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The tribological behaviour of articular cartilage plays a key role in joint motion; however, there is a gap in research on the effect of hyperuricemic joint fluid on cartilage friction behaviour in acute gouty arthritis. In this study, we carried out a fixed-load scratch experiment to compare the friction and wear of articular cartilage under the lubrication of gouty arthritis arthritic fluid and normal human arthritic fluid, and the results showed that the cartilage friction coefficient of patients with acute gouty arthritis was significantly larger than that of normal human beings, and that the cartilage friction coefficient decreased with the elevation of normal load and sliding speed, and the change with the sliding speed varied more differently from that of normal human beings, and that the cartilage surface wear was more severe after prolonged friction. The wear and tear of the cartilage surface is more severe after prolonged friction. Patients with gouty arthritis should reduce the sudden speed changes such as fast running and variable speed running to maintain the stability of the cartilage surface friction coefficient. [ABSTRACT FROM AUTHOR]

The association between short-term ambient air pollution (AAP) exposure and blood lipids is inconsistent across populations. This study aimed to investigate the modifying effects of fasting blood glucose (FBG) levels on the associations between short-term AAP exposure  and blood lipids in 110,637 male participants from Beijing, China. The results showed that FBG modified the association between short-term AAP exposure and blood lipids, especially low-density lipoprotein cholesterol (LDL-C). In the hyperglycemia group, a 10-μg/m3 increase in particles with diameters ≤ 2.5 μm (PM2.5), particles with diameters ≤ 10 μm (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), or a 1-mg/m3 increase in carbon monoxide (CO) was associated with a 0.454%, 0.305%, 1.507%, 0.872%, or 3.961% increase in LDL-C, respectively. In the nonhyperglycemic group, short-term increases in air pollutants were even associated with small decreases in LDL-C. The findings demonstrate that lipids in hyperglycemic individuals are more vulnerable to short-term AAP exposure than those in normal populations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The workshop operation management (WOM) system contains a large amount of multi-source heterogeneous data, but these data often lack effective correlation, posing challenges in extracting valuable information for achieving unified system modelling at the knowledge level. Due to the lack of knowledge active support in the actual WOM system, managers could only passively rely on their own past experiences. To bridge this gap, the knowledge active push method for optimal operation management of the manufacturing cell (OOMMC) is studied based on knowledge graph (KG). Firstly, a dynamic knowledge model is established according to the knowledge application scenario of manufacturing cell, in which the Local Tangent Space Alignment (LTSA) + k-means algorithm is used for active abnormal warning. Secondly, a static knowledge model based on user needs using feature vocabulary and naive Bayesian classifier is established, where managers can obtain knowledge from the KG based on professional noun keywords or simple query questions. These two models complement and promote each other: the dynamic knowledge model using a service-based collaborative filtering method to push the nearest knowledges in the KG to users, effectively inspiring them to retrieve more relevant knowledge. While the static knowledge model evaluates the effectiveness of previously pushed knowledge by setting the knowledge demand index and providing feedback to the system to ensure accuracy of knowledge push. Finally, an active push simulation system is designed and developed to verify the feasibility and effectiveness of the proposed architecture and key enabling technologies. [ABSTRACT FROM AUTHOR]

AbstractHuman-Robot Collaboration (HRC) is key to achieving the flexible automation required by the mass personalization trend, especially towards human-centric intelligent manufacturing. Nevertheless, existing HRC systems suffer from poor task understanding and poor ergonomic satisfaction, which impede empathetic teamwork skills in task execution. To overcome the bottleneck, a Mixed Reality (MR) and visual reasoning-based method is proposed in this research, providing mutual-cognitive task assignment for human and robotic agents’ operations. Firstly, an MR-enabled mutual-cognitive HRC architecture is proposed, with the characteristic of monitoring Digital Twins states, reasoning co-working strategies, and providing cognitive services. Secondly, a visual reasoning approach is introduced, which learns scene interpretation from the visual perception of each agent’s actions and environmental changes to make task planning strategies satisfying human–robot operation needs. Lastly, a safe, ergonomic, and proactive robot motion planning algorithm is proposed to let a robot execute generated co-working strategies, while a human operator is supported with intuitive task operation guidance in the MR environment, achieving empathetic collaboration. Through a demonstration of a disassembly task of aging Electric Vehicle Batteries, the experimental result facilitates cognitive intelligence in Proactive HRC for flexible automation. [ABSTRACT FROM AUTHOR]

In the conceptual design and early embodiment design stages, an effective kinematic prototype can assist in detecting design errors, minimizing unnecessary parameter modifications, and optimizing subsequent resource deployment. Existing methods for creating prototypes, such as 2D sketches, 3D modelling, or analysis software, have their advantages in precise modelling and simulation. However, there is a lack of an efficient approach that can achieve both rapid 3D kinematic idea generation and early on-site interactive verification. To address these challenges, this research proposes a Mixed Reality (MR)-enhanced kinematic prototyping approach for articulated products to enable the rapid generation of 3D kinematic concepts, movement preview, and early interactions with users or the actual workspace. The proposed approach facilitates the quick and rough construction of geometry and kinematic structures through intuitive hand gestures and interactive movement preview within the MR environment. The usability of the proposed approach was evaluated by a comparison experiment with traditional sketch and CAD tools involving eight participants. It is verified that our proposed MR kinematic prototyping system could accelerate the kinematic prototype generation process, improve the user's kinematic understanding, and facilitate on-site interactive verification in advance. [ABSTRACT FROM AUTHOR]

Considering the condition that multiple intermittent faults occur sequentially or simultaneously in hybrid systems, this paper proposes an integrated system-level prognosis scheme. The scheme is built by integrating model-based fault estimation and data-driven system-level remaining useful life (RUL) prediction. As for fault estimation, the level-based learning swarm optimization is utilized to identify state/value changes of intermittently faulty components based on the constructed hybrid system model. With estimation results, the system-level prognosis framework can be designed as follows: 1) The abnormal duration index (ADI) is established to quantify the system degradation extent under multiple intermittent faults. In view of the stochasticity of intermittent fault appearance and disappearance, the ADI is assessed approximately by as pessimistic as possible and as optimistic as possible indicators. 2) A synthetical degradation evaluator is developed to determine the moment to activate predictor, which ensures the obtained fault features can sufficiently reflect the evolutionary trend of the intermittently faulty component when activating RUL predictor. 3) Considering sequential or simultaneous intermittent faults and variations of modes for hybrid systems, the system-level prognosis module is designed, where the optimized support vector regression is adopted for system RUL prediction. Finally, the experimental study is investigated to verify the proposed scheme. • A novel system-level prognosis scheme for hybrid systems with multiple intermittent faults is developed. • The abnormal duration index (ADI) is proposed to quantify the system degradation extent subjected to multiple intermittent faults. • The ADI is assessed by two approximate indicators, i.e., as pessimistic as possible (PP) and as optimistic as possible (OP). • A synthetical degradation evaluator is proposed to determine the moment to activate predictor (MAP) of faulty components. • The general formulations of system RUL in an arbitrary mode of the hybrid system are given based on PP and OP perspectives. [ABSTRACT FROM AUTHOR]

Welding radiographic image defect segmentation (WRIDS) is a key technology to promote the automation and standardization of quality inspection. However, the complexity of scale variability, aggregation and contextual relationships presented by welding defects pose a great challenge to WRIDS, such as porosity, slag and lack of penetration. To address the issue, a multiple scale spaces (MSSs) empowered segmentation method of complex welding defects is proposed. First, a multi-scale feature space is constructed by dilated convolution with different dilated rates. Second, a multi-scale semantic space is constructed by max pooling in different windows. Third, a multi-scale relational space is constructed through a self-attention mechanism. Finally, the proposed MSSs method is validated on the basis of own collected weld inspection data from an aerospace structural component. The results show that the proposed method can effectively segment multiple complex defect types with an average Acc of 99.46% and an average Miou of 74.65% on the test set. [ABSTRACT FROM AUTHOR]

The customized manufacturing process of complex products (with complex structures and production processes) involving randomness and dynamics, is facing the problem of high data management costs and data waste caused by the accumulation of massive amounts of information. Especially in the digital twin-based workshop, the high-fidelity and interactive operation mechanism produces more massive data, aggravating the difficulty of data management. The combined effect of these complex manufacturing processes and dynamic batch production requirements poses a huge challenge to digital twin data management. To overcome this challenge, this paper proposes an updating method for digital twin knowledge based on a memorizing-forgetting model. Firstly, a multi-level representation model is proposed to fuse product, process flow, and manufacturing data. Secondly, the fused memorizing-forgetting model is proposed for dynamically updating digital twin knowledge. Finally, taking ship block manufacturing as an example, the effectiveness of the proposed method in modeling and fusion analysis is proved by the visual analysis of its resources and process knowledge. Considering the dynamic nature of production, it is believed that the data management method will significantly help improve the refined control of workshop resources and manufacturing processes, as well as the efficient use of massive processing data. [ABSTRACT FROM AUTHOR]

Abstract The rapid development and implementation of smart, connected products (SCPs) in the engineering field has triggered a promising manufacturing paradigm of servitization, i.e. smart product-service systems (Smart PSS). As a complex solution bundle in both system and product level, its engineering change management differs from the existing ones mainly in two aspects. Firstly, massive in-context stakeholder-generated/product-sensed data during usage stage can be leveraged to enable its success in a data-driven manner. Secondly, the digitalized services, consisting of both hardware and software solutions, can also be changed in a more flexible way other than the physical components alone. Nevertheless, scarcely any work reports on how to conduct engineering change in such context, let alone a systematic approach to support the automatic generation of its change prediction or recommendation. Aiming to fill these gaps, this work proposes an occurrence-based design structure matrix (DSM) approach together with a three-way based cost-sensitive learning approach for automatic engineering change management in the Smart PSS environment. This informatics-based research, as an explorative study, overcomes the subjectivity and tedious assessment of the experts in the conventional approaches, and can offer useful guidelines to the manufacturing companies for managing their engineering changes for product-service innovation process. [ABSTRACT FROM AUTHOR]

Abstract Recent development and implementation of advanced information and communication technologies (ICT) (e.g. Internet-of-Things (IoT), Cyber-Physical System (CPS)) has triggered a promising market of smart, connected product (SCP), and hence, enabled the prevailing tendency of manufacturing value proposition towards a service-oriented manner, viz. Smart product-service systems (PSS). Smart PSS, as a unique type of PSS, aims to achieve individual customer satisfaction with less environment impact by utilizing SCP as the media and tool, to generate various e-services as a bundle. Its service innovation is enabled by a platform-based approach and generated in a data-driven manner. Moreover, the digitalization of users, things, manufacturers and service providers in the cloud-based environment also provides potential service innovation opportunities. Nevertheless, only few existing works discuss about the Smart PSS, let alone an effective manner with valid cases to support its service innovation. Aiming to fill this gap, this research provides a novel definition of Smart PSS, and proposes a systematic design framework for its service innovation with an elaborated case study of a personalized smart wearable. Meanwhile, other potential industrial applications are discussed as well to offer valuable insights to manufacturers in their servitization for value creation. Graphical abstract Image 1 Highlights • Two criteria-based evaluation for product-service systems classification. • Extended scope of digital twin implementation scenarios. • Platform-based, data-driven and digital twin enabled service innovation. • Four-stage based design approach for service innovation of smart product-service systems. • Smart wearable service innovation and other industrial potentials. [ABSTRACT FROM AUTHOR]

Welding radiographic image analysis (WRIA) is a key technology for welding automated non-destructive testing. Although there already exist some valuable surveys on WRIA, they do not provide a systematic overview of the challenges faced by WRIA and lack a careful distinction and comparison of the core feature techniques in WRIA. With the rapid development of the WRIA area, it is both urgent and challenging to comprehensively review the relevant studies. Therefore, this paper provides an extensive review of 164 papers published in the recent quarter century (1997–2021) ever since its first coined. Three key aspects, namely the challenges faced by WRIA, the evolutionary paths involved in the technology, and the specific application tasks are further discussed in detail. At last, potential future perspectives for WRIA are explored in terms of problem setup, technical improvement, and humanistic care, so as to provide useful insights to both academic researchers and industrial practitioners. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) or 3D printing, as an enabling technology for mass customization or personalization, has been developed rapidly in recent years. Various design tools, materials, machines and service bureaus can be found in the market. Clearly, the choices are abundant, but users can be easily confused as to which AM process they should use. This paper first reviews the existing multi-attribute decision-making methods for AM process selection and assesses their suitability with regard to two aspects, preference rating flexibility and performance evaluation objectivity. We propose that an approach that is capable of handling incomplete attribute information and objective assessment within inherent data has advantages over other approaches. Based on this proposition, this paper proposes a weighted preference graph method for personalized preference evaluation and a rough set based fuzzy axiomatic design approach for performance evaluation and the selection of appropriate AM processes. An example based on the previous research work of AM machine selection is given to validate its robustness for the priori articulation of AM process selection decision support. [ABSTRACT FROM AUTHOR]

Enabled by advanced data analytics and intelligent computing, augmented reality head-up displays (AR-HUDs) are appraised with a certain degree of intelligence towards an in-car assistance system providing more convenience for drivers and ensuring safer traffic. Nevertheless, current AR-HUDs systems fail to analyze perceptual results with recommended driving strategies as the cognitive intelligence, while solely rely on driver's own decision-makings. To pave the way, this work stepwise proposes a visual reasoning-based approach for presenting drivers with perceptual, predictive, and reasoning information onto AR-HUDs toward cognitive intelligence. Firstly, a Driving Scenario Knowledge Graph comprising many road elements and empirical knowledge is established appropriately. Then, by analyzing the video streams and images collected by an in-car visual camera, the driving scene can be perceived comprehensively, including 1) identifying road elements and 2) moving elements' intention recognition. Afterwards, a graph-based driving scenario reasoning model, driving scenario-adaptive KAGNET, is built for achieving driving strategy recommendations. Moreover, the analyzed information is shown on the HUDs via pre-defined AR graphics to support drivers intuitively. A case study is given lastly to prove its feasibility. As an explorative study, some limitations and future work are emphasized to attract further study and open discussion in this area for pursuing the better implementation of AR-HUDs. [ABSTRACT FROM AUTHOR]

Data-driven design (D 3), a new design paradigm benefited from advanced data analytics and computational intelligence, has gradually promoted the research of data-driven product design (DDPD) ever since 2000 s. In today's Intelligence Age, some theoretical and practical studies have tried to achieve the advanced intelligence capabilities in DDPD. However, to the authors' best knowledge, there is still a lack of a holistic summary of DDPD with chronological concern in the intelligence age. To bridge the gap, this research undertakes a literature review of DDPD publications from 2000 to date (19/09/2022), of which 172 relevant papers are discussed via bibliometric analysis and state-of-the-art analysis. The results shown that DDPD has vitality in the Intelligence Age by combining the cutting-edge digital technologies, such as AI, additive manufacturing, digital twin, and so on. Moreover, current DDPD studies could outperform the classical design methods on the well-defined tasks, but it still cannot master the creative/innovative design tasks which require the cognitive capability. This survey further highlights several future research potentials including cognitive intelligence-enabled design, end-to-end design integration, advanced design knowledge support, design for additive manufacturing, and sustainable smart product-service systems. It is hoped that this work can be regarded as a reference to understand the roadmap of DDPD and offer insights for the design practitioners to complete relevant tasks in today's intelligence age. [ABSTRACT FROM AUTHOR]

Data-driven prediction of remaining useful life (RUL) has emerged as one of the most sought-after research in prognostics and health management (PHM). Nevertheless, most RUL prediction methods based on deep learning are black-box models that lack a visual interpretation to understand the RUL degradation process. To remedy the deficiency, we propose an intrinsically interpretable RUL prediction method based on three main modules: a temporal fusion separable convolutional network (TF-SCN), a hierarchical latent space variational auto-encoder (HLS-VAE), and a regressor. TF-SCN is used to extract the local feature information of the temporal signal. HLS-VAE is based on a transformer backbone that mines long-term temporal dependencies and compresses features into a hierarchical latent space. To enhance the streaming representation of the latent space, the temporal degradation information, i.e., health indicators (HI), is incorporated into the latent space in the form of inductive bias by using intermediate latent variables. The latent space can be used as a visual representation with self-interpretation to evaluate RUL degradation patterns visually. Experiments based on turbine engines show that the proposed approach achieves the same high-quality RUL prediction as black-box models while providing a latent space in which degradation rate can be captured to provide the interpretable evaluation. [ABSTRACT FROM AUTHOR]

Concerns are growing over time on the adverse health effects of air pollution. However, the association between ambient air pollution and blood sex hormones in men is poorly understood. We included 72,917 men aged 20–55 years from February 2014 to December 2019 in Beijing, China in this study. Blood testosterone, follicle stimulating hormone, luteinizing hormone, estradiol, and prolactin levels of each participant were measured. We collected exposure data of daily ambient levels of particulate matter ≤10 μm (PM 10) and ≤2.5 μm (PM 2.5), nitrogen dioxide, sulfur dioxide (SO 2), carbon monoxide, and ozone. Generalized linear mixed models were used to analyze the potential association between ambient air pollution exposure and blood sex hormone levels. The results showed that both immediate and short-term cumulative PM 2.5 , PM 10 , and SO 2 exposure was related to altered serum sex hormone levels in men, especially testosterone. An increase of 10 μg/m3 in PM 2.5 and PM 10 in the current day was related to a 1.6% (95% confidence interval [CI]: 0.9%–2.3%) and 1.1% (95% CI: 0.5%–1.6%) decrease in testosterone, respectively, and a decreasing tendency of accumulated effects persisted within lag 0–30 days. The present study demonstrated that it is important to control ambient air pollution exposure to reduce effects on the reproductive health of men. • The association between ambient air pollution exposure and serum sex hormones in male remains unclear. •This study included 72,917 male adults aged 20–55 years. The data of 6 kinds of air pollutants and 5 kinds of hormones was collected. •Increased ambient air pollution exposure were significantly associated with a decreased serum sex hormones level of adult male, especially for the testosterone. [ABSTRACT FROM AUTHOR]

• The definition and a 5-level architecture of smart office (SO) are proposed. • A systematic survey of SO towards employee health promotion (EHP) is conducted. • Three key aspects are synthesized using a quantitative context analysis tool. • Three challenges and future perspectives for EHP in SO are offered. The advancement of Internet-of-Things (IoT) and artificial intelligence contribute to the prevailing development of smart office, which is capable of understanding employees' context and adapting to their demands. The smart office brings numerous opportunities for delivering prevention and control measures of health issues associated with office work (e.g., musculoskeletal disorders and computer vision syndrome). Even though there exist multiple studies across different disciplines, there still lacks a holistic survey on the smart office for employee health promotion. Hence, this paper focuses on three contributions: (1) clarifying the fundamentals of smart office, (2) reviewing the key aspects of this theme based on 60 studies selected from a systematic survey process, and (3) identifying the challenges and future research opportunities. We hope this study can bring an interdisciplinary and collaborative perspective for employee health promotion and encourage more researches in this emerging and promising field. [ABSTRACT FROM AUTHOR]

This paper presents comprehensive research work on the crack initiation behavior of Semi Flexible Pavements (SFP) with the help of a one-way coupled multiscale modelling approach. The study focuses on elucidating the influence of layer interface conditions on potential cracking patterns under different temperature conditions. By integrating material properties at both local (mixture) and global (pavement) scales using a local de-homogenization approach, the interlocking effect was addressed within the asphalt binder's mesoscale properties. CT-image based mesostructure was employed to reconstruct the Representative Volume Element (RVE). Cohesive Zone Model (CZM) was utilized to simulate cracking initiation within the RVEs, with simulation results validated against practical engineering inspections. Additionally, a potential macroscopic failure index was introduced for SFP. It was found that the typical macroscopic failure indexes utilized for AC pavement may not be suitable for SFP due to the higher degree of heterogeneity. Analysis showed that SFP layer's coarse aggregate and cement grout at the bottom may experience compressive stress, while asphalt binder beneath the tire load may face high tensile stresses. Regardless of the interface conditions, top-down cracking was found to be the dominating failure mode in SFP. Besides, the vulnerable area in bonded interface SFP shifts from under tire load to the adjacent area with the rise in pavement temperature, while bottom-up cracking may appear only in unbonded interface SFP under low temperature conditions. Subsequent analysis demonstrated that top-down cracking is prominent in bonded interface SFP, especially at higher temperatures, compared to unbonded interface SFP. It is expected that critical observations for SFP highlighted above will certainly help in understanding critical failure modes and, hence, designing and constructing durable SFP structures. • A one-way coupled multiscale modelling approach considering interlocking effect was developed. • The interlocking effect was addressed using a local de-homogenization method. • The cracking initiation form of SFP was simulated and validated by pavement inspection. • A potential macro-mechanical failure index was proposed for SFP. [ABSTRACT FROM AUTHOR]

The rapid development of advanced technologies provides futuristic option to enhance product value with digital services, which the concept of Smart Product-Service System (SPSS) was proposed. SPSS focuses on usage data, the whole lifecycle, and the iterative optimisation. A large amount of data and context information can be collected during the usage stage, which can be used to elicit requirements to guide further design. How to realise the requirement elicitation with cognitive intelligence based on usage data is the key step for design optimisation. Therefore, this research proposes a cognitive intelligence-enabled requirements elicitation framework to help designers promote the design optimisation process. First, the digital twin-driven data collection architecture of SPSS is proposed by considering multi-sources and multi-stages. Second, the behaviour of SPSS is modelled based on Auto-Encoder which is constructed using deep learning to cognise SPSS performance. Third, the corresponding behaviour model is interpreted based on Shapley Addictive exPlanations (SHAP) to discover key factors with cognitive intelligence. Finally, the domain knowledge graph is used to generate the optimisation requirements for further design. To demonstrate the feasibility and advantages of the proposed framework, this study adopts the case study of wind turbines for illustration and verification. [ABSTRACT FROM AUTHOR]

Mining entities from open Chinese mechanical equipment texts have become prevailing in the intelligent manufacturing field. However, compared to named entities in other domains, it is very hard to determine entity boundaries in mining Chinese mechanical equipment texts because there is no unified standard for entity simplification, and digits and units are mixed in the text. To address the issue, this paper presents an entity boundary-define strategy and constructs a mechanical equipment-oriented corpus called MECorpus using open Chinese mechanical equipment texts by combining domain knowledge. A multi-neural network collaboration model Adaptive-BERT-BiLSTM-CRF-Rating (ABBCR) is then proposed for mechanical equipment named entity recognition. The novelty of ABBCR is characterised by its adaptive input mechanism and rating score ability for identified entities. Various experiments about ABBCR model selection, evaluation and application are conducted on MECorpus. Experimental results show that the ABBCR model provides high-quality mechanical equipment entities for constructing the mechanical equipment knowledge graph. ABBCR combined with the large language model has proved to be a promising method to manage complex mechanical equipment expertise. [ABSTRACT FROM AUTHOR]

Creep-feed grinding is a high-efficiency, high-precision grinding process widely used in the manufacturing of aviation engines. However, the workpiece burn and other quality issues caused by high processing temperature limit the yield of grinding. Therefore, the accurate model of grinding temperature has become the key to improving processing efficiency and quality. Different from the traditional physical or data-driven models, this paper attempts to combine both perspectives based on Physics-Guided Neural Networks (PGNN) to accurately predict grinding temperature with a small number of experiments. At the level of data acquisition, real grinding experiment data was obtained and a data augmentation method had been proposed. At the level of neural network structure, optimisation processes were implemented to enhance prediction performance, and a physics-guided loss function was inserted to guide network training. The experiment results shows that PGNN had better prediction accuracy than the physical model, while also mitigating the limitations of data-driven models on small sample sets. PGNN also performed better with noisy data and predictions out of the training data range, this reveals the benefits of PGNN for small sample problems in processing scenarios. [ABSTRACT FROM AUTHOR]

The rapid development of information and communication technologies (ICT) has enabled the prevailing digital transformation (i.e. digitalization), where physical products can be readily digitized in the virtual space and seamlessly interconnected. Meanwhile, industries are ever increasingly adopting service business models (i.e. servitization), so as to offer not only physical products but also services as a solution bundle to meet individual customer needs. Such convergence of both digitalization and servitization (i.e. digital servitization) has triggered an emerging IT-driven business paradigm, smart product-service systems (Smart PSS). As a novel paradigm coined in 2014, to the authors' knowledge, only 2 conference papers have provided some literature review to date, and many issues remain uncovered or not comprehensively investigated. Aiming to fill this gap, this paper has conducted a systematic review of Smart PSS or related papers published ever since its first brought up to date (30/06/2019), and selected 97 representative items together with other 37 supplementary works to summarize the tendency towards Smart PSS, its business and technical aspects, current challenges, and future perspectives. From the survey, it is found that several hybrid concerns are the key challenges faced, and self-adaptiveness with sustainability, advanced IT infrastructure, human-centric perspectives, and circular lifecycle management are the core future perspectives to explore. It is hoped that this work can attract more open discussions and provide useful insights to both academics and industries in their exploration and implementation of Smart PSS. [ABSTRACT FROM AUTHOR]

[Display omitted] • Lipid metabolism and amino acid metabolism were significantly dysregulated in chromate-exposed workers. • High resolution mass spectrometry and bioinformatics are conducive to characterize metabolic changes. • The decrease of serum l -arginine, PC(18:2/24:4) and PC(14:0/16:0) levels were associated with the WB-Cr in all subjects. • The correlations of WB-Cr levels with key differential metabolites were observed in male-only subgroups. Hexavalent chromium (Cr(VI)) compound is considered as a common environmental and occupational pollutant due to widespread application in industry and agriculture. Cr(VI) as a carcinogen poses a serious threat to human health and the underlying mechanisms need further investigation. Previous studies had demonstrated the characteristic expression profiling after Cr(VI) treatment in vitro and in vivo at the levels of gene and protein. The comprehensive metabolic signatures were also conducive to discover potential biomarkers for effects assessment of Cr(VI) toxicity. In the current study, Ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) non-targeted metabolomics was applied to analyze serum metabolic changes in 77 chromate exposure workers and 62 controls. Thirteen metabolites were found significantly decreased and 41 metabolites were increased, which were involved in arginine and proline metabolism, and glycerophospholipid metabolism by bioinformatic analysis. Furthermore, there were significant negative correlations between blood Cr level and Arginine, PC(18:2/24:4) and PC(14:0/16:0), subgroup analyses indicated that these correlations were observed in male-only subgroups, and were not found among chromate workers and controls separately. Diet could be a potential confounder which was not controlled rigorously in this study. These findings provided preliminary clues to investigate the underlying mechanisms of Cr(VI)-induced toxicity and were required to be further verified in future researches. [ABSTRACT FROM AUTHOR]

Efficient synthesis of β‐hydroxy‐β‐CF3‐phosphonates and β‐hydroxy‐β‐CF3‐phosphine oxides by Mn‐mediated or transition‐metal‐free hydroxyphosphorylation of α‐trifluoromethy)styrenes with H‐phosphonates or H‐phosphine oxides with the assistance of air, respectively, was described. [ABSTRACT FROM AUTHOR]

An efficient and practical method for the synthesis of functionalized trifluoromethyl cyclopropanes via cyclopropanation of α-trifluoromethyl styrenes with chloroacetonitrile or ethyl chloroacetate was developed. In some cases, the mixture of trans- and cis-isomers could be separated by column chromatography. The configurations of the trans-/cis-isomers were further confirmed by heteronuclear 1H–19F NOESY experiments. [ABSTRACT FROM AUTHOR]

A novel and facile method for the synthesis of N‐β‐CF3‐substituted 2‐pyridones via hydroamination of α‐(trifluoromethyl)styrenes with 2‐pyridones was described. The reaction proceeded smoothly at room temperature, affording a variety of N‐(β‐trifluoromethyl‐β‐arylethyl)pyridin‐2(1H)‐ones in moderate to good yields with excellent N‐regioselectivity. [ABSTRACT FROM AUTHOR]

Non-destructive testing of welds based on the radiographic image is crucial for improving the reliability of aerospace structural components. The deep learning method represented by the convolutional neural network (CNN) has received extensive attention in welding radiographic image recognition (WRIR) owing to its powerful feature adaptive extraction ability. However, CNN-based WRIR faces key challenges of small sample size and poor explainability. Inspired by the process of interpreting radiographic film by experts, expert knowledge-empowered CNN for WRIR is proposed. Two self-supervised learning (SSL) tasks for radiographic image deblurring and brightness adjustment are designed to model expert experience. The expert knowledge learned from the SSL process is used to guide the CNN to identify weld defects. The results show that the proposed method improves the inductive bias of the CNN model, owns a faster convergence speed and recognition accuracy under the condition of small sample size, and reaches 97.65% of the comprehensive evaluation index F1-score. Moreover, the expert knowledge learned from the SSL process and the decision-making basis of the CNN model are visualized from both global and local aspects, which improve the explainability of CNN-based WRIR. [ABSTRACT FROM AUTHOR]

A simple and straightforward addition or defluorination of α-(trifluoromethyl)styrenes with 2-nitroimino-imidazolidine (2a), 2-(nitromethylene)imidazolidine (2b), 2-cyanoimino-thiazolidine (2c), and (E)-1-methyl-2-nitroguanidine (2d), in a controlled manner, was developed. The hydroamination of α-(trifluoromethyl)styrenes with 2a, 2b, 2c, and 2d was completed in the presence of DBN at room temperature within 0.5–6 h, affording structurally diverse β-trifluoromethyl-β-arylethyl analogues of neonicotinoids in moderate to good yields. The γ,γ-difluoro-β-arylallyl analogues of neonicotinoids were also successfully synthesized via defluorination of α-(trifluoromethyl)styrenes, with 2a and 2c using NaH as base at an elevated temperature together with a prolonged reaction time of 12 h. The method features simple reaction setup, mild reaction conditions, broad substrate scope, high functional group compatibility, and easy scalability. [ABSTRACT FROM AUTHOR]

Purpose: The contribution of household cooking salt to population iodine status is decreasing in China, the applicability of the coverage rate of iodized salt (IS), proportion of adequately iodized salt (AIS), and salt iodine concentration (SIC) of household cooking salt used for iodine status assessment of residents requires further investigation. Methods: Through the IDD control project, 16,445 children and 4848 pregnant women were recruited from Tianjin, China and the relationship between the coverage rate of IS, proportion of AIS, SIC, and population iodine status was analyzed. Additionally, through the thyroid health survey project, 856 children with IS or noniodized salt were recruited. The effects of different household cooking salts on individual iodine status and thyroid health were analyzed. Results: After adjusting for confounding factors, no relationship was found between the coverage rate of IS, proportion of AIS, SIC of household cooking salt, and iodine status of children and pregnant women (all P > 0.05). No differences in levels of thyroid function and structural indicators were found in children with different household cooking salts (all P > 0.05). Additionally, no relationship was found between noniodized salt exposure and goiter, overt hyperthyroidism, overt hypothyroidism, thyroid nodules, antibody single positivity, or subclinical hypothyroidism (all P > 0.05). Conclusion: Iodine in household cooking salt no longer plays a crucial role in iodine status in Tianjin, China. Other indicators must be identified as beneficial supplements for precise iodine status evaluation not only in Tianjin but also in other large cities in China. [ABSTRACT FROM AUTHOR]

Health concerns surrounding human exposure to phthalates include diminished semen quality. Epidemiological findings remain inconsistent. We have performed a quality appraisal and meta-analysis to quantitatively summarize evidence for associations between phthalate exposures and human semen quality. Pubmed and Web of Science were searched for pertinent studies through October 2014. Cited references were reviewed to identify secondary studies. Studies that reported quantitative estimates of the association between phthalates or their metabolite levels in humans and semen quality were eligible. Random effects models were used to calculate pooled effects estimates. Overall, 20 studies met our inclusion criteria. Subsequently, 14 studies were included in the meta-analysis. Urinary monobutyl phthalate (MBP) and monobenzyl phthalate (MBzP) were associated with reduced sperm concentration (MBP [7.4–25.3 µg/L], pooled odds ratio [OR]=2.60, 95% confidence interval [CI]=1.32–5.15; MBzP [14.0–540.2 µg/L], pooled OR=2.23, 95% CI=1.16–4.30). Both MBP (24.6–14,459.0 µg/L) and MEHP (3.1–208.1 µg/L) were inversely associated with straight line velocity (VSL; MBP, pooled β =–2.51, 95% CI=–4.44, –0.59; MEHP, pooled β =–1.06, 95% CI=–1.99, –0.12). An IQR increase in MBzP and MEP levels (MBzP, IQR=11.35 µg/L; MEP, IQR=449.4 µg/L) was associated with an increase in comet extent (CE; MBzP, pooled β =3.57, 95% CI=0.89–6.25; MEP, pooled β =4.22, 95% CI=1.66–6.77). No associations were observed between monomethyl phthalate and any semen parameters. Our meta-analysis strengthens the evidence that specific phthalates or their metabolite levels may affect semen quality. [ABSTRACT FROM AUTHOR]

With the ever-increasing demand for personalized product functions, product structure becomes more and more complex. To design a complex engineering product, it involves mechanical, electrical, automation and other relevant fields, which requires a closer multidisciplinary collaborative design (MCD) and integration. However, the traditional design method lacks multidisciplinary coordination, which leads to interaction barriers between design stages and disconnection between product design and prototype manufacturing. To bridge the gap, a novel digital twin-enabled MCD approach is proposed. Firstly, the paper explores how to converge the MCD into the digital design process of complex engineering products in a cyber-physical system manner. The multidisciplinary collaborative design is divided into three parts: multidisciplinary knowledge collaboration, multidisciplinary collaborative modeling and multidisciplinary collaborative simulation, and the realization methods are proposed for each part. To be able to describe the complex product in a virtual environment, a systematic MCD framework based on the digital twin is further constructed. Integrate multidisciplinary collaboration into three stages: conceptual design, detailed design and virtual verification. The ability to verify and revise problems arising from multidisciplinary fusions in real-time minimizes the number of iterations and costs in the design process. Meanwhile, it provides a reference value for complex product design. Finally, a design case of an automatic cutting machine is conducted to reveal the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Surface roughness is a critical parameter for quantifying the surface quality of a workpiece. Accurate surface roughness prediction can significantly influence the overall quality of the final components by reducing costs and enhancing productivity. Although various prediction methods have been explored in previous research, less attention has been given to explainable prediction methods for surface roughness. This paper proposes a surface roughness prediction model based on the Differential Evolution (DE) algorithm with ensemble learning. It aims to elucidate the intrinsic correlation between processing parameters and surface roughness values in Multi-Jet Polishing (MJP) using explainable analysis methods. The proposed Ensemble Regression with Differential Evolution (ERDE) algorithm comprises two main modules: data processing and analytics, and ensemble model prediction. These modules investigate the surface roughness of MJP from the data and model levels, respectively. To validate the effectiveness of ERDE, MJP experiments were conducted on three-dimensional printed components of 316L stainless steel. The experimental data indicated that ERDE outperforms other existing algorithms, reducing the mean absolute percentage error by approximately 42 %. [ABSTRACT FROM AUTHOR]

Although nanotoxicology studies have shown that respiratory exposure of titanium dioxide nanoparticles (TiO2 NPs) could induce adverse health effects, limited biomarkers associated with occupational exposure of TiO2 NPs were reported. The purpose of this study is to screen serum biomarkers among workers occupationally exposed to TiO2 NPs using metabolomics. Compared with the control group, a total of 296 serum metabolites were differentially expressed in the TiO2 NPs-exposed group, of which the relative expression of 265 metabolites increased, and the remaining 31 decreased. Three machine learning methods including random forest (RF), support vector machines (SVM), and boruta screened eight potential biomarkers and simultaneously selected a metabolite, Liquoric acid. Through multiple linear regression analysis to adjust the influence of confounding factors such as gender, age, BMI, smoking and drinking, occupational exposure to TiO2 NPs was significantly related to the relative expression of the eight potential biomarkers. Meanwhile, the receiver operating characteristic curves (ROCs) of these potential biomarkers had good sensitivity and specificity. These potential biomarkers were related to lipid peroxidation, and had biological basis for occupational exposure to TiO2 NPs. Therefore, it was demonstrated that the serum metabolites represented by Liquoric acid were good biomarkers of occupational exposure to TiO2 NPs. [ABSTRACT FROM AUTHOR]

To proactively fulfill multiple stakeholders' needs in the engineering solution design process, the knowledge recommendation approach is adopted as a key element in the knowledge management system. Nevertheless, most existing knowledge recommendation approaches cannot simultaneously meet the higher standard of in-context accuracy and diversity. To address the issue, this paper proposes a context-aware diversity-oriented knowledge recommendation approach, thereby assisting stakeholders to accomplish engineering solution design in a smarter manner. Three diversity concerns, namely item-diversity, context-diversity, and user-diversity are addressed by semantic-based content analysis, context definition and awareness, and user profile modeling, respectively. Hence, the proposed approach not only maximizes the diversity of the recommended knowledge but also guarantees its accuracy under multiple problem-solving contexts. Moreover, a practical engineering solution design case on a Smart 3D printer platform is conducted, to validate the efficacy of the proposed approach in providing usable and diverse knowledge items. It is anticipated this work can provide useful insights to practitioners in their knowledge-based engineering solution design process. [ABSTRACT FROM AUTHOR]

Coordinating order acceptance decisions with production scheduling to maximize revenue is challenging for Mass Individualized Prototyping (MIP) service providers. This paper presents a dual deep reinforcement learning agents-based (DDRLA) integrated order acceptance and scheduling (IOAS) for improving revenue. Firstly, a deep reinforcement learning-based virtual production scheduling (VPS) agent together with 8 state features and 11 action rules is designed. The VPS agent quickly and virtually reschedules a dynamically-arriving accepted order to evaluate the overall impact of accepting this order, including consumed capacity and increased revenue. Then, a deep reinforcement learning-based order acceptance decision (OAD) agent is designed. Based on the information guidance resulting from an interaction with the VPS agent, the OAD agent selectively accepts orders to maximize long-term gains, as well as to improve system resilience in the presence of a high ratio of urgent orders. The experiment results show that the proposed DDRLA method has better performance, compared with other IOAS approaches. [ABSTRACT FROM AUTHOR]

• This study focuses on commodity rebalancing and transportation planning with combined distances. • A bi-objective stochastic mixed-integer nonlinear programming model is proposed for the problem. • A linearization approach is developed and an adaptive E -constraint method is applied to obtain the Pareto-optimal solutions. • A solution strategy is proposed to handle large-sized problems. • The trade-off between the objectives and sensitivity analysis of key parameters with managerial insights are obtained. Multi-commodity rebalancing plays a critical role before and during the attack of large-scale disasters. In practice, some relief centers can be out of reach from the ground for vehicles due to the road disruption. Accordingly, alternative transportation systems are essential to maximize fairness and minimize the total transportation time, simultaneously. However, little study has reported on this issue for humanitarian logistics. To address it, a bi-objective stochastic optimization model is proposed to rebalance and transport commodities with the multi-modal transportation system. This work first linearizes the model and then applies an adaptive augmented E -constraint method to obtain a number of Pareto-optimal solutions. Furthermore, a case study of an emergency event is carried out, of which the computational results indicate its decision making effectiveness. Lastly, sensitivity analysis on critical parameters is conducted and the trade-off between the objectives is also analyzed to provide valuable managerial insights. [ABSTRACT FROM AUTHOR]

Smart manufacturing is gradually recognized and widely adopted due to the promising features of sustainability, flexibility, and collaboration. Service discovery and allocation in smart manufacturing aim to provide on-demand manufacturing capabilities for meeting customized production requirements. They are tightly coupled in practice, whereas they are usually considered as two independent processes and investigated separately in most research. Meanwhile, the collaboration relationship and decision autonomy of service providers are rarely taken into account to perform sustainable and flexible production. To deal with these challenges, this paper proposes an integrated framework to holistically describe the active discovery and optimal allocation of smart manufacturing services. A mechanism is designed to consider the collaborative relationship of manufacturing resources and promote collaborative production. The distributed optimization model based on analytical target cascading method is introduced to maintain the decision autonomy of service providers and achieve the optimal allocation of smart manufacturing services. A case study is further provided to demonstrate the effectiveness of the proposed framework. • A framework is proposed to holistically describe service discovery and allocation. • The collaboration relationship of manufacturing resources is considered. • A distributed optimization model is constructed to maintain decision autonomy. • The effectiveness of the proposed framework is illustrated by a case study. [ABSTRACT FROM AUTHOR]

The rapid development and implementation of smart, connected products have triggered a promising manufacturing paradigm of servitization, i.e. smart product-service systems (Smart PSS). Unlike existing product development or service design process, Smart PSS owns the unique design characteristics of IT-driven value co-creation, closed-loop design, and context-awareness enabled by the advanced information and communication technologies. Nevertheless, to the best of authors' knowledge, there is scarcely any discussion on its design theories, which serves as the fundamental basis. To identify the limitations of current design approaches in supporting Smart PSS development and suggest some future research directions, this paper conducts a systematic literature review at existing engineering design methodologies to verify its appropriateness for the Smart PSS development. 9 typical design methods have been chosen based on the systematic review of 101 representative items published ever since the coin of PSS to date (04/05/2020) and 48 supplementary works. They are further compared and evaluated from three aspects, including "smart design" objects, enabling smart technologies and smart application fields. Not surprisingly, the investigation results indicate that none of the existing methodologies can fully meet the design characteristics of Smart PSS, while three research directions of Smart PSS design methodology are provided as the potential solution at last. It is hoped this paper can attract more open discussions and offer useful insights to both academics and industries in their development of Smart PSS. [ABSTRACT FROM AUTHOR]

Smart product-service systems (Smart PSS), as an emerging digital servitization paradigm, leverages smart, connected products and their generated services as a solution bundle to meet individual customer needs. Owing to the advanced information and communication technologies, Smart PSS development differs from the existing product and/or service design mainly in three aspects: (1) closed-loop design/redesign iteration; (2) value co-creation in the context; and (3) design with context-awareness. These unique characteristics bring up new design challenges, and to the authors' best knowledge, none of the existing design theories can address them well. Aiming to fill this gap, a novel design methodology for the Smart PSS development is proposed based on the information theory, where both the system and stakeholders can be regarded as the information containers. Hence, the closed-loop design/redesign iteration can be treated as the dynamic change of information and entropy in a balanced ecosystem. Meanwhile, the value co-creation process is considered as the exchange of accumulated information via the container. Lastly, the design context-awareness represents the process of reducing entropy. As a novel prescriptive design theory, it follows Shannon's information theory to determine the best design/redesign solutions by considering the three characteristics integrally. It is hoped that the proposed design entropy theory can largely facilitate today's Smart PSS development with better performance and user satisfaction. [ABSTRACT FROM AUTHOR]

• A novel approach for context-aware human fatigue management in traffic control centres. • Introduced the fatigue casual causal network representation for fatigue management. • Proposed an artificial immune system and XGBoost-enabled fatigue prediction method. • Presented an adaptive work arrangement algorithm for redesign work schedules. Traffic control operators are usually confronted with a high potential of human fatigue. Existing strategies to manage human fatigue in transportation are primarily by undertaking prescriptive "hours-of-work" regulations. However, these regulations lack certain flexibility and fail to consider dynamic fatigue-inducing factors in the context. To fill this gap, this study makes an explorative first step towards an improved approach for managing human fatigue. First, a fatigue causal network that can adequately represent the context factors and their dynamic interactions of human fatigue is proposed. Moreover, to overcome its problem of high dimension sparse matrix, a novel method based on the artificial immune system and extreme gradient boosting algorithm is introduced. A case study of vessel traffic management showed that the model could predict the fatigue level with high accuracy of 89%. Furthermore, to lower the risk of fatigue occurrence, a novel scheduling algorithm is also provided to adaptively arrange work for operators considering individual differences and work types. The study results showed that 27% of operators could be rearranged to reduce the possibility of human fatigue. Nevertheless, considering that more than half of operator were still fatigue in the case study, human fatigue is still a critical problem. It is hoped this research, as an explorative study, can offer insightful references to traffic management authorities in their safety management process with better operation experience. [ABSTRACT FROM AUTHOR]

Automated surface inspection has become a hot topic with the rapid development of machine vision technologies. Traditional machine vision methods need experts to carefully craft image features for defect detection. This limits their applications to wider areas. The emerging convolutional neural networks (CNN) can automatically extract features and yield good results in many cases. However, the CNN-based image classification methods are more suitable for flat surface texture inspection. It is difficult to accurately locate small defects in geometrically complex products. Furthermore, the computational power required in CNN algorithms is usually high and it is not efficient to be implemented on embedded hardware. To solve these problems, a smart surface inspection system is proposed using faster R-CNN algorithm in the cloud-edge computing environment. The faster R-CNN as a CNN-based object detection method can efficiently identify defects in complex product images and the cloud-edge computing framework can provide fast computation speed and evolving algorithm models. A real industrial case study is presented to illustrate the effectiveness of the proposed method. The results show that the proposed method can provide high detection accuracy within a short time. [ABSTRACT FROM AUTHOR]

• A generic framework of evolving team cognition to examine an accident's evolving context from the perspective of mental model disconnects. • The defective implicit interaction due to task-related disconnects causes the onset of landing risks at outer area. • Passive information dissemination and late initiation for corrective operations make a team hard to prevent unstable approach from occurring under terminal area. Unstable approach is an adverse aviation event, and it is strongly related to the interaction between pilots and air traffic controllers (ATCOs). Mental model disconnects among team members can be a major cause of possible interaction conflicts and defective team cognition. Therefore, to study the negative effects of various mental model disconnects, a framework of evolving team cognition (FETC) is proposed to examine the evolving context of an accident. An agent-based model (ABM) is developed to simulate how mental model disconnects are involved in evolving landing scenarios. The results of a simulation conducted with the ABM indicate that mental model disconnects occur more frequently as an aircraft approaches the terminal area. For landing scenarios in the outer area between 100 and 50 nautical miles (Nm), task-related mental model disconnects occur more frequently, causing incompatibility between parties. The incompatibility reveals the necessity of extra coordination to prevent the potential occurrence of system errors. As for the scenarios around the terminal area (30–15 Nm), the team-related mental model disconnects prevail, leading to passive information dissemination regarding changing conditions and late initiation of urgent coordination. The combination of these factors causes a team to miss the window for preventing an adverse event. [ABSTRACT FROM AUTHOR]

• Better information transparency mitigates possible information and evaluative disconnects occurred. • Information transparency facilitates a team's implicit interaction, reducing possible operational disconnects between parties. • The execution of implicit interaction reduces reactive actions needed to solve errors. • Less demands of reactive actions increases the efficiency of resource utilization. With the ever-increasing demands of air traffic flow management, its landing system requires fundamental improvements to meet the future needs. For the current landing system, information gaps between parties (i.e. pilots and air traffic controllers) cause interaction conflicts, which hinder a system's risks detection and mitigation. Therefore, a well-implementation of data-links can promise a better information transparency, by facilitating the development of team cognition and the execution of implicit interaction between parties to predictively adapt to changing conditions. To achieve it, this research proposes a smart interaction system based on the two agent-based models (ABMs), and further compared them to examine the improvements of a system's reliability and efficiency. Results show that implicit interaction significantly reduces the reactive actions needed to cope with congestions and system errors. Moreover, the efficiency of resource utilization is also improved without causing significant delay, and hence characterizes a more reliable and efficient landing system. [ABSTRACT FROM AUTHOR]