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Embedding dimension determination in phase space reconstruction is difficult. The purpose of this paper is to present a new approach for embedding dimension determination based on empirical mode, showing that embedding dimensions for phase space reconstruction could be easily determined according to the number of intrinsic mode functions decomposed by empirical mode decomposition.

Through the relation analysis of intrinsic mode functions and embedding dimensions, the approach for embedding dimension determination by the number of intrinsic mode functions is presented. First, a time series is decomposed into several intrinsic mode functions. Second, correlation analysis between intrinsic mode functions and original signals is investigated, and then false intrinsic mode functions could be eliminated by the analysis of correlation coefficient thresholds, which makes the embedding dimension precise. Finally, the method presented is applied to the Lorenz system, Chen's system, and the Duffing equation. Simulation results prove this method is feasible.

A new approach for embedding dimension determination based on empirical mode decomposition is presented. Compared with G‐P algorithms, this new method is effective and decreases computational complexity.

This method provides an effective qualitative criterion to the selection of embedding dimensions in phase space reconstruction.

This method could be used to determine embedding dimensions of phase space reconstruction and degree‐of‐freedom of nonlinear dynamical systems.

The paper proposes a new method of embedding dimension determination in phase space reconstruction.

Online question-and-answer (Q&A) communities serve as important channels for knowledge diffusion. The purpose of this study is to investigate the dynamic development process of online knowledge systems and explore the final or progressive state of system development. By measuring the nonlinear characteristics of knowledge systems from the perspective of complexity science, the authors aim to enrich the perspective and method of the research on the dynamics of knowledge systems, and to deeply understand the behavior rules of knowledge systems.

The authors collected data from the programming-related Q&A site Stack Overflow for a ten-year period (2008–2017) and included 48,373 tags in the analyses. The number of tags is taken as the time series, the correlation dimension and the maximum Lyapunov index are used to examine the chaos of the system and the Volterra series multistep forecast method is used to predict the system state.

There are strange attractors in the system, the whole system is complex but bounded and its evolution is bound to approach a relatively stable range. Empirical analyses indicate that chaos exists in the process of knowledge sharing in this social labeling system, and the period of change over time is about one week.

This study contributes to revealing the evolutionary cycle of knowledge stock in online knowledge systems and further indicates how this dynamic evolution can help in the setting of platform mechanics and resource inputs.

The purpose of this research aims at extending the knowledge on whether and how universities include sustainability dimensions in managing their collections. Precisely, the study focusses on the creation of a university museum (UM), as an embryonic stage of life during which management concerns both strategic and operational issues.

Sustainability is envisioned as a multifaceted concept, composed of the economic, cultural, environmental and social dimensions. Resorting to an acknowledged theoretical model for sustainable development in museum management, a qualitative interpretative study is carried out, gathering data from multiple sources. The empirical setting is the University of Pavia, which has recently created a new Museum of Natural History (Kosmos).

Results highlight how sustainability dimensions intertwin in UM creation. Moreover, the economic dimension emerges as a basement for the others. Value for the community, expressed in economic terms, must be ensured in UMs creation as well as throughout its entire life, in order to support cultural, environmental and social sustainability.

Focussing on the embryonic stage of UMs life allowed to consider how sustainability is embedded in relevant strategic and operational decisions. Nevertheless, scholars are encouraged to replicate the study in other stages of UMs' life, in a way to provide insights on its dynamics.

University collections managers can benefit from this research by acknowledging the role played by the economic dimension of sustainability. Notwithstanding their mission, universities should pay attention to extracting economic value from the management of their collections, as a means to ensure innovative and sustainable management on the cultural, environmental and social respects. Furthermore, this research suggests how a higher education system is able to create a new museum by relying on interdisciplinary competencies, which support sustainability since the embryonic stage.

This research contributes to the cultural heritage management literature by proposing an updated version of the sustainable development model for museums, which highlights the different relevance of the sustainability dimensions with particular regard to the UM creation and management.

MITRE and the National Security Agency cooperatively developed and maintained a D3FEND knowledge graph (KG). It provides concepts as an entity from the cybersecurity countermeasure domain, such as dynamic, emulated and file analysis. Those entities are linked by applying relationships such as analyze, may_contains and encrypt. A fundamental challenge for collaborative designers is to encode knowledge and efficiently interrelate the cyber-domain facts generated daily. However, the designers manually update the graph contents with new or missing facts to enrich the knowledge. This paper aims to propose an automated approach to predict the missing facts using the link prediction task, leveraging embedding as representation learning.

D3FEND is available in the resource description framework (RDF) format. In the preprocessing step, the facts in RDF format converted to subject–predicate–object triplet format contain 5,967 entities and 98 relationship types. Progressive distance-based, bilinear and convolutional embedding models are applied to learn the embeddings of entities and relations. This study presents a link prediction task to infer missing facts using learned embeddings.

Experimental results show that the translational model performs well on high-rank results, whereas the bilinear model is superior in capturing the latent semantics of complex relationship types. However, the convolutional model outperforms 44% of the true facts and achieves a 3% improvement in results compared to other models.

Despite the success of embedding models to enrich D3FEND using link prediction under the supervised learning setup, it has some limitations, such as not capturing diversity and hierarchies of relations. The average node degree of D3FEND KG is 16.85, with 12% of entities having a node degree less than 2, especially there are many entities or relations with few or no observed links. This results in sparsity and data imbalance, which affect the model performance even after increasing the embedding vector size. Moreover, KG embedding models consider existing entities and relations and may not incorporate external or contextual information such as textual descriptions, temporal dynamics or domain knowledge, which can enhance the link prediction performance.

Link prediction in the D3FEND KG can benefit cybersecurity countermeasure strategies in several ways, such as it can help to identify gaps or weaknesses in the existing defensive methods and suggest possible ways to improve or augment them; it can help to compare and contrast different defensive methods and understand their trade-offs and synergies; it can help to discover novel or emerging defensive methods by inferring new relations from existing data or external sources; and it can help to generate recommendations or guidance for selecting or deploying appropriate defensive methods based on the characteristics and objectives of the system or network.

The representation learning approach helps to reduce incompleteness using a link prediction that infers possible missing facts by using the existing entities and relations of D3FEND.

In this paper chaos is viewed as an alternative approach to modeling complex and random appearing behavior. The spatial (static) characteristics of weekly returns and price levels for eleven International Indices are quantified. We find evidence that all countries exhibit similar static characteristics. Evidence presented supports the examination of price series instead of returns.

This paper aims to investigate the relationship between the protean career and other variables, including organizational learning climate, individual calling work orientation, and demographic variables.

The research data were obtained from a sample consisting of 292 employees of two South Korean manufacturing companies in the private sector. To collect the research data, this study employed the web survey method.

The study results showed that two organizational learning climates – embedded system and system connection – and calling orientation had significant positive relationship with the protean career. Demographic variables did not relate significantly to the protean career.

This paper provides an empirical approach to related environmental and psychological variables influencing the protean career based on the literature review.

The results have implications for both researchers and practitioners in that the study examines the protean career as it relates to the organizational learning climate and provides suggestions for establishing strategies that foster employees' self‐directed career management attitudes.

This paper offers new and useful insight into the predictors of self‐directed career management by exploring variables related to the protean career.

Research assessment has long been important for directing research funding, rationalizing research organizations and enhancing productivity, including concentrating on specialized subjects. But due to a lack of data, research assessment procedures centered on simple indicators that solely included publications and their citation counts. The Dimensions is one such prodigy of technological evolution like the internet in discovering the research data metrics.

This paper outlines Dimensions, the emergence of Dimensions by partnering with various development partners into a single robust platform and provides directions on implementing a free tool for research insights: Dimensions badge.

The Dimensions platform for research insights pulls together data on financing, publications, policy, patents and grants.

This tool is freely available to libraries worldwide.

Drawing on the natural resource-based view (NRBV) of the firm, this study aims to investigate the mediating role of sustainability performance management (SPM) practices in the relationship between corporate sustainability strategy (SS) and sustainability performance (SP). The conceptualization of SS and SPM practices follow the NRBV resources and capabilities to promote sustainability for competitiveness.

Data for the study were collected through a questionnaire from 114 small-medium to large organizations within environmentally sensitive industries operating in Malaysia.

The results indicate the indirect relationship between SS and SP through SPM practices. The results suggest that SS can only be realized through a broader management accounting control system (such as SPM practices) that provides information to generate, analyze and control environmental, social, economic and governance performance.

As some organizations may face their resource constraints, this study may help managers and management accountants prioritize their focus on SS and adopt the necessary SPM practices to enhance their SP.

This study sheds new light on the role of the SPM practices adopted by firms to manage their SS.

This study aims to establish the friction vibration model.

The friction vibration experiment was carried out on a pin disk friction tester. The causes of friction vibration are discussed, and the friction vibration model is established based on the energy method.

The experimental and simulation results show that the main cause of friction vibration is the nonlinear change of friction coefficient; degree of the friction vibration has a positive relationship with the friction relative velocity and normal contact positive pressure; the proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration.

The proposed friction vibration model is highly consistent in chaotic attractor and time-frequency distribution map and can well predict friction vibration.