Collaborative Framework Research Articles

The Effect of Product Innovation on the Performance of Food SMEs: The Moderator Effect of Quadruple Helix

Small and Medium Enterprises (SMEs) play a vital role in driving Indonesia's economy, contributing significantly to GDP and employment. However, SMEs, particularly in the food sector, face challenges such as changing market demands, limited resources, and restricted access to innovation support systems. This study investigates the influence of product innovation on the performance of food SMEs in Malang Regency and examines the moderating effect of the Quadruple Helix framework, which involves collaboration among academia, business, government, and mass media. Using a quantitative research design, data were collected from 335 food SMEs registered with the SME Business Communication Forum (FKPU) and analyzed using Warp-PLS SEM to evaluate structural and measurement models. The findings reveal that product innovation significantly enhances SME performance, as reflected in improved customer satisfaction, expanded market share, increased revenue, and profitability. The Quadruple Helix actors were found to play critical roles in amplifying this relationship. Academia supports SMEs by bridging research and practice, offering training, and providing operational tools. Government interventions such as subsidies, tax incentives, and promotional activities foster a conducive environment for innovation. Businesses contribute by mentoring, sharing market knowledge, and opening new opportunities. Mass media amplifies these efforts by promoting innovative products, disseminating market information, and connecting SMEs with broader consumer bases. This study highlights the importance of integrating collaborative frameworks into innovation ecosystems to ensure sustainable SME growth. While the research provides valuable insights for policymakers and practitioners, it is limited to food SMEs in Malang Regency. Future studies should explore other sectors and regions to validate these findings and uncover additional dimensions of the Quadruple Helix framework's impact.

Bibliometric Analysis of Silvopasture Systems: Integrating Trees, Livestock, and Crops

Silvopasture systems, which integrate trees, livestock, and crops, are gaining recognition for their ecological and socioeconomic benefits. This bibliometric study examines the progression of silvopasture research, emphasizing key contributors, major thematic areas, and its alignment with the United Nations Sustainable Development Goals (SDGs). The three primary questions it aims to address are: how silvopasture research has evolved in terms of publication trends and key contributors; which thematic areas are most prevalent in the field and how they relate to SDGs, especially those that deal with poverty alleviation, food security, climate action, and biodiversity conservation; and what ecological and socioeconomic impacts are most commonly seen. Data for this research were drawn from Scopus, spanning the years 2013 to 2023. Citation networks, co-authorship relationships, and keyword co-occurrence were analyzed using VOSviewer software to provide a clear depiction of research patterns and collaborative frameworks. The analysis identified influential researchers like Bruno C. Pedreira and Dalton H. Pereira, who have significantly contributed to advancing silvopasture studies.The study identified many important topic clusters, including as carbon sequestration, pasture management, agronomic practices, and biodiversity conservation. The potential of silvopasture to address important global concerns in sustainable agriculture is highlighted by these themes, which are well aligned with SDGs 1, 2, 12, 13, and 15. The relevance of silvopasture in promoting sustainable development is highlighted by its ecological and economic advantages, which include improved soil health, increased biodiversity, and increased resistance to climatic variability. The study highlights the need for more research to fully comprehend these benefits and how they affect rural development. Keywords: Silvopasture; Sustainable Agriculture; Sustainable Development Goals (SDGs); Ecological Impacts; Socioeconomic Benefits

Curriculum Design based on Outcome-Based Education Concept: A Case Study of the C Programming Course

This study, grounded in the principles of Outcome-Based Education (OBE), presents the design and implementation of a practical course syllabus for the C Programming Language within an industry-university collaboration framework. The syllabus development emphasizes the core tenets of "student-centered learning, outcome orientation, and continuous improvement." By aligning curriculum design with market demands, adopting effective teaching methodologies, and establishing a robust evaluation system, the syllabus addresses students' practical needs and professional aspirations, ensuring the comprehensive achievement of course objectives. The study incorporates process evaluation via the PTA platform and summative evaluation through system development projects, employing Rubric scoring criteria to holistically assess students' programming knowledge, skills, and attitudes. The findings reveal that the syllabus design and pedagogical approach significantly enhance students' competencies in programming, providing valuable insights and a reference model for talent cultivation in software engineering education.

Building Inclusive Growth Frameworks through Strategic Community Engagement in Energy Infrastructure Development Projects

Energy infrastructure development is pivotal in driving economic growth and improving societal well-being. However, the benefits of such projects often fail to reach marginalized communities, exacerbating social and economic inequalities. This paper explores the concept of inclusive growth as a framework for equitable development in the energy sector, emphasizing the role of strategic community engagement in fostering participation, mitigating risks, and ensuring sustainable outcomes. The discussion outlines the principles and challenges of inclusivity, identifies key socio-economic and environmental considerations, and proposes actionable frameworks for policy alignment, stakeholder collaboration, and technological integration. By addressing potential risks and highlighting governance structures that facilitate equitable decision-making, the paper underscores the importance of inclusivity as a cornerstone of sustainable energy development. Practical recommendations for policymakers, developers, and community leaders are provided to create energy projects that prioritize shared progress and environmental stewardship.

COMCARE: A Collaborative Ensemble Framework for Context-Aware Medical Named Entity Recognition and Relation Extraction

The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems.

Higher Education and Its Contribution to Economies of African Countries: Move Towards Competence-Based and Skills Demand-Driven Standards in Collaboration with Industry

This study explores the ecosystemic impacts of higher education (HE) on the economies of African countries, emphasizing the need for competence-based, and skills-demand-driven standards in collaboration with industry. HE is vital for equipping individuals with essential knowledge and skills for socio-economic transformation. However, in Africa, this role has weakened, with industry assuming a leading position. Curricula in HE institutions are slow to adapt to the skills needed by industries, leading to a range of challenges such as outdated curriculum delivery, desertion of technical and vocational training, inadequate research resources, insufficient collaboration frameworks between HE and industries, minimal support for entrepreneurship, and poor infrastructure. Aligning HE curricula with industry skills requirements is crucial for enhancing African economic development and competitiveness. Unfortunately, there is a notable lack of partnerships and practical mechanisms for curriculum integration among African HE institutions, which results in graduates possessing skills that do not meet industry demands. This paper reviews the extensive literature on HE's role in African economies, advocating for in-depth collaboration between HE and industry in order to tackle skills mismatches. Accordingly, establishing a healthy partnership between HE institutions and industries could facilitate work-integrated learning, encourage industry-led curriculum development, and prepare graduates with applicable skills and relevant knowledge for the job market. Thus, developing a proactive framework that can facilitate and enforce collaboration between higher education and industries could be critical in addressing the challenges faced by African economic development.

Shifting from Burden Sharing to Task Sharing: Advancing Community-Initiated Care in MHPSS for Refugee Resettlement

The complexities of refugee resettlement in the U.S. require comprehensive mental health and psychosocial support (MHPSS) strategies, yet significant gaps persist due to resettlement policies prioritizing short-term self-sufficiency over long-term mental health and well-being. This study explores the shift from traditional “burden sharing” to “task sharing” models that emphasize community-initiated care (CIC). Using a two-phase qualitative method, 27 refugee leaders and bilingual service providers from 14 refugee communities, including Afghan, Bhutanese, Burmese, Congolese, Somali, and Sudanese communities, across four states participated in focus groups or key informant interviews. Thematic analysis revealed that peer support models play a critical role as bridges between cultures, service sectors, and formal and informal networks. However, peer support remains insufficient without structural reform, as refugees face barriers such as lack of professional development and power imbalances with professionals while managing their own life challenges. This study emphasizes formalizing CIC models that empower refugees to lead the sharing process in collaborative care. Intersectoral collaboration and supportive policy frameworks are necessary to sustain peer support and build long-term leadership capacity. The findings highlight the need for a system-level shift to ensure equitable responsibility for care, fostering sustainable, community-driven MHPSS solutions in refugee resettlement.

Shared Decision-Making in Solid Organ Transplantation: A Review

Solid organ transplantation entails numerous complex medical and ethical decisions. Shared decision-making (SDM) has been advocated as the optimal model for navigating these decisions, providing a collaborative framework that enhances person-centered care. This approach involves patients, caregivers, and healthcare professionals in the decision-making process, ensuring that clinical decisions align with patient preferences, values, and individual circumstances alongside clinical indications. This paper reviews the implementation of SDM throughout the transplantation journey, from diagnosis and transplant referral, pre-transplant assessments, waiting lists, to the organ offer, perioperative period, and long-term follow-up. Barriers to SDM include factors at the patient, provider, and system levels, including inadequate patient–provider communication. Effective SDM requires tailored educational resources, prognostic tools, clinician training, collaborative care models, and supportive policies. Additionally, leveraging technology, such as artificial intelligence and mobile applications, can enhance patient engagement and decision quality. SDM promotes equity by involving all patients—including those from more vulnerable groups—in meaningful conversations about their treatment options, thereby mitigating disparities in access and outcomes. Future research should focus on the long-term impacts of SDM interventions, the development of comprehensive prognostic tools incorporating patient-reported outcomes, and systemic changes to integrate SDM into clinical practice, aiming to improve patient outcomes and person-centered care.

AI-driven knowledge discovery: Developing a human–machine collaborative framework for learning Japanese sentence patterns

AI-driven knowledge discovery: Developing a human–machine collaborative framework for learning Japanese sentence patterns

The Metabolic Cost Analysis of Human Walking with a Robotic Lower-limb Exoskeleton

Lower limb exoskeletons are widely studied to provide assistance for both disabled and healthy person. The assisting efficacy would be more significant if the assisting strategy is more in line with the movement characteristics of different wearers. However, how to optimize the timing and magnitude of the assisting torque so as to provide different wearers with more personalized, comfortable and balanced assistance is still not well tackled. To address the issue, an assisting strategy with adjustable assisting timing and magnitude was designed in this paper. First, we designed a framework for smart human-exoskeleton collaboration in which machine learning methods and human-in-the-loop methodology are combined to optimize the assisting timing and magnitude. Second, an assisting strategy based on hip joints trajectory, which is embedded in the framework was designed to separately plan the assisting torque for hip flexion and extension. Third, the efficacy of the designed method was both verified with quantitative and qualitative results. Quantitative results showed that the designed method reduced the net metabolic cost of walking during load carriage by 2.68% on average. And questionnaire survey results showed that the designed assisting strategy provided subjects with more significant, comfortable and balanced assistance.

Interprofessional education and collaborative practice with practicing radiographers: A mixed methods scoping review.

There is increasing evidence substantiating the advantages of Interprofessional Education and Collaborative Practice (IPECP) in healthcare. Despite this, global adoption is still in its infancy. Whilst there has been some recognition of the importance of collaborative practice in healthcare, implementation of IPECP programs remain limited in many countries. This scoping review aimed to synthesise global evidence for the implementation and effectiveness of IPECP on practicing radiographers and to further identify the enablers and barriers to the implementation of IPECP within radiography. The JBI guidelines for the conduct of scoping reviews and the PRISMA guidelines for reporting scoping reviews were followed. Databases searched included Medline, CINAHL, Scopus, Embase, Cochrane library, and JBI. Grey literature was searched through Google, Google Scholar, and the ProQuest Dissertations and Theses Global. Following full text screening, 21 articles were included in the review, and data was extracted onto a custom-developed template. IPECP competencies identified in the included studies were mapped against the Canadian Interprofessional Health Collaborative (CIHC) framework of competencies. Results indicate that while certain factors such as peer support and interprofessional communication facilitated IPECP, numerous barriers impeded its implementation on a wider scale. Implications for practice, policy and research include the need to prioritise funding for IPECP initiatives and to establish regulatory frameworks that support interprofessional collaboration.

Collaborative Healthcare Data Management Framework using Parallel Computing and the Internet of Things

Healthcare data management has become a critical research area, primarily driven by the widespread adoption of personal health monitoring systems and applications. These systems generate an immense volume of data, necessitating efficient and reliable management solutions for lossless sharing. This article introduces a Collaborative Data Management Framework (CDMF) that leverages the combined strengths of parallel computing and federated learning. The proposed CDMF is designed to achieve two primary objectives: reducing computational complexity in data handling and ensuring high sharing accuracy, regardless of the data generation rate. The framework employs parallel computing to streamline the scheduling and processing of data acquired at various intervals. This approach minimizes processing delays by operating on a less complex scheduling algorithm, making it suitable for handling high-frequency data generation. Federated learning, on the other hand, plays a pivotal role in verifying data distribution and maintaining sharing accuracy. By enabling decentralized learning, federated learning ensures that data remains on local devices while sharing only the necessary model updates. This approach enhances privacy and security, a critical consideration in healthcare data management. It ensures that data distribution and sharing are verified based on appropriate requests while avoiding latency issues. By decentralizing the learning process, federated learning enhances privacy and security, as raw data does not leave the local systems. This cooperative interaction between parallel computing and federated learning operates in a cyclic manner, allowing the framework to adapt dynamically to increasing monitoring intervals and varying data rates. The performance of the CDMF is validated through improvements in two key metrics. First, the framework achieves a 15.08% enhancement in sharing accuracy, which is vital for maintaining data integrity and reliability during transfers. Second, it reduces computation complexity by 9.48%, even when handling maximum data rates. These results highlight the framework’s potential to revolutionize healthcare data management by addressing the dual challenges of scalability and accuracy.

Empowerment and Enlightenment: The Practical Dimensions of Civic Heritage Narrative

Since UNESCOs vision for developing civic engagement tools in heritage management in the 2011 Recommendation on the Historic Urban Landscape, civic engagement has been considered one of the most important ways to promote the sustainable development of heritage. However, two primary obstacles have consistently hindered its effective implementation: the subjugation of authoritative power by international heritage organisations or national governments and conflicts arising from divergent notions of heritage designation and management at the same hierarchical level due to varied heritage concepts. This paper seeks to advance research on the prevalent civic engagement practice of narrating personal heritage stories, with the objective of addressing these existing challenges and enhancing the effective scope of civic engagement within the heritage sector. Through the literature review, which encompasses a thorough examination of contemporary theory and the various case studies of heritage narrative, this paper reveals that civic storytelling practices possess two dimensions: the empowerment narrative and the enlightenment narrative. These two dimensions can significantly mitigate tensions within communities and citizen groups while simultaneously tackling the issue of bureaucratic hegemony over heritage perception. With the additional discussion of these two dimensions, this study proposes The Collaborative Framework of the Civic Heritage Narrative Cycle as a tool for helping planners to develop narrative practices and foster the sustained advancement of heritage within an environment that is more democratic and communicates narratives

Transforming global public health: Climate collaboration, political challenges, and systemic change.

Amidst the global challenges of the 21st century, an urgent call emerges to redefine global public health in response to the multifaceted threats of climate change, political denial, and systemic barriers. This study employs a critical analysis approach, combining a narrative literature review with Action Research, to assess the health impacts of climate change and identify pathways for resilience. Direct and indirect implications-ranging from heat-related illnesses and vector-borne diseases to mental health challenges and displacement-are highlighted, alongside barriers posed by governance structures and economic disparities. A novel collaborative framework, CLIMBED COOL, is introduced, emphasizing adaptation, transformation, and transition as key strategies to address these challenges. Findings also underscore the importance of robust education, simulation-based training, and structured data-sharing mechanisms through regional Centers for Disease Control and Prevention and global databases. This study advocates for a paradigm shift in global governance and collaboration, ensuring holistic and sustainable solutions for future generations.

DualRec: A Collaborative Training Framework for Device and Cloud Recommendation Models

DualRec: A Collaborative Training Framework for Device and Cloud Recommendation Models

Automatic collaborative learning for drug repositioning

Drug repositioning seeks to identify new therapeutic uses for existing drugs, accelerating development and reducing costs. While traditional wet lab experiments are costly, computational methods offer a low-cost, efficient alternative. Despite their potential, most research in this field has uncritically employed the standard message-passing mechanism of Graph Neural Network (GNN), limiting the assessment of collaborative effects on prediction accuracy. In this paper, we introduce a novel model, an automatic collaborative learning framework for drug repositioning. Initially, we propose a metric to measure the interaction levels among neighbors and integrate it with the intrinsic message-passing mechanism of GNN, thereby enhancing the impact of various collaborative effects on prediction accuracy. Furthermore, we introduce an advanced contrastive learning technique to align feature consistency between the disease–drug association space and the customized neighbor space. This approach leverages the inherent regularities across different feature dimensions to minimize feature redundancy. Extensive experiments conducted on three benchmark datasets demonstrate substantial improvements of this novel model over various state-of-the-art methods. Case studies further highlight the practical utility of this model.

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction.

Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for interpretive paths and mechanism explorations that align better with domain-specific knowledge, particularly due to the high costs of experimentation. To address this gap, we propose an iterative Human-AI collaborative framework with two key components: 1) HumanEngaged Knowledge Graph Refinement based on Metapath Strategies, which leverages insights from interpretive paths and domain expertise to refine the knowledge graph through metapath strategies with appropriate granularity. 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis, which aids experts in organizing and comparing predictions and interpretive paths across different granularities, uncovering new SL relationships, enhancing result interpretation, and elucidating potential mechanisms inferred by Graph Neural Network (GNN) models. These components cyclically optimize model predictions and mechanism explorations, enhancing expert involvement and intervention to build trust. Facilitated by SLInterpreter, this framework ensures that newly generated interpretive paths increasingly align with domain knowledge and adhere more closely to real-world biological principles through iterative Human-AI collaboration. We evaluate the framework's efficacy through a case study and expert interviews.

LayoutCopilot: An LLM-Powered Multi-Agent Collaborative Framework for Interactive Analog Layout Design

LayoutCopilot: An LLM-Powered Multi-Agent Collaborative Framework for Interactive Analog Layout Design

Human-AI Collaboration Models: Frameworks for Effective Integration of Human Oversight and AI Insights in Business Processes

Purpose: This paper proposes structured frameworks for effective Human-AI collaboration within business processes. It aims to identify and model optimal task divisions where humans contribute oversight, creativity, and strategic judgment while AI provides computational power, automation, and analytical insights. Methodology: We explore collaboration models based on role-based division, process integration, and task adaptability. We analyze real-world business applications to demonstrate the efficacy of these models in improving productivity, decision-making, and innovation. Findings: We propose three key frameworks: (1) Augmented Creativity, where AI enhances human ideation, (2) Hybrid Decision Systems, where AI assists human judgment through predictive insights, and (3) Oversight-Driven Automation, where humans maintain control over automated tasks. Implications: The study highlights pathways for achieving synergistic Human-AI interactions to optimize business outcomes, enhance agility, and ensure ethical AI deployment

Enhancing modern aviation systems through systems engineering principles and emerging technologies

Modern aviation systems are at the forefront of technological innovation, integrating systems engineering principles to optimize design, efficiency, and safety. This paper explores the research question: "How can systems engineering principles enhance the design, efficiency, and safety of modern aviation systems, from airfoil optimization to intelligent flight management systems?" It delves into the synergistic impact of systems engineering, artificial intelligence (AI), adaptive engineering, and collaborative frameworks on aviation. Key focus areas include airfoil optimization, intelligent flight management systems, and collaborative safety frameworks. Historical insights contextualize these advancements, emphasizing the industry's evolution and future trajectory.