Simulation Solutions (United Kingdom)

Abstract This study investigates the phase behavior of Poloxamer 188 (P188) in aqueous solutions under conditions typical of pharmaceutical lyophilization processes. We utilized heat flux sensors (HFSs) in a freeze-dryer and replicated lyophilization processes using a miniaturized setup in an XRPD climate chamber. HFS data from vials in the freeze-dryer indicated not only the primary crystallization of ice but also detected the secondary crystallization of the P188-water system. This was possible when primary ice nucleation was well-controlled and consistent across all vials, ensuring that primary crystallization signals didn’t overshadow those from secondary crystallization. Our findings reveal that P188 crystallization in aqueous systems is generally slow. Measurements in the XRPD climate chamber, involving a P188-based placebo solution, demonstrated that only a small portion of P188 crystallizes during the annealing step. The majority of P188 crystallizes during the primary drying step, concurrently with ice sublimation. Additional crystallization of P188 is observed even at the final secondary drying stage. These insights provide a valuable foundation for optimizing industrial lyophilization processes for Poloxamer-based lyophilizates. Graphical Abstract This study explores Poloxamer 188 crystallization during typical freeze-drying conditions for biologics. Heat flux sensors and an XRPD climate chamber reveal that P188 crystallizes quite slowly. Insights inform optimization of lyophilization processes of P188-containing formulations.

AI-driven characters in extended reality (XR) healthcare simulations are increasingly used for clinical training, yet their effectiveness, implementation, and quality assurance remain poorly understood. We conducted a systematic review of 132 studies published between January 2015 and July 2025, including 11 randomized controlled trials (RCTs), sourced from biomedical, computing, and education databases and targeted proceedings. Most studies used virtual reality (62.1%) and focused on effectiveness (n = 71), with fewer examining implementation (n = 45) or quality assurance (n = 44). Meta-analysis of two RCTs found a large effect on knowledge and decision-making (Hedges' g = 1.31, 95% CI 0.08-2.54, I2 = 85%), while one RCT reported faster task performance with AI-driven characters (g = -0.68, 95% CI -1.32 to -0.04). Certainty of evidence was low due to small samples and high heterogeneity. Implementation success was often associated with phased roll-outs and faculty training, but quality assurance practices (particularly bias audits and transparency measures) were rarely documented. The review proposes the DASEX framework to address these gaps and guide future integration of AI-driven characters in XR training.

Abstract A new computational framework for large strain elasticity in principal stretches is presented. Distinct from existing literature, the proposed formulation makes direct use of principal stretches rather than their squares that is, eigenvalues of Cauchy‐Green strain tensor. The proposed framework has three key features. First, the eigen‐decomposition of the tangent elasticity and initial (geometric) stiffness operators is obtained in closed‐form from principal information alone. Crucially, these newly found eigenvalues describe the general convexity conditions of isotropic hyperelastic energies. In other words, convexity is postulated concisely through tangent eigenvalues supplementing the original work of Ball (Arch Ration Mech Anal. 1976; 63(4): 337–403). Consequently, this novel finding opens the door for designing efficient automated Newton‐style algorithms with stabilised tangents via closed‐form semipositive definite projection or spectral shifting that converge irrespective of mesh resolution, quality, loading scenario and without relying on path‐following techniques. A critical study of closed‐form tangent stabilisation in the context of isotropic hyperelasticity is therefore undertaken in this work. Second, in addition to high order displacement‐based formulation, mixed Hu‐Washizu variational principles are formulated in terms of principal stretches by introducing stretch work conjugate Lagrange multipliers that enforce principal stretch‐stress compatibility. This is similar to enhanced strain methods. However, the resulting mixed finite element scheme is cost‐efficient, specially compared to approximating the entire strain tensors since the formulation is in the scalar space of singular values. Third, the proposed framework facilitates simulating rigid and stiff systems and those that are nearly‐inextensible in principal directions, a constituent of elasticity that cannot be easily studied using standard formulations.

Information Technology is inextricably linked to virtually all Business processes. And that is exactly where INCONTROL Simulation Software positions its Simulation Software Platform: Enterprise Dynamics. Enterprise Dynamics enables organizations to innovate and improve their business processes e.g. during project preparation & implementation and as an integral part of business operations. At WSC we share our vision, technology insights and business cases on how simulation improved the performance of our clients. INCONTROL Simulation Software is integrated in curricula at Universities, Applied Sciences and R&D Institutes. Together with these partners we implemented solutions in the Process Industry, Transportation, Logistics, Crowd Management and Public Safety.

In this work we present two structurally different mathematical models for the prognostic simulation of Austria’s population: A time-continuous, macroscopic system dynamics approach and a time-discrete, microscopic agent-based approach. Both models were developed as case studies of a series of population concepts in order to support models for decision-support in Austria’s health care system. In the present work we want to focus on the definition, the parametrisation as well as especially the validation process of both population-models. The latter was of special interest as it included a cross-model validation with Statistics Austria’s own prognostic model SIKURS.

Predicting demand for care is necessary to provide sufficient capacities in hospitals and enable better planning to cope with challenges like changes in the structure of the population. Emphasis in this work is put on the analysis of the data and on the parametrization of a simulation model for the paths of patients with mental diseases through the health care system. Survival analysis and model selection methods are used for this purpose. Data on patients and their treatments is analyzed with methods of survival analysis. Different methods for modelling the survival and hazard function are presented and compared. Hereby, the focus is on the cox model. It is used to model the hazard function and can be extended to analyze multiple events. With the use of model selection methods the significant parameters are determined. Only these are included in the simulation model. These methods shall help to raise the quality of the parametrization and therefore the whole simulation model. In the microsimulation model, every patient has a particular set of parameters and can be in one of several predefined, exclusive states. The events are implemented as state changes. The probabilities for the events are calculated using the hazard functions. These are estimated with several extensions of the cox model. The considered events in the simulation are readmissions to hospital and contacts to ambulant psychiatrists. The simulation runs for a predefined time span and the sequence of events of each patient is tracked. After the simulation, the individual paths of the patients as well as aggregated quantities such as the overall numbers of certain events are analyzed. Simulations for different populations are performed. The results for various scenarios are presented. Scenarios with and without contacts to a psychiatrist are considered as well as different maximum numbers of admissions. Also, the subpopulations are compared. For example, differences in the results for diagnosis groups are encountered. These simulations shall lead to an improvement of the prediction of the pathways of the patients and therefore help to evaluate interventions like treatment changes the health care system or the utilization of the capacities in hospitals.

No abstract available.