| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
- Li, Shufei, et al.
(författare)
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Proactive human-robot collaboration : Mutual-cognitive, predictable, and self-organising perspectives
- 2023
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 81, s. 102510-
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Forskningsöversikt (refereegranskat)abstract
- Human-Robot Collaboration (HRC) has a pivotal role in smart manufacturing for strict requirements of human -centricity, sustainability, and resilience. However, existing HRC development mainly undertakes either a human-dominant or robot-dominant manner, where human and robotic agents reactively perform operations by following pre-defined instructions, thus far from an efficient integration of robotic automation and human cognition. The stiff human-robot relations fail to be qualified for complex manufacturing tasks and cannot ease the physical and psychological load of human operators. In response to these realistic needs, this paper presents our arguments on the obvious trend, concept, systematic architecture, and enabling technologies of Proactive HRC, serving as a prospective vision and research topic for future work in the human-centric smart manufacturing era. Human-robot symbiotic relation is evolving with a 5C intelligence - from Connection, Coordination, Cyber, Cognition to Coevolution, and finally embracing mutual-cognitive, predictable, and self -organising intelligent capabilities, i.e., the Proactive HRC. With proactive robot control, multiple human and robotic agents collaboratively operate manufacturing tasks, considering each others' operation needs, desired resources, and qualified complementary capabilities. This paper also highlights current challenges and future research directions, which deserve more research efforts for real-world applications of Proactive HRC. It is hoped that this work can attract more open discussions and provide useful insights to both academic and industrial practitioners in their exploration of human-robot flexible production.
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| 3. |
- Thomas, Minta, et al.
(författare)
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Combining Asian and European genome-wide association studies of colorectal cancer improves risk prediction across racial and ethnic populations
- 2023
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expand PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS are 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1681-3651 cases and 8696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They are significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values < 0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.
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| 4. |
- Thomas, M, et al.
(författare)
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Combining Asian-European Genome-Wide Association Studies of Colorectal Cancer Improves Risk Prediction Across Race and Ethnicity
- 2023
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Polygenic risk scores (PRS) have great potential to guide precision colorectal cancer (CRC) prevention by identifying those at higher risk to undertake targeted screening. However, current PRS using European ancestry data have sub-optimal performance in non-European ancestry populations, limiting their utility among these populations. Towards addressing this deficiency, we expanded PRS development for CRC by incorporating Asian ancestry data (21,731 cases; 47,444 controls) into European ancestry training datasets (78,473 cases; 107,143 controls). The AUC estimates (95% CI) of PRS were 0.63(0.62-0.64), 0.59(0.57-0.61), 0.62(0.60-0.63), and 0.65(0.63-0.66) in independent datasets including 1,681-3,651 cases and 8,696-115,105 controls of Asian, Black/African American, Latinx/Hispanic, and non-Hispanic White, respectively. They were significantly better than the European-centric PRS in all four major US racial and ethnic groups (p-values<0.05). Further inclusion of non-European ancestry populations, especially Black/African American and Latinx/Hispanic, is needed to improve the risk prediction and enhance equity in applying PRS in clinical practice.
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| 5. |
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| 6. |
- Chen, Zhishan, et al.
(författare)
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Fine-mapping analysis including over 254 000 East Asian and European descendants identifies 136 putative colorectal cancer susceptibility genes
- 2024
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Ingår i: Nature Communications. - : Springer Nature. - 2041-1723. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have identified more than 200 common genetic variants independently associated with colorectal cancer (CRC) risk, but the causal variants and target genes are mostly unknown. We sought to fine-map all known CRC risk loci using GWAS data from 100,204 cases and 154,587 controls of East Asian and European ancestry. Our stepwise conditional analyses revealed 238 independent association signals of CRC risk, each with a set of credible causal variants (CCVs), of which 28 signals had a single CCV. Our cis-eQTL/mQTL and colocalization analyses using colorectal tissue-specific transcriptome and methylome data separately from 1299 and 321 individuals, along with functional genomic investigation, uncovered 136 putative CRC susceptibility genes, including 56 genes not previously reported. Analyses of single-cell RNA-seq data from colorectal tissues revealed 17 putative CRC susceptibility genes with distinct expression patterns in specific cell types. Analyses of whole exome sequencing data provided additional support for several target genes identified in this study as CRC susceptibility genes. Enrichment analyses of the 136 genes uncover pathways not previously linked to CRC risk. Our study substantially expanded association signals for CRC and provided additional insight into the biological mechanisms underlying CRC development.
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| 7. |
- de Jong, R. S., et al.
(författare)
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4MOST : Project overview and information for the First Call for Proposals
- 2019
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Ingår i: The Messenger. - : European Southern Observatory. - 0722-6691. ; 175, s. 3-11
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- We introduce the 4-metre Multi-Object Spectroscopic Telescope (4MOST), a new high-multiplex, wide-field spectroscopic survey facility under development for the four-metre-class Visible and Infrared Survey Telescope for Astronomy (VISTA) at Paranal. Its key specifications are: a large field of view (FoV) of 4.2 square degrees and a high multiplex capability, with 1624 fibres feeding two low-resolution spectrographs (R = λ/Δλ ~ 6500), and 812 fibres transferring light to the high-resolution spectrograph (R ~ 20 000). After a description of the instrument and its expected performance, a short overview is given of its operational scheme and planned 4MOST Consortium science; these aspects are covered in more detail in other articles in this edition of The Messenger. Finally, the processes, schedules, and policies concerning the selection of ESO Community Surveys are presented, commencing with a singular opportunity to submit Letters of Intent for Public Surveys during the first five years of 4MOST operations.
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| 8. |
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| 9. |
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| 10. |
- Fernandez-Rozadilla, Ceres, et al.
(författare)
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Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries
- 2023
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 55, s. 89-99
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Tidskriftsartikel (refereegranskat)abstract
- Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.
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| 11. |
- Huang, Sihan, et al.
(författare)
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Industry 5.0 and Society 5.0-Comparison, complementation and co-evolution
- 2022
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 64, s. 424-428
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Tidskriftsartikel (refereegranskat)abstract
- Recently, the futuristic industry and society have caught increasing attention, that is, on Industry 5.0 and Society 5.0. Industry 5.0 is announced by European Commission toward a sustainable, human-centric, and resilient European industry. Society 5.0 is proposed by Japan Cabinet to balance economic advancement with the reso-lution of social problems in Japanese society. Generally, the revolutions of industry and society have profoundly interacted with each other since the first industrial revolution. The coexistence of Industry 5.0 and Society 5.0 could raise varying confusions to be clarified and a series of questions to be answered. Therefore, we attempt to present the comparison, complementation, and co-evolution between Industry 5.0 and Society 5.0 to address the corresponding foundational arguments about Industry 5.0 and Society 5.0, which could be the basic inspiration for future investigation and discussion and accelerate the development of Industry 5.0 and Society 5.0.
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| 12. |
- Leng, Jiewu, et al.
(författare)
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Industry 5.0 : Prospect and retrospect
- 2022
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 65, s. 279-295
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Tidskriftsartikel (refereegranskat)abstract
- Industry 5.0 blows the whistle on global industrial transformation. It aims to place humans' well-being at the center of manufacturing systems, thereby achieving social goals beyond employment and growth to provide prosperity robustly for the sustainable development of all humanity. However, the current exploration of Industry 5.0 is still in its infancy where research findings are relatively scarce and little systematic. This paper first reviews the evolutionary vein of Industry 5.0 and three leading characteristics of Industry 5.0: human-centricity, sustainability, and resiliency. The connotation system of Industry 5.0 is discussed, and its diversified essence is analyzed. Then, this paper constructs a tri-dimension system architecture for implementing Industry 5.0, namely, the technical dimension, reality dimension, and application dimension. The paper further discusses key enablers, the future implementation path, potential applications, and challenges of realistic scenarios of Industry 5.0. Finally, the limitations of the current research are discussed with potential future research directions highlighted. It is expected that this review work will arouse lively discussions and debates, and bring together the strengths of all beings for building a comprehensive system of Industry 5.0.
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| 13. |
- Leng, Jiewu, et al.
(författare)
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Unlocking the power of industrial artificial intelligence towards Industry 5.0: Insights, pathways, and challenges
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 73, s. 349-363
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Forskningsöversikt (refereegranskat)abstract
- With the continuous development of human-centric, resilient, and sustainable manufacturing towards Industry 5.0, Artificial Intelligence (AI) has gradually unveiled new opportunities for additional functionalities, new features, and tendencies in the industrial landscape. On the other hand, the technology-driven Industry 4.0 paradigm is still in full swing. However, there exist many unreasonable designs, configurations, and implementations of Industrial Artificial Intelligence (IndAI) in practice before achieving either Industry 4.0 or Industry 5.0 vision, and a significant gap between the individualized requirement and actual implementation result still exists. To provide insights for designing appropriate models and algorithms in the upgrading process of the industry, this perspective article classifies IndAI by rating the intelligence levels and presents four principles of implementing IndAI. Three significant opportunities of IndAI, namely, collaborative intelligence, self-learning intelligence, and crowd intelligence, towards Industry 5.0 vision are identified to promote the transition from a technology-driven initiative in Industry 4.0 to the coexistence and interplay of Industry 4.0 and a value-oriented proposition in Industry 5.0. Then, pathways for implementing IndAI towards Industry 5.0 together with key empowering techniques are discussed. Social barriers, technology challenges, and future research directions of IndAI are concluded, respectively. We believe that our effort can lay a foundation for unlocking the power of IndAI in futuristic Industry 5.0 research and engineering practice.
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| 14. |
- Li, Chengxi, et al.
(författare)
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Deep reinforcement learning in smart manufacturing : A review and prospects
- 2023
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Ingår i: CIRP - Journal of Manufacturing Science and Technology. - : Elsevier BV. - 1755-5817 .- 1878-0016. ; 40, s. 75-101
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Forskningsöversikt (refereegranskat)abstract
- To facilitate the personalized smart manufacturing paradigm with cognitive automation capabilities, Deep Reinforcement Learning (DRL) has attracted ever-increasing attention by offering an adaptive and flexible solution. DRL takes the advantages of both Deep Neural Networks (DNN) and Reinforcement Learning (RL), by embracing the power of representation learning, to make precise and fast decisions when facing dynamic and complex situations. Ever since the first paper of DRL was published in 2013, its applications have sprung up across the manufacturing field with exponential publication growth year by year. However, there still lacks any comprehensive review of the DRL in the field of smart manufacturing. To fill this gap, a systematic review process was conducted, with 261 relevant publications selected to date (20-Oct-2022), to gain a holistic understanding of the development, application, and challenges of DRL in smart manufacturing along the whole engineering lifecycle. First, the concept and development of DRL are summarized. Then, the typical DRL applications are analyzed in the four engineering lifecycle stages: design, manufacturing, dis-tribution, and maintenance. Finally, the challenges and future directions are illustrated, especially emerging DRL-related technologies and solutions that can improve the manufacturing system's deployment feasi-bility, cognitive capability, and learning efficiency, respectively. It is expected that this work can provide an insightful guide to the research of DRL in the smart manufacturing field and shed light on its future perspectives.
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| 15. |
- Li, Chengxi, et al.
(författare)
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Unleashing mixed-reality capability in Deep Reinforcement Learning-based robot motion generation towards safe human–robot collaboration
- 2024
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Ingår i: Journal of manufacturing systems. - : Elsevier B.V.. - 0278-6125 .- 1878-6642. ; 74, s. 411-421
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Tidskriftsartikel (refereegranskat)abstract
- The integration of human–robot collaboration yields substantial benefits, particularly in terms of enhancing flexibility and efficiency within a range of mass-personalized manufacturing tasks, for example, small-batch customized product inspection and assembly/disassembly. Meanwhile, as human–robot collaboration lands broader in manufacturing, the unstructured scene and operator uncertainties are increasingly involved and considered. Consequently, it becomes imperative for robots to execute in a safe and adaptive manner rather than solely relying on pre-programmed instructions. To tackle it, a systematic solution for safe robot motion generation in human–robot collaborative activities is proposed, leveraging mixed-reality technologies and Deep Reinforcement Learning. This solution covers the entire process of collaboration starting with an intuitive interface that facilitates bare-hand task command transmission and scene coordinate transformation before the collaboration begins. In particular, mixed-reality devices are employed as effective tools for representing the state of humans, robots, and scenes. This enables the learning of an end-to-end Deep Reinforcement Learning policy that addresses both the uncertainties in robot perception and decision-making in an integrated manner. The proposed solution also implements policy simulation-to-reality deployment, along with motion preview and collision detection mechanisms, to ensure safe robot motion execution. It is hoped that this work could inspire further research in human–robot collaboration to unleash and exploit the powerful capabilities of mixed reality.
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| 16. |
- Li, Shufei, et al.
(författare)
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Self-organising multiple human-robot collaboration : A temporal subgraph reasoning-based method
- 2023
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 68, s. 304-312
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Tidskriftsartikel (refereegranskat)abstract
- Multiple Human-Robot Collaboration (HRC) requires self-organising task allocation to adapt to varying operation goals and workspace changes. However, nowadays an HRC system relies on predefined task arrangements for human and robot agents, which fails to accomplish complicated manufacturing tasks consisting of various operation sequences and different mechanical parts. To overcome the bottleneck, this paper proposes a temporal subgraph reasoning-based method for self-organising HRC task planning between multiple agents. Firstly, a tri-layer Knowledge Graph (KG) is defined to depict task-agent-operation relations in HRC tasks. Then, a subgraph mechanism is introduced to learn node embeddings from subregions of the HRC KG, which distills implicit information from local object sets. Thirdly, a temporal reasoning module is leveraged to integrate features from previous records and update the HRC KG for forecasting humans' and robots' subsequent operations. Finally, a car engine assembly task is demonstrated to evaluate the effectiveness of the proposed method, which outperforms other benchmarks in experimental results.
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| 17. |
- Li, Shufei, et al.
(författare)
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Toward Proactive Human-Robot Collaborative Assembly : A Multimodal Transfer-Learning-Enabled Action Prediction Approach
- 2022
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Ingår i: IEEE Transactions on Industrial Electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0278-0046 .- 1557-9948. ; 69:8, s. 8579-8588
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Tidskriftsartikel (refereegranskat)abstract
- Human-robot collaborative assembly (HRCA) is vital for achieving high-level flexible automation for mass personalization in today's smart factories. However, existing works in both industry and academia mainly focus on the adaptive robot planning, while seldom consider human operator's intentions in advance. Hence, it hinders the HRCA transition toward a proactive manner. To overcome the bottleneck, this article proposes a multimodal transfer-learning-enabled action prediction approach, serving as the prerequisite to ensure the proactive HRCA. First, a multimodal intelligence-based action recognition approach is proposed to predict ongoing human actions by leveraging the visual stream and skeleton stream with short-time input frames. Second, a transfer-learning-enabled model is adapted to transfer learnt knowledge from daily activities to industrial assembly operations rapidly for online operator intention analysis. Third, a dynamic decision-making mechanism, including robotic decision and motion control, is described to allow mobile robots to assist operators in a proactive manner. Finally, an aircraft bracket assembly task is demonstrated in the laboratory environment, and the comparative study result shows that the proposed approach outperforms other state-of-the-art ones for efficient action prediction.
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| 18. |
- Li, Shufei, et al.
(författare)
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Towards Mutual-Cognitive Human-Robot Collaboration : A Zero-Shot Visual Reasoning Method
- 2023
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Ingår i: 2023 IEEE 19th International Conference on Automation Science and Engineering, CASE 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
-
Konferensbidrag (refereegranskat)abstract
- Human-Robot Collaboration (HRC) is showing the potential of widespread application in today's human-centric smart manufacturing, as prescribed by Industry 5.0. To enable safe and efficient collaboration, numerous visual perception methods have been explored, which allows the robot to perceive surroundings and plan collision-free, reactive manipulations. However, current visual perception approaches can only convey basic information between robots and humans, falling short of semantic knowledge. With this limitation, HRC cannot guarantee smooth operation when confronted with similar yet unseen situations in real-world applications. Therefore, a mutual-cognitive HRC architecture is proposed to plan human and robot operations based on the learning of knowledge representation of onsite situations and task structures. A zero-shot visual reasoning approach is introduced to derive suitable teamwork strategies in the mutual-cognitive HRC from perceived results, including human actions and detected objects. It assigns adaptive robot path planning and knowledge support for humans by incorporating perception components into a knowledge graph, even when dealing with a new but similar HRC task. Lastly, the significance of the proposed mutual-cognitive HRC system is revealed through its evaluation in collaborative disassembly tasks of aging electric vehicle batteries.
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| 19. |
- Li, Shufei, et al.
(författare)
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Towards proactive human-robot collaboration : A foreseeable cognitive manufacturing paradigm
- 2021
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 60, s. 547-552
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Tidskriftsartikel (refereegranskat)abstract
- Human-robot collaboration (HRC) has attracted strong interests from researchers and engineers for improved operational flexibility and efficiency towards mass personalization. Nevertheless, existing HRC development mainly undertakes either human-centered or robot-centered manner reactively, where operations are conducted by following the pre-defined instructions, thus far from an efficient integration of robotic automation and human cognitions. The prevailing research on human-level information processing of cognitive computing, the industrial IoT, and robot learning creates the possibility of bridging the gap of knowledge distilling and information sharing between onsite operators, robots and other manufacturing systems. Hence, a foreseeable informatics-based cognitive manufacturing paradigm, Proactive HRC, is introduced as an advanced form of Symbiotic HRC with high-level cognitive teamwork skills to be achieved stepwise, including: (1) inter-collaboration cognition, establishing bi-directional empathy in the execution loop based on a holistic understanding of humans and robots' situations; (2) spatio-temporal cooperation prediction, estimating human-robot-object interaction of hierarchical sub-tasks/activities over time for the proactive planning; and (3) self-organizing teamwork, converging knowledge of distributed HRC systems for self-organization learning and task allocation. Except for the description of their technical cores, the main challenges and potential opportunities are further discussed to enable the readiness towards Proactive HRC.
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| 20. |
- Lim, Kendrik Yan Hong, et al.
(författare)
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Graph-enabled cognitive digital twins for causal inference in maintenance processes
- 2024
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Ingår i: International Journal of Production Research. - : Informa UK Limited. - 0020-7543 .- 1366-588X. ; 62:13, s. 4717-4734
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Tidskriftsartikel (refereegranskat)abstract
- The increasing complexity of industrial systems demands more effective and intelligent maintenance approaches to address manufacturing defects arising from faults in multiple asset modules. Traditional digital twin (DT) systems, however, face limitations in interoperability, knowledge sharing, and causal inference. As such, cognitive digital twins (CDTs) can add value by managing a collaborative web of interconnected systems, facilitating advanced cross-domain analysis and dynamic context considerations. This paper introduces a CDT system that leverages industrial knowledge graphs (iKGs) to support maintenance planning and operations. By employing a design structure matrix (DSM) to model dependencies and relationships, a semantic translation approach maps the knowledge into a graph-based representation for reasoning and analysis. An automatic solution generation mechanism, utilising graph sequencing with Louvain and PageRank algorithms, derives feasible solutions, which can be validated via simulation to minimise production disruption impacts. The CDT system can also identify potential disruptions in new product designs, thus enabling preventive actions to be taken. A case study featuring a print production manufacturing line illustrates the CDT system's capabilities in causal inference and solution explainability. The study concludes with a discussion of limitations and future directions, providing valuable guidelines for manufacturers aiming to enhance reactive and predictive maintenance strategies.
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| 21. |
- Shu, Xiang, et al.
(författare)
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Large-scale Integrated Analysis of Genetics and Metabolomic Data Reveals Potential Links Between Lipids and Colorectal Cancer Risk
- 2022
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Ingår i: Cancer Epidemiology, Biomarkers and Prevention. - : American Association for Cancer Research. - 1055-9965 .- 1538-7755. ; 31:6, s. 1216-1226
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Tidskriftsartikel (refereegranskat)abstract
- Background: The etiology of colorectal cancer is not fully understood.Methods: Using genetic variants and metabolomics data including 217 metabolites from the Framingham Heart Study (n = 1,357), we built genetic prediction models for circulating metabolites. Models with prediction R2 > 0.01 (Nmetabolite = 58) were applied to predict levels of metabolites in two large consortia with a combined sample size of approximately 46,300 cases and 59,200 controls of European and approximately 21,700 cases and 47,400 controls of East Asian (EA) descent. Genetically predicted levels of metabolites were evaluated for their associations with colorectal cancer risk in logistic regressions within each racial group, after which the results were combined by meta-analysis.Results: Of the 58 metabolites tested, 24 metabolites were significantly associated with colorectal cancer risk [Benjamini-Hochberg FDR (BH-FDR) < 0.05] in the European population (ORs ranged from 0.91 to 1.06; P values ranged from 0.02 to 6.4 × 10-8). Twenty one of the 24 associations were replicated in the EA population (ORs ranged from 0.26 to 1.69, BH-FDR < 0.05). In addition, the genetically predicted levels of C16:0 cholesteryl ester was significantly associated with colorectal cancer risk in the EA population only (OREA: 1.94, 95% CI, 1.60−2.36, P = 2.6 × 10-11; OREUR: 1.01, 95% CI, 0.99−1.04, P = 0.3). Nineteen of the 25 metabolites were glycerophospholipids and triacylglycerols (TAG). Eighteen associations exhibited significant heterogeneity between the two racial groups (PEUR-EA-Het < 0.005), which were more strongly associated in the EA population. This integrative study suggested a potential role of lipids, especially certain glycerophospholipids and TAGs, in the etiology of colorectal cancer.Conclusions: This study identified potential novel risk biomarkers for colorectal cancer by integrating genetics and circulating metabolomics data.Impact: The identified metabolites could be developed into new tools for risk assessment of colorectal cancer in both European and EA populations.
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| 22. |
- Urgo, Marcello, et al.
(författare)
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AI-Based Pose Estimation of Human Operators in Manufacturing Environments
- 2024
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Ingår i: Lecture Notes in Mechanical Engineering. - : Springer Nature. ; , s. 3-38
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- The fast development of AI-based approaches for image recognition has driven the availability of fast and reliable tools for identifying the human body in captured videos (both 2D and 3D). This has increased the feasibility and effectiveness of approaches for human pose estimation in industrial environments. This essay will cover different approaches for estimating the human pose based on neural networks (e.g., CNN, LSTM, etc.), addressing the workflow and requirements for their implementation and use. A brief analysis and comparison of the existing AI-based frameworks and approaches will be carried out (e.g. OpenPose, MediaPipe) together with a listing of the related hardware and software requirements. Finally, two case studies presenting applications in the manufacturing sector are provided.
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| 23. |
- Wang, Baicun, et al.
(författare)
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Human Digital Twin in the context of Industry 5.0
- 2024
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier Ltd. - 0736-5845 .- 1879-2537. ; 85
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Forskningsöversikt (refereegranskat)abstract
- Human-centricity, a core value of Industry 5.0, places humans in the center of production. It leads to the prioritization of human needs, spanning from health and safety to self-actualization and personal growth. The concept of the Human Digital Twin (HDT) is proposed as a critical method to realize human-centricity in smart manufacturing systems towards Industry 5.0. HDTs are digital representations of humans, aiming to change the practice of human-system integration by coupling humans’ characteristics directly to the system design and its performance. In-depth analysis, critical insights, and application guidelines of HDT are essential to realize the concept of Industry 5.0 in practice and evolve the smart manufacturing paradigm in modern factories. However, the investigation on the development of HDT to evolve humans’ roles and develop humans to their full potential is limited to date. Recent studies are rarely geared towards designing a standardized framework and architecture of HDT for diverse real-world applications. Thus, this work aims to close this research gap by carrying out a comprehensive survey on HDT in the context of Industry 5.0, summarizing the ongoing evolution, and proposing a proper connotation of HDT, before discussing the conceptual framework and system architecture of HDT and analyzing enabling technologies and industrial applications. This work provides guidance on possible avenues as well as challenges for the further development of HDT and its related concepts, allowing humans to reach their potential and accommodating their diverse needs in the futuristic smart manufacturing systems shaped by Industry 5.0.
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| 24. |
- Wang, Baicun, et al.
(författare)
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Toward human-centric smart manufacturing : A human-cyber-physical systems (HCPS) perspective
- 2022
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 63, s. 471-490
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Tidskriftsartikel (refereegranskat)abstract
- Advances in human-centric smart manufacturing (HSM) reflect a trend towards the integration of human-in-the loop with technologies, to address challenges of human-machine relationships. In this context, the human-cyberphysical systems (HCPS), as an emerging human-centric system paradigm, can bring insights to the development and implementation of HSM. This study presents a systematic review of HCPS theories and technologies on HSM with a focus on the human-aspect is conducted. First, the concepts, key components, and taxonomy of HCPS are discussed. HCPS system framework and subsystems are analyzed. Enabling technologies (e.g., domain technologies, unit-level technologies, and system-level technologies) and core features (e.g., connectivity, integration, intelligence, adaptation, and socialization) of HCPS are presented. Applications of HCPS in smart manufacturing are illustrated with the human in the design, production, and service perspectives. This research offers key knowledge and a reference model for the human-centric design, evaluation, and implementation of HCPS-based HSM.
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| 25. |
- Xia, Liqiao, et al.
(författare)
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Toward cognitive predictive maintenance : A survey of graph-based approaches
- 2022
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Ingår i: Journal of manufacturing systems. - : Elsevier BV. - 0278-6125 .- 1878-6642. ; 64, s. 107-120
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Forskningsöversikt (refereegranskat)abstract
- Predictive Maintenance (PdM) has continually attracted interest from the manufacturing community due to its significant potential in reducing unexpected machine downtime and related cost. Much attention to existing PdM research has been paid to perceiving the fault, while the identification and estimation processes are affected by many factors. Many existing approaches have not been able to manage the existing knowledge effectively for reasoning the causal relationship of fault. Meanwhile, complete correlation analysis of identified faults and the corresponding root causes is often missing. To address this problem, graph-based approaches (GbA) with cognitive intelligence are proposed, because the GbA are superior in semantic causal inference, heterogeneous association, and visualized explanation. In addition, GbA can achieve promising performance on PdM's perception tasks by revealing the dependency relationship among parts/components of the equipment. However, despite its advantages, few papers discuss cognitive inference in PdM, let alone GbA. Aiming to fill this gap, this paper concentrates on GbA, and carries out a comprehensive survey organized by the sequential stages in PdM, i. e., anomaly detection, diagnosis, prognosis, and maintenance decision-making. Firstly, GbA and their corresponding graph construction methods are introduced. Secondly, the implementation strategies and instances of GbA in PdM are presented. Finally, challenges and future works toward cognitive PdM are proposed. It is hoped that this work can provide a fundamental basis for researchers and industrial practitioners in adopting GbAbased PdM, and initiate several future research directions to achieve the cognitive PdM.
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| 26. |
- Yin, Yue, et al.
(författare)
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A state-of-the-art survey on Augmented Reality-assisted Digital Twin for futuristic human-centric industry transformation
- 2023
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Ingår i: Robotics and Computer-Integrated Manufacturing. - : Elsevier BV. - 0736-5845 .- 1879-2537. ; 81
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Forskningsöversikt (refereegranskat)abstract
- The combination of Augmented Reality (AR) and Digital Twin (DT) has begun to show its potential nowadays, leading to a growing research interest in both academia and industry. Especially under the current human-centric trend, AR embraces the potential to integrate operators into the new generation of Human Cyber–Physical System (HCPS), in which DT is a pillar component. Some review articles have focused on this topic and discussed the benefits of combining AR and DT, but all of them are limited to a specific domain. To fill the gap, this research conducts a state-of-the-art survey (till 17-July-2022) from the AR-assisted DT perspective across different sectors of the industrial field, covering a total of 118 selected publications. Firstly, application scenarios and functions of AR-assisted DT are summarized by following the engineering lifecycle, among which production process, service design, and Human–Machine Interaction (HMI) are hot topics. Then, improvements specifically brought by AR are analyzed according to three dimensions, namely virtual twin, hybrid twin, and cognitive twin, respectively. Finally, challenges and future perspectives of AR-assisted DT for futuristic human-centric industry transformation are proposed, including promoting product design, robotic-related works, cyber–physical interaction, and human ergonomics.
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| 27. |
- Zheng, Pai, et al.
(författare)
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A collaborative intelligence-based approach for handling human-robot collaboration uncertainties
- 2023
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Ingår i: CIRP annals. - : Elsevier BV. - 0007-8506 .- 1726-0604. ; 72:1, s. 1-4
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Tidskriftsartikel (refereegranskat)abstract
- Human-Robot Collaboration (HRC) has played a pivotal role in today's human-centric smart manufacturing sce-narios. Nevertheless, limited concerns have been given to HRC uncertainties. By integrating both human and artificial intelligence, this paper proposes a Collaborative Intelligence (CI)-based approach for handling three major types of HRC uncertainties (i.e., human, robot and task uncertainties). A fine-grained human digital twin modelling method is introduced to address human uncertainties with better robotic assistance. Meanwhile, a learning from demonstration approach is offered to handle robotic task uncertainties with human intelligence. Lastly, the feasibility of the proposed CI has been demonstrated in an illustrative HRC assembly task.
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| 28. |
- Zheng, Pai, et al.
(författare)
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A visual reasoning-based approach for mutual-cognitive human-robot collaboration
- 2022
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Ingår i: CIRP annals. - : Elsevier BV. - 0007-8506 .- 1726-0604. ; 71:1, s. 377-380
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Tidskriftsartikel (refereegranskat)abstract
- Human-robot collaboration (HRC) allows seamless communication and collaboration between humans and robots to fulfil flexible manufacturing tasks in a shared workspace. Nevertheless, existing HRC systems lack an efficient integration of robotic and human cognitions. Empowered by advanced cognitive computing, this paper proposes a visual reasoning-based approach for mutual-cognitive HRC. Firstly, a domain-specific HRC knowl-edge graph is established. Next, the holistic manufacturing scene is perceived by visual sensors as a temporal graph. Then, a collaborative mode with similar instructions can be inferred by graph embedding. Lastly, mutual-cognitive decisions are immersed into the Augmented Reality execution loop for intuitive HRC support.
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| 29. |
- Zheng, Pai, et al.
(författare)
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Augmented Reality-assisted Mutual Cognitive System for Human-Robot Interaction Safety Concerns
- 2023
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Ingår i: Jixie Gongcheng Xuebao/Journal of Mechanical Engineering. - : Chinese Mechanical Engineering Society. - 0577-6686. ; 59:6, s. 173-184
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Tidskriftsartikel (refereegranskat)abstract
- In modern manufacturing, the interaction and symbiosis between humans and the industrial robot are one of the foci of smart manufacturing. During human-robot interaction(HRI), the potential risk of any injury to workers caused by industrial robots is very critical and should be well-addressed to ensure manufacturing safety. However, in the dynamic and uncertain manufacturing environment, the current HRI safety is still based on the robot's perception of the environment to achieve collision avoidance, lacking adaptable decision-makings under mutual cognition. Therefore, to enhance the cognition of human operators in the working environment and improve the robot's collision avoidance and adaptive motion planning capabilities, this work designs and further implements a mutual cognitive HRI safety system based on augmented reality (AR) in a wearable manner. In the proposed system, the wearable AR device serves as the bridging interface to realize the virtual-real registration of the robot, the virtual-physical mapping of the working environment of the HRI process, and to collect the information of human, robot, and working space. In addition to these, a hierarchical HRI safety strategy is introduced for real-time mutual cognitive assistance to both humans and robots, namely: 1) robot motion speed control and safety area visualization based on human-robot distance, 2) virtual-physical mapping for robot motion preview and collision detection, and 3) deep reinforcement learning-driven motion planning for collision avoidance strategies generation. Lastly, a prototype system is further developed to validate the feasibility and effectiveness of the proposed strategies. By leveraging advanced artificial intelligence and human-robot interaction technologies, it is envisioned this work can bring insightful safety protection mechanisms to better achieve symbiotic human-robot collaboration.
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| 30. |
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| 31. |
- Zheng, Pai, et al.
(författare)
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Smart Product-Service Systems Solution Design via Hybrid Crowd Sensing Approach
- 2019
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Ingår i: IEEE Access. - : IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC. - 2169-3536. ; 7, s. 128463-128473
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Tidskriftsartikel (refereegranskat)abstract
- The third wave of information technology (IT) competition has enabled one promising value co-creation proposition, Smart PSS (smart product-service systems). Manufacturing companies offer smart, connected products with various e-services as a solution bundle to meet individual customer satisfaction, and in return, collect and analyze usage data for evergreen design purposes in a circular manner. Despite a few works discussing such value co-creation business mechanism, scarcely any has been reported from technical aspect to realizing this data-driven manufacturer/service provider-customer interaction cost-effectively. To fill this gap, a novel hybrid crowd sensing approach is proposed, and adopted in the Smart PSS context. It leverages large-scale mobile devices and their massive user-generated/product-sensed data, and converges with reliable static sensing nodes and other data sources in the smart, connected environment for value generation. Both the proposed hybrid crowd sensing conceptual framework and its systematic information modeling process are introduced. An illustrative example of smart water dispenser maintenance service design is given to validate its feasibility. The result shows that the proposed approach can be a promising manner to enable value co-creation process cost-effectively.
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| 32. |
- Thomas, HS, et al.
(författare)
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- 2019
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swepub:Mat__t
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| 33. |
- Bravo, L, et al.
(författare)
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- 2021
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swepub:Mat__t
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| 34. |
- Tabiri, S, et al.
(författare)
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- 2021
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swepub:Mat__t
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