In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to ex...
In the process design and reuse of marine component products, there are a lot of heterogeneous models, causing the problem that the process knowledge and process design experience contained in them are difficult to express and reuse. Therefore, a process knowledge representation model for ship heterogeneous model is proposed in this paper. Firstly, the multi-element process knowledge graph is constructed, and the heterogeneous ship model is described in a unified way. Then, the multi-strategy ontology mapping method is applied, and the semantic expression between the process knowledge graph and the entity model is realized. Finally, by obtaining implicit semantics based on case-based reasoning and checking the similarity of the matching results, the case knowledge reuse is achieved, to achieve rapid design of the process. This method provides reliable technical support for the design of ship component assembly and welding process, greatly shortens the design cycle, and improves the working efficiency. In addition, taking the double-deck bottom segment of a ship as an example, the process knowledge map of the heterogeneous model is constructed to realize the rapid design of ship process, which shows that the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship.
Mechanoluminescence (ML) is one kind of mechanical-tooptical energy conversion, and a strategy toward achieving bright elastic ML emission is strongly anticipated for ML application. Herein, a ternary-host strategy is...
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Mechanoluminescence (ML) is one kind of mechanical-tooptical energy conversion, and a strategy toward achieving bright elastic ML emission is strongly anticipated for ML application. Herein, a ternary-host strategy is proposed for synthesizing heterojunction ML materials with eyevisible elastic ML under an ultralow pressure of 2 N, in which ZnS, CaCO3, and SrCO3 are employed as precursors to prepare the ternary-host (Ca0.5Sr0.5)ZnOS/xZnS. Upon partially substituting the precursor ZnS with MnCO3, the Mn2+ activator could introduce luminescent centers and carrier traps into the ternary-host (Ca0.3Sr0.5)ZnOS/xZnS and enable achieving a series of (Ca0.5Sr0.5)ZnOS/xZnS/Mn2+ phosphors with bright and pressure-sensitive elastic ML. The (Ca0.5Sr0.5)ZnOS/xZnS/Mn2+ phosphors not only exhibit eyevisible red ML under ambient conditions without any preirradiation but can also display a linearly enhanced ML intensity along with the enlarged external force at a 2 N interval as well as regeneratable ML upon repetitively applying an external force 20 times. The maximum emission wavelength of ML can be conveniently modulated from 623 to 658 nm by adjusting the ZnS content of (Ca0.5Sr0.5)ZnOS/xZnS/Mn2+ phosphors.
In recent years, pregnant women in China generally face problems such as unbalanced and excessive nutrition, lack of proper exercise during pregnancy, which shows a significant increase in weight during pregnancy, lea...
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In recent years, pregnant women in China generally face problems such as unbalanced and excessive nutrition, lack of proper exercise during pregnancy, which shows a significant increase in weight during pregnancy, leading to an increasing trend of perinatal complications. At present, there is less sports health management system for pregnant women throughout pregnancy. In view of this, based on the existing research in related fields, this study deeply explores the appropriate monitoring methods of pregnant women's sports in China. In this study, effective and convenient testing methods and evaluation criteria were proposed for pregnant women's sports and psychology. The research idea was a method based on the pulse wave to detect pregnant women's cardiac reserve capacity, and grading the Diastolic/Systolic value of pregnant women as a reference for grading individualized target heart rate range of moderate intensity exercise. The effective time and energy expenditure of pregnant women were assessed by monitoring the exercise process. The purpose is to help and guide pregnant women in the whole process of pregnancy self-movement management, and thus improve the quality of maternal health care services in China.
To construct a nomogram based on clinical factors and paraspinal muscle features to predict vertebral fractures occurring after acute osteoporotic vertebral compression fracture (OVCF). We retrospectively enrolled 307...
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To construct a nomogram based on clinical factors and paraspinal muscle features to predict vertebral fractures occurring after acute osteoporotic vertebral compression fracture (OVCF). We retrospectively enrolled 307 patients with acute OVCF between January 2013 and August 2022, and performed magnetic resonance imaging of the L3/4 and L4/5 intervertebral discs (IVDs) to estimate the cross-sectional area (CSA) and degree of fatty infiltration (FI) of the paraspinal muscles. We also collected clinical and radiographic data. We used univariable and multivariable Cox proportional hazards models to identify factors that should be included in the predictive nomogram. Post-OVCF vertebral fracture occurred within 3, 12, and 24 months in 33, 69, and 98 out of the 307 patients (10.8%, 22.5%, and 31.9%, respectively). Multivariate analysis revealed that this event was associated with percutaneous vertebroplasty treatment, higher FI at the L3/4 IVD levels of the psoas muscle, and lower relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. Area under the curve values for subsequent vertebral fracture at 3, 12, and 24 months were 0.711, 0.724, and 0.737, respectively, indicating remarkable accuracy of the nomogram. We developed a model for predicting post-OVCF vertebral fracture from diagnostic information about prescribed treatment, FI at the L3/4 IVD levels of the psoas muscle, and relative CSA of functional muscle at the L4/5 IVD levels of the multifidus muscle. This model could facilitate personalized predictions and preventive strategies.
The electrochemiluminescence (ECL) bioassay is prominently carried out with the involvement of the coreactant. To remove the detrimental effects of the coreactant on the ECL of luminophores, herein, a promising ECL im...
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The electrochemiluminescence (ECL) bioassay is prominently carried out with the involvement of the coreactant. To remove the detrimental effects of the coreactant on the ECL of luminophores, herein, a promising ECL immunoassay strategy with biocompatible nanoparticles as the luminophore is proposed, which involves directly and electrochemically oxidizing the luminophore methionine-capped Au (Met@Au) nanocrystals (NCs) without the participation of any coreactant. Met@Au NCs are a kind of n-type nanoparticles, and they can be electrochemically injected with valence band (VB) holes around +0.80 and +1.10 V (vs Ag/AgCl). The electrochemically injected exogenous VB hole can recombine with the endogenous conduction band electron of Met@Au NCs and eventually bring out two coreactant-free and near-infrared ECL processes around 0.80 V (ECL-1) and 1.10 V (ECL-2). The intensity of coreactant-free ECL is primarily determined by the electrochemical oxidation-induced hole-injection process. ECL-2 is considerably stronger than ECL-1 and can be exploited for determining the carcinoembryonic antigen (CEA) in a sandwich immunoassay procedure with a linear range from 0.1 to 50 pg/mL as well as a limit of detection of 0.03 pg/mL (S/N = 3).
Background Transformer is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of beginning to use transformer-based models in medical langua...
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Background Transformer is an attention-based architecture proven the state-of-the-art model in natural language processing (NLP). To reduce the difficulty of beginning to use transformer-based models in medical language understanding and expand the capability of the scikit-learn toolkit in deep learning, we proposed an easy to learn Python toolkit named transformers-sklearn. By wrapping the interfaces of transformers in only three functions (i.e., fit, score, and predict), transformers-sklearn combines the advantages of the transformers and scikit-learn toolkits. Methods In transformers-sklearn, three Python classes were implemented, namely, BERTologyClassifier for the classification task, BERTologyNERClassifier for the named entity recognition (NER) task, and BERTologyRegressor for the regression task. Each class contains three methods, i.e., fit for fine-tuning transformer-based models with the training dataset, score for evaluating the performance of the fine-tuned model, and predict for predicting the labels of the test dataset. transformers-sklearn is a user-friendly toolkit that (1) Is customizable via a few parameters (e.g., model_name_or_path and model_type), (2) Supports multilingual NLP tasks, and (3) Requires less coding. The input data format is automatically generated by transformers-sklearn with the annotated corpus. Newcomers only need to prepare the dataset. The model framework and training methods are predefined in transformers-sklearn. Results We collected four open-source medical language datasets, including TrialClassification for Chinese medical trial text multi label classification, BC5CDR for English biomedical text name entity recognition, DiabetesNER for Chinese diabetes entity recognition and BIOSSES for English biomedical sentence similarity estimation. In the four medical NLP tasks, the average code size of our script is 45 lines/task, which is one-sixth the size of transformers' script. The experimental results show that transformers-skl
Constructing three-dimensional (3D) conductive frameworks with high specific surface areas and porous structures is indispensable for their applications as electrocatalysts. In this work, we illustrate for the first t...
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Constructing three-dimensional (3D) conductive frameworks with high specific surface areas and porous structures is indispensable for their applications as electrocatalysts. In this work, we illustrate for the first time that 3D N-doped porous carbon nanosheets (3D-NS), which are synthesized via a facile one-pot pyrolysis reaction using glucose and melamine as raw materials, can serve as high performance and green electrocatalysts for the reduction of H2O2. Moreover, a series of 3D-NS samples with a controllable content of nitrogen were obtained by adjusting the calcination temperature. From our research, the 3D-NS obtained at 900 degrees C possessed high specific surface areas, porous structures, proper dosages of N atoms, suitable degrees of graphitization and defects. Furthermore, we also illustrate their application in H2O2 electrochemical sensing in physiological environments. Under optimum conditions, the 3D-NS-based sensor displays a wide linear scope in the range of 0.5-14 000 mu M and a low detection limit of 0.18 mu M (S/N = 3). Therefore, with desirable selectivity, stability and anti-interference performance, the proposed sensor can be feasibly applied to detect H2O2 in human serum samples.
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