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Copyright of Progress in Modern Biomedicine is the property of Publishing House of Progress in Modern Biomedicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Obejective: To explore the predictive value of Caprini thrombosis risk score and serum D-dimer (D-D), vascular endothelial growth factor (VEGF), platelet-derived growth factor B (PDGF-B) for postoperative deep vein thrombosis (DVT) in patients with traumatic fracture, and to construct risk prediction model. Methods: 216 hospitalized patients with traumatic fracture admitted to The Affiliated Hospital of Xuzhou Medical University from June 2019 to May 2022 were selected as the research objects. According to the occurrence of postoperative DVT, they were divided into DVT group (62 cases) and non-DVT group (154 cases). The differences of Caprini thrombosis risk score, serum D-D, VEGF, PDGF-B and clinical data between the two groups were compared. Multivariate Logistic regression analysis was used to identify the risk factors of DVT, the prediction model was established, and the model was validated, and the receiver operating characteristic (ROC) curve was drawn for efficacy evaluation. Results: The occurrence rate of postoperative DVT in patients with traumatic fracture was 28.70%. The proportion of patients in the DVT group with the time from fracture to admission greater than 24 hours were more than those in the non-DVT group, and the prothrombin time (PT), activated partial thromboplastin time (APTT), D-D, VEGF, PDGF-B and Caprini thrombosis risk score were higher than those in the non-DVT group (P<0.05). Multivariate Logistic regression analysis showed that the time from fracture to admission greater than 24 hours, high levels of serum D-D, VEGF, PDGF-B and increased Caprini thrombosis risk score were independent risk factors for the occurrence of postoperative DVT in patients with traumatic fracture (P<0.05). Logistic regression analysis was used to analyze the 5 independent risk factors of the occurrence of postoperative DVT in patients with traumatic fracture, and the prediction model equation was established as follows: Logit (P) =-0.171 +1.170×time from fracture to admission +1.041×serum D-D +0.046×serum VEGF +0.100×serum PDGF-B +0. 080×Caprini thrombosis risk score, the internal verification results showed that the C-index count was 0.825 (95%CI: 0.740~0.892), and the Calibration curve showed that the Calibration curve was close to the ideal curve (P>0.05). External validation used this study sample: the area under curve (AUC) was 0.901, the sensitivity was 0.887, and the specificity was 0.870, which showed that the model had high predictive efficiency, it was significantly higher than the predictive efficacy of D-D, VEGF, PDGF-B and caprini thrombus risk score alone. Conclusion: The time from fracture to admission, serum D-D, VEGF, PDG and Caprini thrombosis risk score were the influencing factors of the occurrence of postoperative DVT in patients with traumatic fracture, the constructed prediction model had good predictive efficiency for the occurrence of DVT. [ABSTRACT FROM AUTHOR]

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Environmental Science & Technology (10036504) is the property of Editorial Board of Environmental Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In space manufacturing system is a kind of exoatmospheric, distributed manufacturing system which coordinates the ground-based factories, in-orbit space factories, and space transportation vehicles and aims at space facilities construction. Key issues to realize intelligent in-orbit space manufacturing are the modeling of the distributed scheduling systems as well as the designing of the efficient and effective optimization methods. In this paper, an in-orbit space intelligent manufacturing system with distributed ground manufacturing and transportation, ground-air batch transportation and in-orbit assembly process is addressed. The scheduling of the system is modeled as multi-stage distributed scheduling problem, which is decomposed into four subproblems, i.e., distributed homogeneous flow shop scheduling, parallel machines scheduling with transportation time, single machine batch scheduling problem with release time, machine available time and machine capacity constraints, and single machine scheduling problem with release time and precedence constraints, with respect to the criterion of minimizing of the maximum completion time. In addition, the recently proposed I Ching philosophy inspired optimization (ICO) which originally focused on continuous optimization is extended to solve combinatorial optimization, and ICO based memetic algorithm (ICO-MA) is proposed to solve the aforementioned scheduling problem. Experimental results on middle scale and large scale instances show the proposed algorithm is effective and efficient compared with the state-of-the-art algorithms, e.g., particle swarm optimization, teaching learning based optimization and water wave optimization. The proposed ICO-MA could be a feasible and effective algorithm to solve distributed multi-stage scheduling problems. To the best of our knowledge, it is the first study on distributed scheduling of in-orbit space intelligent manufacturing system. [ABSTRACT FROM AUTHOR]

In this paper, the bibliometric method is used to sort out and analyze the development of business model study by statistical analysis and citation networks. Empirical results show the latest hot topics in the business model study focus on business model diagnosis, innovation and design. Three future trends are proposed including the quantification of theoretical research, the extension of the research range and the informatization of market research. The perspective and conclusion of business model in this paper will be particularly illuminating for both academic research and enterprises1 practice application. [ABSTRACT FROM AUTHOR]