Processing Resources Research Articles

Advanced Signal Processing Techniques for Real-Time Systems in Edge Computing

With the rapid expansion of edge computing, real-time systems are increasingly deployed in environments such as IoT, autonomous vehicles, and industrial applications, where efficient signal processing is crucial for performance and decision-making. However, real-time signal processing in edge computing environments faces significant challenges, including limited computational resources, data transmission constraints, and strict latency requirements. Objective: This paper aims to explore advanced signal processing techniques specifically tailored for real-time systems in edge computing. The goal is to propose novel algorithms and strategies to enhance processing efficiency, reduce latency, and optimize resource usage in these environments. Methods: We propose a combination of traditional signal processing methods and emerging techniques, including: Adaptive Filtering: Improving real-time performance in dynamic environments. Compressed Sensing: Leveraging sparsity to reduce data processing and transmission overhead. Deep Learning Models: Applying convolutional and recurrent neural networks for accurate signal classification and anomaly detection. Distributed Signal Processing: Offloading computation tasks to distributed edge devices to balance load and reduce latency. Results: Experimental results demonstrate that the proposed techniques outperform traditional methods in terms of signal processing speed, resource utilization, and accuracy. In particular, the integration of deep learning models significantly improves classification accuracy for real-time signal detection, while compressed sensing reduces bandwidth requirements without compromising signal quality. Conclusion: The proposed signal processing methods effectively address the unique challenges posed by real-time systems in edge computing environments. These techniques can be applied to a wide range of applications, such as smart cities, industrial IoT, and autonomous systems, providing a solid foundation for the development of efficient, low-latency real-time systems.

Boosting Weakly Supervised Object Localization and Segmentation With Domain Adaption.

Weakly supervised object localization (WSOL), adopting only image-level annotations to learn the pixel-level localization model, can release human resources in the annotation process. Most one-stage WSOL methods learn the localization model with multi-instance learning, making them only activate discriminative object parts rather than the whole object. In our work, we attribute this problem to the domain shift between the training and test process of WSOL and provide a novel perspective that views WSOL as a domain adaption (DA) task. Under this perspective, a DA-WSOL pipeline is elaborated to better assist WSOL with DA approaches by considering the specificities for the adaption of WSOL. Our DA-WSOL pipeline can discern the source-related and the Universum samples from other target samples based on a proposed target sampling strategy and then utilize them to solve the sample unbalancing and label unmatching between the source and target domain of WSOL. Experiments show that our pipeline outperforms SOTA methods on three WSOL benchmarks and can improve the performance of downstream weakly supervised semantic segmentation tasks.

Sentiment analysis as an information agenda and public opinion research method (on the example of Chinese mass media and social networks)

The information agenda broadcast by Chinese media resources is a source of up-to-date data on public opinion on key issues of social welfare. Due to the technical peculiarities of the organization of Chinese websites and the need to attract additional resources for automatic processing (parsing) of texts in Chinese, this topic is not widely represented in domestic and foreign studies. The purpose of this paper is to demonstrate the methodology and results of public opinion estimation on the example of data collected from Chinese media and social networks based on a trained sentiment analysis model of Chinese text data. The ML model was used to comparatively analyze Chinese language content on urban infrastructure development issues for the period 2020–2022. The results are presented in the format of sentiment distribution charts based on media and social media data by month over a 2-year period. It is revealed that the level of sentiment differs significantly depending on the type of data source. A steady prevalence of positive sentiment in mass media and negative sentiment in social networks was determined, which can be explained by differences in the composition of text authors, restrictions imposed on the content published in the sources, as well as different purposes of resource use by users.

Generation of Entanglement and Perfect One‐Way EPR Steering via Kerr Nonlinearity in Cavity Magnonics System

AbstractA scheme is proposed for generating entanglement and achieving perfect one‐way EPR (Einstein–Podolsky–Rosen) steering in a cross‐shaped cavity magnonics system via the Kerr effect arising from magnetocrystalline anisotropy. The scheme involves two microwave cavities with distinct quality factors that simultaneously couple to a magnon mode of a macroscopic yttrium‐iron‐garnet sphere via the magnetic‐dipole interaction. It is demonstrated that both the bipartite and tripartite entanglement among the three modes will be generated due to Kerr nonlinearity, and the perfect one‐way EPR steering will also be achieved between the cavity with higher quality factor and the magnon mode. Notably, this scheme differs from conventional protocols that produce asymmetric EPR steering by introducing additional unbalanced losses or noises; instead, it can generate and manipulate one‐way EPR steering solely by adjusting the detuning between the magnon mode and the microwave drive field. It is also analyzed that the robustness of the entanglement and EPR steering against the dissipation of magnon and environmental temperature. The present scheme may provide a promising platform and an efficient quantum resource for quantum information processing.

Tooth point cloud resampling method based on divergence index and improved Euclidean clustering rule.

In endodontic therapy, 3D Cone Beam Computerized Tomography (CBCT) and oral scan fusion models allow exact root canal channels and guidance. However, the point cloud model from CBCT has few data points and poor model features, limiting 3D fusion with oral scan data. Our aim to build a sub-regional point cloud resampling method and evaluate the precision of merging it with three-dimensional oral scan data.
Approach: Two molars and four incisors were resampled for this investigation. Based on point cloud density and curvature, the rebuilt model was separated into the crown and cervical cavities. Using crown surface morphology, Divergence Index (DI) was employed to determine resampling points based on point dispersion. Improved Euclidean Clustering Rule (IECR) downsamples each point using its weight and joins the two halves using Iterative Nearest Neighbour (ICP) to create a complete resampled point cloud. After aligning with the oral scanning model, the maximum error, maximum distance, average distance, and other characteristics are calculated to assess resampling. Additionally, a cross-entropy kernel-based point cloud reconstruction depth selection method is given to determine the appropriate reconstruction depth.
Main results: Applying the DI-IECR technique reduces the average distance between the resampled tooth point cloud and the point cloud generated by the dental scanner by around 20%. The maximum error remains same to that of the widely used method. This study also demonstrates that the use of the DI-IECR approach guarantees the complete representation of the coronal characteristics of the resampled reconstructed 3D model, rather than excessively focusing processing resources on pertinent but insignificant areas.
Significance: Point cloud data and crown features are balanced using DI-IECR. When registered with the oral scan model, CBCT-generated point clouds are more accurate and timely, making them a better intraoperative navigation model.

Honey bee inspired resource allocation scheme for IoT-driven smart healthcare applications in fog-cloud paradigm.

The Internet of Things (IoT) paradigm is a foundational and integral factor for the development of smart applications in different sectors. These applications are comprised over set of interconnected modules that exchange data and realize the distributed data flow (DDF) model. The execution of these modules on distant cloud data-center is prone to quality of service (QoS) degradation. This is where fog computing philosophy comes in to bridge this gap and bring the computation closer to the IoT devices. However, resource management in fog and optimal allocation of fog devices to application modules is critical for better resource utilization and achieve QoS. Significant challenge in this regard is to manage the fog network dynamically to determine cost effective placement of application modules on resources. In this study, we propose the optimal placement strategy for smart health-care application modules on fog resources. The objective of this strategy is to ensure optimal execution in terms of latency, bandwidth and earliest completion time as compared to few baseline techniques. A honey bee inspired strategy has been proposed for allocation and utilization of the resource for application module processing. In order to model the application and measure the effectiveness of our strategy, iFogSim Java-based simulation classes have been extended and conduct the experiments that demonstrate the satisfactory results.

Entrepreneurship Skills needed by Youths for Entry into Oil Palm Production for Self-Employment and Job Creation in Delta State, Nigeria

This study focused on entrepreneurship skills needed by youths for entry into oil palm production in Delta State. Three research questions and two hypotheses were formulated and tested at 0.05 level of significance. The population of the study was one thousand five hundred(1500) youth farmers located in the three (3) senatorial districts in Delta State. The study adopted the use of descriptive survey method. Data was analyzed using frequency distribution, percentages, mean and standard deviation. The findings revealed that more males are involved in oil palm production than females. Also, that the combination of agricultural expertise, business skills, adaptability, and networking can empower youths in Delta State, Nigeria, to engage in oil palm production for self-employment, even while balancing other jobs. Findings also showed that combination of processing knowledge, operational skills, quality control, safety measures, resource management, and environmental considerations are needed. Findings from the research revealed that youth entrepreneurs in oil palm production have strong understanding of agronomy, including soil management, pest control and crop maintenance. The study recommended that Delta State youths can benefit from entrepreneurial education through formal programs and vocational trainings, focusing on oil palm production and self-employment. Special loan schemes and financial literacy training was also recommended.

Teacher Identity Discourses in Place—Exploring Discursive Resources in Pre-Service Teachers’ Constructions of Teacher Identity

This methodological article focuses on how to effectively map pre-service teachers’ use of discursive resources in professional identity production. By adopting a discourse analytical approach, this study views identity construction as a situational, real-time process occurring in interaction. The aim is to contribute knowledge about how to systematically map and analyze the resources that pre-service teachers use to construct their teacher identities during their education. Drawing on the framework of Mediated Discourse Analysis, this article presents a model that integrates two key concepts: discourse domains, which refer to the types of discourse commonly used in teacher education, and layers of discourse, which address societal levels in identity construction. The results suggest that using these concepts to map students’ use of discursive resources highlights how their knowledge of the teaching profession, their education and everyday experiences can be assets when constructing their teacher identities. While the model can be further refined and developed to better show the complexity of discursive resources in identity construction processes, it shows promise as a fruitful approach. By mapping and visualizing discursive resources through this model, this study offers valuable methodological insights into how to approach professional identity development among pre-service teachers.

Neurophysiological mechanisms underlying the influence of motivational intensity on emotion regulation choice.

Motivation is considered a crucial factor in determining emotion regulation choice (ER choice). Our previous study revealed that increased motivation led to a preference for distraction over reappraisal due to the narrowed attention, yet neural underpinnings supporting this phenomenon remain unclear. In this study, we used event-related potentials (ERPs) to investigate whether motivation influences ER choice by early attention processing (P2) or late resource engagement (LPP). EEG activities were recorded for the pictorial contexts varying in motivational intensity and direction, while participants (N = 68) were required to decide which strategy from the two options they would use to regulate their emotions. Consistent with previous studies, the results indicated that individuals chose more distraction over reappraisal in high-withdrawal motivated contexts while a reversed pattern was observed in low-withdrawal motivated contexts. Moreover, high-intensity motivated emotional contexts result in enhanced P2 and LPP. Further analysis indicates that only frontal P2 and the early LPP mediate the effect of motivational intensity on ER choice. These findings suggest that heightened motivation leads to a preference for distraction over reappraisal during emotion regulation due to the greater occupation of attentional resources.

Detection of Indian Counterfeit Currency Notes with MATLAB-Based Feature Extraction

Fakes are a major threat to India’s economy hence the need for a solution that will help detect these materials easily and accurately. The detection of counterfeit currency is a major concern for almost all countries, and this research focuses on developing a MATLAB-based counterfeit currency detection system which evaluates a true image of the Indian Currencies with the help of image processing and pattern recognition technique. Benefiting from MATLAB’s powerful image processing resources, the system conducts the necessary preprocessing, feature extraction and classification of vital security elements of currency, such as watermarks, security threads and micro-lettering which play an important role in identifying the genuine currency from the counterfeit. The specific characteristics of edge and texture are statistically and geometrically calculated, and the normal and high-resolution light conditions are at high accuracy with varying resolutions. In order to determine a distinction between actual and fake notes, support vector machine (SVM) classifiers are used. By validating this MATLAB solution, it has been determined to be effective as an easy to use, robust and customizable software that has the potential to work in numerous operations within banking and retail and prevent the spreading of counterfeit money.

Exploring Translation Strategies of Thesis Abstract Written by Non-English Department Students in The University of Mataram

This study investigates the translation problems, strategies, and the most frequently used strategy in translating Indonesian thesis abstracts into English. The subjects of this research comprised eight thesis abstracts written by non-English department students at the University of Mataram during the 2023 academic year. Utilizing Baker’s theory on translation problems and strategies, which encompasses eleven aspects, this study employed a mixed-methods approach, combining quantitative and qualitative analyses. The findings reveal a total of 78 translation problems across the eight Indonesian thesis abstracts. Among Baker's identified problems, only eight were observed, including: culture-specific concepts (31 instances), source language concepts not lexicalized in the target language (4 instances), semantically complex source language words (14 instances), a lack of specific terms in the target language (1 instance), differences in expressive meaning (8 instances), differences in form (2 instances), differences in frequency and purpose of specific forms (6 instances), and the use of loan words in the source text (12 instances). To address these challenges, seven out of eight translation strategies from Baker’s theory were employed in the English thesis abstracts. The most utilized strategy was translation using loan words (35 instances, 45%), followed by translation using a more general word (12 instances, 16%), translation using a neutral word (10 instances, 13%), translation by omission (10 instances, 13%), translation by paraphrase using a related word (4 instances, 5%), and the least employed strategies were translation using cultural substitution (3 instances, 4%) and paraphrase using unrelated words (3 instances, 4%). Of the 78 translation problems identified, 77 were resolved using the seven strategies, while one problem remained unsolved due to an unacceptable translation. This thesis aims to serve as a valuable reference for translators, English department students, and researchers in the field of translation. The findings indicate a significant need for enhanced support and resources in the translation process for non-English department students at the University of Mataram.

Cloud-Native ETL: Integrating Databricks and Azure Data Factory for Scalable Data Processing in Enterprise Environments

This article examines the implementation of cloud-native ETL solutions leveraging Databricks and Azure Data Factory (ADF) for scalable data processing in enterprise environments. The article presents a comprehensive analysis of the architectural design, integration strategies, and performance optimization techniques for combining Databricks' powerful data transformation capabilities with ADF's robust workflow orchestration. Through a series of case studies and empirical evaluations, we demonstrate how this integrated approach addresses the challenges of big data processing, including scalability, flexibility, and cost-effectiveness. Our findings reveal significant improvements in processing efficiency and resource utilization, with observed reductions in ETL pipeline execution times by up to 40% and overall cloud infrastructure costs by 25%. The article also highlights best practices for data governance, security, and quality management within this framework. These insights provide valuable guidance for data engineers and IT professionals seeking to modernize their data processing infrastructure and harness the full potential of cloud-native ETL solutions.

Neighborhood frequency effects in simple and complex span: Do high-frequency neighbors help or hurt?

A word's orthographic neighborhood is the set of words that differ from the target word by one letter. Both Roodenrys (2009) and Robert et al. (Journal of Psycholinguistic Research, 44, 119-125, 2015) posit that orthographic neighbors are activated when the target word is encountered in tasks such as simple and complex span. The two accounts differ in that the former predicts a beneficial effect of this activation, because it produces feedback activation that helps redintegrate the target word, whereas the latter predicts a detrimental effect, because the need to overcome the greater interference from the larger number of higher-frequency items reduces the processing resources available. Four experiments assess the predictions of these two accounts. Experiments 1 and 2 found a beneficial effect of having a higher- compared with a lower-frequency neighborhood in both a simple and a complex span task. Experiments 3 and 4 found no detrimental effect of having one or more neighbors with higher frequency than the target in both a simple and complex span task. Implications for the two theories are discussed.

Leveraging social capital for digital transformation: The role of ambidexterity and familial orientation

In pursuing competitive advantage, businesses are diffusing new digital technologies across their value chains and, by doing so, catalyzing significant organizational changes in operational processes and strategic approaches. This widespread evolution of businesses has prompted significant questions regarding the drivers of digital change, the necessary resources, and the capabilities that need to be developed or reconfigured. Our paper focuses on the role of intangible resources, such as a firm's social capital, and its specific implications for the broader research on this phenomenon. Consistent with the resource-based view, we examine the role of different types of social capital as critical resources in the digital transformation process while also considering the mediating role of the balancing act of the capabilities around business alignment and adaptation encompassed in the concept of ambidexterity. Furthermore, we introduce a contextual dimension to our study by examining family involvement in a firm as a significant factor influencing how businesses shape their digital goals and allocate resources toward digital transformation. Our paper addresses this under-explored research field by drawing on a large, cross-sectional dataset from an online panel study with 582 management-level participants from UK firms. After testing our hypotheses, our study's results indicate that bridging and bonding social capital resources significantly promote digital transformation by indirectly developing an ambidextrous orientation. Moreover, familial orientation strengthens both relationships. This research is currently one of the few studies providing empirical evidence on the relationship between digital transformation, its antecedents, and, specifically, the role of intangible resources.

Study on pyrolysis process and mechanism of organic coating of waste polyesterimide enameled wire based on density functional theory

The pyrolysis treatment of waste polyesterimide enameled wire offers significant advantages such as continuous processing, non-hazardous nature, and high resource recovery rate. However, research on its pyrolysis process and mechanism remains insufficiently explored. In this study, techniques including thermogravimetry (TG), Fourier transform infrared spectroscopy(FT-IR), pyrolysis–gas chromatography-mass spectrometry(Py-GC/MS) were employed to investigate pyrolysis temperature, weight loss rate, pyrolysis and kinetic parameters, composition and distribution of pyrolysis products, and changes in functional groups under different temperatures and heating rates. The pyrolysis of polyesterimide enameled wire can be divided into three stages: volatilization of specific components (314.8 °C,1.6 %), rapid decomposition of polyesterimide (435.2 °C, 44.7 %), and generation of small organic molecules with the fixation of pyrolytic carbon (750 °C, 9.0 %). The main components of the pyrolysis gas products include aromatic compounds, ethers, aromatics, ketones, and carbon dioxide. At 400 °C, a substantial amount of pyrolysis gas products and free radicals are produced, with an increase in aromatic hydrocarbons. The carbon distribution in the pyrolysis products mainly focuses on C6-C10 and C11-C15, and many organic compounds such as benzoic acid, phenol, and aniline are generated. Based on density functional theory, the bond dissociation energies in the polyesterimide were calculated. This clarified the possible thermal decomposition pathways of the polyesterimide and provided the energy barrier values, elucidating the generation mechanism of pyrolysis products during thermal decomposition. The copper atom in specific location reduces the negative ESP of O and increases the positive ESP of H in EPI, which makes H more likely to attach to O. The presence of Cu increases the ESP range and promote the pyrolysis process. This study provides a theoretical basis for the high-value recycling of waste polyesterimide enamelled wire.

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

Whale Optimized Distributed Computing Data Lake for Energy Storage

This paper presents the Whale Seahorse Optimization Distributed Computing (WSODS) algorithm, a novel approach that combines the Whale Optimization Algorithm (WOA) and Seahorse Optimization Algorithm (SOA) within a distributed computing framework. WSODS aims to address complex optimization challenges across various domains, including power storage systems and data lake architectures. The algorithm's performance was evaluated based on key metrics such as data processing time, system throughput, resource utilization, and scalability. The evaluation results indicate that WSODS significantly enhances system performance. In the context of power storage systems, WSODS improves energy efficiency, storage capacity, charging and discharging rates, and round-trip efficiency. For data lake architectures, WSODS achieves lower data processing times, higher system throughput, and competitive resource utilization while demonstrating good scalability with increasing data loads. The evaluation results indicate that WSODS significantly enhances system performance. In the context of power storage systems, WSODS improves energy efficiency from 92% to 98%, storage capacity from 480 MWh to 500 MWh, charging rate from 45 MW to 55 MW, discharging rate from 55 MW to 64 MW, and round-trip efficiency from 88% to 94% over 100 iterations. For data lake architectures, WSODS achieves lower data processing times (450 seconds compared to 600 seconds for GA), higher system throughput (75 MB/s compared to 55 MB/s for GA), and competitive resource utilization (80% CPU and 65% memory), while demonstrating good scalability with processing time for a 2x data load at 920 seconds compared to 1250 seconds for GA. These findings suggest that WSODS is a versatile and robust optimization tool capable of driving advancements in energy efficiency, data analytics, and distributed computing. Further research and real-world applications are recommended to fully explore its potential and capabilities.

FINANCING THE IMPLEMENTATION OF HR ENGINEERING IN MANUFACTURING ENTERPRISES IN THE CONTEXT OF THE COUNTRY'S DIGITAL ECONOMY FORMATION

The article is devoted to the study of possible prospects for financing the implementation of HR engineering at manufacturing enterprises in the conditions of the formation of a digital economy. In the process of research, the authors considered the possibilities of implementing HR engineering at production enterprises and focused attention on the main directions of implementation. The principles of financing enterprises that implement changes and transform are revealed. Approaches and strategies aimed at optimizing human capital management in the company are proposed. The authors noted the following main tools of HR engineering for the development and improvement of HR processes, which are key to building effective human capital management strategies in the organization and require funding: organizational management structure, strategic maps, creation of a portrait of personnel competencies, modelling of the human resources management process resources, development of a goal tree, increased flexibility of HR management processes, implementation of a balanced system of indicators. The authors' comprehensive analysis of the implementation of digital HR solutions by manufacturing enterprises in previous years made it possible to identify the most important obstacles to the successful implementation and financing of innovations and digital solutions, the main of which are: insufficient financial resources for the implementation of innovative projects; lack of sufficient financial assistance and stimulation from the state; large financial costs associated with changes in the infrastructure and introduction of new technologies; low interest of investors, which may limit access to external sources of financing; some innovations may require a long payback period, which becomes a challenge for companies; high interest rates on loans can make it difficult to finance innovative projects. Considering these financial factors, it is important to develop comprehensive strategies and tools to overcome obstacles and stimulate the innovative and digital development of manufacturing enterprises.

Experiences from the European Region: case studies and scenarios to inform ways forward in digital health

Abstract Member States in the WHO European Region face similar challenges when it comes to strengthening their HIS and implementing digital health solutions. From WHO/Europe’s work with its Member States, several important preconditions have become apparent for successful and sustainable digital transformation. Firstly, the importance of recognising that implementing digital tools is not a one-off investment, but that this takes a long-term vision including long-term budgeting. Having business continuity plans also is a key element of sustainable implementation of digital tools, that is often not adequately addressed in practice. Secondly, the importance of comprehensive strategic planning. Implementing digital tools is not only about software and hardware, but also about people and processes. Here it is also crucial for policymakers to understand that, to be able to harvest the benefits of data that are gathered by means of digital tools, resources for data processing and analysis also need to be made available. Last but not least, an important precondition for successful digital transformation is stakeholder involvement. Often, new software is developed without adequately involving the end-user, resulting in for example apps that do not meet patients’ needs. Or in electronic health record systems that are not user-friendly for the healthcare staff that need to enter the data, with incomplete and low quality information as a consequence. Stakeholder involvement is essential in strategic planning for digital health as well. Think for example about standardization and accreditation agencies, and institutes that will be secondary users of data for statistics and research. In this presentation, several case studies from across the European Region will be presented, illustrating how digital transformation in health can be successfully and sustainably implemented.

Implementing a chronic disease self-management program in Moldova: a roadmap for sustainability

Abstract Chronic disease self-management programs are evidence-based interventions but achieving their sustainability can be challenging. We propose a roadmap to sustainability that examines the transferability of intervention components and fosters multi-level and multisectoral partnerships. Since 2019, the Swiss Agency for Development and Cooperation’s Healthy Life project, in cooperation with the Ministry of Health of Moldova, has implemented a self-management program in rural communities. At pilot stage, we achieved a 53% increase in participants’ self-efficacy (Palmeirim et al, 2024). To date, the program has reached 1432 beneficiaries in 70 communities. The intervention cost per participant was approximately 74 Euros. Our evidence supported the inclusion of self-management in the national non-communicable disease program 2023-2027. We designed a roadmap to hand over this innovation to Moldovan public health institutions. Firstly, we unpacked the intervention into transferable resources, processes and roles. These included human resources (a network of 140 facilitators), pedagogical resources (culturally adapted facilitator training and self-management workshop content) and key operational processes (coordination, resource management, partnership building, monitoring and evaluation). Then, we convened a national workshop to discuss the transfer of these components to relevant institutions. We emphasized working in a coordinated partnership across institutions for intervention delivery. Finally, we launched a national working group that will work on the operational modalities of this multi-stakeholder partnership. Achieving an enabling policy environment for self-management created favorable conditions and accountability for institutional partnership building. A strategic reflection on our partner ecosystem was key to leverage local and national interests in continuing self-management approaches. Key messages • We have established priorities among transferable intervention components to match country stakeholders’ ability and readiness to sustain innovative approaches. • We are building an institutional partnership across sectors based on complementarity of experience to sustain the delivery of a self-management intervention.