Collaborative Resource Research Articles

Dual-timescale resource management for multi-type caching placement and multi-user computation offloading in Internet of Vehicle

In Internet of Vehicle (IoV), edge computing can effectively reduce task processing delays and meet the real-time needs of connected-vehicle applications. However, since the requirements for caching and computing resources vary across heterogeneous vehicle requests, a new challenge is posed on the resource management in the three-tier cloud–edge–end architecture, particularly when multi users offload tasks in the same time. Our work comprehensively considers various scenarios involving the deployment of multiple caching types from multi-users and the distinct time scales of offloading and updating, then builds a joint optimization caching placement, computation offloading and computational resource allocation model, aiming to minimize overall latency. Meanwhile, to better solving the model, we propose the Multi-node Collaborative Caching, Offloading, and Resource Allocation Algorithm (MCCO-RAA). MCCO-RAA utilizes dual time scales to optimize the problem: employing a Bellman optimization idea-based multi-node collaborative greedy caching placement strategy at large time scales, and a computational offloading and resource allocation strategy based on a two-tier iterative Deep Deterministic Policy Gradient (DDPG) and cooperative game at small time scales. Experimental results demonstrate that our proposed scheme achieves a 28% reduction in overall system latency compared to the baseline scheme, with smoother latency variations under different parameters.

Data-driven collaborative healthcare resource allocation in pandemics

Severe shortages of healthcare resources are major challenges in pandemics, especially in their early stages. To improve emergency management efficiency, this paper proposes a novel rolling predict-then-optimize framework that includes three interactive modules, i.e., data-driven demand prediction, healthcare resource allocation, and parameter rolling update. Such a framework uses historical data to dynamically update the control parameters of the proposed Net-SEIHRD model, which predicts the healthcare needs of each region by jointly considering government interventions and cross-regional travel behaviors. Based on the forecasted healthcare resource demand in real-time, an optimization model is then formulated to realize coordinated resource allocation across multiple regions by minimizing the total generalized cost. To facilitate model solving, the proposed mixed integer nonlinear programming model is converted into an equivalent mixed integer linear model by using some linearization techniques. Finally, the proposed method is applied to the SARS-CoV-2 emergency response and collaborative allocation of healthcare resources in Shanghai, China. The results show that the proposed prediction model can effectively predict the peak and scale of the spread of the virus. Compared with the traditional LM and SEIHR models, the prediction accuracy of the Net-SEIHRD model is improved by 10.76% and 24.11%, respectively. Moreover, coordinated relief activities across regions, such as patient transfer and drug-sharing can improve the efficiency of pandemic control and save social costs.

Application of melting technology in the treatment of nonferrous hazardous waste residues

ABSTRACT In order to find a more effective treatment method for non-ferrous hazardous waste residues, the research proposed the fusion disposal technology of collaborative resource residues. The empirical results showed that the leaching concentrations of Cr and Pb of the molten products of the resource residue were maintained at 0 mg/L, and the final leaching concentrations of most heavy metals were within 0.10 mg/L, which showed low toxicity to the environment. The above results show that the fusion disposal technology of collaborative resource residue is an effective treatment method. The fusion disposal technology not only reduces the heavy metal leaching concentration of non-ferrous metals, but also promotes the development of hazardous waste residue treatment technology.

Collaborative Design Capacity for Enactment Framework: An Analytic Tool for Conceptualizing Pedagogical Design Capacity Within Social Context

ABSTRACTThis study examines an urban middle school teacher design team's capacity for creating integrated science, technology, engineering, and mathematics curricula. Using Brown's pedagogical design capacity (PDC) theory, which highlights interactions between personal and curricular resources, this paper introduces an extended framework that includes social interactions as key influences on teachers' design abilities. Findings show that individual teachers' spontaneous curriculum modifications were adopted by the team, becoming collective resources for ongoing redesign and improving their design capacity. Teachers effectively used each other as resources to address unexpected challenges in integrating science, engineering, and mathematics concepts. Three types of social interactions were identified as collaborative resources: (i) Storytelling: sharing experiences to make abstract concepts actionable for curriculum development; (ii) Protocols: structured methods to address curricular problems related to integration; and (iii) Assessment “for” curriculum redesign: using assessment tools to inform and improve the curriculum. The Collective Design Capacity for Enactment framework is introduced to describe PDC within a social context, highlighting the importance of social interactions in bridging curriculum use and development, thus extending the literature on PDC.

ENHANCING SUSTAINABLE HOUSING DEVELOPMENT IN MALAYSIA: THE ROLE OF COOPERATIVE FEDERALISM IN FEDERAL-STATE COLLABORATION

Sustainable housing development in Malaysia relies on collaborative governance and resource sharing. Several proposed sustainable housing initiatives have involved federal-state tensions. Cooperative federalism can bring together diverse stakeholders to address complicated environmental, social, and economic variables affecting housing construction, according to this study. The authors analyze federal and state laws and policies doctrinally. To assess the efficacy of collaborative governance and resource sharing, government officials, housing experts, policymakers, and community leaders were interviewed. Fictional international case studies inform this study. The authors find 1) federal or state government agencies lack coordination. 2) policy alignment for sustainable housing development and 3) state resource constraints. The findings show how collaborative governance and resource sharing may transform. Participants understand the importance of federal-state housing policies, which streamline sustainable housing development and prevent conflicting laws. Effective intergovernmental connections inform housing policy decisions, meeting local demands. International case studies also provide many suggestions for adapting and innovating in Malaysia. This study proposes resilient, inclusive, and sustainable housing in Malaysia. By adopting collaborative governance, harmonizing policies, pooling resources, fostering intergovernmental partnerships, and learning from global experiences, Malaysia's housing sector may inspire future generations with innovation and sustainability.

Collaborative resource allocation in computing power networks: A game-theoretic double auction perspective

The growth of global data is increasing exponentially, leading to a greater demand for computing power. To address this requirement, expanding computing power from the cloud to the edge is essential. However, this transformation presents two significant challenges: how to share computing resources more efficiently and how to optimize resource allocation. To tackle these challenges, we propose a three-layer Computing Power Network (CPN) framework that focuses on implementing the collaborative allocation of computing nodes and user tasks. We formulate the resource allocation problem in CPN as a double auction game and use an experience-weighted attraction algorithm that enables participants to adjust bidding strategies based on environmental interactions. We implemented a prototype of our proposed CPN framework and conducted extensive experiments to verify our algorithm’s convergence and evaluate the benefits obtained by buyers (users) and sellers (computing nodes) from the perspective of transaction prices, rewards, and average pricing. The comprehensive experimental results demonstrate the effectiveness of our proposed method. Compared with state-of-the-art pricing strategies, our approach achieves a 20% increase in convergence speed and an 88% increase in overall returns. Furthermore, it also exhibits a 2.5% increase in deal prices and a substantial 83% rise in the income of individual users. These outcomes convincingly prove the superiority of our method in achieving better convergence, improving overall returns, and benefiting both buyers and sellers in the CPN resource auction market.

A new approach to collaborative resource utilization of biological sludge and zinc-bearing dust: Reduction characteristics and mechanisms

This paper proposes an innovative approach for co-processing zinc-bearing dust with biological sludge, using the organic matter in the sludge as a reducing agent to replace traditional fossil fuels (coal/coke). The results show that the reduction process of biological sludge-zinc-bearing dust pellets (BZP) is divided into two stages. In the first stage (25–620 °C), the breakdown of C = C, C-O-C, and C-O bonds present in biological sludge produced carbon oxide (CO) and hydrogen (H2), facilitating the conversion of zinc ferrite (ZnFe2O4) to magnetite (Fe3O4). The second stage (620–1300 ℃), the cleavage of C = C, C–C and C–H bonds and carbon gasification generate more CO and H2, further reducing magnetite (Fe3O4) to metallic iron (Fe) and zinc oxide (ZnO) to zinc monomers (Zn). The activation energy of BZP in the two stages was 32.86 kJ·mol−1 and 73.88 kJ·mol−1, respectively. The metallization rate and the zinc removal rate of BZP achieved 58.47 % and 86.74 %, respectively, indicating the effectiveness and feasibility of the propose of approach. This approach provides a clean and comprehensive utilization way out for zinc-bearing dust and biological sludge.

Data resource profile: the ORIGINS project databank: a collaborative data resource for investigating the developmental origins of health and disease

IntroductionThe ORIGINS Project ("ORIGINS") is a longitudinal, population-level birth cohort with data and biosample collections that aim to facilitate research to reduce non-communicable diseases (NCDs) and encourage 'a healthy start to life'. ORIGINS has gathered millions of datapoints and over 400,000 biosamples over 15 timepoints, antenatally through to five years of age, from mothers, non-birthing partners and the child, across four health and wellness domains: 'Growth and development', 'Medical, biological and genetic', 'Biopsychosocial and cognitive', 'Lifestyle, environment and nutrition'. MethodsMothers, non-birthing partners and their offspring were recruited antenatally (between 18 and 38 weeks' gestation) from the Joondalup and Wanneroo communities of Perth, Western Australia from 2017 to 2024. Data come from several sources, including routine hospital antenatal and birthing data, ORIGINS clinical appointments, and online self-completed surveys comprising several standardised measures. Data are merged using the Medical Record Number (MRN), the ORIGINS Unique Identifier and the ORIGINS Pregnancy Number, as well as additional demographic data (e.g. name and date of birth) when necessary. ResultsThe data are held on an integrated data platform that extracts, links, ingests, integrates and stores ORIGINS' data on an Amazon Web Services (AWS) cloud-based data warehouse. Data are linked, transformed for cleaning and coding, and catalogued, ready to provide to sub-projects (independent researchers that apply to use ORIGINS data) to prepare for their own analyses. ORIGINS maximises data quality by checking and replacing missing and erroneous data across the various data sources. ConclusionAs a wide array of data across several different domains and timepoints has been collected, the options for future research and utilisation of the data and biosamples are broad. As ORIGINS aims to extend into middle childhood, researchers can examine which antenatal and early childhood factors predict middle childhood outcomes. ORIGINS also aims to link to State and Commonwealth data sets (e.g. Medicare, the National Assessment Program -- Literacy and Numeracy, the Pharmaceutical Benefits Scheme) which will cater to a wide array of research questions.

Hybrid, blended, and hyflex learning as future models for education

The education system continuously adapts to address abnormal social fluctuations such as pandemic lockdowns, climate change, disasters, and social distractions during war, migration, and more. Remote learning methods have advantages and disadvantages, a topic of discussion among many educators. Education is a critical part of society, ensuring the well-being of humanity. Future models for education refer to potential changes and advancements in the structure, delivery, and access to education. This includes innovations in teaching methods, learning environments, technology integration, and overall educational systems. Future models for education may involve the adoption of new teaching strategies and approaches that cater to diverse learning styles and preferences. This could include personalized learning, project-based learning, and flipped classrooms, aiming to enhance student engagement, critical thinking skills, and knowledge retention. The future of education may shift towards more dynamic and interactive learning environments, which can involve the use of virtual reality, augmented reality, gamification, and immersive simulations to create attractive educational experiences. Education in the future might incorporate innovative technology as a crucial component of the learning process, the use of artificial intelligence, adaptive learning platforms, online collaboration, and digital e-learning resources for content distribution. Technology integration targets to make education more accessible and flexible to individual student needs. Research plays a crucial role in shaping the future of education by notifying evidence-based practices and guiding decision-making processes. The article is a short review that discusses and analyses the differences and preferences between remote digital methods applying during COVID-19 restrictions.

The Coupling Coordination Degree and Its Driving Factors for Water–Energy–Food Resources in the Yellow River Irrigation Area of Shandong Province

Water resources, energy, and food are essential for the development of society, and they are strongly interdependent. The coupling and coordination relationships of the water–energy–food (WEF) system are important for regional resource security and high-quality development. The Yellow River Irrigation Area in Shandong Province, China, is a grain production base and has a substantial impact on national food security. To examine the water, energy, and food subsystem dynamics in this area, an evaluation system for the WEF system was established. A comprehensive weighting method based on game theory was employed to determine index weights. TOPSIS was used to assess the development level of the WEF system. A coupling coordination degree model was used to analyze the evolution of the coupling coordination degree of the WEF system from 2000 to 2020, and a GWR model was constructed to explore the spatial heterogeneity of its driving factors. The findings indicated that the development level of the WEF system in the study area was moderate, with a gradual upward trend. The coupling coordination degree fluctuated between 0.62 and 0.739. The GWR model revealed that temperature had an overall negative effect on the coupling coordination degree, with the greatest impact on the central irrigation area; the slope and NDVI had a negative effect, with increasing intensity from the southwest to the northeast; and rainfall had an overall positive effect, with the greatest impact on the irrigation area near the estuary in the northeast. Overall, the building area ratio had a negative effect on the coupling coordination degree, with exceptions in some areas. These research outcomes provide theoretical support for sustainable agricultural development in the Yellow River irrigation areas of Shandong Province and methodological reference data for studying collaborative resource utilization in irrigation regions.

Design of an Iterative Method for Collaborative Resource Management using MAPPO and Federated Learning in 5G Networks

Design of an Iterative Method for Collaborative Resource Management using MAPPO and Federated Learning in 5G Networks

Facilitating digital accessibility for students with disabilities into information services at Jordanian academic libraries

PurposeThis study examines the digital accessibility of information services for students with disabilities at the University of Jordan. Despite advancements in disability rights and technology, academic libraries often lag in providing fully accessible digital services.Design/methodology/approachThe mixed-methods research involved quantitative surveys from 173 students with disabilities and a qualitative interview with the head of the information application department, in addition to a focus group interview with 10 students with disabilities. Quantitative analysis included means, standard deviations and four-way ANOVA to identify significant differences in perceptions. Qualitative insights highlighted specific issues and recurring themes, revealing significant gaps in digital accessibility.FindingsThe results revealed a weak level of digital accessibility of students with disabilities to information services in academic libraries in Jordan is very low. The findings indicate a need for improvements in library infrastructure, staff training and policy development. The study also offers insights into challenges in a developing country context and provides recommendations for enhancing library inclusivity and support structures, emphasizing the importance of aligning services with international accessibility standards.Practical implicationsThe findings will primarily be beneficial for library managers to understand their library’s deficiencies and responsibility towards the local community and the enhancement of digital inclusion. This understanding will aid in planning training programs and workshops for employees on supporting students with disabilities. Additionally, it serves as a valuable resource for collaboration between libraries and educators to organize educational sessions for academic librarians in Jordan and other developing nations.Originality/valueIt serves as a valuable resource for collaboration between libraries and educators to organize educational sessions for academic librarians in Jordan and other developing nations. This research adds value to existing literature by highlighting the context in one developing country.

Charting the Ethical Frontier in Newborn Screening Research: Insights from the NBSTRN ELSI Researcher Needs Survey

From 2008 to 2024, the Newborn Screening Translational Research Network (NBSTRN), part of the National Institute of Child Health and Human Development (NICHD) Hunter Kelly Newborn Screening Program, served as a robust infrastructure to facilitate groundbreaking research in newborn screening (NBS), public health, rare disease, and genomics. Over its sixteen years, NBSTRN developed into a significant international network, supporting innovative research on novel technologies to screen, diagnose, treat, manage, and understand the natural history of more than 280 rare diseases. The NBSTRN tools and resources were used by a variety of stakeholders including researchers, clinicians, state NBS programs, parents, families, and policy makers. Resources and expertise for the newborn screening community in ethical, legal, and social issues (ELSI) has been an important area of focus for the NBSTRN and this includes efforts across the NBS system from pilot studies of candidate conditions to public health implementation of screening for new conditions, and the longitudinal follow-up of NBS-identified individuals to inform health outcomes and disease understanding. In 2023, the NBSTRN conducted a survey to explore ELSI issues in NBS research, specifically those encountered by the NBS community. Since NBS research involves collaboration among researchers, state NBS programs, clinicians, and families, the survey was broadly designed and disseminated to engage all key stakeholders. With responses from 88 members of the NBS community, including researchers and state NBS programs, the survey found that individuals rely most on institutional and collegial resources when they encounter ELSI questions. Most survey responses ranked privacy as extremely or very important in NBS research and identified the need for policies that address informed consent in NBS research. The survey results highlight the need for improved collaborative resources and educational programs focused on ELSI for the NBS community. The survey results inform future efforts in ELSI and NBS research in the United States (U.S.) and the rest of the world, including the development of policies and expanded ELSI initiatives and tools that address the needs of all NBS stakeholders.

Incubating innovation: the role of incubators in supporting business model innovation

Purpose The role of incubators in promoting startup growth has received close research attention, but the findings paint a conflicting picture. This study aims to reduce the ambiguity surrounding incubator impact by exploring how incubators can support startups with business model innovations – a significant growth factor for startups neglected in the incubation literature. Design/methodology/approach Using a multiple-case study design, the authors conducted semistructured interviews with incubator directors and startup founders, offering insights into their experiences. The transcripts were coded following the Gioia method. Findings This study shows that incubatees are exposed to and struggle with business model innovation. Therefore, this study explores how incubators can support startups in innovating their business models. Research limitations/implications This research reveals the importance of addressing the psychological needs of entrepreneurs in incubators. By offering emotional support, incubators can create a positive psychological environment, helping entrepreneurs face fears and challenges. This highlights the human side of entrepreneurship, which has not been considered in the incubation literature. Practical implications Incubator directors can strengthen their programmes’ impact by offering tailored support for business model innovation and facilitating network connections. Policymakers should encourage ecosystem collaboration and allocate resources to effective programmes. Originality/value This research fills a gap in the incubation literature by emphasizing the significance and need for support for business model innovation. This study also offers original insights into the psychological dimensions of entrepreneurship.

Gamification-based collective learning for collaborative natural resources management through the testing agricultural performance solutions (TAPS) program in Nebraska, USA

Gamification-based collective learning for collaborative natural resources management through the testing agricultural performance solutions (TAPS) program in Nebraska, USA

Cloud-Edge-End Collaborative Multi-Service Resource Management for IoT-Based Distribution Grid

Cloud-Edge-End Collaborative Multi-Service Resource Management for IoT-Based Distribution Grid

Maximizing the Value of Service Provisioning in Multi-User ISAC Systems Through Fairness Guaranteed Collaborative Resource Allocation

Maximizing the Value of Service Provisioning in Multi-User ISAC Systems Through Fairness Guaranteed Collaborative Resource Allocation

Development of LSLC-Based Collaborative Learning Model Learning Tools And Their Effects on Critical Thinking Skills

The purpose of this study is to develop collaborative learning-based learning resources and their impact on students' capacity for critical thought. This study used a combination of methods, namely development research (R&D) with Thiagarajan 4D model and experimental research. Data was collected through observations, questionnaires, tests, and interviews. The validity coefficients for the Teaching Module, worksheet, and test were 3.80, 3.80, and 3.70, respectively. Observations showed that 96% of the learning tools met practical criteria. The tools were effective, with 93% of student activities rated very good, 96% of students giving positive responses, and 85% achieving learning completion. A t-test (sig = 0.007) confirmed the collaborative learning tool based on LSLC significantly enhanced students' critical thinking skills.

Computer Allied Intelligence in the Education Resource-Sharing Based inContract Deep Learning

A resource-sharing platform is an online space where individuals or organizations can exchange or access various resources, such as knowledge, skills, tools, or assets. These platforms facilitate collaboration and cooperation among users by providing a centralized hub for sharing resources efficiently. In university higher education, a resource-sharing platform serves as a vital tool for students, faculty, and staff to optimize access to a wide array of educational resources. These platforms enable the sharing of academic materials, such as textbooks, lecture notes, and research papers, fostering collaborative learning environments. This paper proposed the proposed method of Distributed Consensus Blockchain Deep Learning (dCBDL) for a resource-sharing deep learning platform in higher education aims to revolutionize collaborative learning and resource utilization. Initially, the dCBDL model uses a blockchain network that serves as the underlying infrastructure for the platform. This blockchain will store transaction records, verify the authenticity of shared resources, and ensure data integrity and immutability. The proposed model uses smart contracts and decentralized storage with a consensus mechanism for data processing in higher education. Finally, the proposed dCBDL model uses the deep learning model for the classification and assessment of student performance in higher education. The proposed model achieves a classification accuracy of 98% which is significantly higher than the conventional techniques such as blockchain architecture and SVM and LSTM classifiers.

Measurement Tools used to Assess Interprofessional Education and Collaborative Practice in Health Professional Students: A COSMIN Systematic and Psychometric Review.

Background: Interprofessional education and collaborative practice (IPE/IPC) are essential for preparing students to work together and respect the unique qualities and abilities of professionals. However, IPE/IPC are highly abstract phenomena and complicated to assess and measure. In consequence, a critical appraisal is needed to evaluate the quality of the instruments. Purpose: This study aimed to critically appraise, compare and summarize the quality of measurement properties of all self-report collaboration questionnaires for health professional students and to provide evidence concerning the psychometric properties of the measurement Methods: A psychometric review was employed, and the COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) approach was applied to assess the methodological quality of the nature of the measurements. Data search using keywords: health professional students, interprofessional, collaboration, teamwork, collaborative, through MEDLINE, EMBASE, and EBSCO-hosted Education Resource Information Center databases. Results: Seven instruments from 10 studies reviewed were identified. Among them, four instruments targeted attitudes toward collaboration. One instrument focused on students' collaborative learning readiness and had been tested in Hong Kong using English, in Iran using Persian, and in Indonesia using Bahasa Indonesia. One instrument measured perceptions about IPE, and two studies measured IPE/IPC competencies related to patient-centered, team-based, and collaborative care. The methodological quality assessment indicated that several instruments were less rigorously developed and validated than COSMIN and Quality Criteria of Measurement Properties recommend. Conclusions: The findings of this psychometric review additionally provide a strategy to select and develop the most appropriate instrument to assess IPE/IPC following their purpose, psychometric properties, and utility.