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Hyperspectral imaging for phenotyping plant drought stress and nitrogen interactions using multivariate modeling and machine learning techniques in wheat
Get PDFAccurate detection of drought stress in plants is essential for water use efficiency and agricultural output. Hyperspectral imaging (HSI) provides a non-invasive method in plant phenotyping, allowing the long-term monitoring of plant health due to sensitivity to subtle changes in leaf constituents. The broad spectral range of HSI enables the development of different vegetation indices (VIs) to analyze plant trait responses to multiple stresses, such as the combination of nutrient and drought stresses. However, known VIs may underperform when subjected to multiple stresses. This study presents new VIs in tandem with machine learning models to identify drought stress in wheat plants under varying nitrogen (N) levels. A pot wheat experiment was set up in the glasshouse with four treatments: well-watered high-N (WWHN), well-watered low-N (WWLN), drought-stress high-N (DSHN) and drought-stress low-N (DSLN). In addition to ensuring that plants were watered according to the experiment design, photosynthetic rate (Pn) and stomatal conductance (gs) (which are used to assess plant drought stress) were taken regularly, serving as the ground truth data for this study. The proposed VIs, together with known VIs, were used to train three classification models: support vector machines (SVM), random forest (RF), and deep neural networks (DNN) to classify plants based on their drought status. The proposed VIs achieved more than 0.94 accuracy across all models, and their performance further increased when combined with known VIs. The combined VIs were used to train three regression models to predict the stomatal conductance and photosynthetic rates of plants. The random forest regression model performed best, suggesting that it could be used as a stand-alone tool to forecast gs and Pn and track drought stress in wheat. This study shows that combining hyperspectral data with machine learning can effectively monitor and predict drought stress in crops, especially in varying nitrogen conditions.Rothamsted ResearchThis research was supported by the OCP S.A. under the University of Mohammed VI Polytechnic, Rothamsted Research and Cranfield University project (FP04).Remote Sensin
Temperature-dependent solid material properties of GRCop-42 for an additively manufactured liquid rocket engine LOx cooling channel
Get PDFXXXІI International Scientific and Practical Conference “Information Technologies: Science, Engineering, Technology, Education, Health (MicroCAD-2024)” 22-25 May 2024, Kharkiv, UkraineRecent technological developments in the field of Additive Manufacturing (AM) provide a number of opportunities for the utilisation of high-performance copper alloys for aerospace applications. The additively manufactured LOx/LNG DemoP1 aerospike engine demonstrator designed by Pangea Aerospace is a characteristic example based on the Direct Metal Laser Sintering (DMLS) technology. The aerospike engine thrust chamber and LOx cooling channels are manufactured using GRCop-42 material powder, a Cu-Cr-Nb based copper alloy developed by the National Aeronautics and Space Administration (NASA) for the regenerative cooling technology of high thermal demand thrust chambers and nozzles. In the current work temperature-dependent correlations are derived for the density, specific heat capacity at constant pressure and thermal conductivity of the GRCop-42 material. The correlations for the solid material properties are then introduced into the ANSYS Fluent 2023 R2 Computational Fluid Dynamics (CFD) package and their capabilities are investigated for the characterisation of the flow-field characteristics of the LOx flow in the cooling channel. The numerical solution of the coolant flow in the AM cooling channel is compared against experimental data of the DemoP1 engine demonstrator. The main objective of this study is to provide a realistic physical description of the temperature-dependent properties of the AM solid material in high heat flux applications where the material properties are mostly considered as constant in previous studies.The present research work was financially supported by the Centre for Propulsion and Thermal Power Engineering and the Cranfield Air and Space Propulsion Institute (CASPI) at Cranfield University, UK in collaboration with Pangea Aerospace, Spain.Multidiszciplináris Tudományok (Multidisciplinary Sciences
Algorithmic routines and dynamic inertia: how organizations avoid adapting to changes in the environment
Get PDFOrganizations often fail to adequately respond to substantive changes in the environment, despite widespread implementation of algorithmic routines designed to enable dynamic adaptation. We develop a theory to explain this phenomenon based on an inductive, historical case study of the credit rating routine of Moody’s, an organization that failed to adapt to substantial changes in its environment leading up to the 2008 financial crisis. Our analysis of changes to the firm’s algorithmic credit rating routine reveals mechanisms whereby organizations dynamically produce inertia by taking actions that fail to produce significant change. Dynamic inertia occurs through bounded retheorization of the algorithmic model, sedimentation of assumptions about inputs to the algorithmic model, simulation of the unknown future, and specialized compartmentalization. We enable a better understanding of organizational inertia as a sociomaterial phenomenon by theorizing how—despite using algorithmic routines to improve organizational agility—organizations dynamically produce inertia, with potentially serious adverse consequences
The path to net-zero in dairy production: are pronounced decreases in enteric methane achievable?
Get PDFAchieving net-zero greenhouse gas (GHG) emissions in dairy production will require >50% reduction in enteric methane (CH4) emissions together with elimination of emissions from feed production, additional carbon sequestration, reduction in manure emissions, anaerobic digestion of manure, and decreased reliance on fossil fuel energy. Over past decades, improved production efficiency has reduced GHG intensity of milk production (i.e., emissions per unit of milk) in the United States, but this trend will continue only if cows are bred for increased efficiency. Genetic selection of low-CH4-producing animals, diet reformulation, use of feed additives, and vaccination show tremendous potential for enteric CH4 mitigation; however, few mitigation strategies are currently available, and added cost without increased revenue is a major barrier to implementation. Complete elimination of CH4 emissions from dairying is likely not possible without negatively affecting milk production; thus, offsets and removals of other GHGs will be needed to achieve net-zero milk production.M.C. was supported by a UK Research and Innovation Future Leaders Fellowship (grant no. MR/W010577/1).Annual Review of Animal Bioscience
Improving racing drones flight analysis: a data-driven approach using motion capture systems
Get PDFThe publication of the previous study, titled “Experimental Study on the Dynamic Behaviour of Drones Designed for Racing Competitions”, highlighted the increasing interest in employing scientific methods for their design and analysis. That study examined the flight data of 15 racing drones within a large flight area, using Doppler-type sensors for data collection. Building on these findings and seeking to enhance them, the current work introduces an upgraded data acquisition system utilising optical sensors, thereby improving measurement accuracy. These enhanced flight data facilitate the development of updated quality indices and conclusions, offering a more precise and definitive analysis than was previously possible.This work has been supported by the Spain government via MCIN/AEI/ 10.13039/501100011033 [Project PID2020-119468RA-I00].Drone
Exploring the effect of sampling density on spatial prediction with spatial interpolation of multiple soil nutrients at a regional scale
Get PDFEssential soil nutrients are dynamic in nature and require timely management in farmers’ fields. Accurate prediction of the spatial distribution of soil nutrients using a suitable sampling density is a prerequisite for improving the practical utility of spatial soil fertility maps. However, practical research is required to address the challenge of selecting an optimal sampling density that is both cost-effective and accurate for preparing digital soil nutrient maps across regional extents. This study examines the impact of sampling density on spatial prediction accuracy for a range of soil fertility parameters over a regional extent of 8303 km2 located in eastern India. Surface soil samples were collected from 1024 sample points. The performance of six levels of sampling densities for spatial prediction of 14 soil properties was compared using ordinary kriging. From the sample points, randomization was used to select 224 points for validation and the remaining 800 for calibration. Goodness-of-fit for the semi-variograms was evaluated by R2 of model fit. Lin’s concordance correlation coefficient (CCC) and root mean square error (RMSE) were evaluated through independent validation as spatial prediction accuracy parameters. Results show that the impact of sampling density on prediction accuracy was unique for each soil property. As a common trend, R2 of model fit and CCC scores improved, and RMSE values declined with the increasing sampling density for all soil properties. On the other hand, the rate of gain in the accuracy metrics with each increment in the sampling density gradually decreased and ultimately plateaued. This indicates that there exists a sampling density threshold beyond which the extra effort on additional sampling adds less to the spatial prediction accuracy. The findings of this study provide a valuable reference for optimizing soil nutrient mapping across regional extents.Department of Science and Technology, Philippine American Educational Foundation, United States Department of StateLan
Metagenomic analysis reveals metabolic mechanism of enhancing lignocellulosic anaerobic digestion mediated by CO2/O2-nanobubble water
Get PDFNanobubble water (NW) has been reported to enhance anaerobic digestion (AD), but its influence on the metabolic pathways of microorganisms remains unclear. In this study, the specific methane yields of rice straw in the CO2NW and O2NW treatments increased by 6.9% and 18.3%, respectively. The electron transport system (ETS) and coenzyme F420 activities were enhanced by the addition of NW. Metagenomic analysis showed that the abundances of most enzymes in the acidification were significantly increased by both CO2NW and O2NW. Regarding methanogenesis, CO2NW promoted the expression of genes encoding enzymes of hydrogenotrophic methanogenesis, while O2NW stimulated both the acetoclastic and hydrogenotrophic methanogenesis. With the addition of O2NW, the expressions of modules related to the tricarboxylic acid (TCA) cycle and oxidative phosphorylation were enhanced, resulting in increased ATP production. This study provided fundamental evidence of the metabolic pathways of microorganisms mediated by NW at each stage of AD.National Natural Science Foundation of ChinaBioresource Technolog
Effect of temperature, relative humidity, and incubation time on the mycotoxin production by Fusarium spp. responsible for dry rot in potato tubers
Get PDFData supporting this study are openly available from CORD, at this link: https://doi.org/10.57996/cran.ceres-2594Potato is the fourth most consumed crop in the world. More than half of the crop is stored for three to nine months at cold temperatures (3–10 °C) for the fresh and seed market. One of the main causes of fresh potato waste in the retail supply chain is the processing of fungal and bacterial rots during storage. Dry rot is a fungal disease that mainly affects the potato crop during storage and is responsible for 1% of tuber losses in the UK. It is produced by Fusarium spp., such as Fusarium sambucinum and F. oxysporum, which can lead to the accumulation of mycotoxins in the potato tuber. Little is known about the impact of environmental factors on the accumulation of mycotoxins in potato tubers. Understanding the ecophysiology of these fungi is key to mitigating their occurrence under commercial storage conditions. Therefore, this work aimed to elucidate the effect of three different temperatures (5, 10, and 15 °C) and two different water activities (aw; 0.97, 0.99) on the ecophysiology and mycotoxin accumulation of F. sambucinum and F. oxysporum in a potato-based semi-synthetic medium. The mycotoxin accumulation was then studied in vivo, in potato tubers cultivated under organic farming conditions, stored for 40 days at 8.5 °C. Results showed that higher temperatures and aw enhanced fungal growth, lag time, and mycotoxin accumulation in vitro. Growth rate was 2 and 3.6 times higher when the temperature increased from 5 to 10 and 15 °C, respectively. Six different mycotoxins (T-2, HT-2, diacetoxyscirpenol, 15-acetoxyscirpenol, neosolaniol, and beauvericin) were detected in vitro and in vivo. T-2 was the most abundant mycotoxin detected in vitro, observing 106 ng of T-2/g media after 21 days of incubation at 10 °C and 0.99 aw. Due to the long period of time that potato tubers spend in storage, the fluctuations of environmental factors, such as temperature and relative humidity, could promote the development of fungal rot, as well as mycotoxin accumulation. This could result in important food and economic losses for the potato market and a threat to food safety.This research was funded by the Interreg North-West Europe program (project number: NWE 363) and BBSRC research grant Oats for the future between the Applied Mycology Group at Cranfield University, UK (BB/P001432/1).Toxin
Digital resilience and firm internationalization: a study of Chinese listed companies
Get PDFPurpose:
The aim of this research is to explore the impact of digital resilience on firms' international performance. Using internal operation efficiency and innovation as channel variables, this paper explores the relationship between digital resilience and international firm performance of Chinese listed firms.
Design/methodology/approach:
The research design follows a quantitative approach. Using firm-level panel data from 2007 to 2020, this paper tests the hypotheses between digital resilience and international firm performance through internal efficiency and innovation.
Findings:
The results note that digital resilience has a positive effect on internationalization while operation efficiency is a channel through which digital resilience promotes internationalization. Digital resilience also facilitates innovation by improving research and development (R&D) efficiency and matching innovation collaborators.
Originality/value:
This paper is one of the first to explore digital resilience in the context of internalization and international firm performance. This paper extends the notion of resource-based view (RBV) to examine the relationship between digital resilience, internal efficiency and innovation on international firm performance.The National Social Science Fund of China, "Research on the Mechanism and Policy of Market Integration driven by Digital Economy under the New Development Pattern" (22CJY065
Parameter quantification for oxygen transport in the human brain
No full textBackground and objective: Oxygen is carried to the brain by blood flow through generations of vessels across a wide range of length scales. This multi-scale nature of blood flow and oxygen transport poses challenges on investigating the mechanisms underlying both healthy and pathological states through imaging techniques alone. Recently, multi-scale models describing whole brain perfusion and oxygen transport have been developed. Such models rely on effective parameters that represent the microscopic properties. While parameters of the perfusion models have been characterised, those for oxygen transport are still lacking. In this study, we set to quantify the parameters associated with oxygen transport and their uncertainties.
Methods: Effective parameter values of a continuum-based porous multi-scale, multi-compartment oxygen transport model are systematically estimated. In particular, geometric parameters that capture the microvascular topologies are obtained through statistically accurate capillary networks. Maximum consumption rates of oxygen are optimised to uniquely define the oxygen distribution over depth. Simulations are then carried out within a one-dimensional tissue column and a three-dimensional patient-specific brain mesh using the finite element method.
Results: Effective values of the geometric parameters, vessel volume fraction and surface area to volume ratio, are found to be 1.42% and 627 [mm^2/mm^3], respectively. These values compare well with those acquired from human and monkey vascular samples. Simulation results of the one-dimensional tissue column show qualitative agreement with experimental measurements of tissue oxygen partial pressure in rats. Differences between the oxygenation level in the tissue column and the brain mesh are observed, which highlights the importance of anatomical accuracy. Finally, one-at-a-time sensitivity analysis reveals that the oxygen model is not sensitive to most of its parameters; however, perturbations in oxygen solubilities and plasma to whole blood oxygen concentration ratio have a considerable impact on the tissue oxygenation.
Conclusions: The findings of this study demonstrate the validity of using a porous continuum approach to model organ-scale oxygen transport and draw attention to the significance of anatomy and parameters associated with inter-compartment diffusion.TIJ was supported by the Amsterdam UMC Postdoc Career Bridging Grant and the European Union’s Horizon 2020 research and innovation program (grant no. 777072, INSIST project).Computer Methods and Programs in Biomedicin