Implementation Of Framework Research Articles

Development of a poly-generation layout centered on the utilization of rice straw triggered by an air-gas turbine cycle; Multi-facet optimization

Multi-generation energy systems that rely on biomass present a hopeful answer to the challenge of sustainable energy production. The availability and accessibility of rice straw as an energy source make it a feasible source for clean energy production. Therefore, this study develops a model for a multi-generational energy system centered on the utilization of rice straw. This system produces fuel, power, heating, and freshwater triggered by an air-gas turbine cycle. To achieve optimal system performance, a response surface methodology is utilized for multi-objective optimization. The system's emission and efficiency are also taken into account during the assessment of its overall performance. This ensures that all factors are considered and the system performs at its best. Also, power production, air heating rate, hydrogen production, oxygen storage, and freshwater are also evaluated in details. The system successfully produced 2.08 kg/h and 16.5 kg/h biohydrogen and biooxygen, respectively. Surprisingly, the output power, produced fresh water, and hot air capacity were 283.9 kW, 159.2 l/h, and 4915 m3/h, respectively. It was turned out that the implementation of poly-generation framework triggered by rice straw is a strength and consistent scheme in terms of sustainability and efficiency.

Commentary With Study Protocol: Implementation and Effectiveness of Mindfulness-Oriented Recovery Enhancement (MORE) as an Adjunct to Methadone Treatment for Opioid Use Disorder (IMPOWR-MORE).

Traditional methadone treatment (MT) for opioid use disorder (OUD) fails to systematically address the physical pain, emotion dysregulation, and reward processing deficits that co-occur with OUD, and novel interventions that address these issues are needed to improve MT outcomes. Mindfulness-Oriented Recovery Enhancement (MORE) remediates the hedonic dysregulation in brain reward systems that is associated with OUD. Our pilot and phase 2 randomized controlled trials of MORE were the first to demonstrate MORE's feasibility, acceptability, and efficacy as delivered in MT clinics; MORE significantly reduced drug use (eg, benzodiazepines, barbiturates, cocaine, marijuana, opioids, and other drugs), craving, depression, anxiety, and pain among people with OUD. However, uptake of novel, efficacious interventions like MORE may be slow in MT because time and resources are often limited. Therefore, to best address potential implementation issues and to optimize future MORE implementation and dissemination, in this study, we will utilize a Type 2, Hybrid Implementation-Effectiveness study design. We will not only evaluate MORE's effectiveness but also assess barriers and facilitators to integrating MORE into MT. MT clinicians will receive training in (1) a higher intensity MORE implementation strategy consisting of training in the full MORE treatment manual or (2) a minimal intensity implementation strategy consisting of a simple, scripted mindfulness practice (SMP) extracted from the MORE treatment manual with minimal training. We aim to: (1) using a Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework, examine barriers and facilitators to implementation of MORE and SMP in MT, and evaluate strategies for optimizing training, fidelity, and engagement, (2) optimize existing MORE and SMP training and implementation toolkits, including adaptable resources that can accelerate the translation of evidence into practice, and (3) compared to usual MT, evaluate the relative effectiveness of MORE plus MT or SMP plus MT (N = 450).

Scrutinizing the Potential Benefits of Subtitling to Language Teaching and Learning Contexts: A Conceptual Framework for Implementation

Scrutinizing the Potential Benefits of Subtitling to Language Teaching and Learning Contexts: A Conceptual Framework for Implementation

Opportunities and challenges in designing and evaluating complex multilevel, multi-stakeholder occupational health interventions in practice

ABSTRACT Extant research suggests the effectiveness of Occupational Health Psychology (OHP) interventions depends on their design in the broader organisational context. While the field recognises that pre- and posttest evaluation do not sufficiently capture the complex dynamics around OHP interventions, complex multi-level OHP interventions are still scarce in the literature. As established intervention implementation frameworks suggest, it remains difficult to address this complexity in practice. The present position paper re-evaluates lessons learned from two complex European OHP intervention projects, by applying the Integrated Process Evaluation Framework (IPEF) and related theories to bridge the gap between the theoretically recognised complexity and practical challenges. The re-evaluations emphasise that programme-multilevel theories rooted in OHP-perspectives contribute to adequately hypothesising around systemic factors and mechanisms relevant to OHP interventions. Concretely, middle range theories that outline how an intervention’s mechanisms work within a specific context to produce certain outcomes are crucial. Additionally, strategically and actively involving key stakeholders at all levels of the system and across the different intervention phases improves the embedding of OHP interventions in organisations. We elaborate on these insights with seven concrete recommendations for complex OHP intervention research.

Education of adult type 1 diabetes patients in a diabetes ward setting: a best practice implementation project.

Type 1 diabetes is an autoimmune disease that destroys insulin-producing cells in the pancreas. Education is the cornerstone of effective diabetes care. In this implementation project, we aimed to improve compliance with best practices regarding type 1 diabetes educational interventions for adult hospitalized patients. This project was guided by the JBI Evidence Implementation Framework. A baseline audit was conducted involving 20 nurses and 20 type 1 diabetes patients who received regular educational measures. Areas of non-compliance were identified and an improvement strategy was implemented. A follow-up audit was then conducted to evaluate the effectiveness of the improvement strategy. The project was conducted in Poland in 2021 in a tertiary referral unit that specializes in the diagnosis and treatment of diabetes. Substantial improvements were noted for all audit criteria after the implementation of strategies to address areas of non-compliance. Use of the education program improved from 0% to 100%. Compliance regarding patients receiving handouts and personalization of the program increased to 100%. We observed a significant improvement from 0% to 80% in the structuring of the program content. This project successfully improved the quality of education provided for type 1 diabetes patients in all relevant areas. We devised an education program, covering important aspects of diabetes education, with the patients reporting increased satisfaction with the personalized educational measures during their hospital stay. http://links.lww.com/IJEBH/A215.

Utilizing intelligent technologies in construction and demolition waste management: From a systematic review to an implementation framework

Utilizing intelligent technologies in construction and demolition waste management: From a systematic review to an implementation framework

The synergy between protected area effectiveness and economic growth

Protected areas conserve biodiversity and ecosystem functions but might impede local economic growth. Understanding the global patterns and predictors of different relationships between protected area effectiveness and neighboring community economic growth can inform better implementation of the Kunming-Montreal Global Biodiversity Framework. We assessed 10,143 protected areas globally with matched samples to address the non-random location of protected areas. Our results show that protected areas resist human-induced land cover changes and do not limit nightlight increases in neighboring settlements. This result is robust, using different matching techniques, parameter settings, and selection of covariates. We identify four types of relationships between land cover changes and nightlight changes for each protected area: "synergy," "retreat," and two tradeoff relationships. About half of the protected areas (47.5%) retain their natural land cover and do so despite an increase of nightlights in the neighboring communities. This synergy relationship is the most common globally but varies between biomes and continents. Synergy is less frequent in the Amazon, Southeast Asia, and some developing areas, where most biodiversity resides and which suffer more from poverty. Smaller protected areas and those with better access to cities, moderate road density, and better baseline economic conditions have a higher probability of reaching synergy. Our results are promising, as the expansion of protected areas and increased species protection will rely more on conserving the human-modified landscape with smaller protected areas. Future interventions should address local development and biodiversity conservation together to achieve more co-benefits.

მდგრადობის აღრიცხვის მნიშვნელობა თანამედროვე პირობებში

Taking into account the European perspective, Georgia prematurely introduced the international standard of corporate sustainability reporting, the purpose of which is to improve the practice of producing management reports provided for by the Law on Accounting, Reporting and Auditing of Georgia, to contribute the establishment of a sustainable and competitive environment, and reduce the negative impact of enterprises on the environment and society. Georgia, together with the UN member states, expressed its desire to share the goals and objectives of sustainable development set for 2030. Sustainable development is a system of development of society, which, taking into account the interests of economic development and environmental protection of society, ensures the increase in the quality of human life and well-being. One of the important challenges facing today is the implementation of sustainability accounting based on sustainability reporting standards, on the basis of which corporate sustainability reporting will be prepared and published by enterprises. The ustainability counting is a tool that businesses use to become more sustainable. Sustainability accounting is considered as a subcategory of financial accounting, which focuses on publishing non-financial information about the enterprise's activities to external stakeholders, such as equity holders, creditors, etc. Sustainability accounting is an activity that affects society, the environment, and the economic activity of an enterprise. Sustainability accounting is different from managerial accounting. In order to ensure the consistency of information for the preparation of sustainability reports, guidelines were created under the name - Global Reporting Initiative (GRI). According to GRI, enterprise reporting on economic, environmental and social performance is as necessary and time-consuming as financial reporting. The goal of sustainability accounting is to make a fundamental transition to sustainable business models and a sustainable economy. The popularity of sustainability accounting has increased especially recently. This is evidenced by the fact that more and more enterprises are using new methods and techniques in the publication of financial information, along with providing external users with information about the enterprise's main activities and environmental impact. Thus, the challenges of sustainable development of enterprises are directly related to the challenges facing the production and presentation of sustainability accounting-reporting in enterprises, which at this stage is caused by the adoption of the European Union corporate reporting directive. That is why we consider the development and implementation of the legal framework of corporate sustainability reporting as one of the directions for overcoming the challenge and greater involvement of the scientific community, highly qualified accountants and auditors, enterprise managers, entrepreneurs, and students in its process, who in the future will ensure the implementation of the best practices in accounting and reporting in the country.

Implementation frameworks, strategies and outcomes in optimizing central venous access device practice in paediatrics: A scoping review.

This scoping review aimed to explore recent interventional research in CVAD management through an implementation lens. This scoping review used the Arksey and O'Malley framework. Studies were included if they were written in English, published in 2012 to July 2023, involved children and were relevant to the study aims. Risk of bias was appraised by the Mixed Methods Appraisal Tool. Searches were undertaken in EMBASE, CINAHL (Ebsco), PubMed, Web of Science and Cochrane Library (CENTRAL). Of the 1769 studies identified in a systematic search, 46 studies were included. Studies mostly focused on health professionals and central venous access device maintenance and had quantitative pre-post study designs. Adherence to implementation frameworks was lacking, with many studies employing quality improvement approaches. Implementation strategies were typically multipronged, using health-professional education, bundles and working groups. Bundle compliance and reductions in central line-associated bloodstream infections were the most featured outcomes, with most studies primarily focusing on effectiveness outcomes. Translation of evidence-based practices to the clinical setting is difficult and current adoption of implementation frameworks (apart from 'quality improvement') is limited. Implementation strategies are diverse and dependent on the local context, and study outcomes typically focus on the effectiveness of the physical intervention, rather than measuring the implementation effort itself. Future intervention research requires a more uniform and deliberate application of implementation frameworks and strategies. Greater exploration of relationships between frameworks and strategies and implementation and service outcomes is required to increase understanding of their role in maximizing resources to improve health care. Adhered to best reporting guidelines as per PRISMA-ScR (Tricco etal., 2018). No patient or public contribution.

An innovative two-stage machine learning-based adaptive robust unit commitment strategy for addressing uncertainty in renewable energy systems

Confronting the challenge of intermittent renewables, current unit commitment practices falter, urging the development of novel short-term generation scheduling techniques for enhanced microgrid stability. This study presents an adaptive robust unit commitment approach using machine learning techniques for renewable power systems, computing the Calinski-Harabasz index to identify prediction inaccuracies related to intermittent sources. The uncertainties are subsequently grouped together using the spatial clustering tool, and the average density of the K-means distribution is calculated. The clustering of points in space, considering noise, discrete uncertainty in renewable energy sources, and outliers within the comprehensive uncertainty set, is addressed via a nonparametric algorithm. The implementation of established methodologies and frameworks, in conjunction with density-based spatial clustering of applications with noise, introduces an innovative method for vulnerability clustering. This methodology guarantees that every cluster aligns with data pertaining to vulnerabilities of renewable energy sources. The performance of the suggested method is showcased by conducting experiments on modified IEEE 39-bus and 118-bus test systems that use intermittentwindpower. The results demonstrate that the proposed framework may lower the cost of robustness by 8–48% compared to traditional robust optimization techniques. The results of stochastic programming showed that the optimized system with a stable economic organization would have 75 % faster calculations.

Artificial Intelligence the Future of Cardiology

Artificial Intelligence (AI) essentially refers to various types of machine learning, often involving deep neural networks. It autocompletes our ideas as we write, enables us to communicate with our phones, and supports language translation.1 According to the 2019 Global Burden of Disease Study, the estimated age-standardized incidence of cardiovascular disease (CVD) in Pakistan was 918.8 per 100,000 people (global: 684.33 per 100,000), and the age-standardized death rate was 357.88 per 100,000 (global: 239.85 per 100,000).2 With AI, new analytical and data-driven approaches could lead to significant advances in understanding multimorbid groups of cardiology patients and potentially improve therapeutic strategies.3 AI has been used to interpret echocardiograms and heart rhythms from ECGs, and to detect indicators of heart disease, such as left ventricular dysfunction, from surface ECGs and nuclear cardiology.4-6 It is a misconception that AI will replace cardiologists. Instead, skilled practitioners will be able to expand their clinical capabilities, make more accurate and prompt diagnoses, and improve management decisions in patient care. As with any statistical application, it is important to understand AI's strengths and limitations. To understand the basics of AI, it starts with developing an algorithm based on human expertise. Programmers create relationships between input and output, known as expert systems. In machine learning, a general algorithm, such as a neural network, approximates a mathematical relationship between input data and expected outputs. In unsupervised learning, such as clustering, only the inputs are fed into the algorithm, which then finds insights in the data using its inner structure and statistics. An AI model can discover new relationships in data that have previously eluded human discovery.1 For research purposes, cardiologists using AI may follow these steps: Type and collection of data. Preprocessing of data. Choosing the right machine learning approach. Validating and evaluating methods and results.3 The application of AI techniques in the healthcare system is still in its infancy and requires more understanding through workshops and integrated learning.7 In conclusion, AI represents a new development in the field of medicine, especially cardiology. However, it is susceptible to significant errors in interpretation and raises safety and ethical concerns. References Lopez-Jimenez F, Attia Z, Arruda-Olson AM, Carter R, Chareonthaitawee P, Jouni H, et al. Artificial Intelligence in Cardiology: Present and Future. Mayo Clin Proc. 2020;95(5):1015-39. Samad Z, Hanif B. Cardiovascular Diseases in Pakistan: Imagining a Postpandemic, Postconflict Future. Circulation. 2023;147(17):1261-3. Gill SK, Karwath A, Uh HW, Cardoso VR, Gu Z, Barsky A, et al. Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare. Eur Heart J. 2023;44(9):713-25. Cheng LT, Zheng J, Savova GK, Erickson BJ. Discerning tumor status from unstructured MRI reports--completeness of information in existing reports and utility of automated natural language processing. J Digit Imaging. 2010; 23(2):119-32. Narula S, Shameer K, Salem Omar AM, Dudley JT, Sengupta PP. Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography. J Am Coll Cardiol. 2016;68(21):2287-95. Garcia EV, Klein JL, Taylor AT. Clinical decision support systems in myocardial perfusion imaging. J Nucl Cardiol. 2014;21(3):427-39. Gama F, Tyskbo D, Nygren J, Barlow J, Reed J, Svedberg P. Implementation frameworks for artificial intelligence translation into health care practice: scoping review. J Med Internet Res 2022;24:e32215.

Facilitators and Barriers Perceived by German Teachers Considering Basic Life Support Education in School-A Qualitative Study.

This qualitative study aims to analyse the personal qualification, attitudes and the pedagogical concepts of German teachers as experts in their profession regarding basic life support (BLS) education in secondary schools. Thirteen (n = 13) secondary school teachers participated in semi-structured expert interviews and were interviewed for at least 20 to 60 min regarding BLS student education. Interviews were semi-structured with guiding questions addressing (1) personal experience, (2) teacher qualification for BLS and (3) implementation factors (e.g., personal, material and organisational). Audio-recorded interviews were analysed by content analysis, generating a coding system. School teachers provided a heterogeneous view on implementation-related processes in BLS education. Many teachers were educated in first aid, acknowledge its importance, but had no experience in teaching BLS. They want to assure being competent for teaching BLS and need tailored trainings, materials, pedagogical information and the incorporation into the curriculum. Also, the management of time constraints, unwilling colleagues, or young students being overwhelmed were commonly mentioned considerations. Concluding, teachers reported to be willing to teach BLS but a stepwise implementation framework incorporating practice-oriented qualification and educational goals is missing.

Prototyping a Zoomorphic Interactive Robot Companion with Emotion Recognition and Affective Voice Interaction for Elderly People

An aging society paired with a skilled labor shortage, particularly in European countries, requires a rethinking of deprecated structures. Intelligent assistive technologies, specifically socially assistive robots, addressing the gap between caretakers and elderly people in need of care have moved into the focus of debate due to their potentials to reduce costs, improve independence, and eventually raise quality of life. In this work, we outline the potentials of zoomorphic robot companions combining intelligent conversational abilities and emotion recognition. We then describe the prototyping of an emotion-sensing zoomorphic interactive robot companion including the development and implementation of a multimodal emotion recognition framework. This framework uses speech emotion recognition, sentiment analysis, and affective voice interaction based on a large language model. The prototyping has been accompanied by two studies on elderly peoples' design preferences regarding the proposed feature set as well as different embodiments to find the appropriate casing for the robot companion. This work provides valuable insights into the prototyping and can thus support future research endeavors in this area.

Enhancing Digital Transformation: A Comprehensive Review of PT Hutama Karya's Information Technology Management in 2023

This paper provides an in-depth analysis of PT Hutama Karya’s Information Technology (IT) Management Report for the year 2023. The study focuses on the company’s strategic initiatives and achievements in aligning IT functions with its business objectives, particularly in the context of Industry 4.0 readiness. Key areas examined include the evolution of IT architecture, implementation of IT governance frameworks, cybersecurity enhancements, ERP integration, and the development of supporting applications. The findings highlight the significant progress made in digital transformation and the proactive steps taken to ensure the company’s IT infrastructure supports its strategic goals.

Implementation of Inclusive Education Framework in Preservice Teacher Preparation Programs

The study was motivated by the global pursuit of educational equity and equality, and the unique challenges of implementing inclusive education in China, a country with a profound educational legacy and rapid modernization. The study aimed to address the research gap of evaluating the adaptability and effectiveness of the current teacher training framework within the Chinese context, offering guidance and references for educational reforms.The study aimed to evaluate the effectiveness and implementation of the inclusive education framework in pre-service teacher training programs in China, and to answer seven research questions related to the profile, perception, and assessment of pre-service teachers.The study employed a descriptive quantitative research method and adopted a descriptive correlational research design. The study concluded that the pre-service teachers had a moderate level of knowledge, skills, attitudes, and beliefs about inclusive education, and that their training and preparation needed to be improved and tailored to the Chinese context. The study also recommended some innovative measures to enhance the effectiveness and implementation of the inclusive education framework in pre-service teacher training programs.

Indonesia's Digital Economic Policy To Increase Economic Resilience

The growth of information and communication technology (ICT) has changed the way people live their lives and conduct business. The era of the Industrial Revolution 4.0, characterized by the use of digital technology and the Internet of Things (IoT), has created a new economic environment known as the “digital economy." In this context, digital economy policy becomes very important to support sustainable economic growth and improve national economic resilience. This research aims to analyze Indonesia's digital economy policy in developing the digital economy in the context of Society 5.0 and Industrial Revolution 4.0. The main focus of the research is how government policies can strengthen the national economy. This research uses a descriptive-qualitative method with secondary data collected through a systematic review. Data were collected from various scholarly sources, including relevant journals, research publications, reports, and websites. This qualitative approach generates descriptive notes and data that are integrated into the text for further analysis. When policies are implemented correctly, that is, by paying attention to and realizing the various aspects that have an impact on the policy implementation process, it will produce the maximum benefits, including the Integration, Interactivity, Transparency, Control, and Accountability (IITCA) factors. The research shows that digital economy policies have a significant positive impact on economic growth and resilience. The implementation of the Digital Economic Agreement Framework (DEFA) is expected to strengthen the MSME sector, attract investment, encourage innovation, increase productivity, and create high-quality jobs. The establishment of the Digital Innovation and Sustainable Economy Center (DISC) also plays an important role in supporting the transformation of businesses and organizations towards a sustainable digital economy. In addition, the Indonesian government has improved digital infrastructure, such as 4G networks, 5G technology, and low-earth orbit satellites, and launched digital literacy and skills programs to reduce the digital divide and strengthen economic resilience. An effective digital economy policy can be a powerful instrument to strengthen a country's economic resilience. Research results confirm that the implementation of digital economy policies not only drives economic growth but also improves social and financial inclusion. Collaboration between the public sector, the business world, and civil society is essential for the successful implementation of this policy. In addition, transparency and accountability in the implementation of digital policies, such as through the use of blockchain technology and digital payment systems, can reduce corruption and ensure that government assistance and support are well-targeted. Overall, digital economy policies play an important role in strengthening national economic resilience amid the challenges and opportunities of the digital era.

A comprehensive review of it governance: effective implementation of COBIT and ITIL frameworks in financial institutions

A comprehensive review of it governance: effective implementation of COBIT and ITIL frameworks in financial institutions

Turbo-RANS: straightforward and efficient Bayesian optimization of turbulence model coefficients

PurposeIndustrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.Design/methodology/approachThe authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.FindingsThe authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.Originality/valueTo the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

Leveraging Science to Advance Health Equity: Preliminary Considerations for Implementing Health Equity Science at State and Local Health Departments.

In 2021, the Centers for Disease Control and Prevention (CDC) launched CORE, an agency-wide strategy to embed health equity as a foundational component across all areas of the agency's work. The CDC established a definition of health equity science (HES) and principles to guide the development, implementation, dissemination, and use of the HES framework to move beyond documenting inequities to investigating root causes and promoting actionable approaches to eliminate health inequities. The HES framework may be used by state and local health departments to advance health equity efforts in their jurisdictions. Identify implementation considerations and opportunities for providing technical assistance and support to state and local public health departments in advancing HES. A series of implementation consultations and multi-jurisdictional facilitated discussions were held with state and local health departments and community partners in 5 states to gather feedback on the current efforts, opportunities, and support needs to advance HES at the state and local levels. The information shared during these activities was analyzed using inductive and deductive methods, validated with partners, and summarized into themes and HES implementation considerations. Five themes emerged regarding current efforts, opportunities, and support needed to implement HES at state and local health departments. These themes included the following criteria: (1) enhancing the existing health equity evidence base; (2) addressing interdisciplinary public health practice and data needs; (3) recognizing the value of qualitative data; (4) evaluating health equity programs and policies; and (5) including impacted communities in the full life cycle of health equity efforts. Within these themes, we identified HES implementation considerations, which may be leveraged to inform future efforts to advance HES at the state and local levels. Health equity efforts at state and local health departments may be strengthened by leveraging the HES framework and implementation considerations.

Virtual Power Plants: Challenges, Opportunities, and Profitability Assessment in Current Energy Markets

The arrival of virtual power plants (VPPs) marks important progress in the energy sector, providing optimistic solutions to the increasing need for energy flexibility, resilience, and improved energy systems’ integration. VPPs harness several characteristics to bring together distributed energy resources (DERs), resulting in economic gains and improved power grid reliability. Nevertheless, VPPs encounter major challenges when it comes to engaging in energy markets, mainly because there is no all-encompassing policy and regulatory framework specifically designed to accommodate their unique characteristics. This underscores the necessity for research endeavours to develop more advanced methods and structures for the long-term viability of VPPs. To address this concern, the study advocates for the implementation of a multi-aspect framework (MAF) as a systematic approach to thoroughly examine each aspect of virtual power plants (VPPs). A STEEP (social, technological, environmental, economic, and political) analytical tool is utilized to evaluate the challenges, opportunities, and benefits of a VPP in the existing energy markets. The proposed approach highlights important factors and actions that need to be taken to tackle the challenges related to VPP’ entry into energy markets. This study suggests that further support is required to promote the fast and widespread adoption of long-term VPP implementations. For this reason, a more favourable policy and regulatory framework based on social, technological, economic, environmental, and policy considerations is necessary to realize the genuine contributions of VPPs.