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This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goals defined in the NEI Strategic Plan as well as opportunities for future activities and breakthroughs in ophthalmology.Ophthalmology is at the forefront of artificial intelligence-based innovations in biomedical research that may lead to improvement in early detection and surveillance of ocular disease, prediction of progression, and improved quality of life. Technological advances have ushered in an era where unprecedented amounts of information can be linked that enable scientific discovery. However, there remains an unmet need to collect, harmonize, and share data in a machine actionable manner. Similarly, there is a need to ensure that efforts promote health and research equity by expanding diversity in the data and workforce.The NIH/NEI has supported the development artificial intelligence-based innovations to advance biomedical research. The NIH/NEI has defined activities to achieve these goals in the NIH Strategic Plan for Data Science and the NEI Strategic Plan and have spearheaded initiatives to facilitate research in these areas.

19th International Conference in Manufacturing Research ICMR 2022 Manufacturing is a knowledge-intensive business in which companies are distinguished for their ability to deliver products and services using numerous forms of information and knowledge. Various information and communication technologies and systems have been created throughout the years to effectively manage information and knowledge in industry. However, capturing knowledge, particularly tacit knowledge, remains a challenge. Virtual Reality (VR) and Augmented Reality (AR) technologies for industrial tasks have been developed by the research and practitioner communities. For example, in manufacturing, VR/AR have been used to aid in different machining and assembly tasks. This paper presents a proposed an AR equipment ‘Head Mounted Device (HMD)’ under development, as a tool for capturing manufacturing expertise’s knowledge. The technology intends to capture tacit knowledge using a 3D camera installed on the operator's HMD. The proposed approach also supplies the operator with information on production and ongoing processes. The EU Interreg V France Channel England Programme provided funding for this research (CoRoT, Ref: 99).

Co-design and co-creation of robotic systems and supporting automated solutions for industrial applications have seen a rapid increase in development efforts in recent years. Research and development activities within collaborative and industrial robotic projects alongside implementations of technical solutions have also shown significant gains in offering small, medium and some large enterprises enough cost benefit justification and improved understanding of technical requirements to adopt them in the ‘servitisation’ of manufacturing and assembly operations. This paper reports findings related to the impact of generating and exchanging technical knowledge within a large multimillion euros project in France-England Channel region over a running duration of over 5 years and the mutual benefits it had on the research institutes, industrial partners and collaborative network established from the project. The results indicated an increase in knowledge acquisition and exchange amongst individuals that can be used to gain significant advantages and a steppingstone in improving new product development activities, speed to market, as well as enhancing methods to establish long term collaborative efforts within industry-academia and improve innovation output.

The CoRoT project is a collaboration of French and UK partners funded by the EU Interreg Programme. The CoRoT research group has developed a mobile robot demonstrating autonomous operations particularly aiming for applications within flexible manufacturing factories of small to medium enterprises (SMEs). The robot’s capability of full separation classifies it as a modular robotic system. Although the separation is possible, it introduces challenges in the areas of staff training, physical labour and workshop planning. This paper introduces a new method resolving the overt issues of lifting the robot, storing the robot and making use of the two systems as well as highlighting emergency stop regulations and maintaining the warranty of the equipment. By using a steel framed pneumatic lift, the top module can be removed and held in place, anchoring the arm for full operations whilst freeing the base to perform delivery tasks enabling seamless separation of modular robotic systems.

As a concept, Industry 4.0 encompasses the total transformation of the ‘traditional’ production environment with the real-time networking of products, processes and infrastructure via the Internet. What is not clear however, is the functional relationship between pre-existing operational practices such as Lean Manufacturing and Industry 4.0, and thus the associated organisational qualities of ‘Lean-ness’ and ‘Smart-ness’ respectively. This work then presents a formulaic approach to the ubiquitous Lean Manufacturing Value Stream Map process, in order to incorporate Digital elements into traditional product value streams. The result is the creation of a Digital Value Stream Map, which may be utilised to rationalise and deploy Industry 4.0 improvement projects.

Defects generated by the UK supply chain is much higher than its global competitors. Defects impact costs and production throughput due to unpredictable disruptions resulting in many non-value adding activities. However, defects data associated knowledge have rarely been considered and implemented as the manufacturing capability in existing design for manufacturing and assembly (DFMA) data/knowledge bases. On the other hand, current ICT systems used in the aerospace industry are not flexible enough to keep up with the new requirements of collaborating to manage knowledge properly, and the use of real-time manufacturing data generated in manufacturing activities. This research was carried out in collaboration with one of the UK’s largest aerospace companies in order to analyse the complexity of design and manufacturing activities of high-value safety-critical aerospace products. The results of the work are presented, and a novel approach and system was developed, that can be used to support DFMA using defects knowledge. The approach was implemented as a knowledge management system using collaborative design principles. Key findings from the main contribution in the context of extended enterprises of high value low volume safety critical product manufacturing are discussed.

Using a combination of active Radio-Frequency Identification tracking and staff interviews with members from an aerospace manufacturing company, it was uncovered that over 80 hours per week was spent in the manual movement of goods between departments. On a site of over 1000 employees that uses dedicated build cells in separated departments, this mixed-use facility proves challenging for the adoption of an autonomous delivery system due to its narrow corridors and high occupancy, however by investigating the concerns of employees and suggesting low-cost retroactive solutions, this project seeks to justify the transition from manual to automated onsite logistics. The conclusion found that indeed the company does have the transport yields to justify the use of Autonomous Mobile Robots, that the robots would supplement rather than replace workers and that safety was a key factor to address when using robots on a site of this configuration.

Copyright © 2021 The Authors. The Aerospace Industry has been undertaking strategic changes towards digital manufacturing. One of the challenges is the lack of rationalisation for a cost-benefit analysis of automating certain manufacturing and assembly processes within a customer order. The rigidness and complexity of aerospace lifecycle, and tight industry restrictions does not leave much room for high risk innovations in manufacturing and production lines. This research addressed this problem by investigating an automation adoption scenario with BAE Systems, Electronic Systems, which is a UK based aeronautical systems integrator. This paper reports findings from the general manufacturing industry via an industrial survey. These findings are compared with original findings from an empirical study carried out with BAE Systems within the New Product Introduction team to automate product transportation logistics in an environmental test facility. The paper describes the challenges particularly related to skills, and labour workforce required to manipulate heavy standing products in and out of a production line and how their requirements can be addressed within an automation solution package. The solution includes key design factors related to intricate handling of aeronautic systems via the gripping interface design, and the rest of the operational issues surrounding the testing objectives such as transportation, and test setup. The findings are presented in the form of a requirements analysis for businesses looking to automate manually-intensive tasks in the future, and provide some insights into the lessons learnt in the development of the solution to benefit UK manufacturing tactics to some similar challenges. European Commission (improving the design of flexible and responsive manufacturing systems involving autonomous and Collaborative Robots (CoRoT, Project No: 99)), in collaboration with and co-sponsored by BAE Systems.

Introduction Although communal smoking of hookah by means of water pipes is perceived to be a safe alternative to cigarette smoking, the effects of hookah smoke in respiratory epithelia have not been well characterized. This study evaluated epigenomic and transcriptomic effects of hookah smoke relative to cigarette smoke in human respiratory epithelial cells. Methods Primary normal human small airway epithelial cells from three donors and cdk4 and hTERT-immortalized small airway epithelial cells and human bronchial epithelial cells were cultured for 5 days in normal media with or without cigarette smoke condensates (CSCs) or water pipe condensates (WPCs). Cell count, immunoblot, RNA sequencing, quantitative real-time reverse-transcriptase polymerase chain reaction, methylation-specific polymerase chain reaction, and quantitative chromatin immunoprecipitation techniques were used to compare effects of hookah and cigarette smoke on cell proliferation, global histone marks, gene expression, and promoter-related chromatin structure. Results CSC and WPC decreased global H4K16ac and H4K20me3 histone marks and mediated distinct and overlapping cancer-associated transcriptome signatures and pathway modulations that were cell line dependent and stratified across lung cancer cells in a histology-specific manner. Epiregulin encoding a master regulator of EGFR signaling that is overexpressed in lung cancers was up-regulated, whereas FILIP1L and ABI3BP encoding mediators of senescence that are repressed in lung cancers were down-regulated by CSC and WPC. Induction of epiregulin and repression of FILIP1L and ABI3BP by these condensates coincided with unique epigenetic alterations within the respective promoters. Conclusions These findings support translational studies to ascertain if hookah-mediated epigenomic and transcriptomic alterations in cultured respiratory epithelia are detectable and clinically relevant in hookah smokers.

Fibre reinforced filament fabrication is a potential additive manufacturing method for certain load-bearing parts in aerospace and automotive products. In current practice, printing paths are planned from part geometry without considering loading conditions. This paper presents a new method for optimizing printing paths to align with the principal stress field of parts in use. Because of the powerful processing capabilities, for the first time grayscale image was adopted to represent the irregular vector field, which can be robustly processed into sub-regions for generating regular printing paths. Preliminary bending test achieved promising increase in tensile strength compared with conventional methods.

Accurate, rapid and automated tool wear inspection is critical to manufacturing quality, cost and efficiency in smart manufacturing systems. However, manual inspection of tool wear is still a common industrial practice which is inefficient, prone to human errors and not suitable for digitized manufacturing. Previously reported automatic tool wear inspection methods were inaccurate because they only used the remaining worn boundary (i.e., the partial-absence original tool boundary) to approximate tool wear. The authors discovered the association principle between the change law of the cutting edge grayscale and the relative position of the original and worn boundary, which was used to establish the probability functions to accurately reconstruct the curved original tool boundary via Bayesian Inference. The experiment results reported in this paper proved higher efficiency and accuracy than previous automatic tool wear inspection methods.

The 5G network takes advantage of the small cells\ud technology. The next logical step is to cover the urban landscape\ud with mobile small cells, to optimize network services. However,\ud the introduction of mobile small cells raises various security\ud challenges. Cryptographic solutions are capable of solving these\ud as long as they are supported by appropriate key management\ud schemes. The threshold-tolerant identity-based cryptosystem\ud forms a solid basis for key management schemes for mobile small\ud cells. However, this approach is unable to sustain security over\ud time. Therefore, we introduce two extensions, proactive secret\ud sharing and private key cloaking, to address this challenge.

Monitoring and predicting tool wear is an important issue in dynamic process control under changing conditions, especially for machining large-sized difficult-to-cut materials used in airplanes. Existing tool wear monitoring and prediction methods are mainly based on given cutting conditions over a period of time. This paper presents a novel method for accurately predicting tool wear under varying cutting conditions based on a proposed new meta-learning model which can be easily trained, updated and adapted to new machining tasks of different cutting conditions. Experiments proved a substantial improvement in the accuracy of predicting tool wear compared with existing deep learning methods.

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a conventional neural network and a recurrent neural network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.

This paper reports the research on the complex process of implementing modular product structures in a Product Lifecycle Management (PLM) system. There are many challenges in implementing the system. One main challenge is organising or mapping existing product data and migrating it to the new PLM system. Companies often use a PLM tool for management of CAD files, documents and drawings, but they do not take advantage of the full potential of the PLM system to support the development activities of modular products. Product data management tools are used mainly for product CAD data management and PLM systems support by automating and managing some of the operational complexity of modular product design. The aim of this research is to propose a data model that can be used for implementing modular product structures in a PLM system and a tool that can formalise the existing data so as to migrate it into the PLM system.

Microwave curing technologies have many advantages in manufacturing fiber reinforced polymer composite materials used in aerospace products, compared with traditional autoclave curing technologies. However, the uneven electromagnetic field of microwave in the cavity of the curing chamber results in uneven temperature on the surface of composite laminates during curing, which has been a major obstacle in industrial applications worldwide. Existing methods attempted to solve the problem by the random superposition of uneven electromagnetic fields, but the results were still not satisfactory to meet the high quality requirements of aerospace parts. This paper reveals the one-to-one correspondence between heating patterns of composite parts and microwave curing system settings, and reports a new concept to solve this problem by continuously monitoring and compensating the uneven temperature distribution in real-time. Experimental results from both fiber optical fluorescence sensors and infrared thermal imagers showed significant improvement in temperature uniformity compared with existing methods.