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A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents
Get PDFSolubility is a physicochemical property that plays a critical role in pharmaceutical formulation and processing. While COSMO-RS offers physics-based solubility estimates, its computational cost limits large-scale application. Building on earlier attempts to incorporate COSMO-RS-derived solubilities into Machine Learning (ML) models, we present a substantially expanded and systematic hybrid QSAR framework that advances the field in several novel ways. The direct comparison between COSMOtherm and openCOSMO revealed consistent hybrid augmentation across COSMO engines and enhanced reproducibility. Three widely used ML algorithms, eXtreme Gradient Boosting, Random Forest, and Support Vector Machine, were benchmarked under both 10-fold and leave-one-solute-out cross-validation. The comparison between four major descriptor sets, including MOE, Mordred, RDKit descriptors, and Morgan Fingerprints, offering the first descriptor-level assessment of how COSMO-RS calculated solubility augmentation interacts with diverse chemical feature space. The statistical Y-scrambling was conducted to confirm that the hybrid improvements are genuine and not artefacts of dimensionality. SHAP-based feature analysis further revealed substructural patterns linked to solubility, providing interpretability and mechanistic insight. This study demonstrates that combining physics-informed features with robust, interpretable ML algorithms enables scalable and generalisable solubility prediction, supporting data-driven pharmaceutical design
Exploring the lived experiences of women with metastatic breast cancer and their HRQoL questionnaire preferences: a qualitative study
Get PDFBackground: Managing health-related quality of life (HRQoL) in patients with metastatic breast cancer (MBC) is crucial due to the physical, emotional, and social burdens of disease and its treatments. This study examined the HRQoL of patients with MBC and compared the patients’ perspectives of two validated tools—the Functional Assessment of Cancer Therapy - Breast (FACT-B) and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire QLQ-C30 with its breast cancer-specific module QLQ-BR42 (EORTC) to determine which is most suitable for assessing HRQoL in patients with MBC. Methods: A qualitative study design was used to explore patients’ HRQoL and their perspectives on the two questionnaires, FACT-B versus EORTC, focusing on what matters most to the patients. Individual interviews were conducted between September and December 2024 at the Department of Oncology, Odense University Hospital, Denmark. Thematic analysis was selected to extract and analyze data. Results: Fifteen women with MBC were included. Median age was 62 years (range 28–79), and the median duration of the interviews was 21 min (range 13–32 min). The analysis identified three main themes: (1) Living with metastatic disease, when incurable cancer is a life condition, (2) Design of the questionnaires, 3) Content of the questionnaires. Participants described impaired HRQoL, as they were hindered in living an everyday life, felt lonely at times, engaged in protective buffering, and lived in fear of the next scan. Overall, the participants found the design of the two questionnaires acceptable; neither was preferred over the other. However, regarding the content, the participants expressed a clear preference for FACT-B over the EORTC, primarily due to its greater relevance to their current situation, reflecting everyday life and need for support. Conclusions: Participants in this study reported impaired HRQoL, marked by disrupted daily life, loneliness, protective buffering, and scanxiety. They preferred the FACT-B over the EORTC questionnaire for measuring HRQoL because of its focus on personal and emotional aspects, which reflected their lived experiences. However, further research is warranted to validate these findings in more diverse populations in the metastatic breast cancer context
ANN-based online parameter correction for PMSM control using sphere decoding algorithm
Get PDFThis work addresses parameter mismatch in Permanent Magnet Synchronous Motor (PMSM) drives, focusing on performance degradation caused by variations in flux linkage and inductance arising under realistic operating uncertainties. An artificial neural network (ANN) is trained to estimate these parameter shifts and update the controller model online. The procedure comprises three steps: (i) data generation using Sphere Decoding Algorithm-based Model Predictive Control (SDA-MPC) across a mismatch range of ±50% ; (ii) offline ANN training to map measured features to parameter estimates; and (iii) online ANN deployment to update model parameters within the SDA-MPC loop. MATLAB /Simulink simulations show that ANN-based compensation can improve current tracking and THD under many mismatch conditions, although in some cases—particularly when inductance is overestimated—THD may increase relative to nominal operation. When parameters return to nominal values the ANN adapts accordingly, steering the controller back toward baseline performance. The data-driven adaptation enhances robustness with modest computational overhead. Future work includes hardware-in-the-loop (HIL) testing and explicit experimental study of temperature-dependent effects
Attitude estimation using AI-based hyperspectral technology for autonomous close-proximity operations
Get PDFAccurate attitude estimation of resident space objects (RSO) is essential for autonomous navigation and closeproximity operations such as in-orbit servicing and active debris removal. To address the challenges of using conventional cameras in terms of illumination conditions, this research developed a model that leverages hyperspectral imaging together with the machine learning approach. It identifies the features and components of the target spacecraft with a given spectral response and then estimates its attitude quaternions, rather than determining the exact geometric shape by using the conventional RGB cameras. The model utilizes sequences of spectral images or time-series data to determine the attitude of the target object over time, by employing the framework consisting of a convolutional neural network (CNN) and a recurrent neural network (RNN). To further enhance robustness, a Bayesian Neural Network was developed and integrated into the framework, allowing investigating the model uncertainties by generating sets of weights and biases of the neural networks rather than a single deterministic estimate. This approach provides a measure of uncertainty, which is crucial for applications where confidence levels in the estimated attitude are necessary for autonomous decision-making. The model was firstly trained and validated with synthetic data generated from open software Blender and then tested with real-world hyperspectral images that were generated in a controlled laboratory environment. This hardware in-the-loop (HIL) test is the first step in validating the performance of the model for real-word applications. The transition to real data required adaptations to the model architecture, including fine-tuning through a transfer learning approach. This method improved the model’s ability to generalize beyond synthetic datasets, and the result demonstrated that hyperspectral imaging can be effectively utilized for real-world attitude estimation tasks. Future work will focus on expanding both synthetic datasets and laboratory datasets to include a wider variety of objects and testing the model on more complex scenarios to further enhance its performance and applicability in real space missions
New solutions for the symmetrical n-body problem through variational approach and optimisation techniques
Get PDFAdvances in the variational approach to the n-body problem have led to significant progress in celestial mechanics, uncovering new types of possible orbits. In this paper, critical points of the Lagrangian action associated with the n-body problem are analysed using evolutionary algorithms to identify periodic and symmetrical solutions of the discretised system. A key objective is to locate minimum points of the action functional, as these correspond to feasible periodic solutions that satisfy the system’s differential equations. By employing both stochastic and deterministic algorithms, we explore the solution space and obtain numerical representations of these orbits. Next, we examine the stability of these orbits by treating them as critical points. One approach is to compute their discrete Morse index to distinguish between minimum points and saddle points. Another is to classify them based on their action levels. Finally, analysing the boundaries of their attraction basins allows us to identify non-minimal critical points via the Ambrosetti–Rabinowitz Mountain Pass Theorem. This leads to an updated version of the algorithm that provides a constructive proof of the theorem, yielding new orbits in specific cases. This paper builds upon and extends the results presented in Introna et al. (75th International Astronautical Conference, Milan, Italy), providing a more detailed theoretical framework and deeper insights into the formulation. Additionally, we present new numerical results and an extended analysis of the critical points found, further enhancing the findings of the previous study
User centred dread : a Lovecraftian critique of design
No full textThis paper offers a speculative critique of contemporary design practice through the lens of Lovecraftian horror by critically interrogating assumptions embedded in user-centered design theory. Drawing on the oeuvre of 'weird fiction' author H.P. Lovecraft’s cosmically pessimistic mythologies, this paper explores three thematic dimensions - control, user unknowability, alien materiality - to reveal the limitations of human-centered design paradigms. Utilising examples from literature, film, and everyday experience, this critique demonstrates how design’s aspirational narratives, centered on mastery, empathy, and progress, are increasingly inadequate in addressing the complexities of a more-than-human world. The concept of 'user-centered dread' emerges as a central provocation, highlighting how users are led into states of incomprehension and even terror through supposedly benign design work; design itself becoming a site horror. By framing design as a speculative interface with the Lovecraftian inhuman, the paper graphically reimages aspects of design-thinking that can potentially challenge this pessimism and slay Lovecraft's monsters
A systematic review of quantitative assessments of traffic-focused urban air quality regulations
No full textThis systematic review follows PRISMA 2020 guidelines to identify whether measurable impacts have been observed following urban air quality interventions targeting private road users. A structured search was conducted on Scopus in February 2025, identifying 2088 studies. After applying strict inclusion criteria - requiring studies to examine urban-scale air quality interventions, target private road users, and apply quantitative ex post causal inference techniques - 59 studies were included for review. Our review examines publication trends, geographic focus, outlets, methods, key variables, and overall findings, classifying impacts on air quality, economic, behavioural, and health outcomes as positive, mixed, or negative. We identify three intervention types: (1) vehicle bans by type/time, (2) access charges for urban areas, and (3) fines for non-compliant vehicles. When splitting the papers by intervention type, outcome studied, and categorisation of impact, sample sizes become small. Only for intervention types (1) and (3), is there a sample of ≥10 studies examining the impacts on one outcome: air quality. Of these papers, a higher proportion examining fines for non-compliance found positive effects on air quality. While studies commonly report improvements in air quality and health, results are mixed for behavioural and economic variables. This review provides an up-to-date synthesis of policy effectiveness and highlights methodological and geographic gaps in the literature, supporting future evidence-based policy design
A comprehensive review and analysis of S355 fatigue crack growth rate data for offshore wind applications : seawater free-corrosion environment
Get PDFThe recommendations for fatigue crack growth rate (FCGR) in British Standard BS 7910 are based on experimental data which are multiple decades old, and the FCGR laws were constructed using data from multiple structural steel grades. The present study sought to gather more recent FCGR data for S355 steel in a marine free-corrosion environment, which can be applied for use in offshore wind turbine structural integrity life prediction. The collected data were filtered to include only valid Stage II (Paris region) data in the analyses. A method for fitting a bilinear regression curve was developed along with bias reducing weighted regression methods. The resulting bilinear trend shows up to 3.6 times lower FCGR relative to the BS 7910 recommendations for the free-corrosion environment. The analysed S355 data indicated that a bilinear fit best described the FCGR in the heat affected zone, whereas a simple linear fit was appropriate for the base metal. The S355 subgrades, J2 + N, G8 + M and G10 + M, presented considerable variance in mean FCGR, with J2 + N having the highest FCGR and G10 + M the lowest. When considered in the context of offshore wind turbine service life, the overall S355 FCGR reduction relative to the BS 7910 free corrosion curve results in a just over double life expectancy for a 4 × 20 mm surface semi-elliptical flaw size, based on the conservative upper bound analysis. This increase in life could prove invaluable for corrosion protection system repair timescales, considering the need to carefully plan the maintenance time window to avoid adverse weather conditions
A novel 3-dimethylaminopropylamine + N-methyl-2-pyrrolidone low-energy water-lean solvent for onboard CO2 capture : Performance, properties, and capture mechanism
Get PDFCO2 emission regulations in the maritime shipping industry have recently gained prominence following the MEPC 83 meeting. To address the challenges of high regeneration energy consumption, insufficient CO2 cyclic capacity, and excessive viscosity of emerging absorbents that currently hinder the application of onboard carbon capture (OCC) systems, a novel water-lean solvent (WLS) was identified from ten combinations of five polyamines and two organic solvents. Experimental results demonstrated that the 3-dimethylaminopropylamine + N-methyl-2-pyrrolidone + H2O (MNH) solvent displayed superior CO2 capture performance compared to conventional solvents. The desorption energy was reduced by 54.12 % relative to monoethanolamine (MEA), while CO2 cyclic loading increased by 55.20 %. The absorbent maintained significantly lower viscosity than other WLSs after the absorption process. Following desorption, the viscosity decreased to 1.355 mPa∙s, representing a 27.35 % reduction compared to MEA. Comprehensive measurements of density, viscosity, and pH were conducted for MNH WLSs under various temperatures and CO2 loading conditions. Visual analysis techniques were employed to investigate the nucleophilic and proton transfer reactive sites during the absorption process, as well as the mechanisms responsible for viscosity variation. The findings presented in this study provide theoretical guidance and empirical data support for the future deployment and optimization of OCC systems in maritime applications
The development of the 100kW fully superconducting axial flux motor : HTS armature tests
Get PDFZero-emission aviation requires electrical motors with high power densities. Superconducting motors offer a promising way to increase the power density of aviation propulsion motors, owing to the large-current carrying capacity of superconductors. Previous research mainly focus on superconducting motors were on marine propulsion applications with superconducting rotors. Recent research driven by aviation needs are more towards superconducting armatures. This paper will demonstrate the first use of superconducting armature for an axial-flux superconducting motors. For the first tests of the Armature PM rotor was used in order to validate simulation data and check operation of armature in motor mode. The design and testing results for superconducting armature will be discussed. The study will pave the way for future research of superconducting propulsion motors with HTS armatures
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