Down syndrome (DS), the most frequent human genetic disorder marked by an extra copy of chromosome 21 (Hsa21) or a portion thereof, leads to physical and cognitive impairments. Following the Lejeune work, researchers focused on a potential anomaly within the folate-mediated one-carbon metabolism (FOCM). Here, we present a FOCM model modified from a previous work with the incorporation of the enzyme cystathionine beta-synthase (CBS), whose encoding gene is located on Hsa21, coupled with the methionine input rate. Systematic perturbation of FOCM enzyme activity rates has been performed to explore possible in silico configurations to simulate the DS condition. The perturbed vs. unperturbed model-derived ratio concentrations of tetrahydrofolate, 5-formyl-tetrahydrofolate, 5-methyl-tetrahydrofolate, S-adenosyl-homocysteine, and S-adenosyl-methionine were compared with the known literature through various statistical approaches. After investigating public transcriptomic databases, the FTS (formate-tetrahydrofolate ligase) perturbation achieved the best overall score. Although the FTS encoding gene (MTHFD1) is not located on Hsa21, it was found to be overexpressed in the DS condition. In addition, an interesting correlation emerged with the PTG (phosphoribosylglycinamide formyltransferase) perturbation and the corresponding encoding gene (GART), located on Hsa21 and notably over-expressed in the DS condition. The model thus identifies key enzyme activities that warrant further investigation.

Antibody-based therapeutics have revolutionized disease treatment, and recent advances in messenger RNA (mRNA) technologies have opened new opportunities for their intracellular production. In particular, in vitro–transcribed mRNA encapsulated in lipid nanoparticles (LNPs) enables targeted delivery to specific cells, where it can enable the synthesis of therapeutic antibodies with prolonged half-lives in a cost-effective manner. Despite rapidly growing experimental data, a modeling framework that integrates mRNA delivery, intracellular expression kinetics, and whole-body antibody disposition remains unavailable. To address this gap, we extended a Physiologically Based Pharmacokinetic model with a novel multiscale layer describing mRNA trafficking, cellular uptake, translation, and degradation. The integrated model was calibrated and validated using five datasets of mRNA-based cancer therapeutics, demonstrating strong predictive performance for the biodistribution of mRNA-encoded antibodies. The newly introduced mRNA layer, while minimally parameterized, effectively represents complex intracellular and systemic processes, enabling quantitative investigation of antibody biodistribution, optimization of dose scheduling, and providing an initial framework for future exploration of how LNP–mRNA formulation influences delivery and pharma-cokinetics.

Dynamical systems play a central role across the quantitative sciences, offering a powerful mathematical framework to describe, analyze, and predict the evolution of complex processes over time. In systems biology, dynamical systems provide a foundation for modeling and predicting the intricate behaviors of biological systems. Recent advances in data-driven approaches, such as Neural Ordinary Differential Equations (NODEs) and Universal Differential Equations (UDEs), have enabled the development of models that are either fully or partially data-driven. Integrating data-driven components into dynamical systems amplifies the challenge of generalization beyond training data, highlighting the need for robust methods to quantify uncertainty in out-of-distribution (OOD) scenarios-i.e., conditions not encountered during training. In this work, we investigate the reliability of uncertainty quantification (UQ) based on ensembles of models in the reconstruction of dynamical systems. We show that standard ensembles (i.e., models trained independently with different random initializations) risk producing overconfident predictions in previously unseen scenarios, as the models in the ensemble tend to exhibit similar behaviors. To address this issue, we propose a novel ensemble construction method for NODEs and UDEs that fosters diversity in the reconstructed vector field across models within specific regions of the state space, while maintaining explicit control over the fit on the training set. We first evaluate our method on numerical test cases derived from three models commonly used as benchmarks for data-driven reconstruction of dynamical systems: the Lotka-Volterra model, the damped oscillator, and the Lorenz system. We then apply the method to a biologically motivated model of cell apoptosis, considering more realistic conditions such as partial observability of the system outputs and noise in the training dataset. Overall, our results show that the proposed method improves the reliability of UQ in previously unseen scenarios compared with standard ensembles, especially where the latter exhibit overconfidence.

mRNA-based therapeutics have emerged as a transformative class of medicines, yet their translation beyond infectious disease vaccines remains challenged by the absence of an integrated pharmacological framework accounting for the tri-component nature of these therapies - the lipid nanoparticle, the mRNA, and the expressed protein. Here, we present a modular, multiscale computational platform integrating two complementary mechanistic models covering the full pharmacological cascade of mRNA-based immunotherapies. The first is a Quantitative Systems Pharmacology (QSP) model describing the immunological response to mRNA vaccines, from antigen expression in antigen-presenting cells through B cell activation and circulating antibody production. The second is a Physiologically Based Pharmacokinetic (PBPK) model tracking whole-body disposition of mRNA-encoded therapeutic antibodies, incorporating a molecular layer resolving LNP uptake, endosomal mRNA escape, and intracellular translation. Both models are informed by a machine learning pipeline that maps IVT-mRNA nucleotide sequences directly onto kinetic parameters, enabling product-specific model simulations. We propose this platform as a step toward the quantitative pharmacological framework that mRNA therapeutics currently lack, and as a practical tool for model-informed design and development of this therapeutic class. ### Competing Interest Statement The authors have declared no competing interest. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study used exclusively publicly available human data obtained from the scientific literature. All original sources have been appropriately cited and are included in the reference list of the manuscript. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript.

Background. Atherosclerosis is increasingly recognized as a chronic immunometabolic disorder involving complex interactions between circulating immune cells, metabolic factors, and the vascular wall. Carotid intima-media thickness (IMT) is widely used as a surrogate marker of subclinical atherosclerosis. Peripheral blood mononuclear cells (PBMCs) provide a systemic readout of immune transcriptional states, but longitudinal evidence linking PBMC transcriptional profiles to long-term vascular remodeling remains limited. Methods. We analyzed the association between PBMC transcriptomic profiles and carotid IMT in the Barilla Offspring Study, a single-center cohort with long-term follow-up. PBMC transcriptomics and carotid IMT were assessed at baseline in 148 participants, and 101 individuals underwent repeat clinical, vascular, and transcriptomic evaluation at follow-up. Three analytical configurations were examined: baseline cross-sectional, follow-up cross-sectional, and a longitudinal model linking baseline transcriptomic profiles to follow-up IMT. Following a comparison of state-of-the-art machine learning regression algorithms and an innovative rank-based method, the most predictive transcriptomic signature from each analytical configuration was used for downstream functional enrichment and network analyses. Results. The rank-based regression method showed the best performance across all analytical configurations. Cross-sectional analyses at both time points consistently revealed enrichment of immune-related pathways, including leukocyte activation, antigen presentation, and receptor-mediated signaling. In contrast, the longitudinal transcriptomic signature was enriched for pathways related to metabolic regulation, redox processes, and cellular structural organization. Despite limited overlap at the single-gene level, functional similarity analysis demonstrated convergence toward shared immunometabolic pathways associated with vascular remodeling. Conclusions. PBMC transcriptional profiles are associated with subclinical vascular remodeling both cross-sectionally and over long-term follow-up, suggesting that systemic immune transcriptional states may contribute to vascular aging. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was funded by the Italian Ministry of University and Research (MUR) under the PRIN 2022 program, Project 'PREDICTive Cardiometabolic transcriptOMIC trajectorieS in the Barilla Offspring Follow-up STUDY: The PREDICT-OMICS Study', Grant No. 2022FZL247, funded by the European Union, NextGenerationEU, Mission 4 'Education and Research', Component 2. CUP D53D23014340006 ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study was approved by the local Institutional Review Board (Comitato Etico di Area Vasta Emilia Nord; Protocol 45543, 14 November 2023). The study was conducted in accordance with the Declaration of Helsinki, and all participants provided written informed consent prior to study entry. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

IntroductionTuberculosis (TB) poses a significant threat to global health, with millions of new infections and approximately one million deaths annually. Various modeling efforts have emerged, offering tailored data-driven and physiologically-based solutions for novel and historical compounds. However, this diverse modeling panorama may lack consistency, limiting result comparability. Drug-specific models are often tied to commercial software and developed on various platforms and languages, potentially hindering access and complicating the comparison of different compounds.MethodsThis work introduces stormTB: SimulaTOr of a muRine Minimal-pbpk model for anti-TB drugs. It is a web-based interface for our minimal physiologically based pharmacokinetic (mPBPK) platform, designed to simulate custom treatment scenarios for tuberculosis in murine models. The app facilitates visual comparisons of pharmacokinetic profiles, aiding in assessing drug-dose combinations.ResultsThe mPBPK model, supporting 11 anti-TB drugs, offers a unified perspective, overcoming the potential inconsistencies arising from diverse modeling efforts. The app, publicly accessible, provides a user-friendly environment for researchers to conduct what-if analyses and contribute to collective TB eradication efforts. The tool generates comprehensive visualizations of drug concentration profiles and pharmacokinetic/pharmacodynamic indices for TB-relevant tissues, empowering researchers in the quest for more effective TB treatments. stormTB is freely available at the link: https://apps.cosbi.eu/stormTB.

Alpha-synuclein (aSyn) plays a crucial role in Parkinson's disease, with various aggregates proposed as pathogenic triggers and therapeutic targets. However, anti-aSyn aggregation compounds often fail due to limited knowledge of the underlying molecular basis. In particular, interactions with lipid membranes are central to both physiological and pathological roles of aSyn, yet their underlying mechanisms remain unclear. Disrupting this balance may drive Parkinson's onset and progression, underscoring the need for a mechanistic understanding of pure and lipid-mediated aggregation. Building on well-established in vitro aggregation studies, we propose a mathematical model of aSyn accumulation incorporating both aggregation routes via a nucleation-conversion-polymerization process with self-amplifying loops and toxic oligomers. Model calibration uses data from in vitro assays mimicking physiologically relevant conditions, providing insights into transient and stable aSyn intermediates. Incorporating aSyn-lipid interactions enables in silico exploration of how lipid-to-aSyn ratio influences aggregation, with possible implications for neurodegeneration. Sensitivity analysis highlights secondary nucleation inhibition as a potential anti-aggregation strategy. Overall, our work contributes to a unified framework for investigating in vitro aSyn aggregation and evaluating Parkinson's therapies by building on existing models. It can serve as a stand-alone tool and a modular component in multiscale models, with potential applications in quantitative systems pharmacology.

In the field of cancer therapy, bispecific T cell engagers (BiTEs) have demonstrated significant potential. However, their clinical application is constrained by challenges in production and limited plasma half-life. In vitro-transcribed (IVT) mRNA formulations emerge as a promising alternative, offering adaptability and cost-efficiency. Yet, the intricate relationship between mRNA dosage, antibody production, and the distribution of mRNA and proteins requires a deeper understanding. To address these issues, we present a novel physiologically based pharmacokinetic (PBPK) model to characterize the pharmacokinetics of BiTEs. This model predicts the distribution patterns of both recombinant and mRNA-encoded BiTEs by extending an established PBPK model with a hierarchical multiscale framework calibrated and validated using preclinical data from existing literature. The extended PBPK model can be adapted to various mRNA-based therapeutic formulations, facilitating in-silico exploration of different drug administration scenarios. It can provide valuable support for optimizing dose and schedule and allows the efficient investigation of drug distribution at a whole-body scale. This approach promises to enhance the personalization and effectiveness of cancer therapies, reduce research time and costs, and significantly advance the development of mRNA-based BiTEs for cancer treatment.

The unprecedented effort to cope with the COVID-19 pandemic has unlocked the potential of mRNA vaccines as a powerful technology, set to become increasingly pervasive in the years to come. As in other areas of drug development, mathematical modeling is a pivotal tool to support and expedite the mRNA vaccine development process. This study introduces a Quantitative Systems Pharmacology (QSP) model that captures key immune responses following mRNA vaccine administration, encompassing both tissue-level and molecular-level events. The model mechanistically describes the biological processes from the uptake of mRNA by antigen-presenting cells at the injection site to the subsequent release of antibodies into the bloodstream. This two-layer model represents a first attempt to link the molecular mechanisms leading to antigen expression with the immune response, paving the way for the future integration of specific vaccine attributes, such as mRNA sequence features and nanotechnology-based delivery systems. Calibrated specifically for the BNT162b2 SARS-CoV-2 vaccine, the model has undergone successful validation across various dosing regimens and administration schedules. The results underscore the model's effectiveness in optimizing dosing strategies and highlighting critical differences in immune responses, particularly among low-responder groups such as the elderly. Furthermore, the model's adaptability has been demonstrated through its calibration for other mRNA vaccines, such as the Moderna mRNA-1273 vaccine, emphasizing its versatility and broad applicability in mRNA vaccine research and development.

Objectives: Neurofilaments (Nf) are a non-specific marker of axonal injury and neurodegeneration. In motor neuron diseases, including Spinal Muscular Atrophy (SMA), Nf levels are prognostic for disease progression and survival and reduced in response to disease modifying therapies. To deepen the understanding of the relationship between Nf and motor function in SMA, we developed computational models of motor function scores in nusinersen-treated and untreated SMA patients, integrated with simulations from a previously developed Quantitative Systems Pharmacology (QSP) model of Nf trafficking [1]. Methods: For participants with infantile-onset SMA (most likely to develop Type I) and presymptomatic SMA, natural history data [2] and data from nusinersen trials (NURTURE [3] and ENDEAR [4]) were used. This included measurements of phosphorylated Nf heavy (pNfH) levels and the Children's Hospital of Philadelphia Infant Test of Neuromuscular Disorders (CHOP-INTEND) score measurements. For patients with later-onset SMA (most likely to develop Type II), Revised Upper Limb Module (RULM) score from CHERISH [5] and SHINE [6] were incorporated. We considered both the experimental time-series and the time-series simulated by a previously developed QSP model [1] to compare patient-specific Nf levels in different treatment scenarios and the untreated case. Results: We developed a mixed-effect model for participants with infantile-onset SMA (age < 2 years). CHOP-INTEND natural history almost always shows a decreasing trend as a function of age. Inclusion of pNfH levels into the model improves agreement with the disease progression data (without treatment). In infantile-onset SMA, nusinersen treatment often results in a reversal of the trend, leading to an improvement of motor function. To link this effect to pNfH, we identify a correlation between the reduction of pNfH levels predicted by our QSP Nf model and the improvement of the score with respect to baseline.In the case of later-onset SMA, natural history was expressed with an age-dependent model valid for participants between 2 and 5 years of age [5-7], when the improvement of motor functions typically stops. The response of RULM to treatment, resulting in a more rapid and prolonged improvement of the score, could be directly expressed in terms of pNfH time-series, correlating the reduction of pNfH with a more prolonged period of functional improvement. Conclusions: We present a preliminary integration of SMA motor function scores with a previously developed QSP framework of Nf trafficking. Our results indicate that it is possible to link the pNfH time-series to the evolution of patient scores in different SMA subtypes, strengthening the use of Nf as a biomarker for SMA and supporting the usefulness of the QSP Nf platform.Citations: [1] Paris A et al. CPT Pharmacometrics Syst Pharmacol. 2023; 12:196-206.[2] Mercuri E et al. Orphanet J Rare Dis. 2020;15(1):84. Epub 20200405[3] Darryl C et al. Neuromuscul Disord.2019;29(11):842-856.[4] Finkel R et al. Eur J Paediatr Neurol. 2017;21:e14-e15.[5] Mercuri E et al. New England Journal of Medicine. 2018;378(7):625-635.[6] Castro D et al. Neurology. 2020; 94(15):1640.[7] Coratti et al. Muscle & Nerve. 2021; 64:552–559.

MOTIVATION:The rise of transformer-based architectures has dramatically improved our ability to analyze natural language. However, the power and flexibility of these general-purpose models come at the cost of highly complex model architectures with billions of parameters that are not always needed. RESULTS:In this work, we present CSpace: a concise word embedding of biomedical concepts that outperforms all alternatives in terms of out-of-vocabulary ratio and semantic textual similarity task, and has comparable performance with respect to transformer-based alternatives in the sentence similarity task. This ability can serve as the foundation for semantic search by enabling efficient retrieval of conceptually related terms. Additionally, CSpace incorporates ontological identifiers (MeSH, NCBI gene and taxonomy IDs), enabling computationally efficient disease, gene or condition relatedness measurement, potentially unlocking previously unknown disease-condition associations. AVAILABILITY AND IMPLEMENTATION:Full and compressed models are available on Zenodo at https://doi.org/10.5281/zenodo.14781672, while training code, examples, interactive visualizations and experiments are available at https://doi.org/10.5281/zenodo.15125706 and on the GitHub repository.

Quorum Sensing (QS) is a bacterial cell-to-cell communication mechanism allowing to share information about cell density, to adjust gene expression accordingly. Pathogens leverage QS to coordinate virulence and antimicrobial resistance, leading to distinctive population-level behaviors. To support rational design of synthetic biology strategies counteracting these mechanisms, we first mathematically model and compare two common QS architectures: one based on a single positive feedback loop to auto-induce signal molecule synthesis, the other including an additional positive feedback to increase signal molecule receptors production. Our comprehensive analysis of these QS structures and their equilibria highlights the differences in their bistable and hysteretic behaviors. An extensive sensitivity analysis is then performed, highlighting how parameter variations may lead to phenotype alterations in system behavior. Finally, building on our sensitivity analysis, we mathematically model four distinct QS inhibition strategies -signal molecule degradation, pharmaceutical inhibition, CRISPRi, and RNAi -which lead to the design of Quorum-Quenching (QQ) therapeutic approaches. Despite the underlying complex mechanisms, we demonstrate that the effect of the proposed QQ strategies can be captured by varying specific parameters within the QS models. We numerically analyze how these strategies affect the steady-state behavior of both QS models, identifying critical parameter thresholds for effective QS suppression.

Parameter estimation is one of the central challenges in computational biology. In this paper, we present an approach to estimate model parameters and assess their identifiability in cases where only partial knowledge of the system structure is available. The partially known model is embedded into a system of hybrid neural ordinary differential equations, with neural networks capturing unknown system components. Integrating neural networks into the model presents two main challenges: global exploration of the mechanistic parameter space during optimization and potential loss of parameter identifiability due to the neural network flexibility. To tackle these challenges, we treat biological parameters as hyperparameters, allowing for global search during hyperparameter tuning. We then conduct a posteriori identifiability analysis, extending a well-established method for mechanistic models. The pipeline performance is evaluated on three test cases designed to replicate real-world conditions, including noisy data and limited system observability.

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Introduction: Understanding drug exposure at disease target sites is pivotal to profiling new drug candidates in terms of tolerability and efficacy. Such quantification is particularly tedious for anti-tuberculosis (TB) compounds as the heterogeneous pulmonary microenvironment due to the infection may alter lung permeability and affect drug disposition. Murine models have been a longstanding support in TB research so far and are here used as human surrogates to unveil the distribution of several anti-TB compounds at the site-of-action via a novel and centralized PBPK design framework.Methods: As an intermediate approach between data-driven pharmacokinetic (PK) models and whole-body physiologically based (PB) PK models, we propose a parsimonious framework for PK investigation (minimal PBPK approach) that retains key physiological processes involved in TB disease, while reducing computational costs and prior knowledge requirements. By lumping together pulmonary TB-unessential organs, our minimal PBPK model counts 9 equations compared to the 36 of published full models, accelerating the simulation more than 3-folds in Matlab 2022b.Results: The model has been successfully tested and validated against 11 anti-TB compounds—rifampicin, rifapentine, pyrazinamide, ethambutol, isoniazid, moxifloxacin, delamanid, pretomanid, bedaquiline, OPC-167832, GSK2556286 - showing robust predictability power in recapitulating PK dynamics in mice. Structural inspections on the proposed design have ensured global identifiability and listed free fraction in plasma and blood-to-plasma ratio as top sensitive parameters for PK metrics. The platform-oriented implementation allows fast comparison of the compounds in terms of exposure and target attainment. Discrepancies in plasma and lung levels for the latest BPaMZ and HPMZ regimens have been analyzed in terms of their impact on preclinical experiment design and on PK/PD indices.Conclusion: The framework we developed requires limited drug- and species-specific information to reconstruct accurate PK dynamics, delivering a unified viewpoint on anti-TB drug distribution at the site-of-action and a flexible fit-for-purpose tool to accelerate model-informed drug design pipelines and facilitate translation into the clinic.

Bacterial quorum sensing (QS) is a cell-to-cell communication mechanism through which bacteria share information about cell density, and tune gene expression accordingly. Pathogens exploit QS to orchestrate virulence and regulate the expression of genes related to antimicrobial resistance. Despite the vast literature on QS, the properties of the underlying molecular network are not entirely clear. We compare two synthetic QS circuit architectures: in the first, a single positive feedback loop autoinduces the synthesis of the signal molecule; the second includes an additional positive feedback loop enhancing the synthesis of the signal molecule receptor. Our comprehensive analysis of the two systems and their equilibria highlights the differences in the bistable and hysteretic behaviors of the alternative QS structures. Finally, we investigate three different QS inhibition approaches; numerical analysis predicts their effect on the steady-state behavior of the two different QS models, revealing critical parameter thresholds that guarantee an effective QS suppression.

Abstract. Increasing the wing aspect ratio is one way to improve aircraft aerodynamic efficiency. This reduces the induced drag term but, at the same time, produces an increment of the wing loads, hence an increase of the structural weight. One design solution to reduce the wing root bending moment, which is the main driver of the weight of the wing, is the addition of a strut. This work deals with the experimental identification of the flutter behavior of an ultra-high aspect ratio (19) strut-braced wing in a wind tunnel. The inherent non-linear behavior of such a structure that has two different effects on the wing when loaded in compression and in tension is coupled with large deformations due to its extreme flexibility. From here derives the extreme importance of experimental tests to understand how different parameters of such a design can impact its aeroelastic behavior.

An overview of the main outcomes of the [U-HARWARD](https://www.u-harward-project.eu) Ultra High Aspect Ratio Wing Advanced Research and Designs project, part of CleanSky2 program. The format of this session will be different than the previous ones. Talks 1, 3-5 be 20 min long and Talk 2 will be 40 min long, all including Q A. U-HARWARD Project: overview of activities and achievements This presentation describes the structure of U-HARWARD project, main tasks and an overview of the achievements up to now. Wind Tunnel Test Campaign Outcomes Investigating the pros and cons of unconventional configurations must pass through experimental validation to ensure unexpected behavior does not appear. In the U-HARWARD project, several wind tunnel tests have been performed to assess the aeroacoustics, aerodynamic and aeroelastic behavior of unconventional aircraft with high aspect ratio wings. Aeroacoustics tests were conducted at the University of Bristol on the interaction between the wing-strut junction and the high lift devices (both trailing and leading edge) for a strut-braced wing configuration. High-fidelity aeroacoustic simulations were also performed by Siemens to further investigate the noise generation mechanisms of wing-strut systems. Other tests were carried out at the large wind tunnel at Politecnico di Milano, testing aeroelastic behavior and flutter identification of a clamped strut-braced wing and low-speed aerodynamic derivatives of an aircraft in a strut-braced configuration. Insights will be given on a gust response test that will be carried out at the end of October on a free-to-plunge and pitch aircraft equipped with a folding wingtip to alleviate the loads. Strut Braced Wing: challenges vs. promises, the ONERA perspective Overview of the work performed within the U-HARWARD project on the high-AR strut-braced wing configuration, both at OAD level and aerodynamic and structural high-fidelity design level. Main conclusions and near-term perspectives will be highlighted. Folding Wing Tips for High Aspect Ratio Wings - Challenges vs. Promises This presentation will overview numerical and experimental investigations at the University of Bristol into the use of folding wing tips to enable HARW aircraft designs. The benefits of such a concept will be discussed along with an overview of the challenges that need to be overcome in order to enable its use on future aircraft. Final assessment and preliminary project outcomes evaluation The presentation will summarize the status of project outcomes and lessons learned.

This paper describes the development of a new state-of-the-art large wind tunnel model for active flutter suppression studies as well as the supporting techniques used in tests focused on the effects of uncertainty. Design guidelines and the resulting aeroelastic characteristics of the model are covered together with representative test results. Those would allow other researchers working in this area to develop control laws for the new model and evaluate them. A number of important lessons and insight are reported regarding the design of the model, the level of success of commonly used mathematical modeling techniques to capture its behavior, sources of analysis/test correlation discrepancies, multifunction utilization of control surfaces for both system identification and flutter suppression, active flutter suppression testing safety, and techniques for estimating the robustness of closed-loop active aeroservoelastic systems by tests. The new system has made it possible to repeatedly push the actively controlled model, using various flutter suppression control laws (safely), to the actual flutter limit in tests numerous times. This capability is just one of the new experimental capabilities that the new system brings to the aeroelastic active control community.