Metabolic syndrome (MetS) is a complex metabolic disorder and a high-risk condition for type 2 diabetes and cardiovascular disease. Rapid screening of at-risk individuals using accurate and ...time-saving tools is effective in disease management. Using the Korea National Health and Nutrition Examination Survey (KNHANES) data, we collected data from 2234 participants suitable for the study design, of which 974 (43.6%) were men and 1260 (56.4%) were women. We used receiver operating characteristic (ROC) curve analysis to estimate the optimal sex-specific neck circumference (NC) cut-off point to predict the MetS risk. To analyze the risk of MetS according to the estimated NC, logistic regression analysis was performed to identify the confounding factors. The result of the ROC analysis showed that the optimal neck cut-off points for predicting the risk of MetS were 38.25 cm (AUC: 0.759, 95% CI: 0.729–0.790) in men and 33.65 cm (AUC: 0.811, 95% CI: 0.782–0.840) in women. In the upper NC cut-off point compared to the lower NC cut-off point, NC was associated with an increased MetS risk by 2.014-fold (p = 0.010) in men and 3.650-fold (p < 0.001) in women, after adjustments. The current study supports NC as an effective anthropometric indicator for predicting the risk of MetS. It is suggested that more studies should be conducted to analyze the disease prediction effect of the combined application of anthropometric indicators currently in use and NC.
China experienced an unprecedented increase in labor strikes until the mid-2010s, and the country continues to experience considerable strike activity. Therefore, it is important to study what ...predicts Chinese workers' attitudes toward strikes. The study applies social exchange theory as an overarching framework to investigate the contributions of three forms of social exchange relations in organizations-employee-employer relations, labor-management relations, and leader-member relations-to account for Chinese employees' attitudes toward strikes in multinational corporations (MNCs) based in China. Using a matched employee-employer sample of more than 1,600 employees in 41 China-based MNCs, the results obtained from hierarchical linear modeling (HLM) analysis show that individual-perceived employee-employer relations indicated by a negative reciprocity norm is positively related to employees' strike attitudes. Labor-management relations indicated by the organizational-level cooperative industrial relations (IR) climate is negatively related to employees' strike attitudes. Leader-member relations indicated by leader-member exchange (LMX) is not significantly related to employees' strike attitudes. Overall, the results indicate that at the individual level, it is more important for companies not to do bad (to engage in negative reciprocity with workers) than to do good to reduce employees' strike attitudes.
•Recursive and collaborative approach where knowledge gained from machine learning models is integrated with ontological knowledge.•Ontology-based semantic knowledge framework supports recursive ...communication with experts for data-driven RST weldability certification.•Extracted RSW concepts from the decision trees formalized by the RSW ontology and converted the decision rules into SWRL rules.•Transformed datasets helped to develop improved machine learning models that work as a new source of weldability prediction.
Data-driven techniques have shown promising results in the analysis and understanding of complex welding processes. Data analytics play a significant role to turn data into valuable insights to assist in the weldability certification decision-making for Resistance Spot Welding (RSW) as well. However, to successfully perform the associated data analytics, domain knowledge is essential to construct more ‘sense-making’ analytics models, as often the models cannot properly capture the nuances of the domain and do not properly indicate the relationship among the RSW concepts and parameters. Thus, machine learning models developed from rough experimental data often do not provide models meaningful and sensible to the domain expert. In this article, we employ a recursive approach between the domain experts and data-driven models so that the knowledge of the domain experts can be integrated into the weldability certification decision-making process. An ontology-based semantic knowledge framework supports this recursive communication while helping the experts to instil more confidence in the developed analytics models. The collaborative and recursive approach implemented in this study helps the domain experts to tap into their domain knowledge and form expert opinions using the formalized semantic RSW concepts and decision rules. The expert opinions are then used to learn new knowledge about the RSW domain and transform the RSW datasets by incorporating significant features that were not included in the earlier models. The transformed datasets help us to develop improved machine learning models, which in turn work as a new source of semantic knowledge, as we have discovered through our pilot implementation.
This paper presents a semantic Resistance Spot Welding (RSW) weldability prediction framework. The framework constructs a shareable weldability knowledge database based on the regression rules from ...inconsistent RSW quality datasets. This research aims to effectively predict the weldability of RSW process for existing or new weldment design. A real welding test dataset collected from an automotive OEM is used to extract decision rules using a decision tree algorithm, Classification and Regression Trees (CART). The extracted decision rules are converted systematically into SWRL rules for capturing the semantics and to increase the shareability of the constructed knowledge. The experiments show that the RSW ontology, along with SWRL rules that contains weldability rules constructed from the datasets, successfully predicts the weldability (nugget width) values for RSW cases. The predicted nugget width values are found to be in-close proximity of the actual values. This paper shows that semantic prediction framework construes an intelligent way for constructing accurate and transparent predictive models for RSW weldability verification.
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•MUGPSO provides efficiency for optimization problems associated with computationally expensive analysis and simulation tasks.•Meta-models are constructed using GRNN and updated as ...the PSO run proceeds for better approximation of the solution space.•MUGPSO is compared with PSO without meta-modeling and other meta-modeling methods using ten benchmark problems.•Solution quality obtained by MUGPSO is promising while providing best results on a few of the ten benchmark problems.
Swarm intelligence (SI) and evolutionary computation (EC) algorithms are often used to solve various optimization problems. SI and EC algorithms generally require a large number of fitness function evaluations (i.e., higher computational requirements) to obtain quality solutions. This requirement becomes more challenging when optimization problems are associated with computationally expensive analyses and/or simulation tasks. To tackle this issue, meta-modeling has shown successful results in improving computational efficiency by approximating the fitness or constraint functions of these complex optimization problems. Meta-modeling approaches typically use polynomial regression, kriging, radial basis function network, and support vector machines. Less attention has been given to the generalized regression neural network approach, and yet, it offers several advantages. Specifically, the model construction process does not require iterations. Its only one parameter is known to be less sensitive and usually requires less effort in selecting an optimal parameter. We use generalized regression neural network in this paper to construct meta-models and to approximate the fitness function in particle swarm optimization. To assess the performance and quality of these solutions, the proposed meta-modeling approach is tested on ten benchmark functions. The results are promising in terms of the solution quality and computational efficiency, especially when compared against the results of particle swarm optimization without meta-modeling and several other meta-modeling methods in previously published literature.
•Realized data-driven cyber-physical system for RSW certification with integrated analytics and optimization capabilities.•Integrating data from analytics lifecycle phases - data collection operation ...to predictive analytics to design visualization.•The framework is based on conceptualization of layers of cyber-physical system and incorporates design and machine changes.•Closed-loop machine parameter optimization implemented considering the target product design.•Case study based on a real industrial weldability certification to illustrate the application of the envisioned framework.
A cyber-physical system is one of the integral parts of the development endeavor of the smart manufacturing domain and the Industry 4.0 wave. With the advances in data analytics, smart manufacturing is gradually transforming the global manufacturing landscape. In the Resistance Spot Welding (RSW) domain, the focus has been more on the physical systems, compared to the virtual systems. The cyber-physical system facilitates the integrated analysis of the design and manufacturing processes by converging the physical and virtual stages to improve product quality in real-time. However, a cyber-physical system integrated RSW weldability certification is still an unmet need. This research is to realize a real-time data-driven cyber-physical system framework with integrated analytics and parameter optimization capabilities for connected RSW weldability certification. The framework is based on the conceptualization of the layers of the cyber-physical system and can incorporate the design and machine changes. It integrates data from the analytics lifecycle phases, starting from the data collection operation, to the predictive analytics operation, and to the visualization of the design. This integrated framework aims to support decision-makers to understand product design and its manufacturing implications. In addition to data analytics, the proposed framework implements a closed-loop machine parameter optimization considering the target product design. The framework visualizes the target product assembly with predicted response parameters along with displaying the process parameters and material design parameters simultaneously. This layer should help the designers in their decision-making process and the engineers to gain knowledge about the manufacturing processes. A case study on the basis of a real industrial case and data is presented in detail to illustrate the application of the envisioned cyber-physical systems framework.
•Novel approach named Similar Weldment Case Selection (SWCS), which predicts welding results of a new material.•Nugget-size weld-current series (NWS) describes the shape of the relation between ...weldcurrent and nugget size.•Similarity between two NWSs of different materials calculated (quantified) with the dynamic time warping (DTW) method.•SWCS yields superior accuracy than the twelve algorithms do when the two materials are similar or different.
Resistance spot welding (RSW) is a critical joining method in sheet-metal industries. The machine-learning technique fueled by the historical experimental data of the existing materials has been used to build the data-driven model (DDM). The DDM is expected to be a promising tool to investigate a new material and its welding behavior because DDM can narrow the range of the test matrix and can thus reduce the number of necessary physical experiments and the cost. However, one of crucial data quality problems with machine learning is that training data sets’ lack of descriptability for test sets causes poor prediction. This research starts by indicating that such data quality problems that exist in the context of weldment design. To resolve this problem, the presented study introduces a novel approach named Similar Weldment Case Selection (SWCS), which predicts the key parameter, the nugget size, of spot welding results of a new material by selecting the most similar one among the existing welding cases and then constructing a prediction model to generate the results. In order to overcome the difficulties with defining the selection criteria only with the material properties and geometric features, this study has come up with another factor, nugget-size weld-current series (NWS), to consider; the NWS is a factor that describes the shape of the relation between weld-current and nugget size. The similarity between two NWSs of different materials is calculated (quantified) with the dynamic time warping (DTW) method. Initially, the twelve conventional algorithms are tested for varying degrees of descriptability between the two weldment designs for test and train datasets; the prediction accuracies are found to be proportional to the train set’s descriptability on the test set. The results are then compared with those from the SWCS. The SWCS yields superior accuracy than the twelve algorithms do when the two materials are similar or different. However, the superiority disappears when the two are the same.
Abstract
This study aimed to assess the trend of the maintenance status and usability of public automated external defibrillators (AEDs). Public AEDs installed in Seoul from 2013 to 2017 were ...included. An inspector checked the maintenance status and usability of the AEDs annually using a checklist. During the study period, 23,619 AEDs were inspected. Access to the AEDs was improved, including the absence of obstacles near the AEDs (from 90.2% in 2013 to 99.1% in 2017, p < 0.0001) and increased AED signs (from 34.3% in 2013 to 91.3% in 2017, p < 0.0001). The rate of AEDs in normal operation (from 94.0% in 2013 to 97.6% in 2017, p < 0.0001), good battery status (from 95.6% in 2013 to 96.8% in 2017, p = 0.0016), and electrode availability increased (from 97.1% in 2013 to 99.0% in 2017, p < 0.0001); the rate of electrode validity decreased (from 90.0% in 2013 to 87.2% in 2017, p < 0.0001). The overall rate of the non-ready-to-use AEDs and AEDs with less than 24-h usability accounted for 15.4% and 44.1% of the total number of AEDs, respectively. Although most AEDs had a relatively good maintenance status, a significant proportion of public AEDs were not available for 24-h use. Invalid electrodes and less than 24-h accessibility were the main reasons that limited the 24-h usability of public AEDs.
This study aimed to compare the performance of established cardiovascular risk algorithms in Korean patients with new-onset rheumatoid arthritis.
This retrospective cohort study identified patients ...newly diagnosed with rheumatoid arthritis without a history of cardiovascular diseases between 2013 and 2019 using the National Health Insurance Service database. The cohort was followed up until 2020 for the development of the first major adverse cardiovascular event. General cardiovascular risk prediction algorithms, such as the systematic coronary risk evaluation model, the Korean risk prediction model for atherosclerotic cardiovascular diseases, the American College of Cardiology/American Heart Association pooled equations, and the Framingham Risk Score, were used. The discrimination and calibration of cardiovascular risk prediction models were evaluated. Hazard ratios were estimated using Cox proportional hazards regression. A total of 611 patients among 24 889 patients experienced a major adverse cardiovascular event during follow-up. The median 10-year atherosclerotic cardiovascular diseases risk score was significantly higher in patients with major adverse cardiovascular events than those without. The C-statistics of risk algorithms ranged between 0.72 and 0.74. Compared with the low-risk group, the actual risk of developing major adverse cardiovascular events increased significantly in the intermediate- and high-risk groups for all algorithms. However, the risk predictions calculated from all algorithms overestimated the observed cardiovascular risk in the middle to high deciles, and only the systematic coronary risk evaluation algorithm showed comparable observed and predicted event rates in the low-intermediate deciles with the highest sensitivity.
The systematic coronary risk evaluation model algorithm and the general risk prediction models discriminated patients with rheumatoid arthritis appropriately. However, overestimation should be considered when applying the cardiovascular risk prediction model in Korean patients.
•Formalization and representation of 4D printing knowledge for shape/property/functionality changing part design.•Development of a domain ontology based on basic formal ontology.•Spatiotemporal ...understanding of transformable parts/objects over time.
Over the last decade, 4D printing paradigm has received intensive research efforts, whether from researchers in additive manufacturing (AM) or in smart materials (SMs) development. Related research works have thereby generated a large number of ad-hoc solutions with relevant disparate and scattered knowledge. This lack of common core knowledge is mainly due to the multiple involved expertise for fabricating stimulus-reactive structures. The scientific issue of federating and reconciling knowledge is also reinforced especially if such technology must be integrated into the product design process, falling under the field of design for 4D printing. To tackle this challenge, it becomes crucial to formalize and represent knowledge relating AM processes/techniques, SMs behaviours, stimuli and transformation functions with the variety of design objects. In such a context, the paper aims at developing an ontology-based framework for the semantic and logical description of transformable objects in the era of 4D printing for product-process design related purposes. This framework – which is built upon a foundational ontology associated with mereotopology for describing dynamical phenomena called basic formal ontology – consists in introducing a domain ontology equipped with reasoning capabilities supported by description logics for SMs selection and distribution, transformation sequence planning and AM process planning purposes.
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