Embodying routine replication dynamics: an ethnographic study on the impact of the body in routine replication in the Royal Air Force

We examine the role of embodiment in routine replication by investigating how the Royal Air Force adapted the loaded march (tabbing) routine from its Ground Combat Training program into Initial Officer Training. Routine replication required adjustments due to bodily differences among trainees, balancing flexibility with recognizability. Using enactive ethnography—where the first author physically participated in the marches—our 30-month ethnographic study reveals that routine replication is shaped by three bodily adaptation mechanisms: ‘playing with rhythm,’ ‘coping with injuries,’ and ‘dealing with emotions’. These mechanisms illustrate how bodies actively shape routine enactment, challenging conventional views of routine replication as a purely cognitive or procedural process. Our study advances Routine Dynamics by integrating an embodied perspective into the replication dilemma and demonstrating how bodily constraints and adaptations influence routine evolution. Additionally, we contribute methodologically by showcasing enactive ethnography as a powerful approach for studying embodiment in organizations.16th International Symposium on Process Organization Studies (PROS

Integrating causal analysis based on system theory with network modelling to enhance accident analysis

This study integrates Causal Analysis based on System Theory (CAST) with network modelling to enhance accident analysis in aviation ground handling. Using 117 Passenger Boarding Steps (PBS)-related incident reports, the CAST analysis identified 74 flaws across 40 control actions, leading to four loss types. Approaching, inspecting, adjusting, and repositioning PBS were the most critical control actions contributing to incidents. Key contributory factors included issues around training, workload management, situational awareness, performance management, recruitment, organisational culture, procedures, equipment maintenance, and financial constraints. The integration of network modelling into CAST enhanced accident analysis by visualising complex interactions, offering deeper insights into accident causation and identifying critical nodes. This study demonstrates that combining CAST with network modelling enhances the understanding of accidents and safety risks, supporting evidence-based decision-making for aviation safety professionals and improving ground handling risk management strategies. Practitioner Summary: This study integrates CAST with network modelling to enhance accident analysis in aviation ground handling. Analysing 117 passenger boarding steps incidents, the study identifies critical control actions and contributory factors. Network modelling enhances CAST by revealing complex interactions, providing deeper insights into incidents, and supporting improved risk management strategies.Ergonomic

Does cutting airport slots reduce climate impact? the case of Amsterdam airport

This study evaluates the effectiveness of airport slot reductions as a strategy for mitigating greenhouse gas (GHG) emissions, focusing on Amsterdam Schiphol Airport. Following the Dutch Government's decision to reduce slots from 500,000 to 440,000, we analyse various risk scenarios using the D'Hondt method for proportional slot allocation and the Fuel Estimation in Air Transportation (FEAT) model to estimate fuel consumption. Strategies include proportional slot cuts, prioritising short-haul flights, and shifting to rail alternatives. Results show that short-term emissions reductions are modest and do not scale with slot reductions unless long-haul flights are significantly curtailed. Moreover, aircraft up-gauging could lead to increased emissions if airline behaviour is not addressed. Our findings challenge the effectiveness of slot reductions as a climate strategy, highlighting the importance of targeting long-haul flights and adopting comprehensive policies to achieve substantial emissions reductions. The study offers critical insights for sustainable aviation policy development.Transportation Research Part D: Transport and Environmen

Stress, strain, or displacement? A novel machine learning based framework to predict mixed mode I/II fracture load and initiation angle

Accurate prediction of fracture load and initiation angle under complex loading conditions, like mixed mode I/II, is essential for reliable failure assessment. This paper aims to develop a machine learning framework for predicting fracture load and crack initiation angles by directly utilizing stress, strain, or displacement distributions represented by selected nodes as input features. Validation is conducted using experimental data across various mode mixities and specimen geometries for brittle materials. Among stress, strain, and displacement fields, it is shown that the stress-based features, when paired with Multilayer Perceptron models, achieve high predictive accuracy with R2 scores exceeding 0.86 for fracture load predictions and 0.94 for angle predictions. A comparison with the Theory of Critical Distances (Generalized Maximum Tangential Stress) demonstrates the high accuracy of the framework. Furthermore, the impact of input parameter selections is studied, and it is demonstrated that advanced feature selection algorithms enable the framework to handle different ranges and densities of the representing field. The framework’s performance was further validated for datasets with a limited number of data points and restricted mode mixities, where it maintained high accuracy. The proposed framework is computationally efficient and practical, and it operates without any supplementary post-processing steps, such as stress intensity factor calculations.Engineering Fracture Mechanic

Detection of Fusarium spp. and T-2 and HT-2 toxins contamination in oats using visible and near-infrared spectroscopy

Fusarium langsethiae (FL) is one of the major contaminants in oats in the United Kingdom (UK) and is a significant producer of T-2 and HT-2 toxins, among the most prevalent mycotoxins in oats. Visible and near-infrared (Vis-NIR) (350–2500 nm) spectroscopy was explored as a non-invasive, rapid method for detecting FL, Fusarium species that produce T-2 and HT-2 toxins, and T-2 and HT-2 toxins content. Oat grains were artificially inoculated with FL and other Fusarium species under controlled water activity (aw) conditions (0.98, 0.90, and 0.80). FL was found to be particularly responsible for producing T-2 and HT-2 toxins. Classification models were developed to distinguish oat grains based on the presence of FL. The best performance was achieved with all the Vis-NIR spectra, with a classification accuracy of 76.2 %. The Vis region (350–995 nm) emerged as the most important range for classification. Additionally, oat grains were classified by T-2 and HT-2 toxin content, distinguishing oats above and below the European Union (EU) threshold with 93.3 % accuracy. For mycotoxin quantification, the best performance was obtained using the Vis region with a coefficient of determination (R2) of 0.875. Key wavelengths such as 464, 568, 575 and 636 nm were relevant for toxin detection. The NIR region (1005–1795 nm) also played a significant role in the models. This study shows that Vis-NIR spectroscopy is a promising, non-destructive tool for detecting Fusarium and type A trichothecenes in oats, though further research is needed to improve model robustness and support food safety monitoring.Biotechnology and Biological Sciences Research Council (BBSRC)This research is supported by a BBSRC-SFI research grant (BB/P001432/1) between the Applied Mycology Group at Cranfield University and the School of Biology and Environmental Science, University College Dublin, Ireland. This work was also supported by the Spanish Ministry of Universities (predoctoral grant FPU21/00073).International Journal of Food Microbiolog

Inclusive professional education for sustainability: the Cranfield MSc Sustainability

There is increasing pressure on business to contribute to solving global social and environmental challenges. Diversity can help companies better represent their stakeholders and find creative solutions to systemic challenges in partnership with others. Business schools play a key role in equipping leaders with key competencies for sustainability including systems thinking, collaboration and integrated problem solving. To do this effectively, business schools themselves need to be inclusive, not only to reduce inequalities, but to facilitate learning environments conducive to developing these competencies, and to bring together diverse talents with the shared purpose of creating value for society. We reflect on how to deliver interdisciplinary and inclusive professional education for sustainability, informed by our experience of designing and running Cranfield’s part-time MSc Sustainability. Iterating between our reflections, which draw on evidence from learners, faculty, staff and employers, and the prior literature, we develop a multi-level framework setting out how inclusive learning and teaching can be embedded at individual, course, institutional and societal level. Focusing on the needs of professionals helps attract learners who are representative of society, and who can create immediate positive impact through their organisations. Institutional enablers are critical to enhancing diversity through interdisciplinarity. Considering the societal level prompts educators to think about the societal context for, and impact of, their programmes. We highlight key lessons and best practice for future development of inclusive professional learning experiences at Cranfield and beyond.Handbook of Inclusive Learning and Teaching in Business and Managemen

Crash-proof and sustainable polymer-based composites in modern day transportation systems: a review on fabrication of lightweight components for land, sea and air travels

The need for advanced materials that are lightweight, durable and sustainable in the transportation sector in the recent times cannot be overemphasized. Previously, the focus was solely on endurance and crashworthiness without environmental considerations. However, due to environmental concerns in recent times, researchers are now focusing on lightweight and biodegradable materials that are sturdy, crash-proof, and sustainable. In other to achieve these, when selecting materials for transportation applications, many criteria are typically taken into consideration to cater for the divergence in the environment that the materials will be exposed to in service. Thus, polymer-based materials have been discovered to be appropriate for these purposes based on their inherent qualities such as lightweight, processing flexibility, acceptable properties, availability, economic viability and aesthetics. Therefore, most components of modern-day transportation gadgets and appliances are primarily developed from polymer-based composite materials. This review presents various parts of these gadgets/appliances such as; automotive, ship, train and airplanes that are being used for transportation systems that were produced from polymer-based composite materials. As emerging materials, components from polymer-based composites improve fuel efficiency and promote viable transportation. Hence, the progressive application and future benefits of polymer-based composites as lightweight materials in the transportation industry were the focus of this review. Key policies and regulatory frameworks influencing material selection across different sectors were provided while polymer-based composite materials in different modern day transportation systems like electric cars, naval ships, high-speed trains, and aircraft such as the Boeing 787 were identified and specified.Next Material

System analysis and design optimization of future aircraft for power management in ground movement process

This study investigates the optimization of aircraft system architecture for power management during ground operations to reduce CO2 emissions and improve energy efficiency. Focusing on the taxiing, take-off, and landing phases, this research evaluates the potential of electrified systems—such as electric taxiing technologies, electro-mechanical actuators (EMAs), and regenerative braking—to minimize environmental impact. A multi-level functional decomposition method is developed, enabling comparisons between system architectures that incorporate conventional and electrified components. Simulation results highlight the energy-saving potential of EMAs over traditional hydraulic and electrohydraulic actuators. Additionally, hybrid propulsion systems using Sustainable Aviation Fuels (SAF) and Liquid Hydrogen (LH2) are analyzed for their suitability in future aircraft platforms. The findings suggest that electrification and hybridization can significantly reduce fuel consumption during ground operations, and that liquid hydrogen offers the most promising long-term potential for net-zero emission aviation. This research provides a structured framework for designing next-generation aircraft systems that align with sustainability targets and support continued advancements in aviation energy management and system integration.This research was funded by Innovate UK grant number 10002411, under the ATI/IUK Project: LANDOne, with Airbus UK as Industrial Lead.AIAA Aviation Forum and Ascend 202

Developing a strategic roadmap toward hydrogen energy economy for energy mix integration in Saudi Arabia

Luk, Patrick Chi-Kwong - Associate SupervisorHydrogen has come to the forefront as a hopeful solution in the transition toward cleaner, more sustainable sources of energy that will meet global decarbonization goals. However, while it is a great potential, a few challenges are found within the hydrogen sector, including fluctuating renewable energy costs, policy uncertainties, and complex issues related to storage, transportation, and market integration. These make efficient production and distribution of hydrogen hard, hence acting as a barrier to large-scale adoption. This research addresses these challenges through developing a strategic DSS intended to optimize the production and distribution of hydrogen. The presented DSS integrates multi-criteria decision-making and decision tree methodologies to obtain a flexible tool based on data that will balance economic feasibility, technological adaptability, environmental sustainability, and compliance with regulations. By putting all these elements together, the DSS provides an integrated approach to making decisions for addressing issues with hydrogen energy systems. This work is motivated by the fact that literature lacks integrated frameworks that might guide decision analyses in hydrogen production. Most works have focused on isolated issues related to the analysis of technology costs or even policy impacts, excluding the important needs for a strategic approach that captures all these significant factors in one fell swoop. This study fills this gap by presenting one unified system able to support relevant, strategic decisions by any stakeholder. A case study undertaken in Saudi Arabia validates the DSS against practical, real-world scenarios for completeness in aligning with the Vision 2030 energy transition pathways of the country. It should not only promote efficiency and costeffectiveness of hydrogen production but also meet green practices by remaining in step with environmental targets and market demand. Besides, the integration of machine learning techniques enhances the predictive capabilities of DSS and hence increases its adaptability toward dynamically changing energy markets. These research findings pinpoint the DSS as a very important tool for furthering hydrogen production and distribution while offering valuable insights for policymakers, industry leaders, and investors in the same instance. Given that this study provides a structured approach to decision-making, it will contribute valuably to the global effort of developing a sustainable hydrogen economy and support broader goals for energy security and environmental stewardship.PhD in Energy and Powe

Optimisation of deep learning techniques on remaining useful life prediction of complex engineering systems

Addepalli, Pavan - Associate SupervisorPredictive maintenance based on performance degradation is a crucial way to reduce the operation and maintenance costs and potential failures in modern complex engineering systems. Reliable remaining useful life (RUL) prediction is the main criterion for predictive maintenance decisions. Data-driven techniques, especially artificial intelligence (AI) such as deep learning (DL) techniques, have attracted more and more attention in the manufacturing sector with the development of Big Data and Internet-of-Things. Different DL techniques recently have been used for RUL prediction and achieved great success. However, in many cases, the RUL prediction results vary greatly due to the measurement noise and selection of model parameters. This PhD research aims to develop a novel framework to optimise the performance of deep learning methods in the context of predicting the RUL of complex engineering systems. The project consists of four stages including literature review, optimisation investigations, construction of the new prognostic framework and validation. In the first stage, state-of-the-art DL-based approaches for RUL prediction are reviewed. Then, the optimisation investigations on different RNN models, feature engineering, model parameters and RUL target functions are carried out trying to improve the RUL prediction accuracy. After that, the prognostic framework is built based on the result of the optimisation investigation. Meanwhile, a novel three-stage feature selection method and a multi-scale RUL prediction approach are proposed in this stage. In addition, to accommodate the multiple operating conditions of complex engineering systems, this thesis presents an operation-based normalisation method to address the different degradation patterns from data. In the last stage, the C-MAPSS dataset is adopted to validate the proposed framework and the prediction performance is compared with the state-of-the-art RUL prediction approaches. A significant improvement can be observed in the RUL prediction performance using the proposed framework on most of the subsets of the C-MAPSS dataset. Therefore, a reliable and flexible RUL prediction strategy can be made based on this DL-based prognostics framework for complex engineering systemsPhD in Manufacturin