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Machine learning for optimal design of circular hollow section stainless steel stub columns: A comparative analysis with Eurocode 3 predictions
Full text linkStainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes
The Digitisation of the Sputter Deposition Process of Transparent Conductive Oxides by Implementing Artificial Intelligence
Full text linkPlasma-based sputtering is extensively employed to fabricating thin film Transparent Conductive Oxides (TCOs), a category of semiconducting material used for a wide variety application from flat panel display to energy harvesting devices. Methods of evaluating the plasma i.e. glow discharge has been greatly studied requiring complex theoretical physics which is not viable for applied/materials scientist who frequently use this method of deposition at an operational level. The aim of the project was to explore new methods of characterizing the plasma sputtering process to evaluate the possibility of simplifying the monitoring and assessment of the sputtering process. The first method involved monitoring the RF-based plasma sputtering process through optical spectroscopy and characterizing the discharge based on its specific colour. The 2nd method involved implementing Artificial intelligence/Machine learning and feeding the emission spectrum of the plasma extracted from an array of depositions to a deep learning model to evaluate the accuracy of predicting not only the properties of the deposited TCO film but also the growth process conditions. Implementation of such methods pave the way for the design of a digital shadow for plasma-based deposition in the material engineering industry. Spectral data from the plasma was obtained by placing an in-vacuum collimator optic probe (Plasus) which featured a unique honeycomb structure capturing photons whilst simultaneously trapping sputtering particles and preventing gradual coating of the collimator’s quartz window. The spectrometer was programmed to calculate the area under the peak of the spectral range based on predesignated segments of the spectrum. In addition to this, the light collected from the plasma was also guided to a 2nd spectrometer (Jeti) that calculates the chromaticity index of the light. The colour of the plasma source was deduced based on conventional chromaticity index analysis and it was compared to the direct spectral data plots of the emission peaks to investigate the possibility of characterizing the plasma based on its specific colour. This technique was demonstrated to be a viable potential for evaluating the plasma in terms of providing information regarding the stability of the plasma, chamber pressure and plasma power. A linear relationship between the colour functions and the plasma power was observed, while the stability of the sputtering plasma can be assessed based on the plasma colour functions. The colour functions also follow a unique pattern when the working gas pressure is increased. The spectral properties and colour functions of a radio frequency (RF)-based sputtering plasma source was also monitored during consecutive sputter deposition of Indium doped zinc oxide (IZO) thin films under argon and argon/hydrogen mix. The effect of target exposure to the hydrogen gas on charge density/mobility and spectral transmittance of the deposited films was investigated. Consecutive exposure to the hydrogen gas during the deposition process progressively affects the properties of thin films with a certain degree of continuous improvement in electrical conductivity while demonstrating that reverting to only argon from argon/hydrogen mix follows a complex pathway. Preparation of highly conductive zinc oxide thin films without indium presence was exhibited eliminating the need for the expensive indium addition. The complexity of the reactive sputtering of highly conductive zinc oxide thin films in the presence of hydrogen at room temperature was investigated. A hypothesis was put forward regarding importance ii of precise geometric positioning of the substrate with respect to the magnetron to achieve maximum conductivity. The electrical properties of the deposited zinc oxide thins films based on their position on the substrate holder relative to the magnetron were examined. Machine Learning/Deep learning models were incorporated to examine the accuracy of predicting a single feature (sheet resistance) of thin films of indium-doped zinc oxide deposited via plasma sputter deposition by feeding the spectral data of the plasma to the deep learning models. It was shown that Artificial Neural networks could be implemented as a model that could predict the sheet resistance of the thin films as they were deposited, taking in only the spectral emission of the plasma as an input. The spectral emission data from the plasma glow of various sputtering targets containing indium oxide, zinc oxide, and tin oxide were obtained. These spectral data were then converted into twodimensional arrays by implementing a basic array-reshaping technique and a more complex procedure utilizing an unsupervised deep-learning technique, known as the self-organizing-maps method. The twodimensional images obtained from each single-emission spectrum of the plasma mimic an image that can then be used to train a convolutional neural network model capable of predicting certain plasma features, such as impurity levels in the sputtering target, working gas composition, plasma power, and chamber pressure during the machine operation. It was demonstrated that that the single-array-to-2D-array conversion technique, coupled with deep-learning techniques and computer vision, can achieve high predictive accuracy and can, therefore, be fundamental to the construction of a sputtering system’s digital twin
Close contacts of xenograft recipients: Ethical considerations due to risk of xenozoonosis
Full text linkWith decades of pre-clinical studies culminating in the recent clinical application of xenotransplantation, it would appear timely to provide recommendations for operationalizing oversight of xenotransplantation clinical trials. Ethical issues with clinical xenotransplantation have been described for decades, largely centering on animal welfare, the risks posed to the recipient, and public health risks posed by potential spread of xenozoonosis. Much less attention has been given to considerations relating to potentially elevated risks faced by those who may care for or otherwise have close contact with xenograft recipients. This paper examines the ethical and logistical issues raised by the potential exposure to xenozoonotic disease faced by close contacts of xenotransplant recipients—defined herein as including but not limited to caregivers, household contacts, and sexual partners— which warrants special attention given their increased risk of exposure to infection compared to the general public. We discuss implications of assent or consent by these close contacts to potentially undergo, along with the recipient, procedures for infection screening and possible quarantine. We then propose several options and recommendations for operationalizing oversight of xenotransplantation clinical trials that could account for and address close contacts’ education on and agency regarding the risk of xenozoonosis
Constrained estimation of intracranial aneurysm surface deformation using 4D-CTA
Full text linkBackground and objective
Intracranial aneurysms are relatively common life-threatening diseases, and assessing aneurysm rupture risk and identifying the associated risk factors is essential. Parameters such as the Oscillatory Shear Index, Pressure Loss Coefficient, and Wall Shear Stress are reliable indicators of intracranial aneurysm development and rupture risk, but aneurysm surface irregular pulsation has also received attention in aneurysm rupture risk assessment.
Methods
The present paper proposed a new approach to estimate aneurysm surface deformation. This method transforms the estimation of aneurysm surface deformation into a constrained optimization problem, which minimizes the error between the displacement estimated by the model and the sparse data point displacements from the four-dimensional CT angiography (4D-CTA) imaging data.
Results
The effect of the number of sparse data points on the results has been discussed in both simulation and experimental results, and it shows that the proposed method can accurately estimate the surface deformation of intracranial aneurysms when using sufficient sparse data points.
Conclusions
Due to a potential association between aneurysm rupture and surface irregular pulsation, the estimation of aneurysm surface deformation is needed. This paper proposed a method based on 4D-CTA imaging data, offering a novel solution for the estimation of intracranial aneurysm surface deformation
Employees’ Entrepreneurial Orientation in Manufacturing Firms: An Empirical Study
Full text linkThis study aims to examine the relationship between organizational characteristics, knowledge management enablers, learning orientation, and employee entrepreneurial orientation (EO) among manufacturing industry employees in Klang Valley, Malaysia. The theoretical model based on the Resource-Based Theory approach to employee EO was developed. To answer the research questions, seven hypotheses were formulated. Self-administered questionnaires were distributed to the manufacturing industry employees. A total of 386 manufacturing employees of all levels were involved in this study, making an overall 25.73% response rate. This study utilized the Partial Least Squares Structural Equation Modelling to establish the validity and reliability of the measurement model and test the relationships. The findings of the study showed that both organizational characteristics and knowledge management enablers have significant influences on employee EO. The results indicated that learning orientation has a mediating role in the relationship between organizational characteristics toward employee EO, and knowledge management enablers toward employee EO. The findings offered several theoretical and practical implications to employees and policymakers. The limitations of the study are addressed and recommendations for future research work are also offered
Offsite Manufacturing for Housing In Emerging Economies: An Evaluation Of Current Implementation Levels
Full text linkHousing supply is at critical limits globally despite being enshrined as a fundamental human right. The implication of this remains nearly oblivious to fostering adequate supply. Compounding that is the added quality requirement for housing to be climate resilient. Offsite manufacturing has been identified as a viable solution to increase the supply of climate-resilient housing; however, there is a contextual gap as implementation in Emerging Economies (EE) where population growth and urbanization are rapidly occurring is less represented in literature. Additionally, while offsite manufacturing is rooted in prefabrication, an evolution of its use in EE is less documented. This study employs a quantitative methodology through a survey questionnaire of 68 construction professionals operating as Small and Medium Enterprises (SMEs) in the housing sector in a typified EE. Participants were selected using stratified random sampling across demographic variables. The study provides insights into technology adoption and design for manufacturing, which show limited adoption of contextual offsite-enhancing technologies. However, other vital aspects crucial to the increased adoption of offsite processes, such as supply chain relations between stakeholders, are established, albeit with room for improvement to attain strategic partnerships. This study's findings suggest a pragmatic approach - leveraging current practices as a starting point and formulating a roadmap for gradually integrating more sophisticated OMPs over time. Further, it contributes to a deeper understanding of how offsite manufacturing can be harnessed to enhance the efficiency and sustainability of housing construction in EE, thereby advancing climate-responsive housing development in these regions
Reactive vs Predictive Live Migration in Edge Cloud
Full text linkMigrating services in an edge-cloud environ- ment poses unique challenges, including heterogeneous en- vironments, potential failures, and uneven resource distri- bution. This paper studies and evaluate reactive and predic- tive migration approaches to support live migration in case of edge cloud computing failures. Telemetry information relate to edge cloud computing have been considered to trigger migration, whereas deadlock prevention algorithm has been used to determine and select the target device to migrate services. The paper evaluates these strategies by comparing resource utilization, assessing differences between predictive and reactive migration and handling multiple migrations for tenants hosting numerous appli- cations. Experimental results have shown that predictive migration can reduce the downtime of the hosted services. Additionally, the total migration cost can be increased for both scenarios where the containers can be migrated to different edge devices due to lack of available resource
Artificial Intelligence and Endo-Histo-OMICs: New Dimensions of Precision Endoscopy and Histology in Inflammatory Bowel Disease
Full text linkIntegrating artificial intelligence into inflammatory bowel disease (IBD) has the potential to revolutionise clinical practice and research. Artificial intelligence harnesses advanced algorithms to deliver accurate assessments of IBD endoscopy and histology, offering precise evaluations of disease activity, standardised scoring, and outcome prediction. Furthermore, artificial intelligence offers the potential for a holistic endo-histo-omics approach by interlacing and harmonising endoscopy, histology, and omics data towards precision medicine. The emerging applications of artificial intelligence could pave the way for personalised medicine in IBD, offering patient stratification for the most beneficial therapy with minimal risk. Although artificial intelligence holds promise, challenges remain, including data quality, standardisation, reproducibility, scarcity of randomised controlled trials, clinical implementation, ethical concerns, legal liability, and regulatory issues. The development of standardised guidelines and interdisciplinary collaboration, including policy makers and regulatory agencies, is crucial for addressing these challenges and advancing artificial intelligence in IBD clinical practice and trials
A–Z of prescribing for children: D – Distribution
Full text linkThis series focuses on aspects of prescribing for neonates, children and young people, from A–Z. Aspects of pharmacokinetics will be considered, alongside legal considerations, consent and medications in schools
A Novel Multiple Camera RGB-D Calibration Approach Using Simulated Annealing
No full textThe development of a cost-effective surface scanning system tailored for live animal image capture can play an important role in biomedical research. The primary aim was to introduce a low-cost system, achieving a surface reconstruction error of less than 2mm, and enabling rapid acquisition speeds of approximately 1 second for a complete 360-degree surface capture. Leveraging a five RGB-D camera configuration, our approach offers a simple, low-cost alternative to conventional lab-based 3D scanning setups. Key to our methodology is a novel calibration strategy aimed at refining intrinsic and extrinsic camera parameters simultaneously for improved accuracy. We introduce a novel 3D calibration object, extending existing techniques employing ArUco markers, and implement a depth correction matrix to enhance depth accuracy. By utilizing Simulated Annealing optimization alongside our custom calibration object, we achieve superior results compared to conventional optimization techniques. Our obtained results show that the proposed depth correction method can reduce the reprojection error from 3.12 to 2.89 pixels. Furthermore, despite the simplicity of our reconstruction method, we observe around a 22% improvement in surface reconstruction compared to factory calibration parameters. Our findings underscore the practicality and efficacy of our proposed system, paving the way for enhanced 3D surface reconstruction for real-world surface capture