerebellar and brainstem malformations, resulting in a complex and heterogeneous motor phenotype. Despite increasing clinical recognition, functional assessment and physiotherapy strategies in this population remain insufficiently characterized. This study aimed to synthesize current rehabilitation evidence and to propose a conceptual framework for functional motor assessment in children with JS. Methods: A structured narrative review was conducted across PubMed, Scopus, Web of Science, EBSCOhost, the Cochrane Library, and PEDro databases, including studies published between 2000 and 2026. Eligible studies involved pediatric patients (0–18 years) with JS and reported physiotherapy or motor-related outcomes. Data were synthesized descriptively, and recurring functional domains were identified to inform the development of a conceptual framework. Results: Ten studies (eight case reports and two case series) were included. Rehabilitation approaches were heterogeneous and predominantly multidisciplinary, focusing on postural control, trunk stability, and motor milestone acquisition. Functional improvements were reported across studies; however, outcome measures were primarily based on generic pediatric tools such as GMFM-88 and WeeFIM. These tools did not fully capture the multidimensional nature of motor impairment, particularly in relation to regulatory and sensorimotor domains. Evidence also suggested that postural control and gross motor performance may not fully correspond, highlighting additional functional components such as axial control and thoracoabdominal organization. Given the absence of formal risk-of-bias assessment and the low methodological quality of included studies, all findings should be interpreted as exploratory. Conclusions: Current functional assessment in JS may not adequately reflect the interaction between regulatory processes, sensorimotor integration, and motor control. The proposed conceptual framework provides a multidimensional, hypothesis-generating perspective that may support clinical reasoning and physiotherapy planning. Further research is required to validate this framework and to develop more sensitive, syndrome-specific assessment tools. Full article

Against the backdrop of a multi-tiered governance system and increasingly institutionalized norms, China’s historical and cultural preservation policies have long emphasized institutional standardization and hierarchical uniformity. Local policy texts are typically viewed as localized replicas of central institutional logic, overlooking internal variations and differences in information structure. Accordingly, this study examines the Regulations on the Protection of Historical and Cultural Cities, Towns, and Villages issued by 13 provincial-level administrative regions in China. It conceptualizes provincial regulatory texts as institutionalized policy information systems, constructs a cross-regional corpus, and develops a comparative information structure analytical framework based on the Latent Dirichlet Allocation (LDA) topic model. This study operationalizes LDA-derived topic-weight distributions into a comparative analytical framework that captures structural prominence, dispersion, concentration, and priority hierarchy in provincial policy texts. The findings reveal that provincial-level historical and cultural preservation regulations in China exhibit a highly institutionalized information backbone, centered on administrative procedures, legal norms, and macro-level planning controls, and demonstrate significant institutional similarity across provinces. However, within this unified institutional framework, provinces exhibit structural differences in the distribution of thematic weights, information prioritization, and internal textual sequencing, resulting in multiple distinguishable information organization patterns. Consequently, this study highlights the coexistence of formal institutional uniformity and structural differentiation in provincial regulatory texts, providing a more precise basis for understanding variation in local policy expression within China’s historical and cultural governance field. Full article

Background: Occupational exposure to cytotoxic drugs remains a major concern for pharmacy personnel, due to their well-established, carcinogenic, mutagenic and organ-specific effects. Despite the existence of robust international guidelines, evidence suggests substantial variability in compliance, training quality and operational conditions across healthcare settings. Objective: This study aimed to characterise current handling practices, assess working conditions, training, safety procedures, exposure patterns, and perceived risk factors among pharmacy technicians involved in the preparation of cytotoxic drugs in Portugal. Methods: A cross-sectional descriptive study was conducted using a structured questionnaire grounded in international standards (ISOPP, NIOSH, ASHP, USP <800>). The instrument was developed through literature review, expert panel validation (n = 42), and pre-testing. Data were collected electronically between April and May 2025 from pharmacy technicians actively handling cytotoxic drugs in Portugal. Results: A total of 124 valid responses were analysed. Most participants were female (78%) and under 50 years, with nearly one-third having less than one year of experience. Prolonged daily exposure (31.5% participants worked ≥ 5 h/day) extended uninterrupted handling periods (28.2% worked > 120 min), and high preparation workloads were common. While adherence to core protective measures—such as reinforced gowns, double gloves, and Class II B2 biological safety cabinets—was high, important gaps were identified, including incomplete use of closed system transfer devices, inconsistent respiratory and foot protection, limited automation, and insufficient environmental monitoring. Structured competency assessment, periodic training, and formal documentation were frequently absent. Institutional policies on reproductive risk showed strong protection for women but less clarity for male workers. Conclusions: Cytotoxic drug handling practices in Portugal demonstrate satisfactory adherence to fundamental protective measures but reveal significant structural and organisational gaps related to workload management, environmental monitoring, and continuous training. The absence of unified national guidance contributes to variability across institutions. These findings highlight the need for greater standardisation of occupational safety practices. Full article

In the early stages of concurrent engineering, the ability to assess design change impact is fundamentally limited by the availability of expert knowledge. Knowledge-Based Engineering (KBE) provides structured approaches for the capture, formalization, management, and diffusion of knowledge within complex organizations. KBE has increasingly turned toward ontology-based methodologies, leveraging their robust framework for shared conceptualization and reasoning capabilities. Integrated with Model-Based Systems Engineering (MBSE), such Ontology-Based Engineering (OBE) methodologies provide the necessary infrastructure for knowledge-driven workflows in a Digital Engineering (DE) context. Such integration is critical for complex engineering sectors such as the aerospace industry. However, the traditional knowledge acquisition process is expert-centric and, consequently, resource-intensive. The digital transformation of the industry has led to an explosion of data volumes, and raised concerns toward statistical approaches. This study implements a hybrid knowledge acquisition method within the OBE framework and MBSE environment. Specifically, this method combines human expertise and interpretable machine learning techniques to formalize knowledge models and instantiate them with concrete design rules. Applied in a real-world use-case involving workload estimation, this paper aims to enhance cross-domain collaboration during the conceptual design phase of new aircrafts. Full article