Development Of Collaborative Model Research Articles

Empirical Investigation into Collaborative Model Development for Resolving Tenure Conflicts in Forested Regions

Tenure conflict is a challenge and dilemma experienced by many countries. The dilemma is because on the one hand, there is an interest in preserving forests, on the other hand, there are claims to land ownership in forest areas long before they are designated as forest areas. The next dilemma is that the party claiming property rights is generally used for agricultural activities which have been the main livelihood for generations. This study tries to examine the incidence of tenure conflicts in various countries to map how these conflicts occur, the factors that encourage land tenure approaches in resolving them, and government (state) policies. Using secondary data resulting from a review of previous research published in reputable international journals and other reliable and credible reports. The review study model used is systematic with meta-synthesis. Apart from journals, secondary data comes from other reliable and credible reports. Data and materials consist of 67 reputable international journals and 14 books/reports published in the last ten years. The results of the analysis found that tenure conflicts were caused by many things, motivated by the ownership and interests of natural resources specifically for agriculture for the lives of the surrounding community. An interesting fact is that the handling of tenure conflicts in several countries is considered successful, marked by the provision of access to forest management ownership. Other studies such as those conducted in Latin America, Africa, and Asia offer partnership models to resolve tenure conflicts. Similar things have also been done in Indonesia but have not been completely successful. Learning from this, this research offers a model for developing forest management partnerships to resolve tenure conflicts in forest areas, while also accommodating special agricultural activities for local communities. The model offered theoretically complements and/or perfects the models used so far which have proven unsuccessful in resolving conflicts. The practical implications are a reference for the government and interested parties to resolve tenure conflicts so that forests are maintained and can provide benefits for community welfare.

The Paradox of Artificial Creativity: Challenges and Opportunities of Generative AI Artistry

ABSTRACT Creativity has long been viewed as the bastion of human expression. With the advent of generative artificial intelligence (AI), there is an emerging notion of artificial creativity that contests traditional perspectives of artistic exploration. This paper explores the complex dynamics of this evolution by examining how generative AI intertwines with and transforms the art world. It presents a comprehensive analysis of the challenges posed by generative AI in art, from questions of authenticity and intellectual property to ethical dilemmas and impacts on conventional art practices. Simultaneously, it investigates the revolutionary opportunities generative AI offers, including the democratization of art creation, the expansion of creative boundaries, and the development of new collaborative and economic models. The paper posits that the integration of generative AI in art is not just a technological advancement but a significant cultural shift, which necessitates a reevaluation of our understanding of art and the artist. It concludes with a forward-looking perspective, advocating for a collaborative approach to harness the potential of this technology in enriching human creativity and ensuring the vibrant evolution of the art world in the era of AI-driven generation.

Engaging Paradoxical Tensions in Cross-Sectoral Collaborative Business Model Development for Sustainability: A Case Study in the Urban Energy Transition

This article presents an in-depth case study on cross-sectoral collaborative business model development (CBMD) that is pressured to produce systemic sustainability transformations. Drawing on paradox theory, we identify three paradoxical tensions—value, creativity, and consumer tensions. While engaging these tensions offers synergy and creativity potential, engagement barriers limit stakeholders’ ability to harness this potential. Stakeholder networks can access synergy potential by engaging meso-level tensions through increased trust and collaboration. Yet, macro-level engagement barriers posed by governments and society lead to a reliance on incumbent patterns and reduce creativity. This research advocates for reconsidering CBMD processes and regulatory frameworks to enable engagement with these paradoxical tensions. Our implications offer insights for industries transitioning from centralized models to more individualized, decentralized approaches. The findings underscore the necessity of promoting reciprocal interactions and engagement across different levels and the early integration and strategic orchestration of stakeholders to cultivate trust and align objectives.

How Do Health Worker Qualities Affect Collaborative Skills? A Qualitative Study in Rural Indonesia Primary Health Services

Health problems are complicated, even more so in remote areas. Collaboration has emerged as a solution to this complex problem. Some specific health worker qualities have been identified as related to the success of the collaboration, but the transformation of these qualities into collaborative behaviour remains unclear. An epistemological model of collaborative skills development was built and it was confirmed by the experiences of health workers. A research model based on interpretivism epistemology was also developed. The model was then generated using a qualitative methodology with a coding framework using deductive and inductive approaches based on the literature review and the interviews. Ten previously assigned health workers were interviewed to list their personal qualities and functions to overcome collaboration problems. The specific personal qualities of health workers determine each type of collaboration. The personality of health workers does not affect their collaborative skills with the community but is directly related to interprofessional collaboration skills.

Collaborative development model and strategies of multi-energy industry clusters: Multi-indicators analysis affecting the development of coastal energy clusters

The paper explores Coastal Energy-Based Industrial Clusters (EBICs) and their role in advancing energy efficiency and sustainability through collaborative innovation. Economic growth theory and energy sustainability have been introduced into industrial clusters to illustrate indicators that have a greater impact on the development of EBICs. This paper proposes an EBICs development model based on the Cobb-Douglas function, in which accounts for various factors that drive the progress of such clusters. The outcomes of the economic model also provide insights into how the interaction of various factors affects the economic growth of EBICs and their eventual dominance in the energy market of coastal regions, dependent on gradually investing in areas such as research and development (R&D). Different development strategies demonstrate that the final development of a cluster has low dependence on the cluster's initial advantages. The study also illustrates how clusters can gradually monopolize the energy market, even with initial disadvantages. Through quantitative analysis, it showcases the transformation process of developing advantageous clusters into sub-clusters. Next, an energy symbiosis framework for coastal is proposed, which places greater emphasis on the multi-energy complementary system and reduces production costs. Finally, this paper also sheds light on shaping energy strategies for public authorities who shape economic policies at various levels of aggregation and in diverse dimensions.

The Collaborative Policy Modeling Paradox: Perceptions of water quality modeling in the Chesapeake Bay Watershed

The Chesapeake Assessment Scenario Tool (CAST) serves multiple key functions in meeting nutrient reduction targets across the Chesapeake Bay Watershed (CBW) and is embedded in the water quality governance system. To investigate contested perspectives regarding the model, we interviewed 59 stakeholders engaged in model governance across the CBW. We recorded statements regarding the accuracy, legitimacy, and credibility of the model, influences on its use, and on challenges and opportunities. We found skepticism regarding the legitimacy of CAST, including suggestions its role facilitates a “paper process” of policy design and that past experience has greater influence on policy decisions than model predictions. However, despite its perceived shortcomings, CAST has been central in helping stakeholders in prioritizing mitigative activities. With respect to credibility, most respondents believe the model underestimates the effects of nutrient-reduction practices, thereby underestimating progress toward TMDL-related goals. Respondents also identified opportunities for model improvement, emphasizing co-benefits of conservation practices over and above nutrient reduction. Overall, our analysis demonstrates a Collaborative Policy Modeling Paradox: collaborative model development is necessary for effective policy modeling, but the political processes of collaborative model development can negatively impact perceptions of salience, credibility, and legitimacy. Although it is important to recognize this paradox, as it is linked to dissatisfaction with the models, our findings also point to areas where improvement has occurred and to future opportunities for development.

Development of a Digital Tourism Model Using Virtual Reality as Sociological Restoration at the Foot of Mount Sinabung

Mount Sinabung is an active volcano located in the Karo Highlands, specifically in the Karo Regency of North Sumatra, Indonesia. Sinabung and Mount Sibayak, located in North Sumatra, are both active volcanoes and rank as the second-highest peaks in the province. However, it saw a dramatic resurgence and became active once more with a volcanic eruption on August 27, 2010. The most recent eruption of this mountain took place on February 19, 2018 and is ongoing. The significance of this situation prompted the study team to engage in the development of an empowerment paradigm. The objective of this study is to create a digital tourism framework for communities impacted by the eruption of Mount Sinabung, utilizing virtual reality technology as a means to facilitate societal repair. The research methodology employed is a hybrid approach. The data results are processed and evaluated using a customized technique that takes into account the specific demands of the community and parties involved in the collaborative model development. This analysis is done both qualitatively and quantitatively. The analytical stage of discussing the desired digital tourist model employs the theory of viewing techniques, which is akin to reading, in the form of the selection stage.

Impact Analysis and Development of Collaborative Communication Models in Public Relations Programs for Mangrove Conservation

Mangrove forests, as coastal ecosystems, play a crucial role in maintaining the sustainability of fishery resources and protecting shores from erosion and storms. However, mangrove degradation continues to escalate, primarily due to human activities such as shrimp pond expansion and uncontrolled logging. Public Relations programs can serve as effective tools to raise awareness and engage communities in mangrove conservation. Impact analysis and the development of a collaborative communication model are key to understanding the effectiveness of such programs. This research aims to analyze the impact of the Public Relations program on mangrove conservation and develop a more effective collaborative communication model. The focus is on understanding behavioral changes, community responses, and the program's contribution to the sustainability of the mangrove ecosystem. The research methodology employed is qualitative-descriptive with an impact analysis approach. Interviews with representatives from local communities, local governments, non-governmental organizations, and Public Relations practitioners were conducted to gather data. Content analysis was also performed on the program's communication materials. The research findings indicate that the Public Relations program has positively impacted the increase in community awareness and behavioral changes related to mangrove conservation. However, challenges persist concerning environmental uncertainties and the lack of participation from certain segments of the community. A collaborative communication model integrated with mangrove conservation theories, impact analysis, and community engagement proves essential for further improvement. The implications of this research reflect the importance of impact analysis and developing collaborative communication models in Public Relations programs for mangrove conservation. These findings underline that active collaboration with various stakeholders is key to ensuring the sustainability of mangrove ecosystems.

High impact sustainable transdisciplinary collaboration model for food justice

ABSTRACT This article summarizes the development of a High Impact Sustainable Transdisciplinary Collaboration Model for Food Justice based on the work done by faculty from different disciplines—engineering, business, and agriculture—with their corresponding courses in the last 5 years. Our experience aims to show the general picture of sustainable entrepreneurship education through our transdisciplinary collaboration model to promote experiential learning, generating interest among students across traditional disciplinary boundaries to explore problems from different perspectives and create sustainable solutions in a concrete, real-life context. Our model requires transdisciplinary collaboration, where students from diverse academic majors develop the knowledge and skills associated with their respective fields and apply them in a unique setting to solve food security issues. This experience has been formalized into a Preparation, Action, Reflection, and Evaluation (P.A.R.E.) Community-Engaged Scholarship Model that infuses sustainability and that can be easily understood and transferred to other institutions with similar contexts and challenges.

Research on the Collaborative Development Model of Industrial Design Education driven by Artificial Intelligence - An Interdisciplinary Approach Based on Student Creativity and Practice

Research on the Collaborative Development Model of Industrial Design Education driven by Artificial Intelligence - An Interdisciplinary Approach Based on Student Creativity and Practice

Investigating privacy-preserving machine learning for healthcare data sharing through federated learning

Privacy-Preserving Machine Learning (PPML) is a pivotal paradigm in healthcare research, offering innovative solutions to the challenges of data sharing and privacy preservation. In the context of Federated Learning, this paper investigates the implementation of PPML for healthcare data sharing, focusing on the dynamic nature of data collection, sample sizes, data modalities, patient demographics, and comorbidity indices. The results reveal substantial variations in sample sizes across substudies, underscoring the need to align data collection with research objectives and available resources. The distribution of measures demonstrates a balanced approach to healthcare data modalities, ensuring data fairness and equity. The interplay between average age and sample size highlights the significance of tailored privacy-preserving strategies. The comorbidity index distribution provides insights into the health status of the studied population and aids in personalized healthcare. Additionally, the fluctuation of sample sizes over substudies emphasizes the adaptability of privacy-preserving machine learning models in diverse healthcare research scenarios. Overall, this investigation contributes to the evolving landscape of healthcare data sharing by addressing the challenges of data heterogeneity, regulatory compliance, and collaborative model development. The findings empower researchers and healthcare professionals to strike a balance between data utility and privacy preservation, ultimately advancing the field of privacy-preserving machine learning in healthcare research.

Looking for related posts on GitHub discussions

Software teams increasingly adopt different tools and communication channels to aid the software collaborative development model and coordinate tasks. Among such resources, software development forums have become widely used by developers. Such environments enable developers to get and share technical information quickly. In line with this trend, GitHub announced GitHub Discussions—a native forum to facilitate collaborative discussions between users and members of communities hosted on the platform. Since GitHub Discussions is a software development forum, it faces challenges similar to those faced by systems used for asynchronous communication, including the problems caused by related posts (duplicated and near-duplicated posts). These related posts can add noise to the platform and compromise project knowledge sharing. Hence, this article addresses the problem of detecting related posts on GitHub Discussions. To achieve this, we propose an approach based on a Sentence-BERT pre-trained general-purpose model: the RD-Detector. We evaluated RD-Detector using data from three communities hosted in GitHub. Our dataset comprises 16,048 discussion posts. Three maintainers and three Software Engineering (SE) researchers manually evaluated the RD-Detector results, achieving 77–100% of precision and 66% of recall. In addition, maintainers pointed out practical applications of the approach, such as providing knowledge to support merging the discussion posts and converting the posts to comments on other related posts. Maintainers can benefit from RD-Detector to address the labor-intensive task of manually detecting related posts.

Green technology diversification, technology vertical spillovers, and energy intensity in Chinese cities

Drawing from patent data spanning 2011-2016 in 249 Chinese prefecture-level cities and urban scale multi-region input-output (MRIO) table data, this paper empirically demonstrates that as green technological diversification levels escalate, the energy intensity of Chinese prefecture-level cities exhibits an initial increase followed by a decline. Green technological diversification indirectly influences urban energy intensity through vertical spillovers of green technology across the upstream and downstream sectors within the supply chain. Medium-sized cities, service industry-oriented cities, and those in the early stages of industrialization need to prioritize development of multiple green technologies to surpass the diversification turning point. The finding suggests a strategic transition for Chinese cities from a single green technology agglomeration model to a cluster model of collaborative development of multiple green technologies.

Fostering Community-Engaged Research on Criminal Legal Innovations with Logic Models.

There is a persistent disconnect between research and practice, both in the social work profession as well as in the criminal legal system. Community-engaged research has been suggested as an approach to bridge this divide, but specific tools are needed to integrate research and practice efforts. This article presents three distinct logic model development processes that occurred in collaborative research and practice efforts in the context of criminal legal programming, including prosecutor-led diversion programs, a high-intensity drug court, and a multiagency justice and mental health collaborative. Logic model development incorporated multiple forms of program information using collaborative reflexivity, an approach focused on understanding the relationship between knowledge and power in the research process. For each program, the authors describe the context and process of logic model development, and how the logic models were used by both practitioners and researchers. The authors discuss how collaborative logic model development can facilitate community-engaged research, strengthen the research-practice connection, and advance applied social work scholarship.

Silo Effects and Financial Performance: Evidence from an Emerging Market

Focusing on identifying the impact of silos, this article addresses a gap in the literature in terms of the mediating role of collaboration between silo-busting techniques and the productivity and financial performance of Saudi firms. Through an investigation of multiple hypotheses tested via partial least-squares structural equation modelling, the relationship between silo-busting techniques and productivity and financial performance is tested in quantitative terms, thereby providing the first evidence for this relationship in the focal context. On the basis of this modelling approach, silo-busting techniques are shown to be a significant predictor of productivity and financial performance. Further, the results show that silo-busting techniques play an additional related role in shaping productivity and financial performance—that is, these techniques foster collaboration within a given firm. In fact, practicing silo-busting techniques can help improve firm performance. In relation to the present business environment, the results indicate that firms should make significant investments in all five silo-busting factors—values, leadership, collaborative environment, collaborative operating model and people reward and development—in order to improve collaboration results and, therefore, productivity and financial performance. However, particular emphasis should be placed on collaborative operating model and people reward and development as the only two factors shown to strongly support firm performance.

Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models

Guest Editorial Special Issue on Federated Learning for Medical Imaging: Enabling Collaborative Development of Robust AI Models

KOMPARASI MODEL COLLABORATIVE COLLECTION DEVELOPMENT DAN JUST-IN-TIME COLLECTION DEVELOPMENT PADA PERPUSTAKAAN PENDIDIKAN

This study aims to answer the question of how the application of collaborative and just-in-time model collection development and how the comparison of the two models that have been applied to educational libraries. The method used in this research is a critical review method with a literature study approach. The writer made comparation and analyze between two articles which adopted in different model of collection development. The research findings are as follows: 1) The collaborative collection development model utilizes library networks to save budgets and accelerate collection availability, while the just-in-time collection development model applies a responsive access-based collection development approach. This policy is to provide the right and fast collection and save funds. 2) The difference between these two models is that the collaborative model requires a network so as to facilitate the process of both procurement together in order to create budget savings, while the just-in-time collection development is a single model that does not really require a network so that it has absolute autonomy.

Development of Collaborative Learning to Improve Student's Writing Skills Class X SMA Negeri 1 Dolok Merawan Academic Year 2022-2023

The problem in this research is how effective is the use of collaborative learning in improving the writing skills of class X students of SMA Negeri 1 Dolok Merawan in the academic year 2022-2023. The purpose of this study was to describe the effectiveness of the use of collaborative learning in improving the writing skills of class X students of SMA Negeri 1 Dolok Merawan for the academic year 2022-2023. The research method used in this research is the research & development method. The research subject of this development product is the validator. The object of research in this research is a writing learning model, namely a collaborative learning model that is implemented in class X students of SMA Negeri 1 Dolok Merawan in the 2022-2023 academic year. The instrument used to obtain research data using non-test instruments and test instruments. The results of this study indicate that the collaborative learning model is effective in improving the skills of writing descriptive text by class X students of SMA Negeri 1 Dolok Merawan in the 2022-2023 academic year, where before the collaborative learning model development process was in the sufficient category and then increased to good after the learning model development process. collaboratively in class X SMA Negeri 1 Dolok Merawan for the academic year 2022-2023.

Blockchain-Federated and Deep-Learning-Based Ensembling of Capsule Network with Incremental Extreme Learning Machines for Classification of COVID-19 Using CT Scans.

Due to the rapid rate of SARS-CoV-2 dissemination, a conversant and effective strategy must be employed to isolate COVID-19. When it comes to determining the identity of COVID-19, one of the most significant obstacles that researchers must overcome is the rapid propagation of the virus, in addition to the dearth of trustworthy testing models. This problem continues to be the most difficult one for clinicians to deal with. The use of AI in image processing has made the formerly insurmountable challenge of finding COVID-19 situations more manageable. In the real world, there is a problem that has to be handled about the difficulties of sharing data between hospitals while still honoring the privacy concerns of the organizations. When training a global deep learning (DL) model, it is crucial to handle fundamental concerns such as user privacy and collaborative model development. For this study, a novel framework is designed that compiles information from five different databases (several hospitals) and edifies a global model using blockchain-based federated learning (FL). The data is validated through the use of blockchain technology (BCT), and FL trains the model on a global scale while maintaining the secrecy of the organizations. The proposed framework is divided into three parts. First, we provide a method of data normalization that can handle the diversity of data collected from five different sources using several computed tomography (CT) scanners. Second, to categorize COVID-19 patients, we ensemble the capsule network (CapsNet) with incremental extreme learning machines (IELMs). Thirdly, we provide a strategy for interactively training a global model using BCT and FL while maintaining anonymity. Extensive tests employing chest CT scans and a comparison of the classification performance of the proposed model to that of five DL algorithms for predicting COVID-19, while protecting the privacy of the data for a variety of users, were undertaken. Our findings indicate improved effectiveness in identifying COVID-19 patients and achieved an accuracy of 98.99%. Thus, our model provides substantial aid to medical practitioners in their diagnosis of COVID-19.

Dynamic Evolution and Collaborative Development Model of Urban Agglomeration in Hexi Corridor from the Perspective of Economic Flow

Under the green goals of the carbon peak and carbon neutrality, understanding how to develop the economy with high quality is an important issue facing regional development. Based on the years 2000, 2010, and 2020, this paper studies the industrial function connection path and economic network characteristics of the Hexi Corridor through an urban flow model, dominant flow analysis, modified gravity model, and social network analysis method, and puts forward an economic synergistic development model. It is of great significance to strengthen the urban connection in the Hexi Corridor and give full play to the overall competitive advantage. The results are as follows. (1) The overall function of the urban agglomeration is weak, the outward function of manufacturing is outstanding, the complementary network is highly complicated and evolving, and the environment and public service and tourism industry have apparent advantages. (2) The backbone correlation axes of the “three industries” show the characteristics of a closed triangular connection, dual-core linkage development, and multi-center multi-axis interaction. (3) The economic network has a greater agglomeration effect than diffusion effect, with prominent grouping characteristics, forming a network structure of “one man, three vices, and many nodes” and a significant spatial proximity effect. (4) Based on geographical proximity, the “one axis, four circles, multiple points, and multiple channels” synergistic development model, which breaks administrative barriers, becomes the endogenous driving force for the evolution of the economic network.