Collaborative Modeling Approach Research Articles

Advancing Care Team Adoption of Electronic Health Record Systems for Cancer Symptom Management: Findings From a Hybrid Type II, Cluster-Randomized, Stepped-Wedge Trial.

The enhanced, electronic health record (EHR)-facilitated cancer symptom control (E2C2) trial is a cohort cluster-randomized, stepped-wedge, hybrid type II trial that leverages EHR systems to facilitate a collaborative care model (CCM) approach with the goal of improving cancer symptom management. Understanding factors that influence care team adoption of EHR systems remains a critical understudied area of research. This study examines how oncology care teams' perceptions regarding the feasibility, acceptability, and appropriateness of E2C2 EHR systems preimplementation were associated with adoption 3 months after implementation and characterizes differences in adoption by individual- and system-level factors. Care team members completed an electronic survey before and 3 months after implementation of E2C2 for their respective sequence. Adoption was defined as frequency of use to statements aligned with care team-directed EHR systems designed to facilitate CCM approaches. Chi-square tests assessed differences in adoption while logistic regression models estimated associations between baseline mean scores of acceptability, feasibility, and appropriateness on care team adoption at 3 months. Results from 94 care team members (37.2% oncologists, 72.6% female, 55.3% in their role for 6+ years) found that adoption rates ranged from 48.9% to 71.7%, with significant differences observed by location (community-based health care systems v tertiary medical center) and professional role. Adjusting for professional role, care team members reporting higher levels of perceived acceptability and appropriateness at baseline had greater odds of adopting EHR systems at 3 months. EHR systems perceived as acceptable and appropriate are more likely to be adopted by oncology care teams in our sample. Future implementation efforts should consider tailored strategies to facilitate adoption of EHR systems designed to promote CCM-based approaches to improve cancer symptom management.

Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England.

Mathematical modelling has played an important role in offering informed advice during the COVID-19 pandemic. In England, a cross government and academia collaboration generated medium-term projections (MTPs) of possible epidemic trajectories over the future 4-6 weeks from a collection of epidemiological models. In this article, we outline this collaborative modelling approach and evaluate the accuracy of the combined and individual model projections against the data over the period November 2021-December 2022 when various Omicron subvariants were spreading across England. Using a number of statistical methods, we quantify the predictive performance of the model projections for both the combined and individual MTPs, by evaluating the point and probabilistic accuracy. Our results illustrate that the combined MTPs, produced from an ensemble of heterogeneous epidemiological models, were a closer fit to the data than the individual models during the periods of epidemic growth or decline, with the 90% confidence intervals widest around the epidemic peaks. We also show that the combined MTPs increase the robustness and reduce the biases associated with a single model projection. Learning from our experience of ensemble modelling during the COVID-19 epidemic, our findings highlight the importance of developing cross-institutional multi-model infectious disease hubs for future outbreak control.

Combining models to generate a consensus effective reproduction number R for the COVID-19 epidemic status in England

Abstract The effective reproduction number $ R $ was widely accepted as a key indicator during the early stages of the COVID-19 pandemic. In the UK, the $ R $ value published on the UK Government Dashboard has been generated as a combined value from an ensemble of epidemiological models via a collaborative initiative between academia and government. In this paper, we outline this collaborative modelling approach and illustrate how, by using an established combination method, a combined $ R $ estimate can be generated from an ensemble of epidemiological models. We analyse the $ R $ values calculated for the period between April 2021 and December 2021, to show that this $ R $ is robust to different model weighting methods and ensemble sizes and that using heterogeneous data sources for validation increases its robustness and reduces the biases and limitations associated with a single source of data. We discuss how $ R $ can be generated from different data sources and show that it is a good summary indicator of the current dynamics in an epidemic.

Privacy-preserving federated learning: Application to behind-the-meter solar photovoltaic generation forecasting

The growing usage of decentralized renewable energy sources has made accurate estimation of their aggregated generation crucial for maintaining grid flexibility and reliability. However, the majority of distributed photovoltaic (PV) systems are behind-the-meter (BTM) and invisible to utilities, leading to three challenges in obtaining an accurate forecast of their aggregated output. Firstly, traditional centralized prediction algorithms used in previous studies may not be appropriate due to privacy concerns. There is therefore a need for decentralized forecasting methods, such as federated learning (FL), to protect privacy. Secondly, there has been no comparison between localized, centralized, and decentralized forecasting methods for BTM PV production, and the trade-off between prediction accuracy and privacy has not been explored. Lastly, the computational time of data-driven prediction algorithms has not been examined. This article presents a FL power forecasting method for PVs, which uses federated learning as a decentralized collaborative modeling approach to train a single model on data from multiple BTM sites. The machine learning network used to design this FL-based BTM PV forecasting model is a multi-layered perceptron, which ensures privacy and security of the data. Comparing the suggested FL forecasting model to non-private centralized and entirely private localized models revealed that it has a high level of accuracy, with an RMSE that is 18.17% lower than localized models and 9.9% higher than centralized models.

Security enhanced collaborative business model approach using blockchain and IoT

Security enhanced collaborative business model approach using blockchain and IoT

A Blockchain-Based Trust Model for Supporting Collaborative Healthcare Data Management

The development of information technology allows the collaborative business process to be run across multiple enterprises in a larger market environment. However, while collaborative business expands the realm of businesses, it also causes various hazards in collaborative Interaction, such as data falsification, inconstancy, and misuse. To solve these issues, a blockchain-based collaborative business modeling approach was proposed and analyzed. However, the existing studies lack the blockchain risk problem-solving specification, and there is no verification technique to examine the process. Consequently, it is difficult to confirm the appropriateness of the approach. Thus, here, we propose and build a blockchain-based trust model to strengthen and verify the integrity and security of the collaborative business process; Integrity and security address the validity of collaborative interactions in terms of a trust, and we construct a blockchain pattern based on trust elements to meet the required the characteristics. Specifically, a trust model can be applied to the healthcare data-sharing process, and then the achievement of the trust-based safe data-sharing process can be proven. Our model can be used as a trust-building guidance tool or for integrity and security verification with the collaborative business process in a distributed environment with blockchain.

Collaborative Modeling to Construct a Hedging Policy for Drought Management in Reservoir Systems

In situations of fluctuating water availability, the optimal reservoir operation policy, resulting from classical models, fails to be implemented for political reasons. This paper proposes a collaborative modeling approach to define the hedging rules of a two-basin reservoir system, in which the acceptable risks, rationing ratio, and reasonable transfer values are defined by the water allocation agents for different phases/drought states. An optimization algorithm is then applied to determine a politically viable hedging operation policy. The methodology showed good results for the Jaguaribe–Metropolitan system in Brazil, and the resulting policy could be applied to the real operation, as the simulated rationing frequencies were close to those defined by the managers’ consensus. The proposed methodology guarantees a politically viable alternative with conflict management possibilities. However, the performance of the resulting hedging rules depends on the negotiation capacity and tacit knowledge of the stakeholders. The proposed approach is a good alternative when the implementation of the mathematically optimum policy fails.

How Individuals With Low Back Pain Conceptualize Their Condition: A Collaborative Modeling Approach

Low back pain (LBP) is complex. This study aimed to use collaborative modeling to evaluate conceptual models that individuals with LBP have of their condition, and to compare these models with those of researchers/clinicians. Twenty-eight individuals with LBP were facilitated to generate mental models, using “fuzzy cognitive maps,” that represented conceptualization of their own LBP and LBP “in general.” “Components” (ie, causes, outcomes and treatments) related to pain, disability and quality of life were proposed, along with the weighted “Connections” between Components. Components were classified into thematic categories. Weighting of Connections were summed for each Component to judge relative importance. Individual models were aggregated into a metamodel. When considering their own condition, participants’ models included 19(SD = 6) Components and 43(18) Connections with greatest weight on “Biomechanical” components. When considering LBP in general, models changed slightly. Patient models contrasted the more complex models of researchers/clinicians (25(7) Components; 77(42) Connections), with most weight on “Psychological” components. This study provides unique insight into how individuals with LBP consider their condition, which is largely biomedical and narrower than clinician/researcher perspectives. Findings highlight challenges for changing public perception of LBP, and provide a method with potential utility to understand how individuals conceptualize their condition. PerspectiveCollaborative modeling was used to understand how individuals with low back pain conceptualize their own condition, the condition in general, and compare this with models of expert researchers/clinicians. Data revealed issues in how individuals with back pain conceptualize their condition, and the method's potential utility for clinical evaluation of patients.

Association between work status and depression in informal caregivers: a collaborative modelling approach.

BackgroundCare is regularly provided on an informal basis by family and friends and it is well established that caregivers experience high rates of depression. The majority of research on caregivers tends to focus on older, full-time caregivers, with less attention paid to working caregivers (in paid employment). The aim of this study is to explore the impact of work status on depression in caregivers.MethodsA sample of individuals from the 2014 European Social Survey dataset, aged 18 and older, who reported being a caregiver, were investigated (n = 11 177). Differences in sociodemographic, mental and physical health and social network variables, between working and non-working caregivers, were investigated. Hierarchical logistic regression models were used to investigate associations between the caregivers’ work status and depression. This study was developed in partnership with a panel of caregivers who contributed to the conceptualization and interpretation of the statistical analysis.ResultsFindings showed that 51% of caregivers reported being in paid employment. Non-working caregivers were more likely to be female, older, widowed, have lower education levels and provide intensive caring hours. They were also more likely to report depressive symptoms than working caregivers after controlling for sociodemographic, social networks and intensity of caring (adjusted odds ratio = 1.77, 95% confidence interval = 1.54–2.03). The panel considered policies to support continued work important as a means of maintaining positive mental health for caregivers.ConclusionsSupportive policies, such as flexible working and care leave, are recommended to allow caregivers to continue in paid work and better manage their health, caring and working responsibilities.

COLLABORATION OF MARINE ECOTOURISM IN RIAU ISLANDS PROVINCE TOWARDS SUSTAINABLE TOURISM

The purpose of this study is to analyze the existence of collaborative governance in the development of marine ecotourism in a sustainable manner in Bintan Regency, Riau Islands Province using a collaborative governance model approach. This study uses a qualitative approach, where the researcher performs one stage of qualitative data collection at a time, simultaneously (concurrently). This research is useful for increasing basic knowledge about the relationship between marine ecotourism development institutions, and also enriching the knowledge base, especially in the field of public administration. Besides, this research does not provide practical applications that can be applied in the short term but can become a basis for knowledge in solving problems and making policies related to improving institutional relations between institutions in the development of marine ecotourism. Then, it can be implemented by stakeholders and also the Regional Government of Bintan Regency in the development of sustainable marine ecotourism.
 The collaborative institutional strengthening model has an important role in building community involvement. This model can strengthen network systems and information flow and reduce disagreements between stakeholders. An important role in the development of local indigenous-based tourism potential, not only in the role of the central or regional government but also the role of all actors in the tourism community who have an important role in developing sustainable tourism.

Fatigue reliability estimation framework for turbine rotor using multi-agent collaborative modeling

To improve the computing accuracy and simulation efficiency of fatigue reliability estimation for turbine rotor, a multi-agent collaborative modelling (MACM) approach is proposed by absorbing the strengths of improved differential evolution (IDE) algorithm and neural network model into decomposed-collaborative strategy. The fatigue reliability estimation framework is presented in respect of MACM approach. Furthermore, the fatigue reliability estimation of a typical turbine rotor is considered as engineering case to evaluate the presented approach with respect to material variabilities, load fluctuations and model randomness. The estimation results reveal that the probabilistic fatigue life of a turbine rotor under reliability 99.87% is 6 050 cycles; fatigue strength coefficient σf′ and strain range Δɛt play a leading role on the fatigue life since their effect probabilities of 45% and 36%, respectively. The comparisons of methods (direct Monte Carlo simulation (MCS), quadratic polynomial (QP), neural network (NN), neural network agent (NNA)) show that the presented MACM approach holds high efficiency and accuracy for fatigue reliability estimation of turbine rotor.

Enhancing the handling qualities analysis by collaborative aerodynamics surrogate modelling and aero-data fusion

In the modern aircraft design process numerical simulation is one of the key enablers. However, computational time increases exponentially with the level of fidelity of the simulation. In the EU Horizon2020 project AGILE different aircraft design analysis tools relative to different levels of fidelity are used. One of the challenges is to reduce the computational time - e.g. to facilitate an efficient optimization process - by processing the analysis data of various fidelity levels in a global surrogate model. This paper focuses on fusion of data sets via an automatic iterative process embedded in the collaborative multidisciplinary analysis (MDA) chains as applied in AGILE. Surrogate modeling techniques are applied, taking into account the optimal sampling and the corresponding fidelities of the samples. This paper will detail the different steps of the proposed collaborative approach. As a test case handling qualities analysis of the AGILE reference conventional aircraft is performed, by fusing the computed aerodynamic coefficients and derivatives. A full set of aerodynamic data computed either with different levels of fidelity or with only a low-fidelity tool has been derived and evaluated. The data set with multiple levels of fidelity significantly improved the accuracy of the flight performance analysis, especially for the transonic region in which the low fidelity aerodynamic method is not reliable. Moreover, the test case shows that by combining a collaborative surrogate modeling approach with fusion of the data sets, the fidelity of the analysis data can be significantly improved giving maximum relative prediction error less than 5% with minimal computing efforts.

A Tutor-Led Collaborative Modelling Approach to Teaching Paraphrasing to International Graduate Students

Language learners are at particular risk of being accused of plagiarism, and this is often due to incorrect paraphrasing and quoting practices. Tertiary institutions tend to provide rudimentary citation resources through their academic integrity initiatives. Handouts, webinars and one-hour workshops may be enough for undergraduate writers who receive more elaborate instruction and practice opportunities in their classes, but for international graduate students with little to no instruction on source use in their undergraduate degrees, these resources are not enough. These writers often need more conceptual and procedural clarity to paraphrase and use sourced information correctly in their writing. This article introduces a student-centred, collaborative modelling approach and a 5-step procedure for teaching paraphrasing to multilingual graduate students in one-to-one writing center tutoring sessions.

Decomposed Collaborative Modeling Approach for Probabilistic Fatigue Life Evaluation of Turbine Rotor.

To improve simulation accuracy and efficiency of probabilistic fatigue life evaluation for turbine rotor, a decomposed collaborative modeling approach is presented. In this approach, the intelligent Kriging modeling (IKM) is firstly proposed by combining the Kriging model (KM) and an intelligent algorithm (named as dynamic multi-island genetic algorithm), to tackle the multi-modality issues for obtaining optimal Kriging parameters. Then, the decomposed collaborative IKM (DCIKM) comes up by fusing the IKM into decomposed collaborative (DC) strategy, to address the high-nonlinearity problems for accelerating simulation efficiency. Moreover, the DCIKM-based probabilistic fatigue life evaluation theory is introduced. The probabilistic fatigue life evaluation of turbine rotor is regarded as case study to verify the presented approach; the evaluation results reveal that the probabilistic fatigue life of turbine rotor is 3296 cycles. The plastic strain range ∆εp and fatigue strength coefficient σf′ are the main affecting factors to fatigue life, whose effect probability are 28% and 22%, respectively. By comparing with direct Monte Carlo method, KM method, IKM method and DC response surface method, the presented DCIKM is validated to hold high efficiency and accuracy in probabilistic fatigue life evaluation.

LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules.

This work presents a quantum mechanical model for predicting octanol-water partition coefficients of small protein-kinase inhibitor fragments as part of the SAMPL6 LogP Prediction Challenge. The model calculates solvation free energy differences using the M06-2X functional with SMD implicit solvation and the def2-SVP basis set. This model was identified as dqxk4 in the SAMPL6 Challenge and was the third highest performing model in the physical methods category with 0.49 log Root Mean Squared Error (RMSE) for predicting the 11 compounds in SAMPL6 blind prediction set. We also collaboratively investigated the use of empirical models to address model deficiencies for halogenated compounds at minimal additional computational cost. A mixed model consisting of the dqxk4 physical and hdpuj empirical models found improved performance at 0.34 log RMSE on the SAMPL6 dataset. This collaborative mixed model approach shows how empirical models can be leveraged to expediently improve performance in chemical spaces that are difficult for ab initio methods to simulate.

REPOSITIONING CUTTING-EDGE RESEARCH IN THE OIL AND GAS INDUSTRY: EXPERIENCE ON PRACTICAL COLLABORATIVE MANAGEMENT MODEL IN NIGERIA.

ABSTRACT: The place of cutting-edge research for both national and industrial growth had been analyzed using descriptive statistical methods from field data of selected research projects in fifteen years. The hands-on practical experience adopted both modern strategic management administration and technology in the formulation, implementation, evaluation and deployment of research products for the use of the industry. Thirty different research projects were analyzed based on scope, industry relevance, expected outcome and value addition and commercial value to the nation and the oil and gas industry over a period of ten years. Critical performance indicators such as time of delivery, quality of products, cost and capacity building were examined to equate the initial research design with the actual end-product. Critical challenges of consistency in concept formulation and end-products, funding constraints, infrastructural and equipment deficits, experienced technical personnel to use available laboratory equipment, industry buy-in and effective policy for actual enforcement of the use of research products in the industry were important issues for success rating. It was noted that the single stream approach could not give satisfactory results compared to the collaborative model approach among the samples identified and selected for analysis. Overall performance was recorded in the cross-border and commissioned research model for speed and accuracy of completion while, the grant and endowment researches were at top with capacity building and infrastructural development at institutional levels. This paper, therefore is useful for policy formulation, research design conceptualization and decision making in the oil and gas industry.

REPOSITIONING CUTTING-EDGE RESEARCH IN THE OIL AND GAS INDUSTRY: EXPERIENCE ON PRACTICAL COLLABORATIVE MANAGEMENT MODEL IN NIGERIA

ABSTRACT: The place of cutting-edge research for both national and industrial growth had been analyzed using descriptive statistical methods from field data of selected research projects in fifteen years. The hands-on practical experience adopted both modern strategic management administration and technology in the formulation, implementation, evaluation and deployment of research products for the use of the industry. Thirty different research projects were analyzed based on scope, industry relevance, expected outcome and value addition and commercial value to the nation and the oil and gas industry over a period of ten years. Critical performance indicators such as time of delivery, quality of products, cost and capacity building were examined to equate the initial research design with the actual end-product. Critical challenges of consistency in concept formulation and end-products, funding constraints, infrastructural and equipment deficits, experienced technical personnel to use available laboratory equipment, industry buy-in and effective policy for actual enforcement of the use of research products in the industry were important issues for success rating. It was noted that the single stream approach could not give satisfactory results compared to the collaborative model approach among the samples identified and selected for analysis. Overall performance was recorded in the cross-border and commissioned research model for speed and accuracy of completion while, the grant and endowment researches were at top with capacity building and infrastructural development at institutional levels. This paper, therefore is useful for policy formulation, research design conceptualization and decision making in the oil and gas industry.

Experiential Learning for Effective Knowledge Transfer and Mission Achievement

Traditional university mission statements are multifaceted. The majority focus on fundamentals: teaching, research, and service. Most core values are holistic learning, freedom of expression, and openness. However, recent trends appear to embrace a consumerist “transactional approach” emphasizing competitiveness, superiority, self-interest, and quantifiable quality with students as primary customers. At a time when students are demanding value for money, universities must endeavor to balance private interests with traditional universities' values. Hence, consideration of some form of experiential learning and knowledge transfer in universities' mission statements is expected to enrich student learning, add value to society, and also meet the transactional approach requirements. This paper explores theoretical and conceptual approaches to the learning process including theories, models, and perspectives. The United States International University's experience is used as a case study with reference to integrative learning experiences in the General Education (GE) courses, the Collaborative Active Learning Model (CALM) Approach to Learning, and the Global Executive MBA Program, and the knowledge transfer experiences in the GAME Center Living Lab, the Agent Net Work, and the Entrepreneurial Simulation.

Three Ways Forward to Improve Regional Information for Extreme Events: An Early Career Perspective

This paper provides an early career researchers (ECRs) perspective on major challenges and opportunities that arise in the study and understanding of, and the provision of regional information for Climate, Weather and Hydrological (CWH) extreme events. This perspective emerged from the discussions of the early career 3-day Young Earth System Scientists - Young Hydrologic Society (YESS-YHS) workshop, which was conjointly held with the Global Energy and Water Exchanges (GEWEX) Open Science Conference. In this paper we discuss three possible ways forward in the field: a stronger interaction between Earth system scientists and users, a collaborative modeling approach between the different modeling communities, and an increased use of unconventional data sources in scientific studies. This paper also demonstrates the important role of ECRs in embracing the above outlined pathways and addressing the long-standing challenges in the field. YESS and YHS networks encourage the global community to support and strengthen their involvement with ECR communities to advance the field of interdisciplinary Earth system science in the upcoming years to decades.

Enforcing fine-grained access control for secure collaborative modelling using bidirectional transformations

Large-scale model-driven system engineering projects are carried out collaboratively. Engineering artefacts stored in model repositories are developed in either offline (checkout–modify–commit) or online (GoogleDoc-style) scenarios. Complex systems frequently integrate models and components developed by different teams, vendors and suppliers. Thus, confidentiality and integrity of design artefacts need to be protected in accordance with access control policies. We propose a secure collaborative modelling approach where fine-grained access control for models is strictly enforced by bidirectional model transformations. Collaborators obtain filtered local copies of the model containing only those model elements which they are allowed to read; write access control policies are checked on the server upon submitting model changes. We present a formal collaboration schema which provenly guarantees certain correctness constraints, and its adaption to online scenarios with on-the-fly change propagation and the integration into existing version control systems to support offline scenarios. The approach is illustrated, and its scalability is evaluated using a case study of the MONDO EU project.