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Additive manufacturing in pharmaceutical supply chain
Get PDFPurpose: A resilient and efficient pharmaceutical supply chain (PSC) ensures access to essential medicines during pandemics and other emergencies. The COVID-19 pandemic has highlighted the need for continued investment and innovation in this area, and concerted efforts by all stakeholders are necessary to achieve this goal. Additive manufacturing (AM), or 3D printing, can enhance PSC resilience and performance, reduce waste, and improve environmental sustainability. 3D printing can help address drug shortages, patient-specific dosages, and personalised medicine in the pharmaceutical industry. Moreover, 3D printing technology enables local production of drugs and medical devices, reducing transportation costs, carbon footprint, and lead times, transforming how products are designed, produced, and delivered to end-users. This study aims to investigate the multifaceted benefits of 3D printing technology on the PSC, including its potential to streamline processes, increase SC efficiency, enhance responsiveness, and improve sustainability. Additionally, the study seeks to identify the interrelationships between these benefits and how they can contribute to the overall success of the PSC.
Research Approach: To achieve this, we comprehensively analyse the potential benefits and shortcomings of 3D printing technology on the PSC by compiling relevant literature and internet sources.
Findings and Originality: The study identifies ways in which 3D printing can positively impact the PSC, including simplifying the supply chain (SC) process, localising production, and transitioning from make-to-stock to make-to-order production. These changes can significantly impact inventory levels, increasing SC sustainability, efficiency, responsiveness, and resilience. However, this study also identifies unique shortcomings and future research opportunities associated with implementing 3D printing in the PSC, providing a holistic view of the technology's potential impact.
Research Impact: The research highlights the potential of 3D printing to revolutionise the PSC by enabling a more streamlined and sustainable manufacturing process.
Practical Impact: The study's findings offer the pharmaceutical industry insights on how to tackle SC shortcomings such as supplier shortages, fluctuating demand, and short response times. As a result, this study offers a valuable resource for both practitioners and researchers who wish to leverage 3D printing technology to enhance the PSC's performance and understand the technology's impact on the PSC
Advancing aviation safety through machine learning and psychophysiological data: a systematic review
Get PDFIn the aviation industry, safety remains vital, often compromised by pilot errors attributed to factors such as workload, fatigue, stress, and emotional disturbances. To address these challenges, recent research has increasingly leveraged psychophysiological data and machine learning techniques, offering the potential to enhance safety by understanding pilot behavior. This systematic literature review rigorously follows a widely accepted methodology, scrutinizing 80 peer-reviewed studies out of 3352 studies from five key electronic databases. The paper focuses on behavioral aspects, data types, preprocessing techniques, machine learning models, and performance metrics used in existing studies. It reveals that the majority of research disproportionately concentrates on workload and fatigue, leaving behavioral aspects like emotional responses and attention dynamics less explored. Machine learning models such as tree-based and support vector machines are most commonly employed, but the utilization of advanced techniques like deep learning remains limited. Traditional preprocessing techniques dominate the landscape, urging the need for advanced methods. Data imbalance and its impact on model performance is identified as a critical, under-researched area. The review uncovers significant methodological gaps, including the unexplored influence of preprocessing on model efficacy, lack of diversification in data collection environments, and limited focus on model explainability. The paper concludes by advocating for targeted future research to address these gaps, thereby promoting both methodological innovation and a more comprehensive understanding of pilot behavior.IEEE Acces
Numerical modelling of hydrogen leakages in confined spaces for domestic applications
Get PDFThis paper was also presented at: DSDS24, Cranfield Defence and Security Doctoral Symposia 2024, 13-14 November 2024, STEAM Museum, Swindon, UKThe UK government tentatively plans to use hydrogen for domestic applications by 2035. While the use of hydrogen aims to reduce the dependence on hydrocarbons, certain factors need consideration. Since hydrogen is much lighter, and more reactive than methane, it is crucial to understand the change in risk for accident scenarios involving hydrogen in a domestic setting. Numerical modelling was used to simulate the leakage of hydrogen and methane in small, enclosed spaces such as kitchen cupboards. The k- ε turbulence model was used along with the species transport model to simulate the leakage of gas for different inlet locations and leak diameters (1.8 mm–7.2 mm). From the modelling study, it was observed that hydrogen and methane both tend to stratify from top of the control volume to the bottom. The key finding was that, under adverse conditions (leak from a 7.2 mm diameter hole) and due to greater volumetric flow, hydrogen tends to reach equilibrium concentration 45s faster than methane for a total leak duration of 600s. Additionally, it was noted that cases with leak inlet locations near corners had 28% lower hydrogen concentrations, and 25% lower methane concentrations as compared to leak inlet locations near the centre of the cupboard.The work was supported by Cranfield University and DNV Energy Systems, UK.International Journal of Hydrogen Energ
Prediction of Flight Delay using Deep Operator Network with Gradient-mayfly Optimisation Algorithm
No full textData: This folder contains: - Datasets called Jan_2021_ontime.csv and Nov_2021_ontime.csv were used to obtain the results presented in the journal paper. Source code: This folder contains two files having the instructions on how to run the code and a list of library requirements and folders for each of the ML models as named exactly as contained in the paper which implements the proposed Deep Operator Network with Gradient-mayfly Optimisation Algorithm and all the algorithms presented and validated in the journal paper. Output: This folder contains: - Figures called Figure_1_MAE.png, Figure_1_MAPE.png, Figure_1_RMSE.png, Figure_1_MSE.png, Figure_2_MAE.png,Figure_2_MAPE.png, Figure_2_RMSE.png, Figure_2_MSE.png which shows results from the models based on different train/test ratios, The models are: (A)DBN, (B) Gradient Boosting Classifier, (C) Information Gain-SVM, (D) Multi-Agent Approach, (E) DeepLSTM, (F) SSDCA-based Deep LSTM, (G) DeepONet and (H) Proposed GMOA-based DeepOnet.- Figures called Figure 6A.jpg and Figure 6B.jpg which show the EEG signals before applying the preprocessing pipeline, and after applying the preprocessing pipeline, respectively. - Figures called Figure_1_Prediction_Result_Jan_2021.png and Figure_2_Prediction_Result_Nov_2021.png which are the plots of the prediction results from presented in the journal paper. - 8 csv files called 1 MAE.csv, 1 MAPE.csv, 1 RMSE.csv, 1 MSE.csv, 2 MAE.csv, 2 MAPE.csv, 2 RMSE.csv, 2 MSE.csv, which contains the evaluation results produced by all algorithms presented and validated in the journal paper. - 2 csv files called Delay Prediction_Jan_2021_ontime_Figure_1 and Delay Prediction_Nov_2021_ontime_Figure_2 which contains the prediction results produced by all algorithms presented and validated in the journal paper.UK Research and Innovatio
Supporting data and code for 'Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with SHAP Interpretability'
No full textData: This folder contains: - PSD (Power Spectral Density) features and labels datasets for individual pilots. These were leveraged to acquire the results presented in Table 2 of our article. For results pertaining to a specific pilot, two files are utilised to train our proposed model: "Pilot_i_EEG_band_power_features.npy" and "Pilot_i_events.npy". In these filenames, 'i' represents the pilot's unique ID number. The file "Pilot_i_EEG_band_power_features.npy" contains power spectral density features extracted from five distinct frequency bands: delta, theta, alpha, beta, and gamma. On the other hand, "Pilot_i_events.npy" contains the class labels indicating the mental state of the pilot: 0 for baseline, 1 for startle/surprise, 2 for channelized attention, and 3 for diverted attention. - A combined dataset named "EEG_band_power_features.npy", which comprises the PSD features for all pilots. Its corresponding class labels are found in the "all_events.npy" file. This combined dataset was instrumental in deriving the results published in our paper. Source code: This folder contains: - A jupyter notebook called EEG_Stats.ipynb which computes the PSD features using the original EEG data for each pilot. It also include the source code to compute the average power in each frequency band for each mental state and the average power in each frequency band for each EEG channel using the combined pilots dataset. - A jupyter notebook called Ind_pilot_conv_model.ipynb which implements the proposed 1D-CNN approach presented and tested in the journal paper for each pilot. - A jupyter notebook called all_pilots.ipynb which implements the proposed 1D-CNN approach presented and tested in the journal paper for all pilots. It also includes the source code to obtain the training accuracy and loss curves, compute the confusion matrix, and obtain the top 10 important features for each mental state. Output: This folder contains: - A figure called "The average power in each frequency band across pilots" which shows the average power in each frequency band for each mental state using the combined pilots dataset. - A figure called "Heatmap for the average power in each frequency band for EEG channels" which shows the average power in each frequency band for each EEG channel using the combined pilots dataset. - A figure called "Confusion Matrix" which shows the confusion matrix results of the proposed 1D-CNN model using the combined pilots dataset. - A figure called "Accuracy and loss curve" which shows the training accuracy and loss curves results of the proposed 1D-CNN model using the combined pilots dataset. - A figure called "Top 10 important features for NE class" which shows the top 10 important features for detecting the baseline state using the combined pilots dataset. - A figure called "Top 10 important features for SS class" which shows the top 10 important features for detecting the Startle/Surprise state using the combined pilots dataset. - A figure called "Top 10 important features for CA class" which shows the top 10 important features for detecting the Channelised Attention state using the combined pilots dataset. - A figure called "Top 10 important features for DA class" which shows the top 10 important features for detecting the Diverted Attention state using the combined pilots dataset. - A text file called "1D-CNN model evaluation" which contain the results produced by all the proposed 1D-CNN model presented and tested in the journal paper
Bioinspired genetic-algorithm optimized ground-effect wing design: flight performance benefits and aircraft stability effects
Get PDFThis paper presents a bioinspired, genetic-algorithm evolutionary process for Ground-Effect vehicle wing design. The study made use of a rapid aerodynamic model generation and results evaluation computational fluid dynamics vortex lattice method software, supervised by a genetic algorithm optimization Python script. The design space for the aircraft wing parametric features drew inspiration from seabirds, under the assumption of their wings being naturally evolved and partially optimized for proximity flight over water surfaces. A case study was based on the A-90 Orlyonok Russian Ekranoplan, where alternative bioinspired wing variations were proposed. The study objective was to investigate the possible increased flight aircraft performance when using bioinspired wings, as well as verify the static and dynamic aircraft stability compliance for Ground-Effect flight. The methodology presented herein along with the study results, provided an incremental step towards advancing Ground-Effect aircraft conceptual designs using computational fluid dynamics.WSEAS Transactions on Fluid Mechanic
Barriers to meeting circular economy objectives using refill business models: A UK and Vietnamese context.
No full text"The replacement of single use plastic packaging with reusable containers is increasingly regarded as a potentially viable sustainable business model if brought to scale. We aimed to study barriers to "on the go" refilling as an alternative reuse business model aligned to circular economy objectives in Vietnam and the UK. Eleven key informant interviews captured sector stakeholder views alongside an online survey (n=326). The 11 non-coded interview transcripts and spreadsheet of original survey data gathered using Qualtrics software are contained in the dataset published here. Eight interlinked structural and commercial barriers were identified. Barriers including communication and clarification of the benefits of reuse were higher in Vietnam but more likely to be overcome than a less flexible UK sector with a dominant recycling infrastructure. This research outcome will alert policymakers to the challenges of a business model that is unlikely to grow to scale without bold and urgent policy support and a move away from a focus on bottom-up barriers that can delay consumer transition to a circular economy."Barriers to meeting circular economy objectives using refill business models: A UK and Vietnamese contex
Bioaugmentation enables enhanced pesticide removal in slow sand filters – impact of dosing on filter microbiome: data
No full textDataset relating to the article: Bioaugmentation of pilot-scale slow sand filters can achieve compliant levels for the micropollutant metaldehyde in a real water matrixDTP 2018-19 Cranfield Universit
Aerodynamic analysis of Saab 340B aircraft with data fusion implementation
Get PDFThis paper conducts an aerodynamic analysis of the Saab 340B passenger aircraft, employing AVL and as numerical methods and DATCOM as a collection of engineering methods and empirical data that provide a set of aerodynamic coefficients for assessment. The investigation is focused on examining the longitudinal aerodynamic behavior of aircraft, specifically considering the impacts of flaps and elevators at varying angles of attack. The outcomes from the clean configuration are compared with the results obtained from computational fluid dynamics studies found in existing literature. While the results may not precisely match, they capture the general trends in the behavior of the aircraft. This aligns with the expected outcomes from preliminary methods like AVL and DATCOM. Data fusion techniques are strategically employed to integrate insights from these diverse sources, enhancing the overall accuracy and reliability of the aerodynamic assessment. The research aims to provide a comprehensive understanding of the clean configuration's aerodynamic performance, contributing significantly to the advancement of aviation technology.AIAA SCITECH 2024 Foru
Yield-SAFE v2 - Biophysical model for tree and crop yields in agroforestry
No full textAgroforestry systems integrate trees with livestock and/or arable crops on the same parcel of land. Compared to monoculture arable or grass systems, agroforestry systems can enhance soil conservation, carbon sequestration, species and habitat diversity, and provide additional sources of farm income. However, as the trees increase in size, the grass and/or arable yields will tend to decline due to light and water competition with the trees. The form of the tree-crop yield relationship will vary with the level of solar radiation, rainfall, the species being grown, management actions such as choice of planting date and pruning, the soil type, and the time from tree planting. The Yield-SAFE v2 provides the opportunity to model the response of tree-only, agroforestry, and crop-only systems in the same worksheet. Yield-SAFE is a spreadsheet-based biophysical model which has been developed in Microsoft® Excel® to enable the prediction of the relationship between tree and crop yields over the rotation of the tree component. The full name for Yield-SAFE is the €œYIeld trategyEstimator for Long term Design of Silvoarable AgroForestry in Europe€ . The original Yield-SAFE model was developed with funding from the European Union through the Silvoarable Agroforestry For Europe project (contract number QLK5-CT-2001-00560). The process of creating a default publicly available version of the model has been enabled through the BioForce project funded by the UK Department for Energy Security and Net Zero