Collaborative Framework Research Articles

A coalition formation framework for horizontal supply chain collaboration

A coalition formation framework for horizontal supply chain collaboration

ASLAM-FD: a multi-sensor adaptive collaborative fusion SLAM framework based on degradation detection and deep reinforcement learning

Abstract Aiming at the difficulty in ensuring accuracy and performance of existing Simultaneous Localization And Mapping (SLAM) technology based on multi-sensor fusion in complex dynamic environments, a multi-sensor adaptive fusion SLAM framework based on degradation detection and deep reinforcement learning (ASLAM-FD) is proposed. This framework can achieve adaptive collaborative precise adjustment of fusion weights (FWs) based on the real-time self-degradation states and relative degradation states quantified by each sensor, and can adapt to different tightly coupled SLAM algorithms based on FWs. In addition, within this framework, the continuous quantification models for the degradation states of internal/external sensors with certain versatility (we refer to them as EX-DM and IN-DM) is proposed. These quantitative models can achieve continuous quantification of degradation states of various mainstream internal/external sensors. Based on the above sensor degradation state quantification models, this paper further proposes a deep reinforcement learning (DRL) network suitable for adaptive collaborative adjustment of FWs. This network focuses more on the temporal nature of sensor observation data and degradation states, making it more suitable for modeling relationships between data with temporal features. In the experimental section, we adapted the proposed ASLAM-FD to different multi-sensor fusion SLAM algorithms on multiple datasets, and compared it with multiple advanced fusion SLAM algorithms. We found that adapting ASLAM-FD can effectively improve the accuracy and performance of fusion SLAM in complex dynamic environments.

Cybersecurity in the Age of IoT: Business Strategies for Managing Emerging Threats

The quick spread of the Internet of Things (IoT) has essentially changed industries by linking devices, systems, and data. This expansion has, in turn, generated exceptional cybersecurity vulnerabilities, which introduces an urgent requirement for detailed security solutions. This paper analyzes new cybersecurity risks present in the IoT ecosystem and looks into successful business approaches for managing these threats. Using a thorough assessment of current literature, practical case studies, and extensive data analysis, the study discovers essential weaknesses in IoT devices, networks, and infrastructure. The method used collects information from latest incidents related to IoT breaches and evaluates several business strategies designed to meet these challenges. Findings imply that using advanced technologies like artificial intelligence (AI) and machine learning (ML) in monitoring, in conjunction with collaborative cybersecurity frameworks, strongly enhances the security of the IoT. The originality of this research is in its thorough review of the evolving threat landscape and its practical recommendations for enterprises to advance their Internet of Things security position in a quickly changing environment.

Aspects of NCBI GenBank as a Biodiversity Information Resource

DNA sequencing of museum specimens, also known as museomics, provides new insights into the study of biodiversity, including taxonomy, phylogeny, and environmental studies. Also, sequencing specimens have led to the rediscovery of extinct species (Suzuki et al. 2016), identification of related species (Waku et al. 2016), and analysis of ancient DNA (Kanzawa-Kiriyama et al. 2016). Nucleotide sequence data have been collected for more than 30 years under the framework of the International Nucleotide Sequence Database Collaboration (INSDC) by three institutes, namely, National Center for Biotechnology Information, US (NCBI), European Bioinformatics Institute (EBI), and DNA Data Bank of Japan (DDBJ) (Arita et al. 2020). NCBI has collated a database of sequence data, GenBank, which contains approximately 494 million sequences as of April 2022 (Sayers et al. 2021). In fact, GenBank is designed with qualifiers to describe various types of biodiversity information such as "/specimen_voucher", "/lat_lon" (latitude and longitude) and "/collection_date". Also, INSDC now requires that all submissions include the sampling location and date (INSDC 2023). I surveyed the biodiversity information assigned to GenBank records to determine the potential of GenBank as a biodiversity resource. I downloaded all GenBank data as of August 2023 from the FTP site. The “/specimen_voucher” qualifier was introduced to describe specimen ID in Release 104 in December 1997. This qualifier was designed to fill the value in free text: for example, /specimen_voucher="Smith s. n. 4-IV-1995 (U. S. Natl. Herbarium)". After Release 162 in October 2007, a method of writing with a structured value of "[<institution-code>: [<collection-code>:]] <specimen_id>" was added (institution-code and collection-code are optional). There are 527,215 records (37.8%) with "/specimen_voucher" qualifier for fish, 3,096,112 records (40.3%) for insects, 1,505,556 records (39.0%) for flowering plants. But fewer than 10% of records have specimen IDs listed using this structured description. To utilize these ambiguous specimen IDs in GenBank, these IDs may need to be cleansed using databases such as NCBI BioCollections, GRSciColl (Global Registry of Scientific Collections) or AI to map them to IDs in databases rich in specimen information such as those of the Global Biodiveristy Information Facility (GBIF) and Barcode of Life System (BOLD). In GenBank, the BOLD ID is listed in the /db_xref qualifier in the “Features” field as the ID of the external database. The 70% of insect sequence data with a specimen ID in the /specimen_voucher qualifier are also assigned a BOLD ID (Nakazato and Jinbo 2022). The correspondence between specimen IDs in biodiversity information databases such as GBIF and specimen IDs in GenBank is expected to further enhance the value of museum specimens. In addition, GenBank provides the /type_material qualifier for describing the type of voucher (e.g., holotype of Asphondylia bursicola). In GenBank insect data, there were over 2,000 records for type material, and approximately 450 species were mentioned, including 269 for holotypes. We found approximately 3,000 records with type information by including “/notes” and “/specimens_voucher” qualifiers in addition to “/type_material”. Thus, GenBank has potential as a biodiversity information resource, but for more effective use, data mining and linkage with other specimen-based biodiversity databases are essential.

Self-aware collaborative edge inference with embedded devices for IIoT

Edge inference and other compute-intensive industrial Internet of Things (IIoT) applications suffer from a bad quality of experience due to the limited and heterogeneous computing and communication resources of embedded devices. To tackle these issues, we propose a model partitioning-based self-aware collaborative edge inference framework. Specifically, the device can adaptively adjust the local model inference scheme by sensing the available computing and communication resources of surrounding devices. When the inference latency requirement cannot be met by local computation, the model should be partitioned for collaborative computation on other devices to improve the inference efficiency. Furthermore, for two typical IIoT scenarios, i.e., bursting and stacking tasks, the latency-aware and throughput-aware collaborative inference algorithms are designed, respectively. Via jointly optimizing the partition layer and collaborative device selection, the optimal inference efficiency, characterized by minimum inference latency and maximum inference throughput, can be obtained. Finally, the performance of our proposal is validated through extensive simulations and tests conducted on 10 Raspberry Pi 4Bs using popular models. Specifically, in the case of two collaborative devices, our platform reaches up to 92.59% latency reduction for bursting tasks and 16.19× throughput growth for stacking tasks. In addition, the divergence between simulations and tests ranges from 1.64% to 9.56% for bursting tasks and from 3.24% to 11.24% for stacking tasks, which indicates that the theoretical performance analyses are solid. For the general case where the data privacy is not considered and the number of collaborative devices is optimally determined, up to 14.76× throughput speed up and 84.04% latency reduction can be obtained.

The implementation of state's rights and obligations in outer space: is it equal?

The provisions of Outer Space Law govern a state's activities in outer space. The implementation of such activities needs advanced technology, which limits the ability of many countries to explore and exploit outer space. Data indicates that developed nations dominate outer space management, leaving countries without access to sophisticated technology unable to participate fully in space exploration and exploitation. This dominance triggers disparity between developed and developing countries, leading to unequal application of rights and obligations as outlined in Outer Space Law. While the law promotes international collaboration to address this issue, it does not explicitly require developed countries to involve developing nations in outer space activities. This study aims to refine the concept of international collaboration to ensure fair management and exploitation of outer space by all countries. This study is normative in nature, focusing on evolving the concept of international collaboration in outer space. Primary and secondary legal materials and non-legal sources were used as secondary data collected through a literature review and analysed qualitatively. The developed concept emphasises international cooperation and mutual assistance, proposing a framework for collaboration between developed and developing countries. This concept seeks to ensure equitable management of outer space. It reshapes the rights and obligations of states by encouraging all launching states to involve developing countries in space exploration and exploitation.

University-business linkages influence on international business competence: An in-service training perspective

Firms often rely on the skills and mindsets of individual employees and managers rather than on organizational structures and capabilities to execute entrepreneurial activities. These individual-level factors, which may originate from educational and business experiences, also support the development of international business competencies when firms engage in international operations. Accordingly, this study investigates educational collaborations in university-business linkages that influence international business competence at the individual level. Survey data were collected from 489 in-service-trained business executives pursuing (Executive) Master of Business Administration degrees while concurrently holding supervisory and managerial responsibilities in firms in Ghana. Factor analysis revealed four dimensions of educational collaborations in university-business linkages. Results from hypothesis testing, conducted using covariance-based structural equation modeling (CB-SEM), indicate that the diversity and internationality of study content and research dimensions positively and significantly influence these executives' global mindset, entrepreneurial orientation, and international marketing skills in performing foreign business operations. Similarly, interactions with international resource persons positively and significantly influence the executives' global mindset and international marketing skills. However, while participation in international affairs shows positive effects, these are not statistically significant. We discuss these findings and highlight their implications for developing international human capital through a university-business educational collaboration framework useful for executives, business schools, academics, and global higher education development partners.

Effective knowledge representation and utilization for sustainable collaborative learning across heterogeneous systems

AbstractThe increasingly decentralized and private nature of data in our digital society has motivated the development of collaborative intelligent systems that enable knowledge aggregation among data owners. However, collaborative learning has only been investigated in simple settings. For example, clients are often assumed to train solution models de novo, disregarding all prior expertise. The learned model is typically represented in task‐specific forms that are not generalizable to unseen, emerging scenarios. Finally, a universal model representation is enforced among collaborators, ignoring their local compute constraints or input representations. These limitations hampers the practicality of prior collaborative systems in learning scenarios with limited task data that demand constant knowledge adaptation and transfer across information silos, tasks, and learning models, as well as the utilization of prior solution expertise. Furthermore, prior collaborative learning frameworks are not sustainable on a macro scale where participants desire fairness allocation of benefits (e.g., access to the combined model) based on their costs of participation (e.g., overhead of model sharing and training synchronization, risk of information breaches, etc.). This necessitates a new perspective of collaborative learning where the server not only aggregates but also conducts valuation of the participant's contribution, and distribute aggregated information to individuals in commensurate to their contribution. To substantiate the above vision, we propose a new research agenda on developing effective and sustainable collaborative learning frameworks across heterogeneous systems, featuring three novel computational capabilities on knowledge organization: model expression, comprehension, and valuation.

Cultivating Collective Armor: Towards a Collaborative Cybersecurity Resilience Framework for SMEs

As Small and Medium-sized Enterprises (SMEs) increasingly integrate digital technologies into their operations, they face escalating cybersecurity threats that challenge their limited financial and human resources. Historically, cybersecurity was not a primary concern for many SMEs due to their minimal reliance on cyberinfrastructure. However, the growing dependency on digital systems has exposed them to significant risks, necessitating robust cybersecurity measures. This research introduces a cybersecurity resilience framework specifically designed to enhance the cybersecurity posture of SMEs through collaborative efforts. Drawing upon principles of collective intelligence, shared resources, and shared responsibility, the proposed framework promotes a holistic approach that merges advanced technological solutions with collaborative practices among SMEs, industry stakeholders, and governmental entities. By enabling SMEs to pool their cybersecurity resources and capabilities, the framework aims to provide a comprehensive defence against cyber threats that could not be achieved by individual entities alone. The research delves into key collaborative literature to ground the framework in proven strategies and existing successful models of collaboration. It highlights how collective action can be a powerful tool in overcoming the inherent vulnerabilities of SMEs to cyber threats. To validate the effectiveness and applicability of the framework, it went through an expert review process involving cybersecurity professionals and stakeholders in the SME sector. This study not only underscores the necessity for SMEs to adapt to the evolving cybersecurity landscape but also provides a practical blueprint for collective defence. The framework’s emphasis on shared responsibility and mutual aid presents a shift from traditional, isolated cybersecurity approaches to a more integrated and cooperative strategy. This has the potential to significantly enhance the resilience of SMEs against cyber threats, safeguarding their operations and contributing to the overall stability of the digital economy. Future research directions include the application of the framework across various industries and global contexts to evaluate its versatility and impact under different regulatory and threat environments.

Innovating Civil Engineering: Strategies for Fostering Stakeholder Engagement in the Circular Economy

Sustainability and circular economy principles are increasingly important in civil engineering, as highlighted by the United Nations Environmental Programme in 2023 the construction activities represented 36% of global energy use in 2020. However, the sector's focus on reducing operational carbon overshadows the need for addressing embodied carbon from materials like cement, steel, and aluminium. To address this, a shift towards incentivizing all supply chain stakeholders and cultivating collaborative models is necessary. This research provides an initial scoping for the development of the Lithuanian Construction Materials Reuse Optimization (LSEPO) platform, a pioneering regional initiative fostering a marketplace for reused building materials. Such platforms are recognized for their potential to overcome sustainability barriers by utilizing digital technologies for efficient data management and stakeholder collaboration. Our research identifies a significant gap in the existing literature concerning effective stakeholder engagement strategies critical for the success of such platforms. To bridge this gap, we employed a three-tiered methodological approach: firstly, a semi-structured literature analysis delved into existing stakeholder engagement strategies within the circular economy. This review highlighted a fragmented understanding of incentives and collaboration dynamics across different sectors. Secondly, a content analysis of global material exchange and circular construction platforms provided insights into best practices, showcasing successful communication methods, innovative incentive models, and robust collaboration frameworks. Lastly, integrating these findings, our study proposes a conceptual stakeholder engagement framework tailored for social innovation in civil engineering, more specifically Lithuanian Construction Materials Reuse Optimization (LSEPO). Practical implications of our findings underscore the necessity for platforms like LSEPO to incorporate adaptive engagement strategies that are context-sensitive and inclusive, facilitating a broad spectrum of stakeholder inputs. The study demonstrates that effective stakeholder engagement goes beyond traditional communication methods; it requires a systemic integration of technological innovation and incentive alignment to promote sustained participation and collaboration. By providing a comprehensive foundation for the development of the LSEPO platform, this research not only aims to advance it as a beacon of social innovation and environmental stewardship within the civil engineering sector but also sets a benchmark for future initiatives aiming to embed circular economy principles effectively.

A multimodal approach to support teacher, researcher and AI collaboration in STEM+C learning environments

AbstractRecent advances in generative artificial intelligence (AI) and multimodal learning analytics (MMLA) have allowed for new and creative ways of leveraging AI to support K12 students' collaborative learning in STEM+C domains. To date, there is little evidence of AI methods supporting students' collaboration in complex, open‐ended environments. AI systems are known to underperform humans in (1) interpreting students' emotions in learning contexts, (2) grasping the nuances of social interactions and (3) understanding domain‐specific information that was not well‐represented in the training data. As such, combined human and AI (ie, hybrid) approaches are needed to overcome the current limitations of AI systems. In this paper, we take a first step towards investigating how a human‐AI collaboration between teachers and researchers using an AI‐generated multimodal timeline can guide and support teachers' feedback while addressing students' STEM+C difficulties as they work collaboratively to build computational models and solve problems. In doing so, we present a framework characterizing the human component of our human‐AI partnership as a collaboration between teachers and researchers. To evaluate our approach, we present our timeline to a high school teacher and discuss the key insights gleaned from our discussions. Our case study analysis reveals the effectiveness of an iterative approach to using human‐AI collaboration to address students' STEM+C challenges: the teacher can use the AI‐generated timeline to guide formative feedback for students, and the researchers can leverage the teacher's feedback to help improve the multimodal timeline. Additionally, we characterize our findings with respect to two events of interest to the teacher: (1) when the students cross a difficulty threshold, and (2) the point of intervention, that is, when the teacher (or system) should intervene to provide effective feedback. It is important to note that the teacher explained that there should be a lag between (1) and (2) to give students a chance to resolve their own difficulties. Typically, such a lag is not implemented in computer‐based learning environments that provide feedback. Practitioner notesWhat is already known about this topic Collaborative, open‐ended learning environments enhance students' STEM+C conceptual understanding and practice, but they introduce additional complexities when students learn concepts spanning multiple domains. Recent advances in generative AI and MMLA allow for integrating multiple datastreams to derive holistic views of students' states, which can support more informed feedback mechanisms to address students' difficulties in complex STEM+C environments. Hybrid human‐AI approaches can help address collaborating students' STEM+C difficulties by combining the domain knowledge, emotional intelligence and social awareness of human experts with the general knowledge and efficiency of AI. What this paper adds We extend a previous human‐AI collaboration framework using a hybrid intelligence approach to characterize the human component of the partnership as a researcher‐teacher partnership and present our approach as a teacher‐researcher‐AI collaboration. We adapt an AI‐generated multimodal timeline to actualize our human‐AI collaboration by pairing the timeline with videos of students encountering difficulties, engaging in active discussions with a high school teacher while watching the videos to discern the timeline's utility in the classroom. From our discussions with the teacher, we define two types of inflection points to address students' STEM+C difficulties—the difficulty threshold and the intervention point—and discuss how the feedback latency interval separating them can inform educator interventions. We discuss two ways in which our teacher‐researcher‐AI collaboration can help teachers support students encountering STEM+C difficulties: (1) teachers using the multimodal timeline to guide feedback for students, and (2) researchers using teachers' input to iteratively refine the multimodal timeline. Implications for practice and/or policy Our case study suggests that timeline gaps (ie, disengaged behaviour identified by off‐screen students, pauses in discourse and lulls in environment actions) are particularly important for identifying inflection points and formulating formative feedback. Human‐AI collaboration exists on a dynamic spectrum and requires varying degrees of human control and AI automation depending on the context of the learning task and students' work in the environment. Our analysis of this human‐AI collaboration using a multimodal timeline can be extended in the future to support students and teachers in additional ways, for example, designing pedagogical agents that interact directly with students, developing intervention and reflection tools for teachers, helping teachers craft daily lesson plans and aiding teachers and administrators in designing curricula.

Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations.

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework's reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.

Enhancing innovation in aviation: Applying the Triple Helix Model and PMBOK in the UAE-Sweden partnership

To sustain economic growth and generate new ideas for innovation, academia, industries, and governments should cooperate to develop successful research and development ecosystems. Indeed, the collaboration between these parties is required to extend beyond the basic funding provided by industries to research projects conducted by universities. Hence, collaboration must occur in an integrated environment so that each party plays its role including problem identification, setting the regulations, funding, conducting the research, etc. On the other hand, due to globalization, collaboration in research and development has become international where teams from different countries or continents collaborate in various ways. Indeed, the integrated collaboration between industry, governments, and academia improves the impact of the research outcome on society by addressing problems of interest to society while involving all the concerned parties. One of the known frameworks in this context, the Triple Helix Model (THM), has been used in this study to foster international collaboration in aviation. The international collaboration involved two universities representing academia, civil aviation authorities, public funding bodies, and air navigation services representing the government bodies, in addition to aviation industries from Sweden and the United Arab Emirates. The focus of the collaboration was the development of knowledge-based open innovation in the form of a traffic management tool that optimizes, simulates, and visualizes delivery scenarios of unmanned aerial vehicles (UAVs).This paper aims to formalize the collaborative framework that has been used in the international collaboration mentioned above, and that combines the THM and PMBOK models so that it can be used for managing innovation projects of similar nature throughout their lifecycle. Moreover, the challenges that have been encountered by the teams involved in the collaboration are discussed in this paper in addition to the mitigation strategies that have been used to overcome them which can be beneficial for other similar collaborations. Finally, the study results encourage industries, governments, and universities to establish further projects based on the same framework to spur and maximize knowledge-based innovation for social and financial growth.

From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology.

Hematoxylin- and eosin-stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology has enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs by using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages that have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of tumors high in MSI. Moreover, we provide an open-source code base, which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge.

Design and practice of human-machine cooperative international Chinese character teaching in ChatGPT application

The incorporation of artificial intelligence (AI) into language education has created new opportunities for improving the instruction and acquisition of Chinese characters. Nevertheless, the cognitive difficulties linked to the acquisition of Chinese characters, such as their intricate visual features and lack of clear meaning, necessitate thoughtful deliberation when developing AI-supported learning interventions. The objective of this project is to explore the capacity of a collaborative method between humans and machines in teaching Chinese characters, utilising the advantages of both human expertise and AI technology. We specifically investigate the utilisation of ChatGPT, a substantial language model, for the creation of instructional materials and evaluation methods aimed at teaching Chinese characters to individuals who are not native speakers. The study utilises a mixed-methods approach, which involves both qualitative examination of lesson plans created by ChatGPT and quantitative evaluation of student learning outcomes. The results indicate that the suggested framework for human-machine collaboration can successfully tackle the cognitive difficulties associated with learning Chinese characters, resulting in enhanced learner involvement and performance. Nevertheless, the research also emphasises the constraints of AI-generated material and the significance of human involvement in guaranteeing the accuracy and dependability of educational interventions. This research adds to the expanding collection of literature on AI-assisted language learning and offers practical insights for educators and instructional designers who aim to use AI tools into Chinese language curriculum. The results emphasise the necessity of employing a multi-disciplinary strategy in AI-supported language learning, incorporating knowledge from cognitive psychology, educational technology, and second language acquisition.

The roles of AI and educational leaders in AI-assisted administrative decision-making: a proposed framework for symbiotic collaboration

AbstractEducation has always been in a state of flux because of technological disruption. As schools head towards a present in which digital technology is normalised as part of the fabric of everyday society, a post-digital paradigm, Artificial Intelligence (AI) is changing the educational administration and leadership. It is crucial to find ways to coexist with AI while striving for a human-centric future. While numerous studies advocate for a collaborative synergy between AI and human leaders in educational governance, there is a gap in explicating the precise roles they undertake. This paper aims to address the void by providing a conceptual framework for the symbiotic roles of AI and educational leaders in the administrative decision-making process. This paper views administrative decision-making in schools as a political process involving negotiations among administrators, teachers, students, and parents. Within the framework, AI shoulders informational roles such as collecting and analysing data with its data analysis capability. Conversely, educational leaders are tasked with establishing the vision, disseminating information, managing conflicts, identifying new opportunities, negotiating and allocating decision-making authority to stakeholders. Moreover, educational leaders should encourage and supervise the implementation of AI in schools. The framework serves as a theoretical lens for prospective studies of AI impacts on education leadership and administration and bears practical implications for the preparation of future school leaders, contributing to the creation of a scenario where AI augments, rather than replaces educational leadership.

A collaborative cybersecurity framework for higher education

Purpose The purpose of this paper is to present the educational computer emergency response team (EduCERT) framework, an integrated response mechanism to bolster national cybersecurity through collaborative efforts in the higher education sector. The EduCERT framework addresses this gap by enhancing cyber security and mitigating cybercrime through collaborative incident management, knowledge sharing and university awareness campaigns. Design/methodology/approach The authors propose an EduCERT framework following the design science methodology. The framework is developed based on literature and input from focus group experts. Moreover, it is grounded in the principles of the technology-organization-environment framework, organizational learning and diffusion of innovations theory. Findings The EduCERT has eight components: infrastructure, governance, knowledge development, awareness, incident management, evaluation and continuous improvement. The framework reinforces national cybersecurity through cooperation between universities and the National Computer Emergency Response Team. The framework has been implemented in Jordan to generate a cybersecurity foundation for higher education. Evaluating the EduCERT framework’s influence on national cybersecurity highlights the importance of adopting comprehensive cyber-security policies and controls. The framework application shows its relevance, effectiveness, adaptability and alignment with best practices. Research limitations/implications Despite the impact of applying the framework in the Jordanian context, it is essential to acknowledge that the proposed EduCERT framework’s practical implementation may encounter challenges specific to diverse international educational environment sectors. However, framework customization for global applicability could address varied educational institutions in other countries. Practical implications Furthermore, the proposed EduCERT framework is designed with universal applicability that extends beyond the specific country’s context. The principles and components presented in the framework can serve as valuable design advice for establishing collaborative and resilient cybersecurity frameworks in educational settings worldwide. Therefore, the research enhances the proposed framework’s practical utility and positions it as an invaluable contribution to the broader discourse on global cybersecurity in academia. Originality/value This paper enhances national cybersecurity in the higher education sector, addressing the need for a more integrated response mechanism. The EduCERT framework demonstrates its effectiveness, adaptability and alignment with best practices, offering valuable guidance for global educational institutions.

A Comparative Analysis of the Regulatory Framework and Collaborative Environment for Pediatric Medical Device Development in Japan and the United States: Identifying Challenges, Support Mechanisms, and Emerging Opportunities.

There are specific obstacles to designing medical devices for children, such as commercial distortions and regulatory hurdles. This study compares the regulatory frameworks and collaborative ecosystems of the United States (US) and Japan, enabling the development of pediatric medical devices. The study illustrates the differences as well as parallels between the two countries by outlining their primary obstacles, channels of assistance, and prospects. The regulatory regimes of the US and Japan pose substantial challenges due to their intricate approval procedures and the dearth of pediatric-specific guidelines. However, while Japan's ecosystem is more dispersed, the US offers more well-established support mechanisms, such as funding initiatives and research alliances. Despite these difficulties, there are still prospects for innovative thinking and collaborative work. The article demonstrates successful partnerships between business, academia, and government in both countries, which have helped propel the advancement of pediatric devices. For stakeholders endeavoring to maneuver through thecomplex terrain of pediatric medical device development, this study offers valuable insights. With a comprehensive understanding of the regulatory and collaborative frameworks in Japan and the US, developers can more effectively leverage resources, overcome obstacles, and bring vital technology to market. The study's findings have implications for researchers, industry leaders, and policy regulators, providing recommendations for strategies aimed at stimulating innovation and enhancing children's healthcare outcomes internationally.

Rethinking service blueprint for digital coopetition: A new framework for networked collaboration

This study adapts traditional service blueprint methodologies for technology-driven coopetition networks, where companies simultaneously collaborate and compete. Integrating insights from service science, we developed an enhanced service blueprint framework with three key components: the cyber frontstage Lane for digital interactions, the physical backstage Lane for physical operations, and the support stage lane for supporting processes. Empirical validation in the Portuguese stone sector demonstrated the framework’s effectiveness in identifying network dysfunctions and its ease of use for industry professionals. Feedback highlights its relevance in capturing the complexities of modern digital coopetition and managing interactions and resources. This research underscores the necessity of updating service blueprint methods to optimize service delivery and value co-creation in digitally evolving sectors.

Teaching Catholic Religion in Croatian Public Schools: Legal Frame and Challenges

This paper explores the Croatian framework of collaboration between the state and the Catholic Church in the educational field, specifically the teaching of Catholic religion in public schools. First, the statute of the subject of religion was analyzed, following what is established in the Agreement with the Holy See and its Development Agreement with the Croatian Conference of Bishops. Some challenges to this peculiar model of cooperation were revised, mainly the ones regarding the enrollment and the co-responsibility of both ecclesiastical and state authorities in the approval and control of programs and contents. Second, the status of religion teachers was also evaluated. The same ecclesiastical and state authorities also have competence over the teaching staff, from their training to the control of their work, and must confront inconvenient situations related to the suitability of these professionals. In the study of each section and its corresponding rules and practices, it was possible to outline singular aspects, in some cases recognized—or rejected—judicially as discriminatory. This approximation to the right to religious education intends to examine the practice observed by the respective authorities in applying such provisions. The obtained findings and proposals about the singular Croatian model provide a useful contribution for comparative purposes.