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This research analyses challenges faced by users at various levels in planning and designing participatory simulation models of cities. It aims to identify issues that hinder experts from maximising the effectiveness of the SUMO tool. Additionally, evaluating current methods highlights their strengths and weaknesses, facilitating the use of participatory simulation advantages to address these issues. Finally, the presented case studies illustrate the diversity of user groups and emphasise the need for further development of blueprints.

In this research, action research was used to assess and improve a step-by-step guideline. The guideline's conceptual design is based on stakeholder analysis results from those involved in developing urban logistics scenarios and feedback from potential users. A two-round process of application and refinement was conducted to evaluate and enhance the guideline's initial version.

The guidelines still demand an advanced skill level in simulation modelling, rendering them less effective for the intended audience. However, they have proven beneficial in a simulation course for students, emphasising the importance of developing accurate conceptual models and the need for careful implementation.

This paper introduces a step-by-step guideline designed to tackle challenges in modelling urban logistics scenarios using SUMO simulation software. The guideline's effectiveness was tested and enhanced through experiments involving diverse groups of students, varying in their experience with simulation modelling. This approach demonstrates the guideline's applicability and adaptability across different skill levels.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

This case study paper aims to explore the complexities and challenges of epidemic response and public health surveillance in Native American and Indigenous American communities in the United States and find viable solutions. This paper explores these topics through the emergence and impact of the hantavirus pulmonary syndrome (HPS) within the Navajo Nation in the United States using critical incident analysis and best practices.

This project is a case study paper based on a topical review of the literature. A topical review of the literature is a comprehensive exploration of the current body of knowledge within a particular research field. It is an important tool used by scholars and practitioners to further the development of existing knowledge as well as to identify potential directions for future research (Fourie, 2020). Such a paper can provide a useful insight into the various aspects of the process that the researcher may have overlooked, as well as highlighting potential areas of improvement (Gall et al., 2020). It can also provide a useful source of ideas and inspiration for the researcher as it can provide an overview of the various approaches used by other researchers in the field (Göpferich, 2009). Case study papers using a topical review of the literature have been used to help frame and inform research topics, problems and best practices for some time. They are typically used to explore a topic in greater depth and to provide an overview of the literature to improve the world of practice to provide a foundation for future comprehensive empirical research. Case study papers can provide research value by helping to identify gaps in the literature and by providing a general direction for further research. They can also be used to provide a starting point for research questions and hypotheses and to help identify potential areas of inquiry.

This study explores best practices in public health surveillance and epidemic response that can help strengthen public health infrastructure by informing the development of effective surveillance systems and emergency response plans, as well as improving data collection and analysis capabilities within Native American and Indigenous American communities in the United States that also have the option to include new technologies like artificial intelligence (AI) with similar outbreaks in the future.

The literature review did not include any primary data collection, so the existing available research may have limited the findings. The scope of the study was limited to published literature, which may not have reported all relevant findings. For example, unpublished studies, field studies and industry reports may have provided additional insights not included in the literature review. This research has significant value based on the limited amount of studies on how infectious diseases can severely impact Native American communities in the United States, leading to unnecessary and preventable suffering and death. As a result, research on viable best practices is needed on the best practices in public health surveillance and epidemic response in Native American and Indigenous American communities through historical events and critical incident analysis.

Research on public health surveillance and epidemic response in Native American communities can provide insights into the challenges faced by these communities and help identify potential solutions to improve their capacity to detect, respond to and prevent infectious diseases using innovative approaches and new technologies like AI.

More research on public health surveillance and epidemic response can inform policies and interventions to improve access to healthcare for Native American populations, such as increasing availability of healthcare services, providing culturally appropriate health education and improving communication between providers and patients. By providing better public health surveillance and response capacity, research can help reduce the burden of infectious diseases in Native American communities and ultimately lead to improved public health outcomes.

This study aims to uncover the ongoing discourse on generative artificial intelligence (AI), literacy and governance while providing nuanced perspectives on stakeholder involvement and recommendations for the effective regulation and utilization of generative AI technologies.

This study chooses generative AI-related online news coverage on BBC News as the case study. Oriented by a case study methodology, this study conducts a qualitative content analysis on 78 news articles related to generative AI.

By analyzing 78 news articles, generative AI is found to be portrayed in the news in the following ways: Generative AI is primarily used in generating texts, images, audio and videos. Generative AI can have both positive and negative impacts on people’s everyday lives. People’s generative AI literacy includes understanding, using and evaluating generative AI and combating generative AI harms. Various stakeholders, encompassing government authorities, industry, organizations/institutions, academia and affected individuals/users, engage in the practice of AI governance concerning generative AI.

Based on the findings, this study constructs a framework of competencies and considerations constituting generative AI literacy. Furthermore, this study underscores the role played by government authorities as coordinators who conduct co-governance with other stakeholders regarding generative AI literacy and who possess the legislative authority to offer robust legal safeguards to protect against harm.

District-university partners increasingly rely on “grow-your-own” licensure programs to address teacher shortages. Because vacancies in special education represent a chronic issue, our district-university partnership developed LEAP – the Licensed Educators’ Accelerated Pathway, successfully preparing 26 paraprofessionals as special education teachers (SEs). We describe a model university-district partnership in which we collaborated to design and implement paraprofessionals’ SE licensure program.

In this general review, we describe a district-university partnership collaboration that resolved barriers experienced by paraprofessionals working toward licensure in special education (Essential #4, Reflection and Innovation). The specialized design and partnership solutions were grounded in SE preparation research literature.

25 (28 entered the program and 25 completed) paraprofessionals from one large urban and several regional districts completed special education licensure through LEAP. Slightly more than half of LEAP participants were Black or Hispanic (see Table 1), contributing to the diversification of SE workforce. University-district partnership was successful in designing and delivering a program that allowed participants: a) to remain employed, b) attend evening classes in their geographic region or online, c) complete all field experiences in sponsoring districts (Essential #2) and d) receive concierge advising from a “completion coach.” We describe solutions to barriers experienced by paraprofessionals and advocate for district-university collaboration to address chronic teacher shortages.

Limitations include lack of data on success of program completers during their first year of teaching as they began this work in Fall 2023. Further, because the participating district was large and urban, generalization of program details for small and rural districts is difficult.

Practical tips for developing grow-your-own special education licensure programs are providing. Detailed descriptions of barriers candidates experienced and ways the district-university partners resolved these issues are included. Programs like the one described has the potential to positively impact teacher pipeline issues.

The program described provided highly-trained teachers to fill chronic vacancies in special education in three participating districts/agencies. Because students receiving special education services are at risk for school failure and are disproportionately impacted by teacher turnover, addressing this area through grow-your-own licensure programs represents a diversity, equity and inclusion initiative. Further, upskilling diverse paraprofessionals to licensed teacher roles represent an economic boost, which they might not otherwise have achieved.

Available research literature signals alarm over persistent teacher shortages in hard-to-staff districts and lack of diversity in the teacher workforce, but few published accounts describe successful programs. Partner collaboration fostered a re-imagining of course formatting and delivery to accommodate adult learners, avoiding problems often reported with alternative programs.

This article focuses on regional-level cooperation in higher education by examining the functional, organizational and political approaches (FOPA) framework for higher education regionalization and using supra-national regional universities as established and successful examples of regional-level higher education cooperation among countries.

A conceptual framework is used to provide the structure for analyzing the key approaches to higher education regionalization, followed by an analysis of supra-national regional universities to demonstrate the application of the model.

The FOPA framework for higher education regionalization includes three approaches. The first is the functional approach, which includes both collaborative academic and research activities among higher education institutions as well strategies and policies to help align systems across a region. The second is the organizational approach, which focuses on networks, organizations, institutions and programs, which facilitate partnerships. The third is the political approach, which includes regional-level agreements, declarations and strategic plans to promote higher education collaboration. Key higher education activities for each approach are discussed in generic terms, with examples provided from major regions of the world.

The research was based on desk research only. No interviews were conducted.

A conceptual analysis and a model were provided for the concept of regionalization of higher education and for regional universities, which can help readers locate their interests and research in the regionalization of higher education. Examples of three different types of regional universities were provided to give concrete illustrations of a regional university.

One of the rationales driving regional universities is to address and increase a sense of regional identify and to meet the social, economic and educational needs of the specified region.

Regional universities, such as the University of West Indies, Arab Open University and the Pan-Africa University, are an understudied phenomenon. Using them as innovative and sustainable examples of higher education regional cooperation and the FOPA model, this study illustrates how single-campus, multiple-campus and virtual regional universities are functioning to meet the diversified needs and priorities across a region through cooperation among countries.

This study empirically tests the use of loan loss provisions (LLPs) for earnings and capital smoothing when emphasis is laid on banks' riskiness and adoption of the International Financial Reporting Standards (IFRSs) in Nigeria.

Annual bank-level data are hand-extracted between 2007 and 2017 from annual reports of a sample 16 deposit money banks (DMBs), and analysed using appropriate panel regression models subsequent to a number of diagnostic tests including heteroscedasticity, autocorrelation and cross-sectional dependence. The use of both reported LLPs (TLLP) and discretionary LLPs (DLLP) for earnings and capital management is tested to advance the practice in the literature.

Generally, the study finds that Nigerian DMBs manage capital via LLPs, while mixed results are obtained for earnings smoothing. However, during IFRS, Nigerian DMBs' management of capital is identifiable with TLLP, while smoothing of earnings is peculiar to DLLP. Additionally, evidence of the improvement in loan loss reporting quality expected during IFRS for riskier Nigerian DMBs, could not be attained. This is corroborated by the study's findings of the use of both TLLP and DLLP for earnings and capital management during IFRS by DMBs in solvency crisis against the only use of TLLP to manage capital found for the entire period.

The evidential capital and earnings lopsidedness may subject Nigerian DMBs' going-concern to a lot of questions.

The study sets a foremost record in the empirical test of managerial opportunistic behaviour embedded in earnings and capital concurrently while accounting for loan losses by all categories of Nigerian DMBs in terms of riskiness, following accounting regime change.