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Uncovering plant root traits and mechanisms that enable penetration, exploration, and exploitation of soil parent materials: a systematic review
No full textBackground and aims
Certain plant species, including some trees, have been observed growing not only in soil but also in soil parent materials. However, the root traits and mechanisms enabling these species to penetrate soil parent materials are not yet thoroughly understood. This systematic review aims to identify and discuss the root traits and mechanisms that allow plant roots to grow into soil parent materials. It will also draw insights from the characteristics and mechanisms that plants employ to overcome the challenges posed by compacted soils.
Methods
We adhered to the 'Preferred Reporting Items for Systematic Reviews and Meta-Analyses' (PRISMA) guidelines for our methodology.
Results
We identified increased root radial pressure, investment in root biomass, fine root development, root trematotropism, mycorrhizal associations, root hairs, and root exudates as key traits aiding plants in soil penetration. The mentioned root traits and mechanisms have also been shown to help plants overcome compacted soil, except for mycorrhizal associations.
Conclusion
The key root traits and mechanisms identified in this review lay the groundwork for a deeper understanding of root-soil parent material interactions and plant adaptations in changing physical environments. This enhances our ability to select the next generation of robust and resilient crops capable of thriving in complex root-soil parent material interactions. Future research on root-parent material interactions in food crops holds promise for improving our understanding of how crops can grow beyond traditional soil limitations (such as soil depth).This research was funded by UKRI BBSRC FoodBioSystems DTP, grant number BB/T008776/1.Plant and Soi
Hybrid solvers for reactor modelling: matrix-based and matrix-free approaches on voxel-dominated meshes
No full textSimulating neutronics and thermal hydraulics within nuclear reactor cores is computationally intensive, not only because of the complexity of the governing equations but also because of the intricate geometries involved. Solving the Boltzmann transport and Navier-Stokes equations for a full core representation typically relies on unstructured meshes, which, while highly flexible, can substantially increase computational costs regarding memory and solving time. Cartesian meshes with Finite Elements (FE) offer a faster alternative, potentially improving computational speed by an order of magnitude due to direct memory addressing. However, they necessitate finer grids to accurately capture the boundary details of non-Cartesian surfaces, which can offset these gains by increasing solver times. To address this challenge, a new meshing algorithm is proposed in conjunction with hybrid, matrix-based and matrix free, solver technologies. It employs a geometry-conforming boundary method using voxel-dominated Cartesian meshes. This method enables accurate boundary representation at arbitrary resolutions, which can be adjusted to resolve the physics to the desired level of accuracy rather than strictly to capture geometric detail. This is combined with a hybrid solver for fluid flows to different regions of a problem in order to increase efficiency when resolving the boundary. This article demonstrates the method’s application to Computational Fluids Dynamics (CFD) and neutronics problems relevant to reactor physics, showcasing its accuracy, convergence, numerical stability, and suitability for handling complex geometries.The author(s) declare that financial support was received for the research and/or publication of this article. Science and Technology Facilities Council (STFC) funded through grant: Parallel solvers for voxel-dominant meshes for the Boltzmann Transport Equation.Frontiers in Nuclear Engineerin
Measurement of strain and vibration, at ambient conditions, on a dynamically pressurised aircraft fuel pump using optical fibre sensors
No full textEver-increasing demands to improve fuel burn efficiency of aero gas turbines lead to rises in fuel system pressures and temperatures, posing challenges for the structural integrity of the pump housing and creating internal deflections that can adversely affect volumetric efficiency. Non-invasive strain and vibration measurements could allow transient effects to be quantified and considered during the design process, leading to more robust fuel pumps. Fuel pumps used on a high bypass turbofan engine were instrumented with optical fibre Bragg grating (FBG) sensors, strain gauges and thermocouples. A hydraulic hand pump was used to facilitate measurements under static conditions, while dynamic measurements were performed on a dedicated fuel pump test rig. The experimental data were compared with the outputs from a finite element (FE) model and, in general, good agreement was observed. Where differences were observed, it was concluded that they arose from the sensitivity of the model to the selection of nodes that best matched the sensor location. Strain and vibration measurements were performed over the frequency range of 0 to 2.5 kHz and demonstrated the ability of surface-mounted FBGs to characterise vibrations originating within the internal sub-components of the pump, offering potential for condition monitoring.United Kingdom Research and Innovation,113095; Engineering and Physical Sciences Research Council, EP/H02252X/1The authors acknowledge funding from the United Kingdom Research and Innovation (UKRI) through the support from Innovate UK, via the Aerospace Technology Institute (ATI) program, End-to-End Equipment Health Management (E2E EHM, Project Reference: 113095) [50] and by the Engineering and Physical Sciences Research Council (EPSRC), UK via a Platform Grant (EP/H02252X/1).Sensor
Can digital twin technology revolutionize wildfire management and energy resilience in Los Angeles?
No full textWildfires increasingly threaten energy and water infrastructure in regions such as Los Angeles (LA), requiring real-time, resilient coordination strategies. This study introduces an integrated framework that combines digital twin technology with distributionally robust optimization (DRO) to manage uncertainty and improve operational resilience. The system dynamically models wildfire spread using terrain and wind data, and jointly simulates energy and water systems under real-time disruption scenarios. Validation is performed using real-world data from the Palisades Fire, benchmarked against deterministic dispatch models without robust adaptation. Simulation results show that the DRO-based method reduces average PV efficiency loss by 22.4% through real-time reconfiguration and increases battery discharge support during evacuation surges by 18.7%. Compared to baseline strategies, the proposed framework shortens average critical load outage duration by 35%, improves firefighting water delivery reliability by 21.8%, and lowers total daily water consumption by 12.5 million gallons. Fire spread prediction achieves a 24-hour localization error below 310 meters, ensuring precise hazard mapping. These outcomes confirm the framework’s ability to enhance system resilience, minimize resource waste, and support post-disaster recovery. The presented approach offers a scalable and adaptive tool for next-generation wildfire response planning under complex, uncertain conditions.This research was funded by Researchers Supporting Project grant number RSPD2025R635, King Saud University, Riyadh, Saudi Arabia.Energy Report
Producing micro-finite element models from real-time clinical CT scanners: calibration, validation and material mapping strategies
No full textFinite element (FE) models from living anatomical structures to produce patient-specific models offer improved diagnosis, precision pre-op planning for surgeries, and reliable biofidelic stress loading analysis. These models require the use of clinical scanners that are safe to use in-vivo but offer relatively lower resolution than in-vitro micro-CT ones. To capitalise on the clinical advantages, this route offers certain technical challenges which must be ironed out to derive a reliable validated route from scanning to in silico modelling. In the present study, sheep vertebrae were used to create biofidelic phantoms for scanning by using one of the latest technology high-resolution (300 micron) clinical standing scanners (HiRise, Curvebeam). Geometric information was used to produce FEA models (Abaqus/CAE), which were then validated under compression loading in the lab. The main challenges had to do first with reading and converting the scan data from voxels to material property assignment for each FE element, which was performed by using a number of different conversion equations from the literature, and second, to a lesser degree, with the minor challenges of seeking convergence and refining the boundary conditions. The fit between the model and the experimental results was best for two equations from the literature, while others were less reliable. The selection of the most suitable and universally applicable material conversion equation is significant because it can streamline the route to produce scanner to computer patient-specific models, and make these widely available and ultimately more easily immediately obtainable post-scans. Some known clinical examples highlight the potential use of this methodology for situations where loading and unloading configurations are equally challenging for modelling (i.e., standing CT scans of feet), and this paper discusses the importance of the approach for such examples. Unlike previous studies using micro-CT or non-clinical setups, this work validates a real-time, weight-bearing CT-based workflow for biomechanically consistent finite element modelling.This work was supported by grant EP/R513027/1 from the EPSRC DTP 2018–2019.Frontiers in Bioengineering and Biotechnolog
Dataset DrivAer hp-F: Surface Pressure Measurements in Yaw Conditions
No full textDataset for the surface pressure measurements conducted on the 35% scale DrivAer hp-F model at various yaw angles in the 8x6 Wind Tunnel at Cranfield University.
The dataset includes the surface pressure coefficient results from measurements on the slant of the following vehicle configurations:
- DrivAer hp-F standard configuration (no spoiler or rear wing)
- DrivAer hp-F spoiler configuration
- DrivAer hp-F rear wing configuration
The measurements on the DrivAer hp-F rear wing configuration have been conducted three times for repeatability. The dataset also includes a log file of the data structure and wind tunnel conditions for each experiment.
In reference to the publication: Steven Rijns, Tom-Robin Teschner, Kim Blackburn, Anderson Ramos Proenca, James Brighton; Experimental and numerical investigation of the aerodynamic characteristics of high-performance vehicle configurations under yaw conditions. Physics of Fluids 1 April 2024; 36 (4): 045112. https://doi.org/10.1063/5.0196979
CAD files for the DrivAer hp-F configurations are available at: Rijns, Steven; Teschner, Tom-Robin; Blackburn, Kim; Ramos Proenca, Anderson; Brighton, James (2024). DrivAer hp-F: Spoiler & Rear Wing Configurations Geometry Pack. Cranfield Online Research Data (CORD). Dataset. https://doi.org/10.17862/cranfield.rd.25715202
Note: The updated datasets retain all original data while adding calibrated data to provide (new) users with an additional reference option
Unmasking deepfakes: a multidisciplinary examination of social impacts and regulatory responses
Get PDFThis paper presents a comprehensive analysis of deepfake technology and its multifaceted impacts on society, privacy, trust, and information integrity. Deepfakes, synthetic media generated using AI-powered algorithms, pose significant challenges to individual privacy, societal trust, and the integrity of information. To explore these issues, we employed a mixed-methods approach that included in-depth expert interviews with professionals from diverse fields such as law, ethics, artificial intelligence, cybersecurity, and social sciences, along with a dichotomous question survey, which provided comprehensive insights from multiple perspectives. This methodological approach facilitated a multidimensional perspective on the potential risks and benefits of deepfakes. Our findings reveal unanimous concern among experts regarding the profound societal implications of deepfakes, particularly their capacity to amplify disinformation, erode public trust, and inflict psychological harm on individuals. Key themes identified include the urgent need for robust regulatory frameworks, the critical role of media literacy in enhancing public resilience, and the varying impacts of deepfakes across different demographic groups. The consensus among experts points out the necessity for an ethically guided approach to the development and deployment of deepfake technology, emphasizing the importance of interdisciplinary collaboration in crafting effective policy responses. This study advances the ongoing discourse on deepfake technology by providing stakeholders and policymakers with evidence-based recommendations aimed at mitigating the associated risks and harnessing potential benefits. These recommendations promote a balanced and informed approach to navigating the complexities of this emerging technological challenge.Human-Intelligent Systems Integratio
Optimization of drying parameters for pretreated green banana slices using response surface methodology
Get PDFIn this study, the influence of drying temperature and pretreatment on the drying kinetics and quality metrics of green banana slices was investigated. The present investigation included the drying of 4 ± 1 mm green banana slices with potassium metabisulfite (KMS) at levels of 0 (control), 0.5%, 1%, and 1.5% in a tray dryer. A numerical optimization method was utilized to optimize the drying rate, color, water activity, rehydration ratio, and attractiveness of the banana slices, using a variety of input factors such as drying temperature and pretreatment. The results revealed that the untreated samples and those treated with 1.0% and 1.5% KMS required a longer drying time than the samples treated with 0.5% KMS. The process parameters were optimized using response surface methodology, adopting an I‐optimal design for this investigation. The best solution yielded a drying air temperature of 60.62°C and a pretreatment level of 0.786% KMS, resulting in a drying time of 346.28 min, a color (L∗) value of 76.23, a water activity (aw) of 0.299, and a rehydration ratio of 2.81. Similar outcomes were obtained when the optimized conditions were tested and compared with experimental data using an experimental validation process.Journal of Food Qualit
A review of material-related mechanical failures and load monitoring-based structural health monitoring (SHM) technologies in aircraft landing gear
Get PDFThe aircraft landing gear system is vital in ensuring the aircraft’s functional completeness and operational safety. The mechanical structures of the landing gear must withstand significant operational forces, including repeated high-intensity impact loads, throughout their service life. At the same time, they must resist environmental degradation, such as corrosion, temperature fluctuations, and humidity, to ensure structural integrity and long-term reliability. Under this premise, investigating material-related mechanical failures in the landing gear is of great significance for preventing landing gear failures and ensuring aviation safety. Compared to failure investigations, structural health monitoring (SHM) plays a more active role in failure prevention for aircraft landing gears. SHM technologies identify the precursors of potential failures and continuously monitor the operational or health conditions of landing gear structures, which facilitates condition-based maintenance. This paper reviews various landing gear material-related failure investigations. The review suggests a significant portion of these failures can be attributed to material fatigue, which is either induced by abnormal high-stress concentration or corrosion. This paper also reviews a series of load monitoring-based landing gear SHM studies. It is revealed that weight and balance measurement, hard landing detection, and structure load monitoring are the most typical monitoring activities in landing gears. An analytical discussion is also presented on the correlation between reviewed landing gear failures and SHM activities, a comparison of sensors, and the potential shift in load-based landing gear SHM in response to the transition of landing gear design philosophy from safe life to damage tolerance.Airbus (United Kingdom)This research was funded by Airbus Operations Limited, and the reference number for the funding is CU01921121.Aerospac
Investigating the impact of coupling process-based and data-driven models on wheat crops in arid and semi-arid regions
No full textWaine, Toby W. - Associate Supervisor
El Alami, Rafiq - Associate SupervisorAccurate prediction of wheat yields in arid and semi-arid regions is challenging
due to water scarcity, varying environmental conditions, and the dynamic nature
of factors influencing crop growth. This thesis aims to enhance scalable wheat
yield prediction by integrating remote sensing (RS) data into process-based and
data-driven models for more precise and accurate yield prediction in these
regions, supporting both tactical and strategic decision-making in agriculture.
AquaCrop was chosen for its robust simulation of crop yield response to water.
Four interlinked research questions are addressed in this study. First, I identify
key factors impacting wheat yield prediction based on sensitivity and SHAP
analysis for process-based and data-driven models, respectively. Second, I
compare the trade-offs between calibrating process-based models using ground-
based hemispherical data and freely available remotely sensed data, highlighting
the trade-offs between accuracy and practicality. Third, I evaluate the
effectiveness of early-season data-driven yield prediction models across two
geographic regions, emphasising the need for region-specific calibrations to
maintain accuracy and quantifying accuracy loss due to model transferability.
Model performance improved as the season progressed, with Support Vector
Regressors achieving an RMSE of 0.23 t ha⁻¹ in the arid regions and Random
Forests achieving 0,50 t ha⁻¹ and 0.46 t ha⁻¹ in semi-arid and global models.
Fourth, I examine the integration of data-driven models into process-based
models through data assimilation techniques, demonstrating how Bayesian
assimilation and high-temporal resolution data improve yield prediction accuracy.
Bayesian assimilation reduced the prediction errors, decreasing RMSE and
MAPE by 25% and 76.5%, respectively, compared to no assimilation
approach. This research contributes to the body of knowledge by providing a
comprehensive framework for integrating remote sensing data into yield
prediction models, supporting precise and timely agricultural decision-making to
optimise productivity in water-limited environments.PhD in Environment and Agrifoo