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With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

This practitioner-focused article highlights a collaborative, school-wide project at a PDS that showcased elementary students’ strengths and talents. Based on the children’s book, The Smart Cookie (John, 2021), teachers and the university professor-in-residence developed professional learning communities, which inspired the creation of a space for all students to demonstrate ways in which they were “smart cookies” that aligned with our comprehensive mission of promoting inclusive practices.

Rooted in professional learning communities, teachers at our PDS spent the first half of the school year learning about chosen topics of social–emotional learning, stamina and neurodiversity. The Smart Cookie Project was created to demonstrate the connections between these topics. Students at the PDS were given the opportunity to create an original project that showcased their creativity, interests and talents. Projects were then displayed during a schoolwide showcase.

The impact of the project and the showcase demonstrated the importance of creating opportunities for both teacher and student innovation. The project brought the community together, allowed students to be viewed through strengths-based perspectives, helped teachers see how their own learning can positively impact their practice and emphasized the need for honoring student choice in the classroom.

The project discussed here can lend itself to fellow PDSs looking to adopt innovative instructional approaches, honor inclusive practices and situate students in places of strength.

The current study is designed to investigate the factors that foster the framing of destination competitiveness and establish the factors that drive the contribution of tourism innovations to economic growth in smart tourism destinations.

A four-year panel data were extracted from the World Economic Forum's travel and tourism competitiveness index and data were analysed using Poisson Pseudo Maximum Likelihood regression model.

The findings demonstrate that both the enabling environment and airport infrastructure significantly affect tourism's impact on the economy of the selected smart European tourism destinations. Conversely, human resources and general infrastructure display a negative correlation with tourism's contribution to the economy. However, no data in the sample support the idea that tourism policies, government prioritization or readiness of tourism information and communication technologies impact tourism's contribution to the economy. Additionally, the marginal effects indicate that improving the enabling environment and airport infrastructure can generate additional benefits for the economy through tourism.

The uniqueness of this study is the integration of smart tourism destinations with the measure of destination competitiveness to provide an empirical bridge that links tourism competitiveness to economic growth.

The purpose of this study is to examine the state of research into adoption of machine learning systems within the health sector, to identify themes that have been studied and observe the important gaps in the literature that can inform a research agenda going forward.

A systematic literature strategy was utilized to identify and analyze scientific papers between 2012 and 2022. A total of 28 articles were identified and reviewed.

The outcomes reveal that while advances in machine learning have the potential to improve service access and delivery, there have been sporadic growth of literature in this area which is perhaps surprising given the immense potential of machine learning within the health sector. The findings further reveal that themes such as recordkeeping, drugs development and streamlining of treatment have primarily been focused on by the majority of authors in this area.

The search was limited to journal articles published in English, resulting in the exclusion of studies disseminated through alternative channels, such as conferences, and those published in languages other than English. Considering that scholars in developing nations may encounter less difficulty in disseminating their work through alternative channels and that numerous emerging nations employ languages other than English, it is plausible that certain research has been overlooked in the present investigation.

This review provides insights into future research avenues for theory, content and context on adoption of machine learning within the health sector.

The proposal is to answer the following question: based on previous studies, which are the new paths and challenges related to the circular economy (CE) and Industry 4.0 (I4.0)? To answer this question, the research objective is to analyze studies approaching the interface between CE and I4.0.

A systematic literature review (SLR) was conducted on previously published studies pertaining to Scopus and Web of Science bases, and 63 articles were found.

The authors present five new paths and challenges amid the relationship between CE and I4.0: applying those technologies to clean production, using blockchain and big data in the circular supply chain, raising additive manufacturing impact on the CE, seek for a better understanding on how I4.0 technologies can properly support the CE in the stakeholders' view and discerning the factors for implementing those theoretical fields onto supply chains.

Previous studies' sample basis is still recent, lacking research depth. Search strings might have minimized the number of selected studies: there could be a bigger sample.

Practical contributions of this study lay on the applicability of the raised propositions into several sectors' industries.

The authors suggest a transition agenda towards CE, using I4.0 technologies for operational, tactical and strategic personnel within organizations, as well as potential utilization strategies in specific study fields, like supply chain management and product manufacturing per se.

The study presents new paths and challenges amid technologies pertaining to I4.0 and its interfaces with the CE. In the result presentation and analysis, the existing interfaces are described.

This study aims to provide a measurement model, and the underlying constructs and items, for Logistics 4.0 in manufacturing companies. Industry 4.0 technology for logistics processes has been termed Logistics 4.0. Logistics 4.0 and its elements have seen varied conceptualizations in the literature. The literature has mainly focused on conceptual and theoretical studies, which supports the notion that Logistics 4.0 is a relatively young area of research. Refinement of constructs and building consensus perspectives and definitions is necessary for practical and theoretical advances in this area.

Based on a detailed literature review and practitioner focus group interviews, items of Logistics 4.0 for manufacturing enterprises were further validated by using a large-scale survey with practicing experts from organizations located in Central Europe, the Northeastern United States of America and Northern Thailand. Exploratory and confirmatory factor analyses were used to define a measurement model for Logistics 4.0.

Based on 239 responses the exploratory and confirmatory factor analyses resulted in nine items and three factors for the final Logistics 4.0 measurement model. It combines “the leveraging of increased organizational capabilities” (factor 1) with “the rise of interconnection and material flow transparency” (factor 2) and “the setting up of autonomization in logistics processes” (factor 3).

Practitioners can use the proposed measurement model to assess their current level of maturity regarding the implementation of Logistics 4.0 practices. They can map the current state and derive appropriate implementation plans as well as benchmark against best practices across or between industries based on these metrics.

Logistics 4.0 is a relatively young research area, which necessitates greater development through empirical validation. To the best of the authors knowledge, an empirically validated multidimensional construct to measure Logistics 4.0 in manufacturing companies does not exist.