Implementation Scenarios Research Articles

Effect of intra-year Landsat scene availability in land cover land use classification in the conterminous United States using deep neural networks

The Landsat archive having consistent revisit times, near global extent and extensive multi-decadal temporal coverage offers a unique opportunity for land cover land use product generation. Along with this vast volume of freely available data, new classification methods based on deep learning have improved modeling capabilities. This manuscript investigates the effect of intra-annual Landsat scene availability in the accuracy of land cover land use classification in the conterminous United States. More specifically, we seek to quantify the effect of: i) increased monthly scene availability, and ii) specific months that may result in higher classification accuracy across different classes. Identifying specific months with comparable classification accuracy to the entire time series could offer significant computational gains for large-scale mapping. Our experiment incorporated deep learning classifiers and a wide range of reference data across the continental United States. Results were contrasted between five large U.S. climatic regions to further differentiate this intra-annual effect. Our findings indicate that the total number of months can have a highly variable effect in the classification accuracy ranging from minor (a few percentage points in terms of class F1 accuracy) to extremely beneficial (approaching 50% F1 improvement moving from four to twelve month observations). The benefit of increased month observations varied among climatic regions and classes: when all climate regions were combined, the grass/shrub and cultivated classes improved their F1 accuracy up to 30%, while the water class saw the least improvement of about 5%, partially due to its limited room for improvement. The effect of specific month combinations was also examined, where the total number of months was kept constant and the included months varied. The difference between the best month combination and the median combination value was estimated to be as high as about 30% for the four monthly observations scenario and the grass/shrub class. Further validation of the month selection importance comes from an example implementation scenario where F1 improvements can be as high as 10%. Our work demonstrated that month selection may offer such benefits that in some classes and climatic regions this time selection optimization is an inevitable choice due to large accuracy improvements. Also, the potential data reduction with targeted month selection would be particularly appealing to large-scale classification tasks. Due to the large extent of the climatic regions further studies are needed to quantify a more localized effect along with explanation of potential drivers.

Collective intelligence-driven 3D printing factory for social manufacturing: implementing a testbed for industrial application

ABSTRACT The emergence of 3D printing technology has imbued the mass customization production model with novel implications. Concurrently, investigations into social manufacturing (SocialM) and collective intelligence present a fresh challenge for the 3D printing industry in their pursuit of realizing customized mass production. However, there is still a lack of investigation on the technical implementation and application scenarios of SocialM, and it hinders the development of SocialM from theory to industrial application. To mitigate this gap, firstly a five-layer framework based on collective intelligence for the configuration of design-production-service integrated 3D printing factory is established, together with the key enabling techniques that support the configuration and operation of the factory from social interaction software perspective and cyber-physical-social interconnection perspective. Secondly, the running logic of the 3D printing factory is demonstrated, and it starts from order generation to order completion. Thirdly, a testbed of the 3D printing factory is built, which contains both social interaction software and physical production hardware environments, and a production order of a 3D printed robotic arm is used to verify the feasibility of the testbed and the configuration and operation theories of SocialM. The work in this paper provides technical solutions for the industrial application of SocialM.

Review of mathematical models of Neisseria gonorrhoeae vaccine impact: Implications for vaccine development

An effective prophylactic vaccine for prevention of Neisseria gonorrhoeae infection would have a major impact on sexual and reproductive health worldwide. Interest in developing gonorrhoea vaccines is growing due to the reported high rates of N. gonorrhoeae infections globally, and the threat of antimicrobial resistance. Several gonorrhoea vaccine candidates are currently under evaluation and various mathematical models have been used to assess the potential population-level impact a gonorrhoea vaccine may have once available. Here we review key aspects of gonorrhoea vaccine mathematical modelling studies, including model structures, populations considered, and assumptions used as well as vaccine characteristics and implementation scenarios investigated. The predicted vaccine impact varied between studies, ranging from as little as ∼17 % reduction in N. gonorrhoeae prevalence after 30 years up to 100 % reduction after 5 years. However, all studies predicted that even a partially effective gonorrhoea vaccine could have a substantial impact in reducing N. gonorrhoeae prevalence or incidence, particularly when high coverage is achieved within either important risk groups or the overall sexually active population. As expected, higher vaccine efficacy against acquisition of N. gonorrhoeae and longer duration of protection were linked to greater reductions in infections. A vaccine that alters onward transmission could also substantially reduce infections. Several gaps and research needs have been identified by researchers in the field and via this narrative literature review. For example, future modelling to inform gonorrhoea vaccine development and implementation should consider additional populations that are at high risk of N. gonorrhoeae infection, especially in low- and middle-income settings, as well as the impact of vaccination on the potential adverse sexual and reproductive health outcomes of infection. In addition, more detailed and robust epidemiological, biological, and behavioural data is needed to enable more accurate and robust modelling of gonorrhoea vaccine impact to inform future scientific and public health decision-making.

Mixed reality based teleoperation and visualization of surgical robotics.

Surgical robotics has revolutionized the field of surgery, facilitating complex procedures in operating rooms. However, the current teleoperation systems often rely on bulky consoles, which limit the mobility of surgeons. This restriction reduces surgeons' awareness of the patient during procedures and narrows the range of implementation scenarios. To address these challenges, an alternative solution is proposed: a mixed reality-based teleoperation system. This system leverages hand gestures, head motion tracking, and speech commands to enable the teleoperation of surgical robots. The implementation focuses on the da Vinci research kit (dVRK) and utilizes the capabilities of Microsoft HoloLens 2. The system's effectiveness is evaluated through camera navigation tasks and peg transfer tasks. The results indicate that, in comparison to manipulator-based teleoperation, the system demonstrates comparable viability in endoscope teleoperation. However, it falls short in instrument teleoperation, highlighting the need for further improvements in hand gesture recognition and video displayquality.

AIoT in Smart Homes: Challenges, Strategic Solutions, and Future Directions

This study delves into the intricacies of AIoT integration within the domain of smart homes, meticulously analyzing the principal challenges and prospective trajectories for its evolution. Initially, the discourse establishes a foundation by explicating relevant theories and key technological pillars underpinning the intelligent lifestyle paradigm sustained by AIoT. Subsequent to this theoretical framework, the paper dissects the synergy between Artificial Intelligence (AI) and the Internet of Things (IoT), emphasizing their confluence in AIoT and its consequential role in fortifying the smart home ecosystem. Furthermore, the research extends its inquiry by presenting a detailed case study, which illustrates the practical implementation scenarios of AIoT within the ambit of an intelligent living environment. In this context, the exposition brings to light the complexities and tangible impediments besieging AIoT integration, concurrently proposing viable resolutions designed to fortify its application. In the concluding analysis, the manuscript provides an in-depth review of emerging industry trends, thereby projecting a strategic trajectory for the future advancement of AIoT in augmenting smart living spaces. This comprehensive examination not only underscores the transformative potential of AIoT but also critically addresses the exigencies and adaptive strategies requisite for its full-scale implementation.

Retrospective genetic testing (Traceback) in women with early-onset breast cancer after revised national guidelines: a clinical implementation study

PurposeThis study focused on identifying a hereditary predisposition in women previously diagnosed with early-onset breast cancer through a retrospective outreach activity (Traceback). The objectives were to evaluate the possible clinical implementation of a simplified Traceback strategy and to identify carriers of pathogenic variants among previously untested women.MethodsThree hundred and fifteen Traceback-eligible women diagnosed with breast cancer at 36–40 years in Southern Sweden between 2000 and 2019 were identified and offered an analysis of the genes ATM, BARD1, BRCA1, BRCA2, CHEK2, PALB2, RAD51C, and RAD51D through a standardized letter. Women who chose to participate were asked about their experiences through a questionnaire. The workload for the study personnel was measured and recorded.ResultsOne hundred and seventy-six women underwent genetic testing and pathogenic variants were identified in 9.7%: ATM (n = 6), BARD1 (n = 1), BRCA1 (n = 3), CHEK2 (n = 5), and PALB2 (n = 2). Women with normal test results were informed through a standardized letter. Carriers of pathogenic variants were contacted by telephone and offered in-person genetic counseling. One hundred and thirty-four women returned the subsequent questionnaire. Most study participants were satisfied with both written pre- and post-test information and many expressed their gratitude. The extra workload as compared to routine clinical genetic counseling was modest (8 min per patient).ConclusionThe insights from the participants’ perspectives and sentiments throughout the process support the notion that the Traceback procedure is a safe and an appreciated complement to routine genetic counseling. The genetic yield of almost 10% also suggests that the associated extra workload for genetic counselors could be viewed as acceptable in clinical implementation scenarios.

Promoting Sustainable Development Goals by Optimizing City-Level Solar Photovoltaic Deployment in China.

Solar photovoltaic (PV) installations, which enable carbon neutrality, are expected to surge in the coming decades. This growth will support sustainable development goals (SDGs) via reductions in power-generation-related environmental emissions and water consumption while generating new jobs. However, where and to what extent PVs should be utilized to support SDGs must be thoroughly addressed. Here, we use multiple PV deployment scenarios to compare the benefits of PVs and related SDGs progress in 366 prefectural-level cities in China. We developed an assessment framework that integrates a PV allocation model, an electricity system optimization model, and a benefit assessment approach. We identify vast differences in PV distribution and electricity transmission and elucidate trade-offs and synergies among the SDGs under various PV implementation scenarios. The water conservation-oriented scenario yields substantial carbon reduction, air pollutant mitigation, and water saving cobenefits, leading to the greatest SDGs improvements. Prioritizing employment creation enhances job-relevant SDGs but inhibits environmental resource benefits. SDGs in less developed cities present greater progress across all scenarios. This study highlights the need to consider spatial heterogeneity and the potential trade-offs between different SDGs and regions when designing energy transition strategies.

Motivating more people to engage in online green behavior: A multi-agent simulation study of Ant Forest

With the rapid development of Internet technology, people's lives are increasingly embedded in online communities. The implementation scenarios of green behavior are extended from offline to online, and individual decision-making is more likely to be influenced by others in the group. However, little is known about the decision-making process of group participation in online green behavior (OLGB). To fill this gap, we took the online environmental project Ant Forest as an example and constructed a multi-agent simulation model incorporating members' gratification types, social network connectivity, and external incentives. Then, we analyzed the decision-making characteristics of groups participating in OLGB. The results showed that: (1) When the ratio of members seeking entertainment, social, and achievement gratifications was 3:3:4, more people chose to accept OLGB and it has the most stable decision status; (2) Under low network connectivity, the group was more inclined to accept OLGB, and the decision status was more stable; (3) When high-intensity financial incentives and medium-intensity non-financial incentives were coupled, the number of people choosing OLGB was the largest, and the decision-making status was the most stable. These findings provide suggestions for policymakers and online green project operators on how to promote more people to engage in OLGB.

Effect of calorie labelling in the out-of-home food sector on adult obesity prevalence, cardiovascular mortality, and social inequalities in England: a modelling study

England implemented a menu calorie labelling policy in large, out-of-home food businesses in 2022. We aimed to model the likely policy impact on population-level obesity and cardiovascular disease mortality, as well as the socioeconomic equity of estimated effects, in the adult population in England. For this modelling analysis, we built a comparative assessment model using two scenarios: the current implementation scenario refers to actual deployment only in large (≥250 employees), out-of-home food businesses, whereas the full implementation scenario refers to deployment in every out-of-home food business. We compared each scenario with a counterfactual: the scenario in which no intervention is implemented (ie, baseline). For both scenarios, we modelled the impact of the policy through assumed changes in energy intake due to either consumer response or product reformulation by retailers. We used data from the Office for National Statistics and the National Diet and Nutrition Survey 2009-19, and modelled the effect over 20 years (ie, 2022-41) to capture the long-term impact of the policy and provided mid-period results after 10 years. We used the Monte Carlo approach (2500 iterations) to estimate the uncertainty of model parameters. For each scenario, the model generated the change in obesity prevalence and the total number of deaths prevented or postponed. The current implementation scenario was estimated to reduce obesity prevalence by 0·31 percentage points (absolute; 95% uncertainty interval [UI] 0·10-0·35), which would prevent or postpone 730 cardiovascular disease deaths (UI 430-1300) of the 830 000 deaths (UI 600 000-1 200 000) expected over 20 years. However, the health benefits would be increased if calorie labelling was implemented in all out-of-home food businesses (2·65 percentage points reduction in obesity prevalence [UI 1·97-3·24] and 9200 cardiovascular disease deaths prevented or postponed [UI 5500-16 000]). Results were similar in the most and the least deprived socioeconomic groups. This study offers the first modelled estimation of the impact of the menu calorie labelling regulation on the adult population in England, although we did not include a cost-effectiveness analysis. Calorie labelling might result in a reduction in obesity prevalence and cardiovascular disease mortality without widening health inequalities. However, our results emphasise the need for the government to be more ambitious by applying this policy to all out-of-home food businesses to maximise impact. European Research Council.

Vientos—A New Satellite Mission Concept for 3D Wind Measurements by Combining Passive Water Vapor Sounders with Doppler Wind Lidar

Abstract It is challenging to accurately characterize the three-dimensional distribution of horizontal wind vectors (known as 3D winds). Feature-matching satellite cloud top or water vapor fields have been used for decades to retrieve atmospheric motion vectors, but this approach is mostly limited to a single and uncertain pressure level at a given time. Satellite wind lidar measurements are expected to provide more accurate data and capture the line-of-sight wind for clear skies, within cirrus clouds, and above thick clouds, but only along a curtain parallel to the satellite track. Here we propose Vientos—a new satellite mission concept that combines two or more passive water vapor sounders with Doppler wind lidar to measure 3D winds. The need for 3D wind observations is highlighted by inconsistencies in reanalysis estimates, particularly under precipitating conditions. Recent studies have shown that 3D winds can be retrieved using water vapor observations from two polar-orbiting satellites separated by 50 min, with the help of advanced optical flow algorithms. These winds can be improved through the incorporation of a small number of collocated higher-accuracy measurements via machine learning. The Vientos concept would enable simultaneous measurements of 3D winds, temperature, and humidity, and is expected to have a significant impact on scientific research, weather prediction, and other applications. For example, it can help better understand and predict the preconditions for organized convection. This article summarizes recent results, presents the Vientos mission architecture, and discusses implementation scenarios for a 3D wind mission under current budget constraints.

Drivers’ behavior confronting fixed and point-to-point speed enforcement camera: agent-based simulation and translation to crash relative risk change

Utilizing a novel microsimulation approach, this study evaluates the impact of fixed and average point-to-point Speed Enforcement Cameras (SEC) on driving safety. Using the SUMO software, agent-based models for a 6-km highway without exits or obstacles were created. Telematics data from 93,160 trips were used to determine the desired free-flow speed. A total of 13,860 scenarios were simulated with 30 random seeds. The ratio of unsafe driving (RUD) is the spatial division of the total distance travelled at an unsafe speed by the total travel distance. The study compared different SEC implementations under different road traffic and community behaviours using the Power Model and calculated crash risk changes. Results showed that adding one or two fixed SECs reduced RUD by 0.20% (0.18–0.23) and 0.57% (0.54–0.59), respectively. However, average SECs significantly lowered RUD by 10.97% (10.95–10.99). Furthermore, a 1% increase in telematics enforcement decreased RUD by 0.22% (0.21–0.22). Point-to-point cameras effectively reduced crash risk in all implementation scenarios, with reductions ranging from − 3.44 to − 11.27%, pointing to their superiority as speed enforcement across various scenarios. Our cost-conscious and replicable approach can provide interim assessments of SEC effectiveness, even in low-income countries.

Prospecting Cooperative Intelligent Transport Systems acceptance by road management teams through activity theory – a qualitative study

ABSTRACT This article aims to contribute to the development of third generation activity theorising to explore the future of professional situations following the implementation of a new technology and more particularly the acceptance of this technology. To do this, we describe formally the current work of subjects (road management teams) engaged in a job located at the boundary of several interacting activity systems. We therefore position ourselves in opposition to the classical approaches of acceptability, which focus mainly on the user-technology link at the individual level. We consider that acceptance is the consequence of an evolution of the activity systems. In the first stage, we collect observable dysfunctions in individual actions, which we translate into contradictions within or between activity systems. Secondly, we propose different scenarios of technological implementation, which we use to imagine the future of the professional situation. Our approach is of particular interest in complex professional contexts with interdependencies between employees from different professions and even different organisations.

The Implementation of Land Use Scenario to Mitigate Erosion Rate in Pidekso Dam Catchment Area

Land use in Pidekso Dam Catchment Area is the largest in moorland and rice fields, so the potential for erosion rates that produce sedimentation is relatively high and dramatically affects the operating life of the dam. This study aims to compare several land use implementation scenarios to obtain a longer operating life at Pidekso Dam. The amount of erosion rate is calculated based on the Universal Soil Loss Equation (USLE) model with the integration of Geographic Information Systems for land use implementation scenarios. The calculation results before the land use implementation scenario showed the magnitude of the erosion rate at Pidekso Dam Catchment Area of 971846.151 tons/year with a sediment yield of 116026.553 tons/year, including a very severe erosion hazard level. The operating life of Pidekso Dam is estimated to last only 33.54 years from the planned life of 50 years. Based on the analysis of several land use scenarios using vegetative and mechanical methods for the longer operating life of Pidekso Dam with the construction of sediment barriers, which reached 53.99 years.

Daily reference evapotranspiration estimation utilizing deep learning models with varied combinations of weather data

Effective irrigation planning pivots on the meticulous monitoring of ETo (the reference evapotranspiration), a fundamental variable in diverse studies. The go-to method for approximate ETo, the FAO-56 Penman-Monteith (FAO-56 PM) equation, demands an array of weather data, encompassing relative humidity, temperature, solar radiation, and wind speed. However, this data-intensive requirement presents challenges in situations where such information is limited, and artificial intelligence is being used to address this challenge, come into play to estimate ET0 with a streamlined set of parameters. The study begins with a comprehensive analysis, comparing the performance of Penman-Monteith (FAO-56 PM) and (ASCE_PM) with deep learning models such as artificial neural networks (ANN) and one-dimensional convolutional neural networks (CNN 1d).The principal aim is to estimate daily reference evapotranspiration (ETo) in the region of Morocco, specifically Meknes, employing a minimal set of meteorological variables across various combinations of measured data on the fundamental variables that constitute ETo. These combinations encompass scenarios involving all four variables, different combinations of three, two, and each variable in isolation. Two implementation scenarios are considered: (i) cross-validation across all datasets and (ii) training with one station and validating with another. Across these varied techniques, commendable results emerge, portraying a favourable comparison against empirical models reliant on minimal meteorological data.

The strategy of Euro-Atlantic integration of Ukraine: the main scenarios of implementation in the conditions of the Russian-Ukrainian war

The strategy of Euro-Atlantic integration of Ukraine: the main scenarios of implementation in the conditions of the Russian-Ukrainian war

Evaluation of energy efficiency and renewable energy utilization in the road transportation sector of indonesia to reduce emissions in accordance with national contribution target

The transportation sector in Indonesia is one of the largest contributors to GHG emissions after the energy sector, which holds 28.4% of total GHG. Based on data from 2019, the road transportation sector contributes for 70-80% of the emissions. The implementation of energy efficiency and the utilisation of renewable energy (RE) in the road transportation sector play a crucial role in reducing GHG emissions and achieving national climate change contribution targets. These policies are outlined in the Minister of Transportation of the Republic of Indonesia Decree (KEPMENHUB RI) No. KM 8 of 2023, which sets out concrete steps to be taken. Despite this policy, decarbonizing the road transport sector requires restructuring the way people move. The transformation should engage in four areas such as activity reduction, modal choice and supporting infrastructure, energy intensity, and fuel carbon intensity, which only can be successful by changing the human behaviour on consumption and intensify innovation and technology development. Carbon pricing can be the alternative to power this shifting. Theoretically, carbon pricing provides economic incentives for transport manufacturers and consumers to adopt mitigation measures. This research aims to analyse the effectiveness of KEPMENHUB RI No. KM 8 of 2023 in reducing road transport emission and identifying the potential complimentary policy such as carbon pricing scheme in transportation. The research methodology includes literature review, analysis of policy implementation data, and GHG emissions assessment model of each regulatory and policy implementation scenario. During the analysis process, the mitigation action policies in KEPMENHUB RI No. KM 8 of 2023 are categorised according to the Avoid, Shift, Improve (ASI) framework. Policy, emission, environment, and market factors are applied to the GHG emissions assessment model. Final results highlight the extent of realisation, level of effectiveness, and the ease of implementation for each scenario.

Designing and evaluating the green infrastructure implementation scenarios while considering the local social environment

Designing and evaluating the green infrastructure implementation scenarios while considering the local social environment

The impact of a plastic ban implementation scenario on residents of Tiruchirappalli region

Plastic was created first for its durability, extended life, and capacity to be moulded into a broad range of items. There are three reasons why plastics are still used in the packaging sector, even though they are harmful to the environment. First, its low economic cost and widespread circulation. The second feature is their capacity to contain air and water while remaining watertight. The third characteristic is its relative inertness. Based on this, respondents were polled to assess their awareness of alternatives and to comprehend the pre- and post-ban situation for plastics. About 370 respondents from different age groups from different sectors from urban and rural dwellers of Trichy were asked about the prohibition of plastic carry bags, its acceptance, the alternatives available and the cost paid. Most respondents were unhappy about the removal of plastic bags and the high cost of cloth or other bags. Due to environmental concerns and their implications, it was revealed that the negative effects of plastics had not entered society sufficiently to allow the people to reject them. However, durability, lightness, flexibility, and low cost still predominate because the consumer requires plastic bags. Strict steps aiming at promoting public knowledge of the damaging consequences of plastics, their negative impact on the environment, and lowering the costs of alternatives should be explored as immediate remedies.

Counter-hegemonic housing constructs for sustainable urbanism and urban resilience in the 21st century

In the face of the multiple environmental and social crises largely caused by the predatory behavior of human beings in their habitat, the main challenge that the human species must deal with is to build and nurture sustainable human communities so that their ways of inhabiting do not interfere with nature’s inherent ability to sustain life. This study aimed to elucidate counter-hegemonic contemporary housing constructs experienced in Intentional Communities (IC) that demonstrate ecological awareness in their ways of inhabiting. For this purpose, three case studies were selected that present international relevance in their ways of life by proposing ways of inhabiting that are alternative to mainstream society and configure different scenarios of project implementation and scale, such as Findhorn in Scotland, Auroville in India, and Christie Walk in Australia. The discussion sought to highlight similarities and differences in the ways of inhabiting experienced in each IC analyzed, besides proposing a correlation with ecological principles to emphasize the potential of such constructs for the resilience and sustainability of contemporary urban habitat. The results of this paper contribute to studies that propose a new epistemological approach to architecture from a systemic perspective, especially to studies dedicated to IC settlements whose underlying intention is to promote the equilibrium of the human in his habitat.

Transforming Home Community Nursing Practice Through Nurse Empowerment: H.O.P.E. Approach to Care

Ageing at home is the expressed wish of the fastest growing older adult population. Life care, not just medical/health care, can holistically address care needs of this population. Receiving care at home provides the best opportunity for care providers to address a person’s care needs within their natural environment- the place they call home. With heightened concerns of COVID-19 transmission, older audits are hesitant to have multiple care providers in the home and staff are overwhelmed with work as several team members are off due to personal and/or medical reasons. Coupled with the current state of the Canadian homecare practice setting at the system level, a change is needed. The pandemic became the catalyst to transform/evolve the way SE Health provides care for the betterment of our clients, staff and organization- thus spearheading the H.O.P.E.- Home, Opportunity, People, Empowerment model of care. This leading organizational model of care provides nurses with optimal autonomy and flexibility to deliver best life, home and community care. Empowerment provides nurses with optimal autonomy and flexibility to deliver excellent life care with clients and their circle of care members. Caseload management and self-directed scheduling are unique home and community care competencies that are fostered and supported by optimization of operational processes. Therefore, operationalization of nursing practice in this model requires both optimization of Service Coordination function and digital enablement. One digital innovation, MySE Life App was created to address this need. The MySE Life App is a bespoke digital application designed by nurses, for nurses that provides access to key information to support holistic care delivery in a single application. The tool empowers informed decision making around planning, coordination, and delivering exceptional quality care by giving nurses access to client information on demand. The components of H.O.P.E. Model of Care brings homecare back to basics and care back to the future. To bring this ambition to life, we employed implementation science to bring rigor to the implementation approach and support the change management of over 8,000 leaders across the organization nationally. As Primary Nursing is the core of this organizational model, first year of implementation has focused on optimizing scope of nursing practice and continuity of care. H.O.P.E. nursing competency development through a Novice to Expert journey was also undertaken. Three key success insights will be shared with real life implementation scenarios: 1) empowering the nurse and the client, 2) implementation science informed execution, and 3) operationalizing nurse practice with enabling technology and redesigned structures, processes, and leadership. Learning insights can inform healthcare organizations in how creating a nurse empowered practice culture impacts client safety, quality care and sustainable business outcomes. As the implementation moves towards sustainability in nursing, the learnings will also be applied to accelerate the implementation across other health care disciplines such as personal support workers and allied health disciplines in the organization. The implementation of H.O.P.E. Model of Care illustrates how home and community care can work better for people and the broader system in a resource constrained environment.