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With the rapid development of information technologies, shared manufacturing is proposed to meet the prevailing tendency of servitization and digitalisation in the industry. As the crucial section for performing shared manufacturing, resource monitoring and maintenance aim to detect production exceptions and ensure normal task execution. Existing research mainly uses a resource-centric strategy to acquire production-related data and make decisions for the management of shared resources. The experience data from the users/customers of the shared resources or its similar resources is rarely acquired actively in a cost-effective manner. However, the user/customer's experience data may contain essential knowledge that can be used for effective production performance identification and maintenance. To fill this gap, a hybrid sensing-based approach is proposed to perform the monitoring and maintenance of the shared manufacturing resources. It leverages both the sensor-sensed production data and user/customer-generated data for value creation in a cost-effective manner. Based on the acquired hybrid data, a service model is constructed to achieve the monitoring of the shared manufacturing resource, and a knowledge-based mechanism is designed to perform efficient maintenance. A case study is further presented to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Pavement crack detection in complex scenes presents a significant challenge in achieving precise pixel-level accuracy due to the presence of intricate backgrounds, such as water stains, shadows, and repair grouting, which closely resemble cracks in terms of morphology and colour. Consequently, existing approaches often struggle to differentiate these elements, resulting in misclassification and decreased accuracy in corresponding areas. This paper proposes a novel approach, called the Dual-Stream U-Network (DSU-Net), specifically designed for complex scene crack detection. The DSU-Net model employs a dual-stream framework, enabling improved focus on challenging regions and effectively addressing the issue of low recognition accuracy. To construct a comprehensive dataset, photogrammetry techniques are employed to acquire images of cracks, resulting in a dataset comprising 5000 images. Experimental evaluations demonstrate that the trained DSU-Net model achieves a mean F1 score of 74.17% on a test set, surpassing the performance of the state-of-the-art methods which achieved a score of 68.09%. These findings validate the effectiveness of the proposed algorithm in accurately distinguishing water stains, shadows, repair grouting, and cracks. Additionally, the algorithm successfully performs fine-grained extraction of cracks. The promising results obtained in this study indicate the potential practical applicability of the proposed approach in engineering scenarios. [ABSTRACT FROM AUTHOR]

The paradigm of Smart product-service systems (Smart PSS) has emerged recently owing to the edge-cutting Information and Communication Technology (ICT) and artificial intelligence (AI) techniques. The unique features of Smart PSS including smartness and connectedness, value co-creation and data-driven design manner, enable the collection and analysis of large volume and heterogeneous contextual data to extract useful knowledge. Therefore, requirement elicitation, as a critical process for new solution (i.e. product-service) design, can be conducted in a rather context-aware manner, assured by those massive user-generated data and product-sensed data during the usage stage. Nevertheless, despite a few works on semantic modelling, scarcely any reports on such mechanism in today's smart, connected environment. Aiming to fill this gap, for the first time, a graph-based context-aware requirement elicitation approach considering contextual information within the Smart PSS is proposed. It leverages the pre-defined product, service, and condition ontologies together with Deepwalk technique, to formulate those concepts as nodes and their relationships as the edge of the proposed requirement graph. Implicit stakeholder requirements within a specific context can be further derived based on such interrelationships in a data-driven manner. To demonstrate its feasibility and effectiveness, an example of smart bike share system is addressed to illustrate the requirement elicitation process. It is hoped that this explorative study can offer valuable insights for the service providers who would like to extract requirements not only from the voice of customers but also from the user-generated data and product-sensed data. [ABSTRACT FROM AUTHOR]

Owing to the discontinuous specificity and complexity of architecture design space, the issue of selecting and combining components comprising the engineered system, numerous constraints and associations need to be accounted for, adding up to a complex and substantial cognitive load on the system architects, which makes it challenging to tackle the current demand of adaptive improvements or innovative upgrading of the existing mature architectural solutions. To this end, this paper proposes an intelligent design exploration method for complex system architecture generation with reinforcement learning. The architectural design space (ADS) is identified by defining the dimensions of ADS, including model, quantity, and design chain, as well as the mathematical boundaries and representation to facilitate computable intelligent design exploration. On this basis, by adopting AI techniques primarily based on reinforcement learning, a massive and reliable architectural scheme is rapidly generated, and a more satisfying and robust architectural solution is selected by accessing the fuzzy Pareto frontier. Validation of the method is demonstrated through a case study of a launch vehicle's first and second-stage separation system. This research contributes valuable insights to overcoming the limitations of traditional techniques and enhancing the efficiency of the generative design and decision-making for complex engineered system architecture. [ABSTRACT FROM AUTHOR]

The current demand for aircraft panel assembly fixtures necessitates agile manufacturing solutions. Traditional welding fixtures struggle to meet diverse panel types, high quantities, and short manufacturing cycles. Conversely, reconfigurable flexible assembly fixtures offer universality, efficiency and ease of maintenance but heavily rely on designers' experience, hindering knowledge reuse. Existing research on computer-aided fixture design (CAFD) primarily addresses specialized fixtures, neglecting reconfigurable flexible ones, lacking fixture design knowledge systems. Aiming at the above problems, the study proposes an intelligent design method for the reconfigurable flexible assembly fixture for aircraft panels based on the smart composite jig model (SCJM) and knowledge graph (KG). Firstly, based on the SCJM of the aircraft panel type assembly fixture (PSCJM), a configuration design method is proposed to carry out the multi-level design of reconfigurable assembly fixture to instantiate PSCJM containing fixture configuration rules and a module library based on model definition (MBD). Then, a knowledge retrieval method is proposed to search the suitable fixture design knowledge based on KG, assisting designers in achieving the flexible design of aircraft panel reconfigurable assembly fixtures. Finally, practical engineering design validation confirms the method's feasibility and effectiveness in obtaining the reconfigurable flexible assembly fixture design scheme for aircraft panels. [ABSTRACT FROM AUTHOR]

The renin-angiotensin-aldosterone system (RAAS) plays a well-characterized role regulating blood pressure in mammals. Pharmacological and genetic manipulation of the RAAS has been shown to extend lifespan in Caenorhabditis elegans, Drosophila and rodents, but its mechanism is not well defined. Here, we investigate the angiotensin-converting enzyme (ACE) inhibitor drug captopril, which extends lifespan in worms and mice. To investigate the mechanism, we performed a forward genetic screen for captopril-hypersensitive mutants. We identified a missense mutation that causes a partial loss of function of the daf-2 receptor tyrosine kinase gene, a powerful regulator of aging. The homologous mutation in the human insulin receptor causes Donohue syndrome, establishing these mutant worms as an invertebrate model of this disease. Captopril functions in C. elegans by inhibiting ACN-1, the worm homolog of ACE. Reducing the activity of acn-1 via captopril or RNA interference promoted dauer larvae formation, suggesting that acn-1 is a daf gene. Captopril-mediated lifespan extension was abrogated by daf-16(lf) and daf-12(lf) mutations. Our results indicate that captopril and acn-1 influence lifespan by modulating dauer formation pathways. We speculate that this represents a conserved mechanism of lifespan control., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)