•The chloride transport in concrete influenced by drying-wetting cycles, stress ratios and exposed ages was studied.•The relationship between chloride concentration in concrete and exposed ages was ...presented.•The simplified transport model of chloride ion in concrete was established.
Alternation of drying and wetting led to the worst corrosion of the engineering structures in marine environments. In this paper, the transport of chloride ions in concrete under loads and drying-wetting cycles was investigated. The influences of drying-wetting cycles, stress ratios and exposed ages of concrete on chloride ions transport in concrete were studied. The relationships between the coefficient of chloride ion diffusion and stress ratios of concrete under the loads and drying-wetting cycles were obtained. The relationship between the chloride ion concentration of concrete surface and exposed ages of concrete was studied. And the simplified transport model of chloride ions in concrete under loads and drying-wetting cycles was established, of which the validity was verified through the good agreement with the experimental data.
Objectives
Hippocampal characterization is one of the most significant hallmarks of Alzheimer’s disease (AD); rather, the single-level feature is insufficient. A comprehensive hippocampal ...characterization is pivotal for developing a well-performing biomarker for AD. To verify whether a comprehensive characterization of hippocampal features of gray matter volume, segmentation probability, and radiomics features could better distinguish AD from normal control (NC), and to investigate whether the classification decision score could serve as a robust and individualized brain signature.
Methods
A total of 3238 participants’ structural MRI from four independent databases were employed to conduct a 3D residual attention network (3DRA-Net) to classify NC, mild cognitive impairment (MCI), and AD. The generalization was validated under inter-database cross-validation. The neurobiological basis of the classification decision score as a neuroimaging biomarker was systematically investigated by association with clinical profiles, as well as longitudinal trajectory analysis to reveal AD progression. All image analyses were performed only upon the single modality of T1-weighted MRI.
Results
Our study exhibited an outstanding performance (ACC = 91.6%, AUC = 0.95) of the comprehensive characterization of hippocampal features in distinguishing AD (
n
= 282) from NC (
n
= 603) in Alzheimer’s Disease Neuroimaging Initiative cohort, and ACC = 89.2% and AUC = 0.93 under external validation. More importantly, the constructed score was significantly correlated with clinical profiles (
p
< 0.05), and dynamically altered over the AD longitudinal progression, provided compelling evidence of a solid neurobiological basis.
Conclusions
This systemic study highlights the potential of the comprehensive characterization of hippocampal features to provide an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.
Key Points
• The comprehensive characterization of hippocampal features exhibited ACC = 91.6% (AUC = 0.95) in classifying AD from NC under intra-database cross-validation, and ACC = 89.2% (AUC = 0.93) in external validation.
• The constructed classification score was significantly associated with clinical profiles, and dynamically altered over the AD longitudinal progression, which highlighted its potential of being an individualized, generalizable, and biologically plausible neuroimaging biomarker for early detection of AD.
With the development of various network technologies and the spread of coronavirus disease 2019, many online learning platforms have been built. However, some of them may negatively impact student ...learning outcomes. Therefore, this study aims to improve the online learning effect of students by comprehensively evaluating their learning behavior by using deep learning algorithms. On this basis, new teaching strategies are proposed. According to the structured deep network embedding model, a network representation learning algorithm is proposed with the help of auto-encoders under deep learning. This study elaborates the concept and structure of the encoder model and tests its performance. After the node labels and dataset are trained, the applicable parameter λ
2
of the model is 0.3. During the teaching process, the model’s reliability in distinguishing users is examined. Therefore, this model can be applied to network teaching, is an innovative teaching strategy, and provides a theoretical basis for improving teaching methods.
Cigarette smoking (CS) leads to significant bone loss, which is recognized as an independent risk factor for osteoporosis. The number of smokers is continuously increasing due to the addictive nature ...of smoking. Therefore it is of great value to effectively prevent CS-induced osteoporosis. However, there are currently no effective interventions to specifically counteract CS-induced osteoporosis, owing to the fact that the specific mechanisms by which CS affects bone metabolism are still elusive. This review summarizes the latest research findings of important pathways between CS exposure and bone metabolism, with the aim of providing new targets and ideas for the prevention of CS-induced osteoporosis, as well as providing theoretical directions for further research in the future.
•Self-supervised deep learning computes E-fields induced by TMS.•E-fields are computed by solving the governing PDE directly.•Self-supervised deep learning obtains E-fields with high accuracy and ...efficiency.
Electric fields (E-fields) induced by transcranial magnetic stimulation (TMS) can be modeled using partial differential equations (PDEs). Using state-of-the-art finite-element methods (FEM), it often takes tens of seconds to solve the PDEs for computing a high-resolution E-field, hampering the wide application of the E-field modeling in practice and research. To improve the E-field modeling's computational efficiency, we developed a self-supervised deep learning (DL) method to compute precise TMS E-fields. Given a head model and the primary E-field generated by TMS coils, a DL model was built to generate a E-field by minimizing a loss function that measures how well the generated E-field fits the governing PDE. The DL model was trained in a self-supervised manner, which does not require any external supervision. We evaluated the DL model using both a simulated sphere head model and realistic head models of 125 individuals and compared the accuracy and computational speed of the DL model with a state-of-the-art FEM. In realistic head models, the DL model obtained accurate E-fields that were significantly correlated with the FEM solutions. The DL model could obtain precise E-fields within seconds for whole head models at a high spatial resolution, faster than the FEM. The DL model built for the simulated sphere head model also obtained an accurate E-field whose average difference from the analytical E-fields was 0.0054, comparable to the FEM solution. These results demonstrated that the self-supervised DL method could obtain precise E-fields comparable to the FEM solutions with improved computational speed.
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Motivated by the call of the International Maritime Organization to meet the emission targets of 2030, this study considers two important practical aspects of quay crane scheduling: efficiency and ...energy consumption. More precisely, we introduce the bi-objective quay crane scheduling problem where the objective is to minimize the vessel’s completion time and the crane’s energy consumption. This is done by formulating a bi-objective mixed-integer programming model. A branch-and-bound algorithm was developed as the exact solution approach to find the full set of Pareto-optimal solutions. We consider (i) various lower bounds for both objectives, (ii) specific upper bounds, (iii) additional branching criteria, and (iv) fathoming criteria to detect Pareto-optimal solutions. Numerical experiments on benchmark instances show that the branch-and-bound algorithm can efficiently solve small- and medium-sized problems.
The purpose of the study is to promote college students to actively respond to the national "Public Entrepreneurship and Mass Innovation" policies and calls, improve college students' entrepreneurial ...enthusiasm and their entrepreneurial ability, and cultivate their good entrepreneurial psychological states. First, the relevant content of entrepreneurship psychology and causal attribution theory is displayed. Second, the questionnaire of college students' entrepreneurship education is formulated and a questionnaire survey is conducted on University N based on the relevant content of entrepreneurship psychology. Subsequently, the management system of new venture A is taken as the research object to construct the management strategy of new ventures and simulate the implementation process. Finally, the questionnaire survey results of college students' entrepreneurship education are analyzed and the corresponding entrepreneurship education path is formulated. Meanwhile, the implementation effect of the management strategy of new ventures is evaluated. After the questionnaire is sorted out, it is found that there are some problems in college students' entrepreneurship education, such as weak awareness of entrepreneurship, insufficient publicity, outdated curriculum, and unqualified teachers. The reasons for these problems are the constraints of traditional concepts, insufficient attention, and incomplete system construction. Therefore, a plan is made for overall entrepreneurship education, the publicity of the concept of entrepreneurship education is strengthened, and the setting of entrepreneurship education curriculum and the ability of the teachers for entrepreneurship education are improved. Through the evaluation of the simulation implementation of a new enterprise management strategy, it is found that the new management strategy can achieve the expected effect. Therefore, this study provides some references for the development of college students' entrepreneurship education and the management strategy of new ventures.
Educational innovation reform is used as the background. In response to the need to propose innovative educational programs, the concepts of Distributed Deep Neural Network (DDNN) and deep learning ...under edge computing are used as the basis. A teaching program for Science Technology Engineering Mathematics (STEM) is proposed. The average training method is used to verify the performance of the model. Sampling rate means the number of samples per second taken from a continuous signal to form a discrete signal. The accuracy and sample ratio obtained are higher than 95%. The communication volume is 309 bytes, which is in a good range. On this basis, a university uses STEM teaching plans and questionnaires to influence the psychological mobilization factors of students' deep learning effects. Challenging learning tasks and learning motivation have the greatest impact on deep learning, and conclusions that both are positive effects are obtained. Therefore, STEM innovative teaching programs can be widely used. The plan provides a reference theory for improving teaching innovation in the context of the basic educational curriculum reform in China. STEM curriculum is the dual subject of teachers and students, and the learning community includes multi-stakeholders. There are hierarchical relationships among the subjects. In terms of financial support, the first two funds come from the school. Learning communities have dedicated sponsorship partners complemented by clear financial planning. There is not much difference in course resources. Still, the learning community will provide more diversified media forms and special websites, and other auxiliary resources are open to all users. They can obtain first-hand resources without applying. In terms of project form, in addition to the core classroom teaching, the latter two can provide richer activities and realize the diversity of time, space, and information exchange.
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