Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
87,767
result(s) for
"Research projects"
Sort by:
Clustering networked funded European research activities through rank-size laws
This paper treats a well-established public evaluation problem, which is the analysis of the funded research projects. We specifically deal with the collection of the research actions funded by the European Union over the 7th Framework Programme for Research and Technological Development and Horizon 2020. The reference period is 2007–2020. The study is developed through three methodological steps. First, we consider the networked scientific institutions by stating a link between two organizations when they are partners in the same funded project. In doing so, we build yearly complex networks. We compute four nodal centrality measures with relevant, informative content for each of them. Second, we implement a rank-size procedure on each network and each centrality measure by testing four meaningful classes of parametric curves to fit the ranked data. At the end of such a step, we derive the best fit curve and the calibrated parameters. Third, we perform a clustering procedure based on the best-fit curves of the ranked data for identifying regularities and deviations among years of research and scientific institutions. The joint employment of the three methodological approaches allows a clear view of the research activity in Europe in recent years.
Journal Article
Real‐life research projects improve student engagement and provide reliable data for academics
Student engagement can have a positive influence on student success. Many methods exist for fostering engagement but tend to be generic and require tailoring to specific contexts, subjects, and students. In the case of undergraduate science students, practical classes are a popular tool for increasing engagement. However, despite strong potential for improvement via links with “real life” research projects (RLRPs), few academic staff incorporate research participation with teaching activities. This is potentially due to poor time availability and low opinions of students' ability to collect reliable data. This study aims to examine whether involvement with RLRPs can generate reliable scientific data and also act as a motivational tool for engaging tertiary science students. A preexisting core activity for first‐year biology and marine biology students was modified to include a short RLRP component. Student‐based data collection and a questionnaire about experiences were used to examine the reliability of student‐collected data and student perceptions of RLRPs. Results indicated that error rate in student‐collected data was minimal. Irrespective of participating in a “normal” practical class or a class with a RLRP component, students collected equally accurate data. However, when the topic aligned specifically with their degree subject, student accuracy was higher. All students surveyed reported high motivation with the idea of RLRP participation, placing high importance on this from an educational and employability perspective. Yet, students were not confident about participating in RLRPs until they had engaged with one, suggesting that introducing such projects into taught sessions early‐on may encourage students to seek further opportunities in the future. In conclusion, incorporating RLRPs into the curriculum of undergraduate science courses has considerable potential benefits for both students and academic staff. Few academic staff involve undergraduate students in research activities, possibly due to low opinions of students' ability to collect reliable data. This study demonstrated that the error rate in student‐collected data was minimal and that students found the idea of participating in research projects highly motivating. Merging research and teaching activities has considerable potential benefits for both students and academics.
Journal Article
Destruction of the North China Craton
A National Science Foundation of China （NSFC） major research project, Destruction of the North China Craton （NCC）, has been carried out in the past few years by Chinese scientists through an in-depth and systematic observations, experiments and theoretical analyses, with an emphasis on the spatio-temporal distribution of the NCC destruction, the structure of deep earth and shallow geological records of the craton evolution, the mechanism and dynamics of the craton destruction. From this work the foUowing conclusions can be drawn： （1） Significant spatial heterogeneity exists in the NCC lithospheric thickness and crustal structure, which constrains the scope of the NCC destruction. （2） The nature of the Paleozoic, Mesozoic and Cenozoic sub-continental lithospheric mantle （CLM） underneath the NCC is characterized in detail. In terms of water content, the late Mesozoic CLM was rich in water, but Cenozoic CLM was highly water deficient. （3） The correlation between magmatism and surface geological response confirms that the geological and tectonic evolution is governed by cratonic destruction processes. （4） Pacific subduction is the main dynamic factor that triggered the destruction of the NCC, which highlights the role of cratonic destruction in plate tectonics.
Journal Article
A case study on the relationship between risk assessment of scientific research projects and related factors under the Naive Bayesian algorithm
This paper delves into the nuanced dynamics influencing the outcomes of risk assessment (RA) in scientific research projects (SRPs), employing the Naive Bayes algorithm. The methodology involves the selection of diverse SRPs cases, gathering data encompassing project scale, budget investment, team experience, and other pertinent factors. The paper advances the application of the Naive Bayes algorithm by introducing enhancements, specifically integrating the Tree-augmented Naive Bayes (TANB) model. This augmentation serves to estimate risk probabilities for different research projects, shedding light on the intricate interplay and contributions of various factors to the RA process. The findings underscore the efficacy of the TANB algorithm, demonstrating commendable accuracy (average accuracy 89.2%) in RA for SRPs. Notably, budget investment (regression coefficient: 0.68, P < 0.05) and team experience (regression coefficient: 0.51, P < 0.05) emerge as significant determinants obviously influencing RA outcomes. Conversely, the impact of project size (regression coefficient: 0.31, P < 0.05) is relatively modest. This paper furnishes a concrete reference framework for project managers, facilitating informed decision-making in SRPs. By comprehensively analyzing the influence of various factors on RA, the paper not only contributes empirical insights to project decision-making but also elucidates the intricate relationships between different factors. The research advocates for heightened attention to budget investment and team experience when formulating risk management strategies. This strategic focus is posited to enhance the precision of RAs and the scientific foundation of decision-making processes.
Journal Article
Toward a more perfect university
Cole \"identifies the ways America's great universities should evolve in the decades ahead to maintain their global preeminence and enhance their intellectual stature and social mission as higher education confronts the twenty-first-century developments in technology, humanities, culture, and economics\"--Dust jacket flap.
Book
A method for managing scientific research project resource conflicts and predicting risks using BP neural networks
This study begins by considering the resource-sharing characteristics of scientific research projects to address the issues of resource misalignment and conflict in scientific research project management. It comprehensively evaluates the tangible and intangible resources required during project execution and establishes a resource conflict risk index system. Subsequently, a resource conflict risk management model for scientific research projects is developed using Back Propagation (BP) neural networks. This model incorporates the Dropout regularization technique to enhance the generalization capacity of the BP neural network. Leveraging the BP neural network’s non-linear fitting capabilities, it captures the intricate relationship between project resource demand and supply. Additionally, the model employs self-learning to continuously adapt to new scenarios based on historical data, enabling more precise resource conflict risk assessments. Finally, the model’s performance is analyzed. The results reveal that risks in scientific research project management primarily fall into six categories: material, equipment, personnel, financial, time, and organizational factors. This study’s model algorithm exhibits the highest accuracy in predicting time-related risks, achieving 97.21%, surpassing convolutional neural network algorithms. Furthermore, the Root Mean Squared Error of the model algorithm remains stable at approximately 0.03, regardless of the number of hidden layer neurons, demonstrating excellent fitting capabilities. The developed BP neural network risk prediction framework in this study, while not directly influencing resource utilization efficiency or mitigating resource conflicts, aims to offer robust data support for research project managers when making decisions on resource allocation. The framework provides valuable insights through sensitivity analysis of organizational risks and other factors, with their relative importance reaching up to 20%. Further research should focus on defining specific strategies for various risk factors to effectively enhance resource utilization efficiency and manage resource conflicts.
Journal Article